From 34af272c54cfe69c1f8cd2a71be30a419f51773a Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:07:24 -0500 Subject: [PATCH 001/625] Initial commit of adapthist modifications. --- doc/examples/plot_equalize.py | 8 +- skimage/exposure/_adapthist.pyx | 40 +++ skimage/exposure/clahe.h | 329 ++++++++++++++++++++++++ skimage/exposure/exposure.py | 58 ++++- skimage/exposure/setup.py | 31 +++ skimage/exposure/tests/test_exposure.py | 71 +++++ skimage/setup.py | 1 + 7 files changed, 536 insertions(+), 2 deletions(-) create mode 100644 skimage/exposure/_adapthist.pyx create mode 100644 skimage/exposure/clahe.h create mode 100644 skimage/exposure/setup.py diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 1e1291d8..2cb9cbea 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -63,9 +63,12 @@ img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) # Equalization img_eq = exposure.equalize(img) +# Adaptive Equalization +img_adapteq = exposure.adapthist(img) + # Display results -f, axes = plt.subplots(2, 3, figsize=(8, 4)) +f, axes = plt.subplots(2, 4, figsize=(8, 4)) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) ax_img.set_title('Low contrast image') @@ -78,6 +81,9 @@ ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Histogram equalization') ax_cdf.set_ylabel('Fraction of total intensity') +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 3]) +ax_img.set_title('Adaptive equalization') +ax_cdf.set_ylabel('Fraction of total intensity') # prevent overlap of y-axis labels plt.subplots_adjust(wspace=0.4) diff --git a/skimage/exposure/_adapthist.pyx b/skimage/exposure/_adapthist.pyx new file mode 100644 index 00000000..16e84336 --- /dev/null +++ b/skimage/exposure/_adapthist.pyx @@ -0,0 +1,40 @@ +import numpy as np +cimport numpy as np + +cdef extern from "clahe.h": + ctypedef unsigned int kz_pixel_t + int CLAHE(kz_pixel_t * pImage, unsigned int uiXRes, unsigned int uiYRes, + kz_pixel_t Min, + kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, + unsigned int uiNrBins, float fCliplimit) + + +def _adapthist(np.ndarray[kz_pixel_t, ndim=2] image, min, max, nx, ny, nbins, + clip_limit=0): + ''' + image - to the input/output image + min - Minimum greyvalue of input image (also becomes min of output image) + max - Maximum greyvalue of input image (also becomes max of output image) + nx - Number of tile regions in the X direction (min 2, max uiMAX_REG_X) + ny - Number of tile regions in the Y direction (min 2, max uiMAX_REG_Y) + nbins - Number of greybins for histogram ("dynamic range") + clip_limit - Normalized cliplimit (higher values give more contrast) + + The number of "effective" greylevels in the output image is set by nbins; + selecting a small value (eg. 128) speeds up processing and still produce + an output image of good quality. The output image will have the same + minimum and maximum value as the input image. A clip limit smaller than 1 + results in standard (non-contrast limited) AHE. + ''' + # we need the image to be divisible by nx and ny + uiYRes = image.shape[0] - image.shape[0] % ny + uiXRes = image.shape[1] - image.shape[1] % nx + + cdef np.ndarray[kz_pixel_t, ndim = 1] flattened + flattened = image[:uiYRes, :uiXRes].ravel() + + ret = CLAHE(< kz_pixel_t * > flattened.data, uiXRes, uiYRes, min, + max, nx, ny, nbins, clip_limit * nbins) + if ret < 0: + print 'clahe error: ', ret + return flattened.reshape((uiYRes, uiXRes)) diff --git a/skimage/exposure/clahe.h b/skimage/exposure/clahe.h new file mode 100644 index 00000000..8b115aa1 --- /dev/null +++ b/skimage/exposure/clahe.h @@ -0,0 +1,329 @@ +/* +ANSI C code from the article + * "Contrast Limited Adaptive Histogram Equalization" + * by Karel Zuiderveld, karel@cv.ruu.nl + * in "Graphics Gems IV", Academic Press, 1994 +============= + +ACM Software License Agreement + +All software, both binary and source published by the Association for Computing Machinery (hereafter, Software) is copyrighted by the Association (hereafter, ACM) and ownership of all right, title and interest in and to the Software remains with ACM. By using or copying the Software, User agrees to abide by the terms of this Agreement. +Noncommercial Use + +The ACM grants to you (hereafter, User) a royalty-free, nonexclusive right to execute, copy, modify and distribute both the binary and source code solely for academic, research and other similar noncommercial uses, subject to the following conditions: + + User acknowledges that the Software is still in the development stage and that it is being supplied "as is," without any support services from ACM. Neither ACM nor the author makes any representations or warranties, express or implied, including, without limitation, any representations or warranties of the merchantability or fitness for any particular purpose, or that the application of the software, will not infringe on any patents or other proprietary rights of others. + ACM shall not be held liable for direct, indirect, incidental or consequential damages arising from any claim by User or any third party with respect to uses allowed under this Agreement, or from any use of the Software. + User agrees to fully indemnify and hold harmless ACM and/or the author(s) of the original work from and against any and all claims, demands, suits, losses, damages, costs and expenses arising out of the User's use of the Software, including, without limitation, arising out of the User's modification of the Software. + User may modify the Software and distribute that modified work to third parties provided that: (a) if posted separately, it clearly acknowledges that it contains material copyrighted by ACM (b) no charge is associated with such copies, (c) User agrees to notify ACM and the Author(s) of the distribution, and (d) User clearly notifies secondary users that such modified work is not the original Software. + User agrees that ACM, the authors of the original work and others may enjoy a royalty-free, non-exclusive license to use, copy, modify and redistribute these modifications to the Software made by the User and distributed to third parties as a derivative work under this agreement. + This agreement will terminate immediately upon User's breach of, or non-compliance with, any of its terms. User may be held liable for any copyright infringement or the infringement of any other proprietary rights in the Software that is caused or facilitated by the User's failure to abide by the terms of this agreement. + This agreement will be construed and enforced in accordance with the law of the state of New York applicable to contracts performed entirely within the State. The parties irrevocably consent to the exclusive jurisdiction of the state or federal courts located in the City of New York for all disputes concerning this agreement. + +Commercial Use + +Any User wishing to make a commercial use of the Software must contact ACM at permissions@acm.org to arrange an appropriate license. Commercial use includes (1) integrating or incorporating all or part of the source code into a product for sale or license by, or on behalf of, User to third parties, or (2) distribution of the binary or source code to third parties for use with a commercial product sold or licensed by, or on behalf of, User. + +Revised 6/98 +*/ + +#ifdef BYTE_IMAGE +typedef unsigned char kz_pixel_t; /* for 8 bit-per-pixel images */ +#define uiNR_OF_GREY (256) +#else +typedef unsigned short kz_pixel_t; /* for 12 bit-per-pixel images (default) */ +# define uiNR_OF_GREY (4096) +#endif + +/* + * ANSI C code from the article + * "Contrast Limited Adaptive Histogram Equalization" + * by Karel Zuiderveld, karel@cv.ruu.nl + * in "Graphics Gems IV", Academic Press, 1994 + * + * + * These functions implement Contrast Limited Adaptive Histogram Equalization. + * The main routine (CLAHE) expects an input image that is stored contiguously in + * memory; the CLAHE output image overwrites the original input image and has the + * same minimum and maximum values (which must be provided by the user). + * This implementation assumes that the X- and Y image resolutions are an integer + * multiple of the X- and Y sizes of the contextual regions. A check on various other + * error conditions is performed. + * + * #define the symbol BYTE_IMAGE to make this implementation suitable for + * 8-bit images. The maximum number of contextual regions can be redefined + * by changing uiMAX_REG_X and/or uiMAX_REG_Y; the use of more than 256 + * contextual regions is not recommended. + * + * The code is ANSI-C and is also C++ compliant. + * + * Author: Karel Zuiderveld, Computer Vision Research Group, + * Utrecht, The Netherlands (karel@cv.ruu.nl) + */ + +/*********************** Local prototypes ************************/ +static void ClipHistogram (unsigned long*, unsigned int, unsigned long); +static void MakeHistogram (kz_pixel_t*, unsigned int, unsigned int, unsigned int, + unsigned long*, unsigned int, kz_pixel_t*); +static void MapHistogram (unsigned long*, kz_pixel_t, kz_pixel_t, + unsigned int, unsigned long); +static void MakeLut (kz_pixel_t*, kz_pixel_t, kz_pixel_t, unsigned int); +static void Interpolate (kz_pixel_t*, int, unsigned long*, unsigned long*, + unsigned long*, unsigned long*, unsigned int, unsigned int, kz_pixel_t*); + +/************** Start of actual code **************/ +#include /* To get prototypes of malloc() and free() */ + + +const unsigned int uiMAX_REG_X = 16; /* max. # contextual regions in x-direction */ +const unsigned int uiMAX_REG_Y = 16; /* max. # contextual regions in y-direction */ + + + +/************************** main function CLAHE ******************/ +int CLAHE (kz_pixel_t* pImage, unsigned int uiXRes, unsigned int uiYRes, + kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, + unsigned int uiNrBins, float fCliplimit) +/* pImage - Pointer to the input/output image + * uiXRes - Image resolution in the X direction + * uiYRes - Image resolution in the Y direction + * Min - Minimum greyvalue of input image (also becomes minimum of output image) + * Max - Maximum greyvalue of input image (also becomes maximum of output image) + * uiNrX - Number of contextial regions in the X direction (min 2, max uiMAX_REG_X) + * uiNrY - Number of contextial regions in the Y direction (min 2, max uiMAX_REG_Y) + * uiNrBins - Number of greybins for histogram ("dynamic range") + * float fCliplimit - Normalized cliplimit (higher values give more contrast) + * The number of "effective" greylevels in the output image is set by uiNrBins; selecting + * a small value (eg. 128) speeds up processing and still produce an output image of + * good quality. The output image will have the same minimum and maximum value as the input + * image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. + */ +{ + unsigned int uiX, uiY; /* counters */ + unsigned int uiXSize, uiYSize, uiSubX, uiSubY; /* size of context. reg. and subimages */ + unsigned int uiXL, uiXR, uiYU, uiYB; /* auxiliary variables interpolation routine */ + unsigned long ulClipLimit, ulNrPixels;/* clip limit and region pixel count */ + kz_pixel_t* pImPointer; /* pointer to image */ + kz_pixel_t aLUT[uiNR_OF_GREY]; /* lookup table used for scaling of input image */ + unsigned long* pulHist, *pulMapArray; /* pointer to histogram and mappings*/ + unsigned long* pulLU, *pulLB, *pulRU, *pulRB; /* auxiliary pointers interpolation */ + + if (uiNrX > uiMAX_REG_X) return -1; /* # of regions x-direction too large */ + if (uiNrY > uiMAX_REG_Y) return -2; /* # of regions y-direction too large */ + if (uiXRes % uiNrX) return -3; /* x-resolution no multiple of uiNrX */ + if (uiYRes % uiNrY) return -4; /* y-resolution no multiple of uiNrY */ + if (Max >= uiNR_OF_GREY) return -5; /* maximum too large */ + if (Min >= Max) return -6; /* minimum equal or larger than maximum */ + if (uiNrX < 2 || uiNrY < 2) return -7;/* at least 4 contextual regions required */ + if (fCliplimit == 1.0) return 0; /* is OK, immediately returns original image. */ + if (uiNrBins == 0) uiNrBins = 128; /* default value when not specified */ + + pulMapArray=(unsigned long *)malloc(sizeof(unsigned long)*uiNrX*uiNrY*uiNrBins); + if (pulMapArray == 0) return -8; /* Not enough memory! (try reducing uiNrBins) */ + + uiXSize = uiXRes/uiNrX; uiYSize = uiYRes/uiNrY; /* Actual size of contextual regions */ + ulNrPixels = (unsigned long)uiXSize * (unsigned long)uiYSize; + + if(fCliplimit > 0.0) { /* Calculate actual cliplimit */ + ulClipLimit = (unsigned long) (fCliplimit * (uiXSize * uiYSize) / uiNrBins); + ulClipLimit = (ulClipLimit < 1UL) ? 1UL : ulClipLimit; + } + else ulClipLimit = 1UL<<14; /* Large value, do not clip (AHE) */ + MakeLut(aLUT, Min, Max, uiNrBins); /* Make lookup table for mapping of greyvalues */ + /* Calculate greylevel mappings for each contextual region */ + for (uiY = 0, pImPointer = pImage; uiY < uiNrY; uiY++) { + for (uiX = 0; uiX < uiNrX; uiX++, pImPointer += uiXSize) { + pulHist = &pulMapArray[uiNrBins * (uiY * uiNrX + uiX)]; + MakeHistogram(pImPointer,uiXRes,uiXSize,uiYSize,pulHist,uiNrBins,aLUT); + ClipHistogram(pulHist, uiNrBins, ulClipLimit); + MapHistogram(pulHist, Min, Max, uiNrBins, ulNrPixels); + } + pImPointer += (uiYSize - 1) * uiXRes; /* skip lines, set pointer */ + } + /* Interpolate greylevel mappings to get CLAHE image */ + for (pImPointer = pImage, uiY = 0; uiY <= uiNrY; uiY++) { + if (uiY == 0) { /* special case: top row */ + uiSubY = uiYSize >> 1; uiYU = 0; uiYB = 0; + } + else { + if (uiY == uiNrY) { /* special case: bottom row */ + uiSubY = uiYSize >> 1; uiYU = uiNrY-1; uiYB = uiYU; + } + else { /* default values */ + uiSubY = uiYSize; uiYU = uiY - 1; uiYB = uiYU + 1; + } + } + for (uiX = 0; uiX <= uiNrX; uiX++) { + if (uiX == 0) { /* special case: left column */ + uiSubX = uiXSize >> 1; uiXL = 0; uiXR = 0; + } + else { + if (uiX == uiNrX) { /* special case: right column */ + uiSubX = uiXSize >> 1; uiXL = uiNrX - 1; uiXR = uiXL; + } + else { /* default values */ + uiSubX = uiXSize; uiXL = uiX - 1; uiXR = uiXL + 1; + } + } + + pulLU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXL)]; + pulRU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXR)]; + pulLB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXL)]; + pulRB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXR)]; + Interpolate(pImPointer,uiXRes,pulLU,pulRU,pulLB,pulRB,uiSubX,uiSubY,aLUT); + pImPointer += uiSubX; /* set pointer on next matrix */ + } + pImPointer += (uiSubY - 1) * uiXRes; + } + free(pulMapArray); /* free space for histograms */ + return 0; /* return status OK */ +} +void ClipHistogram (unsigned long* pulHistogram, unsigned int + uiNrGreylevels, unsigned long ulClipLimit) +/* This function performs clipping of the histogram and redistribution of bins. + * The histogram is clipped and the number of excess pixels is counted. Afterwards + * the excess pixels are equally redistributed across the whole histogram (providing + * the bin count is smaller than the cliplimit). + */ +{ + unsigned long* pulBinPointer, *pulEndPointer, *pulHisto; + unsigned long ulNrExcess, ulUpper, ulBinIncr, ulStepSize, i; + long lBinExcess; + + ulNrExcess = 0; pulBinPointer = pulHistogram; + for (i = 0; i < uiNrGreylevels; i++) { /* calculate total number of excess pixels */ + lBinExcess = (long) pulBinPointer[i] - (long) ulClipLimit; + if (lBinExcess > 0) ulNrExcess += lBinExcess; /* excess in current bin */ + }; + + /* Second part: clip histogram and redistribute excess pixels in each bin */ + ulBinIncr = ulNrExcess / uiNrGreylevels; /* average binincrement */ + ulUpper = ulClipLimit - ulBinIncr; /* Bins larger than ulUpper set to cliplimit */ + + for (i = 0; i < uiNrGreylevels; i++) { + if (pulHistogram[i] > ulClipLimit) pulHistogram[i] = ulClipLimit; /* clip bin */ + else { + if (pulHistogram[i] > ulUpper) { /* high bin count */ + ulNrExcess -= pulHistogram[i] - ulUpper; pulHistogram[i]=ulClipLimit; + } + else { /* low bin count */ + ulNrExcess -= ulBinIncr; pulHistogram[i] += ulBinIncr; + } + } + } + + while (ulNrExcess) { /* Redistribute remaining excess */ + pulEndPointer = &pulHistogram[uiNrGreylevels]; pulHisto = pulHistogram; + + while (ulNrExcess && pulHisto < pulEndPointer) { + ulStepSize = uiNrGreylevels / ulNrExcess; + if (ulStepSize < 1) ulStepSize = 1; /* stepsize at least 1 */ + for (pulBinPointer=pulHisto; pulBinPointer < pulEndPointer && ulNrExcess; + pulBinPointer += ulStepSize) { + if (*pulBinPointer < ulClipLimit) { + (*pulBinPointer)++; ulNrExcess--; /* reduce excess */ + } + } + pulHisto++; /* restart redistributing on other bin location */ + } + } +} +void MakeHistogram (kz_pixel_t* pImage, unsigned int uiXRes, + unsigned int uiSizeX, unsigned int uiSizeY, + unsigned long* pulHistogram, + unsigned int uiNrGreylevels, kz_pixel_t* pLookupTable) +/* This function classifies the greylevels present in the array image into + * a greylevel histogram. The pLookupTable specifies the relationship + * between the greyvalue of the pixel (typically between 0 and 4095) and + * the corresponding bin in the histogram (usually containing only 128 bins). + */ +{ + kz_pixel_t* pImagePointer; + unsigned int i; + + for (i = 0; i < uiNrGreylevels; i++) pulHistogram[i] = 0L; /* clear histogram */ + + for (i = 0; i < uiSizeY; i++) { + pImagePointer = &pImage[uiSizeX]; + while (pImage < pImagePointer) pulHistogram[pLookupTable[*pImage++]]++; + pImagePointer += uiXRes; + pImage = &pImagePointer[-uiSizeX]; + } +} + +void MapHistogram (unsigned long* pulHistogram, kz_pixel_t Min, kz_pixel_t Max, + unsigned int uiNrGreylevels, unsigned long ulNrOfPixels) +/* This function calculates the equalized lookup table (mapping) by + * cumulating the input histogram. Note: lookup table is rescaled in range [Min..Max]. + */ +{ + unsigned int i; unsigned long ulSum = 0; + const float fScale = ((float)(Max - Min)) / ulNrOfPixels; + const unsigned long ulMin = (unsigned long) Min; + + for (i = 0; i < uiNrGreylevels; i++) { + ulSum += pulHistogram[i]; pulHistogram[i]=(unsigned long)(ulMin+ulSum*fScale); + if (pulHistogram[i] > Max) pulHistogram[i] = Max; + } +} + +void MakeLut (kz_pixel_t * pLUT, kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrBins) +/* To speed up histogram clipping, the input image [Min,Max] is scaled down to + * [0,uiNrBins-1]. This function calculates the LUT. + */ +{ + int i; + const kz_pixel_t BinSize = (kz_pixel_t) (1 + (Max - Min) / uiNrBins); + + for (i = Min; i <= Max; i++) pLUT[i] = (i - Min) / BinSize; +} + +void Interpolate (kz_pixel_t * pImage, int uiXRes, unsigned long * pulMapLU, + unsigned long * pulMapRU, unsigned long * pulMapLB, unsigned long * pulMapRB, + unsigned int uiXSize, unsigned int uiYSize, kz_pixel_t * pLUT) +/* pImage - pointer to input/output image + * uiXRes - resolution of image in x-direction + * pulMap* - mappings of greylevels from histograms + * uiXSize - uiXSize of image submatrix + * uiYSize - uiYSize of image submatrix + * pLUT - lookup table containing mapping greyvalues to bins + * This function calculates the new greylevel assignments of pixels within a submatrix + * of the image with size uiXSize and uiYSize. This is done by a bilinear interpolation + * between four different mappings in order to eliminate boundary artifacts. + * It uses a division; since division is often an expensive operation, I added code to + * perform a logical shift instead when feasible. + */ +{ + const unsigned int uiIncr = uiXRes-uiXSize; /* Pointer increment after processing row */ + kz_pixel_t GreyValue; unsigned int uiNum = uiXSize*uiYSize; /* Normalization factor */ + + unsigned int uiXCoef, uiYCoef, uiXInvCoef, uiYInvCoef, uiShift = 0; + + if (uiNum & (uiNum - 1)) /* If uiNum is not a power of two, use division */ + for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; + uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { + for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; + uiXCoef++, uiXInvCoef--) { + GreyValue = pLUT[*pImage]; /* get histogram bin value */ + *pImage++ = (kz_pixel_t ) ((uiYInvCoef * (uiXInvCoef*pulMapLU[GreyValue] + + uiXCoef * pulMapRU[GreyValue]) + + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] + + uiXCoef * pulMapRB[GreyValue])) / uiNum); + } + } + else { /* avoid the division and use a right shift instead */ + while (uiNum >>= 1) uiShift++; /* Calculate 2log of uiNum */ + for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; + uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { + for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; + uiXCoef++, uiXInvCoef--) { + GreyValue = pLUT[*pImage]; /* get histogram bin value */ + *pImage++ = (kz_pixel_t)((uiYInvCoef* (uiXInvCoef * pulMapLU[GreyValue] + + uiXCoef * pulMapRU[GreyValue]) + + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] + + uiXCoef * pulMapRB[GreyValue])) >> uiShift); + } + } + } +} + diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 5a722308..71869915 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -2,10 +2,13 @@ import numpy as np import skimage from skimage.util.dtype import dtype_range +from skimage.color import rgb2gray +from skimage.util.dtype import convert +from _adapthist import _adapthist __all__ = ['histogram', 'cumulative_distribution', 'equalize', - 'rescale_intensity'] + 'rescale_intensity', 'adapthist'] def histogram(image, nbins=256): @@ -183,3 +186,56 @@ def rescale_intensity(image, in_range=None, out_range=None): image = (image - imin) / float(imax - imin) return dtype(image * (omax - omin) + omin) + + +def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): + '''Contrast Limited Adaptive Histogram Equalization + + Parameters + ---------- + image : array-like + original image + nx : int, optional + Tile regions in the X direction (2, 16) + ny : int, optional + Tile regions in the Y direction (2, 16) + clip_limit : float: optional + Normalized cliplimit (higher values give more contrast) + nbins : int, optional + Greybins for histogram ("dynamic range") + out_range : str, optional + Range of the output image data. + - 'original' - Use original image limits + - 'full' - Use full range of image data type + + Returns + ------- + out - np.ndarray : + equalized image - may be a different shape than the original + ''' + in_type = image.dtype.type + if out_range == 'full': + out_range = None + else: + out_range = (image.min(), image.max()) + # must be converted to 12 bit for CLAHE + image = skimage.img_as_uint(image) + MAX_VAL = 2 ** 12 - 1 + image = rescale_intensity(image, out_range=(0, MAX_VAL)) + # handle color images - CLAHE accepts scalar images only + args = [image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] + if image.ndim == 3: + image = image[:, :, :3] + for channel in range(3): + args[0] = image[:, :, channel] + out = _adapthist(*args) + image[:out.shape[0], :out.shape[1], channel] = out + else: + out = _adapthist(*args) + image[:out.shape[0], :out.shape[1]] = out + # restore to desired output type and output limits + image = rescale_intensity(image) + if in_type != np.uint16: + image = convert(image, in_type) + image = rescale_intensity(image, out_range=out_range) + return image diff --git a/skimage/exposure/setup.py b/skimage/exposure/setup.py new file mode 100644 index 00000000..43bab23f --- /dev/null +++ b/skimage/exposure/setup.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python + +import os +from skimage._build import cython + +base_path = os.path.abspath(os.path.dirname(__file__)) + + +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs + + config = Configuration('exposure', parent_package, top_path) + config.add_data_dir('tests') + + cython(['_adapthist.pyx'], working_path=base_path) + + config.add_extension('_adapthist', sources=['_adapthist.c'], + include_dirs=[get_numpy_include_dirs()]) + + return config + +if __name__ == '__main__': + from numpy.distutils.core import setup + setup(maintainer='scikits-image developers', + author='scikits-image developers', + maintainer_email='scikits-image@googlegroups.com', + description='Exposure', + url='https://github.com/scikits-image/scikits-image', + license='SciPy License (BSD Style)', + **(configuration(top_path='').todict()) + ) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index b6ed0b12..fbad207b 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -4,6 +4,7 @@ from numpy.testing import assert_array_almost_equal as assert_close import skimage from skimage import data from skimage import exposure +from skimage.color import rgb2gray # Test histogram equalization @@ -71,6 +72,76 @@ def test_rescale_out_range(): assert_close(out, [0, 63, 127]) +# Test rescale intensity +# ====================== + +def test_adapthist_ubyte(): + '''Test a scalar uint8 image + ''' + img = skimage.img_as_ubyte(data.moon()) + adapted = exposure.adapthist(img, clip_limit=0.02) + assert adapted.min() == 0 + assert adapted.max() == 255 + assert img.shape == adapted.shape + assert peak_snr(img, adapted) > 22 + assert norm_brightness_err(img, adapted) < 0.05 + return img, adapted + + +def test_adapthist_float(): + '''Test an RGB float image + ''' + img = skimage.img_as_float(data.lena()) + adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, + nbins=128, out_range='original') + assert_almost_equal(adapted.min() , img.min()) + assert_almost_equal(adapted.min(), img.min()) + assert img.shape == adapted.shape + assert peak_snr(img, adapted) > 136 + assert norm_brightness_err(img, adapted) < 0.02 + return data, adapted + + +def peak_snr(img1, img2): + '''Peak signal to noise ratio of two images + + Parameters + ---------- + img1 : array-like + img2 : array-like + + Returns + ------- + peak_snr : float + Peak signal to noise ratio + ''' + if img1.ndim == 3: + img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy()) + mse = 1. / img1.size * np.square(img1 - img2).sum() + _, max_ = dtype_range[img1.dtype.type] + return 20 * np.log(max_ / mse) + + +def norm_brightness_err(img1, img2): + '''Normalized Absolute Mean Brightness Error between two images + + Parameters + ---------- + img1 : array-like + img2 : array-like + + Returns + ------- + norm_brightness_error : float + Normalize absolute mean brightness error + ''' + if img1.ndim == 3: + img1, img2 = rgb2gray(img1), rgb2gray(img2) + ambe = np.abs(img1.mean() - img2.mean()) + nbe = ambe / dtype_range[img1.dtype.type][1] + return nbe + + if __name__ == '__main__': from numpy import testing testing.run_module_suite() diff --git a/skimage/setup.py b/skimage/setup.py index 0afc5bc9..80fdc0cb 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -17,6 +17,7 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('morphology') config.add_subpackage('transform') config.add_subpackage('util') + config.add_subpackage('exposure') def add_test_directories(arg, dirname, fnames): if dirname.split(os.path.sep)[-1] == 'tests': From 819332baa3f624637b52a26c89788b0bf4bb5f74 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:27:24 -0500 Subject: [PATCH 002/625] Tests pass and example works on build. --- doc/examples/plot_equalize.py | 4 ++-- skimage/exposure/__init__.py | 2 +- skimage/exposure/tests/test_exposure.py | 2 ++ 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 2cb9cbea..8b06890a 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -64,7 +64,7 @@ img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) img_eq = exposure.equalize(img) # Adaptive Equalization -img_adapteq = exposure.adapthist(img) +img_adapteq = exposure.adapthist(img, clip_limit=0.03) # Display results @@ -81,7 +81,7 @@ ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Histogram equalization') ax_cdf.set_ylabel('Fraction of total intensity') -ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 3]) +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3]) ax_img.set_title('Adaptive equalization') ax_cdf.set_ylabel('Fraction of total intensity') diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index 7e19d317..a5e6c25a 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,2 +1,2 @@ -from .exposure import histogram, equalize, cumulative_distribution +from .exposure import histogram, equalize, cumulative_distribution, adapthist from .exposure import rescale_intensity diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index fbad207b..963758f3 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -5,6 +5,7 @@ import skimage from skimage import data from skimage import exposure from skimage.color import rgb2gray +from skimage.util.dtype import dtype_range # Test histogram equalization @@ -94,6 +95,7 @@ def test_adapthist_float(): img = skimage.img_as_float(data.lena()) adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') + assert_almost_equal = np.testing.assert_almost_equal assert_almost_equal(adapted.min() , img.min()) assert_almost_equal(adapted.min(), img.min()) assert img.shape == adapted.shape From 045f6279b441045abf7aeec5c32cda68fb2fec92 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:36:39 -0500 Subject: [PATCH 003/625] Added Notes to the docstring. --- skimage/exposure/exposure.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 71869915..df98da1f 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -212,6 +212,14 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): ------- out - np.ndarray : equalized image - may be a different shape than the original + + Notes + ----- + * The underlying algorithm relies on an image whose rows and columns are even multiples of + the number of tiles, so the extra rows and columns are left at their original values, thus + preserving the input image shape. + * For RGB or RGBA images, the algorithm is run on each channel. + * For RGBA images, the original alpha channel is removed. ''' in_type = image.dtype.type if out_range == 'full': From 7dc17334a2431fc617f1467cfa47b274c3344159 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:42:44 -0500 Subject: [PATCH 004/625] Pep8 fixes. --- doc/examples/plot_equalize.py | 2 +- skimage/exposure/exposure.py | 2 +- skimage/exposure/tests/test_exposure.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 8b06890a..e09b5a07 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -26,6 +26,7 @@ import matplotlib.pyplot as plt import numpy as np + def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. @@ -88,4 +89,3 @@ ax_cdf.set_ylabel('Fraction of total intensity') # prevent overlap of y-axis labels plt.subplots_adjust(wspace=0.4) plt.show() - diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index df98da1f..fbc2384e 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -212,7 +212,7 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): ------- out - np.ndarray : equalized image - may be a different shape than the original - + Notes ----- * The underlying algorithm relies on an image whose rows and columns are even multiples of diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 963758f3..9d70969a 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -96,7 +96,7 @@ def test_adapthist_float(): adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(adapted.min() , img.min()) + assert_almost_equal(adapted.min(), img.min()) assert_almost_equal(adapted.min(), img.min()) assert img.shape == adapted.shape assert peak_snr(img, adapted) > 136 From 0e957dab3913f1095b11de58cdab081c94300e77 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Mon, 6 Aug 2012 19:06:57 -0500 Subject: [PATCH 005/625] Replaced generic ACM license with the Graphics Gems IV license. --- skimage/exposure/clahe.h | 31 ++++++++++++------------------- 1 file changed, 12 insertions(+), 19 deletions(-) diff --git a/skimage/exposure/clahe.h b/skimage/exposure/clahe.h index 8b115aa1..a2721b98 100644 --- a/skimage/exposure/clahe.h +++ b/skimage/exposure/clahe.h @@ -5,26 +5,19 @@ ANSI C code from the article * in "Graphics Gems IV", Academic Press, 1994 ============= -ACM Software License Agreement +http://tog.acm.org/resources/GraphicsGems/ -All software, both binary and source published by the Association for Computing Machinery (hereafter, Software) is copyrighted by the Association (hereafter, ACM) and ownership of all right, title and interest in and to the Software remains with ACM. By using or copying the Software, User agrees to abide by the terms of this Agreement. -Noncommercial Use +EULA: The Graphics Gems code is copyright-protected. +In other words, you cannot claim the text of the code as your +own and resell it. Using the code is permitted in any program, +product, or library, non-commercial or commercial. +Giving credit is not required, though is a nice gesture. +The code comes as-is, and if there are any flaws or problems +with any Gems code, nobody involved with Gems - authors, editors, +publishers, or webmasters - are to be held responsible. +Basically, don't be a jerk, and remember that anything free +comes with no guarantee. -The ACM grants to you (hereafter, User) a royalty-free, nonexclusive right to execute, copy, modify and distribute both the binary and source code solely for academic, research and other similar noncommercial uses, subject to the following conditions: - - User acknowledges that the Software is still in the development stage and that it is being supplied "as is," without any support services from ACM. Neither ACM nor the author makes any representations or warranties, express or implied, including, without limitation, any representations or warranties of the merchantability or fitness for any particular purpose, or that the application of the software, will not infringe on any patents or other proprietary rights of others. - ACM shall not be held liable for direct, indirect, incidental or consequential damages arising from any claim by User or any third party with respect to uses allowed under this Agreement, or from any use of the Software. - User agrees to fully indemnify and hold harmless ACM and/or the author(s) of the original work from and against any and all claims, demands, suits, losses, damages, costs and expenses arising out of the User's use of the Software, including, without limitation, arising out of the User's modification of the Software. - User may modify the Software and distribute that modified work to third parties provided that: (a) if posted separately, it clearly acknowledges that it contains material copyrighted by ACM (b) no charge is associated with such copies, (c) User agrees to notify ACM and the Author(s) of the distribution, and (d) User clearly notifies secondary users that such modified work is not the original Software. - User agrees that ACM, the authors of the original work and others may enjoy a royalty-free, non-exclusive license to use, copy, modify and redistribute these modifications to the Software made by the User and distributed to third parties as a derivative work under this agreement. - This agreement will terminate immediately upon User's breach of, or non-compliance with, any of its terms. User may be held liable for any copyright infringement or the infringement of any other proprietary rights in the Software that is caused or facilitated by the User's failure to abide by the terms of this agreement. - This agreement will be construed and enforced in accordance with the law of the state of New York applicable to contracts performed entirely within the State. The parties irrevocably consent to the exclusive jurisdiction of the state or federal courts located in the City of New York for all disputes concerning this agreement. - -Commercial Use - -Any User wishing to make a commercial use of the Software must contact ACM at permissions@acm.org to arrange an appropriate license. Commercial use includes (1) integrating or incorporating all or part of the source code into a product for sale or license by, or on behalf of, User to third parties, or (2) distribution of the binary or source code to third parties for use with a commercial product sold or licensed by, or on behalf of, User. - -Revised 6/98 */ #ifdef BYTE_IMAGE @@ -107,7 +100,7 @@ int CLAHE (kz_pixel_t* pImage, unsigned int uiXRes, unsigned int uiYRes, kz_pixel_t aLUT[uiNR_OF_GREY]; /* lookup table used for scaling of input image */ unsigned long* pulHist, *pulMapArray; /* pointer to histogram and mappings*/ unsigned long* pulLU, *pulLB, *pulRU, *pulRB; /* auxiliary pointers interpolation */ - + if (uiNrX > uiMAX_REG_X) return -1; /* # of regions x-direction too large */ if (uiNrY > uiMAX_REG_Y) return -2; /* # of regions y-direction too large */ if (uiXRes % uiNrX) return -3; /* x-resolution no multiple of uiNrX */ From 55a3f22ba293bec479d70079f4b28a147659d568 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 11 Aug 2012 15:48:18 -0500 Subject: [PATCH 006/625] Improved support for color images using lab transform. --- skimage/exposure/exposure.py | 60 ++++++++++++++++++++----- skimage/exposure/tests/test_exposure.py | 38 ++++++++++++---- 2 files changed, 77 insertions(+), 21 deletions(-) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index fbc2384e..47c98584 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -2,7 +2,7 @@ import numpy as np import skimage from skimage.util.dtype import dtype_range -from skimage.color import rgb2gray +import skimage.color as color from skimage.util.dtype import convert from _adapthist import _adapthist @@ -210,7 +210,7 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): Returns ------- - out - np.ndarray : + out : np.ndarray equalized image - may be a different shape than the original Notes @@ -218,8 +218,17 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): * The underlying algorithm relies on an image whose rows and columns are even multiples of the number of tiles, so the extra rows and columns are left at their original values, thus preserving the input image shape. - * For RGB or RGBA images, the algorithm is run on each channel. + * For grayscale images, CLAHE is performed on one channel, and a grayscale is returned + * For color images, the following steps are performed: + - The image is converted to LAB color space + - The CLAHE algorithm is run on the L channel + - The image is converted back to RGB space and returned * For RGBA images, the original alpha channel is removed. + + References + ---------- + .. [1] http://tog.acm.org/resources/GraphicsGems/ + .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE ''' in_type = image.dtype.type if out_range == 'full': @@ -227,23 +236,50 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): else: out_range = (image.min(), image.max()) # must be converted to 12 bit for CLAHE - image = skimage.img_as_uint(image) + int_image = skimage.img_as_uint(image) MAX_VAL = 2 ** 12 - 1 - image = rescale_intensity(image, out_range=(0, MAX_VAL)) + int_image = rescale_intensity(int_image, out_range=(0, MAX_VAL)) # handle color images - CLAHE accepts scalar images only - args = [image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] + args = [int_image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] if image.ndim == 3: - image = image[:, :, :3] - for channel in range(3): - args[0] = image[:, :, channel] + # check for grayscale + if (np.allclose(image[:, :, 0], image[:, :, 1]) and + np.allclose(image[:, :, 2], image[:, :, 3])): + args[0] = image[:, :, 0] out = _adapthist(*args) - image[:out.shape[0], :out.shape[1], channel] = out + image = int_image[:, :, :3] + for channel in range(3): + image[:out.shape[0], :out.shape[1], channel] = out + # for color images, convert to LAB space for processing + else: + lab_img = color.rgb2lab(skimage.img_as_float(image)) + L_chan = lab_img[:, :, 0] + L_chan /= np.max(np.abs(L_chan)) + L_chan = skimage.img_as_uint(L_chan) + args[0] = rescale_intensity(L_chan, out_range=(0, MAX_VAL)) + new_L = _adapthist(*args).astype(float) + new_L = rescale_intensity(new_L, out_range=(0, 100)) + lab_img[:new_L.shape[0], :new_L.shape[1], 0] = new_L + image = color.lab2rgb(lab_img) + image = rescale_intensity(image, out_range=(0, 1)) else: out = _adapthist(*args) + image = int_image image[:out.shape[0], :out.shape[1]] = out # restore to desired output type and output limits image = rescale_intensity(image) - if in_type != np.uint16: - image = convert(image, in_type) + image = convert(image, in_type) image = rescale_intensity(image, out_range=out_range) return image + +if __name__ == '__main__': + from skimage import data + import matplotlib.pyplot as plt + img = skimage.img_as_uint(data.lena()) + adapted = adapthist(img, nx=10, ny=9, clip_limit=0.01, + nbins=128, out_range='original') + plt.imshow(img) + plt.figure(); plt.imshow(skimage.img_as_ubyte(adapted)) + plt.figure(); plt.imshow(color.lab2rgb(color.rgb2lab(img))) + plt.show() + print 'Done' \ No newline at end of file diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 9d70969a..8ce7c8c6 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -76,7 +76,7 @@ def test_rescale_out_range(): # Test rescale intensity # ====================== -def test_adapthist_ubyte(): +def test_adapthist_scalar(): '''Test a scalar uint8 image ''' img = skimage.img_as_ubyte(data.moon()) @@ -84,23 +84,43 @@ def test_adapthist_ubyte(): assert adapted.min() == 0 assert adapted.max() == 255 assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 22 - assert norm_brightness_err(img, adapted) < 0.05 + full_scale = skimage.exposure.rescale_intensity(img) + assert_almost_equal = np.testing.assert_almost_equal + assert_almost_equal(peak_snr(full_scale, adapted), 22.19920073) + assert_almost_equal(norm_brightness_err(full_scale, adapted), + 0.04161278) return img, adapted -def test_adapthist_float(): - '''Test an RGB float image +def test_adapthist_grayscale(): + '''Test a grayscale float image ''' img = skimage.img_as_float(data.lena()) + img = rgb2gray(img) adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') assert_almost_equal = np.testing.assert_almost_equal assert_almost_equal(adapted.min(), img.min()) - assert_almost_equal(adapted.min(), img.min()) + assert_almost_equal(adapted.max(), img.max()) assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 136 - assert norm_brightness_err(img, adapted) < 0.02 + assert_almost_equal(peak_snr(img, adapted), 131.4962063) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0208805) + return data, adapted + + +def test_adapthist_color(): + '''Test a color uint16 image + ''' + img = skimage.img_as_uint(data.lena()) + adapted = exposure.adapthist(img, clip_limit=0.01) + assert_almost_equal = np.testing.assert_almost_equal + assert adapted.min() == 0 + assert adapted.max() == 65535 + assert img.shape == adapted.shape + full_scale = skimage.exposure.rescale_intensity(img) + assert_almost_equal(peak_snr(full_scale, adapted), 64.29546231) + assert_almost_equal(norm_brightness_err(full_scale, adapted), + 0.181473754) return data, adapted @@ -135,7 +155,7 @@ def norm_brightness_err(img1, img2): Returns ------- norm_brightness_error : float - Normalize absolute mean brightness error + Normalized absolute mean brightness error ''' if img1.ndim == 3: img1, img2 = rgb2gray(img1), rgb2gray(img2) From d9926d8de4fb148dc76a38648757f5149df75316 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 11 Aug 2012 21:28:08 -0500 Subject: [PATCH 007/625] Passed all tests with full coverage, PEP8 and Pylint performed. --- skimage/exposure/exposure.py | 44 ++++++++++--------------- skimage/exposure/tests/test_exposure.py | 1 + 2 files changed, 18 insertions(+), 27 deletions(-) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 47c98584..32231b00 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -188,16 +188,17 @@ def rescale_intensity(image, in_range=None, out_range=None): return dtype(image * (omax - omin) + omin) -def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): +def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256, + out_range='full'): '''Contrast Limited Adaptive Histogram Equalization Parameters ---------- image : array-like original image - nx : int, optional + ntiles_x : int, optional Tile regions in the X direction (2, 16) - ny : int, optional + ntiles_y : int, optional Tile regions in the Y direction (2, 16) clip_limit : float: optional Normalized cliplimit (higher values give more contrast) @@ -237,15 +238,16 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): out_range = (image.min(), image.max()) # must be converted to 12 bit for CLAHE int_image = skimage.img_as_uint(image) - MAX_VAL = 2 ** 12 - 1 - int_image = rescale_intensity(int_image, out_range=(0, MAX_VAL)) + max_val = 2 ** 12 - 1 + int_image = rescale_intensity(int_image, out_range=(0, max_val)) # handle color images - CLAHE accepts scalar images only - args = [int_image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] + args = [int_image.copy(), 0, max_val, ntiles_x, ntiles_y, nbins, + clip_limit] if image.ndim == 3: # check for grayscale if (np.allclose(image[:, :, 0], image[:, :, 1]) and - np.allclose(image[:, :, 2], image[:, :, 3])): - args[0] = image[:, :, 0] + np.allclose(image[:, :, 1], image[:, :, 2])): + args[0] = int_image[:, :, 0] out = _adapthist(*args) image = int_image[:, :, :3] for channel in range(3): @@ -253,13 +255,13 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): # for color images, convert to LAB space for processing else: lab_img = color.rgb2lab(skimage.img_as_float(image)) - L_chan = lab_img[:, :, 0] - L_chan /= np.max(np.abs(L_chan)) - L_chan = skimage.img_as_uint(L_chan) - args[0] = rescale_intensity(L_chan, out_range=(0, MAX_VAL)) - new_L = _adapthist(*args).astype(float) - new_L = rescale_intensity(new_L, out_range=(0, 100)) - lab_img[:new_L.shape[0], :new_L.shape[1], 0] = new_L + l_chan = lab_img[:, :, 0] + l_chan /= np.max(np.abs(l_chan)) + l_chan = skimage.img_as_uint(l_chan) + args[0] = rescale_intensity(l_chan, out_range=(0, max_val)) + new_l = _adapthist(*args).astype(float) + new_l = rescale_intensity(new_l, out_range=(0, 100)) + lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l image = color.lab2rgb(lab_img) image = rescale_intensity(image, out_range=(0, 1)) else: @@ -271,15 +273,3 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): image = convert(image, in_type) image = rescale_intensity(image, out_range=out_range) return image - -if __name__ == '__main__': - from skimage import data - import matplotlib.pyplot as plt - img = skimage.img_as_uint(data.lena()) - adapted = adapthist(img, nx=10, ny=9, clip_limit=0.01, - nbins=128, out_range='original') - plt.imshow(img) - plt.figure(); plt.imshow(skimage.img_as_ubyte(adapted)) - plt.figure(); plt.imshow(color.lab2rgb(color.rgb2lab(img))) - plt.show() - print 'Done' \ No newline at end of file diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 8ce7c8c6..2e64e741 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -97,6 +97,7 @@ def test_adapthist_grayscale(): ''' img = skimage.img_as_float(data.lena()) img = rgb2gray(img) + img = np.dstack((img, img, img)) adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') assert_almost_equal = np.testing.assert_almost_equal From 6546460f47eb90bfbeab06faf9c9c9d51765405d Mon Sep 17 00:00:00 2001 From: Andreas Mueller Date: Sun, 30 Sep 2012 15:53:33 +0100 Subject: [PATCH 008/625] COSMIT some pep8 --- skimage/io/collection.py | 4 +++- skimage/transform/pyramids.py | 10 +++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/skimage/io/collection.py b/skimage/io/collection.py index d420b58d..5a5dfff6 100644 --- a/skimage/io/collection.py +++ b/skimage/io/collection.py @@ -11,6 +11,7 @@ from copy import copy import numpy as np from ._io import imread + def concatenate_images(ic): """Concatenate all images in the image collection into an array. @@ -66,6 +67,7 @@ def alphanumeric_key(s): k = [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)] return k + class MultiImage(object): """A class containing a single multi-frame image. @@ -217,6 +219,7 @@ class MultiImage(object): """ return concatenate_images(self) + class ImageCollection(object): """Load and manage a collection of image files. @@ -428,4 +431,3 @@ class ImageCollection(object): If images in the `ImageCollection` don't have identical shapes. """ return concatenate_images(self) - diff --git a/skimage/transform/pyramids.py b/skimage/transform/pyramids.py index dd274834..ccace457 100644 --- a/skimage/transform/pyramids.py +++ b/skimage/transform/pyramids.py @@ -143,9 +143,9 @@ def pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1, the downscaled images. Note that the first image of the pyramid will be the original, unscaled - image. The total number of images is `max_layer + 1`. In case all layers are - computed, the last image is either a one-pixel image or the image where the - reduction does not change its shape. + image. The total number of images is `max_layer + 1`. In case all layers + are computed, the last image is either a one-pixel image or the image where + the reduction does not change its shape. Parameters ---------- @@ -225,8 +225,8 @@ def pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, Note that the first image of the pyramid will be the difference between the original, unscaled image and its smoothed version. The total number of images is `max_layer + 1`. In case all layers are computed, the last image - is either a one-pixel image or the image where the reduction does not change - its shape. + is either a one-pixel image or the image where the reduction does not + change its shape. Parameters ---------- From f4d59a61aec85d38d63946e6da88804b074b885c Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 30 Sep 2012 10:58:33 -0400 Subject: [PATCH 009/625] PKG: Start 0.8 development cycle. --- bento.info | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/bento.info b/bento.info index affd2473..3e630bc0 100644 --- a/bento.info +++ b/bento.info @@ -1,5 +1,5 @@ Name: scikits-image -Version: 0.7.0 +Version: 0.8.dev0 Summary: Image processing routines for SciPy Url: http://scikits-image.org DownloadUrl: http://github.com/scikits-image/scikits-image diff --git a/setup.py b/setup.py index 89e4a4ac..9823352c 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,7 @@ MAINTAINER_EMAIL = 'stefan@sun.ac.za' URL = 'http://scikits-image.org' LICENSE = 'Modified BSD' DOWNLOAD_URL = 'http://github.com/scikits-image/scikits-image' -VERSION = '0.7.0' +VERSION = '0.8dev' PYTHON_VERSION = (2, 5) DEPENDENCIES = { 'numpy': (1, 6), From 0250bb15f6acf6593ef63af0274ec921e16357f2 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 30 Sep 2012 11:01:36 -0400 Subject: [PATCH 010/625] PKG: Clarify instructions for gh-pages. Also, clean up some whitespace. --- doc/gh-pages.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/gh-pages.py b/doc/gh-pages.py index 849b7053..87baddda 100644 --- a/doc/gh-pages.py +++ b/doc/gh-pages.py @@ -108,7 +108,7 @@ if __name__ == '__main__': shutil.copytree(html_dir, dest) # copy pdf file into tree #shutil.copy(pjoin(pdf_dir, 'scikits.image.pdf'), pjoin(dest, 'scikits.image.pdf')) - + try: cd(pages_dir) status = sh2('git status | head -1') @@ -117,7 +117,7 @@ if __name__ == '__main__': e = 'On %r, git branch is %r, MUST be "gh-pages"' % (pages_dir, branch) raise RuntimeError(e) - sh("touch .nojekyll") + sh("touch .nojekyll") sh('git add .nojekyll') sh('git add index.html') sh('git add %s' % tag) @@ -131,4 +131,4 @@ if __name__ == '__main__': print print 'Now verify the build in: %r' % dest - print "If everything looks good, 'git push'" + print "If everything looks good, run 'git push' inside doc/gh-pages." From e1072ae43ab17de89f0d6e23af8ce903f007a56a Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 30 Sep 2012 11:52:48 -0400 Subject: [PATCH 011/625] PKG: Update release notes. Note: added extra spacing around sublists because docutils will complain otherwise. --- RELEASE.txt | 42 ++++++++++++++++++++++++++++++------------ 1 file changed, 30 insertions(+), 12 deletions(-) diff --git a/RELEASE.txt b/RELEASE.txt index 4821d68d..374742c6 100644 --- a/RELEASE.txt +++ b/RELEASE.txt @@ -2,34 +2,52 @@ How to make a new release of ``skimage`` ======================================== - Update release notes. -- Update the version number in setup.py and bento.info and commit + + - To show a list contributors, run ``doc/release/contributors.sh ``, + where ```` is the first commit since the previous release. + +- Update the version number in ``setup.py`` and ``bento.info`` and commit + - Update the docs: + - Edit ``doc/source/themes/agogo/static/docversions.js`` and commit - - Build a clean version of the docs. Run "make" in the root dir, then - ``rm build -rf; make html`` in the docs. Make sure the random.js + - Build a clean version of the docs. Run ``make`` in the root dir, then + ``rm build -rf; make html`` in the docs. Make sure the ``random.js`` - Run ``make html`` again to copy the newly generated ``random.js`` into place. Double check ``random.js``, otherwise the skimage.org front page gets broken! - - Push upstream using "make gh-pages" + - Build using ``make gh-pages``. + - Push upstream: ``git push`` in ``doc/gh-pages``. + - Add the version number as a tag in git:: - git tag v0.6 + git tag v0.X.0 - Push the new meta-data to github:: - git push --tags origin master + git push --tags origin master -- Publish on PyPi: +- Publish on PyPi:: - python setup.py register - python setup.py sdist upload + python setup.py register + python setup.py sdist upload -- Increase the version number in the setup.py file to ``0.Xdev``. +- Increase the version number + + - In ``setup.py``, set to ``0.Xdev``. + - In ``bento.info``, set to ``0.X.dev0``. - Update the web frontpage: The webpage is kept in a separate repo: scikits-image-web - - ``_templates/sidebar_versions.html`` - - ``index.rst`` + + - Sync your branch with the remote repo: ``git pull``. + If you try to ``make gh-pages`` when your branch is out of sync, it + creates headaches. + - Update stable and development version numbers in + ``_templates/sidebar_versions.html``. + - Add release date to ``index.rst`` under "Announcements". + - Build using ``make gh-pages``. + - Push upstream: ``git push`` in ``gh-pages``. - Post release notes on mailing lists, blog, G+, etc. From 186cab916cdbd7d18cbb78437c914c091e8c45aa Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 30 Sep 2012 17:27:59 -0400 Subject: [PATCH 012/625] PKG: Add attributions to contributors.txt --- CONTRIBUTORS.txt | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 0e3f1250..5f18e821 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -2,9 +2,9 @@ Project coordination - Nicolas Pinto - Colour spaces and filters. + Colour spaces and filters, and image resizing. Shape views: ``util.shape.view_as_windows`` and ``util.shape.view_as_blocks`` - Montage helpers: ``util.montage`` + Montage helpers: ``util.montage``. - Damian Eads Morphological operators @@ -103,6 +103,7 @@ - Nicolas Poilvert Shape views: ``util.shape.view_as_windows`` and ``util.shape.view_as_blocks`` + Image resizing. - Johannes Schönberger Drawing functions, adaptive thresholding, regionprops, geometric @@ -113,3 +114,6 @@ - Joshua Warner Multichannel random walker segmentation. + +- Petter Strandmark + Perimeter calculation in regionprops. From 7a90420d9c23450a281258ba2fd4d57c23c8e178 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Tue, 2 Oct 2012 00:38:56 -0400 Subject: [PATCH 013/625] DOC: Change algorithm for depth of API generation Previously, apigen documented all but the bottom-most level of modules in the package tree. This caused problems when different subpackages had different depths. Instead, just document subpackages (ignore python files, whose public functions should already be loaded into the subpackage). --- doc/tools/apigen.py | 17 +---------------- 1 file changed, 1 insertion(+), 16 deletions(-) diff --git a/doc/tools/apigen.py b/doc/tools/apigen.py index f13f2a1b..49c7e6e3 100644 --- a/doc/tools/apigen.py +++ b/doc/tools/apigen.py @@ -381,16 +381,9 @@ class ApiDocWriter(object): modules.append(package_uri) else: dirnames.remove(dirname) - # Check filenames for modules - for filename in filenames: - module_name = filename[:-3] - module_uri = '.'.join((root_uri, module_name)) - if (self._uri2path(module_uri) and - self._survives_exclude(module_uri, 'module')): - modules.append(module_uri) return sorted(modules) - def write_modules_api(self, modules,outdir): + def write_modules_api(self, modules, outdir): # write the list written_modules = [] for m in modules: @@ -427,14 +420,6 @@ class ApiDocWriter(object): os.mkdir(outdir) # compose list of modules modules = self.discover_modules() - # group modules so we have one less level - module_depth = max([len(item.split('.')) for item in modules]) - # modifying modules in-place, so make a copy - for item in modules[:]: - # Do not treat the .py files all as separate modules. - # Like this, only the objects exported in __all__ get picked up. - if not (len(item.split('.')) < module_depth): - modules.remove(item) self.write_modules_api(modules,outdir) def write_index(self, outdir, froot='gen', relative_to=None): From 0d748f19503aa5872aed2279e8e7e081b14fcc62 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 1 Oct 2012 23:58:59 -0700 Subject: [PATCH 014/625] DOC: Fix typo in release instructions. --- RELEASE.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASE.txt b/RELEASE.txt index 374742c6..96cd2abc 100644 --- a/RELEASE.txt +++ b/RELEASE.txt @@ -12,7 +12,7 @@ How to make a new release of ``skimage`` - Edit ``doc/source/themes/agogo/static/docversions.js`` and commit - Build a clean version of the docs. Run ``make`` in the root dir, then - ``rm build -rf; make html`` in the docs. Make sure the ``random.js`` + ``rm build -rf; make html`` in the docs. - Run ``make html`` again to copy the newly generated ``random.js`` into place. Double check ``random.js``, otherwise the skimage.org front page gets broken! From 127907f5bfc80cc7da7359c15425ebef3569fe54 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 2 Oct 2012 18:46:54 +0200 Subject: [PATCH 015/625] Add new doc theme --- doc/source/_static/default.css_t | 144 - .../agogo/static => _static}/docversions.js | 5 +- .../_static/scikits_image_logo_small.png | Bin 32337 -> 0 bytes doc/source/_templates/layout.html | 6 - doc/source/_templates/localtoc.html | 10 +- doc/source/_templates/navbar.html | 5 + doc/source/_templates/navigation.html | 34 +- doc/source/_templates/versions.html | 18 +- doc/source/conf.py | 11 +- doc/source/themes/agogo/layout.html | 58 - doc/source/themes/agogo/static/agogo.css_t | 754 --- doc/source/themes/agogo/theme.conf | 19 - doc/source/themes/scikit-image/layout.html | 113 + doc/source/themes/scikit-image/search.html | 46 + .../static/css/bootstrap-responsive.css | 1058 +++ .../static/css/bootstrap-responsive.min.css | 9 + .../scikit-image/static/css/bootstrap.css | 5774 +++++++++++++++++ .../scikit-image/static/css/bootstrap.min.css | 9 + .../themes/scikit-image/static/css/custom.css | 196 + .../static/img/glyphicons-halflings-white.png | Bin 0 -> 8777 bytes .../static/img/glyphicons-halflings.png | Bin 0 -> 12799 bytes .../themes/scikit-image/static/img/logo.png | Bin 0 -> 45391 bytes .../scikit-image/static/js/bootstrap.js | 2027 ++++++ .../scikit-image/static/js/bootstrap.min.js | 6 + doc/source/themes/scikit-image/theme.conf | 4 + 25 files changed, 9287 insertions(+), 1019 deletions(-) delete mode 100644 doc/source/_static/default.css_t rename doc/source/{themes/agogo/static => _static}/docversions.js (81%) delete mode 100644 doc/source/_static/scikits_image_logo_small.png delete mode 100644 doc/source/_templates/layout.html create mode 100644 doc/source/_templates/navbar.html delete mode 100644 doc/source/themes/agogo/layout.html delete mode 100644 doc/source/themes/agogo/static/agogo.css_t delete mode 100644 doc/source/themes/agogo/theme.conf create mode 100644 doc/source/themes/scikit-image/layout.html create mode 100644 doc/source/themes/scikit-image/search.html create mode 100644 doc/source/themes/scikit-image/static/css/bootstrap-responsive.css create mode 100644 doc/source/themes/scikit-image/static/css/bootstrap-responsive.min.css create mode 100644 doc/source/themes/scikit-image/static/css/bootstrap.css create mode 100644 doc/source/themes/scikit-image/static/css/bootstrap.min.css create mode 100644 doc/source/themes/scikit-image/static/css/custom.css create mode 100644 doc/source/themes/scikit-image/static/img/glyphicons-halflings-white.png create mode 100644 doc/source/themes/scikit-image/static/img/glyphicons-halflings.png create mode 100644 doc/source/themes/scikit-image/static/img/logo.png create mode 100644 doc/source/themes/scikit-image/static/js/bootstrap.js create mode 100644 doc/source/themes/scikit-image/static/js/bootstrap.min.js create mode 100644 doc/source/themes/scikit-image/theme.conf diff --git a/doc/source/_static/default.css_t b/doc/source/_static/default.css_t deleted file mode 100644 index 4497c5e6..00000000 --- a/doc/source/_static/default.css_t +++ /dev/null @@ -1,144 +0,0 @@ -/* This CSS stylesheet is no longer used. Edit agogo.css instead. */ - -/** - * Sphinx stylesheet -- default theme - * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - */ - -@import url("basic.css"); - -/* -- page layout ----------------------------------------------------------- */ - -body { - font-family: {{ theme_bodyfont }}; - font-size: 100%; - background-color: {{ theme_footerbgcolor }}; - color: #000; - margin: 0; - padding: 0; -} - -div.document { - background-color: {{ theme_sidebarbgcolor }}; -} - -div.documentwrapper { - float: left; - width: 100%; -} - -div.bodywrapper { - margin: 0 0 0 230px; -} - -div.body { - background-color: {{ theme_bgcolor }}; - color: {{ theme_textcolor }}; - padding: 0 20px 30px 20px; - overflow: auto; -} - -{%- if theme_rightsidebar|tobool %} -div.bodywrapper { - margin: 0 230px 0 0; -} -{%- endif %} - -div.footer { - color: {{ theme_footertextcolor }}; - width: 100%; - padding: 9px 0 9px 0; - text-align: center; - font-size: 75%; -} - -div.footer a { - color: {{ theme_footertextcolor }}; - text-decoration: underline; -} - -div.related { - background-color: {{ theme_relbarbgcolor }}; - line-height: 30px; - color: {{ theme_relbartextcolor }}; -} - -div.related a { - color: {{ theme_relbarlinkcolor }}; -} - -div.sphinxsidebar { - {%- if theme_stickysidebar|tobool %} - top: 30px; - margin: 0; - position: fixed; - overflow: auto; - height: 100%; - {%- endif %} - {%- if theme_rightsidebar|tobool %} - float: right; - {%- if theme_stickysidebar|tobool %} - right: 0; - {%- endif %} - {%- endif %} -} - -{%- if theme_stickysidebar|tobool %} -/* this is nice, but it it leads to hidden headings when jumping - to an anchor */ -/* -div.related { - position: fixed; -} - -div.documentwrapper { - margin-top: 30px; -} -*/ -{%- endif %} - -div.sphinxsidebar h3 { - font-family: {{ theme_headfont }}; - color: {{ theme_sidebartextcolor }}; - font-size: 1.4em; - font-weight: normal; - margin: 0; - padding: 0; -} - -div.sphinxsidebar h3 a { - color: {{ theme_sidebartextcolor }}; -} - -div.sphinxsidebar h4 { - font-family: {{ theme_headfont }}; - color: {{ theme_sidebartextcolor }}; - font-size: 1.3em; - font-weight: normal; - margin: 5px 0 0 0; - padding: 0; -} - -div.sphinxsidebar p { - color: {{ theme_sidebartextcolor }}; -} - -div.sphinxsidebar p.topless { - margin: 5px 10px 10px 10px; -} - -div.sphinxsidebar ul { - margin: 10px; - padding: 0; - color: {{ theme_sidebartextcolor }}; -} - -div.sphinxsidebar a { - color: {{ theme_sidebarlinkcolor }}; -} - -div.sphinxsidebar input { - border: 1px solid {{ theme_sidebarlinkcolor }}; - font-family: sans-serif; - font-size: 1em; -} diff --git a/doc/source/themes/agogo/static/docversions.js b/doc/source/_static/docversions.js similarity index 81% rename from doc/source/themes/agogo/static/docversions.js rename to doc/source/_static/docversions.js index 30f17f40..0b98d7dc 100644 --- a/doc/source/themes/agogo/static/docversions.js +++ b/doc/source/_static/docversions.js @@ -1,7 +1,5 @@ function insert_version_links() { - var labels = ['dev', '0.7.0', '0.6', '0.5', '0.4', '0.3']; - - document.write('
    \n'); + var labels = ['dev', '0.7', '0.6', '0.5', '0.4', '0.3']; for (i = 0; i < labels.length; i++){ open_list = '
  • ' @@ -16,5 +14,4 @@ function insert_version_links() { .replace('VERSION', labels[i]) .replace('URL', 'http://scikits-image.org/docs/' + labels[i])); } - document.write('
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    • - {{ super() }} -{% endblock %} - diff --git a/doc/source/_templates/localtoc.html b/doc/source/_templates/localtoc.html index 3a26d6fa..6866649c 100644 --- a/doc/source/_templates/localtoc.html +++ b/doc/source/_templates/localtoc.html @@ -1,8 +1,10 @@ {% if pagename != 'index' %} - {%- if display_toc %} -

    Contents

    - {{ toc }} - {%- endif %} + {%- if display_toc %} +

    Contents

    +
    + {{ toc|replace('
      ', '
        ') }} +
    + {%- endif %} {% endif %} diff --git a/doc/source/_templates/navbar.html b/doc/source/_templates/navbar.html new file mode 100644 index 00000000..c1bcfed0 --- /dev/null +++ b/doc/source/_templates/navbar.html @@ -0,0 +1,5 @@ +
  • Home
    • +
  • Download
    • +
  • Gallery
    • +
  • Documentation
    • +
  • Source
    • diff --git a/doc/source/_templates/navigation.html b/doc/source/_templates/navigation.html index 587d95a1..66244695 100644 --- a/doc/source/_templates/navigation.html +++ b/doc/source/_templates/navigation.html @@ -1,12 +1,22 @@ -{%- block navigation %} - - {% if pagename != 'index' %} -

    Navigation

    -

    - Documentation Home -

    -

     

    - {% endif %} - - -{%- endblock %} +

    {{ _('Navigation') }}

    +
    + +
    +{%- if prev %} +

    {{ _('Previous topic') }}

    +
    + +
    +{%- endif %} +{%- if next %} +

    {{ _('Next topic') }}

    +
    + +
    +{%- endif %} diff --git a/doc/source/_templates/versions.html b/doc/source/_templates/versions.html index a0da9d70..6dbb1962 100644 --- a/doc/source/_templates/versions.html +++ b/doc/source/_templates/versions.html @@ -1,9 +1,9 @@ -{%- block versions %} - -

    Version

    - - - -{%- endblock %} +

    {{ _('Versions') }}

    +
    +
      + + +
    +
    diff --git a/doc/source/conf.py b/doc/source/conf.py index cb22e6f0..bcd1ef77 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -103,8 +103,7 @@ pygments_style = 'sphinx' # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. -html_theme = 'agogo' -html_style = 'agogo.css' +html_theme = 'scikit-image' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the @@ -121,10 +120,6 @@ html_title = 'skimage v%s docs' % version # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -html_logo = "scikits_image_logo_small.png" - # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. @@ -147,9 +142,7 @@ html_static_path = ['_static'] html_sidebars = { '**': ['navigation.html', 'localtoc.html', - 'relations.html', - 'versions.html', - 'searchbox.html'], + 'versions.html'], } # Additional templates that should be rendered to pages, maps page names to diff --git a/doc/source/themes/agogo/layout.html b/doc/source/themes/agogo/layout.html deleted file mode 100644 index 260f06bf..00000000 --- a/doc/source/themes/agogo/layout.html +++ /dev/null @@ -1,58 +0,0 @@ -{# - agogo/layout.html - ~~~~~~~~~~~~~~~~~ - - Sphinx layout template for the agogo theme, originally written - by Andi Albrecht. - - :copyright: Copyright 2007-2010 by the Sphinx team, see AUTHORS. - :license: BSD, see LICENSE for details. -#} -{% extends "basic/layout.html" %} -{% set script_files = script_files + ['_static/docversions.js'] %} - -{% block header %} -
    -
    - {%- block headertitle %} - - {%- if logo %} - - {%- endif %} - {#

    {{ shorttitle|e }}

    #} - {%- endblock %} -
    -
    -{% endblock %} - - - -{% block content %} -
    -
    - {%- block sidebar2 %} - {# We don't want the logo here #} - {%- block sidebarlogo %} {% endblock %} - {{ sidebar() }} - {% endblock %} -
    - {%- block document %} - {{ super() }} - {%- endblock %} -
    -
    -
    -
    -{% endblock %} - -{% block footer %} -
    - {{ super() }} -
    -{% endblock %} - -{% block relbar1 %}{% endblock %} - -{% block relbar2 %}{% endblock %} diff --git a/doc/source/themes/agogo/static/agogo.css_t b/doc/source/themes/agogo/static/agogo.css_t deleted file mode 100644 index f47167c0..00000000 --- a/doc/source/themes/agogo/static/agogo.css_t +++ /dev/null @@ -1,754 +0,0 @@ -/* - * agogo.css_t - * ~~~~~~~~~~~ - * - * Sphinx stylesheet -- agogo theme. - * - * :copyright: Copyright 2007-2010 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -* { - margin: 0px; - padding: 0px; -} - - -div.header-wrapper { - border-top: 0px solid #babdb6; - padding: 1em 1em 0; - min-height: 0px; -} - -body { - font-family: {{ theme_bodyfont }}; - font-size: 10pt; - line-height: 1.4em; - color: black; - background-color: {{ theme_bgcolor }}; -} - - -/* Page layout */ - -div.header, div.content, div.footer { - max-width: {{ theme_pagewidth }}; - margin-left: auto; - margin-right: auto; -} - -div.header-wrapper { - border-bottom: 0px solid #2e3436; -} - - -/* Default body styles */ -a { - color: {{ theme_linkcolor }}; -} - -div.bodywrapper a, div.footer a { - text-decoration: underline; -} - -.clearer { - clear: both; -} - -.left { - float: left; -} - -.right { - float: right; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -h1, h2, h3, h4 { - font-weight: normal; - color: {{ theme_headercolor2 }}; - margin-bottom: .8em; - clear: left; -} - -h1 { - color: {{ theme_headercolor1 }}; - line-height: 1.1em; - text-align: left; -} - -h2 { - padding-bottom: .5em; - color: {{ theme_headercolor1 }}; - border-bottom: 1px solid {{ theme_headercolor1 }}; -} - -a.headerlink { - visibility: hidden; - color: #dddddd; - padding-left: .3em; -} - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink { - visibility: visible; -} - -img { - border: 0; -} - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 2px 7px 1px 7px; - border-left: 0.2em solid black; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -dt:target, .highlighted { - background-color: #fbe54e; -} - -/* Header */ - -div.header {} - -div.header h1 { - font-family: "Trebuchet MS", Helvetica, sans-serif; - font-weight: normal; - font-size: 250%; - letter-spacing: .08em; - line-height: 70px; - margin-bottom: 0; -} - -div.header h1 a { - color: white; -} - -div.header h1 a:hover { - text-decoration: none; -} - -div.header div.rel { - margin-top: 1em; - border-top: 1px solid #AAA; - border-bottom: 1px solid #AAA; - border-radius: 5px; - padding: 3px 1em; -} - -div.header div.rel a { - color: {{ theme_linkcolor }}; - letter-spacing: .05em; - font-weight: bold; -} - -div.logo {} - -img.logo { - border: 0; -} - - -/* Content */ -div.content-wrapper { - background-color: white; - padding: 1em; -} - -div.document { - max-width: {{ theme_documentwidth }}; -} - -div.body { - padding-right: 2em; - min-width: 20em; - overflow: hidden; - font-size: 90%; - text-align: {{ theme_textalign }}; -} - -div.document ul { - margin: 1.5em; - list-style-type: square; -} - -div.document dd { - margin-left: 1.2em; - margin-top: .4em; - margin-bottom: 1em; -} - -div.document .section { - margin-top: 1.7em; -} -div.document .section:first-child { - margin-top: 0px; -} - -div.document div.highlight { - padding: 3px; - background-color: #eeeeec; - border-top: 2px solid #dddddd; - border-bottom: 2px solid #dddddd; - margin-top: .8em; - margin-bottom: .8em; -} - -div.document h2 { - margin-top: .7em; -} - -div.document p { - margin-bottom: 1.5em; -} - -div.document li.toctree-l1 { - margin-bottom: 1em; -} - -div.document .descname { - font-weight: bold; -} - -div.document .docutils.literal { - background-color: #eeeeec; - padding: 1px; -} - -div.document .docutils.xref.literal { - background-color: transparent; - padding: 0px; -} - -div.document blockquote { - margin: 1em; -} - -div.document ol { - margin: 1.5em; -} - - -/* Sidebar */ - -div.sphinxsidebar { - width: {{ theme_sidebarwidth }}; - padding: 0 1em; - float: right; - font-size: .93em; - background-color: white; -} - -div.sphinxsidebar a, div.header a { - text-decoration: none; -} - -div.sphinxsidebar a:hover, div.header a:hover { - text-decoration: underline; -} - -div.sphinxsidebar h3 { - color: #2e3436; - text-transform: uppercase; - font-size: 130%; - letter-spacing: .1em; - margin-bottom: .4em; -} - -div.sphinxsidebar h4 { - margin-bottom: 0; - font-weight: bold; -} - -div.sphinxsidebar .tile { - border: 1px solid #D1DDE2; - border-radius: 10px; - background-color: #E1E8EC; - padding-left: 0.5em; - margin: 1em 0; -} - -div.sphinxsidebar ul { - margin-bottom: 1.5em; - list-style-type: none; -} - -div.sphinxsidebar li.toctree-l1 a { - display: block; - padding: 1px; - border: 1px solid #dddddd; - background-color: #eeeeec; - margin-bottom: .4em; - padding-left: 3px; - color: #2e3436; -} - -div.sphinxsidebar li.toctree-l2 a { - background-color: transparent; - border: none; - margin-left: 1em; - border-bottom: 1px solid #dddddd; -} - -div.sphinxsidebar li.toctree-l3 a { - background-color: transparent; - border: none; - margin-left: 2em; - border-bottom: 1px solid #dddddd; -} - -div.sphinxsidebar li.toctree-l2:last-child a { - border-bottom: none; -} - -div.sphinxsidebar li.toctree-l1.current a { - border-right: 5px solid {{ theme_headerlinkcolor }}; -} - -div.sphinxsidebar li.toctree-l1.current li.toctree-l2 a { - border-right: none; -} - -div.sidebarblock { - padding-bottom: .4em; - border-bottom: 1px solid #AAA; - margin-bottom: .8em; -} - -ul.versions li { - color: gray; - list-style: circle inside; -} - -ul.versions li#current { - list-style: disc inside; -} - -/* Footer */ - -div.footer-wrapper { - padding-top: 10px; - padding-bottom: 10px; -} - -div.footer { - border-top: 2px solid #aaa; - text-align: right; -} - -div.footer, div.footer a { - color: #888a85; -} - -.figure { - float: left; - margin: 1em; -} - -.figure img { - display: block; - margin-left: auto; - margin-right: auto; - max-height: 150px; -} - -.figure .caption { - width: 200px; - text-align: center !important; -} - - -/* Styles copied from basic theme */ - -img.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - clear: both; - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li div.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable p.biglink { - line-height: 150%; -} - -table.contentstable, table.contentstable td, table.contentstable th { - border-style: none; -} - -div.body table.contentstable p { - margin: 0.5em; - text-align: left; -} - -table.contentstable a { - text-decoration: none; - font-size: 120%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable dl, table.indextable dd { - margin-top: 0; - margin-bottom: 0; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -/* -- viewcode extension ---------------------------------------------------- */ - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family:: {{ theme_bodyfont }}; -} - -div.viewcode-block:target { - margin: -1px -3px; - padding: 0 3px; - background-color: #f4debf; - border-top: 1px solid #ac9; - border-bottom: 1px solid #ac9; -} - -span.strike { text-decoration: line-through; } - -/* -- body styles ----------------------------------------------------------- */ - -a { - color: {{ theme_linkcolor }}; - text-decoration: none; -} - -a:visited { - color: {{ theme_visitedlinkcolor }}; - text-decoration: none; -} - -a:hover { - text-decoration: underline; -} - -div.body h1, -div.body h2, -div.body h3, -div.body h4, -div.body h5, -div.body h6 { - font-family: {{ theme_headfont }}; - background-color: {{ theme_headbgcolor }}; - font-weight: normal; - color: {{ theme_headtextcolor }}; - border-bottom: 1px solid #ccc; - margin: 20px 20px 10px 0px; - padding: 3px 0 3px 0px; -} - -div.body h1 { margin-top: 0; font-size: 180%; } -div.body h2 { font-size: 150%; } -div.body h3 { font-size: 130%; } -div.body h4 { font-size: 120%; } -div.body h5 { font-size: 110%; } -div.body h6 { font-size: 100%; } - -a.headerlink { - color: {{ theme_headlinkcolor }}; - font-size: 0.8em; - padding: 0 4px 0 4px; - text-decoration: none; -} - -a.headerlink:hover { - background-color: {{ theme_headlinkcolor }}; - color: white; -} - -div.body p, div.body dd, div.body li { - text-align: justify; - line-height: 130%; -} - -div.admonition p.admonition-title + p { - display: inline; -} - -div.note { - background-color: #eee; - border: 1px solid #ccc; -} - -div.seealso { - background-color: #ffc; - border: 1px solid #ff6; -} - -div.topic { - background-color: #eee; -} - -div.warning { - background-color: #ffe4e4; - border: 1px solid #f66; -} - -p.admonition-title { - display: inline; -} - -p.admonition-title:after { - content: ":"; -} - -pre { - padding: 1em; - margin-bottom: 0.5em; - background-color: {{ theme_codebgcolor }}; - color: {{ theme_codetextcolor }}; - line-height: 120%; - border: 0px solid #ac9; - border-left: none; - border-right: none; -} - -sup { - font-size: x-small; - line-height: 0; -} - -tt { - background-color: #ecf0f3; - padding: 0 1px 0 1px; - font-size: 1em; -} - -table { - border-collapse: collapse; - margin-bottom: 1em; - margin-top: 1em; -} - -table, th, td { - border: 1px solid #ccc; -} - -th, td { - padding: 5px; -} - -th { - color: #333; - background-color: #eee; -} - -#api-reference ul:first-child { - float: left; - width: 35em; - margin-top: 0; - padding-top: 0; - list-style: none; - overflow: auto; -} - -#api-reference li { - float: left; - position: relative; - margin-right: 1em; - width: 17em; - padding: 0; -} - -.field-list { - font-size: 80%; -} - -/* ----------------- Example Gallery ----------------- */ - -.gallery { - height: 200px; -} - -.gallery p.caption a{ - text-decoration: none; -} - -/* ----------------- Coverage States ----------------- */ -span.missing{ - color: #000; - background-color: #ff5840; - border-color: #A77272; - font-weight: bold; -} -span.partial{ - color: #806600; - background-color: #ffc343; - font-weight: bold; -} -span.done{ - color: #106600; - background-color: #60f030; - border-color: #4F8530; - font-weight: bold; -} -span.na{ - color: #A8A8A8; - border-color: #4F8530; -} - -table.coverage { - border: solid 1px; -} - -td.missing-bar{ - color: #ff5840; - background-color: #ff5840; - border-color: #A77272; - font-weight: normal; - font-style: normal; -} -td.partial-bar{ - color: #ffc343; - background-color: #ffc343; - font-weight: normal; - font-style: normal; -} -td.done-bar{ - color: #60f030; - background-color: #60f030; - border-color: #4F8530; - font-weight: normal; - font-style: normal; -} -td.na-bar{ - color: #FFF; - background-color: #FFF; - border-color: #4F8530; - font-weight: normal; - font-style: normal; -} - -/* Adjust doc headers such as Notes, References, etc. */ -p.rubric { - font-weight: bold; - font-size: 120%; -} - -/* Math */ -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} diff --git a/doc/source/themes/agogo/theme.conf b/doc/source/themes/agogo/theme.conf deleted file mode 100644 index a991e5c0..00000000 --- a/doc/source/themes/agogo/theme.conf +++ /dev/null @@ -1,19 +0,0 @@ -[theme] -inherit = basic -stylesheet = agogo.css -pygments_style = tango - -[options] -bodyfont = "Verdana", Arial, sans-serif -pagewidth = 70em -documentwidth = 55em -sidebarwidth = 14em -bgcolor = white -headerbg = url(bgtop.png) top left repeat-x -footerbg = url(bgfooter.png) top left repeat-x -linkcolor = #FC852B -headercolor1 = #555 -headercolor2 = #555 -headerlinkcolor = #fcaf3e -codebgcolor = #EEE -textalign = justify diff --git a/doc/source/themes/scikit-image/layout.html b/doc/source/themes/scikit-image/layout.html new file mode 100644 index 00000000..8fc325cb --- /dev/null +++ b/doc/source/themes/scikit-image/layout.html @@ -0,0 +1,113 @@ +{# + scikit-image/layout.html + ~~~~~~~~~~~~~~~~~ + + Sphinx layout template for the scikit-image theme, written by + Johannes Schönberger. + +#} + +{%- set url_root = pathto('', 1) %} +{# XXX necessary? #} +{%- if url_root == '#' %}{% set url_root = '' %}{% endif %} +{%- if not embedded and docstitle %} + {%- set titlesuffix = " — "|safe + docstitle|e %} +{%- else %} + {%- set titlesuffix = "" %} +{%- endif %} + +{%- macro script() %} + + + + {%- for scriptfile in script_files %} + + {%- endfor %} +{%- endmacro %} + +{%- macro css() %} + + + + + {%- for cssfile in css_files %} + + {%- endfor %} +{%- endmacro %} + + + + + {%- block htmltitle %} + {{ title|striptags|e }}{{ titlesuffix }} + {%- endblock %} + {{ metatags }} + {{ css() }} + {{ script() }} + {%- if hasdoc('about') %} + + {%- endif %} + {%- if hasdoc('genindex') %} + + {%- endif %} + {%- if hasdoc('search') %} + + {%- endif %} + {%- if hasdoc('copyright') %} + + {%- endif %} + + {%- if parents %} + + {%- endif %} + {%- if next %} + + {%- endif %} + {%- if prev %} + + {%- endif %} + + + {%- block extrahead %}{% endblock %} + + + +
    +
    +
    +
      + {% include 'navbar.html' %} +
    +
    + + + +
    +
    +
    +
    +
    + {% block body %}{% endblock %} +
    +
    + {%- for sidebartemplate in sidebars %} + {%- include sidebartemplate %} + {%- endfor %} +
    +
    +
    + + © Copyright the scikits-image development team. + Created using Bootstrap and Sphinx. + +
    + + diff --git a/doc/source/themes/scikit-image/search.html b/doc/source/themes/scikit-image/search.html new file mode 100644 index 00000000..61520e6b --- /dev/null +++ b/doc/source/themes/scikit-image/search.html @@ -0,0 +1,46 @@ +{# + basic/search.html + ~~~~~~~~~~~~~~~~~ + + Template for the search page. + + :copyright: Copyright 2007-2011 by the Sphinx team, see AUTHORS. + :license: BSD, see LICENSE for details. +#} +{% extends "layout.html" %} +{% set title = _('Search') %} +{% set script_files = script_files + ['_static/searchtools.js'] %} +{% set script_files = script_files + ['searchindex.js'] %} +{% block body %} +

    {{ _('Search') }}

    +
    + +

    + {% trans %}Please activate JavaScript to enable the search + functionality.{% endtrans %} +

    +
    +

    + {% trans %}From here you can search these documents. Enter your search + words into the box in the navigation bar and press "Enter". Note that the + search function will automatically search for all of the words. Pages + containing fewer words won't appear in the result list.{% endtrans %} +

    + {% if search_performed %} + {% if not search_results %} +

    {{ _('Your search did not match any results.') }}

    + {% endif %} + {% endif %} +
    + {% if search_results %} +
      + {% for href, caption, context in search_results %} +
    • {{ caption }}
      • + {% endfor %} +
    + {% endif %} +
    +{% endblock %} diff --git a/doc/source/themes/scikit-image/static/css/bootstrap-responsive.css b/doc/source/themes/scikit-image/static/css/bootstrap-responsive.css new file mode 100644 index 00000000..9259d26d --- /dev/null +++ b/doc/source/themes/scikit-image/static/css/bootstrap-responsive.css @@ -0,0 +1,1058 @@ +/*! + * Bootstrap Responsive v2.1.1 + * + * Copyright 2012 Twitter, Inc + * Licensed under the Apache License v2.0 + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Designed and built with all the love in the world @twitter by @mdo and @fat. + */ + +.clearfix { + *zoom: 1; +} + +.clearfix:before, +.clearfix:after { + display: table; + line-height: 0; + content: ""; +} + +.clearfix:after { + clear: both; +} + +.hide-text { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} + +.input-block-level { + display: block; + width: 100%; + min-height: 30px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +.hidden { + display: none; + visibility: hidden; +} + +.visible-phone { + display: none !important; +} + +.visible-tablet { + display: none !important; +} + +.hidden-desktop { + display: none !important; +} + +.visible-desktop { + display: inherit !important; +} + +@media (min-width: 768px) and (max-width: 979px) { + .hidden-desktop { + display: inherit !important; + } + .visible-desktop { + display: none !important ; + } + .visible-tablet { + display: inherit !important; + } + .hidden-tablet { + display: none !important; + } +} + +@media (max-width: 767px) { + .hidden-desktop { + display: inherit !important; + } + .visible-desktop { + display: none !important; + } + .visible-phone { + display: inherit !important; + } + .hidden-phone { + display: none !important; + } +} + +@media (min-width: 1200px) { + .row { + margin-left: -30px; + *zoom: 1; + } + .row:before, + .row:after { + display: table; + line-height: 0; + content: ""; + } + .row:after { + clear: both; + } + [class*="span"] { + float: left; + min-height: 1px; + margin-left: 30px; + } + .container, + .navbar-static-top .container, + .navbar-fixed-top .container, + .navbar-fixed-bottom .container { + width: 1170px; + } + .span12 { + width: 1170px; + } + .span11 { + width: 1070px; + } + .span10 { + width: 970px; + } + .span9 { + width: 870px; + } + .span8 { + width: 770px; + } + .span7 { + width: 670px; + } + .span6 { + width: 570px; + } + .span5 { + width: 470px; + } + .span4 { + width: 370px; + } + .span3 { + width: 270px; + } + .span2 { + width: 170px; + } + .span1 { + width: 70px; + } + .offset12 { + margin-left: 1230px; + } + .offset11 { + margin-left: 1130px; + } + .offset10 { + margin-left: 1030px; + } + .offset9 { + margin-left: 930px; + } + .offset8 { + margin-left: 830px; + } + .offset7 { + margin-left: 730px; + } + .offset6 { + margin-left: 630px; + } + .offset5 { + margin-left: 530px; + } + .offset4 { + margin-left: 430px; + } + .offset3 { + margin-left: 330px; + } + .offset2 { + margin-left: 230px; + } + .offset1 { + margin-left: 130px; + } + .row-fluid { + width: 100%; + *zoom: 1; + } + .row-fluid:before, + .row-fluid:after { + display: table; + line-height: 0; + content: ""; + } + .row-fluid:after { + clear: both; + } + .row-fluid [class*="span"] { + display: block; + float: left; + width: 100%; + min-height: 30px; + margin-left: 2.564102564102564%; + *margin-left: 2.5109110747408616%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .row-fluid [class*="span"]:first-child { + margin-left: 0; + } + .row-fluid .span12 { + width: 100%; + *width: 99.94680851063829%; + } + .row-fluid .span11 { + width: 91.45299145299145%; + *width: 91.39979996362975%; + } + .row-fluid .span10 { + width: 82.90598290598291%; + *width: 82.8527914166212%; + } + .row-fluid .span9 { + width: 74.35897435897436%; + *width: 74.30578286961266%; + } + .row-fluid .span8 { + width: 65.81196581196582%; + *width: 65.75877432260411%; + } + .row-fluid .span7 { + width: 57.26495726495726%; + *width: 57.21176577559556%; + } + .row-fluid .span6 { + width: 48.717948717948715%; + *width: 48.664757228587014%; + } + .row-fluid .span5 { + width: 40.17094017094017%; + *width: 40.11774868157847%; + } + .row-fluid .span4 { + width: 31.623931623931625%; + *width: 31.570740134569924%; + } + .row-fluid .span3 { + width: 23.076923076923077%; + *width: 23.023731587561375%; + } + .row-fluid .span2 { + width: 14.52991452991453%; + *width: 14.476723040552828%; + } + .row-fluid .span1 { + width: 5.982905982905983%; + *width: 5.929714493544281%; + } + .row-fluid .offset12 { + margin-left: 105.12820512820512%; + *margin-left: 105.02182214948171%; + } + .row-fluid .offset12:first-child { + margin-left: 102.56410256410257%; + *margin-left: 102.45771958537915%; + } + .row-fluid .offset11 { + margin-left: 96.58119658119658%; + *margin-left: 96.47481360247316%; + } + .row-fluid .offset11:first-child { + margin-left: 94.01709401709402%; + *margin-left: 93.91071103837061%; + } + .row-fluid .offset10 { + margin-left: 88.03418803418803%; + *margin-left: 87.92780505546462%; + } + .row-fluid .offset10:first-child { + margin-left: 85.47008547008548%; + *margin-left: 85.36370249136206%; + } + .row-fluid .offset9 { + margin-left: 79.48717948717949%; + *margin-left: 79.38079650845607%; + } + .row-fluid .offset9:first-child { + margin-left: 76.92307692307693%; + *margin-left: 76.81669394435352%; + } + .row-fluid .offset8 { + margin-left: 70.94017094017094%; + *margin-left: 70.83378796144753%; + } + .row-fluid .offset8:first-child { + margin-left: 68.37606837606839%; + *margin-left: 68.26968539734497%; + } + .row-fluid .offset7 { + margin-left: 62.393162393162385%; + *margin-left: 62.28677941443899%; + } + .row-fluid .offset7:first-child { + margin-left: 59.82905982905982%; + *margin-left: 59.72267685033642%; + } + .row-fluid .offset6 { + margin-left: 53.84615384615384%; + *margin-left: 53.739770867430444%; + } + .row-fluid .offset6:first-child { + margin-left: 51.28205128205128%; + *margin-left: 51.175668303327875%; + } + .row-fluid .offset5 { + margin-left: 45.299145299145295%; + *margin-left: 45.1927623204219%; + } + .row-fluid .offset5:first-child { + margin-left: 42.73504273504273%; + *margin-left: 42.62865975631933%; + } + .row-fluid .offset4 { + margin-left: 36.75213675213675%; + *margin-left: 36.645753773413354%; + } + .row-fluid .offset4:first-child { + margin-left: 34.18803418803419%; + *margin-left: 34.081651209310785%; + } + .row-fluid .offset3 { + margin-left: 28.205128205128204%; + *margin-left: 28.0987452264048%; + } + .row-fluid .offset3:first-child { + margin-left: 25.641025641025642%; + *margin-left: 25.53464266230224%; + } + .row-fluid .offset2 { + margin-left: 19.65811965811966%; + *margin-left: 19.551736679396257%; + } + .row-fluid .offset2:first-child { + margin-left: 17.094017094017094%; + *margin-left: 16.98763411529369%; + } + .row-fluid .offset1 { + margin-left: 11.11111111111111%; + *margin-left: 11.004728132387708%; + } + .row-fluid .offset1:first-child { + margin-left: 8.547008547008547%; + *margin-left: 8.440625568285142%; + } + input, + textarea, + .uneditable-input { + margin-left: 0; + } + .controls-row [class*="span"] + [class*="span"] { + margin-left: 30px; + } + input.span12, + textarea.span12, + .uneditable-input.span12 { + width: 1156px; + } + input.span11, + textarea.span11, + .uneditable-input.span11 { + width: 1056px; + } + input.span10, + textarea.span10, + .uneditable-input.span10 { + width: 956px; + } + input.span9, + textarea.span9, + .uneditable-input.span9 { + width: 856px; + } + input.span8, + textarea.span8, + .uneditable-input.span8 { + width: 756px; + } + input.span7, + textarea.span7, + .uneditable-input.span7 { + width: 656px; + } + input.span6, + textarea.span6, + .uneditable-input.span6 { + width: 556px; + } + input.span5, + textarea.span5, + .uneditable-input.span5 { + width: 456px; + } + input.span4, + textarea.span4, + .uneditable-input.span4 { + width: 356px; + } + input.span3, + textarea.span3, + .uneditable-input.span3 { + width: 256px; + } + input.span2, + textarea.span2, + .uneditable-input.span2 { + width: 156px; + } + input.span1, + textarea.span1, + .uneditable-input.span1 { + width: 56px; + } + .thumbnails { + margin-left: -30px; + } + .thumbnails > li { + margin-left: 30px; + } + .row-fluid .thumbnails { + margin-left: 0; + } +} + +@media (min-width: 768px) and (max-width: 979px) { + .row { + margin-left: -20px; + *zoom: 1; + } + .row:before, + .row:after { + display: table; + line-height: 0; + content: ""; + } + .row:after { + clear: both; + } + [class*="span"] { + float: left; + min-height: 1px; + margin-left: 20px; + } + .container, + .navbar-static-top .container, + .navbar-fixed-top .container, + .navbar-fixed-bottom .container { + width: 724px; + } + .span12 { + width: 724px; + } + .span11 { + width: 662px; + } + .span10 { + width: 600px; + } + .span9 { + width: 538px; + } + .span8 { + width: 476px; + } + .span7 { + width: 414px; + } + .span6 { + width: 352px; + } + .span5 { + width: 290px; + } + .span4 { + width: 228px; + } + .span3 { + width: 166px; + } + .span2 { + width: 104px; + } + .span1 { + width: 42px; + } + .offset12 { + margin-left: 764px; + } + .offset11 { + margin-left: 702px; + } + .offset10 { + margin-left: 640px; + } + .offset9 { + margin-left: 578px; + } + .offset8 { + margin-left: 516px; + } + .offset7 { + margin-left: 454px; + } + .offset6 { + margin-left: 392px; + } + .offset5 { + margin-left: 330px; + } + .offset4 { + margin-left: 268px; + } + .offset3 { + margin-left: 206px; + } + .offset2 { + margin-left: 144px; + } + .offset1 { + margin-left: 82px; + } + .row-fluid { + width: 100%; + *zoom: 1; + } + .row-fluid:before, + .row-fluid:after { + display: table; + line-height: 0; + content: ""; + } + .row-fluid:after { + clear: both; + } + .row-fluid [class*="span"] { + display: block; + float: left; + width: 100%; + min-height: 30px; + margin-left: 2.7624309392265194%; + *margin-left: 2.709239449864817%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .row-fluid [class*="span"]:first-child { + margin-left: 0; + } + .row-fluid .span12 { + width: 100%; + *width: 99.94680851063829%; + } + .row-fluid .span11 { + width: 91.43646408839778%; + *width: 91.38327259903608%; + } + .row-fluid .span10 { + width: 82.87292817679558%; + *width: 82.81973668743387%; + } + .row-fluid .span9 { + width: 74.30939226519337%; + *width: 74.25620077583166%; + } + .row-fluid .span8 { + width: 65.74585635359117%; + *width: 65.69266486422946%; + } + .row-fluid .span7 { + width: 57.18232044198895%; + *width: 57.12912895262725%; + } + .row-fluid .span6 { + width: 48.61878453038674%; + *width: 48.56559304102504%; + } + .row-fluid .span5 { + width: 40.05524861878453%; + *width: 40.00205712942283%; + } + .row-fluid .span4 { + width: 31.491712707182323%; + *width: 31.43852121782062%; + } + .row-fluid .span3 { + width: 22.92817679558011%; + *width: 22.87498530621841%; + } + .row-fluid .span2 { + width: 14.3646408839779%; + *width: 14.311449394616199%; + } + .row-fluid .span1 { + width: 5.801104972375691%; + *width: 5.747913483013988%; + } + .row-fluid .offset12 { + margin-left: 105.52486187845304%; + *margin-left: 105.41847889972962%; + } + .row-fluid .offset12:first-child { + margin-left: 102.76243093922652%; + *margin-left: 102.6560479605031%; + } + .row-fluid .offset11 { + margin-left: 96.96132596685082%; + *margin-left: 96.8549429881274%; + } + .row-fluid .offset11:first-child { + margin-left: 94.1988950276243%; + *margin-left: 94.09251204890089%; + } + .row-fluid .offset10 { + margin-left: 88.39779005524862%; + *margin-left: 88.2914070765252%; + } + .row-fluid .offset10:first-child { + margin-left: 85.6353591160221%; + *margin-left: 85.52897613729868%; + } + .row-fluid .offset9 { + margin-left: 79.8342541436464%; + *margin-left: 79.72787116492299%; + } + .row-fluid .offset9:first-child { + margin-left: 77.07182320441989%; + *margin-left: 76.96544022569647%; + } + .row-fluid .offset8 { + margin-left: 71.2707182320442%; + *margin-left: 71.16433525332079%; + } + .row-fluid .offset8:first-child { + margin-left: 68.50828729281768%; + *margin-left: 68.40190431409427%; + } + .row-fluid .offset7 { + margin-left: 62.70718232044199%; + *margin-left: 62.600799341718584%; + } + .row-fluid .offset7:first-child { + margin-left: 59.94475138121547%; + *margin-left: 59.838368402492065%; + } + .row-fluid .offset6 { + margin-left: 54.14364640883978%; + *margin-left: 54.037263430116376%; + } + .row-fluid .offset6:first-child { + margin-left: 51.38121546961326%; + *margin-left: 51.27483249088986%; + } + .row-fluid .offset5 { + margin-left: 45.58011049723757%; + *margin-left: 45.47372751851417%; + } + .row-fluid .offset5:first-child { + margin-left: 42.81767955801105%; + *margin-left: 42.71129657928765%; + } + .row-fluid .offset4 { + margin-left: 37.01657458563536%; + *margin-left: 36.91019160691196%; + } + .row-fluid .offset4:first-child { + margin-left: 34.25414364640884%; + *margin-left: 34.14776066768544%; + } + .row-fluid .offset3 { + margin-left: 28.45303867403315%; + *margin-left: 28.346655695309746%; + } + .row-fluid .offset3:first-child { + margin-left: 25.69060773480663%; + *margin-left: 25.584224756083227%; + } + .row-fluid .offset2 { + margin-left: 19.88950276243094%; + *margin-left: 19.783119783707537%; + } + .row-fluid .offset2:first-child { + margin-left: 17.12707182320442%; + *margin-left: 17.02068884448102%; + } + .row-fluid .offset1 { + margin-left: 11.32596685082873%; + *margin-left: 11.219583872105325%; + } + .row-fluid .offset1:first-child { + margin-left: 8.56353591160221%; + *margin-left: 8.457152932878806%; + } + input, + textarea, + .uneditable-input { + margin-left: 0; + } + .controls-row [class*="span"] + [class*="span"] { + margin-left: 20px; + } + input.span12, + textarea.span12, + .uneditable-input.span12 { + width: 710px; + } + input.span11, + textarea.span11, + .uneditable-input.span11 { + width: 648px; + } + input.span10, + textarea.span10, + .uneditable-input.span10 { + width: 586px; + } + input.span9, + textarea.span9, + .uneditable-input.span9 { + width: 524px; + } + input.span8, + textarea.span8, + .uneditable-input.span8 { + width: 462px; + } + input.span7, + textarea.span7, + .uneditable-input.span7 { + width: 400px; + } + input.span6, + textarea.span6, + .uneditable-input.span6 { + width: 338px; + } + input.span5, + textarea.span5, + .uneditable-input.span5 { + width: 276px; + } + input.span4, + textarea.span4, + .uneditable-input.span4 { + width: 214px; + } + input.span3, + textarea.span3, + .uneditable-input.span3 { + width: 152px; + } + input.span2, + textarea.span2, + .uneditable-input.span2 { + width: 90px; + } + input.span1, + textarea.span1, + .uneditable-input.span1 { + width: 28px; + } +} + +@media (max-width: 767px) { + body { + padding-right: 20px; + padding-left: 20px; + } + .navbar-fixed-top, + .navbar-fixed-bottom, + .navbar-static-top { + margin-right: -20px; + margin-left: -20px; + } + .container-fluid { + padding: 0; + } + .dl-horizontal dt { + float: none; + width: auto; + clear: none; + text-align: left; + } + .dl-horizontal dd { + margin-left: 0; + } + .container { + width: auto; + } + .row-fluid { + width: 100%; + } + .row, + .thumbnails { + margin-left: 0; + } + .thumbnails > li { + float: none; + margin-left: 0; + } + [class*="span"], + .row-fluid [class*="span"] { + display: block; + float: none; + width: 100%; + margin-left: 0; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .span12, + .row-fluid .span12 { + width: 100%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .input-large, + .input-xlarge, + .input-xxlarge, + input[class*="span"], + select[class*="span"], + textarea[class*="span"], + .uneditable-input { + display: block; + width: 100%; + min-height: 30px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .input-prepend input, + .input-append input, + .input-prepend input[class*="span"], + .input-append input[class*="span"] { + display: inline-block; + width: auto; + } + .controls-row [class*="span"] + [class*="span"] { + margin-left: 0; + } + .modal { + position: fixed; + top: 20px; + right: 20px; + left: 20px; + width: auto; + margin: 0; + } + .modal.fade.in { + top: auto; + } +} + +@media (max-width: 480px) { + .nav-collapse { + -webkit-transform: translate3d(0, 0, 0); + } + .page-header h1 small { + display: block; + line-height: 20px; + } + input[type="checkbox"], + input[type="radio"] { + border: 1px solid #ccc; + } + .form-horizontal .control-label { + float: none; + width: auto; + padding-top: 0; + text-align: left; + } + .form-horizontal .controls { + margin-left: 0; + } + .form-horizontal .control-list { + padding-top: 0; + } + .form-horizontal .form-actions { + padding-right: 10px; + padding-left: 10px; + } + .modal { + top: 10px; + right: 10px; + left: 10px; + } + .modal-header .close { + padding: 10px; + margin: -10px; + } + .carousel-caption { + position: static; + } +} + +@media (max-width: 979px) { + body { + padding-top: 0; + } + .navbar-fixed-top, + .navbar-fixed-bottom { + position: static; + } + .navbar-fixed-top { + margin-bottom: 20px; + } + .navbar-fixed-bottom { + margin-top: 20px; + } + .navbar-fixed-top .navbar-inner, + .navbar-fixed-bottom .navbar-inner { + padding: 5px; + } + .navbar .container { + width: auto; + padding: 0; + } + .navbar .brand { + padding-right: 10px; + padding-left: 10px; + margin: 0 0 0 -5px; + } + .nav-collapse { + clear: both; + } + .nav-collapse .nav { + float: none; + margin: 0 0 10px; + } + .nav-collapse .nav > li { + float: none; + } + .nav-collapse .nav > li > a { + margin-bottom: 2px; + } + .nav-collapse .nav > .divider-vertical { + display: none; + } + .nav-collapse .nav .nav-header { + color: #777777; + text-shadow: none; + } + .nav-collapse .nav > li > a, + .nav-collapse .dropdown-menu a { + padding: 9px 15px; + font-weight: bold; + color: #777777; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + } + .nav-collapse .btn { + padding: 4px 10px 4px; + font-weight: normal; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + } + .nav-collapse .dropdown-menu li + li a { + margin-bottom: 2px; + } + .nav-collapse .nav > li > a:hover, + .nav-collapse .dropdown-menu a:hover { + background-color: #f2f2f2; + } + .navbar-inverse .nav-collapse .nav > li > a:hover, + .navbar-inverse .nav-collapse .dropdown-menu a:hover { + background-color: #111111; + } + .nav-collapse.in .btn-group { + padding: 0; + margin-top: 5px; + } + .nav-collapse .dropdown-menu { + position: static; + top: auto; + left: auto; + display: block; + float: none; + max-width: none; + padding: 0; + margin: 0 15px; + background-color: transparent; + border: none; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; + } + .nav-collapse .dropdown-menu:before, + .nav-collapse .dropdown-menu:after { + display: none; + } + .nav-collapse .dropdown-menu .divider { + display: none; + } + .nav-collapse .nav > li > .dropdown-menu:before, + .nav-collapse .nav > li > .dropdown-menu:after { + display: none; + } + .nav-collapse .navbar-form, + .nav-collapse .navbar-search { + float: none; + padding: 10px 15px; + margin: 10px 0; + border-top: 1px solid #f2f2f2; + border-bottom: 1px solid #f2f2f2; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + } + .navbar-inverse .nav-collapse .navbar-form, + .navbar-inverse .nav-collapse .navbar-search { + border-top-color: #111111; + border-bottom-color: #111111; + } + .navbar .nav-collapse .nav.pull-right { + float: none; + margin-left: 0; + } + .nav-collapse, + .nav-collapse.collapse { + height: 0; + overflow: hidden; + } + .navbar .btn-navbar { + display: block; + } + .navbar-static .navbar-inner { + padding-right: 10px; + padding-left: 10px; + } +} + +@media (min-width: 980px) { + .nav-collapse.collapse { + height: auto !important; + overflow: visible !important; + } +} diff --git a/doc/source/themes/scikit-image/static/css/bootstrap-responsive.min.css b/doc/source/themes/scikit-image/static/css/bootstrap-responsive.min.css new file mode 100644 index 00000000..7b0158da --- /dev/null +++ b/doc/source/themes/scikit-image/static/css/bootstrap-responsive.min.css @@ -0,0 +1,9 @@ +/*! + * Bootstrap Responsive v2.1.1 + * + * Copyright 2012 Twitter, Inc + * Licensed under the Apache License v2.0 + * 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.container, +.navbar-fixed-top .container, +.navbar-fixed-bottom .container { + width: 940px; +} + +.span12 { + width: 940px; +} + +.span11 { + width: 860px; +} + +.span10 { + width: 780px; +} + +.span9 { + width: 700px; +} + +.span8 { + width: 620px; +} + +.span7 { + width: 540px; +} + +.span6 { + width: 460px; +} + +.span5 { + width: 380px; +} + +.span4 { + width: 300px; +} + +.span3 { + width: 220px; +} + +.span2 { + width: 140px; +} + +.span1 { + width: 60px; +} + +.offset12 { + margin-left: 980px; +} + +.offset11 { + margin-left: 900px; +} + +.offset10 { + margin-left: 820px; +} + +.offset9 { + margin-left: 740px; +} + +.offset8 { + margin-left: 660px; +} + +.offset7 { + margin-left: 580px; +} + +.offset6 { + margin-left: 500px; +} + +.offset5 { + margin-left: 420px; +} + +.offset4 { + margin-left: 340px; +} + +.offset3 { + margin-left: 260px; +} + +.offset2 { + margin-left: 180px; +} + +.offset1 { + margin-left: 100px; +} + +.row-fluid { + width: 100%; + *zoom: 1; +} + +.row-fluid:before, +.row-fluid:after { + display: table; + line-height: 0; + content: ""; +} + +.row-fluid:after { + clear: both; +} + +.row-fluid [class*="span"] { + display: block; + float: left; + width: 100%; + min-height: 30px; + margin-left: 2.127659574468085%; + *margin-left: 2.074468085106383%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +.row-fluid [class*="span"]:first-child { + margin-left: 0; +} + +.row-fluid .span12 { + width: 100%; + *width: 99.94680851063829%; +} + +.row-fluid .span11 { + width: 91.48936170212765%; + *width: 91.43617021276594%; +} + +.row-fluid .span10 { + width: 82.97872340425532%; + *width: 82.92553191489361%; +} + +.row-fluid .span9 { + width: 74.46808510638297%; + *width: 74.41489361702126%; +} + +.row-fluid .span8 { + width: 65.95744680851064%; + *width: 65.90425531914893%; +} + +.row-fluid .span7 { + width: 57.44680851063829%; + *width: 57.39361702127659%; +} + +.row-fluid .span6 { + width: 48.93617021276595%; + *width: 48.88297872340425%; +} + +.row-fluid .span5 { + width: 40.42553191489362%; + *width: 40.37234042553192%; +} + +.row-fluid .span4 { + width: 31.914893617021278%; + *width: 31.861702127659576%; +} + +.row-fluid .span3 { + width: 23.404255319148934%; + *width: 23.351063829787233%; +} + +.row-fluid .span2 { + width: 14.893617021276595%; + *width: 14.840425531914894%; +} + +.row-fluid .span1 { + width: 6.382978723404255%; + *width: 6.329787234042553%; +} + +.row-fluid .offset12 { + margin-left: 104.25531914893617%; + *margin-left: 104.14893617021275%; +} + +.row-fluid .offset12:first-child { + margin-left: 102.12765957446808%; + *margin-left: 102.02127659574467%; +} + +.row-fluid .offset11 { + margin-left: 95.74468085106382%; + *margin-left: 95.6382978723404%; +} + +.row-fluid .offset11:first-child { + margin-left: 93.61702127659574%; + *margin-left: 93.51063829787232%; +} + +.row-fluid .offset10 { + margin-left: 87.23404255319149%; + *margin-left: 87.12765957446807%; +} + +.row-fluid .offset10:first-child { + margin-left: 85.1063829787234%; + *margin-left: 84.99999999999999%; +} + +.row-fluid .offset9 { + margin-left: 78.72340425531914%; + *margin-left: 78.61702127659572%; +} + +.row-fluid .offset9:first-child { + margin-left: 76.59574468085106%; + *margin-left: 76.48936170212764%; +} + +.row-fluid .offset8 { + margin-left: 70.2127659574468%; + *margin-left: 70.10638297872339%; +} + +.row-fluid .offset8:first-child { + margin-left: 68.08510638297872%; + *margin-left: 67.9787234042553%; +} + +.row-fluid .offset7 { + margin-left: 61.70212765957446%; + *margin-left: 61.59574468085106%; +} + +.row-fluid .offset7:first-child { + margin-left: 59.574468085106375%; + *margin-left: 59.46808510638297%; +} + +.row-fluid .offset6 { + margin-left: 53.191489361702125%; + *margin-left: 53.085106382978715%; +} + +.row-fluid .offset6:first-child { + margin-left: 51.063829787234035%; + *margin-left: 50.95744680851063%; +} + +.row-fluid .offset5 { + margin-left: 44.68085106382979%; + *margin-left: 44.57446808510638%; +} + +.row-fluid .offset5:first-child { + margin-left: 42.5531914893617%; + *margin-left: 42.4468085106383%; +} + +.row-fluid .offset4 { + margin-left: 36.170212765957444%; + *margin-left: 36.06382978723405%; +} + +.row-fluid .offset4:first-child { + margin-left: 34.04255319148936%; + *margin-left: 33.93617021276596%; +} + +.row-fluid .offset3 { + margin-left: 27.659574468085104%; + *margin-left: 27.5531914893617%; +} + +.row-fluid .offset3:first-child { + margin-left: 25.53191489361702%; + *margin-left: 25.425531914893618%; +} + +.row-fluid .offset2 { + margin-left: 19.148936170212764%; + *margin-left: 19.04255319148936%; +} + +.row-fluid .offset2:first-child { + margin-left: 17.02127659574468%; + *margin-left: 16.914893617021278%; +} + +.row-fluid .offset1 { + margin-left: 10.638297872340425%; + *margin-left: 10.53191489361702%; +} + +.row-fluid .offset1:first-child { + margin-left: 8.51063829787234%; + *margin-left: 8.404255319148938%; +} + +[class*="span"].hide, +.row-fluid [class*="span"].hide { + display: none; +} + +[class*="span"].pull-right, +.row-fluid [class*="span"].pull-right { + float: right; +} + +.container { + margin-right: auto; + margin-left: auto; + *zoom: 1; +} + +.container:before, +.container:after { + display: table; + line-height: 0; + content: ""; +} + +.container:after { + clear: both; +} + +.container-fluid { + padding-right: 20px; + padding-left: 20px; + *zoom: 1; +} + +.container-fluid:before, +.container-fluid:after { + display: table; + line-height: 0; + content: ""; +} + +.container-fluid:after { + clear: both; +} + +p { + margin: 0 0 10px; +} + +.lead { + margin-bottom: 20px; + font-size: 21px; + font-weight: 200; + line-height: 30px; +} + +small { + font-size: 85%; +} + +strong { + font-weight: bold; +} + +em { + font-style: italic; +} + +cite { + font-style: normal; +} + +.muted { + color: #999999; +} + +.text-warning { + color: #c09853; +} + +.text-error { + color: #b94a48; +} + +.text-info { + color: #3a87ad; +} + +.text-success { + color: #468847; +} + +h1, +h2, +h3, +h4, +h5, +h6 { + margin: 10px 0; + font-family: inherit; + font-weight: bold; + line-height: 1; + color: inherit; + text-rendering: optimizelegibility; +} + +h1 small, +h2 small, +h3 small, +h4 small, +h5 small, +h6 small { + font-weight: normal; + line-height: 1; + color: #999999; +} + +h1 { + font-size: 36px; + line-height: 40px; +} + +h2 { + font-size: 30px; + line-height: 40px; +} + +h3 { + font-size: 24px; + line-height: 40px; +} + +h4 { + font-size: 18px; + line-height: 20px; +} + +h5 { + font-size: 14px; + line-height: 20px; +} + +h6 { + font-size: 12px; + line-height: 20px; +} + +h1 small { + font-size: 24px; +} + +h2 small { + font-size: 18px; +} + +h3 small { + font-size: 14px; +} + +h4 small { + font-size: 14px; +} + +.page-header { + padding-bottom: 9px; + margin: 20px 0 30px; + border-bottom: 1px solid #eeeeee; +} + +ul, +ol { + padding: 0; + margin: 0 0 10px 25px; +} + +ul ul, +ul ol, +ol ol, +ol ul { + margin-bottom: 0; +} + +li { + line-height: 20px; +} + +ul.unstyled, +ol.unstyled { + margin-left: 0; + list-style: none; +} + +dl { + margin-bottom: 20px; +} + +dt, +dd { + line-height: 20px; +} + +dt { + font-weight: bold; +} + +dd { + margin-left: 10px; +} + +.dl-horizontal { + *zoom: 1; +} + +.dl-horizontal:before, +.dl-horizontal:after { + display: table; + line-height: 0; + content: ""; +} + +.dl-horizontal:after { + clear: both; +} + +.dl-horizontal dt { + float: left; + width: 160px; + overflow: hidden; + clear: left; + text-align: right; + text-overflow: ellipsis; + white-space: nowrap; +} + +.dl-horizontal dd { + margin-left: 180px; +} + +hr { + margin: 20px 0; + border: 0; + border-top: 1px solid #eeeeee; + border-bottom: 1px solid #ffffff; +} + +abbr[title] { + cursor: help; + border-bottom: 1px dotted #999999; +} + +abbr.initialism { + font-size: 90%; + text-transform: uppercase; +} + +blockquote { + padding: 0 0 0 15px; + margin: 0 0 20px; + border-left: 5px solid #eeeeee; +} + +blockquote p { + margin-bottom: 0; + font-size: 16px; + font-weight: 300; + line-height: 25px; +} + +blockquote small { + display: block; + line-height: 20px; + color: #999999; +} + +blockquote small:before { + content: '\2014 \00A0'; +} + +blockquote.pull-right { + float: right; + padding-right: 15px; + padding-left: 0; + border-right: 5px solid #eeeeee; + border-left: 0; +} + +blockquote.pull-right p, +blockquote.pull-right small { + text-align: right; +} + +blockquote.pull-right small:before { + content: ''; +} + +blockquote.pull-right small:after { + content: '\00A0 \2014'; +} + +q:before, +q:after, +blockquote:before, +blockquote:after { + content: ""; +} + +address { + display: block; + margin-bottom: 20px; + font-style: normal; + line-height: 20px; +} + +code, +pre { + padding: 0 3px 2px; + font-family: Monaco, Menlo, Consolas, "Courier New", monospace; + font-size: 12px; + color: #333333; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +code { + padding: 2px 4px; + color: #d14; + background-color: #f7f7f9; + border: 1px solid #e1e1e8; +} + +pre { + display: block; + padding: 9.5px; + margin: 0 0 10px; + font-size: 13px; + line-height: 20px; + word-break: break-all; + word-wrap: break-word; + white-space: pre; + white-space: pre-wrap; + background-color: #f5f5f5; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.15); + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +pre.prettyprint { + margin-bottom: 20px; +} + +pre code { + padding: 0; + color: inherit; + background-color: transparent; + border: 0; +} + +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} + +form { + margin: 0 0 20px; +} + +fieldset { + padding: 0; + margin: 0; + border: 0; +} + +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 20px; + font-size: 21px; + line-height: 40px; + color: #333333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} + +legend small { + font-size: 15px; + color: #999999; +} + +label, +input, +button, +select, +textarea { + font-size: 14px; + font-weight: normal; + line-height: 20px; +} + +input, +button, +select, +textarea { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; +} + +label { + display: block; + margin-bottom: 5px; +} + +select, +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + display: inline-block; + height: 20px; + padding: 4px 6px; + margin-bottom: 9px; + font-size: 14px; + line-height: 20px; + color: #555555; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +input, +textarea, +.uneditable-input { + width: 206px; +} + +textarea { + height: auto; +} + +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + background-color: #ffffff; + border: 1px solid #cccccc; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border linear 0.2s, box-shadow linear 0.2s; + -moz-transition: border linear 0.2s, box-shadow linear 0.2s; + -o-transition: border linear 0.2s, box-shadow linear 0.2s; + transition: border linear 0.2s, box-shadow linear 0.2s; +} + +textarea:focus, +input[type="text"]:focus, +input[type="password"]:focus, +input[type="datetime"]:focus, +input[type="datetime-local"]:focus, +input[type="date"]:focus, +input[type="month"]:focus, +input[type="time"]:focus, +input[type="week"]:focus, +input[type="number"]:focus, +input[type="email"]:focus, +input[type="url"]:focus, +input[type="search"]:focus, +input[type="tel"]:focus, +input[type="color"]:focus, +.uneditable-input:focus { + border-color: rgba(82, 168, 236, 0.8); + outline: 0; + outline: thin dotted \9; + /* IE6-9 */ + + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); +} + +input[type="radio"], +input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + *margin-top: 0; + line-height: normal; + cursor: pointer; +} + +input[type="file"], +input[type="image"], +input[type="submit"], +input[type="reset"], +input[type="button"], +input[type="radio"], +input[type="checkbox"] { + width: auto; +} + +select, +input[type="file"] { + height: 30px; + /* In IE7, the height of the select element cannot be changed by height, only font-size */ + + *margin-top: 4px; + /* For IE7, add top margin to align select with labels */ + + line-height: 30px; +} + +select { + width: 220px; + background-color: #ffffff; + border: 1px solid #cccccc; +} + +select[multiple], +select[size] { + height: auto; +} + +select:focus, +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.uneditable-input, +.uneditable-textarea { + color: #999999; + cursor: not-allowed; + background-color: #fcfcfc; + border-color: #cccccc; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); +} + +.uneditable-input { + overflow: hidden; + white-space: nowrap; +} + +.uneditable-textarea { + width: auto; + height: auto; +} + +input:-moz-placeholder, +textarea:-moz-placeholder { + color: #999999; +} + +input:-ms-input-placeholder, +textarea:-ms-input-placeholder { + color: #999999; +} + +input::-webkit-input-placeholder, +textarea::-webkit-input-placeholder { + color: #999999; +} + +.radio, +.checkbox { + min-height: 18px; + padding-left: 18px; +} + +.radio input[type="radio"], +.checkbox input[type="checkbox"] { + float: left; + margin-left: -18px; +} + +.controls > .radio:first-child, +.controls > .checkbox:first-child { + padding-top: 5px; +} + +.radio.inline, +.checkbox.inline { + display: inline-block; + padding-top: 5px; + margin-bottom: 0; + vertical-align: middle; +} + +.radio.inline + .radio.inline, +.checkbox.inline + .checkbox.inline { + margin-left: 10px; +} + +.input-mini { + width: 60px; +} + +.input-small { + width: 90px; +} + +.input-medium { + width: 150px; +} + +.input-large { + width: 210px; +} + +.input-xlarge { + width: 270px; +} + +.input-xxlarge { + width: 530px; +} + +input[class*="span"], +select[class*="span"], +textarea[class*="span"], +.uneditable-input[class*="span"], +.row-fluid input[class*="span"], +.row-fluid select[class*="span"], +.row-fluid textarea[class*="span"], +.row-fluid .uneditable-input[class*="span"] { + float: none; + margin-left: 0; +} + +.input-append input[class*="span"], +.input-append .uneditable-input[class*="span"], +.input-prepend input[class*="span"], +.input-prepend .uneditable-input[class*="span"], +.row-fluid input[class*="span"], +.row-fluid select[class*="span"], +.row-fluid textarea[class*="span"], +.row-fluid .uneditable-input[class*="span"], +.row-fluid .input-prepend [class*="span"], +.row-fluid .input-append [class*="span"] { + display: inline-block; +} + +input, +textarea, +.uneditable-input { + margin-left: 0; +} + +.controls-row [class*="span"] + [class*="span"] { + margin-left: 20px; +} + +input.span12, +textarea.span12, +.uneditable-input.span12 { + width: 926px; +} + +input.span11, +textarea.span11, +.uneditable-input.span11 { + width: 846px; +} + +input.span10, +textarea.span10, +.uneditable-input.span10 { + width: 766px; +} + +input.span9, +textarea.span9, +.uneditable-input.span9 { + width: 686px; +} + +input.span8, +textarea.span8, +.uneditable-input.span8 { + width: 606px; +} + +input.span7, +textarea.span7, +.uneditable-input.span7 { + width: 526px; +} + +input.span6, +textarea.span6, +.uneditable-input.span6 { + width: 446px; +} + +input.span5, +textarea.span5, +.uneditable-input.span5 { + width: 366px; +} + +input.span4, +textarea.span4, +.uneditable-input.span4 { + width: 286px; +} + +input.span3, +textarea.span3, +.uneditable-input.span3 { + width: 206px; +} + +input.span2, +textarea.span2, +.uneditable-input.span2 { + width: 126px; +} + +input.span1, +textarea.span1, +.uneditable-input.span1 { + width: 46px; +} + +.controls-row { + *zoom: 1; +} + +.controls-row:before, +.controls-row:after { + display: table; + line-height: 0; + content: ""; +} + +.controls-row:after { + clear: both; +} + +.controls-row [class*="span"] { + float: left; +} + +input[disabled], +select[disabled], +textarea[disabled], +input[readonly], +select[readonly], +textarea[readonly] { + cursor: not-allowed; + background-color: #eeeeee; +} + +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"][readonly], +input[type="checkbox"][readonly] { + background-color: transparent; +} + +.control-group.warning > label, +.control-group.warning .help-block, +.control-group.warning .help-inline { + color: #c09853; +} + +.control-group.warning .checkbox, +.control-group.warning .radio, +.control-group.warning input, +.control-group.warning select, +.control-group.warning textarea { + color: #c09853; +} + +.control-group.warning input, +.control-group.warning select, +.control-group.warning textarea { + border-color: #c09853; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.warning input:focus, +.control-group.warning select:focus, +.control-group.warning textarea:focus { + border-color: #a47e3c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; +} + +.control-group.warning .input-prepend .add-on, +.control-group.warning .input-append .add-on { + color: #c09853; + background-color: #fcf8e3; + border-color: #c09853; +} + +.control-group.error > label, +.control-group.error .help-block, +.control-group.error .help-inline { + color: #b94a48; +} + +.control-group.error .checkbox, +.control-group.error .radio, +.control-group.error input, +.control-group.error select, +.control-group.error textarea { + color: #b94a48; +} + +.control-group.error input, +.control-group.error select, +.control-group.error textarea { + border-color: #b94a48; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.error input:focus, +.control-group.error select:focus, +.control-group.error textarea:focus { + border-color: #953b39; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; +} + +.control-group.error .input-prepend .add-on, +.control-group.error .input-append .add-on { + color: #b94a48; + background-color: #f2dede; + border-color: #b94a48; +} + +.control-group.success > label, +.control-group.success .help-block, +.control-group.success .help-inline { + color: #468847; +} + +.control-group.success .checkbox, +.control-group.success .radio, +.control-group.success input, +.control-group.success select, +.control-group.success textarea { + color: #468847; +} + +.control-group.success input, +.control-group.success select, +.control-group.success textarea { + border-color: #468847; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.success input:focus, +.control-group.success select:focus, +.control-group.success textarea:focus { + border-color: #356635; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; +} + +.control-group.success .input-prepend .add-on, +.control-group.success .input-append .add-on { + color: #468847; + background-color: #dff0d8; + border-color: #468847; +} + +.control-group.info > label, +.control-group.info .help-block, +.control-group.info .help-inline { + color: #3a87ad; +} + +.control-group.info .checkbox, +.control-group.info .radio, +.control-group.info input, +.control-group.info select, +.control-group.info textarea { + color: #3a87ad; +} + +.control-group.info input, +.control-group.info select, +.control-group.info textarea { + border-color: #3a87ad; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.info input:focus, +.control-group.info select:focus, +.control-group.info textarea:focus { + border-color: #2d6987; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; +} + +.control-group.info .input-prepend .add-on, +.control-group.info .input-append .add-on { + color: #3a87ad; + background-color: #d9edf7; + border-color: #3a87ad; +} + +input:focus:required:invalid, +textarea:focus:required:invalid, +select:focus:required:invalid { + color: #b94a48; + border-color: #ee5f5b; +} + +input:focus:required:invalid:focus, +textarea:focus:required:invalid:focus, +select:focus:required:invalid:focus { + border-color: #e9322d; + -webkit-box-shadow: 0 0 6px #f8b9b7; + -moz-box-shadow: 0 0 6px #f8b9b7; + box-shadow: 0 0 6px #f8b9b7; +} + +.form-actions { + padding: 19px 20px 20px; + margin-top: 20px; + margin-bottom: 20px; + background-color: #f5f5f5; + border-top: 1px solid #e5e5e5; + *zoom: 1; +} + +.form-actions:before, +.form-actions:after { + display: table; + line-height: 0; + content: ""; +} + +.form-actions:after { + clear: both; +} + +.help-block, +.help-inline { + color: #595959; +} + +.help-block { + display: block; + margin-bottom: 10px; +} + +.help-inline { + display: inline-block; + *display: inline; + padding-left: 5px; + vertical-align: middle; + *zoom: 1; +} + +.input-append, +.input-prepend { + margin-bottom: 5px; + font-size: 0; + white-space: nowrap; +} + +.input-append input, +.input-prepend input, +.input-append select, +.input-prepend select, +.input-append .uneditable-input, +.input-prepend .uneditable-input { + position: relative; + margin-bottom: 0; + *margin-left: 0; + font-size: 14px; + vertical-align: top; + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.input-append input:focus, +.input-prepend input:focus, +.input-append select:focus, +.input-prepend select:focus, +.input-append .uneditable-input:focus, +.input-prepend .uneditable-input:focus { + z-index: 2; +} + +.input-append .add-on, +.input-prepend .add-on { + display: inline-block; + width: auto; + height: 20px; + min-width: 16px; + padding: 4px 5px; + font-size: 14px; + font-weight: normal; + line-height: 20px; + text-align: center; + text-shadow: 0 1px 0 #ffffff; + background-color: #eeeeee; + border: 1px solid #ccc; +} + +.input-append .add-on, +.input-prepend .add-on, +.input-append .btn, +.input-prepend .btn { + vertical-align: top; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-append .active, +.input-prepend .active { + background-color: #a9dba9; + border-color: #46a546; +} + +.input-prepend .add-on, +.input-prepend .btn { + margin-right: -1px; +} + +.input-prepend .add-on:first-child, +.input-prepend .btn:first-child { + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-append input, +.input-append select, +.input-append .uneditable-input { + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-append .add-on, +.input-append .btn { + margin-left: -1px; +} + +.input-append .add-on:last-child, +.input-append .btn:last-child { + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.input-prepend.input-append input, +.input-prepend.input-append select, +.input-prepend.input-append .uneditable-input { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-prepend.input-append .add-on:first-child, +.input-prepend.input-append .btn:first-child { + margin-right: -1px; + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-prepend.input-append .add-on:last-child, +.input-prepend.input-append .btn:last-child { + margin-left: -1px; + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +input.search-query { + padding-right: 14px; + padding-right: 4px \9; + padding-left: 14px; + padding-left: 4px \9; + /* IE7-8 doesn't have border-radius, so don't indent the padding */ + + margin-bottom: 0; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +/* Allow for input prepend/append in search forms */ + +.form-search .input-append .search-query, +.form-search .input-prepend .search-query { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.form-search .input-append .search-query { + -webkit-border-radius: 14px 0 0 14px; + -moz-border-radius: 14px 0 0 14px; + border-radius: 14px 0 0 14px; +} + +.form-search .input-append .btn { + -webkit-border-radius: 0 14px 14px 0; + -moz-border-radius: 0 14px 14px 0; + border-radius: 0 14px 14px 0; +} + +.form-search .input-prepend .search-query { + -webkit-border-radius: 0 14px 14px 0; + -moz-border-radius: 0 14px 14px 0; + border-radius: 0 14px 14px 0; +} + +.form-search .input-prepend .btn { + -webkit-border-radius: 14px 0 0 14px; + -moz-border-radius: 14px 0 0 14px; + border-radius: 14px 0 0 14px; +} + +.form-search input, +.form-inline input, +.form-horizontal input, +.form-search textarea, +.form-inline textarea, +.form-horizontal textarea, +.form-search select, +.form-inline select, +.form-horizontal select, +.form-search .help-inline, +.form-inline .help-inline, +.form-horizontal .help-inline, +.form-search .uneditable-input, +.form-inline .uneditable-input, +.form-horizontal .uneditable-input, +.form-search .input-prepend, +.form-inline .input-prepend, +.form-horizontal .input-prepend, +.form-search .input-append, +.form-inline .input-append, +.form-horizontal .input-append { + display: inline-block; + *display: inline; + margin-bottom: 0; + vertical-align: middle; + *zoom: 1; +} + +.form-search .hide, +.form-inline .hide, +.form-horizontal .hide { + display: none; +} + +.form-search label, +.form-inline label, +.form-search .btn-group, +.form-inline .btn-group { + display: inline-block; +} + +.form-search .input-append, +.form-inline .input-append, +.form-search .input-prepend, +.form-inline .input-prepend { + margin-bottom: 0; +} + +.form-search .radio, +.form-search .checkbox, +.form-inline .radio, +.form-inline .checkbox { + padding-left: 0; + margin-bottom: 0; + vertical-align: middle; +} + +.form-search .radio input[type="radio"], +.form-search .checkbox input[type="checkbox"], +.form-inline .radio input[type="radio"], +.form-inline .checkbox input[type="checkbox"] { + float: left; + margin-right: 3px; + margin-left: 0; +} + +.control-group { + margin-bottom: 10px; +} + +legend + .control-group { + margin-top: 20px; + -webkit-margin-top-collapse: separate; +} + +.form-horizontal .control-group { + margin-bottom: 20px; + *zoom: 1; +} + +.form-horizontal .control-group:before, +.form-horizontal .control-group:after { + display: table; + line-height: 0; + content: ""; +} + +.form-horizontal .control-group:after { + clear: both; +} + +.form-horizontal .control-label { + float: left; + width: 160px; + padding-top: 5px; + text-align: right; +} + +.form-horizontal .controls { + *display: inline-block; + *padding-left: 20px; + margin-left: 180px; + *margin-left: 0; +} + +.form-horizontal .controls:first-child { + *padding-left: 180px; +} + +.form-horizontal .help-block { + margin-bottom: 0; +} + +.form-horizontal input + .help-block, +.form-horizontal select + .help-block, +.form-horizontal textarea + .help-block { + margin-top: 10px; +} + +.form-horizontal .form-actions { + padding-left: 180px; +} + +table { + max-width: 100%; + background-color: transparent; + border-collapse: collapse; + border-spacing: 0; +} + +.table { + width: 100%; + margin-bottom: 20px; +} + +.table th, +.table td { + padding: 8px; + line-height: 20px; + text-align: left; + vertical-align: top; + border-top: 1px solid #dddddd; +} + +.table th { + font-weight: bold; +} + +.table thead th { + vertical-align: bottom; +} + +.table caption + thead tr:first-child th, +.table caption + thead tr:first-child td, +.table colgroup + thead tr:first-child th, +.table colgroup + thead tr:first-child td, +.table thead:first-child tr:first-child th, +.table thead:first-child tr:first-child td { + border-top: 0; +} + +.table tbody + tbody { + border-top: 2px solid #dddddd; +} + +.table-condensed th, +.table-condensed td { + padding: 4px 5px; +} + +.table-bordered { + border: 1px solid #dddddd; + border-collapse: separate; + *border-collapse: collapse; + border-left: 0; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.table-bordered th, +.table-bordered td { + border-left: 1px solid #dddddd; +} + +.table-bordered caption + thead tr:first-child th, +.table-bordered caption + tbody tr:first-child th, +.table-bordered caption + tbody tr:first-child td, +.table-bordered colgroup + thead tr:first-child th, +.table-bordered colgroup + tbody tr:first-child th, +.table-bordered colgroup + tbody tr:first-child td, +.table-bordered thead:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child td { + border-top: 0; +} + +.table-bordered thead:first-child tr:first-child th:first-child, +.table-bordered tbody:first-child tr:first-child td:first-child { + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-bordered thead:first-child tr:first-child th:last-child, +.table-bordered tbody:first-child tr:first-child td:last-child { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -moz-border-radius-topright: 4px; +} + +.table-bordered thead:last-child tr:last-child th:first-child, +.table-bordered tbody:last-child tr:last-child td:first-child, +.table-bordered tfoot:last-child tr:last-child td:first-child { + -webkit-border-radius: 0 0 0 4px; + -moz-border-radius: 0 0 0 4px; + border-radius: 0 0 0 4px; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; +} + +.table-bordered thead:last-child tr:last-child th:last-child, +.table-bordered tbody:last-child tr:last-child td:last-child, +.table-bordered tfoot:last-child tr:last-child td:last-child { + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-bottomright: 4px; +} + +.table-bordered caption + thead tr:first-child th:first-child, +.table-bordered caption + tbody tr:first-child td:first-child, +.table-bordered colgroup + thead tr:first-child th:first-child, +.table-bordered colgroup + tbody tr:first-child td:first-child { + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-bordered caption + thead tr:first-child th:last-child, +.table-bordered caption + tbody tr:first-child td:last-child, +.table-bordered colgroup + thead tr:first-child th:last-child, +.table-bordered colgroup + tbody tr:first-child td:last-child { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-striped tbody tr:nth-child(odd) td, +.table-striped tbody tr:nth-child(odd) th { + background-color: #f9f9f9; +} + +.table-hover tbody tr:hover td, +.table-hover tbody tr:hover th { + background-color: #f5f5f5; +} + +table [class*=span], +.row-fluid table [class*=span] { + display: table-cell; + float: none; + margin-left: 0; +} + +.table .span1 { + float: none; + width: 44px; + margin-left: 0; +} + +.table .span2 { + float: none; + width: 124px; + margin-left: 0; +} + +.table .span3 { + float: none; + width: 204px; + margin-left: 0; +} + +.table .span4 { + float: none; + width: 284px; + margin-left: 0; +} + +.table .span5 { + float: none; + width: 364px; + margin-left: 0; +} + +.table .span6 { + float: none; + width: 444px; + margin-left: 0; +} + +.table .span7 { + float: none; + width: 524px; + margin-left: 0; +} + +.table .span8 { + float: none; + width: 604px; + margin-left: 0; +} + +.table .span9 { + float: none; + width: 684px; + margin-left: 0; +} + +.table .span10 { + float: none; + width: 764px; + margin-left: 0; +} + +.table .span11 { + float: none; + width: 844px; + margin-left: 0; +} + +.table .span12 { + float: none; + width: 924px; + margin-left: 0; +} + +.table .span13 { + float: none; + width: 1004px; + margin-left: 0; +} + +.table .span14 { + float: none; + width: 1084px; + margin-left: 0; +} + +.table .span15 { + float: none; + width: 1164px; + margin-left: 0; +} + +.table .span16 { + float: none; + width: 1244px; + margin-left: 0; +} + +.table .span17 { + float: none; + width: 1324px; + margin-left: 0; +} + +.table .span18 { + float: none; + width: 1404px; + margin-left: 0; +} + +.table .span19 { + float: none; + width: 1484px; + margin-left: 0; +} + +.table .span20 { + float: none; + width: 1564px; + margin-left: 0; +} + +.table .span21 { + float: none; + width: 1644px; + margin-left: 0; +} + +.table .span22 { + float: none; + width: 1724px; + margin-left: 0; +} + +.table .span23 { + float: none; + width: 1804px; + margin-left: 0; +} + +.table .span24 { + float: none; + width: 1884px; + margin-left: 0; +} + +.table tbody tr.success td { + background-color: #dff0d8; +} + +.table tbody tr.error td { + background-color: #f2dede; +} + +.table tbody tr.warning td { + background-color: #fcf8e3; +} + +.table tbody tr.info td { + background-color: #d9edf7; +} + +.table-hover tbody tr.success:hover td { + background-color: #d0e9c6; +} + +.table-hover tbody tr.error:hover td { + background-color: #ebcccc; +} + +.table-hover tbody tr.warning:hover td { + background-color: #faf2cc; +} + +.table-hover tbody tr.info:hover td { + background-color: #c4e3f3; +} + +[class^="icon-"], +[class*=" icon-"] { + display: inline-block; + width: 14px; + height: 14px; + margin-top: 1px; + *margin-right: .3em; + line-height: 14px; + vertical-align: text-top; + background-image: url("../img/glyphicons-halflings.png"); + background-position: 14px 14px; + background-repeat: no-repeat; +} + +/* White icons with optional class, or on hover/active states of certain elements */ + +.icon-white, +.nav-tabs > .active > a > [class^="icon-"], +.nav-tabs > .active > a > [class*=" icon-"], +.nav-pills > .active > a > [class^="icon-"], +.nav-pills > .active > a > [class*=" icon-"], +.nav-list > .active > a > [class^="icon-"], +.nav-list > .active > a > [class*=" icon-"], +.navbar-inverse .nav > .active > a > [class^="icon-"], +.navbar-inverse .nav > .active > a > [class*=" icon-"], +.dropdown-menu > li > a:hover > [class^="icon-"], +.dropdown-menu > li > a:hover > [class*=" icon-"], +.dropdown-menu > .active > a > [class^="icon-"], +.dropdown-menu > .active > a > [class*=" icon-"] { + background-image: url("../img/glyphicons-halflings-white.png"); +} + +.icon-glass { + background-position: 0 0; +} + +.icon-music { + background-position: -24px 0; +} + +.icon-search { + background-position: -48px 0; +} + +.icon-envelope { + background-position: -72px 0; +} + +.icon-heart { + background-position: -96px 0; +} + +.icon-star { + background-position: -120px 0; +} + +.icon-star-empty { + background-position: -144px 0; +} + +.icon-user { + background-position: -168px 0; +} + +.icon-film { + background-position: -192px 0; +} + +.icon-th-large { + background-position: -216px 0; +} + +.icon-th { + background-position: -240px 0; +} + +.icon-th-list { + background-position: -264px 0; +} + +.icon-ok { + background-position: -288px 0; +} + +.icon-remove { + background-position: -312px 0; +} + +.icon-zoom-in { + background-position: -336px 0; +} + +.icon-zoom-out { + background-position: -360px 0; +} + +.icon-off { + background-position: -384px 0; +} + +.icon-signal { + background-position: -408px 0; +} + +.icon-cog { + background-position: -432px 0; +} + +.icon-trash { + background-position: -456px 0; +} + +.icon-home { + background-position: 0 -24px; +} + +.icon-file { + background-position: -24px -24px; +} + +.icon-time { + background-position: -48px -24px; +} + +.icon-road { + background-position: -72px -24px; +} + +.icon-download-alt { + background-position: -96px -24px; +} + +.icon-download { + background-position: -120px -24px; +} + +.icon-upload { + background-position: -144px -24px; +} + +.icon-inbox { + background-position: -168px -24px; +} + +.icon-play-circle { + background-position: -192px -24px; +} + +.icon-repeat { + background-position: -216px -24px; +} + +.icon-refresh { + background-position: -240px -24px; +} + +.icon-list-alt { + background-position: -264px -24px; +} + +.icon-lock { + background-position: -287px -24px; +} + +.icon-flag { + background-position: -312px -24px; +} + +.icon-headphones { + background-position: -336px -24px; +} + +.icon-volume-off { + background-position: -360px -24px; +} + +.icon-volume-down { + background-position: -384px -24px; +} + +.icon-volume-up { + background-position: -408px -24px; +} + +.icon-qrcode { + background-position: -432px -24px; +} + +.icon-barcode { + background-position: -456px -24px; +} + +.icon-tag { + background-position: 0 -48px; +} + +.icon-tags { + background-position: -25px -48px; +} + +.icon-book { + background-position: -48px -48px; +} + +.icon-bookmark { + background-position: -72px -48px; +} + +.icon-print { + background-position: -96px -48px; +} + +.icon-camera { + background-position: -120px -48px; +} + +.icon-font { + background-position: -144px -48px; +} + +.icon-bold { + background-position: -167px -48px; +} + +.icon-italic { + background-position: -192px -48px; +} + +.icon-text-height { + background-position: -216px -48px; +} + +.icon-text-width { + background-position: -240px -48px; +} + +.icon-align-left { + background-position: -264px -48px; +} + +.icon-align-center { + background-position: -288px -48px; +} + +.icon-align-right { + background-position: -312px -48px; +} + +.icon-align-justify { + background-position: -336px -48px; +} + +.icon-list { + background-position: -360px -48px; +} + +.icon-indent-left { + background-position: -384px -48px; +} + +.icon-indent-right { + background-position: -408px -48px; +} + +.icon-facetime-video { + background-position: -432px -48px; +} + +.icon-picture { + background-position: -456px -48px; +} + +.icon-pencil { + background-position: 0 -72px; +} + +.icon-map-marker { + background-position: -24px -72px; +} + +.icon-adjust { + background-position: -48px -72px; +} + +.icon-tint { + background-position: -72px -72px; +} + +.icon-edit { + background-position: -96px -72px; +} + +.icon-share { + background-position: -120px -72px; +} + +.icon-check { + background-position: -144px -72px; +} + +.icon-move { + background-position: -168px -72px; +} + +.icon-step-backward { + background-position: -192px -72px; +} + +.icon-fast-backward { + background-position: -216px -72px; +} + +.icon-backward { + background-position: -240px -72px; +} + +.icon-play { + background-position: -264px -72px; +} + +.icon-pause { + background-position: -288px -72px; +} + +.icon-stop { + background-position: -312px -72px; +} + +.icon-forward { + background-position: -336px -72px; +} + +.icon-fast-forward { + background-position: -360px -72px; +} + +.icon-step-forward { + background-position: -384px -72px; +} + +.icon-eject { + background-position: -408px -72px; +} + +.icon-chevron-left { + background-position: -432px -72px; +} + +.icon-chevron-right { + background-position: -456px -72px; +} + +.icon-plus-sign { + background-position: 0 -96px; +} + +.icon-minus-sign { + background-position: -24px -96px; +} + +.icon-remove-sign { + background-position: -48px -96px; +} + +.icon-ok-sign { + background-position: -72px -96px; +} + +.icon-question-sign { + background-position: -96px -96px; +} + +.icon-info-sign { + background-position: -120px -96px; +} + +.icon-screenshot { + background-position: -144px -96px; +} + +.icon-remove-circle { + background-position: -168px -96px; +} + +.icon-ok-circle { + background-position: -192px -96px; +} + +.icon-ban-circle { + background-position: -216px -96px; +} + +.icon-arrow-left { + background-position: -240px -96px; +} + +.icon-arrow-right { + background-position: -264px -96px; +} + +.icon-arrow-up { + background-position: -289px -96px; +} + +.icon-arrow-down { + background-position: -312px -96px; +} + +.icon-share-alt { + background-position: -336px -96px; +} + +.icon-resize-full { + background-position: -360px -96px; +} + +.icon-resize-small { + background-position: -384px -96px; +} + +.icon-plus { + background-position: -408px -96px; +} + +.icon-minus { + background-position: -433px -96px; +} + +.icon-asterisk { + background-position: -456px -96px; +} + +.icon-exclamation-sign { + background-position: 0 -120px; +} + +.icon-gift { + background-position: -24px -120px; +} + +.icon-leaf { + background-position: -48px -120px; +} + +.icon-fire { + background-position: -72px -120px; +} + +.icon-eye-open { + background-position: -96px -120px; +} + +.icon-eye-close { + background-position: -120px -120px; +} + +.icon-warning-sign { + background-position: -144px -120px; +} + +.icon-plane { + background-position: -168px -120px; +} + +.icon-calendar { + background-position: -192px -120px; +} + +.icon-random { + width: 16px; + background-position: -216px -120px; +} + +.icon-comment { + background-position: -240px -120px; +} + +.icon-magnet { + background-position: -264px -120px; +} + +.icon-chevron-up { + background-position: -288px -120px; +} + +.icon-chevron-down { + background-position: -313px -119px; +} + +.icon-retweet { + background-position: -336px -120px; +} + +.icon-shopping-cart { + background-position: -360px -120px; +} + +.icon-folder-close { + background-position: -384px -120px; +} + +.icon-folder-open { + width: 16px; + background-position: -408px -120px; +} + +.icon-resize-vertical { + background-position: -432px -119px; +} + +.icon-resize-horizontal { + background-position: -456px -118px; +} + +.icon-hdd { + background-position: 0 -144px; +} + +.icon-bullhorn { + background-position: -24px -144px; +} + +.icon-bell { + background-position: -48px -144px; +} + +.icon-certificate { + background-position: -72px -144px; +} + +.icon-thumbs-up { + background-position: -96px -144px; +} + +.icon-thumbs-down { + background-position: -120px -144px; +} + +.icon-hand-right { + background-position: -144px -144px; +} + +.icon-hand-left { + background-position: -168px -144px; +} + +.icon-hand-up { + background-position: -192px -144px; +} + +.icon-hand-down { + background-position: -216px -144px; +} + +.icon-circle-arrow-right { + background-position: -240px -144px; +} + +.icon-circle-arrow-left { + background-position: -264px -144px; +} + +.icon-circle-arrow-up { + background-position: -288px -144px; +} + +.icon-circle-arrow-down { + background-position: -312px -144px; +} + +.icon-globe { + background-position: -336px -144px; +} + +.icon-wrench { + background-position: -360px -144px; +} + +.icon-tasks { + background-position: -384px -144px; +} + +.icon-filter { + background-position: -408px -144px; +} + +.icon-briefcase { + background-position: -432px -144px; +} + +.icon-fullscreen { + background-position: -456px -144px; +} + +.dropup, +.dropdown { + position: relative; +} + +.dropdown-toggle { + *margin-bottom: -3px; +} + +.dropdown-toggle:active, +.open .dropdown-toggle { + outline: 0; +} + +.caret { + display: inline-block; + width: 0; + height: 0; + vertical-align: top; + border-top: 4px solid #000000; + border-right: 4px solid transparent; + border-left: 4px solid transparent; + content: ""; +} + +.dropdown .caret { + margin-top: 8px; + margin-left: 2px; +} + +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + list-style: none; + background-color: #ffffff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + *border-right-width: 2px; + *border-bottom-width: 2px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -webkit-background-clip: padding-box; + -moz-background-clip: padding; + background-clip: padding-box; +} + +.dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.dropdown-menu .divider { + *width: 100%; + height: 1px; + margin: 9px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.dropdown-menu a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: normal; + line-height: 20px; + color: #333333; + white-space: nowrap; +} + +.dropdown-menu li > a:hover, +.dropdown-menu li > a:focus, +.dropdown-submenu:hover > a { + color: #ffffff; + text-decoration: none; + background-color: #0088cc; + background-color: #0081c2; + background-image: -moz-linear-gradient(top, #0088cc, #0077b3); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); + background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); + background-image: -o-linear-gradient(top, #0088cc, #0077b3); + background-image: linear-gradient(to bottom, #0088cc, #0077b3); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); +} + +.dropdown-menu .active > a, +.dropdown-menu .active > a:hover { + color: #ffffff; + text-decoration: none; + background-color: #0088cc; + background-color: #0081c2; + background-image: linear-gradient(to bottom, #0088cc, #0077b3); + background-image: -moz-linear-gradient(top, #0088cc, #0077b3); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); + background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); + background-image: -o-linear-gradient(top, #0088cc, #0077b3); + background-repeat: repeat-x; + outline: 0; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); +} + +.dropdown-menu .disabled > a, +.dropdown-menu .disabled > a:hover { + color: #999999; +} + +.dropdown-menu .disabled > a:hover { + text-decoration: none; + cursor: default; + background-color: transparent; +} + +.open { + *z-index: 1000; +} + +.open > .dropdown-menu { + display: block; +} + +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} + +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px solid #000000; + content: ""; +} + +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 1px; +} + +.dropdown-submenu { + position: relative; +} + +.dropdown-submenu > .dropdown-menu { + top: 0; + left: 100%; + margin-top: -6px; + margin-left: -1px; + -webkit-border-radius: 0 6px 6px 6px; + -moz-border-radius: 0 6px 6px 6px; + border-radius: 0 6px 6px 6px; +} + +.dropdown-submenu:hover > .dropdown-menu { + display: block; +} + +.dropdown-submenu > a:after { + display: block; + float: right; + width: 0; + height: 0; + margin-top: 5px; + margin-right: -10px; + border-color: transparent; + border-left-color: #cccccc; + border-style: solid; + border-width: 5px 0 5px 5px; + content: " "; +} + +.dropdown-submenu:hover > a:after { + border-left-color: #ffffff; +} + +.dropdown .dropdown-menu .nav-header { + padding-right: 20px; + padding-left: 20px; +} + +.typeahead { + margin-top: 2px; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #e3e3e3; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} + +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} + +.well-large { + padding: 24px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.well-small { + padding: 9px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + -moz-transition: opacity 0.15s linear; + -o-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} + +.fade.in { + opacity: 1; +} + +.collapse { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition: height 0.35s ease; + -moz-transition: height 0.35s ease; + -o-transition: height 0.35s ease; + transition: height 0.35s ease; +} + +.collapse.in { + height: auto; +} + +.close { + float: right; + font-size: 20px; + font-weight: bold; + line-height: 20px; + color: #000000; + text-shadow: 0 1px 0 #ffffff; + opacity: 0.2; + filter: alpha(opacity=20); +} + +.close:hover { + color: #000000; + text-decoration: none; + cursor: pointer; + opacity: 0.4; + filter: alpha(opacity=40); +} + +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} + +.btn { + display: inline-block; + *display: inline; + padding: 4px 14px; + margin-bottom: 0; + *margin-left: .3em; + font-size: 14px; + line-height: 20px; + *line-height: 20px; + color: #333333; + text-align: center; + text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); + vertical-align: middle; + cursor: pointer; + background-color: #f5f5f5; + *background-color: #e6e6e6; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); + background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); + background-image: linear-gradient(to bottom, #ffffff, #e6e6e6); + background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); + background-repeat: repeat-x; + border: 1px solid #bbbbbb; + *border: 0; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + border-color: #e6e6e6 #e6e6e6 #bfbfbf; + border-bottom-color: #a2a2a2; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn:hover, +.btn:active, +.btn.active, +.btn.disabled, +.btn[disabled] { + color: #333333; + background-color: #e6e6e6; + *background-color: #d9d9d9; +} + +.btn:active, +.btn.active { + background-color: #cccccc \9; +} + +.btn:first-child { + *margin-left: 0; +} + +.btn:hover { + color: #333333; + text-decoration: none; + background-color: #e6e6e6; + *background-color: #d9d9d9; + /* Buttons in IE7 don't get borders, so darken on hover */ + + background-position: 0 -15px; + -webkit-transition: background-position 0.1s linear; + -moz-transition: background-position 0.1s linear; + -o-transition: background-position 0.1s linear; + transition: background-position 0.1s linear; +} + +.btn:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.btn.active, +.btn:active { + background-color: #e6e6e6; + background-color: #d9d9d9 \9; + background-image: none; + outline: 0; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn.disabled, +.btn[disabled] { + cursor: default; + background-color: #e6e6e6; + background-image: none; + opacity: 0.65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; +} + +.btn-large { + padding: 9px 14px; + font-size: 16px; + line-height: normal; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.btn-large [class^="icon-"] { + margin-top: 2px; +} + +.btn-small { + padding: 3px 9px; + font-size: 12px; + line-height: 18px; +} + +.btn-small [class^="icon-"] { + margin-top: 0; +} + +.btn-mini { + padding: 2px 6px; + font-size: 11px; + line-height: 17px; +} + +.btn-block { + display: block; + width: 100%; + padding-right: 0; + padding-left: 0; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +.btn-block + .btn-block { + margin-top: 5px; +} + +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} + +.btn-primary.active, +.btn-warning.active, +.btn-danger.active, +.btn-success.active, +.btn-info.active, +.btn-inverse.active { + color: rgba(255, 255, 255, 0.75); +} + +.btn { + border-color: #c5c5c5; + border-color: rgba(0, 0, 0, 0.15) rgba(0, 0, 0, 0.15) rgba(0, 0, 0, 0.25); +} + +.btn-primary { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #006dcc; + *background-color: #0044cc; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc)); + background-image: -webkit-linear-gradient(top, #0088cc, #0044cc); + background-image: -o-linear-gradient(top, #0088cc, #0044cc); + background-image: linear-gradient(to bottom, #0088cc, #0044cc); + background-image: -moz-linear-gradient(top, #0088cc, #0044cc); + background-repeat: repeat-x; + border-color: #0044cc #0044cc #002a80; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-primary:hover, +.btn-primary:active, +.btn-primary.active, +.btn-primary.disabled, +.btn-primary[disabled] { + color: #ffffff; + background-color: #0044cc; + *background-color: #003bb3; +} + +.btn-primary:active, +.btn-primary.active { + background-color: #003399 \9; +} + +.btn-warning { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #faa732; + *background-color: #f89406; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(to bottom, #fbb450, #f89406); + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-repeat: repeat-x; + border-color: #f89406 #f89406 #ad6704; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-warning:hover, +.btn-warning:active, +.btn-warning.active, +.btn-warning.disabled, +.btn-warning[disabled] { + color: #ffffff; + background-color: #f89406; + *background-color: #df8505; +} + +.btn-warning:active, +.btn-warning.active { + background-color: #c67605 \9; +} + +.btn-danger { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #da4f49; + *background-color: #bd362f; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -o-linear-gradient(top, #ee5f5b, #bd362f); + background-image: linear-gradient(to bottom, #ee5f5b, #bd362f); + background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f); + background-repeat: repeat-x; + border-color: #bd362f #bd362f #802420; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-danger:hover, +.btn-danger:active, +.btn-danger.active, +.btn-danger.disabled, +.btn-danger[disabled] { + color: #ffffff; + background-color: #bd362f; + *background-color: #a9302a; +} + +.btn-danger:active, +.btn-danger.active { + background-color: #942a25 \9; +} + +.btn-success { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #5bb75b; + *background-color: #51a351; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351)); + background-image: -webkit-linear-gradient(top, #62c462, #51a351); + background-image: -o-linear-gradient(top, #62c462, #51a351); + background-image: linear-gradient(to bottom, #62c462, #51a351); + background-image: -moz-linear-gradient(top, #62c462, #51a351); + background-repeat: repeat-x; + border-color: #51a351 #51a351 #387038; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-success:hover, +.btn-success:active, +.btn-success.active, +.btn-success.disabled, +.btn-success[disabled] { + color: #ffffff; + background-color: #51a351; + *background-color: #499249; +} + +.btn-success:active, +.btn-success.active { + background-color: #408140 \9; +} + +.btn-info { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #49afcd; + *background-color: #2f96b4; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4)); + background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -o-linear-gradient(top, #5bc0de, #2f96b4); + background-image: linear-gradient(to bottom, #5bc0de, #2f96b4); + background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4); + background-repeat: repeat-x; + border-color: #2f96b4 #2f96b4 #1f6377; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-info:hover, +.btn-info:active, +.btn-info.active, +.btn-info.disabled, +.btn-info[disabled] { + color: #ffffff; + background-color: #2f96b4; + *background-color: #2a85a0; +} + +.btn-info:active, +.btn-info.active { + background-color: #24748c \9; +} + +.btn-inverse { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #363636; + *background-color: #222222; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#444444), to(#222222)); + background-image: -webkit-linear-gradient(top, #444444, #222222); + background-image: -o-linear-gradient(top, #444444, #222222); + background-image: linear-gradient(to bottom, #444444, #222222); + background-image: -moz-linear-gradient(top, #444444, #222222); + background-repeat: repeat-x; + border-color: #222222 #222222 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-inverse:hover, +.btn-inverse:active, +.btn-inverse.active, +.btn-inverse.disabled, +.btn-inverse[disabled] { + color: #ffffff; + background-color: #222222; + *background-color: #151515; +} + +.btn-inverse:active, +.btn-inverse.active { + background-color: #080808 \9; +} + +button.btn, +input[type="submit"].btn { + *padding-top: 3px; + *padding-bottom: 3px; +} + +button.btn::-moz-focus-inner, +input[type="submit"].btn::-moz-focus-inner { + padding: 0; + border: 0; +} + +button.btn.btn-large, +input[type="submit"].btn.btn-large { + *padding-top: 7px; + *padding-bottom: 7px; +} + +button.btn.btn-small, +input[type="submit"].btn.btn-small { + *padding-top: 3px; + *padding-bottom: 3px; +} + +button.btn.btn-mini, +input[type="submit"].btn.btn-mini { + *padding-top: 1px; + *padding-bottom: 1px; +} + +.btn-link, +.btn-link:active, +.btn-link[disabled] { + background-color: transparent; + background-image: none; + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; +} + +.btn-link { + color: #0088cc; + cursor: pointer; + border-color: transparent; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-link:hover { + color: #005580; + text-decoration: underline; + background-color: transparent; +} + +.btn-link[disabled]:hover { + color: #333333; + text-decoration: none; +} + +.btn-group { + position: relative; + *margin-left: .3em; + font-size: 0; + white-space: nowrap; + vertical-align: middle; +} + +.btn-group:first-child { + *margin-left: 0; +} + +.btn-group + .btn-group { + margin-left: 5px; +} + +.btn-toolbar { + margin-top: 10px; + margin-bottom: 10px; + font-size: 0; +} + +.btn-toolbar .btn-group { + display: inline-block; + *display: inline; + /* IE7 inline-block hack */ + + *zoom: 1; +} + +.btn-toolbar .btn + .btn, +.btn-toolbar .btn-group + .btn, +.btn-toolbar .btn + .btn-group { + margin-left: 5px; +} + +.btn-group > .btn { + position: relative; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-group > .btn + .btn { + margin-left: -1px; +} + +.btn-group > .btn, +.btn-group > .dropdown-menu { + font-size: 14px; +} + +.btn-group > .btn-mini { + font-size: 11px; +} + +.btn-group > .btn-small { + font-size: 12px; +} + +.btn-group > .btn-large { + font-size: 16px; +} + +.btn-group > .btn:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-topleft: 4px; +} + +.btn-group > .btn:last-child, +.btn-group > .dropdown-toggle { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-bottomright: 4px; +} + +.btn-group > .btn.large:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 6px; + border-bottom-left-radius: 6px; + -webkit-border-top-left-radius: 6px; + border-top-left-radius: 6px; + -moz-border-radius-bottomleft: 6px; + -moz-border-radius-topleft: 6px; +} + +.btn-group > .btn.large:last-child, +.btn-group > .large.dropdown-toggle { + -webkit-border-top-right-radius: 6px; + border-top-right-radius: 6px; + -webkit-border-bottom-right-radius: 6px; + border-bottom-right-radius: 6px; + -moz-border-radius-topright: 6px; + -moz-border-radius-bottomright: 6px; +} + +.btn-group > .btn:hover, +.btn-group > .btn:focus, +.btn-group > .btn:active, +.btn-group > .btn.active { + z-index: 2; +} + +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} + +.btn-group > .btn + .dropdown-toggle { + *padding-top: 5px; + padding-right: 8px; + *padding-bottom: 5px; + padding-left: 8px; + -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group > .btn-mini + .dropdown-toggle { + *padding-top: 2px; + padding-right: 5px; + *padding-bottom: 2px; + padding-left: 5px; +} + +.btn-group > .btn-small + .dropdown-toggle { + *padding-top: 5px; + *padding-bottom: 4px; +} + +.btn-group > .btn-large + .dropdown-toggle { + *padding-top: 7px; + padding-right: 12px; + *padding-bottom: 7px; + padding-left: 12px; +} + +.btn-group.open .dropdown-toggle { + background-image: none; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group.open .btn.dropdown-toggle { + background-color: #e6e6e6; +} + +.btn-group.open .btn-primary.dropdown-toggle { + background-color: #0044cc; +} + +.btn-group.open .btn-warning.dropdown-toggle { + background-color: #f89406; +} + +.btn-group.open .btn-danger.dropdown-toggle { + background-color: #bd362f; +} + +.btn-group.open .btn-success.dropdown-toggle { + background-color: #51a351; +} + +.btn-group.open .btn-info.dropdown-toggle { + background-color: #2f96b4; +} + +.btn-group.open .btn-inverse.dropdown-toggle { + background-color: #222222; +} + +.btn .caret { + margin-top: 8px; + margin-left: 0; +} + +.btn-mini .caret, +.btn-small .caret, +.btn-large .caret { + margin-top: 6px; +} + +.btn-large .caret { + border-top-width: 5px; + border-right-width: 5px; + border-left-width: 5px; +} + +.dropup .btn-large .caret { + border-top: 0; + border-bottom: 5px solid #000000; +} + +.btn-primary .caret, +.btn-warning .caret, +.btn-danger .caret, +.btn-info .caret, +.btn-success .caret, +.btn-inverse .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.btn-group-vertical { + display: inline-block; + *display: inline; + /* IE7 inline-block hack */ + + *zoom: 1; +} + +.btn-group-vertical .btn { + display: block; + float: none; + width: 100%; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-group-vertical .btn + .btn { + margin-top: -1px; + margin-left: 0; +} + +.btn-group-vertical .btn:first-child { + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.btn-group-vertical .btn:last-child { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.btn-group-vertical .btn-large:first-child { + -webkit-border-radius: 6px 6px 0 0; + -moz-border-radius: 6px 6px 0 0; + border-radius: 6px 6px 0 0; +} + +.btn-group-vertical .btn-large:last-child { + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; +} + +.alert { + padding: 8px 35px 8px 14px; + margin-bottom: 20px; + color: #c09853; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + background-color: #fcf8e3; + border: 1px solid #fbeed5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.alert h4 { + margin: 0; +} + +.alert .close { + position: relative; + top: -2px; + right: -21px; + line-height: 20px; +} + +.alert-success { + color: #468847; + background-color: #dff0d8; + border-color: #d6e9c6; +} + +.alert-danger, +.alert-error { + color: #b94a48; + background-color: #f2dede; + border-color: #eed3d7; +} + +.alert-info { + color: #3a87ad; + background-color: #d9edf7; + border-color: #bce8f1; +} + +.alert-block { + padding-top: 14px; + padding-bottom: 14px; +} + +.alert-block > p, +.alert-block > ul { + margin-bottom: 0; +} + +.alert-block p + p { + margin-top: 5px; +} + +.nav { + margin-bottom: 20px; + margin-left: 0; + list-style: none; +} + +.nav > li > a { + display: block; +} + +.nav > li > a:hover { + text-decoration: none; + background-color: #eeeeee; +} + +.nav > .pull-right { + float: right; +} + +.nav-header { + display: block; + padding: 3px 15px; + font-size: 11px; + font-weight: bold; + line-height: 20px; + color: #999999; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + text-transform: uppercase; +} + +.nav li + .nav-header { + margin-top: 9px; +} + +.nav-list { + padding-right: 15px; + padding-left: 15px; + margin-bottom: 0; +} + +.nav-list > li > a, +.nav-list .nav-header { + margin-right: -15px; + margin-left: -15px; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); +} + +.nav-list > li > a { + padding: 3px 15px; +} + +.nav-list > .active > a, +.nav-list > .active > a:hover { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); + background-color: #0088cc; +} + +.nav-list [class^="icon-"] { + margin-right: 2px; +} + +.nav-list .divider { + *width: 100%; + height: 1px; + margin: 9px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.nav-tabs, +.nav-pills { + *zoom: 1; +} + +.nav-tabs:before, +.nav-pills:before, +.nav-tabs:after, +.nav-pills:after { + display: table; + line-height: 0; + content: ""; +} + +.nav-tabs:after, +.nav-pills:after { + clear: both; +} + +.nav-tabs > li, +.nav-pills > li { + float: left; +} + +.nav-tabs > li > a, +.nav-pills > li > a { + padding-right: 12px; + padding-left: 12px; + margin-right: 2px; + line-height: 14px; +} + +.nav-tabs { + border-bottom: 1px solid #ddd; +} + +.nav-tabs > li { + margin-bottom: -1px; +} + +.nav-tabs > li > a { + padding-top: 8px; + padding-bottom: 8px; + line-height: 20px; + border: 1px solid transparent; + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #dddddd; +} + +.nav-tabs > .active > a, +.nav-tabs > .active > a:hover { + color: #555555; + cursor: default; + background-color: #ffffff; + border: 1px solid #ddd; + border-bottom-color: transparent; +} + +.nav-pills > li > a { + padding-top: 8px; + padding-bottom: 8px; + margin-top: 2px; + margin-bottom: 2px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.nav-pills > .active > a, +.nav-pills > .active > a:hover { + color: #ffffff; + background-color: #0088cc; +} + +.nav-stacked > li { + float: none; +} + +.nav-stacked > li > a { + margin-right: 0; +} + +.nav-tabs.nav-stacked { + border-bottom: 0; +} + +.nav-tabs.nav-stacked > li > a { + border: 1px solid #ddd; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.nav-tabs.nav-stacked > li:first-child > a { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-topleft: 4px; +} + +.nav-tabs.nav-stacked > li:last-child > a { + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -moz-border-radius-bottomright: 4px; + -moz-border-radius-bottomleft: 4px; +} + +.nav-tabs.nav-stacked > li > a:hover { + z-index: 2; + border-color: #ddd; +} + +.nav-pills.nav-stacked > li > a { + margin-bottom: 3px; +} + +.nav-pills.nav-stacked > li:last-child > a { + margin-bottom: 1px; +} + +.nav-tabs .dropdown-menu { + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; +} + +.nav-pills .dropdown-menu { + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.nav .dropdown-toggle .caret { + margin-top: 6px; + border-top-color: #0088cc; + border-bottom-color: #0088cc; +} + +.nav .dropdown-toggle:hover .caret { + border-top-color: #005580; + border-bottom-color: #005580; +} + +/* move down carets for tabs */ + +.nav-tabs .dropdown-toggle .caret { + margin-top: 8px; +} + +.nav .active .dropdown-toggle .caret { + border-top-color: #fff; + border-bottom-color: #fff; +} + +.nav-tabs .active .dropdown-toggle .caret { + border-top-color: #555555; + border-bottom-color: #555555; +} + +.nav > .dropdown.active > a:hover { + cursor: pointer; +} + +.nav-tabs .open .dropdown-toggle, +.nav-pills .open .dropdown-toggle, +.nav > li.dropdown.open.active > a:hover { + color: #ffffff; + background-color: #999999; + border-color: #999999; +} + +.nav li.dropdown.open .caret, +.nav li.dropdown.open.active .caret, +.nav li.dropdown.open a:hover .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; + opacity: 1; + filter: alpha(opacity=100); +} + +.tabs-stacked .open > a:hover { + border-color: #999999; +} + +.tabbable { + *zoom: 1; +} + +.tabbable:before, +.tabbable:after { + display: table; + line-height: 0; + content: ""; +} + +.tabbable:after { + clear: both; +} + +.tab-content { + overflow: auto; +} + +.tabs-below > .nav-tabs, +.tabs-right > .nav-tabs, +.tabs-left > .nav-tabs { + border-bottom: 0; +} + +.tab-content > .tab-pane, +.pill-content > .pill-pane { + display: none; +} + +.tab-content > .active, +.pill-content > .active { + display: block; +} + +.tabs-below > .nav-tabs { + border-top: 1px solid #ddd; +} + +.tabs-below > .nav-tabs > li { + margin-top: -1px; + margin-bottom: 0; +} + +.tabs-below > .nav-tabs > li > a { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.tabs-below > .nav-tabs > li > a:hover { + border-top-color: #ddd; + border-bottom-color: transparent; +} + +.tabs-below > .nav-tabs > .active > a, +.tabs-below > .nav-tabs > .active > a:hover { + border-color: transparent #ddd #ddd #ddd; +} + +.tabs-left > .nav-tabs > li, +.tabs-right > .nav-tabs > li { + float: none; +} + +.tabs-left > .nav-tabs > li > a, +.tabs-right > .nav-tabs > li > a { + min-width: 74px; + margin-right: 0; + margin-bottom: 3px; +} + +.tabs-left > .nav-tabs { + float: left; + margin-right: 19px; + border-right: 1px solid #ddd; +} + +.tabs-left > .nav-tabs > li > a { + margin-right: -1px; + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.tabs-left > .nav-tabs > li > a:hover { + border-color: #eeeeee #dddddd #eeeeee #eeeeee; +} + +.tabs-left > .nav-tabs .active > a, +.tabs-left > .nav-tabs .active > a:hover { + border-color: #ddd transparent #ddd #ddd; + *border-right-color: #ffffff; +} + +.tabs-right > .nav-tabs { + float: right; + margin-left: 19px; + border-left: 1px solid #ddd; +} + +.tabs-right > .nav-tabs > li > a { + margin-left: -1px; + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.tabs-right > .nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #eeeeee #dddddd; +} + +.tabs-right > .nav-tabs .active > a, +.tabs-right > .nav-tabs .active > a:hover { + border-color: #ddd #ddd #ddd transparent; + *border-left-color: #ffffff; +} + +.nav > .disabled > a { + color: #999999; +} + +.nav > .disabled > a:hover { + text-decoration: none; + cursor: default; + background-color: transparent; +} + +.navbar { + *position: relative; + *z-index: 2; + margin-bottom: 20px; + overflow: visible; + color: #777777; +} + +.navbar-inner { + min-height: 40px; + padding-right: 20px; + padding-left: 20px; + background-color: #fafafa; + background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2)); + background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2); + background-image: -o-linear-gradient(top, #ffffff, #f2f2f2); + background-image: linear-gradient(to bottom, #ffffff, #f2f2f2); + background-repeat: repeat-x; + border: 1px solid #d4d4d4; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0); + *zoom: 1; + -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); + -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); + box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); +} + +.navbar-inner:before, +.navbar-inner:after { + display: table; + line-height: 0; + content: ""; +} + +.navbar-inner:after { + clear: both; +} + +.navbar .container { + width: auto; +} + +.nav-collapse.collapse { + height: auto; +} + +.navbar .brand { + display: block; + float: left; + padding: 10px 20px 10px; + margin-left: -20px; + font-size: 20px; + font-weight: 200; + color: #777777; + text-shadow: 0 1px 0 #ffffff; +} + +.navbar .brand:hover { + text-decoration: none; +} + +.navbar-text { + margin-bottom: 0; + line-height: 40px; +} + +.navbar-link { + color: #777777; +} + +.navbar-link:hover { + color: #333333; +} + +.navbar .divider-vertical { + height: 40px; + margin: 0 9px; + border-right: 1px solid #ffffff; + border-left: 1px solid #f2f2f2; +} + +.navbar .btn, +.navbar .btn-group { + margin-top: 5px; +} + +.navbar .btn-group .btn, +.navbar .input-prepend .btn, +.navbar .input-append .btn { + margin-top: 0; +} + +.navbar-form { + margin-bottom: 0; + *zoom: 1; +} + +.navbar-form:before, +.navbar-form:after { + display: table; + line-height: 0; + content: ""; +} + +.navbar-form:after { + clear: both; +} + +.navbar-form input, +.navbar-form select, +.navbar-form .radio, +.navbar-form .checkbox { + margin-top: 5px; +} + +.navbar-form input, +.navbar-form select, +.navbar-form .btn { + display: inline-block; + margin-bottom: 0; +} + +.navbar-form input[type="image"], +.navbar-form input[type="checkbox"], +.navbar-form input[type="radio"] { + margin-top: 3px; +} + +.navbar-form .input-append, +.navbar-form .input-prepend { + margin-top: 6px; + white-space: nowrap; +} + +.navbar-form .input-append input, +.navbar-form .input-prepend input { + margin-top: 0; +} + +.navbar-search { + position: relative; + float: left; + margin-top: 5px; + margin-bottom: 0; +} + +.navbar-search .search-query { + padding: 4px 14px; + margin-bottom: 0; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 13px; + font-weight: normal; + line-height: 1; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +.navbar-static-top { + position: static; + width: 100%; + margin-bottom: 0; +} + +.navbar-static-top .navbar-inner { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; + margin-bottom: 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-static-top .navbar-inner { + border-width: 0 0 1px; +} + +.navbar-fixed-bottom .navbar-inner { + border-width: 1px 0 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-fixed-bottom .navbar-inner { + padding-right: 0; + padding-left: 0; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.navbar-static-top .container, +.navbar-fixed-top .container, +.navbar-fixed-bottom .container { + width: 940px; +} + +.navbar-fixed-top { + top: 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-static-top .navbar-inner { + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.1), 0 1px 10px rgba(0, 0, 0, 0.1); + -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.1), 0 1px 10px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.1), 0 1px 10px rgba(0, 0, 0, 0.1); +} + +.navbar-fixed-bottom { + bottom: 0; +} + +.navbar-fixed-bottom .navbar-inner { + -webkit-box-shadow: inset 0 1px 0 rgba(0, 0, 0, 0.1), 0 -1px 10px rgba(0, 0, 0, 0.1); + -moz-box-shadow: inset 0 1px 0 rgba(0, 0, 0, 0.1), 0 -1px 10px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 0 rgba(0, 0, 0, 0.1), 0 -1px 10px rgba(0, 0, 0, 0.1); +} + +.navbar .nav { + position: relative; + left: 0; + display: block; + float: left; + margin: 0 10px 0 0; +} + +.navbar .nav.pull-right { + float: right; + margin-right: 0; +} + +.navbar .nav > li { + float: left; +} + +.navbar .nav > li > a { + float: none; + padding: 10px 15px 10px; + color: #777777; + text-decoration: none; + text-shadow: 0 1px 0 #ffffff; +} + +.navbar .nav .dropdown-toggle .caret { + margin-top: 8px; +} + +.navbar .nav > li > a:focus, +.navbar .nav > li > a:hover { + color: #333333; + text-decoration: none; + background-color: transparent; +} + +.navbar .nav > .active > a, +.navbar .nav > .active > a:hover, +.navbar .nav > .active > a:focus { + color: #555555; + text-decoration: none; + background-color: #e5e5e5; + -webkit-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); + -moz-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); +} + +.navbar .btn-navbar { + display: none; + float: right; + padding: 7px 10px; + margin-right: 5px; + margin-left: 5px; + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #ededed; + *background-color: #e5e5e5; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5)); + background-image: -webkit-linear-gradient(top, #f2f2f2, #e5e5e5); + background-image: -o-linear-gradient(top, #f2f2f2, #e5e5e5); + background-image: linear-gradient(to bottom, #f2f2f2, #e5e5e5); + background-image: -moz-linear-gradient(top, #f2f2f2, #e5e5e5); + background-repeat: repeat-x; + border-color: #e5e5e5 #e5e5e5 #bfbfbf; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); +} + +.navbar .btn-navbar:hover, +.navbar .btn-navbar:active, +.navbar .btn-navbar.active, +.navbar .btn-navbar.disabled, +.navbar .btn-navbar[disabled] { + color: #ffffff; + background-color: #e5e5e5; + *background-color: #d9d9d9; +} + +.navbar .btn-navbar:active, +.navbar .btn-navbar.active { + background-color: #cccccc \9; +} + +.navbar .btn-navbar .icon-bar { + display: block; + width: 18px; + height: 2px; + background-color: #f5f5f5; + -webkit-border-radius: 1px; + -moz-border-radius: 1px; + border-radius: 1px; + -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); +} + +.btn-navbar .icon-bar + .icon-bar { + margin-top: 3px; +} + +.navbar .nav > li > .dropdown-menu:before { + position: absolute; + top: -7px; + left: 9px; + display: inline-block; + border-right: 7px solid transparent; + border-bottom: 7px solid #ccc; + border-left: 7px solid transparent; + border-bottom-color: rgba(0, 0, 0, 0.2); + content: ''; +} + +.navbar .nav > li > .dropdown-menu:after { + position: absolute; + top: -6px; + left: 10px; + display: inline-block; + border-right: 6px solid transparent; + border-bottom: 6px solid #ffffff; + border-left: 6px solid transparent; + content: ''; +} + +.navbar-fixed-bottom .nav > li > .dropdown-menu:before { + top: auto; + bottom: -7px; + border-top: 7px solid #ccc; + border-bottom: 0; + border-top-color: rgba(0, 0, 0, 0.2); +} + +.navbar-fixed-bottom .nav > li > .dropdown-menu:after { + top: auto; + bottom: -6px; + border-top: 6px solid #ffffff; + border-bottom: 0; +} + +.navbar .nav li.dropdown.open > .dropdown-toggle, +.navbar .nav li.dropdown.active > .dropdown-toggle, +.navbar .nav li.dropdown.open.active > .dropdown-toggle { + color: #555555; + background-color: #e5e5e5; +} + +.navbar .nav li.dropdown > .dropdown-toggle .caret { + border-top-color: #777777; + border-bottom-color: #777777; +} + +.navbar .nav li.dropdown.open > .dropdown-toggle .caret, +.navbar .nav li.dropdown.active > .dropdown-toggle .caret, +.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret { + border-top-color: #555555; + border-bottom-color: #555555; +} + +.navbar .pull-right > li > .dropdown-menu, +.navbar .nav > li > .dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.navbar .pull-right > li > .dropdown-menu:before, +.navbar .nav > li > .dropdown-menu.pull-right:before { + right: 12px; + left: auto; +} + +.navbar .pull-right > li > .dropdown-menu:after, +.navbar .nav > li > .dropdown-menu.pull-right:after { + right: 13px; + left: auto; +} + +.navbar .pull-right > li > .dropdown-menu .dropdown-menu, +.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu { + right: 100%; + left: auto; + margin-right: -1px; + margin-left: 0; + -webkit-border-radius: 6px 0 6px 6px; + -moz-border-radius: 6px 0 6px 6px; + border-radius: 6px 0 6px 6px; +} + +.navbar-inverse { + color: #999999; +} + +.navbar-inverse .navbar-inner { + background-color: #1b1b1b; + background-image: -moz-linear-gradient(top, #222222, #111111); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111)); + background-image: -webkit-linear-gradient(top, #222222, #111111); + background-image: -o-linear-gradient(top, #222222, #111111); + background-image: linear-gradient(to bottom, #222222, #111111); + background-repeat: repeat-x; + border-color: #252525; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0); +} + +.navbar-inverse .brand, +.navbar-inverse .nav > li > a { + color: #999999; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); +} + +.navbar-inverse .brand:hover, +.navbar-inverse .nav > li > a:hover { + color: #ffffff; +} + +.navbar-inverse .nav > li > a:focus, +.navbar-inverse .nav > li > a:hover { + color: #ffffff; + background-color: transparent; +} + +.navbar-inverse .nav .active > a, +.navbar-inverse .nav .active > a:hover, +.navbar-inverse .nav .active > a:focus { + color: #ffffff; + background-color: #111111; +} + +.navbar-inverse .navbar-link { + color: #999999; +} + +.navbar-inverse .navbar-link:hover { + color: #ffffff; +} + +.navbar-inverse .divider-vertical { + border-right-color: #222222; + border-left-color: #111111; +} + +.navbar-inverse .nav li.dropdown.open > .dropdown-toggle, +.navbar-inverse .nav li.dropdown.active > .dropdown-toggle, +.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle { + color: #ffffff; + background-color: #111111; +} + +.navbar-inverse .nav li.dropdown > .dropdown-toggle .caret { + border-top-color: #999999; + border-bottom-color: #999999; +} + +.navbar-inverse .nav li.dropdown.open > .dropdown-toggle .caret, +.navbar-inverse .nav li.dropdown.active > .dropdown-toggle .caret, +.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.navbar-inverse .navbar-search .search-query { + color: #ffffff; + background-color: #515151; + border-color: #111111; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -webkit-transition: none; + -moz-transition: none; + -o-transition: none; + transition: none; +} + +.navbar-inverse .navbar-search .search-query:-moz-placeholder { + color: #cccccc; +} + +.navbar-inverse .navbar-search .search-query:-ms-input-placeholder { + color: #cccccc; +} + +.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder { + color: #cccccc; +} + +.navbar-inverse .navbar-search .search-query:focus, +.navbar-inverse .navbar-search .search-query.focused { + padding: 5px 15px; + color: #333333; + text-shadow: 0 1px 0 #ffffff; + background-color: #ffffff; + border: 0; + outline: 0; + -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); +} + +.navbar-inverse .btn-navbar { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #0e0e0e; + *background-color: #040404; + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404)); + background-image: -webkit-linear-gradient(top, #151515, #040404); + background-image: -o-linear-gradient(top, #151515, #040404); + background-image: linear-gradient(to bottom, #151515, #040404); + background-image: -moz-linear-gradient(top, #151515, #040404); + background-repeat: repeat-x; + border-color: #040404 #040404 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.navbar-inverse .btn-navbar:hover, +.navbar-inverse .btn-navbar:active, +.navbar-inverse .btn-navbar.active, +.navbar-inverse .btn-navbar.disabled, +.navbar-inverse .btn-navbar[disabled] { + color: #ffffff; + background-color: #040404; + *background-color: #000000; +} + +.navbar-inverse .btn-navbar:active, +.navbar-inverse .btn-navbar.active { + background-color: #000000 \9; +} + +.breadcrumb { + padding: 8px 15px; + margin: 0 0 20px; + list-style: none; + background-color: #f5f5f5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.breadcrumb li { + display: inline-block; + *display: inline; + text-shadow: 0 1px 0 #ffffff; + *zoom: 1; +} + +.breadcrumb .divider { + padding: 0 5px; + color: #ccc; +} + +.breadcrumb .active { + color: #999999; +} + +.pagination { + height: 40px; + margin: 20px 0; +} + +.pagination ul { + display: inline-block; + *display: inline; + margin-bottom: 0; + margin-left: 0; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + *zoom: 1; + -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.pagination ul > li { + display: inline; +} + +.pagination ul > li > a, +.pagination ul > li > span { + float: left; + padding: 0 14px; + line-height: 38px; + text-decoration: none; + background-color: #ffffff; + border: 1px solid #dddddd; + border-left-width: 0; +} + +.pagination ul > li > a:hover, +.pagination ul > .active > a, +.pagination ul > .active > span { + background-color: #f5f5f5; +} + +.pagination ul > .active > a, +.pagination ul > .active > span { + color: #999999; + cursor: default; +} + +.pagination ul > .disabled > span, +.pagination ul > .disabled > a, +.pagination ul > .disabled > a:hover { + color: #999999; + cursor: default; + background-color: transparent; +} + +.pagination ul > li:first-child > a, +.pagination ul > li:first-child > span { + border-left-width: 1px; + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.pagination ul > li:last-child > a, +.pagination ul > li:last-child > span { + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.pagination-centered { + text-align: center; +} + +.pagination-right { + text-align: right; +} + +.pager { + margin: 20px 0; + text-align: center; + list-style: none; + *zoom: 1; +} + +.pager:before, +.pager:after { + display: table; + line-height: 0; + content: ""; +} + +.pager:after { + clear: both; +} + +.pager li { + display: inline; +} + +.pager a, +.pager span { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +.pager a:hover { + text-decoration: none; + background-color: #f5f5f5; +} + +.pager .next a, +.pager .next span { + float: right; +} + +.pager .previous a { + float: left; +} + +.pager .disabled a, +.pager .disabled a:hover, +.pager .disabled span { + color: #999999; + cursor: default; + background-color: #fff; +} + +.modal-open .modal .dropdown-menu { + z-index: 2050; +} + +.modal-open .modal .dropdown.open { + *z-index: 2050; +} + +.modal-open .modal .popover { + z-index: 2060; +} + +.modal-open .modal .tooltip { + z-index: 2080; +} + +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000000; +} + +.modal-backdrop.fade { + opacity: 0; +} + +.modal-backdrop, +.modal-backdrop.fade.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.modal { + position: fixed; + top: 50%; + left: 50%; + z-index: 1050; + width: 560px; + margin: -250px 0 0 -280px; + overflow: auto; + background-color: #ffffff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.3); + *border: 1px solid #999; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -webkit-background-clip: padding-box; + -moz-background-clip: padding-box; + background-clip: padding-box; +} + +.modal.fade { + top: -25%; + -webkit-transition: opacity 0.3s linear, top 0.3s ease-out; + -moz-transition: opacity 0.3s linear, top 0.3s ease-out; + -o-transition: opacity 0.3s linear, top 0.3s ease-out; + transition: opacity 0.3s linear, top 0.3s ease-out; +} + +.modal.fade.in { + top: 50%; +} + +.modal-header { + padding: 9px 15px; + border-bottom: 1px solid #eee; +} + +.modal-header .close { + margin-top: 2px; +} + +.modal-header h3 { + margin: 0; + line-height: 30px; +} + +.modal-body { + max-height: 400px; + padding: 15px; + overflow-y: auto; +} + +.modal-form { + margin-bottom: 0; +} + +.modal-footer { + padding: 14px 15px 15px; + margin-bottom: 0; + text-align: right; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 #ffffff; + -moz-box-shadow: inset 0 1px 0 #ffffff; + box-shadow: inset 0 1px 0 #ffffff; +} + +.modal-footer:before, +.modal-footer:after { + display: table; + line-height: 0; + content: ""; +} + +.modal-footer:after { + clear: both; +} + +.modal-footer .btn + .btn { + margin-bottom: 0; + margin-left: 5px; +} + +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} + +.tooltip { + position: absolute; + z-index: 1030; + display: block; + padding: 5px; + font-size: 11px; + opacity: 0; + filter: alpha(opacity=0); + visibility: visible; +} + +.tooltip.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.tooltip.top { + margin-top: -3px; +} + +.tooltip.right { + margin-left: 3px; +} + +.tooltip.bottom { + margin-top: 3px; +} + +.tooltip.left { + margin-left: -3px; +} + +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #ffffff; + text-align: center; + text-decoration: none; + background-color: #000000; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} + +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top-color: #000000; + border-width: 5px 5px 0; +} + +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-right-color: #000000; + border-width: 5px 5px 5px 0; +} + +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-left-color: #000000; + border-width: 5px 0 5px 5px; +} + +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-bottom-color: #000000; + border-width: 0 5px 5px; +} + +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1010; + display: none; + width: 236px; + padding: 1px; + background-color: #ffffff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -webkit-background-clip: padding-box; + -moz-background-clip: padding; + background-clip: padding-box; +} + +.popover.top { + margin-bottom: 10px; +} + +.popover.right { + margin-left: 10px; +} + +.popover.bottom { + margin-top: 10px; +} + +.popover.left { + margin-right: 10px; +} + +.popover-title { + padding: 8px 14px; + margin: 0; + font-size: 14px; + font-weight: normal; + line-height: 18px; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + -webkit-border-radius: 5px 5px 0 0; + -moz-border-radius: 5px 5px 0 0; + border-radius: 5px 5px 0 0; +} + +.popover-content { + padding: 9px 14px; +} + +.popover-content p, +.popover-content ul, +.popover-content ol { + margin-bottom: 0; +} + +.popover .arrow, +.popover .arrow:after { + position: absolute; + display: inline-block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} + +.popover .arrow:after { + z-index: -1; + content: ""; +} + +.popover.top .arrow { + bottom: -10px; + left: 50%; + margin-left: -10px; + border-top-color: #ffffff; + border-width: 10px 10px 0; +} + +.popover.top .arrow:after { + bottom: -1px; + left: -11px; + border-top-color: rgba(0, 0, 0, 0.25); + border-width: 11px 11px 0; +} + +.popover.right .arrow { + top: 50%; + left: -10px; + margin-top: -10px; + border-right-color: #ffffff; + border-width: 10px 10px 10px 0; +} + +.popover.right .arrow:after { + bottom: -11px; + left: -1px; + border-right-color: rgba(0, 0, 0, 0.25); + border-width: 11px 11px 11px 0; +} + +.popover.bottom .arrow { + top: -10px; + left: 50%; + margin-left: -10px; + border-bottom-color: #ffffff; + border-width: 0 10px 10px; +} + +.popover.bottom .arrow:after { + top: -1px; + left: -11px; + border-bottom-color: rgba(0, 0, 0, 0.25); + border-width: 0 11px 11px; +} + +.popover.left .arrow { + top: 50%; + right: -10px; + margin-top: -10px; + border-left-color: #ffffff; + border-width: 10px 0 10px 10px; +} + +.popover.left .arrow:after { + right: -1px; + bottom: -11px; + border-left-color: rgba(0, 0, 0, 0.25); + border-width: 11px 0 11px 11px; +} + +.thumbnails { + margin-left: -20px; + list-style: none; + *zoom: 1; +} + +.thumbnails:before, +.thumbnails:after { + display: table; + line-height: 0; + content: ""; +} + +.thumbnails:after { + clear: both; +} + +.row-fluid .thumbnails { + margin-left: 0; +} + +.thumbnails > li { + float: left; + margin-bottom: 20px; + margin-left: 20px; +} + +.thumbnail { + display: block; + padding: 4px; + line-height: 20px; + border: 1px solid #ddd; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); + -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); + -webkit-transition: all 0.2s ease-in-out; + -moz-transition: all 0.2s ease-in-out; + -o-transition: all 0.2s ease-in-out; + transition: all 0.2s ease-in-out; +} + +a.thumbnail:hover { + border-color: #0088cc; + -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); +} + +.thumbnail > img { + display: block; + max-width: 100%; + margin-right: auto; + margin-left: auto; +} + +.thumbnail .caption { + padding: 9px; + color: #555555; +} + +.label, +.badge { + font-size: 11.844px; + font-weight: bold; + line-height: 14px; + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + white-space: nowrap; + vertical-align: baseline; + background-color: #999999; +} + +.label { + padding: 1px 4px 2px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.badge { + padding: 1px 9px 2px; + -webkit-border-radius: 9px; + -moz-border-radius: 9px; + border-radius: 9px; +} + +a.label:hover, +a.badge:hover { + color: #ffffff; + text-decoration: none; + cursor: pointer; +} + +.label-important, +.badge-important { + background-color: #b94a48; +} + +.label-important[href], +.badge-important[href] { + background-color: #953b39; +} + +.label-warning, +.badge-warning { + background-color: #f89406; +} + +.label-warning[href], +.badge-warning[href] { + background-color: #c67605; +} + +.label-success, +.badge-success { + background-color: #468847; +} + +.label-success[href], +.badge-success[href] { + background-color: #356635; +} + +.label-info, +.badge-info { + background-color: #3a87ad; +} + +.label-info[href], +.badge-info[href] { + background-color: #2d6987; +} + +.label-inverse, +.badge-inverse { + background-color: #333333; +} + +.label-inverse[href], +.badge-inverse[href] { + background-color: #1a1a1a; +} + +.btn .label, +.btn .badge { + position: relative; + top: -1px; +} + +.btn-mini .label, +.btn-mini .badge { + top: 0; +} + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-moz-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-ms-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-o-keyframes progress-bar-stripes { + from { + background-position: 0 0; + } + to { + background-position: 40px 0; + } +} + +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +.progress { + height: 20px; + margin-bottom: 20px; + overflow: hidden; + background-color: #f7f7f7; + background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9)); + background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: linear-gradient(to bottom, #f5f5f5, #f9f9f9); + background-repeat: repeat-x; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0); + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} + +.progress .bar { + float: left; + width: 0; + height: 100%; + font-size: 12px; + color: #ffffff; + text-align: center; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #0e90d2; + background-image: -moz-linear-gradient(top, #149bdf, #0480be); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be)); + background-image: -webkit-linear-gradient(top, #149bdf, #0480be); + background-image: -o-linear-gradient(top, #149bdf, #0480be); + background-image: linear-gradient(to bottom, #149bdf, #0480be); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0); + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + -webkit-transition: width 0.6s ease; + -moz-transition: width 0.6s ease; + -o-transition: width 0.6s ease; + transition: width 0.6s ease; +} + +.progress .bar + .bar { + -webkit-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -moz-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); +} + +.progress-striped .bar { + background-color: #149bdf; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + -webkit-background-size: 40px 40px; + -moz-background-size: 40px 40px; + -o-background-size: 40px 40px; + background-size: 40px 40px; +} + +.progress.active .bar { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -moz-animation: progress-bar-stripes 2s linear infinite; + -ms-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} + +.progress-danger .bar, +.progress .bar-danger { + background-color: #dd514c; + background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -o-linear-gradient(top, #ee5f5b, #c43c35); + background-image: linear-gradient(to bottom, #ee5f5b, #c43c35); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0); +} + +.progress-danger.progress-striped .bar, +.progress-striped .bar-danger { + background-color: #ee5f5b; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-success .bar, +.progress .bar-success { + background-color: #5eb95e; + background-image: -moz-linear-gradient(top, #62c462, #57a957); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957)); + background-image: -webkit-linear-gradient(top, #62c462, #57a957); + background-image: -o-linear-gradient(top, #62c462, #57a957); + background-image: linear-gradient(to bottom, #62c462, #57a957); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0); +} + +.progress-success.progress-striped .bar, +.progress-striped .bar-success { + background-color: #62c462; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-info .bar, +.progress .bar-info { + background-color: #4bb1cf; + background-image: -moz-linear-gradient(top, #5bc0de, #339bb9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9)); + background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9); + background-image: -o-linear-gradient(top, #5bc0de, #339bb9); + background-image: linear-gradient(to bottom, #5bc0de, #339bb9); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0); +} + +.progress-info.progress-striped .bar, +.progress-striped .bar-info { + background-color: #5bc0de; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-warning .bar, +.progress .bar-warning { + background-color: #faa732; + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(to bottom, #fbb450, #f89406); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); +} + +.progress-warning.progress-striped .bar, +.progress-striped .bar-warning { + background-color: #fbb450; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.accordion { + margin-bottom: 20px; +} + +.accordion-group { + margin-bottom: 2px; + border: 1px solid #e5e5e5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.accordion-heading { + border-bottom: 0; +} + +.accordion-heading .accordion-toggle { + display: block; + padding: 8px 15px; +} + +.accordion-toggle { + cursor: pointer; +} + +.accordion-inner { + padding: 9px 15px; + border-top: 1px solid #e5e5e5; +} + +.carousel { + position: relative; + margin-bottom: 20px; + line-height: 1; +} + +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} + +.carousel .item { + position: relative; + display: none; + -webkit-transition: 0.6s ease-in-out left; + -moz-transition: 0.6s ease-in-out left; + -o-transition: 0.6s ease-in-out left; + transition: 0.6s ease-in-out left; +} + +.carousel .item > img { + display: block; + line-height: 1; +} + +.carousel .active, +.carousel .next, +.carousel .prev { + display: block; +} + +.carousel .active { + left: 0; +} + +.carousel .next, +.carousel .prev { + position: absolute; + top: 0; + width: 100%; +} + +.carousel .next { + left: 100%; +} + +.carousel .prev { + left: -100%; +} + +.carousel .next.left, +.carousel .prev.right { + left: 0; +} + +.carousel .active.left { + left: -100%; +} + +.carousel .active.right { + left: 100%; +} + +.carousel-control { + position: absolute; + top: 40%; + left: 15px; + width: 40px; + height: 40px; + margin-top: -20px; + font-size: 60px; + font-weight: 100; + line-height: 30px; + color: #ffffff; + text-align: center; + background: #222222; + border: 3px solid #ffffff; + -webkit-border-radius: 23px; + -moz-border-radius: 23px; + border-radius: 23px; + opacity: 0.5; + filter: alpha(opacity=50); +} + +.carousel-control.right { + right: 15px; + left: auto; +} + 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'next' : 'prev', $(children[pos])) + } + + , pause: function (e) { + if (!e) this.paused = true + if (this.$element.find('.next, .prev').length && $.support.transition.end) { + this.$element.trigger($.support.transition.end) + this.cycle() + } + clearInterval(this.interval) + this.interval = null + return this + } + + , next: function () { + if (this.sliding) return + return this.slide('next') + } + + , prev: function () { + if (this.sliding) return + return this.slide('prev') + } + + , slide: function (type, next) { + var $active = this.$element.find('.item.active') + , $next = next || $active[type]() + , isCycling = this.interval + , direction = type == 'next' ? 'left' : 'right' + , fallback = type == 'next' ? 'first' : 'last' + , that = this + , e = $.Event('slide', { + relatedTarget: $next[0] + }) + + this.sliding = true + + isCycling && this.pause() + + $next = $next.length ? $next : this.$element.find('.item')[fallback]() + + if ($next.hasClass('active')) return + + if ($.support.transition && this.$element.hasClass('slide')) { + this.$element.trigger(e) + if (e.isDefaultPrevented()) return + $next.addClass(type) + $next[0].offsetWidth // force reflow + $active.addClass(direction) + $next.addClass(direction) + this.$element.one($.support.transition.end, function () { + $next.removeClass([type, direction].join(' ')).addClass('active') + $active.removeClass(['active', direction].join(' ')) + that.sliding = false + setTimeout(function () { that.$element.trigger('slid') }, 0) + }) + } else { + this.$element.trigger(e) + if (e.isDefaultPrevented()) return + $active.removeClass('active') + $next.addClass('active') + this.sliding = false + this.$element.trigger('slid') + } + + isCycling && this.cycle() + + return this + } + + } + + + /* CAROUSEL PLUGIN DEFINITION + * ========================== */ + + $.fn.carousel = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('carousel') + , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option) + , action = typeof option == 'string' ? option : options.slide + if (!data) $this.data('carousel', (data = new Carousel(this, options))) + if (typeof option == 'number') data.to(option) + else if (action) data[action]() + else if (options.interval) data.cycle() + }) + } + + $.fn.carousel.defaults = { + interval: 5000 + , pause: 'hover' + } + + $.fn.carousel.Constructor = Carousel + + + /* CAROUSEL DATA-API + * ================= */ + + $(function () { + $('body').on('click.carousel.data-api', '[data-slide]', function ( e ) { + var $this = $(this), href + , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + , options = !$target.data('modal') && $.extend({}, $target.data(), $this.data()) + $target.carousel(options) + e.preventDefault() + }) + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-collapse.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#collapse + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* COLLAPSE PUBLIC CLASS DEFINITION + * ================================ */ + + var Collapse = function (element, options) { + this.$element = $(element) + this.options = $.extend({}, $.fn.collapse.defaults, options) + + if (this.options.parent) { + this.$parent = $(this.options.parent) + } + + this.options.toggle && this.toggle() + } + + Collapse.prototype = { + + constructor: Collapse + + , dimension: function () { + var hasWidth = this.$element.hasClass('width') + return hasWidth ? 'width' : 'height' + } + + , show: function () { + var dimension + , scroll + , actives + , hasData + + if (this.transitioning) return + + dimension = this.dimension() + scroll = $.camelCase(['scroll', dimension].join('-')) + actives = this.$parent && this.$parent.find('> .accordion-group > .in') + + if (actives && actives.length) { + hasData = actives.data('collapse') + if (hasData && hasData.transitioning) return + actives.collapse('hide') + hasData || actives.data('collapse', null) + } + + this.$element[dimension](0) + this.transition('addClass', $.Event('show'), 'shown') + $.support.transition && this.$element[dimension](this.$element[0][scroll]) + } + + , hide: function () { + var dimension + if (this.transitioning) return + dimension = this.dimension() + this.reset(this.$element[dimension]()) + this.transition('removeClass', $.Event('hide'), 'hidden') + this.$element[dimension](0) + } + + , reset: function (size) { + var dimension = this.dimension() + + this.$element + .removeClass('collapse') + [dimension](size || 'auto') + [0].offsetWidth + + this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') + + return this + } + + , transition: function (method, startEvent, completeEvent) { + var that = this + , complete = function () { + if (startEvent.type == 'show') that.reset() + that.transitioning = 0 + that.$element.trigger(completeEvent) + } + + this.$element.trigger(startEvent) + + if (startEvent.isDefaultPrevented()) return + + this.transitioning = 1 + + this.$element[method]('in') + + $.support.transition && this.$element.hasClass('collapse') ? + this.$element.one($.support.transition.end, complete) : + complete() + } + + , toggle: function () { + this[this.$element.hasClass('in') ? 'hide' : 'show']() + } + + } + + + /* COLLAPSIBLE PLUGIN DEFINITION + * ============================== */ + + $.fn.collapse = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('collapse') + , options = typeof option == 'object' && option + if (!data) $this.data('collapse', (data = new Collapse(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.collapse.defaults = { + toggle: true + } + + $.fn.collapse.Constructor = Collapse + + + /* COLLAPSIBLE DATA-API + * ==================== */ + + $(function () { + $('body').on('click.collapse.data-api', '[data-toggle=collapse]', function (e) { + var $this = $(this), href + , target = $this.attr('data-target') + || e.preventDefault() + || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 + , option = $(target).data('collapse') ? 'toggle' : $this.data() + $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed') + $(target).collapse(option) + }) + }) + +}(window.jQuery);/* ============================================================ + * bootstrap-dropdown.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#dropdowns + * ============================================================ + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* DROPDOWN CLASS DEFINITION + * ========================= */ + + var toggle = '[data-toggle=dropdown]' + , Dropdown = function (element) { + var $el = $(element).on('click.dropdown.data-api', this.toggle) + $('html').on('click.dropdown.data-api', function () { + $el.parent().removeClass('open') + }) + } + + Dropdown.prototype = { + + constructor: Dropdown + + , toggle: function (e) { + var $this = $(this) + , $parent + , isActive + + if ($this.is('.disabled, :disabled')) return + + $parent = getParent($this) + + isActive = $parent.hasClass('open') + + clearMenus() + + if (!isActive) { + $parent.toggleClass('open') + $this.focus() + } + + return false + } + + , keydown: function (e) { + var $this + , $items + , $active + , $parent + , isActive + , index + + if (!/(38|40|27)/.test(e.keyCode)) return + + $this = $(this) + + e.preventDefault() + e.stopPropagation() + + if ($this.is('.disabled, :disabled')) return + + $parent = getParent($this) + + isActive = $parent.hasClass('open') + + if (!isActive || (isActive && e.keyCode == 27)) return $this.click() + + $items = $('[role=menu] li:not(.divider) a', $parent) + + if (!$items.length) return + + index = $items.index($items.filter(':focus')) + + if (e.keyCode == 38 && index > 0) index-- // up + if (e.keyCode == 40 && index < $items.length - 1) index++ // down + if (!~index) index = 0 + + $items + .eq(index) + .focus() + } + + } + + function clearMenus() { + getParent($(toggle)) + .removeClass('open') + } + + function getParent($this) { + var selector = $this.attr('data-target') + , $parent + + if (!selector) { + selector = $this.attr('href') + selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + $parent = $(selector) + $parent.length || ($parent = $this.parent()) + + return $parent + } + + + /* DROPDOWN PLUGIN DEFINITION + * ========================== */ + + $.fn.dropdown = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('dropdown') + if (!data) $this.data('dropdown', (data = new Dropdown(this))) + if (typeof option == 'string') data[option].call($this) + }) + } + + $.fn.dropdown.Constructor = Dropdown + + + /* APPLY TO STANDARD DROPDOWN ELEMENTS + * =================================== */ + + $(function () { + $('html') + .on('click.dropdown.data-api touchstart.dropdown.data-api', clearMenus) + $('body') + .on('click.dropdown touchstart.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() }) + .on('click.dropdown.data-api touchstart.dropdown.data-api' , toggle, Dropdown.prototype.toggle) + .on('keydown.dropdown.data-api touchstart.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown) + }) + +}(window.jQuery);/* ========================================================= + * bootstrap-modal.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#modals + * ========================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================= */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* MODAL CLASS DEFINITION + * ====================== */ + + var Modal = function (element, options) { + this.options = options + this.$element = $(element) + .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) + this.options.remote && this.$element.find('.modal-body').load(this.options.remote) + } + + Modal.prototype = { + + constructor: Modal + + , toggle: function () { + return this[!this.isShown ? 'show' : 'hide']() + } + + , show: function () { + var that = this + , e = $.Event('show') + + this.$element.trigger(e) + + if (this.isShown || e.isDefaultPrevented()) return + + $('body').addClass('modal-open') + + this.isShown = true + + this.escape() + + this.backdrop(function () { + var transition = $.support.transition && that.$element.hasClass('fade') + + if (!that.$element.parent().length) { + that.$element.appendTo(document.body) //don't move modals dom position + } + + that.$element + .show() + + if (transition) { + that.$element[0].offsetWidth // force reflow + } + + that.$element + .addClass('in') + .attr('aria-hidden', false) + .focus() + + that.enforceFocus() + + transition ? + that.$element.one($.support.transition.end, function () { that.$element.trigger('shown') }) : + that.$element.trigger('shown') + + }) + } + + , hide: function (e) { + e && e.preventDefault() + + var that = this + + e = $.Event('hide') + + this.$element.trigger(e) + + if (!this.isShown || e.isDefaultPrevented()) return + + this.isShown = false + + $('body').removeClass('modal-open') + + this.escape() + + $(document).off('focusin.modal') + + this.$element + .removeClass('in') + .attr('aria-hidden', true) + + $.support.transition && this.$element.hasClass('fade') ? + this.hideWithTransition() : + this.hideModal() + } + + , enforceFocus: function () { + var that = this + $(document).on('focusin.modal', function (e) { + if (that.$element[0] !== e.target && !that.$element.has(e.target).length) { + that.$element.focus() + } + }) + } + + , escape: function () { + var that = this + if (this.isShown && this.options.keyboard) { + this.$element.on('keyup.dismiss.modal', function ( e ) { + e.which == 27 && that.hide() + }) + } else if (!this.isShown) { + this.$element.off('keyup.dismiss.modal') + } + } + + , hideWithTransition: function () { + var that = this + , timeout = setTimeout(function () { + that.$element.off($.support.transition.end) + that.hideModal() + }, 500) + + this.$element.one($.support.transition.end, function () { + clearTimeout(timeout) + that.hideModal() + }) + } + + , hideModal: function (that) { + this.$element + .hide() + .trigger('hidden') + + this.backdrop() + } + + , removeBackdrop: function () { + this.$backdrop.remove() + this.$backdrop = null + } + + , backdrop: function (callback) { + var that = this + , animate = this.$element.hasClass('fade') ? 'fade' : '' + + if (this.isShown && this.options.backdrop) { + var doAnimate = $.support.transition && animate + + this.$backdrop = $('
    ') + .appendTo(document.body) + + if (this.options.backdrop != 'static') { + this.$backdrop.click($.proxy(this.hide, this)) + } + + if (doAnimate) this.$backdrop[0].offsetWidth // force reflow + + this.$backdrop.addClass('in') + + doAnimate ? + this.$backdrop.one($.support.transition.end, callback) : + callback() + + } else if (!this.isShown && this.$backdrop) { + this.$backdrop.removeClass('in') + + $.support.transition && this.$element.hasClass('fade')? + this.$backdrop.one($.support.transition.end, $.proxy(this.removeBackdrop, this)) : + this.removeBackdrop() + + } else if (callback) { + callback() + } + } + } + + + /* MODAL PLUGIN DEFINITION + * ======================= */ + + $.fn.modal = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('modal') + , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) + if (!data) $this.data('modal', (data = new Modal(this, options))) + if (typeof option == 'string') data[option]() + else if (options.show) data.show() + }) + } + + $.fn.modal.defaults = { + backdrop: true + , keyboard: true + , show: true + } + + $.fn.modal.Constructor = Modal + + + /* MODAL DATA-API + * ============== */ + + $(function () { + $('body').on('click.modal.data-api', '[data-toggle="modal"]', function ( e ) { + var $this = $(this) + , href = $this.attr('href') + , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7 + , option = $target.data('modal') ? 'toggle' : $.extend({ remote: !/#/.test(href) && href }, $target.data(), $this.data()) + + e.preventDefault() + + $target + .modal(option) + .one('hide', function () { + $this.focus() + }) + }) + }) + +}(window.jQuery);/* =========================================================== + * bootstrap-tooltip.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#tooltips + * Inspired by the original jQuery.tipsy by Jason Frame + * =========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* TOOLTIP PUBLIC CLASS DEFINITION + * =============================== */ + + var Tooltip = function (element, options) { + this.init('tooltip', element, options) + } + + Tooltip.prototype = { + + constructor: Tooltip + + , init: function (type, element, options) { + var eventIn + , eventOut + + this.type = type + this.$element = $(element) + this.options = this.getOptions(options) + this.enabled = true + + if (this.options.trigger == 'click') { + this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) + } else if (this.options.trigger != 'manual') { + eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus' + eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur' + this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) + this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) + } + + this.options.selector ? + (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : + this.fixTitle() + } + + , getOptions: function (options) { + options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data()) + + if (options.delay && typeof options.delay == 'number') { + options.delay = { + show: options.delay + , hide: options.delay + } + } + + return options + } + + , enter: function (e) { + var self = $(e.currentTarget)[this.type](this._options).data(this.type) + + if (!self.options.delay || !self.options.delay.show) return self.show() + + clearTimeout(this.timeout) + self.hoverState = 'in' + this.timeout = setTimeout(function() { + if (self.hoverState == 'in') self.show() + }, self.options.delay.show) + } + + , leave: function (e) { + var self = $(e.currentTarget)[this.type](this._options).data(this.type) + + if (this.timeout) clearTimeout(this.timeout) + if (!self.options.delay || !self.options.delay.hide) return self.hide() + + self.hoverState = 'out' + this.timeout = setTimeout(function() { + if (self.hoverState == 'out') self.hide() + }, self.options.delay.hide) + } + + , show: function () { + var $tip + , inside + , pos + , actualWidth + , actualHeight + , placement + , tp + + if (this.hasContent() && this.enabled) { + $tip = this.tip() + this.setContent() + + if (this.options.animation) { + $tip.addClass('fade') + } + + placement = typeof this.options.placement == 'function' ? + this.options.placement.call(this, $tip[0], this.$element[0]) : + this.options.placement + + inside = /in/.test(placement) + + $tip + .remove() + .css({ top: 0, left: 0, display: 'block' }) + .appendTo(inside ? this.$element : document.body) + + pos = this.getPosition(inside) + + actualWidth = $tip[0].offsetWidth + actualHeight = $tip[0].offsetHeight + + switch (inside ? placement.split(' ')[1] : placement) { + case 'bottom': + tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} + break + case 'top': + tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} + break + case 'left': + tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} + break + case 'right': + tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} + break + } + + $tip + .css(tp) + .addClass(placement) + .addClass('in') + } + } + + , setContent: function () { + var $tip = this.tip() + , title = this.getTitle() + + $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title) + $tip.removeClass('fade in top bottom left right') + } + + , hide: function () { + var that = this + , $tip = this.tip() + + $tip.removeClass('in') + + function removeWithAnimation() { + var timeout = setTimeout(function () { + $tip.off($.support.transition.end).remove() + }, 500) + + $tip.one($.support.transition.end, function () { + clearTimeout(timeout) + $tip.remove() + }) + } + + $.support.transition && this.$tip.hasClass('fade') ? + removeWithAnimation() : + $tip.remove() + + return this + } + + , fixTitle: function () { + var $e = this.$element + if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { + $e.attr('data-original-title', $e.attr('title') || '').removeAttr('title') + } + } + + , hasContent: function () { + return this.getTitle() + } + + , getPosition: function (inside) { + return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), { + width: this.$element[0].offsetWidth + , height: this.$element[0].offsetHeight + }) + } + + , getTitle: function () { + var title + , $e = this.$element + , o = this.options + + title = $e.attr('data-original-title') + || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) + + return title + } + + , tip: function () { + return this.$tip = this.$tip || $(this.options.template) + } + + , validate: function () { + if (!this.$element[0].parentNode) { + this.hide() + this.$element = null + this.options = null + } + } + + , enable: function () { + this.enabled = true + } + + , disable: function () { + this.enabled = false + } + + , toggleEnabled: function () { + this.enabled = !this.enabled + } + + , toggle: function () { + this[this.tip().hasClass('in') ? 'hide' : 'show']() + } + + , destroy: function () { + this.hide().$element.off('.' + this.type).removeData(this.type) + } + + } + + + /* TOOLTIP PLUGIN DEFINITION + * ========================= */ + + $.fn.tooltip = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('tooltip') + , options = typeof option == 'object' && option + if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.tooltip.Constructor = Tooltip + + $.fn.tooltip.defaults = { + animation: true + , placement: 'top' + , selector: false + , template: '
    ' + , trigger: 'hover' + , title: '' + , delay: 0 + , html: true + } + +}(window.jQuery); +/* =========================================================== + * bootstrap-popover.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#popovers + * =========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * =========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* POPOVER PUBLIC CLASS DEFINITION + * =============================== */ + + var Popover = function (element, options) { + this.init('popover', element, options) + } + + + /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js + ========================================== */ + + Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { + + constructor: Popover + + , setContent: function () { + var $tip = this.tip() + , title = this.getTitle() + , content = this.getContent() + + $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title) + $tip.find('.popover-content > *')[this.options.html ? 'html' : 'text'](content) + + $tip.removeClass('fade top bottom left right in') + } + + , hasContent: function () { + return this.getTitle() || this.getContent() + } + + , getContent: function () { + var content + , $e = this.$element + , o = this.options + + content = $e.attr('data-content') + || (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) + + return content + } + + , tip: function () { + if (!this.$tip) { + this.$tip = $(this.options.template) + } + return this.$tip + } + + , destroy: function () { + this.hide().$element.off('.' + this.type).removeData(this.type) + } + + }) + + + /* POPOVER PLUGIN DEFINITION + * ======================= */ + + $.fn.popover = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('popover') + , options = typeof option == 'object' && option + if (!data) $this.data('popover', (data = new Popover(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.popover.Constructor = Popover + + $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { + placement: 'right' + , trigger: 'click' + , content: '' + , template: '

    ' + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-scrollspy.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#scrollspy + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* SCROLLSPY CLASS DEFINITION + * ========================== */ + + function ScrollSpy(element, options) { + var process = $.proxy(this.process, this) + , $element = $(element).is('body') ? $(window) : $(element) + , href + this.options = $.extend({}, $.fn.scrollspy.defaults, options) + this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process) + this.selector = (this.options.target + || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + || '') + ' .nav li > a' + this.$body = $('body') + this.refresh() + this.process() + } + + ScrollSpy.prototype = { + + constructor: ScrollSpy + + , refresh: function () { + var self = this + , $targets + + this.offsets = $([]) + this.targets = $([]) + + $targets = this.$body + .find(this.selector) + .map(function () { + var $el = $(this) + , href = $el.data('target') || $el.attr('href') + , $href = /^#\w/.test(href) && $(href) + return ( $href + && $href.length + && [[ $href.position().top, href ]] ) || null + }) + .sort(function (a, b) { return a[0] - b[0] }) + .each(function () { + self.offsets.push(this[0]) + self.targets.push(this[1]) + }) + } + + , process: function () { + var scrollTop = this.$scrollElement.scrollTop() + this.options.offset + , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight + , maxScroll = scrollHeight - this.$scrollElement.height() + , offsets = this.offsets + , targets = this.targets + , activeTarget = this.activeTarget + , i + + if (scrollTop >= maxScroll) { + return activeTarget != (i = targets.last()[0]) + && this.activate ( i ) + } + + for (i = offsets.length; i--;) { + activeTarget != targets[i] + && scrollTop >= offsets[i] + && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) + && this.activate( targets[i] ) + } + } + + , activate: function (target) { + var active + , selector + + this.activeTarget = target + + $(this.selector) + .parent('.active') + .removeClass('active') + + selector = this.selector + + '[data-target="' + target + '"],' + + this.selector + '[href="' + target + '"]' + + active = $(selector) + .parent('li') + .addClass('active') + + if (active.parent('.dropdown-menu').length) { + active = active.closest('li.dropdown').addClass('active') + } + + active.trigger('activate') + } + + } + + + /* SCROLLSPY PLUGIN DEFINITION + * =========================== */ + + $.fn.scrollspy = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('scrollspy') + , options = typeof option == 'object' && option + if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.scrollspy.Constructor = ScrollSpy + + $.fn.scrollspy.defaults = { + offset: 10 + } + + + /* SCROLLSPY DATA-API + * ================== */ + + $(window).on('load', function () { + $('[data-spy="scroll"]').each(function () { + var $spy = $(this) + $spy.scrollspy($spy.data()) + }) + }) + +}(window.jQuery);/* ======================================================== + * bootstrap-tab.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#tabs + * ======================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* TAB CLASS DEFINITION + * ==================== */ + + var Tab = function (element) { + this.element = $(element) + } + + Tab.prototype = { + + constructor: Tab + + , show: function () { + var $this = this.element + , $ul = $this.closest('ul:not(.dropdown-menu)') + , selector = $this.attr('data-target') + , previous + , $target + , e + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + if ( $this.parent('li').hasClass('active') ) return + + previous = $ul.find('.active a').last()[0] + + e = $.Event('show', { + relatedTarget: previous + }) + + $this.trigger(e) + + if (e.isDefaultPrevented()) return + + $target = $(selector) + + this.activate($this.parent('li'), $ul) + this.activate($target, $target.parent(), function () { + $this.trigger({ + type: 'shown' + , relatedTarget: previous + }) + }) + } + + , activate: function ( element, container, callback) { + var $active = container.find('> .active') + , transition = callback + && $.support.transition + && $active.hasClass('fade') + + function next() { + $active + .removeClass('active') + .find('> .dropdown-menu > .active') + .removeClass('active') + + element.addClass('active') + + if (transition) { + element[0].offsetWidth // reflow for transition + element.addClass('in') + } else { + element.removeClass('fade') + } + + if ( element.parent('.dropdown-menu') ) { + element.closest('li.dropdown').addClass('active') + } + + callback && callback() + } + + transition ? + $active.one($.support.transition.end, next) : + next() + + $active.removeClass('in') + } + } + + + /* TAB PLUGIN DEFINITION + * ===================== */ + + $.fn.tab = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('tab') + if (!data) $this.data('tab', (data = new Tab(this))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.tab.Constructor = Tab + + + /* TAB DATA-API + * ============ */ + + $(function () { + $('body').on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { + e.preventDefault() + $(this).tab('show') + }) + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-typeahead.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#typeahead + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function($){ + + "use strict"; // jshint ;_; + + + /* TYPEAHEAD PUBLIC CLASS DEFINITION + * ================================= */ + + var Typeahead = function (element, options) { + this.$element = $(element) + this.options = $.extend({}, $.fn.typeahead.defaults, options) + this.matcher = this.options.matcher || this.matcher + this.sorter = this.options.sorter || this.sorter + this.highlighter = this.options.highlighter || this.highlighter + this.updater = this.options.updater || this.updater + this.$menu = $(this.options.menu).appendTo('body') + this.source = this.options.source + this.shown = false + this.listen() + } + + Typeahead.prototype = { + + constructor: Typeahead + + , select: function () { + var val = this.$menu.find('.active').attr('data-value') + this.$element + .val(this.updater(val)) + .change() + return this.hide() + } + + , updater: function (item) { + return item + } + + , show: function () { + var pos = $.extend({}, this.$element.offset(), { + height: this.$element[0].offsetHeight + }) + + this.$menu.css({ + top: pos.top + pos.height + , left: pos.left + }) + + this.$menu.show() + this.shown = true + return this + } + + , hide: function () { + this.$menu.hide() + this.shown = false + return this + } + + , lookup: function (event) { + var items + + this.query = this.$element.val() + + if (!this.query || this.query.length < this.options.minLength) { + return this.shown ? this.hide() : this + } + + items = $.isFunction(this.source) ? this.source(this.query, $.proxy(this.process, this)) : this.source + + return items ? this.process(items) : this + } + + , process: function (items) { + var that = this + + items = $.grep(items, function (item) { + return that.matcher(item) + }) + + items = this.sorter(items) + + if (!items.length) { + return this.shown ? this.hide() : this + } + + return this.render(items.slice(0, this.options.items)).show() + } + + , matcher: function (item) { + return ~item.toLowerCase().indexOf(this.query.toLowerCase()) + } + + , sorter: function (items) { + var beginswith = [] + , caseSensitive = [] + , caseInsensitive = [] + , item + + while (item = items.shift()) { + if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item) + else if (~item.indexOf(this.query)) caseSensitive.push(item) + else caseInsensitive.push(item) + } + + return beginswith.concat(caseSensitive, caseInsensitive) + } + + , highlighter: function (item) { + var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&') + return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) { + return '' + match + '' + }) + } + + , render: function (items) { + var that = this + + items = $(items).map(function (i, item) { + i = $(that.options.item).attr('data-value', item) + i.find('a').html(that.highlighter(item)) + return i[0] + }) + + items.first().addClass('active') + this.$menu.html(items) + return this + } + + , next: function (event) { + var active = this.$menu.find('.active').removeClass('active') + , next = active.next() + + if (!next.length) { + next = $(this.$menu.find('li')[0]) + } + + next.addClass('active') + } + + , prev: function (event) { + var active = this.$menu.find('.active').removeClass('active') + , prev = active.prev() + + if (!prev.length) { + prev = this.$menu.find('li').last() + } + + prev.addClass('active') + } + + , listen: function () { + this.$element + .on('blur', $.proxy(this.blur, this)) + .on('keypress', $.proxy(this.keypress, this)) + .on('keyup', $.proxy(this.keyup, this)) + + if ($.browser.chrome || $.browser.webkit || $.browser.msie) { + this.$element.on('keydown', $.proxy(this.keydown, this)) + } + + this.$menu + .on('click', $.proxy(this.click, this)) + .on('mouseenter', 'li', $.proxy(this.mouseenter, this)) + } + + , move: function (e) { + if (!this.shown) return + + switch(e.keyCode) { + case 9: // tab + case 13: // enter + case 27: // escape + e.preventDefault() + break + + case 38: // up arrow + e.preventDefault() + this.prev() + break + + case 40: // down arrow + e.preventDefault() + this.next() + break + } + + e.stopPropagation() + } + + , keydown: function (e) { + this.suppressKeyPressRepeat = !~$.inArray(e.keyCode, [40,38,9,13,27]) + this.move(e) + } + + , keypress: function (e) { + if (this.suppressKeyPressRepeat) return + this.move(e) + } + + , keyup: function (e) { + switch(e.keyCode) { + case 40: // down arrow + case 38: // up arrow + break + + case 9: // tab + case 13: // enter + if (!this.shown) return + this.select() + break + + case 27: // escape + if (!this.shown) return + this.hide() + break + + default: + this.lookup() + } + + e.stopPropagation() + e.preventDefault() + } + + , blur: function (e) { + var that = this + setTimeout(function () { that.hide() }, 150) + } + + , click: function (e) { + e.stopPropagation() + e.preventDefault() + this.select() + } + + , mouseenter: function (e) { + this.$menu.find('.active').removeClass('active') + $(e.currentTarget).addClass('active') + } + + } + + + /* TYPEAHEAD PLUGIN DEFINITION + * =========================== */ + + $.fn.typeahead = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('typeahead') + , options = typeof option == 'object' && option + if (!data) $this.data('typeahead', (data = new Typeahead(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.typeahead.defaults = { + source: [] + , items: 8 + , menu: '
      ' + , item: '
      • ' + , minLength: 1 + } + + $.fn.typeahead.Constructor = Typeahead + + + /* TYPEAHEAD DATA-API + * ================== */ + + $(function () { + $('body').on('focus.typeahead.data-api', '[data-provide="typeahead"]', function (e) { + var $this = $(this) + if ($this.data('typeahead')) return + e.preventDefault() + $this.typeahead($this.data()) + }) + }) + +}(window.jQuery); +/* ========================================================== + * bootstrap-affix.js v2.1.1 + * http://twitter.github.com/bootstrap/javascript.html#affix + * ========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* AFFIX CLASS DEFINITION + * ====================== */ + + var Affix = function (element, options) { + this.options = $.extend({}, $.fn.affix.defaults, options) + this.$window = $(window).on('scroll.affix.data-api', $.proxy(this.checkPosition, this)) + this.$element = $(element) + this.checkPosition() + } + + Affix.prototype.checkPosition = function () { + if (!this.$element.is(':visible')) return + + var scrollHeight = $(document).height() + , scrollTop = this.$window.scrollTop() + , position = this.$element.offset() + , offset = this.options.offset + , offsetBottom = offset.bottom + , offsetTop = offset.top + , reset = 'affix affix-top affix-bottom' + , affix + + if (typeof offset != 'object') offsetBottom = offsetTop = offset + if (typeof offsetTop == 'function') offsetTop = offset.top() + if (typeof offsetBottom == 'function') offsetBottom = offset.bottom() + + affix = this.unpin != null && (scrollTop + this.unpin <= position.top) ? + false : offsetBottom != null && (position.top + this.$element.height() >= scrollHeight - offsetBottom) ? + 'bottom' : offsetTop != null && scrollTop <= offsetTop ? + 'top' : false + + if (this.affixed === affix) return + + this.affixed = affix + this.unpin = affix == 'bottom' ? position.top - scrollTop : null + + this.$element.removeClass(reset).addClass('affix' + (affix ? '-' + affix : '')) + } + + + /* AFFIX PLUGIN DEFINITION + * ======================= */ + + $.fn.affix = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('affix') + , options = typeof option == 'object' && option + if (!data) $this.data('affix', (data = new Affix(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.affix.Constructor = Affix + + $.fn.affix.defaults = { + offset: 0 + } + + + /* AFFIX DATA-API + * ============== */ + + $(window).on('load', function () { + $('[data-spy="affix"]').each(function () { + var $spy = $(this) + , data = $spy.data() + + data.offset = data.offset || {} + + data.offsetBottom && (data.offset.bottom = data.offsetBottom) + data.offsetTop && (data.offset.top = data.offsetTop) + + $spy.affix(data) + }) + }) + + +}(window.jQuery); \ No newline at end of file diff --git a/doc/source/themes/scikit-image/static/js/bootstrap.min.js 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- background-color: #222222; - *background-color: #151515; -} - -.btn-inverse:active, -.btn-inverse.active { - background-color: #080808 \9; -} - -button.btn, -input[type="submit"].btn { - *padding-top: 3px; - *padding-bottom: 3px; -} - -button.btn::-moz-focus-inner, -input[type="submit"].btn::-moz-focus-inner { - padding: 0; - border: 0; -} - -button.btn.btn-large, -input[type="submit"].btn.btn-large { - *padding-top: 7px; - *padding-bottom: 7px; -} - -button.btn.btn-small, -input[type="submit"].btn.btn-small { - *padding-top: 3px; - *padding-bottom: 3px; -} - -button.btn.btn-mini, -input[type="submit"].btn.btn-mini { - *padding-top: 1px; - *padding-bottom: 1px; -} - -.btn-link, -.btn-link:active, -.btn-link[disabled] { - background-color: transparent; - background-image: none; - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; -} - -.btn-link { - color: #0088cc; - cursor: pointer; - border-color: transparent; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.btn-link:hover { - color: #005580; - text-decoration: underline; - background-color: transparent; -} - -.btn-link[disabled]:hover { - color: #333333; - text-decoration: none; -} - -.btn-group { - position: relative; - *margin-left: .3em; - font-size: 0; - white-space: nowrap; - vertical-align: middle; -} - -.btn-group:first-child { - *margin-left: 0; -} - -.btn-group + .btn-group { - margin-left: 5px; -} - -.btn-toolbar { - margin-top: 10px; - margin-bottom: 10px; - font-size: 0; -} - -.btn-toolbar .btn-group { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; -} - -.btn-toolbar .btn + .btn, -.btn-toolbar .btn-group + .btn, -.btn-toolbar .btn + .btn-group { - margin-left: 5px; -} - -.btn-group > .btn { - position: relative; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.btn-group > .btn + .btn { - margin-left: -1px; -} - -.btn-group > .btn, -.btn-group > .dropdown-menu { - font-size: 14px; -} - -.btn-group > .btn-mini { - font-size: 11px; -} - -.btn-group > .btn-small { - font-size: 12px; -} - -.btn-group > .btn-large { - font-size: 16px; -} - -.btn-group > .btn:first-child { - margin-left: 0; - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - -moz-border-radius-topleft: 4px; -} - -.btn-group > .btn:last-child, -.btn-group > .dropdown-toggle { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-bottomright: 4px; -} - -.btn-group > .btn.large:first-child { - margin-left: 0; - -webkit-border-bottom-left-radius: 6px; - border-bottom-left-radius: 6px; - -webkit-border-top-left-radius: 6px; - border-top-left-radius: 6px; - -moz-border-radius-bottomleft: 6px; - -moz-border-radius-topleft: 6px; -} - -.btn-group > .btn.large:last-child, -.btn-group > .large.dropdown-toggle { - -webkit-border-top-right-radius: 6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - border-bottom-right-radius: 6px; - -moz-border-radius-topright: 6px; - -moz-border-radius-bottomright: 6px; -} - -.btn-group > .btn:hover, -.btn-group > .btn:focus, -.btn-group > .btn:active, -.btn-group > .btn.active { - z-index: 2; -} - -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} - -.btn-group > .btn + .dropdown-toggle { - *padding-top: 5px; - padding-right: 8px; - *padding-bottom: 5px; - padding-left: 8px; - -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group > .btn-mini + .dropdown-toggle { - *padding-top: 2px; - padding-right: 5px; - *padding-bottom: 2px; - padding-left: 5px; -} - -.btn-group > .btn-small + .dropdown-toggle { - *padding-top: 5px; - *padding-bottom: 4px; -} - -.btn-group > .btn-large + .dropdown-toggle { - *padding-top: 7px; - padding-right: 12px; - *padding-bottom: 7px; - padding-left: 12px; -} - -.btn-group.open .dropdown-toggle { - background-image: none; - -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group.open .btn.dropdown-toggle { - background-color: #e6e6e6; -} - -.btn-group.open .btn-primary.dropdown-toggle { - background-color: #0044cc; -} - -.btn-group.open .btn-warning.dropdown-toggle { - background-color: #f89406; -} - -.btn-group.open .btn-danger.dropdown-toggle { - background-color: #bd362f; -} - -.btn-group.open .btn-success.dropdown-toggle { - background-color: #51a351; -} - -.btn-group.open .btn-info.dropdown-toggle { - background-color: #2f96b4; -} - -.btn-group.open .btn-inverse.dropdown-toggle { - background-color: #222222; -} - -.btn .caret { - margin-top: 8px; - margin-left: 0; -} - -.btn-mini .caret, -.btn-small .caret, -.btn-large .caret { - margin-top: 6px; -} - -.btn-large .caret { - border-top-width: 5px; - border-right-width: 5px; - border-left-width: 5px; -} - -.dropup .btn-large .caret { - border-top: 0; - border-bottom: 5px solid #000000; -} - -.btn-primary .caret, -.btn-warning .caret, -.btn-danger .caret, -.btn-info .caret, -.btn-success .caret, -.btn-inverse .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.btn-group-vertical { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; -} - -.btn-group-vertical .btn { - display: block; - float: none; - width: 100%; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.btn-group-vertical .btn + .btn { - margin-top: -1px; - margin-left: 0; -} - -.btn-group-vertical .btn:first-child { - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} - -.btn-group-vertical .btn:last-child { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} - -.btn-group-vertical .btn-large:first-child { - -webkit-border-radius: 6px 6px 0 0; - -moz-border-radius: 6px 6px 0 0; - border-radius: 6px 6px 0 0; -} - -.btn-group-vertical .btn-large:last-child { - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; -} - -.alert { - padding: 8px 35px 8px 14px; - margin-bottom: 20px; - color: #c09853; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - background-color: #fcf8e3; - border: 1px solid #fbeed5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.alert h4 { - margin: 0; -} - -.alert .close { - position: relative; - top: -2px; - right: -21px; - line-height: 20px; -} - -.alert-success { - color: #468847; - background-color: #dff0d8; - border-color: #d6e9c6; -} - -.alert-danger, -.alert-error { - color: #b94a48; - background-color: #f2dede; - border-color: #eed3d7; -} - -.alert-info { - color: #3a87ad; - background-color: #d9edf7; - border-color: #bce8f1; -} - -.alert-block { - padding-top: 14px; - padding-bottom: 14px; -} - -.alert-block > p, -.alert-block > ul { - margin-bottom: 0; -} - -.alert-block p + p { - margin-top: 5px; -} - -.nav { - margin-bottom: 20px; - margin-left: 0; - list-style: none; -} - -.nav > li > a { - display: block; -} - -.nav > li > a:hover { - text-decoration: none; - background-color: #eeeeee; -} - -.nav > .pull-right { - float: right; -} - -.nav-header { - display: block; - padding: 3px 15px; - font-size: 11px; - font-weight: bold; - line-height: 20px; - color: #999999; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - text-transform: uppercase; -} - -.nav li + .nav-header { - margin-top: 9px; -} - -.nav-list { - padding-right: 15px; - padding-left: 15px; - margin-bottom: 0; -} - -.nav-list > li > a, -.nav-list .nav-header { - margin-right: -15px; - margin-left: -15px; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); -} - -.nav-list > li > a { - padding: 3px 15px; -} - -.nav-list > .active > a, -.nav-list > .active > a:hover { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); - background-color: #0088cc; -} - -.nav-list [class^="icon-"] { - margin-right: 2px; -} - -.nav-list .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} - -.nav-tabs, -.nav-pills { - *zoom: 1; -} - -.nav-tabs:before, -.nav-pills:before, -.nav-tabs:after, -.nav-pills:after { - display: table; - line-height: 0; - content: ""; -} - -.nav-tabs:after, -.nav-pills:after { - clear: both; -} - -.nav-tabs > li, -.nav-pills > li { - float: left; -} - -.nav-tabs > li > a, -.nav-pills > li > a { - padding-right: 12px; - padding-left: 12px; - margin-right: 2px; - line-height: 14px; -} - -.nav-tabs { - border-bottom: 1px solid #ddd; -} - -.nav-tabs > li { - margin-bottom: -1px; -} - -.nav-tabs > li > a { - padding-top: 8px; - padding-bottom: 8px; - line-height: 20px; - border: 1px solid transparent; - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} - -.nav-tabs > li > a:hover { - border-color: #eeeeee #eeeeee #dddddd; -} - -.nav-tabs > .active > a, -.nav-tabs > .active > a:hover { - color: #555555; - cursor: default; - background-color: #ffffff; - border: 1px solid #ddd; - border-bottom-color: transparent; -} - -.nav-pills > li > a { - padding-top: 8px; - padding-bottom: 8px; - margin-top: 2px; - margin-bottom: 2px; - -webkit-border-radius: 5px; - -moz-border-radius: 5px; - border-radius: 5px; -} - -.nav-pills > .active > a, -.nav-pills > .active > a:hover { - color: #ffffff; - background-color: #0088cc; -} - -.nav-stacked > li { - float: none; -} - -.nav-stacked > li > a { - margin-right: 0; -} - -.nav-tabs.nav-stacked { - border-bottom: 0; -} - -.nav-tabs.nav-stacked > li > a { - border: 1px solid #ddd; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.nav-tabs.nav-stacked > li:first-child > a { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-topleft: 4px; -} - -.nav-tabs.nav-stacked > li:last-child > a { - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -moz-border-radius-bottomright: 4px; - -moz-border-radius-bottomleft: 4px; -} - -.nav-tabs.nav-stacked > li > a:hover { - z-index: 2; - border-color: #ddd; -} - -.nav-pills.nav-stacked > li > a { - margin-bottom: 3px; -} - -.nav-pills.nav-stacked > li:last-child > a { - margin-bottom: 1px; -} - -.nav-tabs .dropdown-menu { - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; -} - -.nav-pills .dropdown-menu { - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.nav .dropdown-toggle .caret { - margin-top: 6px; - border-top-color: #0088cc; - border-bottom-color: #0088cc; -} - -.nav .dropdown-toggle:hover .caret { - border-top-color: #005580; - border-bottom-color: #005580; -} - -/* move down carets for tabs */ - -.nav-tabs .dropdown-toggle .caret { - margin-top: 8px; -} - -.nav .active .dropdown-toggle .caret { - border-top-color: #fff; - border-bottom-color: #fff; -} - -.nav-tabs .active .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.nav > .dropdown.active > a:hover { - cursor: pointer; -} - -.nav-tabs .open .dropdown-toggle, -.nav-pills .open .dropdown-toggle, -.nav > li.dropdown.open.active > a:hover { - color: #ffffff; - background-color: #999999; - border-color: #999999; -} - -.nav li.dropdown.open .caret, -.nav li.dropdown.open.active .caret, -.nav li.dropdown.open a:hover .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; - opacity: 1; - filter: alpha(opacity=100); -} - -.tabs-stacked .open > a:hover { - border-color: #999999; -} - -.tabbable { - *zoom: 1; -} - -.tabbable:before, -.tabbable:after { - display: table; - line-height: 0; - content: ""; -} - -.tabbable:after { - clear: both; -} - -.tab-content { - overflow: auto; -} - -.tabs-below > .nav-tabs, -.tabs-right > .nav-tabs, -.tabs-left > .nav-tabs { - border-bottom: 0; -} - -.tab-content > .tab-pane, -.pill-content > .pill-pane { - display: none; -} - -.tab-content > .active, -.pill-content > .active { - display: block; -} - -.tabs-below > .nav-tabs { - border-top: 1px solid #ddd; -} - -.tabs-below > .nav-tabs > li { - margin-top: -1px; - margin-bottom: 0; -} - -.tabs-below > .nav-tabs > li > a { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} - -.tabs-below > .nav-tabs > li > a:hover { - border-top-color: #ddd; - border-bottom-color: transparent; -} - -.tabs-below > .nav-tabs > .active > a, -.tabs-below > .nav-tabs > .active > a:hover { - border-color: transparent #ddd #ddd #ddd; -} - -.tabs-left > .nav-tabs > li, -.tabs-right > .nav-tabs > li { - float: none; -} - -.tabs-left > .nav-tabs > li > a, -.tabs-right > .nav-tabs > li > a { - min-width: 74px; - margin-right: 0; - margin-bottom: 3px; -} - -.tabs-left > .nav-tabs { - float: left; - margin-right: 19px; - border-right: 1px solid #ddd; -} - -.tabs-left > .nav-tabs > li > a { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.tabs-left > .nav-tabs > li > a:hover { - border-color: #eeeeee #dddddd #eeeeee #eeeeee; -} - -.tabs-left > .nav-tabs .active > a, -.tabs-left > .nav-tabs .active > a:hover { - border-color: #ddd transparent #ddd #ddd; - *border-right-color: #ffffff; -} - -.tabs-right > .nav-tabs { - float: right; - margin-left: 19px; - border-left: 1px solid #ddd; -} - -.tabs-right > .nav-tabs > li > a { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.tabs-right > .nav-tabs > li > a:hover { - border-color: #eeeeee #eeeeee #eeeeee #dddddd; -} - -.tabs-right > .nav-tabs .active > a, -.tabs-right > .nav-tabs .active > a:hover { - border-color: #ddd #ddd #ddd transparent; - *border-left-color: #ffffff; -} - -.nav > .disabled > a { - color: #999999; -} - -.nav > .disabled > a:hover { - text-decoration: none; - cursor: default; - background-color: transparent; -} - -.navbar { - *position: relative; - *z-index: 2; - margin-bottom: 20px; - overflow: visible; - color: #777777; -} - -.navbar-inner { - min-height: 40px; - padding-right: 20px; - padding-left: 20px; - background-color: #fafafa; - background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2)); - background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -o-linear-gradient(top, #ffffff, #f2f2f2); - background-image: linear-gradient(to bottom, #ffffff, #f2f2f2); - background-repeat: repeat-x; - border: 1px solid #d4d4d4; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0); - *zoom: 1; - -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); -} - -.navbar-inner:before, -.navbar-inner:after { - display: table; - line-height: 0; - content: ""; -} - -.navbar-inner:after { - clear: both; -} - -.navbar .container { - width: auto; -} - -.nav-collapse.collapse { - height: auto; -} - -.navbar .brand { - display: block; - float: left; - padding: 10px 20px 10px; - margin-left: -20px; - font-size: 20px; - font-weight: 200; - color: #777777; 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- -webkit-border-radius: 23px; - -moz-border-radius: 23px; - border-radius: 23px; - opacity: 0.5; - filter: alpha(opacity=50); -} - -.carousel-control.right { - right: 15px; - left: auto; -} - -.carousel-control:hover { - color: #ffffff; - text-decoration: none; - opacity: 0.9; - filter: alpha(opacity=90); -} - -.carousel-caption { - position: absolute; - right: 0; - bottom: 0; - left: 0; - padding: 15px; - background: #333333; - background: rgba(0, 0, 0, 0.75); -} - -.carousel-caption h4, -.carousel-caption p { - line-height: 20px; - color: #ffffff; -} - -.carousel-caption h4 { - margin: 0 0 5px; -} - -.carousel-caption p { - margin-bottom: 0; -} - -.hero-unit { - padding: 60px; - margin-bottom: 30px; - background-color: #eeeeee; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.hero-unit h1 { - margin-bottom: 0; - font-size: 60px; - line-height: 1; - letter-spacing: -1px; - color: inherit; -} - -.hero-unit p { - font-size: 18px; - font-weight: 200; - line-height: 30px; - color: inherit; -} - -.pull-right { - float: right; -} - -.pull-left { - float: left; -} - -.hide { - display: none; -} - -.show { - display: block; -} - -.invisible { - visibility: hidden; -} - -.affix { - position: fixed; -} diff --git a/doc/source/themes/scikit-image/static/js/bootstrap.js b/doc/source/themes/scikit-image/static/js/bootstrap.js deleted file mode 100644 index f73fcb8e..00000000 --- a/doc/source/themes/scikit-image/static/js/bootstrap.js +++ /dev/null @@ -1,2027 +0,0 @@ -/* =================================================== - * bootstrap-transition.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#transitions - * =================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - $(function () { - - "use strict"; // jshint ;_; - - - /* CSS TRANSITION SUPPORT (http://www.modernizr.com/) - * ======================================================= */ - - $.support.transition = (function () { - - var transitionEnd = (function () { - - var el = document.createElement('bootstrap') - , transEndEventNames = { - 'WebkitTransition' : 'webkitTransitionEnd' - , 'MozTransition' : 'transitionend' - , 'OTransition' : 'oTransitionEnd otransitionend' - , 'transition' : 'transitionend' - } - , name - - for (name in transEndEventNames){ - if (el.style[name] !== undefined) { - return transEndEventNames[name] - } - } - - }()) - - return transitionEnd && { - end: transitionEnd - } - - })() - - }) - -}(window.jQuery);/* ========================================================== - * bootstrap-alert.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#alerts - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* ALERT CLASS DEFINITION - * ====================== */ - - var dismiss = '[data-dismiss="alert"]' - , Alert = function (el) { - $(el).on('click', dismiss, this.close) - } - - Alert.prototype.close = function (e) { - var $this = $(this) - , selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = $(selector) - - e && e.preventDefault() - - $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent()) - - $parent.trigger(e = $.Event('close')) - - if (e.isDefaultPrevented()) return - - $parent.removeClass('in') - - function removeElement() { - $parent - .trigger('closed') - .remove() - } - - $.support.transition && $parent.hasClass('fade') ? - $parent.on($.support.transition.end, removeElement) : - removeElement() - } - - - /* ALERT PLUGIN DEFINITION - * ======================= */ - - $.fn.alert = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('alert') - if (!data) $this.data('alert', (data = new Alert(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.alert.Constructor = Alert - - - /* ALERT DATA-API - * ============== */ - - $(function () { - $('body').on('click.alert.data-api', dismiss, Alert.prototype.close) - }) - -}(window.jQuery);/* ============================================================ - * bootstrap-button.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#buttons - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* BUTTON PUBLIC CLASS DEFINITION - * ============================== */ - - var Button = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.button.defaults, options) - } - - Button.prototype.setState = function (state) { - var d = 'disabled' - , $el = this.$element - , data = $el.data() - , val = $el.is('input') ? 'val' : 'html' - - state = state + 'Text' - data.resetText || $el.data('resetText', $el[val]()) - - $el[val](data[state] || this.options[state]) - - // push to event loop to allow forms to submit - setTimeout(function () { - state == 'loadingText' ? - $el.addClass(d).attr(d, d) : - $el.removeClass(d).removeAttr(d) - }, 0) - } - - Button.prototype.toggle = function () { - var $parent = this.$element.closest('[data-toggle="buttons-radio"]') - - $parent && $parent - .find('.active') - .removeClass('active') - - this.$element.toggleClass('active') - } - - - /* BUTTON PLUGIN DEFINITION - * ======================== */ - - $.fn.button = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('button') - , options = typeof option == 'object' && option - if (!data) $this.data('button', (data = new Button(this, options))) - if (option == 'toggle') data.toggle() - else if (option) data.setState(option) - }) - } - - $.fn.button.defaults = { - loadingText: 'loading...' - } - - $.fn.button.Constructor = Button - - - /* BUTTON DATA-API - * =============== */ - - $(function () { - $('body').on('click.button.data-api', '[data-toggle^=button]', function ( e ) { - var $btn = $(e.target) - if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn') - $btn.button('toggle') - }) - }) - -}(window.jQuery);/* ========================================================== - * bootstrap-carousel.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#carousel - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CAROUSEL CLASS DEFINITION - * ========================= */ - - var Carousel = function (element, options) { - this.$element = $(element) - this.options = options - this.options.slide && this.slide(this.options.slide) - this.options.pause == 'hover' && this.$element - .on('mouseenter', $.proxy(this.pause, this)) - .on('mouseleave', $.proxy(this.cycle, this)) - } - - Carousel.prototype = { - - cycle: function (e) { - if (!e) this.paused = false - this.options.interval - && !this.paused - && (this.interval = setInterval($.proxy(this.next, this), this.options.interval)) - return this - } - - , to: function (pos) { - var $active = this.$element.find('.item.active') - , children = $active.parent().children() - , activePos = children.index($active) - , that = this - - if (pos > (children.length - 1) || pos < 0) return - - if (this.sliding) { - return this.$element.one('slid', function () { - that.to(pos) - }) - } - - if (activePos == pos) { - return this.pause().cycle() - } - - return this.slide(pos > activePos ? 'next' : 'prev', $(children[pos])) - } - - , pause: function (e) { - if (!e) this.paused = true - if (this.$element.find('.next, .prev').length && $.support.transition.end) { - this.$element.trigger($.support.transition.end) - this.cycle() - } - clearInterval(this.interval) - this.interval = null - return this - } - - , next: function () { - if (this.sliding) return - return this.slide('next') - } - - , prev: function () { - if (this.sliding) return - return this.slide('prev') - } - - , slide: function (type, next) { - var $active = this.$element.find('.item.active') - , $next = next || $active[type]() - , isCycling = this.interval - , direction = type == 'next' ? 'left' : 'right' - , fallback = type == 'next' ? 'first' : 'last' - , that = this - , e = $.Event('slide', { - relatedTarget: $next[0] - }) - - this.sliding = true - - isCycling && this.pause() - - $next = $next.length ? $next : this.$element.find('.item')[fallback]() - - if ($next.hasClass('active')) return - - if ($.support.transition && this.$element.hasClass('slide')) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $next.addClass(type) - $next[0].offsetWidth // force reflow - $active.addClass(direction) - $next.addClass(direction) - this.$element.one($.support.transition.end, function () { - $next.removeClass([type, direction].join(' ')).addClass('active') - $active.removeClass(['active', direction].join(' ')) - that.sliding = false - setTimeout(function () { that.$element.trigger('slid') }, 0) - }) - } else { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $active.removeClass('active') - $next.addClass('active') - this.sliding = false - this.$element.trigger('slid') - } - - isCycling && this.cycle() - - return this - } - - } - - - /* CAROUSEL PLUGIN DEFINITION - * ========================== */ - - $.fn.carousel = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('carousel') - , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option) - , action = typeof option == 'string' ? option : options.slide - if (!data) $this.data('carousel', (data = new Carousel(this, options))) - if (typeof option == 'number') data.to(option) - else if (action) data[action]() - else if (options.interval) data.cycle() - }) - } - - $.fn.carousel.defaults = { - interval: 5000 - , pause: 'hover' - } - - $.fn.carousel.Constructor = Carousel - - - /* CAROUSEL DATA-API - * ================= */ - - $(function () { - $('body').on('click.carousel.data-api', '[data-slide]', function ( e ) { - var $this = $(this), href - , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - , options = !$target.data('modal') && $.extend({}, $target.data(), $this.data()) - $target.carousel(options) - e.preventDefault() - }) - }) - -}(window.jQuery);/* ============================================================= - * bootstrap-collapse.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#collapse - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* COLLAPSE PUBLIC CLASS DEFINITION - * ================================ */ - - var Collapse = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.collapse.defaults, options) - - if (this.options.parent) { - this.$parent = $(this.options.parent) - } - - this.options.toggle && this.toggle() - } - - Collapse.prototype = { - - constructor: Collapse - - , dimension: function () { - var hasWidth = this.$element.hasClass('width') - return hasWidth ? 'width' : 'height' - } - - , show: function () { - var dimension - , scroll - , actives - , hasData - - if (this.transitioning) return - - dimension = this.dimension() - scroll = $.camelCase(['scroll', dimension].join('-')) - actives = this.$parent && this.$parent.find('> .accordion-group > .in') - - if (actives && actives.length) { - hasData = actives.data('collapse') - if (hasData && hasData.transitioning) return - actives.collapse('hide') - hasData || actives.data('collapse', null) - } - - this.$element[dimension](0) - this.transition('addClass', $.Event('show'), 'shown') - $.support.transition && this.$element[dimension](this.$element[0][scroll]) - } - - , hide: function () { - var dimension - if (this.transitioning) return - dimension = this.dimension() - this.reset(this.$element[dimension]()) - this.transition('removeClass', $.Event('hide'), 'hidden') - this.$element[dimension](0) - } - - , reset: function (size) { - var dimension = this.dimension() - - this.$element - .removeClass('collapse') - [dimension](size || 'auto') - [0].offsetWidth - - this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') - - return this - } - - , transition: function (method, startEvent, completeEvent) { - var that = this - , complete = function () { - if (startEvent.type == 'show') that.reset() - that.transitioning = 0 - that.$element.trigger(completeEvent) - } - - this.$element.trigger(startEvent) - - if (startEvent.isDefaultPrevented()) return - - this.transitioning = 1 - - this.$element[method]('in') - - $.support.transition && this.$element.hasClass('collapse') ? - this.$element.one($.support.transition.end, complete) : - complete() - } - - , toggle: function () { - this[this.$element.hasClass('in') ? 'hide' : 'show']() - } - - } - - - /* COLLAPSIBLE PLUGIN DEFINITION - * ============================== */ - - $.fn.collapse = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('collapse') - , options = typeof option == 'object' && option - if (!data) $this.data('collapse', (data = new Collapse(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.collapse.defaults = { - toggle: true - } - - $.fn.collapse.Constructor = Collapse - - - /* COLLAPSIBLE DATA-API - * ==================== */ - - $(function () { - $('body').on('click.collapse.data-api', '[data-toggle=collapse]', function (e) { - var $this = $(this), href - , target = $this.attr('data-target') - || e.preventDefault() - || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 - , option = $(target).data('collapse') ? 'toggle' : $this.data() - $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed') - $(target).collapse(option) - }) - }) - -}(window.jQuery);/* ============================================================ - * bootstrap-dropdown.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#dropdowns - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* DROPDOWN CLASS DEFINITION - * ========================= */ - - var toggle = '[data-toggle=dropdown]' - , Dropdown = function (element) { - var $el = $(element).on('click.dropdown.data-api', this.toggle) - $('html').on('click.dropdown.data-api', function () { - $el.parent().removeClass('open') - }) - } - - Dropdown.prototype = { - - constructor: Dropdown - - , toggle: function (e) { - var $this = $(this) - , $parent - , isActive - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - clearMenus() - - if (!isActive) { - $parent.toggleClass('open') - $this.focus() - } - - return false - } - - , keydown: function (e) { - var $this - , $items - , $active - , $parent - , isActive - , index - - if (!/(38|40|27)/.test(e.keyCode)) return - - $this = $(this) - - e.preventDefault() - e.stopPropagation() - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - if (!isActive || (isActive && e.keyCode == 27)) return $this.click() - - $items = $('[role=menu] li:not(.divider) a', $parent) - - if (!$items.length) return - - index = $items.index($items.filter(':focus')) - - if (e.keyCode == 38 && index > 0) index-- // up - if (e.keyCode == 40 && index < $items.length - 1) index++ // down - if (!~index) index = 0 - - $items - .eq(index) - .focus() - } - - } - - function clearMenus() { - getParent($(toggle)) - .removeClass('open') - } - - function getParent($this) { - var selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = $(selector) - $parent.length || ($parent = $this.parent()) - - return $parent - } - - - /* DROPDOWN PLUGIN DEFINITION - * ========================== */ - - $.fn.dropdown = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('dropdown') - if (!data) $this.data('dropdown', (data = new Dropdown(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.dropdown.Constructor = Dropdown - - - /* APPLY TO STANDARD DROPDOWN ELEMENTS - * =================================== */ - - $(function () { - $('html') - .on('click.dropdown.data-api touchstart.dropdown.data-api', clearMenus) - $('body') - .on('click.dropdown touchstart.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() }) - .on('click.dropdown.data-api touchstart.dropdown.data-api' , toggle, Dropdown.prototype.toggle) - .on('keydown.dropdown.data-api touchstart.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown) - }) - -}(window.jQuery);/* ========================================================= - * bootstrap-modal.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#modals - * ========================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================= */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* MODAL CLASS DEFINITION - * ====================== */ - - var Modal = function (element, options) { - this.options = options - this.$element = $(element) - .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) - this.options.remote && this.$element.find('.modal-body').load(this.options.remote) - } - - Modal.prototype = { - - constructor: Modal - - , toggle: function () { - return this[!this.isShown ? 'show' : 'hide']() - } - - , show: function () { - var that = this - , e = $.Event('show') - - this.$element.trigger(e) - - if (this.isShown || e.isDefaultPrevented()) return - - $('body').addClass('modal-open') - - this.isShown = true - - this.escape() - - this.backdrop(function () { - var transition = $.support.transition && that.$element.hasClass('fade') - - if (!that.$element.parent().length) { - that.$element.appendTo(document.body) //don't move modals dom position - } - - that.$element - .show() - - if (transition) { - that.$element[0].offsetWidth // force reflow - } - - that.$element - .addClass('in') - .attr('aria-hidden', false) - .focus() - - that.enforceFocus() - - transition ? - that.$element.one($.support.transition.end, function () { that.$element.trigger('shown') }) : - that.$element.trigger('shown') - - }) - } - - , hide: function (e) { - e && e.preventDefault() - - var that = this - - e = $.Event('hide') - - this.$element.trigger(e) - - if (!this.isShown || e.isDefaultPrevented()) return - - this.isShown = false - - $('body').removeClass('modal-open') - - this.escape() - - $(document).off('focusin.modal') - - this.$element - .removeClass('in') - .attr('aria-hidden', true) - - $.support.transition && this.$element.hasClass('fade') ? - this.hideWithTransition() : - this.hideModal() - } - - , enforceFocus: function () { - var that = this - $(document).on('focusin.modal', function (e) { - if (that.$element[0] !== e.target && !that.$element.has(e.target).length) { - that.$element.focus() - } - }) - } - - , escape: function () { - var that = this - if (this.isShown && this.options.keyboard) { - this.$element.on('keyup.dismiss.modal', function ( e ) { - e.which == 27 && that.hide() - }) - } else if (!this.isShown) { - this.$element.off('keyup.dismiss.modal') - } - } - - , hideWithTransition: function () { - var that = this - , timeout = setTimeout(function () { - that.$element.off($.support.transition.end) - that.hideModal() - }, 500) - - this.$element.one($.support.transition.end, function () { - clearTimeout(timeout) - that.hideModal() - }) - } - - , hideModal: function (that) { - this.$element - .hide() - .trigger('hidden') - - this.backdrop() - } - - , removeBackdrop: function () { - this.$backdrop.remove() - this.$backdrop = null - } - - , backdrop: function (callback) { - var that = this - , animate = this.$element.hasClass('fade') ? 'fade' : '' - - if (this.isShown && this.options.backdrop) { - var doAnimate = $.support.transition && animate - - this.$backdrop = $('
        ') - .appendTo(document.body) - - if (this.options.backdrop != 'static') { - this.$backdrop.click($.proxy(this.hide, this)) - } - - if (doAnimate) this.$backdrop[0].offsetWidth // force reflow - - this.$backdrop.addClass('in') - - doAnimate ? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (!this.isShown && this.$backdrop) { - this.$backdrop.removeClass('in') - - $.support.transition && this.$element.hasClass('fade')? - this.$backdrop.one($.support.transition.end, $.proxy(this.removeBackdrop, this)) : - this.removeBackdrop() - - } else if (callback) { - callback() - } - } - } - - - /* MODAL PLUGIN DEFINITION - * ======================= */ - - $.fn.modal = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('modal') - , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('modal', (data = new Modal(this, options))) - if (typeof option == 'string') data[option]() - else if (options.show) data.show() - }) - } - - $.fn.modal.defaults = { - backdrop: true - , keyboard: true - , show: true - } - - $.fn.modal.Constructor = Modal - - - /* MODAL DATA-API - * ============== */ - - $(function () { - $('body').on('click.modal.data-api', '[data-toggle="modal"]', function ( e ) { - var $this = $(this) - , href = $this.attr('href') - , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7 - , option = $target.data('modal') ? 'toggle' : $.extend({ remote: !/#/.test(href) && href }, $target.data(), $this.data()) - - e.preventDefault() - - $target - .modal(option) - .one('hide', function () { - $this.focus() - }) - }) - }) - -}(window.jQuery);/* =========================================================== - * bootstrap-tooltip.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#tooltips - * Inspired by the original jQuery.tipsy by Jason Frame - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TOOLTIP PUBLIC CLASS DEFINITION - * =============================== */ - - var Tooltip = function (element, options) { - this.init('tooltip', element, options) - } - - Tooltip.prototype = { - - constructor: Tooltip - - , init: function (type, element, options) { - var eventIn - , eventOut - - this.type = type - this.$element = $(element) - this.options = this.getOptions(options) - this.enabled = true - - if (this.options.trigger == 'click') { - this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) - } else if (this.options.trigger != 'manual') { - eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus' - eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur' - this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) - this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) - } - - this.options.selector ? - (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : - this.fixTitle() - } - - , getOptions: function (options) { - options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data()) - - if (options.delay && typeof options.delay == 'number') { - options.delay = { - show: options.delay - , hide: options.delay - } - } - - return options - } - - , enter: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) - - if (!self.options.delay || !self.options.delay.show) return self.show() - - clearTimeout(this.timeout) - self.hoverState = 'in' - this.timeout = setTimeout(function() { - if (self.hoverState == 'in') self.show() - }, self.options.delay.show) - } - - , leave: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) - - if (this.timeout) clearTimeout(this.timeout) - if (!self.options.delay || !self.options.delay.hide) return self.hide() - - self.hoverState = 'out' - this.timeout = setTimeout(function() { - if (self.hoverState == 'out') self.hide() - }, self.options.delay.hide) - } - - , show: function () { - var $tip - , inside - , pos - , actualWidth - , actualHeight - , placement - , tp - - if (this.hasContent() && this.enabled) { - $tip = this.tip() - this.setContent() - - if (this.options.animation) { - $tip.addClass('fade') - } - - placement = typeof this.options.placement == 'function' ? - this.options.placement.call(this, $tip[0], this.$element[0]) : - this.options.placement - - inside = /in/.test(placement) - - $tip - .remove() - .css({ top: 0, left: 0, display: 'block' }) - .appendTo(inside ? this.$element : document.body) - - pos = this.getPosition(inside) - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - switch (inside ? placement.split(' ')[1] : placement) { - case 'bottom': - tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'top': - tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'left': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} - break - case 'right': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} - break - } - - $tip - .css(tp) - .addClass(placement) - .addClass('in') - } - } - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - - $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title) - $tip.removeClass('fade in top bottom left right') - } - - , hide: function () { - var that = this - , $tip = this.tip() - - $tip.removeClass('in') - - function removeWithAnimation() { - var timeout = setTimeout(function () { - $tip.off($.support.transition.end).remove() - }, 500) - - $tip.one($.support.transition.end, function () { - clearTimeout(timeout) - $tip.remove() - }) - } - - $.support.transition && this.$tip.hasClass('fade') ? - removeWithAnimation() : - $tip.remove() - - return this - } - - , fixTitle: function () { - var $e = this.$element - if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { - $e.attr('data-original-title', $e.attr('title') || '').removeAttr('title') - } - } - - , hasContent: function () { - return this.getTitle() - } - - , getPosition: function (inside) { - return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), { - width: this.$element[0].offsetWidth - , height: this.$element[0].offsetHeight - }) - } - - , getTitle: function () { - var title - , $e = this.$element - , o = this.options - - title = $e.attr('data-original-title') - || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) - - return title - } - - , tip: function () { - return this.$tip = this.$tip || $(this.options.template) - } - - , validate: function () { - if (!this.$element[0].parentNode) { - this.hide() - this.$element = null - this.options = null - } - } - - , enable: function () { - this.enabled = true - } - - , disable: function () { - this.enabled = false - } - - , toggleEnabled: function () { - this.enabled = !this.enabled - } - - , toggle: function () { - this[this.tip().hasClass('in') ? 'hide' : 'show']() - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - } - - - /* TOOLTIP PLUGIN DEFINITION - * ========================= */ - - $.fn.tooltip = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tooltip') - , options = typeof option == 'object' && option - if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tooltip.Constructor = Tooltip - - $.fn.tooltip.defaults = { - animation: true - , placement: 'top' - , selector: false - , template: '
        ' - , trigger: 'hover' - , title: '' - , delay: 0 - , html: true - } - -}(window.jQuery); -/* =========================================================== - * bootstrap-popover.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#popovers - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * =========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* POPOVER PUBLIC CLASS DEFINITION - * =============================== */ - - var Popover = function (element, options) { - this.init('popover', element, options) - } - - - /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js - ========================================== */ - - Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { - - constructor: Popover - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - , content = this.getContent() - - $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title) - $tip.find('.popover-content > *')[this.options.html ? 'html' : 'text'](content) - - $tip.removeClass('fade top bottom left right in') - } - - , hasContent: function () { - return this.getTitle() || this.getContent() - } - - , getContent: function () { - var content - , $e = this.$element - , o = this.options - - content = $e.attr('data-content') - || (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) - - return content - } - - , tip: function () { - if (!this.$tip) { - this.$tip = $(this.options.template) - } - return this.$tip - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - }) - - - /* POPOVER PLUGIN DEFINITION - * ======================= */ - - $.fn.popover = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('popover') - , options = typeof option == 'object' && option - if (!data) $this.data('popover', (data = new Popover(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.popover.Constructor = Popover - - $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { - placement: 'right' - , trigger: 'click' - , content: '' - , template: '

        ' - }) - -}(window.jQuery);/* ============================================================= - * bootstrap-scrollspy.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#scrollspy - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* SCROLLSPY CLASS DEFINITION - * ========================== */ - - function ScrollSpy(element, options) { - var process = $.proxy(this.process, this) - , $element = $(element).is('body') ? $(window) : $(element) - , href - this.options = $.extend({}, $.fn.scrollspy.defaults, options) - this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process) - this.selector = (this.options.target - || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - || '') + ' .nav li > a' - this.$body = $('body') - this.refresh() - this.process() - } - - ScrollSpy.prototype = { - - constructor: ScrollSpy - - , refresh: function () { - var self = this - , $targets - - this.offsets = $([]) - this.targets = $([]) - - $targets = this.$body - .find(this.selector) - .map(function () { - var $el = $(this) - , href = $el.data('target') || $el.attr('href') - , $href = /^#\w/.test(href) && $(href) - return ( $href - && $href.length - && [[ $href.position().top, href ]] ) || null - }) - .sort(function (a, b) { return a[0] - b[0] }) - .each(function () { - self.offsets.push(this[0]) - self.targets.push(this[1]) - }) - } - - , process: function () { - var scrollTop = this.$scrollElement.scrollTop() + this.options.offset - , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight - , maxScroll = scrollHeight - this.$scrollElement.height() - , offsets = this.offsets - , targets = this.targets - , activeTarget = this.activeTarget - , i - - if (scrollTop >= maxScroll) { - return activeTarget != (i = targets.last()[0]) - && this.activate ( i ) - } - - for (i = offsets.length; i--;) { - activeTarget != targets[i] - && scrollTop >= offsets[i] - && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) - && this.activate( targets[i] ) - } - } - - , activate: function (target) { - var active - , selector - - this.activeTarget = target - - $(this.selector) - .parent('.active') - .removeClass('active') - - selector = this.selector - + '[data-target="' + target + '"],' - + this.selector + '[href="' + target + '"]' - - active = $(selector) - .parent('li') - .addClass('active') - - if (active.parent('.dropdown-menu').length) { - active = active.closest('li.dropdown').addClass('active') - } - - active.trigger('activate') - } - - } - - - /* SCROLLSPY PLUGIN DEFINITION - * =========================== */ - - $.fn.scrollspy = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('scrollspy') - , options = typeof option == 'object' && option - if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.scrollspy.Constructor = ScrollSpy - - $.fn.scrollspy.defaults = { - offset: 10 - } - - - /* SCROLLSPY DATA-API - * ================== */ - - $(window).on('load', function () { - $('[data-spy="scroll"]').each(function () { - var $spy = $(this) - $spy.scrollspy($spy.data()) - }) - }) - -}(window.jQuery);/* ======================================================== - * bootstrap-tab.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#tabs - * ======================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ======================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TAB CLASS DEFINITION - * ==================== */ - - var Tab = function (element) { - this.element = $(element) - } - - Tab.prototype = { - - constructor: Tab - - , show: function () { - var $this = this.element - , $ul = $this.closest('ul:not(.dropdown-menu)') - , selector = $this.attr('data-target') - , previous - , $target - , e - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - if ( $this.parent('li').hasClass('active') ) return - - previous = $ul.find('.active a').last()[0] - - e = $.Event('show', { - relatedTarget: previous - }) - - $this.trigger(e) - - if (e.isDefaultPrevented()) return - - $target = $(selector) - - this.activate($this.parent('li'), $ul) - this.activate($target, $target.parent(), function () { - $this.trigger({ - type: 'shown' - , relatedTarget: previous - }) - }) - } - - , activate: function ( element, container, callback) { - var $active = container.find('> .active') - , transition = callback - && $.support.transition - && $active.hasClass('fade') - - function next() { - $active - .removeClass('active') - .find('> .dropdown-menu > .active') - .removeClass('active') - - element.addClass('active') - - if (transition) { - element[0].offsetWidth // reflow for transition - element.addClass('in') - } else { - element.removeClass('fade') - } - - if ( element.parent('.dropdown-menu') ) { - element.closest('li.dropdown').addClass('active') - } - - callback && callback() - } - - transition ? - $active.one($.support.transition.end, next) : - next() - - $active.removeClass('in') - } - } - - - /* TAB PLUGIN DEFINITION - * ===================== */ - - $.fn.tab = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tab') - if (!data) $this.data('tab', (data = new Tab(this))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tab.Constructor = Tab - - - /* TAB DATA-API - * ============ */ - - $(function () { - $('body').on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { - e.preventDefault() - $(this).tab('show') - }) - }) - -}(window.jQuery);/* ============================================================= - * bootstrap-typeahead.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#typeahead - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function($){ - - "use strict"; // jshint ;_; - - - /* TYPEAHEAD PUBLIC CLASS DEFINITION - * ================================= */ - - var Typeahead = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.typeahead.defaults, options) - this.matcher = this.options.matcher || this.matcher - this.sorter = this.options.sorter || this.sorter - this.highlighter = this.options.highlighter || this.highlighter - this.updater = this.options.updater || this.updater - this.$menu = $(this.options.menu).appendTo('body') - this.source = this.options.source - this.shown = false - this.listen() - } - - Typeahead.prototype = { - - constructor: Typeahead - - , select: function () { - var val = this.$menu.find('.active').attr('data-value') - this.$element - .val(this.updater(val)) - .change() - return this.hide() - } - - , updater: function (item) { - return item - } - - , show: function () { - var pos = $.extend({}, this.$element.offset(), { - height: this.$element[0].offsetHeight - }) - - this.$menu.css({ - top: pos.top + pos.height - , left: pos.left - }) - - this.$menu.show() - this.shown = true - return this - } - - , hide: function () { - this.$menu.hide() - this.shown = false - return this - } - - , lookup: function (event) { - var items - - this.query = this.$element.val() - - if (!this.query || this.query.length < this.options.minLength) { - return this.shown ? this.hide() : this - } - - items = $.isFunction(this.source) ? this.source(this.query, $.proxy(this.process, this)) : this.source - - return items ? this.process(items) : this - } - - , process: function (items) { - var that = this - - items = $.grep(items, function (item) { - return that.matcher(item) - }) - - items = this.sorter(items) - - if (!items.length) { - return this.shown ? this.hide() : this - } - - return this.render(items.slice(0, this.options.items)).show() - } - - , matcher: function (item) { - return ~item.toLowerCase().indexOf(this.query.toLowerCase()) - } - - , sorter: function (items) { - var beginswith = [] - , caseSensitive = [] - , caseInsensitive = [] - , item - - while (item = items.shift()) { - if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item) - else if (~item.indexOf(this.query)) caseSensitive.push(item) - else caseInsensitive.push(item) - } - - return beginswith.concat(caseSensitive, caseInsensitive) - } - - , highlighter: function (item) { - var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&') - return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) { - return '' + match + '' - }) - } - - , render: function (items) { - var that = this - - items = $(items).map(function (i, item) { - i = $(that.options.item).attr('data-value', item) - i.find('a').html(that.highlighter(item)) - return i[0] - }) - - items.first().addClass('active') - this.$menu.html(items) - return this - } - - , next: function (event) { - var active = this.$menu.find('.active').removeClass('active') - , next = active.next() - - if (!next.length) { - next = $(this.$menu.find('li')[0]) - } - - next.addClass('active') - } - - , prev: function (event) { - var active = this.$menu.find('.active').removeClass('active') - , prev = active.prev() - - if (!prev.length) { - prev = this.$menu.find('li').last() - } - - prev.addClass('active') - } - - , listen: function () { - this.$element - .on('blur', $.proxy(this.blur, this)) - .on('keypress', $.proxy(this.keypress, this)) - .on('keyup', $.proxy(this.keyup, this)) - - if ($.browser.chrome || $.browser.webkit || $.browser.msie) { - this.$element.on('keydown', $.proxy(this.keydown, this)) - } - - this.$menu - .on('click', $.proxy(this.click, this)) - .on('mouseenter', 'li', $.proxy(this.mouseenter, this)) - } - - , move: function (e) { - if (!this.shown) return - - switch(e.keyCode) { - case 9: // tab - case 13: // enter - case 27: // escape - e.preventDefault() - break - - case 38: // up arrow - e.preventDefault() - this.prev() - break - - case 40: // down arrow - e.preventDefault() - this.next() - break - } - - e.stopPropagation() - } - - , keydown: function (e) { - this.suppressKeyPressRepeat = !~$.inArray(e.keyCode, [40,38,9,13,27]) - this.move(e) - } - - , keypress: function (e) { - if (this.suppressKeyPressRepeat) return - this.move(e) - } - - , keyup: function (e) { - switch(e.keyCode) { - case 40: // down arrow - case 38: // up arrow - break - - case 9: // tab - case 13: // enter - if (!this.shown) return - this.select() - break - - case 27: // escape - if (!this.shown) return - this.hide() - break - - default: - this.lookup() - } - - e.stopPropagation() - e.preventDefault() - } - - , blur: function (e) { - var that = this - setTimeout(function () { that.hide() }, 150) - } - - , click: function (e) { - e.stopPropagation() - e.preventDefault() - this.select() - } - - , mouseenter: function (e) { - this.$menu.find('.active').removeClass('active') - $(e.currentTarget).addClass('active') - } - - } - - - /* TYPEAHEAD PLUGIN DEFINITION - * =========================== */ - - $.fn.typeahead = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('typeahead') - , options = typeof option == 'object' && option - if (!data) $this.data('typeahead', (data = new Typeahead(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.typeahead.defaults = { - source: [] - , items: 8 - , menu: '
          ' - , item: '
          • ' - , minLength: 1 - } - - $.fn.typeahead.Constructor = Typeahead - - - /* TYPEAHEAD DATA-API - * ================== */ - - $(function () { - $('body').on('focus.typeahead.data-api', '[data-provide="typeahead"]', function (e) { - var $this = $(this) - if ($this.data('typeahead')) return - e.preventDefault() - $this.typeahead($this.data()) - }) - }) - -}(window.jQuery); -/* ========================================================== - * bootstrap-affix.js v2.1.1 - * http://twitter.github.com/bootstrap/javascript.html#affix - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* AFFIX CLASS DEFINITION - * ====================== */ - - var Affix = function (element, options) { - this.options = $.extend({}, $.fn.affix.defaults, options) - this.$window = $(window).on('scroll.affix.data-api', $.proxy(this.checkPosition, this)) - this.$element = $(element) - this.checkPosition() - } - - Affix.prototype.checkPosition = function () { - if (!this.$element.is(':visible')) return - - var scrollHeight = $(document).height() - , scrollTop = this.$window.scrollTop() - , position = this.$element.offset() - , offset = this.options.offset - , offsetBottom = offset.bottom - , offsetTop = offset.top - , reset = 'affix affix-top affix-bottom' - , affix - - if (typeof offset != 'object') offsetBottom = offsetTop = offset - if (typeof offsetTop == 'function') offsetTop = offset.top() - if (typeof offsetBottom == 'function') offsetBottom = offset.bottom() - - affix = this.unpin != null && (scrollTop + this.unpin <= position.top) ? - false : offsetBottom != null && (position.top + this.$element.height() >= scrollHeight - offsetBottom) ? - 'bottom' : offsetTop != null && scrollTop <= offsetTop ? - 'top' : false - - if (this.affixed === affix) return - - this.affixed = affix - this.unpin = affix == 'bottom' ? position.top - scrollTop : null - - this.$element.removeClass(reset).addClass('affix' + (affix ? '-' + affix : '')) - } - - - /* AFFIX PLUGIN DEFINITION - * ======================= */ - - $.fn.affix = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('affix') - , options = typeof option == 'object' && option - if (!data) $this.data('affix', (data = new Affix(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.affix.Constructor = Affix - - $.fn.affix.defaults = { - offset: 0 - } - - - /* AFFIX DATA-API - * ============== */ - - $(window).on('load', function () { - $('[data-spy="affix"]').each(function () { - var $spy = $(this) - , data = $spy.data() - - data.offset = data.offset || {} - - data.offsetBottom && (data.offset.bottom = data.offsetBottom) - data.offsetTop && (data.offset.top = data.offsetTop) - - $spy.affix(data) - }) - }) - - -}(window.jQuery); \ No newline at end of file From b0f74810dd17e0525a8a94feec17083050a6d413 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 2 Oct 2012 18:54:25 +0200 Subject: [PATCH 017/625] Fix download link in navigation bar --- doc/source/_templates/navbar.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/_templates/navbar.html b/doc/source/_templates/navbar.html index c1bcfed0..333c5548 100644 --- a/doc/source/_templates/navbar.html +++ b/doc/source/_templates/navbar.html @@ -1,5 +1,5 @@
        • Home
          • -
        • Download
          • +
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          • From d690212d1c4cdeb6e00b352f4e3506d973c8941a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 2 Oct 2012 19:02:37 +0200 Subject: [PATCH 018/625] Rename scikits-image to scikit-image --- doc/Makefile | 10 ++++---- doc/ext/plot2rst.py | 2 +- doc/gh-pages.py | 2 +- doc/logo/snake_logo.svg | 2 +- doc/release/release_0.3.txt | 4 ++-- doc/release/release_0.4.txt | 10 ++++---- doc/release/release_0.5.txt | 6 ++--- doc/release/release_0.6.txt | 6 ++--- doc/release/release_0.7.txt | 8 +++---- doc/source/_static/docversions.js | 2 +- doc/source/_templates/navbar.html | 2 +- doc/source/cell_profiler.txt | 2 +- doc/source/conf.py | 2 +- doc/source/gitwash/development_workflow.txt | 18 +++++++------- doc/source/gitwash/following_latest.txt | 12 +++++----- doc/source/gitwash/forking_hell.txt | 10 ++++---- doc/source/gitwash/git_intro.txt | 4 ++-- doc/source/gitwash/index.txt | 2 +- doc/source/gitwash/maintainer_workflow.txt | 4 ++-- doc/source/gitwash/patching.txt | 20 ++++++++-------- doc/source/gitwash/set_up_fork.txt | 26 ++++++++++----------- doc/source/gitwash/this_project.inc | 8 +++---- doc/source/install.txt | 18 +++++++------- doc/source/overview.txt | 8 +++---- doc/source/random_gallery.py | 4 ++-- doc/source/themes/scikit-image/layout.html | 4 ++-- doc/source/user_guide/getting_help.txt | 8 +++---- doc/source/user_guide/viewer.txt | 2 +- doc/tools/plot_pr.py | 2 +- 29 files changed, 104 insertions(+), 104 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index f7866969..f750fb18 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -123,9 +123,9 @@ gh-pages: python gh-pages.py gitwash: - python tools/gitwash/gitwash_dumper.py source scikits-image \ - --project-url=http://scikits-image.org \ - --project-ml-url=http://groups.google.com/group/scikits-image \ - --repo-name=scikits-image \ - --github-user=scikits-image \ + python tools/gitwash/gitwash_dumper.py source scikit-image \ + --project-url=http://scikit-image.org \ + --project-ml-url=http://groups.google.com/group/scikit-image \ + --repo-name=scikit-image \ + --github-user=scikit-image \ --source-suffix=.txt diff --git a/doc/ext/plot2rst.py b/doc/ext/plot2rst.py index 67355289..5933073f 100644 --- a/doc/ext/plot2rst.py +++ b/doc/ext/plot2rst.py @@ -9,7 +9,7 @@ To generate your own examples, add this extension to the list of example directory(ies) in `plot2rst_paths` (see below) points to a directory with examples named `plot_*.py` and include an `index.rst` file. -This code was adapted from scikits-image, which took it from scikits-learn. +This code was adapted from scikit-image, which took it from scikits-learn. Options ------- diff --git a/doc/gh-pages.py b/doc/gh-pages.py index 87baddda..ab29959e 100644 --- a/doc/gh-pages.py +++ b/doc/gh-pages.py @@ -30,7 +30,7 @@ from subprocess import Popen, PIPE, CalledProcessError, check_call pages_dir = 'gh-pages' html_dir = 'build/html' pdf_dir = 'build/latex' -pages_repo = 'git@github.com:scikits-image/docs.git' +pages_repo = 'git@github.com:scikit-image/docs.git' #----------------------------------------------------------------------------- # Functions diff --git a/doc/logo/snake_logo.svg b/doc/logo/snake_logo.svg index caa47694..5f9f634f 100644 --- a/doc/logo/snake_logo.svg +++ b/doc/logo/snake_logo.svg @@ -74,7 +74,7 @@ y="278.58533" x="261.22247" id="tspan3152" - sodipodi:role="line">scikits-image + sodipodi:role="line">scikit-image =2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website - http://scikits-image.org + http://scikit-image.org or the examples gallery at - http://scikits-image.org/docs/0.3/auto_examples/ + http://scikit-image.org/docs/0.3/auto_examples/ New Features ------------ diff --git a/doc/release/release_0.4.txt b/doc/release/release_0.4.txt index 765b4760..04679271 100644 --- a/doc/release/release_0.4.txt +++ b/doc/release/release_0.4.txt @@ -1,26 +1,26 @@ -Announcement: scikits-image 0.4 +Announcement: scikit-image 0.4 =============================== -We're happy to announce the 0.4 release of scikits-image, an image processing +We're happy to announce the 0.4 release of scikit-image, an image processing toolbox for SciPy. Please visit our examples gallery to see what we've been up to: - http://scikits-image.org/docs/0.4/auto_examples/ + http://scikit-image.org/docs/0.4/auto_examples/ Note that, in this release, we renamed the module from ``scikits.image`` to ``skimage``, to work around name space conflicts with other scikits (similarly, the machine learning scikit is now imported as ``sklearn``). A big shout-out also to everyone currently at SciPy India; have fun, and -remember to join the scikits-image sprint! +remember to join the scikit-image sprint! This release runs under all major operating systems where Python (>=2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website - http://scikits-image.org + http://scikit-image.org New Features ------------ diff --git a/doc/release/release_0.5.txt b/doc/release/release_0.5.txt index 74a5b3fe..652f45fa 100644 --- a/doc/release/release_0.5.txt +++ b/doc/release/release_0.5.txt @@ -1,12 +1,12 @@ -Announcement: scikits-image 0.5 +Announcement: scikit-image 0.5 =============================== -We're happy to announce the 0.5 release of scikits-image, our image processing +We're happy to announce the 0.5 release of scikit-image, our image processing toolbox for SciPy. For more information, please visit our website - http://scikits-image.org + http://scikit-image.org New Features ------------ diff --git a/doc/release/release_0.6.txt b/doc/release/release_0.6.txt index 8c525cb7..df2582cc 100644 --- a/doc/release/release_0.6.txt +++ b/doc/release/release_0.6.txt @@ -1,9 +1,9 @@ -Announcement: scikits-image 0.6 +Announcement: scikit-image 0.6 =============================== -We're happy to announce the 6th version of scikits-image! +We're happy to announce the 6th version of scikit-image! -Scikits-image is an image processing toolbox for SciPy that includes algorithms +scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. diff --git a/doc/release/release_0.7.txt b/doc/release/release_0.7.txt index a6b591cb..6637cd51 100644 --- a/doc/release/release_0.7.txt +++ b/doc/release/release_0.7.txt @@ -1,9 +1,9 @@ -Announcement: scikits-image 0.7.0 +Announcement: scikit-image 0.7.0 ================================= -We're happy to announce the 7th version of scikits-image! +We're happy to announce the 7th version of scikit-image! -Scikits-image is an image processing toolbox for SciPy that includes algorithms +scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. @@ -15,7 +15,7 @@ For more information, examples, and documentation, please visit our website New Features ------------ -It's been only 3 months since scikits-image 0.6 was released, but in that short +It's been only 3 months since scikit-image 0.6 was released, but in that short time, we've managed to add plenty of new features and enhancements, including - Geometric image transforms diff --git a/doc/source/_static/docversions.js b/doc/source/_static/docversions.js index 0b98d7dc..660553e0 100644 --- a/doc/source/_static/docversions.js +++ b/doc/source/_static/docversions.js @@ -12,6 +12,6 @@ function insert_version_links() { document.write(open_list); document.write('skimage VERSION \n' .replace('VERSION', labels[i]) - .replace('URL', 'http://scikits-image.org/docs/' + labels[i])); + .replace('URL', 'http://scikit-image.org/docs/' + labels[i])); } } diff --git a/doc/source/_templates/navbar.html b/doc/source/_templates/navbar.html index 333c5548..b0fa8026 100644 --- a/doc/source/_templates/navbar.html +++ b/doc/source/_templates/navbar.html @@ -2,4 +2,4 @@
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          • diff --git a/doc/source/cell_profiler.txt b/doc/source/cell_profiler.txt index cf24f53c..13918596 100644 --- a/doc/source/cell_profiler.txt +++ b/doc/source/cell_profiler.txt @@ -4,7 +4,7 @@ CellProfiler BSD license announcement :: From: Vebjorn Ljosa - To: scikits-image@googlegroups.com + To: scikit-image@googlegroups.com Date: June 3, 2010 We have changed the license of some parts of CellProfiler from GNU GPL diff --git a/doc/source/conf.py b/doc/source/conf.py index bcd1ef77..71033adc 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -43,7 +43,7 @@ master_doc = 'index' # General information about the project. project = u'skimage' -copyright = u'2011, the scikits-image team' +copyright = u'2011, the scikit-image team' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the diff --git a/doc/source/gitwash/development_workflow.txt b/doc/source/gitwash/development_workflow.txt index b5344da8..47bdead0 100644 --- a/doc/source/gitwash/development_workflow.txt +++ b/doc/source/gitwash/development_workflow.txt @@ -4,14 +4,14 @@ Development workflow #################### -You already have your own forked copy of the scikits-image_ repository, by +You already have your own forked copy of the scikit-image_ repository, by following :ref:`forking`. You have :ref:`set-up-fork`. You have configured git by following :ref:`configure-git`. Now you are ready for some real work. Workflow summary ================ -In what follows we'll refer to the upstream scikits-image ``master`` branch, as +In what follows we'll refer to the upstream scikit-image ``master`` branch, as "trunk". * Don't use your ``master`` branch for anything. Consider deleting it. @@ -22,9 +22,9 @@ In what follows we'll refer to the upstream scikits-image ``master`` branch, as * Name your branch for the purpose of the changes - e.g. ``bugfix-for-issue-14`` or ``refactor-database-code``. * If you can possibly avoid it, avoid merging trunk or any other branches into - your feature branch while you are working. + your feature branch while you are working. * If you do find yourself merging from trunk, consider :ref:`rebase-on-trunk` -* Ask on the `scikits-image mailing list`_ if you get stuck. +* Ask on the `scikit-image mailing list`_ if you get stuck. * Ask for code review! This way of working helps to keep work well organized, with readable history. @@ -81,7 +81,7 @@ what the changes in the branch are for. For example ``add-ability-to-fly``, or git checkout my-new-feature Generally, you will want to keep your feature branches on your public github_ -fork of scikits-image_. To do this, you `git push`_ this new branch up to your +fork of scikit-image_. To do this, you `git push`_ this new branch up to your github repo. Generally (if you followed the instructions in these pages, and by default), git will have a link to your github repo, called ``origin``. You push up to your own repo on github with:: @@ -150,7 +150,7 @@ Ask for your changes to be reviewed or merged When you are ready to ask for someone to review your code and consider a merge: #. Go to the URL of your forked repo, say - ``http://github.com/your-user-name/scikits-image``. + ``http://github.com/your-user-name/scikit-image``. #. Use the 'Switch Branches' dropdown menu near the top left of the page to select the branch with your changes: @@ -192,10 +192,10 @@ If you want to work on some stuff with other people, where you are all committing into the same repository, or even the same branch, then just share it via github. -First fork scikits-image into your account, as from :ref:`forking`. +First fork scikit-image into your account, as from :ref:`forking`. Then, go to your forked repository github page, say -``http://github.com/your-user-name/scikits-image`` +``http://github.com/your-user-name/scikit-image`` Click on the 'Admin' button, and add anyone else to the repo as a collaborator: @@ -204,7 +204,7 @@ collaborator: Now all those people can do:: - git clone git@githhub.com:your-user-name/scikits-image.git + git clone git@githhub.com:your-user-name/scikit-image.git Remember that links starting with ``git@`` use the ssh protocol and are read-write; links starting with ``git://`` are read-only. diff --git a/doc/source/gitwash/following_latest.txt b/doc/source/gitwash/following_latest.txt index 69e5ee13..df0842a0 100644 --- a/doc/source/gitwash/following_latest.txt +++ b/doc/source/gitwash/following_latest.txt @@ -5,12 +5,12 @@ ============================= These are the instructions if you just want to follow the latest -*scikits-image* source, but you don't need to do any development for now. +*scikit-image* source, but you don't need to do any development for now. The steps are: * :ref:`install-git` -* get local copy of the `scikits-image github`_ git repository +* get local copy of the `scikit-image github`_ git repository * update local copy from time to time Get the local copy of the code @@ -18,19 +18,19 @@ Get the local copy of the code From the command line:: - git clone git://github.com/scikits-image/scikits-image.git + git clone git://github.com/scikit-image/scikit-image.git -You now have a copy of the code tree in the new ``scikits-image`` directory. +You now have a copy of the code tree in the new ``scikit-image`` directory. Updating the code ================= From time to time you may want to pull down the latest code. Do this with:: - cd scikits-image + cd scikit-image git pull -The tree in ``scikits-image`` will now have the latest changes from the initial +The tree in ``scikit-image`` will now have the latest changes from the initial repository. .. include:: links.inc diff --git a/doc/source/gitwash/forking_hell.txt b/doc/source/gitwash/forking_hell.txt index 58e12fee..02c14b25 100644 --- a/doc/source/gitwash/forking_hell.txt +++ b/doc/source/gitwash/forking_hell.txt @@ -1,13 +1,13 @@ .. _forking: ============================================ -Making your own copy (fork) of scikits-image +Making your own copy (fork) of scikit-image ============================================ You need to do this only once. The instructions here are very similar to the instructions at http://help.github.com/forking/ |emdash| please see that page for more detail. We're repeating some of it here just to give the -specifics for the scikits-image_ project, and to suggest some default names. +specifics for the scikit-image_ project, and to suggest some default names. Set up and configure a github account ====================================== @@ -17,17 +17,17 @@ If you don't have a github account, go to the github page, and make one. You then need to configure your account to allow write access |emdash| see the ``Generating SSH keys`` help on `github help`_. -Create your own forked copy of scikits-image_ +Create your own forked copy of scikit-image_ ============================================= #. Log into your github account. -#. Go to the scikits-image_ github home at `scikits-image github`_. +#. Go to the scikit-image_ github home at `scikit-image github`_. #. Click on the *fork* button: .. image:: forking_button.png Now, after a short pause and some 'Hardcore forking action', you - should find yourself at the home page for your own forked copy of scikits-image_. + should find yourself at the home page for your own forked copy of scikit-image_. .. include:: links.inc diff --git a/doc/source/gitwash/git_intro.txt b/doc/source/gitwash/git_intro.txt index cb8e16cc..8b4d42bc 100644 --- a/doc/source/gitwash/git_intro.txt +++ b/doc/source/gitwash/git_intro.txt @@ -2,11 +2,11 @@ Introduction ============== -These pages describe a git_ and github_ workflow for the scikits-image_ +These pages describe a git_ and github_ workflow for the scikit-image_ project. There are several different workflows here, for different ways of -working with *scikits-image*. +working with *scikit-image*. This is not a comprehensive git reference, it's just a workflow for our own project. It's tailored to the github hosting service. You may well diff --git a/doc/source/gitwash/index.txt b/doc/source/gitwash/index.txt index d0eac669..7d26110e 100644 --- a/doc/source/gitwash/index.txt +++ b/doc/source/gitwash/index.txt @@ -1,6 +1,6 @@ .. _using-git: -Working with *scikits-image* source code +Working with *scikit-image* source code ======================================== Contents: diff --git a/doc/source/gitwash/maintainer_workflow.txt b/doc/source/gitwash/maintainer_workflow.txt index 7e2b21ab..e661784c 100644 --- a/doc/source/gitwash/maintainer_workflow.txt +++ b/doc/source/gitwash/maintainer_workflow.txt @@ -16,7 +16,7 @@ access to the upstream repo. Being a maintainer, you've got read-write access. It's good to have your upstream remote have a scary name, to remind you that it's a read-write remote:: - git remote add upstream-rw git@github.com:scikits-image/scikits-image.git + git remote add upstream-rw git@github.com:scikit-image/scikit-image.git git fetch upstream-rw ******************* @@ -29,7 +29,7 @@ Let's say you have some changes that need to go into trunk The changes are in some branch that you are currently on. For example, you are looking at someone's changes like this:: - git remote add someone git://github.com/someone/scikits-image.git + git remote add someone git://github.com/someone/scikit-image.git git fetch someone git branch cool-feature --track someone/cool-feature git checkout cool-feature diff --git a/doc/source/gitwash/patching.txt b/doc/source/gitwash/patching.txt index acec9fa6..b40118af 100644 --- a/doc/source/gitwash/patching.txt +++ b/doc/source/gitwash/patching.txt @@ -3,7 +3,7 @@ ================ You've discovered a bug or something else you want to change -in scikits-image_ .. |emdash| excellent! +in scikit-image_ .. |emdash| excellent! You've worked out a way to fix it |emdash| even better! @@ -29,9 +29,9 @@ Overview git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" # get the repository if you don't have it - git clone git://github.com/scikits-image/scikits-image.git + git clone git://github.com/scikit-image/scikit-image.git # make a branch for your patching - cd scikits-image + cd scikit-image git branch the-fix-im-thinking-of git checkout the-fix-im-thinking-of # hack, hack, hack @@ -44,7 +44,7 @@ Overview # make the patch files git format-patch -M -C master -Then, send the generated patch files to the `scikits-image +Then, send the generated patch files to the `scikit-image mailing list`_ |emdash| where we will thank you warmly. In detail @@ -57,10 +57,10 @@ In detail git config --global user.name "Your Name Comes Here" #. If you don't already have one, clone a copy of the - scikits-image_ repository:: + scikit-image_ repository:: - git clone git://github.com/scikits-image/scikits-image.git - cd scikits-image + git clone git://github.com/scikit-image/scikit-image.git + cd scikit-image #. Make a 'feature branch'. This will be where you work on your bug fix. It's nice and safe and leaves you with @@ -100,7 +100,7 @@ In detail 0001-BF-added-tests-for-Funny-bug.patch 0002-BF-added-fix-for-Funny-bug.patch - Send these files to the `scikits-image mailing list`_. + Send these files to the `scikit-image mailing list`_. When you are done, to switch back to the main copy of the code, just return to the ``master`` branch:: @@ -115,7 +115,7 @@ more feature branches, you will probably want to switch to development mode. You can do this with the repository you have. -Fork the scikits-image_ repository on github |emdash| :ref:`forking`. +Fork the scikit-image_ repository on github |emdash| :ref:`forking`. Then:: # checkout and refresh master branch from main repo @@ -124,7 +124,7 @@ Then:: # rename pointer to main repository to 'upstream' git remote rename origin upstream # point your repo to default read / write to your fork on github - git remote add origin git@github.com:your-user-name/scikits-image.git + git remote add origin git@github.com:your-user-name/scikit-image.git # push up any branches you've made and want to keep git push origin the-fix-im-thinking-of diff --git a/doc/source/gitwash/set_up_fork.txt b/doc/source/gitwash/set_up_fork.txt index f59aa57a..36153e7d 100644 --- a/doc/source/gitwash/set_up_fork.txt +++ b/doc/source/gitwash/set_up_fork.txt @@ -11,9 +11,9 @@ Overview :: - git clone git@github.com:your-user-name/scikits-image.git - cd scikits-image - git remote add upstream git://github.com/scikits-image/scikits-image.git + git clone git@github.com:your-user-name/scikit-image.git + cd scikit-image + git remote add upstream git://github.com/scikit-image/scikit-image.git In detail ========= @@ -22,8 +22,8 @@ Clone your fork --------------- #. Clone your fork to the local computer with ``git clone - git@github.com:your-user-name/scikits-image.git`` -#. Investigate. Change directory to your new repo: ``cd scikits-image``. Then + git@github.com:your-user-name/scikit-image.git`` +#. Investigate. Change directory to your new repo: ``cd scikit-image``. Then ``git branch -a`` to show you all branches. You'll get something like:: @@ -35,7 +35,7 @@ Clone your fork What remote repository is ``remote/origin``? Try ``git remote -v`` to see the URLs for the remote. They will point to your github fork. - Now you want to connect to the upstream `scikits-image github`_ repository, so + Now you want to connect to the upstream `scikit-image github`_ repository, so you can merge in changes from trunk. .. _linking-to-upstream: @@ -45,11 +45,11 @@ Linking your repository to the upstream repo :: - cd scikits-image - git remote add upstream git://github.com/scikits-image/scikits-image.git + cd scikit-image + git remote add upstream git://github.com/scikit-image/scikit-image.git ``upstream`` here is just the arbitrary name we're using to refer to the -main scikits-image_ repository at `scikits-image github`_. +main scikit-image_ repository at `scikit-image github`_. Note that we've used ``git://`` for the URL rather than ``git@``. The ``git://`` URL is read only. This means we that we can't accidentally @@ -59,10 +59,10 @@ use it to merge into our own code. Just for your own satisfaction, show yourself that you now have a new 'remote', with ``git remote -v show``, giving you something like:: - upstream git://github.com/scikits-image/scikits-image.git (fetch) - upstream git://github.com/scikits-image/scikits-image.git (push) - origin git@github.com:your-user-name/scikits-image.git (fetch) - origin git@github.com:your-user-name/scikits-image.git (push) + upstream git://github.com/scikit-image/scikit-image.git (fetch) + upstream git://github.com/scikit-image/scikit-image.git (push) + origin git@github.com:your-user-name/scikit-image.git (fetch) + origin git@github.com:your-user-name/scikit-image.git (push) .. include:: links.inc diff --git a/doc/source/gitwash/this_project.inc b/doc/source/gitwash/this_project.inc index 0bfd7306..349b92ba 100644 --- a/doc/source/gitwash/this_project.inc +++ b/doc/source/gitwash/this_project.inc @@ -1,5 +1,5 @@ -.. scikits-image -.. _scikits-image: http://scikits-image.org -.. _`scikits-image github`: http://github.com/scikits-image/scikits-image +.. scikit-image +.. _scikit-image: http://scikit-image.org +.. _`scikit-image github`: http://github.com/scikit-image/scikit-image -.. _`scikits-image mailing list`: http://groups.google.com/group/scikits-image +.. _`scikit-image mailing list`: http://groups.google.com/group/scikit-image diff --git a/doc/source/install.txt b/doc/source/install.txt index 5aa3f43f..81652ab1 100644 --- a/doc/source/install.txt +++ b/doc/source/install.txt @@ -1,7 +1,7 @@ Pre-built installation ---------------------- -.. !! Also update scikits-image-web !! +.. !! Also update scikit-image-web !! `Windows binaries `__ @@ -12,37 +12,37 @@ Distribution (EPD) `__ and `Python(x,y) `__. On systems that support setuptools, the package can be installed from the -`Python packaging index `__ using +`Python packaging index `__ using :: - easy_install -U scikits-image + easy_install -U scikit-image or :: - pip -U scikits-image + pip -U scikit-image Installation from source ------------------------ Obtain the source from the git-repository at -`http://github.com/scikits-image/scikits-image -`_. +`http://github.com/scikit-image/scikit-image +`_. by running :: - git clone http://github.com/scikits-image/scikits-image.git + git clone http://github.com/scikit-image/scikit-image.git in a terminal (You will need to have git installed on your machine). If you do not have git installed, you can also download a zipball from -`https://github.com/scikits-image/scikits-image/zipball/master -`_. +`https://github.com/scikit-image/scikit-image/zipball/master +`_. The SciKit can be installed globally using diff --git a/doc/source/overview.txt b/doc/source/overview.txt index 0bd9d658..5a4e6123 100644 --- a/doc/source/overview.txt +++ b/doc/source/overview.txt @@ -1,7 +1,7 @@ Image Processing SciKit ======================= -The `scikits-image `__ SciKit (toolkit for +The `scikit-image `__ SciKit (toolkit for `SciPy `__) extends ``scipy.ndimage`` to provide a versatile set of image processing routines. It is written in the `Python `_ language. @@ -12,15 +12,15 @@ mailing list (address provided below). Homepage -------- -http://scikits-image.org +http://scikit-image.org Source, bugs and patches ------------------------ -http://github.com/scikits-image/scikits-image +http://github.com/scikit-image/scikit-image Mailing List ------------ -http://groups.google.com/group/scikits-image +http://groups.google.com/group/scikit-image Contact ------- diff --git a/doc/source/random_gallery.py b/doc/source/random_gallery.py index 6c5a40a2..47ce89a2 100644 --- a/doc/source/random_gallery.py +++ b/doc/source/random_gallery.py @@ -32,8 +32,8 @@ gallery_div = '''\ examples = glob.glob(os.path.join(example_dir, 'plot_*.py')) images, links = [], [] -image_url = 'http://scikits-image.org/docs/dev/_images/%s.png' -link_url = 'http://scikits-image.org/docs/dev/auto_examples/%s.html' +image_url = 'http://scikit-image.org/docs/dev/_images/%s.png' +link_url = 'http://scikit-image.org/docs/dev/auto_examples/%s.html' for e in examples: e = os.path.basename(e) diff --git a/doc/source/themes/scikit-image/layout.html b/doc/source/themes/scikit-image/layout.html index 8fc325cb..58424f2f 100644 --- a/doc/source/themes/scikit-image/layout.html +++ b/doc/source/themes/scikit-image/layout.html @@ -79,7 +79,7 @@ {%- block extrahead %}{% endblock %} - +
          @@ -105,7 +105,7 @@
          - © Copyright the scikits-image development team. + © Copyright the scikit-image development team. Created using Bootstrap and Sphinx.
          diff --git a/doc/source/user_guide/getting_help.txt b/doc/source/user_guide/getting_help.txt index 6dedb6f2..ea444856 100644 --- a/doc/source/user_guide/getting_help.txt +++ b/doc/source/user_guide/getting_help.txt @@ -44,13 +44,13 @@ functions include one or more examples. Mailing-list ------------ -The scikits-image mailing-list is scikits-image@googlegroups.com (users +The scikit-image mailing-list is scikit-image@googlegroups.com (users should join the `Google Group -`_ before posting). This +`_ before posting). This mailing-list is shared by users and developers, and it is the right place to ask any question about ``skimage``, or in general, image processing using Python. Posting snippets of code with minimal examples -ensures to get more relevant and focused answers. +ensures to get more relevant and focused answers. We would love to hear from how you use ``skimage`` for your work on the -mailing-list! +mailing-list! diff --git a/doc/source/user_guide/viewer.txt b/doc/source/user_guide/viewer.txt index 17387240..82bb5949 100644 --- a/doc/source/user_guide/viewer.txt +++ b/doc/source/user_guide/viewer.txt @@ -65,7 +65,7 @@ slider: denoise_plugin += SaveButtons() Here, we connect a slider widget to the filter's 'weight' argument. We also -added some buttons for saving the image to file or to the ``scikits-image`` +added some buttons for saving the image to file or to the ``scikit-image`` image stack (see ``skimage.io.push`` and ``skimage.io.pop``). All that's left is to create an image viewer and add the plugin to that viewer. diff --git a/doc/tools/plot_pr.py b/doc/tools/plot_pr.py index 63c0ad44..09a9f91e 100644 --- a/doc/tools/plot_pr.py +++ b/doc/tools/plot_pr.py @@ -33,7 +33,7 @@ month_duration = 24 for r in releases: releases[r] = dateutil.parser.parse(releases[r]) -def fetch_PRs(user='scikits-image', repo='scikits-image', state='open'): +def fetch_PRs(user='scikit-image', repo='scikit-image', state='open'): params = {'state': state, 'per_page': 100, 'page': 1} From 236666fd7c8ea5093fc67f351e2de6df052b4fc0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 2 Oct 2012 19:03:24 +0200 Subject: [PATCH 019/625] Rename scikits-learn to scikit-learn --- doc/ext/plot2rst.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/ext/plot2rst.py b/doc/ext/plot2rst.py index 5933073f..92978723 100644 --- a/doc/ext/plot2rst.py +++ b/doc/ext/plot2rst.py @@ -9,7 +9,7 @@ To generate your own examples, add this extension to the list of example directory(ies) in `plot2rst_paths` (see below) points to a directory with examples named `plot_*.py` and include an `index.rst` file. -This code was adapted from scikit-image, which took it from scikits-learn. +This code was adapted from scikit-image, which took it from scikit-learn. Options ------- From 4024969451a99b1ed77936b8612315a2d09a02ec Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Tue, 2 Oct 2012 21:41:36 -0400 Subject: [PATCH 020/625] DOC: Format gallery into a grid Recent changes to the theme display a single column of images in the gallery. --- .../themes/scikit-image/static/css/custom.css | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/doc/source/themes/scikit-image/static/css/custom.css b/doc/source/themes/scikit-image/static/css/custom.css index 005d4317..df60739e 100644 --- a/doc/source/themes/scikit-image/static/css/custom.css +++ b/doc/source/themes/scikit-image/static/css/custom.css @@ -13,6 +13,11 @@ textarea { pre { font-size: 11px; } + +h1, h2, h3, h4 { + clear: left; +} + h1 { font-size: 30px; line-height: 36px; @@ -194,3 +199,27 @@ p.admonition-title { font-weight: bold; text-decoration: underline; } + +/* example gallery */ + +.gallery { + height: 200px; +} + +.figure { + float: left; + margin: 1em; +} + +.figure img { + display: block; + margin-left: auto; + margin-right: auto; + max-height: 150px; + max-width: 200px; +} + +.figure .caption { + width: 200px; + text-align: center !important; +} From dd9e7f3526671c8b05a89b76e14e81e79914cd04 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Tue, 2 Oct 2012 22:36:47 -0400 Subject: [PATCH 021/625] DOC: Save thumbnails with a fixed image shape A fixed shape improves grid alignment of images and caption in the gallery. --- doc/ext/plot2rst.py | 39 +++++++++++++++++++++++++++++++++------ 1 file changed, 33 insertions(+), 6 deletions(-) diff --git a/doc/ext/plot2rst.py b/doc/ext/plot2rst.py index 92978723..65c77521 100644 --- a/doc/ext/plot2rst.py +++ b/doc/ext/plot2rst.py @@ -27,9 +27,10 @@ plot2rst_rcparams : dict plot2rst_default_thumb : str Path (relative to doc root) of default thumbnail image. -plot2rst_thumb_scale : float - Scale factor for thumbnail (e.g., 0.2 to scale plot to 1/5th the - original size). +plot2rst_thumb_shape : float + Shape of thumbnail in pixels. The image is resized to fit within this shape + and the excess is filled with white pixels. This fixed size ensures that + that gallery images are displayed in a grid. plot2rst_plot_tag : str When this tag is found in the example file, the current plot is saved and @@ -73,7 +74,10 @@ import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt -from matplotlib import image + +from skimage import io +from skimage import transform +from skimage.util.dtype import dtype_range LITERALINCLUDE = """ @@ -160,7 +164,7 @@ def setup(app): ('../examples', 'auto_examples'), True) app.add_config_value('plot2rst_rcparams', {}, True) app.add_config_value('plot2rst_default_thumb', None, True) - app.add_config_value('plot2rst_thumb_scale', 0.25, True) + app.add_config_value('plot2rst_thumb_shape', (250, 300), True) app.add_config_value('plot2rst_plot_tag', 'PLOT2RST.current_figure', True) app.add_config_value('plot2rst_index_name', 'index', True) @@ -335,7 +339,8 @@ def write_example(src_name, src_dir, rst_dir, cfg): thumb_path = thumb_dir.pjoin(src_name[:-3] + '.png') first_image_file = image_dir.pjoin(figure_list[0].lstrip('/')) if first_image_file.exists: - image.thumbnail(first_image_file, thumb_path, cfg.plot2rst_thumb_scale) + first_image = io.imread(first_image_file) + save_thumbnail(first_image, thumb_path, cfg.plot2rst_thumb_shape) if not thumb_path.exists: if cfg.plot2rst_default_thumb is None: @@ -345,6 +350,28 @@ def write_example(src_name, src_dir, rst_dir, cfg): shutil.copy(cfg.plot2rst_default_thumb, thumb_path) +def save_thumbnail(image, thumb_path, shape): + """Save image as a thumbnail with the specified shape. + + The image is first resized to fit within the specified shape and then + centered in an array of the specified shape before saving. + """ + rescale = min(float(w_1) / w_2 for w_1, w_2 in zip(shape, image.shape)) + small_shape = (rescale * np.asarray(image.shape[:2])).astype(int) + small_image = transform.resize(image, small_shape) + + if len(image.shape) == 3: + shape = shape + (image.shape[2],) + background_value = dtype_range[small_image.dtype.type][1] + thumb = background_value * np.ones(shape, dtype=small_image.dtype) + + i = (shape[0] - small_shape[0]) // 2 + j = (shape[1] - small_shape[1]) // 2 + thumb[i:i+small_shape[0], j:j+small_shape[1]] = small_image + + io.imsave(thumb_path, thumb) + + def _plots_are_current(src_path, image_path): first_image_file = Path(image_path.format(1)) needs_replot = (not first_image_file.exists or From 527e4b579d01374a9644e00f1ca0636829636d33 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Tue, 2 Oct 2012 23:42:14 -0400 Subject: [PATCH 022/625] DOC: Remove task list from docs Add link to new task list on github wiki. --- TASKS.txt | 110 +----------------------------------------------------- 1 file changed, 1 insertion(+), 109 deletions(-) diff --git a/TASKS.txt b/TASKS.txt index da715573..9a846be9 100644 --- a/TASKS.txt +++ b/TASKS.txt @@ -22,116 +22,8 @@ following challenges you'd like to solve. * Mentoring is available for those new to scientific programming in Python. * The technical detail of the `development process`_ is given below. * :doc:`How to use GitHub ` when developing skimage +* If you're looking something to implement, you can find a list of `requested features on github `__. In addition, you can browse the `open issues on github `__. .. contents:: :local: -Tasks ------ - -.. :doc:`gsoc2011` -.. :doc:`coverage_table` - -Implement Algorithms -```````````````````` -- Graph cut segmentation -- `Image colorization `__ -- Fast 2D convex hull (consider using CellProfiler version) - `Algorithm overview `__. - `One free implementation - `_. - [Compare against current implementation] -- Convex hulls of objects in a labels matrix (simply adapt current convex hull - image code--this one's low hanging fruit). Generalise this solution to also - skeletonize objects in a labels matrix. -- Add binary features (BRIEF, BRISK, FREAK) -- Add `STAR features `__ -- `Blurring kernel estimation `__ - -Drawing (directly on an ndarray) -```````````````````````````````` -- Wu's algorithm for circles -- Text rendering -- Add anti-aliasing - -Infrastructure --------------- -- Add @greyimage decorator to check if input is a greyscale image -- :strike:`Implement a new backend system so that we may start including - PyOpenCL-based algorithms` - -Adapt existing code for use -``````````````````````````` -These snippets and packages have already been written. Some need to be -modified to work as part of the scikit, others may be lacking in documentation -or tests. - - * :strike:`Connected components` - * Nadav's bilateral filtering (first compare against CellProfiler's - code, based on http://groups.csail.mit.edu/graphics/bilagrid/bilagrid_web.pdf) - Also see https://github.com/stefanv/scikits-image/tree/bilateral - * 2D image warping via thin-plate splines [ask Zach Pincus] - -Merge code provided by `CellProfiler `_ team -````````````````````````````````````````````````````````````````````````` -* Roberts filter - convolution with diagonal and anti-diagonal - kernels to detect edges -* Minimum enclosing circles of objects in a labels matrix -* spur removal, thinning, thickening, and other morphological operations on - binary images, framework for creating arbitrary morphological operations - using a 3x3 grid. - -Their SVN repository is read-accessible at - -- https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler - -The files for the above algorithms are - -- https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler/cpmath/cpmorphology.py -- https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler/cpmath/filter.py - -There are test suites for the files at - -- https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler/cpmath/tests/test_cpmorphology.py -- https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler/cpmath/tests/test_filter.py - -Quoting a message from Lee Kamentsky to Stefan van der Walt sent on -5 August 2009:: - - We're part of the Broad Institute which is non-profit. We would be happy - to include our algorithm code in SciPy under the BSD license since that is - more appropriate for a library that might be integrated into a - commercial product whereas CellProfiler needs the more stringent - protection of GPL as an application. - -In 2010, Vebjorn Ljosa officially released parts of the code under a -BSD license (:doc:`cell_profiler` | `original message -`_). - -Thanks to Lee Kamentsky, Thouis Jones and Anne Carpenter and their colleagues -who contributed. - -Rework linear filters -````````````````````` -* Fast, SSE2 convolution (high priority) (see prototype in pull requests) -* Should take kernel or function for parameter (currently only takes function) -* Kernel shape should be specifiable (currently defaults to image shape) - -io -`` -* Update ``qt_plugin.py`` and other plugins to view collections. -* Rewrite GTK backend using GObject Introspection for Py3K compatibility. -* Add DICOM plugin for `GDCM `__. -* Add ``imread_collection`` to all ``imread`` backends -* Better video loading (move to plugin framework, add backends) - -viewer -`````` -* Using the visualization tools, add an FFT-domain image editor - -docs -```` -* Add examples to the gallery -* Write topics for the `user guide - `_ -* Integrate BiBTeX plugin into Sphinx build From 0e1cc75a733fab981be9dc433cb3483f5e6b6750 Mon Sep 17 00:00:00 2001 From: odebeir Date: Wed, 3 Oct 2012 19:43:41 +0200 Subject: [PATCH 023/625] initial commit --- .gitignore | 2 +- skimage/rank/__init__.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 skimage/rank/__init__.py diff --git a/.gitignore b/.gitignore index 1bd4352c..925e5886 100644 --- a/.gitignore +++ b/.gitignore @@ -22,4 +22,4 @@ doc/source/auto_examples/images/plot_*.png doc/source/auto_examples/images/thumb doc/source/auto_examples/applications/ doc/source/_static/random.js - +.idea/ diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py new file mode 100644 index 00000000..bcc43cdc --- /dev/null +++ b/skimage/rank/__init__.py @@ -0,0 +1 @@ +from .rank import * From 26f9186bc945a6abb8181ec8c7c1cdbd0040d2ca Mon Sep 17 00:00:00 2001 From: odebeir Date: Wed, 3 Oct 2012 19:57:30 +0200 Subject: [PATCH 024/625] paste code --- skimage/rank/README.rst | 5 + skimage/rank/app.py | 171 +++++ skimage/rank/cmorph.pyx | 118 +++ skimage/rank/core.pxd | 1054 ++++++++++++++++++++++++++ skimage/rank/crank.pyx | 325 ++++++++ skimage/rank/crank16.pyx | 323 ++++++++ skimage/rank/crank16_percentiles.pyx | 268 +++++++ skimage/rank/crank_percentiles.pyx | 263 +++++++ skimage/rank/setup.py | 15 + skimage/rank/tools.py | 52 ++ 10 files changed, 2594 insertions(+) create mode 100644 skimage/rank/README.rst create mode 100644 skimage/rank/app.py create mode 100644 skimage/rank/cmorph.pyx create mode 100644 skimage/rank/core.pxd create mode 100644 skimage/rank/crank.pyx create mode 100644 skimage/rank/crank16.pyx create mode 100644 skimage/rank/crank16_percentiles.pyx create mode 100644 skimage/rank/crank_percentiles.pyx create mode 100644 skimage/rank/setup.py create mode 100644 skimage/rank/tools.py diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst new file mode 100644 index 00000000..b15001e6 --- /dev/null +++ b/skimage/rank/README.rst @@ -0,0 +1,5 @@ +To use this to build your Cython file use the commandline options: + +.. sourcecode:: text + + $ python setup.py build_ext --inplace \ No newline at end of file diff --git a/skimage/rank/app.py b/skimage/rank/app.py new file mode 100644 index 00000000..acfc33ec --- /dev/null +++ b/skimage/rank/app.py @@ -0,0 +1,171 @@ +import numpy as np +from time import time +import matplotlib.pyplot as plt +from skimage import data + +from tools import log_timing,init_logger + +import crank +import crank16 +import crank_percentiles +import crank16_percentiles +from pyrankfilter import filter +from cmorph import dilate + + +@log_timing +def c_max(image,selem): + return crank.maximum(image=image,selem = selem) + +@log_timing +def w_max(image,selem): + return filter.maximum(image,struct_elem = selem) + +@log_timing +def cm_max(image,selem): + return dilate(image=image,selem = selem) + +def compare(): + """comparison between + - Cython maximum rankfilter implementation + - weaves maximum rankfilter implementation + - cmorph.dilate cython implementation + on increasing structuring element size and increasing image size + """ + a = (np.random.random((500,500))*256).astype('uint8') + + rec = [] + for r in range(1,20,1): + elem = np.ones((r,r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + (rc,ms_rc) = c_max(a,elem) + (rw, ms_rw) = w_max(a,elem) + (rcm,ms_rcm) = cm_max(a,elem) + rec.append((ms_rc,ms_rw,ms_rcm)) + assert (rc==rw).all() + assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.plot(rec) + plt.legend(['sliding cython','sliding weaves','cmorph']) + plt.figure() + plt.imshow(np.hstack((rc,rw,rcm))) + + r = 9 + elem = np.ones((r,r),dtype='uint8') + + rec = [] + for s in range(100,1000,100): + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = c_max(a,elem) + (rw, ms_rw) = w_max(a,elem) + (rcm,ms_rcm) = cm_max(a,elem) + rec.append((ms_rc,ms_rw,ms_rcm)) + assert (rc==rw).all() + assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.plot(rec) + plt.legend(['sliding cython','sliding weaves','cmorph']) + plt.figure() + plt.imshow(np.hstack((rc,rw,rcm))) + + plt.show() + +def test_image_size(): + """try several image sizes to check bounds conditions + """ + niter = 10 + elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') + for m,n in np.random.random_integers(1,100,size=(10,2)): + a = np.ones((m,n),dtype='uint8') + r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=0) + assert a.shape == r.shape + r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=-1) + assert a.shape == r.shape + r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=+1) + assert a.shape == r.shape + r = crank.mean(image=a,selem = elem,shift_x=-1,shift_y=0) + assert a.shape == r.shape + r = crank.mean(image=a,selem = elem,shift_x=+1,shift_y=0) + assert a.shape == r.shape + r = crank.mean(image=a,selem = elem,shift_x=-1,shift_y=-1) + assert a.shape == r.shape + r = crank.mean(image=a,selem = elem,shift_x=+1,shift_y=+1) + assert a.shape == r.shape + + return True + + +if __name__ == '__main__': + + logger = init_logger('app.log') + a = np.zeros((10,10),dtype='uint8') + a[2,2] = 255 +# a[2,3] = 255 +# a[2,4] = 255 + + print a + + mask = np.ones_like(a) +# mask[:3,:3] = 0 + +# elem = np.asarray([[0,1,0],[1,1,1],[0,1,0]],dtype='uint8') + elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') + + niter = 1 + t0 = time() + + for iter in range(niter): + r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=0,mask = mask) + p = crank.pop(image=a,selem = elem,shift_x=0,shift_y=0,mask = mask) + t1 = time() + print '%f msec'%(t1-t0) + + print 'cython mean' + print r + print p + + t0 = time() + for iter in range(niter): + r = filter.mean(a,struct_elem = elem,struct_elem_center=(1,1),mask = mask) + t1 = time() + print '%f msec'%(t1-t0) + + print 'filter.mean:' + print r + + print a + r = crank.maximum(image=a,selem = elem,shift_x=0,shift_y=0,mask = mask) + print r + + r = crank.gradient(image=r,selem = elem,shift_x=0,shift_y=0,mask = mask) + print r + im = np.zeros((10,10),dtype='uint8') + im[2:6,2:6] = 255 + elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') + f = crank.gradient(image=im,selem = elem) + print f + f = crank.egalise(image=im,selem = elem) + print f + +# compare() +# test_image_size() + +# a = (data.coins()).astype('uint8') + a8 = (data.coins()).astype('uint8') + a = (data.coins()).astype('uint16')*16 + selem = np.ones((20,20),dtype='uint8') +# f1 = filter.soft_gradient(a,struct_elem = selem,bitDepth=8,infSup=[.1,.9]) +# f2 = crank16.bottomhat(a,selem = selem,bitdepth=12) + f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f2 = crank16_percentiles.mean(a,selem = selem,bitdepth=12,p0=.1,p1=.9) +# plt.imshow(f2) + plt.imshow(np.hstack((f1,f2))) + plt.colorbar() + plt.show() + + diff --git a/skimage/rank/cmorph.pyx b/skimage/rank/cmorph.pyx new file mode 100644 index 00000000..9b8b3a27 --- /dev/null +++ b/skimage/rank/cmorph.pyx @@ -0,0 +1,118 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + + +def dilate(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) - shift_y + cdef int centre_c = int(selem.shape[1] / 2) - shift_x + + image = np.ascontiguousarray(image) + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + + cdef int r, c, rr, cc, s, value, local_max + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + + s = 0 + for r in range(srows): + for c in range(scols): + if selem[r, c] != 0: + sr[s] = r - centre_r + sc[s] = c - centre_c + s += 1 + + for r in range(rows): + for c in range(cols): + local_max = 0 + for s in range(selem_num): + rr = r + sr[s] + cc = c + sc[s] + if 0 <= rr < rows and 0 <= cc < cols: + value = image_data[rr * cols + cc] + if value > local_max: + local_max = value + + out_data[r * cols + c] = local_max + + free(sr) + free(sc) + + return out + + +def erode(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) - shift_y + cdef int centre_c = int(selem.shape[1] / 2) - shift_x + + image = np.ascontiguousarray(image) + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + + cdef int r, c, rr, cc, s, value, local_min + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + + s = 0 + for r in range(srows): + for c in range(scols): + if selem[r, c] != 0: + sr[s] = r - centre_r + sc[s] = c - centre_c + s += 1 + + for r in range(rows): + for c in range(cols): + local_min = 255 + for s in range(selem_num): + rr = r + sr[s] + cc = c + sc[s] + if 0 <= rr < rows and 0 <= cc < cols: + value = image_data[rr * cols + cc] + if value < local_min: + local_min = value + + out_data[r * cols + c] = local_min + + free(sr) + free(sc) + + return out diff --git a/skimage/rank/core.pxd b/skimage/rank/core.pxd new file mode 100644 index 00000000..72facde3 --- /dev/null +++ b/skimage/rank/core.pxd @@ -0,0 +1,1054 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + + +#--------------------------------------------------------------------------- +# 8 bit core kernel +#--------------------------------------------------------------------------- + +cdef inline rank8(np.uint8_t kernel(int*, float, np.uint8_t), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + eimage = np.ascontiguousarray(eimage) + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(256 * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + +#--------------------------------------------------------------------------- +# 16 bit core, kernel receive extra information about data bitdepth +#--------------------------------------------------------------------------- + +cdef inline rank16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(maxbin * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + +#--------------------------------------------------------------------------- +# 8 bit core kernel receive extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline rank8_percentile(np.uint8_t kernel(int*, float, np.uint8_t, float, float), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + eimage = np.ascontiguousarray(eimage) + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(256 * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + +#--------------------------------------------------------------------------- +# 16 bit core kernel receive extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline rank16_percentile(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint16 + - result is uint16 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(maxbin * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out \ No newline at end of file diff --git a/skimage/rank/crank.pyx b/skimage/rank/crank.pyx new file mode 100644 index 00000000..09048917 --- /dev/null +++ b/skimage/rank/crank.pyx @@ -0,0 +1,325 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a crank.pxd + +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np + +# import main loop +from core cimport rank8 + +# todo +# - manage float output, +# - manage different bit depth input +# - add auxiliary parameters (spectral_interval, infSup) + +# ----------------------------------------------------------------- +# kernels uint8 +# ----------------------------------------------------------------- + +cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): + cdef int i,imin,imax,delta + + if pop: + for i in range(255,-1,-1): + if histo[i]: + imax = i + break + for i in range(256): + if histo[i]: + imin = i + break + delta = imax-imin + if delta>0: + return (255.*(g-imin)/delta) + else: + return (imax-imin) + +cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): + cdef int i + + for i in range(256): + if histo[i]: + break + + return (g-i) + + +cdef inline np.uint8_t kernel_egalise(int* histo, float pop, np.uint8_t g): + cdef int i + cdef float sum = 0. + + if pop: + for i in range(256): + sum += histo[i] + if i>=g: + break + + return ((255*sum)/pop) + else: + return (0) + +cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g): + cdef int i,imin,imax + + + if pop: + for i in range(255,-1,-1): + if histo[i]: + imax = i + break + for i in range(256): + if histo[i]: + imin = i + break + return (imax-imin) + else: + return (0) + +cdef inline np.uint8_t kernel_maximum(int* histo, float pop, np.uint8_t g): + cdef int i + + if pop: + for i in range(255,-1,-1): + if histo[i]: + return (i) + + return (0) + +cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g): + cdef int i + cdef float mean = 0. + + if pop: + for i in range(256): + mean += histo[i]*i + return (mean/pop) + else: + return (0) + +cdef inline np.uint8_t kernel_meansubstraction(int* histo, float pop, np.uint8_t g): + cdef int i + cdef float mean = 0. + + if pop: + for i in range(256): + mean += histo[i]*i + return ((g-mean/pop)/2.+127) + else: + return (0) + +cdef inline np.uint8_t kernel_median(int* histo, float pop, np.uint8_t g): + cdef int i + cdef float sum = pop/2.0 + + if pop: + for i in range(256): + if histo[i]: + sum -= histo[i] + if sum<0: + return (i) + + return (0) + +cdef inline np.uint8_t kernel_minimum(int* histo, float pop, np.uint8_t g): + cdef int i + + if pop: + for i in range(256): + if histo[i]: + return (i) + + return (0) + +cdef inline np.uint8_t kernel_modal(int* histo, float pop, np.uint8_t g): + cdef int hmax=0,imax=0 + + if pop: + for i in range(256): + if histo[i]>hmax: + hmax = histo[i] + imax = i + return (imax) + + return (0) + +cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t g): + cdef int i,imin,imax + + if pop: + for i in range(255,-1,-1): + if histo[i]: + imax = i + break + for i in range(256): + if histo[i]: + imin = i + break + if imax-g < g-imin: + return (imax) + else: + return (imin) + else: + return (0) + +cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g): + return (pop) + +cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g): + cdef int i + cdef float mean = 0. + + if pop: + for i in range(256): + mean += histo[i]*i + return (g>(mean/pop)) + else: + return (0) + +cdef inline np.uint8_t kernel_tophat(int* histo, float pop, np.uint8_t g): + cdef int i + + for i in range(255,-1,-1): + if histo[i]: + break + + return (i-g) + +# ----------------------------------------------------------------- +# python wrappers +# ----------------------------------------------------------------- +def autolevel(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """bottom hat + """ + return rank8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) + +def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """bottom hat + """ + return rank8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) + +def egalise(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """local egalisation of the gray level + """ + return rank8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) + +def gradient(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """local maximum - local minimum gray level + """ + return rank8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) + +def maximum(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """local maximum gray level + """ + return rank8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) + +def mean(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """average gray level (clipped on uint8) + """ + return rank8(kernel_mean,image,selem,mask,out,shift_x,shift_y) + +def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """(g - average gray level)/2+127 (clipped on uint8) + """ + return rank8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) + +def median(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """local median + """ + return rank8(kernel_median,image,selem,mask,out,shift_x,shift_y) + +def minimum(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """local minimum gray level + """ + return rank8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) + +def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """morphological contrast enhancement + """ + return rank8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) + +def modal(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """local mode + """ + return rank8(kernel_modal,image,selem,mask,out,shift_x,shift_y) + +def pop(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """returns the number of actual pixels of the structuring element inside the mask + """ + return rank8(kernel_pop,image,selem,mask,out,shift_x,shift_y) + +def threshold(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """returns 255 if gray level higher than local mean, 0 else + """ + return rank8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) + +def tophat(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """top hat + """ + return rank8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) + diff --git a/skimage/rank/crank16.pyx b/skimage/rank/crank16.pyx new file mode 100644 index 00000000..78e5a2af --- /dev/null +++ b/skimage/rank/crank16.pyx @@ -0,0 +1,323 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a crank.pxd + +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np + +# import main loop +from core cimport rank16 + +# todo +# - manage float output, +# - manage different bit depth input +# - add auxiliary parameters (spectral_interval, infSup) + +# ----------------------------------------------------------------- +# kernels uint16 take extra parameter for defining the bitdepth +# ----------------------------------------------------------------- + +cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i,imin,imax,delta + + if pop: + for i in range(maxbin-1,-1,-1): + if histo[i]: + imax = i + break + for i in range(maxbin): + if histo[i]: + imin = i + break + delta = imax-imin + if delta>0: + return (maxbin*1.*(g-imin)/delta) + else: + return (imax-imin) + +cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + + for i in range(maxbin): + if histo[i]: + break + + return (g-i) + + +cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + cdef float sum = 0. + + if pop: + for i in range(maxbin): + sum += histo[i] + if i>=g: + break + + return ((maxbin*1.*sum)/pop) + else: + return (0) + +cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i,imin,imax + + if pop: + for i in range(maxbin-1,-1,-1): + if histo[i]: + imax = i + break + for i in range(maxbin): + if histo[i]: + imin = i + break + return (imax-imin) + else: + return (0) + +cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + + if pop: + for i in range(maxbin-1,-1,-1): + if histo[i]: + return (i) + + return (0) + +cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + cdef float mean = 0. + + if pop: + for i in range(maxbin): + mean += histo[i]*i + return (mean/pop) + else: + return (0) + +cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + cdef float mean = 0. + + if pop: + for i in range(maxbin): + mean += histo[i]*i + return ((g-mean/pop)/2.+midbin) + else: + return (0) + +cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + cdef float sum = pop/2.0 + + if pop: + for i in range(maxbin): + if histo[i]: + sum -= histo[i] + if sum<0: + return (i) + + return (0) + +cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + + if pop: + for i in range(maxbin): + if histo[i]: + return (i) + + return (0) + +cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int hmax=0,imax=0 + + if pop: + for i in range(maxbin): + if histo[i]>hmax: + hmax = histo[i] + imax = i + return (imax) + + return (0) + +cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i,imin,imax + + if pop: + for i in range(maxbin-1,-1,-1): + if histo[i]: + imax = i + break + for i in range(maxbin): + if histo[i]: + imin = i + break + if imax-g < g-imin: + return (imax) + else: + return (imin) + else: + return (0) + +cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + return (pop) + +cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + cdef float mean = 0. + + if pop: + for i in range(maxbin): + mean += histo[i]*i + return (g>(mean/pop)) + else: + return (0) + +cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): + cdef int i + + for i in range(maxbin-1,-1,-1): + if histo[i]: + break + + return (i-g) + +# ----------------------------------------------------------------- +# python wrappers +# ----------------------------------------------------------------- +def autolevel(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """bottom hat + """ + return rank16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """bottom hat + """ + return rank16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def egalise(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """local egalisation of the gray level + """ + return rank16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def gradient(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """local maximum - local minimum gray level + """ + return rank16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def maximum(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """local maximum gray level + """ + return rank16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def mean(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """average gray level (clipped on uint8) + """ + return rank16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """(g - average gray level)/2+midbin (clipped on uint8) + """ + return rank16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def median(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """local median + """ + return rank16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def minimum(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """local minimum gray level + """ + return rank16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """morphological contrast enhancement + """ + return rank16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def modal(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """local mode + """ + return rank16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def pop(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """returns the number of actual pixels of the structuring element inside the mask + """ + return rank16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def threshold(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """returns maxbin-1 if gray level higher than local mean, 0 else + """ + return rank16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) + +def tophat(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8): + """top hat + """ + return rank16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) diff --git a/skimage/rank/crank16_percentiles.pyx b/skimage/rank/crank16_percentiles.pyx new file mode 100644 index 00000000..b967c61a --- /dev/null +++ b/skimage/rank/crank16_percentiles.pyx @@ -0,0 +1,268 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a crank_percentiles.pxd + +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np + +# import main loop +from core cimport rank16_percentile + +# todo +# - manage float output, +# - manage different bit depth input +# - add auxiliary parameters (spectral_interval, infSup) + +# ----------------------------------------------------------------- +# kernels uint8 (SOFT version using percentiles) +# ----------------------------------------------------------------- + +cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i,imin,imax,sum,delta + + if pop: + sum = 0 + p1 = 1.0-p1 + for i in range(maxbin): + sum += histo[i] + if sum>=p0*pop: + imin = i + break + sum = 0 + for i in range(maxbin-1,-1,-1): + sum += histo[i] + if sum>=p1*pop: + imax = i + break + + delta = imax-imin + if g>imax: + return (maxbin-1) + if g(0) + if delta>0: + return ((maxbin-1)*1.*(g-imin)/delta) + else: + return (0) + else: + return (0) + + +cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i,imin,imax,sum,delta + + if pop: + sum = 0 + p1 = 1.0-p1 + for i in range(maxbin): + sum += histo[i] + if sum>=p0*pop: + imin = i + break + sum = 0 + for i in range((maxbin-1),-1,-1): + sum += histo[i] + if sum>=p1*pop: + imax = i + break + + return (imax-imin) + else: + return (0) + + +cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i,sum,mean,n + + if pop: + sum = 0 + mean = 0 + n = 0 + for i in range(maxbin): + sum += histo[i] + if (sum>=p0*pop) and (sum<=p1*pop): + n += histo[i] + mean += histo[i]*i + + if n>0: + return (1.0*mean/n) + else: + return (0) + else: + return (0) + +cdef inline np.uint16_t kernel_mean_substraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i,sum,mean,n + + if pop: + sum = 0 + mean = 0 + n = 0 + for i in range(maxbin): + sum += histo[i] + if (sum>=p0*pop) and (sum<=p1*pop): + n += histo[i] + mean += histo[i]*i + if n>0: + return ((g-(mean/n))*.5+midbin) + else: + return (0) + else: + return (0) + +cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i,imin,imax,sum,delta + + if pop: + sum = 0 + p1 = 1.0-p1 + for i in range(maxbin): + sum += histo[i] + if sum>=p0*pop: + imin = i + break + sum = 0 + for i in range((maxbin-1),-1,-1): + sum += histo[i] + if sum>=p1*pop: + imax = i + break + if g>imax: + return imax + if gimin + if imax-g < g-imin: + return imax + else: + return imin + else: + return (0) + +cdef inline np.uint16_t kernel_percentile(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i + cdef float sum = 0. + + if pop: + for i in range(maxbin): + sum += histo[i] + if sum>=p0*pop: + break + + return (i) + else: + return (0) + +cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i,sum,n + + if pop: + sum = 0 + n = 0 + for i in range(maxbin): + sum += histo[i] + if (sum>=p0*pop) and (sum<=p1*pop): + n += histo[i] + return (n) + else: + return (0) + +cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): + cdef int i + cdef float sum = 0. + + if pop: + for i in range(maxbin): + sum += histo[i] + if sum>=p0*pop: + break + + return ((maxbin-1)*(g>=i)) + else: + return (0) + +# ----------------------------------------------------------------- +# python wrappers +# ----------------------------------------------------------------- +def autolevel(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """bottom hat + """ + return rank16_percentile(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + + +def gradient(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """return p0,p1 percentile gradient + """ + return rank16_percentile(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + +def mean(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """return mean between [p0 and p1] percentiles + """ + return rank16_percentile(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + +def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """return original - mean between [p0 and p1] percentiles *.5 +127 + """ + return rank16_percentile(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + +def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """reforce contrast using percentiles + """ + return rank16_percentile(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + + +def percentile(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """return p0 percentile + """ + return rank16_percentile(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + + +def pop(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """return nb of pixels between [p0 and p1] + """ + return rank16_percentile(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + +def threshold(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + """return (maxbin-1) if g > percentile p0 + """ + return rank16_percentile(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) diff --git a/skimage/rank/crank_percentiles.pyx b/skimage/rank/crank_percentiles.pyx new file mode 100644 index 00000000..51a9e01f --- /dev/null +++ b/skimage/rank/crank_percentiles.pyx @@ -0,0 +1,263 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a crank_percentiles.pxd + +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np + +# import main loop +from core cimport rank8_percentile + +# todo +# - manage float output, +# - manage different bit depth input +# - add auxiliary parameters (spectral_interval, infSup) + +# ----------------------------------------------------------------- +# kernels uint8 (SOFT version using percentiles) +# ----------------------------------------------------------------- + +cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i,imin,imax,sum,delta + + if pop: + sum = 0 + p1 = 1.0-p1 + for i in range(256): + sum += histo[i] + if sum>=p0*pop: + imin = i + break + sum = 0 + for i in range(255,-1,-1): + sum += histo[i] + if sum>=p1*pop: + imax = i + break + + delta = imax-imin + if delta>0: + return (255.*(g-imin)/delta) + else: + return (0) + else: + return (0) + + +cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i,imin,imax,sum,delta + + if pop: + sum = 0 + p1 = 1.0-p1 + for i in range(256): + sum += histo[i] + if sum>=p0*pop: + imin = i + break + sum = 0 + for i in range(255,-1,-1): + sum += histo[i] + if sum>=p1*pop: + imax = i + break + + return (imax-imin) + else: + return (0) + + +cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i,sum,mean,n + + if pop: + sum = 0 + mean = 0 + n = 0 + for i in range(256): + sum += histo[i] + if (sum>=p0*pop) and (sum<=p1*pop): + n += histo[i] + mean += histo[i]*i + if n>0: + return (1.0*mean/n) + else: + return (0) + else: + return (0) + +cdef inline np.uint8_t kernel_mean_substraction(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i,sum,mean,n + + if pop: + sum = 0 + mean = 0 + n = 0 + for i in range(256): + sum += histo[i] + if (sum>=p0*pop) and (sum<=p1*pop): + n += histo[i] + mean += histo[i]*i + if n>0: + return ((g-(mean/n))*.5+127) + else: + return (0) + else: + return (0) + +cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i,imin,imax,sum,delta + + if pop: + sum = 0 + p1 = 1.0-p1 + for i in range(256): + sum += histo[i] + if sum>=p0*pop: + imin = i + break + sum = 0 + for i in range(255,-1,-1): + sum += histo[i] + if sum>=p1*pop: + imax = i + break + if g>imax: + return imax + if gimin + if imax-g < g-imin: + return imax + else: + return imin + else: + return (0) + +cdef inline np.uint8_t kernel_percentile(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i + cdef float sum = 0. + + if pop: + for i in range(256): + sum += histo[i] + if sum>=p0*pop: + break + + return (i) + else: + return (0) + +cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i,sum,n + + if pop: + sum = 0 + n = 0 + for i in range(256): + sum += histo[i] + if (sum>=p0*pop) and (sum<=p1*pop): + n += histo[i] + return (n) + else: + return (0) + +cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g, float p0, float p1): + cdef int i + cdef float sum = 0. + + if pop: + for i in range(256): + sum += histo[i] + if sum>=p0*pop: + break + + return (255*(g>=i)) + else: + return (0) + +# ----------------------------------------------------------------- +# python wrappers +# ----------------------------------------------------------------- +def autolevel(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """bottom hat + """ + return rank8_percentile(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) + + +def gradient(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """return p0,p1 percentile gradient + """ + return rank8_percentile(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) + +def mean(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """return mean between [p0 and p1] percentiles + """ + return rank8_percentile(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) + +def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """return original - mean between [p0 and p1] percentiles *.5 +127 + """ + return rank8_percentile(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) + +def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """reforce contrast using percentiles + """ + return rank8_percentile(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) + + +def percentile(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """return p0 percentile + """ + return rank8_percentile(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) + + +def pop(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """return nb of pixels between [p0 and p1] + """ + return rank8_percentile(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) + +def threshold(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + """return 255 if g > percentile p0 + """ + return rank8_percentile(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py new file mode 100644 index 00000000..6d18e27f --- /dev/null +++ b/skimage/rank/setup.py @@ -0,0 +1,15 @@ +import numpy as np + +from distutils.core import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext + +setup( + cmdclass = {'build_ext': build_ext}, + ext_modules = [Extension("helloworld", ["helloworld.pyx"]), + Extension("cmorph", ["cmorph.pyx"], include_dirs=[np.get_include()]), + Extension("crank", ["crank.pyx"], include_dirs=[np.get_include()]), + Extension("crank_percentiles", ["crank_percentiles.pyx"], include_dirs=[np.get_include()]), + Extension("crank16", ["crank16.pyx"], include_dirs=[np.get_include()]), + Extension("crank16_percentiles", ["crank16_percentiles.pyx"], include_dirs=[np.get_include()])] +) \ No newline at end of file diff --git a/skimage/rank/tools.py b/skimage/rank/tools.py new file mode 100644 index 00000000..835a8884 --- /dev/null +++ b/skimage/rank/tools.py @@ -0,0 +1,52 @@ +__author__ = 'Olivier Debeir 2021' + +import logging +import time + +def init_logger(logfilename = 'myapp.log'): + """add logger capabilities + """ + FORMAT = '%(asctime)-15s %(processName)s %(process)d %(message)s' + logging.basicConfig(filename=logfilename,format=FORMAT,filemode='wt') + logger = logging.getLogger() + logger.setLevel(logging.DEBUG) + + # create console handler and set level to debug + ch = logging.StreamHandler() + ch.setLevel(logging.DEBUG) + + # add ch to logger + logger.addHandler(ch) + logger.info('start logging in %s' % logfilename) + return logger + + +logger = logging.getLogger() + +def log_timing(func): + + def wrapper(*arg): + log_timing.level += 1 + t1 = time.time() + res = func(*arg) + t2 = time.time() + ms = (t2-t1)*1000.0 + logger.info('%s%s took %0.3f ms' % (log_timing.level*'-',func.func_name, ms)) + log_timing.level -= 1 + return (res,ms) + + return wrapper + +log_timing.level = 0 + +def tumbnail_it(data): + """display image with its histogram + """ + h = np.histogram(data[:],100) + hn = 512*h[0]/np.max(h[0]) + + plt.subplot(1,2,1) + plt.imshow(ima8,interpolation='nearest',cmap=cm.gray) + plt.subplot(1,2,2) + plt.plot(hn) + plt.colorbar() From 5e97f1f1ea0de611b09ffbe2b133cf4f72d683dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 3 Oct 2012 20:01:00 +0200 Subject: [PATCH 025/625] Fix navigation docs link --- doc/source/_templates/navbar.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/_templates/navbar.html b/doc/source/_templates/navbar.html index b0fa8026..7ef84786 100644 --- a/doc/source/_templates/navbar.html +++ b/doc/source/_templates/navbar.html @@ -1,5 +1,5 @@
        • Home
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          • From c6cf768a9cc25c345110bb74eba98798b43bc108 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 3 Oct 2012 20:06:38 +0200 Subject: [PATCH 026/625] Extend CSS rule for all headings --- doc/source/themes/scikit-image/static/css/custom.css | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/source/themes/scikit-image/static/css/custom.css b/doc/source/themes/scikit-image/static/css/custom.css index df60739e..85a174df 100644 --- a/doc/source/themes/scikit-image/static/css/custom.css +++ b/doc/source/themes/scikit-image/static/css/custom.css @@ -14,10 +14,9 @@ pre { font-size: 11px; } -h1, h2, h3, h4 { - clear: left; +h1, h2, h3, h4, h5, h6 { + clear: left; } - h1 { font-size: 30px; line-height: 36px; From 060d92fb576fce31423f2b1e3edf7e53d049503a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 3 Oct 2012 20:31:55 +0200 Subject: [PATCH 027/625] Fix navigation home page link --- doc/source/_templates/navbar.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/_templates/navbar.html b/doc/source/_templates/navbar.html index 7ef84786..267af8e7 100644 --- a/doc/source/_templates/navbar.html +++ b/doc/source/_templates/navbar.html @@ -1,4 +1,4 @@ -
        • Home
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          • From 904803184526e69dad2c3b4044e03a6298cd87f1 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Wed, 3 Oct 2012 17:03:54 -0700 Subject: [PATCH 028/625] DOC: Credit Johannes for website layout. --- CONTRIBUTORS.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 5f18e821..c73b14e2 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -107,7 +107,7 @@ - Johannes Schönberger Drawing functions, adaptive thresholding, regionprops, geometric - transformations, LBPs, polygon approximations, and more. + transformations, LBPs, polygon approximations, web layout, and more. - Pavel Campr Fixes and tests for Histograms of Oriented Gradients. From 413db93d09d48d85d7b623e8199ab14e5ccdc7be Mon Sep 17 00:00:00 2001 From: odebeir Date: Thu, 4 Oct 2012 09:13:33 +0200 Subject: [PATCH 029/625] adjust code for new project --- skimage/rank/app.log | 1 + skimage/rank/app.py | 173 +++++++------- skimage/rank/core16.pxd | 285 +++++++++++++++++++++++ skimage/rank/core16b.pxd | 286 +++++++++++++++++++++++ skimage/rank/core16p.pxd | 287 +++++++++++++++++++++++ skimage/rank/core8.pxd | 271 ++++++++++++++++++++++ skimage/rank/core8p.pxd | 271 ++++++++++++++++++++++ skimage/rank/crank.pyx | 7 +- skimage/rank/crank16.pyx | 9 +- skimage/rank/crank16_bilateral.pyx | 332 +++++++++++++++++++++++++++ skimage/rank/crank16_percentiles.pyx | 7 +- skimage/rank/crank_percentiles.pyx | 7 +- skimage/rank/setup.py | 4 +- 13 files changed, 1832 insertions(+), 108 deletions(-) create mode 100644 skimage/rank/app.log create mode 100644 skimage/rank/core16.pxd create mode 100644 skimage/rank/core16b.pxd create mode 100644 skimage/rank/core16p.pxd create mode 100644 skimage/rank/core8.pxd create mode 100644 skimage/rank/core8p.pxd create mode 100644 skimage/rank/crank16_bilateral.pyx diff --git a/skimage/rank/app.log b/skimage/rank/app.log new file mode 100644 index 00000000..7eae20f5 --- /dev/null +++ b/skimage/rank/app.log @@ -0,0 +1 @@ +2012-10-04 09:12:19,785 MainProcess 5265 start logging in app.log diff --git a/skimage/rank/app.py b/skimage/rank/app.py index acfc33ec..0cf24408 100644 --- a/skimage/rank/app.py +++ b/skimage/rank/app.py @@ -1,3 +1,5 @@ +import unittest + import numpy as np from time import time import matplotlib.pyplot as plt @@ -9,7 +11,7 @@ import crank import crank16 import crank_percentiles import crank16_percentiles -from pyrankfilter import filter +import crank16_bilateral from cmorph import dilate @@ -17,10 +19,6 @@ from cmorph import dilate def c_max(image,selem): return crank.maximum(image=image,selem = selem) -@log_timing -def w_max(image,selem): - return filter.maximum(image,struct_elem = selem) - @log_timing def cm_max(image,selem): return dilate(image=image,selem = selem) @@ -39,10 +37,8 @@ def compare(): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') (rc,ms_rc) = c_max(a,elem) - (rw, ms_rw) = w_max(a,elem) (rcm,ms_rcm) = cm_max(a,elem) rec.append((ms_rc,ms_rw,ms_rcm)) - assert (rc==rw).all() assert (rc==rcm).all() rec = np.asarray(rec) @@ -59,10 +55,8 @@ def compare(): for s in range(100,1000,100): a = (np.random.random((s,s))*256).astype('uint8') (rc,ms_rc) = c_max(a,elem) - (rw, ms_rw) = w_max(a,elem) (rcm,ms_rcm) = cm_max(a,elem) rec.append((ms_rc,ms_rw,ms_rcm)) - assert (rc==rw).all() assert (rc==rcm).all() rec = np.asarray(rec) @@ -71,89 +65,105 @@ def compare(): plt.plot(rec) plt.legend(['sliding cython','sliding weaves','cmorph']) plt.figure() - plt.imshow(np.hstack((rc,rw,rcm))) + plt.imshow(np.hstack((rc,rcm))) plt.show() +class TestSequenceFunctions(unittest.TestCase): -def test_image_size(): - """try several image sizes to check bounds conditions - """ - niter = 10 - elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') - for m,n in np.random.random_integers(1,100,size=(10,2)): - a = np.ones((m,n),dtype='uint8') - r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=0) - assert a.shape == r.shape - r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=-1) - assert a.shape == r.shape - r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=+1) - assert a.shape == r.shape - r = crank.mean(image=a,selem = elem,shift_x=-1,shift_y=0) - assert a.shape == r.shape - r = crank.mean(image=a,selem = elem,shift_x=+1,shift_y=0) - assert a.shape == r.shape - r = crank.mean(image=a,selem = elem,shift_x=-1,shift_y=-1) - assert a.shape == r.shape - r = crank.mean(image=a,selem = elem,shift_x=+1,shift_y=+1) - assert a.shape == r.shape + def setUp(self): + pass - return True + def test_random_sizes(self): + # make sure the size is not a problem + niter = 10 + elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') + for m,n in np.random.random_integers(1,100,size=(10,2)): + a8 = np.ones((m,n),dtype='uint8') + r = crank.mean(image=a8,selem = elem,shift_x=0,shift_y=0) + self.assertTrue(a8.shape == r.shape) + r = crank.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) + self.assertTrue(a8.shape == r.shape) + for m,n in np.random.random_integers(1,100,size=(10,2)): + a16 = np.ones((m,n),dtype='uint16') + r = crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0) + self.assertTrue(a16.shape == r.shape) + r = crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1) + self.assertTrue(a16.shape == r.shape) + + for m,n in np.random.random_integers(1,100,size=(10,2)): + a16 = np.ones((m,n),dtype='uint16') + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9) + self.assertTrue(a16.shape == r.shape) + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) + self.assertTrue(a16.shape == r.shape) + + def test_compare_with_cmorph(self): + #compare the result of maximum filter with dilate + a = (np.random.random((500,500))*256).astype('uint8') + + for r in range(1,20,1): + elem = np.ones((r,r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc = crank.maximum(image=a,selem = elem) + cm = dilate(image=a,selem = elem) + self.assertTrue((rc==cm).all()) + + def test_bitdepth(self): + elem = np.ones((3,3),dtype='uint8') + a16 = np.ones((100,100),dtype='uint16')*255 + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) + a16 = np.ones((100,100),dtype='uint16')*255*2 + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9) + a16 = np.ones((100,100),dtype='uint16')*255*4 + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10) + a16 = np.ones((100,100),dtype='uint16')*255*8 + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11) + a16 = np.ones((100,100),dtype='uint16')*255*16 + r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) + + def test_population(self): + a = np.zeros((5,5),dtype='uint8') + elem = np.ones((3,3),dtype='uint8') + p = crank.pop(image=a,selem = elem) + r = np.asarray([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]]) + np.testing.assert_array_equal(r,p) + + def test_structuring_element(self): + a = np.zeros((6,6),dtype='uint8') + a[2,2] = 255 + elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') + f = crank.maximum(image=a,selem = elem,shift_x=1,shift_y=1) + r = np.asarray([[ 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0, 0], + [ 0, 0, 255, 255, 255, 0], + [ 0, 0, 0, 255, 255, 0], + [ 0, 0, 0, 0, 0, 0]]) + np.testing.assert_array_equal(r,f) + + + @unittest.expectedFailure + def test_fail_on_bitdepth(self): + # should fail because data bitdepth is too high for the function + a16 = np.ones((100,100),dtype='uint16')*255 + elem = np.ones((3,3),dtype='uint8') + f = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) if __name__ == '__main__': logger = init_logger('app.log') - a = np.zeros((10,10),dtype='uint8') - a[2,2] = 255 -# a[2,3] = 255 -# a[2,4] = 255 - print a +# unittest.main() + suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) + unittest.TextTestRunner(verbosity=2).run(suite) - mask = np.ones_like(a) -# mask[:3,:3] = 0 - -# elem = np.asarray([[0,1,0],[1,1,1],[0,1,0]],dtype='uint8') - elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') - - niter = 1 - t0 = time() - - for iter in range(niter): - r = crank.mean(image=a,selem = elem,shift_x=0,shift_y=0,mask = mask) - p = crank.pop(image=a,selem = elem,shift_x=0,shift_y=0,mask = mask) - t1 = time() - print '%f msec'%(t1-t0) - - print 'cython mean' - print r - print p - - t0 = time() - for iter in range(niter): - r = filter.mean(a,struct_elem = elem,struct_elem_center=(1,1),mask = mask) - t1 = time() - print '%f msec'%(t1-t0) - - print 'filter.mean:' - print r - - print a - r = crank.maximum(image=a,selem = elem,shift_x=0,shift_y=0,mask = mask) - print r - - r = crank.gradient(image=r,selem = elem,shift_x=0,shift_y=0,mask = mask) - print r - im = np.zeros((10,10),dtype='uint8') - im[2:6,2:6] = 255 - elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') - f = crank.gradient(image=im,selem = elem) - print f - f = crank.egalise(image=im,selem = elem) - print f # compare() -# test_image_size() # a = (data.coins()).astype('uint8') a8 = (data.coins()).astype('uint8') @@ -162,9 +172,10 @@ if __name__ == '__main__': # f1 = filter.soft_gradient(a,struct_elem = selem,bitDepth=8,infSup=[.1,.9]) # f2 = crank16.bottomhat(a,selem = selem,bitdepth=12) f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) - f2 = crank16_percentiles.mean(a,selem = selem,bitdepth=12,p0=.1,p1=.9) +# f2 = crank16_percentiles.mean(a,selem = selem,bitdepth=12,p0=.1,p1=.9) + f2 = crank16_bilateral.mean(a,selem = selem,bitdepth=12,s0=500,s1=500) # plt.imshow(f2) - plt.imshow(np.hstack((f1,f2))) + plt.imshow(np.hstack((a,f2))) plt.colorbar() plt.show() diff --git a/skimage/rank/core16.pxd b/skimage/rank/core16.pxd new file mode 100644 index 00000000..9973025f --- /dev/null +++ b/skimage/rank/core16.pxd @@ -0,0 +1,285 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data bitdepth +#--------------------------------------------------------------------------- + +cdef inline rank16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(maxbin * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out diff --git a/skimage/rank/core16b.pxd b/skimage/rank/core16b.pxd new file mode 100644 index 00000000..38832c11 --- /dev/null +++ b/skimage/rank/core16b.pxd @@ -0,0 +1,286 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16b.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data bitdepth and bilateral interval +#--------------------------------------------------------------------------- + +cdef inline rank16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, int s0, int s1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + - only pixel inside [s0,s1] centered on g are taken into account + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(maxbin * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out diff --git a/skimage/rank/core16p.pxd b/skimage/rank/core16p.pxd new file mode 100644 index 00000000..dbf51c54 --- /dev/null +++ b/skimage/rank/core16p.pxd @@ -0,0 +1,287 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16p.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline rank16_percentile(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint16 + - result is uint16 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(maxbin * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + + diff --git a/skimage/rank/core8.pxd b/skimage/rank/core8.pxd new file mode 100644 index 00000000..0c901c8b --- /dev/null +++ b/skimage/rank/core8.pxd @@ -0,0 +1,271 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core8.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 8 bit core kernel +#--------------------------------------------------------------------------- + +cdef inline rank8(np.uint8_t kernel(int*, float, np.uint8_t), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + eimage = np.ascontiguousarray(eimage) + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(256 * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + diff --git a/skimage/rank/core8p.pxd b/skimage/rank/core8p.pxd new file mode 100644 index 00000000..25e3ffcf --- /dev/null +++ b/skimage/rank/core8p.pxd @@ -0,0 +1,271 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core8p.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 8 bit core kernel receives extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline rank8_percentile(np.uint8_t kernel(int*, float, np.uint8_t, float, float), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + eimage = np.ascontiguousarray(eimage) + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + cdef int n_se_n, n_se_s, n_se_e, n_se_w + + cdef int selem_num = np.sum(selem != 0) + cdef int* sr = malloc(selem_num * sizeof(int)) + cdef int* sc = malloc(selem_num * sizeof(int)) + cdef int* histo = malloc(256 * sizeof(int)) + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + n_se_n = n_se_s = n_se_e = n_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[n_se_e] = r - centre_r + se_e_c[n_se_e] = c - centre_c + n_se_e += 1 + if t_w[r,c]: + se_w_r[n_se_w] = r - centre_r + se_w_c[n_se_w] = c - centre_c + n_se_w += 1 + if t_n[r,c]: + se_n_r[n_se_n] = r - centre_r + se_n_c[n_se_n] = c - centre_c + n_se_n += 1 + if t_s[r,c]: + se_s_r[n_se_s] = r - centre_r + se_s_c[n_se_s] = c - centre_c + n_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(n_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(n_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(n_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + free(sr) + free(sc) + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + diff --git a/skimage/rank/crank.pyx b/skimage/rank/crank.pyx index 09048917..59016eed 100644 --- a/skimage/rank/crank.pyx +++ b/skimage/rank/crank.pyx @@ -15,12 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core cimport rank8 - -# todo -# - manage float output, -# - manage different bit depth input -# - add auxiliary parameters (spectral_interval, infSup) +from core8 cimport rank8 # ----------------------------------------------------------------- # kernels uint8 diff --git a/skimage/rank/crank16.pyx b/skimage/rank/crank16.pyx index 78e5a2af..cd0bacd4 100644 --- a/skimage/rank/crank16.pyx +++ b/skimage/rank/crank16.pyx @@ -2,7 +2,7 @@ >>> python setup.py build_ext --inplace to generate html report use: ->>> cython -a crank.pxd +>>> cython -a crank16.pxd """ @@ -15,12 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core cimport rank16 - -# todo -# - manage float output, -# - manage different bit depth input -# - add auxiliary parameters (spectral_interval, infSup) +from core16 cimport rank16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth diff --git a/skimage/rank/crank16_bilateral.pyx b/skimage/rank/crank16_bilateral.pyx new file mode 100644 index 00000000..313b83d6 --- /dev/null +++ b/skimage/rank/crank16_bilateral.pyx @@ -0,0 +1,332 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a crank16.pxd + +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np + +# import main loop +from core16b cimport rank16b + +# ----------------------------------------------------------------- +# kernels uint16 take extra parameter for defining the bitdepth +# ----------------------------------------------------------------- + +#cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i,imin,imax,delta +# +# if pop: +# for i in range(maxbin-1,-1,-1): +# if histo[i]: +# imax = i +# break +# for i in range(maxbin): +# if histo[i]: +# imin = i +# break +# delta = imax-imin +# if delta>0: +# return (maxbin*1.*(g-imin)/delta) +# else: +# return (imax-imin) +# +#cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# +# for i in range(maxbin): +# if histo[i]: +# break +# +# return (g-i) +# +# +#cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# cdef float sum = 0. +# +# if pop: +# for i in range(maxbin): +# sum += histo[i] +# if i>=g: +# break +# +# return ((maxbin*1.*sum)/pop) +# else: +# return (0) +# +#cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i,imin,imax +# +# if pop: +# for i in range(maxbin-1,-1,-1): +# if histo[i]: +# imax = i +# break +# for i in range(maxbin): +# if histo[i]: +# imin = i +# break +# return (imax-imin) +# else: +# return (0) +# +#cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# +# if pop: +# for i in range(maxbin-1,-1,-1): +# if histo[i]: +# return (i) +# +# return (0) + +cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): + cdef int i,bilat_pop=0 + cdef float mean = 0. + + if pop: + for i in range(maxbin): + if (g>(i-s0)) and (g<(i+s1)): + bilat_pop += histo[i] + mean += histo[i]*i + if bilat_pop: + return (mean/bilat_pop) + else: + return (0) + else: + return (0) + +#cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# cdef float mean = 0. +# +# if pop: +# for i in range(maxbin): +# mean += histo[i]*i +# return ((g-mean/pop)/2.+midbin) +# else: +# return (0) +# +#cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# cdef float sum = pop/2.0 +# +# if pop: +# for i in range(maxbin): +# if histo[i]: +# sum -= histo[i] +# if sum<0: +# return (i) +# +# return (0) +# +#cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# +# if pop: +# for i in range(maxbin): +# if histo[i]: +# return (i) +# +# return (0) +# +#cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int hmax=0,imax=0 +# +# if pop: +# for i in range(maxbin): +# if histo[i]>hmax: +# hmax = histo[i] +# imax = i +# return (imax) +# +# return (0) +# +#cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i,imin,imax +# +# if pop: +# for i in range(maxbin-1,-1,-1): +# if histo[i]: +# imax = i +# break +# for i in range(maxbin): +# if histo[i]: +# imin = i +# break +# if imax-g < g-imin: +# return (imax) +# else: +# return (imin) +# else: +# return (0) +# +cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): + cdef int i,bilat_pop=0 + + if pop: + for i in range(maxbin): + if (g>(i-s0)) and (g<(i+s1)): + bilat_pop += histo[i] + return (bilat_pop) + else: + return (0) + +# +#cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# cdef float mean = 0. +# +# if pop: +# for i in range(maxbin): +# mean += histo[i]*i +# return (g>(mean/pop)) +# else: +# return (0) +# +#cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +# cdef int i +# +# for i in range(maxbin-1,-1,-1): +# if histo[i]: +# break +# +# return (i-g) + +# ----------------------------------------------------------------- +# python wrappers +# ----------------------------------------------------------------- +#def autolevel(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """bottom hat +# """ +# return rank16b(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """bottom hat +# """ +# return rank16b(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def egalise(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """local egalisation of the gray level +# """ +# return rank16b(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def gradient(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """local maximum - local minimum gray level +# """ +# return rank16b(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def maximum(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """local maximum gray level +# """ +# return rank16b(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + +def mean(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): + """average gray level (clipped on uint8) + """ + return rank16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + +#def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """(g - average gray level)/2+midbin (clipped on uint8) +# """ +# return rank16b(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def median(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """local median +# """ +# return rank16b(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def minimum(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """local minimum gray level +# """ +# return rank16b(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """morphological contrast enhancement +# """ +# return rank16b(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def modal(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """local mode +# """ +# return rank16b(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +def pop(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): + """returns the number of actual pixels of the structuring element inside the mask + """ + return rank16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + +#def threshold(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """returns maxbin-1 if gray level higher than local mean, 0 else +# """ +# return rank16b(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) +# +#def tophat(np.ndarray[np.uint16_t, ndim=2] image, +# np.ndarray[np.uint8_t, ndim=2] selem, +# np.ndarray[np.uint8_t, ndim=2] mask=None, +# np.ndarray[np.uint16_t, ndim=2] out=None, +# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): +# """top hat +# """ +# return rank16b(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) diff --git a/skimage/rank/crank16_percentiles.pyx b/skimage/rank/crank16_percentiles.pyx index b967c61a..7756bc19 100644 --- a/skimage/rank/crank16_percentiles.pyx +++ b/skimage/rank/crank16_percentiles.pyx @@ -15,12 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core cimport rank16_percentile - -# todo -# - manage float output, -# - manage different bit depth input -# - add auxiliary parameters (spectral_interval, infSup) +from core16p cimport rank16_percentile # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) diff --git a/skimage/rank/crank_percentiles.pyx b/skimage/rank/crank_percentiles.pyx index 51a9e01f..d4d6312f 100644 --- a/skimage/rank/crank_percentiles.pyx +++ b/skimage/rank/crank_percentiles.pyx @@ -15,12 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core cimport rank8_percentile - -# todo -# - manage float output, -# - manage different bit depth input -# - add auxiliary parameters (spectral_interval, infSup) +from core8p cimport rank8_percentile # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 6d18e27f..e9af9446 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -6,10 +6,10 @@ from Cython.Distutils import build_ext setup( cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("helloworld", ["helloworld.pyx"]), - Extension("cmorph", ["cmorph.pyx"], include_dirs=[np.get_include()]), + ext_modules = [Extension("cmorph", ["cmorph.pyx"], include_dirs=[np.get_include()]), Extension("crank", ["crank.pyx"], include_dirs=[np.get_include()]), Extension("crank_percentiles", ["crank_percentiles.pyx"], include_dirs=[np.get_include()]), Extension("crank16", ["crank16.pyx"], include_dirs=[np.get_include()]), + Extension("crank16_bilateral", ["crank16_bilateral.pyx"], include_dirs=[np.get_include()]), Extension("crank16_percentiles", ["crank16_percentiles.pyx"], include_dirs=[np.get_include()])] ) \ No newline at end of file From 089f7ffda049395249e5e8057ad21bff8659063e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 4 Oct 2012 09:25:24 +0200 Subject: [PATCH 030/625] Change domain name --- doc/Makefile | 2 +- doc/release/release_0.3.txt | 4 ++-- doc/release/release_0.4.txt | 4 ++-- doc/release/release_0.5.txt | 2 +- doc/source/_static/docversions.js | 2 +- doc/source/gitwash/this_project.inc | 2 +- doc/source/overview.txt | 4 ++-- doc/source/random_gallery.py | 4 ++-- doc/source/themes/scikit-image/layout.html | 2 +- 9 files changed, 13 insertions(+), 13 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index f750fb18..ea6e26e3 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -124,7 +124,7 @@ gh-pages: gitwash: python tools/gitwash/gitwash_dumper.py source scikit-image \ - --project-url=http://scikit-image.org \ + --project-url=http://skimage.org \ --project-ml-url=http://groups.google.com/group/scikit-image \ --repo-name=scikit-image \ --github-user=scikit-image \ diff --git a/doc/release/release_0.3.txt b/doc/release/release_0.3.txt index 617c3614..743e388d 100644 --- a/doc/release/release_0.3.txt +++ b/doc/release/release_0.3.txt @@ -9,11 +9,11 @@ Python (>=2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website - http://scikit-image.org + http://skimage.org or the examples gallery at - http://scikit-image.org/docs/0.3/auto_examples/ + http://skimage.org/docs/0.3/auto_examples/ New Features ------------ diff --git a/doc/release/release_0.4.txt b/doc/release/release_0.4.txt index 04679271..2e8092dd 100644 --- a/doc/release/release_0.4.txt +++ b/doc/release/release_0.4.txt @@ -6,7 +6,7 @@ toolbox for SciPy. Please visit our examples gallery to see what we've been up to: - http://scikit-image.org/docs/0.4/auto_examples/ + http://skimage.org/docs/0.4/auto_examples/ Note that, in this release, we renamed the module from ``scikits.image`` to ``skimage``, to work around name space conflicts with other scikits (similarly, @@ -20,7 +20,7 @@ This release runs under all major operating systems where Python (>=2.6 or For more information, visit our website - http://scikit-image.org + http://skimage.org New Features ------------ diff --git a/doc/release/release_0.5.txt b/doc/release/release_0.5.txt index 652f45fa..4977046a 100644 --- a/doc/release/release_0.5.txt +++ b/doc/release/release_0.5.txt @@ -6,7 +6,7 @@ toolbox for SciPy. For more information, please visit our website - http://scikit-image.org + http://skimage.org New Features ------------ diff --git a/doc/source/_static/docversions.js b/doc/source/_static/docversions.js index 660553e0..7fbc57a0 100644 --- a/doc/source/_static/docversions.js +++ b/doc/source/_static/docversions.js @@ -12,6 +12,6 @@ function insert_version_links() { document.write(open_list); document.write('skimage VERSION \n' .replace('VERSION', labels[i]) - .replace('URL', 'http://scikit-image.org/docs/' + labels[i])); + .replace('URL', 'http://skimage.org/docs/' + labels[i])); } } diff --git a/doc/source/gitwash/this_project.inc b/doc/source/gitwash/this_project.inc index 349b92ba..6010831f 100644 --- a/doc/source/gitwash/this_project.inc +++ b/doc/source/gitwash/this_project.inc @@ -1,5 +1,5 @@ .. scikit-image -.. _scikit-image: http://scikit-image.org +.. _scikit-image: http://skimage.org .. _`scikit-image github`: http://github.com/scikit-image/scikit-image .. _`scikit-image mailing list`: http://groups.google.com/group/scikit-image diff --git a/doc/source/overview.txt b/doc/source/overview.txt index 5a4e6123..3acdeebf 100644 --- a/doc/source/overview.txt +++ b/doc/source/overview.txt @@ -1,7 +1,7 @@ Image Processing SciKit ======================= -The `scikit-image `__ SciKit (toolkit for +The `scikit-image `__ SciKit (toolkit for `SciPy `__) extends ``scipy.ndimage`` to provide a versatile set of image processing routines. It is written in the `Python `_ language. @@ -12,7 +12,7 @@ mailing list (address provided below). Homepage -------- -http://scikit-image.org +http://skimage.org Source, bugs and patches ------------------------ diff --git a/doc/source/random_gallery.py b/doc/source/random_gallery.py index 47ce89a2..3358b3f0 100644 --- a/doc/source/random_gallery.py +++ b/doc/source/random_gallery.py @@ -32,8 +32,8 @@ gallery_div = '''\ examples = glob.glob(os.path.join(example_dir, 'plot_*.py')) images, links = [], [] -image_url = 'http://scikit-image.org/docs/dev/_images/%s.png' -link_url = 'http://scikit-image.org/docs/dev/auto_examples/%s.html' +image_url = 'http://skimage.org/docs/dev/_images/%s.png' +link_url = 'http://skimage.org/docs/dev/auto_examples/%s.html' for e in examples: e = os.path.basename(e) diff --git a/doc/source/themes/scikit-image/layout.html b/doc/source/themes/scikit-image/layout.html index 58424f2f..a1755670 100644 --- a/doc/source/themes/scikit-image/layout.html +++ b/doc/source/themes/scikit-image/layout.html @@ -79,7 +79,7 @@ {%- block extrahead %}{% endblock %} - +
          From 1494bc56008f50f24d9046f7713b27a250b54eeb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 4 Oct 2012 09:29:00 +0200 Subject: [PATCH 031/625] Remove unused fopenmp compile args --- skimage/transform/setup.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/skimage/transform/setup.py b/skimage/transform/setup.py index cd2a0f8a..0e415bad 100644 --- a/skimage/transform/setup.py +++ b/skimage/transform/setup.py @@ -20,9 +20,7 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_warps_cy', sources=['_warps_cy.c'], - include_dirs=[get_numpy_include_dirs(), '../_shared'], - extra_compile_args=['-fopenmp'], - extra_link_args=['-fopenmp']) + include_dirs=[get_numpy_include_dirs(), '../_shared']) return config From 70a509b626523d93a9e48a4a4a5b856361441c7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 4 Oct 2012 10:19:07 +0200 Subject: [PATCH 032/625] Revert changes to release notes --- doc/release/release_0.3.txt | 4 ++-- doc/release/release_0.4.txt | 10 +++++----- doc/release/release_0.5.txt | 6 +++--- doc/release/release_0.6.txt | 6 +++--- doc/release/release_0.7.txt | 8 ++++---- 5 files changed, 17 insertions(+), 17 deletions(-) diff --git a/doc/release/release_0.3.txt b/doc/release/release_0.3.txt index 743e388d..5446003d 100644 --- a/doc/release/release_0.3.txt +++ b/doc/release/release_0.3.txt @@ -9,11 +9,11 @@ Python (>=2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website - http://skimage.org + http://scikits-image.org or the examples gallery at - http://skimage.org/docs/0.3/auto_examples/ + http://scikits-image.org/docs/0.3/auto_examples/ New Features ------------ diff --git a/doc/release/release_0.4.txt b/doc/release/release_0.4.txt index 2e8092dd..765b4760 100644 --- a/doc/release/release_0.4.txt +++ b/doc/release/release_0.4.txt @@ -1,26 +1,26 @@ -Announcement: scikit-image 0.4 +Announcement: scikits-image 0.4 =============================== -We're happy to announce the 0.4 release of scikit-image, an image processing +We're happy to announce the 0.4 release of scikits-image, an image processing toolbox for SciPy. Please visit our examples gallery to see what we've been up to: - http://skimage.org/docs/0.4/auto_examples/ + http://scikits-image.org/docs/0.4/auto_examples/ Note that, in this release, we renamed the module from ``scikits.image`` to ``skimage``, to work around name space conflicts with other scikits (similarly, the machine learning scikit is now imported as ``sklearn``). A big shout-out also to everyone currently at SciPy India; have fun, and -remember to join the scikit-image sprint! +remember to join the scikits-image sprint! This release runs under all major operating systems where Python (>=2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website - http://skimage.org + http://scikits-image.org New Features ------------ diff --git a/doc/release/release_0.5.txt b/doc/release/release_0.5.txt index 4977046a..74a5b3fe 100644 --- a/doc/release/release_0.5.txt +++ b/doc/release/release_0.5.txt @@ -1,12 +1,12 @@ -Announcement: scikit-image 0.5 +Announcement: scikits-image 0.5 =============================== -We're happy to announce the 0.5 release of scikit-image, our image processing +We're happy to announce the 0.5 release of scikits-image, our image processing toolbox for SciPy. For more information, please visit our website - http://skimage.org + http://scikits-image.org New Features ------------ diff --git a/doc/release/release_0.6.txt b/doc/release/release_0.6.txt index df2582cc..8c525cb7 100644 --- a/doc/release/release_0.6.txt +++ b/doc/release/release_0.6.txt @@ -1,9 +1,9 @@ -Announcement: scikit-image 0.6 +Announcement: scikits-image 0.6 =============================== -We're happy to announce the 6th version of scikit-image! +We're happy to announce the 6th version of scikits-image! -scikit-image is an image processing toolbox for SciPy that includes algorithms +Scikits-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. diff --git a/doc/release/release_0.7.txt b/doc/release/release_0.7.txt index 6637cd51..a6b591cb 100644 --- a/doc/release/release_0.7.txt +++ b/doc/release/release_0.7.txt @@ -1,9 +1,9 @@ -Announcement: scikit-image 0.7.0 +Announcement: scikits-image 0.7.0 ================================= -We're happy to announce the 7th version of scikit-image! +We're happy to announce the 7th version of scikits-image! -scikit-image is an image processing toolbox for SciPy that includes algorithms +Scikits-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. @@ -15,7 +15,7 @@ For more information, examples, and documentation, please visit our website New Features ------------ -It's been only 3 months since scikit-image 0.6 was released, but in that short +It's been only 3 months since scikits-image 0.6 was released, but in that short time, we've managed to add plenty of new features and enhancements, including - Geometric image transforms From 98b18da2abb184b8ea48240c08607fa0f0526282 Mon Sep 17 00:00:00 2001 From: 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zPY<6rs;7g6g@enOh1*Vk4fWr&*_6G0j0KTpGg(YnpZ85kuJiId7n!V83vyj_?b{l! z!+N&0u#`)3_=>|=*48fJ@4lgNbCS4OoI_)_M0V_9V`VvwedPZmo#{{FRtz0*)S<7N zD~E9yNVBpWdf|`KNbkXk6I3IEHf|xut`%KAwDbo~h2$-IvP}GUy3m6G3j!yMY*&@a zMj8tOIGzEGpRW8ajR)?gV%jQU8G?XzI~!=|b3Oau_?Rx^P>v9LVeItV;`HJox zrJvEVc{1~^HweTIkU7cLuLkcI5d>sF$;e=MHK)+H)nFar!G>*}nE5zbv_H9sk*zIp zah$x^)W|@0D<@w$#K3scWol;n*xR#z_pPEO%g8RE38%^b7HtW}>FavFUnF5=TN#11U z4fJ$cEu&ZLMZebihNk4s2?ey4Pd8{$%?h@*`zROZc57^)-&$L1tQ5G;K%WU6mraLN zadE4-P0JN*{p;{PfSl7tW?r+)B_L5tTbDLq-t2$oB$>S9B6_<{TUoXh6Y6BA)L;`B7?Xk}cl~{$Zq#w1=5x^+7lv}p#% zuP3{W_2rq{{Jy7Gzb$RnNWOY?Rgzzzu0gn%QxKcCb%-MAnzNgy^|WMxf*>@B(`s~D zX^IG|Zfe2?RUxg2DHxfUiD$n<)1hfh)S_)$bCNbQG5T=`9W7hEs>*i4oT_R<5vO2a zXxxKdy8|5hCRwrA%*@2tJ?592=hk*xyhEs_qMS@9m!W7*LC?T&(tErbYG!8ErcE1q z5+pa8%E8gkcixa;mClq$RSi;+DQLIW(_aguIvJbD$e1wtxC;7@NCSH{(Uhz*`Vrl? z1xlO(c}b^r1Q20h$iaES^9Bape*x|-8Mf>?r;)INefmr$*iL)U5NWk)Memu zdU-UyL=vi3QB_r^DJFkF^)PL19T`ooqfhf95~nmkrL%`eXvV;UZ-Kib1e7Syn$(3| zer6X|i&oaEmDaFZXKr#ym9L95zDJG-;d*L(6VN-7!_tEw_-mUQrgevenh1DOKlCbX(p zhUptWFgvxuB+N-uGBJ>R|CP==^O**xvicV1JbcWZp`y&CEM%PDn~t^xr8!g1!EzUY zqN0Z+_I&<{-3vM^vHXVRFnpeKMhjuq%;?Q!DV&Nx&~;E(QYDfnH3PH$Bn}jghb*yS z=}$9JmP2U{rWF5!IQ_3Tv=`Hm Date: Thu, 4 Oct 2012 11:32:33 +0200 Subject: [PATCH 034/625] fix. gitignore --- .gitignore | 1 + skimage/rank/app.log | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 skimage/rank/app.log diff --git a/.gitignore b/.gitignore index 925e5886..2623ea74 100644 --- a/.gitignore +++ b/.gitignore @@ -23,3 +23,4 @@ doc/source/auto_examples/images/thumb doc/source/auto_examples/applications/ doc/source/_static/random.js .idea/ +*.log diff --git a/skimage/rank/app.log b/skimage/rank/app.log deleted file mode 100644 index 7eae20f5..00000000 --- a/skimage/rank/app.log +++ /dev/null @@ -1 +0,0 @@ -2012-10-04 09:12:19,785 MainProcess 5265 start logging in app.log From cbd1ec0c5fa5a7d7dd891a5ad7f8746e7f78de7f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 16:09:56 +0200 Subject: [PATCH 035/625] add test/ --- skimage/rank/__init__.py | 2 +- skimage/rank/core.pxd | 1 - skimage/rank/test/test_16bitbilateral.py | 23 +++++ skimage/rank/test/test_benchmark.py | 72 ++++++++++++++ skimage/rank/{app.py => test/test_suite.py} | 101 ++------------------ skimage/rank/{ => test}/tools.py | 10 -- 6 files changed, 104 insertions(+), 105 deletions(-) create mode 100644 skimage/rank/test/test_16bitbilateral.py create mode 100644 skimage/rank/test/test_benchmark.py rename skimage/rank/{app.py => test/test_suite.py} (59%) rename skimage/rank/{ => test}/tools.py (78%) diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index bcc43cdc..8b0e3f5b 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -1 +1 @@ -from .rank import * +from .crank import * diff --git a/skimage/rank/core.pxd b/skimage/rank/core.pxd index 72facde3..bb57aec4 100644 --- a/skimage/rank/core.pxd +++ b/skimage/rank/core.pxd @@ -18,7 +18,6 @@ from libc.stdlib cimport malloc, free cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- diff --git a/skimage/rank/test/test_16bitbilateral.py b/skimage/rank/test/test_16bitbilateral.py new file mode 100644 index 00000000..ac078b09 --- /dev/null +++ b/skimage/rank/test/test_16bitbilateral.py @@ -0,0 +1,23 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.rank import crank_percentiles,crank16_bilateral + +if __name__ == '__main__': + a8 = (data.coins()).astype('uint8') + + a16 = (data.coins()).astype('uint16')*16 + selem = np.ones((20,20),dtype='uint8') + f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) + + plt.figure() + plt.imshow(np.hstack((a8,f1))) + plt.colorbar() + + plt.figure() + plt.imshow(np.hstack((a16,f2))) + plt.colorbar() + + plt.show() diff --git a/skimage/rank/test/test_benchmark.py b/skimage/rank/test/test_benchmark.py new file mode 100644 index 00000000..4fee48c1 --- /dev/null +++ b/skimage/rank/test/test_benchmark.py @@ -0,0 +1,72 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.morphology import cmorph +from skimage.rank import crank + +from tools import log_timing + +@log_timing +def cr_max(image,selem): + return crank.maximum(image=image,selem = selem) + +@log_timing +def cm_dil(image,selem): + return cmorph.dilate(image=image,selem = selem) + + +def compare(): + """comparison between + - crank.maximum rankfilter implementation + - cmorph.dilate cython implementation + on increasing structuring element size and increasing image size + """ +# a = (np.random.random((500,500))*256).astype('uint8') + a = data.camera() + + rec = [] + e_range = range(1,20,1) + for r in e_range: + elem = np.ones((r,r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc,ms_rc = cr_max(a,elem) + rcm,ms_rcm = cm_dil(a,elem) + rec.append((ms_rc,ms_rcm)) + # check if results are identical + assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing element size') + plt.plot(e_range,rec) + plt.legend(['crank.maximum','cmorph.dilate']) + plt.figure() + plt.imshow(np.hstack((rc,rcm))) + + r = 9 + elem = np.ones((r,r),dtype='uint8') + + rec = [] + s_range = range(100,1000,100) + for s in s_range: + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = cr_max(a,elem) + (rcm,ms_rcm) = cm_dil(a,elem) + rec.append((ms_rc,ms_rcm)) + assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing image size') + plt.plot(s_range,rec) + plt.legend(['crank.maximum','cmorph.dilate']) + plt.figure() + plt.imshow(np.hstack((rc,rcm))) + + plt.show() + +if __name__ == '__main__': + compare() \ No newline at end of file diff --git a/skimage/rank/app.py b/skimage/rank/test/test_suite.py similarity index 59% rename from skimage/rank/app.py rename to skimage/rank/test/test_suite.py index 0cf24408..61ceab48 100644 --- a/skimage/rank/app.py +++ b/skimage/rank/test/test_suite.py @@ -1,73 +1,10 @@ import unittest import numpy as np -from time import time -import matplotlib.pyplot as plt -from skimage import data -from tools import log_timing,init_logger +from skimage.rank import crank,crank16,crank16_bilateral,crank16_percentiles,crank_percentiles +from skimage.morphology import cmorph -import crank -import crank16 -import crank_percentiles -import crank16_percentiles -import crank16_bilateral -from cmorph import dilate - - -@log_timing -def c_max(image,selem): - return crank.maximum(image=image,selem = selem) - -@log_timing -def cm_max(image,selem): - return dilate(image=image,selem = selem) - -def compare(): - """comparison between - - Cython maximum rankfilter implementation - - weaves maximum rankfilter implementation - - cmorph.dilate cython implementation - on increasing structuring element size and increasing image size - """ - a = (np.random.random((500,500))*256).astype('uint8') - - rec = [] - for r in range(1,20,1): - elem = np.ones((r,r),dtype='uint8') - # elem = (np.random.random((r,r))>.5).astype('uint8') - (rc,ms_rc) = c_max(a,elem) - (rcm,ms_rcm) = cm_max(a,elem) - rec.append((ms_rc,ms_rw,ms_rcm)) - assert (rc==rcm).all() - - rec = np.asarray(rec) - - plt.plot(rec) - plt.legend(['sliding cython','sliding weaves','cmorph']) - plt.figure() - plt.imshow(np.hstack((rc,rw,rcm))) - - r = 9 - elem = np.ones((r,r),dtype='uint8') - - rec = [] - for s in range(100,1000,100): - a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = c_max(a,elem) - (rcm,ms_rcm) = cm_max(a,elem) - rec.append((ms_rc,ms_rw,ms_rcm)) - assert (rc==rcm).all() - - rec = np.asarray(rec) - - plt.figure() - plt.plot(rec) - plt.legend(['sliding cython','sliding weaves','cmorph']) - plt.figure() - plt.imshow(np.hstack((rc,rcm))) - - plt.show() class TestSequenceFunctions(unittest.TestCase): def setUp(self): @@ -106,7 +43,7 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') rc = crank.maximum(image=a,selem = elem) - cm = dilate(image=a,selem = elem) + cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) def test_bitdepth(self): @@ -139,11 +76,11 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') f = crank.maximum(image=a,selem = elem,shift_x=1,shift_y=1) r = np.asarray([[ 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0, 0], - [ 0, 0, 255, 255, 255, 0], - [ 0, 0, 0, 255, 255, 0], - [ 0, 0, 0, 0, 0, 0]]) + [ 0, 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0, 0], + [ 0, 0, 255, 255, 255, 0], + [ 0, 0, 0, 255, 255, 0], + [ 0, 0, 0, 0, 0, 0]]) np.testing.assert_array_equal(r,f) @@ -156,27 +93,5 @@ class TestSequenceFunctions(unittest.TestCase): if __name__ == '__main__': - logger = init_logger('app.log') - -# unittest.main() suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) unittest.TextTestRunner(verbosity=2).run(suite) - - -# compare() - -# a = (data.coins()).astype('uint8') - a8 = (data.coins()).astype('uint8') - a = (data.coins()).astype('uint16')*16 - selem = np.ones((20,20),dtype='uint8') -# f1 = filter.soft_gradient(a,struct_elem = selem,bitDepth=8,infSup=[.1,.9]) -# f2 = crank16.bottomhat(a,selem = selem,bitdepth=12) - f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) -# f2 = crank16_percentiles.mean(a,selem = selem,bitdepth=12,p0=.1,p1=.9) - f2 = crank16_bilateral.mean(a,selem = selem,bitdepth=12,s0=500,s1=500) -# plt.imshow(f2) - plt.imshow(np.hstack((a,f2))) - plt.colorbar() - plt.show() - - diff --git a/skimage/rank/tools.py b/skimage/rank/test/tools.py similarity index 78% rename from skimage/rank/tools.py rename to skimage/rank/test/tools.py index 835a8884..2fba4798 100644 --- a/skimage/rank/tools.py +++ b/skimage/rank/test/tools.py @@ -39,14 +39,4 @@ def log_timing(func): log_timing.level = 0 -def tumbnail_it(data): - """display image with its histogram - """ - h = np.histogram(data[:],100) - hn = 512*h[0]/np.max(h[0]) - plt.subplot(1,2,1) - plt.imshow(ima8,interpolation='nearest',cmap=cm.gray) - plt.subplot(1,2,2) - plt.plot(hn) - plt.colorbar() From f8f11ab8378b1f635845fdbc1ec13e66d23bec1f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 16:12:08 +0200 Subject: [PATCH 036/625] mv test to tests --- skimage/rank/setup.py | 5 ++++- skimage/rank/{test => tests}/test_16bitbilateral.py | 0 skimage/rank/{test => tests}/test_benchmark.py | 0 skimage/rank/{test => tests}/test_suite.py | 0 skimage/rank/{test => tests}/tools.py | 0 5 files changed, 4 insertions(+), 1 deletion(-) rename skimage/rank/{test => tests}/test_16bitbilateral.py (100%) rename skimage/rank/{test => tests}/test_benchmark.py (100%) rename skimage/rank/{test => tests}/test_suite.py (100%) rename skimage/rank/{test => tests}/tools.py (100%) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index e9af9446..8c5a595a 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -12,4 +12,7 @@ setup( Extension("crank16", ["crank16.pyx"], include_dirs=[np.get_include()]), Extension("crank16_bilateral", ["crank16_bilateral.pyx"], include_dirs=[np.get_include()]), Extension("crank16_percentiles", ["crank16_percentiles.pyx"], include_dirs=[np.get_include()])] -) \ No newline at end of file +) + + + diff --git a/skimage/rank/test/test_16bitbilateral.py b/skimage/rank/tests/test_16bitbilateral.py similarity index 100% rename from skimage/rank/test/test_16bitbilateral.py rename to skimage/rank/tests/test_16bitbilateral.py diff --git a/skimage/rank/test/test_benchmark.py b/skimage/rank/tests/test_benchmark.py similarity index 100% rename from skimage/rank/test/test_benchmark.py rename to skimage/rank/tests/test_benchmark.py diff --git a/skimage/rank/test/test_suite.py b/skimage/rank/tests/test_suite.py similarity index 100% rename from skimage/rank/test/test_suite.py rename to skimage/rank/tests/test_suite.py diff --git a/skimage/rank/test/tools.py b/skimage/rank/tests/tools.py similarity index 100% rename from skimage/rank/test/tools.py rename to skimage/rank/tests/tools.py From 5a5cdfca151a955f4a080806e061d11736d9cfc8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 16:35:06 +0200 Subject: [PATCH 037/625] rename using scikits naming conventions --- skimage/rank/__init__.py | 1 - skimage/rank/{core.pxd => _core.pxd} | 0 skimage/rank/{core16.pxd => _core16.pxd} | 2 +- skimage/rank/{core16b.pxd => _core16b.pxd} | 2 +- skimage/rank/{core16p.pxd => _core16p.pxd} | 2 +- skimage/rank/{core8.pxd => _core8.pxd} | 2 +- skimage/rank/{core8p.pxd => _core8p.pxd} | 2 +- skimage/rank/{crank16.pyx => _crank16.pyx} | 30 ++--- ...6_bilateral.pyx => _crank16_bilateral.pyx} | 6 +- ...rcentiles.pyx => _crank16_percentiles.pyx} | 18 +-- skimage/rank/{crank.pyx => _crank8.pyx} | 30 ++--- ...ercentiles.pyx => _crank8_percentiles.pyx} | 18 +-- skimage/rank/cmorph.pyx | 118 ------------------ skimage/rank/setup.py | 46 +++++-- skimage/rank/tests/test_16bitbilateral.py | 4 +- skimage/rank/tests/test_benchmark.py | 4 +- skimage/rank/tests/test_suite.py | 13 +- 17 files changed, 106 insertions(+), 192 deletions(-) rename skimage/rank/{core.pxd => _core.pxd} (100%) rename skimage/rank/{core16.pxd => _core16.pxd} (99%) rename skimage/rank/{core16b.pxd => _core16b.pxd} (99%) rename skimage/rank/{core16p.pxd => _core16p.pxd} (98%) rename skimage/rank/{core8.pxd => _core8.pxd} (99%) rename skimage/rank/{core8p.pxd => _core8p.pxd} (99%) rename skimage/rank/{crank16.pyx => _crank16.pyx} (90%) rename skimage/rank/{crank16_bilateral.pyx => _crank16_bilateral.pyx} (98%) rename skimage/rank/{crank16_percentiles.pyx => _crank16_percentiles.pyx} (90%) rename skimage/rank/{crank.pyx => _crank8.pyx} (90%) rename skimage/rank/{crank_percentiles.pyx => _crank8_percentiles.pyx} (90%) delete mode 100644 skimage/rank/cmorph.pyx diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index 8b0e3f5b..e69de29b 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -1 +0,0 @@ -from .crank import * diff --git a/skimage/rank/core.pxd b/skimage/rank/_core.pxd similarity index 100% rename from skimage/rank/core.pxd rename to skimage/rank/_core.pxd diff --git a/skimage/rank/core16.pxd b/skimage/rank/_core16.pxd similarity index 99% rename from skimage/rank/core16.pxd rename to skimage/rank/_core16.pxd index 9973025f..ddd8c637 100644 --- a/skimage/rank/core16.pxd +++ b/skimage/rank/_core16.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline rank16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core16b.pxd b/skimage/rank/_core16b.pxd similarity index 99% rename from skimage/rank/core16b.pxd rename to skimage/rank/_core16b.pxd index 38832c11..fefac53e 100644 --- a/skimage/rank/core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 16 bit core kernel receives extra information about data bitdepth and bilateral interval #--------------------------------------------------------------------------- -cdef inline rank16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), +cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core16p.pxd b/skimage/rank/_core16p.pxd similarity index 98% rename from skimage/rank/core16p.pxd rename to skimage/rank/_core16p.pxd index dbf51c54..0d698cee 100644 --- a/skimage/rank/core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 16 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline rank16_percentile(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), +cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core8.pxd b/skimage/rank/_core8.pxd similarity index 99% rename from skimage/rank/core8.pxd rename to skimage/rank/_core8.pxd index 0c901c8b..3d5ddac3 100644 --- a/skimage/rank/core8.pxd +++ b/skimage/rank/_core8.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline rank8(np.uint8_t kernel(int*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core8p.pxd b/skimage/rank/_core8p.pxd similarity index 99% rename from skimage/rank/core8p.pxd rename to skimage/rank/_core8p.pxd index 25e3ffcf..dfab17be 100644 --- a/skimage/rank/core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline rank8_percentile(np.uint8_t kernel(int*, float, np.uint8_t, float, float), +cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/crank16.pyx b/skimage/rank/_crank16.pyx similarity index 90% rename from skimage/rank/crank16.pyx rename to skimage/rank/_crank16.pyx index cd0bacd4..e1e64bed 100644 --- a/skimage/rank/crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core16 cimport rank16 +from _core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth @@ -198,7 +198,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """bottom hat """ - return rank16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -207,7 +207,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """bottom hat """ - return rank16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) def egalise(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -216,7 +216,7 @@ def egalise(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local egalisation of the gray level """ - return rank16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -225,7 +225,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local maximum - local minimum gray level """ - return rank16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -234,7 +234,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local maximum gray level """ - return rank16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -243,7 +243,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """average gray level (clipped on uint8) """ - return rank16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -252,7 +252,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ - return rank16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -261,7 +261,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local median """ - return rank16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -270,7 +270,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local minimum gray level """ - return rank16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -279,7 +279,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """morphological contrast enhancement """ - return rank16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -288,7 +288,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local mode """ - return rank16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -297,7 +297,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ - return rank16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -306,7 +306,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ - return rank16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -315,4 +315,4 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """top hat """ - return rank16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) diff --git a/skimage/rank/crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx similarity index 98% rename from skimage/rank/crank16_bilateral.pyx rename to skimage/rank/_crank16_bilateral.pyx index 313b83d6..440d28e3 100644 --- a/skimage/rank/crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core16b cimport rank16b +from _core16b cimport _core16b # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth @@ -257,7 +257,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return rank16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) #def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, # np.ndarray[np.uint8_t, ndim=2] selem, @@ -311,7 +311,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return rank16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) #def threshold(np.ndarray[np.uint16_t, ndim=2] image, # np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/rank/crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx similarity index 90% rename from skimage/rank/crank16_percentiles.pyx rename to skimage/rank/_crank16_percentiles.pyx index 7756bc19..54c25d40 100644 --- a/skimage/rank/crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core16p cimport rank16_percentile +from _core16p cimport _core16p # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) @@ -194,7 +194,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return rank16_percentile(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -204,7 +204,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return rank16_percentile(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -213,7 +213,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return rank16_percentile(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -222,7 +222,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return rank16_percentile(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -231,7 +231,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return rank16_percentile(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -241,7 +241,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return rank16_percentile(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -251,7 +251,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return rank16_percentile(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -260,4 +260,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return rank16_percentile(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) diff --git a/skimage/rank/crank.pyx b/skimage/rank/_crank8.pyx similarity index 90% rename from skimage/rank/crank.pyx rename to skimage/rank/_crank8.pyx index 59016eed..cb74021e 100644 --- a/skimage/rank/crank.pyx +++ b/skimage/rank/_crank8.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core8 cimport rank8 +from _core8 cimport _core8 # ----------------------------------------------------------------- # kernels uint8 @@ -199,7 +199,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return rank8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -208,7 +208,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return rank8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) def egalise(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -217,7 +217,7 @@ def egalise(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return rank8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -226,7 +226,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return rank8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -235,7 +235,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum gray level """ - return rank8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -244,7 +244,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """average gray level (clipped on uint8) """ - return rank8(kernel_mean,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -253,7 +253,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return rank8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -262,7 +262,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local median """ - return rank8(kernel_median,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y) def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -271,7 +271,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local minimum gray level """ - return rank8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -280,7 +280,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return rank8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -289,7 +289,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local mode """ - return rank8(kernel_modal,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y) def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -298,7 +298,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns the number of actual pixels of the structuring element inside the mask """ - return rank8(kernel_pop,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -307,7 +307,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return rank8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -316,5 +316,5 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """top hat """ - return rank8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) diff --git a/skimage/rank/crank_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx similarity index 90% rename from skimage/rank/crank_percentiles.pyx rename to skimage/rank/_crank8_percentiles.pyx index d4d6312f..81730313 100644 --- a/skimage/rank/crank_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core8p cimport rank8_percentile +from _core8p cimport _core8p # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) @@ -189,7 +189,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """bottom hat """ - return rank8_percentile(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -199,7 +199,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return rank8_percentile(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -208,7 +208,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return rank8_percentile(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -217,7 +217,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return rank8_percentile(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -226,7 +226,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return rank8_percentile(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -236,7 +236,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return rank8_percentile(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -246,7 +246,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return rank8_percentile(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -255,4 +255,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return rank8_percentile(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) diff --git a/skimage/rank/cmorph.pyx b/skimage/rank/cmorph.pyx deleted file mode 100644 index 9b8b3a27..00000000 --- a/skimage/rank/cmorph.pyx +++ /dev/null @@ -1,118 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - - -def dilate(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) - shift_y - cdef int centre_c = int(selem.shape[1] / 2) - shift_x - - image = np.ascontiguousarray(image) - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - - cdef int r, c, rr, cc, s, value, local_max - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - - s = 0 - for r in range(srows): - for c in range(scols): - if selem[r, c] != 0: - sr[s] = r - centre_r - sc[s] = c - centre_c - s += 1 - - for r in range(rows): - for c in range(cols): - local_max = 0 - for s in range(selem_num): - rr = r + sr[s] - cc = c + sc[s] - if 0 <= rr < rows and 0 <= cc < cols: - value = image_data[rr * cols + cc] - if value > local_max: - local_max = value - - out_data[r * cols + c] = local_max - - free(sr) - free(sc) - - return out - - -def erode(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) - shift_y - cdef int centre_c = int(selem.shape[1] / 2) - shift_x - - image = np.ascontiguousarray(image) - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - - cdef int r, c, rr, cc, s, value, local_min - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - - s = 0 - for r in range(srows): - for c in range(scols): - if selem[r, c] != 0: - sr[s] = r - centre_r - sc[s] = c - centre_c - s += 1 - - for r in range(rows): - for c in range(cols): - local_min = 255 - for s in range(selem_num): - rr = r + sr[s] - cc = c + sc[s] - if 0 <= rr < rows and 0 <= cc < cols: - value = image_data[rr * cols + cc] - if value < local_min: - local_min = value - - out_data[r * cols + c] = local_min - - free(sr) - free(sc) - - return out diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 8c5a595a..e8b2691d 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -6,13 +6,45 @@ from Cython.Distutils import build_ext setup( cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("cmorph", ["cmorph.pyx"], include_dirs=[np.get_include()]), - Extension("crank", ["crank.pyx"], include_dirs=[np.get_include()]), - Extension("crank_percentiles", ["crank_percentiles.pyx"], include_dirs=[np.get_include()]), - Extension("crank16", ["crank16.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_bilateral", ["crank16_bilateral.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_percentiles", ["crank16_percentiles.pyx"], include_dirs=[np.get_include()])] + ext_modules = [Extension("crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), + Extension("crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), + Extension("crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), + Extension("crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), + Extension("crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] ) - +##!/usr/bin/env python +# +#import os +#from skimage._build import cython +# +#base_path = os.path.abspath(os.path.dirname(__file__)) +# +# +#def configuration(parent_package='', top_path=None): +# from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs +# +# config = Configuration('rank', parent_package, top_path) +# config.add_data_dir('tests') +# +# cython(['_texture.pyx'], working_path=base_path) +# cython(['_template.pyx'], working_path=base_path) +# +# config.add_extension('_texture', sources=['_texture.c'], +# include_dirs=[get_numpy_include_dirs(), '../_shared']) +# config.add_extension('_template', sources=['_template.c'], +# include_dirs=[get_numpy_include_dirs(), '../_shared']) +# +# return config +# +#if __name__ == '__main__': +# from numpy.distutils.core import setup +# setup(maintainer='scikits-image Developers', +# author='scikits-image Developers', +# maintainer_email='scikits-image@googlegroups.com', +# description='Features', +# url='https://github.com/scikits-image/scikits-image', +# license='SciPy License (BSD Style)', +# **(configuration(top_path='').todict()) +# ) diff --git a/skimage/rank/tests/test_16bitbilateral.py b/skimage/rank/tests/test_16bitbilateral.py index ac078b09..98d76573 100644 --- a/skimage/rank/tests/test_16bitbilateral.py +++ b/skimage/rank/tests/test_16bitbilateral.py @@ -2,14 +2,14 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank_percentiles,crank16_bilateral +from skimage.rank import crank8_percentiles,crank16_bilateral if __name__ == '__main__': a8 = (data.coins()).astype('uint8') a16 = (data.coins()).astype('uint16')*16 selem = np.ones((20,20),dtype='uint8') - f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) plt.figure() diff --git a/skimage/rank/tests/test_benchmark.py b/skimage/rank/tests/test_benchmark.py index 4fee48c1..c67742e3 100644 --- a/skimage/rank/tests/test_benchmark.py +++ b/skimage/rank/tests/test_benchmark.py @@ -3,13 +3,13 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import cmorph -from skimage.rank import crank +from skimage.rank import crank8 from tools import log_timing @log_timing def cr_max(image,selem): - return crank.maximum(image=image,selem = selem) + return crank8.maximum(image=image,selem = selem) @log_timing def cm_dil(image,selem): diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 61ceab48..0dcb21ab 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -2,7 +2,8 @@ import unittest import numpy as np -from skimage.rank import crank,crank16,crank16_bilateral,crank16_percentiles,crank_percentiles +from skimage.rank import crank8,crank8_percentiles +from skimage.rank import crank16,crank16_bilateral,crank16_percentiles from skimage.morphology import cmorph class TestSequenceFunctions(unittest.TestCase): @@ -16,9 +17,9 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') for m,n in np.random.random_integers(1,100,size=(10,2)): a8 = np.ones((m,n),dtype='uint8') - r = crank.mean(image=a8,selem = elem,shift_x=0,shift_y=0) + r = crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) self.assertTrue(a8.shape == r.shape) - r = crank.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) + r = crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) self.assertTrue(a8.shape == r.shape) for m,n in np.random.random_integers(1,100,size=(10,2)): @@ -42,7 +43,7 @@ class TestSequenceFunctions(unittest.TestCase): for r in range(1,20,1): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') - rc = crank.maximum(image=a,selem = elem) + rc = crank8.maximum(image=a,selem = elem) cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) @@ -62,7 +63,7 @@ class TestSequenceFunctions(unittest.TestCase): def test_population(self): a = np.zeros((5,5),dtype='uint8') elem = np.ones((3,3),dtype='uint8') - p = crank.pop(image=a,selem = elem) + p = crank8.pop(image=a,selem = elem) r = np.asarray([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -74,7 +75,7 @@ class TestSequenceFunctions(unittest.TestCase): a = np.zeros((6,6),dtype='uint8') a[2,2] = 255 elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') - f = crank.maximum(image=a,selem = elem,shift_x=1,shift_y=1) + f = crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) r = np.asarray([[ 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0], [ 0, 0, 255, 0, 0, 0], From 483507f4dfa02a9c0d43b1c4f1cabb12e225c5b7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 17:15:10 +0200 Subject: [PATCH 038/625] try to fix rank setup --- skimage/rank/setup.py | 27 +++++++++++++++-------- skimage/rank/tests/test_16bitbilateral.py | 3 ++- skimage/setup.py | 1 + 3 files changed, 21 insertions(+), 10 deletions(-) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index e8b2691d..c16f902f 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -26,24 +26,33 @@ setup( # from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs # # config = Configuration('rank', parent_package, top_path) -# config.add_data_dir('tests') +## config.add_data_dir('tests') # -# cython(['_texture.pyx'], working_path=base_path) -# cython(['_template.pyx'], working_path=base_path) +# cython(['_crank8.pyx'], working_path=base_path) +# cython(['_crank8_percentiles.pyx'], working_path=base_path) +# cython(['_crank16.pyx'], working_path=base_path) +# cython(['_crank16_percentiles.pyx'], working_path=base_path) +# cython(['_crank16_bilateral.pyx'], working_path=base_path) # -# config.add_extension('_texture', sources=['_texture.c'], -# include_dirs=[get_numpy_include_dirs(), '../_shared']) -# config.add_extension('_template', sources=['_template.c'], -# include_dirs=[get_numpy_include_dirs(), '../_shared']) +# config.add_extension('crank8', sources=['_crank8.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank8_percentiles', sources=['_crank8_percentiles.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank16', sources=['_crank16.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank16_percentiles', sources=['_crank16_percentiles.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank16_bilateral', sources=['_crank16_bilateral.c'], +# include_dirs=[get_numpy_include_dirs()]) # # return config # #if __name__ == '__main__': # from numpy.distutils.core import setup # setup(maintainer='scikits-image Developers', -# author='scikits-image Developers', +# author='Olivier Debeir', # maintainer_email='scikits-image@googlegroups.com', -# description='Features', +# description='Rank filters', # url='https://github.com/scikits-image/scikits-image', # license='SciPy License (BSD Style)', # **(configuration(top_path='').todict()) diff --git a/skimage/rank/tests/test_16bitbilateral.py b/skimage/rank/tests/test_16bitbilateral.py index 98d76573..8001a25f 100644 --- a/skimage/rank/tests/test_16bitbilateral.py +++ b/skimage/rank/tests/test_16bitbilateral.py @@ -2,7 +2,8 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank8_percentiles,crank16_bilateral +from skimage.rank import crank8_percentiles +from skimage.rank import crank16_bilateral if __name__ == '__main__': a8 = (data.coins()).astype('uint8') diff --git a/skimage/setup.py b/skimage/setup.py index 1082ba07..7ed50b65 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -16,6 +16,7 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('io') config.add_subpackage('measure') config.add_subpackage('morphology') + config.add_subpackage('rank') config.add_subpackage('transform') config.add_subpackage('util') config.add_subpackage('segmentation') From b2c413dad0e0dc01b75ccd33b57a0825e4e71af0 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 17:30:21 +0200 Subject: [PATCH 039/625] rename some test to demo --- ...bitbilateral.py => demo_16bitbilateral.py} | 0 skimage/rank/tests/demo_all.py | 24 +++++++++++++++++++ .../{test_benchmark.py => demo_benchmark.py} | 0 3 files changed, 24 insertions(+) rename skimage/rank/tests/{test_16bitbilateral.py => demo_16bitbilateral.py} (100%) create mode 100644 skimage/rank/tests/demo_all.py rename skimage/rank/tests/{test_benchmark.py => demo_benchmark.py} (100%) diff --git a/skimage/rank/tests/test_16bitbilateral.py b/skimage/rank/tests/demo_16bitbilateral.py similarity index 100% rename from skimage/rank/tests/test_16bitbilateral.py rename to skimage/rank/tests/demo_16bitbilateral.py diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py new file mode 100644 index 00000000..8001a25f --- /dev/null +++ b/skimage/rank/tests/demo_all.py @@ -0,0 +1,24 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.rank import crank8_percentiles +from skimage.rank import crank16_bilateral + +if __name__ == '__main__': + a8 = (data.coins()).astype('uint8') + + a16 = (data.coins()).astype('uint16')*16 + selem = np.ones((20,20),dtype='uint8') + f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) + + plt.figure() + plt.imshow(np.hstack((a8,f1))) + plt.colorbar() + + plt.figure() + plt.imshow(np.hstack((a16,f2))) + plt.colorbar() + + plt.show() diff --git a/skimage/rank/tests/test_benchmark.py b/skimage/rank/tests/demo_benchmark.py similarity index 100% rename from skimage/rank/tests/test_benchmark.py rename to skimage/rank/tests/demo_benchmark.py From 6c19054aba87467bbb0b99e1d19cbe17490e7580 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 17:59:03 +0200 Subject: [PATCH 040/625] add demo all --- skimage/rank/tests/demo_all.py | 66 +++++++++++++++++++++++++++------- 1 file changed, 53 insertions(+), 13 deletions(-) diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index 8001a25f..a509db33 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -2,23 +2,63 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank8_percentiles -from skimage.rank import crank16_bilateral +from skimage.morphology.selem import disk +from skimage.rank import crank8,crank8_percentiles +from skimage.rank import crank16,crank16_percentiles,crank16_bilateral if __name__ == '__main__': - a8 = (data.coins()).astype('uint8') + a8 = data.camera() + a16 = a8.astype('uint16')*16 +# selem = np.ones((30,30),dtype='uint8') + selem = disk(5) - a16 = (data.coins()).astype('uint16')*16 - selem = np.ones((20,20),dtype='uint8') - f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) - f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) +# for n in dir(crank16): +# method = eval('crank16.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a16,selem = selem,bitdepth=12) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a16) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) - plt.figure() - plt.imshow(np.hstack((a8,f1))) - plt.colorbar() +# for n in dir(crank8): +# method = eval('crank8.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a8,selem = selem) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a8) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) - plt.figure() - plt.imshow(np.hstack((a16,f2))) - plt.colorbar() + for n in dir(crank8_percentiles): + method = eval('crank8_percentiles.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a8,selem = selem,p0=.1,p1=.9) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) + + # plt.show() From 8f0a207866e9de4cc8886c04016ef1b2c56f300e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 18:02:54 +0200 Subject: [PATCH 041/625] add demo all (cont.) --- skimage/rank/tests/demo_all.py | 43 ++++++++++++++++++++++++++++++---- 1 file changed, 38 insertions(+), 5 deletions(-) diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index a509db33..d005d61a 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -44,21 +44,54 @@ if __name__ == '__main__': # plt.colorbar() # plt.title(method) - for n in dir(crank8_percentiles): - method = eval('crank8_percentiles.%s'%n) +# for n in dir(crank8_percentiles): +# method = eval('crank8_percentiles.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a8,selem = selem,p0=.1,p1=.9) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a8) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) + +# for n in dir(crank16_percentiles): +# method = eval('crank16_percentiles.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a16) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) + + selem = disk(50) + for n in dir(crank16_bilateral): + method = eval('crank16_bilateral.%s'%n) t = type(method) if t == type(crank8.maximum): print n,t - f = method(a8,selem = selem,p0=.1,p1=.9) + f = method(a16,selem = selem,bitdepth=12,s0=300,s1=300) plt.figure() plt.subplot(1,2,1) - plt.imshow(a8) + plt.imshow(a16) plt.colorbar() plt.subplot(1,2,2) plt.imshow(f) plt.colorbar() plt.title(method) - # + # plt.show() From be4d866a9bf35d0b8ddc7608dd88558ff39ea82a Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 18:03:08 +0200 Subject: [PATCH 042/625] add demo all (cont.) --- skimage/rank/tests/demo_all.py | 120 ++++++++++++++++----------------- 1 file changed, 60 insertions(+), 60 deletions(-) diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index d005d61a..8e09048e 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -12,69 +12,69 @@ if __name__ == '__main__': # selem = np.ones((30,30),dtype='uint8') selem = disk(5) -# for n in dir(crank16): -# method = eval('crank16.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a16,selem = selem,bitdepth=12) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a16) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + for n in dir(crank16): + method = eval('crank16.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a16,selem = selem,bitdepth=12) -# for n in dir(crank8): -# method = eval('crank8.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a8,selem = selem) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a8) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a16) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) -# for n in dir(crank8_percentiles): -# method = eval('crank8_percentiles.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a8,selem = selem,p0=.1,p1=.9) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a8) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + for n in dir(crank8): + method = eval('crank8.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a8,selem = selem) -# for n in dir(crank16_percentiles): -# method = eval('crank16_percentiles.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a16) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) + + for n in dir(crank8_percentiles): + method = eval('crank8_percentiles.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a8,selem = selem,p0=.1,p1=.9) + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) + + for n in dir(crank16_percentiles): + method = eval('crank16_percentiles.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a16) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) selem = disk(50) for n in dir(crank16_bilateral): From 806bbbcb65990ee5fe2b345fb9f010b5cd711a7c Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 18:23:33 +0200 Subject: [PATCH 043/625] add readme --- skimage/rank/README.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst index b15001e6..ef56f997 100644 --- a/skimage/rank/README.rst +++ b/skimage/rank/README.rst @@ -1,5 +1,13 @@ To use this to build your Cython file use the commandline options: +**To do** + +* add simple examples + +* add doc + + + .. sourcecode:: text $ python setup.py build_ext --inplace \ No newline at end of file From c2d308c21afd66880481a686b7c3ec0c099331c2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 06:18:58 +0200 Subject: [PATCH 044/625] Add function to scale images --- skimage/transform/__init__.py | 2 +- skimage/transform/_warps.py | 43 +++++++++++++++++++++++++++ skimage/transform/tests/test_warps.py | 11 ++++++- 3 files changed, 54 insertions(+), 2 deletions(-) diff --git a/skimage/transform/__init__.py b/skimage/transform/__init__.py index a55d5df4..3185bf1d 100644 --- a/skimage/transform/__init__.py +++ b/skimage/transform/__init__.py @@ -6,6 +6,6 @@ from ._geometric import (warp, warp_coords, estimate_transform, SimilarityTransform, AffineTransform, ProjectiveTransform, PolynomialTransform, PiecewiseAffineTransform) -from ._warps import swirl, homography, resize, rotate +from ._warps import swirl, homography, resize, rotate, scale from .pyramids import (pyramid_reduce, pyramid_expand, pyramid_gaussian, pyramid_laplacian) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index 72f90050..2c508763 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -52,6 +52,49 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): mode=mode, cval=cval) +def scale(image, scale, order=1, mode='constant', cval=0.): + """Scale image. + + Parameters + ---------- + image : ndarray + Input image. + scale : {float, tuple of floats} + Scale factors. Separate scale factors can be defined as + `(row_scale, col_scale)`. + + Returns + ------- + scaled : ndarray + Scaled version of the input. + + Other parameters + ---------------- + order : int + Order of splines used in interpolation. See + `scipy.ndimage.map_coordinates` for detail. + mode : string + How to handle values outside the image borders. See + `scipy.ndimage.map_coordinates` for detail. + cval : string + Used in conjunction with mode 'constant', the value outside + the image boundaries. + + """ + + try: + rscale, cscale = scale + except TypeError: + rscale = cscale = scale + + orig_rows, orig_cols = image.shape[0], image.shape[1] + rows = np.round(rscale * orig_rows) + cols = np.round(cscale * orig_cols) + output_shape = (rows, cols) + + return resize(image, output_shape, order=order, mode=mode, cval=cval) + + def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.): """Rotate image by a certain angle around its center. diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index b705ac47..e0a768e8 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -2,7 +2,7 @@ from numpy.testing import assert_array_almost_equal, run_module_suite import numpy as np from scipy.ndimage import map_coordinates -from skimage.transform import (warp, warp_coords, rotate, resize, +from skimage.transform import (warp, warp_coords, rotate, resize, scale, AffineTransform, ProjectiveTransform, SimilarityTransform, homography) @@ -94,6 +94,15 @@ def test_resize(): assert_array_almost_equal(resized, ref) +def test_scale(): + x = np.zeros((5, 5), dtype=np.double) + x[1, 1] = 1 + scaled = scale(x, 2, order=0) + ref = np.zeros((10, 10)) + ref[2:4, 2:4] = 1 + assert_array_almost_equal(scaled, ref) + + def test_swirl(): image = img_as_float(data.checkerboard()) From 8ca068ed19fe2808a13e9cc5973ef8b31564e666 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 06:21:28 +0200 Subject: [PATCH 045/625] Add test case for separate scale factors --- skimage/transform/tests/test_warps.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index e0a768e8..51a62e55 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -95,6 +95,7 @@ def test_resize(): def test_scale(): + # same scale factor x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 scaled = scale(x, 2, order=0) @@ -102,6 +103,14 @@ def test_scale(): ref[2:4, 2:4] = 1 assert_array_almost_equal(scaled, ref) + # different scale factors + x = np.zeros((5, 5), dtype=np.double) + x[1, 1] = 1 + scaled = scale(x, (2, 1), order=0) + ref = np.zeros((10, 5)) + ref[2:4, 1] = 1 + assert_array_almost_equal(scaled, ref) + def test_swirl(): image = img_as_float(data.checkerboard()) From 17c7d10918c742496c53d35a9c022a260091c057 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 10:05:06 +0200 Subject: [PATCH 046/625] Rename scale function to rescale and scale variables --- skimage/transform/__init__.py | 2 +- skimage/transform/_warps.py | 18 +++++++++--------- skimage/transform/tests/test_warps.py | 6 +++--- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/skimage/transform/__init__.py b/skimage/transform/__init__.py index 3185bf1d..befcc23f 100644 --- a/skimage/transform/__init__.py +++ b/skimage/transform/__init__.py @@ -6,6 +6,6 @@ from ._geometric import (warp, warp_coords, estimate_transform, SimilarityTransform, AffineTransform, ProjectiveTransform, PolynomialTransform, PiecewiseAffineTransform) -from ._warps import swirl, homography, resize, rotate, scale +from ._warps import swirl, homography, resize, rotate, rescale from .pyramids import (pyramid_reduce, pyramid_expand, pyramid_gaussian, pyramid_laplacian) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index 2c508763..c64998fc 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -35,15 +35,15 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): rows, cols = output_shape orig_rows, orig_cols = image.shape[0], image.shape[1] - rscale = float(orig_rows) / rows - cscale = float(orig_cols) / cols + row_scale = float(orig_rows) / rows + col_scale = float(orig_cols) / cols # 3 control points necessary to estimate exact AffineTransform src_corners = np.array([[1, 1], [1, rows], [cols, rows]]) - 1 dst_corners = np.zeros(src_corners.shape, dtype=np.double) # take into account that 0th pixel is at position (0.5, 0.5) - dst_corners[:, 0] = cscale * (src_corners[:, 0] + 0.5) - 0.5 - dst_corners[:, 1] = rscale * (src_corners[:, 1] + 0.5) - 0.5 + dst_corners[:, 0] = col_scale * (src_corners[:, 0] + 0.5) - 0.5 + dst_corners[:, 1] = row_scale * (src_corners[:, 1] + 0.5) - 0.5 tform = AffineTransform() tform.estimate(src_corners, dst_corners) @@ -52,7 +52,7 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): mode=mode, cval=cval) -def scale(image, scale, order=1, mode='constant', cval=0.): +def rescale(image, scale, order=1, mode='constant', cval=0.): """Scale image. Parameters @@ -83,13 +83,13 @@ def scale(image, scale, order=1, mode='constant', cval=0.): """ try: - rscale, cscale = scale + row_scale, col_scale = scale except TypeError: - rscale = cscale = scale + row_scale = col_scale = scale orig_rows, orig_cols = image.shape[0], image.shape[1] - rows = np.round(rscale * orig_rows) - cols = np.round(cscale * orig_cols) + rows = np.round(row_scale * orig_rows) + cols = np.round(col_scale * orig_cols) output_shape = (rows, cols) return resize(image, output_shape, order=order, mode=mode, cval=cval) diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index 51a62e55..69980b25 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -2,7 +2,7 @@ from numpy.testing import assert_array_almost_equal, run_module_suite import numpy as np from scipy.ndimage import map_coordinates -from skimage.transform import (warp, warp_coords, rotate, resize, scale, +from skimage.transform import (warp, warp_coords, rotate, resize, rescale, AffineTransform, ProjectiveTransform, SimilarityTransform, homography) @@ -98,7 +98,7 @@ def test_scale(): # same scale factor x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 - scaled = scale(x, 2, order=0) + scaled = rescale(x, 2, order=0) ref = np.zeros((10, 10)) ref[2:4, 2:4] = 1 assert_array_almost_equal(scaled, ref) @@ -106,7 +106,7 @@ def test_scale(): # different scale factors x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 - scaled = scale(x, (2, 1), order=0) + scaled = rescale(x, (2, 1), order=0) ref = np.zeros((10, 5)) ref[2:4, 1] = 1 assert_array_almost_equal(scaled, ref) From 1fa4bd3d9bcd02687d068946c1c97cae538cf2ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 10:05:37 +0200 Subject: [PATCH 047/625] Enhance short description of resize and rescale functions --- skimage/transform/_warps.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index c64998fc..71bc41b8 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -4,7 +4,7 @@ from ._geometric import (warp, SimilarityTransform, AffineTransform, def resize(image, output_shape, order=1, mode='constant', cval=0.): - """Resize image. + """Resize image to match a certain size. Parameters ---------- @@ -53,7 +53,7 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): def rescale(image, scale, order=1, mode='constant', cval=0.): - """Scale image. + """Scale image by a certain factor. Parameters ---------- From 37e8345dc86778b29c75352ad68e4e4fa6902d66 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 10:13:25 +0200 Subject: [PATCH 048/625] Fix doc version 0.7 link --- doc/source/_static/docversions.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/_static/docversions.js b/doc/source/_static/docversions.js index 7fbc57a0..ff910c3f 100644 --- a/doc/source/_static/docversions.js +++ b/doc/source/_static/docversions.js @@ -1,5 +1,5 @@ function insert_version_links() { - var labels = ['dev', '0.7', '0.6', '0.5', '0.4', '0.3']; + var labels = ['dev', '0.7.0', '0.6', '0.5', '0.4', '0.3']; for (i = 0; i < labels.length; i++){ open_list = '
        • ' From db3803fd72b0c136acd1c86e3bce434bdd6cd29d Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 10:56:05 +0200 Subject: [PATCH 049/625] add py wrappers --- skimage/rank/__init__.py | 3 + skimage/rank/bilateral_rank.py | 18 +++++ skimage/rank/percentile_rank.py | 58 ++++++++++++++ skimage/rank/rank.py | 112 +++++++++++++++++++++++++++ skimage/rank/setup.py | 20 ++--- skimage/rank/tests/demo_benchmark.py | 8 +- skimage/rank/tests/test_rank.py | 10 +++ 7 files changed, 215 insertions(+), 14 deletions(-) create mode 100644 skimage/rank/bilateral_rank.py create mode 100644 skimage/rank/percentile_rank.py create mode 100644 skimage/rank/rank.py create mode 100644 skimage/rank/tests/test_rank.py diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index e69de29b..09812649 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -0,0 +1,3 @@ +from .rank import * +from .percentile_rank import * +from .bilateral_rank import * \ No newline at end of file diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py new file mode 100644 index 00000000..eaa6f8d9 --- /dev/null +++ b/skimage/rank/bilateral_rank.py @@ -0,0 +1,18 @@ +""" +:author: Olivier Debeir, 2012 +:license: modified BSD +""" + +__docformat__ = 'restructuredtext en' + +import warnings +from skimage import img_as_ubyte + +__all__ = ['bilateral_mean'] + + + +def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): + pass + + diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py new file mode 100644 index 00000000..49f87dd4 --- /dev/null +++ b/skimage/rank/percentile_rank.py @@ -0,0 +1,58 @@ +""" +:author: Olivier Debeir, 2012 +:license: modified BSD +""" + +__docformat__ = 'restructuredtext en' + +import warnings +from skimage import img_as_ubyte + +__all__ = ['percentile_mean'] + + +def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. Only pixel values contained inside the + percentile interval [p0,p1] are taken into account. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local mean : uint8 array or uint16 array depending on input image + The result of the local mean. + + Examples + -------- + to be updated + >>> # Erosion shrinks bright regions + >>> from skimage.morphology import square + >>> bright_square = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> erosion(bright_square, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + """ + pass + diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py new file mode 100644 index 00000000..d569a12b --- /dev/null +++ b/skimage/rank/rank.py @@ -0,0 +1,112 @@ +""" +:author: Olivier Debeir, 2012 +:license: modified BSD +""" + +__docformat__ = 'restructuredtext en' + +import warnings +from skimage import img_as_ubyte + +__all__ = ['mean','percentile_mean','bilateral_mean'] + + +def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. Only pixel values contained inside the + percentile interval [p0,p1] are taken into account. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local mean : uint8 array or uint16 array depending on input image + The result of the local mean. + + Examples + -------- + to be updated + >>> # Erosion shrinks bright regions + >>> from skimage.morphology import square + >>> bright_square = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> erosion(bright_square, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + """ + pass + +def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): + pass + +def mean(image, selem, out=None, shift_x=False, shift_y=False): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local mean : uint8 array or uint16 array depending on input image + The result of the local mean. + + Examples + -------- + to be updated + >>> # Erosion shrinks bright regions + >>> from skimage.morphology import square + >>> bright_square = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> erosion(bright_square, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + """ + pass +# if image is out: +# raise NotImplementedError("In-place erosion not supported!") +# image = img_as_ubyte(image) +# selem = img_as_ubyte(selem) +# return cmorph.erode(image, selem, out=out, +# shift_x=shift_x, shift_y=shift_y) + + diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index c16f902f..581e64ed 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -6,11 +6,11 @@ from Cython.Distutils import build_ext setup( cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), - Extension("crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), - Extension("crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] + ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), + Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), + Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), + Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), + Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] ) @@ -34,15 +34,15 @@ setup( # cython(['_crank16_percentiles.pyx'], working_path=base_path) # cython(['_crank16_bilateral.pyx'], working_path=base_path) # -# config.add_extension('crank8', sources=['_crank8.c'], +# config.add_extension('_crank8', sources=['_crank8.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank8_percentiles', sources=['_crank8_percentiles.c'], +# config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank16', sources=['_crank16.c'], +# config.add_extension('_crank16', sources=['_crank16.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank16_percentiles', sources=['_crank16_percentiles.c'], +# config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank16_bilateral', sources=['_crank16_bilateral.c'], +# config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], # include_dirs=[get_numpy_include_dirs()]) # # return config diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index c67742e3..f6fe32bb 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -2,18 +2,18 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.morphology import cmorph -from skimage.rank import crank8 +from skimage.morphology import dilation +from skimage.rank import _crank8 from tools import log_timing @log_timing def cr_max(image,selem): - return crank8.maximum(image=image,selem = selem) + return _crank8.maximum(image=image,selem = selem) @log_timing def cm_dil(image,selem): - return cmorph.dilate(image=image,selem = selem) + return dilation(image=image,selem = selem) def compare(): diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/tests/test_rank.py new file mode 100644 index 00000000..2a1e253d --- /dev/null +++ b/skimage/rank/tests/test_rank.py @@ -0,0 +1,10 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +import skimage.rank as rank + +print dir(rank) + + + From 4661dfb0e616ce46537cfc830f69b69566417b04 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 11:26:14 +0200 Subject: [PATCH 050/625] fix bitdepth in rank.py --- skimage/rank/rank.py | 86 ++++++++++++--------------------- skimage/rank/tests/test_rank.py | 21 ++++++++ 2 files changed, 51 insertions(+), 56 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index d569a12b..7e4d16a2 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -7,59 +7,23 @@ __docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte +import numpy as np -__all__ = ['mean','percentile_mean','bilateral_mean'] +import _crank16,_crank8 +__all__ = ['mean'] -def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local mean of an image. - - Mean is computed on the given structuring element. Only pixel values contained inside the - percentile interval [p0,p1] are taken into account. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local mean : uint8 array or uint16 array depending on input image - The result of the local mean. - - Examples - -------- - to be updated - >>> # Erosion shrinks bright regions - >>> from skimage.morphology import square - >>> bright_square = np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> erosion(bright_square, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) - +def find_bitdepth(image): + """returns the max bith depth of a uint16 image """ - pass + umax = np.max(image) + if umax>2: + return int(np.log2(umax)) + else: + return 1 -def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): - pass -def mean(image, selem, out=None, shift_x=False, shift_y=False): +def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. Mean is computed on the given structuring element. @@ -67,7 +31,11 @@ def mean(image, selem, out=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray @@ -101,12 +69,18 @@ def mean(image, selem, out=None, shift_x=False, shift_y=False): [0, 0, 0, 0, 0]], dtype=uint8) """ - pass -# if image is out: -# raise NotImplementedError("In-place erosion not supported!") -# image = img_as_ubyte(image) -# selem = img_as_ubyte(selem) -# return cmorph.erode(image, selem, out=out, -# shift_x=shift_x, shift_y=shift_y) - + if image is out: + raise NotImplementedError("In-place erosion not supported!") + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image are supported!") + return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1) + else: + raise TypeError("only uint8 and uint16 image supported!") diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/tests/test_rank.py index 2a1e253d..77dae32b 100644 --- a/skimage/rank/tests/test_rank.py +++ b/skimage/rank/tests/test_rank.py @@ -2,9 +2,30 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data +from skimage.morphology.selem import disk import skimage.rank as rank print dir(rank) +print rank.mean +print rank.percentile_mean +print rank.bilateral_mean + +a8 = data.camera() +a16 = a8.astype('uint16')*16 +selem = disk(10) + +f8 = rank.mean(a8,selem) +f16 = rank.mean(a16,selem) + +plt.figure() +plt.imshow(np.hstack((a8,f8))) +plt.colorbar() +plt.figure() +plt.imshow(np.hstack((a16,f16))) +plt.colorbar() +plt.show() + + From d9efa0bc6c85317d847de4d8b865c2e41cf75da1 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 11:28:27 +0200 Subject: [PATCH 051/625] update setup to be compatible with scikits-image --- skimage/rank/setup.py | 114 +++++++++++++++++++++--------------------- 1 file changed, 57 insertions(+), 57 deletions(-) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 581e64ed..4f57209a 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -1,59 +1,59 @@ -import numpy as np - -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext - -setup( - cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), - Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), - Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), - Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), - Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] -) +#import numpy as np +# +#from distutils.core import setup +#from distutils.extension import Extension +#from Cython.Distutils import build_ext +# +#setup( +# cmdclass = {'build_ext': build_ext}, +# ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] +#) -##!/usr/bin/env python -# -#import os -#from skimage._build import cython -# -#base_path = os.path.abspath(os.path.dirname(__file__)) -# -# -#def configuration(parent_package='', top_path=None): -# from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs -# -# config = Configuration('rank', parent_package, top_path) -## config.add_data_dir('tests') -# -# cython(['_crank8.pyx'], working_path=base_path) -# cython(['_crank8_percentiles.pyx'], working_path=base_path) -# cython(['_crank16.pyx'], working_path=base_path) -# cython(['_crank16_percentiles.pyx'], working_path=base_path) -# cython(['_crank16_bilateral.pyx'], working_path=base_path) -# -# config.add_extension('_crank8', sources=['_crank8.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank16', sources=['_crank16.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], -# include_dirs=[get_numpy_include_dirs()]) -# -# return config -# -#if __name__ == '__main__': -# from numpy.distutils.core import setup -# setup(maintainer='scikits-image Developers', -# author='Olivier Debeir', -# maintainer_email='scikits-image@googlegroups.com', -# description='Rank filters', -# url='https://github.com/scikits-image/scikits-image', -# license='SciPy License (BSD Style)', -# **(configuration(top_path='').todict()) -# ) +#!/usr/bin/env python + +import os +from skimage._build import cython + +base_path = os.path.abspath(os.path.dirname(__file__)) + + +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs + + config = Configuration('rank', parent_package, top_path) +# config.add_data_dir('tests') + + cython(['_crank8.pyx'], working_path=base_path) + cython(['_crank8_percentiles.pyx'], working_path=base_path) + cython(['_crank16.pyx'], working_path=base_path) + cython(['_crank16_percentiles.pyx'], working_path=base_path) + cython(['_crank16_bilateral.pyx'], working_path=base_path) + + config.add_extension('_crank8', sources=['_crank8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank16', sources=['_crank16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], + include_dirs=[get_numpy_include_dirs()]) + + return config + +if __name__ == '__main__': + from numpy.distutils.core import setup + setup(maintainer='scikits-image Developers', + author='Olivier Debeir', + maintainer_email='scikits-image@googlegroups.com', + description='Rank filters', + url='https://github.com/scikits-image/scikits-image', + license='SciPy License (BSD Style)', + **(configuration(top_path='').todict()) + ) From 5e14bf201bc063d882078a1d7b7e8c22a5d830be Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 12:09:03 +0200 Subject: [PATCH 052/625] add percentile mean --- skimage/rank/generic.py | 10 ++++++ skimage/rank/percentile_rank.py | 30 +++++++++++++++--- skimage/rank/rank.py | 54 +++++++++++++++++---------------- skimage/rank/tests/test_rank.py | 11 +++++++ 4 files changed, 75 insertions(+), 30 deletions(-) create mode 100644 skimage/rank/generic.py diff --git a/skimage/rank/generic.py b/skimage/rank/generic.py new file mode 100644 index 00000000..e8808e5e --- /dev/null +++ b/skimage/rank/generic.py @@ -0,0 +1,10 @@ +import numpy as np + +def find_bitdepth(image): + """returns the max bith depth of a uint16 image + """ + umax = np.max(image) + if umax>2: + return int(np.log2(umax)) + else: + return 1 diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 49f87dd4..840a302f 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -7,11 +7,14 @@ __docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte +import numpy as np + +from .generic import find_bitdepth +import _crank16_percentiles,_crank8_percentiles __all__ = ['percentile_mean'] - -def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. Mean is computed on the given structuring element. Only pixel values contained inside the @@ -20,16 +23,22 @@ def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, Parameters ---------- image : ndarray - Image array (uint8 array or uint16). + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray The array to store the result of the morphology. If None is passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). shift_x, shift_y : bool shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -54,5 +63,18 @@ def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, [0, 0, 0, 0, 0]], dtype=uint8) """ - pass + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1, + out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 7e4d16a2..c26fd033 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -9,21 +9,13 @@ import warnings from skimage import img_as_ubyte import numpy as np +from .generic import find_bitdepth import _crank16,_crank8 __all__ = ['mean'] -def find_bitdepth(image): - """returns the max bith depth of a uint16 image - """ - umax = np.max(image) - if umax>2: - return int(np.log2(umax)) - else: - return 1 - -def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): +def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. Mean is computed on the given structuring element. @@ -33,14 +25,14 @@ def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): image : ndarray Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray The array to store the result of the morphology. If None is passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). shift_x, shift_y : bool shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). @@ -54,33 +46,43 @@ def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): Examples -------- to be updated - >>> # Erosion shrinks bright regions + >>> # Local mean >>> from skimage.morphology import square - >>> bright_square = np.array([[0, 0, 0, 0, 0], + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> erosion(bright_square, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> mean(ima8, square(3)) + array([[ 63, 85, 127, 85, 63], + [ 85, 113, 170, 113, 85], + [127, 170, 255, 170, 127], + [ 85, 113, 170, 113, 85], + [ 63, 85, 127, 85, 63]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> mean(ima16, square(3)) + array([[1023, 1365, 2047, 1365, 1023], + [1365, 1820, 2730, 1820, 1365], + [2047, 2730, 4095, 2730, 2047], + [1365, 1820, 2730, 1820, 1365], + [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ - if image is out: - raise NotImplementedError("In-place erosion not supported!") selem = img_as_ubyte(selem) if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask) + return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) if bitdepth>11: - raise ValueError("only uint16 <4096 image are supported!") - return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1) + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) else: raise TypeError("only uint8 and uint16 image supported!") diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/tests/test_rank.py index 77dae32b..75dd7f65 100644 --- a/skimage/rank/tests/test_rank.py +++ b/skimage/rank/tests/test_rank.py @@ -24,6 +24,17 @@ plt.colorbar() plt.figure() plt.imshow(np.hstack((a16,f16))) plt.colorbar() + +f8 = rank.percentile_mean(a8,selem,p0=.1,p1=.9) +f16 = rank.percentile_mean(a16,selem,p0=.1,p1=.9) + +plt.figure() +plt.imshow(np.hstack((a8,f8))) +plt.colorbar() +plt.figure() +plt.imshow(np.hstack((a16,f16))) +plt.colorbar() + plt.show() From 007e13609cefb58596998c09283f4ef9d5ae2b59 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 14:46:48 +0200 Subject: [PATCH 053/625] add other rank filters --- skimage/rank/rank.py | 927 ++++++++++++++++++++++++++++++++- skimage/rank/tests/demo_all.py | 44 +- 2 files changed, 950 insertions(+), 21 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index c26fd033..9045f04e 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -9,11 +9,365 @@ import warnings from skimage import img_as_ubyte import numpy as np -from .generic import find_bitdepth +from generic import find_bitdepth import _crank16,_crank8 -__all__ = ['mean'] +__all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean' + ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop'] +def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local autolevel of an image. + + Autolevel is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local autolevel : uint8 array or uint16 array depending on input image + The result of the local autolevel. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> autolevel(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> autolevel(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 4096, 0, 4096, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local bottomhat of an image. + + Bottomhat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local bottomhat : uint8 array or uint16 array depending on input image + The result of the local bottomhat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bottomhat(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bottomhat(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local egalise of an image. + + egalise is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local egalise : uint8 array or uint16 array depending on input image + The result of the local egalise. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> egalise(ima8, square(3)) + array([[191, 170, 127, 170, 191], + [170, 255, 255, 255, 170], + [127, 255, 255, 255, 127], + [170, 255, 255, 255, 170], + [191, 170, 127, 170, 191]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> egalise(ima16, square(3)) + array([[3072, 2730, 2048, 2730, 3072], + [2730, 4096, 4096, 4096, 2730], + [2048, 4096, 4096, 4096, 2048], + [2730, 4096, 4096, 4096, 2730], + [3072, 2730, 2048, 2730, 3072]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local gradient of an image. + + gradient is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local gradient : uint8 array or uint16 array depending on input image + The result of the local gradient. + + Examples + -------- + to be updated + >>> # Local gradient + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> gradient(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 0, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> gradient(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 0, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + + +def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local maximum of an image. + + maximum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local maximum : uint8 array or uint16 array depending on input image + The result of the local maximum. + + Examples + -------- + to be updated + >>> # Local maximum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> maximum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> maximum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. @@ -86,3 +440,572 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): else: raise TypeError("only uint8 and uint16 image supported!") +def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local meansubstraction of an image. + + meansubstraction is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local meansubstraction : uint8 array or uint16 array depending on input image + The result of the local meansubstraction. + + Examples + -------- + to be updated + >>> # Local meansubstraction + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> meansubstraction(ima8, square(3)) + array([[ 95, 84, 63, 84, 95], + [ 84, 197, 169, 197, 84], + [ 63, 169, 127, 169, 63], + [ 84, 197, 169, 197, 84], + [ 95, 84, 63, 84, 95]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> meansubstraction(ima16, square(3)) + array([[1536, 1365, 1024, 1365, 1536], + [1365, 3185, 2730, 3185, 1365], + [1024, 2730, 2048, 2730, 1024], + [1365, 3185, 2730, 3185, 1365], + [1536, 1365, 1024, 1365, 1536]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local median of an image. + + median is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local median : uint8 array or uint16 array depending on input image + The result of the local median. + + Examples + -------- + to be updated + >>> # Local median + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> median(ima8, square(3)) + array([[ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0], + [255, 255, 255, 255, 255], + [ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> median(ima16, square(3)) + array([[ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0], + [4095, 4095, 4095, 4095, 4095], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local minimum of an image. + + minimum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local minimum : uint8 array or uint16 array depending on input image + The result of the local minimum. + + Examples + -------- + to be updated + >>> # Local minimum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> minimum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> minimum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local modal of an image. + + modal is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local modal : uint8 array or uint16 array depending on input image + The result of the local modal. + + Examples + -------- + to be updated + >>> # Local modal + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint8) + >>> modal(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 1, 1, 0, 0], + [0, 0, 5, 0, 0], + [0, 0, 5, 0, 0]], dtype=uint8) + + + >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint16) + >>> modal(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 100, 0, 0], + [ 0, 100, 100, 0, 0], + [ 0, 0, 500, 0, 0], + [ 0, 0, 500, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local morph_contr_enh of an image. + + morph_contr_enh is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local morph_contr_enh : uint8 array or uint16 array depending on input image + The result of the local morph_contr_enh. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> morph_contr_enh(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> morph_contr_enh(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local pop of an image. + + pop is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local pop : uint8 array or uint16 array depending on input image + The result of the local pop. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> pop(ima8, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> pop(ima16, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local threshold of an image. + + threshold is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local threshold : uint8 array or uint16 array depending on input image + The result of the local threshold. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> threshold(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> threshold(ima16, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint16) + + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local tophat of an image. + + tophat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local tophat : uint8 array or uint16 array depending on input image + The result of the local tophat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> tophat(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> tophat(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") \ No newline at end of file diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index 8e09048e..da592c3d 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -1,21 +1,23 @@ import numpy as np import matplotlib.pyplot as plt +from pprint import pprint from skimage import data from skimage.morphology.selem import disk -from skimage.rank import crank8,crank8_percentiles -from skimage.rank import crank16,crank16_percentiles,crank16_bilateral +from skimage.rank import _crank8,_crank8_percentiles +from skimage.rank import _crank16,_crank16_percentiles,_crank16_bilateral -if __name__ == '__main__': +def plot_all(): a8 = data.camera() a16 = a8.astype('uint16')*16 -# selem = np.ones((30,30),dtype='uint8') + # selem = np.ones((30,30),dtype='uint8') selem = disk(5) - for n in dir(crank16): - method = eval('crank16.%s'%n) + + for n in dir(_crank16): + method = eval('_crank16.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a16,selem = selem,bitdepth=12) @@ -28,10 +30,10 @@ if __name__ == '__main__': plt.colorbar() plt.title(method) - for n in dir(crank8): - method = eval('crank8.%s'%n) + for n in dir(_crank8): + method = eval('_crank8.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a8,selem = selem) @@ -44,10 +46,10 @@ if __name__ == '__main__': plt.colorbar() plt.title(method) - for n in dir(crank8_percentiles): - method = eval('crank8_percentiles.%s'%n) + for n in dir(_crank8_percentiles): + method = eval('_crank8_percentiles.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a8,selem = selem,p0=.1,p1=.9) @@ -60,10 +62,10 @@ if __name__ == '__main__': plt.colorbar() plt.title(method) - for n in dir(crank16_percentiles): - method = eval('crank16_percentiles.%s'%n) + for n in dir(_crank16_percentiles): + method = eval('_crank16_percentiles.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) @@ -77,10 +79,10 @@ if __name__ == '__main__': plt.title(method) selem = disk(50) - for n in dir(crank16_bilateral): - method = eval('crank16_bilateral.%s'%n) + for n in dir(_crank16_bilateral): + method = eval('_crank16_bilateral.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a16,selem = selem,bitdepth=12,s0=300,s1=300) @@ -95,3 +97,7 @@ if __name__ == '__main__': # plt.show() + +if __name__ == '__main__': +# plot_all() + pprint(dir(_crank8)) \ No newline at end of file From fb6017e46931582cbbd93b6c4062b8ff891f4265 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 15:22:59 +0200 Subject: [PATCH 054/625] add percentile filters - in progress --- skimage/rank/percentile_rank.py | 1034 ++++++++++++++++++++++++++++++- 1 file changed, 1016 insertions(+), 18 deletions(-) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 840a302f..d658dfb4 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -9,16 +9,371 @@ import warnings from skimage import img_as_ubyte import numpy as np -from .generic import find_bitdepth +from generic import find_bitdepth import _crank16_percentiles,_crank8_percentiles -__all__ = ['percentile_mean'] +__all__ = ['percentile_autolevel','percentile_bottomhat','percentile_egalise','percentile_gradient', + 'percentile_maximum','percentile_mean','percentile_meansubstraction','percentile_median', + 'percentile_minimum','percentile_modal','percentile_morph_contr_enh','percentile_pop'] -def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local mean of an image. +def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local autolevel of an image. - Mean is computed on the given structuring element. Only pixel values contained inside the - percentile interval [p0,p1] are taken into account. + Autolevel is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local autolevel : uint8 array or uint16 array depending on input image + The result of the local autolevel. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> percentile_autolevel(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> percentile_eautolevel(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 4096, 0, 4096, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local bottomhat of an image. + + Bottomhat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local bottomhat : uint8 array or uint16 array depending on input image + The result of the local bottomhat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bottomhat(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bottomhat(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local egalise of an image. + + egalise is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local egalise : uint8 array or uint16 array depending on input image + The result of the local egalise. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> egalise(ima8, square(3)) + array([[191, 170, 127, 170, 191], + [170, 255, 255, 255, 170], + [127, 255, 255, 255, 127], + [170, 255, 255, 255, 170], + [191, 170, 127, 170, 191]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> egalise(ima16, square(3)) + array([[3072, 2730, 2048, 2730, 3072], + [2730, 4096, 4096, 4096, 2730], + [2048, 4096, 4096, 4096, 2048], + [2730, 4096, 4096, 4096, 2730], + [3072, 2730, 2048, 2730, 3072]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local gradient of an image. + + gradient is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local gradient : uint8 array or uint16 array depending on input image + The result of the local gradient. + + Examples + -------- + to be updated + >>> # Local gradient + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> gradient(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 0, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> gradient(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 0, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + + +def percentile_maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local maximum of an image. + + maximum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local maximum : uint8 array or uint16 array depending on input image + The result of the local maximum. + + Examples + -------- + to be updated + >>> # Local maximum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> maximum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> maximum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. Parameters ---------- @@ -37,8 +392,6 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -48,19 +401,31 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa Examples -------- to be updated - >>> # Erosion shrinks bright regions + >>> # Local mean >>> from skimage.morphology import square - >>> bright_square = np.array([[0, 0, 0, 0, 0], + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> erosion(bright_square, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> mean(ima8, square(3)) + array([[ 63, 85, 127, 85, 63], + [ 85, 113, 170, 113, 85], + [127, 170, 255, 170, 127], + [ 85, 113, 170, 113, 85], + [ 63, 85, 127, 85, 63]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> mean(ima16, square(3)) + array([[1023, 1365, 2047, 1365, 1023], + [1365, 1820, 2730, 1820, 1365], + [2047, 2730, 4095, 2730, 2047], + [1365, 1820, 2730, 1820, 1365], + [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ selem = img_as_ubyte(selem) @@ -72,9 +437,642 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa bitdepth = find_bitdepth(image) if bitdepth>11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1, - out=out,p0=p0,p1=p1) + return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") +def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local meansubstraction of an image. + meansubstraction is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local meansubstraction : uint8 array or uint16 array depending on input image + The result of the local meansubstraction. + + Examples + -------- + to be updated + >>> # Local meansubstraction + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> meansubstraction(ima8, square(3)) + array([[ 95, 84, 63, 84, 95], + [ 84, 197, 169, 197, 84], + [ 63, 169, 127, 169, 63], + [ 84, 197, 169, 197, 84], + [ 95, 84, 63, 84, 95]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> meansubstraction(ima16, square(3)) + array([[1536, 1365, 1024, 1365, 1536], + [1365, 3185, 2730, 3185, 1365], + [1024, 2730, 2048, 2730, 1024], + [1365, 3185, 2730, 3185, 1365], + [1536, 1365, 1024, 1365, 1536]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_median(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local median of an image. + + median is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local median : uint8 array or uint16 array depending on input image + The result of the local median. + + Examples + -------- + to be updated + >>> # Local median + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> median(ima8, square(3)) + array([[ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0], + [255, 255, 255, 255, 255], + [ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> median(ima16, square(3)) + array([[ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0], + [4095, 4095, 4095, 4095, 4095], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local minimum of an image. + + minimum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local minimum : uint8 array or uint16 array depending on input image + The result of the local minimum. + + Examples + -------- + to be updated + >>> # Local minimum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> minimum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> minimum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local modal of an image. + + modal is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local modal : uint8 array or uint16 array depending on input image + The result of the local modal. + + Examples + -------- + to be updated + >>> # Local modal + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint8) + >>> modal(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 1, 1, 0, 0], + [0, 0, 5, 0, 0], + [0, 0, 5, 0, 0]], dtype=uint8) + + + >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint16) + >>> modal(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 100, 0, 0], + [ 0, 100, 100, 0, 0], + [ 0, 0, 500, 0, 0], + [ 0, 0, 500, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local morph_contr_enh of an image. + + morph_contr_enh is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local morph_contr_enh : uint8 array or uint16 array depending on input image + The result of the local morph_contr_enh. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> morph_contr_enh(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> morph_contr_enh(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local pop of an image. + + pop is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local pop : uint8 array or uint16 array depending on input image + The result of the local pop. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> pop(ima8, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> pop(ima16, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local threshold of an image. + + threshold is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local threshold : uint8 array or uint16 array depending on input image + The result of the local threshold. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> threshold(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> threshold(ima16, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint16) + + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local tophat of an image. + + tophat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local tophat : uint8 array or uint16 array depending on input image + The result of the local tophat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> tophat(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> tophat(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +#__all__ = ['percentile_mean'] + +#def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +# """Return greyscale local mean of an image. +# +# Mean is computed on the given structuring element. Only pixel values contained inside the +# percentile interval [p0,p1] are taken into account. +# +# Parameters +# ---------- +# image : ndarray +# Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, +# an exception will be raised if image has a value > 4095 +# selem : ndarray +# The neighborhood expressed as a 2-D array of 1's and 0's. +# out : ndarray +# The array to store the result of the morphology. If None is +# passed, a new array will be allocated. +# mask : ndarray (uint8) +# Mask array that defines (>0) area of the image included in the local neighborhood. +# If None, the complete image is used (default). +# shift_x, shift_y : bool +# shift structuring element about center point. This only affects +# eccentric structuring elements (i.e. selem with even numbered sides). +# Shift is bounded to the structuring element sizes. +# p0, p1 : float in [0.,...,1.] +# define the [p0,p1] percentile interval to be considered for computing the value. +# +# Returns +# ------- +# local mean : uint8 array or uint16 array depending on input image +# The result of the local mean. +# +# Examples +# -------- +# to be updated +# >>> # Erosion shrinks bright regions +# >>> from skimage.morphology import square +# >>> bright_square = np.array([[0, 0, 0, 0, 0], +# ... [0, 1, 1, 1, 0], +# ... [0, 1, 1, 1, 0], +# ... [0, 1, 1, 1, 0], +# ... [0, 0, 0, 0, 0]], dtype=np.uint8) +# >>> erosion(bright_square, square(3)) +# array([[0, 0, 0, 0, 0], +# [0, 0, 0, 0, 0], +# [0, 0, 1, 0, 0], +# [0, 0, 0, 0, 0], +# [0, 0, 0, 0, 0]], dtype=uint8) +# +# """ +# selem = img_as_ubyte(selem) +# if mask is not None: +# mask = img_as_ubyte(mask) +# if image.dtype == np.uint8: +# return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) +# elif image.dtype == np.uint16: +# bitdepth = find_bitdepth(image) +# if bitdepth>11: +# raise ValueError("only uint16 <4096 image (12bit) supported!") +# return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) +# else: +# raise TypeError("only uint8 and uint16 image supported!") +# +# From f5e4ae9923be3707e8255ffde5ac9e11b5c4e0b0 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 16:08:30 +0200 Subject: [PATCH 055/625] add percentile filters --- skimage/rank/_crank8_percentiles.pyx | 2 +- skimage/rank/percentile_rank.py | 766 +++++---------------------- 2 files changed, 148 insertions(+), 620 deletions(-) diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 81730313..23fe079f 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -187,7 +187,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): - """bottom hat + """autolevel """ return _core8p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index d658dfb4..924bdd7a 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -12,14 +12,14 @@ import numpy as np from generic import find_bitdepth import _crank16_percentiles,_crank8_percentiles -__all__ = ['percentile_autolevel','percentile_bottomhat','percentile_egalise','percentile_gradient', - 'percentile_maximum','percentile_mean','percentile_meansubstraction','percentile_median', +__all__ = ['percentile_autolevel','percentile_gradient', + 'percentile_mean','percentile_mean_substraction','percentile_median', 'percentile_minimum','percentile_modal','percentile_morph_contr_enh','percentile_pop'] def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. - Autolevel is computed on the given structuring element. + Autolevel is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -38,6 +38,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -54,10 +56,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_autolevel(ima8, square(3)) + >>> percentile_autolevel(ima8, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], [ 0, 0, 0, 0, 0]], dtype=uint8) @@ -66,11 +68,11 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_eautolevel(ima16, square(3)) + >>> percentile_autolevel(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], - [ 0, 4096, 4096, 4096, 0], - [ 0, 4096, 0, 4096, 0], - [ 0, 4096, 4096, 4096, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -87,150 +89,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local bottomhat of an image. - - Bottomhat is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local bottomhat : uint8 array or uint16 array depending on input image - The result of the local bottomhat. - - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bottomhat(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bottomhat(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 0, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -def percentile_egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local egalise of an image. - - egalise is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local egalise : uint8 array or uint16 array depending on input image - The result of the local egalise. - - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> egalise(ima8, square(3)) - array([[191, 170, 127, 170, 191], - [170, 255, 255, 255, 170], - [127, 255, 255, 255, 127], - [170, 255, 255, 255, 170], - [191, 170, 127, 170, 191]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> egalise(ima16, square(3)) - array([[3072, 2730, 2048, 2730, 3072], - [2730, 4096, 4096, 4096, 2730], - [2048, 4096, 4096, 4096, 2048], - [2730, 4096, 4096, 4096, 2730], - [3072, 2730, 2048, 2730, 3072]], dtype=uint16) - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local gradient of an image. + """Return greyscale local percentile_gradient of an image. - gradient is computed on the given structuring element. + percentile_gradient is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -249,11 +111,13 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- - local gradient : uint8 array or uint16 array depending on input image - The result of the local gradient. + local percentile_gradient : uint8 array or uint16 array depending on input image + The result of the local percentile_gradient. Examples -------- @@ -265,10 +129,10 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> gradient(ima8, square(3)) + >>> percentile_gradient(ima8, square(3), p0=0.,p1=1.) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], - [255, 255, 0, 255, 255], + [255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 255, 255, 255]], dtype=uint8) @@ -277,10 +141,10 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> gradient(ima16, square(3)) + >>> percentile_gradient(ima16, square(3), p0=0.,p1=1.) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 0, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095]], dtype=uint16) @@ -299,81 +163,10 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ raise TypeError("only uint8 and uint16 image supported!") -def percentile_maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local maximum of an image. - - maximum is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local maximum : uint8 array or uint16 array depending on input image - The result of the local maximum. - - Examples - -------- - to be updated - >>> # Local maximum - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 1, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> maximum(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 1, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> maximum(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. - Mean is computed on the given structuring element. + Mean is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -392,6 +185,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -408,7 +203,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> mean(ima8, square(3)) + >>> percentile_mean(ima8, square(3),p0=0.,p1=1.) array([[ 63, 85, 127, 85, 63], [ 85, 113, 170, 113, 85], [127, 170, 255, 170, 127], @@ -420,7 +215,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> mean(ima16, square(3)) + >>> percentile_mean(ima16, square(3),p0=0.,p1=1.) array([[1023, 1365, 2047, 1365, 1023], [1365, 1820, 2730, 1820, 1365], [2047, 2730, 4095, 2730, 2047], @@ -441,10 +236,10 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local meansubstraction of an image. +def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean_substraction of an image. - meansubstraction is computed on the given structuring element. + mean_substraction is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -463,27 +258,29 @@ def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- - local meansubstraction : uint8 array or uint16 array depending on input image - The result of the local meansubstraction. + local mean_substraction : uint8 array or uint16 array depending on input image + The result of the local mean_substraction. Examples -------- to be updated - >>> # Local meansubstraction + >>> # Local mean_substraction >>> from skimage.morphology import square >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> meansubstraction(ima8, square(3)) + >>> percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) array([[ 95, 84, 63, 84, 95], - [ 84, 197, 169, 197, 84], + [ 84, 198, 169, 198, 84], [ 63, 169, 127, 169, 63], - [ 84, 197, 169, 197, 84], + [ 84, 198, 169, 198, 84], [ 95, 84, 63, 84, 95]], dtype=uint8) >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], @@ -491,7 +288,7 @@ def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> meansubstraction(ima16, square(3)) + >>> percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) array([[1536, 1365, 1024, 1365, 1536], [1365, 3185, 2730, 3185, 1365], [1024, 2730, 2048, 2730, 1024], @@ -503,234 +300,20 @@ def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return _crank8_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + return _crank8_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) if bitdepth>11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + return _crank16_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_median(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local median of an image. - - median is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local median : uint8 array or uint16 array depending on input image - The result of the local median. - - Examples - -------- - to be updated - >>> # Local median - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 0, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> median(ima8, square(3)) - array([[ 0, 0, 255, 0, 0], - [ 0, 0, 255, 0, 0], - [255, 255, 255, 255, 255], - [ 0, 0, 255, 0, 0], - [ 0, 0, 255, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 0, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> median(ima16, square(3)) - array([[ 0, 0, 4095, 0, 0], - [ 0, 0, 4095, 0, 0], - [4095, 4095, 4095, 4095, 4095], - [ 0, 0, 4095, 0, 0], - [ 0, 0, 4095, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -def percentile_minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local minimum of an image. - - minimum is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local minimum : uint8 array or uint16 array depending on input image - The result of the local minimum. - - Examples - -------- - to be updated - >>> # Local minimum - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> minimum(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> minimum(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 4095, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -def percentile_modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local modal of an image. - - modal is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local modal : uint8 array or uint16 array depending on input image - The result of the local modal. - - Examples - -------- - to be updated - >>> # Local modal - >>> from skimage.morphology import square - >>> ima8 = np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 5, 6, 0], - ... [0, 1, 5, 5, 0], - ... [0, 0, 0, 5, 0]], dtype=np.uint8) - >>> modal(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 1, 1, 0, 0], - [0, 0, 5, 0, 0], - [0, 0, 5, 0, 0]], dtype=uint8) - - - >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 5, 6, 0], - ... [0, 1, 5, 5, 0], - ... [0, 0, 0, 5, 0]], dtype=np.uint16) - >>> modal(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 100, 0, 0], - [ 0, 100, 100, 0, 0], - [ 0, 0, 500, 0, 0], - [ 0, 0, 500, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. - morph_contr_enh is computed on the given structuring element. + morph_contr_enh is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -749,6 +332,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -760,24 +345,24 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, to be updated >>> # Local mean >>> from skimage.morphology import square - >>> ima8 = np.array([[0, 0, 0, 0, 0], + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> morph_contr_enh(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 1, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> morph_contr_enh(ima16, square(3)) + >>> percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -798,10 +383,10 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local pop of an image. +def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local percentile of an image. - pop is computed on the given structuring element. + percentile is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -820,6 +405,82 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. + + Returns + ------- + local percentile : uint8 array or uint16 array depending on input image + The result of the local percentile. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> percentile(ima8, square(3), p0=0.,p1=1.) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> percentile(ima16, square(3), p0=0.,p1=1.) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint16) + + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local pop of an image. + + pop is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -836,7 +497,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> pop(ima8, square(3)) + >>> percentile_pop(ima8, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -848,7 +509,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> pop(ima16, square(3)) + >>> percentile_pop(ima16, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -872,7 +533,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. - threshold is computed on the given structuring element. + threshold is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -891,6 +552,8 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -907,24 +570,24 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> threshold(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 0, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> percentile_threshold(ima8, square(3), p0=0.,p1=1.) + array([[255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255]], dtype=uint8) >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> threshold(ima16, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 0, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint16) + >>> percentile_threshold(ima16, square(3), p0=0.,p1=1.) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ @@ -941,138 +604,3 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local tophat of an image. - - tophat is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local tophat : uint8 array or uint16 array depending on input image - The result of the local tophat. - - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> tophat(ima8, square(3)) - array([[255, 255, 255, 255, 255], - [255, 0, 0, 0, 255], - [255, 0, 0, 0, 255], - [255, 0, 0, 0, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> tophat(ima16, square(3)) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 0, 0, 0, 4095], - [4095, 0, 0, 0, 4095], - [4095, 0, 0, 0, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -#__all__ = ['percentile_mean'] - -#def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): -# """Return greyscale local mean of an image. -# -# Mean is computed on the given structuring element. Only pixel values contained inside the -# percentile interval [p0,p1] are taken into account. -# -# Parameters -# ---------- -# image : ndarray -# Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, -# an exception will be raised if image has a value > 4095 -# selem : ndarray -# The neighborhood expressed as a 2-D array of 1's and 0's. -# out : ndarray -# The array to store the result of the morphology. If None is -# passed, a new array will be allocated. -# mask : ndarray (uint8) -# Mask array that defines (>0) area of the image included in the local neighborhood. -# If None, the complete image is used (default). -# shift_x, shift_y : bool -# shift structuring element about center point. This only affects -# eccentric structuring elements (i.e. selem with even numbered sides). -# Shift is bounded to the structuring element sizes. -# p0, p1 : float in [0.,...,1.] -# define the [p0,p1] percentile interval to be considered for computing the value. -# -# Returns -# ------- -# local mean : uint8 array or uint16 array depending on input image -# The result of the local mean. -# -# Examples -# -------- -# to be updated -# >>> # Erosion shrinks bright regions -# >>> from skimage.morphology import square -# >>> bright_square = np.array([[0, 0, 0, 0, 0], -# ... [0, 1, 1, 1, 0], -# ... [0, 1, 1, 1, 0], -# ... [0, 1, 1, 1, 0], -# ... [0, 0, 0, 0, 0]], dtype=np.uint8) -# >>> erosion(bright_square, square(3)) -# array([[0, 0, 0, 0, 0], -# [0, 0, 0, 0, 0], -# [0, 0, 1, 0, 0], -# [0, 0, 0, 0, 0], -# [0, 0, 0, 0, 0]], dtype=uint8) -# -# """ -# selem = img_as_ubyte(selem) -# if mask is not None: -# mask = img_as_ubyte(mask) -# if image.dtype == np.uint8: -# return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) -# elif image.dtype == np.uint16: -# bitdepth = find_bitdepth(image) -# if bitdepth>11: -# raise ValueError("only uint16 <4096 image (12bit) supported!") -# return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) -# else: -# raise TypeError("only uint8 and uint16 image supported!") -# -# From c4b091d4075af11705920d919b1d959cf73c4de4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 16:19:04 +0200 Subject: [PATCH 056/625] =?UTF-8?q?add=20bilateral=20filters=20pop=20and?= =?UTF-8?q?=20mean=C2=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- skimage/rank/bilateral_rank.py | 155 ++++++++++++++++++++++++++++++++- 1 file changed, 153 insertions(+), 2 deletions(-) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index eaa6f8d9..4e8d3b6a 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -8,11 +8,162 @@ __docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte +import numpy as np + +from generic import find_bitdepth +import _crank16_bilateral + + __all__ = ['bilateral_mean'] +def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): + """Return greyscale local bilateral_mean of an image. -def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): - pass + bilateral mean is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + s0, s1 : int + define the [s0,s1] interval to be considered for computing the value. + + Returns + ------- + local bilateral mean : uint16 array (uint8 image are casted to uint16) + The result of the local bilateral mean. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bilateral_mean(ima8, square(3), s0=10,s1=10) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bilateral_mean(ima16, square(3), s0=10,s1=10) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + image = image.astype(np.uint16) + elif image.dtype == np.uint16: + pass + else: + raise TypeError("only uint8 and uint16 image supported!") + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_bilateral.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + + +def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): + """Return greyscale local bilateral_pop of an image. + + bilateral pop is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + s0, s1 : int + define the [s0,s1] interval to be considered for computing the value. + + Returns + ------- + local bilateral pop : uint16 array (uint8 image are casted to uint16) + The result of the local bilateral pop. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bilateral_pop(ima8, square(3), s0=10,s1=10) + array([[3, 4, 3, 4, 3], + [4, 4, 6, 4, 4], + [3, 6, 9, 6, 3], + [4, 4, 6, 4, 4], + [3, 4, 3, 4, 3]], dtype=uint16) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bilateral_pop(ima16, square(3), s0=10,s1=10) + array([[3, 4, 3, 4, 3], + [4, 4, 6, 4, 4], + [3, 6, 9, 6, 3], + [4, 4, 6, 4, 4], + [3, 4, 3, 4, 3]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + image = image.astype(np.uint16) + elif image.dtype == np.uint16: + pass + else: + raise TypeError("only uint8 and uint16 image supported!") + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_bilateral.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) From 9b36d56234c1572f27d278e5f1feeee7e0fac887 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 17:00:26 +0200 Subject: [PATCH 057/625] add full test --- skimage/rank/bilateral_rank.py | 2 +- skimage/rank/percentile_rank.py | 4 +- skimage/rank/tests/demo_all.py | 149 +++++++++++++------------------- 3 files changed, 65 insertions(+), 90 deletions(-) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index 4e8d3b6a..a76133a6 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -14,7 +14,7 @@ from generic import find_bitdepth import _crank16_bilateral -__all__ = ['bilateral_mean'] +__all__ = ['bilateral_mean','bilateral_pop'] def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 924bdd7a..b6af753c 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -13,8 +13,8 @@ from generic import find_bitdepth import _crank16_percentiles,_crank8_percentiles __all__ = ['percentile_autolevel','percentile_gradient', - 'percentile_mean','percentile_mean_substraction','percentile_median', - 'percentile_minimum','percentile_modal','percentile_morph_contr_enh','percentile_pop'] + 'percentile_mean','percentile_mean_substraction', + 'percentile_morph_contr_enh','percentile_pop'] def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index da592c3d..8df5c3e7 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -4,100 +4,75 @@ from pprint import pprint from skimage import data from skimage.morphology.selem import disk -from skimage.rank import _crank8,_crank8_percentiles -from skimage.rank import _crank16,_crank16_percentiles,_crank16_bilateral +import skimage.rank as rank def plot_all(): a8 = data.camera() a16 = a8.astype('uint16')*16 - # selem = np.ones((30,30),dtype='uint8') selem = disk(5) + name_list = sorted([n for n in dir(rank) if n[0] is not '_']) + print name_list - for n in dir(_crank16): - method = eval('_crank16.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a16,selem = selem,bitdepth=12) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a16) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - for n in dir(_crank8): - method = eval('_crank8.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a8,selem = selem) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - for n in dir(_crank8_percentiles): - method = eval('_crank8_percentiles.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a8,selem = selem,p0=.1,p1=.9) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - for n in dir(_crank16_percentiles): - method = eval('_crank16_percentiles.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a16) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - selem = disk(50) - for n in dir(_crank16_bilateral): - method = eval('_crank16_bilateral.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a16,selem = selem,bitdepth=12,s0=300,s1=300) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a16) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - # + for n in name_list: + if n.rfind('bilateral')==0: + print n + method = eval('rank.%s'%n) + if type(method) == type(rank.maximum): + print method + f8 = method(a8,selem = selem,s0=10,s1=10) + f16 = method(a16,selem = selem,s0=10,s1=10) + plt.figure() + plt.subplot(2,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(2,2,2) + plt.imshow(f8) + plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(f16) + plt.colorbar() + plt.title(method) + for n in name_list: + if n.rfind('percentile')==0: + print n + method = eval('rank.%s'%n) + if type(method) == type(rank.maximum): + print method + f8 = method(a8,selem = selem,p0=.1,p1=.9) + f16 = method(a16,selem = selem,p0=.1,p1=.9) + plt.figure() + plt.subplot(2,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(2,2,2) + plt.imshow(f8) + plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(f16) + plt.colorbar() + plt.title(method) + for n in name_list: + if n.find('percentile')==-1 and n.find('bilateral')==-1: + print n + method = eval('rank.%s'%n) + if type(method) == type(rank.maximum): + print method + f8 = method(a8,selem = selem) + f16 = method(a16,selem = selem) + plt.figure() + plt.subplot(2,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(2,2,2) + plt.imshow(f8) + plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(f16) + plt.colorbar() + plt.title(method) plt.show() if __name__ == '__main__': -# plot_all() - pprint(dir(_crank8)) \ No newline at end of file + plot_all() + pprint(dir(rank)) \ No newline at end of file From 15251cdd509420aeb18804c412fbac074cc621f9 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 17:15:33 +0200 Subject: [PATCH 058/625] adapt tests --- skimage/rank/README.rst | 12 ++++---- skimage/rank/rank.py | 2 +- skimage/rank/tests/demo_16bitbilateral.py | 15 +++++++--- skimage/rank/tests/demo_benchmark.py | 4 +-- skimage/rank/tests/test_suite.py | 34 +++++++++++------------ 5 files changed, 37 insertions(+), 30 deletions(-) diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst index ef56f997..68d2a1fe 100644 --- a/skimage/rank/README.rst +++ b/skimage/rank/README.rst @@ -1,13 +1,13 @@ To use this to build your Cython file use the commandline options: +.. sourcecode:: text + + $ python setup.py build_ext --inplace + + **To do** * add simple examples -* add doc +* add/check existing doc - - -.. sourcecode:: text - - $ python setup.py build_ext --inplace \ No newline at end of file diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 9045f04e..51430236 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -13,7 +13,7 @@ from generic import find_bitdepth import _crank16,_crank8 __all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean' - ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop'] + ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local autolevel of an image. diff --git a/skimage/rank/tests/demo_16bitbilateral.py b/skimage/rank/tests/demo_16bitbilateral.py index 8001a25f..2a302e6a 100644 --- a/skimage/rank/tests/demo_16bitbilateral.py +++ b/skimage/rank/tests/demo_16bitbilateral.py @@ -2,16 +2,19 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank8_percentiles -from skimage.rank import crank16_bilateral +from skimage.morphology import disk +import skimage.rank as rank if __name__ == '__main__': a8 = (data.coins()).astype('uint8') a16 = (data.coins()).astype('uint16')*16 selem = np.ones((20,20),dtype='uint8') - f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) - f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) + f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) + f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) + + selem = disk(50) + f3 = rank.egalise(a16,selem = selem) plt.figure() plt.imshow(np.hstack((a8,f1))) @@ -21,4 +24,8 @@ if __name__ == '__main__': plt.imshow(np.hstack((a16,f2))) plt.colorbar() + plt.figure() + plt.imshow(np.hstack((a16,f3))) + plt.colorbar() + plt.show() diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index f6fe32bb..42971133 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -3,13 +3,13 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import dilation -from skimage.rank import _crank8 +import skimage.rank as rank from tools import log_timing @log_timing def cr_max(image,selem): - return _crank8.maximum(image=image,selem = selem) + return rank.maximum(image=image,selem = selem) @log_timing def cm_dil(image,selem): diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 0dcb21ab..51c7f60b 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -2,8 +2,8 @@ import unittest import numpy as np -from skimage.rank import crank8,crank8_percentiles -from skimage.rank import crank16,crank16_bilateral,crank16_percentiles +from skimage.rank import _crank8,_crank8_percentiles +from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles from skimage.morphology import cmorph class TestSequenceFunctions(unittest.TestCase): @@ -17,23 +17,23 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') for m,n in np.random.random_integers(1,100,size=(10,2)): a8 = np.ones((m,n),dtype='uint8') - r = crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) + r = _crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) self.assertTrue(a8.shape == r.shape) - r = crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) + r = _crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) self.assertTrue(a8.shape == r.shape) for m,n in np.random.random_integers(1,100,size=(10,2)): a16 = np.ones((m,n),dtype='uint16') - r = crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0) + r = _crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0) self.assertTrue(a16.shape == r.shape) - r = crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1) + r = _crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1) self.assertTrue(a16.shape == r.shape) for m,n in np.random.random_integers(1,100,size=(10,2)): a16 = np.ones((m,n),dtype='uint16') - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9) self.assertTrue(a16.shape == r.shape) - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) self.assertTrue(a16.shape == r.shape) def test_compare_with_cmorph(self): @@ -43,27 +43,27 @@ class TestSequenceFunctions(unittest.TestCase): for r in range(1,20,1): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') - rc = crank8.maximum(image=a,selem = elem) + rc = _crank8.maximum(image=a,selem = elem) cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) def test_bitdepth(self): elem = np.ones((3,3),dtype='uint8') a16 = np.ones((100,100),dtype='uint16')*255 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) a16 = np.ones((100,100),dtype='uint16')*255*2 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9) a16 = np.ones((100,100),dtype='uint16')*255*4 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10) a16 = np.ones((100,100),dtype='uint16')*255*8 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11) a16 = np.ones((100,100),dtype='uint16')*255*16 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) def test_population(self): a = np.zeros((5,5),dtype='uint8') elem = np.ones((3,3),dtype='uint8') - p = crank8.pop(image=a,selem = elem) + p = _crank8.pop(image=a,selem = elem) r = np.asarray([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -75,7 +75,7 @@ class TestSequenceFunctions(unittest.TestCase): a = np.zeros((6,6),dtype='uint8') a[2,2] = 255 elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') - f = crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) + f = _crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) r = np.asarray([[ 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0], [ 0, 0, 255, 0, 0, 0], @@ -90,7 +90,7 @@ class TestSequenceFunctions(unittest.TestCase): # should fail because data bitdepth is too high for the function a16 = np.ones((100,100),dtype='uint16')*255 elem = np.ones((3,3),dtype='uint8') - f = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) if __name__ == '__main__': From 28cef2e42c3d856e18da1d3d47f5498cec8a020a Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 17:48:15 +0200 Subject: [PATCH 059/625] add examples - to be cont. --- doc/examples/plot_lena_bilateral_denoise.py | 55 +++++++++++++ doc/examples/plot_local_equalize.py | 86 +++++++++++++++++++++ doc/examples/plot_local_threshold.py | 62 +++++++++++++++ 3 files changed, 203 insertions(+) create mode 100644 doc/examples/plot_lena_bilateral_denoise.py create mode 100644 doc/examples/plot_local_equalize.py create mode 100644 doc/examples/plot_local_threshold.py diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py new file mode 100644 index 00000000..fbee7d67 --- /dev/null +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -0,0 +1,55 @@ +""" +==================================================== +Denoising the picture of Lena using total variation +==================================================== + +In this example, we denoise a noisy version of the picture of Lena +using the total variation denoising filter. The result of this filter +is an image that has a minimal total variation norm, while being as +close to the initial image as possible. The total variation is the L1 +norm of the gradient of the image, and minimizing the total variation +typically produces "posterized" images with flat domains separated by +sharp edges. + +It is possible to change the degree of posterization by controlling +the tradeoff between denoising and faithfulness to the original image. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data, color, img_as_ubyte +from skimage.filter import tv_denoise +from skimage.rank import bilateral_mean +from skimage.morphology import disk + +l = img_as_ubyte(color.rgb2gray(data.lena())) +l = l[230:290, 220:320] + +noisy = l + 0.4 * l.std() * np.random.random(l.shape) + +selem = disk(30) +bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) + +plt.figure(figsize=(8, 2)) + +plt.subplot(131) +plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('noisy', fontsize=20) +plt.subplot(132) +plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('bilateral denoising', fontsize=20) + +selem = disk(30) +bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30) +plt.subplot(133) +plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('(more) bilateral denoising', fontsize=20) + +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, + right=1) +plt.show() diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py new file mode 100644 index 00000000..e79ddbed --- /dev/null +++ b/doc/examples/plot_local_equalize.py @@ -0,0 +1,86 @@ +""" +=============================== +Local Histogram Equalization +=============================== + +This examples enhances an image with low contrast, using a method called +*local histogram equalization*, which "spreads out the most frequent intensity +values" in an image . The equalized image has a roughly linear cumulative +distribution function for each pixel neigborhood. + +to be adjusted... + +.. [1] http://en.wikipedia.org/wiki/Histogram_equalization +.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm + +""" + +from skimage import data +from skimage.util.dtype import dtype_range +from skimage import exposure +from skimage.rank import egalise +from skimage.morphology import disk + + +import matplotlib.pyplot as plt + +import numpy as np + +def plot_img_and_hist(img, axes, bins=256): + """Plot an image along with its histogram and cumulative histogram. + + """ + ax_img, ax_hist = axes + ax_cdf = ax_hist.twinx() + + # Display image + ax_img.imshow(img, cmap=plt.cm.gray) + ax_img.set_axis_off() + + # Display histogram + ax_hist.hist(img.ravel(), bins=bins) + ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) + ax_hist.set_xlabel('Pixel intensity') + + xmin, xmax = dtype_range[img.dtype.type] + ax_hist.set_xlim(xmin, xmax) + + # Display cumulative distribution + img_cdf, bins = exposure.cumulative_distribution(img, bins) + ax_cdf.plot(bins, img_cdf, 'r') + + return ax_img, ax_hist, ax_cdf + + +# Load an example image +img = data.moon() + +# Contrast stretching +p2 = np.percentile(img, 2) +p98 = np.percentile(img, 98) +img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) + +# Equalization +selem = disk(30) +img_eq = egalise(img,selem=selem) + + +# Display results +f, axes = plt.subplots(2, 3, figsize=(8, 4)) + +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) +ax_img.set_title('Low contrast image') +ax_hist.set_ylabel('Number of pixels') + +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) +ax_img.set_title('Contrast stretching') + +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) +ax_img.set_title('Local Histogram equalization') +ax_cdf.set_ylabel('Fraction of total intensity') + + +# prevent overlap of y-axis labels +plt.subplots_adjust(wspace=0.4) +plt.show() + diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py new file mode 100644 index 00000000..3b9a42fb --- /dev/null +++ b/doc/examples/plot_local_threshold.py @@ -0,0 +1,62 @@ +""" +===================== +Local Thresholding +===================== + +Thresholding is the simplest way to segment objects from a background. If that +background is relatively uniform, then you can use a global threshold value to +binarize the image by pixel-intensity. If there's large variation in the +background intensity, however, adaptive thresholding (a.k.a. local or dynamic +thresholding) may produce better results. + +Here, we binarize an image using the `threshold_adaptive` function, which +calculates thresholds in regions of size `block_size` surrounding each pixel +(i.e. local neighborhoods). Each threshold value is the weighted mean of the +local neighborhood minus an offset value. + +Added local threshold using rank filter + +to be adjusted ... + +""" +import matplotlib.pyplot as plt + +from skimage import data +from skimage.filter import threshold_otsu, threshold_adaptive + +from skimage.rank import threshold +from skimage.morphology import disk + + +image = data.page() + +global_thresh = threshold_otsu(image) +binary_global = image > global_thresh + +block_size = 40 +binary_adaptive = threshold_adaptive(image, block_size, offset=10) + +selem = disk(10) +loc_thresh = threshold(image,selem=selem) + +fig, axes = plt.subplots(nrows=4, figsize=(7, 8)) +ax0, ax1, ax2, ax3 = axes +plt.gray() + +ax0.imshow(image) +ax0.set_title('Image') + +ax1.imshow(binary_global) +ax1.set_title('Global thresholding') + +ax2.imshow(binary_adaptive) +ax2.set_title('Adaptive thresholding') + +ax3.imshow(loc_thresh) +ax3.set_title('Local thresholding') + + +for ax in axes: + ax.axis('off') + +plt.show() From 31ab620aed8db8420da29aaaea9da1fbc472dcf6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 18:05:48 +0200 Subject: [PATCH 060/625] add readme --- skimage/rank/README.rst | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst index 68d2a1fe..aae62162 100644 --- a/skimage/rank/README.rst +++ b/skimage/rank/README.rst @@ -7,7 +7,29 @@ To use this to build your Cython file use the commandline options: **To do** -* add simple examples +* add simple examples, adapt documentation on existing examples * add/check existing doc +* adapting tests for each type of filter + +**General remarks** + +Basically these filters compute local histogram for each pixel. Histogram is build using a moving window in +order to limit redundant computation. The path followed by the moving window is given hereunder + + ...-----------------------\ +/--------------------------/ +\-------------------------- ... + +A comparison is proposed with cmorph.dilate algorithm to show how computation costs evolve with respect to image size or +structuring element size. This implementation gives better results for large structuring elements. + +A local histogram is update at each pixel by introducing pixel entering the structuring element border and +by removing those leaving it. The histogram size is 8bit (256 bins) for 8 bit images and 2 to 12 bit (up to 4096 bins) +for 16bit image depending on the image maximum value. Image with pixels higher than 4095 raise a ValueError. + +The filter is applied up to the image border, the neighboorhood used is adjusted accordingly. The user may provide +a mask image (same size as input image) where non zero value are the part of the image participating the the +histogram computation. By default all the image is filtered. + From 76f5156168dc5b7715380fa08917964cad38d5f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 15:21:26 +0200 Subject: [PATCH 061/625] Fix bug in warp which caused 1 pixel images not to be clipped --- skimage/transform/_geometric.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py index 6e04a271..1179811d 100644 --- a/skimage/transform/_geometric.py +++ b/skimage/transform/_geometric.py @@ -1015,7 +1015,8 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1, if mode == 'constant' and not (0 <= cval <= 1): clipped[out == cval] = cval - if clipped.shape[0] == 1 or clipped.shape[1] == 1: + if clipped.ndim == 3 and orig_ndim == 2: + # remove singleton dim introduced by atleast_3d + return clipped[..., 0] + else: return clipped - else: # remove singleton dim introduced by atleast_3d - return clipped.squeeze() From 661d50ee205b3eed4728002c1e1d0bba8ad34383 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 15:23:23 +0200 Subject: [PATCH 062/625] Full test coverage for image pyramids --- skimage/transform/tests/test_pyramids.py | 49 +++++++++++++++++++----- 1 file changed, 40 insertions(+), 9 deletions(-) diff --git a/skimage/transform/tests/test_pyramids.py b/skimage/transform/tests/test_pyramids.py index 611ecdec..dd976a7b 100644 --- a/skimage/transform/tests/test_pyramids.py +++ b/skimage/transform/tests/test_pyramids.py @@ -1,41 +1,72 @@ -from numpy.testing import assert_array_equal, run_module_suite +from numpy.testing import assert_array_equal, assert_raises, run_module_suite from skimage import data -from skimage.transform import (pyramid_reduce, pyramid_expand, - pyramid_gaussian, pyramid_laplacian) +from skimage.transform import pyramids image = data.lena() +image_gray = image[..., 0] def test_pyramid_reduce(): + # RGB image rows, cols, dim = image.shape - out = pyramid_reduce(image, downscale=2) + out = pyramids.pyramid_reduce(image, downscale=2) assert_array_equal(out.shape, (rows / 2, cols / 2, dim)) + # grayscale image + rows, cols = image_gray.shape + out = pyramids.pyramid_reduce(image_gray, downscale=2) + assert_array_equal(out.shape, (rows / 2, cols / 2)) + def test_pyramid_expand(): + # RGB image rows, cols, dim = image.shape - out = pyramid_expand(image, upscale=2) + out = pyramids.pyramid_expand(image, upscale=2) assert_array_equal(out.shape, (rows * 2, cols * 2, dim)) + # grayscale image + rows, cols = image_gray.shape + out = pyramids.pyramid_expand(image_gray, upscale=2) + assert_array_equal(out.shape, (rows * 2, cols * 2)) + def test_build_gaussian_pyramid(): + # RGB image rows, cols, dim = image.shape - pyramid = pyramid_gaussian(image, downscale=2) - + pyramid = pyramids.pyramid_gaussian(image, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim) assert_array_equal(out.shape, layer_shape) + # grayscale image + rows, cols = image_gray.shape + pyramid = pyramids.pyramid_gaussian(image_gray, downscale=2) + for layer, out in enumerate(pyramid): + layer_shape = (rows / 2 ** layer, cols / 2 ** layer) + assert_array_equal(out.shape, layer_shape) + def test_build_laplacian_pyramid(): + # RGB image rows, cols, dim = image.shape - pyramid = pyramid_laplacian(image, downscale=2) - + pyramid = pyramids.pyramid_laplacian(image, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim) assert_array_equal(out.shape, layer_shape) + # grayscale image + rows, cols = image_gray.shape + pyramid = pyramids.pyramid_laplacian(image_gray, downscale=2) + for layer, out in enumerate(pyramid): + layer_shape = (rows / 2 ** layer, cols / 2 ** layer) + assert_array_equal(out.shape, layer_shape) + + +def test_check_factor(): + assert_raises(ValueError, pyramids._check_factor, 0.99) + assert_raises(ValueError, pyramids._check_factor, - 2) + if __name__ == "__main__": run_module_suite() From 59d3d842be3ae6082cc894300283cfcf6afde727 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 17:43:20 +0200 Subject: [PATCH 063/625] Full test coveragem for data package --- skimage/data/tests/test_data.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/skimage/data/tests/test_data.py b/skimage/data/tests/test_data.py index 1d85802b..b5f6bcaa 100644 --- a/skimage/data/tests/test_data.py +++ b/skimage/data/tests/test_data.py @@ -15,9 +15,25 @@ def test_camera(): def test_checkerboard(): - """ Test that checkerboard image can be loaded. """ + """ Test that "checkerboard" image can be loaded. """ data.checkerboard() + +def test_text(): + """ Test that "text" image can be loaded. """ + data.text() + + +def test_moon(): + """ Test that "moon" image can be loaded. """ + data.moon() + + +def test_page(): + """ Test that "page" image can be loaded. """ + data.page() + + if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() From 01df23a67d7aedd0ee503c926b1ba8009dbd2278 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 17:47:54 +0200 Subject: [PATCH 064/625] Full test coverage for canny filter --- skimage/filter/tests/test_canny.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/skimage/filter/tests/test_canny.py b/skimage/filter/tests/test_canny.py index 1e406008..2c758edf 100644 --- a/skimage/filter/tests/test_canny.py +++ b/skimage/filter/tests/test_canny.py @@ -61,3 +61,8 @@ class TestCanny(unittest.TestCase): def test_image_shape(self): self.assertRaises(TypeError, F.canny, np.zeros((20, 20, 20)), 4, 0, 0) + + def test_mask_none(self): + result1 = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool)) + result2 = F.canny(np.zeros((20, 20)), 4, 0, 0) + self.assertTrue(np.all(result1 == result2)) From bca962cc8ad765d0e3b8bd97ddd1b025dc827735 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 17:59:55 +0200 Subject: [PATCH 065/625] Improve test coverage for ctmf --- skimage/filter/tests/test_ctmf.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/filter/tests/test_ctmf.py b/skimage/filter/tests/test_ctmf.py index fd820469..c4f6d10e 100644 --- a/skimage/filter/tests/test_ctmf.py +++ b/skimage/filter/tests/test_ctmf.py @@ -117,5 +117,11 @@ def test_insufficient_size(): median_filter(img, radius=1) +@raises(TypeError) +def test_wrong_shape(): + img = np.empty((10, 10, 3)) + median_filter(img) + + if __name__ == "__main__": np.testing.run_module_suite() From fad2813dd8e6fb50e6552a5fbadf1b5e9635f267 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 18:01:40 +0200 Subject: [PATCH 066/625] Fix callable test in LPI filter --- skimage/filter/lpi_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/lpi_filter.py b/skimage/filter/lpi_filter.py index 3826f5e7..2af74d7a 100644 --- a/skimage/filter/lpi_filter.py +++ b/skimage/filter/lpi_filter.py @@ -75,7 +75,7 @@ class LPIFilter2D(object): >>> filter = LPIFilter2D(filt_func) """ - if impulse_response is None: + if not callable(impulse_response): raise ValueError("Impulse response must be a callable.") self.impulse_response = impulse_response From 00dbb5e4e3017514f41ecbd40b00cfc53ec11916 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 18:36:15 +0200 Subject: [PATCH 067/625] Improve test coverage of LPI filter --- skimage/filter/tests/test_lpi_filter.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/skimage/filter/tests/test_lpi_filter.py b/skimage/filter/tests/test_lpi_filter.py index 08e46a58..f6176691 100644 --- a/skimage/filter/tests/test_lpi_filter.py +++ b/skimage/filter/tests/test_lpi_filter.py @@ -8,7 +8,8 @@ from skimage.io import * from skimage.filter import * -class TestLPIFilter2D(): +class TestLPIFilter2D(object): + img = imread(os.path.join(data_dir, 'camera.png'), flatten=True)[:50, :50] @@ -55,5 +56,9 @@ class TestLPIFilter2D(): g1 = wiener(F[::-1, ::-1], self.filt_func) assert ((g - g1[::-1, ::-1]).sum() < 1) + def test_non_callable(self): + assert_raises(ValueError, LPIFilter2D, None) + + if __name__ == "__main__": run_module_suite() From d1629aec0f1a861106d54cb015b238e7fe542936 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 19:05:11 +0200 Subject: [PATCH 068/625] Fix polygon approximation for 0 or less tolerance --- skimage/measure/_polygon.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/measure/_polygon.py b/skimage/measure/_polygon.py index add4b5fb..7b9b920b 100644 --- a/skimage/measure/_polygon.py +++ b/skimage/measure/_polygon.py @@ -28,7 +28,7 @@ def approximate_polygon(coords, tolerance): ---------- .. [1] http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm """ - if tolerance == 0: + if tolerance <= 0: return coords chain = np.zeros(coords.shape[0], 'bool') From bee7223481e6899900087736132da7542a2eea58 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 19:06:17 +0200 Subject: [PATCH 069/625] Full test coverage for polygon approximation and subdivision --- skimage/measure/tests/test_polygon.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/measure/tests/test_polygon.py b/skimage/measure/tests/test_polygon.py index 1907d7cb..c96aed88 100644 --- a/skimage/measure/tests/test_polygon.py +++ b/skimage/measure/tests/test_polygon.py @@ -22,6 +22,8 @@ def test_approximate_polygon(): out = approximate_polygon(square, -1) np.testing.assert_array_equal(out, square) + out = approximate_polygon(square, 0) + np.testing.assert_array_equal(out, square) def test_subdivide_polygon(): @@ -51,6 +53,10 @@ def test_subdivide_polygon(): np.testing.assert_equal(new_square3.shape[0], 2 * (square3.shape[0] - mask_len + 2)) + # not supported B-Spline degree + np.testing.assert_raises(ValueError, subdivide_polygon, square, 0) + np.testing.assert_raises(ValueError, subdivide_polygon, square, 8) + if __name__ == "__main__": np.testing.run_module_suite() From db76b212c736041ed2f6da9ec4bf70993a72bd63 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 19:23:58 +0200 Subject: [PATCH 070/625] Full test coverage for regionprops --- skimage/measure/tests/test_regionprops.py | 31 ++++++++++++++++++++--- 1 file changed, 27 insertions(+), 4 deletions(-) diff --git a/skimage/measure/tests/test_regionprops.py b/skimage/measure/tests/test_regionprops.py index 271c1ec4..524f55ee 100644 --- a/skimage/measure/tests/test_regionprops.py +++ b/skimage/measure/tests/test_regionprops.py @@ -1,9 +1,9 @@ from numpy.testing import assert_array_equal, assert_almost_equal, \ - assert_array_almost_equal + assert_array_almost_equal, assert_raises import numpy as np import math -from skimage.measure import regionprops +from skimage.measure._regionprops import regionprops, PROPS, perimeter SAMPLE = np.array( @@ -22,6 +22,16 @@ INTENSITY_SAMPLE = SAMPLE.copy() INTENSITY_SAMPLE[1, 9:11] = 2 +def test_unsupported_dtype(): + assert_raises(TypeError, regionprops, np.zeros((10, 10), dtype=np.double)) + + +def test_all_props(): + props = regionprops(SAMPLE, 'all', INTENSITY_SAMPLE)[0] + for prop in PROPS: + assert prop in props + + def test_area(): area = regionprops(SAMPLE, ['Area'])[0]['Area'] assert area == np.sum(SAMPLE) @@ -90,6 +100,11 @@ def test_eccentricity(): eps = regionprops(SAMPLE, ['Eccentricity'])[0]['Eccentricity'] assert_almost_equal(eps, 0.814629313427) + img = np.zeros((5, 5), dtype=np.int) + img[2, 2] = 1 + eps = regionprops(img, ['Eccentricity'])[0]['Eccentricity'] + assert_almost_equal(eps, 0) + def test_equiv_diameter(): diameter = regionprops(SAMPLE, ['EquivDiameter'])[0]['EquivDiameter'] @@ -142,6 +157,11 @@ def test_filled_area(): assert area == np.sum(SAMPLE) +def test_filled_image(): + img = regionprops(SAMPLE, ['FilledImage'])[0]['FilledImage'] + assert_array_equal(img, SAMPLE) + + def test_major_axis_length(): length = regionprops(SAMPLE, ['MajorAxisLength'])[0]['MajorAxisLength'] # MATLAB has different interpretation of ellipse than found in literature, @@ -220,8 +240,11 @@ def test_orientation(): assert_almost_equal(orientation_diag, -math.pi / 4) def test_perimeter(): - perimeter = regionprops(SAMPLE, ['Perimeter'])[0]['Perimeter'] - assert_almost_equal(perimeter, 59.2132034355964) + per = regionprops(SAMPLE, ['Perimeter'])[0]['Perimeter'] + assert_almost_equal(per, 59.2132034355964) + + per = perimeter(SAMPLE, neighbourhood=8) + assert_almost_equal(per, 43.1213203436) def test_solidity(): solidity = regionprops(SAMPLE, ['Solidity'])[0]['Solidity'] From aa85b22f6da450d258931efec8f87ac5165cdb29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 19:36:09 +0200 Subject: [PATCH 071/625] Full test coverage for structural similarity --- .../tests/test_structural_similarity.py | 32 ++++++++++++------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/skimage/measure/tests/test_structural_similarity.py b/skimage/measure/tests/test_structural_similarity.py index 3eb2a7e9..d2ddc774 100644 --- a/skimage/measure/tests/test_structural_similarity.py +++ b/skimage/measure/tests/test_structural_similarity.py @@ -1,5 +1,5 @@ import numpy as np -from numpy.testing import assert_equal +from numpy.testing import assert_equal, assert_raises from skimage.measure import structural_similarity as ssim @@ -24,20 +24,18 @@ def test_ssim_image(): S1 = ssim(X, Y, win_size=3) assert(S1 < 0.3) -## Come up with a better way of testing the gradient -## -## def test_ssim_grad(): -## N = 30 -## X = np.random.random((N, N)) * 255 -## Y = np.random.random((N, N)) * 255 -## def func(Y): -## return ssim(X, Y, dynamic_range=255) +def test_ssim_grad(): + N = 30 + X = np.random.random((N, N)) * 255 + Y = np.random.random((N, N)) * 255 -## def grad(Y): -## return ssim(X, Y, dynamic_range=255, gradient=True)[1] + f = ssim(X, Y, dynamic_range=255) + g = ssim(X, Y, dynamic_range=255, gradient=True) -## assert(np.all(opt.check_grad(func, grad, Y) < 0.05)) + assert f < 0.05 + assert g[0] < 0.05 + assert np.all(g[1] < 0.05) def test_ssim_dtype(): @@ -56,5 +54,15 @@ def test_ssim_dtype(): assert S2 < 0.1 +def test_invalid_input(): + X = np.zeros((3, 3), dtype=np.double) + Y = np.zeros((3, 3), dtype=np.int) + assert_raises(ValueError, ssim, X, Y) + + Y = np.zeros((4, 4), dtype=np.double) + assert_raises(ValueError, ssim, X, Y) + + assert_raises(ValueError, ssim, X, X, win_size=8) + if __name__ == "__main__": np.testing.run_module_suite() From 5117803b79ee56b412f550123e158507f759599c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 21:34:41 +0200 Subject: [PATCH 072/625] Improve test coverage of find_contours --- skimage/measure/find_contours.py | 2 +- skimage/measure/tests/test_find_contours.py | 62 ++++++++++++--------- 2 files changed, 36 insertions(+), 28 deletions(-) diff --git a/skimage/measure/find_contours.py b/skimage/measure/find_contours.py index 68e1d53c..eafbd523 100755 --- a/skimage/measure/find_contours.py +++ b/skimage/measure/find_contours.py @@ -98,7 +98,7 @@ def find_contours(array, level, """ array = np.asarray(array, dtype=np.double) if array.ndim != 2: - raise RuntimeError('Only 2D arrays are supported.') + raise TypeError('Only 2D arrays are supported.') level = float(level) if (fully_connected not in _param_options or positive_orientation not in _param_options): diff --git a/skimage/measure/tests/test_find_contours.py b/skimage/measure/tests/test_find_contours.py index 62b39b3f..cde60e4d 100644 --- a/skimage/measure/tests/test_find_contours.py +++ b/skimage/measure/tests/test_find_contours.py @@ -21,34 +21,37 @@ r = np.sqrt(x**2 + y**2) def test_binary(): - contours = find_contours(a, 0.5) + ref = [[6. , 1.5], + [5. , 1.5], + [4. , 1.5], + [3. , 1.5], + [2. , 1.5], + [1.5, 2. ], + [1.5, 3. ], + [1.5, 4. ], + [1.5, 5. ], + [1.5, 6. ], + [1. , 6.5], + [0.5, 6. ], + [0.5, 5. ], + [0.5, 4. ], + [0.5, 3. ], + [0.5, 2. ], + [0.5, 1. ], + [1. , 0.5], + [2. , 0.5], + [3. , 0.5], + [4. , 0.5], + [5. , 0.5], + [6. , 0.5], + [6.5, 1. ], + [6. , 1.5]] + + contours = find_contours(a, 0.5, positive_orientation='high') assert len(contours) == 1 - assert_array_equal(contours[0], - [[6. , 1.5], - [5. , 1.5], - [4. , 1.5], - [3. , 1.5], - [2. , 1.5], - [1.5, 2. ], - [1.5, 3. ], - [1.5, 4. ], - [1.5, 5. ], - [1.5, 6. ], - [1. , 6.5], - [0.5, 6. ], - [0.5, 5. ], - [0.5, 4. ], - [0.5, 3. ], - [0.5, 2. ], - [0.5, 1. ], - [1. , 0.5], - [2. , 0.5], - [3. , 0.5], - [4. , 0.5], - [5. , 0.5], - [6. , 0.5], - [6.5, 1. ], - [6. , 1.5]]) + assert_array_equal(contours[0][::-1], ref) + + def test_float(): @@ -70,6 +73,11 @@ def test_memory_order(): assert len(contours) == 1 +def test_invalid_input(): + assert_raises(ValueError, find_contours, r, 0.5, 'foo', 'bar') + assert_raises(TypeError, find_contours, r[..., None], 0.5) + + if __name__ == '__main__': from numpy.testing import run_module_suite run_module_suite() From 48572fd0a3dfa90404b69916a97cb66a51e44c58 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 21:52:15 +0200 Subject: [PATCH 073/625] Remove obsolete license note as code has been refactored --- skimage/morphology/grey.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/skimage/morphology/grey.py b/skimage/morphology/grey.py index e7e52de4..47ef1d2c 100644 --- a/skimage/morphology/grey.py +++ b/skimage/morphology/grey.py @@ -1,10 +1,3 @@ -""" -:author: Damian Eads, 2009 -:license: modified BSD -""" - -__docformat__ = 'restructuredtext en' - import warnings from skimage import img_as_ubyte From 0ab93bbf366d0e938625cadb775180a6496dd80a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 21:54:12 +0200 Subject: [PATCH 074/625] Remove redundant dtype conversion --- skimage/morphology/grey.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skimage/morphology/grey.py b/skimage/morphology/grey.py index 47ef1d2c..69050773 100644 --- a/skimage/morphology/grey.py +++ b/skimage/morphology/grey.py @@ -254,7 +254,6 @@ def white_tophat(image, selem, out=None): """ if image is out: raise NotImplementedError("Cannot perform white top hat in place.") - image = img_as_ubyte(image) out = opening(image, selem, out=out) out = image - out @@ -304,7 +303,6 @@ def black_tophat(image, selem, out=None): if image is out: raise NotImplementedError("Cannot perform white top hat in place.") - image = img_as_ubyte(image) out = closing(image, selem, out=out) out = out - image From bc745e833aa6f983a2d28f67b0b0f22ead798be1 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 7 Oct 2012 15:07:11 -0400 Subject: [PATCH 075/625] BUG: Pass package directory as positional argument to nose.run When the package directory is passed to nosetest via the "where" argument (-w), that directory becomes the working directory. This causes problems because `skimage.io` will take precedence over the builtin `io` package. --- skimage/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index c8935cb6..cf62ee87 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -65,7 +65,7 @@ except ImportError: def _setup_test(verbose=False): import functools - args = ['', '--exe', '-w', pkg_dir] + args = ['', pkg_dir, '--exe'] if verbose: args.extend(['-v', '-s']) From 500b1c507ceabe21bbc4bb1e7e82ea7e48075755 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 7 Oct 2012 15:33:03 -0400 Subject: [PATCH 076/625] ENH: Add grayscale support and color adjustment to visualize_boundaries --- skimage/segmentation/boundaries.py | 32 ++++++++++++++++++++++++------ 1 file changed, 26 insertions(+), 6 deletions(-) diff --git a/skimage/segmentation/boundaries.py b/skimage/segmentation/boundaries.py index b40ecb01..0d8be0f3 100644 --- a/skimage/segmentation/boundaries.py +++ b/skimage/segmentation/boundaries.py @@ -1,19 +1,39 @@ import numpy as np from ..morphology import dilation, square from ..util import img_as_float +from ..color import gray2rgb def find_boundaries(label_img): + """Return bool array where boundaries between labeled regions are True.""" boundaries = np.zeros(label_img.shape, dtype=np.bool) boundaries[1:, :] += label_img[1:, :] != label_img[:-1, :] boundaries[:, 1:] += label_img[:, 1:] != label_img[:, :-1] return boundaries -def visualize_boundaries(img, label_img): - img = img_as_float(img, force_copy=True) +def visualize_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): + """Return image with boundaries between labeled regions highlighted. + + Parameters + ---------- + image : (M, N[, 3]) array + Grayscale or RGB image. + label_img : (M, N) array + Label array where regions are marked by different integer values. + color : length-3 sequence + RGB color of boundaries in the output image. + outline_color : length-3 sequence + RGB color surrounding boundaries in the output image. If None, no + outline is drawn. + """ + if image.ndim == 2: + image = gray2rgb(image) + image = img_as_float(image, force_copy=True) + boundaries = find_boundaries(label_img) - outer_boundaries = dilation(boundaries.astype(np.uint8), square(2)) - img[outer_boundaries != 0, :] = np.array([0, 0, 0]) # black - img[boundaries, :] = np.array([1, 1, 0]) # yellow - return img + if outline_color is not None: + outer_boundaries = dilation(boundaries.astype(np.uint8), square(2)) + image[outer_boundaries != 0, :] = np.array(outline_color) + image[boundaries, :] = np.array(color) + return image From 694ac11f930639e948080790626172a762d4a434 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 7 Oct 2012 15:43:28 -0400 Subject: [PATCH 077/625] STY: Rename visualize_boundaries to highlight_boundaries Add @deprecated decorator to ease the transition. --- doc/examples/plot_segmentations.py | 10 +++---- skimage/_shared/utils.py | 43 ++++++++++++++++++++++++++++++ skimage/segmentation/__init__.py | 3 ++- skimage/segmentation/boundaries.py | 8 +++++- 4 files changed, 57 insertions(+), 7 deletions(-) create mode 100644 skimage/_shared/utils.py diff --git a/doc/examples/plot_segmentations.py b/doc/examples/plot_segmentations.py index 99bfefcc..31dc38e7 100644 --- a/doc/examples/plot_segmentations.py +++ b/doc/examples/plot_segmentations.py @@ -63,8 +63,8 @@ import matplotlib.pyplot as plt import numpy as np from skimage.data import lena -from skimage.segmentation import felzenszwalb, \ - visualize_boundaries, slic, quickshift +from skimage.segmentation import felzenszwalb, slic, quickshift +from skimage.segmentation import highlight_boundaries from skimage.util import img_as_float img = img_as_float(lena()[::2, ::2]) @@ -80,11 +80,11 @@ fig, ax = plt.subplots(1, 3) fig.set_size_inches(8, 3, forward=True) plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05) -ax[0].imshow(visualize_boundaries(img, segments_fz)) +ax[0].imshow(highlight_boundaries(img, segments_fz)) ax[0].set_title("Felzenszwalbs's method") -ax[1].imshow(visualize_boundaries(img, segments_slic)) +ax[1].imshow(highlight_boundaries(img, segments_slic)) ax[1].set_title("SLIC") -ax[2].imshow(visualize_boundaries(img, segments_quick)) +ax[2].imshow(highlight_boundaries(img, segments_quick)) ax[2].set_title("Quickshift") for a in ax: a.set_xticks(()) diff --git a/skimage/_shared/utils.py b/skimage/_shared/utils.py new file mode 100644 index 00000000..4075ddb4 --- /dev/null +++ b/skimage/_shared/utils.py @@ -0,0 +1,43 @@ +import warnings +import functools + + +__all__ = ['deprecated'] + + +class deprecated(object): + """Decorator to mark deprecated functions with warning. + + Adapted from . + + Parameters + ---------- + alt_func : str + If given, tell user what function to use instead. + behavior : {'warn', 'raise'} + Behavior during call to deprecated function: 'warn' = warn user that + function is deprecated; 'raise' = raise error. + """ + + def __init__(self, alt_func=None, behavior='warn'): + self.alt_func = alt_func + self.behavior = behavior + + def __call__(self, func): + + msg = "Call to deprecated function `%s`." % func.__name__ + if self.alt_func is not None: + msg = msg + " Use `%s` instead." % self.alt_func + + @functools.wraps(func) + def wrapped(*args, **kwargs): + if self.behavior == 'warn': + warnings.warn_explicit(msg, + category=DeprecationWarning, + filename=func.func_code.co_filename, + lineno=func.func_code.co_firstlineno + 1) + elif self.behavior == 'raise': + raise DeprecationWarning(msg) + return func(*args, **kwargs) + + return wrapped diff --git a/skimage/segmentation/__init__.py b/skimage/segmentation/__init__.py index 9dca2bc3..ce0bfd84 100644 --- a/skimage/segmentation/__init__.py +++ b/skimage/segmentation/__init__.py @@ -2,5 +2,6 @@ from .random_walker_segmentation import random_walker from ._felzenszwalb import felzenszwalb from ._slic import slic from ._quickshift import quickshift -from .boundaries import find_boundaries, visualize_boundaries +from .boundaries import (find_boundaries, visualize_boundaries, + highlight_boundaries) from ._clear_border import clear_border diff --git a/skimage/segmentation/boundaries.py b/skimage/segmentation/boundaries.py index 0d8be0f3..d30a8627 100644 --- a/skimage/segmentation/boundaries.py +++ b/skimage/segmentation/boundaries.py @@ -2,6 +2,7 @@ import numpy as np from ..morphology import dilation, square from ..util import img_as_float from ..color import gray2rgb +from .._shared.utils import deprecated def find_boundaries(label_img): @@ -12,7 +13,7 @@ def find_boundaries(label_img): return boundaries -def visualize_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): +def highlight_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): """Return image with boundaries between labeled regions highlighted. Parameters @@ -37,3 +38,8 @@ def visualize_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): image[outer_boundaries != 0, :] = np.array(outline_color) image[boundaries, :] = np.array(color) return image + + +@deprecated('highlight_boundaries') +def visualize_boundaries(*args, **kwargs): + return highlight_boundaries(*args, **kwargs) From b16ba3daf249d954ff52e02d7467f51a1271521d Mon Sep 17 00:00:00 2001 From: Nicolas Pinto Date: Sun, 7 Oct 2012 13:30:07 -0700 Subject: [PATCH 078/625] BUG: fix side-effect in util.montage() --- skimage/util/montage.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/skimage/util/montage.py b/skimage/util/montage.py index 1a8cda23..587c0939 100644 --- a/skimage/util/montage.py +++ b/skimage/util/montage.py @@ -71,6 +71,8 @@ def montage2d(arr_in, fill='mean', rescale_intensity=False): assert arr_in.ndim == 3 n_images, height, width = arr_in.shape + + arr_in = arr_in.copy() # -- rescale intensity if necessary if rescale_intensity: From ad53159d60ed05ebccb3e9d895ead21c9f3de7ad Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 7 Oct 2012 19:23:38 -0400 Subject: [PATCH 079/625] STY: Rename highlight_boundaries to mark_boundaries --- doc/examples/plot_segmentations.py | 8 ++++---- skimage/segmentation/__init__.py | 3 +-- skimage/segmentation/boundaries.py | 6 +++--- 3 files changed, 8 insertions(+), 9 deletions(-) diff --git a/doc/examples/plot_segmentations.py b/doc/examples/plot_segmentations.py index 31dc38e7..418a0903 100644 --- a/doc/examples/plot_segmentations.py +++ b/doc/examples/plot_segmentations.py @@ -64,7 +64,7 @@ import numpy as np from skimage.data import lena from skimage.segmentation import felzenszwalb, slic, quickshift -from skimage.segmentation import highlight_boundaries +from skimage.segmentation import mark_boundaries from skimage.util import img_as_float img = img_as_float(lena()[::2, ::2]) @@ -80,11 +80,11 @@ fig, ax = plt.subplots(1, 3) fig.set_size_inches(8, 3, forward=True) plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05) -ax[0].imshow(highlight_boundaries(img, segments_fz)) +ax[0].imshow(mark_boundaries(img, segments_fz)) ax[0].set_title("Felzenszwalbs's method") -ax[1].imshow(highlight_boundaries(img, segments_slic)) +ax[1].imshow(mark_boundaries(img, segments_slic)) ax[1].set_title("SLIC") -ax[2].imshow(highlight_boundaries(img, segments_quick)) +ax[2].imshow(mark_boundaries(img, segments_quick)) ax[2].set_title("Quickshift") for a in ax: a.set_xticks(()) diff --git a/skimage/segmentation/__init__.py b/skimage/segmentation/__init__.py index ce0bfd84..7bd37026 100644 --- a/skimage/segmentation/__init__.py +++ b/skimage/segmentation/__init__.py @@ -2,6 +2,5 @@ from .random_walker_segmentation import random_walker from ._felzenszwalb import felzenszwalb from ._slic import slic from ._quickshift import quickshift -from .boundaries import (find_boundaries, visualize_boundaries, - highlight_boundaries) +from .boundaries import find_boundaries, visualize_boundaries, mark_boundaries from ._clear_border import clear_border diff --git a/skimage/segmentation/boundaries.py b/skimage/segmentation/boundaries.py index d30a8627..25656a9b 100644 --- a/skimage/segmentation/boundaries.py +++ b/skimage/segmentation/boundaries.py @@ -13,7 +13,7 @@ def find_boundaries(label_img): return boundaries -def highlight_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): +def mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): """Return image with boundaries between labeled regions highlighted. Parameters @@ -40,6 +40,6 @@ def highlight_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): return image -@deprecated('highlight_boundaries') +@deprecated('mark_boundaries') def visualize_boundaries(*args, **kwargs): - return highlight_boundaries(*args, **kwargs) + return mark_boundaries(*args, **kwargs) From 2c36d34f9a109378fca0535027d5768c91582087 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 8 Oct 2012 10:55:19 +0200 Subject: [PATCH 080/625] Change Exception type for wrong input dims --- skimage/measure/find_contours.py | 2 +- skimage/measure/tests/test_find_contours.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/measure/find_contours.py b/skimage/measure/find_contours.py index eafbd523..3a546ccf 100755 --- a/skimage/measure/find_contours.py +++ b/skimage/measure/find_contours.py @@ -98,7 +98,7 @@ def find_contours(array, level, """ array = np.asarray(array, dtype=np.double) if array.ndim != 2: - raise TypeError('Only 2D arrays are supported.') + raise ValueError('Only 2D arrays are supported.') level = float(level) if (fully_connected not in _param_options or positive_orientation not in _param_options): diff --git a/skimage/measure/tests/test_find_contours.py b/skimage/measure/tests/test_find_contours.py index cde60e4d..11b9b443 100644 --- a/skimage/measure/tests/test_find_contours.py +++ b/skimage/measure/tests/test_find_contours.py @@ -75,7 +75,7 @@ def test_memory_order(): def test_invalid_input(): assert_raises(ValueError, find_contours, r, 0.5, 'foo', 'bar') - assert_raises(TypeError, find_contours, r[..., None], 0.5) + assert_raises(ValueError, find_contours, r[..., None], 0.5) if __name__ == '__main__': From fd07bd65ba665378f4e17f8e68f10e3aa351fd20 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 8 Oct 2012 09:37:49 -0700 Subject: [PATCH 081/625] DOC: Rename skimage.org to scikit-image.org. --- doc/source/_static/docversions.js | 2 +- doc/source/gitwash/this_project.inc | 2 +- doc/source/overview.txt | 4 ++-- doc/source/random_gallery.py | 4 ++-- doc/source/themes/scikit-image/layout.html | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/source/_static/docversions.js b/doc/source/_static/docversions.js index ff910c3f..0b414325 100644 --- a/doc/source/_static/docversions.js +++ b/doc/source/_static/docversions.js @@ -12,6 +12,6 @@ function insert_version_links() { document.write(open_list); document.write('skimage VERSION
          • \n' .replace('VERSION', labels[i]) - .replace('URL', 'http://skimage.org/docs/' + labels[i])); + .replace('URL', 'http://scikit-image.org/docs/' + labels[i])); } } diff --git a/doc/source/gitwash/this_project.inc b/doc/source/gitwash/this_project.inc index 6010831f..349b92ba 100644 --- a/doc/source/gitwash/this_project.inc +++ b/doc/source/gitwash/this_project.inc @@ -1,5 +1,5 @@ .. scikit-image -.. _scikit-image: http://skimage.org +.. _scikit-image: http://scikit-image.org .. _`scikit-image github`: http://github.com/scikit-image/scikit-image .. _`scikit-image mailing list`: http://groups.google.com/group/scikit-image diff --git a/doc/source/overview.txt b/doc/source/overview.txt index 3acdeebf..5a4e6123 100644 --- a/doc/source/overview.txt +++ b/doc/source/overview.txt @@ -1,7 +1,7 @@ Image Processing SciKit ======================= -The `scikit-image `__ SciKit (toolkit for +The `scikit-image `__ SciKit (toolkit for `SciPy `__) extends ``scipy.ndimage`` to provide a versatile set of image processing routines. It is written in the `Python `_ language. @@ -12,7 +12,7 @@ mailing list (address provided below). Homepage -------- -http://skimage.org +http://scikit-image.org Source, bugs and patches ------------------------ diff --git a/doc/source/random_gallery.py b/doc/source/random_gallery.py index 3358b3f0..47ce89a2 100644 --- a/doc/source/random_gallery.py +++ b/doc/source/random_gallery.py @@ -32,8 +32,8 @@ gallery_div = '''\ examples = glob.glob(os.path.join(example_dir, 'plot_*.py')) images, links = [], [] -image_url = 'http://skimage.org/docs/dev/_images/%s.png' -link_url = 'http://skimage.org/docs/dev/auto_examples/%s.html' +image_url = 'http://scikit-image.org/docs/dev/_images/%s.png' +link_url = 'http://scikit-image.org/docs/dev/auto_examples/%s.html' for e in examples: e = os.path.basename(e) diff --git a/doc/source/themes/scikit-image/layout.html b/doc/source/themes/scikit-image/layout.html index a1755670..58424f2f 100644 --- a/doc/source/themes/scikit-image/layout.html +++ b/doc/source/themes/scikit-image/layout.html @@ -79,7 +79,7 @@ {%- block extrahead %}{% endblock %} - +
          From 4946222e3a52e7101f5988540f9575356840de21 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 8 Oct 2012 09:52:12 -0700 Subject: [PATCH 082/625] DOC: Remove confusing paragraph about dtype range. --- doc/source/user_guide/data_types.txt | 5 ----- 1 file changed, 5 deletions(-) diff --git a/doc/source/user_guide/data_types.txt b/doc/source/user_guide/data_types.txt index 9801c244..c3b87b2e 100644 --- a/doc/source/user_guide/data_types.txt +++ b/doc/source/user_guide/data_types.txt @@ -71,11 +71,6 @@ issued:: float64 to uint8 array([ 0, 128, 255], dtype=uint8) -Wherever possible, functions should try to handle input without explicit -conversion. For example, there is no need to force values to a specific type -for doing a convolution; a plotting function, on the other hand, needs to know -the range of the input. - Output types ============ From b4b090271d68b89d7881aeb7e63d84361ddbc607 Mon Sep 17 00:00:00 2001 From: Paul Ivanov Date: Mon, 8 Oct 2012 16:29:55 -0700 Subject: [PATCH 083/625] complete singularization of scikit-image I've updated references in the code and documentation to the new name. --- DEVELOPMENT.txt | 8 ++++---- LICENSE.txt | 2 +- README.rst | 4 ++-- RELEASE.txt | 2 +- TASKS.txt | 4 ++-- bento.info | 8 ++++---- doc/Makefile | 8 ++++---- doc/logo/Makefile | 4 ++-- doc/logo/{scikits_image_logo.py => scikit_image_logo.py} | 0 doc/logo/shrink_logo.py | 4 ++-- doc/make.bat | 4 ++-- doc/source/conf.py | 4 ++-- setup.py | 8 ++++---- skimage/__init__.py | 2 +- skimage/_shared/setup.py | 8 ++++---- skimage/draw/setup.py | 8 ++++---- skimage/feature/setup.py | 8 ++++---- skimage/filter/setup.py | 8 ++++---- skimage/graph/setup.py | 6 +++--- skimage/io/_plugins/skivi.py | 4 ++-- skimage/io/setup.py | 6 +++--- skimage/measure/setup.py | 6 +++--- skimage/morphology/setup.py | 6 +++--- skimage/segmentation/setup.py | 6 +++--- skimage/transform/setup.py | 8 ++++---- 25 files changed, 68 insertions(+), 68 deletions(-) rename doc/logo/{scikits_image_logo.py => scikit_image_logo.py} (100%) diff --git a/DEVELOPMENT.txt b/DEVELOPMENT.txt index 0dd0a102..509cf322 100644 --- a/DEVELOPMENT.txt +++ b/DEVELOPMENT.txt @@ -4,8 +4,8 @@ Development process :doc:`Read this overview ` of how to use Git with ``skimage``. Here's the long and short of it: - * Go to `https://github.com/scikits-image/scikits-image - `_ and follow the + * Go to `https://github.com/scikit-image/scikit-image + `_ and follow the instructions on making your own fork. * Create a new branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name @@ -42,7 +42,7 @@ Guidelines * Follow the `Python PEPs `_ where possible. * No major changes should be committed without review. Ask on the - `mailing list `_ if + `mailing list `_ if you get no response to your pull request. * Examples in the gallery should have a maximum figure width of 8 inches. @@ -99,4 +99,4 @@ detailing the test coverage:: Bugs ```` -Please `report bugs on Github `_. +Please `report bugs on Github `_. diff --git a/LICENSE.txt b/LICENSE.txt index b75d150d..6586c853 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,7 +1,7 @@ Unless otherwise specified by LICENSE.txt files in individual directories, all code is -Copyright (C) 2011, the scikits-image team +Copyright (C) 2011, the scikit-image team All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/README.rst b/README.rst index a419b83a..508db396 100644 --- a/README.rst +++ b/README.rst @@ -3,11 +3,11 @@ Image Processing SciKit Source ------ -https://github.com/scikits-image/scikits-image +https://github.com/scikit-image/scikit-image Mailing List ------------ -http://groups.google.com/group/scikits-image +http://groups.google.com/group/scikit-image Installation from source ------------------------ diff --git a/RELEASE.txt b/RELEASE.txt index 96cd2abc..ce05e24e 100644 --- a/RELEASE.txt +++ b/RELEASE.txt @@ -38,7 +38,7 @@ How to make a new release of ``skimage`` - In ``bento.info``, set to ``0.X.dev0``. - Update the web frontpage: - The webpage is kept in a separate repo: scikits-image-web + The webpage is kept in a separate repo: scikit-image-web - Sync your branch with the remote repo: ``git pull``. If you try to ``make gh-pages`` when your branch is out of sync, it diff --git a/TASKS.txt b/TASKS.txt index 9a846be9..a2fcffe2 100644 --- a/TASKS.txt +++ b/TASKS.txt @@ -16,13 +16,13 @@ How to contribute to ``skimage`` Developing Open Source is great fun! Join us on the `skimage mailing -list `_ and tell us which of the +list `_ and tell us which of the following challenges you'd like to solve. * Mentoring is available for those new to scientific programming in Python. * The technical detail of the `development process`_ is given below. * :doc:`How to use GitHub ` when developing skimage -* If you're looking something to implement, you can find a list of `requested features on github `__. In addition, you can browse the `open issues on github `__. +* If you're looking something to implement, you can find a list of `requested features on github `__. In addition, you can browse the `open issues on github `__. .. contents:: :local: diff --git a/bento.info b/bento.info index 3e630bc0..50a2743a 100644 --- a/bento.info +++ b/bento.info @@ -1,15 +1,15 @@ -Name: scikits-image +Name: scikit-image Version: 0.8.dev0 Summary: Image processing routines for SciPy -Url: http://scikits-image.org -DownloadUrl: http://github.com/scikits-image/scikits-image +Url: http://scikit-image.org +DownloadUrl: http://github.com/scikit-image/scikit-image Description: Image Processing SciKit Image processing algorithms for SciPy, including IO, morphology, filtering, warping, color manipulation, object detection, etc. Please refer to the online documentation at - http://scikits-image.org/ + http://scikit-image.org/ Maintainer: Stefan van der Walt MaintainerEmail: stefan@sun.ac.za License: Modified BSD diff --git a/doc/Makefile b/doc/Makefile index ea6e26e3..c21945dd 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -77,17 +77,17 @@ qthelp: @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in build/qthelp, like this:" - @echo "# qcollectiongenerator build/qthelp/scikitsimage.qhcp" + @echo "# qcollectiongenerator build/qthelp/scikitimage.qhcp" @echo "To view the help file:" - @echo "# assistant -collectionFile build/qthelp/scikitsimage.qhc" + @echo "# assistant -collectionFile build/qthelp/scikitimage.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(DEST)/devhelp @echo @echo "Build finished." @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/scikitsimage" - @echo "# ln -s build/devhelp $$HOME/.local/share/devhelp/scikitsimage" + @echo "# mkdir -p $$HOME/.local/share/devhelp/scikitimage" + @echo "# ln -s build/devhelp $$HOME/.local/share/devhelp/scikitimage" @echo "# devhelp" latex: diff --git a/doc/logo/Makefile b/doc/logo/Makefile index 46c36fda..1cf861bd 100644 --- a/doc/logo/Makefile +++ b/doc/logo/Makefile @@ -1,11 +1,11 @@ .PHONY: logo logo: green_orange_snake.png snake_logo.svg - inkscape --export-png=scikits_image_logo.png --export-dpi=100 \ + inkscape --export-png=scikit_image_logo.png --export-dpi=100 \ --export-area-drawing --export-background-opacity=1 \ snake_logo.svg python shrink_logo.py green_orange_snake.png: - python scikits_image_logo.py --no-plot + python scikit_image_logo.py --no-plot diff --git a/doc/logo/scikits_image_logo.py b/doc/logo/scikit_image_logo.py similarity index 100% rename from doc/logo/scikits_image_logo.py rename to doc/logo/scikit_image_logo.py diff --git a/doc/logo/shrink_logo.py b/doc/logo/shrink_logo.py index 3b7d91ba..205e1bf4 100644 --- a/doc/logo/shrink_logo.py +++ b/doc/logo/shrink_logo.py @@ -2,7 +2,7 @@ from skimage import io, transform s = 0.7 -img = io.imread('scikits_image_logo.png') +img = io.imread('scikit_image_logo.png') h, w, c = img.shape print "\nScaling down logo by %.1fx..." % s @@ -13,4 +13,4 @@ img = transform.homography(img, [[s, 0, 0], output_shape=(int(h*s), int(w*s), 4), order=3) -io.imsave('scikits_image_logo_small.png', img) +io.imsave('scikit_image_logo_small.png', img) diff --git a/doc/make.bat b/doc/make.bat index 9d4d3185..e16c8d40 100644 --- a/doc/make.bat +++ b/doc/make.bat @@ -74,9 +74,9 @@ if "%1" == "qthelp" ( echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in build/qthelp, like this: - echo.^> qcollectiongenerator build\qthelp\scikitsimage.qhcp + echo.^> qcollectiongenerator build\qthelp\scikitimage.qhcp echo.To view the help file: - echo.^> assistant -collectionFile build\qthelp\scikitsimage.ghc + echo.^> assistant -collectionFile build\qthelp\scikitimage.ghc goto end ) diff --git a/doc/source/conf.py b/doc/source/conf.py index 71033adc..5071fab1 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -176,7 +176,7 @@ html_sidebars = { #html_file_suffix = '' # Output file base name for HTML help builder. -htmlhelp_basename = 'scikitsimagedoc' +htmlhelp_basename = 'scikitimagedoc' # -- Options for LaTeX output -------------------------------------------------- @@ -190,7 +190,7 @@ htmlhelp_basename = 'scikitsimagedoc' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ - ('contents', 'scikitsimage.tex', u'The Image Scikit Documentation', + ('contents', 'scikitimage.tex', u'The Image Scikit Documentation', u'SciPy Developers', 'manual'), ] diff --git a/setup.py b/setup.py index 9823352c..390f007c 100644 --- a/setup.py +++ b/setup.py @@ -6,17 +6,17 @@ Image processing algorithms for SciPy, including IO, morphology, filtering, warping, color manipulation, object detection, etc. Please refer to the online documentation at -http://scikits-image.org/ +http://scikit-image.org/ """ -DISTNAME = 'scikits-image' +DISTNAME = 'scikit-image' DESCRIPTION = 'Image processing routines for SciPy' LONG_DESCRIPTION = descr MAINTAINER = 'Stefan van der Walt' MAINTAINER_EMAIL = 'stefan@sun.ac.za' -URL = 'http://scikits-image.org' +URL = 'http://scikit-image.org' LICENSE = 'Modified BSD' -DOWNLOAD_URL = 'http://github.com/scikits-image/scikits-image' +DOWNLOAD_URL = 'http://github.com/scikit-image/scikit-image' VERSION = '0.8dev' PYTHON_VERSION = (2, 5) DEPENDENCIES = { diff --git a/skimage/__init__.py b/skimage/__init__.py index cf62ee87..1ac121b3 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -1,6 +1,6 @@ """Image Processing SciKit (Toolbox for SciPy) -``scikits-image`` (a.k.a. ``skimage``) is a collection of algorithms for image +``scikit-image`` (a.k.a. ``skimage``) is a collection of algorithms for image processing and computer vision. The main package of ``skimage`` only provides a few utilities for converting diff --git a/skimage/_shared/setup.py b/skimage/_shared/setup.py index 81113656..765c30de 100644 --- a/skimage/_shared/setup.py +++ b/skimage/_shared/setup.py @@ -28,11 +28,11 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='Scikits-image Developers', - author='Scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + author='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Transforms', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) diff --git a/skimage/draw/setup.py b/skimage/draw/setup.py index 59067f9a..5b8e237d 100644 --- a/skimage/draw/setup.py +++ b/skimage/draw/setup.py @@ -21,11 +21,11 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image developers', - author='scikits-image developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image developers', + author='scikit-image developers', + maintainer_email='scikit-image@googlegroups.com', description='Drawing', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) diff --git a/skimage/feature/setup.py b/skimage/feature/setup.py index 9c9074be..6f820163 100644 --- a/skimage/feature/setup.py +++ b/skimage/feature/setup.py @@ -24,11 +24,11 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - author='scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + author='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Features', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 03ea7def..3e573aec 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -21,11 +21,11 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - author='scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + author='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Filters', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) diff --git a/skimage/graph/setup.py b/skimage/graph/setup.py index 2c432011..463d2739 100644 --- a/skimage/graph/setup.py +++ b/skimage/graph/setup.py @@ -29,10 +29,10 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Graph-based Image-processing Algorithms', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) diff --git a/skimage/io/_plugins/skivi.py b/skimage/io/_plugins/skivi.py index fb652638..fcfe8bc6 100644 --- a/skimage/io/_plugins/skivi.py +++ b/skimage/io/_plugins/skivi.py @@ -3,9 +3,9 @@ Skivi is written/maintained/developed by: S. Chris Colbert - sccolbert@gmail.com -Skivi is free software and is part of the scikits-image project. +Skivi is free software and is part of the scikit-image project. -Skivi is governed by the licenses of the scikits-image project. +Skivi is governed by the licenses of the scikit-image project. Please report any bugs to the author. diff --git a/skimage/io/setup.py b/skimage/io/setup.py index 55526461..4e3c09c3 100644 --- a/skimage/io/setup.py +++ b/skimage/io/setup.py @@ -31,10 +31,10 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Image I/O Routines', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) diff --git a/skimage/measure/setup.py b/skimage/measure/setup.py index 7e4ecf0f..21d9964e 100644 --- a/skimage/measure/setup.py +++ b/skimage/measure/setup.py @@ -24,10 +24,10 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Graph-based Image-processing Algorithms', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) diff --git a/skimage/morphology/setup.py b/skimage/morphology/setup.py index 02c39ae7..b8564ce4 100644 --- a/skimage/morphology/setup.py +++ b/skimage/morphology/setup.py @@ -39,11 +39,11 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', + setup(maintainer='scikit-image Developers', author='Damian Eads', - maintainer_email='scikits-image@googlegroups.com', + maintainer_email='scikit-image@googlegroups.com', description='Morphology Wrapper', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) diff --git a/skimage/segmentation/setup.py b/skimage/segmentation/setup.py index b9e19078..ec4ac4ff 100644 --- a/skimage/segmentation/setup.py +++ b/skimage/segmentation/setup.py @@ -25,10 +25,10 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Segmentation Algorithms', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) diff --git a/skimage/transform/setup.py b/skimage/transform/setup.py index 0e415bad..b0093d87 100644 --- a/skimage/transform/setup.py +++ b/skimage/transform/setup.py @@ -26,11 +26,11 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup - setup(maintainer='Scikits-image Developers', - author='Scikits-image Developers', - maintainer_email='scikits-image@googlegroups.com', + setup(maintainer='scikit-image Developers', + author='scikit-image Developers', + maintainer_email='scikit-image@googlegroups.com', description='Transforms', - url='https://github.com/scikits-image/scikits-image', + url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) From d79d1ff7e3ec6c11db058531d54c1e3ed9bd88c7 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 8 Oct 2012 16:48:15 -0700 Subject: [PATCH 084/625] DOC: Clean up one last skimage -> scikit-image. --- doc/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/Makefile b/doc/Makefile index c21945dd..7552be6b 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -124,7 +124,7 @@ gh-pages: gitwash: python tools/gitwash/gitwash_dumper.py source scikit-image \ - --project-url=http://skimage.org \ + --project-url=http://scikit-image.org \ --project-ml-url=http://groups.google.com/group/scikit-image \ --repo-name=scikit-image \ --github-user=scikit-image \ From 1ef13c124ec5b7bba59193c6ed11d763fe5dd80a Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 8 Oct 2012 16:57:01 -0700 Subject: [PATCH 085/625] PKG: Print something when bento check passes. --- check_bento_build.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/check_bento_build.py b/check_bento_build.py index d085b3ba..108b0aad 100644 --- a/check_bento_build.py +++ b/check_bento_build.py @@ -69,7 +69,8 @@ def print_results(cy_bento, cy_setup): print(text) print('-' * len(text)) - print # blank line; just for aesthetics + print "Bento errors:" + print "-------------" if cy_bento: info("The following extensions in 'bento.info' were not found:") From ab2411cd1913cdacf2eca9603692a1571ce7d3df Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Mon, 8 Oct 2012 20:30:33 -0400 Subject: [PATCH 086/625] DOC: Add imports to doctests to fix errors --- skimage/morphology/grey.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/morphology/grey.py b/skimage/morphology/grey.py index e7e52de4..45d11729 100644 --- a/skimage/morphology/grey.py +++ b/skimage/morphology/grey.py @@ -45,6 +45,7 @@ def erosion(image, selem, out=None, shift_x=False, shift_y=False): Examples -------- >>> # Erosion shrinks bright regions + >>> import numpy as np >>> from skimage.morphology import square >>> bright_square = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], @@ -97,6 +98,7 @@ def dilation(image, selem, out=None, shift_x=False, shift_y=False): Examples -------- >>> # Dilation enlarges bright regions + >>> import numpy as np >>> from skimage.morphology import square >>> bright_pixel = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], @@ -146,6 +148,7 @@ def opening(image, selem, out=None): Examples -------- >>> # Open up gap between two bright regions (but also shrink regions) + >>> import numpy as np >>> from skimage.morphology import square >>> bad_connection = np.array([[1, 0, 0, 0, 1], ... [1, 1, 0, 1, 1], @@ -196,6 +199,7 @@ def closing(image, selem, out=None): Examples -------- >>> # Close a gap between two bright lines + >>> import numpy as np >>> from skimage.morphology import square >>> broken_line = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], @@ -245,6 +249,7 @@ def white_tophat(image, selem, out=None): Examples -------- >>> # Subtract grey background from bright peak + >>> import numpy as np >>> from skimage.morphology import square >>> bright_on_grey = np.array([[2, 3, 3, 3, 2], ... [3, 4, 5, 4, 3], @@ -294,6 +299,7 @@ def black_tophat(image, selem, out=None): Examples -------- >>> # Change dark peak to bright peak and subtract background + >>> import numpy as np >>> from skimage.morphology import square >>> dark_on_grey = np.array([[7, 6, 6, 6, 7], ... [6, 5, 4, 5, 6], From 48210ecb389e6a6b94b58700863298b9f5e7a719 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 07:53:54 +0200 Subject: [PATCH 087/625] Add note about random test failure for some systems --- skimage/measure/tests/test_structural_similarity.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skimage/measure/tests/test_structural_similarity.py b/skimage/measure/tests/test_structural_similarity.py index d2ddc774..ac45072a 100644 --- a/skimage/measure/tests/test_structural_similarity.py +++ b/skimage/measure/tests/test_structural_similarity.py @@ -25,6 +25,7 @@ def test_ssim_image(): assert(S1 < 0.3) +# NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6) def test_ssim_grad(): N = 30 X = np.random.random((N, N)) * 255 From e47411ed4c13379fd648eff0c459c79c57790f5e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 08:02:43 +0200 Subject: [PATCH 088/625] Split tests for different channel numbers into separate functions --- skimage/transform/tests/test_pyramids.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/transform/tests/test_pyramids.py b/skimage/transform/tests/test_pyramids.py index dd976a7b..6d0609e0 100644 --- a/skimage/transform/tests/test_pyramids.py +++ b/skimage/transform/tests/test_pyramids.py @@ -7,39 +7,39 @@ image = data.lena() image_gray = image[..., 0] -def test_pyramid_reduce(): - # RGB image +def test_pyramid_reduce_rgb(): rows, cols, dim = image.shape out = pyramids.pyramid_reduce(image, downscale=2) assert_array_equal(out.shape, (rows / 2, cols / 2, dim)) - # grayscale image + +def test_pyramid_reduce_gray(): rows, cols = image_gray.shape out = pyramids.pyramid_reduce(image_gray, downscale=2) assert_array_equal(out.shape, (rows / 2, cols / 2)) -def test_pyramid_expand(): - # RGB image +def test_pyramid_expand_rgb(): rows, cols, dim = image.shape out = pyramids.pyramid_expand(image, upscale=2) assert_array_equal(out.shape, (rows * 2, cols * 2, dim)) - # grayscale image + +def test_pyramid_expand_gray(): rows, cols = image_gray.shape out = pyramids.pyramid_expand(image_gray, upscale=2) assert_array_equal(out.shape, (rows * 2, cols * 2)) -def test_build_gaussian_pyramid(): - # RGB image +def test_build_gaussian_pyramid_rgb(): rows, cols, dim = image.shape pyramid = pyramids.pyramid_gaussian(image, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim) assert_array_equal(out.shape, layer_shape) - # grayscale image + +def test_build_gaussian_pyramid_gray(): rows, cols = image_gray.shape pyramid = pyramids.pyramid_gaussian(image_gray, downscale=2) for layer, out in enumerate(pyramid): @@ -47,15 +47,15 @@ def test_build_gaussian_pyramid(): assert_array_equal(out.shape, layer_shape) -def test_build_laplacian_pyramid(): - # RGB image +def test_build_laplacian_pyramid_rgb(): rows, cols, dim = image.shape pyramid = pyramids.pyramid_laplacian(image, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim) assert_array_equal(out.shape, layer_shape) - # grayscale image + +def test_build_laplacian_pyramid_gray(): rows, cols = image_gray.shape pyramid = pyramids.pyramid_laplacian(image_gray, downscale=2) for layer, out in enumerate(pyramid): From dc0d4eff74a019bd777e44892659c7b7da3fe56d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 08:04:25 +0200 Subject: [PATCH 089/625] Add missing empty lines between test functions --- skimage/measure/tests/test_regionprops.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/skimage/measure/tests/test_regionprops.py b/skimage/measure/tests/test_regionprops.py index 524f55ee..6c7f5d46 100644 --- a/skimage/measure/tests/test_regionprops.py +++ b/skimage/measure/tests/test_regionprops.py @@ -46,6 +46,7 @@ def test_bbox(): bbox = regionprops(SAMPLE_mod, ['BoundingBox'])[0]['BoundingBox'] assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]-1)) + def test_central_moments(): mu = regionprops(SAMPLE, ['CentralMoments'])[0]['CentralMoments'] #: determined with OpenCV @@ -58,6 +59,7 @@ def test_central_moments(): assert_almost_equal(mu[2,1], 2000.296296296291) assert_almost_equal(mu[3,0], -760.0246913580195) + def test_centroid(): centroid = regionprops(SAMPLE, ['Centroid'])[0]['Centroid'] # determined with MATLAB @@ -208,6 +210,7 @@ def test_moments(): assert_almost_equal(m[2,1], 43882.0) assert_almost_equal(m[3,0], 95588.0) + def test_normalized_moments(): nu = regionprops(SAMPLE, ['NormalizedMoments'])[0]['NormalizedMoments'] #: determined with OpenCV @@ -218,6 +221,7 @@ def test_normalized_moments(): assert_almost_equal(nu[2,1], 0.045473992910668816) assert_almost_equal(nu[3,0], -0.017278118992041805) + def test_orientation(): orientation = regionprops(SAMPLE, ['Orientation'])[0]['Orientation'] # determined with MATLAB @@ -239,6 +243,7 @@ def test_orientation(): )[0]['Orientation'] assert_almost_equal(orientation_diag, -math.pi / 4) + def test_perimeter(): per = regionprops(SAMPLE, ['Perimeter'])[0]['Perimeter'] assert_almost_equal(per, 59.2132034355964) @@ -246,11 +251,13 @@ def test_perimeter(): per = perimeter(SAMPLE, neighbourhood=8) assert_almost_equal(per, 43.1213203436) + def test_solidity(): solidity = regionprops(SAMPLE, ['Solidity'])[0]['Solidity'] # determined with MATLAB assert_almost_equal(solidity, 0.580645161290323) + def test_weighted_central_moments(): wmu = regionprops(SAMPLE, ['WeightedCentralMoments'], INTENSITY_SAMPLE )[0]['WeightedCentralMoments'] @@ -304,6 +311,7 @@ def test_weighted_moments(): ) assert_array_almost_equal(wm, ref) + def test_weighted_normalized_moments(): wnu = regionprops(SAMPLE, ['WeightedNormalizedMoments'], INTENSITY_SAMPLE )[0]['WeightedNormalizedMoments'] @@ -315,6 +323,7 @@ def test_weighted_normalized_moments(): ) assert_array_almost_equal(wnu, ref) + if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() From be9dcdf195da34ffae5f8068e537ad3b3490e362 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 8 Oct 2012 23:57:44 -0700 Subject: [PATCH 090/625] DOC: Fix pip command. --- doc/source/install.txt | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/doc/source/install.txt b/doc/source/install.txt index 81652ab1..7c886549 100644 --- a/doc/source/install.txt +++ b/doc/source/install.txt @@ -1,8 +1,6 @@ Pre-built installation ---------------------- -.. !! Also update scikit-image-web !! - `Windows binaries `__ are kindly provided by Christoph Gohlke. @@ -23,7 +21,7 @@ or :: - pip -U scikit-image + pip install -U scikit-image Installation from source ------------------------ From b6b2eb708c87c3a961cb78c6c24e18c700c1fa22 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Mon, 8 Oct 2012 20:30:33 -0400 Subject: [PATCH 091/625] DOC: Add imports to doctests to fix errors --- skimage/morphology/grey.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/morphology/grey.py b/skimage/morphology/grey.py index e7e52de4..45d11729 100644 --- a/skimage/morphology/grey.py +++ b/skimage/morphology/grey.py @@ -45,6 +45,7 @@ def erosion(image, selem, out=None, shift_x=False, shift_y=False): Examples -------- >>> # Erosion shrinks bright regions + >>> import numpy as np >>> from skimage.morphology import square >>> bright_square = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], @@ -97,6 +98,7 @@ def dilation(image, selem, out=None, shift_x=False, shift_y=False): Examples -------- >>> # Dilation enlarges bright regions + >>> import numpy as np >>> from skimage.morphology import square >>> bright_pixel = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], @@ -146,6 +148,7 @@ def opening(image, selem, out=None): Examples -------- >>> # Open up gap between two bright regions (but also shrink regions) + >>> import numpy as np >>> from skimage.morphology import square >>> bad_connection = np.array([[1, 0, 0, 0, 1], ... [1, 1, 0, 1, 1], @@ -196,6 +199,7 @@ def closing(image, selem, out=None): Examples -------- >>> # Close a gap between two bright lines + >>> import numpy as np >>> from skimage.morphology import square >>> broken_line = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], @@ -245,6 +249,7 @@ def white_tophat(image, selem, out=None): Examples -------- >>> # Subtract grey background from bright peak + >>> import numpy as np >>> from skimage.morphology import square >>> bright_on_grey = np.array([[2, 3, 3, 3, 2], ... [3, 4, 5, 4, 3], @@ -294,6 +299,7 @@ def black_tophat(image, selem, out=None): Examples -------- >>> # Change dark peak to bright peak and subtract background + >>> import numpy as np >>> from skimage.morphology import square >>> dark_on_grey = np.array([[7, 6, 6, 6, 7], ... [6, 5, 4, 5, 6], From 3b67c8204255757391b3a4f76e4cc778a6ad5072 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 2 Sep 2012 22:54:24 +0200 Subject: [PATCH 092/625] Add support for 3-dimensional image resizing --- skimage/transform/_warps.py | 49 ++++++++++++++++++++------- skimage/transform/tests/test_warps.py | 34 ++++++++++++++++++- 2 files changed, 70 insertions(+), 13 deletions(-) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index 71bc41b8..bde13f3d 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -1,4 +1,5 @@ import numpy as np +from scipy import ndimage from ._geometric import (warp, SimilarityTransform, AffineTransform, ProjectiveTransform) @@ -11,7 +12,10 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): image : ndarray Input image. output_shape : tuple or ndarray - Size of the generated output image `(rows, cols)`. + Size of the generated output image `(rows, cols[, dim])`. If `dim` is + not provided, the number of channels are preserved. In case the number + of input channels does not equal the number of output channels a + 3-dimensional interpolation is applied. Returns ------- @@ -32,24 +36,45 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): """ - rows, cols = output_shape + rows, cols = output_shape[0], output_shape[1] orig_rows, orig_cols = image.shape[0], image.shape[1] row_scale = float(orig_rows) / rows col_scale = float(orig_cols) / cols - # 3 control points necessary to estimate exact AffineTransform - src_corners = np.array([[1, 1], [1, rows], [cols, rows]]) - 1 - dst_corners = np.zeros(src_corners.shape, dtype=np.double) - # take into account that 0th pixel is at position (0.5, 0.5) - dst_corners[:, 0] = col_scale * (src_corners[:, 0] + 0.5) - 0.5 - dst_corners[:, 1] = row_scale * (src_corners[:, 1] + 0.5) - 0.5 + # 3-dimensional interpolation + if len(output_shape) == 3 and (image.ndim == 2 + or output_shape[2] != image.shape[2]): + dim = output_shape[2] + orig_dim = 1 if image.ndim == 2 else image.shape[2] + dim_scale = float(orig_dim) / dim - tform = AffineTransform() - tform.estimate(src_corners, dst_corners) + map_rows, map_cols, map_dims = np.mgrid[:rows, :cols, :dim] + map_rows = row_scale * (map_rows + 0.5) - 0.5 + map_cols = col_scale * (map_cols + 0.5) - 0.5 + map_dims = dim_scale * (map_dims + 0.5) - 0.5 - return warp(image, tform, output_shape=output_shape, order=order, - mode=mode, cval=cval) + coord_map = np.array([map_rows, map_cols, map_dims]) + + out = ndimage.map_coordinates(image, coord_map, order=order, mode=mode, + cval=cval) + + else: # 2-dimensional interpolation + + # 3 control points necessary to estimate exact AffineTransform + src_corners = np.array([[1, 1], [1, rows], [cols, rows]]) - 1 + dst_corners = np.zeros(src_corners.shape, dtype=np.double) + # take into account that 0th pixel is at position (0.5, 0.5) + dst_corners[:, 0] = col_scale * (src_corners[:, 0] + 0.5) - 0.5 + dst_corners[:, 1] = row_scale * (src_corners[:, 1] + 0.5) - 0.5 + + tform = AffineTransform() + tform.estimate(src_corners, dst_corners) + + out = warp(image, tform, output_shape=output_shape, order=order, + mode=mode, cval=cval) + + return out def rescale(image, scale, order=1, mode='constant', cval=0.): diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index 69980b25..e40d2bf0 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -85,7 +85,7 @@ def test_rotate(): assert_array_almost_equal(x90, np.rot90(x)) -def test_resize(): +def test_resize2d(): x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 resized = resize(x, (10, 10), order=0) @@ -112,6 +112,38 @@ def test_scale(): assert_array_almost_equal(scaled, ref) +def test_resize3d(): + # keep 3rd dimension + x = np.zeros((5, 5, 3), dtype=np.double) + x[1, 1, :] = 1 + resized = resize(x, (10, 10), order=0) + ref = np.zeros((10, 10, 3)) + ref[2:4, 2:4, :] = 1 + assert_array_almost_equal(resized, ref) + resized = resize(x, (10, 10, 3), order=0) + assert_array_almost_equal(resized, ref) + + # resize 3rd dimension + x = np.zeros((5, 5, 3), dtype=np.double) + x[1, 1, :] = 1 + resized = resize(x, (10, 10, 1), order=0) + ref = np.zeros((10, 10, 1)) + ref[2:4, 2:4] = 1 + assert_array_almost_equal(resized, ref) + + # bilinear 3rd dimension + x = np.zeros((5, 5, 2), dtype=np.double) + x[1, 1, 0] = 0 + x[1, 1, 1] = 1 + resized = resize(x, (10, 10, 1), order=1) + ref = np.zeros((10, 10, 1)) + ref[1:5, 1:5, :] = 0.03125 + ref[1:5, 2:4, :] = 0.09375 + ref[2:4, 1:5, :] = 0.09375 + ref[2:4, 2:4, :] = 0.28125 + assert_array_almost_equal(resized, ref) + + def test_swirl(): image = img_as_float(data.checkerboard()) From 0856dad9889d76768e8b2d452af88db9259cc5d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 11:19:27 +0200 Subject: [PATCH 093/625] Split 3d resize test function --- skimage/transform/_warps.py | 3 +++ skimage/transform/tests/test_warps.py | 26 +++++++++++++++----------- 2 files changed, 18 insertions(+), 11 deletions(-) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index bde13f3d..6689430b 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -348,3 +348,6 @@ def homography(image, H, output_shape=None, order=1, tform = ProjectiveTransform(H) return warp(image, inverse_map=tform.inverse, output_shape=output_shape, order=order, mode=mode, cval=cval) + + return warp(image, inverse_map=tform.inverse, output_shape=output_shape, + order=order, mode=mode, cval=cval) diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index e40d2bf0..1993f932 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -85,16 +85,7 @@ def test_rotate(): assert_array_almost_equal(x90, np.rot90(x)) -def test_resize2d(): - x = np.zeros((5, 5), dtype=np.double) - x[1, 1] = 1 - resized = resize(x, (10, 10), order=0) - ref = np.zeros((10, 10)) - ref[2:4, 2:4] = 1 - assert_array_almost_equal(resized, ref) - - -def test_scale(): +def test_rescale(): # same scale factor x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 @@ -112,7 +103,16 @@ def test_scale(): assert_array_almost_equal(scaled, ref) -def test_resize3d(): +def test_resize2d(): + x = np.zeros((5, 5), dtype=np.double) + x[1, 1] = 1 + resized = resize(x, (10, 10), order=0) + ref = np.zeros((10, 10)) + ref[2:4, 2:4] = 1 + assert_array_almost_equal(resized, ref) + + +def test_resize3d_keep(): # keep 3rd dimension x = np.zeros((5, 5, 3), dtype=np.double) x[1, 1, :] = 1 @@ -123,6 +123,8 @@ def test_resize3d(): resized = resize(x, (10, 10, 3), order=0) assert_array_almost_equal(resized, ref) + +def test_resize3d_resize(): # resize 3rd dimension x = np.zeros((5, 5, 3), dtype=np.double) x[1, 1, :] = 1 @@ -131,6 +133,8 @@ def test_resize3d(): ref[2:4, 2:4] = 1 assert_array_almost_equal(resized, ref) + +def test_resize3d_bilinear(): # bilinear 3rd dimension x = np.zeros((5, 5, 2), dtype=np.double) x[1, 1, 0] = 0 From 7741d906ed1bb5f827f1166e63993ed379c00ea9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 10 Oct 2012 09:09:52 +0200 Subject: [PATCH 094/625] Fix failing perimeter test caused by bug in scipy --- skimage/measure/tests/test_regionprops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/measure/tests/test_regionprops.py b/skimage/measure/tests/test_regionprops.py index 6c7f5d46..bd23d8b3 100644 --- a/skimage/measure/tests/test_regionprops.py +++ b/skimage/measure/tests/test_regionprops.py @@ -248,7 +248,7 @@ def test_perimeter(): per = regionprops(SAMPLE, ['Perimeter'])[0]['Perimeter'] assert_almost_equal(per, 59.2132034355964) - per = perimeter(SAMPLE, neighbourhood=8) + per = perimeter(SAMPLE.astype('double'), neighbourhood=8) assert_almost_equal(per, 43.1213203436) From 0b531c8060673e55d0526ee9dd5f3d27b76c93b3 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:13:58 +0200 Subject: [PATCH 095/625] add ref --- skimage/rank/rank.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 51430236..946f5a49 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1,4 +1,10 @@ -""" +"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc + +The local histogram is computed using a sliding window similar to the method described in + +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", +IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + :author: Olivier Debeir, 2012 :license: modified BSD """ From 203cdbd21f68ca0f624ad8b5fb0556b58f96c2ae Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:29:28 +0200 Subject: [PATCH 096/625] add comment to bilateral denoising example --- doc/examples/plot_lena_bilateral_denoise.py | 23 ++++++++------------- doc/examples/plot_local_threshold.py | 10 ++++++--- skimage/rank/rank.py | 2 +- 3 files changed, 17 insertions(+), 18 deletions(-) diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index fbee7d67..9fd20285 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -1,26 +1,22 @@ """ ==================================================== -Denoising the picture of Lena using total variation +Denoising the picture of Lena using bilateral filter ==================================================== In this example, we denoise a noisy version of the picture of Lena -using the total variation denoising filter. The result of this filter -is an image that has a minimal total variation norm, while being as -close to the initial image as possible. The total variation is the L1 -norm of the gradient of the image, and minimizing the total variation -typically produces "posterized" images with flat domains separated by -sharp edges. - -It is possible to change the degree of posterization by controlling -the tradeoff between denoising and faithfulness to the original image. +using an approximation of a bilateral filter. +The pixels used to compute a local mean respect these conditions: +- be close to the central pixel, i.e. belong to the given structuring element. +- have a similar gray level, similarity is fixed by an interval [-s0,+s1] centered on the central pixel gray level. +The filter used is an approximation of a classical bilateral filter in the sens that kernel are usually gaussian +both in spatial and spectral dimensions. """ import numpy as np import matplotlib.pyplot as plt from skimage import data, color, img_as_ubyte -from skimage.filter import tv_denoise from skimage.rank import bilateral_mean from skimage.morphology import disk @@ -44,12 +40,11 @@ plt.axis('off') plt.title('bilateral denoising', fontsize=20) selem = disk(30) -bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30) +bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) plt.subplot(133) plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('(more) bilateral denoising', fontsize=20) -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, - right=1) +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,right=1) plt.show() diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py index 3b9a42fb..01077571 100644 --- a/doc/examples/plot_local_threshold.py +++ b/doc/examples/plot_local_threshold.py @@ -24,7 +24,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.filter import threshold_otsu, threshold_adaptive -from skimage.rank import threshold +from skimage.rank import threshold,morph_contr_enh from skimage.morphology import disk @@ -38,9 +38,10 @@ binary_adaptive = threshold_adaptive(image, block_size, offset=10) selem = disk(10) loc_thresh = threshold(image,selem=selem) +loc_morph_contr_enh = morph_contr_enh(image,selem=selem) -fig, axes = plt.subplots(nrows=4, figsize=(7, 8)) -ax0, ax1, ax2, ax3 = axes +fig, axes = plt.subplots(nrows=5, figsize=(7, 8)) +ax0, ax1, ax2, ax3, ax4 = axes plt.gray() ax0.imshow(image) @@ -55,6 +56,9 @@ ax2.set_title('Adaptive thresholding') ax3.imshow(loc_thresh) ax3.set_title('Local thresholding') +ax4.imshow(loc_morph_contr_enh) +ax4.set_title('Local morphological contrast enhancement') + for ax in axes: ax.axis('off') diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 946f5a49..42e5c23e 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1,6 +1,6 @@ """rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc -The local histogram is computed using a sliding window similar to the method described in +The local histogram is computed using a sliding window similar to the method described in Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. From 36c12c5cccad24ce7492bed508c0525764e8dba4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:37:22 +0200 Subject: [PATCH 097/625] compare local and global equalise in example --- doc/examples/plot_local_equalize.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index e79ddbed..840f2a1f 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -5,13 +5,14 @@ Local Histogram Equalization This examples enhances an image with low contrast, using a method called *local histogram equalization*, which "spreads out the most frequent intensity -values" in an image . The equalized image has a roughly linear cumulative -distribution function for each pixel neigborhood. +values" in an image . The equalized image [1]_ has a roughly linear cumulative +distribution function for each pixel neighborhood. The local version [2]_ of the histogram +equalization emphasized every local graylevel variations. to be adjusted... .. [1] http://en.wikipedia.org/wiki/Histogram_equalization -.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm +.. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization """ @@ -58,7 +59,7 @@ img = data.moon() # Contrast stretching p2 = np.percentile(img, 2) p98 = np.percentile(img, 98) -img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) +img_rescale = exposure.equalize(img) # Equalization selem = disk(30) @@ -73,10 +74,10 @@ ax_img.set_title('Low contrast image') ax_hist.set_ylabel('Number of pixels') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) -ax_img.set_title('Contrast stretching') +ax_img.set_title('Global equalise') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) -ax_img.set_title('Local Histogram equalization') +ax_img.set_title('Local equalize') ax_cdf.set_ylabel('Fraction of total intensity') From c18f07ede1f00fdc6b945e593ae8c874b27073e9 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:41:18 +0200 Subject: [PATCH 098/625] rename egalise to equalize --- doc/examples/plot_local_equalize.py | 4 ++-- skimage/rank/_crank8.pyx | 6 +++--- skimage/rank/rank.py | 20 ++++++++++---------- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index 840f2a1f..ec28067d 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -19,7 +19,7 @@ to be adjusted... from skimage import data from skimage.util.dtype import dtype_range from skimage import exposure -from skimage.rank import egalise +from skimage import rank from skimage.morphology import disk @@ -63,7 +63,7 @@ img_rescale = exposure.equalize(img) # Equalization selem = disk(30) -img_eq = egalise(img,selem=selem) +img_eq = rank.equalize(img,selem=selem) # Display results diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index cb74021e..12e0577e 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -49,7 +49,7 @@ cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): return (g-i) -cdef inline np.uint8_t kernel_egalise(int* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_equalize(int* histo, float pop, np.uint8_t g): cdef int i cdef float sum = 0. @@ -210,14 +210,14 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, """ return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) -def egalise(np.ndarray[np.uint8_t, ndim=2] image, +def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y) def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 42e5c23e..2a7caab9 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -18,7 +18,7 @@ import numpy as np from generic import find_bitdepth import _crank16,_crank8 -__all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean' +__all__ = ['autolevel','bottomhat','equalize','gradient','maximum','mean' ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -162,10 +162,10 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): else: raise TypeError("only uint8 and uint16 image supported!") -def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local egalise of an image. +def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local equalize of an image. - egalise is computed on the given structuring element. + equalize is computed on the given structuring element. Parameters ---------- @@ -187,8 +187,8 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local egalise : uint8 array or uint16 array depending on input image - The result of the local egalise. + local equalize : uint8 array or uint16 array depending on input image + The result of the local equalize. Examples -------- @@ -200,7 +200,7 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> egalise(ima8, square(3)) + >>> equalize(ima8, square(3)) array([[191, 170, 127, 170, 191], [170, 255, 255, 255, 170], [127, 255, 255, 255, 127], @@ -212,7 +212,7 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> egalise(ima16, square(3)) + >>> equalize(ima16, square(3)) array([[3072, 2730, 2048, 2730, 3072], [2730, 4096, 4096, 4096, 2730], [2048, 4096, 4096, 4096, 2048], @@ -223,12 +223,12 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return _crank8.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + return _crank8.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) if bitdepth>11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + return _crank16.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) else: raise TypeError("only uint8 and uint16 image supported!") From edd39df07c34d17b00a96cba2ef4651c4c60a629 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:44:03 +0200 Subject: [PATCH 099/625] clean-up code --- skimage/rank/setup.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 4f57209a..efe23515 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -1,19 +1,3 @@ -#import numpy as np -# -#from distutils.core import setup -#from distutils.extension import Extension -#from Cython.Distutils import build_ext -# -#setup( -# cmdclass = {'build_ext': build_ext}, -# ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] -#) - - #!/usr/bin/env python import os From 068293962da3d5dc271534f21d98855dcfc8b6f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 11:00:39 +0200 Subject: [PATCH 100/625] Add hough transform peak detection --- skimage/transform/hough_transform.py | 106 +++++++++++++++++- .../transform/tests/test_hough_transform.py | 29 +++++ 2 files changed, 134 insertions(+), 1 deletion(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 4e3acd6e..71454a21 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -1,4 +1,4 @@ -__all__ = ['hough', 'probabilistic_hough'] +__all__ = ['hough', 'hough_peaks', 'probabilistic_hough'] from itertools import izip as zip @@ -135,3 +135,107 @@ def hough(img, theta=None): """ return _hough(img, theta) + + +def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, + threshold_abs=0, threshold_rel=0.5, num_peaks=np.inf): + """Return peaks in hough transform. + + Identifies most prominent lines separated by a certain angle and distance in + a hough transform. Non-maximum suppresion with different sizes is applied + separately in the first (distances) and second (angles) dimension of the + hough space to identify peaks. + + Parameters + ---------- + hspace : (N, M) array + Hough space returned by the `hough` function. + angles : (M,) array + Angles returned by the `hough` function. + dists : (N, ) array + Distances returned by the `hough` function. + min_distance : int + Minimum distance separating lines (maximum filter size for first + dimension of hough space). + min_angle : int + Minimum angle separating lines (maximum filter size for second + dimension of hough space). + threshold_abs : float + Minimum intensity of peaks in hough space. + threshold_rel : float + Minimum intensity of peaks calculated as `max(hspace) * threshold_rel`. + num_peaks : int + Maximum number of peaks. When the number of peaks exceeds `num_peaks`, + return `num_peaks` coordinates based on peak intensity. + + Returns + ------- + hspace, angles, dists : tuple of array + Peak values in hough space, angles and distances. + + Examples + -------- + >>> import numpy as np + >>> from skimage.transform import hough, hough_peaks + >>> from skimage.draw import line + >>> img = np.zeros((15, 15), dtype=np.bool_) + >>> rr, cc = line(0, 0, 14, 14) + >>> img[rr, cc] = 1 + >>> rr, cc = line(0, 14, 14, 0) + >>> img[cc, rr] = 1 + >>> hspace, angles, dists = hough(img) + >>> hspace, angles, dists = hough_peaks(hspace, angles, dists) + >>> angles + array([ 0.74590887, -0.79856126]) + >>> dists + array([ 10.74418605, 0.51162791]) + + """ + + hspace = hspace.copy() + rows, cols = hspace.shape + + threshold = max(threshold_abs, threshold_rel * np.max(hspace)) + + # sort accumulators from large to small + hspace_max = np.argsort(hspace.flat)[::-1] + hspace_max = np.column_stack(np.unravel_index(hspace_max, hspace.shape)) + + hspace_peaks = [] + dist_peaks = [] + angle_peaks = [] + + # relative coordinate grid for local neighbourhood suppresion + dist_ext, angle_ext = np.mgrid[- min_distance:min_distance + 1, + - min_angle:min_angle + 1] + + for dist_idx, angle_idx in hspace_max: + accum = hspace[dist_idx, angle_idx] + if accum > threshold: + # absolute coordinate grid for local neighbourhood suppresion + dist_nh = dist_idx + dist_ext + angle_nh = angle_idx + angle_ext + + # no reflection for distance neighbourhood + dist_in = np.logical_and(dist_nh > 0, dist_nh < rows) + dist_nh = dist_nh[dist_in] + angle_nh = angle_nh[dist_in] + + # reflect angles and assume angles are continuous, e.g. + # (..., 88, 89, -90, -89, ..., 89, -90, -89, ...) + angle_low = angle_nh < 0 + dist_nh[angle_low] = rows - dist_nh[angle_low] + angle_nh[angle_low] += cols + angle_high = angle_nh >= cols + dist_nh[angle_high] = rows - dist_nh[angle_high] + angle_nh[angle_high] -= cols + + # suppress neighbourhood + hspace[dist_nh, angle_nh] = 0 + + # add current line to peaks + hspace_peaks.append(accum) + dist_peaks.append(dists[dist_idx]) + angle_peaks.append(angles[angle_idx]) + + return np.array(hspace_peaks), np.array(dist_peaks), np.array(angle_peaks) diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py index 03b63d07..3fb0a6f3 100644 --- a/skimage/transform/tests/test_hough_transform.py +++ b/skimage/transform/tests/test_hough_transform.py @@ -72,5 +72,34 @@ def test_probabilistic_hough(): assert([(25, 25), (74, 74)] in sorted_lines) +def test_hough_peaks_dist(): + img = np.zeros((100, 100), dtype=np.bool_) + img[:, 30] = True + img[:, 40] = True + hspace, angles, dists = tf.hough(img) + assert len(tf.hough_peaks(hspace, angles, dists, min_distance=5)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_distance=15)[0]) == 1 + + +def test_hough_peaks_angle(): + img = np.zeros((100, 100), dtype=np.bool_) + img[:, 0] = True + img[0, :] = True + + hspace, angles, dists = tf.hough(img) + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 + + theta = np.linspace(0, np.pi, 100) + hspace, angles, dists = tf.hough(img, theta) + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 + + theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100) + hspace, angles, dists = tf.hough(img, theta) + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 + + if __name__ == "__main__": run_module_suite() From f4bdd94452f9dbf204a85c9d01e0bfcf5dc43e5f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 11:07:35 +0200 Subject: [PATCH 101/625] Add note about range of angle values --- skimage/transform/hough_transform.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 71454a21..d4ba37b9 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -151,7 +151,8 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, hspace : (N, M) array Hough space returned by the `hough` function. angles : (M,) array - Angles returned by the `hough` function. + Angles returned by the `hough` function. Assumed to be continuous + (`angles[-1] - angles[0] == PI`). dists : (N, ) array Distances returned by the `hough` function. min_distance : int From 5fbdd917126bcd6ad86ab0e8b7ae8521450751bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 10 Oct 2012 09:15:22 +0200 Subject: [PATCH 102/625] Fix typo --- skimage/transform/hough_transform.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index d4ba37b9..36693f14 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -142,7 +142,7 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, """Return peaks in hough transform. Identifies most prominent lines separated by a certain angle and distance in - a hough transform. Non-maximum suppresion with different sizes is applied + a hough transform. Non-maximum suppression with different sizes is applied separately in the first (distances) and second (angles) dimension of the hough space to identify peaks. @@ -206,14 +206,14 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, dist_peaks = [] angle_peaks = [] - # relative coordinate grid for local neighbourhood suppresion + # relative coordinate grid for local neighbourhood suppression dist_ext, angle_ext = np.mgrid[- min_distance:min_distance + 1, - min_angle:min_angle + 1] for dist_idx, angle_idx in hspace_max: accum = hspace[dist_idx, angle_idx] if accum > threshold: - # absolute coordinate grid for local neighbourhood suppresion + # absolute coordinate grid for local neighbourhood suppression dist_nh = dist_idx + dist_ext angle_nh = angle_idx + angle_ext From d9a7bac999f475916b32eee93f165251f10c6962 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 10 Oct 2012 09:21:37 +0200 Subject: [PATCH 103/625] Refactor non maximum suppression for faster computation --- skimage/transform/hough_transform.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 36693f14..0d47a376 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -3,6 +3,7 @@ __all__ = ['hough', 'hough_peaks', 'probabilistic_hough'] from itertools import izip as zip import numpy as np +from scipy import ndimage from ._hough_transform import _probabilistic_hough @@ -198,9 +199,17 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, threshold = max(threshold_abs, threshold_rel * np.max(hspace)) - # sort accumulators from large to small - hspace_max = np.argsort(hspace.flat)[::-1] - hspace_max = np.column_stack(np.unravel_index(hspace_max, hspace.shape)) + distance_size = 2 * min_distance + 1 + angle_size = 2 * min_angle + 1 + hspace_max = ndimage.maximum_filter1d(hspace, size=distance_size, axis=0, + mode='constant') + hspace_max = ndimage.maximum_filter1d(hspace_max, size=angle_size, axis=1, + mode='constant') + mask = (hspace == hspace_max) + hspace *= mask + hspace_t = hspace > threshold + + coords = np.transpose(hspace_t.nonzero()) hspace_peaks = [] dist_peaks = [] @@ -210,7 +219,7 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, dist_ext, angle_ext = np.mgrid[- min_distance:min_distance + 1, - min_angle:min_angle + 1] - for dist_idx, angle_idx in hspace_max: + for dist_idx, angle_idx in coords: accum = hspace[dist_idx, angle_idx] if accum > threshold: # absolute coordinate grid for local neighbourhood suppression From ede5df6c7030cc5acca7f09c3030efc08ef4db38 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 10 Oct 2012 09:28:47 +0200 Subject: [PATCH 104/625] Add missing support for num_peaks parameter --- skimage/transform/hough_transform.py | 12 +++++++++++- skimage/transform/tests/test_hough_transform.py | 9 +++++++++ 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 0d47a376..9a37cf2e 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -248,4 +248,14 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, dist_peaks.append(dists[dist_idx]) angle_peaks.append(angles[angle_idx]) - return np.array(hspace_peaks), np.array(dist_peaks), np.array(angle_peaks) + hspace_peaks = np.array(hspace_peaks) + dist_peaks = np.array(dist_peaks) + angle_peaks = np.array(angle_peaks) + + if num_peaks < len(hspace_peaks): + idx_maxsort = np.argsort(hspace_peaks)[::-1][:num_peaks] + hspace_peaks = hspace_peaks[idx_maxsort] + dist_peaks = dist_peaks[idx_maxsort] + angle_peaks = angle_peaks[idx_maxsort] + + return hspace_peaks, dist_peaks, angle_peaks diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py index 3fb0a6f3..a38e6e76 100644 --- a/skimage/transform/tests/test_hough_transform.py +++ b/skimage/transform/tests/test_hough_transform.py @@ -101,5 +101,14 @@ def test_hough_peaks_angle(): assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 +def test_hough_peaks_num(): + img = np.zeros((100, 100), dtype=np.bool_) + img[:, 30] = True + img[:, 40] = True + hspace, angles, dists = tf.hough(img) + assert len(tf.hough_peaks(hspace, angles, dists, min_distance=0, + min_angle=0, num_peaks=1)[0]) == 1 + + if __name__ == "__main__": run_module_suite() From ee098224ebcb7b83e6384d2b73b6f6b2180e4b37 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 10 Oct 2012 13:05:24 +0200 Subject: [PATCH 105/625] Set constant value for maximum filter explicitly --- skimage/transform/hough_transform.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 9a37cf2e..9b5c1050 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -202,9 +202,9 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, distance_size = 2 * min_distance + 1 angle_size = 2 * min_angle + 1 hspace_max = ndimage.maximum_filter1d(hspace, size=distance_size, axis=0, - mode='constant') + mode='constant', cval=0) hspace_max = ndimage.maximum_filter1d(hspace_max, size=angle_size, axis=1, - mode='constant') + mode='constant', cval=0) mask = (hspace == hspace_max) hspace *= mask hspace_t = hspace > threshold From 2214123932191f52e42b57e7bbee1aac89658ce3 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 14:14:13 +0200 Subject: [PATCH 106/625] compare ctmf.median_filter with rank.median --- skimage/rank/tests/demo_benchmark.py | 63 +++++++++++++++++++++++++++- 1 file changed, 62 insertions(+), 1 deletion(-) diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index 42971133..74200b70 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -4,6 +4,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import dilation import skimage.rank as rank +from skimage.filter import median_filter from tools import log_timing @@ -11,15 +12,24 @@ from tools import log_timing def cr_max(image,selem): return rank.maximum(image=image,selem = selem) +@log_timing +def cr_med(image,selem): + return rank.median(image=image,selem = selem) + @log_timing def cm_dil(image,selem): return dilation(image=image,selem = selem) +@log_timing +def ctmf_med(image,radius): + return median_filter(image=image,radius=radius) + def compare(): """comparison between - crank.maximum rankfilter implementation - cmorph.dilate cython implementation + on increasing structuring element size and increasing image size """ # a = (np.random.random((500,500))*256).astype('uint8') @@ -68,5 +78,56 @@ def compare(): plt.show() +def compare_median(): + """comparison between + - crank.median rankfilter implementation + - ctmf.median_filter filter + + on increasing structuring element size and increasing image size + """ + a = data.camera() + + rec = [] + e_range = range(2,40,2) + for r in e_range: + elem = np.ones((2*r,2*r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc,ms_rc = cr_med(a,elem) + rctmf,ms_rctmf = ctmf_med(a,r) + rec.append((ms_rc,ms_rctmf)) + # check if results are identical +# assert (rc==rctmf).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing element size') + plt.plot(e_range,rec) + plt.legend(['rank.median','ctmf.median_filter']) + plt.figure() + plt.imshow(np.hstack((rc,rctmf))) + plt.show() + r = 9 + elem = np.ones((r,r),dtype='uint8') + + rec = [] + s_range = range(100,1000,100) + for s in s_range: + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = cr_max(a,elem) + rctmf,ms_rctmf = ctmf_med(a,r) + rec.append((ms_rc,ms_rctmf)) +# assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing image size') + plt.plot(s_range,rec) + plt.legend(['rank.median','ctmf.median_filter']) + plt.figure() + plt.imshow(np.hstack((rc,rctmf))) + + plt.show() if __name__ == '__main__': - compare() \ No newline at end of file + compare_median() \ No newline at end of file From 4f2dde57076164ae84c774751b4d39cabe6c21fb Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 14:30:29 +0200 Subject: [PATCH 107/625] add comment --- skimage/rank/percentile_rank.py | 15 ++++++++++++++- skimage/rank/rank.py | 7 ++++++- skimage/rank/tests/demo_benchmark.py | 15 ++++++++++----- 3 files changed, 30 insertions(+), 7 deletions(-) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index b6af753c..6cc273e3 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -1,4 +1,17 @@ -""" +"""percentile_rank.py - inferior and superior ranks, provided by the user, are passed to the kernel function +to provide a softer version of the rank filters. E.g. percentile_autolevel will stretch image levels between +percentile [p0,p1] instead of using [min,max]. It means that isolate bright or dark pixels will not produce halos. + +The local histogram is computed using a sliding window similar to the method described in + +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", +IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + +input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), +for 16 bit input images, the number of histogram bins is determined from the maximum value present in the image + +result image is 8 or 16 bit with respect to the input image + :author: Olivier Debeir, 2012 :license: modified BSD """ diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 2a7caab9..5dfa85bc 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1,10 +1,15 @@ -"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc +"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, equalization, etc The local histogram is computed using a sliding window similar to the method described in Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), +for 16 bit input images, the number of histogram bins is determined from the maximum value present in the image + +result image is 8 or 16 bit with respect to the input image + :author: Olivier Debeir, 2012 :license: modified BSD """ diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index 74200b70..c3046083 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -25,7 +25,7 @@ def ctmf_med(image,radius): return median_filter(image=image,radius=radius) -def compare(): +def compare_dilate(): """comparison between - crank.maximum rankfilter implementation - cmorph.dilate cython implementation @@ -88,9 +88,9 @@ def compare_median(): a = data.camera() rec = [] - e_range = range(2,40,2) + e_range = range(2,40,4) for r in e_range: - elem = np.ones((2*r,2*r),dtype='uint8') + elem = np.ones((2*r+1,2*r+1),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') rc,ms_rc = cr_med(a,elem) rctmf,ms_rctmf = ctmf_med(a,r) @@ -106,9 +106,11 @@ def compare_median(): plt.legend(['rank.median','ctmf.median_filter']) plt.figure() plt.imshow(np.hstack((rc,rctmf))) - plt.show() + plt.ylabel('time (ms)') + plt.xlabel('element radius') + r = 9 - elem = np.ones((r,r),dtype='uint8') + elem = np.ones((r*2+1,r*2+1),dtype='uint8') rec = [] s_range = range(100,1000,100) @@ -127,7 +129,10 @@ def compare_median(): plt.legend(['rank.median','ctmf.median_filter']) plt.figure() plt.imshow(np.hstack((rc,rctmf))) + plt.ylabel('time (ms)') + plt.xlabel('image size') plt.show() if __name__ == '__main__': +# compare_dilate() compare_median() \ No newline at end of file From 935f424e9fb19a1e0f0a6969d3b5dac50c3d7b8b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:34:54 +0200 Subject: [PATCH 108/625] fix percentile autolevel --- doc/examples/plot_local_autolevels.py | 45 +++++++++++++++++++++++++++ skimage/rank/_core16.pxd | 4 --- skimage/rank/_core16b.pxd | 4 --- skimage/rank/_core8.pxd | 4 --- skimage/rank/_core8p.pxd | 4 +-- skimage/rank/_crank8.pyx | 10 +++--- skimage/rank/_crank8_percentiles.pyx | 18 ++++++----- skimage/rank/tests/test_suite.py | 14 ++++++++- 8 files changed, 77 insertions(+), 26 deletions(-) create mode 100644 doc/examples/plot_local_autolevels.py diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py new file mode 100644 index 00000000..5b3ba758 --- /dev/null +++ b/doc/examples/plot_local_autolevels.py @@ -0,0 +1,45 @@ +""" +===================== +Local Autolevel +===================== + +Local autolevel stretch local histogram between 0 and max_graylevel (e.g. 255 for 8 bit image). +The following code shows the difference between autolevel and percentile auto_level where [min,max] interval +is replaced by [p0,p1] percentiles interval + +""" +import matplotlib.pyplot as plt + +from skimage import data + +from skimage.rank import percentile_autolevel,autolevel +from skimage.morphology import disk + + +image = data.camera() + +selem = disk(20) +loc_autolevel = autolevel(image,selem=selem) +loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.0,p1=1.0) + +assert (loc_autolevel==loc_perc_autolevel).all() + +loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) + +fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) +ax0, ax1, ax2 = axes +plt.gray() + +ax0.imshow(image) +ax0.set_title('Image') + +ax1.imshow(loc_autolevel) +ax1.set_title('Autolevel') + +ax2.imshow(loc_perc_autolevel,vmin=0,vmax=255) +ax2.set_title('percentile autolevel') + +for ax in axes: + ax.axis('off') + +plt.show() diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index ddd8c637..26a8e948 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -14,10 +14,6 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index fefac53e..cf3cb4c4 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -14,10 +14,6 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth and bilateral interval #--------------------------------------------------------------------------- diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 3d5ddac3..4ea92121 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -14,10 +14,6 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index dfab17be..b878143e 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -15,8 +15,8 @@ cimport numpy as np from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b #--------------------------------------------------------------------------- # 8 bit core kernel receives extra information about data inferior and superior percentiles diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 12e0577e..96624eb5 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -33,11 +33,13 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): if histo[i]: imin = i break - delta = imax-imin - if delta>0: - return (255.*(g-imin)/delta) + delta = imax-imin + if delta>0: + return (255.*(g-imin)/delta) + else: + return (imax-imin) else: - return (imax-imin) + return (0) cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): cdef int i diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 23fe079f..3082e23a 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core8p cimport _core8p +from _core8p cimport _core8p,uint8_max,uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) @@ -27,25 +27,29 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, flo if pop: sum = 0 p1 = 1.0-p1 + imin = 0 + imax = 255 + for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum>(p0*pop): imin = i break sum = 0 for i in range(255,-1,-1): sum += histo[i] - if sum>=p1*pop: + if sum>(p1*pop): imax = i break - delta = imax-imin if delta>0: - return (255.*(g-imin)/delta) +# return (255.) +# return (delta) + return (255*(uint8_min(uint8_max(imin,g),imax)-imin)/delta) else: - return (0) + return (imax-imin) else: - return (0) + return (128) cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, float p0, float p1): diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 51c7f60b..5d47138b 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -4,7 +4,10 @@ import numpy as np from skimage.rank import _crank8,_crank8_percentiles from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles -from skimage.morphology import cmorph +from skimage.morphology import cmorph,disk +from skimage import data +from skimage import rank + class TestSequenceFunctions(unittest.TestCase): @@ -92,6 +95,15 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.ones((3,3),dtype='uint8') f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + def test_compare_autolevels(self): + image = data.camera() + + selem = disk(20) + loc_autolevel = rank.autolevel(image,selem=selem) + loc_perc_autolevel = rank.percentile_autolevel(image,selem=selem,p0=.0,p1=1.) + + assert (loc_autolevel==loc_perc_autolevel).all() + if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) From 5fdc11c4fdbf0ebbae918a9d17e5c6f7f320ea8f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:40:05 +0200 Subject: [PATCH 109/625] fix percentile autolevel --- skimage/rank/_crank16_percentiles.pyx | 16 ++++++---------- skimage/rank/_crank8_percentiles.pyx | 2 -- 2 files changed, 6 insertions(+), 12 deletions(-) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 54c25d40..8d0446ff 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -15,10 +15,10 @@ import numpy as np cimport numpy as np # import main loop -from _core16p cimport _core16p +from _core16p cimport _core16p,int_min,int_max # ----------------------------------------------------------------- -# kernels uint8 (SOFT version using percentiles) +# kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): @@ -29,25 +29,21 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in p1 = 1.0-p1 for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum>p0*pop: imin = i break sum = 0 for i in range(maxbin-1,-1,-1): sum += histo[i] - if sum>=p1*pop: + if sum>p1*pop: imax = i break delta = imax-imin - if g>imax: - return (maxbin-1) - if g(0) if delta>0: - return ((maxbin-1)*1.*(g-imin)/delta) + return (255*(int_min(int_max(imin,g),imax)-imin)/delta) else: - return (0) + return (imax-imin) else: return (0) diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 3082e23a..6b81c8bd 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -43,8 +43,6 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, flo break delta = imax-imin if delta>0: -# return (255.) -# return (delta) return (255*(uint8_min(uint8_max(imin,g),imax)-imin)/delta) else: return (imax-imin) From b55044ef1c4ce2ce9846eb8af986e0f625f2e799 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:45:43 +0200 Subject: [PATCH 110/625] adapt autolevel example --- doc/examples/plot_local_autolevels.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py index 5b3ba758..842215d5 100644 --- a/doc/examples/plot_local_autolevels.py +++ b/doc/examples/plot_local_autolevels.py @@ -9,6 +9,7 @@ is replaced by [p0,p1] percentiles interval """ import matplotlib.pyplot as plt +import numpy as np from skimage import data @@ -20,11 +21,12 @@ image = data.camera() selem = disk(20) loc_autolevel = autolevel(image,selem=selem) -loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.0,p1=1.0) +loc_perc_autolevel0 = percentile_autolevel(image,selem=selem,p0=.00,p1=1.0) +loc_perc_autolevel1 = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) +loc_perc_autolevel2 = percentile_autolevel(image,selem=selem,p0=.05,p1=.95) +loc_perc_autolevel3 = percentile_autolevel(image,selem=selem,p0=.1,p1=.9) -assert (loc_autolevel==loc_perc_autolevel).all() - -loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) +loc_perc_autolevel = np.hstack((loc_perc_autolevel0,loc_perc_autolevel1,loc_perc_autolevel2,loc_perc_autolevel3)) fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax0, ax1, ax2 = axes @@ -37,7 +39,7 @@ ax1.imshow(loc_autolevel) ax1.set_title('Autolevel') ax2.imshow(loc_perc_autolevel,vmin=0,vmax=255) -ax2.set_title('percentile autolevel') +ax2.set_title('percentile autolevel 0%,1%,5% and 10%') for ax in axes: ax.axis('off') From ec2278a646763a5360755c2a453627e62c08763a Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:48:36 +0200 Subject: [PATCH 111/625] fix 16bit percentile --- skimage/rank/_crank16_percentiles.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 8d0446ff..6c67a54e 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -118,13 +118,13 @@ cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_ p1 = 1.0-p1 for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum>p0*pop: imin = i break sum = 0 for i in range((maxbin-1),-1,-1): sum += histo[i] - if sum>=p1*pop: + if sum>p1*pop: imax = i break if g>imax: From 7e7b1d4aacbaeafd567dc42bd3d1a07a99e0d996 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:50:50 +0200 Subject: [PATCH 112/625] clean-up code --- skimage/rank/_core.pxd | 7 ------- skimage/rank/_core16p.pxd | 7 ------- skimage/rank/_core8p.pxd | 7 ------- skimage/rank/_crank16_percentiles.pyx | 8 -------- skimage/rank/_crank8_percentiles.pyx | 8 -------- 5 files changed, 37 deletions(-) diff --git a/skimage/rank/_core.pxd b/skimage/rank/_core.pxd index bb57aec4..fbae2b24 100644 --- a/skimage/rank/_core.pxd +++ b/skimage/rank/_core.pxd @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 0d698cee..1ce9b4cd 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16p.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index b878143e..9b4fbd29 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core8p.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 6c67a54e..0f07196e 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank_percentiles.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 6b81c8bd..2299f04a 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank_percentiles.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False From c1eca2525f2d21ad1a2cfc135a927f5e5b2d0ac8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:53:00 +0200 Subject: [PATCH 113/625] fix equalize --- skimage/rank/_crank16.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index e1e64bed..bd60c3e8 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -209,7 +209,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) -def egalise(np.ndarray[np.uint16_t, ndim=2] image, +def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, From c35f6754afb278850dcf793d6198ed57cb4b4036 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:54:55 +0200 Subject: [PATCH 114/625] fix other equalize --- skimage/rank/_crank16.pyx | 4 ++-- skimage/rank/tests/demo_16bitbilateral.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index bd60c3e8..573db50f 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -49,7 +49,7 @@ cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,in return (g-i) -cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): cdef int i cdef float sum = 0. @@ -216,7 +216,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local egalisation of the gray level """ - return _core16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/rank/tests/demo_16bitbilateral.py b/skimage/rank/tests/demo_16bitbilateral.py index 2a302e6a..85fd510c 100644 --- a/skimage/rank/tests/demo_16bitbilateral.py +++ b/skimage/rank/tests/demo_16bitbilateral.py @@ -14,7 +14,7 @@ if __name__ == '__main__': f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) selem = disk(50) - f3 = rank.egalise(a16,selem = selem) + f3 = rank.equalize(a16,selem = selem) plt.figure() plt.imshow(np.hstack((a8,f1))) From 2ada9ef6b896483e5667b492b58006f1e7d6d793 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 16:46:37 +0200 Subject: [PATCH 115/625] add marked watershed example --- doc/examples/plot_lena_bilateral_denoise.py | 1 + doc/examples/plot_marked_watershed.py | 53 +++++++++++++++++++++ 2 files changed, 54 insertions(+) create mode 100644 doc/examples/plot_marked_watershed.py diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index 9fd20285..57593867 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -1,3 +1,4 @@ + """ ==================================================== Denoising the picture of Lena using bilateral filter diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py new file mode 100644 index 00000000..8f65e344 --- /dev/null +++ b/doc/examples/plot_marked_watershed.py @@ -0,0 +1,53 @@ +""" +================================ +Markers for watershed transform +================================ + +The watershed is a classical algorithm used for **segmentation**, that +is, for separating different objects in an image. + +See Wikipedia_ for more details on the algorithm. + +.. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing) + +""" + +import numpy as np +from scipy import ndimage +import matplotlib.pyplot as plt +from skimage.morphology import watershed,disk +from skimage import rank +from skimage import data +from scipy import ndimage + +# Generate an initial image with two overlapping circles +image = data.camera() + +# denoise image +denoised = rank.median(image,disk(2)) + +# find continuous region (low gradient) --> markers +markers = rank.gradient(denoised,disk(5))<10 +markers = ndimage.label(markers)[0] + +#local gradient +gradient = rank.gradient(denoised,disk(2)) + +# process the watershed +labels = watershed(gradient, markers) + +# display results +fig, axes = plt.subplots(ncols=4, figsize=(8, 2.7)) +ax0, ax1, ax2, ax3 = axes + +ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest') +ax1.imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest') +ax2.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') +ax3.imshow(image, cmap=plt.cm.gray, interpolation='nearest') +ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest',alpha=.7) + +for ax in axes: + ax.axis('off') + +plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) +plt.show() From 3d268dd4388f4f68f085a21acde84025c948e522 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 16:47:34 +0200 Subject: [PATCH 116/625] add marked watershed example (cont.) --- doc/examples/plot_marked_watershed.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 8f65e344..1db4f507 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -6,6 +6,8 @@ Markers for watershed transform The watershed is a classical algorithm used for **segmentation**, that is, for separating different objects in an image. +Here a marker image is build from the region of low gradient inside the image. + See Wikipedia_ for more details on the algorithm. .. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing) From fee2df8d424342d64ba1e0575cdb35335f8652cb Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 16:47:57 +0200 Subject: [PATCH 117/625] add marked watershed example (cont.) --- doc/examples/plot_marked_watershed.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 1db4f507..0be25007 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -22,7 +22,7 @@ from skimage import rank from skimage import data from scipy import ndimage -# Generate an initial image with two overlapping circles +# original data image = data.camera() # denoise image From 29d133b290c079ef6491facda5afcac04e8eeede Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 17:32:35 +0200 Subject: [PATCH 118/625] add local test --- doc/examples/plot_watershed.py | 2 +- skimage/rank/tests/test_morph_contr_enh.py | 28 ++++++++++++++++++++++ 2 files changed, 29 insertions(+), 1 deletion(-) create mode 100644 skimage/rank/tests/test_morph_contr_enh.py diff --git a/doc/examples/plot_watershed.py b/doc/examples/plot_watershed.py index a1cd18cf..9fce196f 100644 --- a/doc/examples/plot_watershed.py +++ b/doc/examples/plot_watershed.py @@ -26,7 +26,7 @@ See Wikipedia_ for more details on the algorithm. """ import numpy as np -from scipy import ndimage +e import matplotlib.pyplot as plt from skimage.morphology import watershed, is_local_maximum diff --git a/skimage/rank/tests/test_morph_contr_enh.py b/skimage/rank/tests/test_morph_contr_enh.py new file mode 100644 index 00000000..812e81c5 --- /dev/null +++ b/skimage/rank/tests/test_morph_contr_enh.py @@ -0,0 +1,28 @@ +import numpy as np +import matplotlib.pyplot as plt +import gdal + +from skimage.morphology import disk +import skimage.rank as rank + +filename = 'iko_pan_Ja1.tif' +im16 = gdal.Open(filename).ReadAsArray().astype(np.uint16) + +plt.figure() +plt.imshow(im16,cmap=plt.cm.gray) +plt.colorbar() + +f0 = rank.median(im16,disk(1)) +f1 = rank.bilateral_mean(im16,disk(20),s0=200,s1=200) +f2 = rank.equalize(f1,disk(10)) +f3 = rank.bottomhat(f1,disk(1)) + +plt.figure() +plt.imshow(f2,cmap=plt.cm.gray,interpolation='nearest') +plt.colorbar() + +plt.show() + + + + From 76a02de83afd9173a1ed253fa1a6163821c4d649 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 11:41:37 +0200 Subject: [PATCH 119/625] remove unused _core --- skimage/rank/_core.pxd | 1046 ---------------------------------------- 1 file changed, 1046 deletions(-) delete mode 100644 skimage/rank/_core.pxd diff --git a/skimage/rank/_core.pxd b/skimage/rank/_core.pxd deleted file mode 100644 index fbae2b24..00000000 --- a/skimage/rank/_core.pxd +++ /dev/null @@ -1,1046 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - -#--------------------------------------------------------------------------- -# 8 bit core kernel -#--------------------------------------------------------------------------- - -cdef inline rank8(np.uint8_t kernel(int*, float, np.uint8_t), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - eimage = np.ascontiguousarray(eimage) - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(256 * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - -#--------------------------------------------------------------------------- -# 16 bit core, kernel receive extra information about data bitdepth -#--------------------------------------------------------------------------- - -cdef inline rank16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(maxbin * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - -#--------------------------------------------------------------------------- -# 8 bit core kernel receive extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline rank8_percentile(np.uint8_t kernel(int*, float, np.uint8_t, float, float), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - eimage = np.ascontiguousarray(eimage) - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(256 * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - -#--------------------------------------------------------------------------- -# 16 bit core kernel receive extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline rank16_percentile(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint16 - - result is uint16 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(maxbin * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out \ No newline at end of file From 45cf1d77e6e9cf7864a838921914b50016efdde7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 12:03:34 +0200 Subject: [PATCH 120/625] add exhaustive comparison between 8bit and 16bit filters --- skimage/rank/rank.py | 6 +++++- skimage/rank/tests/test_suite.py | 19 +++++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 5dfa85bc..bc7dadff 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1019,4 +1019,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): raise ValueError("only uint16 <4096 image (12bit) supported!") return _crank16.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) else: - raise TypeError("only uint8 and uint16 image supported!") \ No newline at end of file + raise TypeError("only uint8 and uint16 image supported!") + +if __name__ == "__main__": + import doctest + doctest.testmod(verbose=True) \ No newline at end of file diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 5d47138b..97345f4f 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -104,6 +104,25 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() + def test_compare_8bit_vs_16bit(self): + # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical + i8 = data.camera() + i16 = i8.astype(np.uint16) + methods = ['autolevel','bottomhat','equalize','gradient','maximum','mean' + ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] + for method in methods: + func = eval('rank.%s'%method) + print func + f8 = func(i8,disk(3)) + f16 = func(i16,disk(3)) +# if (f8==f16).all() is False: + if not (f8==f16).all(): + + print f8 + print f16 + + + if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) From 58bc49d9446d6b7d189d770faf12a61be9510f6b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 12:13:01 +0200 Subject: [PATCH 121/625] fix 8bit-16bit discepencies --- skimage/rank/_crank16.pyx | 6 +++--- skimage/rank/tests/test_suite.py | 11 ++++------- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index 573db50f..90a7e8bb 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -35,7 +35,7 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in break delta = imax-imin if delta>0: - return (maxbin*1.*(g-imin)/delta) + return (1.*(maxbin-1)*(g-imin)/delta) else: return (imax-imin) @@ -59,7 +59,7 @@ cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int if i>=g: break - return ((maxbin*1.*sum)/pop) + return (((maxbin-1)*sum)/pop) else: return (0) @@ -107,7 +107,7 @@ cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16 if pop: for i in range(maxbin): mean += histo[i]*i - return ((g-mean/pop)/2.+midbin) + return ((g-mean/pop)/2.+(midbin-1)) else: return (0) diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 97345f4f..0887fa8f 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -108,19 +108,16 @@ class TestSequenceFunctions(unittest.TestCase): # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical i8 = data.camera() i16 = i8.astype(np.uint16) + assert (i8==i16).all() + methods = ['autolevel','bottomhat','equalize','gradient','maximum','mean' ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] + for method in methods: func = eval('rank.%s'%method) - print func f8 = func(i8,disk(3)) f16 = func(i16,disk(3)) -# if (f8==f16).all() is False: - if not (f8==f16).all(): - - print f8 - print f16 - + assert (f8==f16).all() if __name__ == '__main__': From dec07b64fbe5c5f098455d319b4de0dc3c4ea2db Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 12:19:45 +0200 Subject: [PATCH 122/625] fix doctest in rank --- skimage/rank/rank.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index bc7dadff..cefb06ac 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -78,9 +78,9 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> autolevel(ima16, square(3)) array([[ 0, 0, 0, 0, 0], - [ 0, 4096, 4096, 4096, 0], - [ 0, 4096, 0, 4096, 0], - [ 0, 4096, 4096, 4096, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], + [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -218,11 +218,11 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> equalize(ima16, square(3)) - array([[3072, 2730, 2048, 2730, 3072], - [2730, 4096, 4096, 4096, 2730], - [2048, 4096, 4096, 4096, 2048], - [2730, 4096, 4096, 4096, 2730], - [3072, 2730, 2048, 2730, 3072]], dtype=uint16) + array([[3071, 2730, 2047, 2730, 3071], + [2730, 4095, 4095, 4095, 2730], + [2047, 4095, 4095, 4095, 2047], + [2730, 4095, 4095, 4095, 2730], + [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ selem = img_as_ubyte(selem) if mask is not None: @@ -502,11 +502,11 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> meansubstraction(ima16, square(3)) - array([[1536, 1365, 1024, 1365, 1536], - [1365, 3185, 2730, 3185, 1365], - [1024, 2730, 2048, 2730, 1024], - [1365, 3185, 2730, 3185, 1365], - [1536, 1365, 1024, 1365, 1536]], dtype=uint16) + array([[1535, 1364, 1023, 1364, 1535], + [1364, 3184, 2729, 3184, 1364], + [1023, 2729, 2047, 2729, 1023], + [1364, 3184, 2729, 3184, 1364], + [1535, 1364, 1023, 1364, 1535]], dtype=uint16) """ selem = img_as_ubyte(selem) From f10cc429606151963a74f3dac56c5ae70d7dface Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:03:03 +0200 Subject: [PATCH 123/625] remove ascontiguousarray(eimage) --- skimage/rank/_core16.pxd | 1 - skimage/rank/_core16b.pxd | 1 - skimage/rank/_core16p.pxd | 1 - skimage/rank/_core8.pxd | 1 - skimage/rank/_core8p.pxd | 1 - 5 files changed, 5 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 26a8e948..9ead95e0 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -75,7 +75,6 @@ char shift_x, char shift_y,int bitdepth): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index cf3cb4c4..d75611a7 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -76,7 +76,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 1ce9b4cd..9f3a99af 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -72,7 +72,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 4ea92121..d9fbee0f 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -65,7 +65,6 @@ char shift_x, char shift_y): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 9b4fbd29..75ceaf89 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -62,7 +62,6 @@ char shift_x, char shift_y, float p0, float p1): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data From 35e2e60a9be211cdc7e4f02f331dca3cd9a76c9e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:13:35 +0200 Subject: [PATCH 124/625] remoce sr,sc from cores --- skimage/rank/_core16.pxd | 4 ---- skimage/rank/_core16b.pxd | 4 ---- skimage/rank/_core16p.pxd | 4 ---- skimage/rank/_core8.pxd | 4 ---- skimage/rank/_core8p.pxd | 4 ---- 5 files changed, 20 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 9ead95e0..eb9f4886 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -94,8 +94,6 @@ char shift_x, char shift_y,int bitdepth): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -263,8 +261,6 @@ char shift_x, char shift_y,int bitdepth): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index d75611a7..b18b1fd8 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -95,8 +95,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -264,8 +262,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 9f3a99af..19a53b67 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -91,8 +91,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -260,8 +258,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index d9fbee0f..fefa98e4 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -84,8 +84,6 @@ char shift_x, char shift_y): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -248,8 +246,6 @@ char shift_x, char shift_y): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 75ceaf89..347c7ef5 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -81,8 +81,6 @@ char shift_x, char shift_y, float p0, float p1): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -245,8 +243,6 @@ char shift_x, char shift_y, float p0, float p1): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) From b9f016a7d281df3a1ae71f21f769ff684e9891a2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:19:38 +0200 Subject: [PATCH 125/625] remoce selem_num --- skimage/rank/_core16.pxd | 1 - skimage/rank/_core16b.pxd | 1 - skimage/rank/_core16p.pxd | 1 - skimage/rank/_core8.pxd | 1 - skimage/rank/_core8p.pxd | 1 - 5 files changed, 5 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index eb9f4886..3ecb6314 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -93,7 +93,6 @@ char shift_x, char shift_y,int bitdepth): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index b18b1fd8..5f5b3e81 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -94,7 +94,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 19a53b67..bf50ca35 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -90,7 +90,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index fefa98e4..ece629c5 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -83,7 +83,6 @@ char shift_x, char shift_y): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 347c7ef5..675c4a5d 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -80,7 +80,6 @@ char shift_x, char shift_y, float p0, float p1): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) From 32d40f80eebc681e6e493a214ece0237af5baa76 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:34:04 +0200 Subject: [PATCH 126/625] add comment --- skimage/rank/_core16.pxd | 8 ++++++++ skimage/rank/_core16b.pxd | 8 ++++++++ skimage/rank/_core16p.pxd | 8 ++++++++ skimage/rank/_core8.pxd | 8 ++++++++ skimage/rank/_core8p.pxd | 8 ++++++++ 5 files changed, 40 insertions(+) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 3ecb6314..791f5fb9 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -91,9 +91,17 @@ char shift_x, char shift_y,int bitdepth): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 5f5b3e81..3007a2fb 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -92,9 +92,17 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index bf50ca35..484a6a6b 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -88,9 +88,17 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index ece629c5..325f779d 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -81,9 +81,17 @@ char shift_x, char shift_y): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 675c4a5d..5fa278d5 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -78,9 +78,17 @@ char shift_x, char shift_y, float p0, float p1): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) From 894cb13f501deb45ca1f30d926d9c8dae47f22b7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:47:51 +0200 Subject: [PATCH 127/625] rename n_se_n to num_se_n etc, removed commented code --- skimage/rank/_core16.pxd | 44 ++--- skimage/rank/_core16b.pxd | 44 ++--- skimage/rank/_core16p.pxd | 44 ++--- skimage/rank/_core8.pxd | 44 ++--- skimage/rank/_core8p.pxd | 44 ++--- skimage/rank/_crank16_bilateral.pyx | 261 +--------------------------- 6 files changed, 112 insertions(+), 369 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 791f5fb9..1a3194de 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -93,7 +93,7 @@ char shift_x, char shift_y,int bitdepth): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -126,26 +126,26 @@ char shift_x, char shift_y,int bitdepth): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -175,14 +175,14 @@ char shift_x, char shift_y,int bitdepth): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -200,14 +200,14 @@ char shift_x, char shift_y,int bitdepth): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -222,14 +222,14 @@ char shift_x, char shift_y,int bitdepth): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -247,14 +247,14 @@ char shift_x, char shift_y,int bitdepth): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 3007a2fb..163662b5 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -94,7 +94,7 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -127,26 +127,26 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -176,14 +176,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -201,14 +201,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -223,14 +223,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -248,14 +248,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 484a6a6b..9e992b6b 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -90,7 +90,7 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -123,26 +123,26 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -172,14 +172,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -197,14 +197,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -219,14 +219,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -244,14 +244,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 325f779d..d24297cb 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -83,7 +83,7 @@ char shift_x, char shift_y): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -116,26 +116,26 @@ char shift_x, char shift_y): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(256): @@ -164,14 +164,14 @@ char shift_x, char shift_y): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -188,14 +188,14 @@ char shift_x, char shift_y): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -209,14 +209,14 @@ char shift_x, char shift_y): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -233,14 +233,14 @@ char shift_x, char shift_y): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 5fa278d5..b1adac70 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -80,7 +80,7 @@ char shift_x, char shift_y, float p0, float p1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -113,26 +113,26 @@ char shift_x, char shift_y, float p0, float p1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(256): @@ -161,14 +161,14 @@ char shift_x, char shift_y, float p0, float p1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -185,14 +185,14 @@ char shift_x, char shift_y, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -206,14 +206,14 @@ char shift_x, char shift_y, float p0, float p1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -230,14 +230,14 @@ char shift_x, char shift_y, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index 440d28e3..e783d7ea 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -21,73 +21,6 @@ from _core16b cimport _core16b # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -#cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax,delta -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# delta = imax-imin -# if delta>0: -# return (maxbin*1.*(g-imin)/delta) -# else: -# return (imax-imin) -# -#cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# for i in range(maxbin): -# if histo[i]: -# break -# -# return (g-i) -# -# -#cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float sum = 0. -# -# if pop: -# for i in range(maxbin): -# sum += histo[i] -# if i>=g: -# break -# -# return ((maxbin*1.*sum)/pop) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# return (imax-imin) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# return (i) -# -# return (0) cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): cdef int i,bilat_pop=0 @@ -105,71 +38,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -#cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float mean = 0. -# -# if pop: -# for i in range(maxbin): -# mean += histo[i]*i -# return ((g-mean/pop)/2.+midbin) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float sum = pop/2.0 -# -# if pop: -# for i in range(maxbin): -# if histo[i]: -# sum -= histo[i] -# if sum<0: -# return (i) -# -# return (0) -# -#cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# if pop: -# for i in range(maxbin): -# if histo[i]: -# return (i) -# -# return (0) -# -#cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int hmax=0,imax=0 -# -# if pop: -# for i in range(maxbin): -# if histo[i]>hmax: -# hmax = histo[i] -# imax = i -# return (imax) -# -# return (0) -# -#cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# if imax-g < g-imin: -# return (imax) -# else: -# return (imin) -# else: -# return (0) -# + cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): cdef int i,bilat_pop=0 @@ -181,75 +50,10 @@ cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitd else: return (0) -# -#cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float mean = 0. -# -# if pop: -# for i in range(maxbin): -# mean += histo[i]*i -# return (g>(mean/pop)) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# break -# -# return (i-g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- -#def autolevel(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """bottom hat -# """ -# return rank16b(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """bottom hat -# """ -# return rank16b(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def egalise(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local egalisation of the gray level -# """ -# return rank16b(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def gradient(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local maximum - local minimum gray level -# """ -# return rank16b(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def maximum(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local maximum gray level -# """ -# return rank16b(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) - def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -259,51 +63,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -#def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """(g - average gray level)/2+midbin (clipped on uint8) -# """ -# return rank16b(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def median(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local median -# """ -# return rank16b(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def minimum(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local minimum gray level -# """ -# return rank16b(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """morphological contrast enhancement -# """ -# return rank16b(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def modal(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local mode -# """ -# return rank16b(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# + def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -313,20 +73,3 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -#def threshold(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """returns maxbin-1 if gray level higher than local mean, 0 else -# """ -# return rank16b(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def tophat(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """top hat -# """ -# return rank16b(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) From f22a014ffaa07856e33a5bda678d87673fefb3f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 12 Oct 2012 19:10:46 +0200 Subject: [PATCH 128/625] Add scharr edge filter --- skimage/filter/__init__.py | 3 +- skimage/filter/edges.py | 114 +++++++++++++++++++++++++-- skimage/filter/tests/test_edges.py | 120 +++++++++++++++++++++++++++++ 3 files changed, 228 insertions(+), 9 deletions(-) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index bdbbb531..f76556df 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -1,7 +1,8 @@ from .lpi_filter import * from .ctmf import median_filter from ._canny import canny -from .edges import sobel, hsobel, vsobel, hprewitt, vprewitt, prewitt +from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, + hprewitt, vprewitt) from ._tv_denoise import tv_denoise from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/edges.py b/skimage/filter/edges.py index 134aa796..98e4f600 100644 --- a/skimage/filter/edges.py +++ b/skimage/filter/edges.py @@ -1,6 +1,7 @@ -"""edges.py - Sobel edge filter +"""edges.py - Edge filters -Originally part of CellProfiler, code licensed under both GPL and BSD licenses. +Sobel and Prewitt filters originally part of CellProfiler, code licensed under +both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute @@ -48,7 +49,7 @@ def sobel(image, mask=None): Returns ------- output : ndarray - The Sobel edge map. + The Sobel edge map. Notes ----- @@ -77,7 +78,7 @@ def hsobel(image, mask=None): Returns ------- output : ndarray - The Sobel edge map. + The Sobel edge map. Notes ----- @@ -112,7 +113,7 @@ def vsobel(image, mask=None): Returns ------- output : ndarray - The Sobel edge map. + The Sobel edge map. Notes ----- @@ -132,6 +133,103 @@ def vsobel(image, mask=None): return _mask_filter_result(result, mask) +def scharr(image, mask=None): + """Calculate the absolute magnitude Scharr to find edges. + + Parameters + ---------- + image : array_like, dtype=float + Image to process. + mask : array_like, dtype=bool, optional + An optional mask to limit the application to a certain area. + Note that pixels surrounding masked regions are also masked to + prevent masked regions from affecting the result. + + Returns + ------- + output : ndarray + The Scharr edge map. + + Notes + ----- + Take the square root of the sum of the squares of the horizontal and + vertical Scharrs to get a magnitude that's somewhat insensitive to + direction. + + """ + return np.sqrt(hscharr(image, mask)**2 + vscharr(image, mask)**2) + + +def hscharr(image, mask=None): + """Find the horizontal edges of an image using the Scharr transform. + + Parameters + ---------- + image : array_like, dtype=float + Image to process. + mask : array_like, dtype=bool, optional + An optional mask to limit the application to a certain area. + Note that pixels surrounding masked regions are also masked to + prevent masked regions from affecting the result. + + Returns + ------- + output : ndarray + The Scharr edge map. + + Notes + ----- + We use the following kernel and return the absolute value of the + result at each point:: + + 3 10 3 + 0 0 0 + -3 -10 -3 + + """ + image = img_as_float(image) + result = np.abs(convolve(image, + np.array([[ 3, 10, 3], + [ 0, 0, 0], + [-3, -10, -3]]).astype(float) / 16.0)) + return _mask_filter_result(result, mask) + + +def vscharr(image, mask=None): + """Find the vertical edges of an image using the Scharr transform. + + Parameters + ---------- + image : array_like, dtype=float + Image to process + mask : array_like, dtype=bool, optional + An optional mask to limit the application to a certain area + Note that pixels surrounding masked regions are also masked to + prevent masked regions from affecting the result. + + Returns + ------- + output : ndarray + The Scharr edge map. + + Notes + ----- + We use the following kernel and return the absolute value of the + result at each point:: + + 3 0 -3 + 10 0 -10 + 3 0 -3 + + """ + image = img_as_float(image) + result = np.abs(convolve(image, + np.array([[ 3, 0, -3], + [10, 0, -10], + [ 3, 0, -3]]).astype(float) / 16.0)) + return _mask_filter_result(result, mask) + + def prewitt(image, mask=None): """Find the edge magnitude using the Prewitt transform. @@ -147,7 +245,7 @@ def prewitt(image, mask=None): Returns ------- output : ndarray - The Prewitt edge map. + The Prewitt edge map. Notes ----- @@ -172,7 +270,7 @@ def hprewitt(image, mask=None): Returns ------- output : ndarray - The Prewitt edge map. + The Prewitt edge map. Notes ----- @@ -207,7 +305,7 @@ def vprewitt(image, mask=None): Returns ------- output : ndarray - The Prewitt edge map. + The Prewitt edge map. Notes ----- diff --git a/skimage/filter/tests/test_edges.py b/skimage/filter/tests/test_edges.py index a5d52aa5..1edf8914 100644 --- a/skimage/filter/tests/test_edges.py +++ b/skimage/filter/tests/test_edges.py @@ -9,6 +9,7 @@ def test_sobel_zeros(): result = F.sobel(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) + def test_sobel_mask(): """Sobel on a masked array should be zero""" np.random.seed(0) @@ -16,6 +17,7 @@ def test_sobel_mask(): np.zeros((10, 10), bool)) assert (np.all(result == 0)) + def test_sobel_horizontal(): """Sobel on an edge should be a horizontal line""" i, j = np.mgrid[-5:6, -5:6] @@ -26,6 +28,7 @@ def test_sobel_horizontal(): assert (np.all(result[i == 0] == 1)) assert (np.all(result[np.abs(i) > 1] == 0)) + def test_sobel_vertical(): """Sobel on a vertical edge should be a vertical line""" i, j = np.mgrid[-5:6, -5:6] @@ -41,6 +44,7 @@ def test_hsobel_zeros(): result = F.hsobel(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) + def test_hsobel_mask(): """Horizontal Sobel on a masked array should be zero""" np.random.seed(0) @@ -48,6 +52,7 @@ def test_hsobel_mask(): np.zeros((10, 10), bool)) assert (np.all(result == 0)) + def test_hsobel_horizontal(): """Horizontal Sobel on an edge should be a horizontal line""" i, j = np.mgrid[-5:6, -5:6] @@ -58,6 +63,7 @@ def test_hsobel_horizontal(): assert (np.all(result[i == 0] == 1)) assert (np.all(result[np.abs(i) > 1] == 0)) + def test_hsobel_vertical(): """Horizontal Sobel on a vertical edge should be zero""" i, j = np.mgrid[-5:6, -5:6] @@ -71,6 +77,7 @@ def test_vsobel_zeros(): result = F.vsobel(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) + def test_vsobel_mask(): """Vertical Sobel on a masked array should be zero""" np.random.seed(0) @@ -78,6 +85,7 @@ def test_vsobel_mask(): np.zeros((10, 10), bool)) assert (np.all(result == 0)) + def test_vsobel_vertical(): """Vertical Sobel on an edge should be a vertical line""" i, j = np.mgrid[-5:6, -5:6] @@ -88,6 +96,7 @@ def test_vsobel_vertical(): assert (np.all(result[j == 0] == 1)) assert (np.all(result[np.abs(j) > 1] == 0)) + def test_vsobel_horizontal(): """vertical Sobel on a horizontal edge should be zero""" i, j = np.mgrid[-5:6, -5:6] @@ -97,11 +106,114 @@ def test_vsobel_horizontal(): assert (np.all(np.abs(result) < eps)) +def test_scharr_zeros(): + """Sobel on an array of all zeros""" + result = F.scharr(np.zeros((10, 10)), np.ones((10, 10), bool)) + assert (np.all(result == 0)) + + +def test_scharr_mask(): + """Sobel on a masked array should be zero""" + np.random.seed(0) + result = F.scharr(np.random.uniform(size=(10, 10)), + np.zeros((10, 10), bool)) + assert (np.all(result == 0)) + + +def test_scharr_horizontal(): + """Sobel on an edge should be a horizontal line""" + i, j = np.mgrid[-5:6, -5:6] + image = (i >= 0).astype(float) + result = F.scharr(image) + # Fudge the eroded points + i[np.abs(j) == 5] = 10000 + assert (np.all(result[i == 0] == 1)) + assert (np.all(result[np.abs(i) > 1] == 0)) + + +def test_scharr_vertical(): + """Sobel on a vertical edge should be a vertical line""" + i, j = np.mgrid[-5:6, -5:6] + image = (j >= 0).astype(float) + result = F.scharr(image) + j[np.abs(i) == 5] = 10000 + assert (np.all(result[j == 0] == 1)) + assert (np.all(result[np.abs(j) > 1] == 0)) + + +def test_hscharr_zeros(): + """Horizontal sobel on an array of all zeros""" + result = F.hscharr(np.zeros((10, 10)), np.ones((10, 10), bool)) + assert (np.all(result == 0)) + + +def test_hscharr_mask(): + """Horizontal Sobel on a masked array should be zero""" + np.random.seed(0) + result = F.hscharr(np.random.uniform(size=(10, 10)), + np.zeros((10, 10), bool)) + assert (np.all(result == 0)) + + +def test_hscharr_horizontal(): + """Horizontal Sobel on an edge should be a horizontal line""" + i, j = np.mgrid[-5:6, -5:6] + image = (i >= 0).astype(float) + result = F.hscharr(image) + # Fudge the eroded points + i[np.abs(j) == 5] = 10000 + assert (np.all(result[i == 0] == 1)) + assert (np.all(result[np.abs(i) > 1] == 0)) + + +def test_hscharr_vertical(): + """Horizontal Sobel on a vertical edge should be zero""" + i, j = np.mgrid[-5:6, -5:6] + image = (j >= 0).astype(float) + result = F.hscharr(image) + assert (np.all(result == 0)) + + +def test_vscharr_zeros(): + """Vertical sobel on an array of all zeros""" + result = F.vscharr(np.zeros((10, 10)), np.ones((10, 10), bool)) + assert (np.all(result == 0)) + + +def test_vscharr_mask(): + """Vertical Sobel on a masked array should be zero""" + np.random.seed(0) + result = F.vscharr(np.random.uniform(size=(10, 10)), + np.zeros((10, 10), bool)) + assert (np.all(result == 0)) + + +def test_vscharr_vertical(): + """Vertical Sobel on an edge should be a vertical line""" + i, j = np.mgrid[-5:6, -5:6] + image = (j >= 0).astype(float) + result = F.vscharr(image) + # Fudge the eroded points + j[np.abs(i) == 5] = 10000 + assert (np.all(result[j == 0] == 1)) + assert (np.all(result[np.abs(j) > 1] == 0)) + + +def test_vscharr_horizontal(): + """vertical Sobel on a horizontal edge should be zero""" + i, j = np.mgrid[-5:6, -5:6] + image = (i >= 0).astype(float) + result = F.vscharr(image) + eps = .000001 + assert (np.all(np.abs(result) < eps)) + + def test_prewitt_zeros(): """Prewitt on an array of all zeros""" result = F.prewitt(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) + def test_prewitt_mask(): """Prewitt on a masked array should be zero""" np.random.seed(0) @@ -110,6 +222,7 @@ def test_prewitt_mask(): eps = .000001 assert (np.all(np.abs(result) < eps)) + def test_prewitt_horizontal(): """Prewitt on an edge should be a horizontal line""" i, j = np.mgrid[-5:6, -5:6] @@ -121,6 +234,7 @@ def test_prewitt_horizontal(): assert (np.all(result[i == 0] == 1)) assert (np.all(np.abs(result[np.abs(i) > 1]) < eps)) + def test_prewitt_vertical(): """Prewitt on a vertical edge should be a vertical line""" i, j = np.mgrid[-5:6, -5:6] @@ -137,6 +251,7 @@ def test_hprewitt_zeros(): result = F.hprewitt(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) + def test_hprewitt_mask(): """Horizontal prewitt on a masked array should be zero""" np.random.seed(0) @@ -145,6 +260,7 @@ def test_hprewitt_mask(): eps = .000001 assert (np.all(np.abs(result) < eps)) + def test_hprewitt_horizontal(): """Horizontal prewitt on an edge should be a horizontal line""" i, j = np.mgrid[-5:6, -5:6] @@ -156,6 +272,7 @@ def test_hprewitt_horizontal(): assert (np.all(result[i == 0] == 1)) assert (np.all(np.abs(result[np.abs(i) > 1]) < eps)) + def test_hprewitt_vertical(): """Horizontal prewitt on a vertical edge should be zero""" i, j = np.mgrid[-5:6, -5:6] @@ -170,6 +287,7 @@ def test_vprewitt_zeros(): result = F.vprewitt(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) + def test_vprewitt_mask(): """Vertical prewitt on a masked array should be zero""" np.random.seed(0) @@ -177,6 +295,7 @@ def test_vprewitt_mask(): np.zeros((10, 10), bool)) assert (np.all(result == 0)) + def test_vprewitt_vertical(): """Vertical prewitt on an edge should be a vertical line""" i, j = np.mgrid[-5:6, -5:6] @@ -188,6 +307,7 @@ def test_vprewitt_vertical(): eps = .000001 assert (np.all(np.abs(result[np.abs(j) > 1]) < eps)) + def test_vprewitt_horizontal(): """Vertical prewitt on a horizontal edge should be zero""" i, j = np.mgrid[-5:6, -5:6] From 04f21c58419f2937d3a4eb6c975617ec5c140393 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 12 Oct 2012 19:16:04 +0200 Subject: [PATCH 129/625] Fix dtype and dim note for parameters in doc strings --- skimage/filter/edges.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/skimage/filter/edges.py b/skimage/filter/edges.py index 98e4f600..fa11c69c 100644 --- a/skimage/filter/edges.py +++ b/skimage/filter/edges.py @@ -39,9 +39,9 @@ def sobel(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -68,9 +68,9 @@ def hsobel(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -103,9 +103,9 @@ def vsobel(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -138,9 +138,9 @@ def scharr(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -165,9 +165,9 @@ def hscharr(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -200,9 +200,9 @@ def vscharr(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -235,9 +235,9 @@ def prewitt(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -260,9 +260,9 @@ def hprewitt(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. @@ -295,9 +295,9 @@ def vprewitt(image, mask=None): Parameters ---------- - image : array_like, dtype=float + image : 2-D array Image to process. - mask : array_like, dtype=bool, optional + mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. From fbaa883724a2b5a90d92d509bde2d7cf5a51e0ad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 12 Oct 2012 19:32:53 +0200 Subject: [PATCH 130/625] Fix reference links in doc strings --- skimage/transform/pyramids.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/transform/pyramids.py b/skimage/transform/pyramids.py index ccace457..19001de8 100644 --- a/skimage/transform/pyramids.py +++ b/skimage/transform/pyramids.py @@ -57,7 +57,7 @@ def pyramid_reduce(image, downscale=2, sigma=None, order=1, References ---------- - ..[1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf + .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf """ @@ -111,7 +111,7 @@ def pyramid_expand(image, upscale=2, sigma=None, order=1, References ---------- - ..[1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf + .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf """ @@ -176,7 +176,7 @@ def pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1, References ---------- - ..[1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf + .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf """ @@ -257,8 +257,8 @@ def pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, References ---------- - ..[1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf - ..[2] http://sepwww.stanford.edu/~morgan/texturematch/paper_html/node3.html + .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf + .. [2] http://sepwww.stanford.edu/~morgan/texturematch/paper_html/node3.html """ From 1fbd664d5bad1bc17ff6b856478f05e1215cbf29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 13 Oct 2012 09:17:34 +0200 Subject: [PATCH 131/625] Add reference for scharr operator --- skimage/filter/edges.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/skimage/filter/edges.py b/skimage/filter/edges.py index fa11c69c..f11ae33a 100644 --- a/skimage/filter/edges.py +++ b/skimage/filter/edges.py @@ -156,6 +156,11 @@ def scharr(image, mask=None): vertical Scharrs to get a magnitude that's somewhat insensitive to direction. + References + ---------- + .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization + of Kernel Based Image Derivatives. + """ return np.sqrt(hscharr(image, mask)**2 + vscharr(image, mask)**2) @@ -186,6 +191,11 @@ def hscharr(image, mask=None): 0 0 0 -3 -10 -3 + References + ---------- + .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization + of Kernel Based Image Derivatives. + """ image = img_as_float(image) result = np.abs(convolve(image, @@ -221,6 +231,11 @@ def vscharr(image, mask=None): 10 0 -10 3 0 -3 + References + ---------- + .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization + of Kernel Based Image Derivatives. + """ image = img_as_float(image) result = np.abs(convolve(image, From be6f02bd3d8987323242475845ec0d4731205a7c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 13 Oct 2012 09:18:11 +0200 Subject: [PATCH 132/625] Fix stylistic indentation --- skimage/filter/edges.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/filter/edges.py b/skimage/filter/edges.py index f11ae33a..f374634e 100644 --- a/skimage/filter/edges.py +++ b/skimage/filter/edges.py @@ -239,9 +239,9 @@ def vscharr(image, mask=None): """ image = img_as_float(image) result = np.abs(convolve(image, - np.array([[ 3, 0, -3], + np.array([[ 3, 0, -3], [10, 0, -10], - [ 3, 0, -3]]).astype(float) / 16.0)) + [ 3, 0, -3]]).astype(float) / 16.0)) return _mask_filter_result(result, mask) From 37ccc805e9a37dd8ce50d60b29525e484fbe4990 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 13 Oct 2012 09:19:53 +0200 Subject: [PATCH 133/625] Fix test case doc strings --- skimage/filter/tests/test_edges.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/filter/tests/test_edges.py b/skimage/filter/tests/test_edges.py index 1edf8914..c16b953d 100644 --- a/skimage/filter/tests/test_edges.py +++ b/skimage/filter/tests/test_edges.py @@ -107,13 +107,13 @@ def test_vsobel_horizontal(): def test_scharr_zeros(): - """Sobel on an array of all zeros""" + """Scharr on an array of all zeros""" result = F.scharr(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) def test_scharr_mask(): - """Sobel on a masked array should be zero""" + """Scharr on a masked array should be zero""" np.random.seed(0) result = F.scharr(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) @@ -121,7 +121,7 @@ def test_scharr_mask(): def test_scharr_horizontal(): - """Sobel on an edge should be a horizontal line""" + """Scharr on an edge should be a horizontal line""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = F.scharr(image) @@ -132,7 +132,7 @@ def test_scharr_horizontal(): def test_scharr_vertical(): - """Sobel on a vertical edge should be a vertical line""" + """Scharr on a vertical edge should be a vertical line""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = F.scharr(image) @@ -142,13 +142,13 @@ def test_scharr_vertical(): def test_hscharr_zeros(): - """Horizontal sobel on an array of all zeros""" + """Horizontal Scharr on an array of all zeros""" result = F.hscharr(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) def test_hscharr_mask(): - """Horizontal Sobel on a masked array should be zero""" + """Horizontal Scharr on a masked array should be zero""" np.random.seed(0) result = F.hscharr(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) @@ -156,7 +156,7 @@ def test_hscharr_mask(): def test_hscharr_horizontal(): - """Horizontal Sobel on an edge should be a horizontal line""" + """Horizontal Scharr on an edge should be a horizontal line""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = F.hscharr(image) @@ -167,7 +167,7 @@ def test_hscharr_horizontal(): def test_hscharr_vertical(): - """Horizontal Sobel on a vertical edge should be zero""" + """Horizontal Scharr on a vertical edge should be zero""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = F.hscharr(image) @@ -175,13 +175,13 @@ def test_hscharr_vertical(): def test_vscharr_zeros(): - """Vertical sobel on an array of all zeros""" + """Vertical Scharr on an array of all zeros""" result = F.vscharr(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) def test_vscharr_mask(): - """Vertical Sobel on a masked array should be zero""" + """Vertical Scharr on a masked array should be zero""" np.random.seed(0) result = F.vscharr(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) @@ -189,7 +189,7 @@ def test_vscharr_mask(): def test_vscharr_vertical(): - """Vertical Sobel on an edge should be a vertical line""" + """Vertical Scharr on an edge should be a vertical line""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = F.vscharr(image) @@ -200,7 +200,7 @@ def test_vscharr_vertical(): def test_vscharr_horizontal(): - """vertical Sobel on a horizontal edge should be zero""" + """vertical Scharr on a horizontal edge should be zero""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = F.vscharr(image) From 239026a19dd65770639a7f22a000961adcb9af14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 13 Oct 2012 09:21:10 +0200 Subject: [PATCH 134/625] Add scharr filter to mask test cases --- skimage/filter/tests/test_edges.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/filter/tests/test_edges.py b/skimage/filter/tests/test_edges.py index c16b953d..628de16d 100644 --- a/skimage/filter/tests/test_edges.py +++ b/skimage/filter/tests/test_edges.py @@ -329,7 +329,7 @@ def test_horizontal_mask_line(): expected[1:-1, 1:-1] = 0.2 # constant gradient for most of image, expected[4:7, 1:-1] = 0 # but line and neighbors masked - for grad_func in (F.hprewitt, F.hsobel): + for grad_func in (F.hprewitt, F.hsobel, F.hscharr): result = grad_func(vgrad, mask) yield assert_close, result, expected @@ -346,7 +346,7 @@ def test_vertical_mask_line(): expected[1:-1, 1:-1] = 0.2 # constant gradient for most of image, expected[1:-1, 4:7] = 0 # but line and neighbors masked - for grad_func in (F.vprewitt, F.vsobel): + for grad_func in (F.vprewitt, F.vsobel, F.vscharr): result = grad_func(hgrad, mask) yield assert_close, result, expected From e83a456ebccdad73ae6a9d475e408d224c313a3f Mon Sep 17 00:00:00 2001 From: odebeir Date: Sat, 13 Oct 2012 09:47:25 +0200 Subject: [PATCH 135/625] fix pxd-pyx confusion --- skimage/rank/_core16.pxd | 273 +----------------------------------- skimage/rank/_core16.pyx | 283 +++++++++++++++++++++++++++++++++++++ skimage/rank/_core16b.pxd | 274 +----------------------------------- skimage/rank/_core16b.pyx | 284 ++++++++++++++++++++++++++++++++++++++ skimage/rank/_core16p.pxd | 272 +----------------------------------- skimage/rank/_core16p.pyx | 282 +++++++++++++++++++++++++++++++++++++ skimage/rank/_core8.pxd | 259 +--------------------------------- skimage/rank/_core8.pyx | 269 ++++++++++++++++++++++++++++++++++++ skimage/rank/_core8p.pxd | 255 +--------------------------------- skimage/rank/_core8p.pyx | 266 +++++++++++++++++++++++++++++++++++ skimage/rank/setup.py | 18 +++ 11 files changed, 1411 insertions(+), 1324 deletions(-) create mode 100644 skimage/rank/_core16.pyx create mode 100644 skimage/rank/_core16b.pyx create mode 100644 skimage/rank/_core16p.pyx create mode 100644 skimage/rank/_core8.pyx create mode 100644 skimage/rank/_core8p.pyx diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 1a3194de..d00ef37e 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -1,18 +1,4 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth @@ -23,261 +9,4 @@ np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out +char shift_x, char shift_y,int bitdepth) \ No newline at end of file diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx new file mode 100644 index 00000000..1a3194de --- /dev/null +++ b/skimage/rank/_core16.pyx @@ -0,0 +1,283 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data bitdepth +#--------------------------------------------------------------------------- + +cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 163662b5..b972ae9a 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -1,18 +1,4 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16b.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth and bilateral interval @@ -23,262 +9,4 @@ np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, int s0, int s1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - - only pixel inside [s0,s1] centered on g are taken into account - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out +char shift_x, char shift_y,int bitdepth, int s0, int s1) \ No newline at end of file diff --git a/skimage/rank/_core16b.pyx b/skimage/rank/_core16b.pyx new file mode 100644 index 00000000..163662b5 --- /dev/null +++ b/skimage/rank/_core16b.pyx @@ -0,0 +1,284 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16b.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data bitdepth and bilateral interval +#--------------------------------------------------------------------------- + +cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, int s0, int s1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + - only pixel inside [s0,s1] centered on g are taken into account + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 9e992b6b..d162540c 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -1,15 +1,8 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline int int_max(int a, int b) +cdef inline int int_min(int a, int b) #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data inferior and superior percentiles @@ -20,263 +13,4 @@ np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint16 - - result is uint16 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - - +char shift_x, char shift_y,int bitdepth, float p0, float p1) \ No newline at end of file diff --git a/skimage/rank/_core16p.pyx b/skimage/rank/_core16p.pyx new file mode 100644 index 00000000..9e992b6b --- /dev/null +++ b/skimage/rank/_core16p.pyx @@ -0,0 +1,282 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint16 + - result is uint16 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + + diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index d24297cb..aa3fe527 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -1,18 +1,4 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core8.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # 8 bit core kernel @@ -23,247 +9,4 @@ np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - +char shift_x, char shift_y) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx new file mode 100644 index 00000000..d24297cb --- /dev/null +++ b/skimage/rank/_core8.pyx @@ -0,0 +1,269 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core8.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +#--------------------------------------------------------------------------- +# 8 bit core kernel +#--------------------------------------------------------------------------- + +cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index b1adac70..8d3181e8 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -1,15 +1,8 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- # 8 bit core kernel receives extra information about data inferior and superior percentiles @@ -20,247 +13,5 @@ np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out +char shift_x, char shift_y, float p0, float p1) diff --git a/skimage/rank/_core8p.pyx b/skimage/rank/_core8p.pyx new file mode 100644 index 00000000..b1adac70 --- /dev/null +++ b/skimage/rank/_core8p.pyx @@ -0,0 +1,266 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 8 bit core kernel receives extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index efe23515..20a59979 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -5,6 +5,8 @@ from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) +import sys +sys.path.append('.') def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs @@ -12,12 +14,28 @@ def configuration(parent_package='', top_path=None): config = Configuration('rank', parent_package, top_path) # config.add_data_dir('tests') + + cython(['_core8.pyx'], working_path=base_path) + cython(['_core8p.pyx'], working_path=base_path) + cython(['_core16.pyx'], working_path=base_path) + cython(['_core16p.pyx'], working_path=base_path) + cython(['_core16b.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) cython(['_crank8_percentiles.pyx'], working_path=base_path) cython(['_crank16.pyx'], working_path=base_path) cython(['_crank16_percentiles.pyx'], working_path=base_path) cython(['_crank16_bilateral.pyx'], working_path=base_path) + config.add_extension('_core8', sources=['_core8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core8p', sources=['_core8p.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core16', sources=['_core16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core16p', sources=['_core16p.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core16b', sources=['_core16b.c'], + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], From b9df8405c1c62133be448b97a3e12118361682a6 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sat, 13 Oct 2012 10:09:06 +0200 Subject: [PATCH 136/625] restore setupfile --- skimage/rank/rank.py | 3 +++ skimage/rank/setup.py | 3 --- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index cefb06ac..52b3d570 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1022,5 +1022,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): raise TypeError("only uint8 and uint16 image supported!") if __name__ == "__main__": + import sys + sys.path.append('.') + import doctest doctest.testmod(verbose=True) \ No newline at end of file diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 20a59979..207ff18f 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -5,9 +5,6 @@ from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) -import sys -sys.path.append('.') - def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs From ed82b35fc0a3ed5ce5bac241e8347d47b3bbba94 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 13:21:33 +0200 Subject: [PATCH 137/625] remplace int by Py_ssize_t and fix some doctests --- skimage/rank/_core8.pyx | 38 +++++++++++----------- skimage/rank/rank.py | 70 ++++++++++++++++++++++++----------------- 2 files changed, 61 insertions(+), 47 deletions(-) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index d24297cb..cd997d89 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -29,13 +29,13 @@ char shift_x, char shift_y): - result is uint8 casted """ - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -56,8 +56,8 @@ char shift_x, char shift_y): out = np.ascontiguousarray(out) # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 + cdef Py_ssize_t erows = rows+srows-1 + cdef Py_ssize_t ecols = cols+scols-1 cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) @@ -76,14 +76,14 @@ char shift_x, char shift_y): cdef np.uint8_t* mask_data = mask.data # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row cdef float pop # number of pixels actually inside the neighborhood (float) # allocate memory with malloc - cdef int max_se = srows*scols + cdef Py_ssize_t max_se = srows*scols # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w + cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -92,14 +92,14 @@ char shift_x, char shift_y): # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) + cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 52b3d570..dafe0715 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -59,12 +59,13 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> autolevel(ima8, square(3)) + >>> rank.autolevel(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 0, 255, 0], @@ -76,7 +77,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> autolevel(ima16, square(3)) + >>> rank.autolevel(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 0, 4095, 0], @@ -130,12 +131,13 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bottomhat(ima8, square(3)) + >>> rank.bottomhat(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 0, 255, 0], @@ -147,7 +149,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bottomhat(ima16, square(3)) + >>> rank.bottomhat(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 0, 4095, 0], @@ -200,12 +202,13 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> equalize(ima8, square(3)) + >>> rank.equalize(ima8, square(3)) array([[191, 170, 127, 170, 191], [170, 255, 255, 255, 170], [127, 255, 255, 255, 127], @@ -217,7 +220,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> equalize(ima16, square(3)) + >>> rank.equalize(ima16, square(3)) array([[3071, 2730, 2047, 2730, 3071], [2730, 4095, 4095, 4095, 2730], [2047, 4095, 4095, 4095, 2047], @@ -270,12 +273,13 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local gradient >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> gradient(ima8, square(3)) + >>> rank.gradient(ima8, square(3)) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 0, 255, 255], @@ -287,7 +291,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> gradient(ima16, square(3)) + >>> rank.gradient(ima16, square(3)) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 0, 4095, 4095], @@ -342,12 +346,13 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local maximum >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 1, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> maximum(ima8, square(3)) + >>> rank.maximum(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], @@ -359,7 +364,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 0, 1, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> maximum(ima16, square(3)) + >>> rank.maximum(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -413,12 +418,13 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> mean(ima8, square(3)) + >>> rank.mean(ima8, square(3)) array([[ 63, 85, 127, 85, 63], [ 85, 113, 170, 113, 85], [127, 170, 255, 170, 127], @@ -430,7 +436,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> mean(ima16, square(3)) + >>> rank.mean(ima16, square(3)) array([[1023, 1365, 2047, 1365, 1023], [1365, 1820, 2730, 1820, 1365], [2047, 2730, 4095, 2730, 2047], @@ -484,12 +490,13 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F to be updated >>> # Local meansubstraction >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> meansubstraction(ima8, square(3)) + >>> rank.meansubstraction(ima8, square(3)) array([[ 95, 84, 63, 84, 95], [ 84, 197, 169, 197, 84], [ 63, 169, 127, 169, 63], @@ -501,7 +508,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> meansubstraction(ima16, square(3)) + >>> rank.meansubstraction(ima16, square(3)) array([[1535, 1364, 1023, 1364, 1535], [1364, 3184, 2729, 3184, 1364], [1023, 2729, 2047, 2729, 1023], @@ -555,12 +562,13 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local median >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 0, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> median(ima8, square(3)) + >>> rank.median(ima8, square(3)) array([[ 0, 0, 255, 0, 0], [ 0, 0, 255, 0, 0], [255, 255, 255, 255, 255], @@ -572,7 +580,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 0, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> median(ima16, square(3)) + >>> rank.median(ima16, square(3)) array([[ 0, 0, 4095, 0, 0], [ 0, 0, 4095, 0, 0], [4095, 4095, 4095, 4095, 4095], @@ -626,12 +634,13 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local minimum >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> minimum(ima8, square(3)) + >>> rank.minimum(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0], [ 0, 0, 255, 0, 0], @@ -644,7 +653,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> minimum(ima16, square(3)) + >>> rank.minimum(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0], [ 0, 0, 4095, 0, 0], @@ -698,12 +707,13 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local modal >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 5, 6, 0], ... [0, 1, 5, 5, 0], ... [0, 0, 0, 5, 0]], dtype=np.uint8) - >>> modal(ima8, square(3)) + >>> rank.modal(ima8, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 1, 0, 0], @@ -716,7 +726,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 5, 6, 0], ... [0, 1, 5, 5, 0], ... [0, 0, 0, 5, 0]], dtype=np.uint16) - >>> modal(ima16, square(3)) + >>> rank.modal(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 0, 100, 0, 0], [ 0, 100, 100, 0, 0], @@ -770,12 +780,13 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> morph_contr_enh(ima8, square(3)) + >>> rank.morph_contr_enh(ima8, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], @@ -787,7 +798,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> morph_contr_enh(ima16, square(3)) + >>> rank.morph_contr_enh(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -841,12 +852,13 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> pop(ima8, square(3)) + >>> rank.pop(ima8, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -858,7 +870,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> pop(ima16, square(3)) + >>> rank.pop(ima16, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -912,12 +924,13 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> threshold(ima8, square(3)) + >>> rank.threshold(ima8, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], @@ -929,7 +942,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> threshold(ima16, square(3)) + >>> rank.threshold(ima16, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], @@ -984,12 +997,13 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> tophat(ima8, square(3)) + >>> rank.tophat(ima8, square(3)) array([[255, 255, 255, 255, 255], [255, 0, 0, 0, 255], [255, 0, 0, 0, 255], @@ -1001,7 +1015,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> tophat(ima16, square(3)) + >>> rank.tophat(ima16, square(3)) array([[4095, 4095, 4095, 4095, 4095], [4095, 0, 0, 0, 4095], [4095, 0, 0, 0, 4095], From b2e4827e9b3c14dc7d4a6f480603fffe65db4758 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 13:26:28 +0200 Subject: [PATCH 138/625] remplace int by Py_ssize_t and for rank8 --- skimage/rank/_core8.pxd | 2 +- skimage/rank/_core8.pyx | 4 +-- skimage/rank/_crank8.pyx | 54 ++++++++++++++++++++-------------------- 3 files changed, 30 insertions(+), 30 deletions(-) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index aa3fe527..6dca9f61 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -4,7 +4,7 @@ cimport numpy as np # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index cd997d89..6dc59059 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -18,7 +18,7 @@ from libc.stdlib cimport malloc, free # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, @@ -86,7 +86,7 @@ char shift_x, char shift_y): cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) + cdef Py_ssize_t* histo = malloc(256 * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 96624eb5..68fbfba8 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,8 +21,8 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): - cdef int i,imin,imax,delta +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i,imin,imax,delta if pop: for i in range(255,-1,-1): @@ -41,8 +41,8 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i for i in range(256): if histo[i]: @@ -51,8 +51,8 @@ cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): return (g-i) -cdef inline np.uint8_t kernel_equalize(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float sum = 0. if pop: @@ -65,8 +65,8 @@ cdef inline np.uint8_t kernel_equalize(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g): - cdef int i,imin,imax +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i,imin,imax if pop: @@ -82,8 +82,8 @@ cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_maximum(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i if pop: for i in range(255,-1,-1): @@ -92,8 +92,8 @@ cdef inline np.uint8_t kernel_maximum(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -103,8 +103,8 @@ cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_meansubstraction(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -114,8 +114,8 @@ cdef inline np.uint8_t kernel_meansubstraction(int* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_median(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float sum = pop/2.0 if pop: @@ -127,8 +127,8 @@ cdef inline np.uint8_t kernel_median(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_minimum(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i if pop: for i in range(256): @@ -137,8 +137,8 @@ cdef inline np.uint8_t kernel_minimum(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_modal(int* histo, float pop, np.uint8_t g): - cdef int hmax=0,imax=0 +cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t hmax=0,imax=0 if pop: for i in range(256): @@ -149,8 +149,8 @@ cdef inline np.uint8_t kernel_modal(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t g): - cdef int i,imin,imax +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i,imin,imax if pop: for i in range(255,-1,-1): @@ -168,11 +168,11 @@ cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g): return (pop) -cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -182,8 +182,8 @@ cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_tophat(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i for i in range(255,-1,-1): if histo[i]: From e5e01e3b8f148585d487b12ba1eff66dbe8ee1fe Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 13:36:41 +0200 Subject: [PATCH 139/625] remplace int by Py_ssize_t and for rank16 --- skimage/rank/_core16.pxd | 4 +- skimage/rank/_core16.pyx | 46 +++++++++++----------- skimage/rank/_crank16.pyx | 82 +++++++++++++++++++-------------------- 3 files changed, 66 insertions(+), 66 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index d00ef37e..0efd4e04 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -4,9 +4,9 @@ cimport numpy as np # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth) \ No newline at end of file +char shift_x, char shift_y,Py_ssize_t bitdepth) \ No newline at end of file diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 1a3194de..00e229d9 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -18,24 +18,24 @@ from libc.stdlib cimport malloc, free # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth): +char shift_x, char shift_y,Py_ssize_t bitdepth): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted """ - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -49,7 +49,7 @@ char shift_x, char shift_y,int bitdepth): #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + cdef Py_ssize_t maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] assert (imagemask.data # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row cdef float pop # number of pixels actually inside the neighborhood (float) # allocate memory with malloc - cdef int max_se = srows*scols + cdef Py_ssize_t max_se = srows*scols # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w + cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) + cdef Py_ssize_t* histo = malloc(maxbin * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) + cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index 90a7e8bb..eb08326b 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -21,8 +21,8 @@ from _core16 cimport _core16 # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i,imin,imax,delta +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i,imin,imax,delta if pop: for i in range(maxbin-1,-1,-1): @@ -39,8 +39,8 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in else: return (imax-imin) -cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i for i in range(maxbin): if histo[i]: @@ -49,8 +49,8 @@ cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,in return (g-i) -cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float sum = 0. if pop: @@ -63,8 +63,8 @@ cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int else: return (0) -cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i,imin,imax +cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i,imin,imax if pop: for i in range(maxbin-1,-1,-1): @@ -79,8 +79,8 @@ cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int else: return (0) -cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i if pop: for i in range(maxbin-1,-1,-1): @@ -89,8 +89,8 @@ cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int return (0) -cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -100,8 +100,8 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -111,8 +111,8 @@ cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16 else: return (0) -cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float sum = pop/2.0 if pop: @@ -124,8 +124,8 @@ cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int b return (0) -cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i if pop: for i in range(maxbin): @@ -134,8 +134,8 @@ cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int return (0) -cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int hmax=0,imax=0 +cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t hmax=0,imax=0 if pop: for i in range(maxbin): @@ -146,8 +146,8 @@ cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bi return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i,imin,imax +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i,imin,imax if pop: for i in range(maxbin-1,-1,-1): @@ -165,11 +165,11 @@ cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): return (pop) -cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -179,8 +179,8 @@ cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,in else: return (0) -cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i for i in range(maxbin-1,-1,-1): if histo[i]: @@ -195,7 +195,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -204,7 +204,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -213,7 +213,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local egalisation of the gray level """ return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -222,7 +222,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum - local minimum gray level """ return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -231,7 +231,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum gray level """ return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -240,7 +240,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """average gray level (clipped on uint8) """ return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -249,7 +249,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -258,7 +258,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local median """ return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -267,7 +267,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local minimum gray level """ return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -276,7 +276,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """morphological contrast enhancement """ return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -285,7 +285,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local mode """ return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -294,7 +294,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -303,7 +303,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -312,7 +312,7 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """top hat """ return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) From ae73da922f7293a24cad3c974bb83d05fb7960c8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 14:45:25 +0200 Subject: [PATCH 140/625] remplace emask with is_in_mask function --- skimage/rank/_core8.pyx | 103 +++++++++++++++--------------- skimage/rank/tests/demo_single.py | 29 +++++++++ 2 files changed, 79 insertions(+), 53 deletions(-) create mode 100644 skimage/rank/tests/demo_single.py diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 6dc59059..baedd67a 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -18,6 +18,15 @@ from libc.stdlib cimport malloc, free # 8 bit core kernel #--------------------------------------------------------------------------- +cdef inline Py_ssize_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): + if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: + return 0 + else: + if mask[r*cols+c]: + return 1 + else: + return 0 + cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -55,21 +64,9 @@ char shift_x, char shift_y): else: out = np.ascontiguousarray(out) - # create extended image and mask - cdef Py_ssize_t erows = rows+srows-1 - cdef Py_ssize_t ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - mask = np.ascontiguousarray(mask) # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data cdef np.uint8_t* out_data = out.data cdef np.uint8_t* image_data = image.data @@ -145,18 +142,18 @@ char shift_x, char shift_y): for r in range(srows): for c in range(scols): - rr = r - cc = c + rr = r - centre_r + cc = c - centre_c if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. r = 0 c = 0 # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- # main loop @@ -165,22 +162,22 @@ char shift_x, char shift_y): # ---> west to east for c in range(1,cols): for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] - 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -189,43 +186,43 @@ char shift_x, char shift_y): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- # ---> east to west for c in range(cols-2,-1,-1): for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -234,22 +231,22 @@ char shift_x, char shift_y): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py new file mode 100644 index 00000000..028f27b2 --- /dev/null +++ b/skimage/rank/tests/demo_single.py @@ -0,0 +1,29 @@ +import numpy as np +import matplotlib.pyplot as plt +from pprint import pprint + +from skimage import data +from skimage.morphology.selem import disk +import skimage.rank as rank + + +if __name__ == '__main__': + a8 = data.camera() + a16 = data.camera().astype(np.uint16) + selem = disk(10) + + f8= rank.mean(a8,selem) + f16= rank.mean(a16,selem) + + print f8==f16 + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.subplot(1,2,2) + plt.imshow(f8-f16) + plt.show() + + + + From aa131ce67b2cbb1869f9ce65436767d4fcc2606c Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:03:29 +0200 Subject: [PATCH 141/625] add comment --- skimage/rank/_core8.pyx | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index baedd67a..72b0d6aa 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -18,7 +18,11 @@ from libc.stdlib cimport malloc, free # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline Py_ssize_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): + """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and + inside the given mask + returns 0 otherwise + """ if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: @@ -134,7 +138,7 @@ char shift_x, char shift_y): se_s_c[num_se_s] = c - centre_c num_se_s += 1 - # initial population and histogram + # initial population and histogram (kernel is centered on the first row and column) for i in range(256): histo[i] = 0 @@ -152,9 +156,9 @@ char shift_x, char shift_y): r = 0 c = 0 - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- # main loop r = 0 @@ -176,9 +180,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- r += 1 # pass to the next row if r>=rows: @@ -200,9 +204,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- # ---> east to west for c in range(cols-2,-1,-1): @@ -221,9 +225,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- r += 1 # pass to the next row if r>=rows: @@ -245,9 +249,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- # release memory allocated by malloc From 3dd08d71d012700bd31bf71aa5bb8972b9ce7dcc Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:16:26 +0200 Subject: [PATCH 142/625] remplace emask with is_in_mask function in crank16 --- skimage/rank/_core16.pyx | 109 +++++++++++++++--------------- skimage/rank/tests/demo_single.py | 2 +- 2 files changed, 56 insertions(+), 55 deletions(-) diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 00e229d9..6a004da7 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -18,6 +18,20 @@ from libc.stdlib cimport malloc, free # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): + """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and + inside the given mask + returns 0 otherwise + """ + if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: + return 0 + else: + if mask[r*cols+c]: + return 1 + else: + return 0 + + cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -65,22 +79,9 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): else: out = np.ascontiguousarray(out) - # create extended image and mask - cdef Py_ssize_t erows = rows+srows-1 - cdef Py_ssize_t ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint16) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - mask = np.ascontiguousarray(mask) # define pointers to the data - cdef np.uint16_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - cdef np.uint16_t* out_data = out.data cdef np.uint16_t* image_data = image.data cdef np.uint8_t* mask_data = mask.data @@ -155,18 +156,18 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): for r in range(srows): for c in range(scols): - rr = r - cc = c + rr = r - centre_r + cc = c - centre_c if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. r = 0 c = 0 # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], bitdepth,maxbin,midbin) # kernel ------------------------------------------- @@ -176,22 +177,22 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # ---> west to east for c in range(1,cols): for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] - 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], bitdepth,maxbin,midbin) # kernel ------------------------------------------- @@ -201,44 +202,44 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], bitdepth,maxbin,midbin) # kernel ------------------------------------------- # ---> east to west for c in range(cols-2,-1,-1): for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], bitdepth,maxbin,midbin) # kernel ------------------------------------------- @@ -248,22 +249,22 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], bitdepth,maxbin,midbin) # kernel ------------------------------------------- diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 028f27b2..5ec95d7b 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -19,7 +19,7 @@ if __name__ == '__main__': plt.figure() plt.subplot(1,2,1) - plt.imshow(a8) + plt.imshow(f16) plt.subplot(1,2,2) plt.imshow(f8-f16) plt.show() From f62b8d06d2aaecb9361c1831482339884b1b73ef Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:42:31 +0200 Subject: [PATCH 143/625] group core8,8p and 8b --- skimage/rank/_core8.pxd | 11 +++++--- skimage/rank/_core8.pyx | 25 +++++++++++------- skimage/rank/_crank8.pyx | 56 ++++++++++++++++++++-------------------- 3 files changed, 52 insertions(+), 40 deletions(-) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 6dca9f61..c0ac709d 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -1,12 +1,17 @@ cimport numpy as np +# generic cdef functions +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) + #--------------------------------------------------------------------------- -# 8 bit core kernel +# 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y) +char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) + diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 72b0d6aa..3ffbb5b9 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -14,6 +14,11 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free +# generic cdef functions +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b + + #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- @@ -31,12 +36,12 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, else: return 0 -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y): +char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -78,7 +83,9 @@ char shift_x, char shift_y): # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) + + # number of pixels actually inside the neighborhood (float) + cdef float pop # allocate memory with malloc cdef Py_ssize_t max_se = srows*scols @@ -157,7 +164,7 @@ char shift_x, char shift_y): r = 0 c = 0 # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- # main loop @@ -181,14 +188,14 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row if r>=rows: break - # ---> north to south + # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] @@ -205,7 +212,7 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- # ---> east to west @@ -226,7 +233,7 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row @@ -250,7 +257,7 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 68fbfba8..600cb00d 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,7 +21,7 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax,delta if pop: @@ -41,7 +41,7 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(256): @@ -51,7 +51,7 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t return (g-i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -65,7 +65,7 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax @@ -82,7 +82,7 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -92,7 +92,7 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -103,7 +103,7 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -114,7 +114,7 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np. else: return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop/2.0 @@ -127,7 +127,7 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g) return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -137,7 +137,7 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax=0,imax=0 if pop: @@ -149,7 +149,7 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -168,10 +168,10 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.u else: return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -182,7 +182,7 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(255,-1,-1): @@ -201,7 +201,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -210,7 +210,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -219,7 +219,7 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -228,7 +228,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -237,7 +237,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum gray level """ - return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -246,7 +246,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """average gray level (clipped on uint8) """ - return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -255,7 +255,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -264,7 +264,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local median """ - return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -273,7 +273,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local minimum gray level """ - return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -282,7 +282,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -291,7 +291,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local mode """ - return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -300,7 +300,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns the number of actual pixels of the structuring element inside the mask """ - return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -309,7 +309,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -318,5 +318,5 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """top hat """ - return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) From e47ef3b38e49421c4b4d7be3a607032544826714 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:55:21 +0200 Subject: [PATCH 144/625] delete core8p --- skimage/rank/_core8p.pxd | 17 -- skimage/rank/_core8p.pyx | 266 --------------------------- skimage/rank/_crank8_percentiles.pyx | 34 ++-- skimage/rank/setup.py | 3 - skimage/rank/tests/demo_single.py | 6 +- 5 files changed, 20 insertions(+), 306 deletions(-) delete mode 100644 skimage/rank/_core8p.pxd delete mode 100644 skimage/rank/_core8p.pyx diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd deleted file mode 100644 index 8d3181e8..00000000 --- a/skimage/rank/_core8p.pxd +++ /dev/null @@ -1,17 +0,0 @@ -cimport numpy as np - -# generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) - -#--------------------------------------------------------------------------- -# 8 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1) - diff --git a/skimage/rank/_core8p.pyx b/skimage/rank/_core8p.pyx deleted file mode 100644 index b1adac70..00000000 --- a/skimage/rank/_core8p.pyx +++ /dev/null @@ -1,266 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b - -#--------------------------------------------------------------------------- -# 8 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 2299f04a..16ad49d5 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -7,13 +7,13 @@ import numpy as np cimport numpy as np # import main loop -from _core8p cimport _core8p,uint8_max,uint8_min +from _core8 cimport _core8,uint8_max,uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -42,7 +42,7 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, flo return (128) -cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -65,7 +65,7 @@ cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, floa return (0) -cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -84,7 +84,7 @@ cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g, float p0 else: return (0) -cdef inline np.uint8_t kernel_mean_substraction(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -103,7 +103,7 @@ cdef inline np.uint8_t kernel_mean_substraction(int* histo, float pop, np.uint8_ else: return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -131,7 +131,7 @@ cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_percentile(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -145,7 +145,7 @@ cdef inline np.uint8_t kernel_percentile(int* histo, float pop, np.uint8_t g, fl else: return (0) -cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,n if pop: @@ -159,7 +159,7 @@ cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g, float p0, else: return (0) -cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -183,7 +183,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -193,7 +193,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -202,7 +202,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core8p(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -211,7 +211,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -220,7 +220,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -230,7 +230,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -240,7 +240,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core8p(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -249,4 +249,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 207ff18f..854748b8 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -13,7 +13,6 @@ def configuration(parent_package='', top_path=None): cython(['_core8.pyx'], working_path=base_path) - cython(['_core8p.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) cython(['_core16p.pyx'], working_path=base_path) cython(['_core16b.pyx'], working_path=base_path) @@ -25,8 +24,6 @@ def configuration(parent_package='', top_path=None): config.add_extension('_core8', sources=['_core8.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core8p', sources=['_core8p.c'], - include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16p', sources=['_core16p.c'], diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 5ec95d7b..7ff12b28 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -12,8 +12,8 @@ if __name__ == '__main__': a16 = data.camera().astype(np.uint16) selem = disk(10) - f8= rank.mean(a8,selem) - f16= rank.mean(a16,selem) + f8= rank.percentile_autolevel(a8,selem,p0=.0,p1=1.) + f16= rank.autolevel(a16,selem) print f8==f16 @@ -21,7 +21,7 @@ if __name__ == '__main__': plt.subplot(1,2,1) plt.imshow(f16) plt.subplot(1,2,2) - plt.imshow(f8-f16) + plt.imshow(f8) plt.show() From 3ba95a77af73761995249b020b25e18321f683cc Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:35:11 +0200 Subject: [PATCH 145/625] group crank16 and crank16p --- skimage/rank/_core16.pxd | 17 ++++-- skimage/rank/_core16.pyx | 27 +++++---- skimage/rank/_core8.pxd | 10 ++-- skimage/rank/_core8.pyx | 10 ++-- skimage/rank/_crank16.pyx | 84 ++++++++++++++++++--------- skimage/rank/_crank16_percentiles.pyx | 34 +++++------ skimage/rank/_crank8.pyx | 42 +++++++++----- 7 files changed, 138 insertions(+), 86 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 0efd4e04..f9bb47b3 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -4,9 +4,14 @@ cimport numpy as np # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,Py_ssize_t bitdepth) \ No newline at end of file +# generic cdef functions +cdef inline int int_max(int a, int b) +cdef inline int int_min(int a, int b) + +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint16_t, ndim=2] out, + char shift_x, char shift_y,Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) \ No newline at end of file diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 6a004da7..2e55aa8b 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -18,6 +18,10 @@ from libc.stdlib cimport malloc, free # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask @@ -32,12 +36,13 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, return 0 -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,Py_ssize_t bitdepth): +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint16_t, ndim=2] out, + char shift_x, char shift_y,Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -168,7 +173,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): c = 0 # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- # main loop @@ -193,7 +198,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -218,7 +223,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- # ---> east to west @@ -240,7 +245,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -265,7 +270,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index c0ac709d..a677e915 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -9,9 +9,9 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) + np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint8_t, ndim=2] out, + char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 3ffbb5b9..87588813 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -37,11 +37,11 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, return 0 cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint8_t, ndim=2] out, + char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index eb08326b..2fdda1e3 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -21,7 +21,9 @@ from _core16 cimport _core16 # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax,delta if pop: @@ -39,7 +41,9 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16 else: return (imax-imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin): @@ -49,7 +53,9 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16 return (g-i) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -63,7 +69,9 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -79,7 +87,9 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -89,7 +99,9 @@ cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -100,7 +112,9 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, else: return (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -111,7 +125,9 @@ cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np else: return (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop/2.0 @@ -124,7 +140,9 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t return (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -134,7 +152,9 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t return (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax=0,imax=0 if pop: @@ -146,7 +166,9 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -165,10 +187,14 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np. else: return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -179,7 +205,9 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16 else: return (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin-1,-1,-1): @@ -198,7 +226,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -207,7 +235,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -216,7 +244,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local egalisation of the gray level """ - return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -225,7 +253,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum - local minimum gray level """ - return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -234,7 +262,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum gray level """ - return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -243,7 +271,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """average gray level (clipped on uint8) """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -252,7 +280,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ - return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -261,7 +289,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local median """ - return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -270,7 +298,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local minimum gray level """ - return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -279,7 +307,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """morphological contrast enhancement """ - return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -288,7 +316,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local mode """ - return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -297,7 +325,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -306,7 +334,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ - return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -315,4 +343,4 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """top hat """ - return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 0f07196e..b270965b 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -7,13 +7,13 @@ import numpy as np cimport numpy as np # import main loop -from _core16p cimport _core16p,int_min,int_max +from _core16 cimport _core16,int_min,int_max # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -40,7 +40,7 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in return (0) -cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -63,7 +63,7 @@ cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int return (0) -cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -83,7 +83,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -cdef inline np.uint16_t kernel_mean_substraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -102,7 +102,7 @@ cdef inline np.uint16_t kernel_mean_substraction(int* histo, float pop, np.uint1 else: return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -130,7 +130,7 @@ cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_percentile(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -144,7 +144,7 @@ cdef inline np.uint16_t kernel_percentile(int* histo, float pop, np.uint16_t g,i else: return (0) -cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,n if pop: @@ -158,7 +158,7 @@ cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitd else: return (0) -cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -182,7 +182,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return _core16p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -192,7 +192,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -201,7 +201,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16p(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -210,7 +210,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -219,7 +219,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -229,7 +229,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return _core16p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -239,7 +239,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16p(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -248,4 +248,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 600cb00d..a0b20073 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,7 +21,8 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax,delta if pop: @@ -41,7 +42,8 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(256): @@ -51,7 +53,8 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t return (g-i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -65,7 +68,8 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax @@ -82,7 +86,8 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -92,7 +97,8 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -103,7 +109,8 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g,fl else: return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -114,7 +121,8 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np. else: return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop/2.0 @@ -127,7 +135,8 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g, return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -137,7 +146,8 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax=0,imax=0 if pop: @@ -149,7 +159,8 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g,f return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -168,10 +179,12 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.u else: return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -182,7 +195,8 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(255,-1,-1): From 4fd0857b87c3b74179605b48879547fb3d8bf5d2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:36:08 +0200 Subject: [PATCH 146/625] delete core16p --- skimage/rank/_core16p.pxd | 16 --- skimage/rank/_core16p.pyx | 282 -------------------------------------- skimage/rank/setup.py | 3 - 3 files changed, 301 deletions(-) delete mode 100644 skimage/rank/_core16p.pxd delete mode 100644 skimage/rank/_core16p.pyx diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd deleted file mode 100644 index d162540c..00000000 --- a/skimage/rank/_core16p.pxd +++ /dev/null @@ -1,16 +0,0 @@ -cimport numpy as np - -# generic cdef functions -cdef inline int int_max(int a, int b) -cdef inline int int_min(int a, int b) - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1) \ No newline at end of file diff --git a/skimage/rank/_core16p.pyx b/skimage/rank/_core16p.pyx deleted file mode 100644 index 9e992b6b..00000000 --- a/skimage/rank/_core16p.pyx +++ /dev/null @@ -1,282 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint16 - - result is uint16 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - - diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 854748b8..b7cac4dc 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -14,7 +14,6 @@ def configuration(parent_package='', top_path=None): cython(['_core8.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) - cython(['_core16p.pyx'], working_path=base_path) cython(['_core16b.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) cython(['_crank8_percentiles.pyx'], working_path=base_path) @@ -26,8 +25,6 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core16p', sources=['_core16p.c'], - include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16b', sources=['_core16b.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], From 9079d004bbd55058546e2f3c76337c2ff6fb9131 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:46:41 +0200 Subject: [PATCH 147/625] grou rank16 and rank16b, remove core16b --- skimage/rank/_core16b.pxd | 12 -- skimage/rank/_core16b.pyx | 284 ---------------------------- skimage/rank/_crank16_bilateral.pyx | 10 +- skimage/rank/setup.py | 3 - skimage/rank/tests/test_suite.py | 9 + 5 files changed, 14 insertions(+), 304 deletions(-) delete mode 100644 skimage/rank/_core16b.pxd delete mode 100644 skimage/rank/_core16b.pyx diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd deleted file mode 100644 index b972ae9a..00000000 --- a/skimage/rank/_core16b.pxd +++ /dev/null @@ -1,12 +0,0 @@ -cimport numpy as np - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data bitdepth and bilateral interval -#--------------------------------------------------------------------------- - -cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, int s0, int s1) \ No newline at end of file diff --git a/skimage/rank/_core16b.pyx b/skimage/rank/_core16b.pyx deleted file mode 100644 index 163662b5..00000000 --- a/skimage/rank/_core16b.pyx +++ /dev/null @@ -1,284 +0,0 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16b.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data bitdepth and bilateral interval -#--------------------------------------------------------------------------- - -cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, int s0, int s1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - - only pixel inside [s0,s1] centered on g are taken into account - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index e783d7ea..46028ad3 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -15,14 +15,14 @@ import numpy as np cimport numpy as np # import main loop -from _core16b cimport _core16b +from _core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,bilat_pop=0 cdef float mean = 0. @@ -39,7 +39,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit return (0) -cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,bilat_pop=0 if pop: @@ -61,7 +61,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,0.,0.,s0,s1) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -71,5 +71,5 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,s0,s1) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index b7cac4dc..e1f996f7 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -14,7 +14,6 @@ def configuration(parent_package='', top_path=None): cython(['_core8.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) - cython(['_core16b.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) cython(['_crank8_percentiles.pyx'], working_path=base_path) cython(['_crank16.pyx'], working_path=base_path) @@ -25,8 +24,6 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core16b', sources=['_core16b.c'], - include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 0887fa8f..e7d2436b 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -104,6 +104,15 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() + def test_compare_autolevels_16bit(self): + image = data.camera().astype(np.uint16) + + selem = disk(20) + loc_autolevel = rank.autolevel(image,selem=selem) + loc_perc_autolevel = rank.percentile_autolevel(image,selem=selem,p0=.0,p1=1.) + + assert (loc_autolevel==loc_perc_autolevel).all() + def test_compare_8bit_vs_16bit(self): # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical i8 = data.camera() From de164ce72611eb60328ce77c635e51b8040398a6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:56:36 +0200 Subject: [PATCH 148/625] find error in autolevel and percentile autolevel (16bit) --- skimage/rank/tests/demo_single.py | 5 +++-- skimage/rank/tests/test_suite.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 7ff12b28..96f1fbeb 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -9,11 +9,12 @@ import skimage.rank as rank if __name__ == '__main__': a8 = data.camera() - a16 = data.camera().astype(np.uint16) + a16 = data.camera().astype(np.uint16)*4 selem = disk(10) f8= rank.percentile_autolevel(a8,selem,p0=.0,p1=1.) f16= rank.autolevel(a16,selem) + f16p= rank.percentile_autolevel(a16,selem,p0=.0,p1=1.) print f8==f16 @@ -21,7 +22,7 @@ if __name__ == '__main__': plt.subplot(1,2,1) plt.imshow(f16) plt.subplot(1,2,2) - plt.imshow(f8) + plt.imshow(f16p-f16) plt.show() diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index e7d2436b..9ed39956 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -105,7 +105,7 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() def test_compare_autolevels_16bit(self): - image = data.camera().astype(np.uint16) + image = data.camera().astype(np.uint16)*4 selem = disk(20) loc_autolevel = rank.autolevel(image,selem=selem) From 2ce1a029245e1189861a07bb2aa5789788b6124f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 11:13:09 +0200 Subject: [PATCH 149/625] fix error in autolevel and percentile autolevel (16bit) --- skimage/rank/_crank16_percentiles.pyx | 2 +- skimage/rank/tests/demo_single.py | 13 ++++++++++--- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index b270965b..4fa3661c 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -33,7 +33,7 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16 delta = imax-imin if delta>0: - return (255*(int_min(int_max(imin,g),imax)-imin)/delta) + return (1.0*(maxbin-1)*(int_min(int_max(imin,g),imax)-imin)/delta) else: return (imax-imin) else: diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 96f1fbeb..b601b226 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -16,15 +16,22 @@ if __name__ == '__main__': f16= rank.autolevel(a16,selem) f16p= rank.percentile_autolevel(a16,selem,p0=.0,p1=1.) - print f8==f16 + print f16==f16p plt.figure() - plt.subplot(1,2,1) + plt.subplot(1,3,1) plt.imshow(f16) - plt.subplot(1,2,2) + plt.colorbar() + plt.subplot(1,3,2) + plt.imshow(f16p) + plt.colorbar() + plt.subplot(1,3,3) plt.imshow(f16p-f16) + plt.colorbar() plt.show() + print f16 + print f16p From 516cb8ffa9ced3059fedd0dd1d67f819aa8f6076 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 12:12:28 +0200 Subject: [PATCH 150/625] add histogram_increment and decrement to core8 --- skimage/rank/_core8.pyx | 48 ++++++++++++++++++----------------------- 1 file changed, 21 insertions(+), 27 deletions(-) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 87588813..19e09aec 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -23,6 +23,14 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b # 8 bit core kernel #--------------------------------------------------------------------------- +cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint8_t value): + histo[value] += 1 + pop[0] += 1. + +cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint8_t value): + histo[value] -= 1 + pop[0] -= 1. + cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask @@ -157,9 +165,7 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa cc = c - centre_c if selem[r, c]: if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) r = 0 c = 0 @@ -176,16 +182,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) @@ -200,16 +203,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) @@ -221,16 +221,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) @@ -245,16 +242,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) From b35c4a6633312c3d956d059325b4805544aa8e0b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 12:36:05 +0200 Subject: [PATCH 151/625] add histogram_increment and decrement to core16 --- skimage/rank/_core16.pyx | 51 ++++++++++++++--------------- skimage/rank/_core8.pyx | 5 ++- skimage/rank/tests/test_suite.py | 55 ++++++++++++++++++++++++++++++-- 3 files changed, 81 insertions(+), 30 deletions(-) diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 2e55aa8b..edef264b 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -22,6 +22,14 @@ from libc.stdlib cimport malloc, free cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint16_t value): + histo[value] += 1 + pop[0] += 1. + +cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint16_t value): + histo[value] -= 1 + pop[0] -= 1. + cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask @@ -79,6 +87,9 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ else: mask = np.ascontiguousarray(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + if out is None: out = np.zeros((rows, cols), dtype=np.uint16) else: @@ -165,9 +176,7 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ cc = c - centre_c if selem[r, c]: if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) r = 0 c = 0 @@ -185,16 +194,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], @@ -210,16 +216,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], @@ -232,16 +235,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], @@ -257,16 +257,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 19e09aec..86c40a6d 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -76,6 +76,9 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa else: mask = np.ascontiguousarray(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + if out is None: out = np.zeros((rows, cols), dtype=np.uint8) else: @@ -183,7 +186,7 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa cc = c + se_e_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): histogram_increment(histo,&pop,image_data[rr * cols + cc]) - + for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 9ed39956..d0e2c319 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -16,6 +16,7 @@ class TestSequenceFunctions(unittest.TestCase): def test_random_sizes(self): # make sure the size is not a problem + niter = 10 elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') for m,n in np.random.random_integers(1,100,size=(10,2)): @@ -39,8 +40,9 @@ class TestSequenceFunctions(unittest.TestCase): r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) self.assertTrue(a16.shape == r.shape) - def test_compare_with_cmorph(self): + def test_compare_with_cmorph_dilate(self): #compare the result of maximum filter with dilate + a = (np.random.random((500,500))*256).astype('uint8') for r in range(1,20,1): @@ -50,7 +52,21 @@ class TestSequenceFunctions(unittest.TestCase): cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) + def test_compare_with_cmorph_erode(self): + #compare the result of maximum filter with erode + + a = (np.random.random((500,500))*256).astype('uint8') + + for r in range(1,20,1): + elem = np.ones((r,r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc = _crank8.minimum(image=a,selem = elem) + cm = cmorph.erode(image=a,selem = elem) + self.assertTrue((rc==cm).all()) + def test_bitdepth(self): + # test the different bit depth for rank16 + elem = np.ones((3,3),dtype='uint8') a16 = np.ones((100,100),dtype='uint16')*255 r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) @@ -64,6 +80,8 @@ class TestSequenceFunctions(unittest.TestCase): r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) def test_population(self): + # check the number of valid pixels in the neighborhood + a = np.zeros((5,5),dtype='uint8') elem = np.ones((3,3),dtype='uint8') p = _crank8.pop(image=a,selem = elem) @@ -75,6 +93,8 @@ class TestSequenceFunctions(unittest.TestCase): np.testing.assert_array_equal(r,p) def test_structuring_element(self): + # check the output for a custom structuring element + a = np.zeros((6,6),dtype='uint8') a[2,2] = 255 elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') @@ -91,11 +111,37 @@ class TestSequenceFunctions(unittest.TestCase): @unittest.expectedFailure def test_fail_on_bitdepth(self): # should fail because data bitdepth is too high for the function + a16 = np.ones((100,100),dtype='uint16')*255 elem = np.ones((3,3),dtype='uint8') f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + def test_output(self): + #check rank function with external OUT output array + + selem = disk(20) + a = (np.random.random((500,500))*256).astype('uint8') + out = np.zeros_like(a) + f1 = rank.mean(a,selem,out=out) + f2 = rank.mean(a,selem) + np.testing.assert_array_equal(f1,f2) + np.testing.assert_array_equal(out,f2) + + @unittest.expectedFailure + def test_inplace_output(self): + #rank filters are not supposed to filter inplace + + selem = disk(20) + a = (np.random.random((500,500))*256).astype('uint8') + out = a + f = rank.mean(a,selem,out=out) + np.testing.assert_array_equal(f,out) + + def test_compare_autolevels(self): + # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0 + # should returns the same arrays + image = data.camera() selem = disk(20) @@ -105,6 +151,9 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() def test_compare_autolevels_16bit(self): + # compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and p1=1.0 + # should returns the same arrays + image = data.camera().astype(np.uint16)*4 selem = disk(20) @@ -114,7 +163,9 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() def test_compare_8bit_vs_16bit(self): - # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical + # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) + # should be identical + i8 = data.camera() i16 = i8.astype(np.uint16) assert (i8==i16).all() From 64c37c4e0ca43644cda9103efb0721a9386e783e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 14:40:42 +0200 Subject: [PATCH 152/625] keep true test in /test, move temporary tests in local --- skimage/rank/local/__init__.py | 1 + skimage/rank/{tests => local}/demo_16bitbilateral.py | 0 skimage/rank/{tests => local}/demo_all.py | 0 skimage/rank/{tests => local}/demo_benchmark.py | 2 +- skimage/rank/{tests => local}/demo_single.py | 0 skimage/rank/{tests => local}/test_morph_contr_enh.py | 0 skimage/rank/{tests => local}/test_rank.py | 0 skimage/rank/{tests => local}/tools.py | 0 skimage/rank/tests/test_suite.py | 2 +- 9 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 skimage/rank/local/__init__.py rename skimage/rank/{tests => local}/demo_16bitbilateral.py (100%) rename skimage/rank/{tests => local}/demo_all.py (100%) rename skimage/rank/{tests => local}/demo_benchmark.py (98%) rename skimage/rank/{tests => local}/demo_single.py (100%) rename skimage/rank/{tests => local}/test_morph_contr_enh.py (100%) rename skimage/rank/{tests => local}/test_rank.py (100%) rename skimage/rank/{tests => local}/tools.py (100%) diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py new file mode 100644 index 00000000..10b6fb15 --- /dev/null +++ b/skimage/rank/local/__init__.py @@ -0,0 +1 @@ +__author__ = 'olivier' diff --git a/skimage/rank/tests/demo_16bitbilateral.py b/skimage/rank/local/demo_16bitbilateral.py similarity index 100% rename from skimage/rank/tests/demo_16bitbilateral.py rename to skimage/rank/local/demo_16bitbilateral.py diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/local/demo_all.py similarity index 100% rename from skimage/rank/tests/demo_all.py rename to skimage/rank/local/demo_all.py diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/local/demo_benchmark.py similarity index 98% rename from skimage/rank/tests/demo_benchmark.py rename to skimage/rank/local/demo_benchmark.py index c3046083..feae3a8b 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/local/demo_benchmark.py @@ -6,7 +6,7 @@ from skimage.morphology import dilation import skimage.rank as rank from skimage.filter import median_filter -from tools import log_timing +from skimage.rank.local.tools import log_timing @log_timing def cr_max(image,selem): diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/local/demo_single.py similarity index 100% rename from skimage/rank/tests/demo_single.py rename to skimage/rank/local/demo_single.py diff --git a/skimage/rank/tests/test_morph_contr_enh.py b/skimage/rank/local/test_morph_contr_enh.py similarity index 100% rename from skimage/rank/tests/test_morph_contr_enh.py rename to skimage/rank/local/test_morph_contr_enh.py diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/local/test_rank.py similarity index 100% rename from skimage/rank/tests/test_rank.py rename to skimage/rank/local/test_rank.py diff --git a/skimage/rank/tests/tools.py b/skimage/rank/local/tools.py similarity index 100% rename from skimage/rank/tests/tools.py rename to skimage/rank/local/tools.py diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index d0e2c319..9d2bcfea 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -66,7 +66,7 @@ class TestSequenceFunctions(unittest.TestCase): def test_bitdepth(self): # test the different bit depth for rank16 - + elem = np.ones((3,3),dtype='uint8') a16 = np.ones((100,100),dtype='uint16')*255 r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) From 6e396f8598c9ad620297aeb75aeef26cc8cc0409 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 15:03:34 +0200 Subject: [PATCH 153/625] clean up --- doc/examples/plot_local_equalize.py | 9 +-- doc/examples/plot_local_threshold.py | 9 ++- doc/examples/plot_watershed.py | 2 +- skimage/rank/bilateral_rank.py | 14 ++-- skimage/rank/percentile_rank.py | 56 +++++++--------- skimage/rank/rank.py | 98 ++++++++++++---------------- 6 files changed, 82 insertions(+), 106 deletions(-) diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index ec28067d..897989f0 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -5,11 +5,9 @@ Local Histogram Equalization This examples enhances an image with low contrast, using a method called *local histogram equalization*, which "spreads out the most frequent intensity -values" in an image . The equalized image [1]_ has a roughly linear cumulative -distribution function for each pixel neighborhood. The local version [2]_ of the histogram -equalization emphasized every local graylevel variations. - -to be adjusted... +values" in an image . +The equalized image [1]_ has a roughly linear cumulative distribution function for each pixel neighborhood. +The local version [2]_ of the histogram equalization emphasized every local graylevel variations. .. [1] http://en.wikipedia.org/wiki/Histogram_equalization .. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization @@ -22,7 +20,6 @@ from skimage import exposure from skimage import rank from skimage.morphology import disk - import matplotlib.pyplot as plt import numpy as np diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py index 01077571..c5810156 100644 --- a/doc/examples/plot_local_threshold.py +++ b/doc/examples/plot_local_threshold.py @@ -14,9 +14,12 @@ calculates thresholds in regions of size `block_size` surrounding each pixel (i.e. local neighborhoods). Each threshold value is the weighted mean of the local neighborhood minus an offset value. -Added local threshold using rank filter +An other approach is to binarize locally the image using local histogram distribution. -to be adjusted ... +rank.threshold function set pixels higher than the local mean to 1, to 0 otherwize +rank.morph_contr_enh replaces each pixel by the local minimum (or local maximum) if the +pixel gray level is more close to the local minimum (resp. by the local maximum +if the pixel gray level is more close to the local maximum). """ import matplotlib.pyplot as plt @@ -36,7 +39,7 @@ binary_global = image > global_thresh block_size = 40 binary_adaptive = threshold_adaptive(image, block_size, offset=10) -selem = disk(10) +selem = disk(20) loc_thresh = threshold(image,selem=selem) loc_morph_contr_enh = morph_contr_enh(image,selem=selem) diff --git a/doc/examples/plot_watershed.py b/doc/examples/plot_watershed.py index 9fce196f..50857b2c 100644 --- a/doc/examples/plot_watershed.py +++ b/doc/examples/plot_watershed.py @@ -26,7 +26,7 @@ See Wikipedia_ for more details on the algorithm. """ import numpy as np -e + import matplotlib.pyplot as plt from skimage.morphology import watershed, is_local_maximum diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index a76133a6..cde9e736 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -35,10 +35,9 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int define the [s0,s1] interval to be considered for computing the value. @@ -110,10 +109,9 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int define the [s0,s1] interval to be considered for computing the value. diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 6cc273e3..64f76bcb 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -47,10 +47,9 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -120,10 +119,9 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -194,10 +192,9 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -267,10 +264,9 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -341,10 +337,9 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -414,10 +409,9 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -488,10 +482,9 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -561,10 +554,9 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index dafe0715..9d841d76 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -44,10 +44,9 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -116,10 +115,9 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -187,10 +185,9 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -258,10 +255,9 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -331,10 +327,9 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -403,10 +398,9 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -475,10 +469,9 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -547,10 +540,9 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -619,10 +611,9 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -692,10 +683,9 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -765,10 +755,9 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -837,10 +826,9 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -909,10 +897,9 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -982,10 +969,9 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- From 35ebb64c3fa5f119bbce6534d72bd9d216020985 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 16:20:12 +0200 Subject: [PATCH 154/625] compare ndimage.percentile in demo --- skimage/rank/local/demo_benchmark.py | 75 ++++++++++++++++------------ 1 file changed, 44 insertions(+), 31 deletions(-) diff --git a/skimage/rank/local/demo_benchmark.py b/skimage/rank/local/demo_benchmark.py index feae3a8b..d3c3d00a 100644 --- a/skimage/rank/local/demo_benchmark.py +++ b/skimage/rank/local/demo_benchmark.py @@ -1,44 +1,53 @@ import numpy as np import matplotlib.pyplot as plt +import time from skimage import data -from skimage.morphology import dilation -import skimage.rank as rank +from skimage.morphology import dilation,disk from skimage.filter import median_filter +from scipy.ndimage.filters import percentile_filter +import skimage.rank as rank -from skimage.rank.local.tools import log_timing +def log_timing(func): + """ Decorator that returns both function results and execution time + (result, ms) + """ + def wrapper(*arg): + t1 = time.time() + res = func(*arg) + t2 = time.time() + ms = (t2-t1)*1000.0 + print '%s took %0.3f ms' % (func.func_name, ms) + return (res,ms) + return wrapper -@log_timing -def cr_max(image,selem): - return rank.maximum(image=image,selem = selem) @log_timing def cr_med(image,selem): return rank.median(image=image,selem = selem) -@log_timing -def cm_dil(image,selem): - return dilation(image=image,selem = selem) - @log_timing def ctmf_med(image,radius): return median_filter(image=image,radius=radius) +@log_timing +def ndi_med(image,n): + return percentile_filter(image,50,size=n*2-1) def compare_dilate(): - """comparison between + """ Comparison between - crank.maximum rankfilter implementation - cmorph.dilate cython implementation on increasing structuring element size and increasing image size """ -# a = (np.random.random((500,500))*256).astype('uint8') a = data.camera() rec = [] e_range = range(1,20,1) for r in e_range: - elem = np.ones((r,r),dtype='uint8') +# elem = np.ones((r,r),dtype='uint8') + elem = disk(r+1) # elem = (np.random.random((r,r))>.5).astype('uint8') rc,ms_rc = cr_max(a,elem) rcm,ms_rcm = cm_dil(a,elem) @@ -56,7 +65,8 @@ def compare_dilate(): plt.imshow(np.hstack((rc,rcm))) r = 9 - elem = np.ones((r,r),dtype='uint8') +# elem = np.ones((r,r),dtype='uint8') + elem = disk(r+1) rec = [] s_range = range(100,1000,100) @@ -79,7 +89,7 @@ def compare_dilate(): plt.show() def compare_median(): - """comparison between + """ Comparison between - crank.median rankfilter implementation - ctmf.median_filter filter @@ -88,47 +98,50 @@ def compare_median(): a = data.camera() rec = [] - e_range = range(2,40,4) + e_range = range(2,30,4) for r in e_range: - elem = np.ones((2*r+1,2*r+1),dtype='uint8') - # elem = (np.random.random((r,r))>.5).astype('uint8') + elem = disk(r+1) rc,ms_rc = cr_med(a,elem) rctmf,ms_rctmf = ctmf_med(a,r) - rec.append((ms_rc,ms_rctmf)) + rndi,ms_ndi = ndi_med(a,r) + rec.append((ms_rc,ms_rctmf,ms_ndi)) # check if results are identical -# assert (rc==rctmf).all() + # obviously they cannot be identical since structuring element are different (octagon<>disk) + # assert (rc==rctmf).all() rec = np.asarray(rec) plt.figure() plt.title('increasing element size') plt.plot(e_range,rec) - plt.legend(['rank.median','ctmf.median_filter']) - plt.figure() - plt.imshow(np.hstack((rc,rctmf))) + plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('element radius') + plt.figure() + plt.imshow(np.hstack((rc,rctmf,rndi))) + plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') r = 9 - elem = np.ones((r*2+1,r*2+1),dtype='uint8') + elem = disk(r+1) rec = [] - s_range = range(100,1000,100) + s_range = [100,200,500,1000,2000] for s in s_range: a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = cr_max(a,elem) + (rc,ms_rc) = cr_med(a,elem) rctmf,ms_rctmf = ctmf_med(a,r) - rec.append((ms_rc,ms_rctmf)) -# assert (rc==rcm).all() + rndi,ms_ndi = ndi_med(a,r) + rec.append((ms_rc,ms_rctmf,ms_ndi)) + # check if results are identical + # obviously they cannot be identical since structuring element are different (octagon<>disk) + # assert (rc==rctmf).all() rec = np.asarray(rec) plt.figure() plt.title('increasing image size') plt.plot(s_range,rec) - plt.legend(['rank.median','ctmf.median_filter']) - plt.figure() - plt.imshow(np.hstack((rc,rctmf))) + plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('image size') From a9dac3689515a4ee74b6d7f088bab7bf46ef6cda Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 17:03:22 +0200 Subject: [PATCH 155/625] add _apply(func8,func16,...) helper function --- skimage/rank/bilateral_rank.py | 47 ++++--- skimage/rank/percentile_rank.py | 123 +++++-------------- skimage/rank/rank.py | 209 +++++++------------------------- 3 files changed, 89 insertions(+), 290 deletions(-) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index cde9e736..5fd66e21 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -1,4 +1,7 @@ """ + +note: 8 bit images are casted into 16 bit image here + :author: Olivier Debeir, 2012 :license: modified BSD """ @@ -16,6 +19,21 @@ import _crank16_bilateral __all__ = ['bilateral_mean','bilateral_pop'] +def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + image = image.astype(np.uint16) + elif image.dtype == np.uint16: + pass + else: + raise TypeError("only uint8 and uint16 image supported!") + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Return greyscale local bilateral_mean of an image. @@ -76,19 +94,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - image = image.astype(np.uint16) - elif image.dtype == np.uint16: - pass - else: - raise TypeError("only uint8 and uint16 image supported!") - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_bilateral.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + + return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -150,18 +157,6 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals [3, 4, 3, 4, 3]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - image = image.astype(np.uint16) - elif image.dtype == np.uint16: - pass - else: - raise TypeError("only uint8 and uint16 image supported!") - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_bilateral.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 64f76bcb..e7d760da 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -29,6 +29,20 @@ __all__ = ['percentile_autolevel','percentile_gradient', 'percentile_mean','percentile_mean_substraction', 'percentile_morph_contr_enh','percentile_pop'] +def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. @@ -88,18 +102,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile_gradient of an image. @@ -160,19 +164,8 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. @@ -233,18 +226,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean_substraction of an image. @@ -305,19 +288,8 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals [1536, 1365, 1024, 1365, 1536]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. @@ -378,18 +350,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile of an image. @@ -451,18 +413,8 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local pop of an image. @@ -523,18 +475,8 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal [4, 6, 6, 6, 4]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. @@ -596,16 +538,5 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) \ No newline at end of file diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 9d841d76..c080c291 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -26,6 +26,20 @@ import _crank16,_crank8 __all__ = ['autolevel','bottomhat','equalize','gradient','maximum','mean' ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] +def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local autolevel of an image. @@ -84,18 +98,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local bottomhat of an image. @@ -154,18 +158,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local equalize of an image. @@ -224,18 +218,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [2730, 4095, 4095, 4095, 2730], [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local gradient of an image. @@ -295,19 +279,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local maximum of an image. @@ -367,18 +340,8 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. @@ -438,18 +401,8 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local meansubstraction of an image. @@ -509,18 +462,8 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F [1535, 1364, 1023, 1364, 1535]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local median of an image. @@ -580,18 +523,8 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 4095, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.median, _crank16.median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local minimum of an image. @@ -652,18 +585,8 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.minimum, _crank16.minimum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local modal of an image. @@ -724,18 +647,8 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 500, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.modal, _crank16.modal, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local morph_contr_enh of an image. @@ -795,18 +708,8 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local pop of an image. @@ -866,18 +769,8 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [4, 6, 6, 6, 4]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.pop, _crank16.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local threshold of an image. @@ -938,18 +831,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local tophat of an image. @@ -1008,18 +891,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [4095, 0, 0, 0, 4095], [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) if __name__ == "__main__": import sys From a8291352e42c7484e9afe9e7fb605a069fdf56da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 17 Oct 2012 09:00:06 +0200 Subject: [PATCH 156/625] Improve description of scharr and sobel edge filters --- skimage/filter/edges.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/filter/edges.py b/skimage/filter/edges.py index f374634e..fcd9f548 100644 --- a/skimage/filter/edges.py +++ b/skimage/filter/edges.py @@ -35,7 +35,7 @@ def _mask_filter_result(result, mask): def sobel(image, mask=None): - """Calculate the absolute magnitude Sobel to find edges. + """Find the edge magnitude using the Sobel transform. Parameters ---------- @@ -134,7 +134,7 @@ def vsobel(image, mask=None): def scharr(image, mask=None): - """Calculate the absolute magnitude Scharr to find edges. + """Find the edge magnitude using the Scharr transform. Parameters ---------- From 120571b079788703d10905bbbfa0e57781d66ff0 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 15 Oct 2012 22:44:01 -0700 Subject: [PATCH 157/625] BUG: Allow RGB images to be passed to grey2rgb. --- skimage/color/colorconv.py | 13 ++++++++----- skimage/color/tests/test_colorconv.py | 7 +++++++ 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/skimage/color/colorconv.py b/skimage/color/colorconv.py index ec5551d6..2e7f9138 100644 --- a/skimage/color/colorconv.py +++ b/skimage/color/colorconv.py @@ -525,11 +525,14 @@ def gray2rgb(image): If the input is not 2-dimensional. """ - if image.ndim != 2: - raise ValueError('Gray-level image should be two-dimensional.') - - M, N = image.shape - return np.dstack((image, image, image)) + if image.ndim > 2: + return image + elif image.ndim == 2: + M, N = image.shape + return np.dstack((image, image, image)) + else: + raise ValueError('Gray-level image should be two-dimensional, ' + 'RGB or RGBA.') def xyz2lab(xyz): diff --git a/skimage/color/tests/test_colorconv.py b/skimage/color/tests/test_colorconv.py index 316d801a..1fc46b62 100644 --- a/skimage/color/tests/test_colorconv.py +++ b/skimage/color/tests/test_colorconv.py @@ -196,5 +196,12 @@ def test_gray2rgb(): assert_equal(z[..., 0], x) assert_equal(z[0, 1, :], [128, 128, 128]) + +def test_gray2rgb_rgb(): + x = np.random.random((5, 5, 4)) + y = gray2rgb(x) + assert_equal(x, y) + + if __name__ == "__main__": run_module_suite() From 4a0564a7697e6a0ff81163338ab3a28bdd6bddf2 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Tue, 16 Oct 2012 23:33:29 -0700 Subject: [PATCH 158/625] Add is_rgb and is_gray. --- skimage/color/colorconv.py | 51 +++++++++++++++++++-------- skimage/color/tests/test_colorconv.py | 16 +++++++-- 2 files changed, 51 insertions(+), 16 deletions(-) diff --git a/skimage/color/colorconv.py b/skimage/color/colorconv.py index 2e7f9138..38eab995 100644 --- a/skimage/color/colorconv.py +++ b/skimage/color/colorconv.py @@ -45,7 +45,7 @@ from __future__ import division __all__ = ['convert_colorspace', 'rgb2hsv', 'hsv2rgb', 'rgb2xyz', 'xyz2rgb', 'rgb2rgbcie', 'rgbcie2rgb', 'rgb2grey', 'rgb2gray', 'gray2rgb', - 'xyz2lab', 'lab2xyz', 'lab2rgb', 'rgb2lab' + 'xyz2lab', 'lab2xyz', 'lab2rgb', 'rgb2lab', 'is_rgb', 'is_gray' ] __docformat__ = "restructuredtext en" @@ -55,6 +55,31 @@ from scipy import linalg from ..util import dtype +def is_rgb(image): + """Test whether the image is RGB or RGBA. + + Parameters + ---------- + image : ndarray + Input image. + + """ + return (image.ndim == 3 and image.shape[2] in (3, 4)) + + +def is_gray(image): + """Test whether the image is gray (i.e. has only one color band). + + Parameters + ---------- + image : ndarray + Input image. + + """ + return image.ndim == 2 + + + def convert_colorspace(arr, fromspace, tospace): """Convert an image array to a new color space. @@ -281,7 +306,7 @@ rgbcie_from_rgb = np.dot(rgbcie_from_xyz, xyz_from_rgb) rgb_from_rgbcie = np.dot(rgb_from_xyz, xyz_from_rgbcie) -grey_from_rgb = np.array([[0.2125, 0.7154, 0.0721], +gray_from_rgb = np.array([[0.2125, 0.7154, 0.0721], [0, 0, 0], [0, 0, 0]]) @@ -458,7 +483,7 @@ def rgbcie2rgb(rgbcie): return _convert(rgb_from_rgbcie, rgbcie) -def rgb2grey(rgb): +def rgb2gray(rgb): """Compute luminance of an RGB image. Parameters @@ -475,7 +500,7 @@ def rgb2grey(rgb): Raises ------ ValueError - If `rgb2grey` is not a 3-D array of shape (.., .., 3) or + If `rgb2gray` is not a 3-D array of shape (.., .., 3) or (.., .., 4). References @@ -493,21 +518,21 @@ def rgb2grey(rgb): Examples -------- - >>> from skimage.color import rgb2grey + >>> from skimage.color import rgb2gray >>> from skimage import data >>> lena = data.lena() - >>> lena_grey = rgb2grey(lena) + >>> lena_gray = rgb2gray(lena) """ if rgb.ndim == 2: return rgb - return _convert(grey_from_rgb, rgb[:, :, :3])[..., 0] + return _convert(gray_from_rgb, rgb[:, :, :3])[..., 0] -rgb2gray = rgb2grey +rgb2grey = rgb2gray def gray2rgb(image): - """Create an RGB representation of a grey-level image. + """Create an RGB representation of a gray-level image. Parameters ---------- @@ -525,14 +550,12 @@ def gray2rgb(image): If the input is not 2-dimensional. """ - if image.ndim > 2: + if is_rgb(image): return image - elif image.ndim == 2: - M, N = image.shape + elif is_gray(image): return np.dstack((image, image, image)) else: - raise ValueError('Gray-level image should be two-dimensional, ' - 'RGB or RGBA.') + raise ValueError("Input image expected to be RGB, RGBA or gray.") def xyz2lab(xyz): diff --git a/skimage/color/tests/test_colorconv.py b/skimage/color/tests/test_colorconv.py index 1fc46b62..c09c9d22 100644 --- a/skimage/color/tests/test_colorconv.py +++ b/skimage/color/tests/test_colorconv.py @@ -25,10 +25,11 @@ from skimage.color import ( convert_colorspace, rgb2grey, gray2rgb, xyz2lab, lab2xyz, - lab2rgb, rgb2lab + lab2rgb, rgb2lab, + is_rgb, is_gray ) -from skimage import data_dir +from skimage import data_dir, data import colorsys @@ -203,5 +204,16 @@ def test_gray2rgb_rgb(): assert_equal(x, y) +def test_is_rgb(): + color = data.lena() + gray = data.camera() + + assert is_rgb(color) + assert not is_gray(color) + + assert is_gray(gray) + assert not is_gray(color) + + if __name__ == "__main__": run_module_suite() From b0807739efaa87e412d2c0d4a6efe3652e7acb13 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 11:51:08 +0200 Subject: [PATCH 159/625] Use common file for denoising filters --- skimage/filter/__init__.py | 2 +- skimage/filter/{_tv_denoise.py => denoise.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename skimage/filter/{_tv_denoise.py => denoise.py} (100%) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index f76556df..dc5f9896 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,6 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from ._tv_denoise import tv_denoise +from .denoise import tv_denoise from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_tv_denoise.py b/skimage/filter/denoise.py similarity index 100% rename from skimage/filter/_tv_denoise.py rename to skimage/filter/denoise.py From 25867b9dd5f916ac22f60075c4206a4aa82b75e1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 11:55:27 +0200 Subject: [PATCH 160/625] Improve doc string format of TV denoise functions --- skimage/filter/denoise.py | 55 ++++++++++++++++----------------------- 1 file changed, 22 insertions(+), 33 deletions(-) diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index 3302319c..ae4d2f82 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -3,35 +3,32 @@ from skimage import img_as_float def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200): - """ - Perform total-variation denoising on 3-D arrays + """Perform total-variation denoising on 3-D arrays. Parameters ---------- im: ndarray - 3-D input data to be denoised - + 3-D input data to be denoised. weight: float, optional - denoising weight. The greater ``weight``, the more denoising (at - the expense of fidelity to ``input``) - + Denoising weight. The greater ``weight``, the more denoising (at + the expense of fidelity to ``input``). eps: float, optional - relative difference of the value of the cost function that determines + Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when: (E_(n-1) - E_n) < eps * E_0 n_iter_max: int, optional - maximal number of iterations used for the optimization. + Maximal number of iterations used for the optimization. Returns ------- out: ndarray - denoised array of floats + Denoised array of floats. Notes ----- - Rudin, Osher and Fatemi algorithm + Rudin, Osher and Fatemi algorithm. Examples --------- @@ -86,43 +83,39 @@ def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200): def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200): - """ - Perform total-variation denoising + """Perform total-variation denoising. Parameters ---------- im: ndarray - input data to be denoised - + Input data to be denoised. weight: float, optional - denoising weight. The greater ``weight``, the more denoising (at + Denoising weight. The greater ``weight``, the more denoising (at the expense of fidelity to ``input``) - eps: float, optional - relative difference of the value of the cost function that determines + Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when: (E_(n-1) - E_n) < eps * E_0 n_iter_max: int, optional - maximal number of iterations used for the optimization. + Maximal number of iterations used for the optimization. Returns ------- out: ndarray - denoised array of floats + Denoised array of floats. Notes ----- The principle of total variation denoising is explained in - http://en.wikipedia.org/wiki/Total_variation_denoising + http://en.wikipedia.org/wiki/Total_variation_denoising. This code is an implementation of the algorithm of Rudin, Fatemi and Osher that was proposed by Chambolle in [1]_. References ---------- - .. [1] A. Chambolle, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, Springer, 2004, 20, 89-97. @@ -173,33 +166,30 @@ def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200): def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200): - """ - Perform total-variation denoising on 2-d and 3-d images + """Perform total-variation denoising on 2-d and 3-d images. Parameters ---------- im: ndarray (2d or 3d) of ints, uints or floats - input data to be denoised. `im` can be of any numeric type, + Input data to be denoised. `im` can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. - weight: float, optional - denoising weight. The greater ``weight``, the more denoising (at - the expense of fidelity to ``input``) - + Denoising weight. The greater ``weight``, the more denoising (at + the expense of fidelity to ``input``). eps: float, optional - relative difference of the value of the cost function that + Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when: (E_(n-1) - E_n) < eps * E_0 n_iter_max: int, optional - maximal number of iterations used for the optimization. + Maximal number of iterations used for the optimization. Returns ------- out: ndarray - denoised array of floats + Denoised array of floats. Notes ----- @@ -217,7 +207,6 @@ def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200): References ---------- - .. [1] A. Chambolle, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, Springer, 2004, 20, 89-97. From 75d706ca2558e5b19bc7ae08ead10a6bc466ca5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 5 Oct 2012 23:09:34 +0200 Subject: [PATCH 161/625] Add get_pixel function for 3D arrays --- skimage/_shared/interpolation.pxd | 7 ++-- skimage/_shared/interpolation.pyx | 54 +++++++++++++++++++++++++------ 2 files changed, 49 insertions(+), 12 deletions(-) diff --git a/skimage/_shared/interpolation.pxd b/skimage/_shared/interpolation.pxd index f43ff25e..4e00dfb0 100644 --- a/skimage/_shared/interpolation.pxd +++ b/skimage/_shared/interpolation.pxd @@ -18,7 +18,10 @@ cdef double bicubic_interpolation(double* image, int rows, int cols, double r, double c, char mode, double cval) -cdef double get_pixel(double* image, int rows, int cols, int r, int c, - char mode, double cval) +cdef double get_pixel2d(double* image, int rows, int cols, int r, int c, + char mode, double cval) + +cdef double get_pixel3d(double* image, int rows, int cols, int dims, int r, + int c, int d, char mode, double cval) cdef int coord_map(int dim, int coord, char mode) diff --git a/skimage/_shared/interpolation.pyx b/skimage/_shared/interpolation.pyx index e0fd0067..b34ba507 100644 --- a/skimage/_shared/interpolation.pyx +++ b/skimage/_shared/interpolation.pyx @@ -35,8 +35,8 @@ cdef inline double nearest_neighbour_interpolation(double* image, int rows, """ - return get_pixel(image, rows, cols, round(r), round(c), - mode, cval) + return get_pixel2d(image, rows, cols, round(r), round(c), + mode, cval) cdef inline double bilinear_interpolation(double* image, int rows, int cols, @@ -72,10 +72,10 @@ cdef inline double bilinear_interpolation(double* image, int rows, int cols, maxc = ceil(c) dr = r - minr dc = c - minc - top = (1 - dc) * get_pixel(image, rows, cols, minr, minc, mode, cval) \ - + dc * get_pixel(image, rows, cols, minr, maxc, mode, cval) - bottom = (1 - dc) * get_pixel(image, rows, cols, maxr, minc, mode, cval) \ - + dc * get_pixel(image, rows, cols, maxr, maxc, mode, cval) + top = (1 - dc) * get_pixel2d(image, rows, cols, minr, minc, mode, cval) \ + + dc * get_pixel2d(image, rows, cols, minr, maxc, mode, cval) + bottom = (1 - dc) * get_pixel2d(image, rows, cols, maxr, minc, mode, cval) \ + + dc * get_pixel2d(image, rows, cols, maxr, maxc, mode, cval) return (1 - dr) * top + dr * bottom @@ -144,7 +144,7 @@ cdef inline double biquadratic_interpolation(double* image, int rows, int cols, # row-wise cubic interpolation for pr in range(r0, r0 + 3): for pc in range(c0, c0 + 3): - fc[pc - c0] = get_pixel(image, rows, cols, pr, pc, mode, cval) + fc[pc - c0] = get_pixel2d(image, rows, cols, pr, pc, mode, cval) fr[pr - r0] = quadratic_interpolation(xc, fc) # cubic interpolation for interpolated values of each row @@ -216,15 +216,15 @@ cdef inline double bicubic_interpolation(double* image, int rows, int cols, # row-wise cubic interpolation for pr in range(r0, r0 + 4): for pc in range(c0, c0 + 4): - fc[pc - c0] = get_pixel(image, rows, cols, pr, pc, mode, cval) + fc[pc - c0] = get_pixel2d(image, rows, cols, pr, pc, mode, cval) fr[pr - r0] = cubic_interpolation(xc, fc) # cubic interpolation for interpolated values of each row return cubic_interpolation(xr, fr) -cdef inline double get_pixel(double* image, int rows, int cols, int r, int c, - char mode, double cval): +cdef inline double get_pixel2d(double* image, int rows, int cols, int r, int c, + char mode, double cval): """Get a pixel from the image, taking wrapping mode into consideration. Parameters @@ -255,6 +255,40 @@ cdef inline double get_pixel(double* image, int rows, int cols, int r, int c, return image[coord_map(rows, r, mode) * cols + coord_map(cols, c, mode)] +cdef inline double get_pixel3d(double* image, int rows, int cols, int dims, int r, + int c, int d, char mode, double cval): + """Get a pixel from the image, taking wrapping mode into consideration. + + Parameters + ---------- + image : double array + Input image. + rows, cols, dims : int + Shape of image. + r, c, d : int + Position at which to get the pixel. + mode : {'C', 'W', 'R', 'N'} + Wrapping mode. Constant, Wrap, Reflect or Nearest. + cval : double + Constant value to use for constant mode. + + Returns + ------- + value : double + Pixel value at given position. + + """ + if mode == 'C': + if (r < 0) or (r > rows - 1) or (c < 0) or (c > cols - 1): + return cval + else: + return image[r * cols * dims + c * dims + d] + else: + return image[coord_map(rows, r, mode) * cols * dims + + coord_map(cols, c, mode) * dims + + d] + + cdef inline int coord_map(int dim, int coord, char mode): """ Wrap a coordinate, according to a given mode. From f9ecd140d8942f1db6226df4e7a15d56d79d922c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 00:34:05 +0200 Subject: [PATCH 162/625] Add bilateral denoising filter --- skimage/filter/__init__.py | 2 +- skimage/filter/_denoise.pyx | 160 ++++++++++++++++++++++++++++++++++++ skimage/filter/denoise.py | 57 +++++++++++++ skimage/filter/setup.py | 3 + 4 files changed, 221 insertions(+), 1 deletion(-) create mode 100644 skimage/filter/_denoise.pyx diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index dc5f9896..eec88038 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,6 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from .denoise import tv_denoise +from .denoise import tv_denoise, denoise_bilateral from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx new file mode 100644 index 00000000..bf6c372a --- /dev/null +++ b/skimage/filter/_denoise.pyx @@ -0,0 +1,160 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +cimport numpy as cnp +import numpy as np +from libc.math cimport exp, fabs, sqrt +from libc.stdlib cimport malloc, free +from skimage._shared.interpolation cimport get_pixel2d, get_pixel3d + + +cdef inline double _gaussian_weight(double sigma, double value): + return exp(- 0.5 * (value / sigma)**2) + + +cdef double* _compute_color_lut(int bins, double sigma, double max_value): + cdef double* color_lut = malloc(bins * sizeof(double)) + cdef Py_ssize_t b + for b in range(bins): + color_lut[b] = _gaussian_weight(sigma, b * max_value / bins) + return color_lut + + +cdef double* _compute_range_lut(int win_size, double sigma): + cdef double* range_lut = malloc(win_size**2 * sizeof(double)) + cdef Py_ssize_t kr, kc + cdef Py_ssize_t window_ext = (win_size - 1) / 2 + cdef double dist + for kr in range(win_size): + for kc in range(win_size): + dist = sqrt((kr - window_ext)**2 + (kc - window_ext)**2) + range_lut[kr * win_size + kc] = _gaussian_weight(sigma, dist) + return range_lut + + +def _denoise_bilateral2d(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image, + int win_size, double sigma_color, + double sigma_range, int bins, char mode, + double cval): + + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t window_ext = (win_size - 1) / 2 + cdef double max_value = image.max() + + cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \ + np.zeros((rows, cols), dtype=np.double) + + cdef double* image_data = image.data + cdef double* out_data = out.data + + cdef double* color_lut = _compute_color_lut(bins, sigma_color, max_value) + cdef double* range_lut = _compute_range_lut(win_size, sigma_range) + + cdef Py_ssize_t r, c, wr, wc, kr, kc, rr, cc, pixel_addr + cdef double centre, value, weight, total_value, total_weight, \ + color_weight, range_weight, diff + cdef double dist_scale = bins / max_value + + for r in range(rows): + for c in range(cols): + pixel_addr = r * cols + c + total_value = 0 + total_weight = 0 + centre = image_data[pixel_addr] + for wr in range(- window_ext, window_ext + 1): + rr = wr + r + kr = wr + window_ext + for wc in range(- window_ext, window_ext + 1): + cc = wc + c + kc = wc + window_ext + + value = get_pixel2d(image_data, rows, cols, + rr, cc, mode, cval) + diff = fabs(centre - value) + + range_weight = range_lut[kr * win_size + kc] + color_weight = color_lut[(diff * dist_scale)] + + weight = range_weight * color_weight + total_value += value * weight + total_weight += weight + + out_data[pixel_addr] = total_value / total_weight + + free(color_lut) + free(range_lut) + + return out + + +def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image, + int win_size, double sigma_color, + double sigma_range, int bins, char mode, + double cval): + + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t dims = image.shape[2] + cdef Py_ssize_t window_ext = (win_size - 1) / 2 + cdef double max_value = image.max() + + cdef cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] out = \ + np.zeros((rows, cols, dims), dtype=np.double) + + cdef double* image_data = image.data + cdef double* out_data = out.data + + cdef double* color_lut = _compute_color_lut(bins, sigma_color, max_value) + cdef double* range_lut = _compute_range_lut(win_size, sigma_range) + + cdef Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr + cdef double value, weight, dist, total_weight, color_weight, range_weight + cdef double dist_scale = bins / dims / max_value + cdef double* values = malloc(dims * sizeof(double)) + cdef double* centres = malloc(dims * sizeof(double)) + cdef double* total_values = malloc(dims * sizeof(double)) + + for r in range(rows): + for c in range(cols): + pixel_addr = r * cols * dims + c * dims + total_weight = 0 + for d in range(dims): + total_values[d] = 0 + centres[d] = image_data[pixel_addr + d] + for wr in range(- window_ext, window_ext + 1): + rr = wr + r + kr = wr + window_ext + for wc in range(- window_ext, window_ext + 1): + cc = wc + c + kc = wc + window_ext + + # save pixel values for all dims and compute euclidian + # distance between centre stack and current position + dist = 0 + for d in range(dims): + value = get_pixel3d(image_data, rows, cols, dims, + rr, cc, d, mode, cval) + values[d] = value + dist += (centres[d] - value)**2 + dist = sqrt(dist) + + range_weight = range_lut[kr * win_size + kc] + color_weight = color_lut[(dist * dist_scale)] + + weight = range_weight * color_weight + for d in range(dims): + total_values[d] += values[d] * weight + total_weight += weight + for d in range(dims): + out_data[pixel_addr + d] = total_values[d] / total_weight + + free(color_lut) + free(range_lut) + free(values) + free(centres) + free(total_values) + + return out diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index ae4d2f82..f6b30f6f 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -1,5 +1,6 @@ import numpy as np from skimage import img_as_float +import _denoise def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200): @@ -239,3 +240,59 @@ def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200): raise ValueError('only 2-d and 3-d images may be denoised with this ' 'function') return out + + +def denoise_bilateral(image, win_size=5, sigma_color=1, sigma_range=1, bins=1e4, + mode='constant', cval=0): + """Denoise image using bilateral filter. + + Parameters + ---------- + image : ndarray + Input image. + win_size : int + Window size for filtering. + sigma_color : float + Standard deviation for color distance. A larger value results in + averaging of pixels with larger color differences. + sigma_range : float + Standard deviation for range distance. A larger value results in + averaging of pixels with larger spatial differences. + bins : int + Number of discrete values for gaussian weights of color filtering. + A larger value results in improved accuracy. + mode : string + How to handle values outside the image borders. See + `scipy.ndimage.map_coordinates` for detail. + cval : string + Used in conjunction with mode 'constant', the value outside + the image boundaries. + + Returns + ------- + denoised : ndarray + Denoised image. + + References + ---------- + .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf + + """ + + # not using img_as_float to preserve original range of values, which is + # necessary so sigma_color is applied as user desires + image = np.array(image, dtype=np.double) + + if mode not in ('constant', 'wrap', 'reflect', 'nearest'): + raise ValueError("Invalid mode specified. Please use " + "`constant`, `nearest`, `wrap` or `reflect`.") + mode = ord(mode[0].upper()) + + if image.ndim == 2 or (image.ndim == 3 and image.shape[2] == 1): + if image.ndim == 3 and image.shape[2] == 1: + image = np.squeeze(image) + func = _denoise._denoise_bilateral2d + else: + func = _denoise._denoise_bilateral3d + image = np.ascontiguousarray(image) + return func(image, win_size, sigma_color, sigma_range, bins, mode, cval) diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 3e573aec..b996055b 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -13,9 +13,12 @@ def configuration(parent_package='', top_path=None): config.add_data_dir('tests') cython(['_ctmf.pyx'], working_path=base_path) + cython(['_denoise.pyx'], working_path=base_path) config.add_extension('_ctmf', sources=['_ctmf.c'], include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_denoise', sources=['_denoise.c'], + include_dirs=[get_numpy_include_dirs(), '../_shared']) return config From cec7b6055d21ae0a9ac65b10d9fbc73feac70979 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 08:32:08 +0200 Subject: [PATCH 163/625] Restructure cdef declarations --- skimage/filter/_denoise.pyx | 85 +++++++++++++++++++++---------------- 1 file changed, 48 insertions(+), 37 deletions(-) diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index bf6c372a..b0da5ddb 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -15,22 +15,30 @@ cdef inline double _gaussian_weight(double sigma, double value): cdef double* _compute_color_lut(int bins, double sigma, double max_value): - cdef double* color_lut = malloc(bins * sizeof(double)) - cdef Py_ssize_t b + + cdef: + double* color_lut = malloc(bins * sizeof(double)) + Py_ssize_t b + for b in range(bins): color_lut[b] = _gaussian_weight(sigma, b * max_value / bins) + return color_lut cdef double* _compute_range_lut(int win_size, double sigma): - cdef double* range_lut = malloc(win_size**2 * sizeof(double)) - cdef Py_ssize_t kr, kc - cdef Py_ssize_t window_ext = (win_size - 1) / 2 - cdef double dist + + cdef: + double* range_lut = malloc(win_size**2 * sizeof(double)) + Py_ssize_t kr, kc + Py_ssize_t window_ext = (win_size - 1) / 2 + double dist + for kr in range(win_size): for kc in range(win_size): dist = sqrt((kr - window_ext)**2 + (kc - window_ext)**2) range_lut[kr * win_size + kc] = _gaussian_weight(sigma, dist) + return range_lut @@ -39,24 +47,25 @@ def _denoise_bilateral2d(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image double sigma_range, int bins, char mode, double cval): - cdef Py_ssize_t rows = image.shape[0] - cdef Py_ssize_t cols = image.shape[1] - cdef Py_ssize_t window_ext = (win_size - 1) / 2 - cdef double max_value = image.max() + cdef: + Py_ssize_t rows = image.shape[0] + Py_ssize_t cols = image.shape[1] + Py_ssize_t window_ext = (win_size - 1) / 2 + double max_value = image.max() - cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \ - np.zeros((rows, cols), dtype=np.double) + cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \ + np.zeros((rows, cols), dtype=np.double) - cdef double* image_data = image.data - cdef double* out_data = out.data + double* image_data = image.data + double* out_data = out.data - cdef double* color_lut = _compute_color_lut(bins, sigma_color, max_value) - cdef double* range_lut = _compute_range_lut(win_size, sigma_range) + double* color_lut = _compute_color_lut(bins, sigma_color, max_value) + double* range_lut = _compute_range_lut(win_size, sigma_range) - cdef Py_ssize_t r, c, wr, wc, kr, kc, rr, cc, pixel_addr - cdef double centre, value, weight, total_value, total_weight, \ - color_weight, range_weight, diff - cdef double dist_scale = bins / max_value + Py_ssize_t r, c, wr, wc, kr, kc, rr, cc, pixel_addr + double centre, value, weight, total_value, total_weight, \ + color_weight, range_weight, diff + double dist_scale = bins / max_value for r in range(rows): for c in range(cols): @@ -95,27 +104,29 @@ def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image double sigma_range, int bins, char mode, double cval): - cdef Py_ssize_t rows = image.shape[0] - cdef Py_ssize_t cols = image.shape[1] - cdef Py_ssize_t dims = image.shape[2] - cdef Py_ssize_t window_ext = (win_size - 1) / 2 - cdef double max_value = image.max() + cdef: + Py_ssize_t rows = image.shape[0] + Py_ssize_t cols = image.shape[1] + Py_ssize_t dims = image.shape[2] + Py_ssize_t window_ext = (win_size - 1) / 2 - cdef cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] out = \ - np.zeros((rows, cols, dims), dtype=np.double) + double max_value = image.max() - cdef double* image_data = image.data - cdef double* out_data = out.data + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] out = \ + np.zeros((rows, cols, dims), dtype=np.double) - cdef double* color_lut = _compute_color_lut(bins, sigma_color, max_value) - cdef double* range_lut = _compute_range_lut(win_size, sigma_range) + double* image_data = image.data + double* out_data = out.data - cdef Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr - cdef double value, weight, dist, total_weight, color_weight, range_weight - cdef double dist_scale = bins / dims / max_value - cdef double* values = malloc(dims * sizeof(double)) - cdef double* centres = malloc(dims * sizeof(double)) - cdef double* total_values = malloc(dims * sizeof(double)) + double* color_lut = _compute_color_lut(bins, sigma_color, max_value) + double* range_lut = _compute_range_lut(win_size, sigma_range) + + Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr + double value, weight, dist, total_weight, color_weight, range_weight + double dist_scale = bins / dims / max_value + double* values = malloc(dims * sizeof(double)) + double* centres = malloc(dims * sizeof(double)) + double* total_values = malloc(dims * sizeof(double)) for r in range(rows): for c in range(cols): From 3757e5c5eb0f142d6f23dcab3cfae98811c014f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 22:03:04 +0200 Subject: [PATCH 164/625] Use common file for denoising filters --- skimage/filter/tests/{test_tv_denoise.py => test_denoise.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename skimage/filter/tests/{test_tv_denoise.py => test_denoise.py} (100%) diff --git a/skimage/filter/tests/test_tv_denoise.py b/skimage/filter/tests/test_denoise.py similarity index 100% rename from skimage/filter/tests/test_tv_denoise.py rename to skimage/filter/tests/test_denoise.py From 7967d5fb49cd1bb0b1ed8d2417c3ace36f47600d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 22:18:41 +0200 Subject: [PATCH 165/625] Refactor denoise tests and add tests for bilateral filter --- skimage/filter/tests/test_denoise.py | 135 ++++++++++++++++----------- 1 file changed, 78 insertions(+), 57 deletions(-) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index cc4fae7e..2efbba84 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -1,68 +1,89 @@ import numpy as np -from numpy.testing import run_module_suite +from numpy.testing import run_module_suite, assert_raises -from skimage import filter, data, color +from skimage import filter, data, color, img_as_float -class TestTvDenoise(): +lena = img_as_float(data.lena()[:256, :256]) +lena_gray = color.rgb2gray(lena) - def test_tv_denoise_2d(self): - """ - Apply the TV denoising algorithm on the lena image provided - by scipy - """ - # lena image - lena = color.rgb2gray(data.lena())[:256, :256] - # add noise to lena - lena += 0.5 * lena.std() * np.random.randn(*lena.shape) - # clip noise so that it does not exceed allowed range for float images. - lena = np.clip(lena, 0, 1) - # denoise - denoised_lena = filter.tv_denoise(lena, weight=60.0) - # which dtype? - assert denoised_lena.dtype in [np.float, np.float32, np.float64] - from scipy import ndimage - grad = ndimage.morphological_gradient(lena, size=((3, 3))) - grad_denoised = ndimage.morphological_gradient( - denoised_lena, size=((3, 3))) - # test if the total variation has decreased - assert grad_denoised.dtype == np.float - assert (np.sqrt((grad_denoised**2).sum()) - < np.sqrt((grad**2).sum()) / 2) - def test_tv_denoise_float_result_range(self): - # lena image - lena = color.rgb2gray(data.lena())[:256, :256] - int_lena = np.multiply(lena, 255).astype(np.uint8) - assert np.max(int_lena) > 1 - denoised_int_lena = filter.tv_denoise(int_lena, weight=60.0) - # test if the value range of output float data is within [0.0:1.0] - assert denoised_int_lena.dtype == np.float - assert np.max(denoised_int_lena) <= 1.0 - assert np.min(denoised_int_lena) >= 0.0 +def test_tv_denoise_2d(): + # lena image + img = lena_gray + # add noise to lena + img += 0.5 * img.std() * np.random.random(img.shape) + # clip noise so that it does not exceed allowed range for float images. + img = np.clip(img, 0, 1) + # denoise + denoised_lena = filter.tv_denoise(img, weight=60.0) + # which dtype? + assert denoised_lena.dtype in [np.float, np.float32, np.float64] + from scipy import ndimage + grad = ndimage.morphological_gradient(img, size=((3, 3))) + grad_denoised = ndimage.morphological_gradient( + denoised_lena, size=((3, 3))) + # test if the total variation has decreased + assert grad_denoised.dtype == np.float + assert (np.sqrt((grad_denoised**2).sum()) + < np.sqrt((grad**2).sum()) / 2) - def test_tv_denoise_3d(self): - """ - Apply the TV denoising algorithm on a 3D image representing - a sphere. - """ - x, y, z = np.ogrid[0:40, 0:40, 0:40] - mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 - mask = 100 * mask.astype(np.float) - mask += 60 - mask += 20 * np.random.randn(*mask.shape) - mask[mask < 0] = 0 - mask[mask > 255] = 255 - res = filter.tv_denoise(mask.astype(np.uint8), weight=100) - assert res.dtype == np.float - assert res.std() * 255 < mask.std() - # test wrong number of dimensions - a = np.random.random((8, 8, 8, 8)) - try: - res = filter.tv_denoise(a) - except ValueError: - pass +def test_tv_denoise_float_result_range(): + # lena image + img = lena_gray + int_lena = np.multiply(img, 255).astype(np.uint8) + assert np.max(int_lena) > 1 + denoised_int_lena = filter.tv_denoise(int_lena, weight=60.0) + # test if the value range of output float data is within [0.0:1.0] + assert denoised_int_lena.dtype == np.float + assert np.max(denoised_int_lena) <= 1.0 + assert np.min(denoised_int_lena) >= 0.0 + + +def test_tv_denoise_3d(): + """Apply the TV denoising algorithm on a 3D image representing a sphere.""" + x, y, z = np.ogrid[0:40, 0:40, 0:40] + mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 + mask = 100 * mask.astype(np.float) + mask += 60 + mask += 20 * np.random.random(mask.shape) + mask[mask < 0] = 0 + mask[mask > 255] = 255 + res = filter.tv_denoise(mask.astype(np.uint8), weight=100) + assert res.dtype == np.float + assert res.std() * 255 < mask.std() + + # test wrong number of dimensions + assert_raises(ValueError, filter.tv_denoise, np.random.random((8, 8, 8, 8))) + + +def test_denoise_bilateral_2d(): + img = lena_gray + # add some random noise + img += 0.5 * img.std() * np.random.random(img.shape) + img = np.clip(img, 0, 1) + + out1 = filter.denoise_bilateral(img, sigma_color=0.1, sigma_range=20) + out2 = filter.denoise_bilateral(img, sigma_color=0.2, sigma_range=30) + + # make sure noise is reduced + assert img.std() > out1.std() + assert out1.std() > out2.std() + + +def test_denoise_bilateral_3d(): + img = lena + # add some random noise + img += 0.5 * img.std() * np.random.random(img.shape) + img = np.clip(img, 0, 1) + + out1 = filter.denoise_bilateral(img, sigma_color=0.1, sigma_range=20) + out2 = filter.denoise_bilateral(img, sigma_color=0.2, sigma_range=30) + + # make sure noise is reduced + assert img.std() > out1.std() + assert out1.std() > out2.std() if __name__ == "__main__": From e39012aa240736fcc570416f2e0ece8f15c9744a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 22:31:34 +0200 Subject: [PATCH 166/625] Add more detailed description of bilateral filter --- skimage/filter/denoise.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index f6b30f6f..50ca0247 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -246,6 +246,17 @@ def denoise_bilateral(image, win_size=5, sigma_color=1, sigma_range=1, bins=1e4, mode='constant', cval=0): """Denoise image using bilateral filter. + This is an edge-preserving and noise reducing denoising filter. It averages + pixel based on their spatial closeness and radiometric similarity. + + Spatial closeness is measured by the gaussian function of the euclidian + distance between two pixels and a certain standard deviation + (`sigma_range`). + + Radiometric similarity is measured by the gaussian function of the euclidian + distance between two color values and a certain standard deviation + (`sigma_color`). + Parameters ---------- image : ndarray From 8de3ec988de42d9ceb92e6d436f7d332eb5d337f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 22:51:16 +0200 Subject: [PATCH 167/625] Add example script for bilateral denoising --- doc/examples/plot_denoise.py | 64 ++++++++++++++++++++++++++++ doc/examples/plot_lena_tv_denoise.py | 51 ---------------------- 2 files changed, 64 insertions(+), 51 deletions(-) create mode 100644 doc/examples/plot_denoise.py delete mode 100644 doc/examples/plot_lena_tv_denoise.py diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py new file mode 100644 index 00000000..30dfc10c --- /dev/null +++ b/doc/examples/plot_denoise.py @@ -0,0 +1,64 @@ +""" +============================= +Denoising the picture of Lena +============================= + +In this example, we denoise a noisy version of the picture of Lena using the +total variation and bilateral denoising filter. + +These algorithms typically produce "posterized" images with flat domains +separated by sharp edges. It is possible to change the degree of posterization +by controlling the tradeoff between denoising and faithfulness to the original +image. + +Total variation filter +---------------------- + +The result of this filter is an image that has a minimal total variation norm, +while being as close to the initial image as possible. The total variation is +the L1 norm of the gradient of the image, and minimizing the total variation. + +Bilateral filter +---------------- +A bilateral filter is an edge-preserving and noise reducing denoising filter. +It averages pixel based on their spatial closeness and radiometric similarity. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data, color, img_as_float +from skimage.filter import tv_denoise, denoise_bilateral + +lena = img_as_float(data.lena()) +lena = lena[220:300, 220:320] + +noisy = lena + 0.5 * lena.std() * np.random.random(lena.shape) +noisy = np.clip(noisy, 0, 1) + +fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5)) + +ax[0, 0].imshow(lena) +ax[0, 0].axis('off') +ax[0, 0].set_title('original') +ax[0, 1].imshow(tv_denoise(noisy, weight=0.02)) +ax[0, 1].axis('off') +ax[0, 1].set_title('TV') +ax[0, 2].imshow(tv_denoise(noisy, weight=0.05)) +ax[0, 2].axis('off') +ax[0, 2].set_title('(more) TV') + +ax[1, 0].imshow(noisy) +ax[1, 0].axis('off') +ax[1, 0].set_title('original') +ax[1, 1].imshow(denoise_bilateral(noisy, sigma_color=0.02, sigma_range=15)) +ax[1, 1].axis('off') +ax[1, 1].set_title('Bilateral') +ax[1, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_range=15)) +ax[1, 2].axis('off') +ax[1, 2].set_title('(more) Bilateral') + +fig.subplots_adjust(wspace=0.02, hspace=0.2, top=0.9, bottom=0.05, left=0, right=1) + +plt.show() diff --git a/doc/examples/plot_lena_tv_denoise.py b/doc/examples/plot_lena_tv_denoise.py deleted file mode 100644 index bce07845..00000000 --- a/doc/examples/plot_lena_tv_denoise.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -==================================================== -Denoising the picture of Lena using total variation -==================================================== - -In this example, we denoise a noisy version of the picture of Lena -using the total variation denoising filter. The result of this filter -is an image that has a minimal total variation norm, while being as -close to the initial image as possible. The total variation is the L1 -norm of the gradient of the image, and minimizing the total variation -typically produces "posterized" images with flat domains separated by -sharp edges. - -It is possible to change the degree of posterization by controlling -the tradeoff between denoising and faithfulness to the original image. - -""" - -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data, color, img_as_ubyte -from skimage.filter import tv_denoise - -l = img_as_ubyte(color.rgb2gray(data.lena())) -l = l[230:290, 220:320] - -noisy = l + 0.4 * l.std() * np.random.random(l.shape) - -tv_denoised = tv_denoise(noisy, weight=10) - -plt.figure(figsize=(8, 2)) - -plt.subplot(131) -plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) -plt.axis('off') -plt.title('noisy', fontsize=20) -plt.subplot(132) -plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) -plt.axis('off') -plt.title('TV denoising', fontsize=20) - -tv_denoised = tv_denoise(noisy, weight=50) -plt.subplot(133) -plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) -plt.axis('off') -plt.title('(more) TV denoising', fontsize=20) - -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, - right=1) -plt.show() From 415a3c69b368e587aac158ea852785dba472f88b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 6 Oct 2012 22:52:05 +0200 Subject: [PATCH 168/625] Wrap column --- doc/examples/plot_denoise.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index 30dfc10c..4a05e339 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -59,6 +59,7 @@ ax[1, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_range=15)) ax[1, 2].axis('off') ax[1, 2].set_title('(more) Bilateral') -fig.subplots_adjust(wspace=0.02, hspace=0.2, top=0.9, bottom=0.05, left=0, right=1) +fig.subplots_adjust(wspace=0.02, hspace=0.2, + top=0.9, bottom=0.05, left=0, right=1) plt.show() From 52962f30f1215b705bee0beb70b40819ddf0164e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 16 Oct 2012 16:50:54 +0200 Subject: [PATCH 169/625] Reorder subplots --- doc/examples/plot_denoise.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index 4a05e339..45e5e4b5 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -39,25 +39,25 @@ noisy = np.clip(noisy, 0, 1) fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5)) -ax[0, 0].imshow(lena) +ax[0, 0].imshow(noisy) ax[0, 0].axis('off') -ax[0, 0].set_title('original') -ax[0, 1].imshow(tv_denoise(noisy, weight=0.02)) +ax[0, 0].set_title('noisy') +ax[0, 1].imshow(tv_denoise(noisy, weight=0.1)) ax[0, 1].axis('off') ax[0, 1].set_title('TV') -ax[0, 2].imshow(tv_denoise(noisy, weight=0.05)) +ax[0, 2].imshow(denoise_bilateral(noisy, sigma_color=0.03, sigma_range=15)) ax[0, 2].axis('off') -ax[0, 2].set_title('(more) TV') +ax[0, 2].set_title('Bilateral') -ax[1, 0].imshow(noisy) +ax[1, 0].imshow(tv_denoise(noisy, weight=0.2)) ax[1, 0].axis('off') -ax[1, 0].set_title('original') -ax[1, 1].imshow(denoise_bilateral(noisy, sigma_color=0.02, sigma_range=15)) +ax[1, 0].set_title('(more) TV') +ax[1, 1].imshow(denoise_bilateral(noisy, sigma_color=0.06, sigma_range=15)) ax[1, 1].axis('off') -ax[1, 1].set_title('Bilateral') -ax[1, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_range=15)) +ax[1, 1].set_title('(more) Bilateral') +ax[1, 2].imshow(lena) ax[1, 2].axis('off') -ax[1, 2].set_title('(more) Bilateral') +ax[1, 2].set_title('original') fig.subplots_adjust(wspace=0.02, hspace=0.2, top=0.9, bottom=0.05, left=0, right=1) From 62c1beccb59d714b96e9597fbbadd0bddab55972 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 16 Oct 2012 17:28:42 +0200 Subject: [PATCH 170/625] Fix description of denoising filters --- doc/examples/plot_denoise.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index 45e5e4b5..12e8b9b6 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -16,12 +16,13 @@ Total variation filter The result of this filter is an image that has a minimal total variation norm, while being as close to the initial image as possible. The total variation is -the L1 norm of the gradient of the image, and minimizing the total variation. +the L1 norm of the gradient of the image. Bilateral filter ---------------- -A bilateral filter is an edge-preserving and noise reducing denoising filter. -It averages pixel based on their spatial closeness and radiometric similarity. + +A bilateral filter is an edge-preserving and noise reducing filter. It averages +pixels based on their spatial closeness and radiometric similarity. """ From 1a1560f69679240507668878a1c69e794554f42a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 16 Oct 2012 17:29:39 +0200 Subject: [PATCH 171/625] Fix PEP8 issues --- skimage/filter/_denoise.pyx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index b0da5ddb..ad25d9d0 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -11,7 +11,7 @@ from skimage._shared.interpolation cimport get_pixel2d, get_pixel3d cdef inline double _gaussian_weight(double sigma, double value): - return exp(- 0.5 * (value / sigma)**2) + return exp(-0.5 * (value / sigma)**2) cdef double* _compute_color_lut(int bins, double sigma, double max_value): @@ -73,10 +73,10 @@ def _denoise_bilateral2d(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image total_value = 0 total_weight = 0 centre = image_data[pixel_addr] - for wr in range(- window_ext, window_ext + 1): + for wr in range(-window_ext, window_ext + 1): rr = wr + r kr = wr + window_ext - for wc in range(- window_ext, window_ext + 1): + for wc in range(-window_ext, window_ext + 1): cc = wc + c kc = wc + window_ext @@ -135,10 +135,10 @@ def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image for d in range(dims): total_values[d] = 0 centres[d] = image_data[pixel_addr + d] - for wr in range(- window_ext, window_ext + 1): + for wr in range(-window_ext, window_ext + 1): rr = wr + r kr = wr + window_ext - for wc in range(- window_ext, window_ext + 1): + for wc in range(-window_ext, window_ext + 1): cc = wc + c kc = wc + window_ext From 108cbf90f8cbc48d5dc98e2c8d2be88f02c89735 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 16 Oct 2012 17:34:49 +0200 Subject: [PATCH 172/625] Fix typo in doc string of biliteral filter --- skimage/filter/denoise.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index 50ca0247..f5c9e0ee 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -247,7 +247,7 @@ def denoise_bilateral(image, win_size=5, sigma_color=1, sigma_range=1, bins=1e4, """Denoise image using bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages - pixel based on their spatial closeness and radiometric similarity. + pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by the gaussian function of the euclidian distance between two pixels and a certain standard deviation From b2da237e1455fa92e029a3cf164b1ee4ab3cad1f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 17 Oct 2012 12:07:20 +0200 Subject: [PATCH 173/625] autopep8 sources --- skimage/rank/__init__.py | 2 +- skimage/rank/_core16.pxd | 7 +- skimage/rank/_core16.pyx | 150 ++++++------ skimage/rank/_core8.pxd | 4 +- skimage/rank/_core8.pyx | 131 ++++++----- skimage/rank/_crank16.pyx | 327 ++++++++++++++------------ skimage/rank/_crank16_bilateral.pyx | 45 ++-- skimage/rank/_crank16_percentiles.pyx | 212 +++++++++-------- skimage/rank/_crank8.pyx | 301 +++++++++++++----------- skimage/rank/_crank8_percentiles.pyx | 212 +++++++++-------- skimage/rank/bilateral_rank.py | 23 +- skimage/rank/generic.py | 3 +- skimage/rank/percentile_rank.py | 78 ++++-- skimage/rank/rank.py | 29 ++- skimage/rank/setup.py | 33 +-- 15 files changed, 844 insertions(+), 713 deletions(-) diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index 09812649..30d936db 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -1,3 +1,3 @@ from .rank import * from .percentile_rank import * -from .bilateral_rank import * \ No newline at end of file +from .bilateral_rank import * diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index f9bb47b3..a2843f76 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -8,10 +8,11 @@ cimport numpy as np cdef inline int int_max(int a, int b) cdef inline int int_min(int a, int b) -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core16( + np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y,Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) \ No newline at end of file + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index edef264b..81fab0b4 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -19,18 +19,20 @@ from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline int int_max(int a, int b): + return a if a >= b else b +cdef inline int int_min(int a, int b): + return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint16_t value): +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] += 1 pop[0] += 1. -cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint16_t value): +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] -= 1 pop[0] -= 1. -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask returns 0 otherwise @@ -38,19 +40,20 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: - if mask[r*cols+c]: + if mask[r * cols + c]: return 1 else: return 0 -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core16( + np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y,Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -69,16 +72,15 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ assert centre_c >= 0 assert centre_r < srows assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + assert bitdepth in range(2, 13) + maxbin_list = [0, 0, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] + midbin_list = [0, 0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] #set maxbin and midbin - cdef Py_ssize_t maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + cdef Py_ssize_t maxbin = maxbin_list[bitdepth], midbin = midbin_list[bitdepth] - assert (imageout.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data + cdef np.uint16_t * out_data = out.data + cdef np.uint16_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row cdef float pop # number of pixels actually inside the neighborhood (float) # allocate memory with malloc - cdef Py_ssize_t max_se = srows*scols + cdef Py_ssize_t max_se = srows * scols # number of element in each attack border cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t* histo = malloc(maxbin * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 + t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) + t_e = np.diff(t, axis=1) == -1 - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 + t = np.hstack((np.zeros((selem.shape[0], 1)), selem)) + t_w = np.diff(t, axis=1) == 1 - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 + t = np.vstack((selem, np.zeros((1, selem.shape[1])))) + t_s = np.diff(t, axis=0) == -1 - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 + t = np.vstack((np.zeros((1, selem.shape[1])), selem)) + t_n = np.diff(t, axis=0) == 1 num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): - if t_e[r,c]: + if t_e[r, c]: se_e_r[num_se_e] = r - centre_r se_e_c[num_se_e] = c - centre_c num_se_e += 1 - if t_w[r,c]: + if t_w[r, c]: se_w_r[num_se_w] = r - centre_r se_w_c[num_se_w] = c - centre_c num_se_w += 1 - if t_n[r,c]: + if t_n[r, c]: se_n_r[num_se_n] = r - centre_r se_n_c[num_se_n] = c - centre_c num_se_n += 1 - if t_s[r,c]: + if t_s[r, c]: se_s_r[num_se_s] = r - centre_r se_s_c[num_se_s] = c - centre_c num_se_s += 1 @@ -175,99 +177,101 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r - centre_r cc = c - centre_c if selem[r, c]: - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) r = 0 c = 0 # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- # main loop r = 0 - for even_row in range(0,rows,2): + for even_row in range(0, rows, 2): # ---> west to east - for c in range(1,cols): + for c in range(1, cols): for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- # ---> east to west - for c in range(cols-2,-1,-1): + for c in range(cols - 2, -1, -1): for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index a677e915..1a170500 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -8,10 +8,10 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) # 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core8( + np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) - diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 86c40a6d..7851388d 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -15,23 +15,25 @@ cimport numpy as np from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): + return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): + return a if a <= b else b #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint8_t value): +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] += 1 pop[0] += 1. -cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint8_t value): +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] -= 1 pop[0] -= 1. -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask returns 0 otherwise @@ -39,17 +41,18 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: - if mask[r*cols+c]: + if mask[r * cols + c]: return 1 else: return 0 -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core8( + np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, - char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -88,9 +91,9 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa # define pointers to the data - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data + cdef np.uint8_t * out_data = out.data + cdef np.uint8_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row @@ -99,59 +102,59 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa cdef float pop # allocate memory with malloc - cdef Py_ssize_t max_se = srows*scols + cdef Py_ssize_t max_se = srows * scols # number of element in each attack border cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t* histo = malloc(256 * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 + t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) + t_e = np.diff(t, axis=1) == -1 - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 + t = np.hstack((np.zeros((selem.shape[0], 1)), selem)) + t_w = np.diff(t, axis=1) == 1 - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 + t = np.vstack((selem, np.zeros((1, selem.shape[1])))) + t_s = np.diff(t, axis=0) == -1 - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 + t = np.vstack((np.zeros((1, selem.shape[1])), selem)) + t_n = np.diff(t, axis=0) == 1 num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): - if t_e[r,c]: + if t_e[r, c]: se_e_r[num_se_e] = r - centre_r se_e_c[num_se_e] = c - centre_c num_se_e += 1 - if t_w[r,c]: + if t_w[r, c]: se_w_r[num_se_w] = r - centre_r se_w_c[num_se_w] = c - centre_c num_se_w += 1 - if t_n[r,c]: + if t_n[r, c]: se_n_r[num_se_n] = r - centre_r se_n_c[num_se_n] = c - centre_c num_se_n += 1 - if t_s[r,c]: + if t_s[r, c]: se_s_r[num_se_s] = r - centre_r se_s_c[num_se_s] = c - centre_c num_se_s += 1 @@ -167,94 +170,99 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r - centre_r cc = c - centre_c if selem[r, c]: - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) r = 0 c = 0 # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- # main loop r = 0 - for even_row in range(0,rows,2): + for even_row in range(0, rows, 2): # ---> west to east - for c in range(1,cols): + for c in range(1, cols): for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * + cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- # ---> east to west - for c in range(cols-2,-1,-1): + for c in range(cols - 2, -1, -1): for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * + cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- # release memory allocated by malloc @@ -271,4 +279,3 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa free(histo) return out - diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index 2fdda1e3..ce8510db 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -21,13 +21,14 @@ from _core16 cimport _core16 # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax,delta +cdef inline np.uint16_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: imax = i break @@ -35,47 +36,50 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - delta = imax-imin - if delta>0: - return (1.*(maxbin-1)*(g-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint16_t > (1. * (maxbin - 1) * (g - imin) / delta) else: - return (imax-imin) + return < np.uint16_t > (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_bottomhat( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin): if histo[i]: break - return (g-i) + return < np.uint16_t > (g - i) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_equalize( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. if pop: for i in range(maxbin): sum += histo[i] - if i>=g: + if i >= g: break - return (((maxbin-1)*sum)/pop) + return < np.uint16_t > (((maxbin - 1) * sum) / pop) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax +cdef inline np.uint16_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: imax = i break @@ -83,96 +87,103 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - return (imax-imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_maximum( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: - return (i) + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): - mean += histo[i]*i - return (mean/pop) + mean += histo[i] * i + return < np.uint16_t > (mean / pop) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_meansubstraction( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): - mean += histo[i]*i - return ((g-mean/pop)/2.+(midbin-1)) + mean += histo[i] * i + return < np.uint16_t > ((g - mean / pop) / 2. + (midbin - 1)) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_median( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - cdef float sum = pop/2.0 + cdef float sum = pop / 2.0 if pop: for i in range(maxbin): if histo[i]: sum -= histo[i] - if sum<0: - return (i) + if sum < 0: + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_minimum( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(maxbin): if histo[i]: - return (i) + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t hmax=0,imax=0 +cdef inline np.uint16_t kernel_modal( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(maxbin): - if histo[i]>hmax: + if histo[i] > hmax: hmax = histo[i] imax = i - return (imax) + return < np.uint16_t > (imax) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax +cdef inline np.uint16_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: imax = i break @@ -180,80 +191,89 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - if imax-g < g-imin: - return (imax) + if imax - g < g - imin: + return < np.uint16_t > (imax) else: - return (imin) + return < np.uint16_t > (imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return (pop) +cdef inline np.uint16_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + return < np.uint16_t > (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): - mean += histo[i]*i - return (g>(mean/pop)) + mean += histo[i] * i + return < np.uint16_t > (g > (mean / pop)) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_tophat( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: break - return (i-g) + return < np.uint16_t > (i - g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def equalize(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local egalisation of the gray level """ - return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def gradient(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum - local minimum gray level """ - return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -262,34 +282,38 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum gray level """ - return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """average gray level (clipped on uint8) """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ - return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def median(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local median """ - return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -298,49 +322,54 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local minimum gray level """ - return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """morphological contrast enhancement """ - return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def modal(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local mode """ - return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ - return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def tophat(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """top hat """ - return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index 46028ad3..b5103be4 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -22,54 +22,53 @@ from _core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,bilat_pop=0 +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 cdef float mean = 0. if pop: for i in range(maxbin): - if (g>(i-s0)) and (g<(i+s1)): + if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - mean += histo[i]*i + mean += histo[i] * i if bilat_pop: - return (mean/bilat_pop) + return < np.uint16_t > (mean / bilat_pop) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,bilat_pop=0 +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 if pop: for i in range(maxbin): - if (g>(i-s0)) and (g<(i+s1)): + if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - return (bilat_pop) + return < np.uint16_t > (bilat_pop) else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,0.,0.,s0,s1) + return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, 0., 0., s0, s1) def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,s0,s1) - + return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, s0, s1) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 4fa3661c..527d2aed 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -7,64 +7,64 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16,int_min,int_max +from _core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(maxbin): sum += histo[i] - if sum>p0*pop: + if sum > p0 * pop: imin = i break sum = 0 - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): sum += histo[i] - if sum>p1*pop: + if sum > p1 * pop: imax = i break - delta = imax-imin - if delta>0: - return (1.0*(maxbin-1)*(int_min(int_max(imin,g),imax)-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint16_t > (1.0 * (maxbin - 1) * (int_min(int_max(imin, g), imax) - imin) / delta) else: - return (imax-imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: imin = i break sum = 0 - for i in range((maxbin-1),-1,-1): + for i in range((maxbin - 1), -1, -1): sum += histo[i] - if sum>=p1*pop: + if sum >= p1 * pop: imax = i break - return (imax-imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -72,19 +72,19 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, n = 0 for i in range(maxbin): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i + mean += histo[i] * i - if n>0: - return (1.0*mean/n) + if n > 0: + return < np.uint16_t > (1.0 * mean / n) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -92,160 +92,166 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t* histo, float pop, n n = 0 for i in range(maxbin): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i - if n>0: - return ((g-(mean/n))*.5+midbin) + mean += histo[i] * i + if n > 0: + return < np.uint16_t > ((g - (mean / n)) * .5 + midbin) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(maxbin): sum += histo[i] - if sum>p0*pop: + if sum > p0 * pop: imin = i break sum = 0 - for i in range((maxbin-1),-1,-1): + for i in range((maxbin - 1), -1, -1): sum += histo[i] - if sum>p1*pop: + if sum > p1 * pop: imax = i break - if g>imax: - return imax - if gimin - if imax-g < g-imin: - return imax + if g > imax: + return < np.uint16_t > imax + if g < imin: + return < np.uint16_t > imin + if imax - g < g - imin: + return < np.uint16_t > imax else: - return imin + return < np.uint16_t > imin else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return (i) + return < np.uint16_t > (i) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,n +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, n if pop: sum = 0 n = 0 for i in range(maxbin): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return (n) + return < np.uint16_t > (n) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return ((maxbin-1)*(g>=i)) + return < np.uint16_t > ((maxbin - 1) * (g >= i)) else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return _core16(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index a0b20073..94124cf7 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,12 +21,13 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax,delta +cdef inline np.uint8_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: imax = i break @@ -34,47 +35,49 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - delta = imax-imin - if delta>0: - return (255.*(g-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint8_t > (255. * (g - imin) / delta) else: - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_bottomhat( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(256): if histo[i]: break - return (g-i) + return < np.uint8_t > (g - i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_equalize( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. if pop: for i in range(256): sum += histo[i] - if i>=g: + if i >= g: break - return ((255*sum)/pop) + return < np.uint8_t > ((255 * sum) / pop) else: - return (0) - -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax + return < np.uint8_t > (0) +cdef inline np.uint8_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: imax = i break @@ -82,89 +85,95 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_maximum( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: - return (i) + return < np.uint8_t > (i) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(256): - mean += histo[i]*i - return (mean/pop) + mean += histo[i] * i + return < np.uint8_t > (mean / pop) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_meansubstraction( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(256): - mean += histo[i]*i - return ((g-mean/pop)/2.+127) + mean += histo[i] * i + return < np.uint8_t > ((g - mean / pop) / 2. + 127) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_median( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - cdef float sum = pop/2.0 + cdef float sum = pop / 2.0 if pop: for i in range(256): if histo[i]: sum -= histo[i] - if sum<0: - return (i) + if sum < 0: + return < np.uint8_t > (i) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_minimum( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(256): if histo[i]: - return (i) + return < np.uint8_t > (i) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t hmax=0,imax=0 +cdef inline np.uint8_t kernel_modal( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(256): - if histo[i]>hmax: + if histo[i] > hmax: hmax = histo[i] imax = i - return (imax) + return < np.uint8_t > (imax) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax +cdef inline np.uint8_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: imax = i break @@ -172,77 +181,85 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - if imax-g < g-imin: - return (imax) + if imax - g < g - imin: + return < np.uint8_t > (imax) else: - return (imin) + return < np.uint8_t > (imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return (pop) +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + return < np.uint8_t > (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(256): - mean += histo[i]*i - return (g>(mean/pop)) + mean += histo[i] * i + return < np.uint8_t > (g > (mean / pop)) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_tophat( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: break - return (i-g) + return < np.uint8_t > (i - g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def equalize(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def gradient(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -251,34 +268,38 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum gray level """ - return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """average gray level (clipped on uint8) """ - return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def median(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local median """ - return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -287,50 +308,54 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local minimum gray level """ - return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def modal(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local mode """ - return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def pop(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """returns the number of actual pixels of the structuring element inside the mask """ - return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def tophat(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """top hat """ - return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) - + return _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 16ad49d5..5441eac7 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -7,66 +7,66 @@ import numpy as np cimport numpy as np # import main loop -from _core8 cimport _core8,uint8_max,uint8_min +from _core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 imin = 0 imax = 255 for i in range(256): sum += histo[i] - if sum>(p0*pop): + if sum > (p0 * pop): imin = i break sum = 0 - for i in range(255,-1,-1): + for i in range(255, -1, -1): sum += histo[i] - if sum>(p1*pop): + if sum > (p1 * pop): imax = i break - delta = imax-imin - if delta>0: - return (255*(uint8_min(uint8_max(imin,g),imax)-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint8_t > (255 * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) else: - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (128) + return < np.uint8_t > (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: imin = i break sum = 0 - for i in range(255,-1,-1): + for i in range(255, -1, -1): sum += histo[i] - if sum>=p1*pop: + if sum >= p1 * pop: imax = i break - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -74,18 +74,18 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, f n = 0 for i in range(256): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i - if n>0: - return (1.0*mean/n) + mean += histo[i] * i + if n > 0: + return < np.uint8_t > (1.0 * mean / n) else: - return (0) + return < np.uint8_t > (0) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -93,160 +93,166 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np n = 0 for i in range(256): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i - if n>0: - return ((g-(mean/n))*.5+127) + mean += histo[i] * i + if n > 0: + return < np.uint8_t > ((g - (mean / n)) * .5 + 127) else: - return (0) + return < np.uint8_t > (0) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: imin = i break sum = 0 - for i in range(255,-1,-1): + for i in range(255, -1, -1): sum += histo[i] - if sum>=p1*pop: + if sum >= p1 * pop: imax = i break - if g>imax: - return imax - if gimin - if imax-g < g-imin: - return imax + if g > imax: + return < np.uint8_t > imax + if g < imin: + return < np.uint8_t > imin + if imax - g < g - imin: + return < np.uint8_t > imax else: - return imin + return < np.uint8_t > imin else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return (i) + return < np.uint8_t > (i) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,n +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, n if pop: sum = 0 n = 0 for i in range(256): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return (n) + return < np.uint8_t > (n) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return (255*(g>=i)) + return < np.uint8_t > (255 * (g >= i)) else: - return (0) + return < np.uint8_t > (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index 5fd66e21..97db8f99 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -17,7 +17,8 @@ from generic import find_bitdepth import _crank16_bilateral -__all__ = ['bilateral_mean','bilateral_pop'] +__all__ = ['bilateral_mean', 'bilateral_pop'] + def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): selem = img_as_ubyte(selem) @@ -30,9 +31,9 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): else: raise TypeError("only uint8 and uint16 image supported!") bitdepth = find_bitdepth(image) - if bitdepth>11: + if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -69,12 +70,13 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bilateral_mean(ima8, square(3), s0=10,s1=10) + >>> rank.bilateral_mean(ima8, square(3), s0=10,s1=10) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], @@ -86,7 +88,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bilateral_mean(ima16, square(3), s0=10,s1=10) + >>> rank.bilateral_mean(ima16, square(3), s0=10,s1=10) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -132,12 +134,13 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bilateral_pop(ima8, square(3), s0=10,s1=10) + >>> rank.bilateral_pop(ima8, square(3), s0=10,s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], @@ -149,7 +152,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bilateral_pop(ima16, square(3), s0=10,s1=10) + >>> rank.bilateral_pop(ima16, square(3), s0=10,s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], @@ -160,3 +163,9 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) +if __name__ == "__main__": + import sys + sys.path.append('.') + + import doctest + doctest.testmod(verbose=True) diff --git a/skimage/rank/generic.py b/skimage/rank/generic.py index e8808e5e..94fc3130 100644 --- a/skimage/rank/generic.py +++ b/skimage/rank/generic.py @@ -1,10 +1,11 @@ import numpy as np + def find_bitdepth(image): """returns the max bith depth of a uint16 image """ umax = np.max(image) - if umax>2: + if umax > 2: return int(np.log2(umax)) else: return 1 diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index e7d760da..6ceb503d 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -23,26 +23,29 @@ from skimage import img_as_ubyte import numpy as np from generic import find_bitdepth -import _crank16_percentiles,_crank8_percentiles +import _crank16_percentiles +import _crank8_percentiles + +__all__ = ['percentile_autolevel', 'percentile_gradient', + 'percentile_mean', 'percentile_mean_substraction', + 'percentile_morph_contr_enh', 'percentile', 'percentile_pop', 'percentile_threshold'] -__all__ = ['percentile_autolevel','percentile_gradient', - 'percentile_mean','percentile_mean_substraction', - 'percentile_morph_contr_enh','percentile_pop'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): selem = img_as_ubyte(selem) if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, p0=p0, p1=p1) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) - if bitdepth>11: + if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") + def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. @@ -77,15 +80,16 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_autolevel(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_autolevel(ima8, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], [ 0, 255, 255, 255, 0], [ 0, 0, 0, 0, 0]], dtype=uint8) @@ -94,10 +98,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_autolevel(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_autolevel(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) @@ -105,6 +109,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile_gradient of an image. @@ -139,12 +144,13 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ to be updated >>> # Local gradient >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_gradient(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_gradient(ima8, square(3), p0=0.,p1=1.) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 255, 255, 255], @@ -156,7 +162,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_gradient(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_gradient(ima16, square(3), p0=0.,p1=1.) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], @@ -167,6 +173,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. @@ -201,12 +208,13 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_mean(ima8, square(3),p0=0.,p1=1.) + >>> rank.percentile_mean(ima8, square(3),p0=0.,p1=1.) array([[ 63, 85, 127, 85, 63], [ 85, 113, 170, 113, 85], [127, 170, 255, 170, 127], @@ -218,7 +226,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_mean(ima16, square(3),p0=0.,p1=1.) + >>> rank.percentile_mean(ima16, square(3),p0=0.,p1=1.) array([[1023, 1365, 2047, 1365, 1023], [1365, 1820, 2730, 1820, 1365], [2047, 2730, 4095, 2730, 2047], @@ -229,6 +237,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean_substraction of an image. @@ -263,12 +272,13 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals to be updated >>> # Local mean_substraction >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) array([[ 95, 84, 63, 84, 95], [ 84, 198, 169, 198, 84], [ 63, 169, 127, 169, 63], @@ -280,7 +290,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) array([[1536, 1365, 1024, 1365, 1536], [1365, 3185, 2730, 3185, 1365], [1024, 2730, 2048, 2730, 1024], @@ -291,6 +301,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. @@ -325,12 +336,13 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], @@ -342,7 +354,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -353,6 +365,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile of an image. @@ -387,12 +400,13 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile(ima8, square(3), p0=0.,p1=1.) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], @@ -404,7 +418,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile(ima16, square(3), p0=0.,p1=1.) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], @@ -416,6 +430,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local pop of an image. @@ -450,12 +465,13 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_pop(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_pop(ima8, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -467,7 +483,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_pop(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_pop(ima16, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -478,6 +494,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. @@ -512,12 +529,13 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_threshold(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_threshold(ima8, square(3), p0=0.,p1=1.) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 255, 255, 255], @@ -529,7 +547,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_threshold(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_threshold(ima16, square(3), p0=0.,p1=1.) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], @@ -539,4 +557,12 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) \ No newline at end of file + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + + +if __name__ == "__main__": + import sys + sys.path.append('.') + + import doctest + doctest.testmod(verbose=True) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index c080c291..d07c9037 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -21,25 +21,27 @@ from skimage import img_as_ubyte import numpy as np from generic import find_bitdepth -import _crank16,_crank8 +import _crank16 +import _crank8 + +__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] -__all__ = ['autolevel','bottomhat','equalize','gradient','maximum','mean' - ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): selem = img_as_ubyte(selem) if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) - if bitdepth>11: + if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out) else: raise TypeError("only uint8 and uint16 image supported!") + def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local autolevel of an image. @@ -101,6 +103,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local bottomhat of an image. @@ -161,6 +164,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local equalize of an image. @@ -221,6 +225,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local gradient of an image. @@ -282,6 +287,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local maximum of an image. @@ -343,6 +349,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. @@ -404,6 +411,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local meansubstraction of an image. @@ -465,6 +473,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local median of an image. @@ -526,6 +535,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.median, _crank16.median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local minimum of an image. @@ -588,6 +598,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.minimum, _crank16.minimum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local modal of an image. @@ -650,6 +661,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.modal, _crank16.modal, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local morph_contr_enh of an image. @@ -711,6 +723,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local pop of an image. @@ -772,6 +785,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.pop, _crank16.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local threshold of an image. @@ -834,6 +848,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local tophat of an image. @@ -899,4 +914,4 @@ if __name__ == "__main__": sys.path.append('.') import doctest - doctest.testmod(verbose=True) \ No newline at end of file + doctest.testmod(verbose=True) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index e1f996f7..c6a3dbb9 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -5,13 +5,13 @@ from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) + def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('rank', parent_package, top_path) # config.add_data_dir('tests') - cython(['_core8.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) @@ -21,18 +21,21 @@ def configuration(parent_package='', top_path=None): cython(['_crank16_bilateral.pyx'], working_path=base_path) config.add_extension('_core8', sources=['_core8.c'], - include_dirs=[get_numpy_include_dirs()]) + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], - include_dirs=[get_numpy_include_dirs()]) + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + '_crank8_percentiles', sources=['_crank8_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank16', sources=['_crank16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + '_crank16_percentiles', sources=['_crank16_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], + config.add_extension( + '_crank16_bilateral', sources=['_crank16_bilateral.c'], include_dirs=[get_numpy_include_dirs()]) return config @@ -40,10 +43,10 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikits-image Developers', - author='Olivier Debeir', - maintainer_email='scikits-image@googlegroups.com', - description='Rank filters', - url='https://github.com/scikits-image/scikits-image', - license='SciPy License (BSD Style)', - **(configuration(top_path='').todict()) - ) + author='Olivier Debeir', + maintainer_email='scikits-image@googlegroups.com', + description='Rank filters', + url='https://github.com/scikits-image/scikits-image', + license='SciPy License (BSD Style)', + **(configuration(top_path='').todict()) + ) From 5b8f554a2e86f0450301844e9ee93a26ab00ee3b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 17 Oct 2012 12:09:09 +0200 Subject: [PATCH 174/625] Rename tv_denoise to denoise_tv and deprecate --- skimage/filter/__init__.py | 2 +- skimage/filter/denoise.py | 22 +++++++++++++--------- skimage/filter/tests/test_denoise.py | 14 +++++++------- 3 files changed, 21 insertions(+), 17 deletions(-) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index eec88038..02fddf23 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,6 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from .denoise import tv_denoise, denoise_bilateral +from .denoise import tv_denoise, denoise_tv, denoise_bilateral from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index f5c9e0ee..d7c96f90 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -1,9 +1,10 @@ import numpy as np from skimage import img_as_float +from skimage._shared.utils import deprecated import _denoise -def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200): +def _denoise_tv_3d(im, weight=100, eps=2.e-4, n_iter_max=200): """Perform total-variation denoising on 3-D arrays. Parameters @@ -38,7 +39,7 @@ def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200): >>> mask = (x -22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 >>> mask = mask.astype(np.float) >>> mask += 0.2*np.random.randn(*mask.shape) - >>> res = tv_denoise_3d(mask, weight=100) + >>> res = denoise_tv_3d(mask, weight=100) """ px = np.zeros_like(im) py = np.zeros_like(im) @@ -83,7 +84,7 @@ def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200): return out -def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200): +def _denoise_tv_2d(im, weight=50, eps=2.e-4, n_iter_max=200): """Perform total-variation denoising. Parameters @@ -128,7 +129,7 @@ def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200): >>> import scipy >>> lena = scipy.lena().astype(np.float) >>> lena += 0.5 * lena.std()*np.random.randn(*lena.shape) - >>> denoised_lena = tv_denoise(lena, weight=60.0) + >>> denoised_lena = denoise_tv(lena, weight=60.0) """ px = np.zeros_like(im) py = np.zeros_like(im) @@ -166,7 +167,7 @@ def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200): return out -def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200): +def denoise_tv(im, weight=50, eps=2.e-4, n_iter_max=200): """Perform total-variation denoising on 2-d and 3-d images. Parameters @@ -220,28 +221,31 @@ def tv_denoise(im, weight=50, eps=2.e-4, n_iter_max=200): >>> import scipy >>> lena = scipy.lena().astype(np.float) >>> lena += 0.5 * lena.std()*np.random.randn(*lena.shape) - >>> denoised_lena = tv_denoise(lena, weight=60) + >>> denoised_lena = denoise_tv(lena, weight=60) >>> # 3D example on synthetic data >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] >>> mask = (x -22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 >>> mask = mask.astype(np.float) >>> mask += 0.2*np.random.randn(*mask.shape) - >>> res = tv_denoise_3d(mask, weight=100) + >>> res = denoise_tv_3d(mask, weight=100) """ im_type = im.dtype if not im_type.kind == 'f': im = img_as_float(im) if im.ndim == 2: - out = _tv_denoise_2d(im, weight, eps, n_iter_max) + out = _denoise_tv_2d(im, weight, eps, n_iter_max) elif im.ndim == 3: - out = _tv_denoise_3d(im, weight, eps, n_iter_max) + out = _denoise_tv_3d(im, weight, eps, n_iter_max) else: raise ValueError('only 2-d and 3-d images may be denoised with this ' 'function') return out +tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv) + + def denoise_bilateral(image, win_size=5, sigma_color=1, sigma_range=1, bins=1e4, mode='constant', cval=0): """Denoise image using bilateral filter. diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index 2efbba84..b8e6c9ef 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -8,7 +8,7 @@ lena = img_as_float(data.lena()[:256, :256]) lena_gray = color.rgb2gray(lena) -def test_tv_denoise_2d(): +def test_denoise_tv_2d(): # lena image img = lena_gray # add noise to lena @@ -16,7 +16,7 @@ def test_tv_denoise_2d(): # clip noise so that it does not exceed allowed range for float images. img = np.clip(img, 0, 1) # denoise - denoised_lena = filter.tv_denoise(img, weight=60.0) + denoised_lena = filter.denoise_tv(img, weight=60.0) # which dtype? assert denoised_lena.dtype in [np.float, np.float32, np.float64] from scipy import ndimage @@ -29,19 +29,19 @@ def test_tv_denoise_2d(): < np.sqrt((grad**2).sum()) / 2) -def test_tv_denoise_float_result_range(): +def test_denoise_tv_float_result_range(): # lena image img = lena_gray int_lena = np.multiply(img, 255).astype(np.uint8) assert np.max(int_lena) > 1 - denoised_int_lena = filter.tv_denoise(int_lena, weight=60.0) + denoised_int_lena = filter.denoise_tv(int_lena, weight=60.0) # test if the value range of output float data is within [0.0:1.0] assert denoised_int_lena.dtype == np.float assert np.max(denoised_int_lena) <= 1.0 assert np.min(denoised_int_lena) >= 0.0 -def test_tv_denoise_3d(): +def test_denoise_tv_3d(): """Apply the TV denoising algorithm on a 3D image representing a sphere.""" x, y, z = np.ogrid[0:40, 0:40, 0:40] mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 @@ -50,12 +50,12 @@ def test_tv_denoise_3d(): mask += 20 * np.random.random(mask.shape) mask[mask < 0] = 0 mask[mask > 255] = 255 - res = filter.tv_denoise(mask.astype(np.uint8), weight=100) + res = filter.denoise_tv(mask.astype(np.uint8), weight=100) assert res.dtype == np.float assert res.std() * 255 < mask.std() # test wrong number of dimensions - assert_raises(ValueError, filter.tv_denoise, np.random.random((8, 8, 8, 8))) + assert_raises(ValueError, filter.denoise_tv, np.random.random((8, 8, 8, 8))) def test_denoise_bilateral_2d(): From c04f14c12941572c61b3046a8e058dbd9cc16c8f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 17 Oct 2012 16:28:44 +0200 Subject: [PATCH 175/625] Combine 2D and 3D bilateral filter in one function and rename sigma parameters --- doc/examples/plot_denoise.py | 4 +- skimage/filter/__init__.py | 3 +- skimage/filter/_denoise.pyx | 122 ++++++++++++++------------- skimage/filter/denoise.py | 67 --------------- skimage/filter/tests/test_denoise.py | 8 +- 5 files changed, 71 insertions(+), 133 deletions(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index 12e8b9b6..a3e83a65 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -46,14 +46,14 @@ ax[0, 0].set_title('noisy') ax[0, 1].imshow(tv_denoise(noisy, weight=0.1)) ax[0, 1].axis('off') ax[0, 1].set_title('TV') -ax[0, 2].imshow(denoise_bilateral(noisy, sigma_color=0.03, sigma_range=15)) +ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.03, sigma_spatial=15)) ax[0, 2].axis('off') ax[0, 2].set_title('Bilateral') ax[1, 0].imshow(tv_denoise(noisy, weight=0.2)) ax[1, 0].axis('off') ax[1, 0].set_title('(more) TV') -ax[1, 1].imshow(denoise_bilateral(noisy, sigma_color=0.06, sigma_range=15)) +ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.06, sigma_spatial=15)) ax[1, 1].axis('off') ax[1, 1].set_title('(more) Bilateral') ax[1, 2].imshow(lena) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index 02fddf23..f1c1fd49 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,7 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from .denoise import tv_denoise, denoise_tv, denoise_bilateral +from .denoise import tv_denoise, denoise_tv +from ._denoise import denoise_bilateral from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index ad25d9d0..bda46b0d 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -7,7 +7,8 @@ cimport numpy as cnp import numpy as np from libc.math cimport exp, fabs, sqrt from libc.stdlib cimport malloc, free -from skimage._shared.interpolation cimport get_pixel2d, get_pixel3d +from skimage._shared.interpolation cimport get_pixel3d +from skimage.util import img_as_float cdef inline double _gaussian_weight(double sigma, double value): @@ -42,67 +43,57 @@ cdef double* _compute_range_lut(int win_size, double sigma): return range_lut -def _denoise_bilateral2d(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image, - int win_size, double sigma_color, - double sigma_range, int bins, char mode, - double cval): +def denoise_bilateral(image, int win_size=5, sigma_range=None, + double sigma_spatial=1, int bins=10000, mode='constant', + double cval=0): + """Denoise image using bilateral filter. - cdef: - Py_ssize_t rows = image.shape[0] - Py_ssize_t cols = image.shape[1] - Py_ssize_t window_ext = (win_size - 1) / 2 - double max_value = image.max() + This is an edge-preserving and noise reducing denoising filter. It averages + pixels based on their spatial closeness and radiometric similarity. - cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \ - np.zeros((rows, cols), dtype=np.double) + Spatial closeness is measured by the gaussian function of the euclidian + distance between two pixels and a certain standard deviation + (`sigma_spatial`). - double* image_data = image.data - double* out_data = out.data + Radiometric similarity is measured by the gaussian function of the euclidian + distance between two color values and a certain standard deviation + (`sigma_range`). - double* color_lut = _compute_color_lut(bins, sigma_color, max_value) - double* range_lut = _compute_range_lut(win_size, sigma_range) + Parameters + ---------- + image : ndarray + Input image. + win_size : int + Window size for filtering. + sigma_range : float + Standard deviation for grayvalue/color distance (radiometric + similarity). A larger value results in averaging of pixels with larger + radiometric differences. + sigma_spatial : float + Standard deviation for range distance. A larger value results in + averaging of pixels with larger spatial differences. + bins : int + Number of discrete values for gaussian weights of color filtering. + A larger value results in improved accuracy. + mode : string + How to handle values outside the image borders. See + `scipy.ndimage.map_coordinates` for detail. + cval : string + Used in conjunction with mode 'constant', the value outside + the image boundaries. - Py_ssize_t r, c, wr, wc, kr, kc, rr, cc, pixel_addr - double centre, value, weight, total_value, total_weight, \ - color_weight, range_weight, diff - double dist_scale = bins / max_value + Returns + ------- + denoised : ndarray + Denoised image. - for r in range(rows): - for c in range(cols): - pixel_addr = r * cols + c - total_value = 0 - total_weight = 0 - centre = image_data[pixel_addr] - for wr in range(-window_ext, window_ext + 1): - rr = wr + r - kr = wr + window_ext - for wc in range(-window_ext, window_ext + 1): - cc = wc + c - kc = wc + window_ext + References + ---------- + .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf - value = get_pixel2d(image_data, rows, cols, - rr, cc, mode, cval) - diff = fabs(centre - value) + """ - range_weight = range_lut[kr * win_size + kc] - color_weight = color_lut[(diff * dist_scale)] - - weight = range_weight * color_weight - total_value += value * weight - total_weight += weight - - out_data[pixel_addr] = total_value / total_weight - - free(color_lut) - free(range_lut) - - return out - - -def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image, - int win_size, double sigma_color, - double sigma_range, int bins, char mode, - double cval): + image = np.atleast_3d(img_as_float(image)) cdef: Py_ssize_t rows = image.shape[0] @@ -112,22 +103,35 @@ def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image double max_value = image.max() + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] cimage = \ + np.ascontiguousarray(image) cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] out = \ np.zeros((rows, cols, dims), dtype=np.double) - double* image_data = image.data + double* image_data = cimage.data double* out_data = out.data - double* color_lut = _compute_color_lut(bins, sigma_color, max_value) - double* range_lut = _compute_range_lut(win_size, sigma_range) + double* color_lut = _compute_color_lut(bins, sigma_range, max_value) + double* range_lut = _compute_range_lut(win_size, sigma_spatial) Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr - double value, weight, dist, total_weight, color_weight, range_weight + double value, weight, dist, total_weight, csigma_range, color_weight, \ + range_weight double dist_scale = bins / dims / max_value double* values = malloc(dims * sizeof(double)) double* centres = malloc(dims * sizeof(double)) double* total_values = malloc(dims * sizeof(double)) + if sigma_range is None: + csigma_range = image.std() + else: + csigma_range = sigma_range + + if mode not in ('constant', 'wrap', 'reflect', 'nearest'): + raise ValueError("Invalid mode specified. Please use " + "`constant`, `nearest`, `wrap` or `reflect`.") + cdef char cmode = ord(mode[0].upper()) + for r in range(rows): for c in range(cols): pixel_addr = r * cols * dims + c * dims @@ -147,7 +151,7 @@ def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image dist = 0 for d in range(dims): value = get_pixel3d(image_data, rows, cols, dims, - rr, cc, d, mode, cval) + rr, cc, d, cmode, cval) values[d] = value dist += (centres[d] - value)**2 dist = sqrt(dist) diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index d7c96f90..d62f8b27 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -244,70 +244,3 @@ def denoise_tv(im, weight=50, eps=2.e-4, n_iter_max=200): tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv) - - -def denoise_bilateral(image, win_size=5, sigma_color=1, sigma_range=1, bins=1e4, - mode='constant', cval=0): - """Denoise image using bilateral filter. - - This is an edge-preserving and noise reducing denoising filter. It averages - pixels based on their spatial closeness and radiometric similarity. - - Spatial closeness is measured by the gaussian function of the euclidian - distance between two pixels and a certain standard deviation - (`sigma_range`). - - Radiometric similarity is measured by the gaussian function of the euclidian - distance between two color values and a certain standard deviation - (`sigma_color`). - - Parameters - ---------- - image : ndarray - Input image. - win_size : int - Window size for filtering. - sigma_color : float - Standard deviation for color distance. A larger value results in - averaging of pixels with larger color differences. - sigma_range : float - Standard deviation for range distance. A larger value results in - averaging of pixels with larger spatial differences. - bins : int - Number of discrete values for gaussian weights of color filtering. - A larger value results in improved accuracy. - mode : string - How to handle values outside the image borders. See - `scipy.ndimage.map_coordinates` for detail. - cval : string - Used in conjunction with mode 'constant', the value outside - the image boundaries. - - Returns - ------- - denoised : ndarray - Denoised image. - - References - ---------- - .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf - - """ - - # not using img_as_float to preserve original range of values, which is - # necessary so sigma_color is applied as user desires - image = np.array(image, dtype=np.double) - - if mode not in ('constant', 'wrap', 'reflect', 'nearest'): - raise ValueError("Invalid mode specified. Please use " - "`constant`, `nearest`, `wrap` or `reflect`.") - mode = ord(mode[0].upper()) - - if image.ndim == 2 or (image.ndim == 3 and image.shape[2] == 1): - if image.ndim == 3 and image.shape[2] == 1: - image = np.squeeze(image) - func = _denoise._denoise_bilateral2d - else: - func = _denoise._denoise_bilateral3d - image = np.ascontiguousarray(image) - return func(image, win_size, sigma_color, sigma_range, bins, mode, cval) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index b8e6c9ef..63a5a5e0 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -64,8 +64,8 @@ def test_denoise_bilateral_2d(): img += 0.5 * img.std() * np.random.random(img.shape) img = np.clip(img, 0, 1) - out1 = filter.denoise_bilateral(img, sigma_color=0.1, sigma_range=20) - out2 = filter.denoise_bilateral(img, sigma_color=0.2, sigma_range=30) + out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20) + out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30) # make sure noise is reduced assert img.std() > out1.std() @@ -78,8 +78,8 @@ def test_denoise_bilateral_3d(): img += 0.5 * img.std() * np.random.random(img.shape) img = np.clip(img, 0, 1) - out1 = filter.denoise_bilateral(img, sigma_color=0.1, sigma_range=20) - out2 = filter.denoise_bilateral(img, sigma_color=0.2, sigma_range=30) + out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20) + out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30) # make sure noise is reduced assert img.std() > out1.std() From 2cdb3896800a4466e76a29a31138ca026d7a0db6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 17 Oct 2012 16:29:32 +0200 Subject: [PATCH 176/625] Remove unused import --- skimage/filter/denoise.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py index d62f8b27..351b2482 100644 --- a/skimage/filter/denoise.py +++ b/skimage/filter/denoise.py @@ -1,7 +1,6 @@ import numpy as np from skimage import img_as_float from skimage._shared.utils import deprecated -import _denoise def _denoise_tv_3d(im, weight=100, eps=2.e-4, n_iter_max=200): From dd421dcc80e14157a0a4e8f6f557201b425713ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 17 Oct 2012 16:32:31 +0200 Subject: [PATCH 177/625] Add note about range of values of sigma_range parameter --- skimage/filter/_denoise.pyx | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index bda46b0d..b60a5a85 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -68,7 +68,9 @@ def denoise_bilateral(image, int win_size=5, sigma_range=None, sigma_range : float Standard deviation for grayvalue/color distance (radiometric similarity). A larger value results in averaging of pixels with larger - radiometric differences. + radiometric differences. Note, that the image will be converted using + the `img_as_float` function and thus the standard deviation is in + respect to the range `[0, 1]`. sigma_spatial : float Standard deviation for range distance. A larger value results in averaging of pixels with larger spatial differences. From a41163beb0bd63194f58a03faf5563ae8fe60365 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 17 Oct 2012 16:45:25 +0200 Subject: [PATCH 178/625] Add Cython denoise source file to bento file --- bento.info | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/bento.info b/bento.info index 50a2743a..55b3690d 100644 --- a/bento.info +++ b/bento.info @@ -64,7 +64,10 @@ Library: Extension: skimage.filter._ctmf Sources: skimage/filter/_ctmf.pyx - Extension: skimage.morphology.ccomp + Extension: skimage.filter._denoise + Sources: + skimage/filter/_ctmf.pyx + Extension: skimage.morphology.denoise Sources: skimage/morphology/ccomp.pyx Extension: skimage.morphology._watershed From 82d20ca694c0ceb4b6ad3bbc0fb0856acbf629b9 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:18:01 +0200 Subject: [PATCH 179/625] moved example to doc --- doc/examples/plot_16bitbilateral.py | 33 +++++++++++++++ .../examples/plot_benchmark_rank.py | 41 +++++++++++++++---- skimage/rank/local/demo_16bitbilateral.py | 31 -------------- 3 files changed, 65 insertions(+), 40 deletions(-) create mode 100644 doc/examples/plot_16bitbilateral.py rename skimage/rank/local/demo_benchmark.py => doc/examples/plot_benchmark_rank.py (85%) delete mode 100644 skimage/rank/local/demo_16bitbilateral.py diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py new file mode 100644 index 00000000..f61f3c55 --- /dev/null +++ b/doc/examples/plot_16bitbilateral.py @@ -0,0 +1,33 @@ +""" +============================== +Simplified bilateral filtering +============================== + +to complete + +""" +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.morphology import disk +import skimage.rank as rank + +a8 = (data.coins()).astype('uint8') + +a16 = (data.coins()).astype('uint16')*16 +selem = np.ones((20,20),dtype='uint8') +f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) +f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) +selem = disk(50) +f3 = rank.equalize(a16,selem = selem) + +# display results +fig, axes = plt.subplots(nrows=3, figsize=(15,5)) +ax0, ax1, ax2 = axes + +ax0.imshow(np.hstack((a8,f1))) +ax1.imshow(np.hstack((a16,f2))) +ax2.imshow(np.hstack((a16,f3))) + +plt.show() diff --git a/skimage/rank/local/demo_benchmark.py b/doc/examples/plot_benchmark_rank.py similarity index 85% rename from skimage/rank/local/demo_benchmark.py rename to doc/examples/plot_benchmark_rank.py index d3c3d00a..dab2f8d4 100644 --- a/skimage/rank/local/demo_benchmark.py +++ b/doc/examples/plot_benchmark_rank.py @@ -1,3 +1,20 @@ +""" +============================== +Compare execution time for + - skimage.rank.median, + - skimage.filter import median_filter + - scipy.ndimage.filters import percentile_filter, + + and + + - skimage.cmorph.dilate + - skimage.rank.maximum + +============================== + +to complete + +""" import numpy as np import matplotlib.pyplot as plt import time @@ -17,7 +34,6 @@ def log_timing(func): res = func(*arg) t2 = time.time() ms = (t2-t1)*1000.0 - print '%s took %0.3f ms' % (func.func_name, ms) return (res,ms) return wrapper @@ -26,6 +42,14 @@ def log_timing(func): def cr_med(image,selem): return rank.median(image=image,selem = selem) +@log_timing +def cr_max(image,selem): + return rank.maximum(image=image,selem = selem) + +@log_timing +def cm_dil(image,selem): + return dilation(image=image,selem = selem) + @log_timing def ctmf_med(image,radius): return median_filter(image=image,radius=radius) @@ -46,13 +70,12 @@ def compare_dilate(): rec = [] e_range = range(1,20,1) for r in e_range: -# elem = np.ones((r,r),dtype='uint8') elem = disk(r+1) # elem = (np.random.random((r,r))>.5).astype('uint8') rc,ms_rc = cr_max(a,elem) rcm,ms_rcm = cm_dil(a,elem) rec.append((ms_rc,ms_rcm)) - # check if results are identical + # same structuring element, the results must match assert (rc==rcm).all() rec = np.asarray(rec) @@ -65,7 +88,6 @@ def compare_dilate(): plt.imshow(np.hstack((rc,rcm))) r = 9 -# elem = np.ones((r,r),dtype='uint8') elem = disk(r+1) rec = [] @@ -75,6 +97,7 @@ def compare_dilate(): (rc,ms_rc) = cr_max(a,elem) (rcm,ms_rcm) = cm_dil(a,elem) rec.append((ms_rc,ms_rcm)) + # same structuring element, the results must match assert (rc==rcm).all() rec = np.asarray(rec) @@ -86,7 +109,6 @@ def compare_dilate(): plt.figure() plt.imshow(np.hstack((rc,rcm))) - plt.show() def compare_median(): """ Comparison between @@ -145,7 +167,8 @@ def compare_median(): plt.ylabel('time (ms)') plt.xlabel('image size') - plt.show() -if __name__ == '__main__': -# compare_dilate() - compare_median() \ No newline at end of file + + +compare_dilate() +compare_median() +plt.show() diff --git a/skimage/rank/local/demo_16bitbilateral.py b/skimage/rank/local/demo_16bitbilateral.py deleted file mode 100644 index 85fd510c..00000000 --- a/skimage/rank/local/demo_16bitbilateral.py +++ /dev/null @@ -1,31 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data -from skimage.morphology import disk -import skimage.rank as rank - -if __name__ == '__main__': - a8 = (data.coins()).astype('uint8') - - a16 = (data.coins()).astype('uint16')*16 - selem = np.ones((20,20),dtype='uint8') - f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) - f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) - - selem = disk(50) - f3 = rank.equalize(a16,selem = selem) - - plt.figure() - plt.imshow(np.hstack((a8,f1))) - plt.colorbar() - - plt.figure() - plt.imshow(np.hstack((a16,f2))) - plt.colorbar() - - plt.figure() - plt.imshow(np.hstack((a16,f3))) - plt.colorbar() - - plt.show() From 491f53aaa7bb5f767b872ae6afdd7b0783483431 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:19:45 +0200 Subject: [PATCH 180/625] removed useless tools.py --- skimage/rank/local/tools.py | 42 ------------------------------------- 1 file changed, 42 deletions(-) delete mode 100644 skimage/rank/local/tools.py diff --git a/skimage/rank/local/tools.py b/skimage/rank/local/tools.py deleted file mode 100644 index 2fba4798..00000000 --- a/skimage/rank/local/tools.py +++ /dev/null @@ -1,42 +0,0 @@ -__author__ = 'Olivier Debeir 2021' - -import logging -import time - -def init_logger(logfilename = 'myapp.log'): - """add logger capabilities - """ - FORMAT = '%(asctime)-15s %(processName)s %(process)d %(message)s' - logging.basicConfig(filename=logfilename,format=FORMAT,filemode='wt') - logger = logging.getLogger() - logger.setLevel(logging.DEBUG) - - # create console handler and set level to debug - ch = logging.StreamHandler() - ch.setLevel(logging.DEBUG) - - # add ch to logger - logger.addHandler(ch) - logger.info('start logging in %s' % logfilename) - return logger - - -logger = logging.getLogger() - -def log_timing(func): - - def wrapper(*arg): - log_timing.level += 1 - t1 = time.time() - res = func(*arg) - t2 = time.time() - ms = (t2-t1)*1000.0 - logger.info('%s%s took %0.3f ms' % (log_timing.level*'-',func.func_name, ms)) - log_timing.level -= 1 - return (res,ms) - - return wrapper - -log_timing.level = 0 - - From 381fdf92795bf76cf4acc68d0ae5b56d0bc29f8d Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:23:41 +0200 Subject: [PATCH 181/625] modify CONTRIBUTORS.txt --- CONTRIBUTORS.txt | 3 +++ skimage/rank/bilateral_rank.py | 3 --- skimage/rank/local/__init__.py | 1 - skimage/rank/percentile_rank.py | 2 -- skimage/rank/rank.py | 2 -- 5 files changed, 3 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 5f18e821..de933cfc 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -117,3 +117,6 @@ - Petter Strandmark Perimeter calculation in regionprops. + +- Olivier Debeir + Rank filters (8- and 16-bits) using sliding window. \ No newline at end of file diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index 97db8f99..24ccbcd0 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -2,11 +2,8 @@ note: 8 bit images are casted into 16 bit image here -:author: Olivier Debeir, 2012 -:license: modified BSD """ -__docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py index 10b6fb15..e69de29b 100644 --- a/skimage/rank/local/__init__.py +++ b/skimage/rank/local/__init__.py @@ -1 +0,0 @@ -__author__ = 'olivier' diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 6ceb503d..2ed6f011 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -12,8 +12,6 @@ for 16 bit input images, the number of histogram bins is determined from the max result image is 8 or 16 bit with respect to the input image -:author: Olivier Debeir, 2012 -:license: modified BSD """ __docformat__ = 'restructuredtext en' diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index d07c9037..012c046f 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -10,8 +10,6 @@ for 16 bit input images, the number of histogram bins is determined from the max result image is 8 or 16 bit with respect to the input image -:author: Olivier Debeir, 2012 -:license: modified BSD """ __docformat__ = 'restructuredtext en' From 8d219d1427c5447d63f7d0145192f574701eb2df Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:25:18 +0200 Subject: [PATCH 182/625] __docformat__ removed --- skimage/rank/percentile_rank.py | 1 - skimage/rank/rank.py | 1 - 2 files changed, 2 deletions(-) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 2ed6f011..ac19ccd6 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -14,7 +14,6 @@ result image is 8 or 16 bit with respect to the input image """ -__docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 012c046f..51854128 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -12,7 +12,6 @@ result image is 8 or 16 bit with respect to the input image """ -__docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte From e618c1d4548977efa17cc6d6353dde456a7d4f92 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 10:00:10 +0200 Subject: [PATCH 183/625] move rank/ into filter/ --- doc/__init__.py | 1 + .../{ => applications}/plot_benchmark_rank.py | 19 +++++----- doc/examples/plot_16bitbilateral.py | 8 ++-- doc/examples/plot_lena_bilateral_denoise.py | 2 +- doc/examples/plot_local_autolevels.py | 2 +- doc/examples/plot_local_equalize.py | 2 +- doc/examples/plot_local_threshold.py | 2 +- doc/examples/plot_marked_watershed.py | 5 +-- skimage/{ => filter}/rank/README.rst | 0 skimage/{ => filter}/rank/__init__.py | 0 skimage/{ => filter}/rank/_core16.pxd | 0 skimage/{ => filter}/rank/_core16.pyx | 0 skimage/{ => filter}/rank/_core8.pxd | 0 skimage/{ => filter}/rank/_core8.pyx | 0 skimage/{ => filter}/rank/_crank16.pyx | 2 +- .../{ => filter}/rank/_crank16_bilateral.pyx | 2 +- .../rank/_crank16_percentiles.pyx | 2 +- skimage/{ => filter}/rank/_crank8.pyx | 2 +- .../{ => filter}/rank/_crank8_percentiles.pyx | 2 +- skimage/{ => filter}/rank/bilateral_rank.py | 31 ++++++++++++---- skimage/{ => filter}/rank/generic.py | 0 skimage/{ => filter}/rank/local/demo_all.py | 3 +- .../{ => filter}/rank/local/demo_single.py | 3 +- skimage/filter/rank/local/iko_pan_Ja1.tif | Bin 0 -> 129176 bytes .../rank/local/test_morph_contr_enh.py | 2 +- skimage/{ => filter}/rank/local/test_rank.py | 2 +- skimage/{ => filter}/rank/percentile_rank.py | 23 +++++------- skimage/{ => filter}/rank/rank.py | 35 ++++++++---------- skimage/{ => filter}/rank/setup.py | 0 skimage/{ => filter}/rank/tests/test_suite.py | 8 ++-- skimage/rank/local/__init__.py | 0 31 files changed, 83 insertions(+), 75 deletions(-) create mode 100644 doc/__init__.py rename doc/examples/{ => applications}/plot_benchmark_rank.py (96%) rename skimage/{ => filter}/rank/README.rst (100%) rename skimage/{ => filter}/rank/__init__.py (100%) rename skimage/{ => filter}/rank/_core16.pxd (100%) rename skimage/{ => filter}/rank/_core16.pyx (100%) rename skimage/{ => filter}/rank/_core8.pxd (100%) rename skimage/{ => filter}/rank/_core8.pyx (100%) rename skimage/{ => filter}/rank/_crank16.pyx (99%) rename skimage/{ => filter}/rank/_crank16_bilateral.pyx (98%) rename skimage/{ => filter}/rank/_crank16_percentiles.pyx (99%) rename skimage/{ => filter}/rank/_crank8.pyx (99%) rename skimage/{ => filter}/rank/_crank8_percentiles.pyx (99%) rename skimage/{ => filter}/rank/bilateral_rank.py (85%) rename skimage/{ => filter}/rank/generic.py (100%) rename skimage/{ => filter}/rank/local/demo_all.py (98%) rename skimage/{ => filter}/rank/local/demo_single.py (92%) create mode 100644 skimage/filter/rank/local/iko_pan_Ja1.tif rename skimage/{ => filter}/rank/local/test_morph_contr_enh.py (93%) rename skimage/{ => filter}/rank/local/test_rank.py (95%) rename skimage/{ => filter}/rank/percentile_rank.py (98%) rename skimage/{ => filter}/rank/rank.py (98%) rename skimage/{ => filter}/rank/setup.py (100%) rename skimage/{ => filter}/rank/tests/test_suite.py (97%) delete mode 100644 skimage/rank/local/__init__.py diff --git a/doc/__init__.py b/doc/__init__.py new file mode 100644 index 00000000..10b6fb15 --- /dev/null +++ b/doc/__init__.py @@ -0,0 +1 @@ +__author__ = 'olivier' diff --git a/doc/examples/plot_benchmark_rank.py b/doc/examples/applications/plot_benchmark_rank.py similarity index 96% rename from doc/examples/plot_benchmark_rank.py rename to doc/examples/applications/plot_benchmark_rank.py index dab2f8d4..79357768 100644 --- a/doc/examples/plot_benchmark_rank.py +++ b/doc/examples/applications/plot_benchmark_rank.py @@ -19,13 +19,14 @@ import numpy as np import matplotlib.pyplot as plt import time +from scipy.ndimage.filters import percentile_filter + from skimage import data from skimage.morphology import dilation,disk from skimage.filter import median_filter -from scipy.ndimage.filters import percentile_filter -import skimage.rank as rank +import skimage.filter.rank as rank -def log_timing(func): +def exec_and_timeit(func): """ Decorator that returns both function results and execution time (result, ms) """ @@ -38,23 +39,23 @@ def log_timing(func): return wrapper -@log_timing +@exec_and_timeit def cr_med(image,selem): return rank.median(image=image,selem = selem) -@log_timing +@exec_and_timeit def cr_max(image,selem): return rank.maximum(image=image,selem = selem) -@log_timing +@exec_and_timeit def cm_dil(image,selem): return dilation(image=image,selem = selem) -@log_timing +@exec_and_timeit def ctmf_med(image,radius): return median_filter(image=image,radius=radius) -@log_timing +@exec_and_timeit def ndi_med(image,n): return percentile_filter(image,50,size=n*2-1) @@ -84,8 +85,6 @@ def compare_dilate(): plt.title('increasing element size') plt.plot(e_range,rec) plt.legend(['crank.maximum','cmorph.dilate']) - plt.figure() - plt.imshow(np.hstack((rc,rcm))) r = 9 elem = disk(r+1) diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py index f61f3c55..076d03c4 100644 --- a/doc/examples/plot_16bitbilateral.py +++ b/doc/examples/plot_16bitbilateral.py @@ -11,7 +11,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import disk -import skimage.rank as rank +import skimage.filter.rank as rank a8 = (data.coins()).astype('uint8') @@ -23,11 +23,13 @@ selem = disk(50) f3 = rank.equalize(a16,selem = selem) # display results -fig, axes = plt.subplots(nrows=3, figsize=(15,5)) +fig, axes = plt.subplots(nrows=3, figsize=(15,15)) ax0, ax1, ax2 = axes ax0.imshow(np.hstack((a8,f1))) +ax0.set_title('percentile mean') ax1.imshow(np.hstack((a16,f2))) +ax1.set_title('bilateral mean') ax2.imshow(np.hstack((a16,f3))) - +ax2.set_title('local equalization') plt.show() diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index 57593867..969403d0 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -18,7 +18,7 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data, color, img_as_ubyte -from skimage.rank import bilateral_mean +from skimage.filter.rank import bilateral_mean from skimage.morphology import disk l = img_as_ubyte(color.rgb2gray(data.lena())) diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py index 842215d5..7c750141 100644 --- a/doc/examples/plot_local_autolevels.py +++ b/doc/examples/plot_local_autolevels.py @@ -13,7 +13,7 @@ import numpy as np from skimage import data -from skimage.rank import percentile_autolevel,autolevel +from skimage.filter.rank import percentile_autolevel,autolevel from skimage.morphology import disk diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index 897989f0..1a431f5c 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -17,12 +17,12 @@ The local version [2]_ of the histogram equalization emphasized every local gray from skimage import data from skimage.util.dtype import dtype_range from skimage import exposure -from skimage import rank from skimage.morphology import disk import matplotlib.pyplot as plt import numpy as np +from skimage.filter import rank def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py index c5810156..8bc79b30 100644 --- a/doc/examples/plot_local_threshold.py +++ b/doc/examples/plot_local_threshold.py @@ -27,7 +27,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.filter import threshold_otsu, threshold_adaptive -from skimage.rank import threshold,morph_contr_enh +from skimage.filter.rank import threshold,morph_contr_enh from skimage.morphology import disk diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 0be25007..738a3d24 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -14,15 +14,14 @@ See Wikipedia_ for more details on the algorithm. """ -import numpy as np from scipy import ndimage import matplotlib.pyplot as plt from skimage.morphology import watershed,disk -from skimage import rank from skimage import data -from scipy import ndimage # original data +from skimage.filter import rank + image = data.camera() # denoise image diff --git a/skimage/rank/README.rst b/skimage/filter/rank/README.rst similarity index 100% rename from skimage/rank/README.rst rename to skimage/filter/rank/README.rst diff --git a/skimage/rank/__init__.py b/skimage/filter/rank/__init__.py similarity index 100% rename from skimage/rank/__init__.py rename to skimage/filter/rank/__init__.py diff --git a/skimage/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd similarity index 100% rename from skimage/rank/_core16.pxd rename to skimage/filter/rank/_core16.pxd diff --git a/skimage/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx similarity index 100% rename from skimage/rank/_core16.pyx rename to skimage/filter/rank/_core16.pyx diff --git a/skimage/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd similarity index 100% rename from skimage/rank/_core8.pxd rename to skimage/filter/rank/_core8.pxd diff --git a/skimage/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx similarity index 100% rename from skimage/rank/_core8.pyx rename to skimage/filter/rank/_core8.pyx diff --git a/skimage/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx similarity index 99% rename from skimage/rank/_crank16.pyx rename to skimage/filter/rank/_crank16.pyx index ce8510db..57d41563 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16 +from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx similarity index 98% rename from skimage/rank/_crank16_bilateral.pyx rename to skimage/filter/rank/_crank16_bilateral.pyx index b5103be4..24016bbf 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16 +from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx similarity index 99% rename from skimage/rank/_crank16_percentiles.pyx rename to skimage/filter/rank/_crank16_percentiles.pyx index 527d2aed..73ccde68 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -7,7 +7,7 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16, int_min, int_max +from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) diff --git a/skimage/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx similarity index 99% rename from skimage/rank/_crank8.pyx rename to skimage/filter/rank/_crank8.pyx index 94124cf7..045e3645 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core8 cimport _core8 +from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- # kernels uint8 diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx similarity index 99% rename from skimage/rank/_crank8_percentiles.pyx rename to skimage/filter/rank/_crank8_percentiles.pyx index 5441eac7..f882961a 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -7,7 +7,7 @@ import numpy as np cimport numpy as np # import main loop -from _core8 cimport _core8, uint8_max, uint8_min +from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) diff --git a/skimage/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py similarity index 85% rename from skimage/rank/bilateral_rank.py rename to skimage/filter/rank/bilateral_rank.py index 24ccbcd0..1dc7552d 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -1,17 +1,32 @@ -""" +"""bilateral_rank.py - approximate bilateral rankfilter for local (custom kernel) mean -note: 8 bit images are casted into 16 bit image here +The local histogram is computed using a sliding window similar to the method described in + +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", +IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + +input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), +8 bit images are casted in 16 bit +the number of histogram bins is determined from the maximum value present in the image + +The pixel neighborhood is defined by: + +* the given structuring element + +* an interval [g-s0,g+s1] in gray level around g the processed pixel gray level + +The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally) + +result image is 16 bit with respect to the input image """ - -import warnings from skimage import img_as_ubyte import numpy as np +from skimage.filter.rank import _crank16_bilateral -from generic import find_bitdepth -import _crank16_bilateral +from skimage.filter.rank.generic import find_bitdepth __all__ = ['bilateral_mean', 'bilateral_pop'] @@ -67,7 +82,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -131,7 +146,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], diff --git a/skimage/rank/generic.py b/skimage/filter/rank/generic.py similarity index 100% rename from skimage/rank/generic.py rename to skimage/filter/rank/generic.py diff --git a/skimage/rank/local/demo_all.py b/skimage/filter/rank/local/demo_all.py similarity index 98% rename from skimage/rank/local/demo_all.py rename to skimage/filter/rank/local/demo_all.py index 8df5c3e7..038c749b 100644 --- a/skimage/rank/local/demo_all.py +++ b/skimage/filter/rank/local/demo_all.py @@ -1,10 +1,9 @@ -import numpy as np import matplotlib.pyplot as plt from pprint import pprint from skimage import data from skimage.morphology.selem import disk -import skimage.rank as rank +import skimage.filter.rank as rank def plot_all(): a8 = data.camera() diff --git a/skimage/rank/local/demo_single.py b/skimage/filter/rank/local/demo_single.py similarity index 92% rename from skimage/rank/local/demo_single.py rename to skimage/filter/rank/local/demo_single.py index b601b226..39b9acc0 100644 --- a/skimage/rank/local/demo_single.py +++ b/skimage/filter/rank/local/demo_single.py @@ -1,10 +1,9 @@ import numpy as np import matplotlib.pyplot as plt -from pprint import pprint from skimage import data from skimage.morphology.selem import disk -import skimage.rank as rank +import skimage.filter.rank as rank if __name__ == '__main__': diff --git a/skimage/filter/rank/local/iko_pan_Ja1.tif b/skimage/filter/rank/local/iko_pan_Ja1.tif new file mode 100644 index 0000000000000000000000000000000000000000..47201695755a73086ba6d598f3441063ecdf1068 GIT binary patch literal 129176 zcmd44cXU-%`p13Fp%te0trb7kc7_r`JBve&HUE;TkHMz&0RP5+*5XWzE9iFe)hh1-+g|twK5DE z2SHdX2!kLv$UFS6_{9I$ zqa7al>#ZFw{ohYsd};jm@&$aHe)+|hUU>}%e!JrIOXJt&S6y}bzu*5;%MF+O?*<1; z!%bm!m>3QW$Amq@55lLzW?}pAgz$~J>e)w_NH#{x;F31bs4%dWB!)4*haBjGm zCuW4b!&kx%VfXOc@QQGG7)IN}s&H?Z6PAX1!YJAm9v2-AkA?@r!mw7fk87o2QCK@F z=iBbEVN@J$3+qSw!yTO6O)J8vlzVoEB|O_a3VHS*Pk1(n4sh2|o~skprrbT@{;(q4 z7dD7?hMU7W@x42#xj|GnY8fSSe_?oxr}y#HuCSaEef9t?*&kMOre1WA>nBoLHQ&9y zFFZb~;ormA;jD0M_+r>LEDu%$vx410oA8XV3dXG$W(MPfF}zcP2H`J! 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L&V3s<8XWu!riJvI literal 0 HcmV?d00001 diff --git a/skimage/rank/local/test_morph_contr_enh.py b/skimage/filter/rank/local/test_morph_contr_enh.py similarity index 93% rename from skimage/rank/local/test_morph_contr_enh.py rename to skimage/filter/rank/local/test_morph_contr_enh.py index 812e81c5..f2f0f7c9 100644 --- a/skimage/rank/local/test_morph_contr_enh.py +++ b/skimage/filter/rank/local/test_morph_contr_enh.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import gdal from skimage.morphology import disk -import skimage.rank as rank +import skimage.filter.rank as rank filename = 'iko_pan_Ja1.tif' im16 = gdal.Open(filename).ReadAsArray().astype(np.uint16) diff --git a/skimage/rank/local/test_rank.py b/skimage/filter/rank/local/test_rank.py similarity index 95% rename from skimage/rank/local/test_rank.py rename to skimage/filter/rank/local/test_rank.py index 75dd7f65..09cfdcd5 100644 --- a/skimage/rank/local/test_rank.py +++ b/skimage/filter/rank/local/test_rank.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology.selem import disk -import skimage.rank as rank +import skimage.filter.rank as rank print dir(rank) diff --git a/skimage/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py similarity index 98% rename from skimage/rank/percentile_rank.py rename to skimage/filter/rank/percentile_rank.py index ac19ccd6..7908f5ac 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -14,14 +14,11 @@ result image is 8 or 16 bit with respect to the input image """ - -import warnings from skimage import img_as_ubyte import numpy as np -from generic import find_bitdepth -import _crank16_percentiles -import _crank8_percentiles +from skimage.filter.rank.generic import find_bitdepth +from skimage.filter.rank import _crank16_percentiles, _crank8_percentiles __all__ = ['percentile_autolevel', 'percentile_gradient', 'percentile_mean', 'percentile_mean_substraction', @@ -77,7 +74,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -141,7 +138,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ to be updated >>> # Local gradient >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -205,7 +202,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -269,7 +266,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals to be updated >>> # Local mean_substraction >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -333,7 +330,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -397,7 +394,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -462,7 +459,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -526,7 +523,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], diff --git a/skimage/rank/rank.py b/skimage/filter/rank/rank.py similarity index 98% rename from skimage/rank/rank.py rename to skimage/filter/rank/rank.py index 51854128..0a9dee27 100644 --- a/skimage/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -12,14 +12,11 @@ result image is 8 or 16 bit with respect to the input image """ - -import warnings from skimage import img_as_ubyte import numpy as np +from skimage.filter.rank import _crank8, _crank16 -from generic import find_bitdepth -import _crank16 -import _crank8 +from skimage.filter.rank.generic import find_bitdepth __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] @@ -71,7 +68,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -133,7 +130,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -194,7 +191,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -255,7 +252,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local gradient >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -317,7 +314,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local maximum >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 1, 0, 0], @@ -379,7 +376,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -441,7 +438,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F to be updated >>> # Local meansubstraction >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -503,7 +500,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local median >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 0, 1, 0], @@ -565,7 +562,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local minimum >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -628,7 +625,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local modal >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 5, 6, 0], @@ -691,7 +688,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -753,7 +750,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -815,7 +812,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -878,7 +875,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], diff --git a/skimage/rank/setup.py b/skimage/filter/rank/setup.py similarity index 100% rename from skimage/rank/setup.py rename to skimage/filter/rank/setup.py diff --git a/skimage/rank/tests/test_suite.py b/skimage/filter/rank/tests/test_suite.py similarity index 97% rename from skimage/rank/tests/test_suite.py rename to skimage/filter/rank/tests/test_suite.py index 9d2bcfea..4f1e6f81 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/filter/rank/tests/test_suite.py @@ -1,12 +1,12 @@ import unittest import numpy as np +from skimage.filter import rank -from skimage.rank import _crank8,_crank8_percentiles -from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles -from skimage.morphology import cmorph,disk from skimage import data -from skimage import rank +from skimage.morphology import cmorph,disk +from skimage.filter.rank import _crank8, _crank16 +from skimage.filter.rank import _crank16_percentiles class TestSequenceFunctions(unittest.TestCase): diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py deleted file mode 100644 index e69de29b..00000000 From 53deddf5e06616d10d481bb2f0e2fb7e9c22c220 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 10:12:19 +0200 Subject: [PATCH 184/625] cut long lines --- skimage/filter/rank/bilateral_rank.py | 9 ++++++--- skimage/filter/rank/percentile_rank.py | 27 +++++++++++++++++--------- skimage/filter/rank/rank.py | 24 +++++++++++++++-------- 3 files changed, 40 insertions(+), 20 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index 1dc7552d..5ea92ed9 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -45,7 +45,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + s0=s0, s1=s1) def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -109,7 +110,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) + return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + s0=s0, s1=s1) def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -173,7 +175,8 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals """ - return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) + return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + s0=s0, s1=s1) if __name__ == "__main__": import sys diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py index 7908f5ac..77858715 100644 --- a/skimage/filter/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -35,7 +35,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + p0=p0, p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") @@ -101,7 +102,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -165,7 +167,8 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ """ - return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -229,7 +232,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -293,7 +297,8 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals """ - return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -357,7 +362,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, """ - return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -422,7 +428,8 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, """ - return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -486,7 +493,8 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -551,7 +559,8 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) if __name__ == "__main__": diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.py index 0a9dee27..d4df3aef 100644 --- a/skimage/filter/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -18,7 +18,8 @@ from skimage.filter.rank import _crank8, _crank16 from skimage.filter.rank.generic import find_bitdepth -__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] +__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', + 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): @@ -95,7 +96,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -156,7 +158,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -217,7 +220,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ - return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -279,7 +283,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -465,7 +470,8 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F """ - return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y) def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -715,7 +721,8 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -840,7 +847,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): From ce0a609579bdf35b641c223fea52f5b9ef8079cb Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 10:21:34 +0200 Subject: [PATCH 185/625] autopep8 --- skimage/filter/rank/_core16.pyx | 2 +- skimage/filter/rank/_core8.pyx | 2 +- skimage/filter/rank/bilateral_rank.py | 9 ++++++--- skimage/filter/rank/percentile_rank.py | 27 +++++++++++++++++--------- skimage/filter/rank/rank.py | 21 +++++++++++++------- 5 files changed, 40 insertions(+), 21 deletions(-) diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 81fab0b4..e60308ed 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r cdef inline _core16( - np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 7851388d..0d30045f 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r return 0 cdef inline _core8( - np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index 5ea92ed9..ff4e7878 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -45,7 +45,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + return func16( + image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) @@ -110,7 +111,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + return _apply( + None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) @@ -175,7 +177,8 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals """ - return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + return _apply( + None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) if __name__ == "__main__": diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py index 77858715..5191bec4 100644 --- a/skimage/filter/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -35,7 +35,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + return func16( + image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") @@ -102,7 +103,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -167,7 +169,8 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ """ - return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -232,7 +235,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -297,7 +301,8 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals """ - return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, + return _apply( + _crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -362,7 +367,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, """ - return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, + return _apply( + _crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -428,7 +434,8 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, """ - return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -493,7 +500,8 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -559,7 +567,8 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.py index d4df3aef..517ab2cf 100644 --- a/skimage/filter/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -96,7 +96,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -158,7 +159,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -220,7 +222,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ - return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -283,7 +286,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -470,7 +474,8 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F """ - return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, + return _apply( + _crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -721,7 +726,8 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -847,7 +853,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) From 662ac3039ae0b5e4aecb3ca912ae9de3149945f3 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 15:21:14 +0200 Subject: [PATCH 186/625] add example modal --- doc/examples/plot_modal_filter.py | 67 +++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 doc/examples/plot_modal_filter.py diff --git a/doc/examples/plot_modal_filter.py b/doc/examples/plot_modal_filter.py new file mode 100644 index 00000000..a66da0a1 --- /dev/null +++ b/doc/examples/plot_modal_filter.py @@ -0,0 +1,67 @@ +""" +=================== +Label image regions +=================== + +This example shows how to segment an image with image labelling. The following +steps are applied: + +1. Thresholding with automatic Otsu method +2. Close small holes with binary closing +3. Remove artifacts touching image border +4. Measure image regions to filter small objects + +""" + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches + +from skimage import data +from skimage.filter import threshold_otsu + +from skimage.filter.rank import modal + +from skimage.morphology import label, disk +from skimage.measure import find_contours + + +image = data.coins()[50:-50, 50:-50] + +# apply threshold +thresh = threshold_otsu(image) +bw = image > thresh + +# label image regions +label_image = label(bw) + +# filter obtained labels using model filter +mod_label_image = modal(label_image.astype(np.uint16),disk(5)) + +# the background is here 1 +contours = find_contours(mod_label_image==1,0, positive_orientation='low') + +fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(6, 6)) + +print axes + +ax0, ax1, ax2, ax3 = axes.ravel() + +ax0.imshow(bw, cmap='gray') +ax0.set_title('Otsu threshold') +ax1.imshow(label_image, cmap='jet') +ax1.set_title('label image') +ax2.imshow(mod_label_image, cmap='jet') +ax2.set_title('filtered labels (modal)') +ax3.imshow(image, cmap='gray') +ax3.set_title('contour overlay') +ax3.set_xlim((0,image.shape[1])) +ax3.set_ylim((image.shape[0],0)) + + +for n, contour in enumerate(contours): + ax3.plot(contour[:, 1], contour[:, 0], linewidth=2) + + +plt.show() + From 8f7419a227f76f58d8affa30542f39ff6991b71a Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 15:32:24 +0200 Subject: [PATCH 187/625] cut long line, autopep8 --- skimage/filter/rank/_core16.pxd | 2 +- skimage/filter/rank/_core16.pyx | 2 +- skimage/filter/rank/_core8.pxd | 2 +- skimage/filter/rank/_core8.pyx | 2 +- skimage/filter/rank/_crank16_bilateral.pyx | 10 +- skimage/filter/rank/_crank16_percentiles.pyx | 72 ++++++++++---- skimage/filter/rank/_crank8.pyx | 99 +++++++++++++------- skimage/filter/rank/_crank8_percentiles.pyx | 56 ++++++++--- 8 files changed, 174 insertions(+), 71 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index a2843f76..a113f2b0 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -9,7 +9,7 @@ cdef inline int int_max(int a, int b) cdef inline int int_min(int a, int b) cdef inline _core16( - np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index e60308ed..81fab0b4 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r cdef inline _core16( - np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 1a170500..8ecf7263 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -9,7 +9,7 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- cdef inline _core8( - np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 0d30045f..7851388d 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r return 0 cdef inline _core8( - np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 24016bbf..d6fb9c71 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -22,7 +22,10 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 cdef float mean = 0. @@ -39,7 +42,10 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 if pop: diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 73ccde68..0d37b77c 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -13,7 +13,10 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: @@ -40,7 +43,10 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint1 return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: @@ -63,7 +69,10 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16 return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: @@ -83,7 +92,10 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean_substraction( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: @@ -102,7 +114,10 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: @@ -130,7 +145,10 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_percentile( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i cdef float sum = 0. @@ -144,7 +162,10 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, n if pop: @@ -158,7 +179,10 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i cdef float sum = 0. @@ -184,7 +208,9 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -194,7 +220,9 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -204,7 +232,9 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -214,7 +244,9 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -224,7 +256,9 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -234,7 +268,9 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -244,7 +280,9 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -254,4 +292,6 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 045e3645..be00bf86 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -22,8 +22,9 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- cdef inline np.uint8_t kernel_autolevel( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: @@ -44,8 +45,9 @@ cdef inline np.uint8_t kernel_autolevel( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_bottomhat( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i for i in range(256): @@ -56,8 +58,9 @@ cdef inline np.uint8_t kernel_bottomhat( cdef inline np.uint8_t kernel_equalize( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float sum = 0. @@ -72,8 +75,9 @@ cdef inline np.uint8_t kernel_equalize( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_gradient( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: @@ -90,8 +94,9 @@ cdef inline np.uint8_t kernel_gradient( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_maximum( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i if pop: @@ -101,8 +106,10 @@ cdef inline np.uint8_t kernel_maximum( return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. @@ -114,8 +121,9 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, return < np.uint8_t > (0) cdef inline np.uint8_t kernel_meansubstraction( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. @@ -127,8 +135,9 @@ cdef inline np.uint8_t kernel_meansubstraction( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_median( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i cdef float sum = pop / 2.0 @@ -142,8 +151,9 @@ cdef inline np.uint8_t kernel_median( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_minimum( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i if pop: @@ -154,8 +164,8 @@ cdef inline np.uint8_t kernel_minimum( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_modal( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: @@ -168,8 +178,9 @@ cdef inline np.uint8_t kernel_modal( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_morph_contr_enh( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: @@ -188,13 +199,16 @@ cdef inline np.uint8_t kernel_morph_contr_enh( else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + return < np.uint8_t > (pop) cdef inline np.uint8_t kernel_threshold( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. @@ -206,8 +220,9 @@ cdef inline np.uint8_t kernel_threshold( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_tophat( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i for i in range(255, -1, -1): @@ -228,7 +243,9 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -238,7 +255,9 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -248,7 +267,9 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -258,7 +279,9 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -288,7 +311,9 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -318,7 +343,9 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -348,7 +375,9 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index f882961a..618a6452 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -13,7 +13,9 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -42,7 +44,9 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_ return < np.uint8_t > (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -65,7 +69,9 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -84,7 +90,9 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean_substraction( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -103,7 +111,9 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, n else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -131,7 +141,9 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np. else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_percentile( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -145,7 +157,9 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8 else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n if pop: @@ -159,7 +173,9 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, f else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -185,7 +201,9 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -195,7 +213,9 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -215,7 +235,9 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -225,7 +247,9 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -235,7 +259,9 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -255,4 +281,6 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) From 8347bd449fc137cbbc986461b3f4aa5c68c2137e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 18 Oct 2012 17:10:07 +0200 Subject: [PATCH 188/625] Replace deprecated function call --- doc/examples/plot_denoise.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index a3e83a65..8f02f21b 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -30,7 +30,7 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data, color, img_as_float -from skimage.filter import tv_denoise, denoise_bilateral +from skimage.filter import denoise_tv, denoise_bilateral lena = img_as_float(data.lena()) lena = lena[220:300, 220:320] @@ -43,14 +43,14 @@ fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5)) ax[0, 0].imshow(noisy) ax[0, 0].axis('off') ax[0, 0].set_title('noisy') -ax[0, 1].imshow(tv_denoise(noisy, weight=0.1)) +ax[0, 1].imshow(denoise_tv(noisy, weight=0.1)) ax[0, 1].axis('off') ax[0, 1].set_title('TV') ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.03, sigma_spatial=15)) ax[0, 2].axis('off') ax[0, 2].set_title('Bilateral') -ax[1, 0].imshow(tv_denoise(noisy, weight=0.2)) +ax[1, 0].imshow(denoise_tv(noisy, weight=0.2)) ax[1, 0].axis('off') ax[1, 0].set_title('(more) TV') ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.06, sigma_spatial=15)) From ea8e98f707de2eaeafa8fbe4b65424f2bb155554 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Thu, 18 Oct 2012 12:36:25 -0700 Subject: [PATCH 189/625] PKG: Fix bento build. --- bento.info | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bento.info b/bento.info index 55b3690d..bd831849 100644 --- a/bento.info +++ b/bento.info @@ -66,8 +66,8 @@ Library: skimage/filter/_ctmf.pyx Extension: skimage.filter._denoise Sources: - skimage/filter/_ctmf.pyx - Extension: skimage.morphology.denoise + skimage/filter/_denoise.pyx + Extension: skimage.morphology.ccomp Sources: skimage/morphology/ccomp.pyx Extension: skimage.morphology._watershed From cd8c3da8ec1b80ac327eba41fdb6cf2fa52cc3d9 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Thu, 18 Oct 2012 21:51:21 -0400 Subject: [PATCH 190/625] ENH: Allow imread to read URLs --- skimage/io/_io.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/skimage/io/_io.py b/skimage/io/_io.py index eea7e509..e400f6b3 100644 --- a/skimage/io/_io.py +++ b/skimage/io/_io.py @@ -1,6 +1,9 @@ __all__ = ['Image', 'imread', 'imread_collection', 'imsave', 'imshow', 'show', 'push', 'pop'] +import re +import urllib2 +import tempfile from io import BytesIO import numpy as np @@ -12,6 +15,14 @@ from skimage.color import rgb2grey # Shared image queue _image_stack = [] +URL_REGEX = re.compile(r'http://|https://|ftp://') + + +def is_url(filename): + """Return True if string is an http or ftp path.""" + return (isinstance(filename, basestring) and + URL_REGEX.match(filename) is not None) + class Image(np.ndarray): """Class representing Image data. @@ -88,7 +99,7 @@ def imread(fname, as_grey=False, plugin=None, flatten=None, Parameters ---------- fname : string - Image file name, e.g. ``test.jpg``. + Image file name, e.g. ``test.jpg`` or URL. as_grey : bool If True, convert color images to grey-scale (32-bit floats). Images that are already in grey-scale format are not converted. @@ -117,7 +128,15 @@ def imread(fname, as_grey=False, plugin=None, flatten=None, if flatten is not None: as_grey = flatten - img = call_plugin('imread', fname, plugin=plugin, **plugin_args) + if is_url(fname): + with tempfile.NamedTemporaryFile() as f: + u = urllib2.urlopen(fname) + f.write(u.read()) + f.seek(0) + # Note: the temporary file must be used inside the with-block. + img = call_plugin('imread', f.name, plugin=plugin, **plugin_args) + else: + img = call_plugin('imread', fname, plugin=plugin, **plugin_args) if as_grey and getattr(img, 'ndim', 0) >= 3: img = rgb2grey(img) From e5130180526d6eb79b5af56ef859c7ea6fa7f567 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Thu, 18 Oct 2012 22:07:18 -0400 Subject: [PATCH 191/625] Fix handling of temporary file. Opening an already-opened file was unsafe. Close the file, then manually delete it. --- skimage/io/_io.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/skimage/io/_io.py b/skimage/io/_io.py index e400f6b3..bf75cf76 100644 --- a/skimage/io/_io.py +++ b/skimage/io/_io.py @@ -1,6 +1,7 @@ __all__ = ['Image', 'imread', 'imread_collection', 'imsave', 'imshow', 'show', 'push', 'pop'] +import os import re import urllib2 import tempfile @@ -129,12 +130,12 @@ def imread(fname, as_grey=False, plugin=None, flatten=None, as_grey = flatten if is_url(fname): - with tempfile.NamedTemporaryFile() as f: + with tempfile.NamedTemporaryFile(delete=False) as f: u = urllib2.urlopen(fname) f.write(u.read()) - f.seek(0) - # Note: the temporary file must be used inside the with-block. + f.close() img = call_plugin('imread', f.name, plugin=plugin, **plugin_args) + os.remove(f.name) else: img = call_plugin('imread', fname, plugin=plugin, **plugin_args) From 9f7a376e4dcf19886c6f7f9cea806ffafc8bf36d Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Fri, 19 Oct 2012 15:21:31 -0400 Subject: [PATCH 192/625] ENH: Add test and support for reading file URLs --- skimage/io/_io.py | 2 +- skimage/io/tests/test_io.py | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/skimage/io/_io.py b/skimage/io/_io.py index bf75cf76..6986cc0f 100644 --- a/skimage/io/_io.py +++ b/skimage/io/_io.py @@ -16,7 +16,7 @@ from skimage.color import rgb2grey # Shared image queue _image_stack = [] -URL_REGEX = re.compile(r'http://|https://|ftp://') +URL_REGEX = re.compile(r'http://|https://|ftp://|file://') def is_url(filename): diff --git a/skimage/io/tests/test_io.py b/skimage/io/tests/test_io.py index 72f8496a..b29994c5 100644 --- a/skimage/io/tests/test_io.py +++ b/skimage/io/tests/test_io.py @@ -2,6 +2,7 @@ from numpy.testing import * import numpy as np import skimage.io as io +from skimage import data_dir def test_stack_basic(): @@ -15,5 +16,12 @@ def test_stack_basic(): def test_stack_non_array(): io.push([[1, 2, 3]]) + +def test_imread_url(): + image_url = 'file://%s/camera.png' % data_dir + image = io.imread(image_url) + assert image.shape == (512, 512) + + if __name__ == "__main__": run_module_suite() From c451aa0d4b0b07559fc0a3ea07e6a45a467cbc3e Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Fri, 19 Oct 2012 17:52:10 -0400 Subject: [PATCH 193/625] STY: Fix confusing use of context-manager. --- skimage/io/_io.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/skimage/io/_io.py b/skimage/io/_io.py index 6986cc0f..2a177076 100644 --- a/skimage/io/_io.py +++ b/skimage/io/_io.py @@ -133,9 +133,8 @@ def imread(fname, as_grey=False, plugin=None, flatten=None, with tempfile.NamedTemporaryFile(delete=False) as f: u = urllib2.urlopen(fname) f.write(u.read()) - f.close() - img = call_plugin('imread', f.name, plugin=plugin, **plugin_args) - os.remove(f.name) + img = call_plugin('imread', f.name, plugin=plugin, **plugin_args) + os.remove(f.name) else: img = call_plugin('imread', fname, plugin=plugin, **plugin_args) From c778de04ef5b56827cc105222e1681adc4eddc57 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 21 Oct 2012 13:25:18 -0500 Subject: [PATCH 194/625] Translated clahe C code to python, added to list of contributors. --- CONTRIBUTORS.txt | 3 + skimage/exposure/__init__.py | 3 +- skimage/exposure/_adapthist.py | 337 ++++++++++++++++++++++++ skimage/exposure/_adapthist.pyx | 40 --- skimage/exposure/clahe.h | 322 ---------------------- skimage/exposure/exposure.py | 92 +------ skimage/exposure/setup.py | 31 --- skimage/exposure/tests/test_exposure.py | 28 +- 8 files changed, 357 insertions(+), 499 deletions(-) create mode 100644 skimage/exposure/_adapthist.py delete mode 100644 skimage/exposure/_adapthist.pyx delete mode 100644 skimage/exposure/clahe.h delete mode 100644 skimage/exposure/setup.py diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index baaf064b..9b4dadae 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -107,3 +107,6 @@ Polygon, circle and ellipse drawing functions Adaptive thresholding Implementation of Matlab's `regionprops` + +- Steven Silvester, Karel Zuiderveld + Adaptive Histogram Equalization \ No newline at end of file diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index a5e6c25a..f8a4f046 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,2 +1,3 @@ -from .exposure import histogram, equalize, cumulative_distribution, adapthist +from .exposure import histogram, equalize, cumulative_distribution from .exposure import rescale_intensity +from ._adaphist import adapthist diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py new file mode 100644 index 00000000..f35a5da5 --- /dev/null +++ b/skimage/exposure/_adapthist.py @@ -0,0 +1,337 @@ +''' +Adapted code from the article + * "Contrast Limited Adaptive Histogram Equalization" + * by Karel Zuiderveld, karel@cv.ruu.nl + * in "Graphics Gems IV", Academic Press, 1994 +============= + +http://tog.acm.org/resources/GraphicsGems/ + +EULA: The Graphics Gems code is copyright-protected. +In other words, you cannot claim the text of the code as your +own and resell it. Using the code is permitted in any program, +product, or library, non-commercial or commercial. +Giving credit is not required, though is a nice gesture. +The code comes as-is, and if there are any flaws or problems +with any Gems code, nobody involved with Gems - authors, editors, +publishers, or webmasters - are to be held responsible. +Basically, don't be a jerk, and remember that anything free +comes with no guarantee. + * + * Author: Karel Zuiderveld, Computer Vision Research Group, + * Utrecht, The Netherlands (karel@cv.ruu.nl) +''' +import numpy as np +import skimage +from skimage import color +from skimage.exposure import rescale_intensity + + +MAX_REG_X = 16 # max. # contextual regions in x-direction */ +MAX_REG_Y = 16 # max. # contextual regions in y-direction */ +NR_OF_GREY = 1 << 14 # number of grayscale levels to use in CLAHE algorithm + + +def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): + '''Contrast Limited Adaptive Histogram Equalization + + Parameters + ---------- + image : array-like + original image + ntiles_x : int, optional + Tile regions in the X direction (2, 16) + ntiles_y : int, optional + Tile regions in the Y direction (2, 16) + clip_limit : float: optional + Normalized cliplimit (higher values give more contrast) + nbins : int, optional + Greybins for histogram ("dynamic range") + + Returns + ------- + out : np.ndarray + equalized image - grayscale images are uint16, color images are float + + Notes + ----- + * The algorithm relies on an image whose rows and columns are even + multiples of the number of tiles, so the extra rows and columns are left + at their original values, thus preserving the input image shape. + * For grayscale images, CLAHE is performed on one channel, + and a grayscale is returned + * For color images, the following steps are performed: + - The image is converted to LAB color space + - The CLAHE algorithm is run on the L channel + - The image is converted back to RGB space and returned + * For RGBA images, the original alpha channel is removed. + + References + ---------- + .. [1] http://tog.acm.org/resources/GraphicsGems/ + .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE + ''' + # convert to uint image + int_image = skimage.img_as_uint(image) + int_image = rescale_intensity(int_image, out_range=(0, NR_OF_GREY - 1)) + # handle color images - CLAHE accepts scalar images only + args = [int_image.copy(), ntiles_x, ntiles_y, clip_limit * nbins, nbins] + if image.ndim == 3: + # check for grayscale + if (np.allclose(image[:, :, 0], image[:, :, 1]) and + np.allclose(image[:, :, 1], image[:, :, 2])): + args[0] = int_image[:, :, 0] + out = _clahe(*args) + image = int_image[:, :, :3] + for channel in range(3): + image[:out.shape[0], :out.shape[1], channel] = out + # for color images, convert to LAB space for processing + else: + lab_img = color.rgb2lab(skimage.img_as_float(image)) + l_chan = lab_img[:, :, 0] + l_chan /= np.max(np.abs(l_chan)) + l_chan = skimage.img_as_uint(l_chan) + args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1)) + new_l = _clahe(*args).astype(float) + new_l = rescale_intensity(new_l, out_range=(0, 100)) + lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l + image = color.lab2rgb(lab_img) + image = rescale_intensity(image, out_range=(0, 1)) + else: + out = _clahe(*args) + image = int_image + image[:out.shape[0], :out.shape[1]] = out + return rescale_intensity(image) + + +def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): + '''Contrast Limited Adaptive Histogram Equalization + + Parameters + ---------- + image : array-like + original image + ntiles_x : int, optional + Tile regions in the X direction (2, 16) + ntiles_y : int, optional + Tile regions in the Y direction (2, 16) + clip_limit : float: optional + Normalized cliplimit (higher values give more contrast) + nbins : int, optional + Greybins for histogram ("dynamic range") + + Returns + ------- + out : np.ndarray + + The number of "effective" greylevels in the output image is set by nbins; + selecting a small value (eg. 128) speeds up processing and still produce + an output image of good quality. The output image will have the same + minimum and maximum value as the input image. A clip limit smaller than 1 + results in standard (non-contrast limited) AHE. + ''' + ntiles_x = min(ntiles_x, MAX_REG_X) + ntiles_y = min(ntiles_y, MAX_REG_Y) + ntiles_y = max(ntiles_x, 2) + ntiles_x = max(ntiles_y, 2) + + if clip_limit == 1.0: + return image # is OK, immediately returns original image. + + map_array = np.zeros((ntiles_x * ntiles_y, nbins), dtype=int) + + y_res = image.shape[0] - image.shape[0] % ntiles_y + x_res = image.shape[1] - image.shape[1] % ntiles_x + image = image[: y_res, : x_res] + + x_size = image.shape[1] / ntiles_x # Actual size of contextual regions + y_size = image.shape[0] / ntiles_y + n_pixels = x_size * y_size + + if clip_limit > 0.0: # Calculate actual cliplimit + clip_limit = int(clip_limit * (x_size * y_size) / nbins) + if clip_limit < 1: + clip_limit = 1 + else: + clip_limit = NR_OF_GREY # Large value, do not clip (AHE) + bin_size = 1 + NR_OF_GREY / nbins + aLUT = np.arange(NR_OF_GREY) + aLUT /= bin_size + # Calculate greylevel mappings for each contextual region + ystart = 0 + for y in range(ntiles_y): + xstart = 0 + for x in range(ntiles_x): + sub_img = image[ystart: ystart + y_size, + xstart: xstart + x_size] + hist = aLUT[sub_img.ravel()] + hist = np.bincount(hist) + hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int)) + hist = clip_histogram(hist, clip_limit) + hist = map_histogram(hist, 0, NR_OF_GREY, n_pixels) + map_array[y * ntiles_x + x] = hist + xstart += x_size + ystart += y_size + # Interpolate greylevel mappings to get CLAHE image + ystart = 0 + for y in range(ntiles_y + 1): + xstart = 0 + if y == 0: # special case: top row + ystep = y_size / 2 + yU = 0 + yB = 0 + elif y == ntiles_y: # special case: bottom row + ystep = y_size / 2 + yU = ntiles_y - 1 + yB = yU + else: # default values + ystep = y_size + yU = y - 1 + yB = yB + 1 + for x in range(ntiles_x + 1): + if x == 0: # special case: left column + xstep = x_size / 2 + xL = 0 + xR = 0 + elif x == ntiles_x: # special case: right column + xstep = x_size / 2 + xL = ntiles_x - 1 + xR = xL + else: # default values + xstep = x_size + xL = x - 1 + xR = xL + 1 + mapLU = map_array[yU * ntiles_x + xL] + mapRU = map_array[yU * ntiles_x + xR] + mapLB = map_array[yB * ntiles_x + xL] + mapRB = map_array[yB * ntiles_x + xR] + interpolate(image, xstart, xstep, ystart, ystep, + mapLU, mapRU, mapLB, mapRB, aLUT) + xstart += xstep # set pointer on next matrix */ + ystart += ystep + return image + + +def clip_histogram(hist, clip_limit): + '''Perform clipping of the histogram and redistribution of bins. + + The histogram is clipped and the number of excess pixels is counted. + Afterwards the excess pixels are equally redistributed across the + whole histogram (providing the bin count is smaller than the cliplimit). + + Parameters + ---------- + hist : np.ndarray + histogram array + clip_limit : int + maximum allowed bin count + + Returns + ------- + hist : np.ndarray + clipped histogram + ''' + # calculate total number of excess pixels + excess_mask = hist > clip_limit + excess = hist[excess_mask] + n_excess = excess.sum() - excess.size * clip_limit + + # Second part: clip histogram and redistribute excess pixels in each bin + bin_incr = n_excess / hist.size # average binincrement + upper = clip_limit - bin_incr # Bins larger than upper set to cliplimit + + hist[excess_mask] = clip_limit + + low_mask = hist < upper + n_excess -= hist[low_mask].size * bin_incr + hist[low_mask] += bin_incr + + mid_mask = (hist >= upper) & (hist < clip_limit) + mid = hist[mid_mask] + n_excess -= mid.size * clip_limit - mid.sum() + hist[mid_mask] = clip_limit + + while n_excess > 0: # Redistribute remaining excess + index = 0 + while n_excess > 0 and index < hist.size: + step_size = int(hist[hist < clip_limit].size / n_excess) + step_size = max(step_size, 1) + indices = np.arange(index, hist.size, step_size) + under = hist[indices] < clip_limit + hist[under] += 1 + n_excess -= hist[under].size + index += 1 + return hist + + +def map_histogram(hist, min_val, max_val, n_pixels): + '''Calculates the equalized lookup table (mapping) + + It does so by cumulating the input histogram. + + hist : np.ndarray + clipped histogram + min_val : int + min value for mapping + max_val : int + max value for mapping + n_pixels : int + number of pixels in the region + + Returns + ------- + out : np.ndarray + mapped intensity LUT + ''' + out = np.cumsum(hist).astype(float) + scale = ((float)(max_val - min_val)) / n_pixels + out *= scale + out += min_val + out[out > max_val] = max_val + return out.astype(int) + + +def interpolate(image, xstart, xstep, ystart, ystep, + mapLU, mapRU, mapLB, mapRB, aLUT): + '''Find the new grayscale level for a region using bilinear interpolation + + Parameters + ---------- + image : np.ndarray + full image + xstart, xstop : int + indices of xslice + ystart, ystop : int + indices of yslice + map* : np.ndarray + mappings of greylevels from histograms + aLUT : np.ndarray + maps grayscale levels in image to histogram levels + + Returns + ------- + out : np.ndarray + original image with the subregion replaced + + Note + ---- + This function calculates the new greylevel assignments of pixels + within a submatrix of the image. + This is done by a bilinear interpolation between four different + mappings in order to eliminate boundary artifacts. + ''' + norm = xstep * ystep # Normalization factor + + # interpolation weight matrices + x_coef, y_coef = np.meshgrid(np.arange(xstep), + np.arange(ystep)) + x_inv_coef, y_inv_coef = x_coef[:, ::-1] + 1, y_coef[::-1] + 1 + + im_slice = image[ystart: ystart + ystep, xstart: xstart + xstep] + im_slice = aLUT[im_slice] + new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice] + + x_coef * mapRU[im_slice]) + + y_coef * (x_inv_coef * mapLB[im_slice] + + x_coef * mapRB[im_slice])) + / norm) + image[ystart: ystart + ystep, xstart: xstart + xstep] = new diff --git a/skimage/exposure/_adapthist.pyx b/skimage/exposure/_adapthist.pyx deleted file mode 100644 index 16e84336..00000000 --- a/skimage/exposure/_adapthist.pyx +++ /dev/null @@ -1,40 +0,0 @@ -import numpy as np -cimport numpy as np - -cdef extern from "clahe.h": - ctypedef unsigned int kz_pixel_t - int CLAHE(kz_pixel_t * pImage, unsigned int uiXRes, unsigned int uiYRes, - kz_pixel_t Min, - kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, - unsigned int uiNrBins, float fCliplimit) - - -def _adapthist(np.ndarray[kz_pixel_t, ndim=2] image, min, max, nx, ny, nbins, - clip_limit=0): - ''' - image - to the input/output image - min - Minimum greyvalue of input image (also becomes min of output image) - max - Maximum greyvalue of input image (also becomes max of output image) - nx - Number of tile regions in the X direction (min 2, max uiMAX_REG_X) - ny - Number of tile regions in the Y direction (min 2, max uiMAX_REG_Y) - nbins - Number of greybins for histogram ("dynamic range") - clip_limit - Normalized cliplimit (higher values give more contrast) - - The number of "effective" greylevels in the output image is set by nbins; - selecting a small value (eg. 128) speeds up processing and still produce - an output image of good quality. The output image will have the same - minimum and maximum value as the input image. A clip limit smaller than 1 - results in standard (non-contrast limited) AHE. - ''' - # we need the image to be divisible by nx and ny - uiYRes = image.shape[0] - image.shape[0] % ny - uiXRes = image.shape[1] - image.shape[1] % nx - - cdef np.ndarray[kz_pixel_t, ndim = 1] flattened - flattened = image[:uiYRes, :uiXRes].ravel() - - ret = CLAHE(< kz_pixel_t * > flattened.data, uiXRes, uiYRes, min, - max, nx, ny, nbins, clip_limit * nbins) - if ret < 0: - print 'clahe error: ', ret - return flattened.reshape((uiYRes, uiXRes)) diff --git a/skimage/exposure/clahe.h b/skimage/exposure/clahe.h deleted file mode 100644 index a2721b98..00000000 --- a/skimage/exposure/clahe.h +++ /dev/null @@ -1,322 +0,0 @@ -/* -ANSI C code from the article - * "Contrast Limited Adaptive Histogram Equalization" - * by Karel Zuiderveld, karel@cv.ruu.nl - * in "Graphics Gems IV", Academic Press, 1994 -============= - -http://tog.acm.org/resources/GraphicsGems/ - -EULA: The Graphics Gems code is copyright-protected. -In other words, you cannot claim the text of the code as your -own and resell it. Using the code is permitted in any program, -product, or library, non-commercial or commercial. -Giving credit is not required, though is a nice gesture. -The code comes as-is, and if there are any flaws or problems -with any Gems code, nobody involved with Gems - authors, editors, -publishers, or webmasters - are to be held responsible. -Basically, don't be a jerk, and remember that anything free -comes with no guarantee. - -*/ - -#ifdef BYTE_IMAGE -typedef unsigned char kz_pixel_t; /* for 8 bit-per-pixel images */ -#define uiNR_OF_GREY (256) -#else -typedef unsigned short kz_pixel_t; /* for 12 bit-per-pixel images (default) */ -# define uiNR_OF_GREY (4096) -#endif - -/* - * ANSI C code from the article - * "Contrast Limited Adaptive Histogram Equalization" - * by Karel Zuiderveld, karel@cv.ruu.nl - * in "Graphics Gems IV", Academic Press, 1994 - * - * - * These functions implement Contrast Limited Adaptive Histogram Equalization. - * The main routine (CLAHE) expects an input image that is stored contiguously in - * memory; the CLAHE output image overwrites the original input image and has the - * same minimum and maximum values (which must be provided by the user). - * This implementation assumes that the X- and Y image resolutions are an integer - * multiple of the X- and Y sizes of the contextual regions. A check on various other - * error conditions is performed. - * - * #define the symbol BYTE_IMAGE to make this implementation suitable for - * 8-bit images. The maximum number of contextual regions can be redefined - * by changing uiMAX_REG_X and/or uiMAX_REG_Y; the use of more than 256 - * contextual regions is not recommended. - * - * The code is ANSI-C and is also C++ compliant. - * - * Author: Karel Zuiderveld, Computer Vision Research Group, - * Utrecht, The Netherlands (karel@cv.ruu.nl) - */ - -/*********************** Local prototypes ************************/ -static void ClipHistogram (unsigned long*, unsigned int, unsigned long); -static void MakeHistogram (kz_pixel_t*, unsigned int, unsigned int, unsigned int, - unsigned long*, unsigned int, kz_pixel_t*); -static void MapHistogram (unsigned long*, kz_pixel_t, kz_pixel_t, - unsigned int, unsigned long); -static void MakeLut (kz_pixel_t*, kz_pixel_t, kz_pixel_t, unsigned int); -static void Interpolate (kz_pixel_t*, int, unsigned long*, unsigned long*, - unsigned long*, unsigned long*, unsigned int, unsigned int, kz_pixel_t*); - -/************** Start of actual code **************/ -#include /* To get prototypes of malloc() and free() */ - - -const unsigned int uiMAX_REG_X = 16; /* max. # contextual regions in x-direction */ -const unsigned int uiMAX_REG_Y = 16; /* max. # contextual regions in y-direction */ - - - -/************************** main function CLAHE ******************/ -int CLAHE (kz_pixel_t* pImage, unsigned int uiXRes, unsigned int uiYRes, - kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, - unsigned int uiNrBins, float fCliplimit) -/* pImage - Pointer to the input/output image - * uiXRes - Image resolution in the X direction - * uiYRes - Image resolution in the Y direction - * Min - Minimum greyvalue of input image (also becomes minimum of output image) - * Max - Maximum greyvalue of input image (also becomes maximum of output image) - * uiNrX - Number of contextial regions in the X direction (min 2, max uiMAX_REG_X) - * uiNrY - Number of contextial regions in the Y direction (min 2, max uiMAX_REG_Y) - * uiNrBins - Number of greybins for histogram ("dynamic range") - * float fCliplimit - Normalized cliplimit (higher values give more contrast) - * The number of "effective" greylevels in the output image is set by uiNrBins; selecting - * a small value (eg. 128) speeds up processing and still produce an output image of - * good quality. The output image will have the same minimum and maximum value as the input - * image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. - */ -{ - unsigned int uiX, uiY; /* counters */ - unsigned int uiXSize, uiYSize, uiSubX, uiSubY; /* size of context. reg. and subimages */ - unsigned int uiXL, uiXR, uiYU, uiYB; /* auxiliary variables interpolation routine */ - unsigned long ulClipLimit, ulNrPixels;/* clip limit and region pixel count */ - kz_pixel_t* pImPointer; /* pointer to image */ - kz_pixel_t aLUT[uiNR_OF_GREY]; /* lookup table used for scaling of input image */ - unsigned long* pulHist, *pulMapArray; /* pointer to histogram and mappings*/ - unsigned long* pulLU, *pulLB, *pulRU, *pulRB; /* auxiliary pointers interpolation */ - - if (uiNrX > uiMAX_REG_X) return -1; /* # of regions x-direction too large */ - if (uiNrY > uiMAX_REG_Y) return -2; /* # of regions y-direction too large */ - if (uiXRes % uiNrX) return -3; /* x-resolution no multiple of uiNrX */ - if (uiYRes % uiNrY) return -4; /* y-resolution no multiple of uiNrY */ - if (Max >= uiNR_OF_GREY) return -5; /* maximum too large */ - if (Min >= Max) return -6; /* minimum equal or larger than maximum */ - if (uiNrX < 2 || uiNrY < 2) return -7;/* at least 4 contextual regions required */ - if (fCliplimit == 1.0) return 0; /* is OK, immediately returns original image. */ - if (uiNrBins == 0) uiNrBins = 128; /* default value when not specified */ - - pulMapArray=(unsigned long *)malloc(sizeof(unsigned long)*uiNrX*uiNrY*uiNrBins); - if (pulMapArray == 0) return -8; /* Not enough memory! (try reducing uiNrBins) */ - - uiXSize = uiXRes/uiNrX; uiYSize = uiYRes/uiNrY; /* Actual size of contextual regions */ - ulNrPixels = (unsigned long)uiXSize * (unsigned long)uiYSize; - - if(fCliplimit > 0.0) { /* Calculate actual cliplimit */ - ulClipLimit = (unsigned long) (fCliplimit * (uiXSize * uiYSize) / uiNrBins); - ulClipLimit = (ulClipLimit < 1UL) ? 1UL : ulClipLimit; - } - else ulClipLimit = 1UL<<14; /* Large value, do not clip (AHE) */ - MakeLut(aLUT, Min, Max, uiNrBins); /* Make lookup table for mapping of greyvalues */ - /* Calculate greylevel mappings for each contextual region */ - for (uiY = 0, pImPointer = pImage; uiY < uiNrY; uiY++) { - for (uiX = 0; uiX < uiNrX; uiX++, pImPointer += uiXSize) { - pulHist = &pulMapArray[uiNrBins * (uiY * uiNrX + uiX)]; - MakeHistogram(pImPointer,uiXRes,uiXSize,uiYSize,pulHist,uiNrBins,aLUT); - ClipHistogram(pulHist, uiNrBins, ulClipLimit); - MapHistogram(pulHist, Min, Max, uiNrBins, ulNrPixels); - } - pImPointer += (uiYSize - 1) * uiXRes; /* skip lines, set pointer */ - } - /* Interpolate greylevel mappings to get CLAHE image */ - for (pImPointer = pImage, uiY = 0; uiY <= uiNrY; uiY++) { - if (uiY == 0) { /* special case: top row */ - uiSubY = uiYSize >> 1; uiYU = 0; uiYB = 0; - } - else { - if (uiY == uiNrY) { /* special case: bottom row */ - uiSubY = uiYSize >> 1; uiYU = uiNrY-1; uiYB = uiYU; - } - else { /* default values */ - uiSubY = uiYSize; uiYU = uiY - 1; uiYB = uiYU + 1; - } - } - for (uiX = 0; uiX <= uiNrX; uiX++) { - if (uiX == 0) { /* special case: left column */ - uiSubX = uiXSize >> 1; uiXL = 0; uiXR = 0; - } - else { - if (uiX == uiNrX) { /* special case: right column */ - uiSubX = uiXSize >> 1; uiXL = uiNrX - 1; uiXR = uiXL; - } - else { /* default values */ - uiSubX = uiXSize; uiXL = uiX - 1; uiXR = uiXL + 1; - } - } - - pulLU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXL)]; - pulRU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXR)]; - pulLB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXL)]; - pulRB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXR)]; - Interpolate(pImPointer,uiXRes,pulLU,pulRU,pulLB,pulRB,uiSubX,uiSubY,aLUT); - pImPointer += uiSubX; /* set pointer on next matrix */ - } - pImPointer += (uiSubY - 1) * uiXRes; - } - free(pulMapArray); /* free space for histograms */ - return 0; /* return status OK */ -} -void ClipHistogram (unsigned long* pulHistogram, unsigned int - uiNrGreylevels, unsigned long ulClipLimit) -/* This function performs clipping of the histogram and redistribution of bins. - * The histogram is clipped and the number of excess pixels is counted. Afterwards - * the excess pixels are equally redistributed across the whole histogram (providing - * the bin count is smaller than the cliplimit). - */ -{ - unsigned long* pulBinPointer, *pulEndPointer, *pulHisto; - unsigned long ulNrExcess, ulUpper, ulBinIncr, ulStepSize, i; - long lBinExcess; - - ulNrExcess = 0; pulBinPointer = pulHistogram; - for (i = 0; i < uiNrGreylevels; i++) { /* calculate total number of excess pixels */ - lBinExcess = (long) pulBinPointer[i] - (long) ulClipLimit; - if (lBinExcess > 0) ulNrExcess += lBinExcess; /* excess in current bin */ - }; - - /* Second part: clip histogram and redistribute excess pixels in each bin */ - ulBinIncr = ulNrExcess / uiNrGreylevels; /* average binincrement */ - ulUpper = ulClipLimit - ulBinIncr; /* Bins larger than ulUpper set to cliplimit */ - - for (i = 0; i < uiNrGreylevels; i++) { - if (pulHistogram[i] > ulClipLimit) pulHistogram[i] = ulClipLimit; /* clip bin */ - else { - if (pulHistogram[i] > ulUpper) { /* high bin count */ - ulNrExcess -= pulHistogram[i] - ulUpper; pulHistogram[i]=ulClipLimit; - } - else { /* low bin count */ - ulNrExcess -= ulBinIncr; pulHistogram[i] += ulBinIncr; - } - } - } - - while (ulNrExcess) { /* Redistribute remaining excess */ - pulEndPointer = &pulHistogram[uiNrGreylevels]; pulHisto = pulHistogram; - - while (ulNrExcess && pulHisto < pulEndPointer) { - ulStepSize = uiNrGreylevels / ulNrExcess; - if (ulStepSize < 1) ulStepSize = 1; /* stepsize at least 1 */ - for (pulBinPointer=pulHisto; pulBinPointer < pulEndPointer && ulNrExcess; - pulBinPointer += ulStepSize) { - if (*pulBinPointer < ulClipLimit) { - (*pulBinPointer)++; ulNrExcess--; /* reduce excess */ - } - } - pulHisto++; /* restart redistributing on other bin location */ - } - } -} -void MakeHistogram (kz_pixel_t* pImage, unsigned int uiXRes, - unsigned int uiSizeX, unsigned int uiSizeY, - unsigned long* pulHistogram, - unsigned int uiNrGreylevels, kz_pixel_t* pLookupTable) -/* This function classifies the greylevels present in the array image into - * a greylevel histogram. The pLookupTable specifies the relationship - * between the greyvalue of the pixel (typically between 0 and 4095) and - * the corresponding bin in the histogram (usually containing only 128 bins). - */ -{ - kz_pixel_t* pImagePointer; - unsigned int i; - - for (i = 0; i < uiNrGreylevels; i++) pulHistogram[i] = 0L; /* clear histogram */ - - for (i = 0; i < uiSizeY; i++) { - pImagePointer = &pImage[uiSizeX]; - while (pImage < pImagePointer) pulHistogram[pLookupTable[*pImage++]]++; - pImagePointer += uiXRes; - pImage = &pImagePointer[-uiSizeX]; - } -} - -void MapHistogram (unsigned long* pulHistogram, kz_pixel_t Min, kz_pixel_t Max, - unsigned int uiNrGreylevels, unsigned long ulNrOfPixels) -/* This function calculates the equalized lookup table (mapping) by - * cumulating the input histogram. Note: lookup table is rescaled in range [Min..Max]. - */ -{ - unsigned int i; unsigned long ulSum = 0; - const float fScale = ((float)(Max - Min)) / ulNrOfPixels; - const unsigned long ulMin = (unsigned long) Min; - - for (i = 0; i < uiNrGreylevels; i++) { - ulSum += pulHistogram[i]; pulHistogram[i]=(unsigned long)(ulMin+ulSum*fScale); - if (pulHistogram[i] > Max) pulHistogram[i] = Max; - } -} - -void MakeLut (kz_pixel_t * pLUT, kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrBins) -/* To speed up histogram clipping, the input image [Min,Max] is scaled down to - * [0,uiNrBins-1]. This function calculates the LUT. - */ -{ - int i; - const kz_pixel_t BinSize = (kz_pixel_t) (1 + (Max - Min) / uiNrBins); - - for (i = Min; i <= Max; i++) pLUT[i] = (i - Min) / BinSize; -} - -void Interpolate (kz_pixel_t * pImage, int uiXRes, unsigned long * pulMapLU, - unsigned long * pulMapRU, unsigned long * pulMapLB, unsigned long * pulMapRB, - unsigned int uiXSize, unsigned int uiYSize, kz_pixel_t * pLUT) -/* pImage - pointer to input/output image - * uiXRes - resolution of image in x-direction - * pulMap* - mappings of greylevels from histograms - * uiXSize - uiXSize of image submatrix - * uiYSize - uiYSize of image submatrix - * pLUT - lookup table containing mapping greyvalues to bins - * This function calculates the new greylevel assignments of pixels within a submatrix - * of the image with size uiXSize and uiYSize. This is done by a bilinear interpolation - * between four different mappings in order to eliminate boundary artifacts. - * It uses a division; since division is often an expensive operation, I added code to - * perform a logical shift instead when feasible. - */ -{ - const unsigned int uiIncr = uiXRes-uiXSize; /* Pointer increment after processing row */ - kz_pixel_t GreyValue; unsigned int uiNum = uiXSize*uiYSize; /* Normalization factor */ - - unsigned int uiXCoef, uiYCoef, uiXInvCoef, uiYInvCoef, uiShift = 0; - - if (uiNum & (uiNum - 1)) /* If uiNum is not a power of two, use division */ - for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; - uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { - for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; - uiXCoef++, uiXInvCoef--) { - GreyValue = pLUT[*pImage]; /* get histogram bin value */ - *pImage++ = (kz_pixel_t ) ((uiYInvCoef * (uiXInvCoef*pulMapLU[GreyValue] - + uiXCoef * pulMapRU[GreyValue]) - + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] - + uiXCoef * pulMapRB[GreyValue])) / uiNum); - } - } - else { /* avoid the division and use a right shift instead */ - while (uiNum >>= 1) uiShift++; /* Calculate 2log of uiNum */ - for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; - uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { - for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; - uiXCoef++, uiXInvCoef--) { - GreyValue = pLUT[*pImage]; /* get histogram bin value */ - *pImage++ = (kz_pixel_t)((uiYInvCoef* (uiXInvCoef * pulMapLU[GreyValue] - + uiXCoef * pulMapRU[GreyValue]) - + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] - + uiXCoef * pulMapRB[GreyValue])) >> uiShift); - } - } - } -} - diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 32231b00..8899f319 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -5,10 +5,9 @@ from skimage.util.dtype import dtype_range import skimage.color as color from skimage.util.dtype import convert -from _adapthist import _adapthist __all__ = ['histogram', 'cumulative_distribution', 'equalize', - 'rescale_intensity', 'adapthist'] + 'rescale_intensity'] def histogram(image, nbins=256): @@ -185,91 +184,4 @@ def rescale_intensity(image, in_range=None, out_range=None): image = np.clip(image, imin, imax) image = (image - imin) / float(imax - imin) - return dtype(image * (omax - omin) + omin) - - -def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256, - out_range='full'): - '''Contrast Limited Adaptive Histogram Equalization - - Parameters - ---------- - image : array-like - original image - ntiles_x : int, optional - Tile regions in the X direction (2, 16) - ntiles_y : int, optional - Tile regions in the Y direction (2, 16) - clip_limit : float: optional - Normalized cliplimit (higher values give more contrast) - nbins : int, optional - Greybins for histogram ("dynamic range") - out_range : str, optional - Range of the output image data. - - 'original' - Use original image limits - - 'full' - Use full range of image data type - - Returns - ------- - out : np.ndarray - equalized image - may be a different shape than the original - - Notes - ----- - * The underlying algorithm relies on an image whose rows and columns are even multiples of - the number of tiles, so the extra rows and columns are left at their original values, thus - preserving the input image shape. - * For grayscale images, CLAHE is performed on one channel, and a grayscale is returned - * For color images, the following steps are performed: - - The image is converted to LAB color space - - The CLAHE algorithm is run on the L channel - - The image is converted back to RGB space and returned - * For RGBA images, the original alpha channel is removed. - - References - ---------- - .. [1] http://tog.acm.org/resources/GraphicsGems/ - .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE - ''' - in_type = image.dtype.type - if out_range == 'full': - out_range = None - else: - out_range = (image.min(), image.max()) - # must be converted to 12 bit for CLAHE - int_image = skimage.img_as_uint(image) - max_val = 2 ** 12 - 1 - int_image = rescale_intensity(int_image, out_range=(0, max_val)) - # handle color images - CLAHE accepts scalar images only - args = [int_image.copy(), 0, max_val, ntiles_x, ntiles_y, nbins, - clip_limit] - if image.ndim == 3: - # check for grayscale - if (np.allclose(image[:, :, 0], image[:, :, 1]) and - np.allclose(image[:, :, 1], image[:, :, 2])): - args[0] = int_image[:, :, 0] - out = _adapthist(*args) - image = int_image[:, :, :3] - for channel in range(3): - image[:out.shape[0], :out.shape[1], channel] = out - # for color images, convert to LAB space for processing - else: - lab_img = color.rgb2lab(skimage.img_as_float(image)) - l_chan = lab_img[:, :, 0] - l_chan /= np.max(np.abs(l_chan)) - l_chan = skimage.img_as_uint(l_chan) - args[0] = rescale_intensity(l_chan, out_range=(0, max_val)) - new_l = _adapthist(*args).astype(float) - new_l = rescale_intensity(new_l, out_range=(0, 100)) - lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l - image = color.lab2rgb(lab_img) - image = rescale_intensity(image, out_range=(0, 1)) - else: - out = _adapthist(*args) - image = int_image - image[:out.shape[0], :out.shape[1]] = out - # restore to desired output type and output limits - image = rescale_intensity(image) - image = convert(image, in_type) - image = rescale_intensity(image, out_range=out_range) - return image + return dtype(image * (omax - omin) + omin) \ No newline at end of file diff --git a/skimage/exposure/setup.py b/skimage/exposure/setup.py deleted file mode 100644 index 43bab23f..00000000 --- a/skimage/exposure/setup.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python - -import os -from skimage._build import cython - -base_path = os.path.abspath(os.path.dirname(__file__)) - - -def configuration(parent_package='', top_path=None): - from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs - - config = Configuration('exposure', parent_package, top_path) - config.add_data_dir('tests') - - cython(['_adapthist.pyx'], working_path=base_path) - - config.add_extension('_adapthist', sources=['_adapthist.c'], - include_dirs=[get_numpy_include_dirs()]) - - return config - -if __name__ == '__main__': - from numpy.distutils.core import setup - setup(maintainer='scikits-image developers', - author='scikits-image developers', - maintainer_email='scikits-image@googlegroups.com', - description='Exposure', - url='https://github.com/scikits-image/scikits-image', - license='SciPy License (BSD Style)', - **(configuration(top_path='').todict()) - ) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 2e64e741..3cf42a7f 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -1,6 +1,7 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close - +import sys +sys.path.insert(0, '../..') import skimage from skimage import data from skimage import exposure @@ -82,30 +83,27 @@ def test_adapthist_scalar(): img = skimage.img_as_ubyte(data.moon()) adapted = exposure.adapthist(img, clip_limit=0.02) assert adapted.min() == 0 - assert adapted.max() == 255 + assert adapted.max() == (1<<16) - 1 assert img.shape == adapted.shape - full_scale = skimage.exposure.rescale_intensity(img) + full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(peak_snr(full_scale, adapted), 22.19920073) + assert_almost_equal(peak_snr(full_scale, adapted), 28.6262034) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.04161278) + 0.0410010) return img, adapted - def test_adapthist_grayscale(): '''Test a grayscale float image ''' img = skimage.img_as_float(data.lena()) img = rgb2gray(img) img = np.dstack((img, img, img)) - adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, - nbins=128, out_range='original') + adapted = exposure.adapthist(img, 10, 9, clip_limit=0.01, + nbins=128) assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(adapted.min(), img.min()) - assert_almost_equal(adapted.max(), img.max()) assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 131.4962063) - assert_almost_equal(norm_brightness_err(img, adapted), 0.0208805) + assert_almost_equal(peak_snr(img, adapted), 106.3020173) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0218686) return data, adapted @@ -116,12 +114,12 @@ def test_adapthist_color(): adapted = exposure.adapthist(img, clip_limit=0.01) assert_almost_equal = np.testing.assert_almost_equal assert adapted.min() == 0 - assert adapted.max() == 65535 + assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert_almost_equal(peak_snr(full_scale, adapted), 64.29546231) + assert_almost_equal(peak_snr(full_scale, adapted), 64.7167515) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.181473754) + 0.17922429) return data, adapted From 79fb543256ac236ca59d93134935b41d52f5774e Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 21 Oct 2012 13:49:35 -0500 Subject: [PATCH 195/625] Removed sys.path hack in test, fixed spelling of import in __init__.py --- skimage/exposure/__init__.py | 2 +- skimage/exposure/tests/test_exposure.py | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index f8a4f046..d495686a 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,3 +1,3 @@ from .exposure import histogram, equalize, cumulative_distribution from .exposure import rescale_intensity -from ._adaphist import adapthist +from ._adapthist import adapthist diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 3cf42a7f..04c0cf0e 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -1,7 +1,5 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close -import sys -sys.path.insert(0, '../..') import skimage from skimage import data from skimage import exposure From 6c5b40302dc4c36a3cd8fe516fcb200f4b252c50 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Wed, 24 Oct 2012 14:21:47 -0700 Subject: [PATCH 196/625] ENH: Add motion blurred clock image. --- skimage/data/__init__.py | 13 +++++++++++++ skimage/data/clock_motion.png | Bin 0 -> 58784 bytes skimage/data/tests/test_data.py | 5 +++++ 3 files changed, 18 insertions(+) create mode 100644 skimage/data/clock_motion.png diff --git a/skimage/data/__init__.py b/skimage/data/__init__.py index 5e51d353..d2fba7db 100644 --- a/skimage/data/__init__.py +++ b/skimage/data/__init__.py @@ -114,3 +114,16 @@ def page(): """ return load("page.png") + + +def clock(): + """Motion blurred clock. + + This photograph of a wall clock was taken while moving the camera in an + aproximately horizontal direction. It may be used to illustrate + inverse filters and deconvolution. + + Released into the public domain by the photographer (Stefan van der Walt). + + """ + return load("clock_motion.png") diff --git a/skimage/data/clock_motion.png b/skimage/data/clock_motion.png new file mode 100644 index 0000000000000000000000000000000000000000..3a81bd748692b0c4e3a44199c2c9b872db0e3b73 GIT binary patch literal 58784 zcmV)GK)%0;P)$#cv6IrKJN=mMJJ)F$V%_rKOcBk82 zS?R5;^zk+v3`h1hT(!3~dtYB)+gMv$+gP`+H`dozN2}|j)wR*;Dt^~!Fc=Pp{lQ?x z{+#`J`)lm4>UMkGPPg6ZbUU3+yWO&PzBSs-cDvbVHX6-(qfx6i>h(sgUawWF^=hSB zsa5PlxmsN+SC%W4^73+dxxBPgUS3)*EiEsW?5$WVmP&;}$-Wiygn9r*tZv&SX-l zR3ep(+gmIei$)g~=I0mYXXj^UW@e@)r*`eyxnswUZCkc&`TCm)Ge@t}>kayYVShBl z^Bs*=hj`|r^`Ti}eRE^o46(Mpy0$i2vk&;W)zNTmxHdA|4ftnP27`VN28CTaW)aw; z*Y34t5qoR4%o_Ho*=U(b%o6tAun#peL%nJ?F_YBr+3Zp-FI8xiWiy6-x3|Sbvk2b{ zB^jm2XKIhHNQ>lPpqxDhd-8lbcue*f@f9Cgj=z)N%m&56V$psIMzFu82#aL(i~Q^K z+w!m{o;&P-zaKxQar~Ffi2AGXukzpI>!6U!n1x`IER2%Qrp+d)bRwRlO(KbCG_nwh 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zI%9HWVRU5xGB7bPEigGPF*H;%GdeOjIx#XUFfuwYFcFT;yZ`_IC3HntbYx+4Wjbwd xWNBu305UK!FfA}SEip7yGBY|dI65&hD=;!TFfd@#bpQYW002ovPDHLkV1oBJkz@b> literal 0 HcmV?d00001 diff --git a/skimage/data/tests/test_data.py b/skimage/data/tests/test_data.py index b5f6bcaa..f660c69c 100644 --- a/skimage/data/tests/test_data.py +++ b/skimage/data/tests/test_data.py @@ -34,6 +34,11 @@ def test_page(): data.page() +def test_page(): + """ Test that "clock" image can be loaded. """ + data.clock() + + if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() From a9e05e5fd43859fece29db6542933d61fe43fec4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:14:50 +0100 Subject: [PATCH 197/625] remove inlines in pxd --- skimage/filter/rank/_core16.pxd | 6 +++--- skimage/filter/rank/_core8.pxd | 6 +++--- skimage/setup.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index a113f2b0..9590e277 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -5,10 +5,10 @@ cimport numpy as np #--------------------------------------------------------------------------- # generic cdef functions -cdef inline int int_max(int a, int b) -cdef inline int int_min(int a, int b) +cdef int int_max(int a, int b) +cdef int int_min(int a, int b) -cdef inline _core16( +cdef _core16( np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 8ecf7263..9f898faa 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -1,14 +1,14 @@ cimport numpy as np # generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) +cdef np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) +cdef np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- # 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline _core8( +cdef _core8( np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/setup.py b/skimage/setup.py index 7ed50b65..96497fa9 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -12,11 +12,11 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('draw') config.add_subpackage('feature') config.add_subpackage('filter') + config.add_subpackage('filter/rank') config.add_subpackage('graph') config.add_subpackage('io') config.add_subpackage('measure') config.add_subpackage('morphology') - config.add_subpackage('rank') config.add_subpackage('transform') config.add_subpackage('util') config.add_subpackage('segmentation') From 42d46ad08766b3a1e0f577f2f193aa5789856f7e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:21:10 +0100 Subject: [PATCH 198/625] move setup from /filter/rank to /filter --- skimage/filter/rank/setup.py | 52 ------------------------------------ skimage/filter/setup.py | 27 ++++++++++++++++++- 2 files changed, 26 insertions(+), 53 deletions(-) delete mode 100644 skimage/filter/rank/setup.py diff --git a/skimage/filter/rank/setup.py b/skimage/filter/rank/setup.py deleted file mode 100644 index c6a3dbb9..00000000 --- a/skimage/filter/rank/setup.py +++ /dev/null @@ -1,52 +0,0 @@ -#!/usr/bin/env python - -import os -from skimage._build import cython - -base_path = os.path.abspath(os.path.dirname(__file__)) - - -def configuration(parent_package='', top_path=None): - from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs - - config = Configuration('rank', parent_package, top_path) -# config.add_data_dir('tests') - - cython(['_core8.pyx'], working_path=base_path) - cython(['_core16.pyx'], working_path=base_path) - cython(['_crank8.pyx'], working_path=base_path) - cython(['_crank8_percentiles.pyx'], working_path=base_path) - cython(['_crank16.pyx'], working_path=base_path) - cython(['_crank16_percentiles.pyx'], working_path=base_path) - cython(['_crank16_bilateral.pyx'], working_path=base_path) - - config.add_extension('_core8', sources=['_core8.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core16', sources=['_core16.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank8', sources=['_crank8.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension( - '_crank8_percentiles', sources=['_crank8_percentiles.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank16', sources=['_crank16.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension( - '_crank16_percentiles', sources=['_crank16_percentiles.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension( - '_crank16_bilateral', sources=['_crank16_bilateral.c'], - include_dirs=[get_numpy_include_dirs()]) - - return config - -if __name__ == '__main__': - from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - author='Olivier Debeir', - maintainer_email='scikits-image@googlegroups.com', - description='Rank filters', - url='https://github.com/scikits-image/scikits-image', - license='SciPy License (BSD Style)', - **(configuration(top_path='').todict()) - ) diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 03ea7def..34386e36 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -13,9 +13,34 @@ def configuration(parent_package='', top_path=None): config.add_data_dir('tests') cython(['_ctmf.pyx'], working_path=base_path) + cython(['rank/_core8.pyx'], working_path=base_path) + cython(['rank/_core16.pyx'], working_path=base_path) + cython(['rank/_crank8.pyx'], working_path=base_path) + cython(['rank/_crank8_percentiles.pyx'], working_path=base_path) + cython(['rank/_crank16.pyx'], working_path=base_path) + cython(['rank/_crank16_percentiles.pyx'], working_path=base_path) + cython(['rank/_crank16_bilateral.pyx'], working_path=base_path) config.add_extension('_ctmf', sources=['_ctmf.c'], - include_dirs=[get_numpy_include_dirs()]) + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_core8', sources=['rank/_core8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_core16', sources=['rank/_core16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_crank8', sources=['rank/_crank8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/_crank8_percentiles', sources=['rank/_crank8_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_crank16', sources=['rank/_crank16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/_crank16_percentiles', sources=['rank/_crank16_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/_crank16_bilateral', sources=['rank/_crank16_bilateral.c'], + include_dirs=[get_numpy_include_dirs()]) + return config From 5ae4b4286cec5538e8897878aeb38673c87a9aac Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:26:12 +0100 Subject: [PATCH 199/625] remove comments --- skimage/filter/rank/_core16.pyx | 7 ------- skimage/filter/rank/_core8.pyx | 7 ------- skimage/filter/rank/_crank16.pyx | 8 -------- skimage/filter/rank/_crank16_bilateral.pyx | 8 -------- skimage/filter/rank/_crank8.pyx | 8 -------- 5 files changed, 38 deletions(-) diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 81fab0b4..4829792b 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 7851388d..9955a1e1 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core8.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 57d41563..f6ad10e4 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank16.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index d6fb9c71..c013b779 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank16.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index be00bf86..da716bd9 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False From e488e4b7bd30088b0335e853494f9a0a99fec0e1 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:35:28 +0100 Subject: [PATCH 200/625] remove obsolete comments --- doc/__init__.py | 1 - skimage/filter/rank/bilateral_rank.py | 2 -- skimage/filter/rank/percentile_rank.py | 21 +++++++-------------- 3 files changed, 7 insertions(+), 17 deletions(-) diff --git a/doc/__init__.py b/doc/__init__.py index 10b6fb15..e69de29b 100644 --- a/doc/__init__.py +++ b/doc/__init__.py @@ -1 +0,0 @@ -__author__ = 'olivier' diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index ff4e7878..dd2942b4 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -81,7 +81,6 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -147,7 +146,6 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py index 5191bec4..3817e1cd 100644 --- a/skimage/filter/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -73,7 +73,6 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -139,7 +138,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ Examples -------- - to be updated + >>> # Local gradient >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -205,7 +204,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -271,7 +270,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals Examples -------- - to be updated + >>> # Local mean_substraction >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -337,7 +336,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -403,7 +402,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -470,7 +469,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -536,7 +535,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -572,9 +571,3 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) -if __name__ == "__main__": - import sys - sys.path.append('.') - - import doctest - doctest.testmod(verbose=True) From 07f87d42d2d4b7573f4a1e596059cece73dc6812 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 16:32:08 +0100 Subject: [PATCH 201/625] adjust setup --- doc/examples/plot_lena_bilateral_denoise.py | 9 ++++----- skimage/filter/rank/{local => demo}/demo_all.py | 0 skimage/filter/rank/{local => demo}/demo_single.py | 0 skimage/filter/rank/{local => demo}/iko_pan_Ja1.tif | Bin .../rank/{local => demo}/test_morph_contr_enh.py | 0 skimage/filter/rank/{local => demo}/test_rank.py | 0 skimage/setup.py | 1 - 7 files changed, 4 insertions(+), 6 deletions(-) rename skimage/filter/rank/{local => demo}/demo_all.py (100%) rename skimage/filter/rank/{local => demo}/demo_single.py (100%) rename skimage/filter/rank/{local => demo}/iko_pan_Ja1.tif (100%) rename skimage/filter/rank/{local => demo}/test_morph_contr_enh.py (100%) rename skimage/filter/rank/{local => demo}/test_rank.py (100%) diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index 969403d0..03fc61d0 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -1,4 +1,3 @@ - """ ==================================================== Denoising the picture of Lena using bilateral filter @@ -27,7 +26,7 @@ l = l[230:290, 220:320] noisy = l + 0.4 * l.std() * np.random.random(l.shape) selem = disk(30) -bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) +approx_bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) plt.figure(figsize=(8, 2)) @@ -36,14 +35,14 @@ plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('noisy', fontsize=20) plt.subplot(132) -plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.imshow(approx_bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('bilateral denoising', fontsize=20) selem = disk(30) -bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) +approx_bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) plt.subplot(133) -plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.imshow(approx_bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('(more) bilateral denoising', fontsize=20) diff --git a/skimage/filter/rank/local/demo_all.py b/skimage/filter/rank/demo/demo_all.py similarity index 100% rename from skimage/filter/rank/local/demo_all.py rename to skimage/filter/rank/demo/demo_all.py diff --git a/skimage/filter/rank/local/demo_single.py b/skimage/filter/rank/demo/demo_single.py similarity index 100% rename from skimage/filter/rank/local/demo_single.py rename to skimage/filter/rank/demo/demo_single.py diff --git a/skimage/filter/rank/local/iko_pan_Ja1.tif b/skimage/filter/rank/demo/iko_pan_Ja1.tif similarity index 100% rename from skimage/filter/rank/local/iko_pan_Ja1.tif rename to skimage/filter/rank/demo/iko_pan_Ja1.tif diff --git a/skimage/filter/rank/local/test_morph_contr_enh.py b/skimage/filter/rank/demo/test_morph_contr_enh.py similarity index 100% rename from skimage/filter/rank/local/test_morph_contr_enh.py rename to skimage/filter/rank/demo/test_morph_contr_enh.py diff --git a/skimage/filter/rank/local/test_rank.py b/skimage/filter/rank/demo/test_rank.py similarity index 100% rename from skimage/filter/rank/local/test_rank.py rename to skimage/filter/rank/demo/test_rank.py diff --git a/skimage/setup.py b/skimage/setup.py index 96497fa9..1082ba07 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -12,7 +12,6 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('draw') config.add_subpackage('feature') config.add_subpackage('filter') - config.add_subpackage('filter/rank') config.add_subpackage('graph') config.add_subpackage('io') config.add_subpackage('measure') From 8b0613ff0946b9fbf8c3c8a74dea3cdf73024f03 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 17:04:10 +0100 Subject: [PATCH 202/625] removed mains --- skimage/filter/rank/bilateral_rank.py | 6 ------ skimage/filter/rank/rank.py | 6 ------ 2 files changed, 12 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index dd2942b4..69884e16 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -179,9 +179,3 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) -if __name__ == "__main__": - import sys - sys.path.append('.') - - import doctest - doctest.testmod(verbose=True) diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.py index 517ab2cf..fc06d48b 100644 --- a/skimage/filter/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -918,9 +918,3 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) -if __name__ == "__main__": - import sys - sys.path.append('.') - - import doctest - doctest.testmod(verbose=True) From 9a7d9cd161caef4e0cbc1d632746fb5b27080fc4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 11:32:33 +0200 Subject: [PATCH 203/625] fix. gitignore --- .gitignore | 1 + skimage/rank/app.log | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 skimage/rank/app.log diff --git a/.gitignore b/.gitignore index 925e5886..2623ea74 100644 --- a/.gitignore +++ b/.gitignore @@ -23,3 +23,4 @@ doc/source/auto_examples/images/thumb doc/source/auto_examples/applications/ doc/source/_static/random.js .idea/ +*.log diff --git a/skimage/rank/app.log b/skimage/rank/app.log deleted file mode 100644 index 7eae20f5..00000000 --- a/skimage/rank/app.log +++ /dev/null @@ -1 +0,0 @@ -2012-10-04 09:12:19,785 MainProcess 5265 start logging in app.log From 9788400f1f2f5c0de69c7f40e877ef01a809171c Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 16:09:56 +0200 Subject: [PATCH 204/625] add test/ --- skimage/rank/__init__.py | 2 +- skimage/rank/core.pxd | 1 - skimage/rank/test/test_16bitbilateral.py | 23 +++++ skimage/rank/test/test_benchmark.py | 72 ++++++++++++++ skimage/rank/{app.py => test/test_suite.py} | 101 ++------------------ skimage/rank/{ => test}/tools.py | 10 -- 6 files changed, 104 insertions(+), 105 deletions(-) create mode 100644 skimage/rank/test/test_16bitbilateral.py create mode 100644 skimage/rank/test/test_benchmark.py rename skimage/rank/{app.py => test/test_suite.py} (59%) rename skimage/rank/{ => test}/tools.py (78%) diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index bcc43cdc..8b0e3f5b 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -1 +1 @@ -from .rank import * +from .crank import * diff --git a/skimage/rank/core.pxd b/skimage/rank/core.pxd index 72facde3..bb57aec4 100644 --- a/skimage/rank/core.pxd +++ b/skimage/rank/core.pxd @@ -18,7 +18,6 @@ from libc.stdlib cimport malloc, free cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- diff --git a/skimage/rank/test/test_16bitbilateral.py b/skimage/rank/test/test_16bitbilateral.py new file mode 100644 index 00000000..ac078b09 --- /dev/null +++ b/skimage/rank/test/test_16bitbilateral.py @@ -0,0 +1,23 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.rank import crank_percentiles,crank16_bilateral + +if __name__ == '__main__': + a8 = (data.coins()).astype('uint8') + + a16 = (data.coins()).astype('uint16')*16 + selem = np.ones((20,20),dtype='uint8') + f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) + + plt.figure() + plt.imshow(np.hstack((a8,f1))) + plt.colorbar() + + plt.figure() + plt.imshow(np.hstack((a16,f2))) + plt.colorbar() + + plt.show() diff --git a/skimage/rank/test/test_benchmark.py b/skimage/rank/test/test_benchmark.py new file mode 100644 index 00000000..4fee48c1 --- /dev/null +++ b/skimage/rank/test/test_benchmark.py @@ -0,0 +1,72 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.morphology import cmorph +from skimage.rank import crank + +from tools import log_timing + +@log_timing +def cr_max(image,selem): + return crank.maximum(image=image,selem = selem) + +@log_timing +def cm_dil(image,selem): + return cmorph.dilate(image=image,selem = selem) + + +def compare(): + """comparison between + - crank.maximum rankfilter implementation + - cmorph.dilate cython implementation + on increasing structuring element size and increasing image size + """ +# a = (np.random.random((500,500))*256).astype('uint8') + a = data.camera() + + rec = [] + e_range = range(1,20,1) + for r in e_range: + elem = np.ones((r,r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc,ms_rc = cr_max(a,elem) + rcm,ms_rcm = cm_dil(a,elem) + rec.append((ms_rc,ms_rcm)) + # check if results are identical + assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing element size') + plt.plot(e_range,rec) + plt.legend(['crank.maximum','cmorph.dilate']) + plt.figure() + plt.imshow(np.hstack((rc,rcm))) + + r = 9 + elem = np.ones((r,r),dtype='uint8') + + rec = [] + s_range = range(100,1000,100) + for s in s_range: + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = cr_max(a,elem) + (rcm,ms_rcm) = cm_dil(a,elem) + rec.append((ms_rc,ms_rcm)) + assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing image size') + plt.plot(s_range,rec) + plt.legend(['crank.maximum','cmorph.dilate']) + plt.figure() + plt.imshow(np.hstack((rc,rcm))) + + plt.show() + +if __name__ == '__main__': + compare() \ No newline at end of file diff --git a/skimage/rank/app.py b/skimage/rank/test/test_suite.py similarity index 59% rename from skimage/rank/app.py rename to skimage/rank/test/test_suite.py index 0cf24408..61ceab48 100644 --- a/skimage/rank/app.py +++ b/skimage/rank/test/test_suite.py @@ -1,73 +1,10 @@ import unittest import numpy as np -from time import time -import matplotlib.pyplot as plt -from skimage import data -from tools import log_timing,init_logger +from skimage.rank import crank,crank16,crank16_bilateral,crank16_percentiles,crank_percentiles +from skimage.morphology import cmorph -import crank -import crank16 -import crank_percentiles -import crank16_percentiles -import crank16_bilateral -from cmorph import dilate - - -@log_timing -def c_max(image,selem): - return crank.maximum(image=image,selem = selem) - -@log_timing -def cm_max(image,selem): - return dilate(image=image,selem = selem) - -def compare(): - """comparison between - - Cython maximum rankfilter implementation - - weaves maximum rankfilter implementation - - cmorph.dilate cython implementation - on increasing structuring element size and increasing image size - """ - a = (np.random.random((500,500))*256).astype('uint8') - - rec = [] - for r in range(1,20,1): - elem = np.ones((r,r),dtype='uint8') - # elem = (np.random.random((r,r))>.5).astype('uint8') - (rc,ms_rc) = c_max(a,elem) - (rcm,ms_rcm) = cm_max(a,elem) - rec.append((ms_rc,ms_rw,ms_rcm)) - assert (rc==rcm).all() - - rec = np.asarray(rec) - - plt.plot(rec) - plt.legend(['sliding cython','sliding weaves','cmorph']) - plt.figure() - plt.imshow(np.hstack((rc,rw,rcm))) - - r = 9 - elem = np.ones((r,r),dtype='uint8') - - rec = [] - for s in range(100,1000,100): - a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = c_max(a,elem) - (rcm,ms_rcm) = cm_max(a,elem) - rec.append((ms_rc,ms_rw,ms_rcm)) - assert (rc==rcm).all() - - rec = np.asarray(rec) - - plt.figure() - plt.plot(rec) - plt.legend(['sliding cython','sliding weaves','cmorph']) - plt.figure() - plt.imshow(np.hstack((rc,rcm))) - - plt.show() class TestSequenceFunctions(unittest.TestCase): def setUp(self): @@ -106,7 +43,7 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') rc = crank.maximum(image=a,selem = elem) - cm = dilate(image=a,selem = elem) + cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) def test_bitdepth(self): @@ -139,11 +76,11 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') f = crank.maximum(image=a,selem = elem,shift_x=1,shift_y=1) r = np.asarray([[ 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0, 0], - [ 0, 0, 255, 255, 255, 0], - [ 0, 0, 0, 255, 255, 0], - [ 0, 0, 0, 0, 0, 0]]) + [ 0, 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0, 0], + [ 0, 0, 255, 255, 255, 0], + [ 0, 0, 0, 255, 255, 0], + [ 0, 0, 0, 0, 0, 0]]) np.testing.assert_array_equal(r,f) @@ -156,27 +93,5 @@ class TestSequenceFunctions(unittest.TestCase): if __name__ == '__main__': - logger = init_logger('app.log') - -# unittest.main() suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) unittest.TextTestRunner(verbosity=2).run(suite) - - -# compare() - -# a = (data.coins()).astype('uint8') - a8 = (data.coins()).astype('uint8') - a = (data.coins()).astype('uint16')*16 - selem = np.ones((20,20),dtype='uint8') -# f1 = filter.soft_gradient(a,struct_elem = selem,bitDepth=8,infSup=[.1,.9]) -# f2 = crank16.bottomhat(a,selem = selem,bitdepth=12) - f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) -# f2 = crank16_percentiles.mean(a,selem = selem,bitdepth=12,p0=.1,p1=.9) - f2 = crank16_bilateral.mean(a,selem = selem,bitdepth=12,s0=500,s1=500) -# plt.imshow(f2) - plt.imshow(np.hstack((a,f2))) - plt.colorbar() - plt.show() - - diff --git a/skimage/rank/tools.py b/skimage/rank/test/tools.py similarity index 78% rename from skimage/rank/tools.py rename to skimage/rank/test/tools.py index 835a8884..2fba4798 100644 --- a/skimage/rank/tools.py +++ b/skimage/rank/test/tools.py @@ -39,14 +39,4 @@ def log_timing(func): log_timing.level = 0 -def tumbnail_it(data): - """display image with its histogram - """ - h = np.histogram(data[:],100) - hn = 512*h[0]/np.max(h[0]) - plt.subplot(1,2,1) - plt.imshow(ima8,interpolation='nearest',cmap=cm.gray) - plt.subplot(1,2,2) - plt.plot(hn) - plt.colorbar() From a309a354fc74626f607b06fdb5ee4d040a4d7624 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 16:12:08 +0200 Subject: [PATCH 205/625] mv test to tests --- skimage/rank/setup.py | 5 ++++- skimage/rank/{test => tests}/test_16bitbilateral.py | 0 skimage/rank/{test => tests}/test_benchmark.py | 0 skimage/rank/{test => tests}/test_suite.py | 0 skimage/rank/{test => tests}/tools.py | 0 5 files changed, 4 insertions(+), 1 deletion(-) rename skimage/rank/{test => tests}/test_16bitbilateral.py (100%) rename skimage/rank/{test => tests}/test_benchmark.py (100%) rename skimage/rank/{test => tests}/test_suite.py (100%) rename skimage/rank/{test => tests}/tools.py (100%) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index e9af9446..8c5a595a 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -12,4 +12,7 @@ setup( Extension("crank16", ["crank16.pyx"], include_dirs=[np.get_include()]), Extension("crank16_bilateral", ["crank16_bilateral.pyx"], include_dirs=[np.get_include()]), Extension("crank16_percentiles", ["crank16_percentiles.pyx"], include_dirs=[np.get_include()])] -) \ No newline at end of file +) + + + diff --git a/skimage/rank/test/test_16bitbilateral.py b/skimage/rank/tests/test_16bitbilateral.py similarity index 100% rename from skimage/rank/test/test_16bitbilateral.py rename to skimage/rank/tests/test_16bitbilateral.py diff --git a/skimage/rank/test/test_benchmark.py b/skimage/rank/tests/test_benchmark.py similarity index 100% rename from skimage/rank/test/test_benchmark.py rename to skimage/rank/tests/test_benchmark.py diff --git a/skimage/rank/test/test_suite.py b/skimage/rank/tests/test_suite.py similarity index 100% rename from skimage/rank/test/test_suite.py rename to skimage/rank/tests/test_suite.py diff --git a/skimage/rank/test/tools.py b/skimage/rank/tests/tools.py similarity index 100% rename from skimage/rank/test/tools.py rename to skimage/rank/tests/tools.py From 9eadcdf77493ba68c24ed7745ca48c0df43c906f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 16:35:06 +0200 Subject: [PATCH 206/625] rename using scikits naming conventions --- skimage/rank/__init__.py | 1 - skimage/rank/{core.pxd => _core.pxd} | 0 skimage/rank/{core16.pxd => _core16.pxd} | 2 +- skimage/rank/{core16b.pxd => _core16b.pxd} | 2 +- skimage/rank/{core16p.pxd => _core16p.pxd} | 2 +- skimage/rank/{core8.pxd => _core8.pxd} | 2 +- skimage/rank/{core8p.pxd => _core8p.pxd} | 2 +- skimage/rank/{crank16.pyx => _crank16.pyx} | 30 ++--- ...6_bilateral.pyx => _crank16_bilateral.pyx} | 6 +- ...rcentiles.pyx => _crank16_percentiles.pyx} | 18 +-- skimage/rank/{crank.pyx => _crank8.pyx} | 30 ++--- ...ercentiles.pyx => _crank8_percentiles.pyx} | 18 +-- skimage/rank/cmorph.pyx | 118 ------------------ skimage/rank/setup.py | 46 +++++-- skimage/rank/tests/test_16bitbilateral.py | 4 +- skimage/rank/tests/test_benchmark.py | 4 +- skimage/rank/tests/test_suite.py | 13 +- 17 files changed, 106 insertions(+), 192 deletions(-) rename skimage/rank/{core.pxd => _core.pxd} (100%) rename skimage/rank/{core16.pxd => _core16.pxd} (99%) rename skimage/rank/{core16b.pxd => _core16b.pxd} (99%) rename skimage/rank/{core16p.pxd => _core16p.pxd} (98%) rename skimage/rank/{core8.pxd => _core8.pxd} (99%) rename skimage/rank/{core8p.pxd => _core8p.pxd} (99%) rename skimage/rank/{crank16.pyx => _crank16.pyx} (90%) rename skimage/rank/{crank16_bilateral.pyx => _crank16_bilateral.pyx} (98%) rename skimage/rank/{crank16_percentiles.pyx => _crank16_percentiles.pyx} (90%) rename skimage/rank/{crank.pyx => _crank8.pyx} (90%) rename skimage/rank/{crank_percentiles.pyx => _crank8_percentiles.pyx} (90%) delete mode 100644 skimage/rank/cmorph.pyx diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index 8b0e3f5b..e69de29b 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -1 +0,0 @@ -from .crank import * diff --git a/skimage/rank/core.pxd b/skimage/rank/_core.pxd similarity index 100% rename from skimage/rank/core.pxd rename to skimage/rank/_core.pxd diff --git a/skimage/rank/core16.pxd b/skimage/rank/_core16.pxd similarity index 99% rename from skimage/rank/core16.pxd rename to skimage/rank/_core16.pxd index 9973025f..ddd8c637 100644 --- a/skimage/rank/core16.pxd +++ b/skimage/rank/_core16.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline rank16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core16b.pxd b/skimage/rank/_core16b.pxd similarity index 99% rename from skimage/rank/core16b.pxd rename to skimage/rank/_core16b.pxd index 38832c11..fefac53e 100644 --- a/skimage/rank/core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 16 bit core kernel receives extra information about data bitdepth and bilateral interval #--------------------------------------------------------------------------- -cdef inline rank16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), +cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core16p.pxd b/skimage/rank/_core16p.pxd similarity index 98% rename from skimage/rank/core16p.pxd rename to skimage/rank/_core16p.pxd index dbf51c54..0d698cee 100644 --- a/skimage/rank/core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 16 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline rank16_percentile(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), +cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core8.pxd b/skimage/rank/_core8.pxd similarity index 99% rename from skimage/rank/core8.pxd rename to skimage/rank/_core8.pxd index 0c901c8b..3d5ddac3 100644 --- a/skimage/rank/core8.pxd +++ b/skimage/rank/_core8.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline rank8(np.uint8_t kernel(int*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/core8p.pxd b/skimage/rank/_core8p.pxd similarity index 99% rename from skimage/rank/core8p.pxd rename to skimage/rank/_core8p.pxd index 25e3ffcf..dfab17be 100644 --- a/skimage/rank/core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -22,7 +22,7 @@ cdef inline int int_min(int a, int b): return a if a <= b else b # 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline rank8_percentile(np.uint8_t kernel(int*, float, np.uint8_t, float, float), +cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/crank16.pyx b/skimage/rank/_crank16.pyx similarity index 90% rename from skimage/rank/crank16.pyx rename to skimage/rank/_crank16.pyx index cd0bacd4..e1e64bed 100644 --- a/skimage/rank/crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core16 cimport rank16 +from _core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth @@ -198,7 +198,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """bottom hat """ - return rank16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -207,7 +207,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """bottom hat """ - return rank16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) def egalise(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -216,7 +216,7 @@ def egalise(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local egalisation of the gray level """ - return rank16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -225,7 +225,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local maximum - local minimum gray level """ - return rank16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -234,7 +234,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local maximum gray level """ - return rank16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -243,7 +243,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """average gray level (clipped on uint8) """ - return rank16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -252,7 +252,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ - return rank16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -261,7 +261,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local median """ - return rank16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -270,7 +270,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local minimum gray level """ - return rank16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -279,7 +279,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """morphological contrast enhancement """ - return rank16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -288,7 +288,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local mode """ - return rank16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -297,7 +297,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ - return rank16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -306,7 +306,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ - return rank16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -315,4 +315,4 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """top hat """ - return rank16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) diff --git a/skimage/rank/crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx similarity index 98% rename from skimage/rank/crank16_bilateral.pyx rename to skimage/rank/_crank16_bilateral.pyx index 313b83d6..440d28e3 100644 --- a/skimage/rank/crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core16b cimport rank16b +from _core16b cimport _core16b # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth @@ -257,7 +257,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return rank16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) #def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, # np.ndarray[np.uint8_t, ndim=2] selem, @@ -311,7 +311,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return rank16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) #def threshold(np.ndarray[np.uint16_t, ndim=2] image, # np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/rank/crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx similarity index 90% rename from skimage/rank/crank16_percentiles.pyx rename to skimage/rank/_crank16_percentiles.pyx index 7756bc19..54c25d40 100644 --- a/skimage/rank/crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core16p cimport rank16_percentile +from _core16p cimport _core16p # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) @@ -194,7 +194,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return rank16_percentile(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -204,7 +204,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return rank16_percentile(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -213,7 +213,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return rank16_percentile(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -222,7 +222,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return rank16_percentile(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -231,7 +231,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return rank16_percentile(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -241,7 +241,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return rank16_percentile(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -251,7 +251,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return rank16_percentile(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -260,4 +260,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return rank16_percentile(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) diff --git a/skimage/rank/crank.pyx b/skimage/rank/_crank8.pyx similarity index 90% rename from skimage/rank/crank.pyx rename to skimage/rank/_crank8.pyx index 59016eed..cb74021e 100644 --- a/skimage/rank/crank.pyx +++ b/skimage/rank/_crank8.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core8 cimport rank8 +from _core8 cimport _core8 # ----------------------------------------------------------------- # kernels uint8 @@ -199,7 +199,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return rank8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -208,7 +208,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return rank8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) def egalise(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -217,7 +217,7 @@ def egalise(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return rank8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -226,7 +226,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return rank8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -235,7 +235,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum gray level """ - return rank8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -244,7 +244,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """average gray level (clipped on uint8) """ - return rank8(kernel_mean,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -253,7 +253,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return rank8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -262,7 +262,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local median """ - return rank8(kernel_median,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y) def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -271,7 +271,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local minimum gray level """ - return rank8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -280,7 +280,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return rank8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -289,7 +289,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local mode """ - return rank8(kernel_modal,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y) def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -298,7 +298,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns the number of actual pixels of the structuring element inside the mask """ - return rank8(kernel_pop,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -307,7 +307,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return rank8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -316,5 +316,5 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """top hat """ - return rank8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) diff --git a/skimage/rank/crank_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx similarity index 90% rename from skimage/rank/crank_percentiles.pyx rename to skimage/rank/_crank8_percentiles.pyx index d4d6312f..81730313 100644 --- a/skimage/rank/crank_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from core8p cimport rank8_percentile +from _core8p cimport _core8p # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) @@ -189,7 +189,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """bottom hat """ - return rank8_percentile(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -199,7 +199,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return rank8_percentile(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -208,7 +208,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return rank8_percentile(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -217,7 +217,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return rank8_percentile(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -226,7 +226,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return rank8_percentile(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -236,7 +236,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return rank8_percentile(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -246,7 +246,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return rank8_percentile(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -255,4 +255,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return rank8_percentile(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) diff --git a/skimage/rank/cmorph.pyx b/skimage/rank/cmorph.pyx deleted file mode 100644 index 9b8b3a27..00000000 --- a/skimage/rank/cmorph.pyx +++ /dev/null @@ -1,118 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - - -def dilate(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) - shift_y - cdef int centre_c = int(selem.shape[1] / 2) - shift_x - - image = np.ascontiguousarray(image) - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - - cdef int r, c, rr, cc, s, value, local_max - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - - s = 0 - for r in range(srows): - for c in range(scols): - if selem[r, c] != 0: - sr[s] = r - centre_r - sc[s] = c - centre_c - s += 1 - - for r in range(rows): - for c in range(cols): - local_max = 0 - for s in range(selem_num): - rr = r + sr[s] - cc = c + sc[s] - if 0 <= rr < rows and 0 <= cc < cols: - value = image_data[rr * cols + cc] - if value > local_max: - local_max = value - - out_data[r * cols + c] = local_max - - free(sr) - free(sc) - - return out - - -def erode(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) - shift_y - cdef int centre_c = int(selem.shape[1] / 2) - shift_x - - image = np.ascontiguousarray(image) - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - - cdef int r, c, rr, cc, s, value, local_min - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - - s = 0 - for r in range(srows): - for c in range(scols): - if selem[r, c] != 0: - sr[s] = r - centre_r - sc[s] = c - centre_c - s += 1 - - for r in range(rows): - for c in range(cols): - local_min = 255 - for s in range(selem_num): - rr = r + sr[s] - cc = c + sc[s] - if 0 <= rr < rows and 0 <= cc < cols: - value = image_data[rr * cols + cc] - if value < local_min: - local_min = value - - out_data[r * cols + c] = local_min - - free(sr) - free(sc) - - return out diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 8c5a595a..e8b2691d 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -6,13 +6,45 @@ from Cython.Distutils import build_ext setup( cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("cmorph", ["cmorph.pyx"], include_dirs=[np.get_include()]), - Extension("crank", ["crank.pyx"], include_dirs=[np.get_include()]), - Extension("crank_percentiles", ["crank_percentiles.pyx"], include_dirs=[np.get_include()]), - Extension("crank16", ["crank16.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_bilateral", ["crank16_bilateral.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_percentiles", ["crank16_percentiles.pyx"], include_dirs=[np.get_include()])] + ext_modules = [Extension("crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), + Extension("crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), + Extension("crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), + Extension("crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), + Extension("crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] ) - +##!/usr/bin/env python +# +#import os +#from skimage._build import cython +# +#base_path = os.path.abspath(os.path.dirname(__file__)) +# +# +#def configuration(parent_package='', top_path=None): +# from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs +# +# config = Configuration('rank', parent_package, top_path) +# config.add_data_dir('tests') +# +# cython(['_texture.pyx'], working_path=base_path) +# cython(['_template.pyx'], working_path=base_path) +# +# config.add_extension('_texture', sources=['_texture.c'], +# include_dirs=[get_numpy_include_dirs(), '../_shared']) +# config.add_extension('_template', sources=['_template.c'], +# include_dirs=[get_numpy_include_dirs(), '../_shared']) +# +# return config +# +#if __name__ == '__main__': +# from numpy.distutils.core import setup +# setup(maintainer='scikits-image Developers', +# author='scikits-image Developers', +# maintainer_email='scikits-image@googlegroups.com', +# description='Features', +# url='https://github.com/scikits-image/scikits-image', +# license='SciPy License (BSD Style)', +# **(configuration(top_path='').todict()) +# ) diff --git a/skimage/rank/tests/test_16bitbilateral.py b/skimage/rank/tests/test_16bitbilateral.py index ac078b09..98d76573 100644 --- a/skimage/rank/tests/test_16bitbilateral.py +++ b/skimage/rank/tests/test_16bitbilateral.py @@ -2,14 +2,14 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank_percentiles,crank16_bilateral +from skimage.rank import crank8_percentiles,crank16_bilateral if __name__ == '__main__': a8 = (data.coins()).astype('uint8') a16 = (data.coins()).astype('uint16')*16 selem = np.ones((20,20),dtype='uint8') - f1 = crank_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) plt.figure() diff --git a/skimage/rank/tests/test_benchmark.py b/skimage/rank/tests/test_benchmark.py index 4fee48c1..c67742e3 100644 --- a/skimage/rank/tests/test_benchmark.py +++ b/skimage/rank/tests/test_benchmark.py @@ -3,13 +3,13 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import cmorph -from skimage.rank import crank +from skimage.rank import crank8 from tools import log_timing @log_timing def cr_max(image,selem): - return crank.maximum(image=image,selem = selem) + return crank8.maximum(image=image,selem = selem) @log_timing def cm_dil(image,selem): diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 61ceab48..0dcb21ab 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -2,7 +2,8 @@ import unittest import numpy as np -from skimage.rank import crank,crank16,crank16_bilateral,crank16_percentiles,crank_percentiles +from skimage.rank import crank8,crank8_percentiles +from skimage.rank import crank16,crank16_bilateral,crank16_percentiles from skimage.morphology import cmorph class TestSequenceFunctions(unittest.TestCase): @@ -16,9 +17,9 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') for m,n in np.random.random_integers(1,100,size=(10,2)): a8 = np.ones((m,n),dtype='uint8') - r = crank.mean(image=a8,selem = elem,shift_x=0,shift_y=0) + r = crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) self.assertTrue(a8.shape == r.shape) - r = crank.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) + r = crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) self.assertTrue(a8.shape == r.shape) for m,n in np.random.random_integers(1,100,size=(10,2)): @@ -42,7 +43,7 @@ class TestSequenceFunctions(unittest.TestCase): for r in range(1,20,1): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') - rc = crank.maximum(image=a,selem = elem) + rc = crank8.maximum(image=a,selem = elem) cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) @@ -62,7 +63,7 @@ class TestSequenceFunctions(unittest.TestCase): def test_population(self): a = np.zeros((5,5),dtype='uint8') elem = np.ones((3,3),dtype='uint8') - p = crank.pop(image=a,selem = elem) + p = crank8.pop(image=a,selem = elem) r = np.asarray([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -74,7 +75,7 @@ class TestSequenceFunctions(unittest.TestCase): a = np.zeros((6,6),dtype='uint8') a[2,2] = 255 elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') - f = crank.maximum(image=a,selem = elem,shift_x=1,shift_y=1) + f = crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) r = np.asarray([[ 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0], [ 0, 0, 255, 0, 0, 0], From 2d6014e3566ee110658cfd09e7af38e6b1f85848 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 17:15:10 +0200 Subject: [PATCH 207/625] try to fix rank setup --- skimage/rank/setup.py | 27 +++++++++++++++-------- skimage/rank/tests/test_16bitbilateral.py | 3 ++- skimage/setup.py | 1 + 3 files changed, 21 insertions(+), 10 deletions(-) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index e8b2691d..c16f902f 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -26,24 +26,33 @@ setup( # from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs # # config = Configuration('rank', parent_package, top_path) -# config.add_data_dir('tests') +## config.add_data_dir('tests') # -# cython(['_texture.pyx'], working_path=base_path) -# cython(['_template.pyx'], working_path=base_path) +# cython(['_crank8.pyx'], working_path=base_path) +# cython(['_crank8_percentiles.pyx'], working_path=base_path) +# cython(['_crank16.pyx'], working_path=base_path) +# cython(['_crank16_percentiles.pyx'], working_path=base_path) +# cython(['_crank16_bilateral.pyx'], working_path=base_path) # -# config.add_extension('_texture', sources=['_texture.c'], -# include_dirs=[get_numpy_include_dirs(), '../_shared']) -# config.add_extension('_template', sources=['_template.c'], -# include_dirs=[get_numpy_include_dirs(), '../_shared']) +# config.add_extension('crank8', sources=['_crank8.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank8_percentiles', sources=['_crank8_percentiles.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank16', sources=['_crank16.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank16_percentiles', sources=['_crank16_percentiles.c'], +# include_dirs=[get_numpy_include_dirs()]) +# config.add_extension('crank16_bilateral', sources=['_crank16_bilateral.c'], +# include_dirs=[get_numpy_include_dirs()]) # # return config # #if __name__ == '__main__': # from numpy.distutils.core import setup # setup(maintainer='scikits-image Developers', -# author='scikits-image Developers', +# author='Olivier Debeir', # maintainer_email='scikits-image@googlegroups.com', -# description='Features', +# description='Rank filters', # url='https://github.com/scikits-image/scikits-image', # license='SciPy License (BSD Style)', # **(configuration(top_path='').todict()) diff --git a/skimage/rank/tests/test_16bitbilateral.py b/skimage/rank/tests/test_16bitbilateral.py index 98d76573..8001a25f 100644 --- a/skimage/rank/tests/test_16bitbilateral.py +++ b/skimage/rank/tests/test_16bitbilateral.py @@ -2,7 +2,8 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank8_percentiles,crank16_bilateral +from skimage.rank import crank8_percentiles +from skimage.rank import crank16_bilateral if __name__ == '__main__': a8 = (data.coins()).astype('uint8') diff --git a/skimage/setup.py b/skimage/setup.py index 1082ba07..7ed50b65 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -16,6 +16,7 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('io') config.add_subpackage('measure') config.add_subpackage('morphology') + config.add_subpackage('rank') config.add_subpackage('transform') config.add_subpackage('util') config.add_subpackage('segmentation') From 969a8f6e6c2fdc69dee4af926eb035d8d9db4333 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 17:30:21 +0200 Subject: [PATCH 208/625] rename some test to demo --- ...bitbilateral.py => demo_16bitbilateral.py} | 0 skimage/rank/tests/demo_all.py | 24 +++++++++++++++++++ .../{test_benchmark.py => demo_benchmark.py} | 0 3 files changed, 24 insertions(+) rename skimage/rank/tests/{test_16bitbilateral.py => demo_16bitbilateral.py} (100%) create mode 100644 skimage/rank/tests/demo_all.py rename skimage/rank/tests/{test_benchmark.py => demo_benchmark.py} (100%) diff --git a/skimage/rank/tests/test_16bitbilateral.py b/skimage/rank/tests/demo_16bitbilateral.py similarity index 100% rename from skimage/rank/tests/test_16bitbilateral.py rename to skimage/rank/tests/demo_16bitbilateral.py diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py new file mode 100644 index 00000000..8001a25f --- /dev/null +++ b/skimage/rank/tests/demo_all.py @@ -0,0 +1,24 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.rank import crank8_percentiles +from skimage.rank import crank16_bilateral + +if __name__ == '__main__': + a8 = (data.coins()).astype('uint8') + + a16 = (data.coins()).astype('uint16')*16 + selem = np.ones((20,20),dtype='uint8') + f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) + f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) + + plt.figure() + plt.imshow(np.hstack((a8,f1))) + plt.colorbar() + + plt.figure() + plt.imshow(np.hstack((a16,f2))) + plt.colorbar() + + plt.show() diff --git a/skimage/rank/tests/test_benchmark.py b/skimage/rank/tests/demo_benchmark.py similarity index 100% rename from skimage/rank/tests/test_benchmark.py rename to skimage/rank/tests/demo_benchmark.py From 5e0960275c7fa2fe3141b577aef826524304d50b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 17:59:03 +0200 Subject: [PATCH 209/625] add demo all --- skimage/rank/tests/demo_all.py | 66 +++++++++++++++++++++++++++------- 1 file changed, 53 insertions(+), 13 deletions(-) diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index 8001a25f..a509db33 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -2,23 +2,63 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank8_percentiles -from skimage.rank import crank16_bilateral +from skimage.morphology.selem import disk +from skimage.rank import crank8,crank8_percentiles +from skimage.rank import crank16,crank16_percentiles,crank16_bilateral if __name__ == '__main__': - a8 = (data.coins()).astype('uint8') + a8 = data.camera() + a16 = a8.astype('uint16')*16 +# selem = np.ones((30,30),dtype='uint8') + selem = disk(5) - a16 = (data.coins()).astype('uint16')*16 - selem = np.ones((20,20),dtype='uint8') - f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) - f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) +# for n in dir(crank16): +# method = eval('crank16.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a16,selem = selem,bitdepth=12) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a16) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) - plt.figure() - plt.imshow(np.hstack((a8,f1))) - plt.colorbar() +# for n in dir(crank8): +# method = eval('crank8.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a8,selem = selem) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a8) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) - plt.figure() - plt.imshow(np.hstack((a16,f2))) - plt.colorbar() + for n in dir(crank8_percentiles): + method = eval('crank8_percentiles.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a8,selem = selem,p0=.1,p1=.9) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) + + # plt.show() From 1f5c2b9bfeac022009649d91a3214135127014c6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 18:02:54 +0200 Subject: [PATCH 210/625] add demo all (cont.) --- skimage/rank/tests/demo_all.py | 43 ++++++++++++++++++++++++++++++---- 1 file changed, 38 insertions(+), 5 deletions(-) diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index a509db33..d005d61a 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -44,21 +44,54 @@ if __name__ == '__main__': # plt.colorbar() # plt.title(method) - for n in dir(crank8_percentiles): - method = eval('crank8_percentiles.%s'%n) +# for n in dir(crank8_percentiles): +# method = eval('crank8_percentiles.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a8,selem = selem,p0=.1,p1=.9) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a8) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) + +# for n in dir(crank16_percentiles): +# method = eval('crank16_percentiles.%s'%n) +# t = type(method) +# if t == type(crank8.maximum): +# print n,t +# f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) +# +# plt.figure() +# plt.subplot(1,2,1) +# plt.imshow(a16) +# plt.colorbar() +# plt.subplot(1,2,2) +# plt.imshow(f) +# plt.colorbar() +# plt.title(method) + + selem = disk(50) + for n in dir(crank16_bilateral): + method = eval('crank16_bilateral.%s'%n) t = type(method) if t == type(crank8.maximum): print n,t - f = method(a8,selem = selem,p0=.1,p1=.9) + f = method(a16,selem = selem,bitdepth=12,s0=300,s1=300) plt.figure() plt.subplot(1,2,1) - plt.imshow(a8) + plt.imshow(a16) plt.colorbar() plt.subplot(1,2,2) plt.imshow(f) plt.colorbar() plt.title(method) - # + # plt.show() From 27d028e2e99d32fa603a677c581e2a896e83a51a Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 18:03:08 +0200 Subject: [PATCH 211/625] add demo all (cont.) --- skimage/rank/tests/demo_all.py | 120 ++++++++++++++++----------------- 1 file changed, 60 insertions(+), 60 deletions(-) diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index d005d61a..8e09048e 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -12,69 +12,69 @@ if __name__ == '__main__': # selem = np.ones((30,30),dtype='uint8') selem = disk(5) -# for n in dir(crank16): -# method = eval('crank16.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a16,selem = selem,bitdepth=12) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a16) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + for n in dir(crank16): + method = eval('crank16.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a16,selem = selem,bitdepth=12) -# for n in dir(crank8): -# method = eval('crank8.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a8,selem = selem) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a8) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a16) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) -# for n in dir(crank8_percentiles): -# method = eval('crank8_percentiles.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a8,selem = selem,p0=.1,p1=.9) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a8) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + for n in dir(crank8): + method = eval('crank8.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a8,selem = selem) -# for n in dir(crank16_percentiles): -# method = eval('crank16_percentiles.%s'%n) -# t = type(method) -# if t == type(crank8.maximum): -# print n,t -# f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) -# -# plt.figure() -# plt.subplot(1,2,1) -# plt.imshow(a16) -# plt.colorbar() -# plt.subplot(1,2,2) -# plt.imshow(f) -# plt.colorbar() -# plt.title(method) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) + + for n in dir(crank8_percentiles): + method = eval('crank8_percentiles.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a8,selem = selem,p0=.1,p1=.9) + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) + + for n in dir(crank16_percentiles): + method = eval('crank16_percentiles.%s'%n) + t = type(method) + if t == type(crank8.maximum): + print n,t + f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a16) + plt.colorbar() + plt.subplot(1,2,2) + plt.imshow(f) + plt.colorbar() + plt.title(method) selem = disk(50) for n in dir(crank16_bilateral): From 243c8bfabb12563bc3af5fd93cb9694b06aa0a2f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 4 Oct 2012 18:23:33 +0200 Subject: [PATCH 212/625] add readme --- skimage/rank/README.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst index b15001e6..ef56f997 100644 --- a/skimage/rank/README.rst +++ b/skimage/rank/README.rst @@ -1,5 +1,13 @@ To use this to build your Cython file use the commandline options: +**To do** + +* add simple examples + +* add doc + + + .. sourcecode:: text $ python setup.py build_ext --inplace \ No newline at end of file From d4e01c2287c01824cf6394837916d9cd53f10cf3 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 10:56:05 +0200 Subject: [PATCH 213/625] add py wrappers --- skimage/rank/__init__.py | 3 + skimage/rank/bilateral_rank.py | 18 +++++ skimage/rank/percentile_rank.py | 58 ++++++++++++++ skimage/rank/rank.py | 112 +++++++++++++++++++++++++++ skimage/rank/setup.py | 20 ++--- skimage/rank/tests/demo_benchmark.py | 8 +- skimage/rank/tests/test_rank.py | 10 +++ 7 files changed, 215 insertions(+), 14 deletions(-) create mode 100644 skimage/rank/bilateral_rank.py create mode 100644 skimage/rank/percentile_rank.py create mode 100644 skimage/rank/rank.py create mode 100644 skimage/rank/tests/test_rank.py diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index e69de29b..09812649 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -0,0 +1,3 @@ +from .rank import * +from .percentile_rank import * +from .bilateral_rank import * \ No newline at end of file diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py new file mode 100644 index 00000000..eaa6f8d9 --- /dev/null +++ b/skimage/rank/bilateral_rank.py @@ -0,0 +1,18 @@ +""" +:author: Olivier Debeir, 2012 +:license: modified BSD +""" + +__docformat__ = 'restructuredtext en' + +import warnings +from skimage import img_as_ubyte + +__all__ = ['bilateral_mean'] + + + +def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): + pass + + diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py new file mode 100644 index 00000000..49f87dd4 --- /dev/null +++ b/skimage/rank/percentile_rank.py @@ -0,0 +1,58 @@ +""" +:author: Olivier Debeir, 2012 +:license: modified BSD +""" + +__docformat__ = 'restructuredtext en' + +import warnings +from skimage import img_as_ubyte + +__all__ = ['percentile_mean'] + + +def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. Only pixel values contained inside the + percentile interval [p0,p1] are taken into account. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local mean : uint8 array or uint16 array depending on input image + The result of the local mean. + + Examples + -------- + to be updated + >>> # Erosion shrinks bright regions + >>> from skimage.morphology import square + >>> bright_square = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> erosion(bright_square, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + """ + pass + diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py new file mode 100644 index 00000000..d569a12b --- /dev/null +++ b/skimage/rank/rank.py @@ -0,0 +1,112 @@ +""" +:author: Olivier Debeir, 2012 +:license: modified BSD +""" + +__docformat__ = 'restructuredtext en' + +import warnings +from skimage import img_as_ubyte + +__all__ = ['mean','percentile_mean','bilateral_mean'] + + +def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. Only pixel values contained inside the + percentile interval [p0,p1] are taken into account. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local mean : uint8 array or uint16 array depending on input image + The result of the local mean. + + Examples + -------- + to be updated + >>> # Erosion shrinks bright regions + >>> from skimage.morphology import square + >>> bright_square = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> erosion(bright_square, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + """ + pass + +def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): + pass + +def mean(image, selem, out=None, shift_x=False, shift_y=False): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local mean : uint8 array or uint16 array depending on input image + The result of the local mean. + + Examples + -------- + to be updated + >>> # Erosion shrinks bright regions + >>> from skimage.morphology import square + >>> bright_square = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> erosion(bright_square, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + """ + pass +# if image is out: +# raise NotImplementedError("In-place erosion not supported!") +# image = img_as_ubyte(image) +# selem = img_as_ubyte(selem) +# return cmorph.erode(image, selem, out=out, +# shift_x=shift_x, shift_y=shift_y) + + diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index c16f902f..581e64ed 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -6,11 +6,11 @@ from Cython.Distutils import build_ext setup( cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), - Extension("crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), - Extension("crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), - Extension("crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] + ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), + Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), + Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), + Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), + Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] ) @@ -34,15 +34,15 @@ setup( # cython(['_crank16_percentiles.pyx'], working_path=base_path) # cython(['_crank16_bilateral.pyx'], working_path=base_path) # -# config.add_extension('crank8', sources=['_crank8.c'], +# config.add_extension('_crank8', sources=['_crank8.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank8_percentiles', sources=['_crank8_percentiles.c'], +# config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank16', sources=['_crank16.c'], +# config.add_extension('_crank16', sources=['_crank16.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank16_percentiles', sources=['_crank16_percentiles.c'], +# config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], # include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('crank16_bilateral', sources=['_crank16_bilateral.c'], +# config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], # include_dirs=[get_numpy_include_dirs()]) # # return config diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index c67742e3..f6fe32bb 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -2,18 +2,18 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.morphology import cmorph -from skimage.rank import crank8 +from skimage.morphology import dilation +from skimage.rank import _crank8 from tools import log_timing @log_timing def cr_max(image,selem): - return crank8.maximum(image=image,selem = selem) + return _crank8.maximum(image=image,selem = selem) @log_timing def cm_dil(image,selem): - return cmorph.dilate(image=image,selem = selem) + return dilation(image=image,selem = selem) def compare(): diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/tests/test_rank.py new file mode 100644 index 00000000..2a1e253d --- /dev/null +++ b/skimage/rank/tests/test_rank.py @@ -0,0 +1,10 @@ +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +import skimage.rank as rank + +print dir(rank) + + + From 0c8b4157ed639b7a9735fffbcf760b3ff6f75e6e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 11:26:14 +0200 Subject: [PATCH 214/625] fix bitdepth in rank.py --- skimage/rank/rank.py | 86 ++++++++++++--------------------- skimage/rank/tests/test_rank.py | 21 ++++++++ 2 files changed, 51 insertions(+), 56 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index d569a12b..7e4d16a2 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -7,59 +7,23 @@ __docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte +import numpy as np -__all__ = ['mean','percentile_mean','bilateral_mean'] +import _crank16,_crank8 +__all__ = ['mean'] -def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local mean of an image. - - Mean is computed on the given structuring element. Only pixel values contained inside the - percentile interval [p0,p1] are taken into account. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local mean : uint8 array or uint16 array depending on input image - The result of the local mean. - - Examples - -------- - to be updated - >>> # Erosion shrinks bright regions - >>> from skimage.morphology import square - >>> bright_square = np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> erosion(bright_square, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) - +def find_bitdepth(image): + """returns the max bith depth of a uint16 image """ - pass + umax = np.max(image) + if umax>2: + return int(np.log2(umax)) + else: + return 1 -def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): - pass -def mean(image, selem, out=None, shift_x=False, shift_y=False): +def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. Mean is computed on the given structuring element. @@ -67,7 +31,11 @@ def mean(image, selem, out=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray @@ -101,12 +69,18 @@ def mean(image, selem, out=None, shift_x=False, shift_y=False): [0, 0, 0, 0, 0]], dtype=uint8) """ - pass -# if image is out: -# raise NotImplementedError("In-place erosion not supported!") -# image = img_as_ubyte(image) -# selem = img_as_ubyte(selem) -# return cmorph.erode(image, selem, out=out, -# shift_x=shift_x, shift_y=shift_y) - + if image is out: + raise NotImplementedError("In-place erosion not supported!") + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image are supported!") + return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1) + else: + raise TypeError("only uint8 and uint16 image supported!") diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/tests/test_rank.py index 2a1e253d..77dae32b 100644 --- a/skimage/rank/tests/test_rank.py +++ b/skimage/rank/tests/test_rank.py @@ -2,9 +2,30 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data +from skimage.morphology.selem import disk import skimage.rank as rank print dir(rank) +print rank.mean +print rank.percentile_mean +print rank.bilateral_mean + +a8 = data.camera() +a16 = a8.astype('uint16')*16 +selem = disk(10) + +f8 = rank.mean(a8,selem) +f16 = rank.mean(a16,selem) + +plt.figure() +plt.imshow(np.hstack((a8,f8))) +plt.colorbar() +plt.figure() +plt.imshow(np.hstack((a16,f16))) +plt.colorbar() +plt.show() + + From 87db4ee5907d8c5288bd04673555a9381cbfc0eb Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 11:28:27 +0200 Subject: [PATCH 215/625] update setup to be compatible with scikits-image --- skimage/rank/setup.py | 114 +++++++++++++++++++++--------------------- 1 file changed, 57 insertions(+), 57 deletions(-) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 581e64ed..4f57209a 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -1,59 +1,59 @@ -import numpy as np - -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext - -setup( - cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), - Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), - Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), - Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), - Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] -) +#import numpy as np +# +#from distutils.core import setup +#from distutils.extension import Extension +#from Cython.Distutils import build_ext +# +#setup( +# cmdclass = {'build_ext': build_ext}, +# ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), +# Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] +#) -##!/usr/bin/env python -# -#import os -#from skimage._build import cython -# -#base_path = os.path.abspath(os.path.dirname(__file__)) -# -# -#def configuration(parent_package='', top_path=None): -# from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs -# -# config = Configuration('rank', parent_package, top_path) -## config.add_data_dir('tests') -# -# cython(['_crank8.pyx'], working_path=base_path) -# cython(['_crank8_percentiles.pyx'], working_path=base_path) -# cython(['_crank16.pyx'], working_path=base_path) -# cython(['_crank16_percentiles.pyx'], working_path=base_path) -# cython(['_crank16_bilateral.pyx'], working_path=base_path) -# -# config.add_extension('_crank8', sources=['_crank8.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank16', sources=['_crank16.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], -# include_dirs=[get_numpy_include_dirs()]) -# config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], -# include_dirs=[get_numpy_include_dirs()]) -# -# return config -# -#if __name__ == '__main__': -# from numpy.distutils.core import setup -# setup(maintainer='scikits-image Developers', -# author='Olivier Debeir', -# maintainer_email='scikits-image@googlegroups.com', -# description='Rank filters', -# url='https://github.com/scikits-image/scikits-image', -# license='SciPy License (BSD Style)', -# **(configuration(top_path='').todict()) -# ) +#!/usr/bin/env python + +import os +from skimage._build import cython + +base_path = os.path.abspath(os.path.dirname(__file__)) + + +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs + + config = Configuration('rank', parent_package, top_path) +# config.add_data_dir('tests') + + cython(['_crank8.pyx'], working_path=base_path) + cython(['_crank8_percentiles.pyx'], working_path=base_path) + cython(['_crank16.pyx'], working_path=base_path) + cython(['_crank16_percentiles.pyx'], working_path=base_path) + cython(['_crank16_bilateral.pyx'], working_path=base_path) + + config.add_extension('_crank8', sources=['_crank8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank16', sources=['_crank16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], + include_dirs=[get_numpy_include_dirs()]) + + return config + +if __name__ == '__main__': + from numpy.distutils.core import setup + setup(maintainer='scikits-image Developers', + author='Olivier Debeir', + maintainer_email='scikits-image@googlegroups.com', + description='Rank filters', + url='https://github.com/scikits-image/scikits-image', + license='SciPy License (BSD Style)', + **(configuration(top_path='').todict()) + ) From 3f476703f848860f3eb15eb75225d3a8f6e0b6b6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 12:09:03 +0200 Subject: [PATCH 216/625] add percentile mean --- skimage/rank/generic.py | 10 ++++++ skimage/rank/percentile_rank.py | 30 +++++++++++++++--- skimage/rank/rank.py | 54 +++++++++++++++++---------------- skimage/rank/tests/test_rank.py | 11 +++++++ 4 files changed, 75 insertions(+), 30 deletions(-) create mode 100644 skimage/rank/generic.py diff --git a/skimage/rank/generic.py b/skimage/rank/generic.py new file mode 100644 index 00000000..e8808e5e --- /dev/null +++ b/skimage/rank/generic.py @@ -0,0 +1,10 @@ +import numpy as np + +def find_bitdepth(image): + """returns the max bith depth of a uint16 image + """ + umax = np.max(image) + if umax>2: + return int(np.log2(umax)) + else: + return 1 diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 49f87dd4..840a302f 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -7,11 +7,14 @@ __docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte +import numpy as np + +from .generic import find_bitdepth +import _crank16_percentiles,_crank8_percentiles __all__ = ['percentile_mean'] - -def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. Mean is computed on the given structuring element. Only pixel values contained inside the @@ -20,16 +23,22 @@ def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, Parameters ---------- image : ndarray - Image array (uint8 array or uint16). + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray The array to store the result of the morphology. If None is passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). shift_x, shift_y : bool shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -54,5 +63,18 @@ def percentile_mean(image, selem, out=None, shift_x=False, shift_y=False, p0=.0, [0, 0, 0, 0, 0]], dtype=uint8) """ - pass + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1, + out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 7e4d16a2..c26fd033 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -9,21 +9,13 @@ import warnings from skimage import img_as_ubyte import numpy as np +from .generic import find_bitdepth import _crank16,_crank8 __all__ = ['mean'] -def find_bitdepth(image): - """returns the max bith depth of a uint16 image - """ - umax = np.max(image) - if umax>2: - return int(np.log2(umax)) - else: - return 1 - -def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): +def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. Mean is computed on the given structuring element. @@ -33,14 +25,14 @@ def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): image : ndarray Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray The array to store the result of the morphology. If None is passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). shift_x, shift_y : bool shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). @@ -54,33 +46,43 @@ def mean(image, selem, mask=None, out=None, shift_x=False, shift_y=False): Examples -------- to be updated - >>> # Erosion shrinks bright regions + >>> # Local mean >>> from skimage.morphology import square - >>> bright_square = np.array([[0, 0, 0, 0, 0], + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> erosion(bright_square, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> mean(ima8, square(3)) + array([[ 63, 85, 127, 85, 63], + [ 85, 113, 170, 113, 85], + [127, 170, 255, 170, 127], + [ 85, 113, 170, 113, 85], + [ 63, 85, 127, 85, 63]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> mean(ima16, square(3)) + array([[1023, 1365, 2047, 1365, 1023], + [1365, 1820, 2730, 1820, 1365], + [2047, 2730, 4095, 2730, 2047], + [1365, 1820, 2730, 1820, 1365], + [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ - if image is out: - raise NotImplementedError("In-place erosion not supported!") selem = img_as_ubyte(selem) if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask) + return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) if bitdepth>11: - raise ValueError("only uint16 <4096 image are supported!") - return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1) + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) else: raise TypeError("only uint8 and uint16 image supported!") diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/tests/test_rank.py index 77dae32b..75dd7f65 100644 --- a/skimage/rank/tests/test_rank.py +++ b/skimage/rank/tests/test_rank.py @@ -24,6 +24,17 @@ plt.colorbar() plt.figure() plt.imshow(np.hstack((a16,f16))) plt.colorbar() + +f8 = rank.percentile_mean(a8,selem,p0=.1,p1=.9) +f16 = rank.percentile_mean(a16,selem,p0=.1,p1=.9) + +plt.figure() +plt.imshow(np.hstack((a8,f8))) +plt.colorbar() +plt.figure() +plt.imshow(np.hstack((a16,f16))) +plt.colorbar() + plt.show() From 54e22e08020317fbea459ef639b5e9f0aa7122b6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 14:46:48 +0200 Subject: [PATCH 217/625] add other rank filters --- skimage/rank/rank.py | 927 ++++++++++++++++++++++++++++++++- skimage/rank/tests/demo_all.py | 44 +- 2 files changed, 950 insertions(+), 21 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index c26fd033..9045f04e 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -9,11 +9,365 @@ import warnings from skimage import img_as_ubyte import numpy as np -from .generic import find_bitdepth +from generic import find_bitdepth import _crank16,_crank8 -__all__ = ['mean'] +__all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean' + ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop'] +def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local autolevel of an image. + + Autolevel is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local autolevel : uint8 array or uint16 array depending on input image + The result of the local autolevel. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> autolevel(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> autolevel(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 4096, 0, 4096, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local bottomhat of an image. + + Bottomhat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local bottomhat : uint8 array or uint16 array depending on input image + The result of the local bottomhat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bottomhat(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bottomhat(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local egalise of an image. + + egalise is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local egalise : uint8 array or uint16 array depending on input image + The result of the local egalise. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> egalise(ima8, square(3)) + array([[191, 170, 127, 170, 191], + [170, 255, 255, 255, 170], + [127, 255, 255, 255, 127], + [170, 255, 255, 255, 170], + [191, 170, 127, 170, 191]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> egalise(ima16, square(3)) + array([[3072, 2730, 2048, 2730, 3072], + [2730, 4096, 4096, 4096, 2730], + [2048, 4096, 4096, 4096, 2048], + [2730, 4096, 4096, 4096, 2730], + [3072, 2730, 2048, 2730, 3072]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local gradient of an image. + + gradient is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local gradient : uint8 array or uint16 array depending on input image + The result of the local gradient. + + Examples + -------- + to be updated + >>> # Local gradient + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> gradient(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 0, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> gradient(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 0, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + + +def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local maximum of an image. + + maximum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local maximum : uint8 array or uint16 array depending on input image + The result of the local maximum. + + Examples + -------- + to be updated + >>> # Local maximum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> maximum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> maximum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. @@ -86,3 +440,572 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): else: raise TypeError("only uint8 and uint16 image supported!") +def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local meansubstraction of an image. + + meansubstraction is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local meansubstraction : uint8 array or uint16 array depending on input image + The result of the local meansubstraction. + + Examples + -------- + to be updated + >>> # Local meansubstraction + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> meansubstraction(ima8, square(3)) + array([[ 95, 84, 63, 84, 95], + [ 84, 197, 169, 197, 84], + [ 63, 169, 127, 169, 63], + [ 84, 197, 169, 197, 84], + [ 95, 84, 63, 84, 95]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> meansubstraction(ima16, square(3)) + array([[1536, 1365, 1024, 1365, 1536], + [1365, 3185, 2730, 3185, 1365], + [1024, 2730, 2048, 2730, 1024], + [1365, 3185, 2730, 3185, 1365], + [1536, 1365, 1024, 1365, 1536]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local median of an image. + + median is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local median : uint8 array or uint16 array depending on input image + The result of the local median. + + Examples + -------- + to be updated + >>> # Local median + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> median(ima8, square(3)) + array([[ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0], + [255, 255, 255, 255, 255], + [ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> median(ima16, square(3)) + array([[ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0], + [4095, 4095, 4095, 4095, 4095], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local minimum of an image. + + minimum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local minimum : uint8 array or uint16 array depending on input image + The result of the local minimum. + + Examples + -------- + to be updated + >>> # Local minimum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> minimum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> minimum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local modal of an image. + + modal is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local modal : uint8 array or uint16 array depending on input image + The result of the local modal. + + Examples + -------- + to be updated + >>> # Local modal + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint8) + >>> modal(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 1, 1, 0, 0], + [0, 0, 5, 0, 0], + [0, 0, 5, 0, 0]], dtype=uint8) + + + >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint16) + >>> modal(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 100, 0, 0], + [ 0, 100, 100, 0, 0], + [ 0, 0, 500, 0, 0], + [ 0, 0, 500, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local morph_contr_enh of an image. + + morph_contr_enh is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local morph_contr_enh : uint8 array or uint16 array depending on input image + The result of the local morph_contr_enh. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> morph_contr_enh(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> morph_contr_enh(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local pop of an image. + + pop is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local pop : uint8 array or uint16 array depending on input image + The result of the local pop. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> pop(ima8, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> pop(ima16, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local threshold of an image. + + threshold is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local threshold : uint8 array or uint16 array depending on input image + The result of the local threshold. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> threshold(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> threshold(ima16, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint16) + + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local tophat of an image. + + tophat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local tophat : uint8 array or uint16 array depending on input image + The result of the local tophat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> tophat(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> tophat(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") \ No newline at end of file diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index 8e09048e..da592c3d 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -1,21 +1,23 @@ import numpy as np import matplotlib.pyplot as plt +from pprint import pprint from skimage import data from skimage.morphology.selem import disk -from skimage.rank import crank8,crank8_percentiles -from skimage.rank import crank16,crank16_percentiles,crank16_bilateral +from skimage.rank import _crank8,_crank8_percentiles +from skimage.rank import _crank16,_crank16_percentiles,_crank16_bilateral -if __name__ == '__main__': +def plot_all(): a8 = data.camera() a16 = a8.astype('uint16')*16 -# selem = np.ones((30,30),dtype='uint8') + # selem = np.ones((30,30),dtype='uint8') selem = disk(5) - for n in dir(crank16): - method = eval('crank16.%s'%n) + + for n in dir(_crank16): + method = eval('_crank16.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a16,selem = selem,bitdepth=12) @@ -28,10 +30,10 @@ if __name__ == '__main__': plt.colorbar() plt.title(method) - for n in dir(crank8): - method = eval('crank8.%s'%n) + for n in dir(_crank8): + method = eval('_crank8.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a8,selem = selem) @@ -44,10 +46,10 @@ if __name__ == '__main__': plt.colorbar() plt.title(method) - for n in dir(crank8_percentiles): - method = eval('crank8_percentiles.%s'%n) + for n in dir(_crank8_percentiles): + method = eval('_crank8_percentiles.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a8,selem = selem,p0=.1,p1=.9) @@ -60,10 +62,10 @@ if __name__ == '__main__': plt.colorbar() plt.title(method) - for n in dir(crank16_percentiles): - method = eval('crank16_percentiles.%s'%n) + for n in dir(_crank16_percentiles): + method = eval('_crank16_percentiles.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) @@ -77,10 +79,10 @@ if __name__ == '__main__': plt.title(method) selem = disk(50) - for n in dir(crank16_bilateral): - method = eval('crank16_bilateral.%s'%n) + for n in dir(_crank16_bilateral): + method = eval('_crank16_bilateral.%s'%n) t = type(method) - if t == type(crank8.maximum): + if t == type(_crank8.maximum): print n,t f = method(a16,selem = selem,bitdepth=12,s0=300,s1=300) @@ -95,3 +97,7 @@ if __name__ == '__main__': # plt.show() + +if __name__ == '__main__': +# plot_all() + pprint(dir(_crank8)) \ No newline at end of file From 10155e2e4d4cce8e1b655cc64afb158e04b7591b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 15:22:59 +0200 Subject: [PATCH 218/625] add percentile filters - in progress --- skimage/rank/percentile_rank.py | 1034 ++++++++++++++++++++++++++++++- 1 file changed, 1016 insertions(+), 18 deletions(-) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 840a302f..d658dfb4 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -9,16 +9,371 @@ import warnings from skimage import img_as_ubyte import numpy as np -from .generic import find_bitdepth +from generic import find_bitdepth import _crank16_percentiles,_crank8_percentiles -__all__ = ['percentile_mean'] +__all__ = ['percentile_autolevel','percentile_bottomhat','percentile_egalise','percentile_gradient', + 'percentile_maximum','percentile_mean','percentile_meansubstraction','percentile_median', + 'percentile_minimum','percentile_modal','percentile_morph_contr_enh','percentile_pop'] -def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local mean of an image. +def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local autolevel of an image. - Mean is computed on the given structuring element. Only pixel values contained inside the - percentile interval [p0,p1] are taken into account. + Autolevel is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local autolevel : uint8 array or uint16 array depending on input image + The result of the local autolevel. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> percentile_autolevel(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> percentile_eautolevel(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 4096, 0, 4096, 0], + [ 0, 4096, 4096, 4096, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local bottomhat of an image. + + Bottomhat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local bottomhat : uint8 array or uint16 array depending on input image + The result of the local bottomhat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bottomhat(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bottomhat(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local egalise of an image. + + egalise is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local egalise : uint8 array or uint16 array depending on input image + The result of the local egalise. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> egalise(ima8, square(3)) + array([[191, 170, 127, 170, 191], + [170, 255, 255, 255, 170], + [127, 255, 255, 255, 127], + [170, 255, 255, 255, 170], + [191, 170, 127, 170, 191]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> egalise(ima16, square(3)) + array([[3072, 2730, 2048, 2730, 3072], + [2730, 4096, 4096, 4096, 2730], + [2048, 4096, 4096, 4096, 2048], + [2730, 4096, 4096, 4096, 2730], + [3072, 2730, 2048, 2730, 3072]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local gradient of an image. + + gradient is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local gradient : uint8 array or uint16 array depending on input image + The result of the local gradient. + + Examples + -------- + to be updated + >>> # Local gradient + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> gradient(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 0, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> gradient(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 0, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + + +def percentile_maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local maximum of an image. + + maximum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local maximum : uint8 array or uint16 array depending on input image + The result of the local maximum. + + Examples + -------- + to be updated + >>> # Local maximum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> maximum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 1, 0, 0], + ... [0, 0, 0, 0, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> maximum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean of an image. + + Mean is computed on the given structuring element. Parameters ---------- @@ -37,8 +392,6 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -48,19 +401,31 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa Examples -------- to be updated - >>> # Erosion shrinks bright regions + >>> # Local mean >>> from skimage.morphology import square - >>> bright_square = np.array([[0, 0, 0, 0, 0], + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> erosion(bright_square, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> mean(ima8, square(3)) + array([[ 63, 85, 127, 85, 63], + [ 85, 113, 170, 113, 85], + [127, 170, 255, 170, 127], + [ 85, 113, 170, 113, 85], + [ 63, 85, 127, 85, 63]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> mean(ima16, square(3)) + array([[1023, 1365, 2047, 1365, 1023], + [1365, 1820, 2730, 1820, 1365], + [2047, 2730, 4095, 2730, 2047], + [1365, 1820, 2730, 1820, 1365], + [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ selem = img_as_ubyte(selem) @@ -72,9 +437,642 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa bitdepth = find_bitdepth(image) if bitdepth>11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1, - out=out,p0=p0,p1=p1) + return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") +def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local meansubstraction of an image. + meansubstraction is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local meansubstraction : uint8 array or uint16 array depending on input image + The result of the local meansubstraction. + + Examples + -------- + to be updated + >>> # Local meansubstraction + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> meansubstraction(ima8, square(3)) + array([[ 95, 84, 63, 84, 95], + [ 84, 197, 169, 197, 84], + [ 63, 169, 127, 169, 63], + [ 84, 197, 169, 197, 84], + [ 95, 84, 63, 84, 95]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> meansubstraction(ima16, square(3)) + array([[1536, 1365, 1024, 1365, 1536], + [1365, 3185, 2730, 3185, 1365], + [1024, 2730, 2048, 2730, 1024], + [1365, 3185, 2730, 3185, 1365], + [1536, 1365, 1024, 1365, 1536]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_median(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local median of an image. + + median is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local median : uint8 array or uint16 array depending on input image + The result of the local median. + + Examples + -------- + to be updated + >>> # Local median + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> median(ima8, square(3)) + array([[ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0], + [255, 255, 255, 255, 255], + [ 0, 0, 255, 0, 0], + [ 0, 0, 255, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 0, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> median(ima16, square(3)) + array([[ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0], + [4095, 4095, 4095, 4095, 4095], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 4095, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local minimum of an image. + + minimum is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local minimum : uint8 array or uint16 array depending on input image + The result of the local minimum. + + Examples + -------- + to be updated + >>> # Local minimum + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> minimum(ima8, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) + + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> minimum(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 4095, 0, 0], + [ 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local modal of an image. + + modal is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local modal : uint8 array or uint16 array depending on input image + The result of the local modal. + + Examples + -------- + to be updated + >>> # Local modal + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint8) + >>> modal(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 1, 1, 0, 0], + [0, 0, 5, 0, 0], + [0, 0, 5, 0, 0]], dtype=uint8) + + + >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 5, 6, 0], + ... [0, 1, 5, 5, 0], + ... [0, 0, 0, 5, 0]], dtype=np.uint16) + >>> modal(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 0, 100, 0, 0], + [ 0, 100, 100, 0, 0], + [ 0, 0, 500, 0, 0], + [ 0, 0, 500, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local morph_contr_enh of an image. + + morph_contr_enh is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local morph_contr_enh : uint8 array or uint16 array depending on input image + The result of the local morph_contr_enh. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> morph_contr_enh(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> morph_contr_enh(ima16, square(3)) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local pop of an image. + + pop is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local pop : uint8 array or uint16 array depending on input image + The result of the local pop. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> pop(ima8, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> pop(ima16, square(3)) + array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local threshold of an image. + + threshold is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local threshold : uint8 array or uint16 array depending on input image + The result of the local threshold. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> threshold(ima8, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> threshold(ima16, square(3)) + array([[0, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 0, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 0, 0, 0]], dtype=uint16) + + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local tophat of an image. + + tophat is computed on the given structuring element. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + + Returns + ------- + local tophat : uint8 array or uint16 array depending on input image + The result of the local tophat. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> tophat(ima8, square(3)) + array([[255, 255, 255, 255, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 0, 0, 0, 255], + [255, 255, 255, 255, 255]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> tophat(ima16, square(3)) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 0, 0, 0, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +#__all__ = ['percentile_mean'] + +#def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +# """Return greyscale local mean of an image. +# +# Mean is computed on the given structuring element. Only pixel values contained inside the +# percentile interval [p0,p1] are taken into account. +# +# Parameters +# ---------- +# image : ndarray +# Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, +# an exception will be raised if image has a value > 4095 +# selem : ndarray +# The neighborhood expressed as a 2-D array of 1's and 0's. +# out : ndarray +# The array to store the result of the morphology. If None is +# passed, a new array will be allocated. +# mask : ndarray (uint8) +# Mask array that defines (>0) area of the image included in the local neighborhood. +# If None, the complete image is used (default). +# shift_x, shift_y : bool +# shift structuring element about center point. This only affects +# eccentric structuring elements (i.e. selem with even numbered sides). +# Shift is bounded to the structuring element sizes. +# p0, p1 : float in [0.,...,1.] +# define the [p0,p1] percentile interval to be considered for computing the value. +# +# Returns +# ------- +# local mean : uint8 array or uint16 array depending on input image +# The result of the local mean. +# +# Examples +# -------- +# to be updated +# >>> # Erosion shrinks bright regions +# >>> from skimage.morphology import square +# >>> bright_square = np.array([[0, 0, 0, 0, 0], +# ... [0, 1, 1, 1, 0], +# ... [0, 1, 1, 1, 0], +# ... [0, 1, 1, 1, 0], +# ... [0, 0, 0, 0, 0]], dtype=np.uint8) +# >>> erosion(bright_square, square(3)) +# array([[0, 0, 0, 0, 0], +# [0, 0, 0, 0, 0], +# [0, 0, 1, 0, 0], +# [0, 0, 0, 0, 0], +# [0, 0, 0, 0, 0]], dtype=uint8) +# +# """ +# selem = img_as_ubyte(selem) +# if mask is not None: +# mask = img_as_ubyte(mask) +# if image.dtype == np.uint8: +# return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) +# elif image.dtype == np.uint16: +# bitdepth = find_bitdepth(image) +# if bitdepth>11: +# raise ValueError("only uint16 <4096 image (12bit) supported!") +# return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) +# else: +# raise TypeError("only uint8 and uint16 image supported!") +# +# From 304cac7ecdef50bba63188316236b912f784bf4b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 16:08:30 +0200 Subject: [PATCH 219/625] add percentile filters --- skimage/rank/_crank8_percentiles.pyx | 2 +- skimage/rank/percentile_rank.py | 766 +++++---------------------- 2 files changed, 148 insertions(+), 620 deletions(-) diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 81730313..23fe079f 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -187,7 +187,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): - """bottom hat + """autolevel """ return _core8p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index d658dfb4..924bdd7a 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -12,14 +12,14 @@ import numpy as np from generic import find_bitdepth import _crank16_percentiles,_crank8_percentiles -__all__ = ['percentile_autolevel','percentile_bottomhat','percentile_egalise','percentile_gradient', - 'percentile_maximum','percentile_mean','percentile_meansubstraction','percentile_median', +__all__ = ['percentile_autolevel','percentile_gradient', + 'percentile_mean','percentile_mean_substraction','percentile_median', 'percentile_minimum','percentile_modal','percentile_morph_contr_enh','percentile_pop'] def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. - Autolevel is computed on the given structuring element. + Autolevel is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -38,6 +38,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -54,10 +56,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_autolevel(ima8, square(3)) + >>> percentile_autolevel(ima8, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], + [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], [ 0, 0, 0, 0, 0]], dtype=uint8) @@ -66,11 +68,11 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_eautolevel(ima16, square(3)) + >>> percentile_autolevel(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], - [ 0, 4096, 4096, 4096, 0], - [ 0, 4096, 0, 4096, 0], - [ 0, 4096, 4096, 4096, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -87,150 +89,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local bottomhat of an image. - - Bottomhat is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local bottomhat : uint8 array or uint16 array depending on input image - The result of the local bottomhat. - - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bottomhat(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bottomhat(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 0, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -def percentile_egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local egalise of an image. - - egalise is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local egalise : uint8 array or uint16 array depending on input image - The result of the local egalise. - - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> egalise(ima8, square(3)) - array([[191, 170, 127, 170, 191], - [170, 255, 255, 255, 170], - [127, 255, 255, 255, 127], - [170, 255, 255, 255, 170], - [191, 170, 127, 170, 191]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> egalise(ima16, square(3)) - array([[3072, 2730, 2048, 2730, 3072], - [2730, 4096, 4096, 4096, 2730], - [2048, 4096, 4096, 4096, 2048], - [2730, 4096, 4096, 4096, 2730], - [3072, 2730, 2048, 2730, 3072]], dtype=uint16) - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local gradient of an image. + """Return greyscale local percentile_gradient of an image. - gradient is computed on the given structuring element. + percentile_gradient is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -249,11 +111,13 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- - local gradient : uint8 array or uint16 array depending on input image - The result of the local gradient. + local percentile_gradient : uint8 array or uint16 array depending on input image + The result of the local percentile_gradient. Examples -------- @@ -265,10 +129,10 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> gradient(ima8, square(3)) + >>> percentile_gradient(ima8, square(3), p0=0.,p1=1.) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], - [255, 255, 0, 255, 255], + [255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 255, 255, 255]], dtype=uint8) @@ -277,10 +141,10 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> gradient(ima16, square(3)) + >>> percentile_gradient(ima16, square(3), p0=0.,p1=1.) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 0, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095]], dtype=uint16) @@ -299,81 +163,10 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ raise TypeError("only uint8 and uint16 image supported!") -def percentile_maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local maximum of an image. - - maximum is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local maximum : uint8 array or uint16 array depending on input image - The result of the local maximum. - - Examples - -------- - to be updated - >>> # Local maximum - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 1, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> maximum(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 1, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> maximum(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. - Mean is computed on the given structuring element. + Mean is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -392,6 +185,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -408,7 +203,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> mean(ima8, square(3)) + >>> percentile_mean(ima8, square(3),p0=0.,p1=1.) array([[ 63, 85, 127, 85, 63], [ 85, 113, 170, 113, 85], [127, 170, 255, 170, 127], @@ -420,7 +215,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> mean(ima16, square(3)) + >>> percentile_mean(ima16, square(3),p0=0.,p1=1.) array([[1023, 1365, 2047, 1365, 1023], [1365, 1820, 2730, 1820, 1365], [2047, 2730, 4095, 2730, 2047], @@ -441,10 +236,10 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local meansubstraction of an image. +def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local mean_substraction of an image. - meansubstraction is computed on the given structuring element. + mean_substraction is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -463,27 +258,29 @@ def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- - local meansubstraction : uint8 array or uint16 array depending on input image - The result of the local meansubstraction. + local mean_substraction : uint8 array or uint16 array depending on input image + The result of the local mean_substraction. Examples -------- to be updated - >>> # Local meansubstraction + >>> # Local mean_substraction >>> from skimage.morphology import square >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> meansubstraction(ima8, square(3)) + >>> percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) array([[ 95, 84, 63, 84, 95], - [ 84, 197, 169, 197, 84], + [ 84, 198, 169, 198, 84], [ 63, 169, 127, 169, 63], - [ 84, 197, 169, 197, 84], + [ 84, 198, 169, 198, 84], [ 95, 84, 63, 84, 95]], dtype=uint8) >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], @@ -491,7 +288,7 @@ def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> meansubstraction(ima16, square(3)) + >>> percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) array([[1536, 1365, 1024, 1365, 1536], [1365, 3185, 2730, 3185, 1365], [1024, 2730, 2048, 2730, 1024], @@ -503,234 +300,20 @@ def percentile_meansubstraction(image, selem, out=None, mask=None, shift_x=False if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return _crank8_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + return _crank8_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) if bitdepth>11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + return _crank16_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_median(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local median of an image. - - median is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local median : uint8 array or uint16 array depending on input image - The result of the local median. - - Examples - -------- - to be updated - >>> # Local median - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 0, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> median(ima8, square(3)) - array([[ 0, 0, 255, 0, 0], - [ 0, 0, 255, 0, 0], - [255, 255, 255, 255, 255], - [ 0, 0, 255, 0, 0], - [ 0, 0, 255, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 0, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> median(ima16, square(3)) - array([[ 0, 0, 4095, 0, 0], - [ 0, 0, 4095, 0, 0], - [4095, 4095, 4095, 4095, 4095], - [ 0, 0, 4095, 0, 0], - [ 0, 0, 4095, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -def percentile_minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local minimum of an image. - - minimum is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local minimum : uint8 array or uint16 array depending on input image - The result of the local minimum. - - Examples - -------- - to be updated - >>> # Local minimum - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> minimum(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> minimum(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 4095, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -def percentile_modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local modal of an image. - - modal is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local modal : uint8 array or uint16 array depending on input image - The result of the local modal. - - Examples - -------- - to be updated - >>> # Local modal - >>> from skimage.morphology import square - >>> ima8 = np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 5, 6, 0], - ... [0, 1, 5, 5, 0], - ... [0, 0, 0, 5, 0]], dtype=np.uint8) - >>> modal(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 1, 1, 0, 0], - [0, 0, 5, 0, 0], - [0, 0, 5, 0, 0]], dtype=uint8) - - - >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 5, 6, 0], - ... [0, 1, 5, 5, 0], - ... [0, 0, 0, 5, 0]], dtype=np.uint16) - >>> modal(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 100, 0, 0], - [ 0, 100, 100, 0, 0], - [ 0, 0, 500, 0, 0], - [ 0, 0, 500, 0, 0]], dtype=uint16) - - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. - morph_contr_enh is computed on the given structuring element. + morph_contr_enh is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -749,6 +332,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -760,24 +345,24 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, to be updated >>> # Local mean >>> from skimage.morphology import square - >>> ima8 = np.array([[0, 0, 0, 0, 0], + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> morph_contr_enh(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 1, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint8) >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> morph_contr_enh(ima16, square(3)) + >>> percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -798,10 +383,10 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local pop of an image. +def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local percentile of an image. - pop is computed on the given structuring element. + percentile is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -820,6 +405,82 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. + + Returns + ------- + local percentile : uint8 array or uint16 array depending on input image + The result of the local percentile. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> percentile(ima8, square(3), p0=0.,p1=1.) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint8) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> percentile(ima16, square(3), p0=0.,p1=1.) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], dtype=uint16) + + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return _crank8_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + +def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): + """Return greyscale local pop of an image. + + pop is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -836,7 +497,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> pop(ima8, square(3)) + >>> percentile_pop(ima8, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -848,7 +509,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> pop(ima16, square(3)) + >>> percentile_pop(ima16, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -872,7 +533,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. - threshold is computed on the given structuring element. + threshold is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. Parameters ---------- @@ -891,6 +552,8 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Shift is bounded to the structuring element sizes. + p0, p1 : float in [0.,...,1.] + define the [p0,p1] percentile interval to be considered for computing the value. Returns ------- @@ -907,24 +570,24 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> threshold(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 0, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint8) + >>> percentile_threshold(ima8, square(3), p0=0.,p1=1.) + array([[255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255], + [255, 255, 255, 255, 255]], dtype=uint8) >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> threshold(ima16, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 0, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint16) + >>> percentile_threshold(ima16, square(3), p0=0.,p1=1.) + array([[4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095], + [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ @@ -941,138 +604,3 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift else: raise TypeError("only uint8 and uint16 image supported!") -def percentile_tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): - """Return greyscale local tophat of an image. - - tophat is computed on the given structuring element. - - Parameters - ---------- - image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 - selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. - out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. - mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. - - Returns - ------- - local tophat : uint8 array or uint16 array depending on input image - The result of the local tophat. - - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> tophat(ima8, square(3)) - array([[255, 255, 255, 255, 255], - [255, 0, 0, 0, 255], - [255, 0, 0, 0, 255], - [255, 0, 0, 0, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> tophat(ima16, square(3)) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 0, 0, 0, 4095], - [4095, 0, 0, 0, 4095], - [4095, 0, 0, 0, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) - """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") - -#__all__ = ['percentile_mean'] - -#def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): -# """Return greyscale local mean of an image. -# -# Mean is computed on the given structuring element. Only pixel values contained inside the -# percentile interval [p0,p1] are taken into account. -# -# Parameters -# ---------- -# image : ndarray -# Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, -# an exception will be raised if image has a value > 4095 -# selem : ndarray -# The neighborhood expressed as a 2-D array of 1's and 0's. -# out : ndarray -# The array to store the result of the morphology. If None is -# passed, a new array will be allocated. -# mask : ndarray (uint8) -# Mask array that defines (>0) area of the image included in the local neighborhood. -# If None, the complete image is used (default). -# shift_x, shift_y : bool -# shift structuring element about center point. This only affects -# eccentric structuring elements (i.e. selem with even numbered sides). -# Shift is bounded to the structuring element sizes. -# p0, p1 : float in [0.,...,1.] -# define the [p0,p1] percentile interval to be considered for computing the value. -# -# Returns -# ------- -# local mean : uint8 array or uint16 array depending on input image -# The result of the local mean. -# -# Examples -# -------- -# to be updated -# >>> # Erosion shrinks bright regions -# >>> from skimage.morphology import square -# >>> bright_square = np.array([[0, 0, 0, 0, 0], -# ... [0, 1, 1, 1, 0], -# ... [0, 1, 1, 1, 0], -# ... [0, 1, 1, 1, 0], -# ... [0, 0, 0, 0, 0]], dtype=np.uint8) -# >>> erosion(bright_square, square(3)) -# array([[0, 0, 0, 0, 0], -# [0, 0, 0, 0, 0], -# [0, 0, 1, 0, 0], -# [0, 0, 0, 0, 0], -# [0, 0, 0, 0, 0]], dtype=uint8) -# -# """ -# selem = img_as_ubyte(selem) -# if mask is not None: -# mask = img_as_ubyte(mask) -# if image.dtype == np.uint8: -# return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) -# elif image.dtype == np.uint16: -# bitdepth = find_bitdepth(image) -# if bitdepth>11: -# raise ValueError("only uint16 <4096 image (12bit) supported!") -# return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) -# else: -# raise TypeError("only uint8 and uint16 image supported!") -# -# From 8222a58ec8261516abeaf3c3a935cfc3fa270ee8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 16:19:04 +0200 Subject: [PATCH 220/625] =?UTF-8?q?add=20bilateral=20filters=20pop=20and?= =?UTF-8?q?=20mean=C2=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- skimage/rank/bilateral_rank.py | 155 ++++++++++++++++++++++++++++++++- 1 file changed, 153 insertions(+), 2 deletions(-) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index eaa6f8d9..4e8d3b6a 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -8,11 +8,162 @@ __docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte +import numpy as np + +from generic import find_bitdepth +import _crank16_bilateral + + __all__ = ['bilateral_mean'] +def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): + """Return greyscale local bilateral_mean of an image. -def bilateral_mean(image, selem, out=None, shift_x=False, shift_y=False, s0=10, s1=10): - pass + bilateral mean is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + s0, s1 : int + define the [s0,s1] interval to be considered for computing the value. + + Returns + ------- + local bilateral mean : uint16 array (uint8 image are casted to uint16) + The result of the local bilateral mean. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bilateral_mean(ima8, square(3), s0=10,s1=10) + array([[ 0, 0, 0, 0, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 255, 255, 255, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bilateral_mean(ima16, square(3), s0=10,s1=10) + array([[ 0, 0, 0, 0, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 0, 0, 0, 0]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + image = image.astype(np.uint16) + elif image.dtype == np.uint16: + pass + else: + raise TypeError("only uint8 and uint16 image supported!") + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_bilateral.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + + +def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): + """Return greyscale local bilateral_pop of an image. + + bilateral pop is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used. + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : bool + shift structuring element about center point. This only affects + eccentric structuring elements (i.e. selem with even numbered sides). + Shift is bounded to the structuring element sizes. + s0, s1 : int + define the [s0,s1] interval to be considered for computing the value. + + Returns + ------- + local bilateral pop : uint16 array (uint8 image are casted to uint16) + The result of the local bilateral pop. + + Examples + -------- + to be updated + >>> # Local mean + >>> from skimage.morphology import square + >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> bilateral_pop(ima8, square(3), s0=10,s1=10) + array([[3, 4, 3, 4, 3], + [4, 4, 6, 4, 4], + [3, 6, 9, 6, 3], + [4, 4, 6, 4, 4], + [3, 4, 3, 4, 3]], dtype=uint16) + + >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> bilateral_pop(ima16, square(3), s0=10,s1=10) + array([[3, 4, 3, 4, 3], + [4, 4, 6, 4, 4], + [3, 6, 9, 6, 3], + [4, 4, 6, 4, 4], + [3, 4, 3, 4, 3]], dtype=uint16) + + """ + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + image = image.astype(np.uint16) + elif image.dtype == np.uint16: + pass + else: + raise TypeError("only uint8 and uint16 image supported!") + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return _crank16_bilateral.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) From c633fb895398031d1b7451b769bd433900f4cb30 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 17:00:26 +0200 Subject: [PATCH 221/625] add full test --- skimage/rank/bilateral_rank.py | 2 +- skimage/rank/percentile_rank.py | 4 +- skimage/rank/tests/demo_all.py | 149 +++++++++++++------------------- 3 files changed, 65 insertions(+), 90 deletions(-) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index 4e8d3b6a..a76133a6 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -14,7 +14,7 @@ from generic import find_bitdepth import _crank16_bilateral -__all__ = ['bilateral_mean'] +__all__ = ['bilateral_mean','bilateral_pop'] def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 924bdd7a..b6af753c 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -13,8 +13,8 @@ from generic import find_bitdepth import _crank16_percentiles,_crank8_percentiles __all__ = ['percentile_autolevel','percentile_gradient', - 'percentile_mean','percentile_mean_substraction','percentile_median', - 'percentile_minimum','percentile_modal','percentile_morph_contr_enh','percentile_pop'] + 'percentile_mean','percentile_mean_substraction', + 'percentile_morph_contr_enh','percentile_pop'] def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/tests/demo_all.py index da592c3d..8df5c3e7 100644 --- a/skimage/rank/tests/demo_all.py +++ b/skimage/rank/tests/demo_all.py @@ -4,100 +4,75 @@ from pprint import pprint from skimage import data from skimage.morphology.selem import disk -from skimage.rank import _crank8,_crank8_percentiles -from skimage.rank import _crank16,_crank16_percentiles,_crank16_bilateral +import skimage.rank as rank def plot_all(): a8 = data.camera() a16 = a8.astype('uint16')*16 - # selem = np.ones((30,30),dtype='uint8') selem = disk(5) + name_list = sorted([n for n in dir(rank) if n[0] is not '_']) + print name_list - for n in dir(_crank16): - method = eval('_crank16.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a16,selem = selem,bitdepth=12) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a16) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - for n in dir(_crank8): - method = eval('_crank8.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a8,selem = selem) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - for n in dir(_crank8_percentiles): - method = eval('_crank8_percentiles.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a8,selem = selem,p0=.1,p1=.9) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - for n in dir(_crank16_percentiles): - method = eval('_crank16_percentiles.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a16,selem = selem,bitdepth=12,p0=.1,p1=.9) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a16) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - selem = disk(50) - for n in dir(_crank16_bilateral): - method = eval('_crank16_bilateral.%s'%n) - t = type(method) - if t == type(_crank8.maximum): - print n,t - f = method(a16,selem = selem,bitdepth=12,s0=300,s1=300) - - plt.figure() - plt.subplot(1,2,1) - plt.imshow(a16) - plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(f) - plt.colorbar() - plt.title(method) - - # + for n in name_list: + if n.rfind('bilateral')==0: + print n + method = eval('rank.%s'%n) + if type(method) == type(rank.maximum): + print method + f8 = method(a8,selem = selem,s0=10,s1=10) + f16 = method(a16,selem = selem,s0=10,s1=10) + plt.figure() + plt.subplot(2,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(2,2,2) + plt.imshow(f8) + plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(f16) + plt.colorbar() + plt.title(method) + for n in name_list: + if n.rfind('percentile')==0: + print n + method = eval('rank.%s'%n) + if type(method) == type(rank.maximum): + print method + f8 = method(a8,selem = selem,p0=.1,p1=.9) + f16 = method(a16,selem = selem,p0=.1,p1=.9) + plt.figure() + plt.subplot(2,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(2,2,2) + plt.imshow(f8) + plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(f16) + plt.colorbar() + plt.title(method) + for n in name_list: + if n.find('percentile')==-1 and n.find('bilateral')==-1: + print n + method = eval('rank.%s'%n) + if type(method) == type(rank.maximum): + print method + f8 = method(a8,selem = selem) + f16 = method(a16,selem = selem) + plt.figure() + plt.subplot(2,2,1) + plt.imshow(a8) + plt.colorbar() + plt.subplot(2,2,2) + plt.imshow(f8) + plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(f16) + plt.colorbar() + plt.title(method) plt.show() if __name__ == '__main__': -# plot_all() - pprint(dir(_crank8)) \ No newline at end of file + plot_all() + pprint(dir(rank)) \ No newline at end of file From 866ea40fbbed2d3c860b8a34f41fee65b81b5a48 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 17:15:33 +0200 Subject: [PATCH 222/625] adapt tests --- skimage/rank/README.rst | 12 ++++---- skimage/rank/rank.py | 2 +- skimage/rank/tests/demo_16bitbilateral.py | 15 +++++++--- skimage/rank/tests/demo_benchmark.py | 4 +-- skimage/rank/tests/test_suite.py | 34 +++++++++++------------ 5 files changed, 37 insertions(+), 30 deletions(-) diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst index ef56f997..68d2a1fe 100644 --- a/skimage/rank/README.rst +++ b/skimage/rank/README.rst @@ -1,13 +1,13 @@ To use this to build your Cython file use the commandline options: +.. sourcecode:: text + + $ python setup.py build_ext --inplace + + **To do** * add simple examples -* add doc +* add/check existing doc - - -.. sourcecode:: text - - $ python setup.py build_ext --inplace \ No newline at end of file diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 9045f04e..51430236 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -13,7 +13,7 @@ from generic import find_bitdepth import _crank16,_crank8 __all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean' - ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop'] + ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local autolevel of an image. diff --git a/skimage/rank/tests/demo_16bitbilateral.py b/skimage/rank/tests/demo_16bitbilateral.py index 8001a25f..2a302e6a 100644 --- a/skimage/rank/tests/demo_16bitbilateral.py +++ b/skimage/rank/tests/demo_16bitbilateral.py @@ -2,16 +2,19 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data -from skimage.rank import crank8_percentiles -from skimage.rank import crank16_bilateral +from skimage.morphology import disk +import skimage.rank as rank if __name__ == '__main__': a8 = (data.coins()).astype('uint8') a16 = (data.coins()).astype('uint16')*16 selem = np.ones((20,20),dtype='uint8') - f1 = crank8_percentiles.mean(a8,selem = selem,p0=.1,p1=.9) - f2 = crank16_bilateral.mean(a16,selem = selem,bitdepth=12,s0=500,s1=500) + f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) + f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) + + selem = disk(50) + f3 = rank.egalise(a16,selem = selem) plt.figure() plt.imshow(np.hstack((a8,f1))) @@ -21,4 +24,8 @@ if __name__ == '__main__': plt.imshow(np.hstack((a16,f2))) plt.colorbar() + plt.figure() + plt.imshow(np.hstack((a16,f3))) + plt.colorbar() + plt.show() diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index f6fe32bb..42971133 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -3,13 +3,13 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import dilation -from skimage.rank import _crank8 +import skimage.rank as rank from tools import log_timing @log_timing def cr_max(image,selem): - return _crank8.maximum(image=image,selem = selem) + return rank.maximum(image=image,selem = selem) @log_timing def cm_dil(image,selem): diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 0dcb21ab..51c7f60b 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -2,8 +2,8 @@ import unittest import numpy as np -from skimage.rank import crank8,crank8_percentiles -from skimage.rank import crank16,crank16_bilateral,crank16_percentiles +from skimage.rank import _crank8,_crank8_percentiles +from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles from skimage.morphology import cmorph class TestSequenceFunctions(unittest.TestCase): @@ -17,23 +17,23 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') for m,n in np.random.random_integers(1,100,size=(10,2)): a8 = np.ones((m,n),dtype='uint8') - r = crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) + r = _crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) self.assertTrue(a8.shape == r.shape) - r = crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) + r = _crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) self.assertTrue(a8.shape == r.shape) for m,n in np.random.random_integers(1,100,size=(10,2)): a16 = np.ones((m,n),dtype='uint16') - r = crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0) + r = _crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0) self.assertTrue(a16.shape == r.shape) - r = crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1) + r = _crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1) self.assertTrue(a16.shape == r.shape) for m,n in np.random.random_integers(1,100,size=(10,2)): a16 = np.ones((m,n),dtype='uint16') - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9) self.assertTrue(a16.shape == r.shape) - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) self.assertTrue(a16.shape == r.shape) def test_compare_with_cmorph(self): @@ -43,27 +43,27 @@ class TestSequenceFunctions(unittest.TestCase): for r in range(1,20,1): elem = np.ones((r,r),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') - rc = crank8.maximum(image=a,selem = elem) + rc = _crank8.maximum(image=a,selem = elem) cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) def test_bitdepth(self): elem = np.ones((3,3),dtype='uint8') a16 = np.ones((100,100),dtype='uint16')*255 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) a16 = np.ones((100,100),dtype='uint16')*255*2 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9) a16 = np.ones((100,100),dtype='uint16')*255*4 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10) a16 = np.ones((100,100),dtype='uint16')*255*8 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11) a16 = np.ones((100,100),dtype='uint16')*255*16 - r = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) + r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) def test_population(self): a = np.zeros((5,5),dtype='uint8') elem = np.ones((3,3),dtype='uint8') - p = crank8.pop(image=a,selem = elem) + p = _crank8.pop(image=a,selem = elem) r = np.asarray([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -75,7 +75,7 @@ class TestSequenceFunctions(unittest.TestCase): a = np.zeros((6,6),dtype='uint8') a[2,2] = 255 elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') - f = crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) + f = _crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) r = np.asarray([[ 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0], [ 0, 0, 255, 0, 0, 0], @@ -90,7 +90,7 @@ class TestSequenceFunctions(unittest.TestCase): # should fail because data bitdepth is too high for the function a16 = np.ones((100,100),dtype='uint16')*255 elem = np.ones((3,3),dtype='uint8') - f = crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) if __name__ == '__main__': From b83ae7086272c98715c326e7e7fc129686332bc2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 17:48:15 +0200 Subject: [PATCH 223/625] add examples - to be cont. --- doc/examples/plot_lena_bilateral_denoise.py | 55 +++++++++++++ doc/examples/plot_local_equalize.py | 86 +++++++++++++++++++++ doc/examples/plot_local_threshold.py | 62 +++++++++++++++ 3 files changed, 203 insertions(+) create mode 100644 doc/examples/plot_lena_bilateral_denoise.py create mode 100644 doc/examples/plot_local_equalize.py create mode 100644 doc/examples/plot_local_threshold.py diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py new file mode 100644 index 00000000..fbee7d67 --- /dev/null +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -0,0 +1,55 @@ +""" +==================================================== +Denoising the picture of Lena using total variation +==================================================== + +In this example, we denoise a noisy version of the picture of Lena +using the total variation denoising filter. The result of this filter +is an image that has a minimal total variation norm, while being as +close to the initial image as possible. The total variation is the L1 +norm of the gradient of the image, and minimizing the total variation +typically produces "posterized" images with flat domains separated by +sharp edges. + +It is possible to change the degree of posterization by controlling +the tradeoff between denoising and faithfulness to the original image. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data, color, img_as_ubyte +from skimage.filter import tv_denoise +from skimage.rank import bilateral_mean +from skimage.morphology import disk + +l = img_as_ubyte(color.rgb2gray(data.lena())) +l = l[230:290, 220:320] + +noisy = l + 0.4 * l.std() * np.random.random(l.shape) + +selem = disk(30) +bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) + +plt.figure(figsize=(8, 2)) + +plt.subplot(131) +plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('noisy', fontsize=20) +plt.subplot(132) +plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('bilateral denoising', fontsize=20) + +selem = disk(30) +bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30) +plt.subplot(133) +plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('(more) bilateral denoising', fontsize=20) + +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, + right=1) +plt.show() diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py new file mode 100644 index 00000000..e79ddbed --- /dev/null +++ b/doc/examples/plot_local_equalize.py @@ -0,0 +1,86 @@ +""" +=============================== +Local Histogram Equalization +=============================== + +This examples enhances an image with low contrast, using a method called +*local histogram equalization*, which "spreads out the most frequent intensity +values" in an image . The equalized image has a roughly linear cumulative +distribution function for each pixel neigborhood. + +to be adjusted... + +.. [1] http://en.wikipedia.org/wiki/Histogram_equalization +.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm + +""" + +from skimage import data +from skimage.util.dtype import dtype_range +from skimage import exposure +from skimage.rank import egalise +from skimage.morphology import disk + + +import matplotlib.pyplot as plt + +import numpy as np + +def plot_img_and_hist(img, axes, bins=256): + """Plot an image along with its histogram and cumulative histogram. + + """ + ax_img, ax_hist = axes + ax_cdf = ax_hist.twinx() + + # Display image + ax_img.imshow(img, cmap=plt.cm.gray) + ax_img.set_axis_off() + + # Display histogram + ax_hist.hist(img.ravel(), bins=bins) + ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) + ax_hist.set_xlabel('Pixel intensity') + + xmin, xmax = dtype_range[img.dtype.type] + ax_hist.set_xlim(xmin, xmax) + + # Display cumulative distribution + img_cdf, bins = exposure.cumulative_distribution(img, bins) + ax_cdf.plot(bins, img_cdf, 'r') + + return ax_img, ax_hist, ax_cdf + + +# Load an example image +img = data.moon() + +# Contrast stretching +p2 = np.percentile(img, 2) +p98 = np.percentile(img, 98) +img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) + +# Equalization +selem = disk(30) +img_eq = egalise(img,selem=selem) + + +# Display results +f, axes = plt.subplots(2, 3, figsize=(8, 4)) + +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) +ax_img.set_title('Low contrast image') +ax_hist.set_ylabel('Number of pixels') + +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) +ax_img.set_title('Contrast stretching') + +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) +ax_img.set_title('Local Histogram equalization') +ax_cdf.set_ylabel('Fraction of total intensity') + + +# prevent overlap of y-axis labels +plt.subplots_adjust(wspace=0.4) +plt.show() + diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py new file mode 100644 index 00000000..3b9a42fb --- /dev/null +++ b/doc/examples/plot_local_threshold.py @@ -0,0 +1,62 @@ +""" +===================== +Local Thresholding +===================== + +Thresholding is the simplest way to segment objects from a background. If that +background is relatively uniform, then you can use a global threshold value to +binarize the image by pixel-intensity. If there's large variation in the +background intensity, however, adaptive thresholding (a.k.a. local or dynamic +thresholding) may produce better results. + +Here, we binarize an image using the `threshold_adaptive` function, which +calculates thresholds in regions of size `block_size` surrounding each pixel +(i.e. local neighborhoods). Each threshold value is the weighted mean of the +local neighborhood minus an offset value. + +Added local threshold using rank filter + +to be adjusted ... + +""" +import matplotlib.pyplot as plt + +from skimage import data +from skimage.filter import threshold_otsu, threshold_adaptive + +from skimage.rank import threshold +from skimage.morphology import disk + + +image = data.page() + +global_thresh = threshold_otsu(image) +binary_global = image > global_thresh + +block_size = 40 +binary_adaptive = threshold_adaptive(image, block_size, offset=10) + +selem = disk(10) +loc_thresh = threshold(image,selem=selem) + +fig, axes = plt.subplots(nrows=4, figsize=(7, 8)) +ax0, ax1, ax2, ax3 = axes +plt.gray() + +ax0.imshow(image) +ax0.set_title('Image') + +ax1.imshow(binary_global) +ax1.set_title('Global thresholding') + +ax2.imshow(binary_adaptive) +ax2.set_title('Adaptive thresholding') + +ax3.imshow(loc_thresh) +ax3.set_title('Local thresholding') + + +for ax in axes: + ax.axis('off') + +plt.show() From 60e5dc7fe39b89a27d094a9b0d7eada3a7da35d2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 5 Oct 2012 18:05:48 +0200 Subject: [PATCH 224/625] add readme --- skimage/rank/README.rst | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/skimage/rank/README.rst b/skimage/rank/README.rst index 68d2a1fe..aae62162 100644 --- a/skimage/rank/README.rst +++ b/skimage/rank/README.rst @@ -7,7 +7,29 @@ To use this to build your Cython file use the commandline options: **To do** -* add simple examples +* add simple examples, adapt documentation on existing examples * add/check existing doc +* adapting tests for each type of filter + +**General remarks** + +Basically these filters compute local histogram for each pixel. Histogram is build using a moving window in +order to limit redundant computation. The path followed by the moving window is given hereunder + + ...-----------------------\ +/--------------------------/ +\-------------------------- ... + +A comparison is proposed with cmorph.dilate algorithm to show how computation costs evolve with respect to image size or +structuring element size. This implementation gives better results for large structuring elements. + +A local histogram is update at each pixel by introducing pixel entering the structuring element border and +by removing those leaving it. The histogram size is 8bit (256 bins) for 8 bit images and 2 to 12 bit (up to 4096 bins) +for 16bit image depending on the image maximum value. Image with pixels higher than 4095 raise a ValueError. + +The filter is applied up to the image border, the neighboorhood used is adjusted accordingly. The user may provide +a mask image (same size as input image) where non zero value are the part of the image participating the the +histogram computation. By default all the image is filtered. + From a7ff15188fabda230abfb681cf8cd2f92123ac09 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:13:58 +0200 Subject: [PATCH 225/625] add ref --- skimage/rank/rank.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 51430236..946f5a49 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1,4 +1,10 @@ -""" +"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc + +The local histogram is computed using a sliding window similar to the method described in + +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", +IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + :author: Olivier Debeir, 2012 :license: modified BSD """ From d13517035fa1cdb4e29374b699b731d03ffbf031 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:29:28 +0200 Subject: [PATCH 226/625] add comment to bilateral denoising example --- doc/examples/plot_lena_bilateral_denoise.py | 23 ++++++++------------- doc/examples/plot_local_threshold.py | 10 ++++++--- skimage/rank/rank.py | 2 +- 3 files changed, 17 insertions(+), 18 deletions(-) diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index fbee7d67..9fd20285 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -1,26 +1,22 @@ """ ==================================================== -Denoising the picture of Lena using total variation +Denoising the picture of Lena using bilateral filter ==================================================== In this example, we denoise a noisy version of the picture of Lena -using the total variation denoising filter. The result of this filter -is an image that has a minimal total variation norm, while being as -close to the initial image as possible. The total variation is the L1 -norm of the gradient of the image, and minimizing the total variation -typically produces "posterized" images with flat domains separated by -sharp edges. - -It is possible to change the degree of posterization by controlling -the tradeoff between denoising and faithfulness to the original image. +using an approximation of a bilateral filter. +The pixels used to compute a local mean respect these conditions: +- be close to the central pixel, i.e. belong to the given structuring element. +- have a similar gray level, similarity is fixed by an interval [-s0,+s1] centered on the central pixel gray level. +The filter used is an approximation of a classical bilateral filter in the sens that kernel are usually gaussian +both in spatial and spectral dimensions. """ import numpy as np import matplotlib.pyplot as plt from skimage import data, color, img_as_ubyte -from skimage.filter import tv_denoise from skimage.rank import bilateral_mean from skimage.morphology import disk @@ -44,12 +40,11 @@ plt.axis('off') plt.title('bilateral denoising', fontsize=20) selem = disk(30) -bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30) +bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) plt.subplot(133) plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('(more) bilateral denoising', fontsize=20) -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, - right=1) +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,right=1) plt.show() diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py index 3b9a42fb..01077571 100644 --- a/doc/examples/plot_local_threshold.py +++ b/doc/examples/plot_local_threshold.py @@ -24,7 +24,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.filter import threshold_otsu, threshold_adaptive -from skimage.rank import threshold +from skimage.rank import threshold,morph_contr_enh from skimage.morphology import disk @@ -38,9 +38,10 @@ binary_adaptive = threshold_adaptive(image, block_size, offset=10) selem = disk(10) loc_thresh = threshold(image,selem=selem) +loc_morph_contr_enh = morph_contr_enh(image,selem=selem) -fig, axes = plt.subplots(nrows=4, figsize=(7, 8)) -ax0, ax1, ax2, ax3 = axes +fig, axes = plt.subplots(nrows=5, figsize=(7, 8)) +ax0, ax1, ax2, ax3, ax4 = axes plt.gray() ax0.imshow(image) @@ -55,6 +56,9 @@ ax2.set_title('Adaptive thresholding') ax3.imshow(loc_thresh) ax3.set_title('Local thresholding') +ax4.imshow(loc_morph_contr_enh) +ax4.set_title('Local morphological contrast enhancement') + for ax in axes: ax.axis('off') diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 946f5a49..42e5c23e 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1,6 +1,6 @@ """rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc -The local histogram is computed using a sliding window similar to the method described in +The local histogram is computed using a sliding window similar to the method described in Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. From dc838a690f4d572fff2179b34bb489e7da56e1e4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:37:22 +0200 Subject: [PATCH 227/625] compare local and global equalise in example --- doc/examples/plot_local_equalize.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index e79ddbed..840f2a1f 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -5,13 +5,14 @@ Local Histogram Equalization This examples enhances an image with low contrast, using a method called *local histogram equalization*, which "spreads out the most frequent intensity -values" in an image . The equalized image has a roughly linear cumulative -distribution function for each pixel neigborhood. +values" in an image . The equalized image [1]_ has a roughly linear cumulative +distribution function for each pixel neighborhood. The local version [2]_ of the histogram +equalization emphasized every local graylevel variations. to be adjusted... .. [1] http://en.wikipedia.org/wiki/Histogram_equalization -.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm +.. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization """ @@ -58,7 +59,7 @@ img = data.moon() # Contrast stretching p2 = np.percentile(img, 2) p98 = np.percentile(img, 98) -img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) +img_rescale = exposure.equalize(img) # Equalization selem = disk(30) @@ -73,10 +74,10 @@ ax_img.set_title('Low contrast image') ax_hist.set_ylabel('Number of pixels') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) -ax_img.set_title('Contrast stretching') +ax_img.set_title('Global equalise') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) -ax_img.set_title('Local Histogram equalization') +ax_img.set_title('Local equalize') ax_cdf.set_ylabel('Fraction of total intensity') From 9f33242679e449cc2b3c5fc74c1b095f0c5538a6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:41:18 +0200 Subject: [PATCH 228/625] rename egalise to equalize --- doc/examples/plot_local_equalize.py | 4 ++-- skimage/rank/_crank8.pyx | 6 +++--- skimage/rank/rank.py | 20 ++++++++++---------- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index 840f2a1f..ec28067d 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -19,7 +19,7 @@ to be adjusted... from skimage import data from skimage.util.dtype import dtype_range from skimage import exposure -from skimage.rank import egalise +from skimage import rank from skimage.morphology import disk @@ -63,7 +63,7 @@ img_rescale = exposure.equalize(img) # Equalization selem = disk(30) -img_eq = egalise(img,selem=selem) +img_eq = rank.equalize(img,selem=selem) # Display results diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index cb74021e..12e0577e 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -49,7 +49,7 @@ cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): return (g-i) -cdef inline np.uint8_t kernel_egalise(int* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_equalize(int* histo, float pop, np.uint8_t g): cdef int i cdef float sum = 0. @@ -210,14 +210,14 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, """ return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) -def egalise(np.ndarray[np.uint8_t, ndim=2] image, +def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_egalise,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y) def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 42e5c23e..2a7caab9 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -18,7 +18,7 @@ import numpy as np from generic import find_bitdepth import _crank16,_crank8 -__all__ = ['autolevel','bottomhat','egalise','gradient','maximum','mean' +__all__ = ['autolevel','bottomhat','equalize','gradient','maximum','mean' ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -162,10 +162,10 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): else: raise TypeError("only uint8 and uint16 image supported!") -def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local egalise of an image. +def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return greyscale local equalize of an image. - egalise is computed on the given structuring element. + equalize is computed on the given structuring element. Parameters ---------- @@ -187,8 +187,8 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local egalise : uint8 array or uint16 array depending on input image - The result of the local egalise. + local equalize : uint8 array or uint16 array depending on input image + The result of the local equalize. Examples -------- @@ -200,7 +200,7 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> egalise(ima8, square(3)) + >>> equalize(ima8, square(3)) array([[191, 170, 127, 170, 191], [170, 255, 255, 255, 170], [127, 255, 255, 255, 127], @@ -212,7 +212,7 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> egalise(ima16, square(3)) + >>> equalize(ima16, square(3)) array([[3072, 2730, 2048, 2730, 3072], [2730, 4096, 4096, 4096, 2730], [2048, 4096, 4096, 4096, 2048], @@ -223,12 +223,12 @@ def egalise(image, selem, out=None, mask=None, shift_x=False, shift_y=False): if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return _crank8.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + return _crank8.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) if bitdepth>11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.egalise(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + return _crank16.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) else: raise TypeError("only uint8 and uint16 image supported!") From ad0c59f2dbd922e65a8eb762602c8748f8f4e557 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 11:44:03 +0200 Subject: [PATCH 229/625] clean-up code --- skimage/rank/setup.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 4f57209a..efe23515 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -1,19 +1,3 @@ -#import numpy as np -# -#from distutils.core import setup -#from distutils.extension import Extension -#from Cython.Distutils import build_ext -# -#setup( -# cmdclass = {'build_ext': build_ext}, -# ext_modules = [Extension("_crank8", ["_crank8.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank8_percentiles", ["_crank8_percentiles.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank16", ["_crank16.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank16_bilateral", ["_crank16_bilateral.pyx"], include_dirs=[np.get_include()]), -# Extension("_crank16_percentiles", ["_crank16_percentiles.pyx"], include_dirs=[np.get_include()])] -#) - - #!/usr/bin/env python import os From 86449613e4c2a707864f311d4ae5b08051296ca4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 14:14:13 +0200 Subject: [PATCH 230/625] compare ctmf.median_filter with rank.median --- skimage/rank/tests/demo_benchmark.py | 63 +++++++++++++++++++++++++++- 1 file changed, 62 insertions(+), 1 deletion(-) diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index 42971133..74200b70 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -4,6 +4,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import dilation import skimage.rank as rank +from skimage.filter import median_filter from tools import log_timing @@ -11,15 +12,24 @@ from tools import log_timing def cr_max(image,selem): return rank.maximum(image=image,selem = selem) +@log_timing +def cr_med(image,selem): + return rank.median(image=image,selem = selem) + @log_timing def cm_dil(image,selem): return dilation(image=image,selem = selem) +@log_timing +def ctmf_med(image,radius): + return median_filter(image=image,radius=radius) + def compare(): """comparison between - crank.maximum rankfilter implementation - cmorph.dilate cython implementation + on increasing structuring element size and increasing image size """ # a = (np.random.random((500,500))*256).astype('uint8') @@ -68,5 +78,56 @@ def compare(): plt.show() +def compare_median(): + """comparison between + - crank.median rankfilter implementation + - ctmf.median_filter filter + + on increasing structuring element size and increasing image size + """ + a = data.camera() + + rec = [] + e_range = range(2,40,2) + for r in e_range: + elem = np.ones((2*r,2*r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc,ms_rc = cr_med(a,elem) + rctmf,ms_rctmf = ctmf_med(a,r) + rec.append((ms_rc,ms_rctmf)) + # check if results are identical +# assert (rc==rctmf).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing element size') + plt.plot(e_range,rec) + plt.legend(['rank.median','ctmf.median_filter']) + plt.figure() + plt.imshow(np.hstack((rc,rctmf))) + plt.show() + r = 9 + elem = np.ones((r,r),dtype='uint8') + + rec = [] + s_range = range(100,1000,100) + for s in s_range: + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = cr_max(a,elem) + rctmf,ms_rctmf = ctmf_med(a,r) + rec.append((ms_rc,ms_rctmf)) +# assert (rc==rcm).all() + + rec = np.asarray(rec) + + plt.figure() + plt.title('increasing image size') + plt.plot(s_range,rec) + plt.legend(['rank.median','ctmf.median_filter']) + plt.figure() + plt.imshow(np.hstack((rc,rctmf))) + + plt.show() if __name__ == '__main__': - compare() \ No newline at end of file + compare_median() \ No newline at end of file From a07d0f64bbbef874c1a361341acf29bc587e10a4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 14:30:29 +0200 Subject: [PATCH 231/625] add comment --- skimage/rank/percentile_rank.py | 15 ++++++++++++++- skimage/rank/rank.py | 7 ++++++- skimage/rank/tests/demo_benchmark.py | 15 ++++++++++----- 3 files changed, 30 insertions(+), 7 deletions(-) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index b6af753c..6cc273e3 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -1,4 +1,17 @@ -""" +"""percentile_rank.py - inferior and superior ranks, provided by the user, are passed to the kernel function +to provide a softer version of the rank filters. E.g. percentile_autolevel will stretch image levels between +percentile [p0,p1] instead of using [min,max]. It means that isolate bright or dark pixels will not produce halos. + +The local histogram is computed using a sliding window similar to the method described in + +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", +IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + +input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), +for 16 bit input images, the number of histogram bins is determined from the maximum value present in the image + +result image is 8 or 16 bit with respect to the input image + :author: Olivier Debeir, 2012 :license: modified BSD """ diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 2a7caab9..5dfa85bc 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1,10 +1,15 @@ -"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc +"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, equalization, etc The local histogram is computed using a sliding window similar to the method described in Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), +for 16 bit input images, the number of histogram bins is determined from the maximum value present in the image + +result image is 8 or 16 bit with respect to the input image + :author: Olivier Debeir, 2012 :license: modified BSD """ diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/tests/demo_benchmark.py index 74200b70..c3046083 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/tests/demo_benchmark.py @@ -25,7 +25,7 @@ def ctmf_med(image,radius): return median_filter(image=image,radius=radius) -def compare(): +def compare_dilate(): """comparison between - crank.maximum rankfilter implementation - cmorph.dilate cython implementation @@ -88,9 +88,9 @@ def compare_median(): a = data.camera() rec = [] - e_range = range(2,40,2) + e_range = range(2,40,4) for r in e_range: - elem = np.ones((2*r,2*r),dtype='uint8') + elem = np.ones((2*r+1,2*r+1),dtype='uint8') # elem = (np.random.random((r,r))>.5).astype('uint8') rc,ms_rc = cr_med(a,elem) rctmf,ms_rctmf = ctmf_med(a,r) @@ -106,9 +106,11 @@ def compare_median(): plt.legend(['rank.median','ctmf.median_filter']) plt.figure() plt.imshow(np.hstack((rc,rctmf))) - plt.show() + plt.ylabel('time (ms)') + plt.xlabel('element radius') + r = 9 - elem = np.ones((r,r),dtype='uint8') + elem = np.ones((r*2+1,r*2+1),dtype='uint8') rec = [] s_range = range(100,1000,100) @@ -127,7 +129,10 @@ def compare_median(): plt.legend(['rank.median','ctmf.median_filter']) plt.figure() plt.imshow(np.hstack((rc,rctmf))) + plt.ylabel('time (ms)') + plt.xlabel('image size') plt.show() if __name__ == '__main__': +# compare_dilate() compare_median() \ No newline at end of file From 11566ced34b36355921a63b2f0b1c2bd21be6b13 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:34:54 +0200 Subject: [PATCH 232/625] fix percentile autolevel --- doc/examples/plot_local_autolevels.py | 45 +++++++++++++++++++++++++++ skimage/rank/_core16.pxd | 4 --- skimage/rank/_core16b.pxd | 4 --- skimage/rank/_core8.pxd | 4 --- skimage/rank/_core8p.pxd | 4 +-- skimage/rank/_crank8.pyx | 10 +++--- skimage/rank/_crank8_percentiles.pyx | 18 ++++++----- skimage/rank/tests/test_suite.py | 14 ++++++++- 8 files changed, 77 insertions(+), 26 deletions(-) create mode 100644 doc/examples/plot_local_autolevels.py diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py new file mode 100644 index 00000000..5b3ba758 --- /dev/null +++ b/doc/examples/plot_local_autolevels.py @@ -0,0 +1,45 @@ +""" +===================== +Local Autolevel +===================== + +Local autolevel stretch local histogram between 0 and max_graylevel (e.g. 255 for 8 bit image). +The following code shows the difference between autolevel and percentile auto_level where [min,max] interval +is replaced by [p0,p1] percentiles interval + +""" +import matplotlib.pyplot as plt + +from skimage import data + +from skimage.rank import percentile_autolevel,autolevel +from skimage.morphology import disk + + +image = data.camera() + +selem = disk(20) +loc_autolevel = autolevel(image,selem=selem) +loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.0,p1=1.0) + +assert (loc_autolevel==loc_perc_autolevel).all() + +loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) + +fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) +ax0, ax1, ax2 = axes +plt.gray() + +ax0.imshow(image) +ax0.set_title('Image') + +ax1.imshow(loc_autolevel) +ax1.set_title('Autolevel') + +ax2.imshow(loc_perc_autolevel,vmin=0,vmax=255) +ax2.set_title('percentile autolevel') + +for ax in axes: + ax.axis('off') + +plt.show() diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index ddd8c637..26a8e948 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -14,10 +14,6 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index fefac53e..cf3cb4c4 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -14,10 +14,6 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth and bilateral interval #--------------------------------------------------------------------------- diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 3d5ddac3..4ea92121 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -14,10 +14,6 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index dfab17be..b878143e 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -15,8 +15,8 @@ cimport numpy as np from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b #--------------------------------------------------------------------------- # 8 bit core kernel receives extra information about data inferior and superior percentiles diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 12e0577e..96624eb5 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -33,11 +33,13 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): if histo[i]: imin = i break - delta = imax-imin - if delta>0: - return (255.*(g-imin)/delta) + delta = imax-imin + if delta>0: + return (255.*(g-imin)/delta) + else: + return (imax-imin) else: - return (imax-imin) + return (0) cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): cdef int i diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 23fe079f..3082e23a 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core8p cimport _core8p +from _core8p cimport _core8p,uint8_max,uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) @@ -27,25 +27,29 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, flo if pop: sum = 0 p1 = 1.0-p1 + imin = 0 + imax = 255 + for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum>(p0*pop): imin = i break sum = 0 for i in range(255,-1,-1): sum += histo[i] - if sum>=p1*pop: + if sum>(p1*pop): imax = i break - delta = imax-imin if delta>0: - return (255.*(g-imin)/delta) +# return (255.) +# return (delta) + return (255*(uint8_min(uint8_max(imin,g),imax)-imin)/delta) else: - return (0) + return (imax-imin) else: - return (0) + return (128) cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, float p0, float p1): diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 51c7f60b..5d47138b 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -4,7 +4,10 @@ import numpy as np from skimage.rank import _crank8,_crank8_percentiles from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles -from skimage.morphology import cmorph +from skimage.morphology import cmorph,disk +from skimage import data +from skimage import rank + class TestSequenceFunctions(unittest.TestCase): @@ -92,6 +95,15 @@ class TestSequenceFunctions(unittest.TestCase): elem = np.ones((3,3),dtype='uint8') f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + def test_compare_autolevels(self): + image = data.camera() + + selem = disk(20) + loc_autolevel = rank.autolevel(image,selem=selem) + loc_perc_autolevel = rank.percentile_autolevel(image,selem=selem,p0=.0,p1=1.) + + assert (loc_autolevel==loc_perc_autolevel).all() + if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) From 936b7d5d21bac67ba59aa032a7cf35d3b8979480 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:40:05 +0200 Subject: [PATCH 233/625] fix percentile autolevel --- skimage/rank/_crank16_percentiles.pyx | 16 ++++++---------- skimage/rank/_crank8_percentiles.pyx | 2 -- 2 files changed, 6 insertions(+), 12 deletions(-) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 54c25d40..8d0446ff 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -15,10 +15,10 @@ import numpy as np cimport numpy as np # import main loop -from _core16p cimport _core16p +from _core16p cimport _core16p,int_min,int_max # ----------------------------------------------------------------- -# kernels uint8 (SOFT version using percentiles) +# kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): @@ -29,25 +29,21 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in p1 = 1.0-p1 for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum>p0*pop: imin = i break sum = 0 for i in range(maxbin-1,-1,-1): sum += histo[i] - if sum>=p1*pop: + if sum>p1*pop: imax = i break delta = imax-imin - if g>imax: - return (maxbin-1) - if g(0) if delta>0: - return ((maxbin-1)*1.*(g-imin)/delta) + return (255*(int_min(int_max(imin,g),imax)-imin)/delta) else: - return (0) + return (imax-imin) else: return (0) diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 3082e23a..6b81c8bd 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -43,8 +43,6 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, flo break delta = imax-imin if delta>0: -# return (255.) -# return (delta) return (255*(uint8_min(uint8_max(imin,g),imax)-imin)/delta) else: return (imax-imin) From 633f30e23fbb949a429868f68cb711fda3085b7e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:45:43 +0200 Subject: [PATCH 234/625] adapt autolevel example --- doc/examples/plot_local_autolevels.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py index 5b3ba758..842215d5 100644 --- a/doc/examples/plot_local_autolevels.py +++ b/doc/examples/plot_local_autolevels.py @@ -9,6 +9,7 @@ is replaced by [p0,p1] percentiles interval """ import matplotlib.pyplot as plt +import numpy as np from skimage import data @@ -20,11 +21,12 @@ image = data.camera() selem = disk(20) loc_autolevel = autolevel(image,selem=selem) -loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.0,p1=1.0) +loc_perc_autolevel0 = percentile_autolevel(image,selem=selem,p0=.00,p1=1.0) +loc_perc_autolevel1 = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) +loc_perc_autolevel2 = percentile_autolevel(image,selem=selem,p0=.05,p1=.95) +loc_perc_autolevel3 = percentile_autolevel(image,selem=selem,p0=.1,p1=.9) -assert (loc_autolevel==loc_perc_autolevel).all() - -loc_perc_autolevel = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) +loc_perc_autolevel = np.hstack((loc_perc_autolevel0,loc_perc_autolevel1,loc_perc_autolevel2,loc_perc_autolevel3)) fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax0, ax1, ax2 = axes @@ -37,7 +39,7 @@ ax1.imshow(loc_autolevel) ax1.set_title('Autolevel') ax2.imshow(loc_perc_autolevel,vmin=0,vmax=255) -ax2.set_title('percentile autolevel') +ax2.set_title('percentile autolevel 0%,1%,5% and 10%') for ax in axes: ax.axis('off') From 175658e677b127ea583fb7385d47699a402878fa Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:48:36 +0200 Subject: [PATCH 235/625] fix 16bit percentile --- skimage/rank/_crank16_percentiles.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 8d0446ff..6c67a54e 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -118,13 +118,13 @@ cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_ p1 = 1.0-p1 for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum>p0*pop: imin = i break sum = 0 for i in range((maxbin-1),-1,-1): sum += histo[i] - if sum>=p1*pop: + if sum>p1*pop: imax = i break if g>imax: From 7ef083e5a67560d37730f12c4b44b9f79c598ea2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:50:50 +0200 Subject: [PATCH 236/625] clean-up code --- skimage/rank/_core.pxd | 7 ------- skimage/rank/_core16p.pxd | 7 ------- skimage/rank/_core8p.pxd | 7 ------- skimage/rank/_crank16_percentiles.pyx | 8 -------- skimage/rank/_crank8_percentiles.pyx | 8 -------- 5 files changed, 37 deletions(-) diff --git a/skimage/rank/_core.pxd b/skimage/rank/_core.pxd index bb57aec4..fbae2b24 100644 --- a/skimage/rank/_core.pxd +++ b/skimage/rank/_core.pxd @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 0d698cee..1ce9b4cd 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16p.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index b878143e..9b4fbd29 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core8p.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 6c67a54e..0f07196e 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank_percentiles.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 6b81c8bd..2299f04a 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank_percentiles.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False From c4dc33a27d3ba268daea32416cdd3a1b38a5ef4f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:53:00 +0200 Subject: [PATCH 237/625] fix equalize --- skimage/rank/_crank16.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index e1e64bed..bd60c3e8 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -209,7 +209,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) -def egalise(np.ndarray[np.uint16_t, ndim=2] image, +def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, From 7aae3ac6b68592ce99b97cf0ba8e84ea4f6e48c5 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 15:54:55 +0200 Subject: [PATCH 238/625] fix other equalize --- skimage/rank/_crank16.pyx | 4 ++-- skimage/rank/tests/demo_16bitbilateral.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index bd60c3e8..573db50f 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -49,7 +49,7 @@ cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,in return (g-i) -cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): cdef int i cdef float sum = 0. @@ -216,7 +216,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8): """local egalisation of the gray level """ - return _core16(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/rank/tests/demo_16bitbilateral.py b/skimage/rank/tests/demo_16bitbilateral.py index 2a302e6a..85fd510c 100644 --- a/skimage/rank/tests/demo_16bitbilateral.py +++ b/skimage/rank/tests/demo_16bitbilateral.py @@ -14,7 +14,7 @@ if __name__ == '__main__': f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) selem = disk(50) - f3 = rank.egalise(a16,selem = selem) + f3 = rank.equalize(a16,selem = selem) plt.figure() plt.imshow(np.hstack((a8,f1))) From c26d57dcfa246f907244a7f4c1db6f63073bbed7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 16:46:37 +0200 Subject: [PATCH 239/625] add marked watershed example --- doc/examples/plot_lena_bilateral_denoise.py | 1 + doc/examples/plot_marked_watershed.py | 53 +++++++++++++++++++++ 2 files changed, 54 insertions(+) create mode 100644 doc/examples/plot_marked_watershed.py diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index 9fd20285..57593867 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -1,3 +1,4 @@ + """ ==================================================== Denoising the picture of Lena using bilateral filter diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py new file mode 100644 index 00000000..8f65e344 --- /dev/null +++ b/doc/examples/plot_marked_watershed.py @@ -0,0 +1,53 @@ +""" +================================ +Markers for watershed transform +================================ + +The watershed is a classical algorithm used for **segmentation**, that +is, for separating different objects in an image. + +See Wikipedia_ for more details on the algorithm. + +.. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing) + +""" + +import numpy as np +from scipy import ndimage +import matplotlib.pyplot as plt +from skimage.morphology import watershed,disk +from skimage import rank +from skimage import data +from scipy import ndimage + +# Generate an initial image with two overlapping circles +image = data.camera() + +# denoise image +denoised = rank.median(image,disk(2)) + +# find continuous region (low gradient) --> markers +markers = rank.gradient(denoised,disk(5))<10 +markers = ndimage.label(markers)[0] + +#local gradient +gradient = rank.gradient(denoised,disk(2)) + +# process the watershed +labels = watershed(gradient, markers) + +# display results +fig, axes = plt.subplots(ncols=4, figsize=(8, 2.7)) +ax0, ax1, ax2, ax3 = axes + +ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest') +ax1.imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest') +ax2.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') +ax3.imshow(image, cmap=plt.cm.gray, interpolation='nearest') +ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest',alpha=.7) + +for ax in axes: + ax.axis('off') + +plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) +plt.show() From 0a73260d5de6a87eeb88bab597a22374f20a9fa3 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 16:47:34 +0200 Subject: [PATCH 240/625] add marked watershed example (cont.) --- doc/examples/plot_marked_watershed.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 8f65e344..1db4f507 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -6,6 +6,8 @@ Markers for watershed transform The watershed is a classical algorithm used for **segmentation**, that is, for separating different objects in an image. +Here a marker image is build from the region of low gradient inside the image. + See Wikipedia_ for more details on the algorithm. .. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing) From ebfe4b8b95ca14344d461a9a4df4c0f24d9cf135 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 16:47:57 +0200 Subject: [PATCH 241/625] add marked watershed example (cont.) --- doc/examples/plot_marked_watershed.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 1db4f507..0be25007 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -22,7 +22,7 @@ from skimage import rank from skimage import data from scipy import ndimage -# Generate an initial image with two overlapping circles +# original data image = data.camera() # denoise image From cc5e23ef8a0fe41ebc13c02d4d908bd3c5223458 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 10 Oct 2012 17:32:35 +0200 Subject: [PATCH 242/625] add local test --- doc/examples/plot_watershed.py | 2 +- skimage/rank/tests/test_morph_contr_enh.py | 28 ++++++++++++++++++++++ 2 files changed, 29 insertions(+), 1 deletion(-) create mode 100644 skimage/rank/tests/test_morph_contr_enh.py diff --git a/doc/examples/plot_watershed.py b/doc/examples/plot_watershed.py index a1cd18cf..9fce196f 100644 --- a/doc/examples/plot_watershed.py +++ b/doc/examples/plot_watershed.py @@ -26,7 +26,7 @@ See Wikipedia_ for more details on the algorithm. """ import numpy as np -from scipy import ndimage +e import matplotlib.pyplot as plt from skimage.morphology import watershed, is_local_maximum diff --git a/skimage/rank/tests/test_morph_contr_enh.py b/skimage/rank/tests/test_morph_contr_enh.py new file mode 100644 index 00000000..812e81c5 --- /dev/null +++ b/skimage/rank/tests/test_morph_contr_enh.py @@ -0,0 +1,28 @@ +import numpy as np +import matplotlib.pyplot as plt +import gdal + +from skimage.morphology import disk +import skimage.rank as rank + +filename = 'iko_pan_Ja1.tif' +im16 = gdal.Open(filename).ReadAsArray().astype(np.uint16) + +plt.figure() +plt.imshow(im16,cmap=plt.cm.gray) +plt.colorbar() + +f0 = rank.median(im16,disk(1)) +f1 = rank.bilateral_mean(im16,disk(20),s0=200,s1=200) +f2 = rank.equalize(f1,disk(10)) +f3 = rank.bottomhat(f1,disk(1)) + +plt.figure() +plt.imshow(f2,cmap=plt.cm.gray,interpolation='nearest') +plt.colorbar() + +plt.show() + + + + From 90351170d707298fe8c879a9eac52d66ef1ac6c7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 11:41:37 +0200 Subject: [PATCH 243/625] remove unused _core --- skimage/rank/_core.pxd | 1046 ---------------------------------------- 1 file changed, 1046 deletions(-) delete mode 100644 skimage/rank/_core.pxd diff --git a/skimage/rank/_core.pxd b/skimage/rank/_core.pxd deleted file mode 100644 index fbae2b24..00000000 --- a/skimage/rank/_core.pxd +++ /dev/null @@ -1,1046 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - -#--------------------------------------------------------------------------- -# 8 bit core kernel -#--------------------------------------------------------------------------- - -cdef inline rank8(np.uint8_t kernel(int*, float, np.uint8_t), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - eimage = np.ascontiguousarray(eimage) - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(256 * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - -#--------------------------------------------------------------------------- -# 16 bit core, kernel receive extra information about data bitdepth -#--------------------------------------------------------------------------- - -cdef inline rank16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(maxbin * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - -#--------------------------------------------------------------------------- -# 8 bit core kernel receive extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline rank8_percentile(np.uint8_t kernel(int*, float, np.uint8_t, float, float), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - eimage = np.ascontiguousarray(eimage) - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(256 * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - -#--------------------------------------------------------------------------- -# 16 bit core kernel receive extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline rank16_percentile(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint16 - - result is uint16 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - cdef int n_se_n, n_se_s, n_se_e, n_se_w - - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) - cdef int* histo = malloc(maxbin * sizeof(int)) - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - n_se_n = n_se_s = n_se_e = n_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 - if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 - if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 - if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(n_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(n_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(n_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - free(sr) - free(sc) - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out \ No newline at end of file From 42feb70a31cf0ef6f9cf23b82f6b44bf424a67e6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 12:03:34 +0200 Subject: [PATCH 244/625] add exhaustive comparison between 8bit and 16bit filters --- skimage/rank/rank.py | 6 +++++- skimage/rank/tests/test_suite.py | 19 +++++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 5dfa85bc..bc7dadff 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1019,4 +1019,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): raise ValueError("only uint16 <4096 image (12bit) supported!") return _crank16.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) else: - raise TypeError("only uint8 and uint16 image supported!") \ No newline at end of file + raise TypeError("only uint8 and uint16 image supported!") + +if __name__ == "__main__": + import doctest + doctest.testmod(verbose=True) \ No newline at end of file diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 5d47138b..97345f4f 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -104,6 +104,25 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() + def test_compare_8bit_vs_16bit(self): + # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical + i8 = data.camera() + i16 = i8.astype(np.uint16) + methods = ['autolevel','bottomhat','equalize','gradient','maximum','mean' + ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] + for method in methods: + func = eval('rank.%s'%method) + print func + f8 = func(i8,disk(3)) + f16 = func(i16,disk(3)) +# if (f8==f16).all() is False: + if not (f8==f16).all(): + + print f8 + print f16 + + + if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) From b208060cdf96948894f6b2993b88e772ea019bf2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 12:13:01 +0200 Subject: [PATCH 245/625] fix 8bit-16bit discepencies --- skimage/rank/_crank16.pyx | 6 +++--- skimage/rank/tests/test_suite.py | 11 ++++------- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index 573db50f..90a7e8bb 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -35,7 +35,7 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in break delta = imax-imin if delta>0: - return (maxbin*1.*(g-imin)/delta) + return (1.*(maxbin-1)*(g-imin)/delta) else: return (imax-imin) @@ -59,7 +59,7 @@ cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int if i>=g: break - return ((maxbin*1.*sum)/pop) + return (((maxbin-1)*sum)/pop) else: return (0) @@ -107,7 +107,7 @@ cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16 if pop: for i in range(maxbin): mean += histo[i]*i - return ((g-mean/pop)/2.+midbin) + return ((g-mean/pop)/2.+(midbin-1)) else: return (0) diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 97345f4f..0887fa8f 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -108,19 +108,16 @@ class TestSequenceFunctions(unittest.TestCase): # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical i8 = data.camera() i16 = i8.astype(np.uint16) + assert (i8==i16).all() + methods = ['autolevel','bottomhat','equalize','gradient','maximum','mean' ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] + for method in methods: func = eval('rank.%s'%method) - print func f8 = func(i8,disk(3)) f16 = func(i16,disk(3)) -# if (f8==f16).all() is False: - if not (f8==f16).all(): - - print f8 - print f16 - + assert (f8==f16).all() if __name__ == '__main__': From 0992ff6db72f688d64cfdec1f79a77d2877cb96f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 11 Oct 2012 12:19:45 +0200 Subject: [PATCH 246/625] fix doctest in rank --- skimage/rank/rank.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index bc7dadff..cefb06ac 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -78,9 +78,9 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> autolevel(ima16, square(3)) array([[ 0, 0, 0, 0, 0], - [ 0, 4096, 4096, 4096, 0], - [ 0, 4096, 0, 4096, 0], - [ 0, 4096, 4096, 4096, 0], + [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], + [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -218,11 +218,11 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> equalize(ima16, square(3)) - array([[3072, 2730, 2048, 2730, 3072], - [2730, 4096, 4096, 4096, 2730], - [2048, 4096, 4096, 4096, 2048], - [2730, 4096, 4096, 4096, 2730], - [3072, 2730, 2048, 2730, 3072]], dtype=uint16) + array([[3071, 2730, 2047, 2730, 3071], + [2730, 4095, 4095, 4095, 2730], + [2047, 4095, 4095, 4095, 2047], + [2730, 4095, 4095, 4095, 2730], + [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ selem = img_as_ubyte(selem) if mask is not None: @@ -502,11 +502,11 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> meansubstraction(ima16, square(3)) - array([[1536, 1365, 1024, 1365, 1536], - [1365, 3185, 2730, 3185, 1365], - [1024, 2730, 2048, 2730, 1024], - [1365, 3185, 2730, 3185, 1365], - [1536, 1365, 1024, 1365, 1536]], dtype=uint16) + array([[1535, 1364, 1023, 1364, 1535], + [1364, 3184, 2729, 3184, 1364], + [1023, 2729, 2047, 2729, 1023], + [1364, 3184, 2729, 3184, 1364], + [1535, 1364, 1023, 1364, 1535]], dtype=uint16) """ selem = img_as_ubyte(selem) From 9d9a7492bf76ef6fd6b67136fd7d3169587d36f9 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:03:03 +0200 Subject: [PATCH 247/625] remove ascontiguousarray(eimage) --- skimage/rank/_core16.pxd | 1 - skimage/rank/_core16b.pxd | 1 - skimage/rank/_core16p.pxd | 1 - skimage/rank/_core8.pxd | 1 - skimage/rank/_core8p.pxd | 1 - 5 files changed, 5 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 26a8e948..9ead95e0 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -75,7 +75,6 @@ char shift_x, char shift_y,int bitdepth): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index cf3cb4c4..d75611a7 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -76,7 +76,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 1ce9b4cd..9f3a99af 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -72,7 +72,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 4ea92121..d9fbee0f 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -65,7 +65,6 @@ char shift_x, char shift_y): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 9b4fbd29..75ceaf89 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -62,7 +62,6 @@ char shift_x, char shift_y, float p0, float p1): eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - eimage = np.ascontiguousarray(eimage) mask = np.ascontiguousarray(mask) # define pointers to the data From 3ccec228254cba8053062066d1fd209e1fc59a5d Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:13:35 +0200 Subject: [PATCH 248/625] remoce sr,sc from cores --- skimage/rank/_core16.pxd | 4 ---- skimage/rank/_core16b.pxd | 4 ---- skimage/rank/_core16p.pxd | 4 ---- skimage/rank/_core8.pxd | 4 ---- skimage/rank/_core8p.pxd | 4 ---- 5 files changed, 20 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 9ead95e0..eb9f4886 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -94,8 +94,6 @@ char shift_x, char shift_y,int bitdepth): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -263,8 +261,6 @@ char shift_x, char shift_y,int bitdepth): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index d75611a7..b18b1fd8 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -95,8 +95,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -264,8 +262,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 9f3a99af..19a53b67 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -91,8 +91,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -260,8 +258,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index d9fbee0f..fefa98e4 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -84,8 +84,6 @@ char shift_x, char shift_y): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -248,8 +246,6 @@ char shift_x, char shift_y): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 75ceaf89..347c7ef5 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -81,8 +81,6 @@ char shift_x, char shift_y, float p0, float p1): cdef int n_se_n, n_se_s, n_se_e, n_se_w cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) @@ -245,8 +243,6 @@ char shift_x, char shift_y, float p0, float p1): # kernel ------------------------------------------- # release memory allocated by malloc - free(sr) - free(sc) free(se_e_r) free(se_e_c) From 23c8768cfad8f2bcafa32a1dc5c759a228c428b9 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:19:38 +0200 Subject: [PATCH 249/625] remoce selem_num --- skimage/rank/_core16.pxd | 1 - skimage/rank/_core16b.pxd | 1 - skimage/rank/_core16p.pxd | 1 - skimage/rank/_core8.pxd | 1 - skimage/rank/_core8p.pxd | 1 - 5 files changed, 5 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index eb9f4886..3ecb6314 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -93,7 +93,6 @@ char shift_x, char shift_y,int bitdepth): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index b18b1fd8..5f5b3e81 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -94,7 +94,6 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 19a53b67..bf50ca35 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -90,7 +90,6 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(maxbin * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index fefa98e4..ece629c5 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -83,7 +83,6 @@ char shift_x, char shift_y): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 347c7ef5..675c4a5d 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -80,7 +80,6 @@ char shift_x, char shift_y, float p0, float p1): cdef int max_se = srows*scols cdef int n_se_n, n_se_s, n_se_e, n_se_w - cdef int selem_num = np.sum(selem != 0) cdef int* histo = malloc(256 * sizeof(int)) cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) From 1c4e17a4351e97bb8089bdd95be9dbe3ab26f6eb Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:34:04 +0200 Subject: [PATCH 250/625] add comment --- skimage/rank/_core16.pxd | 8 ++++++++ skimage/rank/_core16b.pxd | 8 ++++++++ skimage/rank/_core16p.pxd | 8 ++++++++ skimage/rank/_core8.pxd | 8 ++++++++ skimage/rank/_core8p.pxd | 8 ++++++++ 5 files changed, 40 insertions(+) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 3ecb6314..791f5fb9 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -91,9 +91,17 @@ char shift_x, char shift_y,int bitdepth): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 5f5b3e81..3007a2fb 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -92,9 +92,17 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index bf50ca35..484a6a6b 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -88,9 +88,17 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index ece629c5..325f779d 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -81,9 +81,17 @@ char shift_x, char shift_y): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 675c4a5d..5fa278d5 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -78,9 +78,17 @@ char shift_x, char shift_y, float p0, float p1): # allocate memory with malloc cdef int max_se = srows*scols + + # number of element in each attack border cdef int n_se_n, n_se_s, n_se_e, n_se_w + # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + cdef int* se_e_r = malloc(max_se * sizeof(int)) cdef int* se_e_c = malloc(max_se * sizeof(int)) cdef int* se_w_r = malloc(max_se * sizeof(int)) From 3d973a192e95691d3a8b70c734d6c36f851295c8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:47:51 +0200 Subject: [PATCH 251/625] rename n_se_n to num_se_n etc, removed commented code --- skimage/rank/_core16.pxd | 44 ++--- skimage/rank/_core16b.pxd | 44 ++--- skimage/rank/_core16p.pxd | 44 ++--- skimage/rank/_core8.pxd | 44 ++--- skimage/rank/_core8p.pxd | 44 ++--- skimage/rank/_crank16_bilateral.pyx | 261 +--------------------------- 6 files changed, 112 insertions(+), 369 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 791f5fb9..1a3194de 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -93,7 +93,7 @@ char shift_x, char shift_y,int bitdepth): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -126,26 +126,26 @@ char shift_x, char shift_y,int bitdepth): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -175,14 +175,14 @@ char shift_x, char shift_y,int bitdepth): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -200,14 +200,14 @@ char shift_x, char shift_y,int bitdepth): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -222,14 +222,14 @@ char shift_x, char shift_y,int bitdepth): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -247,14 +247,14 @@ char shift_x, char shift_y,int bitdepth): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 3007a2fb..163662b5 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -94,7 +94,7 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -127,26 +127,26 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -176,14 +176,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -201,14 +201,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -223,14 +223,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -248,14 +248,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 484a6a6b..9e992b6b 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -90,7 +90,7 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -123,26 +123,26 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -172,14 +172,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -197,14 +197,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -219,14 +219,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -244,14 +244,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 325f779d..d24297cb 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -83,7 +83,7 @@ char shift_x, char shift_y): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -116,26 +116,26 @@ char shift_x, char shift_y): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(256): @@ -164,14 +164,14 @@ char shift_x, char shift_y): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -188,14 +188,14 @@ char shift_x, char shift_y): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -209,14 +209,14 @@ char shift_x, char shift_y): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -233,14 +233,14 @@ char shift_x, char shift_y): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 5fa278d5..b1adac70 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -80,7 +80,7 @@ char shift_x, char shift_y, float p0, float p1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -113,26 +113,26 @@ char shift_x, char shift_y, float p0, float p1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(256): @@ -161,14 +161,14 @@ char shift_x, char shift_y, float p0, float p1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -185,14 +185,14 @@ char shift_x, char shift_y, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -206,14 +206,14 @@ char shift_x, char shift_y, float p0, float p1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -230,14 +230,14 @@ char shift_x, char shift_y, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index 440d28e3..e783d7ea 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -21,73 +21,6 @@ from _core16b cimport _core16b # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -#cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax,delta -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# delta = imax-imin -# if delta>0: -# return (maxbin*1.*(g-imin)/delta) -# else: -# return (imax-imin) -# -#cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# for i in range(maxbin): -# if histo[i]: -# break -# -# return (g-i) -# -# -#cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float sum = 0. -# -# if pop: -# for i in range(maxbin): -# sum += histo[i] -# if i>=g: -# break -# -# return ((maxbin*1.*sum)/pop) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# return (imax-imin) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# return (i) -# -# return (0) cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): cdef int i,bilat_pop=0 @@ -105,71 +38,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -#cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float mean = 0. -# -# if pop: -# for i in range(maxbin): -# mean += histo[i]*i -# return ((g-mean/pop)/2.+midbin) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float sum = pop/2.0 -# -# if pop: -# for i in range(maxbin): -# if histo[i]: -# sum -= histo[i] -# if sum<0: -# return (i) -# -# return (0) -# -#cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# if pop: -# for i in range(maxbin): -# if histo[i]: -# return (i) -# -# return (0) -# -#cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int hmax=0,imax=0 -# -# if pop: -# for i in range(maxbin): -# if histo[i]>hmax: -# hmax = histo[i] -# imax = i -# return (imax) -# -# return (0) -# -#cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# if imax-g < g-imin: -# return (imax) -# else: -# return (imin) -# else: -# return (0) -# + cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): cdef int i,bilat_pop=0 @@ -181,75 +50,10 @@ cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitd else: return (0) -# -#cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float mean = 0. -# -# if pop: -# for i in range(maxbin): -# mean += histo[i]*i -# return (g>(mean/pop)) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# break -# -# return (i-g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- -#def autolevel(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """bottom hat -# """ -# return rank16b(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """bottom hat -# """ -# return rank16b(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def egalise(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local egalisation of the gray level -# """ -# return rank16b(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def gradient(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local maximum - local minimum gray level -# """ -# return rank16b(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def maximum(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local maximum gray level -# """ -# return rank16b(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) - def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -259,51 +63,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -#def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """(g - average gray level)/2+midbin (clipped on uint8) -# """ -# return rank16b(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def median(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local median -# """ -# return rank16b(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def minimum(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local minimum gray level -# """ -# return rank16b(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """morphological contrast enhancement -# """ -# return rank16b(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def modal(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local mode -# """ -# return rank16b(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# + def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -313,20 +73,3 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -#def threshold(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """returns maxbin-1 if gray level higher than local mean, 0 else -# """ -# return rank16b(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def tophat(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """top hat -# """ -# return rank16b(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) From 85b552230d8d7845515936495bc6f170bd32acc7 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sat, 13 Oct 2012 09:47:25 +0200 Subject: [PATCH 252/625] fix pxd-pyx confusion --- skimage/rank/_core16.pxd | 273 +----------------------------------- skimage/rank/_core16.pyx | 283 +++++++++++++++++++++++++++++++++++++ skimage/rank/_core16b.pxd | 274 +----------------------------------- skimage/rank/_core16b.pyx | 284 ++++++++++++++++++++++++++++++++++++++ skimage/rank/_core16p.pxd | 272 +----------------------------------- skimage/rank/_core16p.pyx | 282 +++++++++++++++++++++++++++++++++++++ skimage/rank/_core8.pxd | 259 +--------------------------------- skimage/rank/_core8.pyx | 269 ++++++++++++++++++++++++++++++++++++ skimage/rank/_core8p.pxd | 255 +--------------------------------- skimage/rank/_core8p.pyx | 266 +++++++++++++++++++++++++++++++++++ skimage/rank/setup.py | 18 +++ 11 files changed, 1411 insertions(+), 1324 deletions(-) create mode 100644 skimage/rank/_core16.pyx create mode 100644 skimage/rank/_core16b.pyx create mode 100644 skimage/rank/_core16p.pyx create mode 100644 skimage/rank/_core8.pyx create mode 100644 skimage/rank/_core8p.pyx diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 1a3194de..d00ef37e 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -1,18 +1,4 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth @@ -23,261 +9,4 @@ np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out +char shift_x, char shift_y,int bitdepth) \ No newline at end of file diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx new file mode 100644 index 00000000..1a3194de --- /dev/null +++ b/skimage/rank/_core16.pyx @@ -0,0 +1,283 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data bitdepth +#--------------------------------------------------------------------------- + +cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 163662b5..b972ae9a 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -1,18 +1,4 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16b.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data bitdepth and bilateral interval @@ -23,262 +9,4 @@ np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, int s0, int s1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - - only pixel inside [s0,s1] centered on g are taken into account - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out +char shift_x, char shift_y,int bitdepth, int s0, int s1) \ No newline at end of file diff --git a/skimage/rank/_core16b.pyx b/skimage/rank/_core16b.pyx new file mode 100644 index 00000000..163662b5 --- /dev/null +++ b/skimage/rank/_core16b.pyx @@ -0,0 +1,284 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core16b.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data bitdepth and bilateral interval +#--------------------------------------------------------------------------- + +cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, int s0, int s1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + - only pixel inside [s0,s1] centered on g are taken into account + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,s0,s1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 9e992b6b..d162540c 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -1,15 +1,8 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline int int_max(int a, int b) +cdef inline int int_min(int a, int b) #--------------------------------------------------------------------------- # 16 bit core kernel receives extra information about data inferior and superior percentiles @@ -20,263 +13,4 @@ np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint16 - - result is uint16 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - - +char shift_x, char shift_y,int bitdepth, float p0, float p1) \ No newline at end of file diff --git a/skimage/rank/_core16p.pyx b/skimage/rank/_core16p.pyx new file mode 100644 index 00000000..9e992b6b --- /dev/null +++ b/skimage/rank/_core16p.pyx @@ -0,0 +1,282 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 16 bit core kernel receives extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), +np.ndarray[np.uint16_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint16_t, ndim=2] out, +char shift_x, char shift_y,int bitdepth, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint16 + - result is uint16 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + assert bitdepth in range(2,13) + + maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] + midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + + #set maxbin and midbin + cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + + assert (imageeimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint16_t* out_data = out.data + cdef np.uint16_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(maxbin * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(maxbin): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + bitdepth,maxbin,midbin,p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + + diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index d24297cb..aa3fe527 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -1,18 +1,4 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core8.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # 8 bit core kernel @@ -23,247 +9,4 @@ np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - +char shift_x, char shift_y) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx new file mode 100644 index 00000000..d24297cb --- /dev/null +++ b/skimage/rank/_core8.pyx @@ -0,0 +1,269 @@ +""" to compile this use: +>>> python setup.py build_ext --inplace + +to generate html report use: +>>> cython -a core8.pxd +""" + +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +#--------------------------------------------------------------------------- +# 8 bit core kernel +#--------------------------------------------------------------------------- + +cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index b1adac70..8d3181e8 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -1,15 +1,8 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np cimport numpy as np -from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- # 8 bit core kernel receives extra information about data inferior and superior percentiles @@ -20,247 +13,5 @@ np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out +char shift_x, char shift_y, float p0, float p1) diff --git a/skimage/rank/_core8p.pyx b/skimage/rank/_core8p.pyx new file mode 100644 index 00000000..b1adac70 --- /dev/null +++ b/skimage/rank/_core8p.pyx @@ -0,0 +1,266 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# generic cdef functions +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b + +#--------------------------------------------------------------------------- +# 8 bit core kernel receives extra information about data inferior and superior percentiles +#--------------------------------------------------------------------------- + +cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), +np.ndarray[np.uint8_t, ndim=2] image, +np.ndarray[np.uint8_t, ndim=2] selem, +np.ndarray[np.uint8_t, ndim=2] mask, +np.ndarray[np.uint8_t, ndim=2] out, +char shift_x, char shift_y, float p0, float p1): + """ Main loop, this function computes the histogram for each image point + - data is uint8 + - result is uint8 casted + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + cdef int srows = selem.shape[0] + cdef int scols = selem.shape[1] + + cdef int centre_r = int(selem.shape[0] / 2) + shift_y + cdef int centre_c = int(selem.shape[1] / 2) + shift_x + + # check that structuring element center is inside the element bounding box + assert centre_r >= 0 + assert centre_c >= 0 + assert centre_r < srows + assert centre_c < scols + + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones((rows, cols), dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) + + if out is None: + out = np.zeros((rows, cols), dtype=np.uint8) + else: + out = np.ascontiguousarray(out) + + # create extended image and mask + cdef int erows = rows+srows-1 + cdef int ecols = cols+scols-1 + + cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) + cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) + + eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image + emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask + + mask = np.ascontiguousarray(mask) + + # define pointers to the data + cdef np.uint8_t* eimage_data = eimage.data + cdef np.uint8_t* emask_data = emask.data + + cdef np.uint8_t* out_data = out.data + cdef np.uint8_t* image_data = image.data + cdef np.uint8_t* mask_data = mask.data + + # define local variable types + cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef float pop # number of pixels actually inside the neighborhood (float) + + # allocate memory with malloc + cdef int max_se = srows*scols + + # number of element in each attack border + cdef int num_se_n, num_se_s, num_se_e, num_se_w + + # the current local histogram distribution + cdef int* histo = malloc(256 * sizeof(int)) + + # these lists contain the relative pixel row and column for each of the 4 attack borders + # east, west, north and south + # e.g. se_e_r lists the rows of the east structuring element border + + cdef int* se_e_r = malloc(max_se * sizeof(int)) + cdef int* se_e_c = malloc(max_se * sizeof(int)) + cdef int* se_w_r = malloc(max_se * sizeof(int)) + cdef int* se_w_c = malloc(max_se * sizeof(int)) + cdef int* se_n_r = malloc(max_se * sizeof(int)) + cdef int* se_n_c = malloc(max_se * sizeof(int)) + cdef int* se_s_r = malloc(max_se * sizeof(int)) + cdef int* se_s_c = malloc(max_se * sizeof(int)) + + # build attack and release borders + # by using difference along axis + + t = np.hstack((selem,np.zeros((selem.shape[0],1)))) + t_e = np.diff(t,axis=1)==-1 + + t = np.hstack((np.zeros((selem.shape[0],1)),selem)) + t_w = np.diff(t,axis=1)==1 + + t = np.vstack((selem,np.zeros((1,selem.shape[1])))) + t_s = np.diff(t,axis=0)==-1 + + t = np.vstack((np.zeros((1,selem.shape[1])),selem)) + t_n = np.diff(t,axis=0)==1 + + num_se_n = num_se_s = num_se_e = num_se_w = 0 + + for r in range(srows): + for c in range(scols): + if t_e[r,c]: + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 + if t_w[r,c]: + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 + if t_n[r,c]: + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 + if t_s[r,c]: + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 + + # initial population and histogram + for i in range(256): + histo[i] = 0 + + pop = 0 + + for r in range(srows): + for c in range(scols): + rr = r + cc = c + if selem[r, c]: + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + + r = 0 + c = 0 + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # main loop + r = 0 + for even_row in range(0,rows,2): + # ---> west to east + for c in range(1,cols): + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c - 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # ---> east to west + for c in range(cols-2,-1,-1): + for s in range(num_se_w): + rr = r + se_w_r[s] + centre_r + cc = c + se_w_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_e): + rr = r + se_e_r[s] + centre_r + cc = c + se_e_c[s] + centre_c + 1 + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + r += 1 # pass to the next row + if r>=rows: + break + + # ---> north to south + for s in range(num_se_s): + rr = r + se_s_r[s] + centre_r + cc = c + se_s_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] += 1 + pop += 1. + for s in range(num_se_n): + rr = r + se_n_r[s] + centre_r - 1 + cc = c + se_n_c[s] + centre_c + if emask_data[rr * ecols + cc]: + value = eimage_data[rr * ecols + cc] + histo[value] -= 1 + pop -= 1. + + # kernel ------------------------------------------- + out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) + # kernel ------------------------------------------- + + # release memory allocated by malloc + + free(se_e_r) + free(se_e_c) + free(se_w_r) + free(se_w_c) + free(se_n_r) + free(se_n_c) + free(se_s_r) + free(se_s_c) + + free(histo) + + return out + diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index efe23515..20a59979 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -5,6 +5,8 @@ from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) +import sys +sys.path.append('.') def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs @@ -12,12 +14,28 @@ def configuration(parent_package='', top_path=None): config = Configuration('rank', parent_package, top_path) # config.add_data_dir('tests') + + cython(['_core8.pyx'], working_path=base_path) + cython(['_core8p.pyx'], working_path=base_path) + cython(['_core16.pyx'], working_path=base_path) + cython(['_core16p.pyx'], working_path=base_path) + cython(['_core16b.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) cython(['_crank8_percentiles.pyx'], working_path=base_path) cython(['_crank16.pyx'], working_path=base_path) cython(['_crank16_percentiles.pyx'], working_path=base_path) cython(['_crank16_bilateral.pyx'], working_path=base_path) + config.add_extension('_core8', sources=['_core8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core8p', sources=['_core8p.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core16', sources=['_core16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core16p', sources=['_core16p.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('_core16b', sources=['_core16b.c'], + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], From 388bb7fc29a2972d51d3fba07ce23b3ba443b065 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sat, 13 Oct 2012 10:09:06 +0200 Subject: [PATCH 253/625] restore setupfile --- skimage/rank/rank.py | 3 +++ skimage/rank/setup.py | 3 --- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index cefb06ac..52b3d570 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -1022,5 +1022,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): raise TypeError("only uint8 and uint16 image supported!") if __name__ == "__main__": + import sys + sys.path.append('.') + import doctest doctest.testmod(verbose=True) \ No newline at end of file diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 20a59979..207ff18f 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -5,9 +5,6 @@ from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) -import sys -sys.path.append('.') - def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs From 43cd63b133946735cf63f138af06a3d0fc5ee912 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 13:21:33 +0200 Subject: [PATCH 254/625] remplace int by Py_ssize_t and fix some doctests --- skimage/rank/_core8.pyx | 38 +++++++++++----------- skimage/rank/rank.py | 70 ++++++++++++++++++++++++----------------- 2 files changed, 61 insertions(+), 47 deletions(-) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index d24297cb..cd997d89 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -29,13 +29,13 @@ char shift_x, char shift_y): - result is uint8 casted """ - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -56,8 +56,8 @@ char shift_x, char shift_y): out = np.ascontiguousarray(out) # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 + cdef Py_ssize_t erows = rows+srows-1 + cdef Py_ssize_t ecols = cols+scols-1 cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) @@ -76,14 +76,14 @@ char shift_x, char shift_y): cdef np.uint8_t* mask_data = mask.data # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row cdef float pop # number of pixels actually inside the neighborhood (float) # allocate memory with malloc - cdef int max_se = srows*scols + cdef Py_ssize_t max_se = srows*scols # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w + cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -92,14 +92,14 @@ char shift_x, char shift_y): # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) + cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 52b3d570..dafe0715 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -59,12 +59,13 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> autolevel(ima8, square(3)) + >>> rank.autolevel(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 0, 255, 0], @@ -76,7 +77,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> autolevel(ima16, square(3)) + >>> rank.autolevel(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 0, 4095, 0], @@ -130,12 +131,13 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bottomhat(ima8, square(3)) + >>> rank.bottomhat(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 0, 255, 0], @@ -147,7 +149,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bottomhat(ima16, square(3)) + >>> rank.bottomhat(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 0, 4095, 0], @@ -200,12 +202,13 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> equalize(ima8, square(3)) + >>> rank.equalize(ima8, square(3)) array([[191, 170, 127, 170, 191], [170, 255, 255, 255, 170], [127, 255, 255, 255, 127], @@ -217,7 +220,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> equalize(ima16, square(3)) + >>> rank.equalize(ima16, square(3)) array([[3071, 2730, 2047, 2730, 3071], [2730, 4095, 4095, 4095, 2730], [2047, 4095, 4095, 4095, 2047], @@ -270,12 +273,13 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local gradient >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> gradient(ima8, square(3)) + >>> rank.gradient(ima8, square(3)) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 0, 255, 255], @@ -287,7 +291,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> gradient(ima16, square(3)) + >>> rank.gradient(ima16, square(3)) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 0, 4095, 4095], @@ -342,12 +346,13 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local maximum >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 1, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> maximum(ima8, square(3)) + >>> rank.maximum(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], @@ -359,7 +364,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 0, 1, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> maximum(ima16, square(3)) + >>> rank.maximum(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -413,12 +418,13 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> mean(ima8, square(3)) + >>> rank.mean(ima8, square(3)) array([[ 63, 85, 127, 85, 63], [ 85, 113, 170, 113, 85], [127, 170, 255, 170, 127], @@ -430,7 +436,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> mean(ima16, square(3)) + >>> rank.mean(ima16, square(3)) array([[1023, 1365, 2047, 1365, 1023], [1365, 1820, 2730, 1820, 1365], [2047, 2730, 4095, 2730, 2047], @@ -484,12 +490,13 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F to be updated >>> # Local meansubstraction >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> meansubstraction(ima8, square(3)) + >>> rank.meansubstraction(ima8, square(3)) array([[ 95, 84, 63, 84, 95], [ 84, 197, 169, 197, 84], [ 63, 169, 127, 169, 63], @@ -501,7 +508,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> meansubstraction(ima16, square(3)) + >>> rank.meansubstraction(ima16, square(3)) array([[1535, 1364, 1023, 1364, 1535], [1364, 3184, 2729, 3184, 1364], [1023, 2729, 2047, 2729, 1023], @@ -555,12 +562,13 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local median >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 0, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> median(ima8, square(3)) + >>> rank.median(ima8, square(3)) array([[ 0, 0, 255, 0, 0], [ 0, 0, 255, 0, 0], [255, 255, 255, 255, 255], @@ -572,7 +580,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 0, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> median(ima16, square(3)) + >>> rank.median(ima16, square(3)) array([[ 0, 0, 4095, 0, 0], [ 0, 0, 4095, 0, 0], [4095, 4095, 4095, 4095, 4095], @@ -626,12 +634,13 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local minimum >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> minimum(ima8, square(3)) + >>> rank.minimum(ima8, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0], [ 0, 0, 255, 0, 0], @@ -644,7 +653,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> minimum(ima16, square(3)) + >>> rank.minimum(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0], [ 0, 0, 4095, 0, 0], @@ -698,12 +707,13 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local modal >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 5, 6, 0], ... [0, 1, 5, 5, 0], ... [0, 0, 0, 5, 0]], dtype=np.uint8) - >>> modal(ima8, square(3)) + >>> rank.modal(ima8, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 1, 0, 0], @@ -716,7 +726,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 5, 6, 0], ... [0, 1, 5, 5, 0], ... [0, 0, 0, 5, 0]], dtype=np.uint16) - >>> modal(ima16, square(3)) + >>> rank.modal(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 0, 100, 0, 0], [ 0, 100, 100, 0, 0], @@ -770,12 +780,13 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> morph_contr_enh(ima8, square(3)) + >>> rank.morph_contr_enh(ima8, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], @@ -787,7 +798,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> morph_contr_enh(ima16, square(3)) + >>> rank.morph_contr_enh(ima16, square(3)) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -841,12 +852,13 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> pop(ima8, square(3)) + >>> rank.pop(ima8, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -858,7 +870,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> pop(ima16, square(3)) + >>> rank.pop(ima16, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -912,12 +924,13 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> threshold(ima8, square(3)) + >>> rank.threshold(ima8, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], @@ -929,7 +942,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> threshold(ima16, square(3)) + >>> rank.threshold(ima16, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], @@ -984,12 +997,13 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> tophat(ima8, square(3)) + >>> rank.tophat(ima8, square(3)) array([[255, 255, 255, 255, 255], [255, 0, 0, 0, 255], [255, 0, 0, 0, 255], @@ -1001,7 +1015,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> tophat(ima16, square(3)) + >>> rank.tophat(ima16, square(3)) array([[4095, 4095, 4095, 4095, 4095], [4095, 0, 0, 0, 4095], [4095, 0, 0, 0, 4095], From c3a3f39bbc834e4a4f35f7c5c727c3f18f77d179 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 13:26:28 +0200 Subject: [PATCH 255/625] remplace int by Py_ssize_t and for rank8 --- skimage/rank/_core8.pxd | 2 +- skimage/rank/_core8.pyx | 4 +-- skimage/rank/_crank8.pyx | 54 ++++++++++++++++++++-------------------- 3 files changed, 30 insertions(+), 30 deletions(-) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index aa3fe527..6dca9f61 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -4,7 +4,7 @@ cimport numpy as np # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index cd997d89..6dc59059 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -18,7 +18,7 @@ from libc.stdlib cimport malloc, free # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(int*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, @@ -86,7 +86,7 @@ char shift_x, char shift_y): cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) + cdef Py_ssize_t* histo = malloc(256 * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 96624eb5..68fbfba8 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,8 +21,8 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): - cdef int i,imin,imax,delta +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i,imin,imax,delta if pop: for i in range(255,-1,-1): @@ -41,8 +41,8 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i for i in range(256): if histo[i]: @@ -51,8 +51,8 @@ cdef inline np.uint8_t kernel_bottomhat(int* histo, float pop, np.uint8_t g): return (g-i) -cdef inline np.uint8_t kernel_equalize(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float sum = 0. if pop: @@ -65,8 +65,8 @@ cdef inline np.uint8_t kernel_equalize(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g): - cdef int i,imin,imax +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i,imin,imax if pop: @@ -82,8 +82,8 @@ cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_maximum(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i if pop: for i in range(255,-1,-1): @@ -92,8 +92,8 @@ cdef inline np.uint8_t kernel_maximum(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -103,8 +103,8 @@ cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_meansubstraction(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -114,8 +114,8 @@ cdef inline np.uint8_t kernel_meansubstraction(int* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_median(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float sum = pop/2.0 if pop: @@ -127,8 +127,8 @@ cdef inline np.uint8_t kernel_median(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_minimum(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i if pop: for i in range(256): @@ -137,8 +137,8 @@ cdef inline np.uint8_t kernel_minimum(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_modal(int* histo, float pop, np.uint8_t g): - cdef int hmax=0,imax=0 +cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t hmax=0,imax=0 if pop: for i in range(256): @@ -149,8 +149,8 @@ cdef inline np.uint8_t kernel_modal(int* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t g): - cdef int i,imin,imax +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i,imin,imax if pop: for i in range(255,-1,-1): @@ -168,11 +168,11 @@ cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g): return (pop) -cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -182,8 +182,8 @@ cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_tophat(int* histo, float pop, np.uint8_t g): - cdef int i +cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g): + cdef Py_ssize_t i for i in range(255,-1,-1): if histo[i]: From b78a0460fbb43bcc942742b3a6b41f8d315977c8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 13:36:41 +0200 Subject: [PATCH 256/625] remplace int by Py_ssize_t and for rank16 --- skimage/rank/_core16.pxd | 4 +- skimage/rank/_core16.pyx | 46 +++++++++++----------- skimage/rank/_crank16.pyx | 82 +++++++++++++++++++-------------------- 3 files changed, 66 insertions(+), 66 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index d00ef37e..0efd4e04 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -4,9 +4,9 @@ cimport numpy as np # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth) \ No newline at end of file +char shift_x, char shift_y,Py_ssize_t bitdepth) \ No newline at end of file diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 1a3194de..00e229d9 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -18,24 +18,24 @@ from libc.stdlib cimport malloc, free # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline _core16(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int ), +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth): +char shift_x, char shift_y,Py_ssize_t bitdepth): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted """ - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -49,7 +49,7 @@ char shift_x, char shift_y,int bitdepth): #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + cdef Py_ssize_t maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] assert (imagemask.data # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row + cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row cdef float pop # number of pixels actually inside the neighborhood (float) # allocate memory with malloc - cdef int max_se = srows*scols + cdef Py_ssize_t max_se = srows*scols # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w + cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) + cdef Py_ssize_t* histo = malloc(maxbin * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) + cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index 90a7e8bb..eb08326b 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -21,8 +21,8 @@ from _core16 cimport _core16 # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i,imin,imax,delta +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i,imin,imax,delta if pop: for i in range(maxbin-1,-1,-1): @@ -39,8 +39,8 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in else: return (imax-imin) -cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i for i in range(maxbin): if histo[i]: @@ -49,8 +49,8 @@ cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,in return (g-i) -cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float sum = 0. if pop: @@ -63,8 +63,8 @@ cdef inline np.uint16_t kernel_equalize(int* histo, float pop, np.uint16_t g,int else: return (0) -cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i,imin,imax +cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i,imin,imax if pop: for i in range(maxbin-1,-1,-1): @@ -79,8 +79,8 @@ cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int else: return (0) -cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i if pop: for i in range(maxbin-1,-1,-1): @@ -89,8 +89,8 @@ cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int return (0) -cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -100,8 +100,8 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -111,8 +111,8 @@ cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16 else: return (0) -cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float sum = pop/2.0 if pop: @@ -124,8 +124,8 @@ cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int b return (0) -cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i if pop: for i in range(maxbin): @@ -134,8 +134,8 @@ cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int return (0) -cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int hmax=0,imax=0 +cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t hmax=0,imax=0 if pop: for i in range(maxbin): @@ -146,8 +146,8 @@ cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bi return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i,imin,imax +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i,imin,imax if pop: for i in range(maxbin-1,-1,-1): @@ -165,11 +165,11 @@ cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): return (pop) -cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -179,8 +179,8 @@ cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,in else: return (0) -cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): - cdef int i +cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): + cdef Py_ssize_t i for i in range(maxbin-1,-1,-1): if histo[i]: @@ -195,7 +195,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -204,7 +204,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -213,7 +213,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local egalisation of the gray level """ return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -222,7 +222,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum - local minimum gray level """ return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -231,7 +231,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum gray level """ return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -240,7 +240,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """average gray level (clipped on uint8) """ return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -249,7 +249,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -258,7 +258,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local median """ return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -267,7 +267,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local minimum gray level """ return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -276,7 +276,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """morphological contrast enhancement """ return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -285,7 +285,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local mode """ return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -294,7 +294,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -303,7 +303,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) @@ -312,7 +312,7 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """top hat """ return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) From 8b3b42bc02a9db778a13a8d9a824cdd7ff74769e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 14:45:25 +0200 Subject: [PATCH 257/625] remplace emask with is_in_mask function --- skimage/rank/_core8.pyx | 103 +++++++++++++++--------------- skimage/rank/tests/demo_single.py | 29 +++++++++ 2 files changed, 79 insertions(+), 53 deletions(-) create mode 100644 skimage/rank/tests/demo_single.py diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 6dc59059..baedd67a 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -18,6 +18,15 @@ from libc.stdlib cimport malloc, free # 8 bit core kernel #--------------------------------------------------------------------------- +cdef inline Py_ssize_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): + if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: + return 0 + else: + if mask[r*cols+c]: + return 1 + else: + return 0 + cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -55,21 +64,9 @@ char shift_x, char shift_y): else: out = np.ascontiguousarray(out) - # create extended image and mask - cdef Py_ssize_t erows = rows+srows-1 - cdef Py_ssize_t ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - mask = np.ascontiguousarray(mask) # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data cdef np.uint8_t* out_data = out.data cdef np.uint8_t* image_data = image.data @@ -145,18 +142,18 @@ char shift_x, char shift_y): for r in range(srows): for c in range(scols): - rr = r - cc = c + rr = r - centre_r + cc = c - centre_c if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. r = 0 c = 0 # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- # main loop @@ -165,22 +162,22 @@ char shift_x, char shift_y): # ---> west to east for c in range(1,cols): for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] - 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -189,43 +186,43 @@ char shift_x, char shift_y): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- # ---> east to west for c in range(cols-2,-1,-1): for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -234,22 +231,22 @@ char shift_x, char shift_y): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) # kernel ------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py new file mode 100644 index 00000000..028f27b2 --- /dev/null +++ b/skimage/rank/tests/demo_single.py @@ -0,0 +1,29 @@ +import numpy as np +import matplotlib.pyplot as plt +from pprint import pprint + +from skimage import data +from skimage.morphology.selem import disk +import skimage.rank as rank + + +if __name__ == '__main__': + a8 = data.camera() + a16 = data.camera().astype(np.uint16) + selem = disk(10) + + f8= rank.mean(a8,selem) + f16= rank.mean(a16,selem) + + print f8==f16 + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.subplot(1,2,2) + plt.imshow(f8-f16) + plt.show() + + + + From b9c52e02961098b4ea9b0ae9c25a0d2193929551 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:03:29 +0200 Subject: [PATCH 258/625] add comment --- skimage/rank/_core8.pyx | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index baedd67a..72b0d6aa 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -18,7 +18,11 @@ from libc.stdlib cimport malloc, free # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline Py_ssize_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): + """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and + inside the given mask + returns 0 otherwise + """ if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: @@ -134,7 +138,7 @@ char shift_x, char shift_y): se_s_c[num_se_s] = c - centre_c num_se_s += 1 - # initial population and histogram + # initial population and histogram (kernel is centered on the first row and column) for i in range(256): histo[i] = 0 @@ -152,9 +156,9 @@ char shift_x, char shift_y): r = 0 c = 0 - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- # main loop r = 0 @@ -176,9 +180,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- r += 1 # pass to the next row if r>=rows: @@ -200,9 +204,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- # ---> east to west for c in range(cols-2,-1,-1): @@ -221,9 +225,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- r += 1 # pass to the next row if r>=rows: @@ -245,9 +249,9 @@ char shift_x, char shift_y): histo[value] -= 1 pop -= 1. - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) - # kernel ------------------------------------------- + # kernel -------------------------------------------------------------------- # release memory allocated by malloc From 7df19d2810cad63cd550aa786a64dcc52cd06130 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:16:26 +0200 Subject: [PATCH 259/625] remplace emask with is_in_mask function in crank16 --- skimage/rank/_core16.pyx | 109 +++++++++++++++--------------- skimage/rank/tests/demo_single.py | 2 +- 2 files changed, 56 insertions(+), 55 deletions(-) diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 00e229d9..6a004da7 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -18,6 +18,20 @@ from libc.stdlib cimport malloc, free # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): + """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and + inside the given mask + returns 0 otherwise + """ + if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: + return 0 + else: + if mask[r*cols+c]: + return 1 + else: + return 0 + + cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -65,22 +79,9 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): else: out = np.ascontiguousarray(out) - # create extended image and mask - cdef Py_ssize_t erows = rows+srows-1 - cdef Py_ssize_t ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint16) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - mask = np.ascontiguousarray(mask) # define pointers to the data - cdef np.uint16_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - cdef np.uint16_t* out_data = out.data cdef np.uint16_t* image_data = image.data cdef np.uint8_t* mask_data = mask.data @@ -155,18 +156,18 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): for r in range(srows): for c in range(scols): - rr = r - cc = c + rr = r - centre_r + cc = c - centre_c if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. r = 0 c = 0 # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], bitdepth,maxbin,midbin) # kernel ------------------------------------------- @@ -176,22 +177,22 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # ---> west to east for c in range(1,cols): for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] - 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], bitdepth,maxbin,midbin) # kernel ------------------------------------------- @@ -201,44 +202,44 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], bitdepth,maxbin,midbin) # kernel ------------------------------------------- # ---> east to west for c in range(cols-2,-1,-1): for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_w_r[s] + cc = c + se_w_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_e_r[s] + cc = c + se_e_c[s] + 1 + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], bitdepth,maxbin,midbin) # kernel ------------------------------------------- @@ -248,22 +249,22 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # ---> north to south for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_s_r[s] + cc = c + se_s_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] += 1 pop += 1. for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] + rr = r + se_n_r[s] - 1 + cc = c + se_n_c[s] + if is_in_mask(rows,cols,rr,cc,mask_data): + value = image_data[rr * cols + cc] histo[value] -= 1 pop -= 1. # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], bitdepth,maxbin,midbin) # kernel ------------------------------------------- diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 028f27b2..5ec95d7b 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -19,7 +19,7 @@ if __name__ == '__main__': plt.figure() plt.subplot(1,2,1) - plt.imshow(a8) + plt.imshow(f16) plt.subplot(1,2,2) plt.imshow(f8-f16) plt.show() From ef768b22a86f78bf703794b52fea5e718e1a1114 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:42:31 +0200 Subject: [PATCH 260/625] group core8,8p and 8b --- skimage/rank/_core8.pxd | 11 +++++--- skimage/rank/_core8.pyx | 25 +++++++++++------- skimage/rank/_crank8.pyx | 56 ++++++++++++++++++++-------------------- 3 files changed, 52 insertions(+), 40 deletions(-) diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 6dca9f61..c0ac709d 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -1,12 +1,17 @@ cimport numpy as np +# generic cdef functions +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) + #--------------------------------------------------------------------------- -# 8 bit core kernel +# 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y) +char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) + diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 72b0d6aa..3ffbb5b9 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -14,6 +14,11 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free +# generic cdef functions +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b + + #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- @@ -31,12 +36,12 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, else: return 0 -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t), +cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y): +char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -78,7 +83,9 @@ char shift_x, char shift_y): # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) + + # number of pixels actually inside the neighborhood (float) + cdef float pop # allocate memory with malloc cdef Py_ssize_t max_se = srows*scols @@ -157,7 +164,7 @@ char shift_x, char shift_y): r = 0 c = 0 # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- # main loop @@ -181,14 +188,14 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row if r>=rows: break - # ---> north to south + # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] @@ -205,7 +212,7 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- # ---> east to west @@ -226,7 +233,7 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row @@ -250,7 +257,7 @@ char shift_x, char shift_y): pop -= 1. # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c]) + out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) # kernel -------------------------------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 68fbfba8..600cb00d 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,7 +21,7 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax,delta if pop: @@ -41,7 +41,7 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(256): @@ -51,7 +51,7 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t return (g-i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -65,7 +65,7 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax @@ -82,7 +82,7 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -92,7 +92,7 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -103,7 +103,7 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g): else: return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -114,7 +114,7 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np. else: return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop/2.0 @@ -127,7 +127,7 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g) return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -137,7 +137,7 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax=0,imax=0 if pop: @@ -149,7 +149,7 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g): return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -168,10 +168,10 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.u else: return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -182,7 +182,7 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g): +cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(255,-1,-1): @@ -201,7 +201,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -210,7 +210,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -219,7 +219,7 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -228,7 +228,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -237,7 +237,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum gray level """ - return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -246,7 +246,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """average gray level (clipped on uint8) """ - return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -255,7 +255,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -264,7 +264,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local median """ - return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -273,7 +273,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local minimum gray level """ - return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -282,7 +282,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -291,7 +291,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local mode """ - return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -300,7 +300,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns the number of actual pixels of the structuring element inside the mask """ - return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -309,7 +309,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -318,5 +318,5 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """top hat """ - return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y) + return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) From 7862b971be98c8408b121bb5b596360f0c71ceb7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 15:55:21 +0200 Subject: [PATCH 261/625] delete core8p --- skimage/rank/_core8p.pxd | 17 -- skimage/rank/_core8p.pyx | 266 --------------------------- skimage/rank/_crank8_percentiles.pyx | 34 ++-- skimage/rank/setup.py | 3 - skimage/rank/tests/demo_single.py | 6 +- 5 files changed, 20 insertions(+), 306 deletions(-) delete mode 100644 skimage/rank/_core8p.pxd delete mode 100644 skimage/rank/_core8p.pyx diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd deleted file mode 100644 index 8d3181e8..00000000 --- a/skimage/rank/_core8p.pxd +++ /dev/null @@ -1,17 +0,0 @@ -cimport numpy as np - -# generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) - -#--------------------------------------------------------------------------- -# 8 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1) - diff --git a/skimage/rank/_core8p.pyx b/skimage/rank/_core8p.pyx deleted file mode 100644 index b1adac70..00000000 --- a/skimage/rank/_core8p.pyx +++ /dev/null @@ -1,266 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b - -#--------------------------------------------------------------------------- -# 8 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core8p(np.uint8_t kernel(int*, float, np.uint8_t, float, float), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - # create extended image and mask - cdef int erows = rows+srows-1 - cdef int ecols = cols+scols-1 - - cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8) - cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8) - - eimage[centre_r:rows+centre_r,centre_c:cols+centre_c] = image - emask[centre_r:rows+centre_r,centre_c:cols+centre_c] = mask - - mask = np.ascontiguousarray(mask) - - # define pointers to the data - cdef np.uint8_t* eimage_data = eimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(256 * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(256): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c],p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 2299f04a..16ad49d5 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -7,13 +7,13 @@ import numpy as np cimport numpy as np # import main loop -from _core8p cimport _core8p,uint8_max,uint8_min +from _core8 cimport _core8,uint8_max,uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -42,7 +42,7 @@ cdef inline np.uint8_t kernel_autolevel(int* histo, float pop, np.uint8_t g, flo return (128) -cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -65,7 +65,7 @@ cdef inline np.uint8_t kernel_gradient(int* histo, float pop, np.uint8_t g, floa return (0) -cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -84,7 +84,7 @@ cdef inline np.uint8_t kernel_mean(int* histo, float pop, np.uint8_t g, float p0 else: return (0) -cdef inline np.uint8_t kernel_mean_substraction(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -103,7 +103,7 @@ cdef inline np.uint8_t kernel_mean_substraction(int* histo, float pop, np.uint8_ else: return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -131,7 +131,7 @@ cdef inline np.uint8_t kernel_morph_contr_enh(int* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_percentile(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -145,7 +145,7 @@ cdef inline np.uint8_t kernel_percentile(int* histo, float pop, np.uint8_t g, fl else: return (0) -cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,n if pop: @@ -159,7 +159,7 @@ cdef inline np.uint8_t kernel_pop(int* histo, float pop, np.uint8_t g, float p0, else: return (0) -cdef inline np.uint8_t kernel_threshold(int* histo, float pop, np.uint8_t g, float p0, float p1): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -183,7 +183,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -193,7 +193,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -202,7 +202,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core8p(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -211,7 +211,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -220,7 +220,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -230,7 +230,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -240,7 +240,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core8p(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -249,4 +249,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1) + return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 207ff18f..854748b8 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -13,7 +13,6 @@ def configuration(parent_package='', top_path=None): cython(['_core8.pyx'], working_path=base_path) - cython(['_core8p.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) cython(['_core16p.pyx'], working_path=base_path) cython(['_core16b.pyx'], working_path=base_path) @@ -25,8 +24,6 @@ def configuration(parent_package='', top_path=None): config.add_extension('_core8', sources=['_core8.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core8p', sources=['_core8p.c'], - include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16p', sources=['_core16p.c'], diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 5ec95d7b..7ff12b28 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -12,8 +12,8 @@ if __name__ == '__main__': a16 = data.camera().astype(np.uint16) selem = disk(10) - f8= rank.mean(a8,selem) - f16= rank.mean(a16,selem) + f8= rank.percentile_autolevel(a8,selem,p0=.0,p1=1.) + f16= rank.autolevel(a16,selem) print f8==f16 @@ -21,7 +21,7 @@ if __name__ == '__main__': plt.subplot(1,2,1) plt.imshow(f16) plt.subplot(1,2,2) - plt.imshow(f8-f16) + plt.imshow(f8) plt.show() From e29ce3dba02931e0ae88b2cce29504895e59400f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:35:11 +0200 Subject: [PATCH 262/625] group crank16 and crank16p --- skimage/rank/_core16.pxd | 17 ++++-- skimage/rank/_core16.pyx | 27 +++++---- skimage/rank/_core8.pxd | 10 ++-- skimage/rank/_core8.pyx | 10 ++-- skimage/rank/_crank16.pyx | 84 ++++++++++++++++++--------- skimage/rank/_crank16_percentiles.pyx | 34 +++++------ skimage/rank/_crank8.pyx | 42 +++++++++----- 7 files changed, 138 insertions(+), 86 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 0efd4e04..f9bb47b3 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -4,9 +4,14 @@ cimport numpy as np # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,Py_ssize_t bitdepth) \ No newline at end of file +# generic cdef functions +cdef inline int int_max(int a, int b) +cdef inline int int_min(int a, int b) + +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint16_t, ndim=2] out, + char shift_x, char shift_y,Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) \ No newline at end of file diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 6a004da7..2e55aa8b 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -18,6 +18,10 @@ from libc.stdlib cimport malloc, free # 16 bit core kernel receives extra information about data bitdepth #--------------------------------------------------------------------------- +# generic cdef functions +cdef inline int int_max(int a, int b): return a if a >= b else b +cdef inline int int_min(int a, int b): return a if a <= b else b + cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask @@ -32,12 +36,13 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, return 0 -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t, Py_ssize_t ,Py_ssize_t,Py_ssize_t ), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,Py_ssize_t bitdepth): +cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint16_t, ndim=2] out, + char shift_x, char shift_y,Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -168,7 +173,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): c = 0 # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- # main loop @@ -193,7 +198,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -218,7 +223,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- # ---> east to west @@ -240,7 +245,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- r += 1 # pass to the next row @@ -265,7 +270,7 @@ char shift_x, char shift_y,Py_ssize_t bitdepth): # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin) + bitdepth,maxbin,midbin,p0,p1,s0,s1) # kernel ------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index c0ac709d..a677e915 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -9,9 +9,9 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) + np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint8_t, ndim=2] out, + char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 3ffbb5b9..87588813 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -37,11 +37,11 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, return 0 cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), -np.ndarray[np.uint8_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint8_t, ndim=2] out, -char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint8_t, ndim=2] out, + char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index eb08326b..2fdda1e3 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -21,7 +21,9 @@ from _core16 cimport _core16 # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax,delta if pop: @@ -39,7 +41,9 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16 else: return (imax-imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin): @@ -49,7 +53,9 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16 return (g-i) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -63,7 +69,9 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -79,7 +87,9 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -89,7 +99,9 @@ cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -100,7 +112,9 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, else: return (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -111,7 +125,9 @@ cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np else: return (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop/2.0 @@ -124,7 +140,9 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t return (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -134,7 +152,9 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t return (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax=0,imax=0 if pop: @@ -146,7 +166,9 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -165,10 +187,14 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np. else: return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -179,7 +205,9 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16 else: return (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin): +cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, +Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin-1,-1,-1): @@ -198,7 +226,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -207,7 +235,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -216,7 +244,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local egalisation of the gray level """ - return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -225,7 +253,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum - local minimum gray level """ - return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -234,7 +262,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum gray level """ - return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -243,7 +271,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """average gray level (clipped on uint8) """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -252,7 +280,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ - return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -261,7 +289,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local median """ - return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -270,7 +298,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local minimum gray level """ - return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -279,7 +307,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """morphological contrast enhancement """ - return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -288,7 +316,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local mode """ - return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -297,7 +325,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -306,7 +334,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ - return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -315,4 +343,4 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """top hat """ - return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth) + return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 0f07196e..b270965b 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -7,13 +7,13 @@ import numpy as np cimport numpy as np # import main loop -from _core16p cimport _core16p,int_min,int_max +from _core16 cimport _core16,int_min,int_max # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -40,7 +40,7 @@ cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,in return (0) -cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -63,7 +63,7 @@ cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int return (0) -cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -83,7 +83,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -cdef inline np.uint16_t kernel_mean_substraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,mean,n if pop: @@ -102,7 +102,7 @@ cdef inline np.uint16_t kernel_mean_substraction(int* histo, float pop, np.uint1 else: return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,imin,imax,sum,delta if pop: @@ -130,7 +130,7 @@ cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_ else: return (0) -cdef inline np.uint16_t kernel_percentile(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -144,7 +144,7 @@ cdef inline np.uint16_t kernel_percentile(int* histo, float pop, np.uint16_t g,i else: return (0) -cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,sum,n if pop: @@ -158,7 +158,7 @@ cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitd else: return (0) -cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, float p0, float p1): +cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -182,7 +182,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return _core16p(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -192,7 +192,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16p(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -201,7 +201,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16p(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -210,7 +210,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16p(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -219,7 +219,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16p(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -229,7 +229,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return _core16p(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -239,7 +239,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16p(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -248,4 +248,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16p(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1) + return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index 600cb00d..a0b20073 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,7 +21,8 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax,delta if pop: @@ -41,7 +42,8 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(256): @@ -51,7 +53,8 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t return (g-i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -65,7 +68,8 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax @@ -82,7 +86,8 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -92,7 +97,8 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -103,7 +109,8 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g,fl else: return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -114,7 +121,8 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np. else: return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop/2.0 @@ -127,7 +135,8 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g, return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -137,7 +146,8 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax=0,imax=0 if pop: @@ -149,7 +159,8 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g,f return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i,imin,imax if pop: @@ -168,10 +179,12 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.u else: return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -182,7 +195,8 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t else: return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g,float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g, +float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(255,-1,-1): From 096fa018777338c046dca970a211694101395bc6 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:36:08 +0200 Subject: [PATCH 263/625] delete core16p --- skimage/rank/_core16p.pxd | 16 --- skimage/rank/_core16p.pyx | 282 -------------------------------------- skimage/rank/setup.py | 3 - 3 files changed, 301 deletions(-) delete mode 100644 skimage/rank/_core16p.pxd delete mode 100644 skimage/rank/_core16p.pyx diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd deleted file mode 100644 index d162540c..00000000 --- a/skimage/rank/_core16p.pxd +++ /dev/null @@ -1,16 +0,0 @@ -cimport numpy as np - -# generic cdef functions -cdef inline int int_max(int a, int b) -cdef inline int int_min(int a, int b) - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1) \ No newline at end of file diff --git a/skimage/rank/_core16p.pyx b/skimage/rank/_core16p.pyx deleted file mode 100644 index 9e992b6b..00000000 --- a/skimage/rank/_core16p.pyx +++ /dev/null @@ -1,282 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- - -cdef inline _core16p(np.uint16_t kernel(int*, float, np.uint16_t,int,int,int, float, float), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, float p0, float p1): - """ Main loop, this function computes the histogram for each image point - - data is uint16 - - result is uint16 casted - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,p0,p1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out - - diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index 854748b8..b7cac4dc 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -14,7 +14,6 @@ def configuration(parent_package='', top_path=None): cython(['_core8.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) - cython(['_core16p.pyx'], working_path=base_path) cython(['_core16b.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) cython(['_crank8_percentiles.pyx'], working_path=base_path) @@ -26,8 +25,6 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core16p', sources=['_core16p.c'], - include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16b', sources=['_core16b.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], From a13d1bb4ac525b63f411d60af2a87635132d8883 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:46:41 +0200 Subject: [PATCH 264/625] grou rank16 and rank16b, remove core16b --- skimage/rank/_core16b.pxd | 12 -- skimage/rank/_core16b.pyx | 284 ---------------------------- skimage/rank/_crank16_bilateral.pyx | 10 +- skimage/rank/setup.py | 3 - skimage/rank/tests/test_suite.py | 9 + 5 files changed, 14 insertions(+), 304 deletions(-) delete mode 100644 skimage/rank/_core16b.pxd delete mode 100644 skimage/rank/_core16b.pyx diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd deleted file mode 100644 index b972ae9a..00000000 --- a/skimage/rank/_core16b.pxd +++ /dev/null @@ -1,12 +0,0 @@ -cimport numpy as np - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data bitdepth and bilateral interval -#--------------------------------------------------------------------------- - -cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, int s0, int s1) \ No newline at end of file diff --git a/skimage/rank/_core16b.pyx b/skimage/rank/_core16b.pyx deleted file mode 100644 index 163662b5..00000000 --- a/skimage/rank/_core16b.pyx +++ /dev/null @@ -1,284 +0,0 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16b.pxd -""" - -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False - -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data bitdepth and bilateral interval -#--------------------------------------------------------------------------- - -cdef inline _core16b(np.uint16_t kernel(int*, float, np.uint16_t, int ,int,int,int,int), -np.ndarray[np.uint16_t, ndim=2] image, -np.ndarray[np.uint8_t, ndim=2] selem, -np.ndarray[np.uint8_t, ndim=2] mask, -np.ndarray[np.uint16_t, ndim=2] out, -char shift_x, char shift_y,int bitdepth, int s0, int s1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted - - only pixel inside [s0,s1] centered on g are taken into account - """ - - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] - - cdef int centre_r = int(selem.shape[0] / 2) + shift_y - cdef int centre_c = int(selem.shape[1] / 2) + shift_x - - # check that structuring element center is inside the element bounding box - assert centre_r >= 0 - assert centre_c >= 0 - assert centre_r < srows - assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] - - - #set maxbin and midbin - cdef int maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] - - assert (imageeimage.data - cdef np.uint8_t* emask_data = emask.data - - cdef np.uint16_t* out_data = out.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data - - # define local variable types - cdef int r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) - - # allocate memory with malloc - cdef int max_se = srows*scols - - # number of element in each attack border - cdef int num_se_n, num_se_s, num_se_e, num_se_w - - # the current local histogram distribution - cdef int* histo = malloc(maxbin * sizeof(int)) - - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef int* se_e_r = malloc(max_se * sizeof(int)) - cdef int* se_e_c = malloc(max_se * sizeof(int)) - cdef int* se_w_r = malloc(max_se * sizeof(int)) - cdef int* se_w_c = malloc(max_se * sizeof(int)) - cdef int* se_n_r = malloc(max_se * sizeof(int)) - cdef int* se_n_c = malloc(max_se * sizeof(int)) - cdef int* se_s_r = malloc(max_se * sizeof(int)) - cdef int* se_s_c = malloc(max_se * sizeof(int)) - - # build attack and release borders - # by using difference along axis - - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 - - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 - - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 - - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 - - num_se_n = num_se_s = num_se_e = num_se_w = 0 - - for r in range(srows): - for c in range(scols): - if t_e[r,c]: - se_e_r[num_se_e] = r - centre_r - se_e_c[num_se_e] = c - centre_c - num_se_e += 1 - if t_w[r,c]: - se_w_r[num_se_w] = r - centre_r - se_w_c[num_se_w] = c - centre_c - num_se_w += 1 - if t_n[r,c]: - se_n_r[num_se_n] = r - centre_r - se_n_c[num_se_n] = c - centre_c - num_se_n += 1 - if t_s[r,c]: - se_s_r[num_se_s] = r - centre_r - se_s_c[num_se_s] = c - centre_c - num_se_s += 1 - - # initial population and histogram - for i in range(maxbin): - histo[i] = 0 - - pop = 0 - - for r in range(srows): - for c in range(scols): - rr = r - cc = c - if selem[r, c]: - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - - r = 0 - c = 0 - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # main loop - r = 0 - for even_row in range(0,rows,2): - # ---> west to east - for c in range(1,cols): - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # ---> east to west - for c in range(cols-2,-1,-1): - for s in range(num_se_w): - rr = r + se_w_r[s] + centre_r - cc = c + se_w_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_e): - rr = r + se_e_r[s] + centre_r - cc = c + se_e_c[s] + centre_c + 1 - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - r += 1 # pass to the next row - if r>=rows: - break - - # ---> north to south - for s in range(num_se_s): - rr = r + se_s_r[s] + centre_r - cc = c + se_s_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] += 1 - pop += 1. - for s in range(num_se_n): - rr = r + se_n_r[s] + centre_r - 1 - cc = c + se_n_c[s] + centre_c - if emask_data[rr * ecols + cc]: - value = eimage_data[rr * ecols + cc] - histo[value] -= 1 - pop -= 1. - - # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,eimage_data[(r+centre_r) * ecols + c + centre_c], - bitdepth,maxbin,midbin,s0,s1) - # kernel ------------------------------------------- - - # release memory allocated by malloc - - free(se_e_r) - free(se_e_c) - free(se_w_r) - free(se_w_c) - free(se_n_r) - free(se_n_c) - free(se_s_r) - free(se_s_c) - - free(histo) - - return out diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index e783d7ea..46028ad3 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -15,14 +15,14 @@ import numpy as np cimport numpy as np # import main loop -from _core16b cimport _core16b +from _core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): +cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,bilat_pop=0 cdef float mean = 0. @@ -39,7 +39,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit return (0) -cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): +cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i,bilat_pop=0 if pop: @@ -61,7 +61,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,0.,0.,s0,s1) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -71,5 +71,5 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) + return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,s0,s1) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index b7cac4dc..e1f996f7 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -14,7 +14,6 @@ def configuration(parent_package='', top_path=None): cython(['_core8.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) - cython(['_core16b.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) cython(['_crank8_percentiles.pyx'], working_path=base_path) cython(['_crank16.pyx'], working_path=base_path) @@ -25,8 +24,6 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core16b', sources=['_core16b.c'], - include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 0887fa8f..e7d2436b 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -104,6 +104,15 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() + def test_compare_autolevels_16bit(self): + image = data.camera().astype(np.uint16) + + selem = disk(20) + loc_autolevel = rank.autolevel(image,selem=selem) + loc_perc_autolevel = rank.percentile_autolevel(image,selem=selem,p0=.0,p1=1.) + + assert (loc_autolevel==loc_perc_autolevel).all() + def test_compare_8bit_vs_16bit(self): # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical i8 = data.camera() From 0e7271bfb4eff629ff73278a969c663cbcd5d337 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 15 Oct 2012 16:56:36 +0200 Subject: [PATCH 265/625] find error in autolevel and percentile autolevel (16bit) --- skimage/rank/tests/demo_single.py | 5 +++-- skimage/rank/tests/test_suite.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 7ff12b28..96f1fbeb 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -9,11 +9,12 @@ import skimage.rank as rank if __name__ == '__main__': a8 = data.camera() - a16 = data.camera().astype(np.uint16) + a16 = data.camera().astype(np.uint16)*4 selem = disk(10) f8= rank.percentile_autolevel(a8,selem,p0=.0,p1=1.) f16= rank.autolevel(a16,selem) + f16p= rank.percentile_autolevel(a16,selem,p0=.0,p1=1.) print f8==f16 @@ -21,7 +22,7 @@ if __name__ == '__main__': plt.subplot(1,2,1) plt.imshow(f16) plt.subplot(1,2,2) - plt.imshow(f8) + plt.imshow(f16p-f16) plt.show() diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index e7d2436b..9ed39956 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -105,7 +105,7 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() def test_compare_autolevels_16bit(self): - image = data.camera().astype(np.uint16) + image = data.camera().astype(np.uint16)*4 selem = disk(20) loc_autolevel = rank.autolevel(image,selem=selem) From f35cd7aa3af8ab35f6fbc598c32682b86b3fd9c7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 11:13:09 +0200 Subject: [PATCH 266/625] fix error in autolevel and percentile autolevel (16bit) --- skimage/rank/_crank16_percentiles.pyx | 2 +- skimage/rank/tests/demo_single.py | 13 ++++++++++--- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index b270965b..4fa3661c 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -33,7 +33,7 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16 delta = imax-imin if delta>0: - return (255*(int_min(int_max(imin,g),imax)-imin)/delta) + return (1.0*(maxbin-1)*(int_min(int_max(imin,g),imax)-imin)/delta) else: return (imax-imin) else: diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/tests/demo_single.py index 96f1fbeb..b601b226 100644 --- a/skimage/rank/tests/demo_single.py +++ b/skimage/rank/tests/demo_single.py @@ -16,15 +16,22 @@ if __name__ == '__main__': f16= rank.autolevel(a16,selem) f16p= rank.percentile_autolevel(a16,selem,p0=.0,p1=1.) - print f8==f16 + print f16==f16p plt.figure() - plt.subplot(1,2,1) + plt.subplot(1,3,1) plt.imshow(f16) - plt.subplot(1,2,2) + plt.colorbar() + plt.subplot(1,3,2) + plt.imshow(f16p) + plt.colorbar() + plt.subplot(1,3,3) plt.imshow(f16p-f16) + plt.colorbar() plt.show() + print f16 + print f16p From 8f70a1d01ce315a44b9c65c5b516f125c60ace03 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 12:12:28 +0200 Subject: [PATCH 267/625] add histogram_increment and decrement to core8 --- skimage/rank/_core8.pyx | 48 ++++++++++++++++++----------------------- 1 file changed, 21 insertions(+), 27 deletions(-) diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 87588813..19e09aec 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -23,6 +23,14 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b # 8 bit core kernel #--------------------------------------------------------------------------- +cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint8_t value): + histo[value] += 1 + pop[0] += 1. + +cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint8_t value): + histo[value] -= 1 + pop[0] -= 1. + cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask @@ -157,9 +165,7 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa cc = c - centre_c if selem[r, c]: if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) r = 0 c = 0 @@ -176,16 +182,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) @@ -200,16 +203,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) @@ -221,16 +221,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) @@ -245,16 +242,13 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) From b6b202ae7bbb1efcc88ccb86317903e0bb370fe3 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 12:36:05 +0200 Subject: [PATCH 268/625] add histogram_increment and decrement to core16 --- skimage/rank/_core16.pyx | 51 ++++++++++++++--------------- skimage/rank/_core8.pyx | 5 ++- skimage/rank/tests/test_suite.py | 55 ++++++++++++++++++++++++++++++-- 3 files changed, 81 insertions(+), 30 deletions(-) diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index 2e55aa8b..edef264b 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -22,6 +22,14 @@ from libc.stdlib cimport malloc, free cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint16_t value): + histo[value] += 1 + pop[0] += 1. + +cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint16_t value): + histo[value] -= 1 + pop[0] -= 1. + cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask @@ -79,6 +87,9 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ else: mask = np.ascontiguousarray(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + if out is None: out = np.zeros((rows, cols), dtype=np.uint16) else: @@ -165,9 +176,7 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ cc = c - centre_c if selem[r, c]: if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) r = 0 c = 0 @@ -185,16 +194,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], @@ -210,16 +216,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], @@ -232,16 +235,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], @@ -257,16 +257,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] += 1 - pop += 1. + histogram_increment(histo,&pop,image_data[rr * cols + cc]) + for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): - value = image_data[rr * cols + cc] - histo[value] -= 1 - pop -= 1. + histogram_decrement(histo,&pop,image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 19e09aec..86c40a6d 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -76,6 +76,9 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa else: mask = np.ascontiguousarray(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + if out is None: out = np.zeros((rows, cols), dtype=np.uint8) else: @@ -183,7 +186,7 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa cc = c + se_e_c[s] if is_in_mask(rows,cols,rr,cc,mask_data): histogram_increment(histo,&pop,image_data[rr * cols + cc]) - + for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index 9ed39956..d0e2c319 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -16,6 +16,7 @@ class TestSequenceFunctions(unittest.TestCase): def test_random_sizes(self): # make sure the size is not a problem + niter = 10 elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') for m,n in np.random.random_integers(1,100,size=(10,2)): @@ -39,8 +40,9 @@ class TestSequenceFunctions(unittest.TestCase): r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) self.assertTrue(a16.shape == r.shape) - def test_compare_with_cmorph(self): + def test_compare_with_cmorph_dilate(self): #compare the result of maximum filter with dilate + a = (np.random.random((500,500))*256).astype('uint8') for r in range(1,20,1): @@ -50,7 +52,21 @@ class TestSequenceFunctions(unittest.TestCase): cm = cmorph.dilate(image=a,selem = elem) self.assertTrue((rc==cm).all()) + def test_compare_with_cmorph_erode(self): + #compare the result of maximum filter with erode + + a = (np.random.random((500,500))*256).astype('uint8') + + for r in range(1,20,1): + elem = np.ones((r,r),dtype='uint8') + # elem = (np.random.random((r,r))>.5).astype('uint8') + rc = _crank8.minimum(image=a,selem = elem) + cm = cmorph.erode(image=a,selem = elem) + self.assertTrue((rc==cm).all()) + def test_bitdepth(self): + # test the different bit depth for rank16 + elem = np.ones((3,3),dtype='uint8') a16 = np.ones((100,100),dtype='uint16')*255 r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) @@ -64,6 +80,8 @@ class TestSequenceFunctions(unittest.TestCase): r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) def test_population(self): + # check the number of valid pixels in the neighborhood + a = np.zeros((5,5),dtype='uint8') elem = np.ones((3,3),dtype='uint8') p = _crank8.pop(image=a,selem = elem) @@ -75,6 +93,8 @@ class TestSequenceFunctions(unittest.TestCase): np.testing.assert_array_equal(r,p) def test_structuring_element(self): + # check the output for a custom structuring element + a = np.zeros((6,6),dtype='uint8') a[2,2] = 255 elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') @@ -91,11 +111,37 @@ class TestSequenceFunctions(unittest.TestCase): @unittest.expectedFailure def test_fail_on_bitdepth(self): # should fail because data bitdepth is too high for the function + a16 = np.ones((100,100),dtype='uint16')*255 elem = np.ones((3,3),dtype='uint8') f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + def test_output(self): + #check rank function with external OUT output array + + selem = disk(20) + a = (np.random.random((500,500))*256).astype('uint8') + out = np.zeros_like(a) + f1 = rank.mean(a,selem,out=out) + f2 = rank.mean(a,selem) + np.testing.assert_array_equal(f1,f2) + np.testing.assert_array_equal(out,f2) + + @unittest.expectedFailure + def test_inplace_output(self): + #rank filters are not supposed to filter inplace + + selem = disk(20) + a = (np.random.random((500,500))*256).astype('uint8') + out = a + f = rank.mean(a,selem,out=out) + np.testing.assert_array_equal(f,out) + + def test_compare_autolevels(self): + # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0 + # should returns the same arrays + image = data.camera() selem = disk(20) @@ -105,6 +151,9 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() def test_compare_autolevels_16bit(self): + # compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and p1=1.0 + # should returns the same arrays + image = data.camera().astype(np.uint16)*4 selem = disk(20) @@ -114,7 +163,9 @@ class TestSequenceFunctions(unittest.TestCase): assert (loc_autolevel==loc_perc_autolevel).all() def test_compare_8bit_vs_16bit(self): - # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical + # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) + # should be identical + i8 = data.camera() i16 = i8.astype(np.uint16) assert (i8==i16).all() From e7e60a6cdb8f11c460b4a6507986ebf7dc3ab028 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 14:40:42 +0200 Subject: [PATCH 269/625] keep true test in /test, move temporary tests in local --- skimage/rank/local/__init__.py | 1 + skimage/rank/{tests => local}/demo_16bitbilateral.py | 0 skimage/rank/{tests => local}/demo_all.py | 0 skimage/rank/{tests => local}/demo_benchmark.py | 2 +- skimage/rank/{tests => local}/demo_single.py | 0 skimage/rank/{tests => local}/test_morph_contr_enh.py | 0 skimage/rank/{tests => local}/test_rank.py | 0 skimage/rank/{tests => local}/tools.py | 0 skimage/rank/tests/test_suite.py | 2 +- 9 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 skimage/rank/local/__init__.py rename skimage/rank/{tests => local}/demo_16bitbilateral.py (100%) rename skimage/rank/{tests => local}/demo_all.py (100%) rename skimage/rank/{tests => local}/demo_benchmark.py (98%) rename skimage/rank/{tests => local}/demo_single.py (100%) rename skimage/rank/{tests => local}/test_morph_contr_enh.py (100%) rename skimage/rank/{tests => local}/test_rank.py (100%) rename skimage/rank/{tests => local}/tools.py (100%) diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py new file mode 100644 index 00000000..10b6fb15 --- /dev/null +++ b/skimage/rank/local/__init__.py @@ -0,0 +1 @@ +__author__ = 'olivier' diff --git a/skimage/rank/tests/demo_16bitbilateral.py b/skimage/rank/local/demo_16bitbilateral.py similarity index 100% rename from skimage/rank/tests/demo_16bitbilateral.py rename to skimage/rank/local/demo_16bitbilateral.py diff --git a/skimage/rank/tests/demo_all.py b/skimage/rank/local/demo_all.py similarity index 100% rename from skimage/rank/tests/demo_all.py rename to skimage/rank/local/demo_all.py diff --git a/skimage/rank/tests/demo_benchmark.py b/skimage/rank/local/demo_benchmark.py similarity index 98% rename from skimage/rank/tests/demo_benchmark.py rename to skimage/rank/local/demo_benchmark.py index c3046083..feae3a8b 100644 --- a/skimage/rank/tests/demo_benchmark.py +++ b/skimage/rank/local/demo_benchmark.py @@ -6,7 +6,7 @@ from skimage.morphology import dilation import skimage.rank as rank from skimage.filter import median_filter -from tools import log_timing +from skimage.rank.local.tools import log_timing @log_timing def cr_max(image,selem): diff --git a/skimage/rank/tests/demo_single.py b/skimage/rank/local/demo_single.py similarity index 100% rename from skimage/rank/tests/demo_single.py rename to skimage/rank/local/demo_single.py diff --git a/skimage/rank/tests/test_morph_contr_enh.py b/skimage/rank/local/test_morph_contr_enh.py similarity index 100% rename from skimage/rank/tests/test_morph_contr_enh.py rename to skimage/rank/local/test_morph_contr_enh.py diff --git a/skimage/rank/tests/test_rank.py b/skimage/rank/local/test_rank.py similarity index 100% rename from skimage/rank/tests/test_rank.py rename to skimage/rank/local/test_rank.py diff --git a/skimage/rank/tests/tools.py b/skimage/rank/local/tools.py similarity index 100% rename from skimage/rank/tests/tools.py rename to skimage/rank/local/tools.py diff --git a/skimage/rank/tests/test_suite.py b/skimage/rank/tests/test_suite.py index d0e2c319..9d2bcfea 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/rank/tests/test_suite.py @@ -66,7 +66,7 @@ class TestSequenceFunctions(unittest.TestCase): def test_bitdepth(self): # test the different bit depth for rank16 - + elem = np.ones((3,3),dtype='uint8') a16 = np.ones((100,100),dtype='uint16')*255 r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) From f730a644640685a62195b20ddc5027b7b3041254 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 15:03:34 +0200 Subject: [PATCH 270/625] clean up --- doc/examples/plot_local_equalize.py | 9 +-- doc/examples/plot_local_threshold.py | 9 ++- doc/examples/plot_watershed.py | 2 +- skimage/rank/bilateral_rank.py | 14 ++-- skimage/rank/percentile_rank.py | 56 +++++++--------- skimage/rank/rank.py | 98 ++++++++++++---------------- 6 files changed, 82 insertions(+), 106 deletions(-) diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index ec28067d..897989f0 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -5,11 +5,9 @@ Local Histogram Equalization This examples enhances an image with low contrast, using a method called *local histogram equalization*, which "spreads out the most frequent intensity -values" in an image . The equalized image [1]_ has a roughly linear cumulative -distribution function for each pixel neighborhood. The local version [2]_ of the histogram -equalization emphasized every local graylevel variations. - -to be adjusted... +values" in an image . +The equalized image [1]_ has a roughly linear cumulative distribution function for each pixel neighborhood. +The local version [2]_ of the histogram equalization emphasized every local graylevel variations. .. [1] http://en.wikipedia.org/wiki/Histogram_equalization .. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization @@ -22,7 +20,6 @@ from skimage import exposure from skimage import rank from skimage.morphology import disk - import matplotlib.pyplot as plt import numpy as np diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py index 01077571..c5810156 100644 --- a/doc/examples/plot_local_threshold.py +++ b/doc/examples/plot_local_threshold.py @@ -14,9 +14,12 @@ calculates thresholds in regions of size `block_size` surrounding each pixel (i.e. local neighborhoods). Each threshold value is the weighted mean of the local neighborhood minus an offset value. -Added local threshold using rank filter +An other approach is to binarize locally the image using local histogram distribution. -to be adjusted ... +rank.threshold function set pixels higher than the local mean to 1, to 0 otherwize +rank.morph_contr_enh replaces each pixel by the local minimum (or local maximum) if the +pixel gray level is more close to the local minimum (resp. by the local maximum +if the pixel gray level is more close to the local maximum). """ import matplotlib.pyplot as plt @@ -36,7 +39,7 @@ binary_global = image > global_thresh block_size = 40 binary_adaptive = threshold_adaptive(image, block_size, offset=10) -selem = disk(10) +selem = disk(20) loc_thresh = threshold(image,selem=selem) loc_morph_contr_enh = morph_contr_enh(image,selem=selem) diff --git a/doc/examples/plot_watershed.py b/doc/examples/plot_watershed.py index 9fce196f..50857b2c 100644 --- a/doc/examples/plot_watershed.py +++ b/doc/examples/plot_watershed.py @@ -26,7 +26,7 @@ See Wikipedia_ for more details on the algorithm. """ import numpy as np -e + import matplotlib.pyplot as plt from skimage.morphology import watershed, is_local_maximum diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index a76133a6..cde9e736 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -35,10 +35,9 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int define the [s0,s1] interval to be considered for computing the value. @@ -110,10 +109,9 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int define the [s0,s1] interval to be considered for computing the value. diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 6cc273e3..64f76bcb 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -47,10 +47,9 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -120,10 +119,9 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -194,10 +192,9 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -267,10 +264,9 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -341,10 +337,9 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -414,10 +409,9 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -488,10 +482,9 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. @@ -561,10 +554,9 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0.,...,1.] define the [p0,p1] percentile interval to be considered for computing the value. diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index dafe0715..9d841d76 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -44,10 +44,9 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -116,10 +115,9 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -187,10 +185,9 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -258,10 +255,9 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -331,10 +327,9 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -403,10 +398,9 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -475,10 +469,9 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -547,10 +540,9 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -619,10 +611,9 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -692,10 +683,9 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -765,10 +755,9 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -837,10 +826,9 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -909,10 +897,9 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- @@ -982,10 +969,9 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). - shift_x, shift_y : bool - shift structuring element about center point. This only affects - eccentric structuring elements (i.e. selem with even numbered sides). - Shift is bounded to the structuring element sizes. + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- From c42fabc53f1615ac150dab551e08d0d5f651d86c Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 16:20:12 +0200 Subject: [PATCH 271/625] compare ndimage.percentile in demo --- skimage/rank/local/demo_benchmark.py | 75 ++++++++++++++++------------ 1 file changed, 44 insertions(+), 31 deletions(-) diff --git a/skimage/rank/local/demo_benchmark.py b/skimage/rank/local/demo_benchmark.py index feae3a8b..d3c3d00a 100644 --- a/skimage/rank/local/demo_benchmark.py +++ b/skimage/rank/local/demo_benchmark.py @@ -1,44 +1,53 @@ import numpy as np import matplotlib.pyplot as plt +import time from skimage import data -from skimage.morphology import dilation -import skimage.rank as rank +from skimage.morphology import dilation,disk from skimage.filter import median_filter +from scipy.ndimage.filters import percentile_filter +import skimage.rank as rank -from skimage.rank.local.tools import log_timing +def log_timing(func): + """ Decorator that returns both function results and execution time + (result, ms) + """ + def wrapper(*arg): + t1 = time.time() + res = func(*arg) + t2 = time.time() + ms = (t2-t1)*1000.0 + print '%s took %0.3f ms' % (func.func_name, ms) + return (res,ms) + return wrapper -@log_timing -def cr_max(image,selem): - return rank.maximum(image=image,selem = selem) @log_timing def cr_med(image,selem): return rank.median(image=image,selem = selem) -@log_timing -def cm_dil(image,selem): - return dilation(image=image,selem = selem) - @log_timing def ctmf_med(image,radius): return median_filter(image=image,radius=radius) +@log_timing +def ndi_med(image,n): + return percentile_filter(image,50,size=n*2-1) def compare_dilate(): - """comparison between + """ Comparison between - crank.maximum rankfilter implementation - cmorph.dilate cython implementation on increasing structuring element size and increasing image size """ -# a = (np.random.random((500,500))*256).astype('uint8') a = data.camera() rec = [] e_range = range(1,20,1) for r in e_range: - elem = np.ones((r,r),dtype='uint8') +# elem = np.ones((r,r),dtype='uint8') + elem = disk(r+1) # elem = (np.random.random((r,r))>.5).astype('uint8') rc,ms_rc = cr_max(a,elem) rcm,ms_rcm = cm_dil(a,elem) @@ -56,7 +65,8 @@ def compare_dilate(): plt.imshow(np.hstack((rc,rcm))) r = 9 - elem = np.ones((r,r),dtype='uint8') +# elem = np.ones((r,r),dtype='uint8') + elem = disk(r+1) rec = [] s_range = range(100,1000,100) @@ -79,7 +89,7 @@ def compare_dilate(): plt.show() def compare_median(): - """comparison between + """ Comparison between - crank.median rankfilter implementation - ctmf.median_filter filter @@ -88,47 +98,50 @@ def compare_median(): a = data.camera() rec = [] - e_range = range(2,40,4) + e_range = range(2,30,4) for r in e_range: - elem = np.ones((2*r+1,2*r+1),dtype='uint8') - # elem = (np.random.random((r,r))>.5).astype('uint8') + elem = disk(r+1) rc,ms_rc = cr_med(a,elem) rctmf,ms_rctmf = ctmf_med(a,r) - rec.append((ms_rc,ms_rctmf)) + rndi,ms_ndi = ndi_med(a,r) + rec.append((ms_rc,ms_rctmf,ms_ndi)) # check if results are identical -# assert (rc==rctmf).all() + # obviously they cannot be identical since structuring element are different (octagon<>disk) + # assert (rc==rctmf).all() rec = np.asarray(rec) plt.figure() plt.title('increasing element size') plt.plot(e_range,rec) - plt.legend(['rank.median','ctmf.median_filter']) - plt.figure() - plt.imshow(np.hstack((rc,rctmf))) + plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('element radius') + plt.figure() + plt.imshow(np.hstack((rc,rctmf,rndi))) + plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') r = 9 - elem = np.ones((r*2+1,r*2+1),dtype='uint8') + elem = disk(r+1) rec = [] - s_range = range(100,1000,100) + s_range = [100,200,500,1000,2000] for s in s_range: a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = cr_max(a,elem) + (rc,ms_rc) = cr_med(a,elem) rctmf,ms_rctmf = ctmf_med(a,r) - rec.append((ms_rc,ms_rctmf)) -# assert (rc==rcm).all() + rndi,ms_ndi = ndi_med(a,r) + rec.append((ms_rc,ms_rctmf,ms_ndi)) + # check if results are identical + # obviously they cannot be identical since structuring element are different (octagon<>disk) + # assert (rc==rctmf).all() rec = np.asarray(rec) plt.figure() plt.title('increasing image size') plt.plot(s_range,rec) - plt.legend(['rank.median','ctmf.median_filter']) - plt.figure() - plt.imshow(np.hstack((rc,rctmf))) + plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('image size') From 1e72afdb8bd79cb44fb77a2a25070f729a4aaaac Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 16 Oct 2012 17:03:22 +0200 Subject: [PATCH 272/625] add _apply(func8,func16,...) helper function --- skimage/rank/bilateral_rank.py | 47 ++++--- skimage/rank/percentile_rank.py | 123 +++++-------------- skimage/rank/rank.py | 209 +++++++------------------------- 3 files changed, 89 insertions(+), 290 deletions(-) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index cde9e736..5fd66e21 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -1,4 +1,7 @@ """ + +note: 8 bit images are casted into 16 bit image here + :author: Olivier Debeir, 2012 :license: modified BSD """ @@ -16,6 +19,21 @@ import _crank16_bilateral __all__ = ['bilateral_mean','bilateral_pop'] +def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + image = image.astype(np.uint16) + elif image.dtype == np.uint16: + pass + else: + raise TypeError("only uint8 and uint16 image supported!") + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Return greyscale local bilateral_mean of an image. @@ -76,19 +94,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - image = image.astype(np.uint16) - elif image.dtype == np.uint16: - pass - else: - raise TypeError("only uint8 and uint16 image supported!") - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_bilateral.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + + return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -150,18 +157,6 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals [3, 4, 3, 4, 3]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - image = image.astype(np.uint16) - elif image.dtype == np.uint16: - pass - else: - raise TypeError("only uint8 and uint16 image supported!") - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_bilateral.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 64f76bcb..e7d760da 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -29,6 +29,20 @@ __all__ = ['percentile_autolevel','percentile_gradient', 'percentile_mean','percentile_mean_substraction', 'percentile_morph_contr_enh','percentile_pop'] +def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + else: + raise TypeError("only uint8 and uint16 image supported!") + def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. @@ -88,18 +102,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile_gradient of an image. @@ -160,19 +164,8 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. @@ -233,18 +226,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean_substraction of an image. @@ -305,19 +288,8 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals [1536, 1365, 1024, 1365, 1536]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.mean_substraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. @@ -378,18 +350,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile of an image. @@ -451,18 +413,8 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.percentile(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local pop of an image. @@ -523,18 +475,8 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal [4, 6, 6, 6, 4]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. @@ -596,16 +538,5 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16_percentiles.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) \ No newline at end of file diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 9d841d76..c080c291 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -26,6 +26,20 @@ import _crank16,_crank8 __all__ = ['autolevel','bottomhat','equalize','gradient','maximum','mean' ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] +def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): + selem = img_as_ubyte(selem) + if mask is not None: + mask = img_as_ubyte(mask) + if image.dtype == np.uint8: + return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + elif image.dtype == np.uint16: + bitdepth = find_bitdepth(image) + if bitdepth>11: + raise ValueError("only uint16 <4096 image (12bit) supported!") + return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + else: + raise TypeError("only uint8 and uint16 image supported!") + def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local autolevel of an image. @@ -84,18 +98,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.autolevel(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local bottomhat of an image. @@ -154,18 +158,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.bottomhat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local equalize of an image. @@ -224,18 +218,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [2730, 4095, 4095, 4095, 2730], [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.equalize(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local gradient of an image. @@ -295,19 +279,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.gradient(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local maximum of an image. @@ -367,18 +340,8 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.maximum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. @@ -438,18 +401,8 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.mean(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local meansubstraction of an image. @@ -509,18 +462,8 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F [1535, 1364, 1023, 1364, 1535]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.meansubstraction(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local median of an image. @@ -580,18 +523,8 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 4095, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.median(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.median, _crank16.median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local minimum of an image. @@ -652,18 +585,8 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.minimum(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.minimum, _crank16.minimum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local modal of an image. @@ -724,18 +647,8 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 500, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.modal(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.modal, _crank16.modal, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local morph_contr_enh of an image. @@ -795,18 +708,8 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa [ 0, 0, 0, 0, 0]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.morph_contr_enh(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local pop of an image. @@ -866,18 +769,8 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [4, 6, 6, 6, 4]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.pop(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.pop, _crank16.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local threshold of an image. @@ -938,18 +831,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.threshold(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local tophat of an image. @@ -1008,18 +891,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [4095, 0, 0, 0, 4095], [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ - selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - return _crank8.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) - elif image.dtype == np.uint16: - bitdepth = find_bitdepth(image) - if bitdepth>11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return _crank16.tophat(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) - else: - raise TypeError("only uint8 and uint16 image supported!") + + return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) if __name__ == "__main__": import sys From 5b169f2a5e1b459e192256035e156fd0197e1f70 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 17 Oct 2012 12:07:20 +0200 Subject: [PATCH 273/625] autopep8 sources --- skimage/rank/__init__.py | 2 +- skimage/rank/_core16.pxd | 7 +- skimage/rank/_core16.pyx | 150 ++++++------ skimage/rank/_core8.pxd | 4 +- skimage/rank/_core8.pyx | 131 ++++++----- skimage/rank/_crank16.pyx | 327 ++++++++++++++------------ skimage/rank/_crank16_bilateral.pyx | 45 ++-- skimage/rank/_crank16_percentiles.pyx | 212 +++++++++-------- skimage/rank/_crank8.pyx | 301 +++++++++++++----------- skimage/rank/_crank8_percentiles.pyx | 212 +++++++++-------- skimage/rank/bilateral_rank.py | 23 +- skimage/rank/generic.py | 3 +- skimage/rank/percentile_rank.py | 78 ++++-- skimage/rank/rank.py | 29 ++- skimage/rank/setup.py | 33 +-- 15 files changed, 844 insertions(+), 713 deletions(-) diff --git a/skimage/rank/__init__.py b/skimage/rank/__init__.py index 09812649..30d936db 100644 --- a/skimage/rank/__init__.py +++ b/skimage/rank/__init__.py @@ -1,3 +1,3 @@ from .rank import * from .percentile_rank import * -from .bilateral_rank import * \ No newline at end of file +from .bilateral_rank import * diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index f9bb47b3..a2843f76 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -8,10 +8,11 @@ cimport numpy as np cdef inline int int_max(int a, int b) cdef inline int int_min(int a, int b) -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core16( + np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y,Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) \ No newline at end of file + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) diff --git a/skimage/rank/_core16.pyx b/skimage/rank/_core16.pyx index edef264b..81fab0b4 100644 --- a/skimage/rank/_core16.pyx +++ b/skimage/rank/_core16.pyx @@ -19,18 +19,20 @@ from libc.stdlib cimport malloc, free #--------------------------------------------------------------------------- # generic cdef functions -cdef inline int int_max(int a, int b): return a if a >= b else b -cdef inline int int_min(int a, int b): return a if a <= b else b +cdef inline int int_max(int a, int b): + return a if a >= b else b +cdef inline int int_min(int a, int b): + return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint16_t value): +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] += 1 pop[0] += 1. -cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint16_t value): +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] -= 1 pop[0] -= 1. -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask returns 0 otherwise @@ -38,19 +40,20 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: - if mask[r*cols+c]: + if mask[r * cols + c]: return 1 else: return 0 -cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_t,Py_ssize_t,Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core16( + np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y,Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -69,16 +72,15 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ assert centre_c >= 0 assert centre_r < srows assert centre_c < scols - assert bitdepth in range(2,13) - - maxbin_list = [0,0,4,8,16,32,64,128,256,512,1024,2048,4096] - midbin_list = [0,0,2,4,8,16,32,64,128,256,512,1024,2048] + assert bitdepth in range(2, 13) + maxbin_list = [0, 0, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] + midbin_list = [0, 0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] #set maxbin and midbin - cdef Py_ssize_t maxbin=maxbin_list[bitdepth],midbin=midbin_list[bitdepth] + cdef Py_ssize_t maxbin = maxbin_list[bitdepth], midbin = midbin_list[bitdepth] - assert (imageout.data - cdef np.uint16_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data + cdef np.uint16_t * out_data = out.data + cdef np.uint16_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row cdef float pop # number of pixels actually inside the neighborhood (float) # allocate memory with malloc - cdef Py_ssize_t max_se = srows*scols + cdef Py_ssize_t max_se = srows * scols # number of element in each attack border cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t* histo = malloc(maxbin * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 + t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) + t_e = np.diff(t, axis=1) == -1 - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 + t = np.hstack((np.zeros((selem.shape[0], 1)), selem)) + t_w = np.diff(t, axis=1) == 1 - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 + t = np.vstack((selem, np.zeros((1, selem.shape[1])))) + t_s = np.diff(t, axis=0) == -1 - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 + t = np.vstack((np.zeros((1, selem.shape[1])), selem)) + t_n = np.diff(t, axis=0) == 1 num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): - if t_e[r,c]: + if t_e[r, c]: se_e_r[num_se_e] = r - centre_r se_e_c[num_se_e] = c - centre_c num_se_e += 1 - if t_w[r,c]: + if t_w[r, c]: se_w_r[num_se_w] = r - centre_r se_w_c[num_se_w] = c - centre_c num_se_w += 1 - if t_n[r,c]: + if t_n[r, c]: se_n_r[num_se_n] = r - centre_r se_n_c[num_se_n] = c - centre_c num_se_n += 1 - if t_s[r,c]: + if t_s[r, c]: se_s_r[num_se_s] = r - centre_r se_s_c[num_se_s] = c - centre_c num_se_s += 1 @@ -175,99 +177,101 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_ rr = r - centre_r cc = c - centre_c if selem[r, c]: - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) r = 0 c = 0 # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- # main loop r = 0 - for even_row in range(0,rows,2): + for even_row in range(0, rows, 2): # ---> west to east - for c in range(1,cols): + for c in range(1, cols): for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- # ---> east to west - for c in range(cols-2,-1,-1): + for c in range(cols - 2, -1, -1): for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ], - bitdepth,maxbin,midbin,p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + bitdepth, maxbin, midbin, p0, p1, s0, s1) # kernel ------------------------------------------- # release memory allocated by malloc diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index a677e915..1a170500 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -8,10 +8,10 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) # 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core8( + np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) - diff --git a/skimage/rank/_core8.pyx b/skimage/rank/_core8.pyx index 86c40a6d..7851388d 100644 --- a/skimage/rank/_core8.pyx +++ b/skimage/rank/_core8.pyx @@ -15,23 +15,25 @@ cimport numpy as np from libc.stdlib cimport malloc, free # generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b +cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): + return a if a >= b else b +cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): + return a if a <= b else b #--------------------------------------------------------------------------- # 8 bit core kernel #--------------------------------------------------------------------------- -cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint8_t value): +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] += 1 pop[0] += 1. -cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint8_t value): +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] -= 1 pop[0] -= 1. -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask): +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and inside the given mask returns 0 otherwise @@ -39,17 +41,18 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: - if mask[r*cols+c]: + if mask[r * cols + c]: return 1 else: return 0 -cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), +cdef inline _core8( + np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, - char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): """ Main loop, this function computes the histogram for each image point - data is uint8 - result is uint8 casted @@ -88,9 +91,9 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa # define pointers to the data - cdef np.uint8_t* out_data = out.data - cdef np.uint8_t* image_data = image.data - cdef np.uint8_t* mask_data = mask.data + cdef np.uint8_t * out_data = out.data + cdef np.uint8_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row @@ -99,59 +102,59 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa cdef float pop # allocate memory with malloc - cdef Py_ssize_t max_se = srows*scols + cdef Py_ssize_t max_se = srows * scols # number of element in each attack border cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t* histo = malloc(256 * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 attack borders # east, west, north and south # e.g. se_e_r lists the rows of the east structuring element border - cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis - t = np.hstack((selem,np.zeros((selem.shape[0],1)))) - t_e = np.diff(t,axis=1)==-1 + t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) + t_e = np.diff(t, axis=1) == -1 - t = np.hstack((np.zeros((selem.shape[0],1)),selem)) - t_w = np.diff(t,axis=1)==1 + t = np.hstack((np.zeros((selem.shape[0], 1)), selem)) + t_w = np.diff(t, axis=1) == 1 - t = np.vstack((selem,np.zeros((1,selem.shape[1])))) - t_s = np.diff(t,axis=0)==-1 + t = np.vstack((selem, np.zeros((1, selem.shape[1])))) + t_s = np.diff(t, axis=0) == -1 - t = np.vstack((np.zeros((1,selem.shape[1])),selem)) - t_n = np.diff(t,axis=0)==1 + t = np.vstack((np.zeros((1, selem.shape[1])), selem)) + t_n = np.diff(t, axis=0) == 1 num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): - if t_e[r,c]: + if t_e[r, c]: se_e_r[num_se_e] = r - centre_r se_e_c[num_se_e] = c - centre_c num_se_e += 1 - if t_w[r,c]: + if t_w[r, c]: se_w_r[num_se_w] = r - centre_r se_w_c[num_se_w] = c - centre_c num_se_w += 1 - if t_n[r,c]: + if t_n[r, c]: se_n_r[num_se_n] = r - centre_r se_n_c[num_se_n] = c - centre_c num_se_n += 1 - if t_s[r,c]: + if t_s[r, c]: se_s_r[num_se_s] = r - centre_r se_s_c[num_se_s] = c - centre_c num_se_s += 1 @@ -167,94 +170,99 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa rr = r - centre_r cc = c - centre_c if selem[r, c]: - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) r = 0 c = 0 # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- # main loop r = 0 - for even_row in range(0,rows,2): + for even_row in range(0, rows, 2): # ---> west to east - for c in range(1,cols): + for c in range(1, cols): for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * + cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- # ---> east to west - for c in range(cols-2,-1,-1): + for c in range(cols - 2, -1, -1): for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel( + histo, pop, image_data[r * cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- r += 1 # pass to the next row - if r>=rows: + if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_increment(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] - if is_in_mask(rows,cols,rr,cc,mask_data): - histogram_decrement(histo,&pop,image_data[rr * cols + cc]) + if is_in_mask(rows, cols, rr, cc, mask_data): + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],p0,p1,s0,s1) + out_data[r * cols + c] = kernel(histo, pop, image_data[r * + cols + c], p0, p1, s0, s1) # kernel -------------------------------------------------------------------- # release memory allocated by malloc @@ -271,4 +279,3 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa free(histo) return out - diff --git a/skimage/rank/_crank16.pyx b/skimage/rank/_crank16.pyx index 2fdda1e3..ce8510db 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/rank/_crank16.pyx @@ -21,13 +21,14 @@ from _core16 cimport _core16 # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax,delta +cdef inline np.uint16_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: imax = i break @@ -35,47 +36,50 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - delta = imax-imin - if delta>0: - return (1.*(maxbin-1)*(g-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint16_t > (1. * (maxbin - 1) * (g - imin) / delta) else: - return (imax-imin) + return < np.uint16_t > (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_bottomhat( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin): if histo[i]: break - return (g-i) + return < np.uint16_t > (g - i) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_equalize( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. if pop: for i in range(maxbin): sum += histo[i] - if i>=g: + if i >= g: break - return (((maxbin-1)*sum)/pop) + return < np.uint16_t > (((maxbin - 1) * sum) / pop) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax +cdef inline np.uint16_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: imax = i break @@ -83,96 +87,103 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - return (imax-imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_maximum( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: - return (i) + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): - mean += histo[i]*i - return (mean/pop) + mean += histo[i] * i + return < np.uint16_t > (mean / pop) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_meansubstraction( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): - mean += histo[i]*i - return ((g-mean/pop)/2.+(midbin-1)) + mean += histo[i] * i + return < np.uint16_t > ((g - mean / pop) / 2. + (midbin - 1)) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_median( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - cdef float sum = pop/2.0 + cdef float sum = pop / 2.0 if pop: for i in range(maxbin): if histo[i]: sum -= histo[i] - if sum<0: - return (i) + if sum < 0: + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_minimum( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(maxbin): if histo[i]: - return (i) + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t hmax=0,imax=0 +cdef inline np.uint16_t kernel_modal( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(maxbin): - if histo[i]>hmax: + if histo[i] > hmax: hmax = histo[i] imax = i - return (imax) + return < np.uint16_t > (imax) - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax +cdef inline np.uint16_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: imax = i break @@ -180,80 +191,89 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - if imax-g < g-imin: - return (imax) + if imax - g < g - imin: + return < np.uint16_t > (imax) else: - return (imin) + return < np.uint16_t > (imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return (pop) +cdef inline np.uint16_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + return < np.uint16_t > (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): - mean += histo[i]*i - return (g>(mean/pop)) + mean += histo[i] * i + return < np.uint16_t > (g > (mean / pop)) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint16_t g, -Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_tophat( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): if histo[i]: break - return (i-g) + return < np.uint16_t > (i - g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """bottom hat """ - return _core16(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def equalize(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local egalisation of the gray level """ - return _core16(kernel_equalize,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def gradient(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum - local minimum gray level """ - return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -262,34 +282,38 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local maximum gray level """ - return _core16(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """average gray level (clipped on uint8) """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """(g - average gray level)/2+midbin (clipped on uint8) """ - return _core16(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def median(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local median """ - return _core16(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -298,49 +322,54 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local minimum gray level """ - return _core16(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """morphological contrast enhancement """ - return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def modal(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """local mode """ - return _core16(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """returns maxbin-1 if gray level higher than local mean, 0 else """ - return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def tophat(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): """top hat """ - return _core16(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,0,0) + return _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index 46028ad3..b5103be4 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -22,54 +22,53 @@ from _core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,bilat_pop=0 +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 cdef float mean = 0. if pop: for i in range(maxbin): - if (g>(i-s0)) and (g<(i+s1)): + if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - mean += histo[i]*i + mean += histo[i] * i if bilat_pop: - return (mean/bilat_pop) + return < np.uint16_t > (mean / bilat_pop) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,bilat_pop=0 +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 if pop: for i in range(maxbin): - if (g>(i-s0)) and (g<(i+s1)): + if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - return (bilat_pop) + return < np.uint16_t > (bilat_pop) else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,0.,0.,s0,s1) + return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, 0., 0., s0, s1) def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,.0,.0,s0,s1) - + return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, s0, s1) diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/rank/_crank16_percentiles.pyx index 4fa3661c..527d2aed 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/rank/_crank16_percentiles.pyx @@ -7,64 +7,64 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16,int_min,int_max +from _core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(maxbin): sum += histo[i] - if sum>p0*pop: + if sum > p0 * pop: imin = i break sum = 0 - for i in range(maxbin-1,-1,-1): + for i in range(maxbin - 1, -1, -1): sum += histo[i] - if sum>p1*pop: + if sum > p1 * pop: imax = i break - delta = imax-imin - if delta>0: - return (1.0*(maxbin-1)*(int_min(int_max(imin,g),imax)-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint16_t > (1.0 * (maxbin - 1) * (int_min(int_max(imin, g), imax) - imin) / delta) else: - return (imax-imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: imin = i break sum = 0 - for i in range((maxbin-1),-1,-1): + for i in range((maxbin - 1), -1, -1): sum += histo[i] - if sum>=p1*pop: + if sum >= p1 * pop: imax = i break - return (imax-imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -72,19 +72,19 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t* histo, float pop, np.uint16_t g, n = 0 for i in range(maxbin): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i + mean += histo[i] * i - if n>0: - return (1.0*mean/n) + if n > 0: + return < np.uint16_t > (1.0 * mean / n) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -92,160 +92,166 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t* histo, float pop, n n = 0 for i in range(maxbin): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i - if n>0: - return ((g-(mean/n))*.5+midbin) + mean += histo[i] * i + if n > 0: + return < np.uint16_t > ((g - (mean / n)) * .5 + midbin) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(maxbin): sum += histo[i] - if sum>p0*pop: + if sum > p0 * pop: imin = i break sum = 0 - for i in range((maxbin-1),-1,-1): + for i in range((maxbin - 1), -1, -1): sum += histo[i] - if sum>p1*pop: + if sum > p1 * pop: imax = i break - if g>imax: - return imax - if gimin - if imax-g < g-imin: - return imax + if g > imax: + return < np.uint16_t > imax + if g < imin: + return < np.uint16_t > imin + if imax - g < g - imin: + return < np.uint16_t > imax else: - return imin + return < np.uint16_t > imin else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return (i) + return < np.uint16_t > (i) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,n +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, n if pop: sum = 0 n = 0 for i in range(maxbin): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return (n) + return < np.uint16_t > (n) else: - return (0) + return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint16_t g,Py_ssize_t bitdepth,Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(maxbin): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return ((maxbin-1)*(g>=i)) + return < np.uint16_t > ((maxbin - 1) * (g >= i)) else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return _core16(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return _core16(kernel_percentile,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,p0,p1,0,0) + return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/_crank8.pyx b/skimage/rank/_crank8.pyx index a0b20073..94124cf7 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/rank/_crank8.pyx @@ -21,12 +21,13 @@ from _core8 cimport _core8 # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax,delta +cdef inline np.uint8_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: imax = i break @@ -34,47 +35,49 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - delta = imax-imin - if delta>0: - return (255.*(g-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint8_t > (255. * (g - imin) / delta) else: - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_bottomhat( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(256): if histo[i]: break - return (g-i) + return < np.uint8_t > (g - i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_equalize( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. if pop: for i in range(256): sum += histo[i] - if i>=g: + if i >= g: break - return ((255*sum)/pop) + return < np.uint8_t > ((255 * sum) / pop) else: - return (0) - -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax + return < np.uint8_t > (0) +cdef inline np.uint8_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: imax = i break @@ -82,89 +85,95 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_maximum( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: - return (i) + return < np.uint8_t > (i) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(256): - mean += histo[i]*i - return (mean/pop) + mean += histo[i] * i + return < np.uint8_t > (mean / pop) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_meansubstraction( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(256): - mean += histo[i]*i - return ((g-mean/pop)/2.+127) + mean += histo[i] * i + return < np.uint8_t > ((g - mean / pop) / 2. + 127) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_median( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - cdef float sum = pop/2.0 + cdef float sum = pop / 2.0 if pop: for i in range(256): if histo[i]: sum -= histo[i] - if sum<0: - return (i) + if sum < 0: + return < np.uint8_t > (i) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_minimum( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(256): if histo[i]: - return (i) + return < np.uint8_t > (i) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t hmax=0,imax=0 +cdef inline np.uint8_t kernel_modal( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(256): - if histo[i]>hmax: + if histo[i] > hmax: hmax = histo[i] imax = i - return (imax) + return < np.uint8_t > (imax) - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i,imin,imax +cdef inline np.uint8_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: imax = i break @@ -172,77 +181,85 @@ float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): if histo[i]: imin = i break - if imax-g < g-imin: - return (imax) + if imax - g < g - imin: + return < np.uint8_t > (imax) else: - return (imin) + return < np.uint8_t > (imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return (pop) +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + return < np.uint8_t > (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(256): - mean += histo[i]*i - return (g>(mean/pop)) + mean += histo[i] * i + return < np.uint8_t > (g > (mean / pop)) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t* histo, float pop, np.uint8_t g, -float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_tophat( + Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - for i in range(255,-1,-1): + for i in range(255, -1, -1): if histo[i]: break - return (i-g) + return < np.uint8_t > (i - g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def equalize(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_equalize,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def gradient(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -251,34 +268,38 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum gray level """ - return _core8(kernel_maximum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """average gray level (clipped on uint8) """ - return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return _core8(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def median(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local median """ - return _core8(kernel_median,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -287,50 +308,54 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local minimum gray level """ - return _core8(kernel_minimum,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def modal(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """local mode """ - return _core8(kernel_modal,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def pop(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """returns the number of actual pixels of the structuring element inside the mask """ - return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) + return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def tophat(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): """top hat """ - return _core8(kernel_tophat,image,selem,mask,out,shift_x,shift_y,.0,.0,0,0) - + return _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/rank/_crank8_percentiles.pyx index 16ad49d5..5441eac7 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/rank/_crank8_percentiles.pyx @@ -7,66 +7,66 @@ import numpy as np cimport numpy as np # import main loop -from _core8 cimport _core8,uint8_max,uint8_min +from _core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 imin = 0 imax = 255 for i in range(256): sum += histo[i] - if sum>(p0*pop): + if sum > (p0 * pop): imin = i break sum = 0 - for i in range(255,-1,-1): + for i in range(255, -1, -1): sum += histo[i] - if sum>(p1*pop): + if sum > (p1 * pop): imax = i break - delta = imax-imin - if delta>0: - return (255*(uint8_min(uint8_max(imin,g),imax)-imin)/delta) + delta = imax - imin + if delta > 0: + return < np.uint8_t > (255 * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) else: - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (128) + return < np.uint8_t > (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: imin = i break sum = 0 - for i in range(255,-1,-1): + for i in range(255, -1, -1): sum += histo[i] - if sum>=p1*pop: + if sum >= p1 * pop: imax = i break - return (imax-imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -74,18 +74,18 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t* histo, float pop, np.uint8_t g, f n = 0 for i in range(256): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i - if n>0: - return (1.0*mean/n) + mean += histo[i] * i + if n > 0: + return < np.uint8_t > (1.0 * mean / n) else: - return (0) + return < np.uint8_t > (0) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,mean,n +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: sum = 0 @@ -93,160 +93,166 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t* histo, float pop, np n = 0 for i in range(256): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - mean += histo[i]*i - if n>0: - return ((g-(mean/n))*.5+127) + mean += histo[i] * i + if n > 0: + return < np.uint8_t > ((g - (mean / n)) * .5 + 127) else: - return (0) + return < np.uint8_t > (0) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,imin,imax,sum,delta +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: sum = 0 - p1 = 1.0-p1 + p1 = 1.0 - p1 for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: imin = i break sum = 0 - for i in range(255,-1,-1): + for i in range(255, -1, -1): sum += histo[i] - if sum>=p1*pop: + if sum >= p1 * pop: imax = i break - if g>imax: - return imax - if gimin - if imax-g < g-imin: - return imax + if g > imax: + return < np.uint8_t > imax + if g < imin: + return < np.uint8_t > imin + if imax - g < g - imin: + return < np.uint8_t > imax else: - return imin + return < np.uint8_t > imin else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return (i) + return < np.uint8_t > (i) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): - cdef int i,sum,n +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, n if pop: sum = 0 n = 0 for i in range(256): sum += histo[i] - if (sum>=p0*pop) and (sum<=p1*pop): + if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return (n) + return < np.uint8_t > (n) else: - return (0) + return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t* histo, float pop, np.uint8_t g, float p0, float p1,Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. if pop: for i in range(256): sum += histo[i] - if sum>=p0*pop: + if sum >= p0 * pop: break - return (255*(g>=i)) + return < np.uint8_t > (255 * (g >= i)) else: - return (0) + return < np.uint8_t > (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def autolevel(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8(kernel_gradient,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core8(kernel_mean,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8(kernel_mean_substraction,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8(kernel_percentile,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core8(kernel_pop,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + def threshold(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, float p0=0., float p1=0.): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8(kernel_threshold,image,selem,mask,out,shift_x,shift_y,p0,p1,0,0) + return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index 5fd66e21..97db8f99 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -17,7 +17,8 @@ from generic import find_bitdepth import _crank16_bilateral -__all__ = ['bilateral_mean','bilateral_pop'] +__all__ = ['bilateral_mean', 'bilateral_pop'] + def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): selem = img_as_ubyte(selem) @@ -30,9 +31,9 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): else: raise TypeError("only uint8 and uint16 image supported!") bitdepth = find_bitdepth(image) - if bitdepth>11: + if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,s0=s0,s1=s1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -69,12 +70,13 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bilateral_mean(ima8, square(3), s0=10,s1=10) + >>> rank.bilateral_mean(ima8, square(3), s0=10,s1=10) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], @@ -86,7 +88,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bilateral_mean(ima16, square(3), s0=10,s1=10) + >>> rank.bilateral_mean(ima16, square(3), s0=10,s1=10) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -132,12 +134,13 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> bilateral_pop(ima8, square(3), s0=10,s1=10) + >>> rank.bilateral_pop(ima8, square(3), s0=10,s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], @@ -149,7 +152,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> bilateral_pop(ima16, square(3), s0=10,s1=10) + >>> rank.bilateral_pop(ima16, square(3), s0=10,s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], @@ -160,3 +163,9 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) +if __name__ == "__main__": + import sys + sys.path.append('.') + + import doctest + doctest.testmod(verbose=True) diff --git a/skimage/rank/generic.py b/skimage/rank/generic.py index e8808e5e..94fc3130 100644 --- a/skimage/rank/generic.py +++ b/skimage/rank/generic.py @@ -1,10 +1,11 @@ import numpy as np + def find_bitdepth(image): """returns the max bith depth of a uint16 image """ umax = np.max(image) - if umax>2: + if umax > 2: return int(np.log2(umax)) else: return 1 diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index e7d760da..6ceb503d 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -23,26 +23,29 @@ from skimage import img_as_ubyte import numpy as np from generic import find_bitdepth -import _crank16_percentiles,_crank8_percentiles +import _crank16_percentiles +import _crank8_percentiles + +__all__ = ['percentile_autolevel', 'percentile_gradient', + 'percentile_mean', 'percentile_mean_substraction', + 'percentile_morph_contr_enh', 'percentile', 'percentile_pop', 'percentile_threshold'] -__all__ = ['percentile_autolevel','percentile_gradient', - 'percentile_mean','percentile_mean_substraction', - 'percentile_morph_contr_enh','percentile_pop'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): selem = img_as_ubyte(selem) if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out,p0=p0,p1=p1) + return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, p0=p0, p1=p1) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) - if bitdepth>11: + if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out,p0=p0,p1=p1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") + def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. @@ -77,15 +80,16 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_autolevel(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_autolevel(ima8, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], + [ 0, 255, 0, 255, 0], [ 0, 255, 255, 255, 0], [ 0, 0, 0, 0, 0]], dtype=uint8) @@ -94,10 +98,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_autolevel(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_autolevel(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], + [ 0, 4095, 0, 4095, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 0, 0, 0, 0]], dtype=uint16) @@ -105,6 +109,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile_gradient of an image. @@ -139,12 +144,13 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ to be updated >>> # Local gradient >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_gradient(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_gradient(ima8, square(3), p0=0.,p1=1.) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 255, 255, 255], @@ -156,7 +162,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_gradient(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_gradient(ima16, square(3), p0=0.,p1=1.) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], @@ -167,6 +173,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. @@ -201,12 +208,13 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_mean(ima8, square(3),p0=0.,p1=1.) + >>> rank.percentile_mean(ima8, square(3),p0=0.,p1=1.) array([[ 63, 85, 127, 85, 63], [ 85, 113, 170, 113, 85], [127, 170, 255, 170, 127], @@ -218,7 +226,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_mean(ima16, square(3),p0=0.,p1=1.) + >>> rank.percentile_mean(ima16, square(3),p0=0.,p1=1.) array([[1023, 1365, 2047, 1365, 1023], [1365, 1820, 2730, 1820, 1365], [2047, 2730, 4095, 2730, 2047], @@ -229,6 +237,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean_substraction of an image. @@ -263,12 +272,13 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals to be updated >>> # Local mean_substraction >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) array([[ 95, 84, 63, 84, 95], [ 84, 198, 169, 198, 84], [ 63, 169, 127, 169, 63], @@ -280,7 +290,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) array([[1536, 1365, 1024, 1365, 1536], [1365, 3185, 2730, 3185, 1365], [1024, 2730, 2048, 2730, 1024], @@ -291,6 +301,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. @@ -325,12 +336,13 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 255, 255, 255, 0], [ 0, 255, 255, 255, 0], @@ -342,7 +354,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) array([[ 0, 0, 0, 0, 0], [ 0, 4095, 4095, 4095, 0], [ 0, 4095, 4095, 4095, 0], @@ -353,6 +365,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile of an image. @@ -387,12 +400,13 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile(ima8, square(3), p0=0.,p1=1.) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], @@ -404,7 +418,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile(ima16, square(3), p0=0.,p1=1.) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], @@ -416,6 +430,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local pop of an image. @@ -450,12 +465,13 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_pop(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_pop(ima8, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -467,7 +483,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_pop(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_pop(ima16, square(3), p0=0.,p1=1.) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -478,6 +494,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. @@ -512,12 +529,13 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square + >>> import skimage.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> percentile_threshold(ima8, square(3), p0=0.,p1=1.) + >>> rank.percentile_threshold(ima8, square(3), p0=0.,p1=1.) array([[255, 255, 255, 255, 255], [255, 255, 255, 255, 255], [255, 255, 255, 255, 255], @@ -529,7 +547,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> percentile_threshold(ima16, square(3), p0=0.,p1=1.) + >>> rank.percentile_threshold(ima16, square(3), p0=0.,p1=1.) array([[4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], [4095, 4095, 4095, 4095, 4095], @@ -539,4 +557,12 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) \ No newline at end of file + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + + +if __name__ == "__main__": + import sys + sys.path.append('.') + + import doctest + doctest.testmod(verbose=True) diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index c080c291..d07c9037 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -21,25 +21,27 @@ from skimage import img_as_ubyte import numpy as np from generic import find_bitdepth -import _crank16,_crank8 +import _crank16 +import _crank8 + +__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] -__all__ = ['autolevel','bottomhat','equalize','gradient','maximum','mean' - ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): selem = img_as_ubyte(selem) if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: - return func8(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,out=out) + return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out) elif image.dtype == np.uint16: bitdepth = find_bitdepth(image) - if bitdepth>11: + if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image,selem,shift_x=shift_x,shift_y=shift_y,mask=mask,bitdepth=bitdepth+1,out=out) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out) else: raise TypeError("only uint8 and uint16 image supported!") + def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local autolevel of an image. @@ -101,6 +103,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local bottomhat of an image. @@ -161,6 +164,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local equalize of an image. @@ -221,6 +225,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local gradient of an image. @@ -282,6 +287,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local maximum of an image. @@ -343,6 +349,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. @@ -404,6 +411,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local meansubstraction of an image. @@ -465,6 +473,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local median of an image. @@ -526,6 +535,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.median, _crank16.median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local minimum of an image. @@ -588,6 +598,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.minimum, _crank16.minimum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local modal of an image. @@ -650,6 +661,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.modal, _crank16.modal, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local morph_contr_enh of an image. @@ -711,6 +723,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local pop of an image. @@ -772,6 +785,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.pop, _crank16.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local threshold of an image. @@ -834,6 +848,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local tophat of an image. @@ -899,4 +914,4 @@ if __name__ == "__main__": sys.path.append('.') import doctest - doctest.testmod(verbose=True) \ No newline at end of file + doctest.testmod(verbose=True) diff --git a/skimage/rank/setup.py b/skimage/rank/setup.py index e1f996f7..c6a3dbb9 100644 --- a/skimage/rank/setup.py +++ b/skimage/rank/setup.py @@ -5,13 +5,13 @@ from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) + def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('rank', parent_package, top_path) # config.add_data_dir('tests') - cython(['_core8.pyx'], working_path=base_path) cython(['_core16.pyx'], working_path=base_path) cython(['_crank8.pyx'], working_path=base_path) @@ -21,18 +21,21 @@ def configuration(parent_package='', top_path=None): cython(['_crank16_bilateral.pyx'], working_path=base_path) config.add_extension('_core8', sources=['_core8.c'], - include_dirs=[get_numpy_include_dirs()]) + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_core16', sources=['_core16.c'], - include_dirs=[get_numpy_include_dirs()]) + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank8', sources=['_crank8.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank8_percentiles', sources=['_crank8_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + '_crank8_percentiles', sources=['_crank8_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_crank16', sources=['_crank16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + '_crank16_percentiles', sources=['_crank16_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank16_percentiles', sources=['_crank16_percentiles.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank16_bilateral', sources=['_crank16_bilateral.c'], + config.add_extension( + '_crank16_bilateral', sources=['_crank16_bilateral.c'], include_dirs=[get_numpy_include_dirs()]) return config @@ -40,10 +43,10 @@ def configuration(parent_package='', top_path=None): if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikits-image Developers', - author='Olivier Debeir', - maintainer_email='scikits-image@googlegroups.com', - description='Rank filters', - url='https://github.com/scikits-image/scikits-image', - license='SciPy License (BSD Style)', - **(configuration(top_path='').todict()) - ) + author='Olivier Debeir', + maintainer_email='scikits-image@googlegroups.com', + description='Rank filters', + url='https://github.com/scikits-image/scikits-image', + license='SciPy License (BSD Style)', + **(configuration(top_path='').todict()) + ) From 32b9cba23c79e17a486550d113c3f72386c13188 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:18:01 +0200 Subject: [PATCH 274/625] moved example to doc --- doc/examples/plot_16bitbilateral.py | 33 +++++++++++++++ .../examples/plot_benchmark_rank.py | 41 +++++++++++++++---- skimage/rank/local/demo_16bitbilateral.py | 31 -------------- 3 files changed, 65 insertions(+), 40 deletions(-) create mode 100644 doc/examples/plot_16bitbilateral.py rename skimage/rank/local/demo_benchmark.py => doc/examples/plot_benchmark_rank.py (85%) delete mode 100644 skimage/rank/local/demo_16bitbilateral.py diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py new file mode 100644 index 00000000..f61f3c55 --- /dev/null +++ b/doc/examples/plot_16bitbilateral.py @@ -0,0 +1,33 @@ +""" +============================== +Simplified bilateral filtering +============================== + +to complete + +""" +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.morphology import disk +import skimage.rank as rank + +a8 = (data.coins()).astype('uint8') + +a16 = (data.coins()).astype('uint16')*16 +selem = np.ones((20,20),dtype='uint8') +f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) +f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) +selem = disk(50) +f3 = rank.equalize(a16,selem = selem) + +# display results +fig, axes = plt.subplots(nrows=3, figsize=(15,5)) +ax0, ax1, ax2 = axes + +ax0.imshow(np.hstack((a8,f1))) +ax1.imshow(np.hstack((a16,f2))) +ax2.imshow(np.hstack((a16,f3))) + +plt.show() diff --git a/skimage/rank/local/demo_benchmark.py b/doc/examples/plot_benchmark_rank.py similarity index 85% rename from skimage/rank/local/demo_benchmark.py rename to doc/examples/plot_benchmark_rank.py index d3c3d00a..dab2f8d4 100644 --- a/skimage/rank/local/demo_benchmark.py +++ b/doc/examples/plot_benchmark_rank.py @@ -1,3 +1,20 @@ +""" +============================== +Compare execution time for + - skimage.rank.median, + - skimage.filter import median_filter + - scipy.ndimage.filters import percentile_filter, + + and + + - skimage.cmorph.dilate + - skimage.rank.maximum + +============================== + +to complete + +""" import numpy as np import matplotlib.pyplot as plt import time @@ -17,7 +34,6 @@ def log_timing(func): res = func(*arg) t2 = time.time() ms = (t2-t1)*1000.0 - print '%s took %0.3f ms' % (func.func_name, ms) return (res,ms) return wrapper @@ -26,6 +42,14 @@ def log_timing(func): def cr_med(image,selem): return rank.median(image=image,selem = selem) +@log_timing +def cr_max(image,selem): + return rank.maximum(image=image,selem = selem) + +@log_timing +def cm_dil(image,selem): + return dilation(image=image,selem = selem) + @log_timing def ctmf_med(image,radius): return median_filter(image=image,radius=radius) @@ -46,13 +70,12 @@ def compare_dilate(): rec = [] e_range = range(1,20,1) for r in e_range: -# elem = np.ones((r,r),dtype='uint8') elem = disk(r+1) # elem = (np.random.random((r,r))>.5).astype('uint8') rc,ms_rc = cr_max(a,elem) rcm,ms_rcm = cm_dil(a,elem) rec.append((ms_rc,ms_rcm)) - # check if results are identical + # same structuring element, the results must match assert (rc==rcm).all() rec = np.asarray(rec) @@ -65,7 +88,6 @@ def compare_dilate(): plt.imshow(np.hstack((rc,rcm))) r = 9 -# elem = np.ones((r,r),dtype='uint8') elem = disk(r+1) rec = [] @@ -75,6 +97,7 @@ def compare_dilate(): (rc,ms_rc) = cr_max(a,elem) (rcm,ms_rcm) = cm_dil(a,elem) rec.append((ms_rc,ms_rcm)) + # same structuring element, the results must match assert (rc==rcm).all() rec = np.asarray(rec) @@ -86,7 +109,6 @@ def compare_dilate(): plt.figure() plt.imshow(np.hstack((rc,rcm))) - plt.show() def compare_median(): """ Comparison between @@ -145,7 +167,8 @@ def compare_median(): plt.ylabel('time (ms)') plt.xlabel('image size') - plt.show() -if __name__ == '__main__': -# compare_dilate() - compare_median() \ No newline at end of file + + +compare_dilate() +compare_median() +plt.show() diff --git a/skimage/rank/local/demo_16bitbilateral.py b/skimage/rank/local/demo_16bitbilateral.py deleted file mode 100644 index 85fd510c..00000000 --- a/skimage/rank/local/demo_16bitbilateral.py +++ /dev/null @@ -1,31 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data -from skimage.morphology import disk -import skimage.rank as rank - -if __name__ == '__main__': - a8 = (data.coins()).astype('uint8') - - a16 = (data.coins()).astype('uint16')*16 - selem = np.ones((20,20),dtype='uint8') - f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) - f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) - - selem = disk(50) - f3 = rank.equalize(a16,selem = selem) - - plt.figure() - plt.imshow(np.hstack((a8,f1))) - plt.colorbar() - - plt.figure() - plt.imshow(np.hstack((a16,f2))) - plt.colorbar() - - plt.figure() - plt.imshow(np.hstack((a16,f3))) - plt.colorbar() - - plt.show() From 6814f5945488a1c8ffe2a883b9806134a22f16b5 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:19:45 +0200 Subject: [PATCH 275/625] removed useless tools.py --- skimage/rank/local/tools.py | 42 ------------------------------------- 1 file changed, 42 deletions(-) delete mode 100644 skimage/rank/local/tools.py diff --git a/skimage/rank/local/tools.py b/skimage/rank/local/tools.py deleted file mode 100644 index 2fba4798..00000000 --- a/skimage/rank/local/tools.py +++ /dev/null @@ -1,42 +0,0 @@ -__author__ = 'Olivier Debeir 2021' - -import logging -import time - -def init_logger(logfilename = 'myapp.log'): - """add logger capabilities - """ - FORMAT = '%(asctime)-15s %(processName)s %(process)d %(message)s' - logging.basicConfig(filename=logfilename,format=FORMAT,filemode='wt') - logger = logging.getLogger() - logger.setLevel(logging.DEBUG) - - # create console handler and set level to debug - ch = logging.StreamHandler() - ch.setLevel(logging.DEBUG) - - # add ch to logger - logger.addHandler(ch) - logger.info('start logging in %s' % logfilename) - return logger - - -logger = logging.getLogger() - -def log_timing(func): - - def wrapper(*arg): - log_timing.level += 1 - t1 = time.time() - res = func(*arg) - t2 = time.time() - ms = (t2-t1)*1000.0 - logger.info('%s%s took %0.3f ms' % (log_timing.level*'-',func.func_name, ms)) - log_timing.level -= 1 - return (res,ms) - - return wrapper - -log_timing.level = 0 - - From 9546c344a0317e3696382459e3bf250b0c4fb3fc Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:23:41 +0200 Subject: [PATCH 276/625] modify CONTRIBUTORS.txt --- CONTRIBUTORS.txt | 3 +++ skimage/rank/bilateral_rank.py | 3 --- skimage/rank/local/__init__.py | 1 - skimage/rank/percentile_rank.py | 2 -- skimage/rank/rank.py | 2 -- 5 files changed, 3 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index c73b14e2..8ba62e0b 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -117,3 +117,6 @@ - Petter Strandmark Perimeter calculation in regionprops. + +- Olivier Debeir + Rank filters (8- and 16-bits) using sliding window. \ No newline at end of file diff --git a/skimage/rank/bilateral_rank.py b/skimage/rank/bilateral_rank.py index 97db8f99..24ccbcd0 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/rank/bilateral_rank.py @@ -2,11 +2,8 @@ note: 8 bit images are casted into 16 bit image here -:author: Olivier Debeir, 2012 -:license: modified BSD """ -__docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py index 10b6fb15..e69de29b 100644 --- a/skimage/rank/local/__init__.py +++ b/skimage/rank/local/__init__.py @@ -1 +0,0 @@ -__author__ = 'olivier' diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 6ceb503d..2ed6f011 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -12,8 +12,6 @@ for 16 bit input images, the number of histogram bins is determined from the max result image is 8 or 16 bit with respect to the input image -:author: Olivier Debeir, 2012 -:license: modified BSD """ __docformat__ = 'restructuredtext en' diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index d07c9037..012c046f 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -10,8 +10,6 @@ for 16 bit input images, the number of histogram bins is determined from the max result image is 8 or 16 bit with respect to the input image -:author: Olivier Debeir, 2012 -:license: modified BSD """ __docformat__ = 'restructuredtext en' From f2f8b68a1a11afd982e5f167defe492bf62f2057 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 09:25:18 +0200 Subject: [PATCH 277/625] __docformat__ removed --- skimage/rank/percentile_rank.py | 1 - skimage/rank/rank.py | 1 - 2 files changed, 2 deletions(-) diff --git a/skimage/rank/percentile_rank.py b/skimage/rank/percentile_rank.py index 2ed6f011..ac19ccd6 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/rank/percentile_rank.py @@ -14,7 +14,6 @@ result image is 8 or 16 bit with respect to the input image """ -__docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py index 012c046f..51854128 100644 --- a/skimage/rank/rank.py +++ b/skimage/rank/rank.py @@ -12,7 +12,6 @@ result image is 8 or 16 bit with respect to the input image """ -__docformat__ = 'restructuredtext en' import warnings from skimage import img_as_ubyte From 117b6d187bbdd34c02042ecbadaae6af0df7c465 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 10:00:10 +0200 Subject: [PATCH 278/625] move rank/ into filter/ --- doc/__init__.py | 1 + .../{ => applications}/plot_benchmark_rank.py | 19 +++++----- doc/examples/plot_16bitbilateral.py | 8 ++-- doc/examples/plot_lena_bilateral_denoise.py | 2 +- doc/examples/plot_local_autolevels.py | 2 +- doc/examples/plot_local_equalize.py | 2 +- doc/examples/plot_local_threshold.py | 2 +- doc/examples/plot_marked_watershed.py | 5 +-- skimage/{ => filter}/rank/README.rst | 0 skimage/{ => filter}/rank/__init__.py | 0 skimage/{ => filter}/rank/_core16.pxd | 0 skimage/{ => filter}/rank/_core16.pyx | 0 skimage/{ => filter}/rank/_core8.pxd | 0 skimage/{ => filter}/rank/_core8.pyx | 0 skimage/{ => filter}/rank/_crank16.pyx | 2 +- .../{ => filter}/rank/_crank16_bilateral.pyx | 2 +- .../rank/_crank16_percentiles.pyx | 2 +- skimage/{ => filter}/rank/_crank8.pyx | 2 +- .../{ => filter}/rank/_crank8_percentiles.pyx | 2 +- skimage/{ => filter}/rank/bilateral_rank.py | 31 ++++++++++++---- skimage/{ => filter}/rank/generic.py | 0 skimage/{ => filter}/rank/local/demo_all.py | 3 +- .../{ => filter}/rank/local/demo_single.py | 3 +- skimage/filter/rank/local/iko_pan_Ja1.tif | Bin 0 -> 129176 bytes .../rank/local/test_morph_contr_enh.py | 2 +- skimage/{ => filter}/rank/local/test_rank.py | 2 +- skimage/{ => filter}/rank/percentile_rank.py | 23 +++++------- skimage/{ => filter}/rank/rank.py | 35 ++++++++---------- skimage/{ => filter}/rank/setup.py | 0 skimage/{ => filter}/rank/tests/test_suite.py | 8 ++-- skimage/rank/local/__init__.py | 0 31 files changed, 83 insertions(+), 75 deletions(-) create mode 100644 doc/__init__.py rename doc/examples/{ => applications}/plot_benchmark_rank.py (96%) rename skimage/{ => filter}/rank/README.rst (100%) rename skimage/{ => filter}/rank/__init__.py (100%) rename skimage/{ => filter}/rank/_core16.pxd (100%) rename skimage/{ => filter}/rank/_core16.pyx (100%) rename skimage/{ => filter}/rank/_core8.pxd (100%) rename skimage/{ => filter}/rank/_core8.pyx (100%) rename skimage/{ => filter}/rank/_crank16.pyx (99%) rename skimage/{ => filter}/rank/_crank16_bilateral.pyx (98%) rename skimage/{ => filter}/rank/_crank16_percentiles.pyx (99%) rename skimage/{ => filter}/rank/_crank8.pyx (99%) rename skimage/{ => filter}/rank/_crank8_percentiles.pyx (99%) rename skimage/{ => filter}/rank/bilateral_rank.py (85%) rename skimage/{ => filter}/rank/generic.py (100%) rename skimage/{ => filter}/rank/local/demo_all.py (98%) rename skimage/{ => filter}/rank/local/demo_single.py (92%) create mode 100644 skimage/filter/rank/local/iko_pan_Ja1.tif rename skimage/{ => filter}/rank/local/test_morph_contr_enh.py (93%) rename skimage/{ => filter}/rank/local/test_rank.py (95%) rename skimage/{ => filter}/rank/percentile_rank.py (98%) rename skimage/{ => filter}/rank/rank.py (98%) rename skimage/{ => filter}/rank/setup.py (100%) rename skimage/{ => filter}/rank/tests/test_suite.py (97%) delete mode 100644 skimage/rank/local/__init__.py diff --git a/doc/__init__.py b/doc/__init__.py new file mode 100644 index 00000000..10b6fb15 --- /dev/null +++ b/doc/__init__.py @@ -0,0 +1 @@ +__author__ = 'olivier' diff --git a/doc/examples/plot_benchmark_rank.py b/doc/examples/applications/plot_benchmark_rank.py similarity index 96% rename from doc/examples/plot_benchmark_rank.py rename to doc/examples/applications/plot_benchmark_rank.py index dab2f8d4..79357768 100644 --- a/doc/examples/plot_benchmark_rank.py +++ b/doc/examples/applications/plot_benchmark_rank.py @@ -19,13 +19,14 @@ import numpy as np import matplotlib.pyplot as plt import time +from scipy.ndimage.filters import percentile_filter + from skimage import data from skimage.morphology import dilation,disk from skimage.filter import median_filter -from scipy.ndimage.filters import percentile_filter -import skimage.rank as rank +import skimage.filter.rank as rank -def log_timing(func): +def exec_and_timeit(func): """ Decorator that returns both function results and execution time (result, ms) """ @@ -38,23 +39,23 @@ def log_timing(func): return wrapper -@log_timing +@exec_and_timeit def cr_med(image,selem): return rank.median(image=image,selem = selem) -@log_timing +@exec_and_timeit def cr_max(image,selem): return rank.maximum(image=image,selem = selem) -@log_timing +@exec_and_timeit def cm_dil(image,selem): return dilation(image=image,selem = selem) -@log_timing +@exec_and_timeit def ctmf_med(image,radius): return median_filter(image=image,radius=radius) -@log_timing +@exec_and_timeit def ndi_med(image,n): return percentile_filter(image,50,size=n*2-1) @@ -84,8 +85,6 @@ def compare_dilate(): plt.title('increasing element size') plt.plot(e_range,rec) plt.legend(['crank.maximum','cmorph.dilate']) - plt.figure() - plt.imshow(np.hstack((rc,rcm))) r = 9 elem = disk(r+1) diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py index f61f3c55..076d03c4 100644 --- a/doc/examples/plot_16bitbilateral.py +++ b/doc/examples/plot_16bitbilateral.py @@ -11,7 +11,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import disk -import skimage.rank as rank +import skimage.filter.rank as rank a8 = (data.coins()).astype('uint8') @@ -23,11 +23,13 @@ selem = disk(50) f3 = rank.equalize(a16,selem = selem) # display results -fig, axes = plt.subplots(nrows=3, figsize=(15,5)) +fig, axes = plt.subplots(nrows=3, figsize=(15,15)) ax0, ax1, ax2 = axes ax0.imshow(np.hstack((a8,f1))) +ax0.set_title('percentile mean') ax1.imshow(np.hstack((a16,f2))) +ax1.set_title('bilateral mean') ax2.imshow(np.hstack((a16,f3))) - +ax2.set_title('local equalization') plt.show() diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index 57593867..969403d0 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -18,7 +18,7 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data, color, img_as_ubyte -from skimage.rank import bilateral_mean +from skimage.filter.rank import bilateral_mean from skimage.morphology import disk l = img_as_ubyte(color.rgb2gray(data.lena())) diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py index 842215d5..7c750141 100644 --- a/doc/examples/plot_local_autolevels.py +++ b/doc/examples/plot_local_autolevels.py @@ -13,7 +13,7 @@ import numpy as np from skimage import data -from skimage.rank import percentile_autolevel,autolevel +from skimage.filter.rank import percentile_autolevel,autolevel from skimage.morphology import disk diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index 897989f0..1a431f5c 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -17,12 +17,12 @@ The local version [2]_ of the histogram equalization emphasized every local gray from skimage import data from skimage.util.dtype import dtype_range from skimage import exposure -from skimage import rank from skimage.morphology import disk import matplotlib.pyplot as plt import numpy as np +from skimage.filter import rank def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py index c5810156..8bc79b30 100644 --- a/doc/examples/plot_local_threshold.py +++ b/doc/examples/plot_local_threshold.py @@ -27,7 +27,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.filter import threshold_otsu, threshold_adaptive -from skimage.rank import threshold,morph_contr_enh +from skimage.filter.rank import threshold,morph_contr_enh from skimage.morphology import disk diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 0be25007..738a3d24 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -14,15 +14,14 @@ See Wikipedia_ for more details on the algorithm. """ -import numpy as np from scipy import ndimage import matplotlib.pyplot as plt from skimage.morphology import watershed,disk -from skimage import rank from skimage import data -from scipy import ndimage # original data +from skimage.filter import rank + image = data.camera() # denoise image diff --git a/skimage/rank/README.rst b/skimage/filter/rank/README.rst similarity index 100% rename from skimage/rank/README.rst rename to skimage/filter/rank/README.rst diff --git a/skimage/rank/__init__.py b/skimage/filter/rank/__init__.py similarity index 100% rename from skimage/rank/__init__.py rename to skimage/filter/rank/__init__.py diff --git a/skimage/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd similarity index 100% rename from skimage/rank/_core16.pxd rename to skimage/filter/rank/_core16.pxd diff --git a/skimage/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx similarity index 100% rename from skimage/rank/_core16.pyx rename to skimage/filter/rank/_core16.pyx diff --git a/skimage/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd similarity index 100% rename from skimage/rank/_core8.pxd rename to skimage/filter/rank/_core8.pxd diff --git a/skimage/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx similarity index 100% rename from skimage/rank/_core8.pyx rename to skimage/filter/rank/_core8.pyx diff --git a/skimage/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx similarity index 99% rename from skimage/rank/_crank16.pyx rename to skimage/filter/rank/_crank16.pyx index ce8510db..57d41563 100644 --- a/skimage/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16 +from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx similarity index 98% rename from skimage/rank/_crank16_bilateral.pyx rename to skimage/filter/rank/_crank16_bilateral.pyx index b5103be4..24016bbf 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16 +from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx similarity index 99% rename from skimage/rank/_crank16_percentiles.pyx rename to skimage/filter/rank/_crank16_percentiles.pyx index 527d2aed..73ccde68 100644 --- a/skimage/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -7,7 +7,7 @@ import numpy as np cimport numpy as np # import main loop -from _core16 cimport _core16, int_min, int_max +from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) diff --git a/skimage/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx similarity index 99% rename from skimage/rank/_crank8.pyx rename to skimage/filter/rank/_crank8.pyx index 94124cf7..045e3645 100644 --- a/skimage/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -15,7 +15,7 @@ import numpy as np cimport numpy as np # import main loop -from _core8 cimport _core8 +from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- # kernels uint8 diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx similarity index 99% rename from skimage/rank/_crank8_percentiles.pyx rename to skimage/filter/rank/_crank8_percentiles.pyx index 5441eac7..f882961a 100644 --- a/skimage/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -7,7 +7,7 @@ import numpy as np cimport numpy as np # import main loop -from _core8 cimport _core8, uint8_max, uint8_min +from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) diff --git a/skimage/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py similarity index 85% rename from skimage/rank/bilateral_rank.py rename to skimage/filter/rank/bilateral_rank.py index 24ccbcd0..1dc7552d 100644 --- a/skimage/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -1,17 +1,32 @@ -""" +"""bilateral_rank.py - approximate bilateral rankfilter for local (custom kernel) mean -note: 8 bit images are casted into 16 bit image here +The local histogram is computed using a sliding window similar to the method described in + +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", +IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + +input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), +8 bit images are casted in 16 bit +the number of histogram bins is determined from the maximum value present in the image + +The pixel neighborhood is defined by: + +* the given structuring element + +* an interval [g-s0,g+s1] in gray level around g the processed pixel gray level + +The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally) + +result image is 16 bit with respect to the input image """ - -import warnings from skimage import img_as_ubyte import numpy as np +from skimage.filter.rank import _crank16_bilateral -from generic import find_bitdepth -import _crank16_bilateral +from skimage.filter.rank.generic import find_bitdepth __all__ = ['bilateral_mean', 'bilateral_pop'] @@ -67,7 +82,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -131,7 +146,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], diff --git a/skimage/rank/generic.py b/skimage/filter/rank/generic.py similarity index 100% rename from skimage/rank/generic.py rename to skimage/filter/rank/generic.py diff --git a/skimage/rank/local/demo_all.py b/skimage/filter/rank/local/demo_all.py similarity index 98% rename from skimage/rank/local/demo_all.py rename to skimage/filter/rank/local/demo_all.py index 8df5c3e7..038c749b 100644 --- a/skimage/rank/local/demo_all.py +++ b/skimage/filter/rank/local/demo_all.py @@ -1,10 +1,9 @@ -import numpy as np import matplotlib.pyplot as plt from pprint import pprint from skimage import data from skimage.morphology.selem import disk -import skimage.rank as rank +import skimage.filter.rank as rank def plot_all(): a8 = data.camera() diff --git a/skimage/rank/local/demo_single.py b/skimage/filter/rank/local/demo_single.py similarity index 92% rename from skimage/rank/local/demo_single.py rename to skimage/filter/rank/local/demo_single.py index b601b226..39b9acc0 100644 --- a/skimage/rank/local/demo_single.py +++ b/skimage/filter/rank/local/demo_single.py @@ -1,10 +1,9 @@ import numpy as np import matplotlib.pyplot as plt -from pprint import pprint from skimage import data from skimage.morphology.selem import disk -import skimage.rank as rank +import skimage.filter.rank as rank if __name__ == '__main__': diff --git a/skimage/filter/rank/local/iko_pan_Ja1.tif b/skimage/filter/rank/local/iko_pan_Ja1.tif new file mode 100644 index 0000000000000000000000000000000000000000..47201695755a73086ba6d598f3441063ecdf1068 GIT binary patch literal 129176 zcmd44cXU-%`p13Fp%te0trb7kc7_r`JBve&HUE;TkHMz&0RP5+*5XWzE9iFe)hh1-+g|twK5DE z2SHdX2!kLv$UFS6_{9I$ zqa7al>#ZFw{ohYsd};jm@&$aHe)+|hUU>}%e!JrIOXJt&S6y}bzu*5;%MF+O?*<1; z!%bm!m>3QW$Amq@55lLzW?}pAgz$~J>e)w_NH#{x;F31bs4%dWB!)4*haBjGm zCuW4b!&kx%VfXOc@QQGG7)IN}s&H?Z6PAX1!YJAm9v2-AkA?@r!mw7fk87o2QCK@F z=iBbEVN@J$3+qSw!yTO6O)J8vlzVoEB|O_a3VHS*Pk1(n4sh2|o~skprrbT@{;(q4 z7dD7?hMU7W@x42#xj|GnY8fSSe_?oxr}y#HuCSaEef9t?*&kMOre1WA>nBoLHQ&9y zFFZb~;ormA;jD0M_+r>LEDu%$vx410oA8XV3dXG$W(MPfF}zcP2H`J! 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L&V3s<8XWu!riJvI literal 0 HcmV?d00001 diff --git a/skimage/rank/local/test_morph_contr_enh.py b/skimage/filter/rank/local/test_morph_contr_enh.py similarity index 93% rename from skimage/rank/local/test_morph_contr_enh.py rename to skimage/filter/rank/local/test_morph_contr_enh.py index 812e81c5..f2f0f7c9 100644 --- a/skimage/rank/local/test_morph_contr_enh.py +++ b/skimage/filter/rank/local/test_morph_contr_enh.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import gdal from skimage.morphology import disk -import skimage.rank as rank +import skimage.filter.rank as rank filename = 'iko_pan_Ja1.tif' im16 = gdal.Open(filename).ReadAsArray().astype(np.uint16) diff --git a/skimage/rank/local/test_rank.py b/skimage/filter/rank/local/test_rank.py similarity index 95% rename from skimage/rank/local/test_rank.py rename to skimage/filter/rank/local/test_rank.py index 75dd7f65..09cfdcd5 100644 --- a/skimage/rank/local/test_rank.py +++ b/skimage/filter/rank/local/test_rank.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology.selem import disk -import skimage.rank as rank +import skimage.filter.rank as rank print dir(rank) diff --git a/skimage/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py similarity index 98% rename from skimage/rank/percentile_rank.py rename to skimage/filter/rank/percentile_rank.py index ac19ccd6..7908f5ac 100644 --- a/skimage/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -14,14 +14,11 @@ result image is 8 or 16 bit with respect to the input image """ - -import warnings from skimage import img_as_ubyte import numpy as np -from generic import find_bitdepth -import _crank16_percentiles -import _crank8_percentiles +from skimage.filter.rank.generic import find_bitdepth +from skimage.filter.rank import _crank16_percentiles, _crank8_percentiles __all__ = ['percentile_autolevel', 'percentile_gradient', 'percentile_mean', 'percentile_mean_substraction', @@ -77,7 +74,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -141,7 +138,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ to be updated >>> # Local gradient >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -205,7 +202,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -269,7 +266,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals to be updated >>> # Local mean_substraction >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -333,7 +330,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -397,7 +394,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -462,7 +459,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -526,7 +523,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], diff --git a/skimage/rank/rank.py b/skimage/filter/rank/rank.py similarity index 98% rename from skimage/rank/rank.py rename to skimage/filter/rank/rank.py index 51854128..0a9dee27 100644 --- a/skimage/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -12,14 +12,11 @@ result image is 8 or 16 bit with respect to the input image """ - -import warnings from skimage import img_as_ubyte import numpy as np +from skimage.filter.rank import _crank8, _crank16 -from generic import find_bitdepth -import _crank16 -import _crank8 +from skimage.filter.rank.generic import find_bitdepth __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] @@ -71,7 +68,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -133,7 +130,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -194,7 +191,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -255,7 +252,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local gradient >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -317,7 +314,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local maximum >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 1, 0, 0], @@ -379,7 +376,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -441,7 +438,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F to be updated >>> # Local meansubstraction >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -503,7 +500,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local median >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 0, 1, 0], @@ -565,7 +562,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local minimum >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -628,7 +625,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local modal >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 5, 6, 0], @@ -691,7 +688,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -753,7 +750,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -815,7 +812,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], @@ -878,7 +875,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): to be updated >>> # Local mean >>> from skimage.morphology import square - >>> import skimage.rank as rank + >>> import skimage.filter.rank as rank >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], diff --git a/skimage/rank/setup.py b/skimage/filter/rank/setup.py similarity index 100% rename from skimage/rank/setup.py rename to skimage/filter/rank/setup.py diff --git a/skimage/rank/tests/test_suite.py b/skimage/filter/rank/tests/test_suite.py similarity index 97% rename from skimage/rank/tests/test_suite.py rename to skimage/filter/rank/tests/test_suite.py index 9d2bcfea..4f1e6f81 100644 --- a/skimage/rank/tests/test_suite.py +++ b/skimage/filter/rank/tests/test_suite.py @@ -1,12 +1,12 @@ import unittest import numpy as np +from skimage.filter import rank -from skimage.rank import _crank8,_crank8_percentiles -from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles -from skimage.morphology import cmorph,disk from skimage import data -from skimage import rank +from skimage.morphology import cmorph,disk +from skimage.filter.rank import _crank8, _crank16 +from skimage.filter.rank import _crank16_percentiles class TestSequenceFunctions(unittest.TestCase): diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py deleted file mode 100644 index e69de29b..00000000 From 2c1bed000d192b6b081cb53bed8ebb0cd7d55365 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 10:12:19 +0200 Subject: [PATCH 279/625] cut long lines --- skimage/filter/rank/bilateral_rank.py | 9 ++++++--- skimage/filter/rank/percentile_rank.py | 27 +++++++++++++++++--------- skimage/filter/rank/rank.py | 24 +++++++++++++++-------- 3 files changed, 40 insertions(+), 20 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index 1dc7552d..5ea92ed9 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -45,7 +45,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + s0=s0, s1=s1) def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -109,7 +110,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) + return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + s0=s0, s1=s1) def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): @@ -173,7 +175,8 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals """ - return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) + return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + s0=s0, s1=s1) if __name__ == "__main__": import sys diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py index 7908f5ac..77858715 100644 --- a/skimage/filter/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -35,7 +35,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) + return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + p0=p0, p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") @@ -101,7 +102,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -165,7 +167,8 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ """ - return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -229,7 +232,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -293,7 +297,8 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals """ - return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -357,7 +362,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, """ - return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -422,7 +428,8 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, """ - return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -486,7 +493,8 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): @@ -551,7 +559,8 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) if __name__ == "__main__": diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.py index 0a9dee27..d4df3aef 100644 --- a/skimage/filter/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -18,7 +18,8 @@ from skimage.filter.rank import _crank8, _crank16 from skimage.filter.rank.generic import find_bitdepth -__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] +__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', + 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): @@ -95,7 +96,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -156,7 +158,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -217,7 +220,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ - return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -279,7 +283,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -465,7 +470,8 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F """ - return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, + shift_x=shift_x, shift_y=shift_y) def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -715,7 +721,8 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -840,7 +847,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y) def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): From c5fb9b4b7222eab63276711411340e48a60f7761 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 10:21:34 +0200 Subject: [PATCH 280/625] autopep8 --- skimage/filter/rank/_core16.pyx | 2 +- skimage/filter/rank/_core8.pyx | 2 +- skimage/filter/rank/bilateral_rank.py | 9 ++++++--- skimage/filter/rank/percentile_rank.py | 27 +++++++++++++++++--------- skimage/filter/rank/rank.py | 21 +++++++++++++------- 5 files changed, 40 insertions(+), 21 deletions(-) diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 81fab0b4..e60308ed 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r cdef inline _core16( - np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 7851388d..0d30045f 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r return 0 cdef inline _core8( - np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index 5ea92ed9..ff4e7878 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -45,7 +45,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + return func16( + image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) @@ -110,7 +111,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + return _apply( + None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) @@ -175,7 +177,8 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals """ - return _apply(None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, + return _apply( + None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) if __name__ == "__main__": diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py index 77858715..5191bec4 100644 --- a/skimage/filter/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -35,7 +35,8 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, + return func16( + image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) else: raise TypeError("only uint8 and uint16 image supported!") @@ -102,7 +103,8 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -167,7 +169,8 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ """ - return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -232,7 +235,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -297,7 +301,8 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals """ - return _apply(_crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, + return _apply( + _crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -362,7 +367,8 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, """ - return _apply(_crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, + return _apply( + _crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -428,7 +434,8 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, """ - return _apply(_crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -493,7 +500,8 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal """ - return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) @@ -559,7 +567,8 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, + return _apply( + _crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.py index d4df3aef..517ab2cf 100644 --- a/skimage/filter/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -96,7 +96,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -158,7 +159,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [ 0, 0, 0, 0, 0]], dtype=uint16) """ - return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -220,7 +222,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [3071, 2730, 2047, 2730, 3071]], dtype=uint16) """ - return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -283,7 +286,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -470,7 +474,8 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F """ - return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, + return _apply( + _crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -721,7 +726,8 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa """ - return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -847,7 +853,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, + return _apply( + _crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) From 2844db6025d551a228347982e12da83d8992d926 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 15:21:14 +0200 Subject: [PATCH 281/625] add example modal --- doc/examples/plot_modal_filter.py | 67 +++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 doc/examples/plot_modal_filter.py diff --git a/doc/examples/plot_modal_filter.py b/doc/examples/plot_modal_filter.py new file mode 100644 index 00000000..a66da0a1 --- /dev/null +++ b/doc/examples/plot_modal_filter.py @@ -0,0 +1,67 @@ +""" +=================== +Label image regions +=================== + +This example shows how to segment an image with image labelling. The following +steps are applied: + +1. Thresholding with automatic Otsu method +2. Close small holes with binary closing +3. Remove artifacts touching image border +4. Measure image regions to filter small objects + +""" + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches + +from skimage import data +from skimage.filter import threshold_otsu + +from skimage.filter.rank import modal + +from skimage.morphology import label, disk +from skimage.measure import find_contours + + +image = data.coins()[50:-50, 50:-50] + +# apply threshold +thresh = threshold_otsu(image) +bw = image > thresh + +# label image regions +label_image = label(bw) + +# filter obtained labels using model filter +mod_label_image = modal(label_image.astype(np.uint16),disk(5)) + +# the background is here 1 +contours = find_contours(mod_label_image==1,0, positive_orientation='low') + +fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(6, 6)) + +print axes + +ax0, ax1, ax2, ax3 = axes.ravel() + +ax0.imshow(bw, cmap='gray') +ax0.set_title('Otsu threshold') +ax1.imshow(label_image, cmap='jet') +ax1.set_title('label image') +ax2.imshow(mod_label_image, cmap='jet') +ax2.set_title('filtered labels (modal)') +ax3.imshow(image, cmap='gray') +ax3.set_title('contour overlay') +ax3.set_xlim((0,image.shape[1])) +ax3.set_ylim((image.shape[0],0)) + + +for n, contour in enumerate(contours): + ax3.plot(contour[:, 1], contour[:, 0], linewidth=2) + + +plt.show() + From b8d9227d85e9c723805d5dc16eaacd3bab707335 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 18 Oct 2012 15:32:24 +0200 Subject: [PATCH 282/625] cut long line, autopep8 --- skimage/filter/rank/_core16.pxd | 2 +- skimage/filter/rank/_core16.pyx | 2 +- skimage/filter/rank/_core8.pxd | 2 +- skimage/filter/rank/_core8.pyx | 2 +- skimage/filter/rank/_crank16_bilateral.pyx | 10 +- skimage/filter/rank/_crank16_percentiles.pyx | 72 ++++++++++---- skimage/filter/rank/_crank8.pyx | 99 +++++++++++++------- skimage/filter/rank/_crank8_percentiles.pyx | 56 ++++++++--- 8 files changed, 174 insertions(+), 71 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index a2843f76..a113f2b0 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -9,7 +9,7 @@ cdef inline int int_max(int a, int b) cdef inline int int_min(int a, int b) cdef inline _core16( - np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index e60308ed..81fab0b4 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r cdef inline _core16( - np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 1a170500..8ecf7263 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -9,7 +9,7 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- cdef inline _core8( - np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 0d30045f..7851388d 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -47,7 +47,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r return 0 cdef inline _core8( - np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), + np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 24016bbf..d6fb9c71 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -22,7 +22,10 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 cdef float mean = 0. @@ -39,7 +42,10 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, bilat_pop = 0 if pop: diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 73ccde68..0d37b77c 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -13,7 +13,10 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: @@ -40,7 +43,10 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint1 return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: @@ -63,7 +69,10 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16 return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: @@ -83,7 +92,10 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean_substraction( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, mean, n if pop: @@ -102,7 +114,10 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, imin, imax, sum, delta if pop: @@ -130,7 +145,10 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_percentile( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i cdef float sum = 0. @@ -144,7 +162,10 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i, sum, n if pop: @@ -158,7 +179,10 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef int i cdef float sum = 0. @@ -184,7 +208,9 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat """ - return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -194,7 +220,9 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -204,7 +232,9 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -214,7 +244,9 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -224,7 +256,9 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -234,7 +268,9 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile """ - return _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -244,7 +280,9 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -254,4 +292,6 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core16( + kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, + < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 045e3645..be00bf86 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -22,8 +22,9 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- cdef inline np.uint8_t kernel_autolevel( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: @@ -44,8 +45,9 @@ cdef inline np.uint8_t kernel_autolevel( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_bottomhat( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i for i in range(256): @@ -56,8 +58,9 @@ cdef inline np.uint8_t kernel_bottomhat( cdef inline np.uint8_t kernel_equalize( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float sum = 0. @@ -72,8 +75,9 @@ cdef inline np.uint8_t kernel_equalize( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_gradient( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: @@ -90,8 +94,9 @@ cdef inline np.uint8_t kernel_gradient( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_maximum( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i if pop: @@ -101,8 +106,10 @@ cdef inline np.uint8_t kernel_maximum( return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. @@ -114,8 +121,9 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, return < np.uint8_t > (0) cdef inline np.uint8_t kernel_meansubstraction( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. @@ -127,8 +135,9 @@ cdef inline np.uint8_t kernel_meansubstraction( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_median( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i cdef float sum = pop / 2.0 @@ -142,8 +151,9 @@ cdef inline np.uint8_t kernel_median( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_minimum( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i if pop: @@ -154,8 +164,8 @@ cdef inline np.uint8_t kernel_minimum( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_modal( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: @@ -168,8 +178,9 @@ cdef inline np.uint8_t kernel_modal( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_morph_contr_enh( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: @@ -188,13 +199,16 @@ cdef inline np.uint8_t kernel_morph_contr_enh( else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + return < np.uint8_t > (pop) cdef inline np.uint8_t kernel_threshold( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. @@ -206,8 +220,9 @@ cdef inline np.uint8_t kernel_threshold( return < np.uint8_t > (0) cdef inline np.uint8_t kernel_tophat( - Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i for i in range(255, -1, -1): @@ -228,7 +243,9 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -238,7 +255,9 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """bottom hat """ - return _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -248,7 +267,9 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local egalisation of the gray level """ - return _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -258,7 +279,9 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """local maximum - local minimum gray level """ - return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -288,7 +311,9 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """(g - average gray level)/2+127 (clipped on uint8) """ - return _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -318,7 +343,9 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """morphological contrast enhancement """ - return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -348,7 +375,9 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0): """returns 255 if gray level higher than local mean, 0 else """ - return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, + < Py_ssize_t > 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index f882961a..618a6452 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -13,7 +13,9 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_autolevel( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -42,7 +44,9 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_ return < np.uint8_t > (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_gradient( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -65,7 +69,9 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -84,7 +90,9 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean_substraction( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -103,7 +111,9 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, n else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_morph_contr_enh( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -131,7 +141,9 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np. else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_percentile( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -145,7 +157,9 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8 else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_pop( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n if pop: @@ -159,7 +173,9 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, f else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_threshold( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -185,7 +201,9 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -195,7 +213,9 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -215,7 +235,9 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -225,7 +247,9 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -235,7 +259,9 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -255,4 +281,6 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + return _core8( + kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, + < Py_ssize_t > 0) From 27793ddecc97cc62326347408cea906afbdbbe34 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:14:50 +0100 Subject: [PATCH 283/625] remove inlines in pxd --- skimage/filter/rank/_core16.pxd | 6 +++--- skimage/filter/rank/_core8.pxd | 6 +++--- skimage/setup.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index a113f2b0..9590e277 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -5,10 +5,10 @@ cimport numpy as np #--------------------------------------------------------------------------- # generic cdef functions -cdef inline int int_max(int a, int b) -cdef inline int int_min(int a, int b) +cdef int int_max(int a, int b) +cdef int int_min(int a, int b) -cdef inline _core16( +cdef _core16( np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 8ecf7263..9f898faa 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -1,14 +1,14 @@ cimport numpy as np # generic cdef functions -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) +cdef np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) +cdef np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) #--------------------------------------------------------------------------- # 8 bit core kernel receives extra information about data inferior and superior percentiles #--------------------------------------------------------------------------- -cdef inline _core8( +cdef _core8( np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/setup.py b/skimage/setup.py index 7ed50b65..96497fa9 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -12,11 +12,11 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('draw') config.add_subpackage('feature') config.add_subpackage('filter') + config.add_subpackage('filter/rank') config.add_subpackage('graph') config.add_subpackage('io') config.add_subpackage('measure') config.add_subpackage('morphology') - config.add_subpackage('rank') config.add_subpackage('transform') config.add_subpackage('util') config.add_subpackage('segmentation') From cdfa2a6d90befbb84c38af013f0f25960d45043c Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:21:10 +0100 Subject: [PATCH 284/625] move setup from /filter/rank to /filter --- skimage/filter/rank/setup.py | 52 ------------------------------------ skimage/filter/setup.py | 28 +++++++++++++++++-- 2 files changed, 26 insertions(+), 54 deletions(-) delete mode 100644 skimage/filter/rank/setup.py diff --git a/skimage/filter/rank/setup.py b/skimage/filter/rank/setup.py deleted file mode 100644 index c6a3dbb9..00000000 --- a/skimage/filter/rank/setup.py +++ /dev/null @@ -1,52 +0,0 @@ -#!/usr/bin/env python - -import os -from skimage._build import cython - -base_path = os.path.abspath(os.path.dirname(__file__)) - - -def configuration(parent_package='', top_path=None): - from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs - - config = Configuration('rank', parent_package, top_path) -# config.add_data_dir('tests') - - cython(['_core8.pyx'], working_path=base_path) - cython(['_core16.pyx'], working_path=base_path) - cython(['_crank8.pyx'], working_path=base_path) - cython(['_crank8_percentiles.pyx'], working_path=base_path) - cython(['_crank16.pyx'], working_path=base_path) - cython(['_crank16_percentiles.pyx'], working_path=base_path) - cython(['_crank16_bilateral.pyx'], working_path=base_path) - - config.add_extension('_core8', sources=['_core8.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_core16', sources=['_core16.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank8', sources=['_crank8.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension( - '_crank8_percentiles', sources=['_crank8_percentiles.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_crank16', sources=['_crank16.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension( - '_crank16_percentiles', sources=['_crank16_percentiles.c'], - include_dirs=[get_numpy_include_dirs()]) - config.add_extension( - '_crank16_bilateral', sources=['_crank16_bilateral.c'], - include_dirs=[get_numpy_include_dirs()]) - - return config - -if __name__ == '__main__': - from numpy.distutils.core import setup - setup(maintainer='scikits-image Developers', - author='Olivier Debeir', - maintainer_email='scikits-image@googlegroups.com', - description='Rank filters', - url='https://github.com/scikits-image/scikits-image', - license='SciPy License (BSD Style)', - **(configuration(top_path='').todict()) - ) diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index b996055b..79755e87 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -14,11 +14,35 @@ def configuration(parent_package='', top_path=None): cython(['_ctmf.pyx'], working_path=base_path) cython(['_denoise.pyx'], working_path=base_path) + cython(['rank/_core8.pyx'], working_path=base_path) + cython(['rank/_core16.pyx'], working_path=base_path) + cython(['rank/_crank8.pyx'], working_path=base_path) + cython(['rank/_crank8_percentiles.pyx'], working_path=base_path) + cython(['rank/_crank16.pyx'], working_path=base_path) + cython(['rank/_crank16_percentiles.pyx'], working_path=base_path) + cython(['rank/_crank16_bilateral.pyx'], working_path=base_path) config.add_extension('_ctmf', sources=['_ctmf.c'], - include_dirs=[get_numpy_include_dirs()]) + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_denoise', sources=['_denoise.c'], - include_dirs=[get_numpy_include_dirs(), '../_shared']) + include_dirs=[get_numpy_include_dirs(), '../_shared']) + config.add_extension('rank/_core8', sources=['rank/_core8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_core16', sources=['rank/_core16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_crank8', sources=['rank/_crank8.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/_crank8_percentiles', sources=['rank/_crank8_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension('rank/_crank16', sources=['rank/_crank16.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/_crank16_percentiles', sources=['rank/_crank16_percentiles.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/_crank16_bilateral', sources=['rank/_crank16_bilateral.c'], + include_dirs=[get_numpy_include_dirs()]) return config From 4ec4174b44ab10140e911e2a7ea890c95371dce2 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:26:12 +0100 Subject: [PATCH 285/625] remove comments --- skimage/filter/rank/_core16.pyx | 7 ------- skimage/filter/rank/_core8.pyx | 7 ------- skimage/filter/rank/_crank16.pyx | 8 -------- skimage/filter/rank/_crank16_bilateral.pyx | 8 -------- skimage/filter/rank/_crank8.pyx | 8 -------- 5 files changed, 38 deletions(-) diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 81fab0b4..4829792b 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core16.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 7851388d..9955a1e1 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -1,10 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a core8.pxd -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 57d41563..f6ad10e4 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank16.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index d6fb9c71..c013b779 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank16.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index be00bf86..da716bd9 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -1,11 +1,3 @@ -""" to compile this use: ->>> python setup.py build_ext --inplace - -to generate html report use: ->>> cython -a crank.pxd - -""" - #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False From b6527080751e16922be9f7b02f18a9c14b1f77ad Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 09:35:28 +0100 Subject: [PATCH 286/625] remove obsolete comments --- doc/__init__.py | 1 - skimage/filter/rank/bilateral_rank.py | 2 -- skimage/filter/rank/percentile_rank.py | 21 +++++++-------------- 3 files changed, 7 insertions(+), 17 deletions(-) diff --git a/doc/__init__.py b/doc/__init__.py index 10b6fb15..e69de29b 100644 --- a/doc/__init__.py +++ b/doc/__init__.py @@ -1 +0,0 @@ -__author__ = 'olivier' diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index ff4e7878..dd2942b4 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -81,7 +81,6 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -147,7 +146,6 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py index 5191bec4..3817e1cd 100644 --- a/skimage/filter/rank/percentile_rank.py +++ b/skimage/filter/rank/percentile_rank.py @@ -73,7 +73,6 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -139,7 +138,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ Examples -------- - to be updated + >>> # Local gradient >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -205,7 +204,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -271,7 +270,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals Examples -------- - to be updated + >>> # Local mean_substraction >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -337,7 +336,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -403,7 +402,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -470,7 +469,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -536,7 +535,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift Examples -------- - to be updated + >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank @@ -572,9 +571,3 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) -if __name__ == "__main__": - import sys - sys.path.append('.') - - import doctest - doctest.testmod(verbose=True) From 1d1696f11d1da6ee52071e5ba333b53c0b8e5d3f Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 16:32:08 +0100 Subject: [PATCH 287/625] adjust setup --- doc/examples/plot_lena_bilateral_denoise.py | 9 ++++----- skimage/filter/rank/{local => demo}/demo_all.py | 0 skimage/filter/rank/{local => demo}/demo_single.py | 0 skimage/filter/rank/{local => demo}/iko_pan_Ja1.tif | Bin .../rank/{local => demo}/test_morph_contr_enh.py | 0 skimage/filter/rank/{local => demo}/test_rank.py | 0 skimage/setup.py | 1 - 7 files changed, 4 insertions(+), 6 deletions(-) rename skimage/filter/rank/{local => demo}/demo_all.py (100%) rename skimage/filter/rank/{local => demo}/demo_single.py (100%) rename skimage/filter/rank/{local => demo}/iko_pan_Ja1.tif (100%) rename skimage/filter/rank/{local => demo}/test_morph_contr_enh.py (100%) rename skimage/filter/rank/{local => demo}/test_rank.py (100%) diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py index 969403d0..03fc61d0 100644 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ b/doc/examples/plot_lena_bilateral_denoise.py @@ -1,4 +1,3 @@ - """ ==================================================== Denoising the picture of Lena using bilateral filter @@ -27,7 +26,7 @@ l = l[230:290, 220:320] noisy = l + 0.4 * l.std() * np.random.random(l.shape) selem = disk(30) -bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) +approx_bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) plt.figure(figsize=(8, 2)) @@ -36,14 +35,14 @@ plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('noisy', fontsize=20) plt.subplot(132) -plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.imshow(approx_bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('bilateral denoising', fontsize=20) selem = disk(30) -bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) +approx_bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) plt.subplot(133) -plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.imshow(approx_bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('(more) bilateral denoising', fontsize=20) diff --git a/skimage/filter/rank/local/demo_all.py b/skimage/filter/rank/demo/demo_all.py similarity index 100% rename from skimage/filter/rank/local/demo_all.py rename to skimage/filter/rank/demo/demo_all.py diff --git a/skimage/filter/rank/local/demo_single.py b/skimage/filter/rank/demo/demo_single.py similarity index 100% rename from skimage/filter/rank/local/demo_single.py rename to skimage/filter/rank/demo/demo_single.py diff --git a/skimage/filter/rank/local/iko_pan_Ja1.tif b/skimage/filter/rank/demo/iko_pan_Ja1.tif similarity index 100% rename from skimage/filter/rank/local/iko_pan_Ja1.tif rename to skimage/filter/rank/demo/iko_pan_Ja1.tif diff --git a/skimage/filter/rank/local/test_morph_contr_enh.py b/skimage/filter/rank/demo/test_morph_contr_enh.py similarity index 100% rename from skimage/filter/rank/local/test_morph_contr_enh.py rename to skimage/filter/rank/demo/test_morph_contr_enh.py diff --git a/skimage/filter/rank/local/test_rank.py b/skimage/filter/rank/demo/test_rank.py similarity index 100% rename from skimage/filter/rank/local/test_rank.py rename to skimage/filter/rank/demo/test_rank.py diff --git a/skimage/setup.py b/skimage/setup.py index 96497fa9..1082ba07 100644 --- a/skimage/setup.py +++ b/skimage/setup.py @@ -12,7 +12,6 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('draw') config.add_subpackage('feature') config.add_subpackage('filter') - config.add_subpackage('filter/rank') config.add_subpackage('graph') config.add_subpackage('io') config.add_subpackage('measure') From 528718bb6d2ff45bf34560fe43b7b460aba15f87 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 17:04:10 +0100 Subject: [PATCH 288/625] removed mains --- skimage/filter/rank/bilateral_rank.py | 6 ------ skimage/filter/rank/rank.py | 6 ------ 2 files changed, 12 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py index dd2942b4..69884e16 100644 --- a/skimage/filter/rank/bilateral_rank.py +++ b/skimage/filter/rank/bilateral_rank.py @@ -179,9 +179,3 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) -if __name__ == "__main__": - import sys - sys.path.append('.') - - import doctest - doctest.testmod(verbose=True) diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.py index 517ab2cf..fc06d48b 100644 --- a/skimage/filter/rank/rank.py +++ b/skimage/filter/rank/rank.py @@ -918,9 +918,3 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) -if __name__ == "__main__": - import sys - sys.path.append('.') - - import doctest - doctest.testmod(verbose=True) From 262e7f78f4ffe9145be2e1fcd8717cd4dde8da5d Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 29 Oct 2012 18:29:48 +0100 Subject: [PATCH 289/625] small comparison with denoise_bilateral --- skimage/filter/rank/demo/demo_single.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py index 39b9acc0..652f2cc8 100644 --- a/skimage/filter/rank/demo/demo_single.py +++ b/skimage/filter/rank/demo/demo_single.py @@ -5,6 +5,7 @@ from skimage import data from skimage.morphology.selem import disk import skimage.filter.rank as rank +from skimage.filter import denoise_bilateral if __name__ == '__main__': a8 = data.camera() @@ -15,6 +16,16 @@ if __name__ == '__main__': f16= rank.autolevel(a16,selem) f16p= rank.percentile_autolevel(a16,selem,p0=.0,p1=1.) + den = denoise_bilateral(a8,win_size=10,sigma_range=10,sigma_spatial=2)[:,:,0] + f16b= rank.bilateral_mean(a8.astype(np.uint16),disk(10),s0=10,s1=10) + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(den) + plt.subplot(1,2,2) + plt.imshow(f16b) + plt.show() + print f16==f16p plt.figure() From 1460d7f1b1f1a66b90f3c35e9db52c957eef42b1 Mon Sep 17 00:00:00 2001 From: odebeir Date: Tue, 30 Oct 2012 16:55:05 +0100 Subject: [PATCH 290/625] compare bilateral --- doc/examples/plot_16bitbilateral.py | 34 +++++++++----- doc/examples/plot_compare_bilateral.py | 63 ++++++++++++++++++++++++++ 2 files changed, 86 insertions(+), 11 deletions(-) create mode 100644 doc/examples/plot_compare_bilateral.py diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py index 076d03c4..fc30aa6b 100644 --- a/doc/examples/plot_16bitbilateral.py +++ b/doc/examples/plot_16bitbilateral.py @@ -1,9 +1,23 @@ """ ============================== -Simplified bilateral filtering +Bilateral mean ============================== +This example compares -to complete +* local mean +* percentile mean +* bilateral mean + +build on the local histogram distribution +local mean uses all pixels belonging to the structuring element to compute average gray level, +percentile mean uses only values between percentiles p0 and p1 (here 10% and 90%), +whereas bilateral mean uses only pixels of the structuring element having a gray level situated inside +g-s0 and g+s1 (here g-500 and g+500). +The filters are applied on a 16 bit image (actual bitdepth is 12bit). + +Percentile and usual mean give here similar results, these filters smooth the complete image (background and details). +Bilateral mean exhibits a high filtering rate for continuous area (i.e. background) while image higher frequencies +remains untouched. """ import numpy as np @@ -13,23 +27,21 @@ from skimage import data from skimage.morphology import disk import skimage.filter.rank as rank -a8 = (data.coins()).astype('uint8') - a16 = (data.coins()).astype('uint16')*16 -selem = np.ones((20,20),dtype='uint8') -f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9) +selem = disk(20) + +f1 = rank.percentile_mean(a16,selem = selem,p0=.1,p1=.9) f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) -selem = disk(50) -f3 = rank.equalize(a16,selem = selem) +f3 = rank.mean(a16,selem = selem) # display results -fig, axes = plt.subplots(nrows=3, figsize=(15,15)) +fig, axes = plt.subplots(nrows=3, figsize=(15,10)) ax0, ax1, ax2 = axes -ax0.imshow(np.hstack((a8,f1))) +ax0.imshow(np.hstack((a16,f1))) ax0.set_title('percentile mean') ax1.imshow(np.hstack((a16,f2))) ax1.set_title('bilateral mean') ax2.imshow(np.hstack((a16,f3))) -ax2.set_title('local equalization') +ax2.set_title('local mean') plt.show() diff --git a/doc/examples/plot_compare_bilateral.py b/doc/examples/plot_compare_bilateral.py new file mode 100644 index 00000000..977b7704 --- /dev/null +++ b/doc/examples/plot_compare_bilateral.py @@ -0,0 +1,63 @@ +""" +==================================================== +Bilateral comparison +==================================================== + +In this example, we compare both bilateral implementation + +* filter.denoise_bilateral +* filter.rank.bilateral_mean + +The first filter implements a spatial-gaussian and spectral-gaussian kernel bilateral filter whereas the latter implements +a cylindrical kernel bilateral filter i.e. spatial-flat and spectral-flat kernel. + +The timing comparison is just for information since the kernel are not the same. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data +from skimage.filter._denoise import denoise_bilateral +from skimage.filter.rank import bilateral_mean +from skimage.morphology import disk +from skimage.filter import denoise_bilateral +import time + +def exec_and_timeit(func): + """ Decorator that returns both function results and execution time + (result, ms) + """ + def wrapper(*arg): + t1 = time.time() + res = func(*arg) + t2 = time.time() + ms = (t2-t1)*1000.0 + return (res,ms) + return wrapper + + +@exec_and_timeit +def den_bil(image): + return denoise_bilateral(a8,win_size=20,sigma_range=255,sigma_spatial=1)[:,:,0]*255 + +@exec_and_timeit +def rank_bil(image): + return bilateral_mean(a8.astype(np.uint16),disk(20),s0=10,s1=10) + +a8 = data.camera() +selem = disk(10) + +f1,t1 = den_bil(a8) +f2,t2 = rank_bil(a8) + +# display results +fig, axes = plt.subplots(nrows=2, figsize=(15,10)) +ax0, ax1= axes + +ax0.imshow(np.hstack((f1,a8-f1))) +ax0.set_title('denoise bilateral (%f ms)'%t1) +ax1.imshow(np.hstack((f2,a8-f1))) +ax1.set_title('bilateral mean (%f ms)'%t2) +plt.show() From 1221a98a4252bdc09146d6b301803610519c6194 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 31 Oct 2012 09:24:09 +0100 Subject: [PATCH 291/625] Fix ordering of return values --- skimage/transform/hough_transform.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 9b5c1050..a5987d5d 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -216,8 +216,8 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, angle_peaks = [] # relative coordinate grid for local neighbourhood suppression - dist_ext, angle_ext = np.mgrid[- min_distance:min_distance + 1, - - min_angle:min_angle + 1] + dist_ext, angle_ext = np.mgrid[-min_distance:min_distance + 1, + -min_angle:min_angle + 1] for dist_idx, angle_idx in coords: accum = hspace[dist_idx, angle_idx] @@ -258,4 +258,4 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, dist_peaks = dist_peaks[idx_maxsort] angle_peaks = angle_peaks[idx_maxsort] - return hspace_peaks, dist_peaks, angle_peaks + return hspace_peaks, angle_peaks, dist_peaks From 5be7f628138602e2692f526730402321316a6227 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 31 Oct 2012 09:26:47 +0100 Subject: [PATCH 292/625] Fix hough transform return value ordering in doc string --- skimage/transform/hough_transform.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index a5987d5d..5e77954d 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -111,10 +111,10 @@ def hough(img, theta=None): ------- H : 2-D ndarray of uint64 Hough transform accumulator. - distances : ndarray - Distance values. theta : ndarray Angles at which the transform was computed. + distances : ndarray + Distance values. Examples -------- From 531060ec2054f06958f66c90eb37e4c2fdffe95f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 1 Nov 2012 15:01:14 +0100 Subject: [PATCH 293/625] Reduce to one threshold paramater --- skimage/transform/hough_transform.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 5e77954d..3c27e346 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -139,7 +139,7 @@ def hough(img, theta=None): def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, - threshold_abs=0, threshold_rel=0.5, num_peaks=np.inf): + threshold=None, num_peaks=np.inf): """Return peaks in hough transform. Identifies most prominent lines separated by a certain angle and distance in @@ -162,10 +162,8 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, min_angle : int Minimum angle separating lines (maximum filter size for second dimension of hough space). - threshold_abs : float - Minimum intensity of peaks in hough space. - threshold_rel : float - Minimum intensity of peaks calculated as `max(hspace) * threshold_rel`. + threshold : float + Minimum intensity of peaks calculated as `0.5 * max(hspace)`. num_peaks : int Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` coordinates based on peak intensity. @@ -197,7 +195,8 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, hspace = hspace.copy() rows, cols = hspace.shape - threshold = max(threshold_abs, threshold_rel * np.max(hspace)) + if threshold is None: + threshold = 0.5 * np.max(hspace) distance_size = 2 * min_distance + 1 angle_size = 2 * min_angle + 1 From bc08be76a8fca4a150642235a58c1970aa07d976 Mon Sep 17 00:00:00 2001 From: odebeir Date: Fri, 2 Nov 2012 09:50:07 +0100 Subject: [PATCH 294/625] rename rank.py to rank.pyx + adjust setup --- .../rank/{bilateral_rank.py => bilateral_rank.pyx} | 0 .../rank/{percentile_rank.py => percentile_rank.pyx} | 0 skimage/filter/rank/{rank.py => rank.pyx} | 0 skimage/filter/setup.py | 12 ++++++++++++ 4 files changed, 12 insertions(+) rename skimage/filter/rank/{bilateral_rank.py => bilateral_rank.pyx} (100%) rename skimage/filter/rank/{percentile_rank.py => percentile_rank.pyx} (100%) rename skimage/filter/rank/{rank.py => rank.pyx} (100%) diff --git a/skimage/filter/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.pyx similarity index 100% rename from skimage/filter/rank/bilateral_rank.py rename to skimage/filter/rank/bilateral_rank.pyx diff --git a/skimage/filter/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.pyx similarity index 100% rename from skimage/filter/rank/percentile_rank.py rename to skimage/filter/rank/percentile_rank.pyx diff --git a/skimage/filter/rank/rank.py b/skimage/filter/rank/rank.pyx similarity index 100% rename from skimage/filter/rank/rank.py rename to skimage/filter/rank/rank.pyx diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 79755e87..56c1e9e5 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -21,6 +21,9 @@ def configuration(parent_package='', top_path=None): cython(['rank/_crank16.pyx'], working_path=base_path) cython(['rank/_crank16_percentiles.pyx'], working_path=base_path) cython(['rank/_crank16_bilateral.pyx'], working_path=base_path) + cython(['rank/rank.pyx'], working_path=base_path) + cython(['rank/percentile_rank.pyx'], working_path=base_path) + cython(['rank/bilateral_rank.pyx'], working_path=base_path) config.add_extension('_ctmf', sources=['_ctmf.c'], include_dirs=[get_numpy_include_dirs()]) @@ -43,6 +46,15 @@ def configuration(parent_package='', top_path=None): config.add_extension( 'rank/_crank16_bilateral', sources=['rank/_crank16_bilateral.c'], include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/rank', sources=['rank/rank.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/percentile_rank', sources=['rank/percentile_rank.c'], + include_dirs=[get_numpy_include_dirs()]) + config.add_extension( + 'rank/bilateral_rank', sources=['rank/bilateral_rank.c'], + include_dirs=[get_numpy_include_dirs()]) return config From 10a6c23ff848cd66f81ac73e5940da47b7629773 Mon Sep 17 00:00:00 2001 From: odebeir Date: Fri, 2 Nov 2012 10:10:39 +0100 Subject: [PATCH 295/625] doc bilateral_rank --- skimage/filter/rank/bilateral_rank.pyx | 47 +++++--------------------- 1 file changed, 8 insertions(+), 39 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 69884e16..d72a84c9 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -81,33 +81,14 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal Examples -------- - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.bilateral_mean(ima8, square(3), s0=10,s1=10) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.bilateral_mean(ima16, square(3), s0=10,s1=10) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - + >>> from skimage import data + >>> from skimage.morphology import disk + >>> from skimage.filter.rank import bilateral_mean + >>> # bilateral filtering of cameraman image using a flat kernel + >>> # Load test image + >>> a8 = data.camera() + >>> # Apply bilateral filter + >>> bl8 = bilateral_mean(a8, disk(20), s0=10,s1=10) """ return _apply( @@ -161,18 +142,6 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals [4, 4, 6, 4, 4], [3, 4, 3, 4, 3]], dtype=uint16) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.bilateral_pop(ima16, square(3), s0=10,s1=10) - array([[3, 4, 3, 4, 3], - [4, 4, 6, 4, 4], - [3, 6, 9, 6, 3], - [4, 4, 6, 4, 4], - [3, 4, 3, 4, 3]], dtype=uint16) - """ return _apply( From 29f187885b2a432cc33a51b99316ffb264342d43 Mon Sep 17 00:00:00 2001 From: odebeir Date: Fri, 2 Nov 2012 15:07:19 +0100 Subject: [PATCH 296/625] doc --- skimage/filter/rank/bilateral_rank.pyx | 41 +- skimage/filter/rank/rank.pyx | 502 ++++--------------------- 2 files changed, 111 insertions(+), 432 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index d72a84c9..80349b0b 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -51,9 +51,17 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): - """Return greyscale local bilateral_mean of an image. + """Apply a flat kernel bilateral filter. - bilateral mean is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used. + This is an edge-preserving and noise reducing denoising filter. It averages + pixels based on their spatial closeness and radiometric similarity. + + Spatial closeness is measured by considering only the local pixel neighborhood given by a + structuring element (selem). + + Radiometric similarity is defined by the gray level interval [g-s0,g+s1] where g is the current pixel gray level. + Only pixels belonging to the structuring element AND having a gray level inside this interval are averaged. + Return greyscale local bilateral_mean of an image. Parameters ---------- @@ -76,19 +84,29 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal Returns ------- - local bilateral mean : uint16 array (uint8 image are casted to uint16) + out : uint16 array (uint8 image are casted to uint16) The result of the local bilateral mean. + See also + -------- + skimage.filter.denoise_bilateral() for a gaussian bilateral filter. + + Notes + ----- + + * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) + + * 8 bit images are casted in 16 bit + Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filter.rank import bilateral_mean - >>> # bilateral filtering of cameraman image using a flat kernel >>> # Load test image - >>> a8 = data.camera() - >>> # Apply bilateral filter - >>> bl8 = bilateral_mean(a8, disk(20), s0=10,s1=10) + >>> ima = data.camera() + >>> # bilateral filtering of cameraman image using a flat kernel + >>> bilat_ima = bilateral_mean(ima, disk(20), s0=10,s1=10) """ return _apply( @@ -97,9 +115,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): - """Return greyscale local bilateral_pop of an image. - - bilateral pop is computed on the given structuring element. Only levels between [g-s0,g+s1] ,are used. + """Return the number (population) of pixels actually inside the bilateral neighborhood, + i.e. being inside the structuring element AND having a gray level inside the interval [g-s0,g+s1]. Parameters ---------- @@ -122,8 +139,8 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals Returns ------- - local bilateral pop : uint16 array (uint8 image are casted to uint16) - The result of the local bilateral pop. + out : uint16 array (uint8 image are casted to uint16) + the local number of pixels inside the bilateral neighborhood Examples -------- diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index fc06d48b..730d37c7 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -38,9 +38,7 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local autolevel of an image. - - Autolevel is computed on the given structuring element. + """Autolevel image using local histogram. Parameters ---------- @@ -61,38 +59,18 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local autolevel : uint8 array or uint16 array depending on input image + out : uint8 array or uint16 array (same as input image) The result of the local autolevel. Examples -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.autolevel(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.autolevel(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 0, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) + >>> from skimage import data + >>> from skimage.morphology import disk + >>> from skimage.filter.rank import autolevel + >>> # Load test image + >>> ima = data.camera() + >>> # Stretch image contrast locally + >>> auto = autolevel(ima, disk(20)) """ @@ -102,9 +80,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local bottomhat of an image. - - Bottomhat is computed on the given structuring element. + """Returns greyscale local bottomhat of an image. Parameters ---------- @@ -128,35 +104,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): local bottomhat : uint8 array or uint16 array depending on input image The result of the local bottomhat. - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.bottomhat(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.bottomhat(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 0, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) """ return _apply( @@ -165,9 +113,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local equalize of an image. - - equalize is computed on the given structuring element. + """Equalize image using local histogram. Parameters ---------- @@ -188,38 +134,18 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local equalize : uint8 array or uint16 array depending on input image + out : uint8 array or uint16 array (same as input image) The result of the local equalize. Examples -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.equalize(ima8, square(3)) - array([[191, 170, 127, 170, 191], - [170, 255, 255, 255, 170], - [127, 255, 255, 255, 127], - [170, 255, 255, 255, 170], - [191, 170, 127, 170, 191]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.equalize(ima16, square(3)) - array([[3071, 2730, 2047, 2730, 3071], - [2730, 4095, 4095, 4095, 2730], - [2047, 4095, 4095, 4095, 2047], - [2730, 4095, 4095, 4095, 2730], - [3071, 2730, 2047, 2730, 3071]], dtype=uint16) + >>> from skimage import data + >>> from skimage.morphology import disk + >>> from skimage.filter.rank import equalize + >>> # Load test image + >>> ima = data.camera() + >>> # Local equalization + >>> equ = equalize(ima, disk(20)) """ return _apply( @@ -228,9 +154,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local gradient of an image. + """Return greyscale local gradient of an image (i.e. local maximum - local minimum). - gradient is computed on the given structuring element. Parameters ---------- @@ -251,38 +176,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local gradient : uint8 array or uint16 array depending on input image - The result of the local gradient. - - Examples - -------- - to be updated - >>> # Local gradient - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.gradient(ima8, square(3)) - array([[255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 0, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.gradient(ima16, square(3)) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 0, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) + out : uint8 array or uint16 array (same as input image) + The local gradient. """ @@ -294,7 +189,6 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local maximum of an image. - maximum is computed on the given structuring element. Parameters ---------- @@ -315,38 +209,18 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local maximum : uint8 array or uint16 array depending on input image - The result of the local maximum. + out : uint8 array or uint16 array (same as input image) + The local maximum. - Examples + See also -------- - to be updated - >>> # Local maximum - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 1, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.maximum(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) + skimage.morphology.dilation() - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 1, 0, 0], - ... [0, 0, 0, 0, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.maximum(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) + Note + ---- + * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) + + * the lower algorithm complexity makes the rank.maximum() more efficient for larger images and structuring elements """ @@ -356,8 +230,6 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local mean of an image. - Mean is computed on the given structuring element. - Parameters ---------- image : ndarray @@ -377,48 +249,25 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local mean : uint8 array or uint16 array depending on input image - The result of the local mean. + out : uint8 array or uint16 array (same as input image) + The local mean. Examples -------- - to be updated + >>> from skimage import data + >>> from skimage.morphology import disk + >>> from skimage.filter.rank import mean + >>> # Load test image + >>> ima = data.camera() >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.mean(ima8, square(3)) - array([[ 63, 85, 127, 85, 63], - [ 85, 113, 170, 113, 85], - [127, 170, 255, 170, 127], - [ 85, 113, 170, 113, 85], - [ 63, 85, 127, 85, 63]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.mean(ima16, square(3)) - array([[1023, 1365, 2047, 1365, 1023], - [1365, 1820, 2730, 1820, 1365], - [2047, 2730, 4095, 2730, 2047], - [1365, 1820, 2730, 1820, 1365], - [1023, 1365, 2047, 1365, 1023]], dtype=uint16) - + >>> avg = mean(ima, disk(20)) """ return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local meansubstraction of an image. - - meansubstraction is computed on the given structuring element. + """Return image substracted from its local mean. Parameters ---------- @@ -439,38 +288,10 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F Returns ------- - local meansubstraction : uint8 array or uint16 array depending on input image + out : uint8 array or uint16 array (same as input image) The result of the local meansubstraction. - Examples - -------- - to be updated - >>> # Local meansubstraction - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.meansubstraction(ima8, square(3)) - array([[ 95, 84, 63, 84, 95], - [ 84, 197, 169, 197, 84], - [ 63, 169, 127, 169, 63], - [ 84, 197, 169, 197, 84], - [ 95, 84, 63, 84, 95]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.meansubstraction(ima16, square(3)) - array([[1535, 1364, 1023, 1364, 1535], - [1364, 3184, 2729, 3184, 1364], - [1023, 2729, 2047, 2729, 1023], - [1364, 3184, 2729, 3184, 1364], - [1535, 1364, 1023, 1364, 1535]], dtype=uint16) """ @@ -482,7 +303,6 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local median of an image. - median is computed on the given structuring element. Parameters ---------- @@ -503,39 +323,18 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local median : uint8 array or uint16 array depending on input image - The result of the local median. + out : uint8 array or uint16 array (same as input image) + The local median. Examples -------- - to be updated - >>> # Local median - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 0, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.median(ima8, square(3)) - array([[ 0, 0, 255, 0, 0], - [ 0, 0, 255, 0, 0], - [255, 255, 255, 255, 255], - [ 0, 0, 255, 0, 0], - [ 0, 0, 255, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 0, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.median(ima16, square(3)) - array([[ 0, 0, 4095, 0, 0], - [ 0, 0, 4095, 0, 0], - [4095, 4095, 4095, 4095, 4095], - [ 0, 0, 4095, 0, 0], - [ 0, 0, 4095, 0, 0]], dtype=uint16) - + >>> from skimage import data + >>> from skimage.morphology import disk + >>> from skimage.filter.rank import median + >>> # Load test image + >>> ima = data.camera() + >>> # Local mean + >>> avg = median(ima, disk(20)) """ return _apply(_crank8.median, _crank16.median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @@ -544,8 +343,6 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local minimum of an image. - minimum is computed on the given structuring element. - Parameters ---------- image : ndarray @@ -565,39 +362,18 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local minimum : uint8 array or uint16 array depending on input image - The result of the local minimum. + out : uint8 array or uint16 array (same as input image) + The local minimum. - Examples + See also -------- - to be updated - >>> # Local minimum - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.minimum(ima8, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) + skimage.morphology.erosion() + Note + ---- + * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.minimum(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 4095, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) + * the lower algorithm complexity makes the rank.minimum() more efficient for larger images and structuring elements """ @@ -605,9 +381,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local modal of an image. - - modal is computed on the given structuring element. + """Return greyscale local mode of an image. Parameters ---------- @@ -628,39 +402,9 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local modal : uint8 array or uint16 array depending on input image - The result of the local modal. + out : uint8 array or uint16 array (same as input image) + The local modal. - Examples - -------- - to be updated - >>> # Local modal - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 5, 6, 0], - ... [0, 1, 5, 5, 0], - ... [0, 0, 0, 5, 0]], dtype=np.uint8) - >>> rank.modal(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 1, 1, 0, 0], - [0, 0, 5, 0, 0], - [0, 0, 5, 0, 0]], dtype=uint8) - - - >>> ima16 = 100*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 5, 6, 0], - ... [0, 1, 5, 5, 0], - ... [0, 0, 0, 5, 0]], dtype=np.uint16) - >>> rank.modal(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 100, 0, 0], - [ 0, 100, 100, 0, 0], - [ 0, 0, 500, 0, 0], - [ 0, 0, 500, 0, 0]], dtype=uint16) """ @@ -668,9 +412,8 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local morph_contr_enh of an image. - - morph_contr_enh is computed on the given structuring element. + """Enhance an image replacing each pixel by the local maximum if pixel graylevel is closest to maximimum + than local minimum OR local minimum otherwise. Parameters ---------- @@ -691,39 +434,18 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa Returns ------- - local morph_contr_enh : uint8 array or uint16 array depending on input image + out : uint8 array or uint16 array (same as input image) The result of the local morph_contr_enh. Examples -------- - to be updated + >>> from skimage import data + >>> from skimage.morphology import disk + >>> from skimage.filter.rank import morph_contr_enh + >>> # Load test image + >>> ima = data.camera() >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.morph_contr_enh(ima8, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 1, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.morph_contr_enh(ima16, square(3)) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) - + >>> avg = morph_contr_enh(ima, disk(20)) """ return _apply( @@ -732,9 +454,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local pop of an image. - - pop is computed on the given structuring element. + """Return the number (population) of pixels actually inside the neighborhood. Parameters ---------- @@ -755,38 +475,26 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local pop : uint8 array or uint16 array depending on input image - The result of the local pop. + out : uint8 array or uint16 array (same as input image) + The number of pixels belonging to the neighborhood. Examples -------- - to be updated >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + >>> ima = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.pop(ima8, square(3)) + >>> rank.pop(ima, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.pop(ima16, square(3)) - array([[4, 6, 6, 6, 4], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [4, 6, 6, 6, 4]], dtype=uint16) """ @@ -796,8 +504,6 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local threshold of an image. - threshold is computed on the given structuring element. - Parameters ---------- image : ndarray @@ -817,39 +523,26 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local threshold : uint8 array or uint16 array depending on input image + out : uint8 array or uint16 array (same as input image) The result of the local threshold. Examples -------- - to be updated - >>> # Local mean + >>> # Local threshold >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], + >>> from skimage.filter.rank import threshold + >>> ima = 255*np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.threshold(ima8, square(3)) + >>> threshold(ima, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.threshold(ima16, square(3)) - array([[0, 0, 0, 0, 0], - [0, 1, 1, 1, 0], - [0, 1, 0, 1, 0], - [0, 1, 1, 1, 0], - [0, 0, 0, 0, 0]], dtype=uint16) - """ @@ -861,8 +554,6 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local tophat of an image. - tophat is computed on the given structuring element. - Parameters ---------- image : ndarray @@ -882,38 +573,9 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - local tophat : uint8 array or uint16 array depending on input image - The result of the local tophat. + out : uint8 array or uint16 array (same as input image) + The image tophat. - Examples - -------- - to be updated - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.tophat(ima8, square(3)) - array([[255, 255, 255, 255, 255], - [255, 0, 0, 0, 255], - [255, 0, 0, 0, 255], - [255, 0, 0, 0, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.tophat(ima16, square(3)) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 0, 0, 0, 4095], - [4095, 0, 0, 0, 4095], - [4095, 0, 0, 0, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) From d0415e6df475157705fd1ef2af69b16e4f7e38cc Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Fri, 2 Nov 2012 16:02:06 -0700 Subject: [PATCH 297/625] Update README to Markdown. --- README.rst => README.md | 5 ----- 1 file changed, 5 deletions(-) rename README.rst => README.md (91%) diff --git a/README.rst b/README.md similarity index 91% rename from README.rst rename to README.md index 508db396..64a7dfe9 100644 --- a/README.rst +++ b/README.md @@ -29,8 +29,3 @@ this path to your PYTHONPATH variable and compiling the extensions: License ------- Please read LICENSE.txt in this directory. - -Contact -------- -Stefan van der Walt - From 890c473afe3f47f84af21ed9cebd8c48045317f8 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sat, 3 Nov 2012 17:15:13 +0100 Subject: [PATCH 298/625] start filter bigger example --- .../applications/plot_image_filtering.py | 210 ++++++++++++++++++ skimage/filter/rank/tests/test_suite.py | 5 + 2 files changed, 215 insertions(+) create mode 100644 doc/examples/applications/plot_image_filtering.py diff --git a/doc/examples/applications/plot_image_filtering.py b/doc/examples/applications/plot_image_filtering.py new file mode 100644 index 00000000..a58d59aa --- /dev/null +++ b/doc/examples/applications/plot_image_filtering.py @@ -0,0 +1,210 @@ +""" +=============================================================== +Image filtering +=============================================================== + +Filtering is a common operation on images, it serves several purposes such as: + +* image quality enhancement + e.g. image smoothing, sharpening + +* image pre-processing + e.g. noise reduction, contrast enhancement + +* feature extraction + e.g. border detection, isolated point detection + +* post-processing + e.g. small object removal, object grouping, contour smoothing + + +Filters usually operate on image using a neighborhood situated around the pixel current pixel being treated. + +Depending on the type of operation traditionally filters fall into one of the following (not exclusive) types: + +* linear filter + where filtered pixel grey value results in a linear function of its neighborhood, linear filters are in fact + convolution and may be implemented using the Fourier transform (not discussed here). + +* non-linear + where relation may involve non-linear function such as logical test or grey level rank. + +* morphological filter + these filters belong to Mathematical morphology (MM) which "is a theory and technique for the analysis and processing + of geometrical structures" [1]_ + +.. [1] http://en.wikipedia.org/wiki/Mathematical_morphology + +Skimage implement several filters in ``skimage.filter``, ``skimage.filter.rank``, ``skimage.morphology``, some of +these filters are redundant for historical reasons, since there implementation are not identical, there respective +algorithm complexity may differ and therefore the choice may be function of image size or bitdepth +and filter parameters. + +In this example, we will see how to filter a grey level image using some of the linear and non-linear filters +availables in skimage. We use the ``camera`` image from ``skimage.data``. + +""" + +import numpy as np +import matplotlib.pyplot as plt + +from skimage import data + +ima = data.camera() +hist = np.histogram(ima, bins=np.arange(0, 256)) + +plt.figure(figsize=(8, 3)) +plt.subplot(121) +plt.imshow(ima, cmap=plt.cm.gray, interpolation='nearest') +plt.axis('off') +plt.subplot(122) +plt.plot(hist[1][:-1], hist[0], lw=2) +plt.title('histogram of grey values') + +""" +.. image:: PLOT2RST.current_figure + +Noise removal +============== + +some noise is added to the image, 1% of pixels are randomly set to 255, %1% are randomly set to 0. +The **median** filter is applied to remove the noise. + +""" + +noise = np.random.random(ima.shape) +nima = data.camera() +nima[noise>.99] = 255 +nima[noise<.01] = 0 + +from skimage.filter.rank import median +from skimage.morphology import disk + +fig = plt.figure(figsize=[10,7]) + +lo = median(nima,disk(1)) +hi = median(nima,disk(5)) +ext = median(nima,disk(20)) +plt.subplot(2,2,1) +plt.imshow(nima,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.xlabel('noised image') +plt.subplot(2,2,2) +plt.imshow(lo,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.xlabel('median $r=1$') +plt.subplot(2,2,3) +plt.imshow(hi,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.xlabel('median $r=5$') +plt.subplot(2,2,4) +plt.imshow(ext,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.xlabel('median $r=20$') + +""" +.. image:: PLOT2RST.current_figure + +The added noise is efficiently removed, as the image defaults are small (1 pixel wide), a small filter radius is +sufficient. As the radius is increasing, objects with a bigger size are filtered too such as the camera tripod. +Median filter is commonly used for noise removal because borders are preserved. + +Image smoothing +================ + +The example hereunder shows how a local **mean** smooth the cameraman image. + +""" +from skimage.filter.rank import mean + +fig = plt.figure(figsize=[10,7]) + +loc_mean = mean(nima,disk(10)) +plt.subplot(1,2,1) +plt.imshow(ima,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.xlabel('original') +plt.subplot(1,2,2) +plt.imshow(loc_mean,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.xlabel('local mean $r=10$') + +""" +.. image:: PLOT2RST.current_figure + +One may be interested in smoothing an image while preserving important borders (median filters already achieved this), +here we use the **bilateral** filter that restrict the local neighborhood to pixel having a grey level similar to the +central one. + +rem: a different implementations is available for color images in ``skimage.filter.denoise_bilateral``. + +""" + +from skimage.filter.rank import bilateral_mean + +ima = data.camera() +selem = disk(10) + +bilat = bilateral_mean(ima.astype(np.uint16),disk(20),s0=10,s1=10) + +# display results +fig = plt.figure(figsize=[10,7]) +plt.subplot(1,2,1) +plt.imshow(ima) +plt.xlabel('original') +plt.subplot(1,2,2) +plt.imshow(bilat) +plt.xlabel('bilateral mean') + +""" +One can see that the large continuous part of the image (e.g.sky) are smoothed whereas other details are preserved. + + +Contrast enhancement +==================== + +We compare here how the global histogram equalization is applied locally. + +The equalized image [2]_ has a roughly linear cumulative distribution function for each pixel neighborhood. +The local version [3]_ of the histogram equalization emphasized every local graylevel variations. + +.. [2] http://en.wikipedia.org/wiki/Histogram_equalization +.. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization + +""" + +from skimage.exposure import equalize as global_equalize +from skimage.filter.rank import equalize as local_equalize + +ima = data.camera() +# equalize globally and locally +loc = local_equalize(ima,disk(20)) +glob = global_equalize(ima) + +# extract histogram for each image +hist = np.histogram(ima, bins=np.arange(0, 256)) +glob_hist = np.histogram(glob, bins=np.arange(0, 256)) +loc_hist = np.histogram(loc, bins=np.arange(0, 256)) + +plt.figure(figsize=(10, 10)) +plt.subplot(321) +plt.imshow(ima, cmap=plt.cm.gray, interpolation='nearest') +plt.axis('off') +plt.subplot(322) +plt.plot(hist[1][:-1], hist[0], lw=2) +plt.title('histogram of grey values') +plt.subplot(323) +plt.imshow(glob, cmap=plt.cm.gray, interpolation='nearest') +plt.axis('off') +plt.subplot(324) +plt.plot(glob_hist[1][:-1], glob_hist[0], lw=2) +plt.title('histogram of grey values') +plt.subplot(325) +plt.imshow(loc, cmap=plt.cm.gray, interpolation='nearest') +plt.axis('off') +plt.subplot(326) +plt.plot(loc_hist[1][:-1], loc_hist[0], lw=2) +plt.title('histogram of grey values') + +""" +.. image:: PLOT2RST.current_figure + +Image morphology +================ + +""" +plt.show() diff --git a/skimage/filter/rank/tests/test_suite.py b/skimage/filter/rank/tests/test_suite.py index 4f1e6f81..66b6537c 100644 --- a/skimage/filter/rank/tests/test_suite.py +++ b/skimage/filter/rank/tests/test_suite.py @@ -1,3 +1,8 @@ +import sys +print sys.path +import skimage +print skimage + import unittest import numpy as np From acc1e1f7e4ef953a05885df3a51b1bde0f5aa181 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sat, 3 Nov 2012 17:38:54 +0100 Subject: [PATCH 299/625] example continued --- .../applications/plot_image_filtering.py | 53 ++++++++++++++++--- 1 file changed, 46 insertions(+), 7 deletions(-) diff --git a/doc/examples/applications/plot_image_filtering.py b/doc/examples/applications/plot_image_filtering.py index a58d59aa..e0ebea44 100644 --- a/doc/examples/applications/plot_image_filtering.py +++ b/doc/examples/applications/plot_image_filtering.py @@ -70,6 +70,8 @@ Noise removal some noise is added to the image, 1% of pixels are randomly set to 255, %1% are randomly set to 0. The **median** filter is applied to remove the noise. +.. note:: there is different implementations of median filter : ``skimage.filter.median_filter``and +`skimage.filter.rank.median`` """ noise = np.random.random(ima.shape) @@ -130,7 +132,7 @@ One may be interested in smoothing an image while preserving important borders ( here we use the **bilateral** filter that restrict the local neighborhood to pixel having a grey level similar to the central one. -rem: a different implementations is available for color images in ``skimage.filter.denoise_bilateral``. +.. note:: a different implementations is available for color images in ``skimage.filter.denoise_bilateral``. """ @@ -144,13 +146,15 @@ bilat = bilateral_mean(ima.astype(np.uint16),disk(20),s0=10,s1=10) # display results fig = plt.figure(figsize=[10,7]) plt.subplot(1,2,1) -plt.imshow(ima) +plt.imshow(ima,cmap=plt.cm.gray) plt.xlabel('original') plt.subplot(1,2,2) -plt.imshow(bilat) +plt.imshow(bilat,cmap=plt.cm.gray) plt.xlabel('bilateral mean') """ +.. image:: PLOT2RST.current_figure + One can see that the large continuous part of the image (e.g.sky) are smoothed whereas other details are preserved. @@ -167,13 +171,13 @@ The local version [3]_ of the histogram equalization emphasized every local gray """ -from skimage.exposure import equalize as global_equalize -from skimage.filter.rank import equalize as local_equalize +from skimage import exposure +from skimage.filter import rank ima = data.camera() # equalize globally and locally -loc = local_equalize(ima,disk(20)) -glob = global_equalize(ima) +glob = exposure.equalize(ima)*255 +loc = rank.equalize(ima,disk(20)) # extract histogram for each image hist = np.histogram(ima, bins=np.arange(0, 256)) @@ -199,6 +203,39 @@ plt.axis('off') plt.subplot(326) plt.plot(loc_hist[1][:-1], loc_hist[0], lw=2) plt.title('histogram of grey values') +""" +.. image:: PLOT2RST.current_figure + +an other way to maximize the number of grey level used for an image is to apply a local auto-leveling, +i.e. here a pixel grey level is proportionally remapped between local minimum and local maximum. + +The following example show how local autolevel enhance the camaraman picture. +""" +from skimage.filter.rank import autolevel + +ima = data.camera() +selem = disk(10) + +auto = autolevel(ima.astype(np.uint16),disk(20)) + +# display results +fig = plt.figure(figsize=[10,7]) +plt.subplot(1,2,1) +plt.imshow(ima,cmap=plt.cm.gray) +plt.xlabel('original') +plt.subplot(1,2,2) +plt.imshow(auto,cmap=plt.cm.gray) +plt.xlabel('local autolevel') +""" +.. image:: PLOT2RST.current_figure + +This filter is very sensitive to local outlayers, see the little white spot in the sky left part. This is due +to a local maximum which is very high comparing to the rest of the neighborhood. One can moderate this +using the percentile version of the autolevel filter which uses to given percentiles (one inferior, one superior) +in place of local minimum and maximim. The example bellow illustrate how the percentile parameters influence the +local autolevel result. + +""" """ .. image:: PLOT2RST.current_figure @@ -206,5 +243,7 @@ plt.title('histogram of grey values') Image morphology ================ + + """ plt.show() From e6199c78cfa1cf705421b2ef3d4e4961adeee78b Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 09:39:58 +0100 Subject: [PATCH 300/625] doc cont. --- ...mage_filtering.py => plot_rank_filters.py} | 119 ++++++++++++++---- 1 file changed, 94 insertions(+), 25 deletions(-) rename doc/examples/applications/{plot_image_filtering.py => plot_rank_filters.py} (66%) diff --git a/doc/examples/applications/plot_image_filtering.py b/doc/examples/applications/plot_rank_filters.py similarity index 66% rename from doc/examples/applications/plot_image_filtering.py rename to doc/examples/applications/plot_rank_filters.py index e0ebea44..19305fdc 100644 --- a/doc/examples/applications/plot_image_filtering.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -1,9 +1,14 @@ """ =============================================================== -Image filtering +Rank filters =============================================================== -Filtering is a common operation on images, it serves several purposes such as: +Rank filters are non-linear filters using the local grey levels ordering to compute the filtered value. +This ensemble of filters share a common base: the local grey-level histogram extraction computed on +the neighborhood of a pixel (defined by a 2D structuring element). +If the filtered value is taken as the middle value of the histogram, we get the classical median filter. + +Rank filters can be used for several purposes such as: * image quality enhancement e.g. image smoothing, sharpening @@ -17,32 +22,15 @@ Filtering is a common operation on images, it serves several purposes such as: * post-processing e.g. small object removal, object grouping, contour smoothing +Some well known filters are specific cases of rank filters [1]_ e.g. morphological dilation, morphological erosion, +median filters. -Filters usually operate on image using a neighborhood situated around the pixel current pixel being treated. - -Depending on the type of operation traditionally filters fall into one of the following (not exclusive) types: - -* linear filter - where filtered pixel grey value results in a linear function of its neighborhood, linear filters are in fact - convolution and may be implemented using the Fourier transform (not discussed here). - -* non-linear - where relation may involve non-linear function such as logical test or grey level rank. - -* morphological filter - these filters belong to Mathematical morphology (MM) which "is a theory and technique for the analysis and processing - of geometrical structures" [1]_ - -.. [1] http://en.wikipedia.org/wiki/Mathematical_morphology - -Skimage implement several filters in ``skimage.filter``, ``skimage.filter.rank``, ``skimage.morphology``, some of -these filters are redundant for historical reasons, since there implementation are not identical, there respective -algorithm complexity may differ and therefore the choice may be function of image size or bitdepth -and filter parameters. +The different implementation availables in ``skimage`` are compared compare. In this example, we will see how to filter a grey level image using some of the linear and non-linear filters availables in skimage. We use the ``camera`` image from ``skimage.data``. +.. [1] Pierre Soille, On morphological operators based on rank filters, Pattern Recognition 35 (2002) 527-535. """ import numpy as np @@ -206,7 +194,7 @@ plt.title('histogram of grey values') """ .. image:: PLOT2RST.current_figure -an other way to maximize the number of grey level used for an image is to apply a local auto-leveling, +an other way to maximize the number of grey level used for an image is to apply a local autoleveling, i.e. here a pixel grey level is proportionally remapped between local minimum and local maximum. The following example show how local autolevel enhance the camaraman picture. @@ -232,10 +220,81 @@ plt.xlabel('local autolevel') This filter is very sensitive to local outlayers, see the little white spot in the sky left part. This is due to a local maximum which is very high comparing to the rest of the neighborhood. One can moderate this using the percentile version of the autolevel filter which uses to given percentiles (one inferior, one superior) -in place of local minimum and maximim. The example bellow illustrate how the percentile parameters influence the +in place of local minimum and maximum. The example bellow illustrate how the percentile parameters influence the local autolevel result. """ +from skimage.filter.rank import percentile_autolevel + +image = data.camera() + +selem = disk(20) +loc_autolevel = autolevel(image,selem=selem) +loc_perc_autolevel0 = percentile_autolevel(image,selem=selem,p0=.00,p1=1.0) +loc_perc_autolevel1 = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) +loc_perc_autolevel2 = percentile_autolevel(image,selem=selem,p0=.05,p1=.95) +loc_perc_autolevel3 = percentile_autolevel(image,selem=selem,p0=.1,p1=.9) + +fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) +ax0, ax1, ax2 = axes +plt.gray() + +ax0.imshow(np.hstack((image,loc_autolevel))) +ax0.set_title('original / autolevel') + +ax1.imshow(np.hstack((loc_perc_autolevel0,loc_perc_autolevel1)),vmin=0,vmax=255) +ax1.set_title('percentile autolevel 0%,1%') +ax2.imshow(np.hstack((loc_perc_autolevel2,loc_perc_autolevel3)),vmin=0,vmax=255) +ax2.set_title('percentile autolevel 5% and 10%') + +for ax in axes: + ax.axis('off') + +""" +.. image:: PLOT2RST.current_figure + +Morphological contrast enhancement filter replaces the central pixel by local maximum +if the original grey level value if closest to local maximum, by the minimum local otherwise. + +""" + +from skimage.filter.rank import morph_contr_enh + +ima = data.camera() + +enh = morph_contr_enh(ima,disk(5)) + +# display results +fig = plt.figure(figsize=[10,7]) +plt.subplot(1,2,1) +plt.imshow(ima,cmap=plt.cm.gray) +plt.xlabel('original') +plt.subplot(1,2,2) +plt.imshow(enh,cmap=plt.cm.gray) +plt.xlabel('local morphlogical contrast enhancement') + +""" +.. image:: PLOT2RST.current_figure + +The percentile version of the local morphological contrast enhancement, uses percentile p0 and p1 instead of local +minimum and local maximum. + +""" + +from skimage.filter.rank import percentile_morph_contr_enh + +ima = data.camera() + +penh = percentile_morph_contr_enh(ima,disk(5),p0=.1,p1=.9) + +# display results +fig = plt.figure(figsize=[10,7]) +plt.subplot(1,2,1) +plt.imshow(ima,cmap=plt.cm.gray) +plt.xlabel('original') +plt.subplot(1,2,2) +plt.imshow(penh,cmap=plt.cm.gray) +plt.xlabel('local morphlogical contrast enhancement') """ .. image:: PLOT2RST.current_figure @@ -243,7 +302,17 @@ local autolevel result. Image morphology ================ +Local maximum and local minimum are the base operators for grey level morphology. +""" + +""" +.. image:: PLOT2RST.current_figure + +Implementation +================ + +Implementation comparison w.r.t. image size and structuring element size. """ plt.show() From 8839c1d8f74a4effa810b0ccd5e67c129f6303c8 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 14:51:43 +0100 Subject: [PATCH 301/625] doc add perf.comp. --- .../applications/plot_rank_filters.py | 209 +++++++++++++++++- skimage/filter/rank/rank.pyx | 28 +-- 2 files changed, 213 insertions(+), 24 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 19305fdc..dfc72052 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -120,7 +120,7 @@ One may be interested in smoothing an image while preserving important borders ( here we use the **bilateral** filter that restrict the local neighborhood to pixel having a grey level similar to the central one. -.. note:: a different implementations is available for color images in ``skimage.filter.denoise_bilateral``. +.. note:: a different implementation is available for color images in ``skimage.filter.denoise_bilateral``. """ @@ -133,12 +133,16 @@ bilat = bilateral_mean(ima.astype(np.uint16),disk(20),s0=10,s1=10) # display results fig = plt.figure(figsize=[10,7]) -plt.subplot(1,2,1) +plt.subplot(2,2,1) plt.imshow(ima,cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(1,2,2) +plt.subplot(2,2,3) plt.imshow(bilat,cmap=plt.cm.gray) plt.xlabel('bilateral mean') +plt.subplot(2,2,2) +plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) +plt.subplot(2,2,4) +plt.imshow(bilat[200:350,350:450],cmap=plt.cm.gray) """ .. image:: PLOT2RST.current_figure @@ -266,17 +270,21 @@ enh = morph_contr_enh(ima,disk(5)) # display results fig = plt.figure(figsize=[10,7]) -plt.subplot(1,2,1) +plt.subplot(2,2,1) plt.imshow(ima,cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(1,2,2) +plt.subplot(2,2,3) plt.imshow(enh,cmap=plt.cm.gray) plt.xlabel('local morphlogical contrast enhancement') +plt.subplot(2,2,2) +plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) +plt.subplot(2,2,4) +plt.imshow(enh[200:350,350:450],cmap=plt.cm.gray) """ .. image:: PLOT2RST.current_figure -The percentile version of the local morphological contrast enhancement, uses percentile p0 and p1 instead of local +The percentile version of the local morphological contrast enhancement, uses percentile *p0* and *p1* instead of local minimum and local maximum. """ @@ -289,12 +297,16 @@ penh = percentile_morph_contr_enh(ima,disk(5),p0=.1,p1=.9) # display results fig = plt.figure(figsize=[10,7]) -plt.subplot(1,2,1) +plt.subplot(2,2,1) plt.imshow(ima,cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(1,2,2) +plt.subplot(2,2,3) plt.imshow(penh,cmap=plt.cm.gray) -plt.xlabel('local morphlogical contrast enhancement') +plt.xlabel('local percentile morphlogical\n contrast enhancement') +plt.subplot(2,2,2) +plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) +plt.subplot(2,2,4) +plt.imshow(penh[200:350,350:450],cmap=plt.cm.gray) """ .. image:: PLOT2RST.current_figure @@ -304,15 +316,192 @@ Image morphology Local maximum and local minimum are the base operators for grey level morphology. +.. note:: ``skimage.dilate`` and ``skimage.erode`` are equivalent filters (see below for comparison). + +Here is an example of classical morphological grey level filters : opening, closing and morphological gradient. + """ +from skimage.filter.rank import maximum,minimum,gradient + +ima = data.camera() + +closing = maximum(minimum(ima,disk(5)),disk(5)) +opening = minimum(maximum(ima,disk(5)),disk(5)) +grad = gradient(ima,disk(5)) + +# display results +fig = plt.figure(figsize=[10,7]) +plt.subplot(2,2,1) +plt.imshow(ima,cmap=plt.cm.gray) +plt.xlabel('original') +plt.subplot(2,2,2) +plt.imshow(closing,cmap=plt.cm.gray) +plt.xlabel('grey level closing') +plt.subplot(2,2,3) +plt.imshow(opening,cmap=plt.cm.gray) +plt.xlabel('grey level opening') +plt.subplot(2,2,4) +plt.imshow(grad,cmap=plt.cm.gray) +plt.xlabel('morphological gradient') + """ .. image:: PLOT2RST.current_figure Implementation ================ -Implementation comparison w.r.t. image size and structuring element size. +The central part of the ``skimage.rank``filters is build on a sliding window that update local grey level histogram. +This approach limits the algorithm complexity to O(n) where n is the number of image pixels. The complexity is also +limited with respect to the structuring element size. """ +from time import time + +from scipy.ndimage.filters import percentile_filter +from skimage.morphology import dilation +from skimage.filter import median_filter +from skimage.filter.rank import median,maximum + +def exec_and_timeit(func): + """ Decorator that returns both function results and execution time + (result, ms) + """ + def wrapper(*arg): + t1 = time() + res = func(*arg) + t2 = time() + ms = (t2-t1)*1000.0 + return (res,ms) + return wrapper + + +@exec_and_timeit +def cr_med(image,selem): + return median(image=image,selem = selem) + +@exec_and_timeit +def cr_max(image,selem): + return maximum(image=image,selem = selem) + +@exec_and_timeit +def cm_dil(image,selem): + return dilation(image=image,selem = selem) + +@exec_and_timeit +def ctmf_med(image,radius): + return median_filter(image=image,radius=radius) + +@exec_and_timeit +def ndi_med(image,n): + return percentile_filter(image,50,size=n*2-1) + +""" +.. image:: PLOT2RST.current_figure + +Comparison between + +* rank.maximum +* cmorph.dilate + +on increasing structuring element size and increasing image size +""" + +a = data.camera() + +rec = [] +e_range = range(1,20,1) +for r in e_range: + elem = disk(r+1) + rc,ms_rc = cr_max(a,elem) + rcm,ms_rcm = cm_dil(a,elem) + rec.append((ms_rc,ms_rcm)) + # same structuring element, the results must match + assert (rc==rcm).all() + +rec = np.asarray(rec) + +plt.figure() +plt.title('increasing element size') +plt.plot(e_range,rec) +plt.legend(['crank.maximum','cmorph.dilate']) + +r = 9 +elem = disk(r+1) + +rec = [] +s_range = range(100,1000,100) +for s in s_range: + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = cr_max(a,elem) + (rcm,ms_rcm) = cm_dil(a,elem) + rec.append((ms_rc,ms_rcm)) + # same structuring element, the results must match + assert (rc==rcm).all() + +rec = np.asarray(rec) + +plt.figure() +plt.title('increasing image size') +plt.plot(s_range,rec) +plt.legend(['crank.maximum','cmorph.dilate']) + + +""" +.. image:: PLOT2RST.current_figure + +Comparison between: + +* rank.median +* ctmf.median_filter +* ndimage.percentile + +on increasing structuring element size and increasing image size +""" + + +a = data.camera() + +rec = [] +e_range = range(2,30,4) +for r in e_range: + elem = disk(r+1) + rc,ms_rc = cr_med(a,elem) + rctmf,ms_rctmf = ctmf_med(a,r) + rndi,ms_ndi = ndi_med(a,r) + rec.append((ms_rc,ms_rctmf,ms_ndi)) + +rec = np.asarray(rec) + +plt.figure() +plt.title('increasing element size') +plt.plot(e_range,rec) +plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) +plt.ylabel('time (ms)') +plt.xlabel('element radius') +plt.figure() +plt.imshow(np.hstack((rc,rctmf,rndi))) +plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') + +r = 9 +elem = disk(r+1) + +rec = [] +s_range = [100,200,500,1000] +for s in s_range: + a = (np.random.random((s,s))*256).astype('uint8') + (rc,ms_rc) = cr_med(a,elem) + rctmf,ms_rctmf = ctmf_med(a,r) + rndi,ms_ndi = ndi_med(a,r) + rec.append((ms_rc,ms_rctmf,ms_ndi)) + +rec = np.asarray(rec) + +plt.figure() +plt.title('increasing image size') +plt.plot(s_range,rec) +plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) +plt.ylabel('time (ms)') +plt.xlabel('image size') + plt.show() diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 730d37c7..2f144e98 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -43,7 +43,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -85,7 +85,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -118,7 +118,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -160,7 +160,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -193,7 +193,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -233,7 +233,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -272,7 +272,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -307,7 +307,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -346,7 +346,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -386,7 +386,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -418,7 +418,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -459,7 +459,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -507,7 +507,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. @@ -557,7 +557,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. From 14d86ccb337d0e379cedee67f5d488d55efd7090 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 15:45:36 +0100 Subject: [PATCH 302/625] document rank filters --- .../applications/plot_rank_filters.py | 51 +++++++++++++------ 1 file changed, 36 insertions(+), 15 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index dfc72052..2ebb7b92 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -351,7 +351,7 @@ plt.xlabel('morphological gradient') Implementation ================ -The central part of the ``skimage.rank``filters is build on a sliding window that update local grey level histogram. +The central part of the ``skimage.rank`` filters is build on a sliding window that update local grey level histogram. This approach limits the algorithm complexity to O(n) where n is the number of image pixels. The complexity is also limited with respect to the structuring element size. @@ -397,35 +397,39 @@ def ndi_med(image,n): return percentile_filter(image,50,size=n*2-1) """ -.. image:: PLOT2RST.current_figure - Comparison between -* rank.maximum -* cmorph.dilate +* ``rank.maximum`` +* ``cmorph.dilate`` -on increasing structuring element size and increasing image size +on increasing structuring element size """ a = data.camera() rec = [] -e_range = range(1,20,1) +e_range = range(1,20,2) for r in e_range: elem = disk(r+1) rc,ms_rc = cr_max(a,elem) rcm,ms_rcm = cm_dil(a,elem) rec.append((ms_rc,ms_rcm)) - # same structuring element, the results must match - assert (rc==rcm).all() rec = np.asarray(rec) plt.figure() plt.title('increasing element size') +plt.ylabel('time (ms)') +plt.xlabel('element radius') plt.plot(e_range,rec) plt.legend(['crank.maximum','cmorph.dilate']) +""" +and increasing image size + +.. image:: PLOT2RST.current_figure +""" + r = 9 elem = disk(r+1) @@ -436,13 +440,13 @@ for s in s_range: (rc,ms_rc) = cr_max(a,elem) (rcm,ms_rcm) = cm_dil(a,elem) rec.append((ms_rc,ms_rcm)) - # same structuring element, the results must match - assert (rc==rcm).all() rec = np.asarray(rec) plt.figure() plt.title('increasing image size') +plt.ylabel('time (ms)') +plt.xlabel('image size') plt.plot(s_range,rec) plt.legend(['crank.maximum','cmorph.dilate']) @@ -452,11 +456,11 @@ plt.legend(['crank.maximum','cmorph.dilate']) Comparison between: -* rank.median -* ctmf.median_filter -* ndimage.percentile +* ``rank.median`` +* ``ctmf.median_filter`` +* ``ndimage.percentile`` -on increasing structuring element size and increasing image size +on increasing structuring element size """ @@ -479,10 +483,23 @@ plt.plot(e_range,rec) plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('element radius') +""" +.. image:: PLOT2RST.current_figure + +comparison of outcome of the three methods + +""" plt.figure() plt.imshow(np.hstack((rc,rctmf,rndi))) plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') +""" +.. image:: PLOT2RST.current_figure + +and increasing image size + +""" + r = 9 elem = disk(r+1) @@ -504,4 +521,8 @@ plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('image size') +""" +.. image:: PLOT2RST.current_figure +""" + plt.show() From 271ea14c0e0f5454171e3d738c6529b3f911e0e3 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 15:49:54 +0100 Subject: [PATCH 303/625] remove trivial examples from perentile_rank --- skimage/filter/rank/percentile_rank.pyx | 228 ------------------------ 1 file changed, 228 deletions(-) diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 3817e1cd..90e24892 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -71,34 +71,6 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift local autolevel : uint8 array or uint16 array depending on input image The result of the local autolevel. - Examples - -------- - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_autolevel(ima8, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_autolevel(ima16, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 0, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -136,35 +108,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ local percentile_gradient : uint8 array or uint16 array depending on input image The result of the local percentile_gradient. - Examples - -------- - - >>> # Local gradient - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_gradient(ima8, square(3), p0=0.,p1=1.) - array([[255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_gradient(ima16, square(3), p0=0.,p1=1.) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ @@ -202,35 +146,6 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa local mean : uint8 array or uint16 array depending on input image The result of the local mean. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_mean(ima8, square(3),p0=0.,p1=1.) - array([[ 63, 85, 127, 85, 63], - [ 85, 113, 170, 113, 85], - [127, 170, 255, 170, 127], - [ 85, 113, 170, 113, 85], - [ 63, 85, 127, 85, 63]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_mean(ima16, square(3),p0=0.,p1=1.) - array([[1023, 1365, 2047, 1365, 1023], - [1365, 1820, 2730, 1820, 1365], - [2047, 2730, 4095, 2730, 2047], - [1365, 1820, 2730, 1820, 1365], - [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ @@ -268,35 +183,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals local mean_substraction : uint8 array or uint16 array depending on input image The result of the local mean_substraction. - Examples - -------- - - >>> # Local mean_substraction - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) - array([[ 95, 84, 63, 84, 95], - [ 84, 198, 169, 198, 84], - [ 63, 169, 127, 169, 63], - [ 84, 198, 169, 198, 84], - [ 95, 84, 63, 84, 95]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) - array([[1536, 1365, 1024, 1365, 1536], - [1365, 3185, 2730, 3185, 1365], - [1024, 2730, 2048, 2730, 1024], - [1365, 3185, 2730, 3185, 1365], - [1536, 1365, 1024, 1365, 1536]], dtype=uint16) """ @@ -334,35 +221,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, local morph_contr_enh : uint8 array or uint16 array depending on input image The result of the local morph_contr_enh. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -400,35 +259,6 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, local percentile : uint8 array or uint16 array depending on input image The result of the local percentile. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile(ima8, square(3), p0=0.,p1=1.) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile(ima16, square(3), p0=0.,p1=1.) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint16) """ @@ -467,35 +297,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal local pop : uint8 array or uint16 array depending on input image The result of the local pop. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_pop(ima8, square(3), p0=0.,p1=1.) - array([[4, 6, 6, 6, 4], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [4, 6, 6, 6, 4]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_pop(ima16, square(3), p0=0.,p1=1.) - array([[4, 6, 6, 6, 4], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [4, 6, 6, 6, 4]], dtype=uint16) """ @@ -533,37 +335,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift local threshold : uint8 array or uint16 array depending on input image The result of the local threshold. - Examples - -------- - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_threshold(ima8, square(3), p0=0.,p1=1.) - array([[255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_threshold(ima16, square(3), p0=0.,p1=1.) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) - - """ return _apply( From df02bf893a3c048071ae81fb5684566ccc45d0fc Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 16:05:08 +0100 Subject: [PATCH 304/625] remove duplicate examples --- .../applications/plot_benchmark_rank.py | 173 ------------------ doc/examples/plot_compare_bilateral.py | 63 ------- doc/examples/plot_lena_bilateral_denoise.py | 50 ----- doc/examples/plot_local_autolevels.py | 47 ----- doc/examples/plot_local_threshold.py | 69 ------- skimage/filter/rank/percentile_rank.pyx | 2 +- 6 files changed, 1 insertion(+), 403 deletions(-) delete mode 100644 doc/examples/applications/plot_benchmark_rank.py delete mode 100644 doc/examples/plot_compare_bilateral.py delete mode 100644 doc/examples/plot_lena_bilateral_denoise.py delete mode 100644 doc/examples/plot_local_autolevels.py delete mode 100644 doc/examples/plot_local_threshold.py diff --git a/doc/examples/applications/plot_benchmark_rank.py b/doc/examples/applications/plot_benchmark_rank.py deleted file mode 100644 index 79357768..00000000 --- a/doc/examples/applications/plot_benchmark_rank.py +++ /dev/null @@ -1,173 +0,0 @@ -""" -============================== -Compare execution time for - - skimage.rank.median, - - skimage.filter import median_filter - - scipy.ndimage.filters import percentile_filter, - - and - - - skimage.cmorph.dilate - - skimage.rank.maximum - -============================== - -to complete - -""" -import numpy as np -import matplotlib.pyplot as plt -import time - -from scipy.ndimage.filters import percentile_filter - -from skimage import data -from skimage.morphology import dilation,disk -from skimage.filter import median_filter -import skimage.filter.rank as rank - -def exec_and_timeit(func): - """ Decorator that returns both function results and execution time - (result, ms) - """ - def wrapper(*arg): - t1 = time.time() - res = func(*arg) - t2 = time.time() - ms = (t2-t1)*1000.0 - return (res,ms) - return wrapper - - -@exec_and_timeit -def cr_med(image,selem): - return rank.median(image=image,selem = selem) - -@exec_and_timeit -def cr_max(image,selem): - return rank.maximum(image=image,selem = selem) - -@exec_and_timeit -def cm_dil(image,selem): - return dilation(image=image,selem = selem) - -@exec_and_timeit -def ctmf_med(image,radius): - return median_filter(image=image,radius=radius) - -@exec_and_timeit -def ndi_med(image,n): - return percentile_filter(image,50,size=n*2-1) - -def compare_dilate(): - """ Comparison between - - crank.maximum rankfilter implementation - - cmorph.dilate cython implementation - - on increasing structuring element size and increasing image size - """ - a = data.camera() - - rec = [] - e_range = range(1,20,1) - for r in e_range: - elem = disk(r+1) - # elem = (np.random.random((r,r))>.5).astype('uint8') - rc,ms_rc = cr_max(a,elem) - rcm,ms_rcm = cm_dil(a,elem) - rec.append((ms_rc,ms_rcm)) - # same structuring element, the results must match - assert (rc==rcm).all() - - rec = np.asarray(rec) - - plt.figure() - plt.title('increasing element size') - plt.plot(e_range,rec) - plt.legend(['crank.maximum','cmorph.dilate']) - - r = 9 - elem = disk(r+1) - - rec = [] - s_range = range(100,1000,100) - for s in s_range: - a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = cr_max(a,elem) - (rcm,ms_rcm) = cm_dil(a,elem) - rec.append((ms_rc,ms_rcm)) - # same structuring element, the results must match - assert (rc==rcm).all() - - rec = np.asarray(rec) - - plt.figure() - plt.title('increasing image size') - plt.plot(s_range,rec) - plt.legend(['crank.maximum','cmorph.dilate']) - plt.figure() - plt.imshow(np.hstack((rc,rcm))) - - -def compare_median(): - """ Comparison between - - crank.median rankfilter implementation - - ctmf.median_filter filter - - on increasing structuring element size and increasing image size - """ - a = data.camera() - - rec = [] - e_range = range(2,30,4) - for r in e_range: - elem = disk(r+1) - rc,ms_rc = cr_med(a,elem) - rctmf,ms_rctmf = ctmf_med(a,r) - rndi,ms_ndi = ndi_med(a,r) - rec.append((ms_rc,ms_rctmf,ms_ndi)) - # check if results are identical - # obviously they cannot be identical since structuring element are different (octagon<>disk) - # assert (rc==rctmf).all() - - rec = np.asarray(rec) - - plt.figure() - plt.title('increasing element size') - plt.plot(e_range,rec) - plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) - plt.ylabel('time (ms)') - plt.xlabel('element radius') - plt.figure() - plt.imshow(np.hstack((rc,rctmf,rndi))) - plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') - - r = 9 - elem = disk(r+1) - - rec = [] - s_range = [100,200,500,1000,2000] - for s in s_range: - a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = cr_med(a,elem) - rctmf,ms_rctmf = ctmf_med(a,r) - rndi,ms_ndi = ndi_med(a,r) - rec.append((ms_rc,ms_rctmf,ms_ndi)) - # check if results are identical - # obviously they cannot be identical since structuring element are different (octagon<>disk) - # assert (rc==rctmf).all() - - rec = np.asarray(rec) - - plt.figure() - plt.title('increasing image size') - plt.plot(s_range,rec) - plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) - plt.ylabel('time (ms)') - plt.xlabel('image size') - - - -compare_dilate() -compare_median() -plt.show() diff --git a/doc/examples/plot_compare_bilateral.py b/doc/examples/plot_compare_bilateral.py deleted file mode 100644 index 977b7704..00000000 --- a/doc/examples/plot_compare_bilateral.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -==================================================== -Bilateral comparison -==================================================== - -In this example, we compare both bilateral implementation - -* filter.denoise_bilateral -* filter.rank.bilateral_mean - -The first filter implements a spatial-gaussian and spectral-gaussian kernel bilateral filter whereas the latter implements -a cylindrical kernel bilateral filter i.e. spatial-flat and spectral-flat kernel. - -The timing comparison is just for information since the kernel are not the same. - -""" - -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data -from skimage.filter._denoise import denoise_bilateral -from skimage.filter.rank import bilateral_mean -from skimage.morphology import disk -from skimage.filter import denoise_bilateral -import time - -def exec_and_timeit(func): - """ Decorator that returns both function results and execution time - (result, ms) - """ - def wrapper(*arg): - t1 = time.time() - res = func(*arg) - t2 = time.time() - ms = (t2-t1)*1000.0 - return (res,ms) - return wrapper - - -@exec_and_timeit -def den_bil(image): - return denoise_bilateral(a8,win_size=20,sigma_range=255,sigma_spatial=1)[:,:,0]*255 - -@exec_and_timeit -def rank_bil(image): - return bilateral_mean(a8.astype(np.uint16),disk(20),s0=10,s1=10) - -a8 = data.camera() -selem = disk(10) - -f1,t1 = den_bil(a8) -f2,t2 = rank_bil(a8) - -# display results -fig, axes = plt.subplots(nrows=2, figsize=(15,10)) -ax0, ax1= axes - -ax0.imshow(np.hstack((f1,a8-f1))) -ax0.set_title('denoise bilateral (%f ms)'%t1) -ax1.imshow(np.hstack((f2,a8-f1))) -ax1.set_title('bilateral mean (%f ms)'%t2) -plt.show() diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py deleted file mode 100644 index 03fc61d0..00000000 --- a/doc/examples/plot_lena_bilateral_denoise.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -==================================================== -Denoising the picture of Lena using bilateral filter -==================================================== - -In this example, we denoise a noisy version of the picture of Lena -using an approximation of a bilateral filter. -The pixels used to compute a local mean respect these conditions: -- be close to the central pixel, i.e. belong to the given structuring element. -- have a similar gray level, similarity is fixed by an interval [-s0,+s1] centered on the central pixel gray level. - -The filter used is an approximation of a classical bilateral filter in the sens that kernel are usually gaussian -both in spatial and spectral dimensions. -""" - -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data, color, img_as_ubyte -from skimage.filter.rank import bilateral_mean -from skimage.morphology import disk - -l = img_as_ubyte(color.rgb2gray(data.lena())) -l = l[230:290, 220:320] - -noisy = l + 0.4 * l.std() * np.random.random(l.shape) - -selem = disk(30) -approx_bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10) - -plt.figure(figsize=(8, 2)) - -plt.subplot(131) -plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) -plt.axis('off') -plt.title('noisy', fontsize=20) -plt.subplot(132) -plt.imshow(approx_bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) -plt.axis('off') -plt.title('bilateral denoising', fontsize=20) - -selem = disk(30) -approx_bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40) -plt.subplot(133) -plt.imshow(approx_bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) -plt.axis('off') -plt.title('(more) bilateral denoising', fontsize=20) - -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,right=1) -plt.show() diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py deleted file mode 100644 index 7c750141..00000000 --- a/doc/examples/plot_local_autolevels.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -===================== -Local Autolevel -===================== - -Local autolevel stretch local histogram between 0 and max_graylevel (e.g. 255 for 8 bit image). -The following code shows the difference between autolevel and percentile auto_level where [min,max] interval -is replaced by [p0,p1] percentiles interval - -""" -import matplotlib.pyplot as plt -import numpy as np - -from skimage import data - -from skimage.filter.rank import percentile_autolevel,autolevel -from skimage.morphology import disk - - -image = data.camera() - -selem = disk(20) -loc_autolevel = autolevel(image,selem=selem) -loc_perc_autolevel0 = percentile_autolevel(image,selem=selem,p0=.00,p1=1.0) -loc_perc_autolevel1 = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) -loc_perc_autolevel2 = percentile_autolevel(image,selem=selem,p0=.05,p1=.95) -loc_perc_autolevel3 = percentile_autolevel(image,selem=selem,p0=.1,p1=.9) - -loc_perc_autolevel = np.hstack((loc_perc_autolevel0,loc_perc_autolevel1,loc_perc_autolevel2,loc_perc_autolevel3)) - -fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) -ax0, ax1, ax2 = axes -plt.gray() - -ax0.imshow(image) -ax0.set_title('Image') - -ax1.imshow(loc_autolevel) -ax1.set_title('Autolevel') - -ax2.imshow(loc_perc_autolevel,vmin=0,vmax=255) -ax2.set_title('percentile autolevel 0%,1%,5% and 10%') - -for ax in axes: - ax.axis('off') - -plt.show() diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py deleted file mode 100644 index 8bc79b30..00000000 --- a/doc/examples/plot_local_threshold.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -===================== -Local Thresholding -===================== - -Thresholding is the simplest way to segment objects from a background. If that -background is relatively uniform, then you can use a global threshold value to -binarize the image by pixel-intensity. If there's large variation in the -background intensity, however, adaptive thresholding (a.k.a. local or dynamic -thresholding) may produce better results. - -Here, we binarize an image using the `threshold_adaptive` function, which -calculates thresholds in regions of size `block_size` surrounding each pixel -(i.e. local neighborhoods). Each threshold value is the weighted mean of the -local neighborhood minus an offset value. - -An other approach is to binarize locally the image using local histogram distribution. - -rank.threshold function set pixels higher than the local mean to 1, to 0 otherwize -rank.morph_contr_enh replaces each pixel by the local minimum (or local maximum) if the -pixel gray level is more close to the local minimum (resp. by the local maximum -if the pixel gray level is more close to the local maximum). - -""" -import matplotlib.pyplot as plt - -from skimage import data -from skimage.filter import threshold_otsu, threshold_adaptive - -from skimage.filter.rank import threshold,morph_contr_enh -from skimage.morphology import disk - - -image = data.page() - -global_thresh = threshold_otsu(image) -binary_global = image > global_thresh - -block_size = 40 -binary_adaptive = threshold_adaptive(image, block_size, offset=10) - -selem = disk(20) -loc_thresh = threshold(image,selem=selem) -loc_morph_contr_enh = morph_contr_enh(image,selem=selem) - -fig, axes = plt.subplots(nrows=5, figsize=(7, 8)) -ax0, ax1, ax2, ax3, ax4 = axes -plt.gray() - -ax0.imshow(image) -ax0.set_title('Image') - -ax1.imshow(binary_global) -ax1.set_title('Global thresholding') - -ax2.imshow(binary_adaptive) -ax2.set_title('Adaptive thresholding') - -ax3.imshow(loc_thresh) -ax3.set_title('Local thresholding') - -ax4.imshow(loc_morph_contr_enh) -ax4.set_title('Local morphological contrast enhancement') - - -for ax in axes: - ax.axis('off') - -plt.show() diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 90e24892..d45bcfe3 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -335,7 +335,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift local threshold : uint8 array or uint16 array depending on input image The result of the local threshold. - + """ return _apply( From 298d2f9cdbc3872c532e7051b598e9e0c2782b5b Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 16:16:09 +0100 Subject: [PATCH 305/625] remove other duplicate examples --- doc/examples/plot_modal_filter.py | 67 ------------------------------- 1 file changed, 67 deletions(-) delete mode 100644 doc/examples/plot_modal_filter.py diff --git a/doc/examples/plot_modal_filter.py b/doc/examples/plot_modal_filter.py deleted file mode 100644 index a66da0a1..00000000 --- a/doc/examples/plot_modal_filter.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -=================== -Label image regions -=================== - -This example shows how to segment an image with image labelling. The following -steps are applied: - -1. Thresholding with automatic Otsu method -2. Close small holes with binary closing -3. Remove artifacts touching image border -4. Measure image regions to filter small objects - -""" - -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.patches as mpatches - -from skimage import data -from skimage.filter import threshold_otsu - -from skimage.filter.rank import modal - -from skimage.morphology import label, disk -from skimage.measure import find_contours - - -image = data.coins()[50:-50, 50:-50] - -# apply threshold -thresh = threshold_otsu(image) -bw = image > thresh - -# label image regions -label_image = label(bw) - -# filter obtained labels using model filter -mod_label_image = modal(label_image.astype(np.uint16),disk(5)) - -# the background is here 1 -contours = find_contours(mod_label_image==1,0, positive_orientation='low') - -fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(6, 6)) - -print axes - -ax0, ax1, ax2, ax3 = axes.ravel() - -ax0.imshow(bw, cmap='gray') -ax0.set_title('Otsu threshold') -ax1.imshow(label_image, cmap='jet') -ax1.set_title('label image') -ax2.imshow(mod_label_image, cmap='jet') -ax2.set_title('filtered labels (modal)') -ax3.imshow(image, cmap='gray') -ax3.set_title('contour overlay') -ax3.set_xlim((0,image.shape[1])) -ax3.set_ylim((image.shape[0],0)) - - -for n, contour in enumerate(contours): - ax3.plot(contour[:, 1], contour[:, 0], linewidth=2) - - -plt.show() - From f61ffcaf53a4ee48710969a906f9eacf68c81e87 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 7 Nov 2012 17:26:59 +0100 Subject: [PATCH 306/625] test noise filter --- skimage/filter/rank/_crank8.pyx | 29 ++++++++++++++++++++++++ skimage/filter/rank/demo/demo_single.py | 21 +++++++++++++++++ skimage/filter/rank/rank.pyx | 30 ++++++++++++++++++++++++- 3 files changed, 79 insertions(+), 1 deletion(-) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index da716bd9..a0c3cc35 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -223,6 +223,26 @@ cdef inline np.uint8_t kernel_tophat( return < np.uint8_t > (i - g) +cdef inline np.uint8_t kernel_noise_filter( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + + cdef Py_ssize_t i + cdef Py_ssize_t min_i + + for i in range(255, g, -1): + if histo[i]: + break + min_i = i-g + for i in range(0, g): + if histo[i]: + break + if g-i < min_i: + return < np.uint8_t > (g-i) + else: + return < np.uint8_t > min_i + + # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- @@ -380,3 +400,12 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, """top hat """ return _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + +def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + """top hat + """ + return _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py index 652f2cc8..3b7eaa95 100644 --- a/skimage/filter/rank/demo/demo_single.py +++ b/skimage/filter/rank/demo/demo_single.py @@ -19,6 +19,27 @@ if __name__ == '__main__': den = denoise_bilateral(a8,win_size=10,sigma_range=10,sigma_spatial=2)[:,:,0] f16b= rank.bilateral_mean(a8.astype(np.uint16),disk(10),s0=10,s1=10) + + selem = np.ones((3,3)) + selem[1,1] = 0 + radius = 3 + selem = disk(radius) + selem[radius,radius] = 0 + print selem + noise = rank.noise_filter(a8,selem) + plt.imsave('noise.png',noise,cmap=plt.cm.gray) + plt.imsave('cam.png',a8,cmap=plt.cm.gray) + print noise + + plt.figure() + plt.subplot(1,2,1) + plt.imshow(a8) + plt.subplot(1,2,2) + plt.imshow(noise) + plt.colorbar() + plt.show() + + plt.figure() plt.subplot(1,2,1) plt.imshow(den) diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 2f144e98..40ceecad 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -19,7 +19,7 @@ from skimage.filter.rank import _crank8, _crank16 from skimage.filter.rank.generic import find_bitdepth __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', - 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat'] + 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): @@ -580,3 +580,31 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) +def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Return minimum absolute diffirence between a pixel and its neighborhood + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + + Returns + ------- + out : uint8 array or uint16 array (same as input image) + The image noise . + + """ + + return _apply(_crank8.noise_filter, None, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file From a8a5e33425f73cb7e02f4054a95ec65320111a93 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 8 Nov 2012 10:37:55 +0100 Subject: [PATCH 307/625] fix noise_filter --- skimage/filter/rank/_crank8.pyx | 46 +++++--------------- skimage/filter/rank/demo/demo_single.py | 9 +--- skimage/filter/rank/rank.pyx | 20 ++++++++- skimage/filter/rank/tests/test_histo.py | 57 +++++++++++++++++++++++++ 4 files changed, 88 insertions(+), 44 deletions(-) create mode 100644 skimage/filter/rank/tests/test_histo.py diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index a0c3cc35..9ddcb12d 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -230,21 +230,26 @@ cdef inline np.uint8_t kernel_noise_filter( cdef Py_ssize_t i cdef Py_ssize_t min_i - for i in range(255, g, -1): + # early stop if at least one pixel of the neighborhood has the same g + if histo[g]>0: + return < np.uint8_t > 0 + + for i in range(g, -1, -1): if histo[i]: break - min_i = i-g - for i in range(0, g): + min_i = g-i + for i in range(g, 256): if histo[i]: break - if g-i < min_i: - return < np.uint8_t > (g-i) + if i-g < min_i: + return < np.uint8_t > (i-g) else: return < np.uint8_t > min_i # ----------------------------------------------------------------- # python wrappers +# used only internally # ----------------------------------------------------------------- @@ -253,8 +258,6 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """bottom hat - """ return _core8( kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -265,8 +268,6 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """bottom hat - """ return _core8( kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -277,8 +278,6 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """local egalisation of the gray level - """ return _core8( kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -289,8 +288,6 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """local maximum - local minimum gray level - """ return _core8( kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -301,8 +298,6 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """local maximum gray level - """ return _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -311,8 +306,6 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """average gray level (clipped on uint8) - """ return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -321,8 +314,6 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """(g - average gray level)/2+127 (clipped on uint8) - """ return _core8( kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -333,8 +324,6 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """local median - """ return _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -343,8 +332,6 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """local minimum gray level - """ return _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -353,8 +340,6 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """morphological contrast enhancement - """ return _core8( kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -365,8 +350,6 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """local mode - """ return _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -375,8 +358,6 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """returns the number of actual pixels of the structuring element inside the mask - """ return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -385,8 +366,6 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """returns 255 if gray level higher than local mean, 0 else - """ return _core8( kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -397,15 +376,12 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """top hat - """ return _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - """top hat - """ return _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py index 3b7eaa95..e3fe9c1e 100644 --- a/skimage/filter/rank/demo/demo_single.py +++ b/skimage/filter/rank/demo/demo_single.py @@ -20,16 +20,11 @@ if __name__ == '__main__': f16b= rank.bilateral_mean(a8.astype(np.uint16),disk(10),s0=10,s1=10) - selem = np.ones((3,3)) - selem[1,1] = 0 - radius = 3 - selem = disk(radius) - selem[radius,radius] = 0 - print selem +# selem = np.ones((3,3),dtype=np.uint8) noise = rank.noise_filter(a8,selem) plt.imsave('noise.png',noise,cmap=plt.cm.gray) plt.imsave('cam.png',a8,cmap=plt.cm.gray) - print noise + plt.figure() plt.subplot(1,2,1) diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 40ceecad..74875d1c 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -27,8 +27,12 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): if mask is not None: mask = img_as_ubyte(mask) if image.dtype == np.uint8: + if func8 is None: + raise TypeError("uint8 image not supported for this filter") return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out) elif image.dtype == np.uint16: + if func16 is None: + raise TypeError("uint16 image not supported for this filter") bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") @@ -581,7 +585,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return minimum absolute diffirence between a pixel and its neighborhood + """Returns the noise feature as described in [1]_ Parameters ---------- @@ -600,11 +604,23 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Reference + ---------- + + .. [1] N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9. + + Returns ------- out : uint8 array or uint16 array (same as input image) The image noise . """ + # ensure that the central pixel in the structuring element is empty + centre_r = int(selem.shape[0] / 2) + shift_y + centre_c = int(selem.shape[1] / 2) + shift_x + # make a local copy + selem_cpy = selem.copy() + selem_cpy[centre_r,centre_c] = 0 - return _apply(_crank8.noise_filter, None, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file + return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file diff --git a/skimage/filter/rank/tests/test_histo.py b/skimage/filter/rank/tests/test_histo.py new file mode 100644 index 00000000..b6a7bbca --- /dev/null +++ b/skimage/filter/rank/tests/test_histo.py @@ -0,0 +1,57 @@ +import sys +print sys.path +import skimage +print skimage + +import unittest + +import numpy as np +from skimage.filter import rank + +from skimage import data +from skimage.morphology import cmorph,disk +from skimage.filter.rank import _crank8, _crank16 +from skimage.filter.rank import _crank16_percentiles + + +class TestSequenceFunctions(unittest.TestCase): + + def setUp(self): + pass + + def test_trivial_selem(self): + # check that min, max and mean returns identity if structuring element contains only central pixel + + a = np.zeros((5,5),dtype='uint8') + a[2,2] = 255 + a[2,3] = 128 + a[1,2] = 16 + elem = np.asarray([[0,0,0],[0,1,0],[0,0,0]],dtype='uint8') + f = _crank8.mean(image=a,selem = elem,shift_x=0,shift_y=0) + np.testing.assert_array_equal(a,f) + f = _crank8.minimum(image=a,selem = elem,shift_x=0,shift_y=0) + np.testing.assert_array_equal(a,f) + f = _crank8.maximum(image=a,selem = elem,shift_x=0,shift_y=0) + np.testing.assert_array_equal(a,f) + + def test_smallest_selem(self): + # check that min, max and mean returns identity if structuring element contains only central pixel + + a = np.zeros((5,5),dtype='uint8') + a[2,2] = 255 + a[2,3] = 128 + a[1,2] = 16 + elem = np.asarray([[1]],dtype='uint8') + f = _crank8.mean(image=a,selem = elem,shift_x=0,shift_y=0) + np.testing.assert_array_equal(a,f) + f = _crank8.minimum(image=a,selem = elem,shift_x=0,shift_y=0) + np.testing.assert_array_equal(a,f) + f = _crank8.maximum(image=a,selem = elem,shift_x=0,shift_y=0) + np.testing.assert_array_equal(a,f) + + + +if __name__ == '__main__': + + suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) + unittest.TextTestRunner(verbosity=2).run(suite) From e804f96f02a53864400dd40a8796c371b0e15647 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 8 Nov 2012 10:45:20 +0100 Subject: [PATCH 308/625] fix noise_filter --- skimage/filter/rank/_crank8.pyx | 23 +++++++++++++++++++++ skimage/filter/rank/rank.pyx | 36 ++++++++++++++++++++++++++++++--- 2 files changed, 56 insertions(+), 3 deletions(-) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 9ddcb12d..5cb0b5ed 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -246,6 +246,22 @@ cdef inline np.uint8_t kernel_noise_filter( else: return < np.uint8_t > min_i +cdef inline np.uint8_t kernel_entropy( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + + cdef Py_ssize_t i + cdef Py_ssize_t min_i + cdef float e,p + + e = 0 + + for i in range(256): + p = histo[i]/pop + if p>0: + e -= p*np.log2(p) + + return < np.uint8_t > e # ----------------------------------------------------------------- # python wrappers @@ -385,3 +401,10 @@ def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): return _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + +def entropy(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + return _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 74875d1c..12517cfa 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -28,11 +28,11 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): mask = img_as_ubyte(mask) if image.dtype == np.uint8: if func8 is None: - raise TypeError("uint8 image not supported for this filter") + raise TypeError("not implemented for uint8 image") return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out) elif image.dtype == np.uint16: if func16 is None: - raise TypeError("uint16 image not supported for this filter") + raise TypeError("not implemented for uint16 image") bitdepth = find_bitdepth(image) if bitdepth > 11: raise ValueError("only uint16 <4096 image (12bit) supported!") @@ -623,4 +623,34 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False selem_cpy = selem.copy() selem_cpy[centre_r,centre_c] = 0 - return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file + return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + +def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Returns the entropy (in bit) computed locally (precision is limited due to image type used 8- or 16-bit) + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + The array to store the result of the morphology. If None is + passed, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + + + Returns + ------- + out : uint8 array or uint16 array (same as input image) + local entropy (in bit) + + """ + + return _apply(_crank8.entropy, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file From 665e05ff35b9750ea6a63b0b43ba1bef98239c84 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 8 Nov 2012 12:15:47 +0100 Subject: [PATCH 309/625] doc entropy --- .../applications/plot_rank_filters.py | 55 ++++++++++++++ doc/examples/plot_entropy.py | 44 +++++++++++ skimage/filter/rank/_crank16.pyx | 53 ++++++------- skimage/filter/rank/_crank8.pyx | 11 +-- skimage/filter/rank/bilateral_rank.pyx | 6 +- skimage/filter/rank/demo/demo_single.py | 5 +- skimage/filter/rank/percentile_rank.pyx | 24 ++---- skimage/filter/rank/rank.pyx | 76 ++++++++++--------- 8 files changed, 183 insertions(+), 91 deletions(-) create mode 100644 doc/examples/plot_entropy.py diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 2ebb7b92..7b28d776 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -348,6 +348,61 @@ plt.xlabel('morphological gradient') """ .. image:: PLOT2RST.current_figure +Feature extraction +=================== + +Local histogram can be exploited to compute local entropy, which is related to the local image complexity. +Entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode local +greylevel distribution. + +``skimage.rank.entropy`` returns local entropy on a given structuring element. +The following example shows this filter applied on 8- and 16- bit images. + +.. note:: to better use the available image bit, the function returns 10x entropy for 8-bit images and 1000x entropy + for 16-bit images. + +""" + +from skimage import data +from skimage.filter.rank import entropy +from skimage.morphology import disk +import numpy as np +import matplotlib.pyplot as plt + +# defining a 8- and a 16-bit test images +a8 = data.camera() +a16 = data.camera().astype(np.uint16)*4 + +ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy +ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy + +# display results +plt.figure(figsize=(10, 10)) + +plt.subplot(2,2,1) +plt.imshow(a8, cmap=plt.cm.gray) +plt.xlabel('8-bit image') +plt.colorbar() + +plt.subplot(2,2,2) +plt.imshow(ent8, cmap=plt.cm.jet) +plt.xlabel('entropy*10') +plt.colorbar() + +plt.subplot(2,2,3) +plt.imshow(a16, cmap=plt.cm.gray) +plt.xlabel('16-bit image') +plt.colorbar() + +plt.subplot(2,2,4) +plt.imshow(ent16, cmap=plt.cm.jet) +plt.xlabel('entropy*1000') +plt.colorbar() +plt.show() + +""" +.. image:: PLOT2RST.current_figure + Implementation ================ diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py new file mode 100644 index 00000000..f019d79c --- /dev/null +++ b/doc/examples/plot_entropy.py @@ -0,0 +1,44 @@ +""" +=================== +Entropy +=================== + + +""" +from skimage import data +from skimage.filter.rank import entropy +from skimage.morphology import disk +import numpy as np +import matplotlib.pyplot as plt + +# defining a 8- and a 16-bit test images +a8 = data.camera() +a16 = data.camera().astype(np.uint16)*4 + +ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy +ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy + +# display results +plt.figure(figsize=(10, 10)) + +plt.subplot(2,2,1) +plt.imshow(a8, cmap=plt.cm.gray) +plt.xlabel('8-bit image') +plt.colorbar() + +plt.subplot(2,2,2) +plt.imshow(ent8, cmap=plt.cm.jet) +plt.xlabel('entropy*10') +plt.colorbar() + +plt.subplot(2,2,3) +plt.imshow(a16, cmap=plt.cm.gray) +plt.xlabel('16-bit image') +plt.colorbar() + +plt.subplot(2,2,4) +plt.imshow(ent16, cmap=plt.cm.jet) +plt.xlabel('entropy*1000') +plt.colorbar() +plt.show() + diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index f6ad10e4..73e8e0bd 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -5,6 +5,7 @@ import numpy as np cimport numpy as np +from libc.math cimport log2 # import main loop from skimage.filter.rank._core16 cimport _core16 @@ -222,6 +223,23 @@ cdef inline np.uint16_t kernel_tophat( return < np.uint16_t > (i - g) + +cdef inline np.uint16_t kernel_entropy( + Py_ssize_t * histo, float pop, np.uint16_t g, + Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i + cdef float e,p + + e = 0. + + for i in range(maxbin): + p = histo[i]/pop + if p>0: + e -= p*log2(p) + + return < np.uint16_t > e*1000 + # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- @@ -232,8 +250,6 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """bottom hat - """ return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -242,8 +258,6 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """bottom hat - """ return _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -252,8 +266,6 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """local egalisation of the gray level - """ return _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -262,8 +274,6 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """local maximum - local minimum gray level - """ return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -272,8 +282,6 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """local maximum gray level - """ return _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -282,8 +290,6 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """average gray level (clipped on uint8) - """ return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -292,8 +298,6 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """(g - average gray level)/2+midbin (clipped on uint8) - """ return _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -302,8 +306,6 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """local median - """ return _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -312,8 +314,6 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """local minimum gray level - """ return _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -322,8 +322,6 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """morphological contrast enhancement - """ return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -332,8 +330,6 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """local mode - """ return _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -342,8 +338,6 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """returns the number of actual pixels of the structuring element inside the mask - """ return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -352,8 +346,6 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """returns maxbin-1 if gray level higher than local mean, 0 else - """ return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) @@ -362,6 +354,11 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - """top hat - """ return _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + +def entropy(np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + return _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 5cb0b5ed..84ad9204 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -6,6 +6,8 @@ import numpy as np cimport numpy as np +from libc.math cimport log2 + # import main loop from skimage.filter.rank._core8 cimport _core8 @@ -251,17 +253,16 @@ cdef inline np.uint8_t kernel_entropy( Py_ssize_t s1): cdef Py_ssize_t i - cdef Py_ssize_t min_i cdef float e,p - e = 0 + e = 0. for i in range(256): - p = histo[i]/pop + p = histo[i]/pop if p>0: - e -= p*np.log2(p) + e -= p*log2(p) - return < np.uint8_t > e + return < np.uint8_t > e*10 # ----------------------------------------------------------------- # python wrappers diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 80349b0b..f181735b 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -71,8 +71,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -126,8 +125,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py index e3fe9c1e..4afa546b 100644 --- a/skimage/filter/rank/demo/demo_single.py +++ b/skimage/filter/rank/demo/demo_single.py @@ -25,12 +25,15 @@ if __name__ == '__main__': plt.imsave('noise.png',noise,cmap=plt.cm.gray) plt.imsave('cam.png',a8,cmap=plt.cm.gray) + selem = disk(3) + ent = rank.entropy(a16,selem) + plt.figure() plt.subplot(1,2,1) plt.imshow(a8) plt.subplot(1,2,2) - plt.imshow(noise) + plt.imshow(ent) plt.colorbar() plt.show() diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index d45bcfe3..1bd89eb8 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -55,8 +55,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -92,8 +91,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -130,8 +128,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -167,8 +164,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -205,8 +201,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -243,8 +238,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -281,8 +275,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -319,8 +312,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 12517cfa..2c9b7326 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -19,7 +19,7 @@ from skimage.filter.rank import _crank8, _crank16 from skimage.filter.rank.generic import find_bitdepth __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', - 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter'] + 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter','entropy'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): @@ -52,8 +52,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -94,8 +93,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -127,8 +125,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -169,8 +166,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -202,8 +198,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -242,8 +237,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -281,8 +275,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -316,8 +309,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -355,8 +347,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -395,8 +386,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -427,8 +417,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -468,8 +457,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -516,8 +504,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -566,8 +553,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -595,8 +581,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -626,7 +611,12 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Returns the entropy (in bit) computed locally (precision is limited due to image type used 8- or 16-bit) + """Returns the entropy [wiki_entropy]_ computed locally. Entropy is computed using base 2 logarithm i.e. + the filter returns the minimum number of bits needed to encode local greylevel distribution. + + References + ---------- + .. [wiki_entropy] http://en.wikipedia.org/wiki/Entropy_(information_theory) Parameters ---------- @@ -636,8 +626,7 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray - The array to store the result of the morphology. If None is - passed, a new array will be allocated. + If None, a new array will be allocated. mask : ndarray (uint8) Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). @@ -645,12 +634,25 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - Returns ------- out : uint8 array or uint16 array (same as input image) - local entropy (in bit) + entropy x10 (uint8 images) and entropy x1000 (uint16 images) + + + Examples + -------- + + >>> # Local entropy + >>> from skimage import data + >>> from skimage.filter.rank import entropy + >>> from skimage.morphology import disk + >>> # defining a 8- and a 16-bit test images + >>> a8 = data.camera() + >>> a16 = data.camera().astype(np.uint16)*4 + >>> ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy + >>> ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy """ - return _apply(_crank8.entropy, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file + return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file From 8cf762f132b45a8b4b241d59138c034ac70edc9b Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 8 Nov 2012 12:17:22 +0100 Subject: [PATCH 310/625] doc noise filter --- skimage/filter/rank/rank.pyx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 2c9b7326..ad66fc65 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -571,7 +571,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Returns the noise feature as described in [1]_ + """Returns the noise feature as described in [Hashimoto12]_ Parameters ---------- @@ -579,7 +579,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, an exception will be raised if image has a value > 4095 selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. + The neighborhood expressed as a 2-D array of 1's and 0's. Central element is removed during the filtering. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) @@ -592,7 +592,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False Reference ---------- - .. [1] N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9. + .. [Hashimoto12] N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9. Returns From 12209a50dec9f3457476fa9f0a192cd1c335e5ee Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 8 Nov 2012 15:36:09 +0100 Subject: [PATCH 311/625] modify example entropy --- doc/examples/applications/plot_rank_filters.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 7b28d776..d52595f2 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -25,7 +25,7 @@ Rank filters can be used for several purposes such as: Some well known filters are specific cases of rank filters [1]_ e.g. morphological dilation, morphological erosion, median filters. -The different implementation availables in ``skimage`` are compared compare. +The different implementation availables in ``skimage`` are compared. In this example, we will see how to filter a grey level image using some of the linear and non-linear filters availables in skimage. We use the ``camera`` image from ``skimage.data``. @@ -363,6 +363,8 @@ The following example shows this filter applied on 8- and 16- bit images. """ + + from skimage import data from skimage.filter.rank import entropy from skimage.morphology import disk From 692cfe7e8f88f95520e3486a01d4e09d9fe0b97a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 8 Nov 2012 16:56:24 +0100 Subject: [PATCH 312/625] Use center of mass for peaks with equal accumulator values --- skimage/transform/hough_transform.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 3c27e346..79facb62 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -5,6 +5,7 @@ from itertools import izip as zip import numpy as np from scipy import ndimage from ._hough_transform import _probabilistic_hough +from skimage import measure, morphology def _hough(img, theta=None): @@ -208,7 +209,9 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, hspace *= mask hspace_t = hspace > threshold - coords = np.transpose(hspace_t.nonzero()) + label_hspace = morphology.label(hspace_t) + props = measure.regionprops(label_hspace, ['Centroid']) + coords = np.array([np.round(p['Centroid']) for p in props], dtype=int) hspace_peaks = [] dist_peaks = [] From 44ada6cb70501208e2a64364e1a373d084f10325 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Thu, 8 Nov 2012 17:10:00 +0100 Subject: [PATCH 313/625] add local Otsu threshold --- skimage/filter/rank/_crank8.pyx | 47 ++++++++++++++++++++++++ skimage/filter/rank/demo/demo_single.py | 46 +++-------------------- skimage/filter/rank/rank.pyx | 49 ++++++++++++++++++++++++- 3 files changed, 100 insertions(+), 42 deletions(-) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 84ad9204..4efd2bb0 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -264,6 +264,46 @@ cdef inline np.uint8_t kernel_entropy( return < np.uint8_t > e*10 +cdef inline np.uint8_t kernel_otsu( + Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + + cdef Py_ssize_t i + cdef Py_ssize_t max_i + cdef float P, mu1, mu2, q1,new_q1, sigma_b, max_sigma_b + cdef float mu = 0. + + # compute local mean + + if pop: + for i in range(256): + mu += histo[i] * i + mu = (mu / pop) + else: + return < np.uint8_t > (0) + + # maximizing the between class variance + max_i = 0 + q1 = histo[0]/pop + m1 = 0. + max_sigma_b = 0. + + for i in range(1,256): + P = histo[i]/pop + new_q1 = q1 + P + if new_q1>0: + mu1 = (q1*mu1 + i*P)/new_q1 + mu2 = (mu-new_q1*mu1)/(1.-new_q1) + sigma_b = new_q1*(1.-new_q1)*(mu1-mu2)**2 + if sigma_b>max_sigma_b: + max_sigma_b = sigma_b + max_i = i + q1 = new_q1 + + if g>max_i: + return < np.uint8_t > 255 + else: + return < np.uint8_t > 0 # ----------------------------------------------------------------- # python wrappers # used only internally @@ -409,3 +449,10 @@ def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): return _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + +def otsu(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): + return _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py index 4afa546b..7e4ef405 100644 --- a/skimage/filter/rank/demo/demo_single.py +++ b/skimage/filter/rank/demo/demo_single.py @@ -10,57 +10,23 @@ from skimage.filter import denoise_bilateral if __name__ == '__main__': a8 = data.camera() a16 = data.camera().astype(np.uint16)*4 - selem = disk(10) - f8= rank.percentile_autolevel(a8,selem,p0=.0,p1=1.) - f16= rank.autolevel(a16,selem) - f16p= rank.percentile_autolevel(a16,selem,p0=.0,p1=1.) + p8 = data.page() - den = denoise_bilateral(a8,win_size=10,sigma_range=10,sigma_spatial=2)[:,:,0] - f16b= rank.bilateral_mean(a8.astype(np.uint16),disk(10),s0=10,s1=10) + selem = disk(20) - -# selem = np.ones((3,3),dtype=np.uint8) - noise = rank.noise_filter(a8,selem) - plt.imsave('noise.png',noise,cmap=plt.cm.gray) - plt.imsave('cam.png',a8,cmap=plt.cm.gray) - - selem = disk(3) - ent = rank.entropy(a16,selem) + otsu = rank.otsu(p8,selem) plt.figure() plt.subplot(1,2,1) - plt.imshow(a8) + plt.imshow(p8) + plt.colorbar() plt.subplot(1,2,2) - plt.imshow(ent) + plt.imshow(otsu) plt.colorbar() plt.show() - plt.figure() - plt.subplot(1,2,1) - plt.imshow(den) - plt.subplot(1,2,2) - plt.imshow(f16b) - plt.show() - - print f16==f16p - - plt.figure() - plt.subplot(1,3,1) - plt.imshow(f16) - plt.colorbar() - plt.subplot(1,3,2) - plt.imshow(f16p) - plt.colorbar() - plt.subplot(1,3,3) - plt.imshow(f16p-f16) - plt.colorbar() - plt.show() - - print f16 - print f16p - diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index ad66fc65..6a35920b 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -19,7 +19,7 @@ from skimage.filter.rank import _crank8, _crank16 from skimage.filter.rank.generic import find_bitdepth __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', - 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter','entropy'] + 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter','entropy','otsu'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): @@ -655,4 +655,49 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file + return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + +def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): + """Returns the image threshold using a the Otsu [otsu]_ locally . + + References + ---------- + + .. [otsu] http://en.wikipedia.org/wiki/Otsu's_method + + Parameters + ---------- + image : ndarray + Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 + selem : ndarray + The neighborhood expressed as a 2-D array of 1's and 0's. + out : ndarray + If None, a new array will be allocated. + mask : ndarray (uint8) + Mask array that defines (>0) area of the image included in the local neighborhood. + If None, the complete image is used (default). + shift_x, shift_y : (int) + Offset added to the structuring element center point. + Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + + Returns + ------- + out : uint8 array or uint16 array (same as input image) + threshold image + + + Examples + -------- + + >>> # Local entropy + >>> from skimage import data + >>> from skimage.filter.rank import otsu + >>> from skimage.morphology import disk + >>> # defining a 8- and a 16-bit test images + >>> a8 = data.camera() + >>> loc_otsu = otsu(a8,disk(5)) + + """ + + return _apply(_crank8.otsu, None, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file From 6df06b46dd793159f90b7003ef711f6add00e348 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 8 Nov 2012 17:14:30 +0100 Subject: [PATCH 314/625] Fix threshold default value description --- skimage/transform/hough_transform.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 79facb62..05d5cfbe 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -164,7 +164,7 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, Minimum angle separating lines (maximum filter size for second dimension of hough space). threshold : float - Minimum intensity of peaks calculated as `0.5 * max(hspace)`. + Minimum intensity of peaks. Default is `0.5 * max(hspace)`. num_peaks : int Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` coordinates based on peak intensity. From 4a14a217b951c7c83b9c12f07dec52efc1dd42fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 9 Nov 2012 19:39:30 +0100 Subject: [PATCH 315/625] Improve overall code style --- skimage/filter/rank/_core16.pxd | 23 +- skimage/filter/rank/_core16.pyx | 125 ++--- skimage/filter/rank/_core8.pxd | 27 +- skimage/filter/rank/_core8.pyx | 154 +++--- skimage/filter/rank/_crank16.pyx | 46 +- skimage/filter/rank/_crank16_bilateral.pyx | 6 +- skimage/filter/rank/_crank16_percentiles.pyx | 64 +-- skimage/filter/rank/_crank8.pyx | 65 +-- skimage/filter/rank/_crank8_percentiles.pyx | 36 +- skimage/filter/rank/bilateral_rank.pyx | 135 +++--- skimage/filter/rank/percentile_rank.pyx | 361 ++++++++------ skimage/filter/rank/rank.pyx | 475 +++++++++++-------- 12 files changed, 812 insertions(+), 705 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index 9590e277..b236d982 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -1,18 +1,17 @@ cimport numpy as np -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data bitdepth -#--------------------------------------------------------------------------- -# generic cdef functions cdef int int_max(int a, int b) cdef int int_min(int a, int b) -cdef _core16( - np.uint16_t kernel(Py_ssize_t * , float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y, Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) + +# 16 bit core kernel receives extra information about data bitdepth +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, + Py_ssize_t, Py_ssize_t, Py_ssize_t, float, + float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint16_t, ndim=2] out, + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 4829792b..671a92fc 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -6,50 +6,40 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free +from _core8 cimport is_in_mask -#--------------------------------------------------------------------------- -# 16 bit core kernel receives extra information about data bitdepth -#--------------------------------------------------------------------------- -# generic cdef functions cdef inline int int_max(int a, int b): return a if a >= b else b + + cdef inline int int_min(int a, int b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint16_t value): + +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, + np.uint16_t value): histo[value] += 1 pop[0] += 1. -cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint16_t value): + +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, + np.uint16_t value): histo[value] -= 1 pop[0] -= 1. -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): - """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and - inside the given mask - returns 0 otherwise - """ - if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: - return 0 - else: - if mask[r * cols + c]: - return 1 - else: - return 0 - -cdef inline _core16( - np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y, Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, + Py_ssize_t, Py_ssize_t, Py_ssize_t, float, + float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint16_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint16_t, ndim=2] out, + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + """Compute histogram for each pixel neighborhood, apply kernel function and + use kernel function return value for output image. """ cdef Py_ssize_t rows = image.shape[0] @@ -71,35 +61,20 @@ cdef inline _core16( midbin_list = [0, 0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] #set maxbin and midbin - cdef Py_ssize_t maxbin = maxbin_list[bitdepth], midbin = midbin_list[bitdepth] + cdef Py_ssize_t maxbin = maxbin_list[bitdepth] + cdef Py_ssize_t midbin = midbin_list[bitdepth] assert (image < maxbin).all() - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if image is out: - raise NotImplementedError("Cannot perform rank operation in place.") - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint16) - else: - out = np.ascontiguousarray(out) - - mask = np.ascontiguousarray(mask) - # define pointers to the data - cdef np.uint16_t * out_data = out.data - cdef np.uint16_t * image_data = image.data - cdef np.uint8_t * mask_data = mask.data + cdef np.uint16_t * out_data = out.data + cdef np.uint16_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row - cdef float pop # number of pixels actually inside the neighborhood (float) + # number of pixels actually inside the neighborhood (float) + cdef float pop # allocate memory with malloc cdef Py_ssize_t max_se = srows * scols @@ -108,24 +83,22 @@ cdef inline _core16( cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + # these lists contain the relative pixel row and column for each of the 4 + # attack borders east, west, north and south e.g. se_e_r lists the rows of + # the east structuring element border + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis - t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) t_e = np.diff(t, axis=1) == -1 @@ -171,7 +144,7 @@ cdef inline _core16( cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) r = 0 c = 0 @@ -189,13 +162,13 @@ cdef inline _core16( rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel( @@ -212,13 +185,13 @@ cdef inline _core16( rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], @@ -231,13 +204,13 @@ cdef inline _core16( rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel( @@ -254,13 +227,13 @@ cdef inline _core16( rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], @@ -279,5 +252,3 @@ cdef inline _core16( free(se_s_c) free(histo) - - return out diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 9f898faa..d724c417 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -1,17 +1,22 @@ cimport numpy as np -# generic cdef functions + cdef np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) cdef np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) -#--------------------------------------------------------------------------- -# 8 bit core kernel receives extra information about data inferior and superior percentiles -#--------------------------------------------------------------------------- -cdef _core8( - np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint8_t, ndim=2] out, - char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) +cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, + np.uint8_t * mask) + + +# 8 bit core kernel receives extra information about data inferior and superior +# percentiles +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, + float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint8_t, ndim=2] out, + char shift_x, char shift_y, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1) diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 9955a1e1..17e5f64e 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -7,30 +7,31 @@ import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free -# generic cdef functions + cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): return a if a >= b else b + + cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b -#--------------------------------------------------------------------------- -# 8 bit core kernel -#--------------------------------------------------------------------------- - -cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint8_t value): +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, + np.uint8_t value): histo[value] += 1 pop[0] += 1. -cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint8_t value): + +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, + np.uint8_t value): histo[value] -= 1 pop[0] -= 1. -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): - """ returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and - inside the given mask - returns 0 otherwise - """ + +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, + np.uint8_t * mask): + """Check whether given coordinate is within image and mask is true.""" if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: @@ -39,16 +40,17 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r else: return 0 -cdef inline _core8( - np.uint8_t kernel(Py_ssize_t * , float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint8_t, ndim=2] out, - char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - """ Main loop, this function computes the histogram for each image point - - data is uint8 - - result is uint8 casted + +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, + float, Py_ssize_t, Py_ssize_t), + np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask, + np.ndarray[np.uint8_t, ndim=2] out, + char shift_x, char shift_y, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + """Compute histogram for each pixel neighborhood, apply kernel function and + use kernel function return value for output image. """ cdef Py_ssize_t rows = image.shape[0] @@ -65,28 +67,11 @@ cdef inline _core8( assert centre_r < srows assert centre_c < scols - image = np.ascontiguousarray(image) - - if mask is None: - mask = np.ones((rows, cols), dtype=np.uint8) - else: - mask = np.ascontiguousarray(mask) - - if image is out: - raise NotImplementedError("Cannot perform rank operation in place.") - - if out is None: - out = np.zeros((rows, cols), dtype=np.uint8) - else: - out = np.ascontiguousarray(out) - - mask = np.ascontiguousarray(mask) - # define pointers to the data - cdef np.uint8_t * out_data = out.data - cdef np.uint8_t * image_data = image.data - cdef np.uint8_t * mask_data = mask.data + cdef np.uint8_t * out_data = out.data + cdef np.uint8_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row @@ -101,24 +86,22 @@ cdef inline _core8( cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) - # these lists contain the relative pixel row and column for each of the 4 attack borders - # east, west, north and south - # e.g. se_e_r lists the rows of the east structuring element border - - cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + # these lists contain the relative pixel row and column for each of the 4 + # attack borders east, west, north and south e.g. se_e_r lists the rows of + # the east structuring element border + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis - t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) t_e = np.diff(t, axis=1) == -1 @@ -152,7 +135,8 @@ cdef inline _core8( se_s_c[num_se_s] = c - centre_c num_se_s += 1 - # initial population and histogram (kernel is centered on the first row and column) + # initial population and histogram (kernel is centered on the first row and + # column) for i in range(256): histo[i] = 0 @@ -164,14 +148,14 @@ cdef inline _core8( cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) r = 0 c = 0 - # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + - c], p0, p1, s0, s1) - # kernel -------------------------------------------------------------------- + # kernel ------------------------------------------------------------------- + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + p0, p1, s0, s1) + # kernel ------------------------------------------------------------------- # main loop r = 0 @@ -182,18 +166,18 @@ cdef inline _core8( rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) - # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel( - histo, pop, image_data[r * cols + c], p0, p1, s0, s1) - # kernel -------------------------------------------------------------------- + # kernel ----------------------------------------------------------- + out_data[r * cols + c] = \ + kernel(histo, pop, image_data[r * cols + c], p0, p1, s0, s1) + # kernel ----------------------------------------------------------- r += 1 # pass to the next row if r >= rows: @@ -204,18 +188,18 @@ cdef inline _core8( rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) - # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo, pop, image_data[r * - cols + c], p0, p1, s0, s1) - # kernel -------------------------------------------------------------------- + # kernel --------------------------------------------------------------- + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + p0, p1, s0, s1) + # kernel --------------------------------------------------------------- # ---> east to west for c in range(cols - 2, -1, -1): @@ -223,18 +207,18 @@ cdef inline _core8( rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) - # kernel -------------------------------------------------------------------- + # kernel ----------------------------------------------------------- out_data[r * cols + c] = kernel( histo, pop, image_data[r * cols + c], p0, p1, s0, s1) - # kernel -------------------------------------------------------------------- + # kernel ----------------------------------------------------------- r += 1 # pass to the next row if r >= rows: @@ -245,18 +229,18 @@ cdef inline _core8( rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) - # kernel -------------------------------------------------------------------- - out_data[r * cols + c] = kernel(histo, pop, image_data[r * - cols + c], p0, p1, s0, s1) - # kernel -------------------------------------------------------------------- + # kernel --------------------------------------------------------------- + out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], + p0, p1, s0, s1) + # kernel --------------------------------------------------------------- # release memory allocated by malloc @@ -270,5 +254,3 @@ cdef inline _core8( free(se_s_c) free(histo) - - return out diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 73e8e0bd..ab5b6015 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -240,6 +240,7 @@ cdef inline np.uint16_t kernel_entropy( return < np.uint16_t > e*1000 + # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- @@ -250,7 +251,8 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, @@ -258,7 +260,8 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, @@ -266,7 +269,8 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -274,7 +278,8 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, @@ -282,7 +287,8 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -290,7 +296,8 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -298,7 +305,8 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def median(np.ndarray[np.uint16_t, ndim=2] image, @@ -306,7 +314,8 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, @@ -314,7 +323,8 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -322,7 +332,8 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def modal(np.ndarray[np.uint16_t, ndim=2] image, @@ -330,7 +341,8 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -338,7 +350,8 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -346,7 +359,8 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, @@ -354,11 +368,13 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) def entropy(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): - return _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index c013b779..cf68115e 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -59,7 +59,8 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average gray level (clipped on uint8) """ - return _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, 0., 0., s0, s1) + _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, + bitdepth, 0., 0., s0, s1) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -69,4 +70,5 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask """ - return _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, s0, s1) + _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, + bitdepth, .0, .0, s0, s1) diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 0d37b77c..3b50c86c 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -205,93 +205,93 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """bottom hat """ - return _core16( - kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core16( - kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core16( - kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core16( - kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core16( - kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """return p0 percentile """ - return _core16( - kernel_percentile, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core16( - kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): + char shift_x=0, char shift_y=0, int bitdepth=8, + float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 """ - return _core16( - kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, 0, 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 4efd2bb0..5b2cba01 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -315,9 +315,8 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_autolevel, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -325,9 +324,8 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -335,9 +333,8 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_equalize, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -345,9 +342,8 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_gradient, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -355,7 +351,8 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -363,7 +360,8 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -371,9 +369,8 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -381,7 +378,8 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, @@ -389,7 +387,8 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -397,9 +396,8 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -407,7 +405,8 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -415,7 +414,8 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -423,9 +423,8 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8( - kernel_threshold, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, 0, 0, + 0, 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, @@ -433,7 +432,8 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, @@ -441,18 +441,21 @@ def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) def otsu(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - return _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, + 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 618a6452..636295fd 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -201,9 +201,8 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ - return _core8( - kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, + 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -213,9 +212,8 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ - return _core8( - kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, + 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -225,7 +223,8 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ - return _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, + 0, 0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -235,9 +234,8 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - return _core8( - kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, + p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -247,9 +245,8 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ - return _core8( - kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, + p0, p1, 0, 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -259,9 +256,8 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ - return _core8( - kernel_percentile, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, + p0, p1, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -271,7 +267,8 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ - return _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, + 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -281,6 +278,5 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ - return _core8( - kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, < Py_ssize_t > 0, - < Py_ssize_t > 0) + _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, + 0, 0) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index f181735b..39afa71a 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -1,31 +1,31 @@ -"""bilateral_rank.py - approximate bilateral rankfilter for local (custom kernel) mean +"""Approximate bilateral rankfilter for local (custom kernel) mean. -The local histogram is computed using a sliding window similar to the method described in +The local histogram is computed using a sliding window similar to the method +described in: -Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", -IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median +filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal +Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. -input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), -8 bit images are casted in 16 bit -the number of histogram bins is determined from the maximum value present in the image +Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), 8 bit +images are casted in 16 bit the number of histogram bins is determined from the +maximum value present in the image. The pixel neighborhood is defined by: * the given structuring element - * an interval [g-s0,g+s1] in gray level around g the processed pixel gray level -The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally) +The kernel is flat (i.e. each pixel belonging to the neighborhood contributes +equally). -result image is 16 bit with respect to the input image +Result image is 16 bit with respect to the input image. """ -from skimage import img_as_ubyte - import numpy as np +from skimage import img_as_ubyte from skimage.filter.rank import _crank16_bilateral - from skimage.filter.rank.generic import find_bitdepth @@ -34,52 +34,74 @@ __all__ = ['bilateral_mean', 'bilateral_pop'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): selem = img_as_ubyte(selem) - if mask is not None: - mask = img_as_ubyte(mask) - if image.dtype == np.uint8: - image = image.astype(np.uint16) - elif image.dtype == np.uint16: - pass + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones(image.shape, dtype=np.uint8) else: - raise TypeError("only uint8 and uint16 image supported!") - bitdepth = find_bitdepth(image) - if bitdepth > 11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16( - image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, - s0=s0, s1=s1) + mask = np.ascontiguousarray(mask) + mask = img_as_ubyte(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + + mask = np.ascontiguousarray(mask) + if image.dtype == np.uint8: + if func8 is None: + raise TypeError("Not implemented for uint8 image.") + if out is None: + out = np.zeros(image.shape, dtype=np.uint8) + func8(image, selem, shift_x=shift_x, shift_y=shift_y, + mask=mask, out=out, s0=s0, s1=s1) + elif image.dtype == np.uint16: + if func16 is None: + raise TypeError("Not implemented for uint16 image.") + if out is None: + out = np.zeros(image.shape, dtype=np.uint16) + bitdepth = find_bitdepth(image) + if bitdepth > 11: + raise ValueError("Only uint16 <4096 image (12bit) supported.") + func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, + bitdepth=bitdepth + 1, out=out, s0=s0, s1=s1) + else: + raise TypeError("Only uint8 and uint16 image supported.") + + return out -def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): +def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, s0=10, s1=10): """Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. - Spatial closeness is measured by considering only the local pixel neighborhood given by a - structuring element (selem). + Spatial closeness is measured by considering only the local pixel + neighborhood given by a structuring element (selem). - Radiometric similarity is defined by the gray level interval [g-s0,g+s1] where g is the current pixel gray level. - Only pixels belonging to the structuring element AND having a gray level inside this interval are averaged. - Return greyscale local bilateral_mean of an image. + Radiometric similarity is defined by the gray level interval [g-s0,g+s1] + where g is the current pixel gray level. Only pixels belonging to the + structuring element AND having a gray level inside this interval are + averaged. Return greyscale local bilateral_mean of an image. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). s0, s1 : int - define the [s0,s1] interval to be considered for computing the value. + define the [s0, s1] interval to be considered for computing the value. Returns ------- @@ -108,32 +130,35 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal >>> bilat_ima = bilateral_mean(ima, disk(20), s0=10,s1=10) """ - return _apply( - None, _crank16_bilateral.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, - s0=s0, s1=s1) + return _apply(None, _crank16_bilateral.mean, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) -def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): - """Return the number (population) of pixels actually inside the bilateral neighborhood, - i.e. being inside the structuring element AND having a gray level inside the interval [g-s0,g+s1]. +def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, s0=10, s1=10): + """Return the number (population) of pixels actually inside the bilateral + neighborhood, i.e. being inside the structuring element AND having a gray + level inside the interval [g-s0, g+s1]. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). s0, s1 : int - define the [s0,s1] interval to be considered for computing the value. + define the [s0, s1] interval to be considered for computing the value. Returns ------- @@ -159,7 +184,5 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals """ - return _apply( - None, _crank16_bilateral.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, - s0=s0, s1=s1) - + return _apply(None, _crank16_bilateral.pop, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 1bd89eb8..c5a5a594 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -1,337 +1,390 @@ -"""percentile_rank.py - inferior and superior ranks, provided by the user, are passed to the kernel function -to provide a softer version of the rank filters. E.g. percentile_autolevel will stretch image levels between -percentile [p0,p1] instead of using [min,max]. It means that isolate bright or dark pixels will not produce halos. +"""Inferior and superior ranks, provided by the user, are passed to the kernel +function to provide a softer version of the rank filters. E.g. +percentile_autolevel will stretch image levels between percentile [p0, p1] +instead of using [min,max]. It means that isolate bright or dark pixels will not +produce halos. -The local histogram is computed using a sliding window similar to the method described in +The local histogram is computed using a sliding window similar to the method +described in: -Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", -IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median +filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal +Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. -input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), -for 16 bit input images, the number of histogram bins is determined from the maximum value present in the image +Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), for 16 bit +input images, the number of histogram bins is determined from the maximum value +present in the image. -result image is 8 or 16 bit with respect to the input image +Result image is 8 or 16 bit with respect to the input image. """ -from skimage import img_as_ubyte import numpy as np - +from skimage import img_as_ubyte from skimage.filter.rank.generic import find_bitdepth from skimage.filter.rank import _crank16_percentiles, _crank8_percentiles + __all__ = ['percentile_autolevel', 'percentile_gradient', 'percentile_mean', 'percentile_mean_substraction', - 'percentile_morph_contr_enh', 'percentile', 'percentile_pop', 'percentile_threshold'] + 'percentile_morph_contr_enh', 'percentile', 'percentile_pop', + 'percentile_threshold'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): selem = img_as_ubyte(selem) - if mask is not None: + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones(image.shape, dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) mask = img_as_ubyte(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + + mask = np.ascontiguousarray(mask) if image.dtype == np.uint8: - return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, p0=p0, p1=p1) + if func8 is None: + raise TypeError("Not implemented for uint8 image.") + if out is None: + out = np.zeros(image.shape, dtype=np.uint8) + func8(image, selem, shift_x=shift_x, shift_y=shift_y, + mask=mask, out=out, p0=p0, p1=p1) elif image.dtype == np.uint16: + if func16 is None: + raise TypeError("Not implemented for uint16 image.") + if out is None: + out = np.zeros(image.shape, dtype=np.uint16) bitdepth = find_bitdepth(image) if bitdepth > 11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16( - image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out, - p0=p0, p1=p1) + raise ValueError("Only uint16 <4096 image (12bit) supported.") + func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, + bitdepth=bitdepth + 1, out=out, p0=p0, p1=p1) else: - raise TypeError("only uint8 and uint16 image supported!") + raise TypeError("Only uint8 and uint16 image supported.") + + return out -def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local autolevel of an image. - Autolevel is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + Autolevel is computed on the given structuring element. Only levels between + percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local autolevel : uint8 array or uint16 array depending on input image + local autolevel : uint8 array or uint16 The result of the local autolevel. - """ - return _apply( - _crank8_percentiles.autolevel, _crank16_percentiles.autolevel, image, selem, out=out, mask=mask, - shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local percentile_gradient of an image. - percentile_gradient is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + percentile_gradient is computed on the given structuring element. Only + levels between percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local percentile_gradient : uint8 array or uint16 array depending on input image + local percentile_gradient : uint8 array or uint16 The result of the local percentile_gradient. - - """ - return _apply( - _crank8_percentiles.gradient, _crank16_percentiles.gradient, image, selem, out=out, mask=mask, - shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.gradient, _crank16_percentiles.gradient, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_mean(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local mean of an image. - Mean is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + Mean is computed on the given structuring element. Only levels between + percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local mean : uint8 array or uint16 array depending on input image + local mean : uint8 array or uint16 The result of the local mean. - """ - return _apply( - _crank8_percentiles.mean, _crank16_percentiles.mean, image, selem, out=out, mask=mask, - shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.mean, _crank16_percentiles.mean, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_mean_substraction(image, selem, out=None, mask=None, + shift_x=False, shift_y=False, p0=.0, p1=1.): """Return greyscale local mean_substraction of an image. - mean_substraction is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + mean_substraction is computed on the given structuring element. Only levels + between percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local mean_substraction : uint8 array or uint16 array depending on input image + local mean_substraction : uint8 array or uint16 The result of the local mean_substraction. - - """ - return _apply( - _crank8_percentiles.mean_substraction, _crank16_percentiles.mean_substraction, image, selem, out=out, - mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.mean_substraction, + _crank16_percentiles.mean_substraction, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. - morph_contr_enh is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + morph_contr_enh is computed on the given structuring element. Only levels + between percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local morph_contr_enh : uint8 array or uint16 array depending on input image + local morph_contr_enh : uint8 array or uint16 The result of the local morph_contr_enh. - - """ - return _apply( - _crank8_percentiles.morph_contr_enh, _crank16_percentiles.morph_contr_enh, image, selem, out=out, - mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.morph_contr_enh, + _crank16_percentiles.morph_contr_enh, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, + p0=.0, p1=1.): """Return greyscale local percentile of an image. - percentile is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + percentile is computed on the given structuring element. Only levels between + percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local percentile : uint8 array or uint16 array depending on input image + local percentile : uint8 array or uint16 The result of the local percentile. - - """ - return _apply( - _crank8_percentiles.percentile, _crank16_percentiles.percentile, image, selem, out=out, mask=mask, - shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.percentile, + _crank16_percentiles.percentile, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_pop(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local pop of an image. - pop is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + pop is computed on the given structuring element. Only levels between + percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local pop : uint8 array or uint16 array depending on input image + local pop : uint8 array or uint16 The result of the local pop. - - """ - return _apply( - _crank8_percentiles.pop, _crank16_percentiles.pop, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y, p0=p0, p1=p1) + return _apply(_crank8_percentiles.pop, _crank16_percentiles.pop, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) -def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=.0, p1=1.): +def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local threshold of an image. - threshold is computed on the given structuring element. Only levels between percentiles [p0,p1] ,are used. + threshold is computed on the given structuring element. Only levels between + percentiles [p0, p1] are used. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, as the + algorithm uses max. 12bit histogram, an exception will be raised if + image has a value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). - p0, p1 : float in [0.,...,1.] - define the [p0,p1] percentile interval to be considered for computing the value. + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). + p0, p1 : float in [0, ..., 1] + Define the [p0, p1] percentile interval to be considered for computing + the value. Returns ------- - local threshold : uint8 array or uint16 array depending on input image + local threshold : uint8 array or uint16 The result of the local threshold. - """ - return _apply( - _crank8_percentiles.threshold, _crank16_percentiles.threshold, image, selem, out=out, mask=mask, - shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) - - + return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 6a35920b..8ac98f81 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -1,44 +1,63 @@ -"""rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, equalization, etc +"""The local histogram is computed using a sliding window similar to the method +described in: -The local histogram is computed using a sliding window similar to the method described in +Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median +filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal +Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. -Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", -IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), for 16 bit +input images, the number of histogram bins is determined from the maximum value +present in the image. -input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), -for 16 bit input images, the number of histogram bins is determined from the maximum value present in the image - -result image is 8 or 16 bit with respect to the input image +Result image is 8 or 16 bit with respect to the input image. """ -from skimage import img_as_ubyte import numpy as np +from skimage import img_as_ubyte from skimage.filter.rank import _crank8, _crank16 - from skimage.filter.rank.generic import find_bitdepth -__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', - 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter','entropy','otsu'] + +__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', + 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', + 'pop', 'threshold', 'tophat','noise_filter','entropy','otsu'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): selem = img_as_ubyte(selem) - if mask is not None: + image = np.ascontiguousarray(image) + + if mask is None: + mask = np.ones(image.shape, dtype=np.uint8) + else: + mask = np.ascontiguousarray(mask) mask = img_as_ubyte(mask) + if image is out: + raise NotImplementedError("Cannot perform rank operation in place.") + + mask = np.ascontiguousarray(mask) if image.dtype == np.uint8: if func8 is None: - raise TypeError("not implemented for uint8 image") - return func8(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out) + raise TypeError("Not implemented for uint8 image.") + if out is None: + out = np.zeros(image.shape, dtype=np.uint8) + func8(image, selem, shift_x=shift_x, shift_y=shift_y, + mask=mask, out=out) elif image.dtype == np.uint16: if func16 is None: - raise TypeError("not implemented for uint16 image") + raise TypeError("Not implemented for uint16 image.") + if out is None: + out = np.zeros(image.shape, dtype=np.uint16) bitdepth = find_bitdepth(image) if bitdepth > 11: - raise ValueError("only uint16 <4096 image (12bit) supported!") - return func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, bitdepth=bitdepth + 1, out=out) + raise ValueError("Only uint16 <4096 image (12bit) supported.") + func16(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, + bitdepth=bitdepth + 1, out=out) else: - raise TypeError("only uint8 and uint16 image supported!") + raise TypeError("Only uint8 and uint16 image supported.") + + return out def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -47,18 +66,20 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -77,9 +98,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply( - _crank8.autolevel, _crank16.autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y) + return _apply(_crank8.autolevel, _crank16.autolevel, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -88,30 +108,30 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- local bottomhat : uint8 array or uint16 array depending on input image The result of the local bottomhat. - """ - return _apply( - _crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y) + return _apply(_crank8.bottomhat, _crank16.bottomhat, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -120,18 +140,20 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -147,32 +169,35 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> ima = data.camera() >>> # Local equalization >>> equ = equalize(ima, disk(20)) + """ - return _apply( - _crank8.equalize, _crank16.equalize, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y) + return _apply(_crank8.equalize, _crank16.equalize, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return greyscale local gradient of an image (i.e. local maximum - local minimum). + """Return greyscale local gradient of an image (i.e. local maximum - local + minimum). Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -181,9 +206,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply( - _crank8.gradient, _crank16.gradient, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y) + return _apply(_crank8.gradient, _crank16.gradient, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -193,18 +217,20 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -213,17 +239,18 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): See also -------- - skimage.morphology.dilation() + skimage.morphology.dilation Note ---- * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) - - * the lower algorithm complexity makes the rank.maximum() more efficient for larger images and structuring elements + * the lower algorithm complexity makes the rank.maximum() more efficient for + larger images and structuring elements """ - return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.maximum, _crank16.maximum, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -232,18 +259,20 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -259,63 +288,66 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> ima = data.camera() >>> # Local mean >>> avg = mean(ima, disk(20)) + """ - return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.mean, _crank16.mean, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) -def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=False): +def meansubstraction(image, selem, out=None, mask=None, shift_x=False, + shift_y=False): """Return image substracted from its local mean. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- out : uint8 array or uint16 array (same as input image) The result of the local meansubstraction. - - """ - return _apply( - _crank8.meansubstraction, _crank16.meansubstraction, image, selem, out=out, mask=mask, - shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image, + selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return greyscale local median of an image. - Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -331,9 +363,11 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> ima = data.camera() >>> # Local mean >>> avg = median(ima, disk(20)) + """ - return _apply(_crank8.median, _crank16.median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.median, _crank16.median, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -342,18 +376,20 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -362,17 +398,18 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): See also -------- - skimage.morphology.erosion() + skimage.morphology.erosion Note ---- * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) - - * the lower algorithm complexity makes the rank.minimum() more efficient for larger images and structuring elements + * the lower algorithm complexity makes the rank.minimum() more efficient + for larger images and structuring elements """ - return _apply(_crank8.minimum, _crank16.minimum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.minimum, _crank16.minimum, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -381,49 +418,55 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- out : uint8 array or uint16 array (same as input image) The local modal. - """ - return _apply(_crank8.modal, _crank16.modal, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.modal, _crank16.modal, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) -def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Enhance an image replacing each pixel by the local maximum if pixel graylevel is closest to maximimum - than local minimum OR local minimum otherwise. +def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, + shift_y=False): + """Enhance an image replacing each pixel by the local maximum if pixel + graylevel is closest to maximimum than local minimum OR local minimum + otherwise. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -439,31 +482,34 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa >>> ima = data.camera() >>> # Local mean >>> avg = morph_contr_enh(ima, disk(20)) + """ - return _apply( - _crank8.morph_contr_enh, _crank16.morph_contr_enh, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y) + return _apply(_crank8.morph_contr_enh, _crank16.morph_contr_enh, image, + selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Return the number (population) of pixels actually inside the neighborhood. + """Return the number (population) of pixels actually inside the + neighborhood. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -487,10 +533,10 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]], dtype=uint8) - """ - return _apply(_crank8.pop, _crank16.pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.pop, _crank16.pop, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -499,18 +545,20 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -534,12 +582,10 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=uint8) - """ - return _apply( - _crank8.threshold, _crank16.threshold, image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y) + return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): @@ -548,18 +594,20 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- @@ -568,32 +616,35 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) -def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False): +def noise_filter(image, selem, out=None, mask=None, shift_x=False, + shift_y=False): """Returns the noise feature as described in [Hashimoto12]_ Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray - The neighborhood expressed as a 2-D array of 1's and 0's. Central element is removed during the filtering. + The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). - Reference + References ---------- - - .. [Hashimoto12] N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9. - + .. [Hashimoto12] N. Hashimoto et al. Referenceless image quality evaluation + for whole slide imaging. J Pathol Inform 2012;3:9. Returns ------- @@ -601,6 +652,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False The image noise . """ + # ensure that the central pixel in the structuring element is empty centre_r = int(selem.shape[0] / 2) + shift_y centre_c = int(selem.shape[1] / 2) + shift_x @@ -608,41 +660,43 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False selem_cpy = selem.copy() selem_cpy[centre_r,centre_c] = 0 - return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Returns the entropy [wiki_entropy]_ computed locally. Entropy is computed using base 2 logarithm i.e. - the filter returns the minimum number of bits needed to encode local greylevel distribution. - - References - ---------- - .. [wiki_entropy] http://en.wikipedia.org/wiki/Entropy_(information_theory) + """Returns the entropy [wiki_entropy]_ computed locally. Entropy is computed + using base 2 logarithm i.e. the filter returns the minimum number of + bits needed to encode local greylevel distribution. Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- out : uint8 array or uint16 array (same as input image) entropy x10 (uint8 images) and entropy x1000 (uint16 images) + References + ---------- + .. [wiki_entropy] http://en.wikipedia.org/wiki/Entropy_(information_theory) Examples -------- - >>> # Local entropy >>> from skimage import data >>> from skimage.filter.rank import entropy @@ -650,46 +704,48 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> # defining a 8- and a 16-bit test images >>> a8 = data.camera() >>> a16 = data.camera().astype(np.uint16)*4 - >>> ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy - >>> ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy + >>> # pixel values contain 10x the local entropy + >>> ent8 = entropy(a8,disk(5)) + >>> # pixel values contain 1000x the local entropy + >>> ent16 = entropy(a16,disk(5)) """ - return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Returns the image threshold using a the Otsu [otsu]_ locally . - References - ---------- - - .. [otsu] http://en.wikipedia.org/wiki/Otsu's_method - Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram, - an exception will be raised if image has a value > 4095 + Image array (uint8 array or uint16). If image is uint16, the algorithm + uses max. 12bit histogram, an exception will be raised if image has a + value > 4095. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray (uint8) - Mask array that defines (>0) area of the image included in the local neighborhood. - If None, the complete image is used (default). - shift_x, shift_y : (int) - Offset added to the structuring element center point. - Shift is bounded to the structuring element sizes (center must be inside the given structuring element). + Mask array that defines (>0) area of the image included in the local + neighborhood. If None, the complete image is used (default). + shift_x, shift_y : int + Offset added to the structuring element center point. Shift is bounded + to the structuring element sizes (center must be inside the given + structuring element). Returns ------- out : uint8 array or uint16 array (same as input image) threshold image + References + ---------- + .. [otsu] http://en.wikipedia.org/wiki/Otsu's_method Examples -------- - >>> # Local entropy >>> from skimage import data >>> from skimage.filter.rank import otsu @@ -700,4 +756,5 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """ - return _apply(_crank8.otsu, None, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) \ No newline at end of file + return _apply(_crank8.otsu, None, image, selem, out=out, + mask=mask, shift_x=shift_x, shift_y=shift_y) From 28e7b19c93524fad824c0f3747f76aac626b0f81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 08:24:44 +0100 Subject: [PATCH 316/625] Improve code layout of kernel functions --- skimage/filter/rank/_crank16.pyx | 205 ++++++++++-------- skimage/filter/rank/_crank16_bilateral.pyx | 34 +-- skimage/filter/rank/_crank16_percentiles.pyx | 119 ++++++---- skimage/filter/rank/_crank8.pyx | 217 ++++++++++--------- skimage/filter/rank/_crank8_percentiles.pyx | 103 +++++---- 5 files changed, 378 insertions(+), 300 deletions(-) diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index ab5b6015..602a60f4 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -6,18 +6,19 @@ import numpy as np cimport numpy as np from libc.math cimport log2 - -# import main loop from skimage.filter.rank._core16 cimport _core16 + # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax, delta if pop: @@ -31,27 +32,30 @@ cdef inline np.uint16_t kernel_autolevel( break delta = imax - imin if delta > 0: - return < np.uint16_t > (1. * (maxbin - 1) * (g - imin) / delta) + return (1. * (maxbin - 1) * (g - imin) / delta) else: - return < np.uint16_t > (imax - imin) + return (imax - imin) -cdef inline np.uint16_t kernel_bottomhat( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin): if histo[i]: break - return < np.uint16_t > (g - i) + return (g - i) -cdef inline np.uint16_t kernel_equalize( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -61,14 +65,16 @@ cdef inline np.uint16_t kernel_equalize( if i >= g: break - return < np.uint16_t > (((maxbin - 1) * sum) / pop) + return (((maxbin - 1) * sum) / pop) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_gradient( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax if pop: @@ -80,55 +86,63 @@ cdef inline np.uint16_t kernel_gradient( if histo[i]: imin = i break - return < np.uint16_t > (imax - imin) + return (imax - imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_maximum( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(maxbin - 1, -1, -1): if histo[i]: - return < np.uint16_t > (i) + return (i) - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_mean( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): mean += histo[i] * i - return < np.uint16_t > (mean / pop) + return (mean / pop) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_meansubstraction( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): mean += histo[i] * i - return < np.uint16_t > ((g - mean / pop) / 2. + (midbin - 1)) + return ((g - mean / pop) / 2. + (midbin - 1)) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_median( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop / 2.0 @@ -137,27 +151,31 @@ cdef inline np.uint16_t kernel_median( if histo[i]: sum -= histo[i] if sum < 0: - return < np.uint16_t > (i) + return (i) - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_minimum( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(maxbin): if histo[i]: - return < np.uint16_t > (i) + return (i) - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_modal( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax = 0, imax = 0 if pop: @@ -165,14 +183,16 @@ cdef inline np.uint16_t kernel_modal( if histo[i] > hmax: hmax = histo[i] imax = i - return < np.uint16_t > (imax) + return (imax) - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_morph_contr_enh( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax if pop: @@ -185,49 +205,56 @@ cdef inline np.uint16_t kernel_morph_contr_enh( imin = i break if imax - g < g - imin: - return < np.uint16_t > (imax) + return (imax) else: - return < np.uint16_t > (imin) + return (imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_pop( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return < np.uint16_t > (pop) -cdef inline np.uint16_t kernel_threshold( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + return (pop) + + +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): mean += histo[i] * i - return < np.uint16_t > (g > (mean / pop)) + return (g > (mean / pop)) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_tophat( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i for i in range(maxbin - 1, -1, -1): if histo[i]: break - return < np.uint16_t > (i - g) + return (i - g) -cdef inline np.uint16_t kernel_entropy( - Py_ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float e,p @@ -238,7 +265,7 @@ cdef inline np.uint16_t kernel_entropy( if p>0: e -= p*log2(p) - return < np.uint16_t > e*1000 + return e*1000 # ----------------------------------------------------------------- diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index cf68115e..1fb81a2d 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -5,18 +5,19 @@ import numpy as np cimport numpy as np - -# import main loop from skimage.filter.rank._core16 cimport _core16 + # ----------------------------------------------------------------- # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, bilat_pop = 0 cdef float mean = 0. @@ -27,16 +28,18 @@ cdef inline np.uint16_t kernel_mean( bilat_pop += histo[i] mean += histo[i] * i if bilat_pop: - return < np.uint16_t > (mean / bilat_pop) + return (mean / bilat_pop) else: - return < np.uint16_t > (0) + return (0) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_pop( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, - Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, bilat_pop = 0 @@ -44,14 +47,16 @@ cdef inline np.uint16_t kernel_pop( for i in range(maxbin): if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - return < np.uint16_t > (bilat_pop) + return (bilat_pop) else: - return < np.uint16_t > (0) + return (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- + + def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -68,7 +73,8 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): - """returns the number of actual pixels of the structuring element inside the mask + """returns the number of actual pixels of the structuring element inside + the mask """ _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, .0, .0, s0, s1) diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 3b50c86c..4c2525be 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -5,17 +5,19 @@ import numpy as np cimport numpy as np - -# import main loop from skimage.filter.rank._core16 cimport _core16, int_min, int_max + # ----------------------------------------------------------------- # kernels uint16 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -36,16 +38,19 @@ cdef inline np.uint16_t kernel_autolevel( delta = imax - imin if delta > 0: - return < np.uint16_t > (1.0 * (maxbin - 1) * (int_min(int_max(imin, g), imax) - imin) / delta) + return (1.0 * (maxbin - 1) \ + * (int_min(int_max(imin, g), imax) - imin) / delta) else: - return < np.uint16_t > (imax - imin) + return (imax - imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_gradient( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -64,14 +69,16 @@ cdef inline np.uint16_t kernel_gradient( imax = i break - return < np.uint16_t > (imax - imin) + return (imax - imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_mean( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -86,15 +93,18 @@ cdef inline np.uint16_t kernel_mean( mean += histo[i] * i if n > 0: - return < np.uint16_t > (1.0 * mean / n) + return (1.0 * mean / n) else: - return < np.uint16_t > (0) + return (0) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_mean_substraction( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -108,15 +118,18 @@ cdef inline np.uint16_t kernel_mean_substraction( n += histo[i] mean += histo[i] * i if n > 0: - return < np.uint16_t > ((g - (mean / n)) * .5 + midbin) + return ((g - (mean / n)) * .5 + midbin) else: - return < np.uint16_t > (0) + return (0) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_morph_contr_enh( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -135,19 +148,22 @@ cdef inline np.uint16_t kernel_morph_contr_enh( imax = i break if g > imax: - return < np.uint16_t > imax + return imax if g < imin: - return < np.uint16_t > imin + return imin if imax - g < g - imin: - return < np.uint16_t > imax + return imax else: - return < np.uint16_t > imin + return imin else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_percentile( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -158,13 +174,16 @@ cdef inline np.uint16_t kernel_percentile( if sum >= p0 * pop: break - return < np.uint16_t > (i) + return (i) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_pop( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n @@ -175,13 +194,16 @@ cdef inline np.uint16_t kernel_pop( sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return < np.uint16_t > (n) + return (n) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_threshold( - Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -192,9 +214,10 @@ cdef inline np.uint16_t kernel_threshold( if sum >= p0 * pop: break - return < np.uint16_t > ((maxbin - 1) * (g >= i)) + return ((maxbin - 1) * (g >= i)) else: - return < np.uint16_t > (0) + return (0) + # ----------------------------------------------------------------- # python wrappers diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 5b2cba01..7398b26f 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -5,19 +5,18 @@ import numpy as np cimport numpy as np - from libc.math cimport log2 - -# import main loop from skimage.filter.rank._core8 cimport _core8 + # ----------------------------------------------------------------- # kernels uint8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax, delta @@ -32,15 +31,16 @@ cdef inline np.uint8_t kernel_autolevel( break delta = imax - imin if delta > 0: - return < np.uint8_t > (255. * (g - imin) / delta) + return (255. * (g - imin) / delta) else: - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_bottomhat( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -48,12 +48,12 @@ cdef inline np.uint8_t kernel_bottomhat( if histo[i]: break - return < np.uint8_t > (g - i) + return (g - i) -cdef inline np.uint8_t kernel_equalize( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -64,13 +64,14 @@ cdef inline np.uint8_t kernel_equalize( if i >= g: break - return < np.uint8_t > ((255 * sum) / pop) + return ((255 * sum) / pop) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_gradient( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax @@ -83,26 +84,28 @@ cdef inline np.uint8_t kernel_gradient( if histo[i]: imin = i break - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_maximum( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(255, -1, -1): if histo[i]: - return < np.uint8_t > (i) + return (i) - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_mean( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -110,13 +113,14 @@ cdef inline np.uint8_t kernel_mean( if pop: for i in range(256): mean += histo[i] * i - return < np.uint8_t > (mean / pop) + return (mean / pop) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_meansubstraction( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -124,13 +128,14 @@ cdef inline np.uint8_t kernel_meansubstraction( if pop: for i in range(256): mean += histo[i] * i - return < np.uint8_t > ((g - mean / pop) / 2. + 127) + return ((g - mean / pop) / 2. + 127) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_median( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop / 2.0 @@ -140,25 +145,28 @@ cdef inline np.uint8_t kernel_median( if histo[i]: sum -= histo[i] if sum < 0: - return < np.uint8_t > (i) + return (i) - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_minimum( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(256): if histo[i]: - return < np.uint8_t > (i) + return (i) - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_modal( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax = 0, imax = 0 @@ -167,13 +175,14 @@ cdef inline np.uint8_t kernel_modal( if histo[i] > hmax: hmax = histo[i] imax = i - return < np.uint8_t > (imax) + return (imax) - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_morph_contr_enh( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax @@ -187,21 +196,23 @@ cdef inline np.uint8_t kernel_morph_contr_enh( imin = i break if imax - g < g - imin: - return < np.uint8_t > (imax) + return (imax) else: - return < np.uint8_t > (imin) + return (imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_pop( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): - return < np.uint8_t > (pop) +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): -cdef inline np.uint8_t kernel_threshold( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + return (pop) + + +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -209,13 +220,14 @@ cdef inline np.uint8_t kernel_threshold( if pop: for i in range(256): mean += histo[i] * i - return < np.uint8_t > (g > (mean / pop)) + return (g > (mean / pop)) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_tophat( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -223,64 +235,64 @@ cdef inline np.uint8_t kernel_tophat( if histo[i]: break - return < np.uint8_t > (i - g) + return (i - g) -cdef inline np.uint8_t kernel_noise_filter( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef Py_ssize_t min_i # early stop if at least one pixel of the neighborhood has the same g - if histo[g]>0: - return < np.uint8_t > 0 + if histo[g] > 0: + return 0 for i in range(g, -1, -1): if histo[i]: break - min_i = g-i + min_i = g - i for i in range(g, 256): if histo[i]: break - if i-g < min_i: - return < np.uint8_t > (i-g) + if i - g < min_i: + return (i - g) else: - return < np.uint8_t > min_i + return min_i -cdef inline np.uint8_t kernel_entropy( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): +cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float e,p e = 0. for i in range(256): - p = histo[i]/pop - if p>0: - e -= p*log2(p) + p = histo[i] / pop + if p > 0: + e -= p * log2(p) - return < np.uint8_t > e*10 + return e*10 -cdef inline np.uint8_t kernel_otsu( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): +cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef Py_ssize_t i cdef Py_ssize_t max_i cdef float P, mu1, mu2, q1,new_q1, sigma_b, max_sigma_b cdef float mu = 0. # compute local mean - if pop: for i in range(256): mu += histo[i] * i mu = (mu / pop) else: - return < np.uint8_t > (0) + return (0) # maximizing the between class variance max_i = 0 @@ -289,21 +301,24 @@ cdef inline np.uint8_t kernel_otsu( max_sigma_b = 0. for i in range(1,256): - P = histo[i]/pop + P = histo[i] / pop new_q1 = q1 + P - if new_q1>0: - mu1 = (q1*mu1 + i*P)/new_q1 - mu2 = (mu-new_q1*mu1)/(1.-new_q1) - sigma_b = new_q1*(1.-new_q1)*(mu1-mu2)**2 - if sigma_b>max_sigma_b: + if new_q1 > 0: + mu1 = (q1 * mu1 + i * P) / new_q1 + mu2 = (mu - new_q1*mu1) / (1. - new_q1) + sigma_b = new_q1 * (1. - new_q1) * (mu1 - mu2)**2 + if sigma_b > max_sigma_b: max_sigma_b = sigma_b max_i = i q1 = new_q1 - if g>max_i: - return < np.uint8_t > 255 + if g > max_i: + return 255 else: - return < np.uint8_t > 0 + return 0 + + + # ----------------------------------------------------------------- # python wrappers # used only internally diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 636295fd..777aec64 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -5,17 +5,17 @@ import numpy as np cimport numpy as np - -# import main loop from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min + # ----------------------------------------------------------------- # kernels uint8 (SOFT version using percentiles) # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -37,16 +37,17 @@ cdef inline np.uint8_t kernel_autolevel( break delta = imax - imin if delta > 0: - return < np.uint8_t > (255 * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) + return (255 \ + * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) else: - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (128) + return (128) -cdef inline np.uint8_t kernel_gradient( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -64,14 +65,14 @@ cdef inline np.uint8_t kernel_gradient( imax = i break - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_mean( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -84,15 +85,16 @@ cdef inline np.uint8_t kernel_mean( n += histo[i] mean += histo[i] * i if n > 0: - return < np.uint8_t > (1.0 * mean / n) + return (1.0 * mean / n) else: - return < np.uint8_t > (0) + return (0) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_mean_substraction( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -105,15 +107,16 @@ cdef inline np.uint8_t kernel_mean_substraction( n += histo[i] mean += histo[i] * i if n > 0: - return < np.uint8_t > ((g - (mean / n)) * .5 + 127) + return ((g - (mean / n)) * .5 + 127) else: - return < np.uint8_t > (0) + return (0) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_morph_contr_enh( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -131,19 +134,20 @@ cdef inline np.uint8_t kernel_morph_contr_enh( imax = i break if g > imax: - return < np.uint8_t > imax + return imax if g < imin: - return < np.uint8_t > imin + return imin if imax - g < g - imin: - return < np.uint8_t > imax + return imax else: - return < np.uint8_t > imin + return imin else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_percentile( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -153,13 +157,14 @@ cdef inline np.uint8_t kernel_percentile( if sum >= p0 * pop: break - return < np.uint8_t > (i) + return (i) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_pop( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n if pop: @@ -169,13 +174,14 @@ cdef inline np.uint8_t kernel_pop( sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return < np.uint8_t > (n) + return (n) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_threshold( - Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): + +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, + np.uint8_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -185,9 +191,10 @@ cdef inline np.uint8_t kernel_threshold( if sum >= p0 * pop: break - return < np.uint8_t > (255 * (g >= i)) + return (255 * (g >= i)) else: - return < np.uint8_t > (0) + return (0) + # ----------------------------------------------------------------- # python wrappers From 045eb88e9f030aabbdadff2fe7fdc3071bfda677 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 08:31:20 +0100 Subject: [PATCH 317/625] Change type of pop variable from float to Py_ssize_t --- skimage/filter/rank/_core16.pxd | 2 +- skimage/filter/rank/_core16.pyx | 12 +++---- skimage/filter/rank/_core8.pxd | 2 +- skimage/filter/rank/_core8.pyx | 12 +++---- skimage/filter/rank/_crank16.pyx | 30 ++++++++--------- skimage/filter/rank/_crank16_bilateral.pyx | 4 +-- skimage/filter/rank/_crank16_percentiles.pyx | 16 ++++----- skimage/filter/rank/_crank8.pyx | 34 ++++++++++---------- skimage/filter/rank/_crank8_percentiles.pyx | 16 ++++----- 9 files changed, 64 insertions(+), 64 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index b236d982..0ff5d518 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -6,7 +6,7 @@ cdef int int_min(int a, int b) # 16 bit core kernel receives extra information about data bitdepth -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 671a92fc..30d0a5f2 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -17,19 +17,19 @@ cdef inline int int_min(int a, int b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, +cdef inline void histogram_increment(Py_ssize_t * histo, Py_ssize_t * pop, np.uint16_t value): histo[value] += 1 - pop[0] += 1. + pop[0] += 1 -cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, +cdef inline void histogram_decrement(Py_ssize_t * histo, Py_ssize_t * pop, np.uint16_t value): histo[value] -= 1 - pop[0] -= 1. + pop[0] -= 1 -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, @@ -74,7 +74,7 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) - cdef float pop + cdef Py_ssize_t pop # allocate memory with malloc cdef Py_ssize_t max_se = srows * scols diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index d724c417..6a6bdafa 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -12,7 +12,7 @@ cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, # 8 bit core kernel receives extra information about data inferior and superior # percentiles -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 17e5f64e..fc1b64b6 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -16,16 +16,16 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, +cdef inline void histogram_increment(Py_ssize_t * histo, Py_ssize_t * pop, np.uint8_t value): histo[value] += 1 - pop[0] += 1. + pop[0] += 1 -cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, +cdef inline void histogram_decrement(Py_ssize_t * histo, Py_ssize_t * pop, np.uint8_t value): histo[value] -= 1 - pop[0] -= 1. + pop[0] -= 1 cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, @@ -41,7 +41,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, return 0 -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -77,7 +77,7 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) - cdef float pop + cdef Py_ssize_t pop # allocate memory with malloc cdef Py_ssize_t max_se = srows * scols diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 602a60f4..bb3f7c7d 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -14,7 +14,7 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -37,7 +37,7 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, return (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -51,7 +51,7 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, return (g - i) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -70,7 +70,7 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -91,7 +91,7 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -106,7 +106,7 @@ cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -122,7 +122,7 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -138,7 +138,7 @@ cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -156,7 +156,7 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -171,7 +171,7 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -188,7 +188,7 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -212,7 +212,7 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -220,7 +220,7 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, return (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -236,7 +236,7 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -250,7 +250,7 @@ cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, return (i - g) -cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 1fb81a2d..98bc5683 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -13,7 +13,7 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -35,7 +35,7 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 4c2525be..8f8585e7 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -13,7 +13,7 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -46,7 +46,7 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -74,7 +74,7 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -100,7 +100,7 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -125,7 +125,7 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -159,7 +159,7 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -179,7 +179,7 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -199,7 +199,7 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 7398b26f..6fee8a9f 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -14,7 +14,7 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -38,7 +38,7 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -51,7 +51,7 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, return (g - i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -69,7 +69,7 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -89,7 +89,7 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -103,7 +103,7 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -118,7 +118,7 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -133,7 +133,7 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -150,7 +150,7 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -164,7 +164,7 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -180,7 +180,7 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -203,14 +203,14 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -225,7 +225,7 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -238,7 +238,7 @@ cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, return (i - g) -cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -262,7 +262,7 @@ cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, return min_i -cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -278,7 +278,7 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, return e*10 -cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, +cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 777aec64..0bc81aac 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -13,7 +13,7 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -45,7 +45,7 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, return (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -70,7 +70,7 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -92,7 +92,7 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -114,7 +114,7 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -145,7 +145,7 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i @@ -162,7 +162,7 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n @@ -179,7 +179,7 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, return (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i From 804b426787869932a1d5dbb1977045321acb9684 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 08:35:23 +0100 Subject: [PATCH 318/625] Clean Readme --- skimage/filter/rank/README.rst | 41 ++++++++++++++++------------------ 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/skimage/filter/rank/README.rst b/skimage/filter/rank/README.rst index aae62162..29a68edb 100644 --- a/skimage/filter/rank/README.rst +++ b/skimage/filter/rank/README.rst @@ -1,35 +1,32 @@ -To use this to build your Cython file use the commandline options: - -.. sourcecode:: text - - $ python setup.py build_ext --inplace - - -**To do** +To do +----- * add simple examples, adapt documentation on existing examples - * add/check existing doc - * adapting tests for each type of filter -**General remarks** +General remarks +--------------- -Basically these filters compute local histogram for each pixel. Histogram is build using a moving window in -order to limit redundant computation. The path followed by the moving window is given hereunder +Basically these filters compute local histogram for each pixel. Histogram is +build using a moving window in order to limit redundant computation. The path +followed by the moving window is given hereunder ...-----------------------\ /--------------------------/ \-------------------------- ... -A comparison is proposed with cmorph.dilate algorithm to show how computation costs evolve with respect to image size or -structuring element size. This implementation gives better results for large structuring elements. +A comparison is proposed with cmorph.dilate algorithm to show how computation +costs evolve with respect to image size or structuring element size. This +implementation gives better results for large structuring elements. -A local histogram is update at each pixel by introducing pixel entering the structuring element border and -by removing those leaving it. The histogram size is 8bit (256 bins) for 8 bit images and 2 to 12 bit (up to 4096 bins) -for 16bit image depending on the image maximum value. Image with pixels higher than 4095 raise a ValueError. - -The filter is applied up to the image border, the neighboorhood used is adjusted accordingly. The user may provide -a mask image (same size as input image) where non zero value are the part of the image participating the the -histogram computation. By default all the image is filtered. +A local histogram is update at each pixel by introducing pixel entering the +structuring element border and by removing those leaving it. The histogram size +is 8bit (256 bins) for 8 bit images and 2 to 12 bit (up to 4096 bins) for 16bit +image depending on the image maximum value. Image with pixels higher than 4095 +raise a ValueError. +The filter is applied up to the image border, the neighboorhood used is adjusted +accordingly. The user may provide a mask image (same size as input image) where +non zero value are the part of the image participating the the histogram +computation. By default all the image is filtered. From 2bb139fb03a8443e7e01cfa9fa9eccb3e2d405db Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 08:39:27 +0100 Subject: [PATCH 319/625] Wrap lines --- skimage/filter/rank/_crank16.pyx | 18 ++++++++++++------ skimage/filter/rank/_crank16_percentiles.pyx | 18 ++++++++++++------ skimage/filter/rank/_crank8_percentiles.pyx | 9 ++++++--- 3 files changed, 30 insertions(+), 15 deletions(-) diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index bb3f7c7d..9c4b8fe1 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -122,9 +122,12 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, Py_ssize_t pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, + Py_ssize_t pop, + np.uint16_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -188,9 +191,12 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, + Py_ssize_t pop, + np.uint16_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 8f8585e7..e5dc2ab7 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -100,9 +100,12 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, Py_ssize_t pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, + Py_ssize_t pop, + np.uint16_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -125,9 +128,12 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, Py_ssize_t return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, + Py_ssize_t pop, + np.uint16_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 0bc81aac..4a11bdc4 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -92,8 +92,10 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, Py_ssize_t pop, - np.uint8_t g, float p0, float p1, +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, + Py_ssize_t pop, + np.uint8_t g, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -114,7 +116,8 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, Py_ssize_t p return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, + Py_ssize_t pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta From 7ea3a754e0584cb5057f7928d0714ab1cd1ebef8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 08:59:48 +0100 Subject: [PATCH 320/625] Remove duplicate contiguous conversion --- skimage/filter/rank/bilateral_rank.pyx | 2 +- skimage/filter/rank/percentile_rank.pyx | 2 +- skimage/filter/rank/rank.pyx | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 39afa71a..73434bb5 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -41,10 +41,10 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, s0, s1): else: mask = np.ascontiguousarray(mask) mask = img_as_ubyte(mask) + if image is out: raise NotImplementedError("Cannot perform rank operation in place.") - mask = np.ascontiguousarray(mask) if image.dtype == np.uint8: if func8 is None: raise TypeError("Not implemented for uint8 image.") diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index c5a5a594..5e506035 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -40,10 +40,10 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y, p0, p1): else: mask = np.ascontiguousarray(mask) mask = img_as_ubyte(mask) + if image is out: raise NotImplementedError("Cannot perform rank operation in place.") - mask = np.ascontiguousarray(mask) if image.dtype == np.uint8: if func8 is None: raise TypeError("Not implemented for uint8 image.") diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 8ac98f81..74add3d7 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -33,10 +33,10 @@ def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): else: mask = np.ascontiguousarray(mask) mask = img_as_ubyte(mask) + if image is out: raise NotImplementedError("Cannot perform rank operation in place.") - mask = np.ascontiguousarray(mask) if image.dtype == np.uint8: if func8 is None: raise TypeError("Not implemented for uint8 image.") From c00fedde6cff84949cd7a5826af46980a45ebf99 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 09:43:15 +0100 Subject: [PATCH 321/625] Fix, improve and extend histogram test cases --- skimage/filter/rank/tests/test_histo.py | 135 ++++++++++++++++-------- 1 file changed, 89 insertions(+), 46 deletions(-) diff --git a/skimage/filter/rank/tests/test_histo.py b/skimage/filter/rank/tests/test_histo.py index b6a7bbca..a943176d 100644 --- a/skimage/filter/rank/tests/test_histo.py +++ b/skimage/filter/rank/tests/test_histo.py @@ -1,57 +1,100 @@ -import sys -print sys.path -import skimage -print skimage - -import unittest - import numpy as np -from skimage.filter import rank +from numpy.testing import run_module_suite, assert_array_equal -from skimage import data -from skimage.morphology import cmorph,disk from skimage.filter.rank import _crank8, _crank16 -from skimage.filter.rank import _crank16_percentiles -class TestSequenceFunctions(unittest.TestCase): +def test_trivial_selem8(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel - def setUp(self): - pass + image = np.zeros((5, 5), dtype=np.uint8) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 - def test_trivial_selem(self): - # check that min, max and mean returns identity if structuring element contains only central pixel - - a = np.zeros((5,5),dtype='uint8') - a[2,2] = 255 - a[2,3] = 128 - a[1,2] = 16 - elem = np.asarray([[0,0,0],[0,1,0],[0,0,0]],dtype='uint8') - f = _crank8.mean(image=a,selem = elem,shift_x=0,shift_y=0) - np.testing.assert_array_equal(a,f) - f = _crank8.minimum(image=a,selem = elem,shift_x=0,shift_y=0) - np.testing.assert_array_equal(a,f) - f = _crank8.maximum(image=a,selem = elem,shift_x=0,shift_y=0) - np.testing.assert_array_equal(a,f) - - def test_smallest_selem(self): - # check that min, max and mean returns identity if structuring element contains only central pixel - - a = np.zeros((5,5),dtype='uint8') - a[2,2] = 255 - a[2,3] = 128 - a[1,2] = 16 - elem = np.asarray([[1]],dtype='uint8') - f = _crank8.mean(image=a,selem = elem,shift_x=0,shift_y=0) - np.testing.assert_array_equal(a,f) - f = _crank8.minimum(image=a,selem = elem,shift_x=0,shift_y=0) - np.testing.assert_array_equal(a,f) - f = _crank8.maximum(image=a,selem = elem,shift_x=0,shift_y=0) - np.testing.assert_array_equal(a,f) + elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) + _crank8.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) +def test_trivial_selem16(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel -if __name__ == '__main__': + image = np.zeros((5, 5), dtype=np.uint16) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 - suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) - unittest.TextTestRunner(verbosity=2).run(suite) + elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) + _crank16.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + +def test_smallest_selem8(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel + + image = np.zeros((5, 5), dtype=np.uint8) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[1]], dtype=np.uint8) + _crank8.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + +def test_smallest_selem16(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel + + image = np.zeros((5, 5), dtype=np.uint16) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[1]], dtype=np.uint8) + _crank16.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + +if __name__ == "__main__": + run_module_suite() From 8c55640d6092a018f1c4628090fdbdef45da3b29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 11:20:56 +0100 Subject: [PATCH 322/625] Do not ignore exceptions in core functions --- skimage/filter/rank/_core16.pxd | 2 +- skimage/filter/rank/_core16.pyx | 4 ++-- skimage/filter/rank/_core8.pxd | 2 +- skimage/filter/rank/_core8.pyx | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index 0ff5d518..f6f85202 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -14,4 +14,4 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, char shift_x, char shift_y, Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except * diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 30d0a5f2..79fefe2b 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -37,7 +37,7 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, char shift_x, char shift_y, Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ @@ -60,7 +60,7 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, maxbin_list = [0, 0, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] midbin_list = [0, 0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] - #set maxbin and midbin + # set maxbin and midbin cdef Py_ssize_t maxbin = maxbin_list[bitdepth] cdef Py_ssize_t midbin = midbin_list[bitdepth] diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 6a6bdafa..08391eaf 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -19,4 +19,4 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1) + Py_ssize_t s0, Py_ssize_t s1) except * diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index fc1b64b6..3dabe9ec 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -48,7 +48,7 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ From c9f95db67f425bf249269896a3c4ee92a313218b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 11:21:55 +0100 Subject: [PATCH 323/625] Fix, improve and extend test cases --- skimage/filter/rank/tests/test_suite.py | 353 ++++++++++++------------ 1 file changed, 182 insertions(+), 171 deletions(-) diff --git a/skimage/filter/rank/tests/test_suite.py b/skimage/filter/rank/tests/test_suite.py index 66b6537c..dd7c7703 100644 --- a/skimage/filter/rank/tests/test_suite.py +++ b/skimage/filter/rank/tests/test_suite.py @@ -1,191 +1,202 @@ -import sys -print sys.path -import skimage -print skimage - +import numpy as np +from numpy.testing import run_module_suite, assert_array_equal, assert_raises import unittest -import numpy as np -from skimage.filter import rank - from skimage import data -from skimage.morphology import cmorph,disk -from skimage.filter.rank import _crank8, _crank16 -from skimage.filter.rank import _crank16_percentiles +from skimage.morphology import cmorph, disk +from skimage.filter import rank +from skimage.filter.rank import _crank8, _crank16, _crank16_percentiles -class TestSequenceFunctions(unittest.TestCase): +def test_random_sizes(): + # make sure the size is not a problem - def setUp(self): - pass + niter = 10 + elem = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8) + for m, n in np.random.random_integers(1, 100, size=(10, 2)): + mask = np.ones((m, n), dtype=np.uint8) - def test_random_sizes(self): - # make sure the size is not a problem + image8 = np.ones((m, n), dtype=np.uint8) + out8 = np.empty_like(image8) + _crank8.mean(image=image8, selem=elem, mask=mask, out=out8, + shift_x=0, shift_y=0) + assert_array_equal(image8.shape, out8.shape) + _crank8.mean(image=image8, selem=elem, mask=mask, out=out8, + shift_x=+1, shift_y=+1) + assert_array_equal(image8.shape, out8.shape) - niter = 10 - elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8') - for m,n in np.random.random_integers(1,100,size=(10,2)): - a8 = np.ones((m,n),dtype='uint8') - r = _crank8.mean(image=a8,selem = elem,shift_x=0,shift_y=0) - self.assertTrue(a8.shape == r.shape) - r = _crank8.mean(image=a8,selem = elem,shift_x=+1,shift_y=+1) - self.assertTrue(a8.shape == r.shape) + image16 = np.ones((m, n), dtype=np.uint16) + out16 = np.empty_like(image8, dtype=np.uint16) + _crank16.mean(image=image16, selem=elem, mask=mask, out=out16, + shift_x=0, shift_y=0) + assert_array_equal(image16.shape, out16.shape) + _crank16.mean(image=image16, selem=elem, mask=mask, out=out16, + shift_x=+1, shift_y=+1) + assert_array_equal(image16.shape, out16.shape) - for m,n in np.random.random_integers(1,100,size=(10,2)): - a16 = np.ones((m,n),dtype='uint16') - r = _crank16.mean(image=a16,selem = elem,shift_x=0,shift_y=0) - self.assertTrue(a16.shape == r.shape) - r = _crank16.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1) - self.assertTrue(a16.shape == r.shape) - - for m,n in np.random.random_integers(1,100,size=(10,2)): - a16 = np.ones((m,n),dtype='uint16') - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9) - self.assertTrue(a16.shape == r.shape) - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9) - self.assertTrue(a16.shape == r.shape) - - def test_compare_with_cmorph_dilate(self): - #compare the result of maximum filter with dilate - - a = (np.random.random((500,500))*256).astype('uint8') - - for r in range(1,20,1): - elem = np.ones((r,r),dtype='uint8') - # elem = (np.random.random((r,r))>.5).astype('uint8') - rc = _crank8.maximum(image=a,selem = elem) - cm = cmorph.dilate(image=a,selem = elem) - self.assertTrue((rc==cm).all()) - - def test_compare_with_cmorph_erode(self): - #compare the result of maximum filter with erode - - a = (np.random.random((500,500))*256).astype('uint8') - - for r in range(1,20,1): - elem = np.ones((r,r),dtype='uint8') - # elem = (np.random.random((r,r))>.5).astype('uint8') - rc = _crank8.minimum(image=a,selem = elem) - cm = cmorph.erode(image=a,selem = elem) - self.assertTrue((rc==cm).all()) - - def test_bitdepth(self): - # test the different bit depth for rank16 - - elem = np.ones((3,3),dtype='uint8') - a16 = np.ones((100,100),dtype='uint16')*255 - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8) - a16 = np.ones((100,100),dtype='uint16')*255*2 - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=9) - a16 = np.ones((100,100),dtype='uint16')*255*4 - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=10) - a16 = np.ones((100,100),dtype='uint16')*255*8 - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=11) - a16 = np.ones((100,100),dtype='uint16')*255*16 - r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12) - - def test_population(self): - # check the number of valid pixels in the neighborhood - - a = np.zeros((5,5),dtype='uint8') - elem = np.ones((3,3),dtype='uint8') - p = _crank8.pop(image=a,selem = elem) - r = np.asarray([[4, 6, 6, 6, 4], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [4, 6, 6, 6, 4]]) - np.testing.assert_array_equal(r,p) - - def test_structuring_element(self): - # check the output for a custom structuring element - - a = np.zeros((6,6),dtype='uint8') - a[2,2] = 255 - elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8') - f = _crank8.maximum(image=a,selem = elem,shift_x=1,shift_y=1) - r = np.asarray([[ 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0, 0], - [ 0, 0, 255, 255, 255, 0], - [ 0, 0, 0, 255, 255, 0], - [ 0, 0, 0, 0, 0, 0]]) - np.testing.assert_array_equal(r,f) + _crank16_percentiles.mean(image=image16, mask=mask, out=out16, + selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9) + assert_array_equal(image16.shape, out16.shape) + _crank16_percentiles.mean(image=image16, mask=mask, out=out16, + selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9) + assert_array_equal(image16.shape, out16.shape) - @unittest.expectedFailure - def test_fail_on_bitdepth(self): - # should fail because data bitdepth is too high for the function +def test_compare_with_cmorph_dilate(): + # compare the result of maximum filter with dilate - a16 = np.ones((100,100),dtype='uint16')*255 - elem = np.ones((3,3),dtype='uint8') - f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4) + image = (np.random.random((100, 100)) * 256).astype(np.uint8) + out = np.empty_like(image) + mask = np.ones(image.shape, dtype=np.uint8) - def test_output(self): - #check rank function with external OUT output array - - selem = disk(20) - a = (np.random.random((500,500))*256).astype('uint8') - out = np.zeros_like(a) - f1 = rank.mean(a,selem,out=out) - f2 = rank.mean(a,selem) - np.testing.assert_array_equal(f1,f2) - np.testing.assert_array_equal(out,f2) - - @unittest.expectedFailure - def test_inplace_output(self): - #rank filters are not supposed to filter inplace - - selem = disk(20) - a = (np.random.random((500,500))*256).astype('uint8') - out = a - f = rank.mean(a,selem,out=out) - np.testing.assert_array_equal(f,out) + for r in range(1, 20, 1): + elem = np.ones((r, r), dtype=np.uint8) + _crank8.maximum(image=image, selem=elem, out=out, mask=mask) + cm = cmorph.dilate(image=image, selem=elem) + assert_array_equal(out, cm) - def test_compare_autolevels(self): - # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0 - # should returns the same arrays +def test_compare_with_cmorph_erode(): + # compare the result of maximum filter with erode - image = data.camera() + image = (np.random.random((100, 100)) * 256).astype(np.uint8) + out = np.empty_like(image) + mask = np.ones(image.shape, dtype=np.uint8) - selem = disk(20) - loc_autolevel = rank.autolevel(image,selem=selem) - loc_perc_autolevel = rank.percentile_autolevel(image,selem=selem,p0=.0,p1=1.) - - assert (loc_autolevel==loc_perc_autolevel).all() - - def test_compare_autolevels_16bit(self): - # compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and p1=1.0 - # should returns the same arrays - - image = data.camera().astype(np.uint16)*4 - - selem = disk(20) - loc_autolevel = rank.autolevel(image,selem=selem) - loc_perc_autolevel = rank.percentile_autolevel(image,selem=selem,p0=.0,p1=1.) - - assert (loc_autolevel==loc_perc_autolevel).all() - - def test_compare_8bit_vs_16bit(self): - # filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) - # should be identical - - i8 = data.camera() - i16 = i8.astype(np.uint16) - assert (i8==i16).all() - - methods = ['autolevel','bottomhat','equalize','gradient','maximum','mean' - ,'meansubstraction','median','minimum','modal','morph_contr_enh','pop','threshold', 'tophat'] - - for method in methods: - func = eval('rank.%s'%method) - f8 = func(i8,disk(3)) - f16 = func(i16,disk(3)) - assert (f8==f16).all() + for r in range(1, 20, 1): + elem = np.ones((r, r), dtype=np.uint8) + _crank8.minimum(image=image, selem=elem, out=out, mask=mask) + cm = cmorph.erode(image=image, selem=elem) + assert_array_equal(out, cm) -if __name__ == '__main__': +def test_bitdepth(): + # test the different bit depth for rank16 - suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions) - unittest.TextTestRunner(verbosity=2).run(suite) + elem = np.ones((3, 3), dtype=np.uint8) + out = np.empty((100, 100), dtype=np.uint16) + mask = np.ones((100, 100), dtype=np.uint8) + + for i in range(5): + image = np.ones((100, 100),dtype=np.uint16) * 255 * 2**i + r = _crank16_percentiles.mean(image=image, selem=elem, mask=mask, + out=out, shift_x=0, shift_y=0, p0=.1, p1=.9, bitdepth=8 + i) + + +def test_population(): + # check the number of valid pixels in the neighborhood + + image = np.zeros((5, 5), dtype=np.uint8) + elem = np.ones((3, 3), dtype=np.uint8) + out = np.empty_like(image) + mask = np.ones(image.shape, dtype=np.uint8) + + _crank8.pop(image=image, selem=elem, out=out, mask=mask) + r = np.array([[4, 6, 6, 6, 4], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [6, 9, 9, 9, 6], + [4, 6, 6, 6, 4]]) + assert_array_equal(r, out) + + +def test_structuring_element8(): + # check the output for a custom structuring element + + r = np.array([[ 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0], + [ 0, 0, 255, 0, 0, 0], + [ 0, 0, 255, 255, 255, 0], + [ 0, 0, 0, 255, 255, 0], + [ 0, 0, 0, 0, 0, 0]]) + + # 8bit + image = np.zeros((6, 6), dtype=np.uint8) + image[2, 2] = 255 + elem = np.asarray([[1, 1, 0], [1, 1, 1], [0, 0, 1]], dtype=np.uint8) + out = np.empty_like(image) + mask = np.ones(image.shape, dtype=np.uint8) + + _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=1, shift_y=1) + assert_array_equal(r, out) + + # 16bit + image = np.zeros((6, 6), dtype=np.uint16) + image[2, 2] = 255 + out = np.empty_like(image) + + _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=1, shift_y=1) + assert_array_equal(r, out) + + +def test_fail_on_bitdepth(): + # should fail because data bitdepth is too high for the function + + image = np.ones((100, 100), dtype=np.uint16) * 255 + elem = np.ones((3, 3), dtype=np.uint8) + out = np.empty_like(image) + mask = np.ones(image.shape, dtype=np.uint8) + assert_raises(AssertionError, _crank16_percentiles.mean, image=image, + selem=elem, out=out, mask=mask, shift_x=0, shift_y=0, bitdepth=4) + + +def test_inplace_output(): + # rank filters are not supposed to filter inplace + + selem = disk(20) + image = (np.random.random((500,500))*256).astype(np.uint8) + out = image + assert_raises(NotImplementedError, rank.mean, image, selem, out=out) + + +def test_compare_autolevels(): + # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0 + # should returns the same arrays + + image = data.camera() + + selem = disk(20) + loc_autolevel = rank.autolevel(image, selem=selem) + loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem, + p0=.0, p1=1.) + + assert_array_equal(loc_autolevel, loc_perc_autolevel) + + +def test_compare_autolevels_16bit(): + # compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and + # p1=1.0 should returns the same arrays + + image = data.camera().astype(np.uint16) * 4 + + selem = disk(20) + loc_autolevel = rank.autolevel(image, selem=selem) + loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem, + p0=.0, p1=1.) + + assert_array_equal(loc_autolevel, loc_perc_autolevel) + + +def test_compare_8bit_vs_16bit(): + # filters applied on 8bit image ore 16bit image (having only real 8bit of + # dynamic) should be identical + + image8 = data.camera() + image16 = image8.astype(np.uint16) + assert_array_equal(image8, image16) + + methods = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', + 'mean', 'meansubstraction', 'median', 'minimum', 'modal', + 'morph_contr_enh', 'pop', 'threshold', 'tophat'] + + for method in methods: + func = getattr(rank, method) + f8 = func(image8, disk(3)) + f16 = func(image16, disk(3)) + assert_array_equal(f8, f16) + + +if __name__ == "__main__": + run_module_suite() From f5ad204aca5912fddeefd6c6386aac6b12765678 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 11:23:43 +0100 Subject: [PATCH 324/625] Use single file for test cases and rename file --- skimage/filter/rank/tests/test_histo.py | 100 ------------------ .../tests/{test_suite.py => test_rank.py} | 92 ++++++++++++++++ 2 files changed, 92 insertions(+), 100 deletions(-) delete mode 100644 skimage/filter/rank/tests/test_histo.py rename skimage/filter/rank/tests/{test_suite.py => test_rank.py} (68%) diff --git a/skimage/filter/rank/tests/test_histo.py b/skimage/filter/rank/tests/test_histo.py deleted file mode 100644 index a943176d..00000000 --- a/skimage/filter/rank/tests/test_histo.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -from numpy.testing import run_module_suite, assert_array_equal - -from skimage.filter.rank import _crank8, _crank16 - - -def test_trivial_selem8(): - # check that min, max and mean returns identity if structuring element - # contains only central pixel - - image = np.zeros((5, 5), dtype=np.uint8) - out = np.zeros_like(image) - mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 - - elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) - _crank8.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank8.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank8.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - - -def test_trivial_selem16(): - # check that min, max and mean returns identity if structuring element - # contains only central pixel - - image = np.zeros((5, 5), dtype=np.uint16) - out = np.zeros_like(image) - mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 - - elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) - _crank16.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank16.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank16.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - - -def test_smallest_selem8(): - # check that min, max and mean returns identity if structuring element - # contains only central pixel - - image = np.zeros((5, 5), dtype=np.uint8) - out = np.zeros_like(image) - mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 - - elem = np.array([[1]], dtype=np.uint8) - _crank8.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank8.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank8.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - - -def test_smallest_selem16(): - # check that min, max and mean returns identity if structuring element - # contains only central pixel - - image = np.zeros((5, 5), dtype=np.uint16) - out = np.zeros_like(image) - mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 - - elem = np.array([[1]], dtype=np.uint8) - _crank16.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank16.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - _crank16.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) - assert_array_equal(image, out) - - -if __name__ == "__main__": - run_module_suite() diff --git a/skimage/filter/rank/tests/test_suite.py b/skimage/filter/rank/tests/test_rank.py similarity index 68% rename from skimage/filter/rank/tests/test_suite.py rename to skimage/filter/rank/tests/test_rank.py index dd7c7703..e92f386c 100644 --- a/skimage/filter/rank/tests/test_suite.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -198,5 +198,97 @@ def test_compare_8bit_vs_16bit(): assert_array_equal(f8, f16) +def test_trivial_selem8(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel + + image = np.zeros((5, 5), dtype=np.uint8) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) + _crank8.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + +def test_trivial_selem16(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel + + image = np.zeros((5, 5), dtype=np.uint16) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) + _crank16.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + +def test_smallest_selem8(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel + + image = np.zeros((5, 5), dtype=np.uint8) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[1]], dtype=np.uint8) + _crank8.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + +def test_smallest_selem16(): + # check that min, max and mean returns identity if structuring element + # contains only central pixel + + image = np.zeros((5, 5), dtype=np.uint16) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[1]], dtype=np.uint8) + _crank16.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(image, out) + + if __name__ == "__main__": run_module_suite() From 8fea5d15d1900df7cfb36e8a1b65d07b2fbe65ab Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 10 Nov 2012 13:49:52 +0100 Subject: [PATCH 325/625] Change data type of pop variable to float --- skimage/filter/rank/_core16.pxd | 2 +- skimage/filter/rank/_core16.pyx | 8 ++--- skimage/filter/rank/_core8.pxd | 2 +- skimage/filter/rank/_core8.pyx | 8 ++--- skimage/filter/rank/_crank16.pyx | 30 ++++++++--------- skimage/filter/rank/_crank16_bilateral.pyx | 4 +-- skimage/filter/rank/_crank16_percentiles.pyx | 16 ++++----- skimage/filter/rank/_crank8.pyx | 34 ++++++++++---------- skimage/filter/rank/_crank8_percentiles.pyx | 16 ++++----- 9 files changed, 60 insertions(+), 60 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index f6f85202..92c6f7d6 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -6,7 +6,7 @@ cdef int int_min(int a, int b) # 16 bit core kernel receives extra information about data bitdepth -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 79fefe2b..d3715f47 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -17,19 +17,19 @@ cdef inline int int_min(int a, int b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, Py_ssize_t * pop, +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] += 1 pop[0] += 1 -cdef inline void histogram_decrement(Py_ssize_t * histo, Py_ssize_t * pop, +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] -= 1 pop[0] -= 1 -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, @@ -74,7 +74,7 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, Py_ssize_t, np.uint16_t, # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) - cdef Py_ssize_t pop + cdef float pop # allocate memory with malloc cdef Py_ssize_t max_se = srows * scols diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 08391eaf..503d2df0 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -12,7 +12,7 @@ cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, # 8 bit core kernel receives extra information about data inferior and superior # percentiles -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 3dabe9ec..cfb011ac 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -16,13 +16,13 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, Py_ssize_t * pop, +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] += 1 pop[0] += 1 -cdef inline void histogram_decrement(Py_ssize_t * histo, Py_ssize_t * pop, +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] -= 1 pop[0] -= 1 @@ -41,7 +41,7 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, return 0 -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, @@ -77,7 +77,7 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, Py_ssize_t, np.uint8_t, float, cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) - cdef Py_ssize_t pop + cdef float pop # allocate memory with malloc cdef Py_ssize_t max_se = srows * scols diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 9c4b8fe1..ce300cdd 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -14,7 +14,7 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -37,7 +37,7 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, return (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -51,7 +51,7 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, Py_ssize_t pop, return (g - i) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -70,7 +70,7 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -91,7 +91,7 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -106,7 +106,7 @@ cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -123,7 +123,7 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, - Py_ssize_t pop, + float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, @@ -141,7 +141,7 @@ cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, return (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -159,7 +159,7 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -174,7 +174,7 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -192,7 +192,7 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, - Py_ssize_t pop, + float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, @@ -218,7 +218,7 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -226,7 +226,7 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, return (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -242,7 +242,7 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -256,7 +256,7 @@ cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, Py_ssize_t pop, return (i - g) -cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 98bc5683..1fb81a2d 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -13,7 +13,7 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -35,7 +35,7 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index e5dc2ab7..8b45d46a 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -13,7 +13,7 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -46,7 +46,7 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -74,7 +74,7 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -101,7 +101,7 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, - Py_ssize_t pop, + float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, @@ -129,7 +129,7 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, - Py_ssize_t pop, + float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, @@ -165,7 +165,7 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, return (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -185,7 +185,7 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, @@ -205,7 +205,7 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 6fee8a9f..7398b26f 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -14,7 +14,7 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -38,7 +38,7 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -51,7 +51,7 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, Py_ssize_t pop, return (g - i) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -69,7 +69,7 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -89,7 +89,7 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -103,7 +103,7 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -118,7 +118,7 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -133,7 +133,7 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, Py_ssize_t po return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -150,7 +150,7 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -164,7 +164,7 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -180,7 +180,7 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -203,14 +203,14 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, Py_ssize_t pop return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -225,7 +225,7 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -238,7 +238,7 @@ cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, Py_ssize_t pop, return (i - g) -cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -262,7 +262,7 @@ cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, Py_ssize_t pop, return min_i -cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -278,7 +278,7 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, Py_ssize_t pop, return e*10 -cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, Py_ssize_t pop, np.uint8_t g, +cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 4a11bdc4..d5072c81 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -13,7 +13,7 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -45,7 +45,7 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, Py_ssize_t pop, return (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -70,7 +70,7 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -93,7 +93,7 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, Py_ssize_t pop, cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, - Py_ssize_t pop, + float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): @@ -117,7 +117,7 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, - Py_ssize_t pop, + float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -148,7 +148,7 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, return (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i @@ -165,7 +165,7 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n @@ -182,7 +182,7 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, Py_ssize_t pop, return (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, Py_ssize_t pop, +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef int i From 08b748c379bf468dad76bb1f56a802fe437a104b Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 11 Nov 2012 14:39:09 +0100 Subject: [PATCH 326/625] local Otsu returns now the threshold values --- .../applications/plot_rank_filters.py | 50 +++++++++++++++++++ doc/examples/plot_local_otsu.py | 49 ++++++++++++++++++ skimage/filter/rank/_crank8.pyx | 6 +-- skimage/filter/rank/demo/demo_single.py | 29 +++++++---- skimage/filter/rank/rank.pyx | 5 +- 5 files changed, 121 insertions(+), 18 deletions(-) create mode 100644 doc/examples/plot_local_otsu.py diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index d52595f2..0efaf17b 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -308,6 +308,56 @@ plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) plt.subplot(2,2,4) plt.imshow(penh[200:350,350:450],cmap=plt.cm.gray) +""" +.. image:: PLOT2RST.current_figure + +Image threshold +=============== +The Otsu's threshold [1]_ method can be applied locally using local greylevel distribution. +In the example below, for each pixel, an "optimal" threshold is determined by maximizing +the variance between two classes of pixels of the local neighborhood defined by a structuring element. + +The example compares the local threshold with the global threshold `skimage.filter.threshold_otsu``. + +.. note: local threshold is much slower than global one. + +.. [1] http://en.wikipedia.org/wiki/Otsu's_method + +""" +from skimage.filter.rank import otsu +from skimage.filter import threshold_otsu + +p8 = data.page() + +radius = 10 +selem = disk(radius) + +# t_loc_otsu is an image +t_loc_otsu = otsu(p8,selem) +loc_otsu = p8>=t_loc_otsu + +# t_glob_otsu is a scalar +t_glob_otsu = threshold_otsu(p8) +glob_otsu = p8>=t_glob_otsu + +plt.figure() +plt.subplot(2,2,1) +plt.imshow(p8,cmap=plt.cm.gray) +plt.xlabel('original') +plt.colorbar() +plt.subplot(2,2,2) +plt.imshow(loc_otsu,cmap=plt.cm.gray) +plt.xlabel('local Otsu ($radius=%d$)'%radius) +plt.colorbar() +plt.subplot(2,2,3) +plt.imshow(p8>=loc_otsu,cmap=plt.cm.gray) +plt.xlabel('original>=local Otsu'%t_glob_otsu) +plt.subplot(2,2,4) +plt.imshow(glob_otsu,cmap=plt.cm.gray) +plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) +plt.show() + + """ .. image:: PLOT2RST.current_figure diff --git a/doc/examples/plot_local_otsu.py b/doc/examples/plot_local_otsu.py new file mode 100644 index 00000000..ae1ead48 --- /dev/null +++ b/doc/examples/plot_local_otsu.py @@ -0,0 +1,49 @@ +""" +===================== +Local Otsu Threshold +===================== +This example shows how Otsu's threshold [1]_ method can be applied locally. +For each pixel, an "optimal" threshold is determined by maximizing the variance between two classes of pixels +of the local neighborhood defined by a structuring element. + +The example compares the local threshold with the global threshold. + +.. note: local threshold is much slower than global one. + +.. [1] http://en.wikipedia.org/wiki/Otsu's_method + +""" +import matplotlib.pyplot as plt + +from skimage import data +from skimage.morphology.selem import disk +import skimage.filter.rank as rank +from skimage.filter import threshold_otsu + + +p8 = data.page() + +radius = 10 +selem = disk(radius) + +loc_otsu = rank.otsu(p8,selem) +t_glob_otsu = threshold_otsu(p8) +glob_otsu = p8>=t_glob_otsu + + +plt.figure() +plt.subplot(2,2,1) +plt.imshow(p8,cmap=plt.cm.gray) +plt.xlabel('original') +plt.colorbar() +plt.subplot(2,2,2) +plt.imshow(loc_otsu,cmap=plt.cm.gray) +plt.xlabel('local Otsu ($radius=%d$)'%radius) +plt.colorbar() +plt.subplot(2,2,3) +plt.imshow(p8>=loc_otsu,cmap=plt.cm.gray) +plt.xlabel('original>=local Otsu'%t_glob_otsu) +plt.subplot(2,2,4) +plt.imshow(glob_otsu,cmap=plt.cm.gray) +plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) +plt.show() diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 7398b26f..53076217 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -312,11 +312,7 @@ cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, max_i = i q1 = new_q1 - if g > max_i: - return 255 - else: - return 0 - + return max_i # ----------------------------------------------------------------- diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py index 7e4ef405..053f7328 100644 --- a/skimage/filter/rank/demo/demo_single.py +++ b/skimage/filter/rank/demo/demo_single.py @@ -4,27 +4,34 @@ import matplotlib.pyplot as plt from skimage import data from skimage.morphology.selem import disk import skimage.filter.rank as rank - -from skimage.filter import denoise_bilateral +from skimage.filter import threshold_otsu if __name__ == '__main__': - a8 = data.camera() - a16 = data.camera().astype(np.uint16)*4 - p8 = data.page() - selem = disk(20) + radius = 10 + selem = disk(radius) - otsu = rank.otsu(p8,selem) + loc_otsu = rank.otsu(p8,selem) + t_glob_otsu = threshold_otsu(p8) + glob_otsu = p8>=t_glob_otsu plt.figure() - plt.subplot(1,2,1) - plt.imshow(p8) + plt.subplot(2,2,1) + plt.imshow(p8,cmap=plt.cm.gray) + plt.xlabel('original') plt.colorbar() - plt.subplot(1,2,2) - plt.imshow(otsu) + plt.subplot(2,2,2) + plt.imshow(loc_otsu,cmap=plt.cm.gray) + plt.xlabel('local Otsu ($radius=%d$)'%radius) plt.colorbar() + plt.subplot(2,2,3) + plt.imshow(p8>=loc_otsu,cmap=plt.cm.gray) + plt.xlabel('original>=local Otsu'%t_glob_otsu) + plt.subplot(2,2,4) + plt.imshow(glob_otsu,cmap=plt.cm.gray) + plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) plt.show() diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 74add3d7..763a702f 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -715,7 +715,7 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): mask=mask, shift_x=shift_x, shift_y=shift_y) def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): - """Returns the image threshold using a the Otsu [otsu]_ locally . + """Returns the Otsu's threshold value for each pixel. Parameters ---------- @@ -738,7 +738,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- out : uint8 array or uint16 array (same as input image) - threshold image + Otsu's threshold values References ---------- @@ -753,6 +753,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> # defining a 8- and a 16-bit test images >>> a8 = data.camera() >>> loc_otsu = otsu(a8,disk(5)) + >>> thresh_image = a8 >= loc_otsu """ From 47b0a72b3299fb0ad1ce9f965f18adaf18b0092c Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 09:02:40 +0100 Subject: [PATCH 327/625] fix: plt.show() removed in doc --- doc/examples/applications/plot_rank_filters.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 0efaf17b..3fdd5a94 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -355,7 +355,6 @@ plt.xlabel('original>=local Otsu'%t_glob_otsu) plt.subplot(2,2,4) plt.imshow(glob_otsu,cmap=plt.cm.gray) plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) -plt.show() """ @@ -450,7 +449,6 @@ plt.subplot(2,2,4) plt.imshow(ent16, cmap=plt.cm.jet) plt.xlabel('entropy*1000') plt.colorbar() -plt.show() """ .. image:: PLOT2RST.current_figure From a09ec78977956ab91a283913b728e2efdf45e4c9 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 09:34:33 +0100 Subject: [PATCH 328/625] remove local demo from git --- skimage/filter/rank/.gitignore | 1 + skimage/filter/rank/demo/demo_all.py | 77 ------------------ skimage/filter/rank/demo/demo_single.py | 39 --------- skimage/filter/rank/demo/iko_pan_Ja1.tif | Bin 129176 -> 0 bytes .../filter/rank/demo/test_morph_contr_enh.py | 28 ------- skimage/filter/rank/demo/test_rank.py | 42 ---------- 6 files changed, 1 insertion(+), 186 deletions(-) create mode 100644 skimage/filter/rank/.gitignore delete mode 100644 skimage/filter/rank/demo/demo_all.py delete mode 100644 skimage/filter/rank/demo/demo_single.py delete mode 100644 skimage/filter/rank/demo/iko_pan_Ja1.tif delete mode 100644 skimage/filter/rank/demo/test_morph_contr_enh.py delete mode 100644 skimage/filter/rank/demo/test_rank.py diff --git a/skimage/filter/rank/.gitignore b/skimage/filter/rank/.gitignore new file mode 100644 index 00000000..08a24d5c --- /dev/null +++ b/skimage/filter/rank/.gitignore @@ -0,0 +1 @@ +demo/ \ No newline at end of file diff --git a/skimage/filter/rank/demo/demo_all.py b/skimage/filter/rank/demo/demo_all.py deleted file mode 100644 index 038c749b..00000000 --- a/skimage/filter/rank/demo/demo_all.py +++ /dev/null @@ -1,77 +0,0 @@ -import matplotlib.pyplot as plt -from pprint import pprint - -from skimage import data -from skimage.morphology.selem import disk -import skimage.filter.rank as rank - -def plot_all(): - a8 = data.camera() - a16 = a8.astype('uint16')*16 - selem = disk(5) - - name_list = sorted([n for n in dir(rank) if n[0] is not '_']) - print name_list - - for n in name_list: - if n.rfind('bilateral')==0: - print n - method = eval('rank.%s'%n) - if type(method) == type(rank.maximum): - print method - f8 = method(a8,selem = selem,s0=10,s1=10) - f16 = method(a16,selem = selem,s0=10,s1=10) - plt.figure() - plt.subplot(2,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(2,2,2) - plt.imshow(f8) - plt.colorbar() - plt.subplot(2,2,3) - plt.imshow(f16) - plt.colorbar() - plt.title(method) - for n in name_list: - if n.rfind('percentile')==0: - print n - method = eval('rank.%s'%n) - if type(method) == type(rank.maximum): - print method - f8 = method(a8,selem = selem,p0=.1,p1=.9) - f16 = method(a16,selem = selem,p0=.1,p1=.9) - plt.figure() - plt.subplot(2,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(2,2,2) - plt.imshow(f8) - plt.colorbar() - plt.subplot(2,2,3) - plt.imshow(f16) - plt.colorbar() - plt.title(method) - for n in name_list: - if n.find('percentile')==-1 and n.find('bilateral')==-1: - print n - method = eval('rank.%s'%n) - if type(method) == type(rank.maximum): - print method - f8 = method(a8,selem = selem) - f16 = method(a16,selem = selem) - plt.figure() - plt.subplot(2,2,1) - plt.imshow(a8) - plt.colorbar() - plt.subplot(2,2,2) - plt.imshow(f8) - plt.colorbar() - plt.subplot(2,2,3) - plt.imshow(f16) - plt.colorbar() - plt.title(method) - plt.show() - -if __name__ == '__main__': - plot_all() - pprint(dir(rank)) \ No newline at end of file diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py deleted file mode 100644 index 053f7328..00000000 --- a/skimage/filter/rank/demo/demo_single.py +++ /dev/null @@ -1,39 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data -from skimage.morphology.selem import disk -import skimage.filter.rank as rank -from skimage.filter import threshold_otsu - -if __name__ == '__main__': - p8 = data.page() - - radius = 10 - selem = disk(radius) - - loc_otsu = rank.otsu(p8,selem) - t_glob_otsu = threshold_otsu(p8) - glob_otsu = p8>=t_glob_otsu - - - plt.figure() - plt.subplot(2,2,1) - plt.imshow(p8,cmap=plt.cm.gray) - plt.xlabel('original') - plt.colorbar() - plt.subplot(2,2,2) - plt.imshow(loc_otsu,cmap=plt.cm.gray) - plt.xlabel('local Otsu ($radius=%d$)'%radius) - plt.colorbar() - plt.subplot(2,2,3) - plt.imshow(p8>=loc_otsu,cmap=plt.cm.gray) - plt.xlabel('original>=local Otsu'%t_glob_otsu) - plt.subplot(2,2,4) - plt.imshow(glob_otsu,cmap=plt.cm.gray) - plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) - plt.show() - - - - diff --git a/skimage/filter/rank/demo/iko_pan_Ja1.tif b/skimage/filter/rank/demo/iko_pan_Ja1.tif deleted file mode 100644 index 47201695755a73086ba6d598f3441063ecdf1068..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 129176 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L&V3s<8XWu!riJvI diff --git a/skimage/filter/rank/demo/test_morph_contr_enh.py b/skimage/filter/rank/demo/test_morph_contr_enh.py deleted file mode 100644 index f2f0f7c9..00000000 --- a/skimage/filter/rank/demo/test_morph_contr_enh.py +++ /dev/null @@ -1,28 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import gdal - -from skimage.morphology import disk -import skimage.filter.rank as rank - -filename = 'iko_pan_Ja1.tif' -im16 = gdal.Open(filename).ReadAsArray().astype(np.uint16) - -plt.figure() -plt.imshow(im16,cmap=plt.cm.gray) -plt.colorbar() - -f0 = rank.median(im16,disk(1)) -f1 = rank.bilateral_mean(im16,disk(20),s0=200,s1=200) -f2 = rank.equalize(f1,disk(10)) -f3 = rank.bottomhat(f1,disk(1)) - -plt.figure() -plt.imshow(f2,cmap=plt.cm.gray,interpolation='nearest') -plt.colorbar() - -plt.show() - - - - diff --git a/skimage/filter/rank/demo/test_rank.py b/skimage/filter/rank/demo/test_rank.py deleted file mode 100644 index 09cfdcd5..00000000 --- a/skimage/filter/rank/demo/test_rank.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -from skimage import data -from skimage.morphology.selem import disk -import skimage.filter.rank as rank - -print dir(rank) - -print rank.mean -print rank.percentile_mean -print rank.bilateral_mean - -a8 = data.camera() -a16 = a8.astype('uint16')*16 -selem = disk(10) - -f8 = rank.mean(a8,selem) -f16 = rank.mean(a16,selem) - -plt.figure() -plt.imshow(np.hstack((a8,f8))) -plt.colorbar() -plt.figure() -plt.imshow(np.hstack((a16,f16))) -plt.colorbar() - -f8 = rank.percentile_mean(a8,selem,p0=.1,p1=.9) -f16 = rank.percentile_mean(a16,selem,p0=.1,p1=.9) - -plt.figure() -plt.imshow(np.hstack((a8,f8))) -plt.colorbar() -plt.figure() -plt.imshow(np.hstack((a16,f16))) -plt.colorbar() - -plt.show() - - - - From fd82ac9959b184a64d183917ec74f25b1ec62edb Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 09:54:10 +0100 Subject: [PATCH 329/625] add new tests --- skimage/filter/rank/tests/test_rank.py | 66 ++++++++++++++------------ 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index e92f386c..10aeb5ac 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -1,11 +1,9 @@ import numpy as np from numpy.testing import run_module_suite, assert_array_equal, assert_raises -import unittest from skimage import data from skimage.morphology import cmorph, disk from skimage.filter import rank -from skimage.filter.rank import _crank8, _crank16, _crank16_percentiles def test_random_sizes(): @@ -18,26 +16,26 @@ def test_random_sizes(): image8 = np.ones((m, n), dtype=np.uint8) out8 = np.empty_like(image8) - _crank8.mean(image=image8, selem=elem, mask=mask, out=out8, + rank.mean(image=image8, selem=elem, mask=mask, out=out8, shift_x=0, shift_y=0) assert_array_equal(image8.shape, out8.shape) - _crank8.mean(image=image8, selem=elem, mask=mask, out=out8, + rank.mean(image=image8, selem=elem, mask=mask, out=out8, shift_x=+1, shift_y=+1) assert_array_equal(image8.shape, out8.shape) image16 = np.ones((m, n), dtype=np.uint16) out16 = np.empty_like(image8, dtype=np.uint16) - _crank16.mean(image=image16, selem=elem, mask=mask, out=out16, + rank.mean(image=image16, selem=elem, mask=mask, out=out16, shift_x=0, shift_y=0) assert_array_equal(image16.shape, out16.shape) - _crank16.mean(image=image16, selem=elem, mask=mask, out=out16, + rank.mean(image=image16, selem=elem, mask=mask, out=out16, shift_x=+1, shift_y=+1) assert_array_equal(image16.shape, out16.shape) - _crank16_percentiles.mean(image=image16, mask=mask, out=out16, + rank.percentile_mean(image=image16, mask=mask, out=out16, selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9) assert_array_equal(image16.shape, out16.shape) - _crank16_percentiles.mean(image=image16, mask=mask, out=out16, + rank.percentile_mean(image=image16, mask=mask, out=out16, selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9) assert_array_equal(image16.shape, out16.shape) @@ -51,7 +49,7 @@ def test_compare_with_cmorph_dilate(): for r in range(1, 20, 1): elem = np.ones((r, r), dtype=np.uint8) - _crank8.maximum(image=image, selem=elem, out=out, mask=mask) + rank.maximum(image=image, selem=elem, out=out, mask=mask) cm = cmorph.dilate(image=image, selem=elem) assert_array_equal(out, cm) @@ -65,7 +63,7 @@ def test_compare_with_cmorph_erode(): for r in range(1, 20, 1): elem = np.ones((r, r), dtype=np.uint8) - _crank8.minimum(image=image, selem=elem, out=out, mask=mask) + rank.minimum(image=image, selem=elem, out=out, mask=mask) cm = cmorph.erode(image=image, selem=elem) assert_array_equal(out, cm) @@ -79,8 +77,8 @@ def test_bitdepth(): for i in range(5): image = np.ones((100, 100),dtype=np.uint16) * 255 * 2**i - r = _crank16_percentiles.mean(image=image, selem=elem, mask=mask, - out=out, shift_x=0, shift_y=0, p0=.1, p1=.9, bitdepth=8 + i) + r = rank.percentile_mean(image=image, selem=elem, mask=mask, + out=out, shift_x=0, shift_y=0, p0=.1, p1=.9) def test_population(): @@ -91,7 +89,7 @@ def test_population(): out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) - _crank8.pop(image=image, selem=elem, out=out, mask=mask) + rank.pop(image=image, selem=elem, out=out, mask=mask) r = np.array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], @@ -117,7 +115,7 @@ def test_structuring_element8(): out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) - _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=1, shift_y=1) assert_array_equal(r, out) @@ -126,7 +124,7 @@ def test_structuring_element8(): image[2, 2] = 255 out = np.empty_like(image) - _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=1, shift_y=1) assert_array_equal(r, out) @@ -134,12 +132,20 @@ def test_structuring_element8(): def test_fail_on_bitdepth(): # should fail because data bitdepth is too high for the function - image = np.ones((100, 100), dtype=np.uint16) * 255 + image = np.ones((100, 100), dtype=np.uint16) * 2**12 + elem = np.ones((3, 3), dtype=np.uint8) + out = np.empty_like(image) + mask = np.ones(image.shape, dtype=np.uint8) + assert_raises(ValueError, rank.percentile_mean, image=image, + selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) + +def test_pass_on_bitdepth(): + # should pass because data bitdepth is not too high for the function + + image = np.ones((100, 100), dtype=np.uint16) * 2**11 elem = np.ones((3, 3), dtype=np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) - assert_raises(AssertionError, _crank16_percentiles.mean, image=image, - selem=elem, out=out, mask=mask, shift_x=0, shift_y=0, bitdepth=4) def test_inplace_output(): @@ -210,13 +216,13 @@ def test_trivial_selem8(): image[1,2] = 16 elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) - _crank8.mean(image=image, selem=elem, out=out, mask=mask, + rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank8.minimum(image=image, selem=elem, out=out, mask=mask, + rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) @@ -233,13 +239,13 @@ def test_trivial_selem16(): image[1,2] = 16 elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) - _crank16.mean(image=image, selem=elem, out=out, mask=mask, + rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank16.minimum(image=image, selem=elem, out=out, mask=mask, + rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) @@ -256,13 +262,13 @@ def test_smallest_selem8(): image[1,2] = 16 elem = np.array([[1]], dtype=np.uint8) - _crank8.mean(image=image, selem=elem, out=out, mask=mask, + rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank8.minimum(image=image, selem=elem, out=out, mask=mask, + rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank8.maximum(image=image, selem=elem, out=out, mask=mask, + rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) @@ -279,13 +285,13 @@ def test_smallest_selem16(): image[1,2] = 16 elem = np.array([[1]], dtype=np.uint8) - _crank16.mean(image=image, selem=elem, out=out, mask=mask, + rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank16.minimum(image=image, selem=elem, out=out, mask=mask, + rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) - _crank16.maximum(image=image, selem=elem, out=out, mask=mask, + rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_array_equal(image, out) From c9f1b67c7b029b65fa9a6c53b91717efd61b3eb8 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 10:05:52 +0100 Subject: [PATCH 330/625] fix: check pop==0 for some kernels --- skimage/filter/rank/_crank16.pyx | 52 +++++++++++++----------- skimage/filter/rank/_crank8.pyx | 55 +++++++++++++++----------- skimage/filter/rank/tests/test_rank.py | 22 +++++++++++ 3 files changed, 82 insertions(+), 47 deletions(-) diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index ce300cdd..67db0c69 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -44,12 +44,14 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - for i in range(maxbin): - if histo[i]: - break - - return (g - i) + if pop: + for i in range(maxbin): + if histo[i]: + break + return (g - i) + else: + return (0) cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, @@ -155,8 +157,8 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, sum -= histo[i] if sum < 0: return (i) - - return (0) + else: + return (0) cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, @@ -170,8 +172,8 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, for i in range(maxbin): if histo[i]: return (i) - - return (0) + else: + return (0) cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, @@ -187,8 +189,8 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, hmax = histo[i] imax = i return (imax) - - return (0) + else: + return (0) cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, @@ -249,12 +251,14 @@ cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - for i in range(maxbin - 1, -1, -1): - if histo[i]: - break - - return (i - g) + if pop: + for i in range(maxbin - 1, -1, -1): + if histo[i]: + break + return (i - g) + else: + return (0) cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, @@ -264,15 +268,17 @@ cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, cdef Py_ssize_t i cdef float e,p - e = 0. + if pop: + e = 0. - for i in range(maxbin): - p = histo[i]/pop - if p>0: - e -= p*log2(p) - - return e*1000 + for i in range(maxbin): + p = histo[i]/pop + if p>0: + e -= p*log2(p) + return e*1000 + else: + return (0) # ----------------------------------------------------------------- # python wrappers diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 53076217..9b69296b 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -44,11 +44,14 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, cdef Py_ssize_t i - for i in range(256): - if histo[i]: - break + if pop: + for i in range(256): + if histo[i]: + break - return (g - i) + return (g - i) + else: + return (0) cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, @@ -99,8 +102,8 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, for i in range(255, -1, -1): if histo[i]: return (i) - - return (0) + else: + return (0) cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, @@ -146,8 +149,8 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, sum -= histo[i] if sum < 0: return (i) - - return (0) + else: + return (0) cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, @@ -160,8 +163,8 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, for i in range(256): if histo[i]: return (i) - - return (0) + else: + return (0) cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, @@ -176,8 +179,8 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, hmax = histo[i] imax = i return (imax) - - return (0) + else: + return (0) cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, @@ -231,12 +234,14 @@ cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, cdef Py_ssize_t i - for i in range(255, -1, -1): - if histo[i]: - break - - return (i - g) + if pop: + for i in range(255, -1, -1): + if histo[i]: + break + return (i - g) + else: + return (0) cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, @@ -268,15 +273,17 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, cdef Py_ssize_t i cdef float e,p - e = 0. + if pop: + e = 0. - for i in range(256): - p = histo[i] / pop - if p > 0: - e -= p * log2(p) - - return e*10 + for i in range(256): + p = histo[i] / pop + if p > 0: + e -= p * log2(p) + return e*10 + else: + return (0) cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index 10aeb5ac..7603fee1 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -295,6 +295,28 @@ def test_smallest_selem16(): shift_x=0, shift_y=0) assert_array_equal(image, out) +def test_empty_selem(): + # check that min, max and mean returns zeros if structuring element is empty + + image = np.zeros((5, 5), dtype=np.uint16) + out = np.zeros_like(image) + mask = np.ones_like(image, dtype=np.uint8) + res = np.zeros_like(image) + image[2,2] = 255 + image[2,3] = 128 + image[1,2] = 16 + + elem = np.array([[0,0,0],[0,0,0]], dtype=np.uint8) + + rank.mean(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(res, out) + rank.minimum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(res, out) + rank.maximum(image=image, selem=elem, out=out, mask=mask, + shift_x=0, shift_y=0) + assert_array_equal(res, out) if __name__ == "__main__": run_module_suite() From add437a9f2357e3bbd4e35957c7376ac75141463 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 10:39:12 +0100 Subject: [PATCH 331/625] add: test entropy 8- & 16-bit --- skimage/filter/rank/tests/test_rank.py | 38 ++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index 7603fee1..c40c91d7 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -318,5 +318,43 @@ def test_empty_selem(): shift_x=0, shift_y=0) assert_array_equal(res, out) +def test_otsu(): + # + pass + +def test_entropy(): + # verify that entropy is coherent with bitdepth of the input data + + selem = np.ones((16,16), dtype=np.uint8) + # 1 bit per pixel + data = np.tile(np.asarray([0,1]),(100,100)).astype(np.uint8) + assert(np.max(rank.entropy(data,selem))==10) + + # 2 bit per pixel + data = np.tile(np.asarray([[0,1],[2,3]]),(10,10)).astype(np.uint8) + assert(np.max(rank.entropy(data,selem))==20) + + # 3 bit per pixel + data = np.tile(np.asarray([[0,1,2,3],[4,5,6,7]]),(10,10)).astype(np.uint8) + assert(np.max(rank.entropy(data,selem))==30) + + # 4 bit per pixel + data = np.tile(np.reshape(np.arange(16),(4,4)),(10,10)).astype(np.uint8) + assert(np.max(rank.entropy(data,selem))==40) + + # 6 bit per pixel + data = np.tile(np.reshape(np.arange(64),(8,8)),(10,10)).astype(np.uint8) + assert(np.max(rank.entropy(data,selem))==60) + + # 8 bit per pixel + data = np.tile(np.reshape(np.arange(256),(16,16)),(10,10)).astype(np.uint8) + assert(np.max(rank.entropy(data,selem))==80) + + # 12 bit per pixel + selem = np.ones((64,64), dtype=np.uint8) + data = np.tile(np.reshape(np.arange(4096),(64,64)),(2,2)).astype(np.uint16) + assert(np.max(rank.entropy(data,selem))==12000) + + if __name__ == "__main__": run_module_suite() From db2e066f4cbefe95cd203818adc62f98db487da1 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 14:40:49 +0100 Subject: [PATCH 332/625] add: test otsu --- skimage/filter/rank/tests/test_rank.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index c40c91d7..73f3a5c3 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -320,7 +320,13 @@ def test_empty_selem(): def test_otsu(): # - pass + test = np.tile([128, 145, 103, 127, 165, 83, 127, 185, 63, 127, 205, 43, 127, 225, 23, 127],(16,1)) + test = test.astype(np.uint8) + res = np.tile([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1],(16,1)) + selem = np.ones((6,6), dtype=np.uint8) + th = 1*(test>=rank.otsu(test,selem)) + assert_array_equal(th,res) + def test_entropy(): # verify that entropy is coherent with bitdepth of the input data @@ -358,3 +364,4 @@ def test_entropy(): if __name__ == "__main__": run_module_suite() + \ No newline at end of file From da391089cbc25ad6db097d3896a184bff786f55e Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 14:48:38 +0100 Subject: [PATCH 333/625] add: local Otsu synthetic example --- .../applications/plot_rank_filters.py | 21 +++++++++++++++++++ skimage/filter/rank/tests/test_rank.py | 1 - 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 3fdd5a94..75806486 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -356,6 +356,27 @@ plt.subplot(2,2,4) plt.imshow(glob_otsu,cmap=plt.cm.gray) plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) +""" +.. image:: PLOT2RST.current_figure + +The following example, shows on a synthetic image how local Otsu's threshold handle a global level shift. + """ + +n = 100 +theta = np.linspace(0,10*np.pi,n) +x = np.sin(theta) +m = (np.tile(x,(n,1))* np.linspace(0.1,1,n)*128+128).astype(np.uint8) + +radius = 10 +t = rank.otsu(m,disk(radius)) +plt.figure() +plt.subplot(1,2,1) +plt.imshow(m) +plt.xlabel('original') +plt.subplot(1,2,2) +plt.imshow(m>=t,interpolation='nearest') +plt.xlabel('local Otsu ($radius=%d$)'%radius) + """ .. image:: PLOT2RST.current_figure diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index 73f3a5c3..6a8a19d4 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -364,4 +364,3 @@ def test_entropy(): if __name__ == "__main__": run_module_suite() - \ No newline at end of file From 542ec6fdc53426fc5fae585f44ff1492abc6ca11 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Mon, 12 Nov 2012 14:57:08 +0100 Subject: [PATCH 334/625] fix: local otsu doc --- doc/examples/applications/plot_rank_filters.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 75806486..ee89a4e7 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -346,11 +346,11 @@ plt.imshow(p8,cmap=plt.cm.gray) plt.xlabel('original') plt.colorbar() plt.subplot(2,2,2) -plt.imshow(loc_otsu,cmap=plt.cm.gray) +plt.imshow(t_loc_otsu,cmap=plt.cm.gray) plt.xlabel('local Otsu ($radius=%d$)'%radius) plt.colorbar() plt.subplot(2,2,3) -plt.imshow(p8>=loc_otsu,cmap=plt.cm.gray) +plt.imshow(p8>=t_loc_otsu,cmap=plt.cm.gray) plt.xlabel('original>=local Otsu'%t_glob_otsu) plt.subplot(2,2,4) plt.imshow(glob_otsu,cmap=plt.cm.gray) From f724b97f90c8ed0771c53ee510c40eff0d5db9fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 19:24:02 +0100 Subject: [PATCH 335/625] Fix typos and spelling of readme --- skimage/filter/rank/README.rst | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/filter/rank/README.rst b/skimage/filter/rank/README.rst index 29a68edb..cdf8205c 100644 --- a/skimage/filter/rank/README.rst +++ b/skimage/filter/rank/README.rst @@ -8,25 +8,25 @@ To do General remarks --------------- -Basically these filters compute local histogram for each pixel. Histogram is -build using a moving window in order to limit redundant computation. The path +Basically these filters compute local histogram for each pixel. A histogram is +built using a moving window in order to limit redundant computation. The path followed by the moving window is given hereunder ...-----------------------\ /--------------------------/ \-------------------------- ... -A comparison is proposed with cmorph.dilate algorithm to show how computation -costs evolve with respect to image size or structuring element size. This -implementation gives better results for large structuring elements. +We compare cmorph.dilate to this histogram based method to show how +computational costs increase with respect to image size or structuring element +size. This implementation gives better results for large structuring elements. -A local histogram is update at each pixel by introducing pixel entering the -structuring element border and by removing those leaving it. The histogram size -is 8bit (256 bins) for 8 bit images and 2 to 12 bit (up to 4096 bins) for 16bit -image depending on the image maximum value. Image with pixels higher than 4095 -raise a ValueError. +The local histogram is updated at each pixel as the structuring element window +moves by, i.e. only those pixels entering and leaving the structuring element +update the local histogram. The histogram size is 8-bit (256 bins) for 8-bit +images and 2 to 12-bit (up to 4096 bins) for 16-bit images depending on the +maximum value of the image. Pixel values higher than 4095 raise a ValueError. The filter is applied up to the image border, the neighboorhood used is adjusted accordingly. The user may provide a mask image (same size as input image) where -non zero value are the part of the image participating the the histogram -computation. By default all the image is filtered. +non zero values are the part of the image participating in the histogram +computation. By default the entire image is filtered. From fce2ae3f5f13c8fff42f1e316c8a83f1d865f3ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 19:25:46 +0100 Subject: [PATCH 336/625] Fix spelling of X-bit --- skimage/filter/rank/_core16.pxd | 2 +- skimage/filter/rank/_core8.pxd | 2 +- skimage/filter/rank/bilateral_rank.pyx | 10 +++++----- skimage/filter/rank/percentile_rank.pyx | 4 ++-- skimage/filter/rank/rank.pyx | 8 ++++---- skimage/filter/rank/tests/test_rank.py | 14 +++++++------- 6 files changed, 20 insertions(+), 20 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index 92c6f7d6..5f7de9df 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -5,7 +5,7 @@ cdef int int_max(int a, int b) cdef int int_min(int a, int b) -# 16 bit core kernel receives extra information about data bitdepth +# 16-bit core kernel receives extra information about data bitdepth cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, Py_ssize_t, Py_ssize_t, Py_ssize_t, float, float, Py_ssize_t, Py_ssize_t), diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 503d2df0..38236b87 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -10,7 +10,7 @@ cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, np.uint8_t * mask) -# 8 bit core kernel receives extra information about data inferior and superior +# 8-bit core kernel receives extra information about data inferior and superior # percentiles cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, float, Py_ssize_t, Py_ssize_t), diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 73434bb5..0ec299b0 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -7,8 +7,8 @@ Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. -Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), 8 bit -images are casted in 16 bit the number of histogram bins is determined from the +Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit +images are casted in 16-bit the number of histogram bins is determined from the maximum value present in the image. The pixel neighborhood is defined by: @@ -19,7 +19,7 @@ The pixel neighborhood is defined by: The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally). -Result image is 16 bit with respect to the input image. +Result image is 16-bit with respect to the input image. """ @@ -115,9 +115,9 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, Notes ----- - * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) + * input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit) - * 8 bit images are casted in 16 bit + * 8-bit images are casted in 16-bit Examples -------- diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 5e506035..1c194f4b 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -11,11 +11,11 @@ Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. -Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), for 16 bit +Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value present in the image. -Result image is 8 or 16 bit with respect to the input image. +Result image is 8 or 16-bit with respect to the input image. """ diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 763a702f..02720216 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -5,11 +5,11 @@ Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. -Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), for 16 bit +Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value present in the image. -Result image is 8 or 16 bit with respect to the input image. +Result image is 8 or 16-bit with respect to the input image. """ @@ -243,7 +243,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Note ---- - * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) + * input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit) * the lower algorithm complexity makes the rank.maximum() more efficient for larger images and structuring elements @@ -402,7 +402,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Note ---- - * input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit) + * input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit) * the lower algorithm complexity makes the rank.minimum() more efficient for larger images and structuring elements diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index 6a8a19d4..d8bcba52 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -108,7 +108,7 @@ def test_structuring_element8(): [ 0, 0, 0, 255, 255, 0], [ 0, 0, 0, 0, 0, 0]]) - # 8bit + # 8-bit image = np.zeros((6, 6), dtype=np.uint8) image[2, 2] = 255 elem = np.asarray([[1, 1, 0], [1, 1, 1], [0, 0, 1]], dtype=np.uint8) @@ -119,7 +119,7 @@ def test_structuring_element8(): shift_x=1, shift_y=1) assert_array_equal(r, out) - # 16bit + # 16-bit image = np.zeros((6, 6), dtype=np.uint16) image[2, 2] = 255 out = np.empty_like(image) @@ -171,8 +171,8 @@ def test_compare_autolevels(): assert_array_equal(loc_autolevel, loc_perc_autolevel) -def test_compare_autolevels_16bit(): - # compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and +def test_compare_autolevels_16-bit(): + # compare autolevel(16-bit) and percentile autolevel(16-bit) with p0=0.0 and # p1=1.0 should returns the same arrays image = data.camera().astype(np.uint16) * 4 @@ -185,8 +185,8 @@ def test_compare_autolevels_16bit(): assert_array_equal(loc_autolevel, loc_perc_autolevel) -def test_compare_8bit_vs_16bit(): - # filters applied on 8bit image ore 16bit image (having only real 8bit of +def test_compare_8-bit_vs_16-bit(): + # filters applied on 8-bit image ore 16-bit image (having only real 8-bit of # dynamic) should be identical image8 = data.camera() @@ -352,7 +352,7 @@ def test_entropy(): data = np.tile(np.reshape(np.arange(64),(8,8)),(10,10)).astype(np.uint8) assert(np.max(rank.entropy(data,selem))==60) - # 8 bit per pixel + # 8-bit per pixel data = np.tile(np.reshape(np.arange(256),(16,16)),(10,10)).astype(np.uint8) assert(np.max(rank.entropy(data,selem))==80) From a376a07c3b8ac65f0fd3650bb86be8e70bce3980 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 19:29:00 +0100 Subject: [PATCH 337/625] PEP8 fixes in doc string examples --- skimage/filter/rank/bilateral_rank.pyx | 10 ++++----- skimage/filter/rank/rank.pyx | 28 +++++++++++++------------- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 0ec299b0..818f0c64 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -170,11 +170,11 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> ima8 = 255 * np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.bilateral_pop(ima8, square(3), s0=10,s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 02720216..c1878f34 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -521,11 +521,11 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank - >>> ima = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> ima = 255 * np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.pop(ima, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], @@ -570,11 +570,11 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> # Local threshold >>> from skimage.morphology import square >>> from skimage.filter.rank import threshold - >>> ima = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) + >>> ima = 255 * np.array([[0, 0, 0, 0, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 1, 1, 1, 0], + ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> threshold(ima, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], @@ -703,11 +703,11 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> from skimage.morphology import disk >>> # defining a 8- and a 16-bit test images >>> a8 = data.camera() - >>> a16 = data.camera().astype(np.uint16)*4 + >>> a16 = data.camera().astype(np.uint16) * 4 >>> # pixel values contain 10x the local entropy - >>> ent8 = entropy(a8,disk(5)) + >>> ent8 = entropy(a8, disk(5)) >>> # pixel values contain 1000x the local entropy - >>> ent16 = entropy(a16,disk(5)) + >>> ent16 = entropy(a16, disk(5)) """ @@ -752,7 +752,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): >>> from skimage.morphology import disk >>> # defining a 8- and a 16-bit test images >>> a8 = data.camera() - >>> loc_otsu = otsu(a8,disk(5)) + >>> loc_otsu = otsu(a8, disk(5)) >>> thresh_image = a8 >= loc_otsu """ From eaf48a71e931c06080b3cc68e82d2598468037e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 19:31:06 +0100 Subject: [PATCH 338/625] Use numpy reference style --- skimage/filter/rank/bilateral_rank.pyx | 8 ++++---- skimage/filter/rank/percentile_rank.pyx | 10 +++++----- skimage/filter/rank/rank.pyx | 6 +++--- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 818f0c64..1a528143 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -1,11 +1,11 @@ -"""Approximate bilateral rankfilter for local (custom kernel) mean. +"""Approximate bilateral rank filter for local (custom kernel) mean. The local histogram is computed using a sliding window similar to the method described in: -Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median -filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal -Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit images are casted in 16-bit the number of histogram bins is determined from the diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 1c194f4b..5ad94af4 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -1,15 +1,15 @@ """Inferior and superior ranks, provided by the user, are passed to the kernel function to provide a softer version of the rank filters. E.g. percentile_autolevel will stretch image levels between percentile [p0, p1] -instead of using [min,max]. It means that isolate bright or dark pixels will not -produce halos. +instead of using [min, max]. It means that isolated bright or dark pixels will +not produce halos. The local histogram is computed using a sliding window similar to the method described in: -Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median -filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal -Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index c1878f34..3ce2c223 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -1,9 +1,9 @@ """The local histogram is computed using a sliding window similar to the method described in: -Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median -filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal -Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value From d0d5df0d6815eecac0d11628afbc23dbba7e759f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 20:04:42 +0100 Subject: [PATCH 339/625] Improve description of long rank filter example --- .../applications/plot_rank_filters.py | 199 +++++++++++------- 1 file changed, 128 insertions(+), 71 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index ee89a4e7..782131ca 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -1,12 +1,13 @@ """ -=============================================================== +============ Rank filters -=============================================================== +============ -Rank filters are non-linear filters using the local grey levels ordering to compute the filtered value. -This ensemble of filters share a common base: the local grey-level histogram extraction computed on -the neighborhood of a pixel (defined by a 2D structuring element). -If the filtered value is taken as the middle value of the histogram, we get the classical median filter. +Rank filters are non-linear filters using the local grey levels ordering to +compute the filtered value. This ensemble of filters share a common base: the +local grey-level histogram extraction computed on the neighborhood of a pixel +(defined by a 2D structuring element). If the filtered value is taken as the +middle value of the histogram, we get the classical median filter. Rank filters can be used for several purposes such as: @@ -22,15 +23,18 @@ Rank filters can be used for several purposes such as: * post-processing e.g. small object removal, object grouping, contour smoothing -Some well known filters are specific cases of rank filters [1]_ e.g. morphological dilation, morphological erosion, -median filters. +Some well known filters are specific cases of rank filters [1]_ e.g. +morphological dilation, morphological erosion, median filters. -The different implementation availables in ``skimage`` are compared. +The different implementation availables in `skimage` are compared. -In this example, we will see how to filter a grey level image using some of the linear and non-linear filters -availables in skimage. We use the ``camera`` image from ``skimage.data``. +In this example, we will see how to filter a grey level image using some of the +linear and non-linear filters availables in skimage. We use the `camera` +image from `skimage.data`. + +.. [1] Pierre Soille, On morphological operators based on rank filters, Pattern + Recognition 35 (2002) 527-535. -.. [1] Pierre Soille, On morphological operators based on rank filters, Pattern Recognition 35 (2002) 527-535. """ import numpy as np @@ -50,16 +54,20 @@ plt.plot(hist[1][:-1], hist[0], lw=2) plt.title('histogram of grey values') """ + .. image:: PLOT2RST.current_figure Noise removal -============== +============= -some noise is added to the image, 1% of pixels are randomly set to 255, %1% are randomly set to 0. -The **median** filter is applied to remove the noise. +Some noise is added to the image, 1% of pixels are randomly set to 255, 1% are +randomly set to 0. The **median** filter is applied to remove the noise. + +.. note:: + + there are different implementations of median filter : + `skimage.filter.median_filter` and `skimage.filter.rank.median` -.. note:: there is different implementations of median filter : ``skimage.filter.median_filter``and -`skimage.filter.rank.median`` """ noise = np.random.random(ima.shape) @@ -91,16 +99,18 @@ plt.xlabel('median $r=20$') """ .. image:: PLOT2RST.current_figure -The added noise is efficiently removed, as the image defaults are small (1 pixel wide), a small filter radius is -sufficient. As the radius is increasing, objects with a bigger size are filtered too such as the camera tripod. -Median filter is commonly used for noise removal because borders are preserved. +The added noise is efficiently removed, as the image defaults are small (1 pixel +wide), a small filter radius is sufficient. As the radius is increasing, objects +with a bigger size are filtered as well, such as the camera tripod. The median +filter is commonly used for noise removal because borders are preserved. Image smoothing ================ -The example hereunder shows how a local **mean** smooth the cameraman image. +The example hereunder shows how a local **mean** smoothes the camera man image. """ + from skimage.filter.rank import mean fig = plt.figure(figsize=[10,7]) @@ -114,13 +124,18 @@ plt.imshow(loc_mean,cmap=plt.cm.gray,vmin=0,vmax=255) plt.xlabel('local mean $r=10$') """ + .. image:: PLOT2RST.current_figure -One may be interested in smoothing an image while preserving important borders (median filters already achieved this), -here we use the **bilateral** filter that restrict the local neighborhood to pixel having a grey level similar to the -central one. +One may be interested in smoothing an image while preserving important borders +(median filters already achieved this), here we use the **bilateral** filter +that restricts the local neighborhood to pixel having a grey level similar to +the central one. -.. note:: a different implementation is available for color images in ``skimage.filter.denoise_bilateral``. +.. note:: + + a different implementation is available for color images in + `skimage.filter.denoise_bilateral`. """ @@ -145,9 +160,11 @@ plt.subplot(2,2,4) plt.imshow(bilat[200:350,350:450],cmap=plt.cm.gray) """ + .. image:: PLOT2RST.current_figure -One can see that the large continuous part of the image (e.g.sky) are smoothed whereas other details are preserved. +One can see that the large continuous part of the image (e.g. sky) is smoothed +whereas other details are preserved. Contrast enhancement @@ -155,8 +172,9 @@ Contrast enhancement We compare here how the global histogram equalization is applied locally. -The equalized image [2]_ has a roughly linear cumulative distribution function for each pixel neighborhood. -The local version [3]_ of the histogram equalization emphasized every local graylevel variations. +The equalized image [2]_ has a roughly linear cumulative distribution function +for each pixel neighborhood. The local version [3]_ of the histogram +equalization emphasizes every local graylevel variations. .. [2] http://en.wikipedia.org/wiki/Histogram_equalization .. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization @@ -195,14 +213,19 @@ plt.axis('off') plt.subplot(326) plt.plot(loc_hist[1][:-1], loc_hist[0], lw=2) plt.title('histogram of grey values') + """ + .. image:: PLOT2RST.current_figure -an other way to maximize the number of grey level used for an image is to apply a local autoleveling, -i.e. here a pixel grey level is proportionally remapped between local minimum and local maximum. +another way to maximize the number of grey levels used for an image is to apply +a local autoleveling, i.e. here a pixel grey level is proportionally remapped +between local minimum and local maximum. + +The following example shows how local autolevel enhances the camara man picture. -The following example show how local autolevel enhance the camaraman picture. """ + from skimage.filter.rank import autolevel ima = data.camera() @@ -218,16 +241,20 @@ plt.xlabel('original') plt.subplot(1,2,2) plt.imshow(auto,cmap=plt.cm.gray) plt.xlabel('local autolevel') + """ + .. image:: PLOT2RST.current_figure -This filter is very sensitive to local outlayers, see the little white spot in the sky left part. This is due -to a local maximum which is very high comparing to the rest of the neighborhood. One can moderate this -using the percentile version of the autolevel filter which uses to given percentiles (one inferior, one superior) -in place of local minimum and maximum. The example bellow illustrate how the percentile parameters influence the -local autolevel result. +This filter is very sensitive to local outlayers, see the little white spot in +the sky left part. This is due to a local maximum which is very high comparing +to the rest of the neighborhood. One can moderate this using the percentile +version of the autolevel filter which uses given percentiles (one inferior, +one superior) in place of local minimum and maximum. The example below +illustrates how the percentile parameters influence the local autolevel result. """ + from skimage.filter.rank import percentile_autolevel image = data.camera() @@ -255,10 +282,12 @@ for ax in axes: ax.axis('off') """ + .. image:: PLOT2RST.current_figure -Morphological contrast enhancement filter replaces the central pixel by local maximum -if the original grey level value if closest to local maximum, by the minimum local otherwise. +The morphological contrast enhancement filter replaces the central pixel by the +local maximum if the original pixel value is closest to local maximum, otherwise +by the minimum local. """ @@ -282,10 +311,11 @@ plt.subplot(2,2,4) plt.imshow(enh[200:350,350:450],cmap=plt.cm.gray) """ + .. image:: PLOT2RST.current_figure -The percentile version of the local morphological contrast enhancement, uses percentile *p0* and *p1* instead of local -minimum and local maximum. +The percentile version of the local morphological contrast enhancement uses +percentile *p0* and *p1* instead of the local minimum and maximum. """ @@ -309,21 +339,29 @@ plt.subplot(2,2,4) plt.imshow(penh[200:350,350:450],cmap=plt.cm.gray) """ + .. image:: PLOT2RST.current_figure Image threshold =============== -The Otsu's threshold [1]_ method can be applied locally using local greylevel distribution. -In the example below, for each pixel, an "optimal" threshold is determined by maximizing -the variance between two classes of pixels of the local neighborhood defined by a structuring element. -The example compares the local threshold with the global threshold `skimage.filter.threshold_otsu``. +The Otsu's threshold [1]_ method can be applied locally using the local +greylevel distribution. In the example below, for each pixel, an "optimal" +threshold is determined by maximizing the variance between two classes of pixels +of the local neighborhood defined by a structuring element. -.. note: local threshold is much slower than global one. +The example compares the local threshold with the global threshold +`skimage.filter.threshold_otsu`. + +.. note:: + + Local thresholding is much slower than global one. There exists a function + for global Otsu thresholding: `skimage.filter.threshold_otsu`. .. [1] http://en.wikipedia.org/wiki/Otsu's_method """ + from skimage.filter.rank import otsu from skimage.filter import threshold_otsu @@ -357,10 +395,13 @@ plt.imshow(glob_otsu,cmap=plt.cm.gray) plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) """ + .. image:: PLOT2RST.current_figure -The following example, shows on a synthetic image how local Otsu's threshold handle a global level shift. - """ +The following example shows how local Otsu's threshold handles a global level +shift applied to a synthetic image . + +""" n = 100 theta = np.linspace(0,10*np.pi,n) @@ -377,18 +418,23 @@ plt.subplot(1,2,2) plt.imshow(m>=t,interpolation='nearest') plt.xlabel('local Otsu ($radius=%d$)'%radius) - """ + .. image:: PLOT2RST.current_figure Image morphology ================ -Local maximum and local minimum are the base operators for grey level morphology. +Local maximum and local minimum are the base operators for grey level +morphology. -.. note:: ``skimage.dilate`` and ``skimage.erode`` are equivalent filters (see below for comparison). +.. note:: -Here is an example of classical morphological grey level filters : opening, closing and morphological gradient. + `skimage.dilate` and `skimage.erode` are equivalent filters (see below for + comparison). + +Here is an example of the classical morphological grey level filters: opening, +closing and morphological gradient. """ @@ -416,25 +462,27 @@ plt.imshow(grad,cmap=plt.cm.gray) plt.xlabel('morphological gradient') """ + .. image:: PLOT2RST.current_figure Feature extraction =================== -Local histogram can be exploited to compute local entropy, which is related to the local image complexity. -Entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode local -greylevel distribution. +Local histogram can be exploited to compute local entropy, which is related to +the local image complexity. Entropy is computed using base 2 logarithm i.e. the +filter returns the minimum number of bits needed to encode local greylevel +distribution. -``skimage.rank.entropy`` returns local entropy on a given structuring element. +`skimage.rank.entropy` returns local entropy on a given structuring element. The following example shows this filter applied on 8- and 16- bit images. -.. note:: to better use the available image bit, the function returns 10x entropy for 8-bit images and 1000x entropy - for 16-bit images. +.. note:: + + to better use the available image bit, the function returns 10x entropy for + 8-bit images and 1000x entropy for 16-bit images. """ - - from skimage import data from skimage.filter.rank import entropy from skimage.morphology import disk @@ -472,16 +520,19 @@ plt.xlabel('entropy*1000') plt.colorbar() """ + .. image:: PLOT2RST.current_figure Implementation ================ -The central part of the ``skimage.rank`` filters is build on a sliding window that update local grey level histogram. -This approach limits the algorithm complexity to O(n) where n is the number of image pixels. The complexity is also -limited with respect to the structuring element size. +The central part of the `skimage.rank` filters is build on a sliding window that +update local grey level histogram. This approach limits the algorithm complexity +to O(n) where n is the number of image pixels. The complexity is also limited +with respect to the structuring element size. """ + from time import time from scipy.ndimage.filters import percentile_filter @@ -490,9 +541,7 @@ from skimage.filter import median_filter from skimage.filter.rank import median,maximum def exec_and_timeit(func): - """ Decorator that returns both function results and execution time - (result, ms) - """ + """Decorator that returns both function results and execution time.""" def wrapper(*arg): t1 = time() res = func(*arg) @@ -523,12 +572,14 @@ def ndi_med(image,n): return percentile_filter(image,50,size=n*2-1) """ + Comparison between -* ``rank.maximum`` -* ``cmorph.dilate`` +* `rank.maximum` +* `cmorph.dilate` on increasing structuring element size + """ a = data.camera() @@ -551,9 +602,11 @@ plt.plot(e_range,rec) plt.legend(['crank.maximum','cmorph.dilate']) """ + and increasing image size .. image:: PLOT2RST.current_figure + """ r = 9 @@ -578,17 +631,18 @@ plt.legend(['crank.maximum','cmorph.dilate']) """ + .. image:: PLOT2RST.current_figure Comparison between: -* ``rank.median`` -* ``ctmf.median_filter`` -* ``ndimage.percentile`` +* `rank.median` +* `ctmf.median_filter` +* `ndimage.percentile` on increasing structuring element size -""" +""" a = data.camera() @@ -609,12 +663,14 @@ plt.plot(e_range,rec) plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('element radius') + """ .. image:: PLOT2RST.current_figure comparison of outcome of the three methods """ + plt.figure() plt.imshow(np.hstack((rc,rctmf,rndi))) plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') @@ -649,6 +705,7 @@ plt.xlabel('image size') """ .. image:: PLOT2RST.current_figure + """ plt.show() From 4a0b22aff52ff299fb6d6ee6caf034f4da2cada9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 20:07:02 +0100 Subject: [PATCH 340/625] Use common spelling for greylevel --- .../applications/plot_rank_filters.py | 22 +++++++++---------- skimage/filter/rank/_crank16_bilateral.pyx | 2 +- skimage/filter/rank/bilateral_rank.pyx | 8 +++---- skimage/filter/rank/rank.pyx | 2 +- 4 files changed, 17 insertions(+), 17 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 782131ca..0331ac2d 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -3,7 +3,7 @@ Rank filters ============ -Rank filters are non-linear filters using the local grey levels ordering to +Rank filters are non-linear filters using the local greylevels ordering to compute the filtered value. This ensemble of filters share a common base: the local grey-level histogram extraction computed on the neighborhood of a pixel (defined by a 2D structuring element). If the filtered value is taken as the @@ -28,7 +28,7 @@ morphological dilation, morphological erosion, median filters. The different implementation availables in `skimage` are compared. -In this example, we will see how to filter a grey level image using some of the +In this example, we will see how to filter a greylevel image using some of the linear and non-linear filters availables in skimage. We use the `camera` image from `skimage.data`. @@ -129,7 +129,7 @@ plt.xlabel('local mean $r=10$') One may be interested in smoothing an image while preserving important borders (median filters already achieved this), here we use the **bilateral** filter -that restricts the local neighborhood to pixel having a grey level similar to +that restricts the local neighborhood to pixel having a greylevel similar to the central one. .. note:: @@ -174,7 +174,7 @@ We compare here how the global histogram equalization is applied locally. The equalized image [2]_ has a roughly linear cumulative distribution function for each pixel neighborhood. The local version [3]_ of the histogram -equalization emphasizes every local graylevel variations. +equalization emphasizes every local greylevel variations. .. [2] http://en.wikipedia.org/wiki/Histogram_equalization .. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization @@ -218,8 +218,8 @@ plt.title('histogram of grey values') .. image:: PLOT2RST.current_figure -another way to maximize the number of grey levels used for an image is to apply -a local autoleveling, i.e. here a pixel grey level is proportionally remapped +another way to maximize the number of greylevels used for an image is to apply +a local autoleveling, i.e. here a pixel greylevel is proportionally remapped between local minimum and local maximum. The following example shows how local autolevel enhances the camara man picture. @@ -425,7 +425,7 @@ plt.xlabel('local Otsu ($radius=%d$)'%radius) Image morphology ================ -Local maximum and local minimum are the base operators for grey level +Local maximum and local minimum are the base operators for greylevel morphology. .. note:: @@ -433,7 +433,7 @@ morphology. `skimage.dilate` and `skimage.erode` are equivalent filters (see below for comparison). -Here is an example of the classical morphological grey level filters: opening, +Here is an example of the classical morphological greylevel filters: opening, closing and morphological gradient. """ @@ -453,10 +453,10 @@ plt.imshow(ima,cmap=plt.cm.gray) plt.xlabel('original') plt.subplot(2,2,2) plt.imshow(closing,cmap=plt.cm.gray) -plt.xlabel('grey level closing') +plt.xlabel('greylevel closing') plt.subplot(2,2,3) plt.imshow(opening,cmap=plt.cm.gray) -plt.xlabel('grey level opening') +plt.xlabel('greylevel opening') plt.subplot(2,2,4) plt.imshow(grad,cmap=plt.cm.gray) plt.xlabel('morphological gradient') @@ -527,7 +527,7 @@ Implementation ================ The central part of the `skimage.rank` filters is build on a sliding window that -update local grey level histogram. This approach limits the algorithm complexity +update local greylevel histogram. This approach limits the algorithm complexity to O(n) where n is the number of image pixels. The complexity is also limited with respect to the structuring element size. diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 1fb81a2d..0174b926 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -62,7 +62,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): - """average gray level (clipped on uint8) + """average greylevel (clipped on uint8) """ _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, 0., 0., s0, s1) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 1a528143..0d3cdd88 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -14,7 +14,7 @@ maximum value present in the image. The pixel neighborhood is defined by: * the given structuring element -* an interval [g-s0,g+s1] in gray level around g the processed pixel gray level +* an interval [g-s0,g+s1] in greylevel around g the processed pixel greylevel The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally). @@ -78,9 +78,9 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem). - Radiometric similarity is defined by the gray level interval [g-s0,g+s1] - where g is the current pixel gray level. Only pixels belonging to the - structuring element AND having a gray level inside this interval are + Radiometric similarity is defined by the greylevel interval [g-s0,g+s1] + where g is the current pixel greylevel. Only pixels belonging to the + structuring element AND having a greylevel inside this interval are averaged. Return greyscale local bilateral_mean of an image. Parameters diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 3ce2c223..da2f35bc 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -447,7 +447,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Enhance an image replacing each pixel by the local maximum if pixel - graylevel is closest to maximimum than local minimum OR local minimum + greylevel is closest to maximimum than local minimum OR local minimum otherwise. Parameters From af55d712ee24925cc1c671ed34dc8032a480466b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 12 Nov 2012 20:10:56 +0100 Subject: [PATCH 341/625] PEP8 fixes for long rank filter example --- .../applications/plot_rank_filters.py | 318 +++++++++--------- 1 file changed, 163 insertions(+), 155 deletions(-) diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 0331ac2d..c182671d 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -46,10 +46,10 @@ ima = data.camera() hist = np.histogram(ima, bins=np.arange(0, 256)) plt.figure(figsize=(8, 3)) -plt.subplot(121) +plt.subplot(1, 2, 1) plt.imshow(ima, cmap=plt.cm.gray, interpolation='nearest') plt.axis('off') -plt.subplot(122) +plt.subplot(1, 2, 2) plt.plot(hist[1][:-1], hist[0], lw=2) plt.title('histogram of grey values') @@ -72,31 +72,32 @@ randomly set to 0. The **median** filter is applied to remove the noise. noise = np.random.random(ima.shape) nima = data.camera() -nima[noise>.99] = 255 -nima[noise<.01] = 0 +nima[noise > 0.99] = 255 +nima[noise < 0.01] = 0 from skimage.filter.rank import median from skimage.morphology import disk -fig = plt.figure(figsize=[10,7]) +fig = plt.figure(figsize=[10, 7]) -lo = median(nima,disk(1)) -hi = median(nima,disk(5)) -ext = median(nima,disk(20)) -plt.subplot(2,2,1) -plt.imshow(nima,cmap=plt.cm.gray,vmin=0,vmax=255) +lo = median(nima, disk(1)) +hi = median(nima, disk(5)) +ext = median(nima, disk(20)) +plt.subplot(2, 2, 1) +plt.imshow(nima, cmap=plt.cm.gray, vmin=0, vmax=255) plt.xlabel('noised image') -plt.subplot(2,2,2) -plt.imshow(lo,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.subplot(2, 2, 2) +plt.imshow(lo, cmap=plt.cm.gray, vmin=0, vmax=255) plt.xlabel('median $r=1$') -plt.subplot(2,2,3) -plt.imshow(hi,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.subplot(2, 2, 3) +plt.imshow(hi, cmap=plt.cm.gray, vmin=0, vmax=255) plt.xlabel('median $r=5$') -plt.subplot(2,2,4) -plt.imshow(ext,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.subplot(2, 2, 4) +plt.imshow(ext, cmap=plt.cm.gray, vmin=0, vmax=255) plt.xlabel('median $r=20$') """ + .. image:: PLOT2RST.current_figure The added noise is efficiently removed, as the image defaults are small (1 pixel @@ -113,14 +114,14 @@ The example hereunder shows how a local **mean** smoothes the camera man image. from skimage.filter.rank import mean -fig = plt.figure(figsize=[10,7]) +fig = plt.figure(figsize=[10, 7]) -loc_mean = mean(nima,disk(10)) -plt.subplot(1,2,1) -plt.imshow(ima,cmap=plt.cm.gray,vmin=0,vmax=255) +loc_mean = mean(nima, disk(10)) +plt.subplot(1, 2, 1) +plt.imshow(ima, cmap=plt.cm.gray, vmin=0, vmax=255) plt.xlabel('original') -plt.subplot(1,2,2) -plt.imshow(loc_mean,cmap=plt.cm.gray,vmin=0,vmax=255) +plt.subplot(1, 2, 2) +plt.imshow(loc_mean, cmap=plt.cm.gray, vmin=0, vmax=255) plt.xlabel('local mean $r=10$') """ @@ -144,20 +145,20 @@ from skimage.filter.rank import bilateral_mean ima = data.camera() selem = disk(10) -bilat = bilateral_mean(ima.astype(np.uint16),disk(20),s0=10,s1=10) +bilat = bilateral_mean(ima.astype(np.uint16), disk(20), s0=10, s1=10) # display results -fig = plt.figure(figsize=[10,7]) -plt.subplot(2,2,1) -plt.imshow(ima,cmap=plt.cm.gray) +fig = plt.figure(figsize=[10, 7]) +plt.subplot(2, 2, 1) +plt.imshow(ima, cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(2,2,3) -plt.imshow(bilat,cmap=plt.cm.gray) +plt.subplot(2, 2, 3) +plt.imshow(bilat, cmap=plt.cm.gray) plt.xlabel('bilateral mean') -plt.subplot(2,2,2) -plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) -plt.subplot(2,2,4) -plt.imshow(bilat[200:350,350:450],cmap=plt.cm.gray) +plt.subplot(2, 2, 2) +plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray) +plt.subplot(2, 2, 4) +plt.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray) """ @@ -186,8 +187,8 @@ from skimage.filter import rank ima = data.camera() # equalize globally and locally -glob = exposure.equalize(ima)*255 -loc = rank.equalize(ima,disk(20)) +glob = exposure.equalize(ima) * 255 +loc = rank.equalize(ima, disk(20)) # extract histogram for each image hist = np.histogram(ima, bins=np.arange(0, 256)) @@ -231,15 +232,15 @@ from skimage.filter.rank import autolevel ima = data.camera() selem = disk(10) -auto = autolevel(ima.astype(np.uint16),disk(20)) +auto = autolevel(ima.astype(np.uint16), disk(20)) # display results -fig = plt.figure(figsize=[10,7]) -plt.subplot(1,2,1) -plt.imshow(ima,cmap=plt.cm.gray) +fig = plt.figure(figsize=[10, 7]) +plt.subplot(1, 2, 1) +plt.imshow(ima, cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(1,2,2) -plt.imshow(auto,cmap=plt.cm.gray) +plt.subplot(1, 2, 2) +plt.imshow(auto, cmap=plt.cm.gray) plt.xlabel('local autolevel') """ @@ -260,22 +261,24 @@ from skimage.filter.rank import percentile_autolevel image = data.camera() selem = disk(20) -loc_autolevel = autolevel(image,selem=selem) -loc_perc_autolevel0 = percentile_autolevel(image,selem=selem,p0=.00,p1=1.0) -loc_perc_autolevel1 = percentile_autolevel(image,selem=selem,p0=.01,p1=.99) -loc_perc_autolevel2 = percentile_autolevel(image,selem=selem,p0=.05,p1=.95) -loc_perc_autolevel3 = percentile_autolevel(image,selem=selem,p0=.1,p1=.9) +loc_autolevel = autolevel(image, selem=selem) +loc_perc_autolevel0 = percentile_autolevel(image, selem=selem, p0=.00, p1=1.0) +loc_perc_autolevel1 = percentile_autolevel(image, selem=selem, p0=.01, p1=.99) +loc_perc_autolevel2 = percentile_autolevel(image, selem=selem, p0=.05, p1=.95) +loc_perc_autolevel3 = percentile_autolevel(image, selem=selem, p0=.1, p1=.9) fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax0, ax1, ax2 = axes plt.gray() -ax0.imshow(np.hstack((image,loc_autolevel))) +ax0.imshow(np.hstack((image, loc_autolevel))) ax0.set_title('original / autolevel') -ax1.imshow(np.hstack((loc_perc_autolevel0,loc_perc_autolevel1)),vmin=0,vmax=255) +ax1.imshow( + np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255) ax1.set_title('percentile autolevel 0%,1%') -ax2.imshow(np.hstack((loc_perc_autolevel2,loc_perc_autolevel3)),vmin=0,vmax=255) +ax2.imshow( + np.hstack((loc_perc_autolevel2, loc_perc_autolevel3)), vmin=0, vmax=255) ax2.set_title('percentile autolevel 5% and 10%') for ax in axes: @@ -295,20 +298,20 @@ from skimage.filter.rank import morph_contr_enh ima = data.camera() -enh = morph_contr_enh(ima,disk(5)) +enh = morph_contr_enh(ima, disk(5)) # display results -fig = plt.figure(figsize=[10,7]) -plt.subplot(2,2,1) -plt.imshow(ima,cmap=plt.cm.gray) +fig = plt.figure(figsize=[10, 7]) +plt.subplot(2, 2, 1) +plt.imshow(ima, cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(2,2,3) -plt.imshow(enh,cmap=plt.cm.gray) +plt.subplot(2, 2, 3) +plt.imshow(enh, cmap=plt.cm.gray) plt.xlabel('local morphlogical contrast enhancement') -plt.subplot(2,2,2) -plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) -plt.subplot(2,2,4) -plt.imshow(enh[200:350,350:450],cmap=plt.cm.gray) +plt.subplot(2, 2, 2) +plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray) +plt.subplot(2, 2, 4) +plt.imshow(enh[200:350, 350:450], cmap=plt.cm.gray) """ @@ -323,20 +326,20 @@ from skimage.filter.rank import percentile_morph_contr_enh ima = data.camera() -penh = percentile_morph_contr_enh(ima,disk(5),p0=.1,p1=.9) +penh = percentile_morph_contr_enh(ima, disk(5), p0=.1, p1=.9) # display results -fig = plt.figure(figsize=[10,7]) -plt.subplot(2,2,1) -plt.imshow(ima,cmap=plt.cm.gray) +fig = plt.figure(figsize=[10, 7]) +plt.subplot(2, 2, 1) +plt.imshow(ima, cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(2,2,3) -plt.imshow(penh,cmap=plt.cm.gray) +plt.subplot(2, 2, 3) +plt.imshow(penh, cmap=plt.cm.gray) plt.xlabel('local percentile morphlogical\n contrast enhancement') -plt.subplot(2,2,2) -plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray) -plt.subplot(2,2,4) -plt.imshow(penh[200:350,350:450],cmap=plt.cm.gray) +plt.subplot(2, 2, 2) +plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray) +plt.subplot(2, 2, 4) +plt.imshow(penh[200:350, 350:450], cmap=plt.cm.gray) """ @@ -371,28 +374,28 @@ radius = 10 selem = disk(radius) # t_loc_otsu is an image -t_loc_otsu = otsu(p8,selem) -loc_otsu = p8>=t_loc_otsu +t_loc_otsu = otsu(p8, selem) +loc_otsu = p8 >= t_loc_otsu # t_glob_otsu is a scalar t_glob_otsu = threshold_otsu(p8) -glob_otsu = p8>=t_glob_otsu +glob_otsu = p8 >= t_glob_otsu plt.figure() -plt.subplot(2,2,1) -plt.imshow(p8,cmap=plt.cm.gray) +plt.subplot(2, 2, 1) +plt.imshow(p8, cmap=plt.cm.gray) plt.xlabel('original') plt.colorbar() -plt.subplot(2,2,2) -plt.imshow(t_loc_otsu,cmap=plt.cm.gray) -plt.xlabel('local Otsu ($radius=%d$)'%radius) +plt.subplot(2, 2, 2) +plt.imshow(t_loc_otsu, cmap=plt.cm.gray) +plt.xlabel('local Otsu ($radius=%d$)' % radius) plt.colorbar() -plt.subplot(2,2,3) -plt.imshow(p8>=t_loc_otsu,cmap=plt.cm.gray) -plt.xlabel('original>=local Otsu'%t_glob_otsu) -plt.subplot(2,2,4) -plt.imshow(glob_otsu,cmap=plt.cm.gray) -plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) +plt.subplot(2, 2, 3) +plt.imshow(p8 >= t_loc_otsu, cmap=plt.cm.gray) +plt.xlabel('original>=local Otsu' % t_glob_otsu) +plt.subplot(2, 2, 4) +plt.imshow(glob_otsu, cmap=plt.cm.gray) +plt.xlabel('global Otsu ($t=%d$)' % t_glob_otsu) """ @@ -404,19 +407,19 @@ shift applied to a synthetic image . """ n = 100 -theta = np.linspace(0,10*np.pi,n) +theta = np.linspace(0, 10 * np.pi, n) x = np.sin(theta) -m = (np.tile(x,(n,1))* np.linspace(0.1,1,n)*128+128).astype(np.uint8) +m = (np.tile(x, (n, 1)) * np.linspace(0.1, 1, n) * 128 + 128).astype(np.uint8) radius = 10 -t = rank.otsu(m,disk(radius)) +t = rank.otsu(m, disk(radius)) plt.figure() -plt.subplot(1,2,1) +plt.subplot(1, 2, 1) plt.imshow(m) plt.xlabel('original') -plt.subplot(1,2,2) -plt.imshow(m>=t,interpolation='nearest') -plt.xlabel('local Otsu ($radius=%d$)'%radius) +plt.subplot(1, 2, 2) +plt.imshow(m >= t, interpolation='nearest') +plt.xlabel('local Otsu ($radius=%d$)' % radius) """ @@ -438,27 +441,27 @@ closing and morphological gradient. """ -from skimage.filter.rank import maximum,minimum,gradient +from skimage.filter.rank import maximum, minimum, gradient ima = data.camera() -closing = maximum(minimum(ima,disk(5)),disk(5)) -opening = minimum(maximum(ima,disk(5)),disk(5)) -grad = gradient(ima,disk(5)) +closing = maximum(minimum(ima, disk(5)), disk(5)) +opening = minimum(maximum(ima, disk(5)), disk(5)) +grad = gradient(ima, disk(5)) # display results -fig = plt.figure(figsize=[10,7]) -plt.subplot(2,2,1) -plt.imshow(ima,cmap=plt.cm.gray) +fig = plt.figure(figsize=[10, 7]) +plt.subplot(2, 2, 1) +plt.imshow(ima, cmap=plt.cm.gray) plt.xlabel('original') -plt.subplot(2,2,2) -plt.imshow(closing,cmap=plt.cm.gray) +plt.subplot(2, 2, 2) +plt.imshow(closing, cmap=plt.cm.gray) plt.xlabel('greylevel closing') -plt.subplot(2,2,3) -plt.imshow(opening,cmap=plt.cm.gray) +plt.subplot(2, 2, 3) +plt.imshow(opening, cmap=plt.cm.gray) plt.xlabel('greylevel opening') -plt.subplot(2,2,4) -plt.imshow(grad,cmap=plt.cm.gray) +plt.subplot(2, 2, 4) +plt.imshow(grad, cmap=plt.cm.gray) plt.xlabel('morphological gradient') """ @@ -491,30 +494,30 @@ import matplotlib.pyplot as plt # defining a 8- and a 16-bit test images a8 = data.camera() -a16 = data.camera().astype(np.uint16)*4 +a16 = data.camera().astype(np.uint16) * 4 -ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy -ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy +ent8 = entropy(a8, disk(5)) # pixel value contain 10x the local entropy +ent16 = entropy(a16, disk(5)) # pixel value contain 1000x the local entropy # display results plt.figure(figsize=(10, 10)) -plt.subplot(2,2,1) +plt.subplot(2, 2, 1) plt.imshow(a8, cmap=plt.cm.gray) plt.xlabel('8-bit image') plt.colorbar() -plt.subplot(2,2,2) +plt.subplot(2, 2, 2) plt.imshow(ent8, cmap=plt.cm.jet) plt.xlabel('entropy*10') plt.colorbar() -plt.subplot(2,2,3) +plt.subplot(2, 2, 3) plt.imshow(a16, cmap=plt.cm.gray) plt.xlabel('16-bit image') plt.colorbar() -plt.subplot(2,2,4) +plt.subplot(2, 2, 4) plt.imshow(ent16, cmap=plt.cm.jet) plt.xlabel('entropy*1000') plt.colorbar() @@ -538,7 +541,8 @@ from time import time from scipy.ndimage.filters import percentile_filter from skimage.morphology import dilation from skimage.filter import median_filter -from skimage.filter.rank import median,maximum +from skimage.filter.rank import median, maximum + def exec_and_timeit(func): """Decorator that returns both function results and execution time.""" @@ -546,30 +550,34 @@ def exec_and_timeit(func): t1 = time() res = func(*arg) t2 = time() - ms = (t2-t1)*1000.0 - return (res,ms) + ms = (t2 - t1) * 1000.0 + return (res, ms) return wrapper @exec_and_timeit -def cr_med(image,selem): - return median(image=image,selem = selem) +def cr_med(image, selem): + return median(image=image, selem=selem) + @exec_and_timeit -def cr_max(image,selem): - return maximum(image=image,selem = selem) +def cr_max(image, selem): + return maximum(image=image, selem=selem) + @exec_and_timeit -def cm_dil(image,selem): - return dilation(image=image,selem = selem) +def cm_dil(image, selem): + return dilation(image=image, selem=selem) + @exec_and_timeit -def ctmf_med(image,radius): - return median_filter(image=image,radius=radius) +def ctmf_med(image, radius): + return median_filter(image=image, radius=radius) + @exec_and_timeit -def ndi_med(image,n): - return percentile_filter(image,50,size=n*2-1) +def ndi_med(image, n): + return percentile_filter(image, 50, size=n * 2 - 1) """ @@ -585,12 +593,12 @@ on increasing structuring element size a = data.camera() rec = [] -e_range = range(1,20,2) +e_range = range(1, 20, 2) for r in e_range: - elem = disk(r+1) - rc,ms_rc = cr_max(a,elem) - rcm,ms_rcm = cm_dil(a,elem) - rec.append((ms_rc,ms_rcm)) + elem = disk(r + 1) + rc, ms_rc = cr_max(a, elem) + rcm, ms_rcm = cm_dil(a, elem) + rec.append((ms_rc, ms_rcm)) rec = np.asarray(rec) @@ -598,8 +606,8 @@ plt.figure() plt.title('increasing element size') plt.ylabel('time (ms)') plt.xlabel('element radius') -plt.plot(e_range,rec) -plt.legend(['crank.maximum','cmorph.dilate']) +plt.plot(e_range, rec) +plt.legend(['crank.maximum', 'cmorph.dilate']) """ @@ -610,15 +618,15 @@ and increasing image size """ r = 9 -elem = disk(r+1) +elem = disk(r + 1) rec = [] -s_range = range(100,1000,100) +s_range = range(100, 1000, 100) for s in s_range: - a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = cr_max(a,elem) - (rcm,ms_rcm) = cm_dil(a,elem) - rec.append((ms_rc,ms_rcm)) + a = (np.random.random((s, s)) * 256).astype('uint8') + (rc, ms_rc) = cr_max(a, elem) + (rcm, ms_rcm) = cm_dil(a, elem) + rec.append((ms_rc, ms_rcm)) rec = np.asarray(rec) @@ -626,8 +634,8 @@ plt.figure() plt.title('increasing image size') plt.ylabel('time (ms)') plt.xlabel('image size') -plt.plot(s_range,rec) -plt.legend(['crank.maximum','cmorph.dilate']) +plt.plot(s_range, rec) +plt.legend(['crank.maximum', 'cmorph.dilate']) """ @@ -647,20 +655,20 @@ on increasing structuring element size a = data.camera() rec = [] -e_range = range(2,30,4) +e_range = range(2, 30, 4) for r in e_range: - elem = disk(r+1) - rc,ms_rc = cr_med(a,elem) - rctmf,ms_rctmf = ctmf_med(a,r) - rndi,ms_ndi = ndi_med(a,r) - rec.append((ms_rc,ms_rctmf,ms_ndi)) + elem = disk(r + 1) + rc, ms_rc = cr_med(a, elem) + rctmf, ms_rctmf = ctmf_med(a, r) + rndi, ms_ndi = ndi_med(a, r) + rec.append((ms_rc, ms_rctmf, ms_ndi)) rec = np.asarray(rec) plt.figure() plt.title('increasing element size') -plt.plot(e_range,rec) -plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) +plt.plot(e_range, rec) +plt.legend(['rank.median', 'ctmf.median_filter', 'ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('element radius') @@ -672,7 +680,7 @@ comparison of outcome of the three methods """ plt.figure() -plt.imshow(np.hstack((rc,rctmf,rndi))) +plt.imshow(np.hstack((rc, rctmf, rndi))) plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile') """ @@ -683,23 +691,23 @@ and increasing image size """ r = 9 -elem = disk(r+1) +elem = disk(r + 1) rec = [] -s_range = [100,200,500,1000] +s_range = [100, 200, 500, 1000] for s in s_range: - a = (np.random.random((s,s))*256).astype('uint8') - (rc,ms_rc) = cr_med(a,elem) - rctmf,ms_rctmf = ctmf_med(a,r) - rndi,ms_ndi = ndi_med(a,r) - rec.append((ms_rc,ms_rctmf,ms_ndi)) + a = (np.random.random((s, s)) * 256).astype('uint8') + (rc, ms_rc) = cr_med(a, elem) + rctmf, ms_rctmf = ctmf_med(a, r) + rndi, ms_ndi = ndi_med(a, r) + rec.append((ms_rc, ms_rctmf, ms_ndi)) rec = np.asarray(rec) plt.figure() plt.title('increasing image size') -plt.plot(s_range,rec) -plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile']) +plt.plot(s_range, rec) +plt.legend(['rank.median', 'ctmf.median_filter', 'ndimage.percentile']) plt.ylabel('time (ms)') plt.xlabel('image size') From f1331587921ce447029e70f1ab6b210c94a562d4 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 13 Nov 2012 09:37:58 +0100 Subject: [PATCH 342/625] autopep8 for plot_* and rank sources --- doc/examples/plot_16bitbilateral.py | 16 +-- doc/examples/plot_local_equalize.py | 4 +- doc/examples/plot_local_otsu.py | 26 ++-- doc/examples/plot_marked_watershed.py | 10 +- skimage/filter/rank/_core16.pyx | 42 +++--- skimage/filter/rank/_core8.pyx | 42 +++--- skimage/filter/rank/_crank16.pyx | 107 +++++++------- skimage/filter/rank/_crank16_bilateral.pyx | 10 +- skimage/filter/rank/_crank16_percentiles.pyx | 65 ++++----- skimage/filter/rank/_crank8.pyx | 138 ++++++++++--------- skimage/filter/rank/_crank8_percentiles.pyx | 62 ++++----- skimage/filter/rank/percentile_rank.pyx | 19 +-- skimage/filter/rank/rank.pyx | 7 +- skimage/filter/rank/tests/test_rank.py | 4 +- 14 files changed, 281 insertions(+), 271 deletions(-) diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py index fc30aa6b..473fcadd 100644 --- a/doc/examples/plot_16bitbilateral.py +++ b/doc/examples/plot_16bitbilateral.py @@ -27,21 +27,21 @@ from skimage import data from skimage.morphology import disk import skimage.filter.rank as rank -a16 = (data.coins()).astype('uint16')*16 +a16 = (data.coins()).astype('uint16') * 16 selem = disk(20) -f1 = rank.percentile_mean(a16,selem = selem,p0=.1,p1=.9) -f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500) -f3 = rank.mean(a16,selem = selem) +f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9) +f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500) +f3 = rank.mean(a16, selem=selem) # display results -fig, axes = plt.subplots(nrows=3, figsize=(15,10)) +fig, axes = plt.subplots(nrows=3, figsize=(15, 10)) ax0, ax1, ax2 = axes -ax0.imshow(np.hstack((a16,f1))) +ax0.imshow(np.hstack((a16, f1))) ax0.set_title('percentile mean') -ax1.imshow(np.hstack((a16,f2))) +ax1.imshow(np.hstack((a16, f2))) ax1.set_title('bilateral mean') -ax2.imshow(np.hstack((a16,f3))) +ax2.imshow(np.hstack((a16, f3))) ax2.set_title('local mean') plt.show() diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index 1a431f5c..bc458505 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -24,6 +24,7 @@ import matplotlib.pyplot as plt import numpy as np from skimage.filter import rank + def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. @@ -60,7 +61,7 @@ img_rescale = exposure.equalize(img) # Equalization selem = disk(30) -img_eq = rank.equalize(img,selem=selem) +img_eq = rank.equalize(img, selem=selem) # Display results @@ -81,4 +82,3 @@ ax_cdf.set_ylabel('Fraction of total intensity') # prevent overlap of y-axis labels plt.subplots_adjust(wspace=0.4) plt.show() - diff --git a/doc/examples/plot_local_otsu.py b/doc/examples/plot_local_otsu.py index ae1ead48..968ce6e1 100644 --- a/doc/examples/plot_local_otsu.py +++ b/doc/examples/plot_local_otsu.py @@ -26,24 +26,24 @@ p8 = data.page() radius = 10 selem = disk(radius) -loc_otsu = rank.otsu(p8,selem) +loc_otsu = rank.otsu(p8, selem) t_glob_otsu = threshold_otsu(p8) -glob_otsu = p8>=t_glob_otsu +glob_otsu = p8 >= t_glob_otsu plt.figure() -plt.subplot(2,2,1) -plt.imshow(p8,cmap=plt.cm.gray) +plt.subplot(2, 2, 1) +plt.imshow(p8, cmap=plt.cm.gray) plt.xlabel('original') plt.colorbar() -plt.subplot(2,2,2) -plt.imshow(loc_otsu,cmap=plt.cm.gray) -plt.xlabel('local Otsu ($radius=%d$)'%radius) +plt.subplot(2, 2, 2) +plt.imshow(loc_otsu, cmap=plt.cm.gray) +plt.xlabel('local Otsu ($radius=%d$)' % radius) plt.colorbar() -plt.subplot(2,2,3) -plt.imshow(p8>=loc_otsu,cmap=plt.cm.gray) -plt.xlabel('original>=local Otsu'%t_glob_otsu) -plt.subplot(2,2,4) -plt.imshow(glob_otsu,cmap=plt.cm.gray) -plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu) +plt.subplot(2, 2, 3) +plt.imshow(p8 >= loc_otsu, cmap=plt.cm.gray) +plt.xlabel('original>=local Otsu' % t_glob_otsu) +plt.subplot(2, 2, 4) +plt.imshow(glob_otsu, cmap=plt.cm.gray) +plt.xlabel('global Otsu ($t=%d$)' % t_glob_otsu) plt.show() diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py index 738a3d24..e97280c7 100644 --- a/doc/examples/plot_marked_watershed.py +++ b/doc/examples/plot_marked_watershed.py @@ -16,7 +16,7 @@ See Wikipedia_ for more details on the algorithm. from scipy import ndimage import matplotlib.pyplot as plt -from skimage.morphology import watershed,disk +from skimage.morphology import watershed, disk from skimage import data # original data @@ -25,14 +25,14 @@ from skimage.filter import rank image = data.camera() # denoise image -denoised = rank.median(image,disk(2)) +denoised = rank.median(image, disk(2)) # find continuous region (low gradient) --> markers -markers = rank.gradient(denoised,disk(5))<10 +markers = rank.gradient(denoised, disk(5)) < 10 markers = ndimage.label(markers)[0] #local gradient -gradient = rank.gradient(denoised,disk(2)) +gradient = rank.gradient(denoised, disk(2)) # process the watershed labels = watershed(gradient, markers) @@ -45,7 +45,7 @@ ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest') ax1.imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest') ax2.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') ax3.imshow(image, cmap=plt.cm.gray, interpolation='nearest') -ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest',alpha=.7) +ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest', alpha=.7) for ax in axes: ax.axis('off') diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index d3715f47..1575e24e 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -67,9 +67,9 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, assert (image < maxbin).all() # define pointers to the data - cdef np.uint16_t * out_data = out.data - cdef np.uint16_t * image_data = image.data - cdef np.uint8_t * mask_data = mask.data + cdef np.uint16_t * out_data = out.data + cdef np.uint16_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row @@ -83,19 +83,19 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border - cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis @@ -144,7 +144,7 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) r = 0 c = 0 @@ -162,13 +162,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel( @@ -185,13 +185,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], @@ -204,13 +204,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel( @@ -227,13 +227,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index cfb011ac..6a9701a0 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -69,9 +69,9 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, # define pointers to the data - cdef np.uint8_t * out_data = out.data - cdef np.uint8_t * image_data = image.data - cdef np.uint8_t * mask_data = mask.data + cdef np.uint8_t * out_data = out.data + cdef np.uint8_t * image_data = image.data + cdef np.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row @@ -86,19 +86,19 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) + cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border - cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis @@ -148,7 +148,7 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) r = 0 c = 0 @@ -166,13 +166,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ----------------------------------------------------------- out_data[r * cols + c] = \ @@ -188,13 +188,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel --------------------------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], @@ -207,13 +207,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel ----------------------------------------------------------- out_data[r * cols + c] = kernel( @@ -229,13 +229,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, &pop, image_data[rr * cols + cc]) + histogram_increment(histo, & pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, &pop, image_data[rr * cols + cc]) + histogram_decrement(histo, & pop, image_data[rr * cols + cc]) # kernel --------------------------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 67db0c69..3a9b5775 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -32,9 +32,9 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, break delta = imax - imin if delta > 0: - return (1. * (maxbin - 1) * (g - imin) / delta) + return < np.uint16_t > (1. * (maxbin - 1) * (g - imin) / delta) else: - return (imax - imin) + return < np.uint16_t > (imax - imin) cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, @@ -49,9 +49,9 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, if histo[i]: break - return (g - i) + return < np.uint16_t > (g - i) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, @@ -67,9 +67,9 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, if i >= g: break - return (((maxbin - 1) * sum) / pop) + return < np.uint16_t > (((maxbin - 1) * sum) / pop) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, @@ -88,9 +88,9 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, if histo[i]: imin = i break - return (imax - imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, @@ -103,9 +103,9 @@ cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, if pop: for i in range(maxbin - 1, -1, -1): if histo[i]: - return (i) + return < np.uint16_t > (i) - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, @@ -119,9 +119,9 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, if pop: for i in range(maxbin): mean += histo[i] * i - return (mean / pop) + return < np.uint16_t > (mean / pop) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, @@ -138,9 +138,9 @@ cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, if pop: for i in range(maxbin): mean += histo[i] * i - return ((g - mean / pop) / 2. + (midbin - 1)) + return < np.uint16_t > ((g - mean / pop) / 2. + (midbin - 1)) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, @@ -156,9 +156,9 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, if histo[i]: sum -= histo[i] if sum < 0: - return (i) + return < np.uint16_t > (i) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, @@ -171,9 +171,9 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, if pop: for i in range(maxbin): if histo[i]: - return (i) + return < np.uint16_t > (i) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, @@ -188,9 +188,9 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, if histo[i] > hmax: hmax = histo[i] imax = i - return (imax) + return < np.uint16_t > (imax) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, @@ -213,11 +213,11 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, imin = i break if imax - g < g - imin: - return (imax) + return < np.uint16_t > (imax) else: - return (imin) + return < np.uint16_t > (imin) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, @@ -225,7 +225,7 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return (pop) + return < np.uint16_t > (pop) cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, @@ -239,9 +239,9 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, if pop: for i in range(maxbin): mean += histo[i] * i - return (g > (mean / pop)) + return < np.uint16_t > (g > (mean / pop)) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, @@ -256,9 +256,9 @@ cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, if histo[i]: break - return (i - g) + return < np.uint16_t > (i - g) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, np.uint16_t g, Py_ssize_t bitdepth, @@ -266,19 +266,19 @@ cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - cdef float e,p + cdef float e, p if pop: e = 0. for i in range(maxbin): - p = histo[i]/pop - if p>0: - e -= p*log2(p) + p = histo[i] / pop + if p > 0: + e -= p * log2(p) - return e*1000 + return < np.uint16_t > e * 1000 else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- # python wrappers @@ -291,7 +291,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, @@ -300,7 +300,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, @@ -309,7 +309,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -318,7 +318,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, @@ -327,7 +327,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -336,7 +336,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -345,7 +345,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def median(np.ndarray[np.uint16_t, ndim=2] image, @@ -354,7 +354,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, @@ -363,7 +363,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -372,7 +372,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def modal(np.ndarray[np.uint16_t, ndim=2] image, @@ -381,7 +381,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -390,7 +390,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -399,7 +399,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, @@ -408,12 +408,13 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + def entropy(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint16_t, ndim=2] out=None, + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 0174b926..c71ccc5c 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -28,11 +28,11 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, bilat_pop += histo[i] mean += histo[i] * i if bilat_pop: - return (mean / bilat_pop) + return < np.uint16_t > (mean / bilat_pop) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, @@ -47,9 +47,9 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, for i in range(maxbin): if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - return (bilat_pop) + return < np.uint16_t > (bilat_pop) else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 8b45d46a..72d48e53 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -38,12 +38,12 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, delta = imax - imin if delta > 0: - return (1.0 * (maxbin - 1) \ - * (int_min(int_max(imin, g), imax) - imin) / delta) + return < np.uint16_t > (1.0 * (maxbin - 1) + * (int_min(int_max(imin, g), imax) - imin) / delta) else: - return (imax - imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, @@ -69,9 +69,9 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, imax = i break - return (imax - imin) + return < np.uint16_t > (imax - imin) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, @@ -93,11 +93,11 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, mean += histo[i] * i if n > 0: - return (1.0 * mean / n) + return < np.uint16_t > (1.0 * mean / n) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, @@ -121,11 +121,11 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, n += histo[i] mean += histo[i] * i if n > 0: - return ((g - (mean / n)) * .5 + midbin) + return < np.uint16_t > ((g - (mean / n)) * .5 + midbin) else: - return (0) + return < np.uint16_t > (0) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, @@ -154,15 +154,15 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, imax = i break if g > imax: - return imax + return < np.uint16_t > imax if g < imin: - return imin + return < np.uint16_t > imin if imax - g < g - imin: - return imax + return < np.uint16_t > imax else: - return imin + return < np.uint16_t > imin else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, @@ -180,9 +180,9 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return (i) + return < np.uint16_t > (i) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, @@ -200,9 +200,9 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return (n) + return < np.uint16_t > (n) else: - return (0) + return < np.uint16_t > (0) cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, @@ -220,9 +220,9 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return ((maxbin - 1) * (g >= i)) + return < np.uint16_t > ((maxbin - 1) * (g >= i)) else: - return (0) + return < np.uint16_t > (0) # ----------------------------------------------------------------- @@ -239,7 +239,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, """bottom hat """ _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -251,7 +251,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, """return p0,p1 percentile gradient """ _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -263,7 +263,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -274,8 +274,9 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ - _core16(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + _core16( + kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -287,7 +288,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, """reforce contrast using percentiles """ _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -299,7 +300,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, """return p0 percentile """ _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -311,7 +312,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -323,4 +324,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, """return (maxbin-1) if g > percentile p0 """ _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 9b69296b..4a25e974 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -31,11 +31,11 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, break delta = imax - imin if delta > 0: - return (255. * (g - imin) / delta) + return < np.uint8_t > (255. * (g - imin) / delta) else: - return (imax - imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, @@ -49,9 +49,9 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, if histo[i]: break - return (g - i) + return < np.uint8_t > (g - i) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, @@ -67,9 +67,9 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, if i >= g: break - return ((255 * sum) / pop) + return < np.uint8_t > ((255 * sum) / pop) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, @@ -87,9 +87,9 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, if histo[i]: imin = i break - return (imax - imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, @@ -101,9 +101,9 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, if pop: for i in range(255, -1, -1): if histo[i]: - return (i) + return < np.uint8_t > (i) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, @@ -116,9 +116,9 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, if pop: for i in range(256): mean += histo[i] * i - return (mean / pop) + return < np.uint8_t > (mean / pop) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, @@ -131,9 +131,9 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, if pop: for i in range(256): mean += histo[i] * i - return ((g - mean / pop) / 2. + 127) + return < np.uint8_t > ((g - mean / pop) / 2. + 127) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, @@ -148,9 +148,9 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, if histo[i]: sum -= histo[i] if sum < 0: - return (i) + return < np.uint8_t > (i) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, @@ -162,9 +162,9 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, if pop: for i in range(256): if histo[i]: - return (i) + return < np.uint8_t > (i) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, @@ -178,9 +178,9 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, if histo[i] > hmax: hmax = histo[i] imax = i - return (imax) + return < np.uint8_t > (imax) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, @@ -199,18 +199,18 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, imin = i break if imax - g < g - imin: - return (imax) + return < np.uint8_t > (imax) else: - return (imin) + return < np.uint8_t > (imin) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): - return (pop) + return < np.uint8_t > (pop) cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, @@ -223,9 +223,9 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, if pop: for i in range(256): mean += histo[i] * i - return (g > (mean / pop)) + return < np.uint8_t > (g > (mean / pop)) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, @@ -239,9 +239,9 @@ cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, if histo[i]: break - return (i - g) + return < np.uint8_t > (i - g) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, @@ -252,7 +252,7 @@ cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, # early stop if at least one pixel of the neighborhood has the same g if histo[g] > 0: - return 0 + return < np.uint8_t > 0 for i in range(g, -1, -1): if histo[i]: @@ -262,16 +262,16 @@ cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, if histo[i]: break if i - g < min_i: - return (i - g) + return < np.uint8_t > (i - g) else: - return min_i + return < np.uint8_t > min_i cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i - cdef float e,p + cdef float e, p if pop: e = 0. @@ -281,16 +281,16 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, if p > 0: e -= p * log2(p) - return e*10 + return < np.uint8_t > e * 10 else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef Py_ssize_t max_i - cdef float P, mu1, mu2, q1,new_q1, sigma_b, max_sigma_b + cdef float P, mu1, mu2, q1, new_q1, sigma_b, max_sigma_b cdef float mu = 0. # compute local mean @@ -299,27 +299,27 @@ cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, mu += histo[i] * i mu = (mu / pop) else: - return (0) + return < np.uint8_t > (0) # maximizing the between class variance max_i = 0 - q1 = histo[0]/pop + q1 = histo[0] / pop m1 = 0. max_sigma_b = 0. - for i in range(1,256): + for i in range(1, 256): P = histo[i] / pop new_q1 = q1 + P if new_q1 > 0: mu1 = (q1 * mu1 + i * P) / new_q1 - mu2 = (mu - new_q1*mu1) / (1. - new_q1) - sigma_b = new_q1 * (1. - new_q1) * (mu1 - mu2)**2 + mu2 = (mu - new_q1 * mu1) / (1. - new_q1) + sigma_b = new_q1 * (1. - new_q1) * (mu1 - mu2) ** 2 if sigma_b > max_sigma_b: max_sigma_b = sigma_b max_i = i q1 = new_q1 - return max_i + return < np.uint8_t > max_i # ----------------------------------------------------------------- @@ -334,7 +334,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -343,7 +343,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -352,7 +352,7 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -361,7 +361,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -370,7 +370,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -379,7 +379,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -388,7 +388,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -397,7 +397,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, @@ -406,7 +406,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -415,7 +415,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -424,7 +424,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -433,7 +433,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -442,7 +442,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, 0, 0, - 0, 0) + < Py_ssize_t > 0, < Py_ssize_t > 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, @@ -451,29 +451,31 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, - char shift_x=0, char shift_y=0): - _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) - -def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) -def otsu(np.ndarray[np.uint8_t, ndim=2] image, + +def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): + _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + + +def otsu(np.ndarray[np.uint8_t, ndim=2] image, + np.ndarray[np.uint8_t, ndim=2] selem, + np.ndarray[np.uint8_t, ndim=2] mask=None, + np.ndarray[np.uint8_t, ndim=2] out=None, + char shift_x=0, char shift_y=0): _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index d5072c81..ddaec290 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -37,12 +37,12 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, break delta = imax - imin if delta > 0: - return (255 \ - * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) + return < np.uint8_t > (255 + * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) else: - return (imax - imin) + return < np.uint8_t > (imax - imin) else: - return (128) + return < np.uint8_t > (128) cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, @@ -65,9 +65,9 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, imax = i break - return (imax - imin) + return < np.uint8_t > (imax - imin) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, @@ -85,11 +85,11 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, n += histo[i] mean += histo[i] * i if n > 0: - return (1.0 * mean / n) + return < np.uint8_t > (1.0 * mean / n) else: - return (0) + return < np.uint8_t > (0) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, @@ -109,11 +109,11 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, n += histo[i] mean += histo[i] * i if n > 0: - return ((g - (mean / n)) * .5 + 127) + return < np.uint8_t > ((g - (mean / n)) * .5 + 127) else: - return (0) + return < np.uint8_t > (0) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, @@ -137,15 +137,15 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, imax = i break if g > imax: - return imax + return < np.uint8_t > imax if g < imin: - return imin + return < np.uint8_t > imin if imax - g < g - imin: - return imax + return < np.uint8_t > imax else: - return imin + return < np.uint8_t > imin else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, @@ -160,9 +160,9 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return (i) + return < np.uint8_t > (i) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, @@ -177,9 +177,9 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return (n) + return < np.uint8_t > (n) else: - return (0) + return < np.uint8_t > (0) cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, @@ -194,9 +194,9 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return (255 * (g >= i)) + return < np.uint8_t > (255 * (g >= i)) else: - return (0) + return < np.uint8_t > (0) # ----------------------------------------------------------------- @@ -212,7 +212,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, """autolevel """ _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + < Py_ssize_t > 0, < Py_ssize_t > 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -223,7 +223,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, """return p0,p1 percentile gradient """ _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + < Py_ssize_t > 0, < Py_ssize_t > 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -234,7 +234,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + < Py_ssize_t > 0, < Py_ssize_t > 0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -245,7 +245,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, """return original - mean between [p0 and p1] percentiles *.5 +127 """ _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -256,7 +256,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, """reforce contrast using percentiles """ _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -267,7 +267,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, """return p0 percentile """ _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -278,7 +278,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + < Py_ssize_t > 0, < Py_ssize_t > 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -289,4 +289,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, """return 255 if g > percentile p0 """ _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + < Py_ssize_t > 0, < Py_ssize_t > 0) diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 5ad94af4..eee83a74 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -102,9 +102,10 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, """ - return _apply(_crank8_percentiles.autolevel, _crank16_percentiles.autolevel, - image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y, p0=p0, p1=p1) + return _apply( + _crank8_percentiles.autolevel, _crank16_percentiles.autolevel, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, @@ -228,8 +229,9 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_y=shift_y, p0=p0, p1=p1) -def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, - shift_y=False, p0=.0, p1=1.): +def percentile_morph_contr_enh( + image, selem, out=None, mask=None, shift_x=False, + shift_y=False, p0=.0, p1=1.): """Return greyscale local morph_contr_enh of an image. morph_contr_enh is computed on the given structuring element. Only levels @@ -385,6 +387,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, """ - return _apply(_crank8_percentiles.threshold, _crank16_percentiles.threshold, - image, selem, out=out, mask=mask, shift_x=shift_x, - shift_y=shift_y, p0=p0, p1=p1) + return _apply( + _crank8_percentiles.threshold, _crank16_percentiles.threshold, + image, selem, out=out, mask=mask, shift_x=shift_x, + shift_y=shift_y, p0=p0, p1=p1) diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index da2f35bc..be078ddd 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -21,7 +21,7 @@ from skimage.filter.rank.generic import find_bitdepth __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', - 'pop', 'threshold', 'tophat','noise_filter','entropy','otsu'] + 'pop', 'threshold', 'tophat', 'noise_filter', 'entropy', 'otsu'] def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y): @@ -619,6 +619,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Returns the noise feature as described in [Hashimoto12]_ @@ -658,11 +659,12 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, centre_c = int(selem.shape[1] / 2) + shift_x # make a local copy selem_cpy = selem.copy() - selem_cpy[centre_r,centre_c] = 0 + selem_cpy[centre_r, centre_c] = 0 return _apply(_crank8.noise_filter, None, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Returns the entropy [wiki_entropy]_ computed locally. Entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of @@ -714,6 +716,7 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) + def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Returns the Otsu's threshold value for each pixel. diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index d8bcba52..c5d42f51 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -171,7 +171,7 @@ def test_compare_autolevels(): assert_array_equal(loc_autolevel, loc_perc_autolevel) -def test_compare_autolevels_16-bit(): +def test_compare_autolevels_16bit(): # compare autolevel(16-bit) and percentile autolevel(16-bit) with p0=0.0 and # p1=1.0 should returns the same arrays @@ -185,7 +185,7 @@ def test_compare_autolevels_16-bit(): assert_array_equal(loc_autolevel, loc_perc_autolevel) -def test_compare_8-bit_vs_16-bit(): +def test_compare_8bit_vs_16bit(): # filters applied on 8-bit image ore 16-bit image (having only real 8-bit of # dynamic) should be identical From 411d4686d5f24346bb6adabda03009e14bd6a391 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 13 Nov 2012 17:02:08 +0100 Subject: [PATCH 343/625] fix:wrap lines in tests --- skimage/filter/rank/tests/test_rank.py | 166 +++++++++++++------------ 1 file changed, 89 insertions(+), 77 deletions(-) diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index c5d42f51..1c0481a5 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -17,26 +17,26 @@ def test_random_sizes(): image8 = np.ones((m, n), dtype=np.uint8) out8 = np.empty_like(image8) rank.mean(image=image8, selem=elem, mask=mask, out=out8, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image8.shape, out8.shape) rank.mean(image=image8, selem=elem, mask=mask, out=out8, - shift_x=+1, shift_y=+1) + shift_x=+1, shift_y=+1) assert_array_equal(image8.shape, out8.shape) image16 = np.ones((m, n), dtype=np.uint16) out16 = np.empty_like(image8, dtype=np.uint16) rank.mean(image=image16, selem=elem, mask=mask, out=out16, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image16.shape, out16.shape) rank.mean(image=image16, selem=elem, mask=mask, out=out16, - shift_x=+1, shift_y=+1) + shift_x=+1, shift_y=+1) assert_array_equal(image16.shape, out16.shape) rank.percentile_mean(image=image16, mask=mask, out=out16, - selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9) + selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9) assert_array_equal(image16.shape, out16.shape) rank.percentile_mean(image=image16, mask=mask, out=out16, - selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9) + selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9) assert_array_equal(image16.shape, out16.shape) @@ -76,9 +76,9 @@ def test_bitdepth(): mask = np.ones((100, 100), dtype=np.uint8) for i in range(5): - image = np.ones((100, 100),dtype=np.uint16) * 255 * 2**i + image = np.ones((100, 100), dtype=np.uint16) * 255 * 2 ** i r = rank.percentile_mean(image=image, selem=elem, mask=mask, - out=out, shift_x=0, shift_y=0, p0=.1, p1=.9) + out=out, shift_x=0, shift_y=0, p0=.1, p1=.9) def test_population(): @@ -101,12 +101,12 @@ def test_population(): def test_structuring_element8(): # check the output for a custom structuring element - r = np.array([[ 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0], - [ 0, 0, 255, 0, 0, 0], - [ 0, 0, 255, 255, 255, 0], - [ 0, 0, 0, 255, 255, 0], - [ 0, 0, 0, 0, 0, 0]]) + r = np.array([[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 255, 0, 0, 0], + [0, 0, 255, 255, 255, 0], + [0, 0, 0, 255, 255, 0], + [0, 0, 0, 0, 0, 0]]) # 8-bit image = np.zeros((6, 6), dtype=np.uint8) @@ -116,7 +116,7 @@ def test_structuring_element8(): mask = np.ones(image.shape, dtype=np.uint8) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=1, shift_y=1) + shift_x=1, shift_y=1) assert_array_equal(r, out) # 16-bit @@ -125,24 +125,25 @@ def test_structuring_element8(): out = np.empty_like(image) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=1, shift_y=1) + shift_x=1, shift_y=1) assert_array_equal(r, out) def test_fail_on_bitdepth(): # should fail because data bitdepth is too high for the function - image = np.ones((100, 100), dtype=np.uint16) * 2**12 + image = np.ones((100, 100), dtype=np.uint16) * 2 ** 12 elem = np.ones((3, 3), dtype=np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) assert_raises(ValueError, rank.percentile_mean, image=image, - selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) + selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) + def test_pass_on_bitdepth(): # should pass because data bitdepth is not too high for the function - image = np.ones((100, 100), dtype=np.uint16) * 2**11 + image = np.ones((100, 100), dtype=np.uint16) * 2 ** 11 elem = np.ones((3, 3), dtype=np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) @@ -152,7 +153,7 @@ def test_inplace_output(): # rank filters are not supposed to filter inplace selem = disk(20) - image = (np.random.random((500,500))*256).astype(np.uint8) + image = (np.random.random((500, 500)) * 256).astype(np.uint8) out = image assert_raises(NotImplementedError, rank.mean, image, selem, out=out) @@ -166,7 +167,7 @@ def test_compare_autolevels(): selem = disk(20) loc_autolevel = rank.autolevel(image, selem=selem) loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem, - p0=.0, p1=1.) + p0=.0, p1=1.) assert_array_equal(loc_autolevel, loc_perc_autolevel) @@ -180,7 +181,7 @@ def test_compare_autolevels_16bit(): selem = disk(20) loc_autolevel = rank.autolevel(image, selem=selem) loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem, - p0=.0, p1=1.) + p0=.0, p1=1.) assert_array_equal(loc_autolevel, loc_perc_autolevel) @@ -195,7 +196,7 @@ def test_compare_8bit_vs_16bit(): methods = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', - 'morph_contr_enh', 'pop', 'threshold', 'tophat'] + 'morph_contr_enh', 'pop', 'threshold', 'tophat'] for method in methods: func = getattr(rank, method) @@ -211,19 +212,19 @@ def test_trivial_selem8(): image = np.zeros((5, 5), dtype=np.uint8) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 + image[2, 2] = 255 + image[2, 3] = 128 + image[1, 2] = 16 - elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) + elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) @@ -234,19 +235,19 @@ def test_trivial_selem16(): image = np.zeros((5, 5), dtype=np.uint16) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 + image[2, 2] = 255 + image[2, 3] = 128 + image[1, 2] = 16 - elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8) + elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) @@ -257,19 +258,19 @@ def test_smallest_selem8(): image = np.zeros((5, 5), dtype=np.uint8) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 + image[2, 2] = 255 + image[2, 3] = 128 + image[1, 2] = 16 elem = np.array([[1]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) @@ -280,21 +281,22 @@ def test_smallest_selem16(): image = np.zeros((5, 5), dtype=np.uint16) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 + image[2, 2] = 255 + image[2, 3] = 128 + image[1, 2] = 16 elem = np.array([[1]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(image, out) + def test_empty_selem(): # check that min, max and mean returns zeros if structuring element is empty @@ -302,64 +304,74 @@ def test_empty_selem(): out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) res = np.zeros_like(image) - image[2,2] = 255 - image[2,3] = 128 - image[1,2] = 16 + image[2, 2] = 255 + image[2, 3] = 128 + image[1, 2] = 16 - elem = np.array([[0,0,0],[0,0,0]], dtype=np.uint8) + elem = np.array([[0, 0, 0], [0, 0, 0]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(res, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(res, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, - shift_x=0, shift_y=0) + shift_x=0, shift_y=0) assert_array_equal(res, out) + def test_otsu(): # - test = np.tile([128, 145, 103, 127, 165, 83, 127, 185, 63, 127, 205, 43, 127, 225, 23, 127],(16,1)) + test = np.tile( + [128, 145, 103, 127, 165, 83, 127, 185, 63, 127, 205, 43, + 127, 225, 23, 127], + (16, 1)) test = test.astype(np.uint8) - res = np.tile([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1],(16,1)) - selem = np.ones((6,6), dtype=np.uint8) - th = 1*(test>=rank.otsu(test,selem)) - assert_array_equal(th,res) + res = np.tile([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1], + (16, 1)) + selem = np.ones((6, 6), dtype=np.uint8) + th = 1 * (test >= rank.otsu(test, selem)) + assert_array_equal(th, res) def test_entropy(): # verify that entropy is coherent with bitdepth of the input data - selem = np.ones((16,16), dtype=np.uint8) + selem = np.ones((16, 16), dtype=np.uint8) # 1 bit per pixel - data = np.tile(np.asarray([0,1]),(100,100)).astype(np.uint8) - assert(np.max(rank.entropy(data,selem))==10) + data = np.tile(np.asarray([0, 1]), (100, 100)).astype(np.uint8) + assert(np.max(rank.entropy(data, selem)) == 10) # 2 bit per pixel - data = np.tile(np.asarray([[0,1],[2,3]]),(10,10)).astype(np.uint8) - assert(np.max(rank.entropy(data,selem))==20) + data = np.tile(np.asarray([[0, 1], [2, 3]]), (10, 10)).astype(np.uint8) + assert(np.max(rank.entropy(data, selem)) == 20) # 3 bit per pixel - data = np.tile(np.asarray([[0,1,2,3],[4,5,6,7]]),(10,10)).astype(np.uint8) - assert(np.max(rank.entropy(data,selem))==30) + data = np.tile( + np.asarray([[0, 1, 2, 3], [4, 5, 6, 7]]), (10, 10)).astype(np.uint8) + assert(np.max(rank.entropy(data, selem)) == 30) # 4 bit per pixel - data = np.tile(np.reshape(np.arange(16),(4,4)),(10,10)).astype(np.uint8) - assert(np.max(rank.entropy(data,selem))==40) + data = np.tile( + np.reshape(np.arange(16), (4, 4)), (10, 10)).astype(np.uint8) + assert(np.max(rank.entropy(data, selem)) == 40) # 6 bit per pixel - data = np.tile(np.reshape(np.arange(64),(8,8)),(10,10)).astype(np.uint8) - assert(np.max(rank.entropy(data,selem))==60) + data = np.tile( + np.reshape(np.arange(64), (8, 8)), (10, 10)).astype(np.uint8) + assert(np.max(rank.entropy(data, selem)) == 60) # 8-bit per pixel - data = np.tile(np.reshape(np.arange(256),(16,16)),(10,10)).astype(np.uint8) - assert(np.max(rank.entropy(data,selem))==80) + data = np.tile( + np.reshape(np.arange(256), (16, 16)), (10, 10)).astype(np.uint8) + assert(np.max(rank.entropy(data, selem)) == 80) # 12 bit per pixel - selem = np.ones((64,64), dtype=np.uint8) - data = np.tile(np.reshape(np.arange(4096),(64,64)),(2,2)).astype(np.uint16) - assert(np.max(rank.entropy(data,selem))==12000) + selem = np.ones((64, 64), dtype=np.uint8) + data = np.tile( + np.reshape(np.arange(4096), (64, 64)), (2, 2)).astype(np.uint16) + assert(np.max(rank.entropy(data, selem)) == 12000) if __name__ == "__main__": From 698d6ae13f690d660b7d276c56ce4cfd05b65a7d Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Tue, 13 Nov 2012 17:07:21 +0100 Subject: [PATCH 344/625] fix:< Py_ssize_t > 0 and & pop --- skimage/filter/rank/_core16.pyx | 18 +++++------ skimage/filter/rank/_core8.pyx | 18 +++++------ skimage/filter/rank/_crank16.pyx | 30 ++++++++--------- skimage/filter/rank/_crank16_percentiles.pyx | 16 ++++----- skimage/filter/rank/_crank8.pyx | 34 ++++++++++---------- skimage/filter/rank/_crank8_percentiles.pyx | 16 ++++----- 6 files changed, 66 insertions(+), 66 deletions(-) diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 1575e24e..aa959fd0 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -144,7 +144,7 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) r = 0 c = 0 @@ -162,13 +162,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel( @@ -185,13 +185,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], @@ -204,13 +204,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel( @@ -227,13 +227,13 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 6a9701a0..9f4bc693 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -148,7 +148,7 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) r = 0 c = 0 @@ -166,13 +166,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ----------------------------------------------------------- out_data[r * cols + c] = \ @@ -188,13 +188,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel --------------------------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], @@ -207,13 +207,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel ----------------------------------------------------------- out_data[r * cols + c] = kernel( @@ -229,13 +229,13 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_increment(histo, & pop, image_data[rr * cols + cc]) + histogram_increment(histo, &pop, image_data[rr * cols + cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): - histogram_decrement(histo, & pop, image_data[rr * cols + cc]) + histogram_decrement(histo, &pop, image_data[rr * cols + cc]) # kernel --------------------------------------------------------------- out_data[r * cols + c] = kernel(histo, pop, image_data[r * cols + c], diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 3a9b5775..e81bf81f 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -291,7 +291,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, @@ -300,7 +300,7 @@ def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, @@ -309,7 +309,7 @@ def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -318,7 +318,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, @@ -327,7 +327,7 @@ def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -336,7 +336,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -345,7 +345,7 @@ def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def median(np.ndarray[np.uint16_t, ndim=2] image, @@ -354,7 +354,7 @@ def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, @@ -363,7 +363,7 @@ def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -372,7 +372,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def modal(np.ndarray[np.uint16_t, ndim=2] image, @@ -381,7 +381,7 @@ def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -390,7 +390,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -399,7 +399,7 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, @@ -408,7 +408,7 @@ def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) def entropy(np.ndarray[np.uint16_t, ndim=2] image, @@ -417,4 +417,4 @@ def entropy(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint16_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 72d48e53..220d2386 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -239,7 +239,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, """bottom hat """ _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -251,7 +251,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, """return p0,p1 percentile gradient """ _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -263,7 +263,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -276,7 +276,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, """ _core16( kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -288,7 +288,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, """reforce contrast using percentiles """ _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -300,7 +300,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, """return p0 percentile """ _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -312,7 +312,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -324,4 +324,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, """return (maxbin-1) if g > percentile p0 """ _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + bitdepth, p0, p1, 0, 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 4a25e974..8bff9703 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -334,7 +334,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -343,7 +343,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -352,7 +352,7 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -361,7 +361,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -370,7 +370,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -379,7 +379,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -388,7 +388,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -397,7 +397,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, @@ -406,7 +406,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -415,7 +415,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -424,7 +424,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -433,7 +433,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -442,7 +442,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, 0, 0, - < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, @@ -451,7 +451,7 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, @@ -460,7 +460,7 @@ def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def entropy(np.ndarray[np.uint8_t, ndim=2] image, @@ -469,7 +469,7 @@ def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) def otsu(np.ndarray[np.uint8_t, ndim=2] image, @@ -478,4 +478,4 @@ def otsu(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, - 0, 0, < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index ddaec290..31b00742 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -212,7 +212,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, """autolevel """ _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -223,7 +223,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, """return p0,p1 percentile gradient """ _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -234,7 +234,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -245,7 +245,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, """return original - mean between [p0 and p1] percentiles *.5 +127 """ _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -256,7 +256,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, """reforce contrast using percentiles """ _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + p0, p1, 0, 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -267,7 +267,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, """return p0 percentile """ _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - p0, p1, < Py_ssize_t > 0, < Py_ssize_t > 0) + p0, p1, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -278,7 +278,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -289,4 +289,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, """return 255 if g > percentile p0 """ _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, - < Py_ssize_t > 0, < Py_ssize_t > 0) + 0, 0) From 70f2b67ef7e251b9e758d28aa3fd80353ce5629a Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Wed, 14 Nov 2012 08:00:59 +0100 Subject: [PATCH 345/625] fix: ref and test_otsu doc --- skimage/filter/rank/percentile_rank.pyx | 7 +++++-- skimage/filter/rank/tests/test_rank.py | 4 +++- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index eee83a74..62cc4576 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -5,9 +5,12 @@ instead of using [min, max]. It means that isolated bright or dark pixels will not produce halos. The local histogram is computed using a sliding window similar to the method -described in: +described in [1]. -.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional +References +========== + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. diff --git a/skimage/filter/rank/tests/test_rank.py b/skimage/filter/rank/tests/test_rank.py index 1c0481a5..43ff030f 100644 --- a/skimage/filter/rank/tests/test_rank.py +++ b/skimage/filter/rank/tests/test_rank.py @@ -322,7 +322,9 @@ def test_empty_selem(): def test_otsu(): - # + # test the local Otsu segmentation on a synthetic image + # (left to right ramp * sinus) + test = np.tile( [128, 145, 103, 127, 165, 83, 127, 185, 63, 127, 205, 43, 127, 225, 23, 127], From 80a9a5aba5f6e629219acb0bd44a35567e00b5c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 14 Nov 2012 08:33:27 +0100 Subject: [PATCH 346/625] Apply numpy reference style to rank filter descriptions --- skimage/filter/rank/bilateral_rank.pyx | 13 ++++++++----- skimage/filter/rank/percentile_rank.pyx | 16 ++++++++-------- skimage/filter/rank/rank.pyx | 13 ++++++++----- 3 files changed, 24 insertions(+), 18 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 0d3cdd88..dec07c28 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -1,11 +1,7 @@ """Approximate bilateral rank filter for local (custom kernel) mean. The local histogram is computed using a sliding window similar to the method -described in: - -.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional - median filtering algorithm", IEEE Transactions on Acoustics, Speech and - Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +described in [1]_. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit images are casted in 16-bit the number of histogram bins is determined from the @@ -21,6 +17,13 @@ equally). Result image is 16-bit with respect to the input image. +References +---------- + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + """ import numpy as np diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 62cc4576..7deae623 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -5,14 +5,7 @@ instead of using [min, max]. It means that isolated bright or dark pixels will not produce halos. The local histogram is computed using a sliding window similar to the method -described in [1]. - -References -========== - -.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional - median filtering algorithm", IEEE Transactions on Acoustics, Speech and - Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +described in [1]_. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value @@ -20,6 +13,13 @@ present in the image. Result image is 8 or 16-bit with respect to the input image. +References +---------- + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + """ import numpy as np diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index be078ddd..43f03133 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -1,9 +1,5 @@ """The local histogram is computed using a sliding window similar to the method -described in: - -.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional - median filtering algorithm", IEEE Transactions on Acoustics, Speech and - Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +described in [1]_. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value @@ -11,6 +7,13 @@ present in the image. Result image is 8 or 16-bit with respect to the input image. +References +---------- + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + """ import numpy as np From 22a73fc0cb8ab7c11d0aaf37c19caf7ad2257d57 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Wed, 14 Nov 2012 20:08:16 -0500 Subject: [PATCH 347/625] BUG: Adapt to whitespace in text search --- check_bento_build.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/check_bento_build.py b/check_bento_build.py index 108b0aad..00e4d659 100644 --- a/check_bento_build.py +++ b/check_bento_build.py @@ -5,7 +5,7 @@ import os import re -RE_CYTHON = re.compile("config.add_extension\(['\"]([\S]+)['\"]") +RE_CYTHON = re.compile("config.add_extension\(\s*['\"]([\S]+)['\"]") BENTO_TEMPLATE = """ Extension: {module_path} From 2a8466a5490dab8ea02c1480ac95ebba2e4260f5 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Wed, 14 Nov 2012 20:43:52 -0500 Subject: [PATCH 348/625] ENH: Compare full paths of cython extensions. If setup.py defined an extension in a subdirectory, the extension name in bento.info didn't match the one found in setup.py. Comparing the full extension path fixes the issue. --- check_bento_build.py | 47 ++++++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 24 deletions(-) diff --git a/check_bento_build.py b/check_bento_build.py index 00e4d659..34b2272a 100644 --- a/check_bento_build.py +++ b/check_bento_build.py @@ -23,7 +23,7 @@ def each_setup_in_pkg(top_dir): def each_cy_in_setup(top_dir): - """Yield path and name for each cython extension package's setup file.""" + """Yield path for each cython extension package's setup file.""" for dir_path, f in each_setup_in_pkg(top_dir): text = f.read() match = RE_CYTHON.findall(text) @@ -38,30 +38,27 @@ def each_cy_in_setup(top_dir): else: path = dir_path full_path = os.path.join(path, cy_file) - yield full_path, cy_file + yield full_path def each_cy_in_bento(bento_file='bento.info'): - """Yield path and name for each cython extension in bento info file.""" + """Yield path for each cython extension in bento info file.""" with open(bento_file) as f: for line in f: line = line.strip() if line.startswith('Extension:'): - parts = line.split('.') - ext_name = parts[-1] path = line.lstrip('Extension:').strip() - yield path, ext_name + yield path def remove_common_extensions(cy_bento, cy_setup): - for ext_name in cy_bento.keys(): - if ext_name in cy_setup: - spath = cy_setup.pop(ext_name) - bpath = cy_bento.pop(ext_name) - if not spath.replace(os.path.sep, '.') == bpath: - print "Mismatched paths:" - print " setup.py: ", spath - print " bento.info:", bpath + # normalize so that cy_setup and cy_bento have the same separator + cy_setup = set(ext.replace('/', '.') for ext in cy_setup) + cy_setup_diff = cy_setup.difference(cy_bento) + cy_setup_diff = set(ext.replace('.', '/') for ext in cy_setup_diff) + cy_bento_diff = cy_bento.difference(cy_setup) + return cy_bento_diff, cy_setup_diff + def print_results(cy_bento, cy_setup): def info(text): @@ -69,28 +66,30 @@ def print_results(cy_bento, cy_setup): print(text) print('-' * len(text)) - print "Bento errors:" - print "-------------" + if not (cy_bento or cy_setup): + print "bento.info and setup.py files match." if cy_bento: - info("The following extensions in 'bento.info' were not found:") - print('\n'.join(cy_bento.keys())) + info("Extensions found in 'bento.info' but not in any 'setup.py:") + print('\n'.join(cy_bento)) if cy_setup: - info("The following cython files exist but were not in 'bento.info':") + info("Extensions found in a 'setup.py' but not in any 'bento.info:") print('\n'.join(cy_setup)) info("Consider adding the following to the 'bento.info' Library:") - for ext_name, dir_path in cy_setup.iteritems(): - print BENTO_TEMPLATE.format(module_path=dir_path.replace('/', '.'), + for dir_path in cy_setup: + module_path = dir_path.replace('/', '.') + print BENTO_TEMPLATE.format(module_path=module_path, dir_path=dir_path) + if __name__ == '__main__': # All cython extensions defined in 'setup.py' files. - cy_setup = dict((ext, path) for path, ext in each_cy_in_setup('skimage')) + cy_setup = set(each_cy_in_setup('skimage')) # All cython extensions defined 'bento.info' file. - cy_bento = dict((ext, path) for path, ext in each_cy_in_bento()) + cy_bento = set(each_cy_in_bento()) - remove_common_extensions(cy_bento, cy_setup) + cy_bento, cy_setup = remove_common_extensions(cy_bento, cy_setup) print_results(cy_bento, cy_setup) From d6a0b088597a439c6fb7cc04dfcf6d3db3c53503 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Wed, 14 Nov 2012 20:46:47 -0500 Subject: [PATCH 349/625] ENH: Add filter.rank extensions to bento.info --- bento.info | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/bento.info b/bento.info index bd831849..8e001582 100644 --- a/bento.info +++ b/bento.info @@ -118,6 +118,36 @@ Library: Extension: skimage._shared.geometry Sources: skimage/_shared/geometry.pyx + Extension: skimage.filter.rank._core16 + Sources: + skimage/filter/rank/_core16.pyx + Extension: skimage.filter.rank._crank8 + Sources: + skimage/filter/rank/_crank8.pyx + Extension: skimage.filter.rank._crank16 + Sources: + skimage/filter/rank/_crank16.pyx + Extension: skimage.filter.rank._core8 + Sources: + skimage/filter/rank/_core8.pyx + Extension: skimage.filter.rank.rank + Sources: + skimage/filter/rank/rank.pyx + Extension: skimage.filter.rank.bilateral_rank + Sources: + skimage/filter/rank/bilateral_rank.pyx + Extension: skimage.filter.rank._crank16_percentiles + Sources: + skimage/filter/rank/_crank16_percentiles.pyx + Extension: skimage.filter.rank.percentile_rank + Sources: + skimage/filter/rank/percentile_rank.pyx + Extension: skimage.filter.rank._crank8_percentiles + Sources: + skimage/filter/rank/_crank8_percentiles.pyx + Extension: skimage.filter.rank._crank16_bilateral + Sources: + skimage/filter/rank/_crank16_bilateral.pyx Executable: skivi Module: skimage.scripts.skivi From a7527adf31e464b93daaa2d4d8da6db70187aba9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 17 Nov 2012 22:27:11 +0100 Subject: [PATCH 350/625] Add example for hough_peaks function --- doc/examples/plot_hough_transform.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/doc/examples/plot_hough_transform.py b/doc/examples/plot_hough_transform.py index 5416d662..12931c74 100644 --- a/doc/examples/plot_hough_transform.py +++ b/doc/examples/plot_hough_transform.py @@ -59,7 +59,7 @@ References ''' -from skimage.transform import hough, probabilistic_hough +from skimage.transform import hough, hough_peaks, probabilistic_hough from skimage.filter import canny from skimage import data @@ -81,11 +81,11 @@ h, theta, d = hough(image) plt.figure(figsize=(8, 4)) -plt.subplot(121) +plt.subplot(131) plt.imshow(image, cmap=plt.cm.gray) plt.title('Input image') -plt.subplot(122) +plt.subplot(132) plt.imshow(np.log(1 + h), extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]), d[-1], d[0]], @@ -94,6 +94,14 @@ plt.title('Hough transform') plt.xlabel('Angles (degrees)') plt.ylabel('Distance (pixels)') +plt.subplot(133) +plt.imshow(image, cmap=plt.cm.gray) +rows, cols = image.shape +for _, angle, dist in zip(*hough_peaks(h, theta, d)): + y0 = (dist - 0 * np.cos(angle)) / np.sin(angle) + y1 = (dist - cols * np.cos(angle)) / np.sin(angle) + plt.plot((0, cols), (y0, y1), '-r') +plt.axis((0, cols, rows, 0)) # Line finding, using the Probabilistic Hough Transform @@ -121,4 +129,3 @@ for line in lines: plt.title('Lines found with PHT') plt.axis('image') plt.show() - From 63b5c5a4a0bee2aea185b31af67da97fe751a2c2 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Mon, 19 Nov 2012 23:38:58 -0600 Subject: [PATCH 351/625] FEAT - combined API from is_local_maximum() into peak_local_max() is_local_maximum() is a wrapper function for peak_local_max() is_local_maximum() runs much faster (~20% of prior runtime, nearly = to peak_local_max()) All tests in .feature and .morphology subpackages pass as written with these changes. Todo: * Fully document API * remove commented-out old algorithm in is_local_maximum() * add new tests for full coverage of new, more complex peak_local_max() --- skimage/feature/peak.py | 85 +++++++++++++++++------- skimage/morphology/watershed.py | 112 +++++++++++++++++--------------- 2 files changed, 120 insertions(+), 77 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 4765974e..b1148c9a 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -1,10 +1,9 @@ -import warnings import numpy as np -from scipy import ndimage +import scipy.ndimage as ndi - -def peak_local_max(image, min_distance=10, threshold='deprecated', - threshold_abs=0, threshold_rel=0.1, num_peaks=np.inf): +def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, + exclude_border=True, indices=True, num_peaks=np.inf, + footprint=None, labels=None, **kwargs): """Return coordinates of peaks in an image. Peaks are the local maxima in a region of `2 * min_distance + 1` @@ -36,11 +35,11 @@ def peak_local_max(image, min_distance=10, threshold='deprecated', Notes ----- - The peak local maximum function returns the coordinates of local peaks (maxima) - in a image. A maximum filter is used for finding local maxima. This operation - dilates the original image. After comparison between dilated and original image, - peak_local_max function returns the coordinates of peaks where - dilated image = original. + The peak local maximum function returns the coordinates of local peaks + (maxima) in a image. A maximum filter is used for finding local maxima. + This operation dilates the original image. After comparison between + dilated and original image, peak_local_max function returns the + coordinates of peaks where dilated image = original. Examples -------- @@ -64,25 +63,60 @@ def peak_local_max(image, min_distance=10, threshold='deprecated', array([[3, 2]]) """ + # In the case of labels, recursively build and return an output + # operating on each label separately; for API compatibility with + # ..watershed.is_local_maximum() + if labels is not None: + label_values = np.unique(labels) + # Reorder label values to have consecutive integers (no gaps) + if np.any(np.diff(label_values) != 1): + mask = labels >= 0 + labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype) + labels = labels.astype(np.int32) + + out = np.zeros_like(image) + for label in labels: + out += peak_local_max(image, min_distance=min_distance, + threshold_abs=threshold_abs, + threshold_rel=threshold_rel, + exclude_border=exclude_border, + indices=False, num_peaks=np.inf, + footprint=footprint, labels=None, + **kwargs) + + if indices is True: + return np.transpose(out.nonzero()) + else: + return out + + if np.all(image == image.flat[0]): - return [] + if indices is True: + return [] + else: + return np.zeros_like(image) + image = image.copy() # Non maximum filter - size = 2 * min_distance + 1 - image_max = ndimage.maximum_filter(image, size=size, mode='constant') + if footprint is not None: + image_max = ndi.maximum_filter(image, footprint=footprint, + mode='constant') + else: + size = 2 * min_distance + 1 + image_max = ndi.maximum_filter(image, size=size, mode='constant') mask = (image == image_max) image *= mask - # Remove the image borders - image[:min_distance] = 0 - image[-min_distance:] = 0 - image[:, :min_distance] = 0 - image[:, -min_distance:] = 0 + if exclude_border: + # Remove the image borders + image[:min_distance] = 0 + image[-min_distance:] = 0 + image[:, :min_distance] = 0 + image[:, -min_distance:] = 0 + + if kwargs.has_key('threshold'): + threshold_rel = kwargs['threshold'] - if not threshold == 'deprecated': - msg = "`threshold` parameter deprecated; use `threshold_rel instead." - warnings.warn(msg, DeprecationWarning) - threshold_rel = threshold # find top peak candidates above a threshold peak_threshold = max(np.max(image.ravel()) * threshold_rel, threshold_abs) image_t = (image > peak_threshold) * 1 @@ -95,4 +129,9 @@ def peak_local_max(image, min_distance=10, threshold='deprecated', idx_maxsort = np.argsort(intensities)[::-1] coordinates = coordinates[idx_maxsort][:num_peaks] - return coordinates + if indices is True: + return coordinates + else: + out = np.zeros_like(image) + out[coordinates[:, 0], coordinates[:, 1]] = 1 + return out diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index 8a08fafc..840d4c90 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -28,6 +28,7 @@ from _heapq import heappush, heappop import numpy as np import scipy.ndimage from ..filter import rank_order +from ..feature import peak_local_max from . import _watershed @@ -281,62 +282,65 @@ def is_local_maximum(image, labels=None, footprint=None): [False, False, False, False], [False, True, False, True]], dtype=bool) """ - if labels is None: - labels = np.ones(image.shape, dtype=np.uint8) - if footprint is None: - footprint = np.ones([3] * image.ndim, dtype=np.uint8) - assert((np.all(footprint.shape) & 1) == 1) - footprint = (footprint != 0) - footprint_extent = (np.array(footprint.shape) - 1) // 2 - if np.all(footprint_extent == 0): - return labels > 0 - result = (labels > 0).copy() - # - # Create a labels matrix with zeros at the borders that might be - # hit by the footprint. - # - big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2, - labels.dtype) - big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels - # - # Find the relative indexes of each footprint element - # - image_strides = np.array(image.strides) // image.dtype.itemsize - big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize - result_strides = np.array(result.strides) // result.dtype.itemsize - footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]] + # if labels is None: + # labels = np.ones(image.shape, dtype=np.uint8) + # if footprint is None: + # footprint = np.ones([3] * image.ndim, dtype=np.uint8) + # assert((np.all(footprint.shape) & 1) == 1) + # footprint = (footprint != 0) + # footprint_extent = (np.array(footprint.shape) - 1) // 2 + # if np.all(footprint_extent == 0): + # return labels > 0 + # result = (labels > 0).copy() + # # + # # Create a labels matrix with zeros at the borders that might be + # # hit by the footprint. + # # + # big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2, + # labels.dtype) + # big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels + # # + # # Find the relative indexes of each footprint element + # # + # image_strides = np.array(image.strides) // image.dtype.itemsize + # big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize + # result_strides = np.array(result.strides) // result.dtype.itemsize + # footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]] - fp_image_offsets = np.sum(image_strides[:, np.newaxis] * - footprint_offsets[:, footprint], 0) - fp_big_offsets = np.sum(big_strides[:, np.newaxis] * - footprint_offsets[:, footprint], 0) - # - # Get the index of each labeled pixel in the image and big_labels arrays - # - indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0] - image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0) - big_indexes = np.sum(big_strides[:, np.newaxis] * - (indexes + footprint_extent[:, np.newaxis]), 0) - result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0) - # - # Now operate on the raveled images - # - big_labels_raveled = big_labels.ravel() - image_raveled = image.ravel() - result_raveled = result.ravel() - # - # A hit is a hit if the label at the offset matches the label at the pixel - # and if the intensity at the pixel is greater or equal to the intensity - # at the offset. - # - for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets): - same_label = (big_labels_raveled[big_indexes + fp_big_offset] == - big_labels_raveled[big_indexes]) - less_than = (image_raveled[image_indexes[same_label]] < - image_raveled[image_indexes[same_label] + fp_image_offset]) - result_raveled[result_indexes[same_label][less_than]] = False + # fp_image_offsets = np.sum(image_strides[:, np.newaxis] * + # footprint_offsets[:, footprint], 0) + # fp_big_offsets = np.sum(big_strides[:, np.newaxis] * + # footprint_offsets[:, footprint], 0) + # # + # # Get the index of each labeled pixel in the image and big_labels arrays + # # + # indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0] + # image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0) + # big_indexes = np.sum(big_strides[:, np.newaxis] * + # (indexes + footprint_extent[:, np.newaxis]), 0) + # result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0) + # # + # # Now operate on the raveled images + # # + # big_labels_raveled = big_labels.ravel() + # image_raveled = image.ravel() + # result_raveled = result.ravel() + # # + # # A hit is a hit if the label at the offset matches the label at the pixel + # # and if the intensity at the pixel is greater or equal to the intensity + # # at the offset. + # # + # for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets): + # same_label = (big_labels_raveled[big_indexes + fp_big_offset] == + # big_labels_raveled[big_indexes]) + # less_than = (image_raveled[image_indexes[same_label]] < + # image_raveled[image_indexes[same_label] + fp_image_offset]) + # result_raveled[result_indexes[same_label][less_than]] = False - return result + # return result + return peak_local_max(image, labels=labels, min_distance=1, + footprint=footprint, indices=False, + exclude_border=False) # ---------------------- deprecated ------------------------------ From 226eaaff9250da4e73e4f0589cea41a13d961130 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Tue, 20 Nov 2012 00:12:47 -0600 Subject: [PATCH 352/625] FEAT - Unified peak finder backend Detailed changelist: * Fully documented new API for peak_local_max() * Removed commented-out old code for is_local_maximum() * Added "See also" and "Notes" to is_local_maximum() redirecting to peak_local_max() --- skimage/feature/peak.py | 58 +++++++++++++++++++++------- skimage/morphology/watershed.py | 67 ++++++--------------------------- 2 files changed, 56 insertions(+), 69 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index b1148c9a..cd740656 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -4,10 +4,10 @@ import scipy.ndimage as ndi def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=np.inf, footprint=None, labels=None, **kwargs): - """Return coordinates of peaks in an image. + """ + Find peaks in an image, and return them as coordinates or a boolean array. - Peaks are the local maxima in a region of `2 * min_distance + 1` - (i.e. peaks are separated by at least `min_distance`). + Peaks are the local maxima NOTE: If peaks are flat (i.e. multiple pixels have exact same intensity), the coordinates of all pixels are returned. @@ -16,22 +16,54 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, ---------- image : ndarray of floats Input image. - min_distance : int - Minimum number of pixels separating peaks and image boundary. - threshold : float - Deprecated. See `threshold_rel`. - threshold_abs : float + + min_distance : int, default 10. + Minimum number of pixels separating peaks in a region of `2 * + min_distance + 1` (i.e. peaks are separated by at least + `min_distance`). + If `exclude_border` is True, this value also excludes a border + `min_distance` from the image boundary. + To find the maximum number of points, use `min_distance=1`. + + threshold_abs : float, default 0. Minimum intensity of peaks. - threshold_rel : float + + threshold_rel : float, default 0.1 Minimum intensity of peaks calculated as `max(image) * threshold_rel`. - num_peaks : int + + exclude_border : bool, default True + If True, `min_distance` excludes peaks from the border of the image as + well as from each other. + + indices : bool, default True + If True, the output will be a matrix representing peak coordinates. + If False, the output will be a boolean matrix shaped as `image.shape` + with peaks present at True elements. + + num_peaks : int, default np.inf Maximum number of peaks. When the number of peaks exceeds `num_peaks`, - return `num_peaks` coordinates based on peak intensity. + return `num_peaks` peaks based on highest peak intensity. + + footprint : ndarray of bools, optional + If provided, `footprint == 1` represents the local region within which + to search for peaks at every point in `image`. + Overrides `min_distance`, except for border exclusion if + `exclude_border` is True. + + labels : ndarray of ints, optional + If provided, each unique region `labels == value` represents a unique + region to search for peaks. Zero is reserved for background. + + threshold : float, optional + Deprecated. If provided as a kwarg, will override `threshold_rel`. + See `threshold_rel`. Returns ------- - coordinates : (N, 2) array - (row, column) coordinates of peaks. + output : (N, 2) array or ndarray of bools + If `exclude_border = True` : (row, column) coordinates of peaks. + If `exclude_border = False` : Boolean array shaped like `image`, + with peaks represented by True values. Notes ----- diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index 840d4c90..f4c5a816 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -248,6 +248,16 @@ def is_local_maximum(image, labels=None, footprint=None): result: ndarray of bools mask that is True for pixels that are local maxima of `image` + See also + -------- + skimage.feature.peak_local_max: Unified peak finding backend. + The more capable backend for finding local maxima. + + Notes + ----- + This function is now a wrapper for skimage.feature.peak_local_max() and is + retained only for convenience and backward compatibility. + Examples -------- >>> image = np.zeros((4, 4)) @@ -281,63 +291,8 @@ def is_local_maximum(image, labels=None, footprint=None): [False, True, False, True], [False, False, False, False], [False, True, False, True]], dtype=bool) + """ - # if labels is None: - # labels = np.ones(image.shape, dtype=np.uint8) - # if footprint is None: - # footprint = np.ones([3] * image.ndim, dtype=np.uint8) - # assert((np.all(footprint.shape) & 1) == 1) - # footprint = (footprint != 0) - # footprint_extent = (np.array(footprint.shape) - 1) // 2 - # if np.all(footprint_extent == 0): - # return labels > 0 - # result = (labels > 0).copy() - # # - # # Create a labels matrix with zeros at the borders that might be - # # hit by the footprint. - # # - # big_labels = np.zeros(np.array(labels.shape) + footprint_extent * 2, - # labels.dtype) - # big_labels[[slice(fe, -fe) for fe in footprint_extent]] = labels - # # - # # Find the relative indexes of each footprint element - # # - # image_strides = np.array(image.strides) // image.dtype.itemsize - # big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize - # result_strides = np.array(result.strides) // result.dtype.itemsize - # footprint_offsets = np.mgrid[[slice(-fe, fe + 1) for fe in footprint_extent]] - - # fp_image_offsets = np.sum(image_strides[:, np.newaxis] * - # footprint_offsets[:, footprint], 0) - # fp_big_offsets = np.sum(big_strides[:, np.newaxis] * - # footprint_offsets[:, footprint], 0) - # # - # # Get the index of each labeled pixel in the image and big_labels arrays - # # - # indexes = np.mgrid[[slice(0, x) for x in labels.shape]][:, labels > 0] - # image_indexes = np.sum(image_strides[:, np.newaxis] * indexes, 0) - # big_indexes = np.sum(big_strides[:, np.newaxis] * - # (indexes + footprint_extent[:, np.newaxis]), 0) - # result_indexes = np.sum(result_strides[:, np.newaxis] * indexes, 0) - # # - # # Now operate on the raveled images - # # - # big_labels_raveled = big_labels.ravel() - # image_raveled = image.ravel() - # result_raveled = result.ravel() - # # - # # A hit is a hit if the label at the offset matches the label at the pixel - # # and if the intensity at the pixel is greater or equal to the intensity - # # at the offset. - # # - # for fp_image_offset, fp_big_offset in zip(fp_image_offsets, fp_big_offsets): - # same_label = (big_labels_raveled[big_indexes + fp_big_offset] == - # big_labels_raveled[big_indexes]) - # less_than = (image_raveled[image_indexes[same_label]] < - # image_raveled[image_indexes[same_label] + fp_image_offset]) - # result_raveled[result_indexes[same_label][less_than]] = False - - # return result return peak_local_max(image, labels=labels, min_distance=1, footprint=footprint, indices=False, exclude_border=False) From 33c95140626374a68a8547114052aa25b8f3630d Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Tue, 20 Nov 2012 00:36:12 -0600 Subject: [PATCH 353/625] BUG - Fixed a bug where, when `indices` = False, output was not Boolean. --- skimage/feature/peak.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index cd740656..ffa942c4 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -119,7 +119,7 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, if indices is True: return np.transpose(out.nonzero()) else: - return out + return out.astype(bool) if np.all(image == image.flat[0]): @@ -164,6 +164,6 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, if indices is True: return coordinates else: - out = np.zeros_like(image) - out[coordinates[:, 0], coordinates[:, 1]] = 1 + out = np.zeros_like(image, dtype=bool) + out[coordinates[:, 0], coordinates[:, 1]] = True return out From 233b368195d6beaeba84794b5bfd8663e2bf45f2 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Tue, 20 Nov 2012 00:45:01 -0600 Subject: [PATCH 354/625] BUG - Fixed a bug where the image was not properly masked for each unique label. --- skimage/feature/peak.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index ffa942c4..fcd088c6 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -108,13 +108,14 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, out = np.zeros_like(image) for label in labels: - out += peak_local_max(image, min_distance=min_distance, - threshold_abs=threshold_abs, - threshold_rel=threshold_rel, - exclude_border=exclude_border, - indices=False, num_peaks=np.inf, - footprint=footprint, labels=None, - **kwargs) + maskim = (labels == label) + out += peak_local_max(image * maskim, min_distance=min_distance, + threshold_abs=threshold_abs, + threshold_rel=threshold_rel, + exclude_border=exclude_border, + indices=False, num_peaks=np.inf, + footprint=footprint, labels=None, + **kwargs) if indices is True: return np.transpose(out.nonzero()) From da5a2a82213e01fc9b9d98f31cf740c6d3aa7767 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Tue, 20 Nov 2012 01:11:31 -0600 Subject: [PATCH 355/625] BUG - Now parsing all unique labels != 0 properly, and set threshold_rel=0 in is_local_maximum() wrapper. --- skimage/feature/peak.py | 4 +++- skimage/morphology/watershed.py | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index fcd088c6..ea2ace3e 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -106,8 +106,10 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype) labels = labels.astype(np.int32) + # New values for new ordering + label_values = np.unique(labels) out = np.zeros_like(image) - for label in labels: + for label in label_values[label_values != 0]: maskim = (labels == label) out += peak_local_max(image * maskim, min_distance=min_distance, threshold_abs=threshold_abs, diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index f4c5a816..b6ae2d07 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -294,8 +294,8 @@ def is_local_maximum(image, labels=None, footprint=None): """ return peak_local_max(image, labels=labels, min_distance=1, - footprint=footprint, indices=False, - exclude_border=False) + threshold_rel=0, footprint=footprint, + indices=False, exclude_border=False) # ---------------------- deprecated ------------------------------ From e7288f9140b1a1a8a545f19434ec5805006b4a1a Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Tue, 20 Nov 2012 01:17:42 -0600 Subject: [PATCH 356/625] BUG - Dtype conversion to Boolean in a new code path not included in current tests. --- skimage/feature/peak.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index ea2ace3e..2d40ded6 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -129,7 +129,7 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, if indices is True: return [] else: - return np.zeros_like(image) + return np.zeros_like(image, dtype=bool) image = image.copy() # Non maximum filter From 6ebc43bad8e1e94660ab7978cdd51098d125bb91 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 5 Nov 2012 22:17:42 +0000 Subject: [PATCH 357/625] ENH Add imread io plugin This relies on imread (https://github.com/luispedro/imread) which supports a few microscopy formats otherwise hard to read (LSM and MetaMorph STK) as well as basic ones. --- skimage/io/_plugins/imread_plugin.ini | 3 + skimage/io/_plugins/imread_plugin.py | 43 ++++++++++++++ skimage/io/tests/test_imread.py | 85 +++++++++++++++++++++++++++ 3 files changed, 131 insertions(+) create mode 100644 skimage/io/_plugins/imread_plugin.ini create mode 100644 skimage/io/_plugins/imread_plugin.py create mode 100644 skimage/io/tests/test_imread.py diff --git a/skimage/io/_plugins/imread_plugin.ini b/skimage/io/_plugins/imread_plugin.ini new file mode 100644 index 00000000..a6a5ddb1 --- /dev/null +++ b/skimage/io/_plugins/imread_plugin.ini @@ -0,0 +1,3 @@ +[imread] +description = Image reading and writing via imread +provides = imread, imsave diff --git a/skimage/io/_plugins/imread_plugin.py b/skimage/io/_plugins/imread_plugin.py new file mode 100644 index 00000000..7e7681b0 --- /dev/null +++ b/skimage/io/_plugins/imread_plugin.py @@ -0,0 +1,43 @@ +__all__ = ['imread', 'imsave'] + +import numpy as np + +try: + import imread as _imread +except ImportError: + raise ImportError("Imread could not be found" + "Please refer to http://pypi.python.org/pypi/imread/ " + "for further instructions.") + +def imread(fname, dtype=None): + """Load an image from file. + + Parameters + ---------- + fname : str + Name of input file + + """ + im = _imread.imread(fname) + if dtype is not None: + im = im.asdtype(dtype) + return im + +def imsave(fname, arr, format_str=None): + """Save an image to disk. + + Parameters + ---------- + fname : str + Name of destination file. + arr : ndarray of uint8 or uint16 + Array (image) to save. + format_str: str,optional + Format to save as. + + Notes + ----- + Currently, only 8-bit precision is supported. + """ + return _imread.imsave(fname, arr, formatstr=format_str) + diff --git a/skimage/io/tests/test_imread.py b/skimage/io/tests/test_imread.py new file mode 100644 index 00000000..8dd6ea6f --- /dev/null +++ b/skimage/io/tests/test_imread.py @@ -0,0 +1,85 @@ +import os.path +import numpy as np +from numpy.testing import * +from numpy.testing.decorators import skipif + +from tempfile import NamedTemporaryFile + +from skimage import data_dir +from skimage.io import imread, imsave, use_plugin, reset_plugins + +try: + import imread as _imread + use_plugin('imread') +except ImportError: + imread_available = False +else: + imread_available = True + + +def teardown(): + reset_plugins() + + +def setup_module(self): + """The effect of the `plugin.use` call may be overridden by later imports. + Call `use_plugin` directly before the tests to ensure that imread is used. + + """ + try: + use_plugin('imread') + except ImportError: + pass + + + +@skipif(not imread_available) +def test_imread_flatten(): + # a color image is flattened + img = imread(os.path.join(data_dir, 'color.png'), flatten=True) + assert img.ndim == 2 + assert img.dtype == np.float64 + img = imread(os.path.join(data_dir, 'camera.png'), flatten=True) + # check that flattening does not occur for an image that is grey already. + assert np.sctype2char(img.dtype) in np.typecodes['AllInteger'] + + +@skipif(not imread_available) +def test_imread_palette(): + img = imread(os.path.join(data_dir, 'palette_color.png')) + assert img.ndim == 3 + + +@skipif(not imread_available) +def test_bilevel(): + expected = np.zeros((10, 10), bool) + expected[::2] = 1 + + img = imread(os.path.join(data_dir, 'checker_bilevel.png')) + assert_array_equal(img, expected) + + +class TestSave: + def roundtrip(self, x, scaling=1): + f = NamedTemporaryFile(suffix='.png') + fname = f.name + f.close() + imsave(fname, x) + y = imread(fname) + + assert_array_almost_equal((x * scaling).astype(np.int32), y) + + @skipif(not imread_available) + def test_imsave_roundtrip(self): + dtype = np.uint8 + for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: + x = np.ones(shape, dtype=dtype) * np.random.random(shape) + + if np.issubdtype(dtype, float): + yield self.roundtrip, x, 255 + else: + x = (x * 255).astype(dtype) + yield self.roundtrip, x + +if __name__ == "__main__": + run_module_suite() From 10ce9b89abaf7f2d9bb914771bb77d7f1ded55f5 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 5 Nov 2012 22:32:43 +0000 Subject: [PATCH 358/625] RFCT Use `skimage.utils.convert` for type conversion --- skimage/io/_plugins/imread_plugin.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skimage/io/_plugins/imread_plugin.py b/skimage/io/_plugins/imread_plugin.py index 7e7681b0..323fbec8 100644 --- a/skimage/io/_plugins/imread_plugin.py +++ b/skimage/io/_plugins/imread_plugin.py @@ -1,6 +1,7 @@ __all__ = ['imread', 'imsave'] import numpy as np +from skimage.utils.dtype import convert try: import imread as _imread @@ -20,7 +21,7 @@ def imread(fname, dtype=None): """ im = _imread.imread(fname) if dtype is not None: - im = im.asdtype(dtype) + im = convert(im, dtype) return im def imsave(fname, arr, format_str=None): From 1f84c5e35fb6bc981af7c2938e3a997e2d965b6c Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Mon, 5 Nov 2012 22:33:40 +0000 Subject: [PATCH 359/625] DOC Add myself to CONTRIBUTORS.txt --- CONTRIBUTORS.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 8ba62e0b..012fd53d 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -119,4 +119,7 @@ Perimeter calculation in regionprops. - Olivier Debeir - Rank filters (8- and 16-bits) using sliding window. \ No newline at end of file + Rank filters (8- and 16-bits) using sliding window. + +- Luis Pedro Coelho + imread plugin From 38f699471eab29259a075bcc52fef4514aed81d7 Mon Sep 17 00:00:00 2001 From: Luis Pedro Coelho Date: Sat, 24 Nov 2012 11:12:17 +0000 Subject: [PATCH 360/625] TST Remove unneeded test Per stefanv's comment on github, this was testing functionality higher up in skimage.io --- skimage/io/tests/test_imread.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/skimage/io/tests/test_imread.py b/skimage/io/tests/test_imread.py index 8dd6ea6f..afb9eac9 100644 --- a/skimage/io/tests/test_imread.py +++ b/skimage/io/tests/test_imread.py @@ -21,18 +21,6 @@ def teardown(): reset_plugins() -def setup_module(self): - """The effect of the `plugin.use` call may be overridden by later imports. - Call `use_plugin` directly before the tests to ensure that imread is used. - - """ - try: - use_plugin('imread') - except ImportError: - pass - - - @skipif(not imread_available) def test_imread_flatten(): # a color image is flattened From d2262227d92bd135d44c712afa36f4660cf29425 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Sat, 24 Nov 2012 17:24:14 -0600 Subject: [PATCH 361/625] BUG - `rank_order` now imported. Also changed (peak.py): * Standardized documentation as requested. * Removed `threshold` as an optional kwarg. * Removed extra line break incorrect by PEP8 standards. Also changed (watershed.py): * Added @deprecated decorator and import statement --- skimage/feature/peak.py | 51 +++++++++++---------------------- skimage/morphology/watershed.py | 2 ++ 2 files changed, 19 insertions(+), 34 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 2d40ded6..181dd600 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -1,9 +1,11 @@ import numpy as np import scipy.ndimage as ndi +from ..filter import rank_order + def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=np.inf, - footprint=None, labels=None, **kwargs): + footprint=None, labels=None): """ Find peaks in an image, and return them as coordinates or a boolean array. @@ -16,48 +18,34 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, ---------- image : ndarray of floats Input image. - - min_distance : int, default 10. + min_distance : int Minimum number of pixels separating peaks in a region of `2 * min_distance + 1` (i.e. peaks are separated by at least - `min_distance`). - If `exclude_border` is True, this value also excludes a border - `min_distance` from the image boundary. + `min_distance`). If `exclude_border` is True, this value also excludes + a border `min_distance` from the image boundary. To find the maximum number of points, use `min_distance=1`. - - threshold_abs : float, default 0. + threshold_abs : float Minimum intensity of peaks. - - threshold_rel : float, default 0.1 + threshold_rel : float Minimum intensity of peaks calculated as `max(image) * threshold_rel`. - - exclude_border : bool, default True + exclude_border : bool If True, `min_distance` excludes peaks from the border of the image as well as from each other. - - indices : bool, default True + indices : bool If True, the output will be a matrix representing peak coordinates. If False, the output will be a boolean matrix shaped as `image.shape` - with peaks present at True elements. - - num_peaks : int, default np.inf + with peaks present at True elements. + num_peaks : int Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` peaks based on highest peak intensity. - footprint : ndarray of bools, optional If provided, `footprint == 1` represents the local region within which - to search for peaks at every point in `image`. - Overrides `min_distance`, except for border exclusion if - `exclude_border` is True. - + to search for peaks at every point in `image`. Overrides + `min_distance`, except for border exclusion if `exclude_border=True`. labels : ndarray of ints, optional If provided, each unique region `labels == value` represents a unique region to search for peaks. Zero is reserved for background. - threshold : float, optional - Deprecated. If provided as a kwarg, will override `threshold_rel`. - See `threshold_rel`. - Returns ------- output : (N, 2) array or ndarray of bools @@ -116,15 +104,14 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, threshold_rel=threshold_rel, exclude_border=exclude_border, indices=False, num_peaks=np.inf, - footprint=footprint, labels=None, - **kwargs) + footprint=footprint, labels=None) + del maskim if indices is True: return np.transpose(out.nonzero()) else: return out.astype(bool) - if np.all(image == image.flat[0]): if indices is True: return [] @@ -149,15 +136,11 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, image[:, :min_distance] = 0 image[:, -min_distance:] = 0 - if kwargs.has_key('threshold'): - threshold_rel = kwargs['threshold'] - # find top peak candidates above a threshold peak_threshold = max(np.max(image.ravel()) * threshold_rel, threshold_abs) - image_t = (image > peak_threshold) * 1 # get coordinates of peaks - coordinates = np.transpose(image_t.nonzero()) + coordinates = np.transpose((image > peak_threshold).nonzero()) if coordinates.shape[0] > num_peaks: intensities = image[coordinates[:, 0], coordinates[:, 1]] diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index b6ae2d07..5c4ffd1a 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -29,6 +29,7 @@ import numpy as np import scipy.ndimage from ..filter import rank_order from ..feature import peak_local_max +from .._shared.utils import deprecated from . import _watershed @@ -226,6 +227,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None): return c_output +@deprecated('filter.peak_local_max') def is_local_maximum(image, labels=None, footprint=None): """ Return a boolean array of points that are local maxima From 45a91aa9c5d9bdc2184440badd9ae2478fb296bd Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Sun, 25 Nov 2012 22:56:45 -0600 Subject: [PATCH 362/625] doc: deprecated string and docstring formatting fixes in `is_local_maximum` --- skimage/morphology/watershed.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index 5c4ffd1a..6d5a8371 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -227,7 +227,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None): return c_output -@deprecated('filter.peak_local_max') +@deprecated('feature.peak_local_max') def is_local_maximum(image, labels=None, footprint=None): """ Return a boolean array of points that are local maxima @@ -236,10 +236,8 @@ def is_local_maximum(image, labels=None, footprint=None): ---------- image: ndarray (2-D, 3-D, ...) intensity image - labels: ndarray, optional find maxima only within labels. Zero is reserved for background. - footprint: ndarray of bools, optional binary mask indicating the neighborhood to be examined `footprint` must be a matrix with odd dimensions, the center is taken From 98ce4751134b01f15c153a9f23ca6bdd48a259c3 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Sun, 25 Nov 2012 23:09:06 -0600 Subject: [PATCH 363/625] style: initialize `out` at beginning of function * remove API compatibility comment * remove unnecessary `del maskim` --- skimage/feature/peak.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 181dd600..f68bd925 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -83,9 +83,9 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, array([[3, 2]]) """ + out = np.zeros_like(image, dtype=np.bool) # In the case of labels, recursively build and return an output - # operating on each label separately; for API compatibility with - # ..watershed.is_local_maximum() + # operating on each label separately if labels is not None: label_values = np.unique(labels) # Reorder label values to have consecutive integers (no gaps) @@ -96,7 +96,6 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, # New values for new ordering label_values = np.unique(labels) - out = np.zeros_like(image) for label in label_values[label_values != 0]: maskim = (labels == label) out += peak_local_max(image * maskim, min_distance=min_distance, @@ -105,18 +104,17 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, exclude_border=exclude_border, indices=False, num_peaks=np.inf, footprint=footprint, labels=None) - del maskim if indices is True: return np.transpose(out.nonzero()) else: - return out.astype(bool) + return out.astype(np.bool) if np.all(image == image.flat[0]): if indices is True: return [] else: - return np.zeros_like(image, dtype=bool) + return out image = image.copy() # Non maximum filter @@ -150,6 +148,5 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, if indices is True: return coordinates else: - out = np.zeros_like(image, dtype=bool) out[coordinates[:, 0], coordinates[:, 1]] = True return out From dc377970f520f8f04a2b3d879983c3a1171518e7 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Wed, 28 Nov 2012 12:22:14 +1100 Subject: [PATCH 364/625] Add functions to join and relabel segmentations The join of two segmentations is the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both input segmentations. --- skimage/segmentation/__init__.py | 1 + skimage/segmentation/_join.py | 93 ++++++++++++++++++++++++++++++++ 2 files changed, 94 insertions(+) create mode 100644 skimage/segmentation/_join.py diff --git a/skimage/segmentation/__init__.py b/skimage/segmentation/__init__.py index 7bd37026..a0fab77a 100644 --- a/skimage/segmentation/__init__.py +++ b/skimage/segmentation/__init__.py @@ -4,3 +4,4 @@ from ._slic import slic from ._quickshift import quickshift from .boundaries import find_boundaries, visualize_boundaries, mark_boundaries from ._clear_border import clear_border +from ._join import join_segmentations, relabel_from_one diff --git a/skimage/segmentation/_join.py b/skimage/segmentation/_join.py new file mode 100644 index 00000000..5cd25b80 --- /dev/null +++ b/skimage/segmentation/_join.py @@ -0,0 +1,93 @@ +import numpy as np + +def join_segmentations(s1, s2): + """Return the join of the two input segmentations. + + The join J of S1 and S2 is defined as the segmentation in which two voxels + are in the same segment in J if and only if they are in the same segment + in *both* S1 and S2. + + Parameters + ---------- + s1, s2 : numpy arrays + s1 and s2 are label fields of the same shape. + + Returns + ------- + j : numpy array + The join segmentation of s1 and s2. + + Examples + -------- + >>> import numpy as np + >>> from skimage.segmentation import join_segmentations + >>> s1 = np.array([[0, 0, 1, 1], + ... [0, 2, 1, 1], + ... [2, 2, 2, 1]]) + >>> s2 = np.array([[0, 1, 1, 0], + ... [0, 1, 1, 0], + ... [0, 1, 1, 1]]) + >>> join_segmentations(s1, s2) + array([[0, 1, 3, 2], + [0, 5, 3, 2], + [4, 5, 5, 3]]) + """ + if s1.shape != s2.shape: + raise ValueError("Cannot join segmentations of different shape. " + + "s1.shape: %s, s2.shape: %s" % (s1.shape, s2.shape)) + s1 = relabel_from_one(s1)[0] + s2 = relabel_from_one(s2)[0] + j = (s2.max() + 1) * s1 + s2 + j = relabel_from_one(j)[0] + return j + +def relabel_from_one(ar): + """Convert array ar of arbitrary labels to labels 1...len(np.unique(ar))+1 + + This function also returns the forward map (mapping the original labels to + the reduced labels) and the inverse map (mapping the reduced labels back + to the original ones). + + Parameters + ---------- + ar : numpy ndarray (integer type) + + Returns + ------- + ar_relabeled : numpy array of same shape as ar + forward_map : 1d numpy array of length np.unique(ar) + 1 + inverse_map : 1d numpy array of length len(np.unique(ar)) + The length is len(np.unique(ar)) + 1 if 0 is not in np.unique(ar) + + Examples + -------- + >>> import numpy as np + >>> from skimage.segmentation import relabel_from_one + >>> ar = array([1, 1, 5, 5, 8, 99, 42]) + >>> ar_relab, fw, inv = relabel_from_one(ar) + >>> ar_relab + array([1, 1, 2, 2, 3, 5, 4]) + >>> fw + array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 5]) + >>> inv + array([ 0, 1, 5, 8, 42, 99]) + >>> (fw[ar] == ar_relab).all() + True + >>> (inv[ar_relab] == ar).all() + True + """ + labels = np.unique(ar) + labels0 = labels[labels != 0] + m = labels.max() + if m == len(labels0): # nothing to do, already 1...n labels + return ar, labels, labels + forward_map = np.zeros(m+1, int) + forward_map[labels0] = np.arange(1, len(labels0) + 1) + if not (labels == 0).any(): + labels = np.concatenate(([0], labels)) + inverse_map = labels + return forward_map[ar], forward_map, inverse_map From b906082034822a825ec2963864b32d6619cf938a Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Wed, 28 Nov 2012 12:48:13 +1100 Subject: [PATCH 365/625] Add testing functions for join and relabel --- skimage/segmentation/tests/test_join.py | 40 +++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 skimage/segmentation/tests/test_join.py diff --git a/skimage/segmentation/tests/test_join.py b/skimage/segmentation/tests/test_join.py new file mode 100644 index 00000000..f03244e9 --- /dev/null +++ b/skimage/segmentation/tests/test_join.py @@ -0,0 +1,40 @@ +import numpy as np +from numpy.testing import assert_array_equal, assert_raises +from skimage.segmentation import join_segmentations, relabel_from_one + +def test_join_segmentations(): + s1 = np.array([[0, 0, 1, 1], + [0, 2, 1, 1], + [2, 2, 2, 1]]) + s2 = np.array([[0, 1, 1, 0], + [0, 1, 1, 0], + [0, 1, 1, 1]]) + + # test correct join + # NOTE: technically, equality to j_ref is not required, only that there + # is a one-to-one mapping between j and j_ref. I don't know of an easy way + # to check this (i.e. not as error-prone as the function being tested) + j = join_segmentations(s1, s2) + j_ref = np.array([[0, 1, 3, 2], + [0, 5, 3, 2], + [4, 5, 5, 3]]) + assert_array_equal(j, j_ref) + + # test correct exception when arrays are different shapes + s3 = np.array([[0, 0, 1, 1], [0, 2, 2, 1]]) + assert_raises(ValueError, join_segmentations, s1, s3) + +def test_relabel_from_one(): + ar = np.array([1, 1, 5, 5, 8, 99, 42]) + ar_relab, fw, inv = relabel_from_one(ar) + ar_relab_ref = np.array([1, 1, 2, 2, 3, 5, 4]) + assert_array_equal(ar_relab, ar_relab_ref) + fw_ref = np.zeros(100, int) + fw_ref[1] = 1; fw_ref[5] = 2; fw_ref[8] = 3; fw_ref[42] = 4; fw_ref[99] = 5 + assert_array_equal(fw, fw_ref) + inv_ref = np.array([0, 1, 5, 8, 42, 99]) + assert_array_equal(inv, inv_ref) + + +if __name__ == "__main__": + np.testing.run_module_suite() From 5108d8c346abb72b459bb25f20a37493875c6d38 Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Thu, 29 Nov 2012 12:06:01 -0800 Subject: [PATCH 366/625] Fixed RGB<->XYZ conversion. Now correctly follows the algorithm from EasyRGB. Also fixed a XYZ<->Lab conversion issue by adding a .copy(). --- skimage/color/colorconv.py | 20 ++++++++++++++++---- skimage/color/tests/test_colorconv.py | 6 +++++- 2 files changed, 21 insertions(+), 5 deletions(-) diff --git a/skimage/color/colorconv.py b/skimage/color/colorconv.py index 38eab995..123e547c 100644 --- a/skimage/color/colorconv.py +++ b/skimage/color/colorconv.py @@ -379,8 +379,14 @@ def xyz2rgb(xyz): >>> lena_xyz = rgb2xyz(lena) >>> lena_rgb = xyz2rgb(lena_xyz) """ - return _convert(rgb_from_xyz, xyz) - + # Follow the algorithm from http://www.easyrgb.com/index.php?X=MATH&H=01#text1 + # except we don't multiply/divide by 100 in the conversion + arr = _prepare_colorarray(xyz) + arr = _convert(rgb_from_xyz, arr) + mask = arr>0.0031308 + arr[mask] = 1.055 * np.power(arr[mask], 1/2.4) - 0.055 + arr[~mask] *= 12.92 + return arr def rgb2xyz(rgb): """RGB to XYZ color space conversion. @@ -415,7 +421,13 @@ def rgb2xyz(rgb): >>> lena = data.lena() >>> lena_xyz = rgb2xyz(lena) """ - return _convert(xyz_from_rgb, rgb) + # Follow the algorithm from http://www.easyrgb.com/index.php?X=MATH&H=02#text2 + # except we don't multiply/divide by 100 in the conversion + arr = _prepare_colorarray(rgb).copy() + mask = arr>0.04045 + arr[mask] = np.power((arr[mask]+0.055)/1.055, 2.4) + arr[~mask] /= 12.92 + return _convert(xyz_from_rgb, arr) def rgb2rgbcie(rgb): @@ -594,7 +606,7 @@ def xyz2lab(xyz): >>> lena_xyz = rgb2xyz(lena) >>> lena_lab = xyz2lab(lena_xyz) """ - arr = _prepare_colorarray(xyz) + arr = _prepare_colorarray(xyz).copy() # scale by CIE XYZ tristimulus values of the reference white point arr = arr / lab_ref_white diff --git a/skimage/color/tests/test_colorconv.py b/skimage/color/tests/test_colorconv.py index c09c9d22..d1edf47f 100644 --- a/skimage/color/tests/test_colorconv.py +++ b/skimage/color/tests/test_colorconv.py @@ -113,10 +113,14 @@ class TestColorconv(TestCase): # XYZ to RGB def test_xyz2rgb_conversion(self): - # only roundtrip test, we checked rgb2xyz above already assert_almost_equal(xyz2rgb(rgb2xyz(self.colbars_array)), self.colbars_array) + # RGB<->XYZ roundtrip on another image + def test_xyz_rgb_roundtrip(self): + img_rgb = img_as_float(self.img_rgb) + assert_array_almost_equal(xyz2rgb(rgb2xyz(img_rgb)), img_rgb) + # RGB to RGB CIE def test_rgb2rgbcie_conversion(self): gt = np.array([[[ 0.1488856 , 0.18288098, 0.19277574], From c5e047ff0e1cfe35692838365b907db8c3746c4b Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Fri, 30 Nov 2012 15:25:00 +1100 Subject: [PATCH 367/625] Add join_segmentations example to the gallery --- doc/examples/plot_join_segmentations.py | 68 +++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 doc/examples/plot_join_segmentations.py diff --git a/doc/examples/plot_join_segmentations.py b/doc/examples/plot_join_segmentations.py new file mode 100644 index 00000000..af06cef3 --- /dev/null +++ b/doc/examples/plot_join_segmentations.py @@ -0,0 +1,68 @@ +""" +========================================== +Find the intersection of two segmentations +========================================== + +When segmenting an image, you may want to combine multiple alternative +segmentations. The `skimage.segmentation.join_segmentations` function +computes the join of two segmentations, in which a pixel is placed in +the same segment if and only if it is in the same segment in _both_ +segmentations. +""" + +import numpy as np +from scipy import ndimage as nd +import matplotlib.pyplot as plt +import matplotlib as mpl + +from skimage.filter import sobel +from skimage.segmentation import slic, join_segmentations +from skimage.morphology import watershed + +from skimage import data + +coins = data.coins() + +# make segmentation using edge-detection and watershed +edges = sobel(coins) +markers = np.zeros_like(coins) +foreground, background = 1, 2 +markers[coins < 30] = background +markers[coins > 150] = foreground + +ws = watershed(edges, markers) +seg1 = nd.label(ws == foreground)[0] + +# make segmentation using SLIC superpixels + +# make the RGB equivalent of `coins` +coins_colour = np.tile(coins[..., np.newaxis], (1, 1, 3)) +seg2 = slic(coins_colour, max_iter=20, sigma=0, convert2lab=False) + +# combine the two +segj = join_segmentations(seg1, seg2) + +### Display the result ### + +# make a random colormap for a set number of values +def random_cmap(im): + np.random.seed(9) + cmap_array = np.concatenate( + (np.zeros((1, 3)), np.random.rand(np.ceil(im.max()), 3))) + return mpl.colors.ListedColormap(cmap_array) + +# show the segmentations +fig, axes = plt.subplots(ncols=4, figsize=(9, 2.5)) +axes[0].imshow(coins, cmap=plt.cm.gray, interpolation='nearest') +axes[0].set_title('Image') +axes[1].imshow(seg1, cmap=random_cmap(seg1), interpolation='nearest') +axes[1].set_title('Sobel+Watershed') +axes[2].imshow(seg2, cmap=random_cmap(seg2), interpolation='nearest') +axes[2].set_title('SLIC superpixels') +axes[3].imshow(segj, cmap=random_cmap(segj), interpolation='nearest') +axes[3].set_title('Join') + +for ax in axes: + ax.axis('off') +plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) +plt.show() From 43eb33d26b2e775f75058c40ea950a369e709e34 Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Fri, 30 Nov 2012 01:30:59 -0800 Subject: [PATCH 368/625] flake8 on colorconv.py and removed unneeded .copy() --- skimage/color/colorconv.py | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/skimage/color/colorconv.py b/skimage/color/colorconv.py index 123e547c..4186682d 100644 --- a/skimage/color/colorconv.py +++ b/skimage/color/colorconv.py @@ -79,7 +79,6 @@ def is_gray(image): return image.ndim == 2 - def convert_colorspace(arr, fromspace, tospace): """Convert an image array to a new color space. @@ -288,8 +287,8 @@ sb_primaries = np.array([1. / 155, 1. / 190, 1. / 225]) * 1e5 # From sRGB specification xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423], - [0.212671, 0.715160, 0.072169], - [0.019334, 0.119193, 0.950227]]) + [0.212671, 0.715160, 0.072169], + [0.019334, 0.119193, 0.950227]]) rgb_from_xyz = linalg.inv(xyz_from_rgb) @@ -379,15 +378,15 @@ def xyz2rgb(xyz): >>> lena_xyz = rgb2xyz(lena) >>> lena_rgb = xyz2rgb(lena_xyz) """ - # Follow the algorithm from http://www.easyrgb.com/index.php?X=MATH&H=01#text1 + # Follow the algorithm from http://www.easyrgb.com/index.php # except we don't multiply/divide by 100 in the conversion - arr = _prepare_colorarray(xyz) - arr = _convert(rgb_from_xyz, arr) - mask = arr>0.0031308 - arr[mask] = 1.055 * np.power(arr[mask], 1/2.4) - 0.055 + arr = _convert(rgb_from_xyz, xyz) + mask = arr > 0.0031308 + arr[mask] = 1.055 * np.power(arr[mask], 1 / 2.4) - 0.055 arr[~mask] *= 12.92 return arr + def rgb2xyz(rgb): """RGB to XYZ color space conversion. @@ -421,11 +420,11 @@ def rgb2xyz(rgb): >>> lena = data.lena() >>> lena_xyz = rgb2xyz(lena) """ - # Follow the algorithm from http://www.easyrgb.com/index.php?X=MATH&H=02#text2 + # Follow the algorithm from http://www.easyrgb.com/index.php # except we don't multiply/divide by 100 in the conversion arr = _prepare_colorarray(rgb).copy() - mask = arr>0.04045 - arr[mask] = np.power((arr[mask]+0.055)/1.055, 2.4) + mask = arr > 0.04045 + arr[mask] = np.power((arr[mask] + 0.055) / 1.055, 2.4) arr[~mask] /= 12.92 return _convert(xyz_from_rgb, arr) @@ -606,7 +605,7 @@ def xyz2lab(xyz): >>> lena_xyz = rgb2xyz(lena) >>> lena_lab = xyz2lab(lena_xyz) """ - arr = _prepare_colorarray(xyz).copy() + arr = _prepare_colorarray(xyz) # scale by CIE XYZ tristimulus values of the reference white point arr = arr / lab_ref_white From b5d1e9f209d143169195514f80b74e1350799b03 Mon Sep 17 00:00:00 2001 From: Andreas Mueller Date: Fri, 30 Nov 2012 15:36:01 +0100 Subject: [PATCH 369/625] FIX quickshift nosetest with different xyz color conversion --- skimage/segmentation/tests/test_quickshift.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/segmentation/tests/test_quickshift.py b/skimage/segmentation/tests/test_quickshift.py index d43d7559..a940f859 100644 --- a/skimage/segmentation/tests/test_quickshift.py +++ b/skimage/segmentation/tests/test_quickshift.py @@ -34,10 +34,10 @@ def test_color(): seg = quickshift(img, random_seed=0, max_dist=30, kernel_size=10, sigma=0) # we expect 4 segments: assert_equal(len(np.unique(seg)), 4) - assert_array_equal(seg[:10, :10], 0) - assert_array_equal(seg[10:, :10], 3) - assert_array_equal(seg[:10, 10:], 1) - assert_array_equal(seg[10:, 10:], 2) + assert_array_equal(seg[:10, :10], 1) + assert_array_equal(seg[10:, :10], 2) + assert_array_equal(seg[:10, 10:], 0) + assert_array_equal(seg[10:, 10:], 3) seg2 = quickshift(img, kernel_size=1, max_dist=2, random_seed=0, convert2lab=False, sigma=0) From 22f94d8707d3d9d2a36493403cbaeb61213dd68b Mon Sep 17 00:00:00 2001 From: Christos Psaltis Date: Sat, 1 Dec 2012 12:30:40 +0200 Subject: [PATCH 370/625] Fix resize argument handling in rotate. It used to have exactly the opposite effect that it supposed to. --- skimage/transform/_warps.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index 6689430b..0ace0937 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -163,7 +163,7 @@ def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.): tform = tform1 + tform2 + tform3 output_shape = None - if not resize: + if resize: # determine shape of output image corners = np.array([[1, 1], [1, rows], [cols, rows], [cols, 1]]) corners = tform(corners - 1) From 2e1729a9fbbc21fc0b04df8e68efbab9cfd6dada Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 1 Dec 2012 23:46:52 -0600 Subject: [PATCH 371/625] Cleaned up adapthist func to use LAB for all RGB->required rest result change --- skimage/exposure/_adapthist.py | 43 +++++++++---------------- skimage/exposure/tests/test_exposure.py | 4 +-- 2 files changed, 18 insertions(+), 29 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index f35a5da5..091514b6 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -71,37 +71,26 @@ def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): .. [1] http://tog.acm.org/resources/GraphicsGems/ .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE ''' - # convert to uint image - int_image = skimage.img_as_uint(image) - int_image = rescale_intensity(int_image, out_range=(0, NR_OF_GREY - 1)) # handle color images - CLAHE accepts scalar images only - args = [int_image.copy(), ntiles_x, ntiles_y, clip_limit * nbins, nbins] - if image.ndim == 3: - # check for grayscale - if (np.allclose(image[:, :, 0], image[:, :, 1]) and - np.allclose(image[:, :, 1], image[:, :, 2])): - args[0] = int_image[:, :, 0] - out = _clahe(*args) - image = int_image[:, :, :3] - for channel in range(3): - image[:out.shape[0], :out.shape[1], channel] = out - # for color images, convert to LAB space for processing - else: - lab_img = color.rgb2lab(skimage.img_as_float(image)) - l_chan = lab_img[:, :, 0] - l_chan /= np.max(np.abs(l_chan)) - l_chan = skimage.img_as_uint(l_chan) - args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1)) - new_l = _clahe(*args).astype(float) - new_l = rescale_intensity(new_l, out_range=(0, 100)) - lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l - image = color.lab2rgb(lab_img) - image = rescale_intensity(image, out_range=(0, 1)) + args = [None, ntiles_x, ntiles_y, clip_limit * nbins, nbins] + if image.ndim > 2: + lab_img = color.rgb2lab(skimage.img_as_float(image)) + l_chan = lab_img[:, :, 0] + l_chan /= np.max(np.abs(l_chan)) + l_chan = skimage.img_as_uint(l_chan) + args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1)) + new_l = _clahe(*args).astype(float) + new_l = rescale_intensity(new_l, out_range=(0, 100)) + lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l + image = color.lab2rgb(lab_img) + image = rescale_intensity(image, out_range=(0, 1)) else: + image = skimage.img_as_uint(image) + args[0] = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1)) out = _clahe(*args) - image = int_image image[:out.shape[0], :out.shape[1]] = out - return rescale_intensity(image) + image = rescale_intensity(image) + return image def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 04c0cf0e..dc48a3d8 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -100,8 +100,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 106.3020173) - assert_almost_equal(norm_brightness_err(img, adapted), 0.0218686) + assert_almost_equal(peak_snr(img, adapted), 77.5841845) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0376632) return data, adapted From bf28a38f6e50e3bf806b07b533683a76ebdfbd84 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sun, 2 Dec 2012 16:02:27 -0500 Subject: [PATCH 372/625] ENH: Make black the default boundary color for mark_boundaries. This improves visibility in light-colored image regions. Note also that the new behavior matches the behavior from the 0.7 release. --- skimage/segmentation/boundaries.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/segmentation/boundaries.py b/skimage/segmentation/boundaries.py index 25656a9b..c5bf985b 100644 --- a/skimage/segmentation/boundaries.py +++ b/skimage/segmentation/boundaries.py @@ -13,7 +13,7 @@ def find_boundaries(label_img): return boundaries -def mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None): +def mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=(0, 0, 0)): """Return image with boundaries between labeled regions highlighted. Parameters From 6953785eabc57079650329e7add662edf8c13de8 Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Sun, 2 Dec 2012 22:51:16 -0800 Subject: [PATCH 373/625] added rgb2lab test --- skimage/color/tests/test_colorconv.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/skimage/color/tests/test_colorconv.py b/skimage/color/tests/test_colorconv.py index d1edf47f..962fa9fc 100644 --- a/skimage/color/tests/test_colorconv.py +++ b/skimage/color/tests/test_colorconv.py @@ -179,11 +179,29 @@ class TestColorconv(TestCase): assert_array_almost_equal(lab2xyz(self.lab_array), self.xyz_array, decimal=3) + def test_rgb2lab_brucelindbloom(self): + """ + Test the RGB->Lab conversion by comparing to the calculator on the + authoritative Bruce Lindbloom + [website](http://brucelindbloom.com/index.html?ColorCalculator.html). + """ + # Obtained with D65 white point, sRGB model and gamma + gt_for_colbars = np.array([ + [100,0,0], + [97.1393, -21.5537, 94.4780], + [91.1132, -48.0875, -14.1312], + [87.7347, -86.1827, 83.1793], + [60.3242, 98.2343, -60.8249], + [53.2408, 80.0925, 67.2032], + [32.2970, 79.1875, -107.8602], + [0,0,0]]).T + gt_array = np.swapaxes(gt_for_colbars.reshape(3, 4, 2), 0, 2) + assert_array_almost_equal(rgb2lab(self.colbars_array), gt_array, decimal=2) + def test_lab_rgb_roundtrip(self): img_rgb = img_as_float(self.img_rgb) assert_array_almost_equal(lab2rgb(rgb2lab(img_rgb)), img_rgb) - def test_gray2rgb(): x = np.array([0, 0.5, 1]) assert_raises(ValueError, gray2rgb, x) From 49e06c97552173a42071be424a017455f283f8b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 3 Dec 2012 14:37:26 +0100 Subject: [PATCH 374/625] Add title to hough peak detection example plot --- doc/examples/plot_hough_transform.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/examples/plot_hough_transform.py b/doc/examples/plot_hough_transform.py index 12931c74..b74132da 100644 --- a/doc/examples/plot_hough_transform.py +++ b/doc/examples/plot_hough_transform.py @@ -102,6 +102,7 @@ for _, angle, dist in zip(*hough_peaks(h, theta, d)): y1 = (dist - cols * np.cos(angle)) / np.sin(angle) plt.plot((0, cols), (y0, y1), '-r') plt.axis((0, cols, rows, 0)) +plt.title('Detected lines') # Line finding, using the Probabilistic Hough Transform @@ -126,6 +127,6 @@ for line in lines: p0, p1 = line plt.plot((p0[0], p1[0]), (p0[1], p1[1])) -plt.title('Lines found with PHT') +plt.title('Probabilistic Hough') plt.axis('image') plt.show() From 298abe1ac0b4fe67495dd01bec58d62abf109a2a Mon Sep 17 00:00:00 2001 From: Christos Psaltis Date: Mon, 3 Dec 2012 15:54:26 +0200 Subject: [PATCH 375/625] Add test for resize option in rotate --- skimage/transform/tests/test_warps.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index 1993f932..2cdda001 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -85,6 +85,17 @@ def test_rotate(): assert_array_almost_equal(x90, np.rot90(x)) +def test_rotate_resize(): + x = np.zeros((10, 10), dtype=np.double) + + x45 = rotate(x, 45, resize=False) + assert x45.shape == (10, 10) + + x45 = rotate(x, 45, resize=True) + # new dimension should be d = sqrt(2 * (10/2)^2) + assert x45.shape == (14, 14) + + def test_rescale(): # same scale factor x = np.zeros((5, 5), dtype=np.double) From 68c2ab205e590cbd50a79dc09c1dee1c9a892334 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 3 Dec 2012 08:49:35 -0800 Subject: [PATCH 376/625] BUG: WindowsError not defined under Linux. --- skimage/_build.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/skimage/_build.py b/skimage/_build.py index 8f255f29..771b04b9 100644 --- a/skimage/_build.py +++ b/skimage/_build.py @@ -4,6 +4,13 @@ import hashlib import subprocess +# WindowsError is not defined on unix systems +try: + WindowsError +except NameError: + WindowsError = None + + def cython(pyx_files, working_path=''): """Use Cython to convert the given files to C. From 4f68f1cd36ea3844fc7d8101e8416eca85379381 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Mon, 3 Dec 2012 20:23:01 -0600 Subject: [PATCH 377/625] doc: fix and clarify `peak_local_max` docstring --- skimage/feature/peak.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index f68bd925..85d2cb4a 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -9,10 +9,11 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, """ Find peaks in an image, and return them as coordinates or a boolean array. - Peaks are the local maxima + Peaks are the local maxima in a region of `2 * min_distance + 1` + (i.e. peaks are separated by at least `min_distance`). - NOTE: If peaks are flat (i.e. multiple pixels have exact same intensity), - the coordinates of all pixels are returned. + NOTE: If peaks are flat (i.e. multiple adjacent pixels have identical + intensities), the coordinates of all such pixels are returned. Parameters ---------- @@ -23,7 +24,7 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, min_distance + 1` (i.e. peaks are separated by at least `min_distance`). If `exclude_border` is True, this value also excludes a border `min_distance` from the image boundary. - To find the maximum number of points, use `min_distance=1`. + To find the maximum number of peaks, use `min_distance=1`. threshold_abs : float Minimum intensity of peaks. threshold_rel : float From c6d0acfdabd5f14d2da353e76571638f3cc1fb0e Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:07:24 -0500 Subject: [PATCH 378/625] Initial commit of adapthist modifications. --- doc/examples/plot_equalize.py | 8 +- skimage/exposure/_adapthist.pyx | 40 +++ skimage/exposure/clahe.h | 329 ++++++++++++++++++++++++ skimage/exposure/exposure.py | 58 ++++- skimage/exposure/setup.py | 31 +++ skimage/exposure/tests/test_exposure.py | 71 +++++ 6 files changed, 535 insertions(+), 2 deletions(-) create mode 100644 skimage/exposure/_adapthist.pyx create mode 100644 skimage/exposure/clahe.h create mode 100644 skimage/exposure/setup.py diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 1e1291d8..2cb9cbea 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -63,9 +63,12 @@ img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) # Equalization img_eq = exposure.equalize(img) +# Adaptive Equalization +img_adapteq = exposure.adapthist(img) + # Display results -f, axes = plt.subplots(2, 3, figsize=(8, 4)) +f, axes = plt.subplots(2, 4, figsize=(8, 4)) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) ax_img.set_title('Low contrast image') @@ -78,6 +81,9 @@ ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Histogram equalization') ax_cdf.set_ylabel('Fraction of total intensity') +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 3]) +ax_img.set_title('Adaptive equalization') +ax_cdf.set_ylabel('Fraction of total intensity') # prevent overlap of y-axis labels plt.subplots_adjust(wspace=0.4) diff --git a/skimage/exposure/_adapthist.pyx b/skimage/exposure/_adapthist.pyx new file mode 100644 index 00000000..16e84336 --- /dev/null +++ b/skimage/exposure/_adapthist.pyx @@ -0,0 +1,40 @@ +import numpy as np +cimport numpy as np + +cdef extern from "clahe.h": + ctypedef unsigned int kz_pixel_t + int CLAHE(kz_pixel_t * pImage, unsigned int uiXRes, unsigned int uiYRes, + kz_pixel_t Min, + kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, + unsigned int uiNrBins, float fCliplimit) + + +def _adapthist(np.ndarray[kz_pixel_t, ndim=2] image, min, max, nx, ny, nbins, + clip_limit=0): + ''' + image - to the input/output image + min - Minimum greyvalue of input image (also becomes min of output image) + max - Maximum greyvalue of input image (also becomes max of output image) + nx - Number of tile regions in the X direction (min 2, max uiMAX_REG_X) + ny - Number of tile regions in the Y direction (min 2, max uiMAX_REG_Y) + nbins - Number of greybins for histogram ("dynamic range") + clip_limit - Normalized cliplimit (higher values give more contrast) + + The number of "effective" greylevels in the output image is set by nbins; + selecting a small value (eg. 128) speeds up processing and still produce + an output image of good quality. The output image will have the same + minimum and maximum value as the input image. A clip limit smaller than 1 + results in standard (non-contrast limited) AHE. + ''' + # we need the image to be divisible by nx and ny + uiYRes = image.shape[0] - image.shape[0] % ny + uiXRes = image.shape[1] - image.shape[1] % nx + + cdef np.ndarray[kz_pixel_t, ndim = 1] flattened + flattened = image[:uiYRes, :uiXRes].ravel() + + ret = CLAHE(< kz_pixel_t * > flattened.data, uiXRes, uiYRes, min, + max, nx, ny, nbins, clip_limit * nbins) + if ret < 0: + print 'clahe error: ', ret + return flattened.reshape((uiYRes, uiXRes)) diff --git a/skimage/exposure/clahe.h b/skimage/exposure/clahe.h new file mode 100644 index 00000000..8b115aa1 --- /dev/null +++ b/skimage/exposure/clahe.h @@ -0,0 +1,329 @@ +/* +ANSI C code from the article + * "Contrast Limited Adaptive Histogram Equalization" + * by Karel Zuiderveld, karel@cv.ruu.nl + * in "Graphics Gems IV", Academic Press, 1994 +============= + +ACM Software License Agreement + +All software, both binary and source published by the Association for Computing Machinery (hereafter, Software) is copyrighted by the Association (hereafter, ACM) and ownership of all right, title and interest in and to the Software remains with ACM. By using or copying the Software, User agrees to abide by the terms of this Agreement. +Noncommercial Use + +The ACM grants to you (hereafter, User) a royalty-free, nonexclusive right to execute, copy, modify and distribute both the binary and source code solely for academic, research and other similar noncommercial uses, subject to the following conditions: + + User acknowledges that the Software is still in the development stage and that it is being supplied "as is," without any support services from ACM. Neither ACM nor the author makes any representations or warranties, express or implied, including, without limitation, any representations or warranties of the merchantability or fitness for any particular purpose, or that the application of the software, will not infringe on any patents or other proprietary rights of others. + ACM shall not be held liable for direct, indirect, incidental or consequential damages arising from any claim by User or any third party with respect to uses allowed under this Agreement, or from any use of the Software. + User agrees to fully indemnify and hold harmless ACM and/or the author(s) of the original work from and against any and all claims, demands, suits, losses, damages, costs and expenses arising out of the User's use of the Software, including, without limitation, arising out of the User's modification of the Software. + User may modify the Software and distribute that modified work to third parties provided that: (a) if posted separately, it clearly acknowledges that it contains material copyrighted by ACM (b) no charge is associated with such copies, (c) User agrees to notify ACM and the Author(s) of the distribution, and (d) User clearly notifies secondary users that such modified work is not the original Software. + User agrees that ACM, the authors of the original work and others may enjoy a royalty-free, non-exclusive license to use, copy, modify and redistribute these modifications to the Software made by the User and distributed to third parties as a derivative work under this agreement. + This agreement will terminate immediately upon User's breach of, or non-compliance with, any of its terms. User may be held liable for any copyright infringement or the infringement of any other proprietary rights in the Software that is caused or facilitated by the User's failure to abide by the terms of this agreement. + This agreement will be construed and enforced in accordance with the law of the state of New York applicable to contracts performed entirely within the State. The parties irrevocably consent to the exclusive jurisdiction of the state or federal courts located in the City of New York for all disputes concerning this agreement. + +Commercial Use + +Any User wishing to make a commercial use of the Software must contact ACM at permissions@acm.org to arrange an appropriate license. Commercial use includes (1) integrating or incorporating all or part of the source code into a product for sale or license by, or on behalf of, User to third parties, or (2) distribution of the binary or source code to third parties for use with a commercial product sold or licensed by, or on behalf of, User. + +Revised 6/98 +*/ + +#ifdef BYTE_IMAGE +typedef unsigned char kz_pixel_t; /* for 8 bit-per-pixel images */ +#define uiNR_OF_GREY (256) +#else +typedef unsigned short kz_pixel_t; /* for 12 bit-per-pixel images (default) */ +# define uiNR_OF_GREY (4096) +#endif + +/* + * ANSI C code from the article + * "Contrast Limited Adaptive Histogram Equalization" + * by Karel Zuiderveld, karel@cv.ruu.nl + * in "Graphics Gems IV", Academic Press, 1994 + * + * + * These functions implement Contrast Limited Adaptive Histogram Equalization. + * The main routine (CLAHE) expects an input image that is stored contiguously in + * memory; the CLAHE output image overwrites the original input image and has the + * same minimum and maximum values (which must be provided by the user). + * This implementation assumes that the X- and Y image resolutions are an integer + * multiple of the X- and Y sizes of the contextual regions. A check on various other + * error conditions is performed. + * + * #define the symbol BYTE_IMAGE to make this implementation suitable for + * 8-bit images. The maximum number of contextual regions can be redefined + * by changing uiMAX_REG_X and/or uiMAX_REG_Y; the use of more than 256 + * contextual regions is not recommended. + * + * The code is ANSI-C and is also C++ compliant. + * + * Author: Karel Zuiderveld, Computer Vision Research Group, + * Utrecht, The Netherlands (karel@cv.ruu.nl) + */ + +/*********************** Local prototypes ************************/ +static void ClipHistogram (unsigned long*, unsigned int, unsigned long); +static void MakeHistogram (kz_pixel_t*, unsigned int, unsigned int, unsigned int, + unsigned long*, unsigned int, kz_pixel_t*); +static void MapHistogram (unsigned long*, kz_pixel_t, kz_pixel_t, + unsigned int, unsigned long); +static void MakeLut (kz_pixel_t*, kz_pixel_t, kz_pixel_t, unsigned int); +static void Interpolate (kz_pixel_t*, int, unsigned long*, unsigned long*, + unsigned long*, unsigned long*, unsigned int, unsigned int, kz_pixel_t*); + +/************** Start of actual code **************/ +#include /* To get prototypes of malloc() and free() */ + + +const unsigned int uiMAX_REG_X = 16; /* max. # contextual regions in x-direction */ +const unsigned int uiMAX_REG_Y = 16; /* max. # contextual regions in y-direction */ + + + +/************************** main function CLAHE ******************/ +int CLAHE (kz_pixel_t* pImage, unsigned int uiXRes, unsigned int uiYRes, + kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, + unsigned int uiNrBins, float fCliplimit) +/* pImage - Pointer to the input/output image + * uiXRes - Image resolution in the X direction + * uiYRes - Image resolution in the Y direction + * Min - Minimum greyvalue of input image (also becomes minimum of output image) + * Max - Maximum greyvalue of input image (also becomes maximum of output image) + * uiNrX - Number of contextial regions in the X direction (min 2, max uiMAX_REG_X) + * uiNrY - Number of contextial regions in the Y direction (min 2, max uiMAX_REG_Y) + * uiNrBins - Number of greybins for histogram ("dynamic range") + * float fCliplimit - Normalized cliplimit (higher values give more contrast) + * The number of "effective" greylevels in the output image is set by uiNrBins; selecting + * a small value (eg. 128) speeds up processing and still produce an output image of + * good quality. The output image will have the same minimum and maximum value as the input + * image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. + */ +{ + unsigned int uiX, uiY; /* counters */ + unsigned int uiXSize, uiYSize, uiSubX, uiSubY; /* size of context. reg. and subimages */ + unsigned int uiXL, uiXR, uiYU, uiYB; /* auxiliary variables interpolation routine */ + unsigned long ulClipLimit, ulNrPixels;/* clip limit and region pixel count */ + kz_pixel_t* pImPointer; /* pointer to image */ + kz_pixel_t aLUT[uiNR_OF_GREY]; /* lookup table used for scaling of input image */ + unsigned long* pulHist, *pulMapArray; /* pointer to histogram and mappings*/ + unsigned long* pulLU, *pulLB, *pulRU, *pulRB; /* auxiliary pointers interpolation */ + + if (uiNrX > uiMAX_REG_X) return -1; /* # of regions x-direction too large */ + if (uiNrY > uiMAX_REG_Y) return -2; /* # of regions y-direction too large */ + if (uiXRes % uiNrX) return -3; /* x-resolution no multiple of uiNrX */ + if (uiYRes % uiNrY) return -4; /* y-resolution no multiple of uiNrY */ + if (Max >= uiNR_OF_GREY) return -5; /* maximum too large */ + if (Min >= Max) return -6; /* minimum equal or larger than maximum */ + if (uiNrX < 2 || uiNrY < 2) return -7;/* at least 4 contextual regions required */ + if (fCliplimit == 1.0) return 0; /* is OK, immediately returns original image. */ + if (uiNrBins == 0) uiNrBins = 128; /* default value when not specified */ + + pulMapArray=(unsigned long *)malloc(sizeof(unsigned long)*uiNrX*uiNrY*uiNrBins); + if (pulMapArray == 0) return -8; /* Not enough memory! (try reducing uiNrBins) */ + + uiXSize = uiXRes/uiNrX; uiYSize = uiYRes/uiNrY; /* Actual size of contextual regions */ + ulNrPixels = (unsigned long)uiXSize * (unsigned long)uiYSize; + + if(fCliplimit > 0.0) { /* Calculate actual cliplimit */ + ulClipLimit = (unsigned long) (fCliplimit * (uiXSize * uiYSize) / uiNrBins); + ulClipLimit = (ulClipLimit < 1UL) ? 1UL : ulClipLimit; + } + else ulClipLimit = 1UL<<14; /* Large value, do not clip (AHE) */ + MakeLut(aLUT, Min, Max, uiNrBins); /* Make lookup table for mapping of greyvalues */ + /* Calculate greylevel mappings for each contextual region */ + for (uiY = 0, pImPointer = pImage; uiY < uiNrY; uiY++) { + for (uiX = 0; uiX < uiNrX; uiX++, pImPointer += uiXSize) { + pulHist = &pulMapArray[uiNrBins * (uiY * uiNrX + uiX)]; + MakeHistogram(pImPointer,uiXRes,uiXSize,uiYSize,pulHist,uiNrBins,aLUT); + ClipHistogram(pulHist, uiNrBins, ulClipLimit); + MapHistogram(pulHist, Min, Max, uiNrBins, ulNrPixels); + } + pImPointer += (uiYSize - 1) * uiXRes; /* skip lines, set pointer */ + } + /* Interpolate greylevel mappings to get CLAHE image */ + for (pImPointer = pImage, uiY = 0; uiY <= uiNrY; uiY++) { + if (uiY == 0) { /* special case: top row */ + uiSubY = uiYSize >> 1; uiYU = 0; uiYB = 0; + } + else { + if (uiY == uiNrY) { /* special case: bottom row */ + uiSubY = uiYSize >> 1; uiYU = uiNrY-1; uiYB = uiYU; + } + else { /* default values */ + uiSubY = uiYSize; uiYU = uiY - 1; uiYB = uiYU + 1; + } + } + for (uiX = 0; uiX <= uiNrX; uiX++) { + if (uiX == 0) { /* special case: left column */ + uiSubX = uiXSize >> 1; uiXL = 0; uiXR = 0; + } + else { + if (uiX == uiNrX) { /* special case: right column */ + uiSubX = uiXSize >> 1; uiXL = uiNrX - 1; uiXR = uiXL; + } + else { /* default values */ + uiSubX = uiXSize; uiXL = uiX - 1; uiXR = uiXL + 1; + } + } + + pulLU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXL)]; + pulRU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXR)]; + pulLB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXL)]; + pulRB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXR)]; + Interpolate(pImPointer,uiXRes,pulLU,pulRU,pulLB,pulRB,uiSubX,uiSubY,aLUT); + pImPointer += uiSubX; /* set pointer on next matrix */ + } + pImPointer += (uiSubY - 1) * uiXRes; + } + free(pulMapArray); /* free space for histograms */ + return 0; /* return status OK */ +} +void ClipHistogram (unsigned long* pulHistogram, unsigned int + uiNrGreylevels, unsigned long ulClipLimit) +/* This function performs clipping of the histogram and redistribution of bins. + * The histogram is clipped and the number of excess pixels is counted. Afterwards + * the excess pixels are equally redistributed across the whole histogram (providing + * the bin count is smaller than the cliplimit). + */ +{ + unsigned long* pulBinPointer, *pulEndPointer, *pulHisto; + unsigned long ulNrExcess, ulUpper, ulBinIncr, ulStepSize, i; + long lBinExcess; + + ulNrExcess = 0; pulBinPointer = pulHistogram; + for (i = 0; i < uiNrGreylevels; i++) { /* calculate total number of excess pixels */ + lBinExcess = (long) pulBinPointer[i] - (long) ulClipLimit; + if (lBinExcess > 0) ulNrExcess += lBinExcess; /* excess in current bin */ + }; + + /* Second part: clip histogram and redistribute excess pixels in each bin */ + ulBinIncr = ulNrExcess / uiNrGreylevels; /* average binincrement */ + ulUpper = ulClipLimit - ulBinIncr; /* Bins larger than ulUpper set to cliplimit */ + + for (i = 0; i < uiNrGreylevels; i++) { + if (pulHistogram[i] > ulClipLimit) pulHistogram[i] = ulClipLimit; /* clip bin */ + else { + if (pulHistogram[i] > ulUpper) { /* high bin count */ + ulNrExcess -= pulHistogram[i] - ulUpper; pulHistogram[i]=ulClipLimit; + } + else { /* low bin count */ + ulNrExcess -= ulBinIncr; pulHistogram[i] += ulBinIncr; + } + } + } + + while (ulNrExcess) { /* Redistribute remaining excess */ + pulEndPointer = &pulHistogram[uiNrGreylevels]; pulHisto = pulHistogram; + + while (ulNrExcess && pulHisto < pulEndPointer) { + ulStepSize = uiNrGreylevels / ulNrExcess; + if (ulStepSize < 1) ulStepSize = 1; /* stepsize at least 1 */ + for (pulBinPointer=pulHisto; pulBinPointer < pulEndPointer && ulNrExcess; + pulBinPointer += ulStepSize) { + if (*pulBinPointer < ulClipLimit) { + (*pulBinPointer)++; ulNrExcess--; /* reduce excess */ + } + } + pulHisto++; /* restart redistributing on other bin location */ + } + } +} +void MakeHistogram (kz_pixel_t* pImage, unsigned int uiXRes, + unsigned int uiSizeX, unsigned int uiSizeY, + unsigned long* pulHistogram, + unsigned int uiNrGreylevels, kz_pixel_t* pLookupTable) +/* This function classifies the greylevels present in the array image into + * a greylevel histogram. The pLookupTable specifies the relationship + * between the greyvalue of the pixel (typically between 0 and 4095) and + * the corresponding bin in the histogram (usually containing only 128 bins). + */ +{ + kz_pixel_t* pImagePointer; + unsigned int i; + + for (i = 0; i < uiNrGreylevels; i++) pulHistogram[i] = 0L; /* clear histogram */ + + for (i = 0; i < uiSizeY; i++) { + pImagePointer = &pImage[uiSizeX]; + while (pImage < pImagePointer) pulHistogram[pLookupTable[*pImage++]]++; + pImagePointer += uiXRes; + pImage = &pImagePointer[-uiSizeX]; + } +} + +void MapHistogram (unsigned long* pulHistogram, kz_pixel_t Min, kz_pixel_t Max, + unsigned int uiNrGreylevels, unsigned long ulNrOfPixels) +/* This function calculates the equalized lookup table (mapping) by + * cumulating the input histogram. Note: lookup table is rescaled in range [Min..Max]. + */ +{ + unsigned int i; unsigned long ulSum = 0; + const float fScale = ((float)(Max - Min)) / ulNrOfPixels; + const unsigned long ulMin = (unsigned long) Min; + + for (i = 0; i < uiNrGreylevels; i++) { + ulSum += pulHistogram[i]; pulHistogram[i]=(unsigned long)(ulMin+ulSum*fScale); + if (pulHistogram[i] > Max) pulHistogram[i] = Max; + } +} + +void MakeLut (kz_pixel_t * pLUT, kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrBins) +/* To speed up histogram clipping, the input image [Min,Max] is scaled down to + * [0,uiNrBins-1]. This function calculates the LUT. + */ +{ + int i; + const kz_pixel_t BinSize = (kz_pixel_t) (1 + (Max - Min) / uiNrBins); + + for (i = Min; i <= Max; i++) pLUT[i] = (i - Min) / BinSize; +} + +void Interpolate (kz_pixel_t * pImage, int uiXRes, unsigned long * pulMapLU, + unsigned long * pulMapRU, unsigned long * pulMapLB, unsigned long * pulMapRB, + unsigned int uiXSize, unsigned int uiYSize, kz_pixel_t * pLUT) +/* pImage - pointer to input/output image + * uiXRes - resolution of image in x-direction + * pulMap* - mappings of greylevels from histograms + * uiXSize - uiXSize of image submatrix + * uiYSize - uiYSize of image submatrix + * pLUT - lookup table containing mapping greyvalues to bins + * This function calculates the new greylevel assignments of pixels within a submatrix + * of the image with size uiXSize and uiYSize. This is done by a bilinear interpolation + * between four different mappings in order to eliminate boundary artifacts. + * It uses a division; since division is often an expensive operation, I added code to + * perform a logical shift instead when feasible. + */ +{ + const unsigned int uiIncr = uiXRes-uiXSize; /* Pointer increment after processing row */ + kz_pixel_t GreyValue; unsigned int uiNum = uiXSize*uiYSize; /* Normalization factor */ + + unsigned int uiXCoef, uiYCoef, uiXInvCoef, uiYInvCoef, uiShift = 0; + + if (uiNum & (uiNum - 1)) /* If uiNum is not a power of two, use division */ + for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; + uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { + for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; + uiXCoef++, uiXInvCoef--) { + GreyValue = pLUT[*pImage]; /* get histogram bin value */ + *pImage++ = (kz_pixel_t ) ((uiYInvCoef * (uiXInvCoef*pulMapLU[GreyValue] + + uiXCoef * pulMapRU[GreyValue]) + + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] + + uiXCoef * pulMapRB[GreyValue])) / uiNum); + } + } + else { /* avoid the division and use a right shift instead */ + while (uiNum >>= 1) uiShift++; /* Calculate 2log of uiNum */ + for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; + uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { + for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; + uiXCoef++, uiXInvCoef--) { + GreyValue = pLUT[*pImage]; /* get histogram bin value */ + *pImage++ = (kz_pixel_t)((uiYInvCoef* (uiXInvCoef * pulMapLU[GreyValue] + + uiXCoef * pulMapRU[GreyValue]) + + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] + + uiXCoef * pulMapRB[GreyValue])) >> uiShift); + } + } + } +} + diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index bffe660a..1c4c69da 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -2,10 +2,13 @@ import numpy as np from skimage import img_as_float from skimage.util.dtype import dtype_range +from skimage.color import rgb2gray +from skimage.util.dtype import convert +from _adapthist import _adapthist __all__ = ['histogram', 'cumulative_distribution', 'equalize', - 'rescale_intensity'] + 'rescale_intensity', 'adapthist'] def histogram(image, nbins=256): @@ -189,3 +192,56 @@ def rescale_intensity(image, in_range=None, out_range=None): image = (image - imin) / float(imax - imin) return dtype(image * (omax - omin) + omin) + + +def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): + '''Contrast Limited Adaptive Histogram Equalization + + Parameters + ---------- + image : array-like + original image + nx : int, optional + Tile regions in the X direction (2, 16) + ny : int, optional + Tile regions in the Y direction (2, 16) + clip_limit : float: optional + Normalized cliplimit (higher values give more contrast) + nbins : int, optional + Greybins for histogram ("dynamic range") + out_range : str, optional + Range of the output image data. + - 'original' - Use original image limits + - 'full' - Use full range of image data type + + Returns + ------- + out - np.ndarray : + equalized image - may be a different shape than the original + ''' + in_type = image.dtype.type + if out_range == 'full': + out_range = None + else: + out_range = (image.min(), image.max()) + # must be converted to 12 bit for CLAHE + image = skimage.img_as_uint(image) + MAX_VAL = 2 ** 12 - 1 + image = rescale_intensity(image, out_range=(0, MAX_VAL)) + # handle color images - CLAHE accepts scalar images only + args = [image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] + if image.ndim == 3: + image = image[:, :, :3] + for channel in range(3): + args[0] = image[:, :, channel] + out = _adapthist(*args) + image[:out.shape[0], :out.shape[1], channel] = out + else: + out = _adapthist(*args) + image[:out.shape[0], :out.shape[1]] = out + # restore to desired output type and output limits + image = rescale_intensity(image) + if in_type != np.uint16: + image = convert(image, in_type) + image = rescale_intensity(image, out_range=out_range) + return image diff --git a/skimage/exposure/setup.py b/skimage/exposure/setup.py new file mode 100644 index 00000000..43bab23f --- /dev/null +++ b/skimage/exposure/setup.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python + +import os +from skimage._build import cython + +base_path = os.path.abspath(os.path.dirname(__file__)) + + +def configuration(parent_package='', top_path=None): + from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs + + config = Configuration('exposure', parent_package, top_path) + config.add_data_dir('tests') + + cython(['_adapthist.pyx'], working_path=base_path) + + config.add_extension('_adapthist', sources=['_adapthist.c'], + include_dirs=[get_numpy_include_dirs()]) + + return config + +if __name__ == '__main__': + from numpy.distutils.core import setup + setup(maintainer='scikits-image developers', + author='scikits-image developers', + maintainer_email='scikits-image@googlegroups.com', + description='Exposure', + url='https://github.com/scikits-image/scikits-image', + license='SciPy License (BSD Style)', + **(configuration(top_path='').todict()) + ) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index b6ed0b12..fbad207b 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -4,6 +4,7 @@ from numpy.testing import assert_array_almost_equal as assert_close import skimage from skimage import data from skimage import exposure +from skimage.color import rgb2gray # Test histogram equalization @@ -71,6 +72,76 @@ def test_rescale_out_range(): assert_close(out, [0, 63, 127]) +# Test rescale intensity +# ====================== + +def test_adapthist_ubyte(): + '''Test a scalar uint8 image + ''' + img = skimage.img_as_ubyte(data.moon()) + adapted = exposure.adapthist(img, clip_limit=0.02) + assert adapted.min() == 0 + assert adapted.max() == 255 + assert img.shape == adapted.shape + assert peak_snr(img, adapted) > 22 + assert norm_brightness_err(img, adapted) < 0.05 + return img, adapted + + +def test_adapthist_float(): + '''Test an RGB float image + ''' + img = skimage.img_as_float(data.lena()) + adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, + nbins=128, out_range='original') + assert_almost_equal(adapted.min() , img.min()) + assert_almost_equal(adapted.min(), img.min()) + assert img.shape == adapted.shape + assert peak_snr(img, adapted) > 136 + assert norm_brightness_err(img, adapted) < 0.02 + return data, adapted + + +def peak_snr(img1, img2): + '''Peak signal to noise ratio of two images + + Parameters + ---------- + img1 : array-like + img2 : array-like + + Returns + ------- + peak_snr : float + Peak signal to noise ratio + ''' + if img1.ndim == 3: + img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy()) + mse = 1. / img1.size * np.square(img1 - img2).sum() + _, max_ = dtype_range[img1.dtype.type] + return 20 * np.log(max_ / mse) + + +def norm_brightness_err(img1, img2): + '''Normalized Absolute Mean Brightness Error between two images + + Parameters + ---------- + img1 : array-like + img2 : array-like + + Returns + ------- + norm_brightness_error : float + Normalize absolute mean brightness error + ''' + if img1.ndim == 3: + img1, img2 = rgb2gray(img1), rgb2gray(img2) + ambe = np.abs(img1.mean() - img2.mean()) + nbe = ambe / dtype_range[img1.dtype.type][1] + return nbe + + if __name__ == '__main__': from numpy import testing testing.run_module_suite() From 9eeb99089821a5be54bfe75e726414cdfd4a23b6 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:27:24 -0500 Subject: [PATCH 379/625] Tests pass and example works on build. --- doc/examples/plot_equalize.py | 4 ++-- skimage/exposure/__init__.py | 2 +- skimage/exposure/tests/test_exposure.py | 2 ++ 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 2cb9cbea..8b06890a 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -64,7 +64,7 @@ img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) img_eq = exposure.equalize(img) # Adaptive Equalization -img_adapteq = exposure.adapthist(img) +img_adapteq = exposure.adapthist(img, clip_limit=0.03) # Display results @@ -81,7 +81,7 @@ ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Histogram equalization') ax_cdf.set_ylabel('Fraction of total intensity') -ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 3]) +ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3]) ax_img.set_title('Adaptive equalization') ax_cdf.set_ylabel('Fraction of total intensity') diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index 7e19d317..a5e6c25a 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,2 +1,2 @@ -from .exposure import histogram, equalize, cumulative_distribution +from .exposure import histogram, equalize, cumulative_distribution, adapthist from .exposure import rescale_intensity diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index fbad207b..963758f3 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -5,6 +5,7 @@ import skimage from skimage import data from skimage import exposure from skimage.color import rgb2gray +from skimage.util.dtype import dtype_range # Test histogram equalization @@ -94,6 +95,7 @@ def test_adapthist_float(): img = skimage.img_as_float(data.lena()) adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') + assert_almost_equal = np.testing.assert_almost_equal assert_almost_equal(adapted.min() , img.min()) assert_almost_equal(adapted.min(), img.min()) assert img.shape == adapted.shape From ea850757c8232e575de5c00031856f6dfedc04b7 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:36:39 -0500 Subject: [PATCH 380/625] Added Notes to the docstring. --- skimage/exposure/exposure.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 1c4c69da..c37afcc3 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -218,6 +218,14 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): ------- out - np.ndarray : equalized image - may be a different shape than the original + + Notes + ----- + * The underlying algorithm relies on an image whose rows and columns are even multiples of + the number of tiles, so the extra rows and columns are left at their original values, thus + preserving the input image shape. + * For RGB or RGBA images, the algorithm is run on each channel. + * For RGBA images, the original alpha channel is removed. ''' in_type = image.dtype.type if out_range == 'full': From 715271bde9edab2747ebfac6ab8e5c500ced67a2 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 5 Aug 2012 23:42:44 -0500 Subject: [PATCH 381/625] Pep8 fixes. --- doc/examples/plot_equalize.py | 2 +- skimage/exposure/exposure.py | 2 +- skimage/exposure/tests/test_exposure.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 8b06890a..e09b5a07 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -26,6 +26,7 @@ import matplotlib.pyplot as plt import numpy as np + def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. @@ -88,4 +89,3 @@ ax_cdf.set_ylabel('Fraction of total intensity') # prevent overlap of y-axis labels plt.subplots_adjust(wspace=0.4) plt.show() - diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index c37afcc3..0a2a253d 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -218,7 +218,7 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): ------- out - np.ndarray : equalized image - may be a different shape than the original - + Notes ----- * The underlying algorithm relies on an image whose rows and columns are even multiples of diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 963758f3..9d70969a 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -96,7 +96,7 @@ def test_adapthist_float(): adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(adapted.min() , img.min()) + assert_almost_equal(adapted.min(), img.min()) assert_almost_equal(adapted.min(), img.min()) assert img.shape == adapted.shape assert peak_snr(img, adapted) > 136 From 20f4cee5da839edf68bded25d198f809e81cab05 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Mon, 6 Aug 2012 19:06:57 -0500 Subject: [PATCH 382/625] Replaced generic ACM license with the Graphics Gems IV license. --- skimage/exposure/clahe.h | 31 ++++++++++++------------------- 1 file changed, 12 insertions(+), 19 deletions(-) diff --git a/skimage/exposure/clahe.h b/skimage/exposure/clahe.h index 8b115aa1..a2721b98 100644 --- a/skimage/exposure/clahe.h +++ b/skimage/exposure/clahe.h @@ -5,26 +5,19 @@ ANSI C code from the article * in "Graphics Gems IV", Academic Press, 1994 ============= -ACM Software License Agreement +http://tog.acm.org/resources/GraphicsGems/ -All software, both binary and source published by the Association for Computing Machinery (hereafter, Software) is copyrighted by the Association (hereafter, ACM) and ownership of all right, title and interest in and to the Software remains with ACM. 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User may be held liable for any copyright infringement or the infringement of any other proprietary rights in the Software that is caused or facilitated by the User's failure to abide by the terms of this agreement. - This agreement will be construed and enforced in accordance with the law of the state of New York applicable to contracts performed entirely within the State. The parties irrevocably consent to the exclusive jurisdiction of the state or federal courts located in the City of New York for all disputes concerning this agreement. - -Commercial Use - -Any User wishing to make a commercial use of the Software must contact ACM at permissions@acm.org to arrange an appropriate license. Commercial use includes (1) integrating or incorporating all or part of the source code into a product for sale or license by, or on behalf of, User to third parties, or (2) distribution of the binary or source code to third parties for use with a commercial product sold or licensed by, or on behalf of, User. - -Revised 6/98 */ #ifdef BYTE_IMAGE @@ -107,7 +100,7 @@ int CLAHE (kz_pixel_t* pImage, unsigned int uiXRes, unsigned int uiYRes, kz_pixel_t aLUT[uiNR_OF_GREY]; /* lookup table used for scaling of input image */ unsigned long* pulHist, *pulMapArray; /* pointer to histogram and mappings*/ unsigned long* pulLU, *pulLB, *pulRU, *pulRB; /* auxiliary pointers interpolation */ - + if (uiNrX > uiMAX_REG_X) return -1; /* # of regions x-direction too large */ if (uiNrY > uiMAX_REG_Y) return -2; /* # of regions y-direction too large */ if (uiXRes % uiNrX) return -3; /* x-resolution no multiple of uiNrX */ From c7c4db6e3c2556991695f81c679113e111139254 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 11 Aug 2012 15:48:18 -0500 Subject: [PATCH 383/625] Improved support for color images using lab transform. --- skimage/exposure/exposure.py | 60 ++++++++++++++++++++----- skimage/exposure/tests/test_exposure.py | 38 ++++++++++++---- 2 files changed, 77 insertions(+), 21 deletions(-) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 0a2a253d..d34068f6 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -2,7 +2,7 @@ import numpy as np from skimage import img_as_float from skimage.util.dtype import dtype_range -from skimage.color import rgb2gray +import skimage.color as color from skimage.util.dtype import convert from _adapthist import _adapthist @@ -216,7 +216,7 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): Returns ------- - out - np.ndarray : + out : np.ndarray equalized image - may be a different shape than the original Notes @@ -224,8 +224,17 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): * The underlying algorithm relies on an image whose rows and columns are even multiples of the number of tiles, so the extra rows and columns are left at their original values, thus preserving the input image shape. - * For RGB or RGBA images, the algorithm is run on each channel. + * For grayscale images, CLAHE is performed on one channel, and a grayscale is returned + * For color images, the following steps are performed: + - The image is converted to LAB color space + - The CLAHE algorithm is run on the L channel + - The image is converted back to RGB space and returned * For RGBA images, the original alpha channel is removed. + + References + ---------- + .. [1] http://tog.acm.org/resources/GraphicsGems/ + .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE ''' in_type = image.dtype.type if out_range == 'full': @@ -233,23 +242,50 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): else: out_range = (image.min(), image.max()) # must be converted to 12 bit for CLAHE - image = skimage.img_as_uint(image) + int_image = skimage.img_as_uint(image) MAX_VAL = 2 ** 12 - 1 - image = rescale_intensity(image, out_range=(0, MAX_VAL)) + int_image = rescale_intensity(int_image, out_range=(0, MAX_VAL)) # handle color images - CLAHE accepts scalar images only - args = [image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] + args = [int_image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] if image.ndim == 3: - image = image[:, :, :3] - for channel in range(3): - args[0] = image[:, :, channel] + # check for grayscale + if (np.allclose(image[:, :, 0], image[:, :, 1]) and + np.allclose(image[:, :, 2], image[:, :, 3])): + args[0] = image[:, :, 0] out = _adapthist(*args) - image[:out.shape[0], :out.shape[1], channel] = out + image = int_image[:, :, :3] + for channel in range(3): + image[:out.shape[0], :out.shape[1], channel] = out + # for color images, convert to LAB space for processing + else: + lab_img = color.rgb2lab(skimage.img_as_float(image)) + L_chan = lab_img[:, :, 0] + L_chan /= np.max(np.abs(L_chan)) + L_chan = skimage.img_as_uint(L_chan) + args[0] = rescale_intensity(L_chan, out_range=(0, MAX_VAL)) + new_L = _adapthist(*args).astype(float) + new_L = rescale_intensity(new_L, out_range=(0, 100)) + lab_img[:new_L.shape[0], :new_L.shape[1], 0] = new_L + image = color.lab2rgb(lab_img) + image = rescale_intensity(image, out_range=(0, 1)) else: out = _adapthist(*args) + image = int_image image[:out.shape[0], :out.shape[1]] = out # restore to desired output type and output limits image = rescale_intensity(image) - if in_type != np.uint16: - image = convert(image, in_type) + image = convert(image, in_type) image = rescale_intensity(image, out_range=out_range) return image + +if __name__ == '__main__': + from skimage import data + import matplotlib.pyplot as plt + img = skimage.img_as_uint(data.lena()) + adapted = adapthist(img, nx=10, ny=9, clip_limit=0.01, + nbins=128, out_range='original') + plt.imshow(img) + plt.figure(); plt.imshow(skimage.img_as_ubyte(adapted)) + plt.figure(); plt.imshow(color.lab2rgb(color.rgb2lab(img))) + plt.show() + print 'Done' diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 9d70969a..8ce7c8c6 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -76,7 +76,7 @@ def test_rescale_out_range(): # Test rescale intensity # ====================== -def test_adapthist_ubyte(): +def test_adapthist_scalar(): '''Test a scalar uint8 image ''' img = skimage.img_as_ubyte(data.moon()) @@ -84,23 +84,43 @@ def test_adapthist_ubyte(): assert adapted.min() == 0 assert adapted.max() == 255 assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 22 - assert norm_brightness_err(img, adapted) < 0.05 + full_scale = skimage.exposure.rescale_intensity(img) + assert_almost_equal = np.testing.assert_almost_equal + assert_almost_equal(peak_snr(full_scale, adapted), 22.19920073) + assert_almost_equal(norm_brightness_err(full_scale, adapted), + 0.04161278) return img, adapted -def test_adapthist_float(): - '''Test an RGB float image +def test_adapthist_grayscale(): + '''Test a grayscale float image ''' img = skimage.img_as_float(data.lena()) + img = rgb2gray(img) adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') assert_almost_equal = np.testing.assert_almost_equal assert_almost_equal(adapted.min(), img.min()) - assert_almost_equal(adapted.min(), img.min()) + assert_almost_equal(adapted.max(), img.max()) assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 136 - assert norm_brightness_err(img, adapted) < 0.02 + assert_almost_equal(peak_snr(img, adapted), 131.4962063) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0208805) + return data, adapted + + +def test_adapthist_color(): + '''Test a color uint16 image + ''' + img = skimage.img_as_uint(data.lena()) + adapted = exposure.adapthist(img, clip_limit=0.01) + assert_almost_equal = np.testing.assert_almost_equal + assert adapted.min() == 0 + assert adapted.max() == 65535 + assert img.shape == adapted.shape + full_scale = skimage.exposure.rescale_intensity(img) + assert_almost_equal(peak_snr(full_scale, adapted), 64.29546231) + assert_almost_equal(norm_brightness_err(full_scale, adapted), + 0.181473754) return data, adapted @@ -135,7 +155,7 @@ def norm_brightness_err(img1, img2): Returns ------- norm_brightness_error : float - Normalize absolute mean brightness error + Normalized absolute mean brightness error ''' if img1.ndim == 3: img1, img2 = rgb2gray(img1), rgb2gray(img2) From 269929333b3c72e9b5a7da34fb2e8a99607f1aab Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 11 Aug 2012 21:28:08 -0500 Subject: [PATCH 384/625] Passed all tests with full coverage, PEP8 and Pylint performed. --- skimage/exposure/exposure.py | 44 ++++++++++--------------- skimage/exposure/tests/test_exposure.py | 1 + 2 files changed, 18 insertions(+), 27 deletions(-) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index d34068f6..a85a209d 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -194,16 +194,17 @@ def rescale_intensity(image, in_range=None, out_range=None): return dtype(image * (omax - omin) + omin) -def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): +def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256, + out_range='full'): '''Contrast Limited Adaptive Histogram Equalization Parameters ---------- image : array-like original image - nx : int, optional + ntiles_x : int, optional Tile regions in the X direction (2, 16) - ny : int, optional + ntiles_y : int, optional Tile regions in the Y direction (2, 16) clip_limit : float: optional Normalized cliplimit (higher values give more contrast) @@ -243,15 +244,16 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): out_range = (image.min(), image.max()) # must be converted to 12 bit for CLAHE int_image = skimage.img_as_uint(image) - MAX_VAL = 2 ** 12 - 1 - int_image = rescale_intensity(int_image, out_range=(0, MAX_VAL)) + max_val = 2 ** 12 - 1 + int_image = rescale_intensity(int_image, out_range=(0, max_val)) # handle color images - CLAHE accepts scalar images only - args = [int_image.copy(), 0, MAX_VAL, nx, ny, nbins, clip_limit] + args = [int_image.copy(), 0, max_val, ntiles_x, ntiles_y, nbins, + clip_limit] if image.ndim == 3: # check for grayscale if (np.allclose(image[:, :, 0], image[:, :, 1]) and - np.allclose(image[:, :, 2], image[:, :, 3])): - args[0] = image[:, :, 0] + np.allclose(image[:, :, 1], image[:, :, 2])): + args[0] = int_image[:, :, 0] out = _adapthist(*args) image = int_image[:, :, :3] for channel in range(3): @@ -259,13 +261,13 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): # for color images, convert to LAB space for processing else: lab_img = color.rgb2lab(skimage.img_as_float(image)) - L_chan = lab_img[:, :, 0] - L_chan /= np.max(np.abs(L_chan)) - L_chan = skimage.img_as_uint(L_chan) - args[0] = rescale_intensity(L_chan, out_range=(0, MAX_VAL)) - new_L = _adapthist(*args).astype(float) - new_L = rescale_intensity(new_L, out_range=(0, 100)) - lab_img[:new_L.shape[0], :new_L.shape[1], 0] = new_L + l_chan = lab_img[:, :, 0] + l_chan /= np.max(np.abs(l_chan)) + l_chan = skimage.img_as_uint(l_chan) + args[0] = rescale_intensity(l_chan, out_range=(0, max_val)) + new_l = _adapthist(*args).astype(float) + new_l = rescale_intensity(new_l, out_range=(0, 100)) + lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l image = color.lab2rgb(lab_img) image = rescale_intensity(image, out_range=(0, 1)) else: @@ -277,15 +279,3 @@ def adapthist(image, nx=8, ny=8, clip_limit=0.01, nbins=256, out_range='full'): image = convert(image, in_type) image = rescale_intensity(image, out_range=out_range) return image - -if __name__ == '__main__': - from skimage import data - import matplotlib.pyplot as plt - img = skimage.img_as_uint(data.lena()) - adapted = adapthist(img, nx=10, ny=9, clip_limit=0.01, - nbins=128, out_range='original') - plt.imshow(img) - plt.figure(); plt.imshow(skimage.img_as_ubyte(adapted)) - plt.figure(); plt.imshow(color.lab2rgb(color.rgb2lab(img))) - plt.show() - print 'Done' diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 8ce7c8c6..2e64e741 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -97,6 +97,7 @@ def test_adapthist_grayscale(): ''' img = skimage.img_as_float(data.lena()) img = rgb2gray(img) + img = np.dstack((img, img, img)) adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, nbins=128, out_range='original') assert_almost_equal = np.testing.assert_almost_equal From 94af01cdbde63b6d4a55b4235732c084fd696241 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 21 Oct 2012 13:25:18 -0500 Subject: [PATCH 385/625] Translated clahe C code to python, added to list of contributors. --- CONTRIBUTORS.txt | 7 + skimage/exposure/__init__.py | 3 +- skimage/exposure/_adapthist.py | 337 ++++++++++++++++++++++++ skimage/exposure/_adapthist.pyx | 40 --- skimage/exposure/clahe.h | 322 ---------------------- skimage/exposure/exposure.py | 90 +------ skimage/exposure/setup.py | 31 --- skimage/exposure/tests/test_exposure.py | 28 +- 8 files changed, 360 insertions(+), 498 deletions(-) create mode 100644 skimage/exposure/_adapthist.py delete mode 100644 skimage/exposure/_adapthist.pyx delete mode 100644 skimage/exposure/clahe.h delete mode 100644 skimage/exposure/setup.py diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 012fd53d..5f796b3f 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -123,3 +123,10 @@ - Luis Pedro Coelho imread plugin +======= + Polygon, circle and ellipse drawing functions + Adaptive thresholding + Implementation of Matlab's `regionprops` + +- Steven Silvester, Karel Zuiderveld + Adaptive Histogram Equalization diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index a5e6c25a..f8a4f046 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,2 +1,3 @@ -from .exposure import histogram, equalize, cumulative_distribution, adapthist +from .exposure import histogram, equalize, cumulative_distribution from .exposure import rescale_intensity +from ._adaphist import adapthist diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py new file mode 100644 index 00000000..f35a5da5 --- /dev/null +++ b/skimage/exposure/_adapthist.py @@ -0,0 +1,337 @@ +''' +Adapted code from the article + * "Contrast Limited Adaptive Histogram Equalization" + * by Karel Zuiderveld, karel@cv.ruu.nl + * in "Graphics Gems IV", Academic Press, 1994 +============= + +http://tog.acm.org/resources/GraphicsGems/ + +EULA: The Graphics Gems code is copyright-protected. +In other words, you cannot claim the text of the code as your +own and resell it. Using the code is permitted in any program, +product, or library, non-commercial or commercial. +Giving credit is not required, though is a nice gesture. +The code comes as-is, and if there are any flaws or problems +with any Gems code, nobody involved with Gems - authors, editors, +publishers, or webmasters - are to be held responsible. +Basically, don't be a jerk, and remember that anything free +comes with no guarantee. + * + * Author: Karel Zuiderveld, Computer Vision Research Group, + * Utrecht, The Netherlands (karel@cv.ruu.nl) +''' +import numpy as np +import skimage +from skimage import color +from skimage.exposure import rescale_intensity + + +MAX_REG_X = 16 # max. # contextual regions in x-direction */ +MAX_REG_Y = 16 # max. # contextual regions in y-direction */ +NR_OF_GREY = 1 << 14 # number of grayscale levels to use in CLAHE algorithm + + +def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): + '''Contrast Limited Adaptive Histogram Equalization + + Parameters + ---------- + image : array-like + original image + ntiles_x : int, optional + Tile regions in the X direction (2, 16) + ntiles_y : int, optional + Tile regions in the Y direction (2, 16) + clip_limit : float: optional + Normalized cliplimit (higher values give more contrast) + nbins : int, optional + Greybins for histogram ("dynamic range") + + Returns + ------- + out : np.ndarray + equalized image - grayscale images are uint16, color images are float + + Notes + ----- + * The algorithm relies on an image whose rows and columns are even + multiples of the number of tiles, so the extra rows and columns are left + at their original values, thus preserving the input image shape. + * For grayscale images, CLAHE is performed on one channel, + and a grayscale is returned + * For color images, the following steps are performed: + - The image is converted to LAB color space + - The CLAHE algorithm is run on the L channel + - The image is converted back to RGB space and returned + * For RGBA images, the original alpha channel is removed. + + References + ---------- + .. [1] http://tog.acm.org/resources/GraphicsGems/ + .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE + ''' + # convert to uint image + int_image = skimage.img_as_uint(image) + int_image = rescale_intensity(int_image, out_range=(0, NR_OF_GREY - 1)) + # handle color images - CLAHE accepts scalar images only + args = [int_image.copy(), ntiles_x, ntiles_y, clip_limit * nbins, nbins] + if image.ndim == 3: + # check for grayscale + if (np.allclose(image[:, :, 0], image[:, :, 1]) and + np.allclose(image[:, :, 1], image[:, :, 2])): + args[0] = int_image[:, :, 0] + out = _clahe(*args) + image = int_image[:, :, :3] + for channel in range(3): + image[:out.shape[0], :out.shape[1], channel] = out + # for color images, convert to LAB space for processing + else: + lab_img = color.rgb2lab(skimage.img_as_float(image)) + l_chan = lab_img[:, :, 0] + l_chan /= np.max(np.abs(l_chan)) + l_chan = skimage.img_as_uint(l_chan) + args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1)) + new_l = _clahe(*args).astype(float) + new_l = rescale_intensity(new_l, out_range=(0, 100)) + lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l + image = color.lab2rgb(lab_img) + image = rescale_intensity(image, out_range=(0, 1)) + else: + out = _clahe(*args) + image = int_image + image[:out.shape[0], :out.shape[1]] = out + return rescale_intensity(image) + + +def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): + '''Contrast Limited Adaptive Histogram Equalization + + Parameters + ---------- + image : array-like + original image + ntiles_x : int, optional + Tile regions in the X direction (2, 16) + ntiles_y : int, optional + Tile regions in the Y direction (2, 16) + clip_limit : float: optional + Normalized cliplimit (higher values give more contrast) + nbins : int, optional + Greybins for histogram ("dynamic range") + + Returns + ------- + out : np.ndarray + + The number of "effective" greylevels in the output image is set by nbins; + selecting a small value (eg. 128) speeds up processing and still produce + an output image of good quality. The output image will have the same + minimum and maximum value as the input image. A clip limit smaller than 1 + results in standard (non-contrast limited) AHE. + ''' + ntiles_x = min(ntiles_x, MAX_REG_X) + ntiles_y = min(ntiles_y, MAX_REG_Y) + ntiles_y = max(ntiles_x, 2) + ntiles_x = max(ntiles_y, 2) + + if clip_limit == 1.0: + return image # is OK, immediately returns original image. + + map_array = np.zeros((ntiles_x * ntiles_y, nbins), dtype=int) + + y_res = image.shape[0] - image.shape[0] % ntiles_y + x_res = image.shape[1] - image.shape[1] % ntiles_x + image = image[: y_res, : x_res] + + x_size = image.shape[1] / ntiles_x # Actual size of contextual regions + y_size = image.shape[0] / ntiles_y + n_pixels = x_size * y_size + + if clip_limit > 0.0: # Calculate actual cliplimit + clip_limit = int(clip_limit * (x_size * y_size) / nbins) + if clip_limit < 1: + clip_limit = 1 + else: + clip_limit = NR_OF_GREY # Large value, do not clip (AHE) + bin_size = 1 + NR_OF_GREY / nbins + aLUT = np.arange(NR_OF_GREY) + aLUT /= bin_size + # Calculate greylevel mappings for each contextual region + ystart = 0 + for y in range(ntiles_y): + xstart = 0 + for x in range(ntiles_x): + sub_img = image[ystart: ystart + y_size, + xstart: xstart + x_size] + hist = aLUT[sub_img.ravel()] + hist = np.bincount(hist) + hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int)) + hist = clip_histogram(hist, clip_limit) + hist = map_histogram(hist, 0, NR_OF_GREY, n_pixels) + map_array[y * ntiles_x + x] = hist + xstart += x_size + ystart += y_size + # Interpolate greylevel mappings to get CLAHE image + ystart = 0 + for y in range(ntiles_y + 1): + xstart = 0 + if y == 0: # special case: top row + ystep = y_size / 2 + yU = 0 + yB = 0 + elif y == ntiles_y: # special case: bottom row + ystep = y_size / 2 + yU = ntiles_y - 1 + yB = yU + else: # default values + ystep = y_size + yU = y - 1 + yB = yB + 1 + for x in range(ntiles_x + 1): + if x == 0: # special case: left column + xstep = x_size / 2 + xL = 0 + xR = 0 + elif x == ntiles_x: # special case: right column + xstep = x_size / 2 + xL = ntiles_x - 1 + xR = xL + else: # default values + xstep = x_size + xL = x - 1 + xR = xL + 1 + mapLU = map_array[yU * ntiles_x + xL] + mapRU = map_array[yU * ntiles_x + xR] + mapLB = map_array[yB * ntiles_x + xL] + mapRB = map_array[yB * ntiles_x + xR] + interpolate(image, xstart, xstep, ystart, ystep, + mapLU, mapRU, mapLB, mapRB, aLUT) + xstart += xstep # set pointer on next matrix */ + ystart += ystep + return image + + +def clip_histogram(hist, clip_limit): + '''Perform clipping of the histogram and redistribution of bins. + + The histogram is clipped and the number of excess pixels is counted. + Afterwards the excess pixels are equally redistributed across the + whole histogram (providing the bin count is smaller than the cliplimit). + + Parameters + ---------- + hist : np.ndarray + histogram array + clip_limit : int + maximum allowed bin count + + Returns + ------- + hist : np.ndarray + clipped histogram + ''' + # calculate total number of excess pixels + excess_mask = hist > clip_limit + excess = hist[excess_mask] + n_excess = excess.sum() - excess.size * clip_limit + + # Second part: clip histogram and redistribute excess pixels in each bin + bin_incr = n_excess / hist.size # average binincrement + upper = clip_limit - bin_incr # Bins larger than upper set to cliplimit + + hist[excess_mask] = clip_limit + + low_mask = hist < upper + n_excess -= hist[low_mask].size * bin_incr + hist[low_mask] += bin_incr + + mid_mask = (hist >= upper) & (hist < clip_limit) + mid = hist[mid_mask] + n_excess -= mid.size * clip_limit - mid.sum() + hist[mid_mask] = clip_limit + + while n_excess > 0: # Redistribute remaining excess + index = 0 + while n_excess > 0 and index < hist.size: + step_size = int(hist[hist < clip_limit].size / n_excess) + step_size = max(step_size, 1) + indices = np.arange(index, hist.size, step_size) + under = hist[indices] < clip_limit + hist[under] += 1 + n_excess -= hist[under].size + index += 1 + return hist + + +def map_histogram(hist, min_val, max_val, n_pixels): + '''Calculates the equalized lookup table (mapping) + + It does so by cumulating the input histogram. + + hist : np.ndarray + clipped histogram + min_val : int + min value for mapping + max_val : int + max value for mapping + n_pixels : int + number of pixels in the region + + Returns + ------- + out : np.ndarray + mapped intensity LUT + ''' + out = np.cumsum(hist).astype(float) + scale = ((float)(max_val - min_val)) / n_pixels + out *= scale + out += min_val + out[out > max_val] = max_val + return out.astype(int) + + +def interpolate(image, xstart, xstep, ystart, ystep, + mapLU, mapRU, mapLB, mapRB, aLUT): + '''Find the new grayscale level for a region using bilinear interpolation + + Parameters + ---------- + image : np.ndarray + full image + xstart, xstop : int + indices of xslice + ystart, ystop : int + indices of yslice + map* : np.ndarray + mappings of greylevels from histograms + aLUT : np.ndarray + maps grayscale levels in image to histogram levels + + Returns + ------- + out : np.ndarray + original image with the subregion replaced + + Note + ---- + This function calculates the new greylevel assignments of pixels + within a submatrix of the image. + This is done by a bilinear interpolation between four different + mappings in order to eliminate boundary artifacts. + ''' + norm = xstep * ystep # Normalization factor + + # interpolation weight matrices + x_coef, y_coef = np.meshgrid(np.arange(xstep), + np.arange(ystep)) + x_inv_coef, y_inv_coef = x_coef[:, ::-1] + 1, y_coef[::-1] + 1 + + im_slice = image[ystart: ystart + ystep, xstart: xstart + xstep] + im_slice = aLUT[im_slice] + new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice] + + x_coef * mapRU[im_slice]) + + y_coef * (x_inv_coef * mapLB[im_slice] + + x_coef * mapRB[im_slice])) + / norm) + image[ystart: ystart + ystep, xstart: xstart + xstep] = new diff --git a/skimage/exposure/_adapthist.pyx b/skimage/exposure/_adapthist.pyx deleted file mode 100644 index 16e84336..00000000 --- a/skimage/exposure/_adapthist.pyx +++ /dev/null @@ -1,40 +0,0 @@ -import numpy as np -cimport numpy as np - -cdef extern from "clahe.h": - ctypedef unsigned int kz_pixel_t - int CLAHE(kz_pixel_t * pImage, unsigned int uiXRes, unsigned int uiYRes, - kz_pixel_t Min, - kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, - unsigned int uiNrBins, float fCliplimit) - - -def _adapthist(np.ndarray[kz_pixel_t, ndim=2] image, min, max, nx, ny, nbins, - clip_limit=0): - ''' - image - to the input/output image - min - Minimum greyvalue of input image (also becomes min of output image) - max - Maximum greyvalue of input image (also becomes max of output image) - nx - Number of tile regions in the X direction (min 2, max uiMAX_REG_X) - ny - Number of tile regions in the Y direction (min 2, max uiMAX_REG_Y) - nbins - Number of greybins for histogram ("dynamic range") - clip_limit - Normalized cliplimit (higher values give more contrast) - - The number of "effective" greylevels in the output image is set by nbins; - selecting a small value (eg. 128) speeds up processing and still produce - an output image of good quality. The output image will have the same - minimum and maximum value as the input image. A clip limit smaller than 1 - results in standard (non-contrast limited) AHE. - ''' - # we need the image to be divisible by nx and ny - uiYRes = image.shape[0] - image.shape[0] % ny - uiXRes = image.shape[1] - image.shape[1] % nx - - cdef np.ndarray[kz_pixel_t, ndim = 1] flattened - flattened = image[:uiYRes, :uiXRes].ravel() - - ret = CLAHE(< kz_pixel_t * > flattened.data, uiXRes, uiYRes, min, - max, nx, ny, nbins, clip_limit * nbins) - if ret < 0: - print 'clahe error: ', ret - return flattened.reshape((uiYRes, uiXRes)) diff --git a/skimage/exposure/clahe.h b/skimage/exposure/clahe.h deleted file mode 100644 index a2721b98..00000000 --- a/skimage/exposure/clahe.h +++ /dev/null @@ -1,322 +0,0 @@ -/* -ANSI C code from the article - * "Contrast Limited Adaptive Histogram Equalization" - * by Karel Zuiderveld, karel@cv.ruu.nl - * in "Graphics Gems IV", Academic Press, 1994 -============= - -http://tog.acm.org/resources/GraphicsGems/ - -EULA: The Graphics Gems code is copyright-protected. -In other words, you cannot claim the text of the code as your -own and resell it. Using the code is permitted in any program, -product, or library, non-commercial or commercial. -Giving credit is not required, though is a nice gesture. -The code comes as-is, and if there are any flaws or problems -with any Gems code, nobody involved with Gems - authors, editors, -publishers, or webmasters - are to be held responsible. -Basically, don't be a jerk, and remember that anything free -comes with no guarantee. - -*/ - -#ifdef BYTE_IMAGE -typedef unsigned char kz_pixel_t; /* for 8 bit-per-pixel images */ -#define uiNR_OF_GREY (256) -#else -typedef unsigned short kz_pixel_t; /* for 12 bit-per-pixel images (default) */ -# define uiNR_OF_GREY (4096) -#endif - -/* - * ANSI C code from the article - * "Contrast Limited Adaptive Histogram Equalization" - * by Karel Zuiderveld, karel@cv.ruu.nl - * in "Graphics Gems IV", Academic Press, 1994 - * - * - * These functions implement Contrast Limited Adaptive Histogram Equalization. - * The main routine (CLAHE) expects an input image that is stored contiguously in - * memory; the CLAHE output image overwrites the original input image and has the - * same minimum and maximum values (which must be provided by the user). - * This implementation assumes that the X- and Y image resolutions are an integer - * multiple of the X- and Y sizes of the contextual regions. A check on various other - * error conditions is performed. - * - * #define the symbol BYTE_IMAGE to make this implementation suitable for - * 8-bit images. The maximum number of contextual regions can be redefined - * by changing uiMAX_REG_X and/or uiMAX_REG_Y; the use of more than 256 - * contextual regions is not recommended. - * - * The code is ANSI-C and is also C++ compliant. - * - * Author: Karel Zuiderveld, Computer Vision Research Group, - * Utrecht, The Netherlands (karel@cv.ruu.nl) - */ - -/*********************** Local prototypes ************************/ -static void ClipHistogram (unsigned long*, unsigned int, unsigned long); -static void MakeHistogram (kz_pixel_t*, unsigned int, unsigned int, unsigned int, - unsigned long*, unsigned int, kz_pixel_t*); -static void MapHistogram (unsigned long*, kz_pixel_t, kz_pixel_t, - unsigned int, unsigned long); -static void MakeLut (kz_pixel_t*, kz_pixel_t, kz_pixel_t, unsigned int); -static void Interpolate (kz_pixel_t*, int, unsigned long*, unsigned long*, - unsigned long*, unsigned long*, unsigned int, unsigned int, kz_pixel_t*); - -/************** Start of actual code **************/ -#include /* To get prototypes of malloc() and free() */ - - -const unsigned int uiMAX_REG_X = 16; /* max. # contextual regions in x-direction */ -const unsigned int uiMAX_REG_Y = 16; /* max. # contextual regions in y-direction */ - - - -/************************** main function CLAHE ******************/ -int CLAHE (kz_pixel_t* pImage, unsigned int uiXRes, unsigned int uiYRes, - kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrX, unsigned int uiNrY, - unsigned int uiNrBins, float fCliplimit) -/* pImage - Pointer to the input/output image - * uiXRes - Image resolution in the X direction - * uiYRes - Image resolution in the Y direction - * Min - Minimum greyvalue of input image (also becomes minimum of output image) - * Max - Maximum greyvalue of input image (also becomes maximum of output image) - * uiNrX - Number of contextial regions in the X direction (min 2, max uiMAX_REG_X) - * uiNrY - Number of contextial regions in the Y direction (min 2, max uiMAX_REG_Y) - * uiNrBins - Number of greybins for histogram ("dynamic range") - * float fCliplimit - Normalized cliplimit (higher values give more contrast) - * The number of "effective" greylevels in the output image is set by uiNrBins; selecting - * a small value (eg. 128) speeds up processing and still produce an output image of - * good quality. The output image will have the same minimum and maximum value as the input - * image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. - */ -{ - unsigned int uiX, uiY; /* counters */ - unsigned int uiXSize, uiYSize, uiSubX, uiSubY; /* size of context. reg. and subimages */ - unsigned int uiXL, uiXR, uiYU, uiYB; /* auxiliary variables interpolation routine */ - unsigned long ulClipLimit, ulNrPixels;/* clip limit and region pixel count */ - kz_pixel_t* pImPointer; /* pointer to image */ - kz_pixel_t aLUT[uiNR_OF_GREY]; /* lookup table used for scaling of input image */ - unsigned long* pulHist, *pulMapArray; /* pointer to histogram and mappings*/ - unsigned long* pulLU, *pulLB, *pulRU, *pulRB; /* auxiliary pointers interpolation */ - - if (uiNrX > uiMAX_REG_X) return -1; /* # of regions x-direction too large */ - if (uiNrY > uiMAX_REG_Y) return -2; /* # of regions y-direction too large */ - if (uiXRes % uiNrX) return -3; /* x-resolution no multiple of uiNrX */ - if (uiYRes % uiNrY) return -4; /* y-resolution no multiple of uiNrY */ - if (Max >= uiNR_OF_GREY) return -5; /* maximum too large */ - if (Min >= Max) return -6; /* minimum equal or larger than maximum */ - if (uiNrX < 2 || uiNrY < 2) return -7;/* at least 4 contextual regions required */ - if (fCliplimit == 1.0) return 0; /* is OK, immediately returns original image. */ - if (uiNrBins == 0) uiNrBins = 128; /* default value when not specified */ - - pulMapArray=(unsigned long *)malloc(sizeof(unsigned long)*uiNrX*uiNrY*uiNrBins); - if (pulMapArray == 0) return -8; /* Not enough memory! (try reducing uiNrBins) */ - - uiXSize = uiXRes/uiNrX; uiYSize = uiYRes/uiNrY; /* Actual size of contextual regions */ - ulNrPixels = (unsigned long)uiXSize * (unsigned long)uiYSize; - - if(fCliplimit > 0.0) { /* Calculate actual cliplimit */ - ulClipLimit = (unsigned long) (fCliplimit * (uiXSize * uiYSize) / uiNrBins); - ulClipLimit = (ulClipLimit < 1UL) ? 1UL : ulClipLimit; - } - else ulClipLimit = 1UL<<14; /* Large value, do not clip (AHE) */ - MakeLut(aLUT, Min, Max, uiNrBins); /* Make lookup table for mapping of greyvalues */ - /* Calculate greylevel mappings for each contextual region */ - for (uiY = 0, pImPointer = pImage; uiY < uiNrY; uiY++) { - for (uiX = 0; uiX < uiNrX; uiX++, pImPointer += uiXSize) { - pulHist = &pulMapArray[uiNrBins * (uiY * uiNrX + uiX)]; - MakeHistogram(pImPointer,uiXRes,uiXSize,uiYSize,pulHist,uiNrBins,aLUT); - ClipHistogram(pulHist, uiNrBins, ulClipLimit); - MapHistogram(pulHist, Min, Max, uiNrBins, ulNrPixels); - } - pImPointer += (uiYSize - 1) * uiXRes; /* skip lines, set pointer */ - } - /* Interpolate greylevel mappings to get CLAHE image */ - for (pImPointer = pImage, uiY = 0; uiY <= uiNrY; uiY++) { - if (uiY == 0) { /* special case: top row */ - uiSubY = uiYSize >> 1; uiYU = 0; uiYB = 0; - } - else { - if (uiY == uiNrY) { /* special case: bottom row */ - uiSubY = uiYSize >> 1; uiYU = uiNrY-1; uiYB = uiYU; - } - else { /* default values */ - uiSubY = uiYSize; uiYU = uiY - 1; uiYB = uiYU + 1; - } - } - for (uiX = 0; uiX <= uiNrX; uiX++) { - if (uiX == 0) { /* special case: left column */ - uiSubX = uiXSize >> 1; uiXL = 0; uiXR = 0; - } - else { - if (uiX == uiNrX) { /* special case: right column */ - uiSubX = uiXSize >> 1; uiXL = uiNrX - 1; uiXR = uiXL; - } - else { /* default values */ - uiSubX = uiXSize; uiXL = uiX - 1; uiXR = uiXL + 1; - } - } - - pulLU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXL)]; - pulRU = &pulMapArray[uiNrBins * (uiYU * uiNrX + uiXR)]; - pulLB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXL)]; - pulRB = &pulMapArray[uiNrBins * (uiYB * uiNrX + uiXR)]; - Interpolate(pImPointer,uiXRes,pulLU,pulRU,pulLB,pulRB,uiSubX,uiSubY,aLUT); - pImPointer += uiSubX; /* set pointer on next matrix */ - } - pImPointer += (uiSubY - 1) * uiXRes; - } - free(pulMapArray); /* free space for histograms */ - return 0; /* return status OK */ -} -void ClipHistogram (unsigned long* pulHistogram, unsigned int - uiNrGreylevels, unsigned long ulClipLimit) -/* This function performs clipping of the histogram and redistribution of bins. - * The histogram is clipped and the number of excess pixels is counted. Afterwards - * the excess pixels are equally redistributed across the whole histogram (providing - * the bin count is smaller than the cliplimit). - */ -{ - unsigned long* pulBinPointer, *pulEndPointer, *pulHisto; - unsigned long ulNrExcess, ulUpper, ulBinIncr, ulStepSize, i; - long lBinExcess; - - ulNrExcess = 0; pulBinPointer = pulHistogram; - for (i = 0; i < uiNrGreylevels; i++) { /* calculate total number of excess pixels */ - lBinExcess = (long) pulBinPointer[i] - (long) ulClipLimit; - if (lBinExcess > 0) ulNrExcess += lBinExcess; /* excess in current bin */ - }; - - /* Second part: clip histogram and redistribute excess pixels in each bin */ - ulBinIncr = ulNrExcess / uiNrGreylevels; /* average binincrement */ - ulUpper = ulClipLimit - ulBinIncr; /* Bins larger than ulUpper set to cliplimit */ - - for (i = 0; i < uiNrGreylevels; i++) { - if (pulHistogram[i] > ulClipLimit) pulHistogram[i] = ulClipLimit; /* clip bin */ - else { - if (pulHistogram[i] > ulUpper) { /* high bin count */ - ulNrExcess -= pulHistogram[i] - ulUpper; pulHistogram[i]=ulClipLimit; - } - else { /* low bin count */ - ulNrExcess -= ulBinIncr; pulHistogram[i] += ulBinIncr; - } - } - } - - while (ulNrExcess) { /* Redistribute remaining excess */ - pulEndPointer = &pulHistogram[uiNrGreylevels]; pulHisto = pulHistogram; - - while (ulNrExcess && pulHisto < pulEndPointer) { - ulStepSize = uiNrGreylevels / ulNrExcess; - if (ulStepSize < 1) ulStepSize = 1; /* stepsize at least 1 */ - for (pulBinPointer=pulHisto; pulBinPointer < pulEndPointer && ulNrExcess; - pulBinPointer += ulStepSize) { - if (*pulBinPointer < ulClipLimit) { - (*pulBinPointer)++; ulNrExcess--; /* reduce excess */ - } - } - pulHisto++; /* restart redistributing on other bin location */ - } - } -} -void MakeHistogram (kz_pixel_t* pImage, unsigned int uiXRes, - unsigned int uiSizeX, unsigned int uiSizeY, - unsigned long* pulHistogram, - unsigned int uiNrGreylevels, kz_pixel_t* pLookupTable) -/* This function classifies the greylevels present in the array image into - * a greylevel histogram. The pLookupTable specifies the relationship - * between the greyvalue of the pixel (typically between 0 and 4095) and - * the corresponding bin in the histogram (usually containing only 128 bins). - */ -{ - kz_pixel_t* pImagePointer; - unsigned int i; - - for (i = 0; i < uiNrGreylevels; i++) pulHistogram[i] = 0L; /* clear histogram */ - - for (i = 0; i < uiSizeY; i++) { - pImagePointer = &pImage[uiSizeX]; - while (pImage < pImagePointer) pulHistogram[pLookupTable[*pImage++]]++; - pImagePointer += uiXRes; - pImage = &pImagePointer[-uiSizeX]; - } -} - -void MapHistogram (unsigned long* pulHistogram, kz_pixel_t Min, kz_pixel_t Max, - unsigned int uiNrGreylevels, unsigned long ulNrOfPixels) -/* This function calculates the equalized lookup table (mapping) by - * cumulating the input histogram. Note: lookup table is rescaled in range [Min..Max]. - */ -{ - unsigned int i; unsigned long ulSum = 0; - const float fScale = ((float)(Max - Min)) / ulNrOfPixels; - const unsigned long ulMin = (unsigned long) Min; - - for (i = 0; i < uiNrGreylevels; i++) { - ulSum += pulHistogram[i]; pulHistogram[i]=(unsigned long)(ulMin+ulSum*fScale); - if (pulHistogram[i] > Max) pulHistogram[i] = Max; - } -} - -void MakeLut (kz_pixel_t * pLUT, kz_pixel_t Min, kz_pixel_t Max, unsigned int uiNrBins) -/* To speed up histogram clipping, the input image [Min,Max] is scaled down to - * [0,uiNrBins-1]. This function calculates the LUT. - */ -{ - int i; - const kz_pixel_t BinSize = (kz_pixel_t) (1 + (Max - Min) / uiNrBins); - - for (i = Min; i <= Max; i++) pLUT[i] = (i - Min) / BinSize; -} - -void Interpolate (kz_pixel_t * pImage, int uiXRes, unsigned long * pulMapLU, - unsigned long * pulMapRU, unsigned long * pulMapLB, unsigned long * pulMapRB, - unsigned int uiXSize, unsigned int uiYSize, kz_pixel_t * pLUT) -/* pImage - pointer to input/output image - * uiXRes - resolution of image in x-direction - * pulMap* - mappings of greylevels from histograms - * uiXSize - uiXSize of image submatrix - * uiYSize - uiYSize of image submatrix - * pLUT - lookup table containing mapping greyvalues to bins - * This function calculates the new greylevel assignments of pixels within a submatrix - * of the image with size uiXSize and uiYSize. This is done by a bilinear interpolation - * between four different mappings in order to eliminate boundary artifacts. - * It uses a division; since division is often an expensive operation, I added code to - * perform a logical shift instead when feasible. - */ -{ - const unsigned int uiIncr = uiXRes-uiXSize; /* Pointer increment after processing row */ - kz_pixel_t GreyValue; unsigned int uiNum = uiXSize*uiYSize; /* Normalization factor */ - - unsigned int uiXCoef, uiYCoef, uiXInvCoef, uiYInvCoef, uiShift = 0; - - if (uiNum & (uiNum - 1)) /* If uiNum is not a power of two, use division */ - for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; - uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { - for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; - uiXCoef++, uiXInvCoef--) { - GreyValue = pLUT[*pImage]; /* get histogram bin value */ - *pImage++ = (kz_pixel_t ) ((uiYInvCoef * (uiXInvCoef*pulMapLU[GreyValue] - + uiXCoef * pulMapRU[GreyValue]) - + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] - + uiXCoef * pulMapRB[GreyValue])) / uiNum); - } - } - else { /* avoid the division and use a right shift instead */ - while (uiNum >>= 1) uiShift++; /* Calculate 2log of uiNum */ - for (uiYCoef = 0, uiYInvCoef = uiYSize; uiYCoef < uiYSize; - uiYCoef++, uiYInvCoef--,pImage+=uiIncr) { - for (uiXCoef = 0, uiXInvCoef = uiXSize; uiXCoef < uiXSize; - uiXCoef++, uiXInvCoef--) { - GreyValue = pLUT[*pImage]; /* get histogram bin value */ - *pImage++ = (kz_pixel_t)((uiYInvCoef* (uiXInvCoef * pulMapLU[GreyValue] - + uiXCoef * pulMapRU[GreyValue]) - + uiYCoef * (uiXInvCoef * pulMapLB[GreyValue] - + uiXCoef * pulMapRB[GreyValue])) >> uiShift); - } - } - } -} - diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index a85a209d..6ef7ff66 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -5,10 +5,9 @@ from skimage.util.dtype import dtype_range import skimage.color as color from skimage.util.dtype import convert -from _adapthist import _adapthist __all__ = ['histogram', 'cumulative_distribution', 'equalize', - 'rescale_intensity', 'adapthist'] + 'rescale_intensity'] def histogram(image, nbins=256): @@ -192,90 +191,3 @@ def rescale_intensity(image, in_range=None, out_range=None): image = (image - imin) / float(imax - imin) return dtype(image * (omax - omin) + omin) - - -def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256, - out_range='full'): - '''Contrast Limited Adaptive Histogram Equalization - - Parameters - ---------- - image : array-like - original image - ntiles_x : int, optional - Tile regions in the X direction (2, 16) - ntiles_y : int, optional - Tile regions in the Y direction (2, 16) - clip_limit : float: optional - Normalized cliplimit (higher values give more contrast) - nbins : int, optional - Greybins for histogram ("dynamic range") - out_range : str, optional - Range of the output image data. - - 'original' - Use original image limits - - 'full' - Use full range of image data type - - Returns - ------- - out : np.ndarray - equalized image - may be a different shape than the original - - Notes - ----- - * The underlying algorithm relies on an image whose rows and columns are even multiples of - the number of tiles, so the extra rows and columns are left at their original values, thus - preserving the input image shape. - * For grayscale images, CLAHE is performed on one channel, and a grayscale is returned - * For color images, the following steps are performed: - - The image is converted to LAB color space - - The CLAHE algorithm is run on the L channel - - The image is converted back to RGB space and returned - * For RGBA images, the original alpha channel is removed. - - References - ---------- - .. [1] http://tog.acm.org/resources/GraphicsGems/ - .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE - ''' - in_type = image.dtype.type - if out_range == 'full': - out_range = None - else: - out_range = (image.min(), image.max()) - # must be converted to 12 bit for CLAHE - int_image = skimage.img_as_uint(image) - max_val = 2 ** 12 - 1 - int_image = rescale_intensity(int_image, out_range=(0, max_val)) - # handle color images - CLAHE accepts scalar images only - args = [int_image.copy(), 0, max_val, ntiles_x, ntiles_y, nbins, - clip_limit] - if image.ndim == 3: - # check for grayscale - if (np.allclose(image[:, :, 0], image[:, :, 1]) and - np.allclose(image[:, :, 1], image[:, :, 2])): - args[0] = int_image[:, :, 0] - out = _adapthist(*args) - image = int_image[:, :, :3] - for channel in range(3): - image[:out.shape[0], :out.shape[1], channel] = out - # for color images, convert to LAB space for processing - else: - lab_img = color.rgb2lab(skimage.img_as_float(image)) - l_chan = lab_img[:, :, 0] - l_chan /= np.max(np.abs(l_chan)) - l_chan = skimage.img_as_uint(l_chan) - args[0] = rescale_intensity(l_chan, out_range=(0, max_val)) - new_l = _adapthist(*args).astype(float) - new_l = rescale_intensity(new_l, out_range=(0, 100)) - lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l - image = color.lab2rgb(lab_img) - image = rescale_intensity(image, out_range=(0, 1)) - else: - out = _adapthist(*args) - image = int_image - image[:out.shape[0], :out.shape[1]] = out - # restore to desired output type and output limits - image = rescale_intensity(image) - image = convert(image, in_type) - image = rescale_intensity(image, out_range=out_range) - return image diff --git a/skimage/exposure/setup.py b/skimage/exposure/setup.py deleted file mode 100644 index 43bab23f..00000000 --- a/skimage/exposure/setup.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python - -import os -from skimage._build import cython - -base_path = os.path.abspath(os.path.dirname(__file__)) - - -def configuration(parent_package='', top_path=None): - from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs - - config = Configuration('exposure', parent_package, top_path) - config.add_data_dir('tests') - - cython(['_adapthist.pyx'], working_path=base_path) - - config.add_extension('_adapthist', sources=['_adapthist.c'], - include_dirs=[get_numpy_include_dirs()]) - - return config - -if __name__ == '__main__': - from numpy.distutils.core import setup - setup(maintainer='scikits-image developers', - author='scikits-image developers', - maintainer_email='scikits-image@googlegroups.com', - description='Exposure', - url='https://github.com/scikits-image/scikits-image', - license='SciPy License (BSD Style)', - **(configuration(top_path='').todict()) - ) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 2e64e741..3cf42a7f 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -1,6 +1,7 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close - +import sys +sys.path.insert(0, '../..') import skimage from skimage import data from skimage import exposure @@ -82,30 +83,27 @@ def test_adapthist_scalar(): img = skimage.img_as_ubyte(data.moon()) adapted = exposure.adapthist(img, clip_limit=0.02) assert adapted.min() == 0 - assert adapted.max() == 255 + assert adapted.max() == (1<<16) - 1 assert img.shape == adapted.shape - full_scale = skimage.exposure.rescale_intensity(img) + full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(peak_snr(full_scale, adapted), 22.19920073) + assert_almost_equal(peak_snr(full_scale, adapted), 28.6262034) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.04161278) + 0.0410010) return img, adapted - def test_adapthist_grayscale(): '''Test a grayscale float image ''' img = skimage.img_as_float(data.lena()) img = rgb2gray(img) img = np.dstack((img, img, img)) - adapted = exposure.adapthist(img, nx=10, ny=9, clip_limit=0.01, - nbins=128, out_range='original') + adapted = exposure.adapthist(img, 10, 9, clip_limit=0.01, + nbins=128) assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(adapted.min(), img.min()) - assert_almost_equal(adapted.max(), img.max()) assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 131.4962063) - assert_almost_equal(norm_brightness_err(img, adapted), 0.0208805) + assert_almost_equal(peak_snr(img, adapted), 106.3020173) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0218686) return data, adapted @@ -116,12 +114,12 @@ def test_adapthist_color(): adapted = exposure.adapthist(img, clip_limit=0.01) assert_almost_equal = np.testing.assert_almost_equal assert adapted.min() == 0 - assert adapted.max() == 65535 + assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert_almost_equal(peak_snr(full_scale, adapted), 64.29546231) + assert_almost_equal(peak_snr(full_scale, adapted), 64.7167515) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.181473754) + 0.17922429) return data, adapted From 7f86b0c40068e22bdb6ef65d1c9b03686fc4a47e Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 21 Oct 2012 13:49:35 -0500 Subject: [PATCH 386/625] Removed sys.path hack in test, fixed spelling of import in __init__.py --- skimage/exposure/__init__.py | 2 +- skimage/exposure/tests/test_exposure.py | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index f8a4f046..d495686a 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,3 +1,3 @@ from .exposure import histogram, equalize, cumulative_distribution from .exposure import rescale_intensity -from ._adaphist import adapthist +from ._adapthist import adapthist diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 3cf42a7f..04c0cf0e 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -1,7 +1,5 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close -import sys -sys.path.insert(0, '../..') import skimage from skimage import data from skimage import exposure From b8844beb793f2341677ba4411307ea1624fb6c80 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 1 Dec 2012 23:46:52 -0600 Subject: [PATCH 387/625] Cleaned up adapthist func to use LAB for all RGB->required rest result change --- skimage/exposure/_adapthist.py | 43 +++++++++---------------- skimage/exposure/tests/test_exposure.py | 4 +-- 2 files changed, 18 insertions(+), 29 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index f35a5da5..091514b6 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -71,37 +71,26 @@ def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): .. [1] http://tog.acm.org/resources/GraphicsGems/ .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE ''' - # convert to uint image - int_image = skimage.img_as_uint(image) - int_image = rescale_intensity(int_image, out_range=(0, NR_OF_GREY - 1)) # handle color images - CLAHE accepts scalar images only - args = [int_image.copy(), ntiles_x, ntiles_y, clip_limit * nbins, nbins] - if image.ndim == 3: - # check for grayscale - if (np.allclose(image[:, :, 0], image[:, :, 1]) and - np.allclose(image[:, :, 1], image[:, :, 2])): - args[0] = int_image[:, :, 0] - out = _clahe(*args) - image = int_image[:, :, :3] - for channel in range(3): - image[:out.shape[0], :out.shape[1], channel] = out - # for color images, convert to LAB space for processing - else: - lab_img = color.rgb2lab(skimage.img_as_float(image)) - l_chan = lab_img[:, :, 0] - l_chan /= np.max(np.abs(l_chan)) - l_chan = skimage.img_as_uint(l_chan) - args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1)) - new_l = _clahe(*args).astype(float) - new_l = rescale_intensity(new_l, out_range=(0, 100)) - lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l - image = color.lab2rgb(lab_img) - image = rescale_intensity(image, out_range=(0, 1)) + args = [None, ntiles_x, ntiles_y, clip_limit * nbins, nbins] + if image.ndim > 2: + lab_img = color.rgb2lab(skimage.img_as_float(image)) + l_chan = lab_img[:, :, 0] + l_chan /= np.max(np.abs(l_chan)) + l_chan = skimage.img_as_uint(l_chan) + args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1)) + new_l = _clahe(*args).astype(float) + new_l = rescale_intensity(new_l, out_range=(0, 100)) + lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l + image = color.lab2rgb(lab_img) + image = rescale_intensity(image, out_range=(0, 1)) else: + image = skimage.img_as_uint(image) + args[0] = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1)) out = _clahe(*args) - image = int_image image[:out.shape[0], :out.shape[1]] = out - return rescale_intensity(image) + image = rescale_intensity(image) + return image def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 04c0cf0e..dc48a3d8 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -100,8 +100,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 106.3020173) - assert_almost_equal(norm_brightness_err(img, adapted), 0.0218686) + assert_almost_equal(peak_snr(img, adapted), 77.5841845) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0376632) return data, adapted From 2eb03c0fc95c8edd61c4e14fea7aee26919223af Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 8 Dec 2012 18:26:13 +0100 Subject: [PATCH 388/625] Add dense DAISY feature description. --- skimage/feature/__init__.py | 1 + skimage/feature/_daisy.py | 167 ++++++++++++++++++++++++++++ skimage/feature/tests/test_daisy.py | 88 +++++++++++++++ 3 files changed, 256 insertions(+) create mode 100644 skimage/feature/_daisy.py create mode 100644 skimage/feature/tests/test_daisy.py diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index 1597e9a8..b1dabad3 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,3 +1,4 @@ +from ._daisy import daisy from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py new file mode 100644 index 00000000..7fb0364c --- /dev/null +++ b/skimage/feature/_daisy.py @@ -0,0 +1,167 @@ +import numpy as np +import scipy as sp +from numpy.linalg import norm +from scipy import sqrt, pi, arctan2, cos, sin, exp +from scipy.ndimage import gaussian_filter +from scipy.special import iv + +def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, + normalization='l1', sigmas=None, ring_radii=None): + '''Extract DAISY feature descriptors densely for the given image. + + DAISY is a feature descriptor similar to SIFT formulated in a way + that allows for fast dense extraction. Typically, this is practical + for bag-of-features image representations. + + The implementation follows Tola et al. [1] but deviate on the + following points: + * Histogram bin contribution are smoothed with a circular + Gaussian window over the tonal range (the angular range). + * The sigma values of the spatial Gaussian smoothing in this code + do not match the sigma values in the original code by Tola et + al. [2]. In their code, spatial smoothing is applied to both the + input image and the center histogram. However, this smoothing is + not documented in [1] and, therefore, it is omitted. + + Parameters + ---------- + img : (M, N) array + Input image (greyscale). + step : int, optional + Distance between descriptor sampling points. + radius : int, optional + Radius (in pixels) of the outermost ring. + rings : int, optional + Number of rings. + histograms : int, optional + Number of histograms sampled per ring. + orientations : int, optional + Number of orientations (bins) per histogram. + normalization : {'l1', 'l2', 'daisy', 'off'}, optional + How to normalize the descriptors: + * 'l1': L1-normalization of each descriptor. + * 'l2': L2-normalization of each descriptor. + * 'daisy': L2-normalization of individual histograms. + * 'off': Disable normalization. + sigmas : 1D array of float, optional + Standard deviation of spatial Gaussian smoothing for the center + histogram and for each ring of histograms. The array of sigmas + should be sorted from the center and out. I.e. the first sigma + value specifies the spatial smoothing of the center histogram + and the last sigma value specifies the spatial smoothing of the + outermost ring. + Specifying sigmas overrides the following parameter: + rings = len(sigmas)-1 + ring_radii : 1D array of int, optional + Radius (in pixels) for each ring. + Specifying ring_radii overrides the following two parameters: + rings = len(ring_radii) + radius = ring_radii[-1] + If both sigmas and ring_radii are given, they must satisfy + len(ring_radii) == len(sigmas)+1 + since no radius is needed for the center histogram. + + Returns + ------- + descs : array + Grid of DAISY descriptors for the given image as an array + dimensionality (P, Q, R) where + P = ceil((M-radius*2)/step) + Q = ceil((N-radius*2)/step) + R = (rings*histograms + 1)*orientations + + References + ---------- + [1] Tola et al. "Daisy: An efficient dense descriptor applied to + wide-baseline stereo." Pattern Analysis and Machine Intelligence, + IEEE Transactions on 32.5 (2010): 815-830. + [2] http://cvlab.epfl.ch/alumni/tola/daisy.html + ''' + + # Validate image format. + if img.ndim > 2: + raise ValueError('Only grey-level images are supported.') + if img.dtype.kind == 'u': + img = img.astype(float) + img = img/255. + + # Validate parameters. + if sigmas != None and ring_radii != None \ + and len(sigmas)-1 != len(ring_radii): + raise ValueError('len(sigmas)-1 != len(ring_radii)') + if ring_radii != None: + rings = len(ring_radii) + radius = ring_radii[-1] + if sigmas != None: + rings = len(sigmas)-1 + if sigmas == None: + sigmas = [radius*(i+1)/float(2*rings) for i in range(rings)] + if ring_radii == None: + ring_radii = [radius*(i+1)/float(rings) for i in range(rings)] + if normalization not in ['l1', 'l2', 'daisy', 'off']: + raise ValueError('Invalid normalization method.') + + # Compute image derivatives. + dx = np.zeros(img.shape) + dy = np.zeros(img.shape) + dx[:, :-1] = np.diff(img, n=1, axis=1) + dy[:-1, :] = np.diff(img, n=1, axis=0) + + # Compute gradient orientation and magnitude and their contribution + # to the histograms. + grad_mag = sqrt(dx**2 + dy**2) + grad_ori = arctan2(dy, dx) + hist_sigma = pi/orientations + kappa = 1./hist_sigma; + bessel = iv(0, kappa) + hist = np.empty((orientations,) + img.shape, dtype=float) + for i in range(orientations): + mu = 2*i*pi/orientations-pi + # Weigh bin contribution by the circular normal distribution + hist[i,:,:] = exp(kappa*cos(grad_ori-mu))/(2*pi*bessel) + # Weigh bin contribution by the gradient magnitude + hist[i,:,:] = np.multiply(hist[i,:,:], grad_mag) + + # Smooth orientation histograms for the center and all rings. + sigmas = [sigmas[0]] + sigmas + hist_smooth = np.empty((rings+1,)+hist.shape, dtype=float) + for i in range(rings+1): + for j in range(orientations): + hist_smooth[i,j,:,:] = \ + gaussian_filter(hist[j,:,:], sigma=sigmas[i]) + + # Assemble descriptor grid. + theta = [2*pi*j/histograms for j in range(histograms)] + desc_dims = (rings*histograms + 1)*orientations + descs = np.empty((desc_dims, img.shape[0]-2*radius, + img.shape[1]-2*radius)) + descs[:orientations,:,:] = \ + hist_smooth[0,:,radius:-radius,radius:-radius] + idx = orientations + for i in range(rings): + for j in range(histograms): + y_min = radius + int(round(ring_radii[i]*sin(theta[j]))) + y_max = descs.shape[1] + y_min + x_min = radius + int(round(ring_radii[i]*cos(theta[j]))) + x_max = descs.shape[2] + x_min + descs[idx:idx+orientations,:,:] = \ + hist_smooth[i+1,:,y_min:y_max,x_min:x_max] + idx += orientations + descs = descs[:,::step,::step] + descs = descs.swapaxes(0,1).swapaxes(1,2) + + # Normalize descriptors. + if normalization != 'off': + descs += 1e-10 + if normalization == 'l1': + descs /= np.sum(descs, axis=2)[:,:,np.newaxis] + elif normalization == 'l2': + descs /= sqrt(np.sum(descs**2, axis=2))[:,:,np.newaxis] + elif normalization == 'daisy': + for i in range(0, desc_dims, orientations): + norms = sqrt(np.sum(descs[:,:,i:i+orientations]**2, + axis=2)) + descs[:,:,i:i+orientations] /= norms[:,:,np.newaxis] + + return descs + diff --git a/skimage/feature/tests/test_daisy.py b/skimage/feature/tests/test_daisy.py new file mode 100644 index 00000000..9e4c663a --- /dev/null +++ b/skimage/feature/tests/test_daisy.py @@ -0,0 +1,88 @@ +import numpy as np +from numpy.testing import assert_raises, assert_almost_equal +from numpy import sqrt, ceil + +from skimage import data +from skimage import img_as_float +from skimage.feature import daisy + + +def test_daisy_color_image_unsupported_error(): + img = np.zeros((20, 20, 3)) + assert_raises(ValueError, daisy, img) + + +def test_daisy_desc_dims(): + img = img_as_float(data.lena()[:128, :128].mean(axis=2)) + rings = 2 + histograms = 4 + orientations = 3 + descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) + assert(descs.shape[2] == (rings*histograms + 1)*orientations) + + rings = 4 + histograms = 5 + orientations = 13 + descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) + assert(descs.shape[2] == (rings*histograms + 1)*orientations) + + +def test_descs_shape(): + img = img_as_float(data.lena()[:256, :256].mean(axis=2)) + radius = 20 + step = 8 + descs = daisy(img, radius=radius, step=step) + assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) + assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + + img = img[:-1,:-2] + radius = 5 + step = 3 + descs = daisy(img, radius=radius, step=step) + assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) + assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + + +def test_daisy_incompatible_sigmas_and_radii(): + img = img_as_float(data.lena()[:128, :128].mean(axis=2)) + sigmas = [1, 2] + radii = [1, 2] + assert_raises(ValueError, daisy, img, sigmas=sigmas, ring_radii=radii) + + +def test_daisy_normalization(): + img = img_as_float(data.lena()[:64, :64].mean(axis=2)) + + descs = daisy(img, normalization='l1') + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + assert_almost_equal(np.sum(descs[i,j,:]), 1) + descs_ = daisy(img) + assert_almost_equal(descs, descs_) + + descs = daisy(img, normalization='l2') + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + assert_almost_equal(sqrt(np.sum(descs[i,j,:]**2)), 1) + + orientations = 8 + descs = daisy(img, orientations=orientations, normalization='daisy') + desc_dims = descs.shape[2] + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + for k in range(0, desc_dims, orientations): + assert_almost_equal(sqrt(np.sum( + descs[i,j,k:k+orientations]**2)), 1) + + img = np.zeros((50, 50)) + descs = daisy(img, normalization='off') + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + assert_almost_equal(np.sum(descs[i,j,:]), 0) + + assert_raises(ValueError, daisy, img, normalization='does_not_exist') + + +if __name__ == '__main__': + from numpy import testing + testing.run_module_suite() From 08ef1e9b7541fb1465f73539400442ebaf929deb Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 8 Dec 2012 14:29:36 -0600 Subject: [PATCH 389/625] Rebased to master, incorporated view_as_blocks, cleaned up interpolate method and example --- CONTRIBUTORS.txt | 4 -- doc/examples/plot_equalize.py | 7 ++-- skimage/exposure/_adapthist.py | 49 +++++++++++-------------- skimage/exposure/tests/test_exposure.py | 5 ++- 4 files changed, 30 insertions(+), 35 deletions(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 5f796b3f..fd3ff235 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -123,10 +123,6 @@ - Luis Pedro Coelho imread plugin -======= - Polygon, circle and ellipse drawing functions - Adaptive thresholding - Implementation of Matlab's `regionprops` - Steven Silvester, Karel Zuiderveld Adaptive Histogram Equalization diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index e09b5a07..14e4b9f2 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -18,7 +18,7 @@ that fall within the 2nd and 98th percentiles [2]_. """ -from skimage import data +from skimage import data, img_as_ubyte from skimage.util.dtype import dtype_range from skimage import exposure @@ -39,7 +39,7 @@ def plot_img_and_hist(img, axes, bins=256): ax_img.set_axis_off() # Display histogram - ax_hist.hist(img.ravel(), bins=bins) + ax_hist.hist(img.ravel(), bins=bins, histtype='stepfilled') ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) ax_hist.set_xlabel('Pixel intensity') @@ -63,10 +63,11 @@ img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) # Equalization img_eq = exposure.equalize(img) +img_eq = img_as_ubyte(img_eq) # Adaptive Equalization img_adapteq = exposure.adapthist(img, clip_limit=0.03) - +img_adapteq = img_as_ubyte(img_adapteq) # Display results f, axes = plt.subplots(2, 4, figsize=(8, 4)) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 091514b6..ff144700 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -25,6 +25,7 @@ import numpy as np import skimage from skimage import color from skimage.exposure import rescale_intensity +from skimage.util import view_as_blocks MAX_REG_X = 16 # max. # contextual regions in x-direction */ @@ -58,8 +59,6 @@ def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): * The algorithm relies on an image whose rows and columns are even multiples of the number of tiles, so the extra rows and columns are left at their original values, thus preserving the input image shape. - * For grayscale images, CLAHE is performed on one channel, - and a grayscale is returned * For color images, the following steps are performed: - The image is converted to LAB color space - The CLAHE algorithm is run on the L channel @@ -127,7 +126,7 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): if clip_limit == 1.0: return image # is OK, immediately returns original image. - map_array = np.zeros((ntiles_x * ntiles_y, nbins), dtype=int) + map_array = np.zeros((ntiles_y, ntiles_x, nbins), dtype=int) y_res = image.shape[0] - image.shape[0] % ntiles_y x_res = image.shape[1] - image.shape[1] % ntiles_x @@ -146,21 +145,17 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): bin_size = 1 + NR_OF_GREY / nbins aLUT = np.arange(NR_OF_GREY) aLUT /= bin_size + img_blocks = view_as_blocks(image, (y_size, x_size)) # Calculate greylevel mappings for each contextual region - ystart = 0 for y in range(ntiles_y): - xstart = 0 for x in range(ntiles_x): - sub_img = image[ystart: ystart + y_size, - xstart: xstart + x_size] + sub_img = img_blocks[y, x] hist = aLUT[sub_img.ravel()] hist = np.bincount(hist) hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int)) hist = clip_histogram(hist, clip_limit) hist = map_histogram(hist, 0, NR_OF_GREY, n_pixels) - map_array[y * ntiles_x + x] = hist - xstart += x_size - ystart += y_size + map_array[y, x] = hist # Interpolate greylevel mappings to get CLAHE image ystart = 0 for y in range(ntiles_y + 1): @@ -190,11 +185,13 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): xstep = x_size xL = x - 1 xR = xL + 1 - mapLU = map_array[yU * ntiles_x + xL] - mapRU = map_array[yU * ntiles_x + xR] - mapLB = map_array[yB * ntiles_x + xL] - mapRB = map_array[yB * ntiles_x + xR] - interpolate(image, xstart, xstep, ystart, ystep, + mapLU = map_array[yU, xL] + mapRU = map_array[yU, xR] + mapLB = map_array[yB, xL] + mapRB = map_array[yB, xR] + xslice = np.arange(xstart, xstart + xstep) + yslice = np.arange(ystart, ystart + ystep) + interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, aLUT) xstart += xstep # set pointer on next matrix */ ystart += ystep @@ -280,7 +277,7 @@ def map_histogram(hist, min_val, max_val, n_pixels): return out.astype(int) -def interpolate(image, xstart, xstep, ystart, ystep, +def interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, aLUT): '''Find the new grayscale level for a region using bilinear interpolation @@ -288,10 +285,8 @@ def interpolate(image, xstart, xstep, ystart, ystep, ---------- image : np.ndarray full image - xstart, xstop : int - indices of xslice - ystart, ystop : int - indices of yslice + xslice, yslice : array-like + indices of the region map* : np.ndarray mappings of greylevels from histograms aLUT : np.ndarray @@ -309,18 +304,18 @@ def interpolate(image, xstart, xstep, ystart, ystep, This is done by a bilinear interpolation between four different mappings in order to eliminate boundary artifacts. ''' - norm = xstep * ystep # Normalization factor - + norm = xslice.size * yslice.size # Normalization factor # interpolation weight matrices - x_coef, y_coef = np.meshgrid(np.arange(xstep), - np.arange(ystep)) + x_coef, y_coef = np.meshgrid(np.arange(xslice.size), + np.arange(yslice.size)) x_inv_coef, y_inv_coef = x_coef[:, ::-1] + 1, y_coef[::-1] + 1 - im_slice = image[ystart: ystart + ystep, xstart: xstart + xstep] - im_slice = aLUT[im_slice] + view = image[yslice[0]: yslice[-1] + 1, xslice[0]: xslice[-1] + 1] + im_slice = aLUT[view] new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice] + x_coef * mapRU[im_slice]) + y_coef * (x_inv_coef * mapLB[im_slice] + x_coef * mapRB[im_slice])) / norm) - image[ystart: ystart + ystep, xstart: xstart + xstep] = new + view[:, :] = new + return image diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index dc48a3d8..91b15b09 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -1,11 +1,13 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close import skimage +from skimage import io from skimage import data from skimage import exposure from skimage.color import rgb2gray from skimage.util.dtype import dtype_range +io.use_plugin('qt') # Test histogram equalization # =========================== @@ -81,7 +83,7 @@ def test_adapthist_scalar(): img = skimage.img_as_ubyte(data.moon()) adapted = exposure.adapthist(img, clip_limit=0.02) assert adapted.min() == 0 - assert adapted.max() == (1<<16) - 1 + assert adapted.max() == (1 << 16) - 1 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal @@ -90,6 +92,7 @@ def test_adapthist_scalar(): 0.0410010) return img, adapted + def test_adapthist_grayscale(): '''Test a grayscale float image ''' From e91129de537088e616178ae12084ea380b50bac3 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 8 Dec 2012 14:53:34 -0600 Subject: [PATCH 390/625] Renamed functions to equalize_hist/adapthist, with a deprecation warning for equalize --- skimage/exposure/__init__.py | 6 +++--- skimage/exposure/_adapthist.py | 3 ++- skimage/exposure/exposure.py | 6 ++++++ 3 files changed, 11 insertions(+), 4 deletions(-) diff --git a/skimage/exposure/__init__.py b/skimage/exposure/__init__.py index d495686a..ae75c982 100644 --- a/skimage/exposure/__init__.py +++ b/skimage/exposure/__init__.py @@ -1,3 +1,3 @@ -from .exposure import histogram, equalize, cumulative_distribution -from .exposure import rescale_intensity -from ._adapthist import adapthist +from .exposure import histogram, equalize, equalize_hist +from .exposure import rescale_intensity, cumulative_distribution +from ._adapthist import equalize_adapthist diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 57541de1..30d92d48 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -33,7 +33,8 @@ MAX_REG_Y = 16 # max. # contextual regions in y-direction */ NR_OF_GREY = 1 << 14 # number of grayscale levels to use in CLAHE algorithm -def adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): +def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, + nbins=256): '''Contrast Limited Adaptive Histogram Equalization Parameters diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 6ef7ff66..465b9182 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -4,6 +4,7 @@ from skimage import img_as_float from skimage.util.dtype import dtype_range import skimage.color as color from skimage.util.dtype import convert +from skimage._shared.utils import deprecated __all__ = ['histogram', 'cumulative_distribution', 'equalize', @@ -78,7 +79,12 @@ def cumulative_distribution(image, nbins=256): return img_cdf, bin_centers +@deprecated('equalize_hist') def equalize(image, nbins=256): + equalize_hist(image, nbins) + + +def equalize_hist(image, nbins=256): """Return image after histogram equalization. Parameters From 71f505f71c3f6bbf2b9e8dd486586e5a17b9803a Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 8 Dec 2012 15:03:11 -0600 Subject: [PATCH 391/625] Used the new function names in example and test. --- doc/examples/plot_equalize.py | 5 ++--- skimage/exposure/tests/test_exposure.py | 14 ++++++-------- 2 files changed, 8 insertions(+), 11 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 14e4b9f2..13ffa320 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -23,7 +23,6 @@ from skimage.util.dtype import dtype_range from skimage import exposure import matplotlib.pyplot as plt - import numpy as np @@ -62,11 +61,11 @@ p98 = np.percentile(img, 98) img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) # Equalization -img_eq = exposure.equalize(img) +img_eq = exposure.equalize_hist(img) img_eq = img_as_ubyte(img_eq) # Adaptive Equalization -img_adapteq = exposure.adapthist(img, clip_limit=0.03) +img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03) img_adapteq = img_as_ubyte(img_adapteq) # Display results diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 17206d1b..17633cf9 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -1,13 +1,11 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close import skimage -from skimage import io from skimage import data from skimage import exposure from skimage.color import rgb2gray from skimage.util.dtype import dtype_range -io.use_plugin('qt') # Test histogram equalization # =========================== @@ -18,7 +16,7 @@ test_img = exposure.rescale_intensity(data.camera() / 5. + 100) def test_equalize_ubyte(): img = skimage.img_as_ubyte(test_img) - img_eq = exposure.equalize(img) + img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_eq) check_cdf_slope(cdf) @@ -26,7 +24,7 @@ def test_equalize_ubyte(): def test_equalize_float(): img = skimage.img_as_float(test_img) - img_eq = exposure.equalize(img) + img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_eq) check_cdf_slope(cdf) @@ -81,8 +79,8 @@ def test_adapthist_scalar(): '''Test a scalar uint8 image ''' img = skimage.img_as_ubyte(data.moon()) - adapted = exposure.adapthist(img, clip_limit=0.02) - assert adapted.min() == 0= + adapted = exposure.equalize_adapthist(img, clip_limit=0.02) + assert adapted.min() == 0 assert adapted.max() == (1 << 16) - 1 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) @@ -99,7 +97,7 @@ def test_adapthist_grayscale(): img = skimage.img_as_float(data.lena()) img = rgb2gray(img) img = np.dstack((img, img, img)) - adapted = exposure.adapthist(img, 10, 9, clip_limit=0.01, + adapted = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01, nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape @@ -112,7 +110,7 @@ def test_adapthist_color(): '''Test a color uint16 image ''' img = skimage.img_as_uint(data.lena()) - adapted = exposure.adapthist(img, clip_limit=0.01) + adapted = exposure.equalize_adapthist(img, clip_limit=0.01) assert_almost_equal = np.testing.assert_almost_equal assert adapted.min() == 0 assert adapted.max() == 1.0 From 979a15cd9a417b030ef42b386646b12b8db218f5 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 8 Dec 2012 22:37:36 +0100 Subject: [PATCH 392/625] PEP8 style corrections. --- skimage/feature/_daisy.py | 150 ++++++++++++++-------------- skimage/feature/tests/test_daisy.py | 28 +++--- 2 files changed, 89 insertions(+), 89 deletions(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 7fb0364c..afe059be 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -1,27 +1,26 @@ import numpy as np -import scipy as sp -from numpy.linalg import norm from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from scipy.special import iv + def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, - normalization='l1', sigmas=None, ring_radii=None): + normalization='l1', sigmas=None, ring_radii=None): '''Extract DAISY feature descriptors densely for the given image. - DAISY is a feature descriptor similar to SIFT formulated in a way - that allows for fast dense extraction. Typically, this is practical - for bag-of-features image representations. + DAISY is a feature descriptor similar to SIFT formulated in a way that + allows for fast dense extraction. Typically, this is practical for + bag-of-features image representations. - The implementation follows Tola et al. [1] but deviate on the - following points: - * Histogram bin contribution are smoothed with a circular - Gaussian window over the tonal range (the angular range). - * The sigma values of the spatial Gaussian smoothing in this code - do not match the sigma values in the original code by Tola et - al. [2]. In their code, spatial smoothing is applied to both the - input image and the center histogram. However, this smoothing is - not documented in [1] and, therefore, it is omitted. + The implementation follows Tola et al. [1] but deviate on the following + points: + * Histogram bin contribution are smoothed with a circular Gaussian + window over the tonal range (the angular range). + * The sigma values of the spatial Gaussian smoothing in this code do + not match the sigma values in the original code by Tola et al. [2]. + In their code, spatial smoothing is applied to both the input image + and the center histogram. However, this smoothing is not documented + in [1] and, therefore, it is omitted. Parameters ---------- @@ -39,26 +38,25 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, Number of orientations (bins) per histogram. normalization : {'l1', 'l2', 'daisy', 'off'}, optional How to normalize the descriptors: - * 'l1': L1-normalization of each descriptor. - * 'l2': L2-normalization of each descriptor. - * 'daisy': L2-normalization of individual histograms. - * 'off': Disable normalization. + * 'l1': L1-normalization of each descriptor. + * 'l2': L2-normalization of each descriptor. + * 'daisy': L2-normalization of individual histograms. + * 'off': Disable normalization. sigmas : 1D array of float, optional Standard deviation of spatial Gaussian smoothing for the center - histogram and for each ring of histograms. The array of sigmas - should be sorted from the center and out. I.e. the first sigma - value specifies the spatial smoothing of the center histogram - and the last sigma value specifies the spatial smoothing of the - outermost ring. - Specifying sigmas overrides the following parameter: - rings = len(sigmas)-1 + histogram and for each ring of histograms. The array of sigmas should + be sorted from the center and out. I.e. the first sigma value defines + the spatial smoothing of the center histogram and the last sigma value + defines the spatial smoothing of the outermost ring. Specifying sigmas + overrides the following parameter: + rings = len(sigmas)-1 ring_radii : 1D array of int, optional - Radius (in pixels) for each ring. - Specifying ring_radii overrides the following two parameters: - rings = len(ring_radii) - radius = ring_radii[-1] + Radius (in pixels) for each ring. Specifying ring_radii overrides the + following two parameters: + rings = len(ring_radii) + radius = ring_radii[-1] If both sigmas and ring_radii are given, they must satisfy - len(ring_radii) == len(sigmas)+1 + len(ring_radii) == len(sigmas)+1 since no radius is needed for the center histogram. Returns @@ -66,16 +64,16 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, descs : array Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where - P = ceil((M-radius*2)/step) - Q = ceil((N-radius*2)/step) - R = (rings*histograms + 1)*orientations + P = ceil((M-radius*2)/step) + Q = ceil((N-radius*2)/step) + R = (rings*histograms + 1)*orientations References ---------- - [1] Tola et al. "Daisy: An efficient dense descriptor applied to - wide-baseline stereo." Pattern Analysis and Machine Intelligence, - IEEE Transactions on 32.5 (2010): 815-830. - [2] http://cvlab.epfl.ch/alumni/tola/daisy.html + [1]: Tola et al. "Daisy: An efficient dense descriptor applied to + wide-baseline stereo." Pattern Analysis and Machine Intelligence, + IEEE Transactions on 32.5 (2010): 815-830. + [2]: http://cvlab.epfl.ch/alumni/tola/daisy.html ''' # Validate image format. @@ -83,21 +81,21 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, raise ValueError('Only grey-level images are supported.') if img.dtype.kind == 'u': img = img.astype(float) - img = img/255. + img = img / 255. # Validate parameters. - if sigmas != None and ring_radii != None \ - and len(sigmas)-1 != len(ring_radii): + if sigmas is not None and ring_radii is not None \ + and len(sigmas) - 1 != len(ring_radii): raise ValueError('len(sigmas)-1 != len(ring_radii)') - if ring_radii != None: + if ring_radii is not None: rings = len(ring_radii) radius = ring_radii[-1] - if sigmas != None: - rings = len(sigmas)-1 - if sigmas == None: - sigmas = [radius*(i+1)/float(2*rings) for i in range(rings)] - if ring_radii == None: - ring_radii = [radius*(i+1)/float(rings) for i in range(rings)] + if sigmas is not None: + rings = len(sigmas) - 1 + if sigmas is None: + sigmas = [radius * (i + 1) / float(2 * rings) for i in range(rings)] + if ring_radii is None: + ring_radii = [radius * (i + 1) / float(rings) for i in range(rings)] if normalization not in ['l1', 'l2', 'daisy', 'off']: raise ValueError('Invalid normalization method.') @@ -109,59 +107,59 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, # Compute gradient orientation and magnitude and their contribution # to the histograms. - grad_mag = sqrt(dx**2 + dy**2) + grad_mag = sqrt(dx ** 2 + dy ** 2) grad_ori = arctan2(dy, dx) - hist_sigma = pi/orientations - kappa = 1./hist_sigma; + hist_sigma = pi / orientations + kappa = 1. / hist_sigma bessel = iv(0, kappa) hist = np.empty((orientations,) + img.shape, dtype=float) for i in range(orientations): - mu = 2*i*pi/orientations-pi + mu = 2 * i * pi / orientations - pi # Weigh bin contribution by the circular normal distribution - hist[i,:,:] = exp(kappa*cos(grad_ori-mu))/(2*pi*bessel) + hist[i, :, :] = exp(kappa * cos(grad_ori - mu)) / (2 * pi * bessel) # Weigh bin contribution by the gradient magnitude - hist[i,:,:] = np.multiply(hist[i,:,:], grad_mag) + hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) # Smooth orientation histograms for the center and all rings. sigmas = [sigmas[0]] + sigmas - hist_smooth = np.empty((rings+1,)+hist.shape, dtype=float) - for i in range(rings+1): + hist_smooth = np.empty((rings + 1,) + hist.shape, dtype=float) + for i in range(rings + 1): for j in range(orientations): - hist_smooth[i,j,:,:] = \ - gaussian_filter(hist[j,:,:], sigma=sigmas[i]) + hist_smooth[i, j, :, :] = gaussian_filter(hist[j, :, :], + sigma=sigmas[i]) # Assemble descriptor grid. - theta = [2*pi*j/histograms for j in range(histograms)] - desc_dims = (rings*histograms + 1)*orientations - descs = np.empty((desc_dims, img.shape[0]-2*radius, - img.shape[1]-2*radius)) - descs[:orientations,:,:] = \ - hist_smooth[0,:,radius:-radius,radius:-radius] + theta = [2 * pi * j / histograms for j in range(histograms)] + desc_dims = (rings * histograms + 1) * orientations + descs = np.empty((desc_dims, img.shape[0] - 2 * radius, + img.shape[1] - 2 * radius)) + descs[:orientations, :, :] = hist_smooth[0, :, radius:-radius, + radius:-radius] idx = orientations for i in range(rings): for j in range(histograms): - y_min = radius + int(round(ring_radii[i]*sin(theta[j]))) + y_min = radius + int(round(ring_radii[i] * sin(theta[j]))) y_max = descs.shape[1] + y_min - x_min = radius + int(round(ring_radii[i]*cos(theta[j]))) + x_min = radius + int(round(ring_radii[i] * cos(theta[j]))) x_max = descs.shape[2] + x_min - descs[idx:idx+orientations,:,:] = \ - hist_smooth[i+1,:,y_min:y_max,x_min:x_max] + descs[idx:idx + orientations, :, :] = hist_smooth[i + 1, :, + y_min:y_max, + x_min:x_max] idx += orientations - descs = descs[:,::step,::step] - descs = descs.swapaxes(0,1).swapaxes(1,2) + descs = descs[:, ::step, ::step] + descs = descs.swapaxes(0, 1).swapaxes(1, 2) # Normalize descriptors. if normalization != 'off': descs += 1e-10 if normalization == 'l1': - descs /= np.sum(descs, axis=2)[:,:,np.newaxis] + descs /= np.sum(descs, axis=2)[:, :, np.newaxis] elif normalization == 'l2': - descs /= sqrt(np.sum(descs**2, axis=2))[:,:,np.newaxis] + descs /= sqrt(np.sum(descs ** 2, axis=2))[:, :, np.newaxis] elif normalization == 'daisy': for i in range(0, desc_dims, orientations): - norms = sqrt(np.sum(descs[:,:,i:i+orientations]**2, - axis=2)) - descs[:,:,i:i+orientations] /= norms[:,:,np.newaxis] + norms = sqrt(np.sum(descs[:, :, i:i + orientations] ** 2, + axis=2)) + descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] return descs - diff --git a/skimage/feature/tests/test_daisy.py b/skimage/feature/tests/test_daisy.py index 9e4c663a..b0dd28de 100644 --- a/skimage/feature/tests/test_daisy.py +++ b/skimage/feature/tests/test_daisy.py @@ -17,14 +17,16 @@ def test_daisy_desc_dims(): rings = 2 histograms = 4 orientations = 3 - descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) - assert(descs.shape[2] == (rings*histograms + 1)*orientations) + descs = daisy(img, rings=rings, histograms=histograms, + orientations=orientations) + assert(descs.shape[2] == (rings * histograms + 1) * orientations) rings = 4 histograms = 5 orientations = 13 - descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) - assert(descs.shape[2] == (rings*histograms + 1)*orientations) + descs = daisy(img, rings=rings, histograms=histograms, + orientations=orientations) + assert(descs.shape[2] == (rings * histograms + 1) * orientations) def test_descs_shape(): @@ -32,15 +34,15 @@ def test_descs_shape(): radius = 20 step = 8 descs = daisy(img, radius=radius, step=step) - assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) - assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))) + assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))) - img = img[:-1,:-2] + img = img[:-1, :-2] radius = 5 step = 3 descs = daisy(img, radius=radius, step=step) - assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) - assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))) + assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))) def test_daisy_incompatible_sigmas_and_radii(): @@ -56,14 +58,14 @@ def test_daisy_normalization(): descs = daisy(img, normalization='l1') for i in range(descs.shape[0]): for j in range(descs.shape[1]): - assert_almost_equal(np.sum(descs[i,j,:]), 1) + assert_almost_equal(np.sum(descs[i, j, :]), 1) descs_ = daisy(img) assert_almost_equal(descs, descs_) descs = daisy(img, normalization='l2') for i in range(descs.shape[0]): for j in range(descs.shape[1]): - assert_almost_equal(sqrt(np.sum(descs[i,j,:]**2)), 1) + assert_almost_equal(sqrt(np.sum(descs[i, j, :] ** 2)), 1) orientations = 8 descs = daisy(img, orientations=orientations, normalization='daisy') @@ -72,13 +74,13 @@ def test_daisy_normalization(): for j in range(descs.shape[1]): for k in range(0, desc_dims, orientations): assert_almost_equal(sqrt(np.sum( - descs[i,j,k:k+orientations]**2)), 1) + descs[i, j, k:k + orientations] ** 2)), 1) img = np.zeros((50, 50)) descs = daisy(img, normalization='off') for i in range(descs.shape[0]): for j in range(descs.shape[1]): - assert_almost_equal(np.sum(descs[i,j,:]), 0) + assert_almost_equal(np.sum(descs[i, j, :]), 0) assert_raises(ValueError, daisy, img, normalization='does_not_exist') From 5cba69a4fad59b062910dc7dca1c53b489c2a0a8 Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Sat, 8 Dec 2012 21:12:26 -0600 Subject: [PATCH 393/625] test: add additional tests for 100% coverage of new `peak_local_max` Also fix minor bug in label reordering. --- skimage/feature/peak.py | 4 +-- skimage/feature/tests/test_peak.py | 49 +++++++++++++++++++++++++++++- 2 files changed, 50 insertions(+), 3 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 85d2cb4a..4ff70be1 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -91,8 +91,8 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, label_values = np.unique(labels) # Reorder label values to have consecutive integers (no gaps) if np.any(np.diff(label_values) != 1): - mask = labels >= 0 - labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype) + mask = labels >= 1 + labels[mask] = 1 + rank_order(labels[mask])[0].astype(labels.dtype) labels = labels.astype(np.int32) # New values for new ordering diff --git a/skimage/feature/tests/test_peak.py b/skimage/feature/tests/test_peak.py index 13457781..3ef1f12d 100644 --- a/skimage/feature/tests/test_peak.py +++ b/skimage/feature/tests/test_peak.py @@ -1,9 +1,17 @@ import numpy as np from numpy.testing import assert_array_almost_equal as assert_close - +import scipy.ndimage from skimage.feature import peak +def test_trivial_case(): + trivial = np.zeros((25, 25)) + peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True) + assert not peak_indices # inherent boolean-ness of empty list + peaks = peak.peak_local_max(trivial, min_distance=1, indices=False) + assert (peaks.astype(np.bool) == trivial).all() + + def test_noisy_peaks(): peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)] @@ -70,6 +78,45 @@ def test_num_peaks(): assert (3, 5) in peaks_limited +def test_reorder_labels(): + np.random.seed(21) + image = np.random.uniform(size=(40, 60)) + i, j = np.mgrid[0:40, 0:60] + labels = 1 + (i >= 20) + (j >= 30) * 2 + labels[labels == 4] = 5 + i, j = np.mgrid[-3:4, -3:4] + footprint = (i * i + j * j <= 9) + expected = np.zeros(image.shape, float) + for imin, imax in ((0, 20), (20, 40)): + for jmin, jmax in ((0, 30), (30, 60)): + expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter( + image[imin:imax, jmin:jmax], footprint=footprint) + expected = (expected == image) + result = peak.peak_local_max(image, labels=labels, min_distance=1, + threshold_rel=0, footprint=footprint, + indices=False, exclude_border=False) + assert (result == expected).all() + + +def test_indices_with_labels(): + np.random.seed(21) + image = np.random.uniform(size=(40, 60)) + i, j = np.mgrid[0:40, 0:60] + labels = 1 + (i >= 20) + (j >= 30) * 2 + i, j = np.mgrid[-3:4, -3:4] + footprint = (i * i + j * j <= 9) + expected = np.zeros(image.shape, float) + for imin, imax in ((0, 20), (20, 40)): + for jmin, jmax in ((0, 30), (30, 60)): + expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter( + image[imin:imax, jmin:jmax], footprint=footprint) + expected = (expected == image) + result = peak.peak_local_max(image, labels=labels, min_distance=1, + threshold_rel=0, footprint=footprint, + indices=True, exclude_border=False) + assert (result == np.transpose(expected.nonzero())).all() + + if __name__ == '__main__': from numpy import testing testing.run_module_suite() From e5fb8a2a72023f7ea7bdae390e559a7bf6e8cb7c Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 9 Dec 2012 12:53:52 +0100 Subject: [PATCH 394/625] Better Sphinx documentation. --- skimage/feature/_daisy.py | 69 +++++++++++++++++++++------------------ 1 file changed, 38 insertions(+), 31 deletions(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index afe059be..b0cc6748 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -2,6 +2,7 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from scipy.special import iv +from skimage import img_as_float def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, @@ -12,15 +13,16 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, allows for fast dense extraction. Typically, this is practical for bag-of-features image representations. - The implementation follows Tola et al. [1] but deviate on the following + The implementation follows Tola et al. [1]_ but deviate on the following points: - * Histogram bin contribution are smoothed with a circular Gaussian - window over the tonal range (the angular range). - * The sigma values of the spatial Gaussian smoothing in this code do - not match the sigma values in the original code by Tola et al. [2]. - In their code, spatial smoothing is applied to both the input image - and the center histogram. However, this smoothing is not documented - in [1] and, therefore, it is omitted. + + * Histogram bin contribution are smoothed with a circular Gaussian + window over the tonal range (the angular range). + * The sigma values of the spatial Gaussian smoothing in this code do not + match the sigma values in the original code by Tola et al. [2]_. In + their code, spatial smoothing is applied to both the input image and + the center histogram. However, this smoothing is not documented in [1]_ + and, therefore, it is omitted. Parameters ---------- @@ -36,52 +38,57 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, Number of histograms sampled per ring. orientations : int, optional Number of orientations (bins) per histogram. - normalization : {'l1', 'l2', 'daisy', 'off'}, optional - How to normalize the descriptors: - * 'l1': L1-normalization of each descriptor. - * 'l2': L2-normalization of each descriptor. - * 'daisy': L2-normalization of individual histograms. - * 'off': Disable normalization. + normalization : [ 'l1' | 'l2' | 'daisy' | 'off' ], optional + How to normalize the descriptors + + * 'l1': L1-normalization of each descriptor. + * 'l2': L2-normalization of each descriptor. + * 'daisy': L2-normalization of individual histograms. + * 'off': Disable normalization. + sigmas : 1D array of float, optional Standard deviation of spatial Gaussian smoothing for the center histogram and for each ring of histograms. The array of sigmas should be sorted from the center and out. I.e. the first sigma value defines the spatial smoothing of the center histogram and the last sigma value defines the spatial smoothing of the outermost ring. Specifying sigmas - overrides the following parameter: - rings = len(sigmas)-1 + overrides the following parameter. + ``rings = len(sigmas)-1`` + ring_radii : 1D array of int, optional Radius (in pixels) for each ring. Specifying ring_radii overrides the - following two parameters: - rings = len(ring_radii) - radius = ring_radii[-1] - If both sigmas and ring_radii are given, they must satisfy - len(ring_radii) == len(sigmas)+1 - since no radius is needed for the center histogram. + following two parameters. + | ``rings = len(ring_radii)`` + | ``radius = ring_radii[-1]`` + + If both sigmas and ring_radii are given, they must satisfy the + following predicate since no radius is needed for the center + histogram. + ``len(ring_radii) == len(sigmas)+1`` + Returns ------- descs : array Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where - P = ceil((M-radius*2)/step) - Q = ceil((N-radius*2)/step) - R = (rings*histograms + 1)*orientations + | ``P = ceil((M-radius*2)/step)`` + | ``Q = ceil((N-radius*2)/step)`` + | ``R = (rings*histograms + 1)*orientations`` References ---------- - [1]: Tola et al. "Daisy: An efficient dense descriptor applied to - wide-baseline stereo." Pattern Analysis and Machine Intelligence, - IEEE Transactions on 32.5 (2010): 815-830. - [2]: http://cvlab.epfl.ch/alumni/tola/daisy.html + .. [1] Tola et al. "Daisy\: An efficient dense descriptor applied to wide- + baseline stereo." Pattern Analysis and Machine Intelligence, IEEE + Transactions on 32.5 (2010): 815-830. + .. [2] http://cvlab.epfl.ch/alumni/tola/daisy.html ''' # Validate image format. if img.ndim > 2: raise ValueError('Only grey-level images are supported.') if img.dtype.kind == 'u': - img = img.astype(float) - img = img / 255. + img = img_as_float(img) # Validate parameters. if sigmas is not None and ring_radii is not None \ From fbf51a950897a6139288cfa3fc89bd4ff9552664 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 16:33:17 +0100 Subject: [PATCH 395/625] Fix dependency check in setup Previous version failed when there were characters other than digits or dots. --- setup.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 390f007c..e1f6c0dc 100644 --- a/setup.py +++ b/setup.py @@ -27,6 +27,7 @@ DEPENDENCIES = { import os import sys +import re import setuptools from numpy.distutils.core import setup try: @@ -72,8 +73,8 @@ def get_package_version(package): for version_attr in ('version', 'VERSION', '__version__'): if hasattr(package, version_attr) \ and isinstance(getattr(package, version_attr), str): - version_info = getattr(package, version_attr) - for part in version_info.split('.'): + version_info = getattr(package, version_attr, '') + for part in re.split('\D+', version_info): try: version.append(int(part)) except ValueError: From 8c4ba00b12133494dc7bca45d5275ee9ca770342 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 20:59:19 +0200 Subject: [PATCH 396/625] Move harris file to a combined interest point file --- skimage/feature/__init__.py | 2 +- skimage/feature/{_harris.py => interest.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename skimage/feature/{_harris.py => interest.py} (100%) diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index 1597e9a8..4beca93e 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,5 +1,5 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max -from ._harris import harris +from .interest import harris from .template import match_template diff --git a/skimage/feature/_harris.py b/skimage/feature/interest.py similarity index 100% rename from skimage/feature/_harris.py rename to skimage/feature/interest.py From 32a8442d613665f3c0cffdac0edc00fe2ee3c5db Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:01:03 +0200 Subject: [PATCH 397/625] Refactor harris corner detection, so it returns harris response image --- skimage/feature/interest.py | 103 +++++++++++------------------------- 1 file changed, 32 insertions(+), 71 deletions(-) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index ae30a29e..87163247 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -1,85 +1,31 @@ -""" -Harris corner detector - -Inspired from Solem's implementation -http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html -""" +import numpy as np from scipy import ndimage - +from skimage.color import rgb2grey +from skimage.util import img_as_float from . import peak -def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1): - """Compute the Harris corner detector response function - for each pixel in the image +def harris(image, eps=1e-6, gaussian_deviation=1): + """Compute Harris response image. Parameters ---------- image : ndarray of floats Input image. - eps : float, optional Normalisation factor. - - gaussian_deviation : integer, optional - Standard deviation used for the Gaussian kernel. - - Returns - -------- - image : (M, N) ndarray - Harris image response - """ - if len(image.shape) == 3: - image = image.mean(axis=2) - - # derivatives - image = ndimage.gaussian_filter(image, gaussian_deviation) - imx = ndimage.sobel(image, axis=0, mode='constant') - imy = ndimage.sobel(image, axis=1, mode='constant') - - Wxx = ndimage.gaussian_filter(imx * imx, 1.5, mode='constant') - Wxy = ndimage.gaussian_filter(imx * imy, 1.5, mode='constant') - Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant') - - # determinant and trace - Wdet = Wxx * Wyy - Wxy**2 - Wtr = Wxx + Wyy - # Alternate formula for Harris response. - # Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989) - harris = Wdet / (Wtr + eps) - - return harris - - -def harris(image, min_distance=10, threshold=0.1, eps=1e-6, - gaussian_deviation=1): - """Return corners from a Harris response image - - Parameters - ---------- - image : ndarray of floats - Input image. - - min_distance : int, optional - Minimum number of pixels separating interest points and image boundary. - - threshold : float, optional - Relative threshold impacting the number of interest points. - - eps : float, optional - Normalisation factor. - gaussian_deviation : integer, optional Standard deviation used for the Gaussian kernel. Returns ------- coordinates : (N, 2) array - (row, column) coordinates of interest points. + `(row, column)` coordinates of interest points. Examples ------- - >>> square = np.zeros([10,10]) + >>> from skimage.feature import harris, peak_local_max + >>> square = np.zeros([10, 10]) >>> square[2:8,2:8] = 1 >>> square array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], @@ -92,18 +38,33 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6, [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) - >>> harris(square, min_distance=1) - - Corners of the square - + >>> peak_local_max(harris(square), min_distance=1) array([[3, 3], [3, 6], [6, 3], [6, 6]]) + """ - harrisim = _compute_harris_response(image, eps=eps, - gaussian_deviation=gaussian_deviation) - coordinates = peak.peak_local_max(harrisim, min_distance=min_distance, - threshold_rel=threshold) - return coordinates + if image.ndim == 3: + image = rgb2grey(image) + + # derivatives + image = ndimage.gaussian_filter(image, gaussian_deviation, mode='constant', + cval=0) + imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) + imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) + + Wxx = ndimage.gaussian_filter(imx * imx, 1.5, mode='constant', cval=0) + Wxy = ndimage.gaussian_filter(imx * imy, 1.5, mode='constant', cval=0) + Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant', cval=0) + + # determinant and trace + Wdet = Wxx * Wyy - Wxy**2 + Wtr = Wxx + Wyy + + # Alternate formula for Harris response. + # Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989) + harris = Wdet / (Wtr + eps) + + return harris From 4991acf34ead450778303777bb93b35affb85b4c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:02:45 +0200 Subject: [PATCH 398/625] Add Moravec interest point detection --- skimage/feature/_interest.pyx | 39 ++++++++++++++++++++++++++ skimage/feature/interest.py | 53 ++++++++++++++++++++++++++++++++++- skimage/feature/setup.py | 3 ++ 3 files changed, 94 insertions(+), 1 deletion(-) create mode 100644 skimage/feature/_interest.pyx diff --git a/skimage/feature/_interest.pyx b/skimage/feature/_interest.pyx new file mode 100644 index 00000000..7af5aa7e --- /dev/null +++ b/skimage/feature/_interest.pyx @@ -0,0 +1,39 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False +import numpy as np +cimport numpy as cnp +from libc.float cimport DBL_MAX + + +def moravec(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image, + int block_size): + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + + cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \ + np.zeros_like(image) + + cdef double* image_data = image.data + cdef double* out_data = out.data + + cdef double msum, min_msum + cdef int r, c, br, bc, mr, mc, a, b + for r in range(2 * block_size, rows - 2 * block_size): + for c in range(2 * block_size, cols - 2 * block_size): + min_msum = DBL_MAX + for br in range(r - block_size, r + block_size + 1): + for bc in range(c - block_size, c + block_size + 1): + if br != r and bc != c: + msum = 0 + for mr in range(- block_size, block_size + 1): + for mc in range(- block_size, block_size + 1): + a = (r + mr) * cols + c + mc + b = (br + mr) * cols + bc + mc + msum += (image_data[a] - image_data[b]) ** 2 + min_msum = min(msum, min_msum) + + out_data[r * cols + c] = min_msum + + return out \ No newline at end of file diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index 87163247..cddd6039 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -2,7 +2,58 @@ import numpy as np from scipy import ndimage from skimage.color import rgb2grey from skimage.util import img_as_float -from . import peak +from . import peak, _interest + + +def moravec(image, block_size=3, mode='constant', cval=0): + """Compute Moravec response image. + + This interest operator is comparatively fast but not rotation invariant. + + Parameters + ---------- + image : ndarray + Input image. + block_size : int, optional + Block size for mean filtering the squared gradients. + mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional + The mode parameter determines how the array borders are handled, where + cval is the value when mode is equal to 'constant'. + cval : double, optional + Constant value to use for constant mode. + + Returns + ------- + coordinates : (N, 2) array + `(row, column)` coordinates of interest points. + + Examples + ------- + >>> from skimage.feature import moravec, peak_local_max + >>> square = np.zeros([10, 10]) + >>> square[1:9,1:9] = 1 + >>> square + array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) + >>> moravec(square)), square.shape) + (2, 6) + + """ + + if image.ndim == 3: + image = rgb2grey(image) + + image = np.ascontiguousarray(img_as_float(image)) + + return _corner._moravec(image, block_size) def harris(image, eps=1e-6, gaussian_deviation=1): diff --git a/skimage/feature/setup.py b/skimage/feature/setup.py index 6f820163..905f5b99 100644 --- a/skimage/feature/setup.py +++ b/skimage/feature/setup.py @@ -12,9 +12,12 @@ def configuration(parent_package='', top_path=None): config = Configuration('feature', parent_package, top_path) config.add_data_dir('tests') + cython(['_interest.pyx'], working_path=base_path) cython(['_texture.pyx'], working_path=base_path) cython(['_template.pyx'], working_path=base_path) + config.add_extension('_interest', sources=['_interest.c'], + include_dirs=[get_numpy_include_dirs()]) config.add_extension('_texture', sources=['_texture.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) config.add_extension('_template', sources=['_template.c'], From 1d96b29d775c3c11bde519aad44d70736da51218 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:03:46 +0200 Subject: [PATCH 399/625] Rename harris test case file --- skimage/feature/interest.py | 2 +- skimage/feature/tests/{test_harris.py => test_interest.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename skimage/feature/tests/{test_harris.py => test_interest.py} (100%) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index cddd6039..b3855c3a 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -53,7 +53,7 @@ def moravec(image, block_size=3, mode='constant', cval=0): image = np.ascontiguousarray(img_as_float(image)) - return _corner._moravec(image, block_size) + return _interest._moravec(image, block_size) def harris(image, eps=1e-6, gaussian_deviation=1): diff --git a/skimage/feature/tests/test_harris.py b/skimage/feature/tests/test_interest.py similarity index 100% rename from skimage/feature/tests/test_harris.py rename to skimage/feature/tests/test_interest.py From 2edb33f9500cfe25133b84ef7203b89d30003a3b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:17:21 +0200 Subject: [PATCH 400/625] Make moravec standalone cython function and fix doc string --- skimage/feature/__init__.py | 1 + skimage/feature/_interest.pyx | 39 --------------- skimage/feature/interest.py | 55 +-------------------- skimage/feature/interest_cy.pyx | 85 +++++++++++++++++++++++++++++++++ skimage/feature/setup.py | 4 +- 5 files changed, 89 insertions(+), 95 deletions(-) delete mode 100644 skimage/feature/_interest.pyx create mode 100644 skimage/feature/interest_cy.pyx diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index 4beca93e..233cb2f5 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -2,4 +2,5 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max from .interest import harris +from .interest_cy import moravec from .template import match_template diff --git a/skimage/feature/_interest.pyx b/skimage/feature/_interest.pyx deleted file mode 100644 index 7af5aa7e..00000000 --- a/skimage/feature/_interest.pyx +++ /dev/null @@ -1,39 +0,0 @@ -#cython: cdivision=True -#cython: boundscheck=False -#cython: nonecheck=False -#cython: wraparound=False -import numpy as np -cimport numpy as cnp -from libc.float cimport DBL_MAX - - -def moravec(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image, - int block_size): - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - - cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \ - np.zeros_like(image) - - cdef double* image_data = image.data - cdef double* out_data = out.data - - cdef double msum, min_msum - cdef int r, c, br, bc, mr, mc, a, b - for r in range(2 * block_size, rows - 2 * block_size): - for c in range(2 * block_size, cols - 2 * block_size): - min_msum = DBL_MAX - for br in range(r - block_size, r + block_size + 1): - for bc in range(c - block_size, c + block_size + 1): - if br != r and bc != c: - msum = 0 - for mr in range(- block_size, block_size + 1): - for mc in range(- block_size, block_size + 1): - a = (r + mr) * cols + c + mc - b = (br + mr) * cols + bc + mc - msum += (image_data[a] - image_data[b]) ** 2 - min_msum = min(msum, min_msum) - - out_data[r * cols + c] = min_msum - - return out \ No newline at end of file diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index b3855c3a..1ce19ce6 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -1,59 +1,6 @@ import numpy as np from scipy import ndimage -from skimage.color import rgb2grey -from skimage.util import img_as_float -from . import peak, _interest - - -def moravec(image, block_size=3, mode='constant', cval=0): - """Compute Moravec response image. - - This interest operator is comparatively fast but not rotation invariant. - - Parameters - ---------- - image : ndarray - Input image. - block_size : int, optional - Block size for mean filtering the squared gradients. - mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional - The mode parameter determines how the array borders are handled, where - cval is the value when mode is equal to 'constant'. - cval : double, optional - Constant value to use for constant mode. - - Returns - ------- - coordinates : (N, 2) array - `(row, column)` coordinates of interest points. - - Examples - ------- - >>> from skimage.feature import moravec, peak_local_max - >>> square = np.zeros([10, 10]) - >>> square[1:9,1:9] = 1 - >>> square - array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) - >>> moravec(square)), square.shape) - (2, 6) - - """ - - if image.ndim == 3: - image = rgb2grey(image) - - image = np.ascontiguousarray(img_as_float(image)) - - return _interest._moravec(image, block_size) +from . import peak def harris(image, eps=1e-6, gaussian_deviation=1): diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/interest_cy.pyx new file mode 100644 index 00000000..e3fa72db --- /dev/null +++ b/skimage/feature/interest_cy.pyx @@ -0,0 +1,85 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False +import numpy as np +cimport numpy as cnp +from libc.float cimport DBL_MAX + +from skimage.color import rgb2grey +from skimage.util import img_as_float + + +def moravec(image, int block_size=1): + """Compute Moravec response image. + + This interest operator is comparatively fast but not rotation invariant. + + Parameters + ---------- + image : ndarray + Input image. + block_size : int, optional + Block size for mean filtering the squared gradients. + + Returns + ------- + coordinates : (N, 2) array + `(row, column)` coordinates of interest points. + + Examples + ------- + >>> from skimage.feature import moravec, peak_local_max + >>> square = np.zeros([7, 7]) + >>> square[3, 3] = 1 + >>> square + array([[ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 1., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.]]) + >>> moravec(square) + array([[ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 2., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0.]]) + """ + + cdef int rows = image.shape[0] + cdef int cols = image.shape[1] + + cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] cimage, out + + if image.ndim == 3: + cimage = rgb2grey(image) + cimage = np.ascontiguousarray(img_as_float(image)) + + out = np.zeros_like(image) + + cdef double* image_data = cimage.data + cdef double* out_data = out.data + + cdef double msum, min_msum + cdef int r, c, br, bc, mr, mc, a, b + for r in range(2 * block_size, rows - 2 * block_size): + for c in range(2 * block_size, cols - 2 * block_size): + min_msum = DBL_MAX + for br in range(r - block_size, r + block_size + 1): + for bc in range(c - block_size, c + block_size + 1): + if br != r and bc != c: + msum = 0 + for mr in range(- block_size, block_size + 1): + for mc in range(- block_size, block_size + 1): + a = (r + mr) * cols + c + mc + b = (br + mr) * cols + bc + mc + msum += (image_data[a] - image_data[b]) ** 2 + min_msum = min(msum, min_msum) + + out_data[r * cols + c] = min_msum + + return out diff --git a/skimage/feature/setup.py b/skimage/feature/setup.py index 905f5b99..20195500 100644 --- a/skimage/feature/setup.py +++ b/skimage/feature/setup.py @@ -12,11 +12,11 @@ def configuration(parent_package='', top_path=None): config = Configuration('feature', parent_package, top_path) config.add_data_dir('tests') - cython(['_interest.pyx'], working_path=base_path) + cython(['interest_cy.pyx'], working_path=base_path) cython(['_texture.pyx'], working_path=base_path) cython(['_template.pyx'], working_path=base_path) - config.add_extension('_interest', sources=['_interest.c'], + config.add_extension('interest_cy', sources=['interest_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_texture', sources=['_texture.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) From ecd2687226716074308c8ab155c97f0e2dde364c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:18:30 +0200 Subject: [PATCH 401/625] Fix return types in doc string of interest point detectors --- skimage/feature/interest.py | 6 +++--- skimage/feature/interest_cy.pyx | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index 1ce19ce6..d1a9a76c 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -8,7 +8,7 @@ def harris(image, eps=1e-6, gaussian_deviation=1): Parameters ---------- - image : ndarray of floats + image : ndarray Input image. eps : float, optional Normalisation factor. @@ -17,8 +17,8 @@ def harris(image, eps=1e-6, gaussian_deviation=1): Returns ------- - coordinates : (N, 2) array - `(row, column)` coordinates of interest points. + response : ndarray + Moravec response image. Examples ------- diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/interest_cy.pyx index e3fa72db..8d6867ed 100644 --- a/skimage/feature/interest_cy.pyx +++ b/skimage/feature/interest_cy.pyx @@ -24,8 +24,8 @@ def moravec(image, int block_size=1): Returns ------- - coordinates : (N, 2) array - `(row, column)` coordinates of interest points. + response : ndarray + Moravec response image. Examples ------- From f692055a25e3b746e8667cf8edddfd4cc658f541 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:19:46 +0200 Subject: [PATCH 402/625] Add reference to moravec operator --- skimage/feature/interest_cy.pyx | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/interest_cy.pyx index 8d6867ed..1974f8d7 100644 --- a/skimage/feature/interest_cy.pyx +++ b/skimage/feature/interest_cy.pyx @@ -27,6 +27,10 @@ def moravec(image, int block_size=1): response : ndarray Moravec response image. + References + ---------- + ..[1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm + Examples ------- >>> from skimage.feature import moravec, peak_local_max From 3529afe0d2a7256cad89a25d09733ad9d59fad4a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:21:06 +0200 Subject: [PATCH 403/625] Rename window size parameter of moravec operator --- skimage/feature/interest_cy.pyx | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/interest_cy.pyx index 1974f8d7..04a0e180 100644 --- a/skimage/feature/interest_cy.pyx +++ b/skimage/feature/interest_cy.pyx @@ -10,7 +10,7 @@ from skimage.color import rgb2grey from skimage.util import img_as_float -def moravec(image, int block_size=1): +def moravec(image, int window_size=1): """Compute Moravec response image. This interest operator is comparatively fast but not rotation invariant. @@ -19,8 +19,8 @@ def moravec(image, int block_size=1): ---------- image : ndarray Input image. - block_size : int, optional - Block size for mean filtering the squared gradients. + window_size : int, optional + Window size. Returns ------- @@ -70,15 +70,15 @@ def moravec(image, int block_size=1): cdef double msum, min_msum cdef int r, c, br, bc, mr, mc, a, b - for r in range(2 * block_size, rows - 2 * block_size): - for c in range(2 * block_size, cols - 2 * block_size): + for r in range(2 * window_size, rows - 2 * window_size): + for c in range(2 * window_size, cols - 2 * window_size): min_msum = DBL_MAX - for br in range(r - block_size, r + block_size + 1): - for bc in range(c - block_size, c + block_size + 1): + for br in range(r - window_size, r + window_size + 1): + for bc in range(c - window_size, c + window_size + 1): if br != r and bc != c: msum = 0 - for mr in range(- block_size, block_size + 1): - for mc in range(- block_size, block_size + 1): + for mr in range(- window_size, window_size + 1): + for mc in range(- window_size, window_size + 1): a = (r + mr) * cols + c + mc b = (br + mr) * cols + bc + mc msum += (image_data[a] - image_data[b]) ** 2 From 2ae23b427edfc0b1f3ddc1200b5256cca93ae0be Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 21:47:32 +0200 Subject: [PATCH 404/625] Fix missing sigma for gaussian filter in harris operator --- skimage/feature/interest.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index d1a9a76c..15b249bc 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -3,7 +3,7 @@ from scipy import ndimage from . import peak -def harris(image, eps=1e-6, gaussian_deviation=1): +def harris(image, eps=1e-6, sigma=1): """Compute Harris response image. Parameters @@ -12,7 +12,7 @@ def harris(image, eps=1e-6, gaussian_deviation=1): Input image. eps : float, optional Normalisation factor. - gaussian_deviation : integer, optional + sigma : float, optional Standard deviation used for the Gaussian kernel. Returns @@ -48,14 +48,16 @@ def harris(image, eps=1e-6, gaussian_deviation=1): image = rgb2grey(image) # derivatives - image = ndimage.gaussian_filter(image, gaussian_deviation, mode='constant', - cval=0) + image = ndimage.gaussian_filter(image, sigma, mode='constant', cval=0) imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) - Wxx = ndimage.gaussian_filter(imx * imx, 1.5, mode='constant', cval=0) - Wxy = ndimage.gaussian_filter(imx * imy, 1.5, mode='constant', cval=0) - Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant', cval=0) + Wxx = ndimage.gaussian_filter(imx * imx, sigma, + mode='constant', cval=0) + Wxy = ndimage.gaussian_filter(imx * imy, sigma, + mode='constant', cval=0) + Wyy = ndimage.gaussian_filter(imy * imy, sigma, + mode='constant', cval=0) # determinant and trace Wdet = Wxx * Wyy - Wxy**2 From c4ca726566f391b010bbb91ed79c3bd4d56d99a1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 22:20:35 +0200 Subject: [PATCH 405/625] Fix harris corner bug and add alternative corner measure param --- doc/examples/plot_harris.py | 6 ++--- skimage/feature/interest.py | 36 ++++++++++++++++---------- skimage/feature/tests/test_interest.py | 12 ++++----- 3 files changed, 31 insertions(+), 23 deletions(-) diff --git a/doc/examples/plot_harris.py b/doc/examples/plot_harris.py index 6212ac4a..39c1f29e 100644 --- a/doc/examples/plot_harris.py +++ b/doc/examples/plot_harris.py @@ -13,7 +13,7 @@ import numpy as np from matplotlib import pyplot as plt from skimage import data, img_as_float -from skimage.feature import harris +from skimage.feature import harris, peak_local_max def plot_harris_points(image, filtered_coords): @@ -29,12 +29,12 @@ plt.figure(figsize=(8, 3)) im_lena = img_as_float(data.lena()) im_text = img_as_float(data.text()) -filtered_coords = harris(im_lena, min_distance=4) +filtered_coords = peak_local_max(harris(im_lena), min_distance=4) plt.axes([0, 0, 0.3, 0.95]) plot_harris_points(im_lena, filtered_coords) -filtered_coords = harris(im_text, min_distance=4) +filtered_coords = peak_local_max(harris(im_text), min_distance=4) plt.axes([0.2, 0, 0.77, 1]) plot_harris_points(im_text, filtered_coords) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index 15b249bc..88280a27 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -1,24 +1,31 @@ import numpy as np from scipy import ndimage +from skimage.color import rgb2grey from . import peak -def harris(image, eps=1e-6, sigma=1): +def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """Compute Harris response image. Parameters ---------- image : ndarray Input image. + method : {'k', 'eps'}, optional + Method to + k : float, optional + Sensitivity factor to separate corners from edges, typically in range + `[0, 0.2]`. Small values of k result in detection of sharp corners. eps : float, optional - Normalisation factor. + Normalisation factor (Noble's corner measure). sigma : float, optional - Standard deviation used for the Gaussian kernel. + Standard deviation used for the Gaussian kernel, which is used as + weighting function for the auto-correlation matrix. Returns ------- response : ndarray - Moravec response image. + Harris response image. Examples ------- @@ -48,23 +55,24 @@ def harris(image, eps=1e-6, sigma=1): image = rgb2grey(image) # derivatives - image = ndimage.gaussian_filter(image, sigma, mode='constant', cval=0) imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) - Wxx = ndimage.gaussian_filter(imx * imx, sigma, + Axx = ndimage.gaussian_filter(imx * imx, sigma, mode='constant', cval=0) - Wxy = ndimage.gaussian_filter(imx * imy, sigma, + Axy = ndimage.gaussian_filter(imx * imy, sigma, mode='constant', cval=0) - Wyy = ndimage.gaussian_filter(imy * imy, sigma, + Ayy = ndimage.gaussian_filter(imy * imy, sigma, mode='constant', cval=0) - # determinant and trace - Wdet = Wxx * Wyy - Wxy**2 - Wtr = Wxx + Wyy + # determinant + detA = Axx * Ayy - Axy**2 + # trace + traceA = Axx + Ayy - # Alternate formula for Harris response. - # Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989) - harris = Wdet / (Wtr + eps) + if method == 'k': + harris = detA - k * traceA**2 + else: + harris = 2 * detA / (traceA + eps) return harris diff --git a/skimage/feature/tests/test_interest.py b/skimage/feature/tests/test_interest.py index 43bf28a3..22de33c3 100644 --- a/skimage/feature/tests/test_interest.py +++ b/skimage/feature/tests/test_interest.py @@ -3,13 +3,13 @@ import numpy as np from skimage import data from skimage import img_as_float -from skimage.feature import harris +from skimage.feature import moravec, harris, peak_local_max def test_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. - results = harris(im) + results = peak_local_max(harris(im)) assert results.any() assert len(results) == 1 @@ -18,7 +18,7 @@ def test_noisy_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. im = im + np.random.uniform(size=im.shape) * .5 - results = harris(im) + results = peak_local_max(harris(im)) assert results.any() assert len(results) == 1 @@ -27,7 +27,7 @@ def test_squared_dot(): im = np.zeros((50, 50)) im[4:8, 4:8] = 1 im = img_as_float(im) - results = harris(im, min_distance=3) + results = peak_local_max(harris(im)) assert (results == np.array([[6, 6]])).all() @@ -37,9 +37,9 @@ def test_rotated_lena(): rotation. """ im = img_as_float(data.lena().mean(axis=2)) - results = harris(im) + results = peak_local_max(harris(im)) im_rotated = im.T - results_rotated = harris(im_rotated) + results_rotated = peak_local_max(harris(im_rotated)) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() From 43393860b2fad0c92420b6e68fb2b70183da6311 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 10 Sep 2012 22:23:54 +0200 Subject: [PATCH 406/625] Add comment to explain meaning of structure matrix of harris --- skimage/feature/interest.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index 88280a27..cf9b3ed0 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -58,6 +58,7 @@ def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) + # sum of squared differences / structure tensore Axx = ndimage.gaussian_filter(imx * imx, sigma, mode='constant', cval=0) Axy = ndimage.gaussian_filter(imx * imy, sigma, From 4a75c649e32a59dc15857329e3d6c464fcea07f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:19:04 +0200 Subject: [PATCH 407/625] Add Shi-Tomasi corner detector and fix bug in auto-correlation matrix --- skimage/feature/__init__.py | 2 +- skimage/feature/interest.py | 94 ++++++++++++++++++++++++++++++------- 2 files changed, 77 insertions(+), 19 deletions(-) diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index 233cb2f5..ba1d8c76 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,6 +1,6 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max -from .interest import harris +from .interest import harris, shi_tomasi from .interest_cy import moravec from .template import match_template diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index cf9b3ed0..307f8ded 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -4,15 +4,57 @@ from skimage.color import rgb2grey from . import peak +def _compute_auto_correlation(image, sigma): + """Compute auto-correlation matrix using sum of squared differences. + + Parameters + ---------- + image : ndarray + Input image. + sigma : float + Standard deviation used for the Gaussian kernel, which is used as + weighting function for the auto-correlation matrix. + + Returns + ------- + Axx, Axy, Ayy : arrays + Elements of the auto-correlation matrix for each pixel in input image. + + """ + + if image.ndim == 3: + image = rgb2grey(image) + + # derivatives + gradient_weights = np.array([-1, 0, 1]) + imx = ndimage.convolve1d(image, gradient_weights, axis=0, + mode='constant', cval=0) + imy = ndimage.convolve1d(image, gradient_weights, axis=1, + mode='constant', cval=0) + + # structure tensore + Axx = ndimage.gaussian_filter(imx * imx, sigma, + mode='constant', cval=0) + Axy = ndimage.gaussian_filter(imx * imy, sigma, + mode='constant', cval=0) + Ayy = ndimage.gaussian_filter(imy * imy, sigma, + mode='constant', cval=0) + + return Axx, Axy, Ayy + + def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """Compute Harris response image. + This corner detector uses information in the auto-correlation matrix + (sum of squared differences) to make assumptions about the type of point. + Parameters ---------- image : ndarray Input image. method : {'k', 'eps'}, optional - Method to + Method to compute the response image from the auto-correlation matrix. k : float, optional Sensitivity factor to separate corners from edges, typically in range `[0, 0.2]`. Small values of k result in detection of sharp corners. @@ -51,20 +93,7 @@ def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """ - if image.ndim == 3: - image = rgb2grey(image) - - # derivatives - imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) - imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) - - # sum of squared differences / structure tensore - Axx = ndimage.gaussian_filter(imx * imx, sigma, - mode='constant', cval=0) - Axy = ndimage.gaussian_filter(imx * imy, sigma, - mode='constant', cval=0) - Ayy = ndimage.gaussian_filter(imy * imy, sigma, - mode='constant', cval=0) + Axx, Axy, Ayy = _compute_auto_correlation(image, sigma) # determinant detA = Axx * Ayy - Axy**2 @@ -72,8 +101,37 @@ def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): traceA = Axx + Ayy if method == 'k': - harris = detA - k * traceA**2 + response = detA - k * traceA**2 else: - harris = 2 * detA / (traceA + eps) + response = 2 * detA / (traceA + eps) - return harris + return response + + +def shi_tomasi(image, sigma=1): + """Compute Shi-Tomasi (Kanade-Tomasi) response image. + + This corner detector uses information in the auto-correlation matrix + (sum of squared differences) to make assumptions about the type of point. + It is computationally more expensive than the harris corner detector as + it directly computes the minimum eigenvalue of the auto-correlation matrix. + + Parameters + ---------- + image : ndarray + Input image. + sigma : float, optional + Standard deviation used for the Gaussian kernel, which is used as + weighting function for the auto-correlation matrix. + + response : ndarray + Shi-Tomasi response image. + + """ + + Axx, Axy, Ayy = _compute_auto_correlation(image, sigma) + + # minimum eigenvalue of A + response = ((Axx + Ayy) - np.sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2 + + return response From 82089f7fc8607f71dabb1cf17997181f2f5e280a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:37:25 +0200 Subject: [PATCH 408/625] Update test cases for shi-tomasi and moravec operator --- skimage/feature/tests/test_interest.py | 54 +++++++++++++++++++++++--- 1 file changed, 49 insertions(+), 5 deletions(-) diff --git a/skimage/feature/tests/test_interest.py b/skimage/feature/tests/test_interest.py index 22de33c3..2877e81d 100644 --- a/skimage/feature/tests/test_interest.py +++ b/skimage/feature/tests/test_interest.py @@ -3,23 +3,45 @@ import numpy as np from skimage import data from skimage import img_as_float -from skimage.feature import moravec, harris, peak_local_max +from skimage.feature import moravec, harris, shi_tomasi, peak_local_max def test_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. + + # Moravec + results = peak_local_max(moravec(im)) + # interest points along edge + assert len(results) == 57 + + # Harris results = peak_local_max(harris(im)) - assert results.any() + # interest at corner + assert len(results) == 1 + + # Shi-Tomasi + results = peak_local_max(shi_tomasi(im)) + # interest at corner assert len(results) == 1 def test_noisy_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. - im = im + np.random.uniform(size=im.shape) * .5 - results = peak_local_max(harris(im)) + im = im + np.random.uniform(size=im.shape) * .2 + + # Moravec + results = peak_local_max(moravec(im)) + # undefined number of interest points assert results.any() + + # Harris + results = peak_local_max(harris(im, sigma=1.5)) + assert len(results) == 1 + + # Shi-Tomasi + results = peak_local_max(shi_tomasi(im, sigma=1.5)) assert len(results) == 1 @@ -27,9 +49,17 @@ def test_squared_dot(): im = np.zeros((50, 50)) im[4:8, 4:8] = 1 im = img_as_float(im) + + # Moravec fails + + # Harris results = peak_local_max(harris(im)) assert (results == np.array([[6, 6]])).all() + # Shi-Tomasi + results = peak_local_max(shi_tomasi(im)) + assert (results == np.array([[6, 6]])).all() + def test_rotated_lena(): """ @@ -37,12 +67,26 @@ def test_rotated_lena(): rotation. """ im = img_as_float(data.lena().mean(axis=2)) - results = peak_local_max(harris(im)) im_rotated = im.T + + # Moravec + results = peak_local_max(moravec(im)) + results_rotated = peak_local_max(moravec(im_rotated)) + assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() + assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() + + # Harris + results = peak_local_max(harris(im)) results_rotated = peak_local_max(harris(im_rotated)) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() + # Shi-Tomasi + results = peak_local_max(shi_tomasi(im)) + results_rotated = peak_local_max(shi_tomasi(im_rotated)) + assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() + assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() + if __name__ == '__main__': from numpy import testing From 6593e27b4d9ba305a8b0d4ef246fed64b54edaf0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:37:45 +0200 Subject: [PATCH 409/625] Fix contiguous bug in moravec operator --- skimage/feature/interest_cy.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/interest_cy.pyx index 04a0e180..b14e3f6a 100644 --- a/skimage/feature/interest_cy.pyx +++ b/skimage/feature/interest_cy.pyx @@ -63,7 +63,7 @@ def moravec(image, int window_size=1): cimage = rgb2grey(image) cimage = np.ascontiguousarray(img_as_float(image)) - out = np.zeros_like(image) + out = np.zeros(image.shape, dtype=np.double) cdef double* image_data = cimage.data cdef double* out_data = out.data From 3508aa90321f2098433bb6f605310f8f35727606 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:43:01 +0200 Subject: [PATCH 410/625] Add example to doc string of shi-tomasi --- skimage/feature/interest.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index 307f8ded..1e2a86af 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -127,6 +127,28 @@ def shi_tomasi(image, sigma=1): response : ndarray Shi-Tomasi response image. + Examples + ------- + >>> from skimage.feature import shi_tomasi, peak_local_max + >>> square = np.zeros([10, 10]) + >>> square[2:8,2:8] = 1 + >>> square + array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) + >>> peak_local_max(shi_tomasi(square), min_distance=1) + array([[3, 3], + [3, 6], + [6, 3], + [6, 6]]) + """ Axx, Axy, Ayy = _compute_auto_correlation(image, sigma) From 410280f4a9c6cde82f515d9d54c7bab157a0c674 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:52:10 +0200 Subject: [PATCH 411/625] Add some more references to interest point functions --- skimage/feature/interest.py | 10 ++++++++++ skimage/feature/interest_cy.pyx | 1 + 2 files changed, 11 insertions(+) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index 1e2a86af..ef47e7cf 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -69,6 +69,11 @@ def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): response : ndarray Harris response image. + References + ---------- + ..[1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm + ..[2] http://en.wikipedia.org/wiki/Corner_detection + Examples ------- >>> from skimage.feature import harris, peak_local_max @@ -127,6 +132,11 @@ def shi_tomasi(image, sigma=1): response : ndarray Shi-Tomasi response image. + References + ---------- + ..[1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm + ..[2] http://en.wikipedia.org/wiki/Corner_detection + Examples ------- >>> from skimage.feature import shi_tomasi, peak_local_max diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/interest_cy.pyx index b14e3f6a..ae49faf1 100644 --- a/skimage/feature/interest_cy.pyx +++ b/skimage/feature/interest_cy.pyx @@ -30,6 +30,7 @@ def moravec(image, int window_size=1): References ---------- ..[1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm + ..[2] http://en.wikipedia.org/wiki/Corner_detection Examples ------- From d55ef7ede8dbe0163de9bf7c8cf066b441b1b708 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:52:51 +0200 Subject: [PATCH 412/625] Add missing doc string return section --- skimage/feature/interest.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index ef47e7cf..f4075d57 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -129,6 +129,8 @@ def shi_tomasi(image, sigma=1): Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. + Returns + ------- response : ndarray Shi-Tomasi response image. From 3b9ffe1cd89316f226dd437fdd2b3fa0246769dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 08:53:33 +0200 Subject: [PATCH 413/625] Improve code layout --- skimage/feature/interest.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/skimage/feature/interest.py b/skimage/feature/interest.py index f4075d57..cc63acae 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/interest.py @@ -33,12 +33,9 @@ def _compute_auto_correlation(image, sigma): mode='constant', cval=0) # structure tensore - Axx = ndimage.gaussian_filter(imx * imx, sigma, - mode='constant', cval=0) - Axy = ndimage.gaussian_filter(imx * imy, sigma, - mode='constant', cval=0) - Ayy = ndimage.gaussian_filter(imy * imy, sigma, - mode='constant', cval=0) + Axx = ndimage.gaussian_filter(imx * imx, sigma, mode='constant', cval=0) + Axy = ndimage.gaussian_filter(imx * imy, sigma, mode='constant', cval=0) + Ayy = ndimage.gaussian_filter(imy * imy, sigma, mode='constant', cval=0) return Axx, Axy, Ayy From 16f09d358dc7debc7b122bd8e8bc0ceeb68c24c6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 18:58:45 +0200 Subject: [PATCH 414/625] Rename corner operators and file --- skimage/feature/__init__.py | 4 +-- skimage/feature/{interest.py => corner.py} | 4 +-- .../{interest_cy.pyx => corner_cy.pyx} | 2 +- skimage/feature/setup.py | 4 +-- .../{test_interest.py => test_corner.py} | 31 ++++++++++--------- 5 files changed, 23 insertions(+), 22 deletions(-) rename skimage/feature/{interest.py => corner.py} (98%) rename skimage/feature/{interest_cy.pyx => corner_cy.pyx} (98%) rename skimage/feature/tests/{test_interest.py => test_corner.py} (66%) diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index ba1d8c76..5b421c9c 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,6 +1,6 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max -from .interest import harris, shi_tomasi -from .interest_cy import moravec +from .corner import corner_harris, corner_shi_tomasi +from .corner_cy import corner_moravec from .template import match_template diff --git a/skimage/feature/interest.py b/skimage/feature/corner.py similarity index 98% rename from skimage/feature/interest.py rename to skimage/feature/corner.py index cc63acae..e8f6aa2a 100644 --- a/skimage/feature/interest.py +++ b/skimage/feature/corner.py @@ -40,7 +40,7 @@ def _compute_auto_correlation(image, sigma): return Axx, Axy, Ayy -def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): +def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """Compute Harris response image. This corner detector uses information in the auto-correlation matrix @@ -110,7 +110,7 @@ def harris(image, method='k', k=0.05, eps=1e-6, sigma=1): return response -def shi_tomasi(image, sigma=1): +def corner_shi_tomasi(image, sigma=1): """Compute Shi-Tomasi (Kanade-Tomasi) response image. This corner detector uses information in the auto-correlation matrix diff --git a/skimage/feature/interest_cy.pyx b/skimage/feature/corner_cy.pyx similarity index 98% rename from skimage/feature/interest_cy.pyx rename to skimage/feature/corner_cy.pyx index ae49faf1..6094d937 100644 --- a/skimage/feature/interest_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -10,7 +10,7 @@ from skimage.color import rgb2grey from skimage.util import img_as_float -def moravec(image, int window_size=1): +def corner_moravec(image, int window_size=1): """Compute Moravec response image. This interest operator is comparatively fast but not rotation invariant. diff --git a/skimage/feature/setup.py b/skimage/feature/setup.py index 20195500..e769621d 100644 --- a/skimage/feature/setup.py +++ b/skimage/feature/setup.py @@ -12,11 +12,11 @@ def configuration(parent_package='', top_path=None): config = Configuration('feature', parent_package, top_path) config.add_data_dir('tests') - cython(['interest_cy.pyx'], working_path=base_path) + cython(['corner_cy.pyx'], working_path=base_path) cython(['_texture.pyx'], working_path=base_path) cython(['_template.pyx'], working_path=base_path) - config.add_extension('interest_cy', sources=['interest_cy.c'], + config.add_extension('corner_cy', sources=['corner_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_texture', sources=['_texture.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) diff --git a/skimage/feature/tests/test_interest.py b/skimage/feature/tests/test_corner.py similarity index 66% rename from skimage/feature/tests/test_interest.py rename to skimage/feature/tests/test_corner.py index 2877e81d..2c75442c 100644 --- a/skimage/feature/tests/test_interest.py +++ b/skimage/feature/tests/test_corner.py @@ -3,7 +3,8 @@ import numpy as np from skimage import data from skimage import img_as_float -from skimage.feature import moravec, harris, shi_tomasi, peak_local_max +from skimage.feature import (corner_moravec, corner_harris, corner_shi_tomasi, + peak_local_max) def test_square_image(): @@ -11,17 +12,17 @@ def test_square_image(): im[:25, :25] = 1. # Moravec - results = peak_local_max(moravec(im)) + results = peak_local_max(corner_moravec(im)) # interest points along edge assert len(results) == 57 # Harris - results = peak_local_max(harris(im)) + results = peak_local_max(corner_harris(im)) # interest at corner assert len(results) == 1 # Shi-Tomasi - results = peak_local_max(shi_tomasi(im)) + results = peak_local_max(corner_shi_tomasi(im)) # interest at corner assert len(results) == 1 @@ -32,16 +33,16 @@ def test_noisy_square_image(): im = im + np.random.uniform(size=im.shape) * .2 # Moravec - results = peak_local_max(moravec(im)) + results = peak_local_max(corner_moravec(im)) # undefined number of interest points assert results.any() # Harris - results = peak_local_max(harris(im, sigma=1.5)) + results = peak_local_max(corner_harris(im, sigma=1.5)) assert len(results) == 1 # Shi-Tomasi - results = peak_local_max(shi_tomasi(im, sigma=1.5)) + results = peak_local_max(corner_shi_tomasi(im, sigma=1.5)) assert len(results) == 1 @@ -53,11 +54,11 @@ def test_squared_dot(): # Moravec fails # Harris - results = peak_local_max(harris(im)) + results = peak_local_max(corner_harris(im)) assert (results == np.array([[6, 6]])).all() # Shi-Tomasi - results = peak_local_max(shi_tomasi(im)) + results = peak_local_max(corner_shi_tomasi(im)) assert (results == np.array([[6, 6]])).all() @@ -70,20 +71,20 @@ def test_rotated_lena(): im_rotated = im.T # Moravec - results = peak_local_max(moravec(im)) - results_rotated = peak_local_max(moravec(im_rotated)) + results = peak_local_max(corner_moravec(im)) + results_rotated = peak_local_max(corner_moravec(im_rotated)) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() # Harris - results = peak_local_max(harris(im)) - results_rotated = peak_local_max(harris(im_rotated)) + results = peak_local_max(corner_harris(im)) + results_rotated = peak_local_max(corner_harris(im_rotated)) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() # Shi-Tomasi - results = peak_local_max(shi_tomasi(im)) - results_rotated = peak_local_max(shi_tomasi(im_rotated)) + results = peak_local_max(corner_shi_tomasi(im)) + results_rotated = peak_local_max(corner_shi_tomasi(im_rotated)) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() From e26dc02ead617887b6a6c29011e7d0d4a95374b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 19:20:28 +0200 Subject: [PATCH 415/625] Add Kitchen and Rosenfeld corner detector --- skimage/feature/__init__.py | 2 +- skimage/feature/corner.py | 59 +++++++++++++++++++++++++++++++++---- 2 files changed, 54 insertions(+), 7 deletions(-) diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index 5b421c9c..f74f31a8 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,6 +1,6 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max -from .corner import corner_harris, corner_shi_tomasi +from .corner import corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi from .corner_cy import corner_moravec from .template import match_template diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index e8f6aa2a..9ebee9f2 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -4,6 +4,30 @@ from skimage.color import rgb2grey from . import peak +def _compute_derivatives(image): + """Compute derivatives in x and y direction. + + Parameters + ---------- + image : ndarray + Input image. + + Returns + ------- + imx, imy : arrays + Derivatives in x and y direction. + + """ + + gradient_weights = np.array([-1, 0, 1]) + imx = ndimage.convolve1d(image, gradient_weights, axis=0, + mode='constant', cval=0) + imy = ndimage.convolve1d(image, gradient_weights, axis=1, + mode='constant', cval=0) + + return imx, imy + + def _compute_auto_correlation(image, sigma): """Compute auto-correlation matrix using sum of squared differences. @@ -25,12 +49,7 @@ def _compute_auto_correlation(image, sigma): if image.ndim == 3: image = rgb2grey(image) - # derivatives - gradient_weights = np.array([-1, 0, 1]) - imx = ndimage.convolve1d(image, gradient_weights, axis=0, - mode='constant', cval=0) - imy = ndimage.convolve1d(image, gradient_weights, axis=1, - mode='constant', cval=0) + imx, imy = _compute_derivatives(image) # structure tensore Axx = ndimage.gaussian_filter(imx * imx, sigma, mode='constant', cval=0) @@ -40,6 +59,34 @@ def _compute_auto_correlation(image, sigma): return Axx, Axy, Ayy +def corner_kitchen_rosenfeld(image): + """Compute Kitchen and Rosenfeld response image. + + This corner detector uses information in the auto-correlation matrix + (sum of squared differences) to make assumptions about the type of point. + + Parameters + ---------- + image : ndarray + Input image. + + Returns + ------- + response : ndarray + Kitchen and Rosenfeld response image. + + """ + + imx, imy = _compute_derivatives(image) + imxx, imxy = _compute_derivatives(imx) + imyx, imyy = _compute_derivatives(imy) + + response = (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy) \ + / (imx**2 + imy**2) + + return response + + def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """Compute Harris response image. From 4366462033fd797210213cf438dbba4205aaa148 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 19:23:15 +0200 Subject: [PATCH 416/625] Use sobel for image derivatives --- skimage/feature/corner.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 9ebee9f2..c7e64233 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -5,7 +5,7 @@ from . import peak def _compute_derivatives(image): - """Compute derivatives in x and y direction. + """Compute derivatives in x and y direction using the Sobel operator. Parameters ---------- @@ -19,11 +19,8 @@ def _compute_derivatives(image): """ - gradient_weights = np.array([-1, 0, 1]) - imx = ndimage.convolve1d(image, gradient_weights, axis=0, - mode='constant', cval=0) - imy = ndimage.convolve1d(image, gradient_weights, axis=1, - mode='constant', cval=0) + imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) + imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) return imx, imy From 2d3cc8e0a047f23a747cbef991138a04db4b077c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 19:39:27 +0200 Subject: [PATCH 417/625] Add more detailed description of corner detectors in doc strings --- skimage/feature/corner.py | 37 +++++++++++++++++++++++++++-------- skimage/feature/corner_cy.pyx | 3 ++- 2 files changed, 31 insertions(+), 9 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index c7e64233..4302e8d0 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -59,8 +59,13 @@ def _compute_auto_correlation(image, sigma): def corner_kitchen_rosenfeld(image): """Compute Kitchen and Rosenfeld response image. - This corner detector uses information in the auto-correlation matrix - (sum of squared differences) to make assumptions about the type of point. + The corner measure is calculated as follows:: + + (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy) + ------------------------------------------------------ + (imx**2 + imy**2) + + Where imx and imy are the first and imxx, imxy, imyy the second derivatives. Parameters ---------- @@ -87,8 +92,19 @@ def corner_kitchen_rosenfeld(image): def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """Compute Harris response image. - This corner detector uses information in the auto-correlation matrix - (sum of squared differences) to make assumptions about the type of point. + This corner detector uses information from the auto-correlation matrix A:: + + A = [(imx**2) (imx*imy)] = [Axx Axy] + [(imx*imy) (imy**2)] [Axy Ayy] + + Where imx and imy are the first derivatives averaged with a gaussian filter. + The corner measure is then defined as:: + + det(A) - k * trace(A)**2 + + or:: + + 2 * det(A) / (trace(A) + eps) Parameters ---------- @@ -157,10 +173,15 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): def corner_shi_tomasi(image, sigma=1): """Compute Shi-Tomasi (Kanade-Tomasi) response image. - This corner detector uses information in the auto-correlation matrix - (sum of squared differences) to make assumptions about the type of point. - It is computationally more expensive than the harris corner detector as - it directly computes the minimum eigenvalue of the auto-correlation matrix. + This corner detector uses information from the auto-correlation matrix A:: + + A = [(imx**2) (imx*imy)] = [Axx Axy] + [(imx*imy) (imy**2)] [Axy Ayy] + + Where imx and imy are the first derivatives averaged with a gaussian filter. + The corner measure is then defined as the smaller eigenvalue of A:: + + ((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2 Parameters ---------- diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx index 6094d937..94d69772 100644 --- a/skimage/feature/corner_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -13,7 +13,8 @@ from skimage.util import img_as_float def corner_moravec(image, int window_size=1): """Compute Moravec response image. - This interest operator is comparatively fast but not rotation invariant. + This is one of the simplest corner detectors and is comparatively fast but + has several limitations (e.g. not rotation invariant). Parameters ---------- From 0f639e4989d5856d7aac18159e774e7c8571ce61 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 20:06:50 +0200 Subject: [PATCH 418/625] Add foerstner corner detector --- skimage/feature/__init__.py | 3 +- skimage/feature/corner.py | 57 +++++++++++++++++++++++++++++++++++-- 2 files changed, 56 insertions(+), 4 deletions(-) diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index f74f31a8..c1e7f845 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,6 +1,7 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max -from .corner import corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi +from .corner import (corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi, + corner_foerstner) from .corner_cy import corner_moravec from .template import match_template diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 4302e8d0..0d35c93a 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -57,7 +57,7 @@ def _compute_auto_correlation(image, sigma): def corner_kitchen_rosenfeld(image): - """Compute Kitchen and Rosenfeld response image. + """Compute Kitchen and Rosenfeld corner measure response image. The corner measure is calculated as follows:: @@ -90,7 +90,7 @@ def corner_kitchen_rosenfeld(image): def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): - """Compute Harris response image. + """Compute Harris corner measure response image. This corner detector uses information from the auto-correlation matrix A:: @@ -171,7 +171,7 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): def corner_shi_tomasi(image, sigma=1): - """Compute Shi-Tomasi (Kanade-Tomasi) response image. + """Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image. This corner detector uses information from the auto-correlation matrix A:: @@ -231,3 +231,54 @@ def corner_shi_tomasi(image, sigma=1): response = ((Axx + Ayy) - np.sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2 return response + + +def corner_foerstner(image, sigma=1): + """Compute Foerstner corner measure response image. + + This corner detector uses information from the auto-correlation matrix A:: + + A = [(imx**2) (imx*imy)] = [Axx Axy] + [(imx*imy) (imy**2)] [Axy Ayy] + + Where imx and imy are the first derivatives averaged with a gaussian filter. + The corner measure is then defined as:: + + w = det(A) / trace(A) (size of error ellipse) + q = 4 * det(A) / trace(A)**2 (roundness of error ellipse) + w * q (corner measure) + + Parameters + ---------- + image : ndarray + Input image. + sigma : float, optional + Standard deviation used for the Gaussian kernel, which is used as + weighting function for the auto-correlation matrix. + + Returns + ------- + response : ndarray + Foerstner response image. + + References + ---------- + ..[1] http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/\ + foerstner87.fast.pdf + ..[2] http://en.wikipedia.org/wiki/Corner_detection + + """ + + Axx, Axy, Ayy = _compute_auto_correlation(image, sigma) + + # determinant + detA = Axx * Ayy - Axy**2 + # trace + traceA = Axx + Ayy + + w = detA / traceA + q = 4 * detA / traceA**2 + + response = w * q + + return response From 6e193175f92857e1585cf068f7089471dc12e1f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 11 Sep 2012 20:19:24 +0200 Subject: [PATCH 419/625] Fix short description of moravec corner detector --- skimage/feature/corner_cy.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx index 94d69772..2f001ea4 100644 --- a/skimage/feature/corner_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -11,7 +11,7 @@ from skimage.util import img_as_float def corner_moravec(image, int window_size=1): - """Compute Moravec response image. + """Compute Moravec corner measure response image. This is one of the simplest corner detectors and is comparatively fast but has several limitations (e.g. not rotation invariant). From bf3d34ccf27e251c2d36a5bf005218454add5bd5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 12 Sep 2012 08:24:12 +0200 Subject: [PATCH 420/625] Let foerstner return both corner measures --- skimage/feature/corner.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 0d35c93a..4c1dd89c 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -246,7 +246,6 @@ def corner_foerstner(image, sigma=1): w = det(A) / trace(A) (size of error ellipse) q = 4 * det(A) / trace(A)**2 (roundness of error ellipse) - w * q (corner measure) Parameters ---------- @@ -258,8 +257,8 @@ def corner_foerstner(image, sigma=1): Returns ------- - response : ndarray - Foerstner response image. + w, q : ndarray + Foerstner response images. References ---------- @@ -279,6 +278,4 @@ def corner_foerstner(image, sigma=1): w = detA / traceA q = 4 * detA / traceA**2 - response = w * q - - return response + return w, q From cea889c99761ee612f9e9d1f3a79f6fb68fd74cd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 10:48:18 +0200 Subject: [PATCH 421/625] Add subpixel corner detection --- skimage/feature/__init__.py | 2 +- skimage/feature/corner.py | 128 +++++++++++++++++++++++++++++++++++- 2 files changed, 126 insertions(+), 4 deletions(-) diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index c1e7f845..ec81849f 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -2,6 +2,6 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max from .corner import (corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi, - corner_foerstner) + corner_foerstner, corner_subpix) from .corner_cy import corner_moravec from .template import match_template diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 4c1dd89c..9c7a2fdf 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -1,6 +1,8 @@ import numpy as np from scipy import ndimage +from scipy import stats from skimage.color import rgb2grey +from skimage.util import img_as_float from . import peak @@ -19,8 +21,8 @@ def _compute_derivatives(image): """ - imx = ndimage.sobel(image, axis=0, mode='constant', cval=0) - imy = ndimage.sobel(image, axis=1, mode='constant', cval=0) + imy = ndimage.sobel(image, axis=0, mode='constant', cval=0) + imx = ndimage.sobel(image, axis=1, mode='constant', cval=0) return imx, imy @@ -44,7 +46,7 @@ def _compute_auto_correlation(image, sigma): """ if image.ndim == 3: - image = rgb2grey(image) + image = img_as_float(rgb2grey(image)) imx, imy = _compute_derivatives(image) @@ -279,3 +281,123 @@ def corner_foerstner(image, sigma=1): q = 4 * detA / traceA**2 return w, q + + +def corner_subpix(image, corners, window_size=11, alpha=0.99): + """Determine subpixel position of corners. + + Parameters + ---------- + image : ndarray + Input image. + corners : (N, 2) ndarray + Corner coordinates `(row, cols)`. + window_size : int, optional + Search window size for subpixel estimation. + alpha : float, optional + Significance level for point classification. + + Returns + ------- + positions : (N, 2) ndarray + Subpixel corner positions. NaN for "not classified" corners. + + """ + + # window extent in one direction + wext = (window_size - 1) / 2 + + # normal equation arrays + N_dot = np.zeros((2, 2), dtype=np.double) + N_edge = np.zeros((2, 2), dtype=np.double) + b_dot = np.zeros((2, ), dtype=np.double) + b_edge = np.zeros((2, ), dtype=np.double) + + # critical statistical test values + redundancy = window_size**2 - 2 + t_crit_dot = stats.f.isf(1 - alpha, redundancy, redundancy) + t_crit_edge = stats.f.isf(alpha, redundancy, redundancy) + + # coordinates of pixels within window + y, x = np.mgrid[- wext:wext + 1, - wext:wext + 1] + + # output arrays + corners_subpix = np.zeros_like(corners, dtype=np.double) + corners_class = np.zeros(corners.shape[0], dtype=np.int8) + + for i, (y0, x0) in enumerate(corners): + + # crop window around corner + border for sobel operator + miny = y0 - wext - 1 + maxy = y0 + wext + 2 + minx = x0 - wext - 1 + maxx = x0 + wext + 2 + window = image[miny:maxy, minx:maxx] + + winx, winy = _compute_derivatives(window) + + # compute gradient suares and remove border + winx_winx = (winx * winx)[1:-1, 1:-1] + winx_winy = (winx * winy)[1:-1, 1:-1] + winy_winy = (winy * winy)[1:-1, 1:-1] + + # sum of squared differences (mean instead of gaussian filter) + Axx = np.sum(winx_winx) + Axy = np.sum(winx_winy) + Ayy = np.sum(winy_winy) + + # sum of squared differences weighted with coordinates + # (mean instead of gaussian filter) + bxx_x = np.sum(winx_winx * x) + bxx_y = np.sum(winx_winx * y) + bxy_x = np.sum(winx_winy * x) + bxy_y = np.sum(winx_winy * y) + byy_x = np.sum(winy_winy * x) + byy_y = np.sum(winy_winy * y) + + # normal equations for subpixel position + N_dot[0, 0] = Axx + N_dot[0, 1] = N_dot[1, 0] = - Axy + N_dot[1, 1] = Ayy + + N_edge[0, 0] = Ayy + N_edge[0, 1] = N_edge[1, 0] = Axy + N_edge[1, 1] = Axx + + b_dot[:] = bxx_y - bxy_x, byy_x - bxy_y + b_edge[:] = byy_y + bxy_x, bxx_x + bxy_y + + # estimated positions + est_dot = np.linalg.solve(N_dot, b_dot) + est_edge = np.linalg.solve(N_edge, b_edge) + + # residuals + ry_dot = y - est_dot[0] + rx_dot = x - est_dot[1] + ry_edge = y - est_edge[0] + rx_edge = x - est_edge[1] + # squared residuals + rxx_dot = rx_dot * rx_dot + rxy_dot = rx_dot * ry_dot + ryy_dot = ry_dot * ry_dot + rxx_edge = rx_edge * rx_edge + rxy_edge = rx_edge * ry_edge + ryy_edge = ry_edge * ry_edge + + # determine corner class (dot or edge) + w_dot = np.sum(winx_winx * ryy_dot - 2 * winx_winy * rxy_dot \ + + winy_winy * rxx_dot) + w_edge = np.sum(winy_winy * ryy_edge + 2 * winx_winy * rxy_edge \ + + winx_winx * rxx_edge) + t = w_edge / w_dot + # 1 for edge, -1 for dot, 0 for "not classified" + corner_class = (t < t_crit_edge) - (t > t_crit_dot) + + if corner_class == - 1: + corners_subpix[i, :] = y0 + est_dot[0], x0 + est_dot[1] + elif corner_class == 0: + corners_subpix[i, :] = np.nan, np.nan + elif corner_class == 1: + corners_subpix[i, :] = y0 + est_edge[0], x0 + est_edge[1] + + return corners_subpix From 2273b73d99f39f010216726afe3b5ca9ebf62efe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 11:04:19 +0200 Subject: [PATCH 422/625] Add references for subpixel corner detection --- skimage/feature/corner.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 9c7a2fdf..aa5211bf 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -302,6 +302,12 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99): positions : (N, 2) ndarray Subpixel corner positions. NaN for "not classified" corners. + References + ---------- + ..[1] http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/\ + foerstner87.fast.pdf + ..[2] http://en.wikipedia.org/wiki/Corner_detection + """ # window extent in one direction From 8af43f2d4940044cc98f206ca735a91b9343d9bb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 11:13:31 +0200 Subject: [PATCH 423/625] Add test case for subpixel corner detection --- skimage/feature/tests/test_corner.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/skimage/feature/tests/test_corner.py b/skimage/feature/tests/test_corner.py index 2c75442c..600d743e 100644 --- a/skimage/feature/tests/test_corner.py +++ b/skimage/feature/tests/test_corner.py @@ -1,10 +1,11 @@ import numpy as np +from numpy.testing import assert_array_equal from skimage import data from skimage import img_as_float from skimage.feature import (corner_moravec, corner_harris, corner_shi_tomasi, - peak_local_max) + corner_subpix, peak_local_max) def test_square_image(): @@ -89,6 +90,15 @@ def test_rotated_lena(): assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() +def test_subpix(): + img = np.zeros((50, 50)) + img[:25,:25] = 255 + img[25:,25:] = 255 + corner = peak_local_max(corner_harris(img), num_peaks=1) + subpix = corner_subpix(img, corner) + assert_array_equal(subpix[0], (24.5, 24.5)) + + if __name__ == '__main__': from numpy import testing testing.run_module_suite() From c1f9336c2ac25bcd91f4d4f0ffad6b220f015192 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 11:22:27 +0200 Subject: [PATCH 424/625] Update examples of corner detection in doc string --- skimage/feature/corner.py | 78 ++++++++++++++++++++++++++------------- 1 file changed, 52 insertions(+), 26 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index aa5211bf..69511dc7 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -135,21 +135,21 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): Examples ------- - >>> from skimage.feature import harris, peak_local_max + >>> from skimage.feature import corner_harris, peak_local_max >>> square = np.zeros([10, 10]) - >>> square[2:8,2:8] = 1 + >>> square[2:8, 2:8] = 1 >>> square - array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) - >>> peak_local_max(harris(square), min_distance=1) + array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) + >>> peak_local_max(corner_harris(square), min_distance=1) array([[3, 3], [3, 6], [6, 3], @@ -205,21 +205,21 @@ def corner_shi_tomasi(image, sigma=1): Examples ------- - >>> from skimage.feature import shi_tomasi, peak_local_max + >>> from skimage.feature import corner_shi_tomasi, peak_local_max >>> square = np.zeros([10, 10]) - >>> square[2:8,2:8] = 1 + >>> square[2:8, 2:8] = 1 >>> square - array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) - >>> peak_local_max(shi_tomasi(square), min_distance=1) + array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) + >>> peak_local_max(corner_shi_tomasi(square), min_distance=1) array([[3, 3], [3, 6], [6, 3], @@ -268,6 +268,32 @@ def corner_foerstner(image, sigma=1): foerstner87.fast.pdf ..[2] http://en.wikipedia.org/wiki/Corner_detection + Examples + ------- + >>> from skimage.feature import corner_foerstner, peak_local_max + >>> square = np.zeros([10, 10]) + >>> square[2:8, 2:8] = 1 + >>> square + array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) + >>> w, q = corner_foerstner(square) + >>> accuracy_thresh = 0.5 + >>> roundness_thresh = 0.3 + >>> foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w + >>> peak_local_max(foerstner, min_distance=1) + array([[2, 2], + [2, 7], + [7, 2], + [7, 7]]) + """ Axx, Axy, Ayy = _compute_auto_correlation(image, sigma) From f95e93669658d586eccaf9dc00f144a8d48c036f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 15:39:25 +0200 Subject: [PATCH 425/625] Refactor corner detection example --- doc/examples/plot_corner.py | 38 +++++++++++++++++++++++++++++++++ doc/examples/plot_harris.py | 42 ------------------------------------- 2 files changed, 38 insertions(+), 42 deletions(-) create mode 100644 doc/examples/plot_corner.py delete mode 100644 doc/examples/plot_harris.py diff --git a/doc/examples/plot_corner.py b/doc/examples/plot_corner.py new file mode 100644 index 00000000..7c9caa34 --- /dev/null +++ b/doc/examples/plot_corner.py @@ -0,0 +1,38 @@ +""" +================ +Corner detection +================ + +Detect corner points using the Harris corner detector and determine subpixel +position of corners. + +.. [1] http://en.wikipedia.org/wiki/Corner_detection +.. [2] http://en.wikipedia.org/wiki/Interest_point_detection + +""" + +import numpy as np +from matplotlib import pyplot as plt + +from skimage import data +from skimage.feature import corner_harris, corner_subpix, peak_local_max +from skimage.transform import warp, AffineTransform +from skimage.draw import ellipse + +tform = AffineTransform(scale=(1.3, 1.1), rotation=1, shear=0.7, + translation=(210, 50)) +image = warp(data.checkerboard(), tform.inverse, output_shape=(350, 350)) +rr, cc = ellipse(310, 175, 10, 100) +image[rr, cc] = 1 +image[180:230, 10:60] = 1 +image[230:280, 60:110] = 1 + +coords = peak_local_max(corner_harris(image), min_distance=5) +coords_subpix = corner_subpix(image, coords, window_size=13) + +plt.gray() +plt.imshow(image, interpolation='nearest') +plt.plot(coords[:, 1], coords[:, 0], '.b', markersize=3) +plt.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15) +plt.axis((0, 350, 350, 0)) +plt.show() diff --git a/doc/examples/plot_harris.py b/doc/examples/plot_harris.py deleted file mode 100644 index 39c1f29e..00000000 --- a/doc/examples/plot_harris.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -=============================================================================== -Harris Corner detector -=============================================================================== - -The Harris corner filter [1]_ detects "interest points" [2]_ using edge -detection in multiple directions. - -.. [1] http://en.wikipedia.org/wiki/Corner_detection -.. [2] http://en.wikipedia.org/wiki/Interest_point_detection -""" -import numpy as np -from matplotlib import pyplot as plt - -from skimage import data, img_as_float -from skimage.feature import harris, peak_local_max - - -def plot_harris_points(image, filtered_coords): - """ plots corners found in image""" - - plt.imshow(image) - y, x = np.transpose(filtered_coords) - plt.plot(x, y, 'b.') - plt.axis('off') - -# display results -plt.figure(figsize=(8, 3)) -im_lena = img_as_float(data.lena()) -im_text = img_as_float(data.text()) - -filtered_coords = peak_local_max(harris(im_lena), min_distance=4) - -plt.axes([0, 0, 0.3, 0.95]) -plot_harris_points(im_lena, filtered_coords) - -filtered_coords = peak_local_max(harris(im_text), min_distance=4) - -plt.axes([0.2, 0, 0.77, 1]) -plot_harris_points(im_text, filtered_coords) - -plt.show() From 5b72c5d52c1ce3e4cd8f810a1d4adbb863fc6846 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 20:10:00 +0200 Subject: [PATCH 426/625] Fix doc string for multiple return values --- skimage/feature/corner.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 69511dc7..0363ee55 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -16,8 +16,10 @@ def _compute_derivatives(image): Returns ------- - imx, imy : arrays - Derivatives in x and y direction. + imx : ndarray + Derivative in x-direction. + imy : ndarray + Derivative in y-direction. """ @@ -40,8 +42,12 @@ def _compute_auto_correlation(image, sigma): Returns ------- - Axx, Axy, Ayy : arrays - Elements of the auto-correlation matrix for each pixel in input image. + Axx : ndarray + Element of the auto-correlation matrix for each pixel in input image. + Axy : ndarray + Element of the auto-correlation matrix for each pixel in input image. + Ayy : ndarray + Element of the auto-correlation matrix for each pixel in input image. """ @@ -259,8 +265,10 @@ def corner_foerstner(image, sigma=1): Returns ------- - w, q : ndarray - Foerstner response images. + w : ndarray + Error ellipse sizes. + q : ndarray + Roundness of error ellipse. References ---------- From 32cbf8e22609d97d801ab9967e307231107d2a68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 20:10:46 +0200 Subject: [PATCH 427/625] Fix typo in doc string --- skimage/feature/corner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 0363ee55..a62dcc73 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -325,7 +325,7 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99): image : ndarray Input image. corners : (N, 2) ndarray - Corner coordinates `(row, cols)`. + Corner coordinates `(row, col)`. window_size : int, optional Search window size for subpixel estimation. alpha : float, optional From b22d570ff988a34c9b0008a2d9c9d26414b4b6fa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 23:18:16 +0200 Subject: [PATCH 428/625] Add comments and rename variables for better readability --- skimage/feature/corner.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index a62dcc73..64ccfb63 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -425,11 +425,13 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99): ryy_edge = ry_edge * ry_edge # determine corner class (dot or edge) - w_dot = np.sum(winx_winx * ryy_dot - 2 * winx_winy * rxy_dot \ - + winy_winy * rxx_dot) - w_edge = np.sum(winy_winy * ryy_edge + 2 * winx_winy * rxy_edge \ - + winx_winx * rxx_edge) - t = w_edge / w_dot + # variance for different models + var_dot = np.sum(winx_winx * ryy_dot - 2 * winx_winy * rxy_dot \ + + winy_winy * rxx_dot) + var_edge = np.sum(winy_winy * ryy_edge + 2 * winx_winy * rxy_edge \ + + winx_winx * rxx_edge) + # test value (F-distributed) + t = var_edge / var_dot # 1 for edge, -1 for dot, 0 for "not classified" corner_class = (t < t_crit_edge) - (t > t_crit_dot) From 04fbee189e8b2eefaba1ea346561bb4601537911 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 16 Sep 2012 23:19:32 +0200 Subject: [PATCH 429/625] Remove unused array --- skimage/feature/corner.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 64ccfb63..e48a0b41 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -361,9 +361,7 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99): # coordinates of pixels within window y, x = np.mgrid[- wext:wext + 1, - wext:wext + 1] - # output arrays corners_subpix = np.zeros_like(corners, dtype=np.double) - corners_class = np.zeros(corners.shape[0], dtype=np.int8) for i, (y0, x0) in enumerate(corners): From 81c01eac0fb332d7ceeb94226ab03b1fea3797fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 17:39:15 +0100 Subject: [PATCH 430/625] Fix confused parameters in doc string of peak_local_max --- skimage/feature/peak.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 4ff70be1..4b0f6b4f 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -50,8 +50,8 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, Returns ------- output : (N, 2) array or ndarray of bools - If `exclude_border = True` : (row, column) coordinates of peaks. - If `exclude_border = False` : Boolean array shaped like `image`, + If `indices = True` : (row, column) coordinates of peaks. + If `indices = False` : Boolean array shaped like `image`, with peaks represented by True values. Notes From 60c8e95905d11e1b2b1c852fb61d662826958f71 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 18:14:36 +0100 Subject: [PATCH 431/625] Add function to detect corner peaks as a wrapper to peak_local_max --- doc/examples/plot_corner.py | 4 +- skimage/feature/__init__.py | 2 +- skimage/feature/corner.py | 87 ++++++++++++++++++++++++++++++------- 3 files changed, 75 insertions(+), 18 deletions(-) diff --git a/doc/examples/plot_corner.py b/doc/examples/plot_corner.py index 7c9caa34..4d01d177 100644 --- a/doc/examples/plot_corner.py +++ b/doc/examples/plot_corner.py @@ -15,7 +15,7 @@ import numpy as np from matplotlib import pyplot as plt from skimage import data -from skimage.feature import corner_harris, corner_subpix, peak_local_max +from skimage.feature import corner_harris, corner_subpix, corner_peaks from skimage.transform import warp, AffineTransform from skimage.draw import ellipse @@ -27,7 +27,7 @@ image[rr, cc] = 1 image[180:230, 10:60] = 1 image[230:280, 60:110] = 1 -coords = peak_local_max(corner_harris(image), min_distance=5) +coords = corner_peaks(corner_harris(image), min_distance=5) coords_subpix = corner_subpix(image, coords, window_size=13) plt.gray() diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index ec81849f..e0756bfe 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -2,6 +2,6 @@ from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max from .corner import (corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi, - corner_foerstner, corner_subpix) + corner_foerstner, corner_subpix, corner_peaks) from .corner_cy import corner_moravec from .template import match_template diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index e48a0b41..cbdbf881 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -3,7 +3,7 @@ from scipy import ndimage from scipy import stats from skimage.color import rgb2grey from skimage.util import img_as_float -from . import peak +from skimage.feature import peak_local_max def _compute_derivatives(image): @@ -141,7 +141,7 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): Examples ------- - >>> from skimage.feature import corner_harris, peak_local_max + >>> from skimage.feature import corner_harris, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square @@ -155,11 +155,11 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) - >>> peak_local_max(corner_harris(square), min_distance=1) - array([[3, 3], - [3, 6], - [6, 3], - [6, 6]]) + >>> corner_peaks(corner_harris(square), min_distance=1) + array([[2, 2], + [2, 7], + [7, 2], + [7, 7]]) """ @@ -211,7 +211,7 @@ def corner_shi_tomasi(image, sigma=1): Examples ------- - >>> from skimage.feature import corner_shi_tomasi, peak_local_max + >>> from skimage.feature import corner_shi_tomasi, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square @@ -225,11 +225,11 @@ def corner_shi_tomasi(image, sigma=1): [ 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) - >>> peak_local_max(corner_shi_tomasi(square), min_distance=1) - array([[3, 3], - [3, 6], - [6, 3], - [6, 6]]) + >>> corner_peaks(corner_shi_tomasi(square), min_distance=1) + array([[2, 2], + [2, 7], + [7, 2], + [7, 7]]) """ @@ -278,7 +278,7 @@ def corner_foerstner(image, sigma=1): Examples ------- - >>> from skimage.feature import corner_foerstner, peak_local_max + >>> from skimage.feature import corner_foerstner, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square @@ -296,7 +296,7 @@ def corner_foerstner(image, sigma=1): >>> accuracy_thresh = 0.5 >>> roundness_thresh = 0.3 >>> foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w - >>> peak_local_max(foerstner, min_distance=1) + >>> corner_peaks(foerstner, min_distance=1) array([[2, 2], [2, 7], [7, 2], @@ -441,3 +441,60 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99): corners_subpix[i, :] = y0 + est_edge[0], x0 + est_edge[1] return corners_subpix + + +def corner_peaks(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, + exclude_border=True, indices=True, num_peaks=np.inf, + footprint=None, labels=None): + """Find corners in corner measure response image. + + This differs from `skimage.feature.peak_local_max` in that it suppresses + multiple connected peaks with the same accumulator value. + + Parameters + ---------- + See `skimage.feature.peak_local_max`. + + Returns + ------- + See `skimage.feature.peak_local_max`. + + Examples + -------- + >>> from skimage.feature import peak_local_max, corner_peaks + >>> response = np.zeros((5, 5)) + >>> response[2:4, 2:4] = 1 + >>> response + array([[ 0., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0.], + [ 0., 0., 1., 1., 0.], + [ 0., 0., 1., 1., 0.], + [ 0., 0., 0., 0., 0.]]) + >>> peak_local_max(response, exclude_border=False) + array([[2, 2], + [2, 3], + [3, 2], + [3, 3]]) + >>> corner_peaks(response, exclude_border=False) + array([[2, 2]]) + + """ + + peaks = peak_local_max(image, min_distance=min_distance, + threshold_abs=threshold_abs, + threshold_rel=threshold_rel, + exclude_border=exclude_border, + indices=False, num_peaks=np.inf, + footprint=footprint, labels=labels) + if min_distance > 0: + coords = np.transpose(peaks.nonzero()) + for r, c in coords: + if peaks[r, c]: + peaks[r - min_distance:r + min_distance + 1, + c - min_distance:c + min_distance + 1] = False + peaks[r, c] = True + + if indices is True: + return np.transpose(peaks.nonzero()) + else: + return peaks From a7411bc67853aa6573a06765210891f6025cede0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 18:21:31 +0100 Subject: [PATCH 432/625] Extend example of corner_peaks to highlight difference to peak_local_max --- skimage/feature/corner.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index cbdbf881..867acb5d 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -477,6 +477,11 @@ def corner_peaks(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, [3, 3]]) >>> corner_peaks(response, exclude_border=False) array([[2, 2]]) + >>> corner_peaks(response, exclude_border=False, min_distance=0) + array([[2, 2], + [2, 3], + [3, 2], + [3, 3]]) """ From 377ae3b968ef860ede7158919600d7cd614d1ff4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 18:26:09 +0100 Subject: [PATCH 433/625] Add test case for corner_peaks function --- skimage/feature/tests/test_corner.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/skimage/feature/tests/test_corner.py b/skimage/feature/tests/test_corner.py index 600d743e..46e5466f 100644 --- a/skimage/feature/tests/test_corner.py +++ b/skimage/feature/tests/test_corner.py @@ -5,7 +5,7 @@ from skimage import data from skimage import img_as_float from skimage.feature import (corner_moravec, corner_harris, corner_shi_tomasi, - corner_subpix, peak_local_max) + corner_subpix, peak_local_max, corner_peaks) def test_square_image(): @@ -99,6 +99,17 @@ def test_subpix(): assert_array_equal(subpix[0], (24.5, 24.5)) +def test_corner_peaks(): + response = np.zeros((5, 5)) + response[2:4, 2:4] = 1 + + corners = corner_peaks(response, exclude_border=False) + assert len(corners) == 1 + + corners = corner_peaks(response, exclude_border=False, min_distance=0) + assert len(corners) == 4 + + if __name__ == '__main__': from numpy import testing testing.run_module_suite() From a410f1c47e87077d9670cfcf2bc36f4f1807bf5b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 23:17:06 +0100 Subject: [PATCH 434/625] UmfPack warning is now only raised if random walker is explicitly called --- .../segmentation/random_walker_segmentation.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/skimage/segmentation/random_walker_segmentation.py b/skimage/segmentation/random_walker_segmentation.py index 6eb7def8..397c4299 100644 --- a/skimage/segmentation/random_walker_segmentation.py +++ b/skimage/segmentation/random_walker_segmentation.py @@ -14,13 +14,9 @@ import numpy as np from scipy import sparse, ndimage try: from scipy.sparse.linalg.dsolve import umfpack - u = umfpack.UmfpackContext() + UmfpackContext = umfpack.UmfpackContext() except: - warnings.warn("""Scipy was built without UMFPACK. Consider rebuilding - Scipy with UMFPACK, this will greatly speed up the random walker - functions. You may also install pyamg and run the random walker function - in cg_mg mode (see the docstrings) - """) + UmfpackContext = None try: from pyamg import ruge_stuben_solver amg_loaded = True @@ -338,6 +334,14 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32) """ + + if UmfpackContext is None: + warnings.warn('SciPy was built without UMFPACK. Consider rebuilding ' + 'SciPy with UMFPACK, this will greatly speed up the ' + 'random walker functions. You may also install pyamg ' + 'and run the random walker function in cg_mg mode ' + '(see the docstrings)') + # Parse input data if not multichannel: # We work with 4-D arrays of floats From 33c58ad8547fd3d65937a7bb8ac26ed7cf0e0d5b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 9 Dec 2012 23:19:42 +0100 Subject: [PATCH 435/625] Fix some doc string formatting issues of random walker --- .../random_walker_segmentation.py | 23 ++++--------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/skimage/segmentation/random_walker_segmentation.py b/skimage/segmentation/random_walker_segmentation.py index 397c4299..32baf59c 100644 --- a/skimage/segmentation/random_walker_segmentation.py +++ b/skimage/segmentation/random_walker_segmentation.py @@ -172,20 +172,19 @@ def _build_laplacian(data, mask=None, beta=50, depth=1., multichannel=False): def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, multichannel=False, return_full_prob=False, depth=1.): - """ - Random walker algorithm for segmentation from markers, for gray-level or - multichannel images. + """Random walker algorithm for segmentation from markers. + + Random walker algorithm is implemented for gray-level or multichannel + images. Parameters ---------- - data : array_like Image to be segmented in phases. Gray-level `data` can be two- or three-dimensional; multichannel data can be three- or four- dimensional (multichannel=True) with the highest dimension denoting channels. Data spacing is assumed isotropic unless depth keyword argument is used. - labels : array of ints, of same shape as `data` without channels dimension Array of seed markers labeled with different positive integers for different phases. Zero-labeled pixels are unlabeled pixels. @@ -195,49 +194,39 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, labels are consecutive. In the multichannel case, `labels` should have the same shape as a single channel of `data`, i.e. without the final dimension denoting channels. - beta : float Penalization coefficient for the random walker motion (the greater `beta`, the more difficult the diffusion). - mode : {'bf', 'cg_mg', 'cg'} (default: 'bf') Mode for solving the linear system in the random walker algorithm. - - 'bf' (brute force, default): an LU factorization of the Laplacian is computed. This is fast for small images (<1024x1024), but very slow (due to the memory cost) and memory-consuming for big images (in 3-D for example). - - 'cg' (conjugate gradient): the linear system is solved iteratively using the Conjugate Gradient method from scipy.sparse.linalg. This is less memory-consuming than the brute force method for large images, but it is quite slow. - - 'cg_mg' (conjugate gradient with multigrid preconditioner): a preconditioner is computed using a multigrid solver, then the solution is computed with the Conjugate Gradient method. This mode requires that the pyamg module (http://code.google.com/p/pyamg/) is installed. For images of size > 512x512, this is the recommended (fastest) mode. - tol : float tolerance to achieve when solving the linear system, in cg' and 'cg_mg' modes. - copy : bool If copy is False, the `labels` array will be overwritten with the result of the segmentation. Use copy=False if you want to save on memory. - multichannel : bool, default False If True, input data is parsed as multichannel data (see 'data' above for proper input format in this case) - return_full_prob : bool, default False If True, the probability that a pixel belongs to each of the labels will be returned, instead of only the most likely label. - depth : float, default 1. Correction for non-isotropic voxel depths in 3D volumes. Default (1.) implies isotropy. This factor is derived as follows: @@ -247,7 +236,6 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, Returns ------- - output : ndarray If `return_full_prob` is False, array of ints of same shape as `data`, in which each pixel has been labeled according to the marker that @@ -258,14 +246,12 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, See also -------- - skimage.morphology.watershed: watershed segmentation A segmentation algorithm based on mathematical morphology and "flooding" of regions from markers. Notes ----- - Multichannel inputs are scaled with all channel data combined. Ensure all channels are separately normalized prior to running this algorithm. @@ -315,7 +301,6 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, Examples -------- - >>> a = np.zeros((10, 10)) + 0.2*np.random.random((10, 10)) >>> a[5:8, 5:8] += 1 >>> b = np.zeros_like(a) From ac09463cf8b7571e65f51fc892aaa3cc0d9fde70 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Mon, 10 Dec 2012 20:19:23 -0600 Subject: [PATCH 436/625] Fixed comment header in test file. --- skimage/exposure/tests/test_exposure.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 17633cf9..1517eb11 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -72,8 +72,8 @@ def test_rescale_out_range(): assert_close(out, [0, 63, 127]) -# Test rescale intensity -# ====================== +# Test adaptive histogram equalization +# ==================================== def test_adapthist_scalar(): '''Test a scalar uint8 image From bb4f2b96dfe25e07558d6d1836c10e0563402416 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Mon, 10 Dec 2012 20:37:01 -0600 Subject: [PATCH 437/625] Modified test to pass with matplotlib plugin. --- skimage/exposure/tests/test_exposure.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 1517eb11..f0bdde37 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -5,7 +5,9 @@ from skimage import data from skimage import exposure from skimage.color import rgb2gray from skimage.util.dtype import dtype_range +from skimage.io import use_plugin +use_plugin('matplotlib') # Test histogram equalization # =========================== @@ -101,7 +103,7 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 77.5841845) + assert_almost_equal(peak_snr(img, adapted), 77.58419, 5) assert_almost_equal(norm_brightness_err(img, adapted), 0.0376632) return data, adapted From 5444867fd09e19d39ffd200a89ccc43f82994a30 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Mon, 10 Dec 2012 20:38:42 -0600 Subject: [PATCH 438/625] Removed explicit io_plugin from test --- skimage/exposure/tests/test_exposure.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index f0bdde37..30f51fc5 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -5,9 +5,7 @@ from skimage import data from skimage import exposure from skimage.color import rgb2gray from skimage.util.dtype import dtype_range -from skimage.io import use_plugin -use_plugin('matplotlib') # Test histogram equalization # =========================== From 0e34c91e37f7ffbe401012a7f8a161bc95ce7fea Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Tue, 11 Dec 2012 18:08:27 -0600 Subject: [PATCH 439/625] Changed all test to use 3 significant digits. --- skimage/exposure/tests/test_exposure.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 30f51fc5..26607f61 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -85,9 +85,9 @@ def test_adapthist_scalar(): assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(peak_snr(full_scale, adapted), 28.6262034) + assert_almost_equal(peak_snr(full_scale, adapted), 28.626, 3) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.0410010) + 0.041, 3) return img, adapted @@ -101,8 +101,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 77.58419, 5) - assert_almost_equal(norm_brightness_err(img, adapted), 0.0376632) + assert_almost_equal(peak_snr(img, adapted), 77.584, 5) + assert_almost_equal(norm_brightness_err(img, adapted), 0.038, 3) return data, adapted @@ -116,9 +116,9 @@ def test_adapthist_color(): assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert_almost_equal(peak_snr(full_scale, adapted), 64.7167515) + assert_almost_equal(peak_snr(full_scale, adapted), 64.717, 3) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.17922429) + 0.179, 3) return data, adapted From 9adae8b0c231fe79a6599946b2ebc4f2aebc4acf Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Tue, 11 Dec 2012 18:57:24 -0600 Subject: [PATCH 440/625] Fixed last remaining sig figure problem. --- skimage/exposure/tests/test_exposure.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 26607f61..b41f53fe 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -101,7 +101,7 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 77.584, 5) + assert_almost_equal(peak_snr(img, adapted), 77.584, 3) assert_almost_equal(norm_brightness_err(img, adapted), 0.038, 3) return data, adapted From 530fbbb2fc4cb739c8af075570246f2db3e0398a Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Tue, 27 Nov 2012 16:26:47 -0800 Subject: [PATCH 441/625] DOC: Minor clean-ups of equalization demo. --- doc/examples/plot_equalize.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 13ffa320..f57cea9c 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -18,7 +18,7 @@ that fall within the 2nd and 98th percentiles [2]_. """ -from skimage import data, img_as_ubyte +from skimage import data, img_as_float from skimage.util.dtype import dtype_range from skimage import exposure @@ -30,6 +30,7 @@ def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. """ + img = img_as_float(img) ax_img, ax_hist = axes ax_cdf = ax_hist.twinx() @@ -38,16 +39,16 @@ def plot_img_and_hist(img, axes, bins=256): ax_img.set_axis_off() # Display histogram - ax_hist.hist(img.ravel(), bins=bins, histtype='stepfilled') + ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black') ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) ax_hist.set_xlabel('Pixel intensity') - - xmin, xmax = dtype_range[img.dtype.type] - ax_hist.set_xlim(xmin, xmax) + ax_hist.set_xlim(0, 1) + ax_hist.set_yticks([]) # Display cumulative distribution img_cdf, bins = exposure.cumulative_distribution(img, bins) ax_cdf.plot(bins, img_cdf, 'r') + ax_cdf.set_yticks([]) return ax_img, ax_hist, ax_cdf @@ -62,29 +63,31 @@ img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) # Equalization img_eq = exposure.equalize_hist(img) -img_eq = img_as_ubyte(img_eq) # Adaptive Equalization img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03) -img_adapteq = img_as_ubyte(img_adapteq) # Display results f, axes = plt.subplots(2, 4, figsize=(8, 4)) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) ax_img.set_title('Low contrast image') + +y_min, y_max = ax_hist.get_ylim() ax_hist.set_ylabel('Number of pixels') +ax_hist.set_yticks(np.linspace(0, y_max, 5)) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) ax_img.set_title('Contrast stretching') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Histogram equalization') -ax_cdf.set_ylabel('Fraction of total intensity') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3]) ax_img.set_title('Adaptive equalization') + ax_cdf.set_ylabel('Fraction of total intensity') +ax_cdf.set_yticks(np.linspace(0, 1, 5)) # prevent overlap of y-axis labels plt.subplots_adjust(wspace=0.4) From 7ebbdfd75fb0cd38153698815746974c12588742 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Tue, 11 Dec 2012 18:14:45 -0800 Subject: [PATCH 442/625] Minor code cleanup. --- skimage/exposure/_adapthist.py | 147 ++++++++++++++++----------------- 1 file changed, 72 insertions(+), 75 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 30d92d48..667d2cb2 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -1,26 +1,18 @@ -''' -Adapted code from the article - * "Contrast Limited Adaptive Histogram Equalization" - * by Karel Zuiderveld, karel@cv.ruu.nl - * in "Graphics Gems IV", Academic Press, 1994 -============= +""" +Adapted code from "Contrast Limited Adaptive Histogram Equalization" by Karel +Zuiderveld , Graphics Gems IV, Academic Press, 1994. -http://tog.acm.org/resources/GraphicsGems/ +http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi -EULA: The Graphics Gems code is copyright-protected. -In other words, you cannot claim the text of the code as your -own and resell it. Using the code is permitted in any program, -product, or library, non-commercial or commercial. -Giving credit is not required, though is a nice gesture. -The code comes as-is, and if there are any flaws or problems -with any Gems code, nobody involved with Gems - authors, editors, -publishers, or webmasters - are to be held responsible. -Basically, don't be a jerk, and remember that anything free +The Graphics Gems code is copyright-protected. In other words, you cannot +claim the text of the code as your own and resell it. Using the code is +permitted in any program, product, or library, non-commercial or commercial. +Giving credit is not required, though is a nice gesture. The code comes as-is, +and if there are any flaws or problems with any Gems code, nobody involved with +Gems - authors, editors, publishers, or webmasters - are to be held +responsible. Basically, don't be a jerk, and remember that anything free comes with no guarantee. - * - * Author: Karel Zuiderveld, Computer Vision Research Group, - * Utrecht, The Netherlands (karel@cv.ruu.nl) -''' +""" import numpy as np import skimage from skimage import color @@ -30,41 +22,37 @@ from skimage.util import view_as_blocks MAX_REG_X = 16 # max. # contextual regions in x-direction */ MAX_REG_Y = 16 # max. # contextual regions in y-direction */ -NR_OF_GREY = 1 << 14 # number of grayscale levels to use in CLAHE algorithm +NR_OF_GREY = 16384 # number of grayscale levels to use in CLAHE algorithm def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256): - '''Contrast Limited Adaptive Histogram Equalization + """Contrast Limited Adaptive Histogram Equalization. Parameters ---------- image : array-like - original image + Input image. ntiles_x : int, optional - Tile regions in the X direction (2, 16) + Number of tile regions in the X direction. Ranges between 2 and 16. ntiles_y : int, optional - Tile regions in the Y direction (2, 16) + Number of tile regions in the Y direction. Ranges between 2 and 16. clip_limit : float: optional - Normalized cliplimit (higher values give more contrast) + Clipping limit, normalized between 0 and 1 (higher values give more + contrast). nbins : int, optional - Greybins for histogram ("dynamic range") + Number of gray bins for histogram ("dynamic range"). Returns ------- - out : np.ndarray - equalized image - grayscale images are uint16, color images are float + out : ndarray + Equalized image. Notes ----- * The algorithm relies on an image whose rows and columns are even multiples of the number of tiles, so the extra rows and columns are left at their original values, thus preserving the input image shape. -<<<<<<< HEAD -======= - * For grayscale images, CLAHE is performed on one channel, - and a grayscale is returned ->>>>>>> 2e1729a9fbbc21fc0b04df8e68efbab9cfd6dada * For color images, the following steps are performed: - The image is converted to LAB color space - The CLAHE algorithm is run on the L channel @@ -73,10 +61,9 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, References ---------- - .. [1] http://tog.acm.org/resources/GraphicsGems/ + .. [1] http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE - ''' - # handle color images - CLAHE accepts scalar images only + """ args = [None, ntiles_x, ntiles_y, clip_limit * nbins, nbins] if image.ndim > 2: lab_img = color.rgb2lab(skimage.img_as_float(image)) @@ -99,31 +86,32 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): - '''Contrast Limited Adaptive Histogram Equalization + """Contrast Limited Adaptive Histogram Equalization. Parameters ---------- image : array-like - original image + Input image. ntiles_x : int, optional - Tile regions in the X direction (2, 16) + Number of tile regions in the X direction. Ranges between 2 and 16. ntiles_y : int, optional - Tile regions in the Y direction (2, 16) - clip_limit : float: optional - Normalized cliplimit (higher values give more contrast) + Number of tile regions in the Y direction. Ranges between 2 and 16. + clip_limit : float, optional + Normalized clipping limit (higher values give more contrast). nbins : int, optional - Greybins for histogram ("dynamic range") + Number of gray bins for histogram ("dynamic range"). Returns ------- - out : np.ndarray + out : ndarray + Equalized image. - The number of "effective" greylevels in the output image is set by nbins; + The number of "effective" greylevels in the output image is set by `nbins`; selecting a small value (eg. 128) speeds up processing and still produce an output image of good quality. The output image will have the same minimum and maximum value as the input image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. - ''' + """ ntiles_x = min(ntiles_x, MAX_REG_X) ntiles_y = min(ntiles_y, MAX_REG_Y) ntiles_y = max(ntiles_x, 2) @@ -148,10 +136,12 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): clip_limit = 1 else: clip_limit = NR_OF_GREY # Large value, do not clip (AHE) + bin_size = 1 + NR_OF_GREY / nbins aLUT = np.arange(NR_OF_GREY) aLUT /= bin_size img_blocks = view_as_blocks(image, (y_size, x_size)) + # Calculate greylevel mappings for each contextual region for y in range(ntiles_y): for x in range(ntiles_x): @@ -162,6 +152,7 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): hist = clip_histogram(hist, clip_limit) hist = map_histogram(hist, 0, NR_OF_GREY, n_pixels) map_array[y, x] = hist + # Interpolate greylevel mappings to get CLAHE image ystart = 0 for y in range(ntiles_y + 1): @@ -178,6 +169,7 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): ystep = y_size yU = y - 1 yB = yB + 1 + for x in range(ntiles_x + 1): if x == 0: # special case: left column xstep = x_size / 2 @@ -191,21 +183,26 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): xstep = x_size xL = x - 1 xR = xL + 1 + mapLU = map_array[yU, xL] mapRU = map_array[yU, xR] mapLB = map_array[yB, xL] mapRB = map_array[yB, xR] + xslice = np.arange(xstart, xstart + xstep) yslice = np.arange(ystart, ystart + ystep) interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, aLUT) + xstart += xstep # set pointer on next matrix */ + ystart += ystep + return image def clip_histogram(hist, clip_limit): - '''Perform clipping of the histogram and redistribution of bins. + """Perform clipping of the histogram and redistribution of bins. The histogram is clipped and the number of excess pixels is counted. Afterwards the excess pixels are equally redistributed across the @@ -213,16 +210,16 @@ def clip_histogram(hist, clip_limit): Parameters ---------- - hist : np.ndarray - histogram array + hist : ndarray + Histogram array. clip_limit : int - maximum allowed bin count + Maximum allowed bin count. Returns ------- - hist : np.ndarray - clipped histogram - ''' + hist : ndarray + Clipped histogram. + """ # calculate total number of excess pixels excess_mask = hist > clip_limit excess = hist[excess_mask] @@ -253,28 +250,29 @@ def clip_histogram(hist, clip_limit): hist[under] += 1 n_excess -= hist[under].size index += 1 + return hist def map_histogram(hist, min_val, max_val, n_pixels): - '''Calculates the equalized lookup table (mapping) + """Calculate the equalized lookup table (mapping). It does so by cumulating the input histogram. - hist : np.ndarray - clipped histogram + hist : ndarray + Clipped histogram. min_val : int - min value for mapping + Minimum value for mapping. max_val : int - max value for mapping + Maximum value for mapping. n_pixels : int - number of pixels in the region + Number of pixels in the region. Returns ------- - out : np.ndarray - mapped intensity LUT - ''' + out : ndarray + Mapped intensity LUT. + """ out = np.cumsum(hist).astype(float) scale = ((float)(max_val - min_val)) / n_pixels out *= scale @@ -285,23 +283,23 @@ def map_histogram(hist, min_val, max_val, n_pixels): def interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, aLUT): - '''Find the new grayscale level for a region using bilinear interpolation + """Find the new grayscale level for a region using bilinear interpolation. Parameters ---------- - image : np.ndarray - full image + image : ndarray + Full image. xslice, yslice : array-like - indices of the region - map* : np.ndarray - mappings of greylevels from histograms - aLUT : np.ndarray - maps grayscale levels in image to histogram levels + Indices of the region. + map* : ndarray + Mappings of greylevels from histograms. + aLUT : ndarray + Maps grayscale levels in image to histogram levels. Returns ------- - out : np.ndarray - original image with the subregion replaced + out : ndarray + Original image with the subregion replaced. Note ---- @@ -309,7 +307,7 @@ def interpolate(image, xslice, yslice, within a submatrix of the image. This is done by a bilinear interpolation between four different mappings in order to eliminate boundary artifacts. - ''' + """ norm = xslice.size * yslice.size # Normalization factor # interpolation weight matrices x_coef, y_coef = np.meshgrid(np.arange(xslice.size), @@ -325,4 +323,3 @@ def interpolate(image, xslice, yslice, / norm) view[:, :] = new return image - From e3951a2a7841ff77b64139229076707ca5424b9c Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Wed, 12 Dec 2012 14:31:05 +1100 Subject: [PATCH 443/625] Rename ar_relabeled to relabeled for simplicity --- skimage/segmentation/_join.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/segmentation/_join.py b/skimage/segmentation/_join.py index 5cd25b80..09a8361b 100644 --- a/skimage/segmentation/_join.py +++ b/skimage/segmentation/_join.py @@ -54,7 +54,7 @@ def relabel_from_one(ar): Returns ------- - ar_relabeled : numpy array of same shape as ar + relabeled : numpy array of same shape as ar forward_map : 1d numpy array of length np.unique(ar) + 1 inverse_map : 1d numpy array of length len(np.unique(ar)) The length is len(np.unique(ar)) + 1 if 0 is not in np.unique(ar) @@ -64,8 +64,8 @@ def relabel_from_one(ar): >>> import numpy as np >>> from skimage.segmentation import relabel_from_one >>> ar = array([1, 1, 5, 5, 8, 99, 42]) - >>> ar_relab, fw, inv = relabel_from_one(ar) - >>> ar_relab + >>> relab, fw, inv = relabel_from_one(ar) + >>> relab array([1, 1, 2, 2, 3, 5, 4]) >>> fw array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, @@ -75,9 +75,9 @@ def relabel_from_one(ar): 0, 0, 0, 0, 0, 0, 0, 5]) >>> inv array([ 0, 1, 5, 8, 42, 99]) - >>> (fw[ar] == ar_relab).all() + >>> (fw[ar] == relab).all() True - >>> (inv[ar_relab] == ar).all() + >>> (inv[relab] == ar).all() True """ labels = np.unique(ar) From 82c3526f307141fddad3e8c83978997853c81818 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Wed, 12 Dec 2012 14:37:43 +1100 Subject: [PATCH 444/625] Tweak join_segmentations example for clarity --- doc/examples/plot_join_segmentations.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/examples/plot_join_segmentations.py b/doc/examples/plot_join_segmentations.py index af06cef3..02600ba6 100644 --- a/doc/examples/plot_join_segmentations.py +++ b/doc/examples/plot_join_segmentations.py @@ -37,7 +37,8 @@ seg1 = nd.label(ws == foreground)[0] # make the RGB equivalent of `coins` coins_colour = np.tile(coins[..., np.newaxis], (1, 1, 3)) -seg2 = slic(coins_colour, max_iter=20, sigma=0, convert2lab=False) +seg2 = slic(coins_colour, n_segments=30, max_iter=160, sigma=1, ratio=9, + convert2lab=False) # combine the two segj = join_segmentations(seg1, seg2) From 73f5e9dccc375f930642a9166abb6d46a3d366fa Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Tue, 11 Dec 2012 23:14:30 -0600 Subject: [PATCH 445/625] Fixed error in peak_snr, now uses float arithmetic throughout. --- skimage/exposure/tests/test_exposure.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index b41f53fe..258b4224 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -85,7 +85,7 @@ def test_adapthist_scalar(): assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(peak_snr(full_scale, adapted), 28.626, 3) + assert_almost_equal(peak_snr(full_scale, adapted), 101.231, 3) assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.041, 3) return img, adapted @@ -137,8 +137,11 @@ def peak_snr(img1, img2): ''' if img1.ndim == 3: img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy()) + img1 = skimage.img_as_float(img1) + img2 = skimage.img_as_float(img2) mse = 1. / img1.size * np.square(img1 - img2).sum() _, max_ = dtype_range[img1.dtype.type] + print mse, max_ return 20 * np.log(max_ / mse) From a6d4b6e7fabc835c978d956e7fd38ef8ba1a03df Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Wed, 12 Dec 2012 11:06:15 -0500 Subject: [PATCH 446/625] BUG: Fix file separators for windows --- skimage/io/_io.py | 2 +- skimage/io/tests/test_io.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/skimage/io/_io.py b/skimage/io/_io.py index 2a177076..f8f395bd 100644 --- a/skimage/io/_io.py +++ b/skimage/io/_io.py @@ -16,7 +16,7 @@ from skimage.color import rgb2grey # Shared image queue _image_stack = [] -URL_REGEX = re.compile(r'http://|https://|ftp://|file://') +URL_REGEX = re.compile(r'http://|https://|ftp://|file://|file:\\') def is_url(filename): diff --git a/skimage/io/tests/test_io.py b/skimage/io/tests/test_io.py index b29994c5..aa48ac1a 100644 --- a/skimage/io/tests/test_io.py +++ b/skimage/io/tests/test_io.py @@ -1,3 +1,5 @@ +import os + from numpy.testing import * import numpy as np @@ -18,7 +20,7 @@ def test_stack_non_array(): def test_imread_url(): - image_url = 'file://%s/camera.png' % data_dir + image_url = 'file:{0}{0}{1}{0}camera.png'.format(os.path.sep, data_dir) image = io.imread(image_url) assert image.shape == (512, 512) From abc1d2095f18f6c7ff129f3b8bf9eae2d7a6239a Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Thu, 13 Dec 2012 13:39:32 -0500 Subject: [PATCH 447/625] BUG: Fix file URI in test (2nd attempt) --- skimage/io/tests/test_io.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/skimage/io/tests/test_io.py b/skimage/io/tests/test_io.py index aa48ac1a..484784ee 100644 --- a/skimage/io/tests/test_io.py +++ b/skimage/io/tests/test_io.py @@ -20,7 +20,10 @@ def test_stack_non_array(): def test_imread_url(): - image_url = 'file:{0}{0}{1}{0}camera.png'.format(os.path.sep, data_dir) + # tweak data path so that file URI works on both unix and windows. + data_path = data_dir.lstrip(os.path.sep) + data_path = data_path.replace(os.path.sep, '/') + image_url = 'file:///{0}/camera.png'.format(data_path) image = io.imread(image_url) assert image.shape == (512, 512) From 380e27e60d59144552baad3d602a2136346595fb Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Fri, 14 Dec 2012 16:38:48 -0600 Subject: [PATCH 448/625] Lazily import nose for testing, makes skimge load in 1/3 time. --- skimage/__init__.py | 43 ++++++++++++++++++++----------------------- 1 file changed, 20 insertions(+), 23 deletions(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 1ac121b3..e962b558 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -52,6 +52,8 @@ img_as_ubyte """ import os.path as _osp +import imp +import functools pkg_dir = _osp.abspath(_osp.dirname(__file__)) data_dir = _osp.join(pkg_dir, 'data') @@ -62,30 +64,25 @@ except ImportError: __version__ = "unbuilt-dev" -def _setup_test(verbose=False): - import functools +try: + imp.find_module('nose') +except ImportError: + def test(verbose=False): + """This would invoke the skimage test suite, but nose couldn't be + imported so the test suite can not run. + """ + raise ImportError("Could not load nose. Unit tests not available.") +else: + def test(verbose=False): + """Invoke the skimage test suite.""" + import nose + args = ['', pkg_dir, '--exe'] + if verbose: + args.extend(['-v', '-s']) + nose.run('skimage', argv=args) - args = ['', pkg_dir, '--exe'] - if verbose: - args.extend(['-v', '-s']) - - try: - import nose as _nose - except ImportError: - def broken_test_func(): - """This would invoke the skimage test suite, but nose couldn't be - imported so the test suite can not run. - """ - raise ImportError("Could not load nose. Unit tests not available.") - return broken_test_func - else: - f = functools.partial(_nose.run, 'skimage', argv=args) - f.__doc__ = 'Invoke the skimage test suite.' - return f - - -test = _setup_test() -test_verbose = _setup_test(verbose=True) +test_verbose = functools.partial(test, verbose=True) +test_verbose.__doc__ = test.__doc__ def get_log(name=None): From cb1527bbe8dffc16abab2eb45bdf8263be977e81 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 15 Dec 2012 07:24:31 -0600 Subject: [PATCH 449/625] STY: Fixed indent on docstring. --- skimage/__init__.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index e962b558..3a4f60b1 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -68,18 +68,18 @@ try: imp.find_module('nose') except ImportError: def test(verbose=False): - """This would invoke the skimage test suite, but nose couldn't be - imported so the test suite can not run. - """ - raise ImportError("Could not load nose. Unit tests not available.") + """This would invoke the skimage test suite, but nose couldn't be + imported so the test suite can not run. + """ + raise ImportError("Could not load nose. Unit tests not available.") else: def test(verbose=False): - """Invoke the skimage test suite.""" - import nose - args = ['', pkg_dir, '--exe'] - if verbose: - args.extend(['-v', '-s']) - nose.run('skimage', argv=args) + """Invoke the skimage test suite.""" + import nose + args = ['', pkg_dir, '--exe'] + if verbose: + args.extend(['-v', '-s']) + nose.run('skimage', argv=args) test_verbose = functools.partial(test, verbose=True) test_verbose.__doc__ = test.__doc__ @@ -122,7 +122,7 @@ def _setup_log(): formatter = logging.Formatter( '%(name)s: %(levelname)s: %(message)s' - ) + ) try: handler = logging.StreamHandler(stream=sys.stdout) @@ -137,4 +137,4 @@ def _setup_log(): _setup_log() -from .util.dtype import * +from .util.dtype import * \ No newline at end of file From b620c0ade667a174d5070680c8c263d1c0757ea4 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 15 Dec 2012 07:40:04 -0600 Subject: [PATCH 450/625] STY: Added newline at the end of file --- skimage/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 3a4f60b1..1cd17744 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -137,4 +137,4 @@ def _setup_log(): _setup_log() -from .util.dtype import * \ No newline at end of file +from .util.dtype import * From 73d9ee9b614dff630c7099fc83ed4e778cf2f90d Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Fri, 14 Dec 2012 16:38:48 -0600 Subject: [PATCH 451/625] Lazily import nose for testing, makes skimge load in 1/3 time. STY: Fixed indent on docstring. STY: Added newline at the end of file STY: Use original indention --- skimage/__init__.py | 43 ++++++++++++++++++++----------------------- 1 file changed, 20 insertions(+), 23 deletions(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 1ac121b3..56ab6de0 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -52,6 +52,8 @@ img_as_ubyte """ import os.path as _osp +import imp +import functools pkg_dir = _osp.abspath(_osp.dirname(__file__)) data_dir = _osp.join(pkg_dir, 'data') @@ -62,30 +64,25 @@ except ImportError: __version__ = "unbuilt-dev" -def _setup_test(verbose=False): - import functools +try: + imp.find_module('nose') +except ImportError: + def test(verbose=False): + """This would invoke the skimage test suite, but nose couldn't be + imported so the test suite can not run. + """ + raise ImportError("Could not load nose. Unit tests not available.") +else: + def test(verbose=False): + """Invoke the skimage test suite.""" + import nose + args = ['', pkg_dir, '--exe'] + if verbose: + args.extend(['-v', '-s']) + nose.run('skimage', argv=args) - args = ['', pkg_dir, '--exe'] - if verbose: - args.extend(['-v', '-s']) - - try: - import nose as _nose - except ImportError: - def broken_test_func(): - """This would invoke the skimage test suite, but nose couldn't be - imported so the test suite can not run. - """ - raise ImportError("Could not load nose. Unit tests not available.") - return broken_test_func - else: - f = functools.partial(_nose.run, 'skimage', argv=args) - f.__doc__ = 'Invoke the skimage test suite.' - return f - - -test = _setup_test() -test_verbose = _setup_test(verbose=True) +test_verbose = functools.partial(test, verbose=True) +test_verbose.__doc__ = test.__doc__ def get_log(name=None): From 49119524e343fdad3d3a93f2961af6ca6413158e Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 15 Dec 2012 09:17:39 -0600 Subject: [PATCH 452/625] Fixed indent on nose.run, so it will run when verbose is False --- skimage/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 1cd17744..b8d97dc4 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -79,7 +79,7 @@ else: args = ['', pkg_dir, '--exe'] if verbose: args.extend(['-v', '-s']) - nose.run('skimage', argv=args) + nose.run('skimage', argv=args) test_verbose = functools.partial(test, verbose=True) test_verbose.__doc__ = test.__doc__ From a8dcb32b60a7b4d9ac7c03768c78d226d83a296e Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sat, 15 Dec 2012 09:19:26 -0600 Subject: [PATCH 453/625] STY: Replaced indent with original formatting, no need to change. --- skimage/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index b8d97dc4..3277919e 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -122,7 +122,7 @@ def _setup_log(): formatter = logging.Formatter( '%(name)s: %(levelname)s: %(message)s' - ) + ) try: handler = logging.StreamHandler(stream=sys.stdout) From 2849cbf8587de52d7d3090c3a8a96da2e51badb4 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 15 Dec 2012 18:43:33 +0100 Subject: [PATCH 454/625] Add circle perimeter to draw module. --- skimage/draw/__init__.py | 2 +- skimage/draw/_draw.pyx | 39 +++++++++++++++++++++++++++++++++ skimage/draw/tests/test_draw.py | 33 +++++++++++++++++++++++++++- 3 files changed, 72 insertions(+), 2 deletions(-) diff --git a/skimage/draw/__init__.py b/skimage/draw/__init__.py index 4eac9b63..b213f93f 100644 --- a/skimage/draw/__init__.py +++ b/skimage/draw/__init__.py @@ -1,2 +1,2 @@ -from ._draw import line, polygon, ellipse, circle +from ._draw import line, polygon, ellipse, circle, circle_perimeter bresenham = line diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index ca0c502a..b5b96a2b 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -187,3 +187,42 @@ def circle(double cy, double cx, double radius, shape=None): ``img[rr, cc] = 1``. """ return ellipse(cy, cx, radius, radius, shape) + + +def circle_perimeter(int cy, int cx, int radius): + """Generate circle perimeter coordinates. + + Parameters + ---------- + cy, cx : int + Centre coordinate of circle. + radius: int + Radius of circle. + + Returns + ------- + rr, cc : (N,) ndarray of int + Indices of pixels that belong to the circle perimeter. + May be used to directly index into an array, e.g. + ``img[rr, cc] = 1``. + + """ + + cdef list rr = list() + cdef list cc = list() + + cdef int x = 0 + cdef int y = radius + cdef int d = 3 - 2 * radius + + while y >= x: + rr.extend([y, -y, y, -y, x, -x, x, -x]) + cc.extend([x, x, -x, -x, y, y, -y, -y]) + if d < 0: + d += 4 * x + 6 + else: + d += 4 * (x - y) + 10 + y -= 1 + x += 1 + + return np.array(rr) + cy, np.array(cc) + cx diff --git a/skimage/draw/tests/test_draw.py b/skimage/draw/tests/test_draw.py index 9e6ca8e8..04cbae92 100644 --- a/skimage/draw/tests/test_draw.py +++ b/skimage/draw/tests/test_draw.py @@ -1,7 +1,7 @@ from numpy.testing import assert_array_equal import numpy as np -from skimage.draw import line, polygon, circle, ellipse +from skimage.draw import line, polygon, circle, circle_perimeter, ellipse def test_line_horizontal(): @@ -150,6 +150,37 @@ def test_circle(): assert_array_equal(img, img_) +def test_circle_perimeter(): + img = np.zeros((15, 15), 'uint8') + rr, cc = circle_perimeter(7, 7, 0) + img[rr, cc] = 1 + assert(np.sum(img) == 1) + + img = np.zeros((17, 15), 'uint8') + rr, cc = circle_perimeter(7, 7, 7) + img[rr, cc] = 1 + img_ = np.array( + [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] + ) + assert_array_equal(img, img_) + + def test_ellipse(): img = np.zeros((15, 15), 'uint8') From 6c19cf0693ca85eff0d43ff68693fcf3f2d83b48 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 15 Dec 2012 18:44:23 +0100 Subject: [PATCH 455/625] Add DAISY visualization + example plot. --- doc/examples/plot_daisy.py | 28 ++++++++++++ doc/examples/plot_shapes.py | 8 +++- skimage/feature/_daisy.py | 70 ++++++++++++++++++++++++++--- skimage/feature/tests/test_daisy.py | 5 +++ 4 files changed, 103 insertions(+), 8 deletions(-) create mode 100644 doc/examples/plot_daisy.py diff --git a/doc/examples/plot_daisy.py b/doc/examples/plot_daisy.py new file mode 100644 index 00000000..af78103d --- /dev/null +++ b/doc/examples/plot_daisy.py @@ -0,0 +1,28 @@ +""" +=============================== +Dense DAISY feature description +=============================== + +The DAISY local image descriptor is based on gradient orientation histograms +similar to the SIFT descriptor. It is formulated in a way that allows for fast +dense extraction which is useful for e.g. bag-of-features image +representations. + +In this example a limited number of DAISY descriptors are extracted at a large +scale for illustrative purposes. +""" + +from skimage.feature import daisy +from skimage import data +import matplotlib.pyplot as plt + + +img = data.camera() +descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6, + orientations=8, visualize=True) + +plt.axis('off') +plt.imshow(descs_img) +descs_num = descs.shape[0] * descs.shape[1] +plt.title('%i DAISY descriptors extracted:' % descs_num) +plt.show() diff --git a/doc/examples/plot_shapes.py b/doc/examples/plot_shapes.py index 9e586209..25a48ebe 100644 --- a/doc/examples/plot_shapes.py +++ b/doc/examples/plot_shapes.py @@ -13,7 +13,7 @@ This example shows how to fill several different shapes: import matplotlib.pyplot as plt -from skimage.draw import line, polygon, circle, ellipse +from skimage.draw import line, polygon, circle, circle_perimeter, ellipse import numpy as np @@ -42,5 +42,9 @@ img[rr,cc,:] = (255, 255, 0) rr, cc = ellipse(300, 300, 100, 200, img.shape) img[rr,cc,2] = 255 +# circle +rr, cc = circle_perimeter(120, 400, 50) +img[rr, cc, :] = (255, 0, 255) + plt.imshow(img) -plt.show() \ No newline at end of file +plt.show() diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index b0cc6748..85e259d0 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -6,7 +6,7 @@ from skimage import img_as_float def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, - normalization='l1', sigmas=None, ring_radii=None): + normalization='l1', sigmas=None, ring_radii=None, visualize=False): '''Extract DAISY feature descriptors densely for the given image. DAISY is a feature descriptor similar to SIFT formulated in a way that @@ -65,6 +65,8 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, following predicate since no radius is needed for the center histogram. ``len(ring_radii) == len(sigmas)+1`` + visualize : bool, optional + Generate a visualization of the DAISY descriptors Returns @@ -75,10 +77,12 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, | ``P = ceil((M-radius*2)/step)`` | ``Q = ceil((N-radius*2)/step)`` | ``R = (rings*histograms + 1)*orientations`` + descs_img : (M, N, 3) array (only if visualize==True) + Visualization of the DAISY descriptors. References ---------- - .. [1] Tola et al. "Daisy\: An efficient dense descriptor applied to wide- + .. [1] Tola et al. "Daisy: An efficient dense descriptor applied to wide- baseline stereo." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.5 (2010): 815-830. .. [2] http://cvlab.epfl.ch/alumni/tola/daisy.html @@ -120,10 +124,11 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, kappa = 1. / hist_sigma bessel = iv(0, kappa) hist = np.empty((orientations,) + img.shape, dtype=float) - for i in range(orientations): - mu = 2 * i * pi / orientations - pi + orientation_angles = [2 * o * pi / orientations - pi + for o in range(orientations)] + for i, o in enumerate(orientation_angles): # Weigh bin contribution by the circular normal distribution - hist[i, :, :] = exp(kappa * cos(grad_ori - mu)) / (2 * pi * bessel) + hist[i, :, :] = exp(kappa * cos(grad_ori - o)) / (2 * pi * bessel) # Weigh bin contribution by the gradient magnitude hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) @@ -169,4 +174,57 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, axis=2)) descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] - return descs + if visualize: + from skimage import draw + from skimage import color + descs_img = color.gray2rgb(img) + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + # Draw center histogram sigma + color = (1, 0, 0) + desc_y = i * step + radius + desc_x = j * step + radius + coords = draw.circle_perimeter(desc_y, desc_x, sigmas[0]) + set_color(descs_img, coords, color) + max_bin = np.max(descs[i, j, :]) + for o_num, o in enumerate(orientation_angles): + # Draw center histogram bins + bin_size = descs[i, j, o_num] / max_bin + dy = sigmas[0] * bin_size * sin(o) + dx = sigmas[0] * bin_size * cos(o) + coords = draw.line(desc_y, desc_x, desc_y + dy, + desc_x + dx) + set_color(descs_img, coords, color) + for r_num, r in enumerate(ring_radii): + color_offset = float(1 + r_num) / rings + color = (1 - color_offset, 1, color_offset) + for t_num, t in enumerate(theta): + # Draw ring histogram sigmas + hist_y = desc_y + int(round(r * sin(t))) + hist_x = desc_x + int(round(r * cos(t))) + coords = draw.circle_perimeter(hist_y, hist_x, + sigmas[r_num + 1]) + set_color(descs_img, coords, color) + for o_num, o in enumerate(orientation_angles): + # Draw histogram bins + bin_size = descs[i, j, orientations + r_num * + histograms * orientations + + t_num * orientations + o_num] + bin_size /= max_bin + dy = sigmas[r_num + 1] * bin_size * sin(o) + dx = sigmas[r_num + 1] * bin_size * cos(o) + coords = draw.line(hist_y, hist_x, hist_y + dy, + hist_x + dx) + set_color(descs_img, coords, color) + return descs, descs_img + else: + return descs + + +def set_color(img, coords, color): + ''' Set pixel color at the given coordiantes in the image.''' + coords = filter(lambda (y, x): y >= 0 and y < img.shape[0] and x >= 0 + and x < img.shape[1], zip(*coords)) + if coords: + rr, cc = zip(*coords) + img[rr, cc, :] = color diff --git a/skimage/feature/tests/test_daisy.py b/skimage/feature/tests/test_daisy.py index b0dd28de..40781a64 100644 --- a/skimage/feature/tests/test_daisy.py +++ b/skimage/feature/tests/test_daisy.py @@ -85,6 +85,11 @@ def test_daisy_normalization(): assert_raises(ValueError, daisy, img, normalization='does_not_exist') +def test_daisy_visualization(): + img = img_as_float(data.lena()[:128, :128].mean(axis=2)) + descs, descs_img = daisy(img, visualize=True) + assert(descs_img.shape == (128, 128, 3)) + if __name__ == '__main__': from numpy import testing testing.run_module_suite() From 450eaee4d9d16a77331354c9adc7a4456fe01dd1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 15 Dec 2012 19:35:17 +0100 Subject: [PATCH 456/625] Hide functools and imp packages in global skimage namespace --- skimage/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 3277919e..5a84dd68 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -52,8 +52,8 @@ img_as_ubyte """ import os.path as _osp -import imp -import functools +import imp as _imp +import functools as _functools pkg_dir = _osp.abspath(_osp.dirname(__file__)) data_dir = _osp.join(pkg_dir, 'data') @@ -65,7 +65,7 @@ except ImportError: try: - imp.find_module('nose') + _imp.find_module('nose') except ImportError: def test(verbose=False): """This would invoke the skimage test suite, but nose couldn't be @@ -81,7 +81,7 @@ else: args.extend(['-v', '-s']) nose.run('skimage', argv=args) -test_verbose = functools.partial(test, verbose=True) +test_verbose = _functools.partial(test, verbose=True) test_verbose.__doc__ = test.__doc__ From e0f4cd097817342ed0ed5cf4b40bd429f8782221 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 15 Dec 2012 19:35:49 +0100 Subject: [PATCH 457/625] Fix double space in warning message --- skimage/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 5a84dd68..1435892c 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -71,7 +71,7 @@ except ImportError: """This would invoke the skimage test suite, but nose couldn't be imported so the test suite can not run. """ - raise ImportError("Could not load nose. Unit tests not available.") + raise ImportError("Could not load nose. Unit tests not available.") else: def test(verbose=False): """Invoke the skimage test suite.""" From 746d815dfa48e2cd5e99525ed00e906ad0fe3119 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 15 Dec 2012 20:08:18 +0100 Subject: [PATCH 458/625] Fix recursion problem with nose test suite --- skimage/__init__.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/skimage/__init__.py b/skimage/__init__.py index 1435892c..daac238c 100644 --- a/skimage/__init__.py +++ b/skimage/__init__.py @@ -67,13 +67,13 @@ except ImportError: try: _imp.find_module('nose') except ImportError: - def test(verbose=False): + def _test(verbose=False): """This would invoke the skimage test suite, but nose couldn't be imported so the test suite can not run. """ raise ImportError("Could not load nose. Unit tests not available.") else: - def test(verbose=False): + def _test(verbose=False): """Invoke the skimage test suite.""" import nose args = ['', pkg_dir, '--exe'] @@ -81,6 +81,9 @@ else: args.extend(['-v', '-s']) nose.run('skimage', argv=args) +# do not use `test` as function name as this leads to a recursion problem with +# the nose test suite +test = _test test_verbose = _functools.partial(test, verbose=True) test_verbose.__doc__ = test.__doc__ From a641ce6a0e236fd00b2371f9252a64d1bfc3ef78 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sat, 15 Dec 2012 15:59:14 -0500 Subject: [PATCH 459/625] BUG: Move import inside deprecated function Fixes circular import --- skimage/morphology/watershed.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index 6d5a8371..b6f2a4fb 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -28,7 +28,6 @@ from _heapq import heappush, heappop import numpy as np import scipy.ndimage from ..filter import rank_order -from ..feature import peak_local_max from .._shared.utils import deprecated from . import _watershed @@ -293,6 +292,8 @@ def is_local_maximum(image, labels=None, footprint=None): [False, True, False, True]], dtype=bool) """ + # call import here to prevent circular imports + from ..feature import peak_local_max return peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, footprint=footprint, indices=False, exclude_border=False) From 7aa943530144fb9478e0b09d57c94ea42efc5980 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 16 Dec 2012 13:47:35 +0100 Subject: [PATCH 460/625] Handle all non-float images. --- skimage/feature/_daisy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 85e259d0..d011d693 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -91,7 +91,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, # Validate image format. if img.ndim > 2: raise ValueError('Only grey-level images are supported.') - if img.dtype.kind == 'u': + if img.dtype.kind != 'f': img = img_as_float(img) # Validate parameters. From 097f25dbb5fe599491b067b5263f350990ada5d5 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 16 Dec 2012 16:44:56 +0100 Subject: [PATCH 461/625] Move set_color() to draw module. --- skimage/draw/__init__.py | 2 +- skimage/draw/_draw.pyx | 27 +++++++++++++++++++++++++++ skimage/feature/_daisy.py | 24 +++++++----------------- 3 files changed, 35 insertions(+), 18 deletions(-) diff --git a/skimage/draw/__init__.py b/skimage/draw/__init__.py index b213f93f..4b30ea18 100644 --- a/skimage/draw/__init__.py +++ b/skimage/draw/__init__.py @@ -1,2 +1,2 @@ -from ._draw import line, polygon, ellipse, circle, circle_perimeter +from ._draw import line, polygon, ellipse, circle, circle_perimeter, set_color bresenham = line diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index b5b96a2b..509796bb 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -226,3 +226,30 @@ def circle_perimeter(int cy, int cx, int radius): x += 1 return np.array(rr) + cy, np.array(cc) + cx + + +@cython.boundscheck(False) +@cython.wraparound(False) +def set_color(img, coords, color): + """Set pixel color in the image at the given coordiantes. Coordinates that + exceeed the shape of the image will be ignored. + + Parameters + ---------- + img : (M, N, D) ndarray + Image + coords : ((P,) ndarray, (P,) ndarray) + Coordinates of pixels to be colored. + color : (D,) ndarray + Color to be assigned to coordinates in the image. + + Returns + ------- + img : (M, N, D) ndarray + The updated image. + """ + rr, cc = coords + rr_inside = np.logical_and(rr >= 0, rr < img.shape[0]) + cc_inside = np.logical_and(cc >= 0, cc < img.shape[1]) + inside = np.logical_and(rr_inside, cc_inside) + img[rr[inside], cc[inside]] = color diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index d011d693..07caf22b 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -2,7 +2,8 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from scipy.special import iv -from skimage import img_as_float +import skimage +from skimage import img_as_float, draw def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, @@ -175,9 +176,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] if visualize: - from skimage import draw - from skimage import color - descs_img = color.gray2rgb(img) + descs_img = skimage.color.gray2rgb(img) for i in range(descs.shape[0]): for j in range(descs.shape[1]): # Draw center histogram sigma @@ -185,7 +184,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, desc_y = i * step + radius desc_x = j * step + radius coords = draw.circle_perimeter(desc_y, desc_x, sigmas[0]) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) max_bin = np.max(descs[i, j, :]) for o_num, o in enumerate(orientation_angles): # Draw center histogram bins @@ -194,7 +193,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, dx = sigmas[0] * bin_size * cos(o) coords = draw.line(desc_y, desc_x, desc_y + dy, desc_x + dx) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) for r_num, r in enumerate(ring_radii): color_offset = float(1 + r_num) / rings color = (1 - color_offset, 1, color_offset) @@ -204,7 +203,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, hist_x = desc_x + int(round(r * cos(t))) coords = draw.circle_perimeter(hist_y, hist_x, sigmas[r_num + 1]) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) for o_num, o in enumerate(orientation_angles): # Draw histogram bins bin_size = descs[i, j, orientations + r_num * @@ -215,16 +214,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, dx = sigmas[r_num + 1] * bin_size * cos(o) coords = draw.line(hist_y, hist_x, hist_y + dy, hist_x + dx) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) return descs, descs_img else: return descs - - -def set_color(img, coords, color): - ''' Set pixel color at the given coordiantes in the image.''' - coords = filter(lambda (y, x): y >= 0 and y < img.shape[0] and x >= 0 - and x < img.shape[1], zip(*coords)) - if coords: - rr, cc = zip(*coords) - img[rr, cc, :] = color From 3b43cf0dcfa96b453c7ab3f3b47d4ea9475ae567 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 16 Dec 2012 17:26:32 +0100 Subject: [PATCH 462/625] No need to normalize angular smoothing. --- skimage/feature/_daisy.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 07caf22b..f5599dd5 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -1,7 +1,6 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter -from scipy.special import iv import skimage from skimage import img_as_float, draw @@ -121,15 +120,13 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, # to the histograms. grad_mag = sqrt(dx ** 2 + dy ** 2) grad_ori = arctan2(dy, dx) - hist_sigma = pi / orientations - kappa = 1. / hist_sigma - bessel = iv(0, kappa) - hist = np.empty((orientations,) + img.shape, dtype=float) + orientation_kappa = orientations / pi orientation_angles = [2 * o * pi / orientations - pi for o in range(orientations)] + hist = np.empty((orientations,) + img.shape, dtype=float) for i, o in enumerate(orientation_angles): # Weigh bin contribution by the circular normal distribution - hist[i, :, :] = exp(kappa * cos(grad_ori - o)) / (2 * pi * bessel) + hist[i, :, :] = exp(orientation_kappa * cos(grad_ori - o)) # Weigh bin contribution by the gradient magnitude hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) From 2ebac38c4dde70a5605acf63786db7f079419414 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Mon, 17 Dec 2012 16:37:41 +1100 Subject: [PATCH 463/625] Rename `ar` to `label_field` for clarity --- skimage/segmentation/_join.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/skimage/segmentation/_join.py b/skimage/segmentation/_join.py index 09a8361b..454da71e 100644 --- a/skimage/segmentation/_join.py +++ b/skimage/segmentation/_join.py @@ -41,8 +41,8 @@ def join_segmentations(s1, s2): j = relabel_from_one(j)[0] return j -def relabel_from_one(ar): - """Convert array ar of arbitrary labels to labels 1...len(np.unique(ar))+1 +def relabel_from_one(label_field): + """Convert labels in an arbitrary label field to {1, ... number_of_labels}. This function also returns the forward map (mapping the original labels to the reduced labels) and the inverse map (mapping the reduced labels back @@ -50,7 +50,7 @@ def relabel_from_one(ar): Parameters ---------- - ar : numpy ndarray (integer type) + label_field : numpy ndarray (integer type) Returns ------- @@ -63,8 +63,8 @@ def relabel_from_one(ar): -------- >>> import numpy as np >>> from skimage.segmentation import relabel_from_one - >>> ar = array([1, 1, 5, 5, 8, 99, 42]) - >>> relab, fw, inv = relabel_from_one(ar) + >>> label_field = array([1, 1, 5, 5, 8, 99, 42]) + >>> relab, fw, inv = relabel_from_one(label_field) >>> relab array([1, 1, 2, 2, 3, 5, 4]) >>> fw @@ -75,19 +75,19 @@ def relabel_from_one(ar): 0, 0, 0, 0, 0, 0, 0, 5]) >>> inv array([ 0, 1, 5, 8, 42, 99]) - >>> (fw[ar] == relab).all() + >>> (fw[label_field] == relab).all() True - >>> (inv[relab] == ar).all() + >>> (inv[relab] == label_field).all() True """ - labels = np.unique(ar) + labels = np.unique(label_field) labels0 = labels[labels != 0] m = labels.max() if m == len(labels0): # nothing to do, already 1...n labels - return ar, labels, labels + return label_field, labels, labels forward_map = np.zeros(m+1, int) forward_map[labels0] = np.arange(1, len(labels0) + 1) if not (labels == 0).any(): labels = np.concatenate(([0], labels)) inverse_map = labels - return forward_map[ar], forward_map, inverse_map + return forward_map[label_field], forward_map, inverse_map From 35e8ad2ccc7c32d08ae9f063183747a2154cb673 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Mon, 17 Dec 2012 16:45:23 +1100 Subject: [PATCH 464/625] Allow grayscale input images to SLIC segmentation There is no reason for SLIC to be restricted to colour/RGB images. I have added a few lines that allow single-channel input. --- skimage/segmentation/_slic.pyx | 11 ++++++----- skimage/segmentation/tests/test_slic.py | 16 ++++++++++++++++ 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index d0e4c2a3..dfb37a7c 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -3,7 +3,7 @@ cimport numpy as np from time import time from scipy import ndimage from ..util import img_as_float -from ..color import rgb2lab +from ..color import rgb2lab, gray2rgb def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, @@ -12,7 +12,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, Parameters ---------- - image : (width, height, 3) ndarray + image : (width, height [, 3]) ndarray Input image. n_segments : int The (approximate) number of labels in the segmented output image. @@ -53,9 +53,10 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, >>> # Increasing the ratio parameter yields more square regions >>> segments = slic(img, n_segments=100, ratio=20) """ - image = np.atleast_3d(image) - if image.shape[2] != 3: - ValueError("Only 3-channel 2D images are supported.") + if image.ndim == 2: + image = gray2rgb(image) + if image.ndim != 3 or image.shape[2] != 3: + ValueError("Only 1- or 3-channel 2D images are supported.") image = ndimage.gaussian_filter(img_as_float(image), [sigma, sigma, 0]) if convert2lab: image = rgb2lab(image) diff --git a/skimage/segmentation/tests/test_slic.py b/skimage/segmentation/tests/test_slic.py index f2d6698d..59088fcd 100644 --- a/skimage/segmentation/tests/test_slic.py +++ b/skimage/segmentation/tests/test_slic.py @@ -21,6 +21,22 @@ def test_color(): assert_array_equal(seg[:10, 10:], 1) assert_array_equal(seg[10:, 10:], 3) +def test_gray(): + rnd = np.random.RandomState(0) + img = np.zeros((20, 21)) + img[:10, :10] = 0.33 + img[10:, :10] = 0.67 + img[10:, 10:] = 1.00 + img += 0.0033 * rnd.normal(size=img.shape) + img[img > 1] = 1 + img[img < 0] = 0 + seg = slic(img, sigma=0, n_segments=4, ratio=50.0) + print(seg) + assert_equal(len(np.unique(seg)), 4) + assert_array_equal(seg[:10, :10], 0) + assert_array_equal(seg[10:, :10], 2) + assert_array_equal(seg[:10, 10:], 1) + assert_array_equal(seg[10:, 10:], 3) if __name__ == '__main__': from numpy import testing From c276ec06342b194fffb2cfd5efa4a87416bfcbaa Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Mon, 17 Dec 2012 11:54:37 +0100 Subject: [PATCH 465/625] Fixed submodule import. --- skimage/feature/_daisy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index f5599dd5..f83605b5 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -1,7 +1,7 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter -import skimage +import skimage.color from skimage import img_as_float, draw From 112eb41c1fcb2e0f89836f63112b8a9bed5923a7 Mon Sep 17 00:00:00 2001 From: Marianne Corvellec Date: Wed, 19 Dec 2012 23:28:52 +0100 Subject: [PATCH 466/625] more intuitive description for beginner contributor to get her hands on documentation --- DEVELOPMENT.txt | 30 ++++++++++++++++++------------ TASKS.txt | 8 ++++---- 2 files changed, 22 insertions(+), 16 deletions(-) diff --git a/DEVELOPMENT.txt b/DEVELOPMENT.txt index 509cf322..8726394b 100644 --- a/DEVELOPMENT.txt +++ b/DEVELOPMENT.txt @@ -1,8 +1,7 @@ Development process ------------------- -:doc:`Read this overview ` of how to use Git with -``skimage``. Here's the long and short of it: +Here's the long and short of it: * Go to `https://github.com/scikit-image/scikit-image `_ and follow the @@ -11,9 +10,12 @@ Development process branch name will appear in the merge message, use a sensible name such as 'transform-speedups'. * Commit locally as you progress. - * Push your changes back to github and create a pull request by - clicking "request pull" in GitHub. - * Optionally, mail the mailing list, explaining your changes. + * Push your changes back to GitHub and create a Pull Request by + clicking 'Pull Request' in GitHub. + * Optionally, post on the `mailing list `_ to explain your changes. + +Read these :doc:`detailed documents ` on how to use Git with +``scikit-image`` (``_). .. note:: @@ -33,8 +35,9 @@ project on how they manage to keep review overhead to a minimum: http://mail.scipy.org/pipermail/ipython-dev/2010-October/006746.html Guidelines -`````````` - * All code should have tests (see "Test coverage" below for more details). +---------- + + * All code should have tests (see `test coverage`_ below for more details). * All code should be documented, to the same `standard `_ as NumPy and SciPy. For new functionality, always add an example to the @@ -47,7 +50,8 @@ Guidelines * Examples in the gallery should have a maximum figure width of 8 inches. Stylistic Guidelines -```````````````````` +-------------------- + * Use numpy data types instead of strings (``np.uint8`` instead of ``"uint8"``). @@ -78,9 +82,10 @@ Stylistic Guidelines hough(canny(my_image)) Test coverage -````````````` +------------- + Tests for a module should ideally cover all code in that module, -i.e. statement coverage should be at 100%. +i.e., statement coverage should be at 100%. To measure the test coverage, install `coverage.py `__ @@ -98,5 +103,6 @@ detailing the test coverage:: ... Bugs -```` -Please `report bugs on Github `_. +---- + +Please `report bugs on GitHub `_. diff --git a/TASKS.txt b/TASKS.txt index a2fcffe2..0046b6a3 100644 --- a/TASKS.txt +++ b/TASKS.txt @@ -15,14 +15,14 @@ How to contribute to ``skimage`` cell_profiler -Developing Open Source is great fun! Join us on the `skimage mailing +Developing Open Source is great fun! Join us on the `scikit-image mailing list `_ and tell us which of the following challenges you'd like to solve. * Mentoring is available for those new to scientific programming in Python. -* The technical detail of the `development process`_ is given below. -* :doc:`How to use GitHub ` when developing skimage -* If you're looking something to implement, you can find a list of `requested features on github `__. In addition, you can browse the `open issues on github `__. +* If you're looking for something to implement, you can find a list of `requested features on GitHub `__. In addition, you can browse the `open issues on GitHub `__. +* The technical detail of the `development process`_ is summed up below. + Refer to the :doc:`gitwash ` for a step-by-step tutorial. .. contents:: :local: From 4c4ba8cd1ac84e6ff6ea27dd7204c6ed0889c91e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 20 Dec 2012 15:08:23 +0100 Subject: [PATCH 467/625] Add default value for polynomial order --- skimage/transform/_geometric.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py index 1179811d..75ee541a 100644 --- a/skimage/transform/_geometric.py +++ b/skimage/transform/_geometric.py @@ -604,7 +604,7 @@ class PolynomialTransform(GeometricTransform): raise ValueError("invalid shape of transformation parameters") self._params = params - def estimate(self, src, dst, order): + def estimate(self, src, dst, order=2): """Set the transformation matrix with the explicit transformation parameters. @@ -645,7 +645,7 @@ class PolynomialTransform(GeometricTransform): Source coordinates. dst : (N, 2) array Destination coordinates. - order : int + order : int, optional Polynomial order (number of coefficients is order + 1). """ From 5fb518b89b76363b8427cad6d3581203fc49ba19 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 20 Dec 2012 15:08:53 +0100 Subject: [PATCH 468/625] Add test case for default value of polynomial order --- skimage/transform/tests/test_geometric.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/transform/tests/test_geometric.py b/skimage/transform/tests/test_geometric.py index d3a75f7b..c7f9f832 100644 --- a/skimage/transform/tests/test_geometric.py +++ b/skimage/transform/tests/test_geometric.py @@ -159,6 +159,12 @@ def test_polynomial_init(): assert_array_almost_equal(tform2._params, tform._params) +def test_polynomial_default_order(): + tform = estimate_transform('polynomial', SRC, DST) + tform2 = estimate_transform('polynomial', SRC, DST, order=2) + assert_array_almost_equal(tform2._params, tform._params) + + def test_union(): tform1 = SimilarityTransform(scale=0.1, rotation=0.3) tform2 = SimilarityTransform(scale=0.1, rotation=0.9) From 257cf9585b1d98bfb2667afd1f31b938878df43c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 22 Dec 2012 20:07:36 +0100 Subject: [PATCH 469/625] Fix typo bug in perimeter function --- skimage/measure/_regionprops.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/measure/_regionprops.py b/skimage/measure/_regionprops.py index d285d453..b523e2e0 100644 --- a/skimage/measure/_regionprops.py +++ b/skimage/measure/_regionprops.py @@ -440,14 +440,14 @@ def perimeter(image, neighbourhood=4): strel = STREL_4 else: strel = STREL_8 - eroded_image = ndimage.binary_erosion(image, strel) + eroded_image = ndimage.binary_erosion(image, strel, border_value=0) border_image = image - eroded_image # perimeter contribution: corresponding values in convolved image perimeter_weights = { - 1: (5, 7, 15, 17, 25, 27), - sqrt(2): (21, 33), - 1 + sqrt(2) / 2: (13, 23) + 1: (5, 7, 15, 17, 25, 27), + sqrt(2): (21, 33), + (1 + sqrt(2)) / 2: (13, 23) } perimeter_image = ndimage.convolve(border_image, np.array([[10, 2, 10], [ 2, 1, 2], From 61ab5ed07755d03bfb2e6d63f508ae01995f26db Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 22 Dec 2012 20:08:06 +0100 Subject: [PATCH 470/625] Fix convolution bug in perimeter function --- skimage/measure/_regionprops.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skimage/measure/_regionprops.py b/skimage/measure/_regionprops.py index b523e2e0..72d1e2f4 100644 --- a/skimage/measure/_regionprops.py +++ b/skimage/measure/_regionprops.py @@ -451,7 +451,8 @@ def perimeter(image, neighbourhood=4): } perimeter_image = ndimage.convolve(border_image, np.array([[10, 2, 10], [ 2, 1, 2], - [10, 2, 10]])) + [10, 2, 10]]), + mode='constant', cval=0) total_perimeter = 0 for weight, values in perimeter_weights.items(): num_values = 0 From fb9099eec3ae5ebabc70b1bcd0da59920d5ee194 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 22 Dec 2012 20:09:29 +0100 Subject: [PATCH 471/625] Update perimeter test case which was correct for buggy previous version --- skimage/measure/tests/test_regionprops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/measure/tests/test_regionprops.py b/skimage/measure/tests/test_regionprops.py index bd23d8b3..8185bf67 100644 --- a/skimage/measure/tests/test_regionprops.py +++ b/skimage/measure/tests/test_regionprops.py @@ -246,10 +246,10 @@ def test_orientation(): def test_perimeter(): per = regionprops(SAMPLE, ['Perimeter'])[0]['Perimeter'] - assert_almost_equal(per, 59.2132034355964) + assert_almost_equal(per, 55.2487373415) per = perimeter(SAMPLE.astype('double'), neighbourhood=8) - assert_almost_equal(per, 43.1213203436) + assert_almost_equal(per, 46.8284271247) def test_solidity(): From 06b852778bad00735a8fd39798baab2f14148c4b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 22 Dec 2012 22:58:32 +0100 Subject: [PATCH 472/625] Add note about default polynomial order to estimate_transform --- skimage/transform/_geometric.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py index 75ee541a..96ea4687 100644 --- a/skimage/transform/_geometric.py +++ b/skimage/transform/_geometric.py @@ -750,7 +750,8 @@ def estimate_transform(ttype, src, dst, **kwargs): 'affine' `src, `dst` 'piecewise-affine' `src, `dst` 'projective' `src, `dst` - 'polynomial' `src, `dst`, `order` (polynomial order) + 'polynomial' `src, `dst`, `order` (polynomial order, + default order is 2) Also see examples below. From 1c589908a8aa6548f8a39f05aa03d737343e6955 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 23 Dec 2012 10:54:09 -0600 Subject: [PATCH 473/625] BUG: Not all plugins would pass the adapthist test --- skimage/exposure/tests/test_exposure.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 258b4224..e87bdeb4 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -12,6 +12,7 @@ from skimage.util.dtype import dtype_range # squeeze image intensities to lower image contrast test_img = exposure.rescale_intensity(data.camera() / 5. + 100) +test_img = skimage.img_as_float(test_img) def test_equalize_ubyte(): @@ -101,8 +102,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 77.584, 3) - assert_almost_equal(norm_brightness_err(img, adapted), 0.038, 3) + assert_almost_equal(peak_snr(img, adapted), 97.949, 3) + assert_almost_equal(norm_brightness_err(img, adapted), 0.03077, 3) return data, adapted @@ -116,9 +117,9 @@ def test_adapthist_color(): assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert_almost_equal(peak_snr(full_scale, adapted), 64.717, 3) + assert_almost_equal(peak_snr(full_scale, adapted), 102.940, 3) assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.179, 3) + 0.0110, 3) return data, adapted From 6a0ab4e3fbe04b19b920e422a91746fe639d4150 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Sun, 23 Dec 2012 15:45:10 -0600 Subject: [PATCH 474/625] BUG: Fixed exposure test to cast data on load. --- skimage/exposure/tests/test_exposure.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index e87bdeb4..43e1017d 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -11,8 +11,8 @@ from skimage.util.dtype import dtype_range # =========================== # squeeze image intensities to lower image contrast -test_img = exposure.rescale_intensity(data.camera() / 5. + 100) -test_img = skimage.img_as_float(test_img) +test_img = skimage.img_as_float(data.camera()) +test_img = exposure.rescale_intensity(test_img / 5. + 100) def test_equalize_ubyte(): From 875270cb78aac8b543f41b03f3940ed6d8cbeff9 Mon Sep 17 00:00:00 2001 From: Adam Ginsburg Date: Tue, 25 Dec 2012 14:25:17 -0700 Subject: [PATCH 475/625] very slight change to warp_coords docstring, but I think slightly more illustrative of the shape of the array --- skimage/transform/_geometric.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py index 1179811d..11effb8a 100644 --- a/skimage/transform/_geometric.py +++ b/skimage/transform/_geometric.py @@ -874,18 +874,18 @@ def warp_coords(coord_map, shape, dtype=np.float64): Examples -------- - Produce a coordinate map that Shifts an image to the right: + Produce a coordinate map that Shifts an image up and to the right: >>> from skimage import data >>> from scipy.ndimage import map_coordinates >>> - >>> def shift_right(xy): - ... xy[:, 0] -= 10 - ... return xy + >>> def shift_up1_left2(xy): + ... return xy-np.array([-2,1])[:,None] >>> >>> image = data.lena().astype(np.float32) >>> coords = warp_coords(shift_right, image.shape) >>> warped_image = map_coordinates(image, coords) + >>> """ rows, cols = shape[0], shape[1] From 0824af0e3ac01aae3ca8a19e31de781b942d8059 Mon Sep 17 00:00:00 2001 From: Adam Ginsburg Date: Tue, 25 Dec 2012 14:27:49 -0700 Subject: [PATCH 476/625] looks like I didn't save on the previous commit; these should be squashed with the previous commit message --- skimage/transform/_geometric.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py index 11effb8a..6c5cb2c0 100644 --- a/skimage/transform/_geometric.py +++ b/skimage/transform/_geometric.py @@ -848,6 +848,8 @@ def warp_coords(coord_map, shape, dtype=np.float64): ---------- coord_map : callable like GeometricTransform.inverse Return input coordinates for given output coordinates. + Coordinates are in the shape (P,2), where P is the number + of coordinates and each element is an x,y pair shape : tuple Shape of output image ``(rows, cols[, bands])``. dtype : np.dtype or string @@ -879,11 +881,11 @@ def warp_coords(coord_map, shape, dtype=np.float64): >>> from skimage import data >>> from scipy.ndimage import map_coordinates >>> - >>> def shift_up1_left2(xy): - ... return xy-np.array([-2,1])[:,None] + >>> def shift_up10_left20(xy): + ... return xy-np.array([-20,10])[None,:] >>> >>> image = data.lena().astype(np.float32) - >>> coords = warp_coords(shift_right, image.shape) + >>> coords = warp_coords(shift_up10_left20, image.shape) >>> warped_image = map_coordinates(image, coords) >>> From 4f37418406a58e380abe357357e4a27c5685b5d0 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Tue, 25 Dec 2012 23:00:46 -0600 Subject: [PATCH 477/625] ENH: Added support for python3 in travis build --- .travis.yml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/.travis.yml b/.travis.yml index 28104a17..f50ba1ac 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,16 +7,17 @@ language: erlang env: - - PYTHON=python PYSUF='' - # - PYTHON=python3 PYSUF=3 : python3-numpy not currently available + - PYTHON=python PYSUF='' PYVER=2.7 + - PYTHON=python3 PYSUF='3' PYVER=3.2 install: - # - sudo apt-get build-dep $PYTHON-numpy + - sudo apt-get update # needed for python3-numpy - sudo apt-get install $PYTHON-dev - sudo apt-get install $PYTHON-numpy - sudo apt-get install $PYTHON-scipy - sudo apt-get install $PYTHON-setuptools - sudo apt-get install $PYTHON-nose - - sudo apt-get install cython + - sudo easy_install$PYSUF pip + - sudo pip-$PYVER install cython - sudo apt-get install libfreeimage3 - $PYTHON setup.py build - sudo $PYTHON setup.py install @@ -24,5 +25,5 @@ script: # Change into an innocuous directory and find tests from installation - mkdir for_test - cd for_test - - nosetests --exe -v --cover-package=skimage skimage + - nosetests-$PYVER --exe -v --cover-package=skimage skimage From aef305a0d3064ddbd9438180d1ab321a4bf7790b Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Tue, 25 Dec 2012 23:02:27 -0600 Subject: [PATCH 478/625] BUG: Python 3 Travis build caught this error --- skimage/exposure/tests/test_exposure.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 43e1017d..08f59145 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -142,7 +142,6 @@ def peak_snr(img1, img2): img2 = skimage.img_as_float(img2) mse = 1. / img1.size * np.square(img1 - img2).sum() _, max_ = dtype_range[img1.dtype.type] - print mse, max_ return 20 * np.log(max_ / mse) From a0a05954c2776fe22cc28258604f83730e11270e Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Wed, 26 Dec 2012 00:34:17 -0600 Subject: [PATCH 479/625] BUG: Failed exposure tests on Python3, fixed --- skimage/exposure/_adapthist.py | 2 +- skimage/exposure/tests/test_exposure.py | 15 +++++++-------- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 667d2cb2..88729ce2 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -150,7 +150,7 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): hist = np.bincount(hist) hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int)) hist = clip_histogram(hist, clip_limit) - hist = map_histogram(hist, 0, NR_OF_GREY, n_pixels) + hist = map_histogram(hist, 0, NR_OF_GREY - 1, n_pixels) map_array[y, x] = hist # Interpolate greylevel mappings to get CLAHE image diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 08f59145..12ef2acd 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -85,10 +85,10 @@ def test_adapthist_scalar(): assert adapted.max() == (1 << 16) - 1 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) + assert_almost_equal = np.testing.assert_almost_equal - assert_almost_equal(peak_snr(full_scale, adapted), 101.231, 3) - assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.041, 3) + assert peak_snr(full_scale, adapted) > 95 + assert norm_brightness_err(full_scale, adapted) < 0.05 return img, adapted @@ -102,8 +102,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 97.949, 3) - assert_almost_equal(norm_brightness_err(img, adapted), 0.03077, 3) + assert peak_snr(img, adapted) > 95 + assert norm_brightness_err(img, adapted) < 0.05 return data, adapted @@ -117,9 +117,8 @@ def test_adapthist_color(): assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert_almost_equal(peak_snr(full_scale, adapted), 102.940, 3) - assert_almost_equal(norm_brightness_err(full_scale, adapted), - 0.0110, 3) + assert peak_snr(img, adapted) > 95 + assert norm_brightness_err(img, adapted) < 0.05 return data, adapted From 5e44e7848803f13b4fcd06c6b3940b57f8c5f2bf Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Wed, 26 Dec 2012 00:45:41 -0600 Subject: [PATCH 480/625] STY: Used float comparison in test_exposure --- skimage/exposure/tests/test_exposure.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 12ef2acd..12338e79 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -87,7 +87,7 @@ def test_adapthist_scalar(): full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert peak_snr(full_scale, adapted) > 95 + assert peak_snr(full_scale, adapted) > 95. assert norm_brightness_err(full_scale, adapted) < 0.05 return img, adapted @@ -102,7 +102,7 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 95 + assert peak_snr(img, adapted) > 95. assert norm_brightness_err(img, adapted) < 0.05 return data, adapted @@ -117,7 +117,7 @@ def test_adapthist_color(): assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert peak_snr(img, adapted) > 95 + assert peak_snr(img, adapted) > 95. assert norm_brightness_err(img, adapted) < 0.05 return data, adapted From 6a54c569004f786d34d262615e71be114c778b05 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Wed, 26 Dec 2012 00:59:05 -0600 Subject: [PATCH 481/625] STY: Change minor formatting to test travis build --- skimage/exposure/tests/test_exposure.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 12338e79..a6323ea8 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -87,7 +87,7 @@ def test_adapthist_scalar(): full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert peak_snr(full_scale, adapted) > 95. + assert peak_snr(full_scale, adapted) > 95.0 assert norm_brightness_err(full_scale, adapted) < 0.05 return img, adapted @@ -102,7 +102,7 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 95. + assert peak_snr(img, adapted) > 95.0 assert norm_brightness_err(img, adapted) < 0.05 return data, adapted @@ -117,7 +117,7 @@ def test_adapthist_color(): assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert peak_snr(img, adapted) > 95. + assert peak_snr(img, adapted) > 95.0 assert norm_brightness_err(img, adapted) < 0.05 return data, adapted From 4ff97da8b15c987b5eb5b2944395cff812715e0e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 26 Dec 2012 09:24:30 +0100 Subject: [PATCH 482/625] Fix PEP8 issues and doc string formatting --- skimage/transform/_geometric.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py index 86a95438..448829de 100644 --- a/skimage/transform/_geometric.py +++ b/skimage/transform/_geometric.py @@ -849,8 +849,8 @@ def warp_coords(coord_map, shape, dtype=np.float64): ---------- coord_map : callable like GeometricTransform.inverse Return input coordinates for given output coordinates. - Coordinates are in the shape (P,2), where P is the number - of coordinates and each element is an x,y pair + Coordinates are in the shape (P, 2), where P is the number + of coordinates and each element is a ``(x, y)`` pair. shape : tuple Shape of output image ``(rows, cols[, bands])``. dtype : np.dtype or string @@ -883,12 +883,11 @@ def warp_coords(coord_map, shape, dtype=np.float64): >>> from scipy.ndimage import map_coordinates >>> >>> def shift_up10_left20(xy): - ... return xy-np.array([-20,10])[None,:] + ... return xy - np.array([-20, 10])[None, :] >>> >>> image = data.lena().astype(np.float32) >>> coords = warp_coords(shift_up10_left20, image.shape) >>> warped_image = map_coordinates(image, coords) - >>> """ rows, cols = shape[0], shape[1] From 24b49fc8ee7693f2a6c2f3648a3dd66ab39c54c5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 2 Nov 2012 20:10:45 +0100 Subject: [PATCH 483/625] Improve total-varation denoising algorithm Implementation of fast split-Bregman optimization algorithm in Cython. This implementation also fixes the previously broken 3D version, which darkened the images. --- skimage/filter/__init__.py | 4 +- skimage/filter/_denoise.pyx | 169 +++++++++++++++++++++++++++++++++++- 2 files changed, 170 insertions(+), 3 deletions(-) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index f1c1fd49..8894ff1a 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,7 +3,7 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from .denoise import tv_denoise, denoise_tv -from ._denoise import denoise_bilateral +from .denoise import tv_denoise +from ._denoise import denoise_bilateral, denoise_tv from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index b60a5a85..ca75ab4f 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -7,6 +7,7 @@ cimport numpy as cnp import numpy as np from libc.math cimport exp, fabs, sqrt from libc.stdlib cimport malloc, free +from libc.float cimport DBL_MAX from skimage._shared.interpolation cimport get_pixel3d from skimage.util import img_as_float @@ -174,4 +175,170 @@ def denoise_bilateral(image, int win_size=5, sigma_range=None, free(centres) free(total_values) - return out + return np.squeeze(out) + + +cdef inline double _get_elem(double* image, Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, + Py_ssize_t k): + return image[r * cols * dims + c * dims + k] + + +cdef inline void _set_elem(double* image, Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, + Py_ssize_t k, double value): + image[r * cols * dims + c * dims + k] = value + + +cdef inline void _incr_elem(double* image, Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, + Py_ssize_t k, double value): + image[r * cols * dims + c * dims + k] += value + + +def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): + """Perform total-variation denoising using split-Bregman optimization. + + Total-variation denoising (also know as total-variation regularization) + tries to find an image with less total total-variation under the constraint + of being similar to the input image, which is controlled by the + regularization parameter. + + Parameters + ---------- + image : ndarray + Input data to be denoised (converted using img_as_float`). + weight : float, optional + Denoising weight. The smaller the `weight`, the more denoising (at + the expense of less similarity to the `input`). The regularization + parameter `lambda` is chosen as `2 * weight`. + eps : float, optional + Relative difference of the value of the cost function that determines + the stop criterion. The algorithm stops when:: + + SUM((u(n) - u(n-1))**2) < eps + + max_iter: int, optional + Maximal number of iterations used for the optimization. + + Returns + ------- + u : ndarray + Denoised image. + + References + ---------- + .. [1] http://en.wikipedia.org/wiki/Total_variation_denoising + .. [2] ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf + .. [3] http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf + + """ + + image = np.atleast_3d(img_as_float(image)) + + cdef: + Py_ssize_t rows = image.shape[0] + Py_ssize_t cols = image.shape[1] + Py_ssize_t dims = image.shape[2] + Py_ssize_t rows2 = rows + 2 + Py_ssize_t cols2 = cols + 2 + Py_ssize_t r, c, k + + Py_ssize_t total = rows * cols * dims + + shape_ext = (rows2, cols2, dims) + + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] cimage = \ + np.ascontiguousarray(image) + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] u = \ + np.zeros(shape_ext, dtype=np.double) + + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] dx = \ + np.zeros(shape_ext, dtype=np.double) + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] dy = \ + np.zeros(shape_ext, dtype=np.double) + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] bx = \ + np.zeros(shape_ext, dtype=np.double) + cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] by = \ + np.zeros(shape_ext, dtype=np.double) + + double* image_data = cimage.data + double* u_data = u.data + + double* dx_data = dx.data + double* dy_data = dy.data + double* bx_data = bx.data + double* by_data = by.data + + double ux, uy, uprev, unew, bxx, byy, dxx, dyy, s + int i = 0 + double lam = 2 * weight + double rmse = DBL_MAX + double norm = (weight + 4 * lam) + + u[1:-1, 1:-1] = image + + # reflect image + u[0, 1:-1] = image[1, :] + u[1:-1, 0] = image[:, 1] + u[-1, 1:-1] = image[-2, :] + u[1:-1, -1] = image[:, -2] + + while i < max_iter and rmse > eps: + + rmse = 0 + + for k in range(dims): + for r in range(1, rows + 1): + for c in range(1, cols + 1): + + uprev = _get_elem(u_data, rows2, cols2, dims, r, c, k) + + # forward derivatives + ux = _get_elem(u_data, rows2, cols2, dims, + r, c+1, k) - uprev + uy = _get_elem(u_data, rows2, cols2, dims, + r+1, c, k) - uprev + + # Gauss-Seidel method + unew = ( + lam * ( + + _get_elem(u_data, rows2, cols2, dims, r+1, c, k) + + _get_elem(u_data, rows2, cols2, dims, r-1, c, k) + + _get_elem(u_data, rows2, cols2, dims, r, c+1, k) + + _get_elem(u_data, rows2, cols2, dims, r, c-1, k) + + + _get_elem(dx_data, rows2, cols2, dims, r, c-1, k) + - _get_elem(dx_data, rows2, cols2, dims, r, c, k) + + _get_elem(dy_data, rows2, cols2, dims, r-1, c, k) + - _get_elem(dy_data, rows2, cols2, dims, r, c, k) + + - _get_elem(bx_data, rows2, cols2, dims, r, c-1, k) + + _get_elem(bx_data, rows2, cols2, dims, r, c, k) + - _get_elem(by_data, rows2, cols2, dims, r-1, c, k) + + _get_elem(by_data, rows2, cols2, dims, r, c, k) + ) + weight * _get_elem(image_data, rows, cols, dims, + r-1, c-1, k) + ) / norm + _set_elem(u_data, rows2, cols2, dims, r, c, k, unew) + + # update root mean square error + rmse += (unew - uprev)**2 + + bxx = _get_elem(bx_data, rows2, cols2, dims, r, c, k) + byy = _get_elem(by_data, rows2, cols2, dims, r, c, k) + + s = sqrt((ux + bxx)**2 + (uy + byy)**2) + dxx = s * lam * (ux + bxx) / (s * lam + 1) + dyy = s * lam * (uy + byy) / (s * lam + 1) + + _set_elem(dx_data, rows2, cols2, dims, r, c, k, dxx) + _set_elem(dy_data, rows2, cols2, dims, r, c, k, dyy) + + _incr_elem(bx_data, rows2, cols2, dims, r, c, k, ux - dxx) + _incr_elem(by_data, rows2, cols2, dims, r, c, k, uy - dyy) + + rmse = sqrt(rmse / total) + i += 1 + + return np.squeeze(u[1:-1, 1:-1]) From cda03cfba4920a3c526424614305ae7e6a421e82 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 2 Nov 2012 20:17:48 +0100 Subject: [PATCH 484/625] Fix test cases for TV denoising --- skimage/filter/tests/test_denoise.py | 46 +++++++++++----------------- 1 file changed, 18 insertions(+), 28 deletions(-) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index 63a5a5e0..03c8c58a 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -9,24 +9,17 @@ lena_gray = color.rgb2gray(lena) def test_denoise_tv_2d(): - # lena image img = lena_gray - # add noise to lena + # add some random noise img += 0.5 * img.std() * np.random.random(img.shape) - # clip noise so that it does not exceed allowed range for float images. img = np.clip(img, 0, 1) - # denoise - denoised_lena = filter.denoise_tv(img, weight=60.0) - # which dtype? - assert denoised_lena.dtype in [np.float, np.float32, np.float64] - from scipy import ndimage - grad = ndimage.morphological_gradient(img, size=((3, 3))) - grad_denoised = ndimage.morphological_gradient( - denoised_lena, size=((3, 3))) - # test if the total variation has decreased - assert grad_denoised.dtype == np.float - assert (np.sqrt((grad_denoised**2).sum()) - < np.sqrt((grad**2).sum()) / 2) + + out1 = filter.denoise_tv(img, weight=10) + out2 = filter.denoise_tv(img, weight=5) + + # make sure noise is reduced + assert img.std() > out1.std() + assert out1.std() > out2.std() def test_denoise_tv_float_result_range(): @@ -42,20 +35,17 @@ def test_denoise_tv_float_result_range(): def test_denoise_tv_3d(): - """Apply the TV denoising algorithm on a 3D image representing a sphere.""" - x, y, z = np.ogrid[0:40, 0:40, 0:40] - mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 - mask = 100 * mask.astype(np.float) - mask += 60 - mask += 20 * np.random.random(mask.shape) - mask[mask < 0] = 0 - mask[mask > 255] = 255 - res = filter.denoise_tv(mask.astype(np.uint8), weight=100) - assert res.dtype == np.float - assert res.std() * 255 < mask.std() + img = lena + # add some random noise + img += 0.5 * img.std() * np.random.random(img.shape) + img = np.clip(img, 0, 1) - # test wrong number of dimensions - assert_raises(ValueError, filter.denoise_tv, np.random.random((8, 8, 8, 8))) + out1 = filter.denoise_tv(img, weight=10) + out2 = filter.denoise_tv(img, weight=5) + + # make sure noise is reduced + assert img.std() > out1.std() + assert out1.std() > out2.std() def test_denoise_bilateral_2d(): From dd34097fe4d65b24a83fafa7c8348672a688f474 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 2 Nov 2012 20:18:28 +0100 Subject: [PATCH 485/625] Remove old implementation if TV filter --- skimage/filter/__init__.py | 3 +- skimage/filter/_denoise.pyx | 3 + skimage/filter/denoise.py | 245 ------------------------------------ 3 files changed, 4 insertions(+), 247 deletions(-) delete mode 100644 skimage/filter/denoise.py diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index 8894ff1a..4f8b129a 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,7 +3,6 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from .denoise import tv_denoise -from ._denoise import denoise_bilateral, denoise_tv +from ._denoise import denoise_bilateral, denoise_tv, tv_denoise from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index ca75ab4f..2e519b8f 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -10,6 +10,7 @@ from libc.stdlib cimport malloc, free from libc.float cimport DBL_MAX from skimage._shared.interpolation cimport get_pixel3d from skimage.util import img_as_float +from skimage._shared.utils import deprecated cdef inline double _gaussian_weight(double sigma, double value): @@ -342,3 +343,5 @@ def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): i += 1 return np.squeeze(u[1:-1, 1:-1]) + +tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv) diff --git a/skimage/filter/denoise.py b/skimage/filter/denoise.py deleted file mode 100644 index 351b2482..00000000 --- a/skimage/filter/denoise.py +++ /dev/null @@ -1,245 +0,0 @@ -import numpy as np -from skimage import img_as_float -from skimage._shared.utils import deprecated - - -def _denoise_tv_3d(im, weight=100, eps=2.e-4, n_iter_max=200): - """Perform total-variation denoising on 3-D arrays. - - Parameters - ---------- - im: ndarray - 3-D input data to be denoised. - weight: float, optional - Denoising weight. The greater ``weight``, the more denoising (at - the expense of fidelity to ``input``). - eps: float, optional - Relative difference of the value of the cost function that determines - the stop criterion. The algorithm stops when: - - (E_(n-1) - E_n) < eps * E_0 - - n_iter_max: int, optional - Maximal number of iterations used for the optimization. - - Returns - ------- - out: ndarray - Denoised array of floats. - - Notes - ----- - Rudin, Osher and Fatemi algorithm. - - Examples - --------- - First build synthetic noisy data - >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] - >>> mask = (x -22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 - >>> mask = mask.astype(np.float) - >>> mask += 0.2*np.random.randn(*mask.shape) - >>> res = denoise_tv_3d(mask, weight=100) - """ - px = np.zeros_like(im) - py = np.zeros_like(im) - pz = np.zeros_like(im) - gx = np.zeros_like(im) - gy = np.zeros_like(im) - gz = np.zeros_like(im) - d = np.zeros_like(im) - i = 0 - while i < n_iter_max: - d = - px - py - pz - d[1:] += px[:-1] - d[:, 1:] += py[:, :-1] - d[:, :, 1:] += pz[:, :, :-1] - - out = im + d - E = (d**2).sum() - - gx[:-1] = np.diff(out, axis=0) - gy[:, :-1] = np.diff(out, axis=1) - gz[:, :, :-1] = np.diff(out, axis=2) - norm = np.sqrt(gx**2 + gy**2 + gz**2) - E += weight * norm.sum() - norm *= 0.5 / weight - norm += 1. - px -= 1. / 6. * gx - px /= norm - py -= 1. / 6. * gy - py /= norm - pz -= 1 / 6. * gz - pz /= norm - E /= float(im.size) - if i == 0: - E_init = E - E_previous = E - else: - if np.abs(E_previous - E) < eps * E_init: - break - else: - E_previous = E - i += 1 - return out - - -def _denoise_tv_2d(im, weight=50, eps=2.e-4, n_iter_max=200): - """Perform total-variation denoising. - - Parameters - ---------- - im: ndarray - Input data to be denoised. - weight: float, optional - Denoising weight. The greater ``weight``, the more denoising (at - the expense of fidelity to ``input``) - eps: float, optional - Relative difference of the value of the cost function that determines - the stop criterion. The algorithm stops when: - - (E_(n-1) - E_n) < eps * E_0 - - n_iter_max: int, optional - Maximal number of iterations used for the optimization. - - Returns - ------- - out: ndarray - Denoised array of floats. - - Notes - ----- - The principle of total variation denoising is explained in - http://en.wikipedia.org/wiki/Total_variation_denoising. - - This code is an implementation of the algorithm of Rudin, Fatemi and Osher - that was proposed by Chambolle in [1]_. - - References - ---------- - .. [1] A. Chambolle, An algorithm for total variation minimization and - applications, Journal of Mathematical Imaging and Vision, - Springer, 2004, 20, 89-97. - - Examples - --------- - >>> import scipy - >>> lena = scipy.lena() - >>> import scipy - >>> lena = scipy.lena().astype(np.float) - >>> lena += 0.5 * lena.std()*np.random.randn(*lena.shape) - >>> denoised_lena = denoise_tv(lena, weight=60.0) - """ - px = np.zeros_like(im) - py = np.zeros_like(im) - gx = np.zeros_like(im) - gy = np.zeros_like(im) - d = np.zeros_like(im) - i = 0 - while i < n_iter_max: - d = -px - py - d[1:] += px[:-1] - d[:, 1:] += py[:, :-1] - - out = im + d - E = (d**2).sum() - gx[:-1] = np.diff(out, axis=0) - gy[:, :-1] = np.diff(out, axis=1) - norm = np.sqrt(gx**2 + gy**2) - E += weight * norm.sum() - norm *= 0.5 / weight - norm += 1 - px -= 0.25 * gx - px /= norm - py -= 0.25 * gy - py /= norm - E /= float(im.size) - if i == 0: - E_init = E - E_previous = E - else: - if np.abs(E_previous - E) < eps * E_init: - break - else: - E_previous = E - i += 1 - return out - - -def denoise_tv(im, weight=50, eps=2.e-4, n_iter_max=200): - """Perform total-variation denoising on 2-d and 3-d images. - - Parameters - ---------- - im: ndarray (2d or 3d) of ints, uints or floats - Input data to be denoised. `im` can be of any numeric type, - but it is cast into an ndarray of floats for the computation - of the denoised image. - weight: float, optional - Denoising weight. The greater ``weight``, the more denoising (at - the expense of fidelity to ``input``). - eps: float, optional - Relative difference of the value of the cost function that - determines the stop criterion. The algorithm stops when: - - (E_(n-1) - E_n) < eps * E_0 - - n_iter_max: int, optional - Maximal number of iterations used for the optimization. - - Returns - ------- - out: ndarray - Denoised array of floats. - - Notes - ----- - The principle of total variation denoising is explained in - http://en.wikipedia.org/wiki/Total_variation_denoising - - The principle of total variation denoising is to minimize the - total variation of the image, which can be roughly described as - the integral of the norm of the image gradient. Total variation - denoising tends to produce "cartoon-like" images, that is, - piecewise-constant images. - - This code is an implementation of the algorithm of Rudin, Fatemi and Osher - that was proposed by Chambolle in [1]_. - - References - ---------- - .. [1] A. Chambolle, An algorithm for total variation minimization and - applications, Journal of Mathematical Imaging and Vision, - Springer, 2004, 20, 89-97. - - Examples - --------- - >>> import scipy - >>> # 2D example using lena - >>> lena = scipy.lena() - >>> import scipy - >>> lena = scipy.lena().astype(np.float) - >>> lena += 0.5 * lena.std()*np.random.randn(*lena.shape) - >>> denoised_lena = denoise_tv(lena, weight=60) - >>> # 3D example on synthetic data - >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] - >>> mask = (x -22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 - >>> mask = mask.astype(np.float) - >>> mask += 0.2*np.random.randn(*mask.shape) - >>> res = denoise_tv_3d(mask, weight=100) - """ - im_type = im.dtype - if not im_type.kind == 'f': - im = img_as_float(im) - - if im.ndim == 2: - out = _denoise_tv_2d(im, weight, eps, n_iter_max) - elif im.ndim == 3: - out = _denoise_tv_3d(im, weight, eps, n_iter_max) - else: - raise ValueError('only 2-d and 3-d images may be denoised with this ' - 'function') - return out - - -tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv) From ccd2f4264308979b56867388e7b5cc7a1beea26f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 2 Nov 2012 20:18:49 +0100 Subject: [PATCH 486/625] Update denoising example for new TV implementation --- doc/examples/plot_denoise.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index 8f02f21b..cfcec5e4 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -32,7 +32,7 @@ import matplotlib.pyplot as plt from skimage import data, color, img_as_float from skimage.filter import denoise_tv, denoise_bilateral -lena = img_as_float(data.lena()) +lena = color.rgb2gray(img_as_float(data.lena())) lena = lena[220:300, 220:320] noisy = lena + 0.5 * lena.std() * np.random.random(lena.shape) @@ -40,17 +40,19 @@ noisy = np.clip(noisy, 0, 1) fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5)) +plt.gray() + ax[0, 0].imshow(noisy) ax[0, 0].axis('off') ax[0, 0].set_title('noisy') -ax[0, 1].imshow(denoise_tv(noisy, weight=0.1)) +ax[0, 1].imshow(denoise_tv(noisy, weight=40)) ax[0, 1].axis('off') ax[0, 1].set_title('TV') ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.03, sigma_spatial=15)) ax[0, 2].axis('off') ax[0, 2].set_title('Bilateral') -ax[1, 0].imshow(denoise_tv(noisy, weight=0.2)) +ax[1, 0].imshow(denoise_tv(noisy, weight=20)) ax[1, 0].axis('off') ax[1, 0].set_title('(more) TV') ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.06, sigma_spatial=15)) From fff33702eaf1d12f14f967d4c3b15164664a99a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 4 Nov 2012 13:39:24 +0100 Subject: [PATCH 487/625] Replace manual array with builting Cython buffer indexing --- skimage/filter/_denoise.pyx | 75 ++++++++++++------------------------- 1 file changed, 23 insertions(+), 52 deletions(-) diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index 2e519b8f..436097f6 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -179,24 +179,6 @@ def denoise_bilateral(image, int win_size=5, sigma_range=None, return np.squeeze(out) -cdef inline double _get_elem(double* image, Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, - Py_ssize_t k): - return image[r * cols * dims + c * dims + k] - - -cdef inline void _set_elem(double* image, Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, - Py_ssize_t k, double value): - image[r * cols * dims + c * dims + k] = value - - -cdef inline void _incr_elem(double* image, Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, - Py_ssize_t k, double value): - image[r * cols * dims + c * dims + k] += value - - def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): """Perform total-variation denoising using split-Bregman optimization. @@ -263,14 +245,6 @@ def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] by = \ np.zeros(shape_ext, dtype=np.double) - double* image_data = cimage.data - double* u_data = u.data - - double* dx_data = dx.data - double* dy_data = dy.data - double* bx_data = bx.data - double* by_data = by.data - double ux, uy, uprev, unew, bxx, byy, dxx, dyy, s int i = 0 double lam = 2 * weight @@ -293,51 +267,48 @@ def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): for r in range(1, rows + 1): for c in range(1, cols + 1): - uprev = _get_elem(u_data, rows2, cols2, dims, r, c, k) + uprev = u[r, c, k] # forward derivatives - ux = _get_elem(u_data, rows2, cols2, dims, - r, c+1, k) - uprev - uy = _get_elem(u_data, rows2, cols2, dims, - r+1, c, k) - uprev + ux = u[r, c + 1, k] - uprev + uy = u[r + 1, c, k] - uprev # Gauss-Seidel method unew = ( lam * ( - + _get_elem(u_data, rows2, cols2, dims, r+1, c, k) - + _get_elem(u_data, rows2, cols2, dims, r-1, c, k) - + _get_elem(u_data, rows2, cols2, dims, r, c+1, k) - + _get_elem(u_data, rows2, cols2, dims, r, c-1, k) + + u[r + 1, c, k] + + u[r - 1, c, k] + + u[r, c + 1, k] + + u[r, c - 1, k] - + _get_elem(dx_data, rows2, cols2, dims, r, c-1, k) - - _get_elem(dx_data, rows2, cols2, dims, r, c, k) - + _get_elem(dy_data, rows2, cols2, dims, r-1, c, k) - - _get_elem(dy_data, rows2, cols2, dims, r, c, k) + + dx[r, c - 1, k] + - dx[r, c, k] + + dy[r - 1, c, k] + - dy[r, c, k] - - _get_elem(bx_data, rows2, cols2, dims, r, c-1, k) - + _get_elem(bx_data, rows2, cols2, dims, r, c, k) - - _get_elem(by_data, rows2, cols2, dims, r-1, c, k) - + _get_elem(by_data, rows2, cols2, dims, r, c, k) - ) + weight * _get_elem(image_data, rows, cols, dims, - r-1, c-1, k) + - bx[r, c - 1, k] + + bx[r, c, k] + - by[r - 1, c, k] + + by[r, c, k] + ) + weight * cimage[r - 1, c - 1, k] ) / norm - _set_elem(u_data, rows2, cols2, dims, r, c, k, unew) + u[r, c, k] = unew # update root mean square error rmse += (unew - uprev)**2 - bxx = _get_elem(bx_data, rows2, cols2, dims, r, c, k) - byy = _get_elem(by_data, rows2, cols2, dims, r, c, k) + bxx = bx[r, c, k] + byy = by[r, c, k] s = sqrt((ux + bxx)**2 + (uy + byy)**2) dxx = s * lam * (ux + bxx) / (s * lam + 1) dyy = s * lam * (uy + byy) / (s * lam + 1) - _set_elem(dx_data, rows2, cols2, dims, r, c, k, dxx) - _set_elem(dy_data, rows2, cols2, dims, r, c, k, dyy) + dx[r, c, k] = dxx + dy[r, c, k] = dyy - _incr_elem(bx_data, rows2, cols2, dims, r, c, k, ux - dxx) - _incr_elem(by_data, rows2, cols2, dims, r, c, k, uy - dyy) + bx[r, c, k] += ux - dxx + by[r, c, k] += uy - dyy rmse = sqrt(rmse / total) i += 1 From 780d886db95f45ff17c2535f30bd1ec026357792 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 10:44:19 +0100 Subject: [PATCH 488/625] Rename denoise_tv to denoise_tv_bregman --- skimage/filter/__init__.py | 2 +- skimage/filter/_denoise.pyx | 4 +--- skimage/filter/tests/test_denoise.py | 10 +++++----- 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index 4f8b129a..39d50376 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,6 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from ._denoise import denoise_bilateral, denoise_tv, tv_denoise +from ._denoise import denoise_bilateral, denoise_tv_bregman from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx index 436097f6..ea046a0a 100644 --- a/skimage/filter/_denoise.pyx +++ b/skimage/filter/_denoise.pyx @@ -179,7 +179,7 @@ def denoise_bilateral(image, int win_size=5, sigma_range=None, return np.squeeze(out) -def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): +def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3): """Perform total-variation denoising using split-Bregman optimization. Total-variation denoising (also know as total-variation regularization) @@ -314,5 +314,3 @@ def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3): i += 1 return np.squeeze(u[1:-1, 1:-1]) - -tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index 03c8c58a..6f77fdc7 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -14,8 +14,8 @@ def test_denoise_tv_2d(): img += 0.5 * img.std() * np.random.random(img.shape) img = np.clip(img, 0, 1) - out1 = filter.denoise_tv(img, weight=10) - out2 = filter.denoise_tv(img, weight=5) + out1 = filter.denoise_tv_bregman(img, weight=10) + out2 = filter.denoise_tv_bregman(img, weight=5) # make sure noise is reduced assert img.std() > out1.std() @@ -27,7 +27,7 @@ def test_denoise_tv_float_result_range(): img = lena_gray int_lena = np.multiply(img, 255).astype(np.uint8) assert np.max(int_lena) > 1 - denoised_int_lena = filter.denoise_tv(int_lena, weight=60.0) + denoised_int_lena = filter.denoise_tv_bregman(int_lena, weight=60.0) # test if the value range of output float data is within [0.0:1.0] assert denoised_int_lena.dtype == np.float assert np.max(denoised_int_lena) <= 1.0 @@ -40,8 +40,8 @@ def test_denoise_tv_3d(): img += 0.5 * img.std() * np.random.random(img.shape) img = np.clip(img, 0, 1) - out1 = filter.denoise_tv(img, weight=10) - out2 = filter.denoise_tv(img, weight=5) + out1 = filter.denoise_tv_bregman(img, weight=10) + out2 = filter.denoise_tv_bregman(img, weight=5) # make sure noise is reduced assert img.std() > out1.std() From f2840ba9067872f7f217d145d52ef91ed9ba1576 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 10:55:46 +0100 Subject: [PATCH 489/625] Rename denoising cython source file --- skimage/filter/__init__.py | 2 +- skimage/filter/{_denoise.pyx => _denoise_cy.pyx} | 0 skimage/filter/setup.py | 4 ++-- 3 files changed, 3 insertions(+), 3 deletions(-) rename skimage/filter/{_denoise.pyx => _denoise_cy.pyx} (100%) diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index 39d50376..80d637cf 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,6 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) -from ._denoise import denoise_bilateral, denoise_tv_bregman +from ._denoise_cy import denoise_bilateral, denoise_tv_bregman from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise_cy.pyx similarity index 100% rename from skimage/filter/_denoise.pyx rename to skimage/filter/_denoise_cy.pyx diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 56c1e9e5..650a26a6 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -13,7 +13,7 @@ def configuration(parent_package='', top_path=None): config.add_data_dir('tests') cython(['_ctmf.pyx'], working_path=base_path) - cython(['_denoise.pyx'], working_path=base_path) + cython(['_denoise_cy.pyx'], working_path=base_path) cython(['rank/_core8.pyx'], working_path=base_path) cython(['rank/_core16.pyx'], working_path=base_path) cython(['rank/_crank8.pyx'], working_path=base_path) @@ -27,7 +27,7 @@ def configuration(parent_package='', top_path=None): config.add_extension('_ctmf', sources=['_ctmf.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('_denoise', sources=['_denoise.c'], + config.add_extension('_denoise_cy', sources=['_denoise_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) config.add_extension('rank/_core8', sources=['rank/_core8.c'], include_dirs=[get_numpy_include_dirs()]) From dfd8cb4fc203594df6ed14dede882381041e5930 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:00:38 +0100 Subject: [PATCH 490/625] Fix typo in denoise_tv_bregman doc string --- skimage/filter/_denoise_cy.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/_denoise_cy.pyx b/skimage/filter/_denoise_cy.pyx index ea046a0a..47e2a6d5 100644 --- a/skimage/filter/_denoise_cy.pyx +++ b/skimage/filter/_denoise_cy.pyx @@ -183,7 +183,7 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3): """Perform total-variation denoising using split-Bregman optimization. Total-variation denoising (also know as total-variation regularization) - tries to find an image with less total total-variation under the constraint + tries to find an image with less total-variation under the constraint of being similar to the input image, which is controlled by the regularization parameter. From ae5488a2b189af807d008bf4e23ccba167ac8907 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:04:39 +0100 Subject: [PATCH 491/625] Rename test functions for bregman algorithm --- skimage/filter/tests/test_denoise.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index 6f77fdc7..10745b72 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -8,7 +8,7 @@ lena = img_as_float(data.lena()[:256, :256]) lena_gray = color.rgb2gray(lena) -def test_denoise_tv_2d(): +def test_denoise_tv_bregman_2d(): img = lena_gray # add some random noise img += 0.5 * img.std() * np.random.random(img.shape) @@ -22,7 +22,7 @@ def test_denoise_tv_2d(): assert out1.std() > out2.std() -def test_denoise_tv_float_result_range(): +def test_denoise_tv_bregman_float_result_range(): # lena image img = lena_gray int_lena = np.multiply(img, 255).astype(np.uint8) @@ -34,7 +34,7 @@ def test_denoise_tv_float_result_range(): assert np.min(denoised_int_lena) >= 0.0 -def test_denoise_tv_3d(): +def test_denoise_tv_bregman_3d(): img = lena # add some random noise img += 0.5 * img.std() * np.random.random(img.shape) From 236ef48ec97fe389aeaf5bfeb195863d59dd928f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:15:41 +0100 Subject: [PATCH 492/625] Add original (Chambolle) implementation of TV denoising --- skimage/filter/__init__.py | 1 + skimage/filter/_denoise.py | 261 +++++++++++++++++++++++++++++++++++++ 2 files changed, 262 insertions(+) create mode 100644 skimage/filter/_denoise.py diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py index 80d637cf..f32c3b23 100644 --- a/skimage/filter/__init__.py +++ b/skimage/filter/__init__.py @@ -3,6 +3,7 @@ from .ctmf import median_filter from ._canny import canny from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt, hprewitt, vprewitt) +from ._denoise import denoise_tv_chambolle, tv_denoise from ._denoise_cy import denoise_bilateral, denoise_tv_bregman from ._rank_order import rank_order from .thresholding import threshold_otsu, threshold_adaptive diff --git a/skimage/filter/_denoise.py b/skimage/filter/_denoise.py new file mode 100644 index 00000000..315efc05 --- /dev/null +++ b/skimage/filter/_denoise.py @@ -0,0 +1,261 @@ +import numpy as np +from skimage import img_as_float +from skimage._shared.utils import deprecated + + +def _denoise_tv_chambolle_3d(im, weight=100, eps=2.e-4, n_iter_max=200): + """Perform total-variation denoising on 3D images. + + Parameters + ---------- + im : ndarray + 3-D input data to be denoised. + weight : float, optional + Denoising weight. The greater `weight`, the more denoising (at + the expense of fidelity to `input`). + eps : float, optional + Relative difference of the value of the cost function that determines + the stop criterion. The algorithm stops when: + + (E_(n-1) - E_n) < eps * E_0 + + n_iter_max : int, optional + Maximal number of iterations used for the optimization. + + Returns + ------- + out : ndarray + Denoised array of floats. + + Notes + ----- + Rudin, Osher and Fatemi algorithm. + + Examples + --------- + >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] + >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 + >>> mask = mask.astype(np.float) + >>> mask += 0.2 * np.random.randn(*mask.shape) + >>> res = denoise_tv(mask, weight=100) + + """ + + px = np.zeros_like(im) + py = np.zeros_like(im) + pz = np.zeros_like(im) + gx = np.zeros_like(im) + gy = np.zeros_like(im) + gz = np.zeros_like(im) + d = np.zeros_like(im) + i = 0 + while i < n_iter_max: + d = - px - py - pz + d[1:] += px[:-1] + d[:, 1:] += py[:, :-1] + d[:, :, 1:] += pz[:, :, :-1] + + out = im + d + E = (d**2).sum() + + gx[:-1] = np.diff(out, axis=0) + gy[:, :-1] = np.diff(out, axis=1) + gz[:, :, :-1] = np.diff(out, axis=2) + norm = np.sqrt(gx**2 + gy**2 + gz**2) + E += weight * norm.sum() + norm *= 0.5 / weight + norm += 1. + px -= 1. / 6. * gx + px /= norm + py -= 1. / 6. * gy + py /= norm + pz -= 1 / 6. * gz + pz /= norm + E /= float(im.size) + if i == 0: + E_init = E + E_previous = E + else: + if np.abs(E_previous - E) < eps * E_init: + break + else: + E_previous = E + i += 1 + return out + + +def _denoise_tv_chambolle_2d(im, weight=50, eps=2.e-4, n_iter_max=200): + """Perform total-variation denoising on 2D images. + + Parameters + ---------- + im : ndarray + Input data to be denoised. + weight : float, optional + Denoising weight. The greater `weight`, the more denoising (at + the expense of fidelity to `input`) + eps : float, optional + Relative difference of the value of the cost function that determines + the stop criterion. The algorithm stops when: + + (E_(n-1) - E_n) < eps * E_0 + + n_iter_max : int, optional + Maximal number of iterations used for the optimization. + + Returns + ------- + out : ndarray + Denoised array of floats. + + Notes + ----- + The principle of total variation denoising is explained in + http://en.wikipedia.org/wiki/Total_variation_denoising. + + This code is an implementation of the algorithm of Rudin, Fatemi and Osher + that was proposed by Chambolle in [1]_. + + References + ---------- + .. [1] A. Chambolle, An algorithm for total variation minimization and + applications, Journal of Mathematical Imaging and Vision, + Springer, 2004, 20, 89-97. + + Examples + --------- + >>> from skimage import color, data + >>> lena = color.rgb2gray(data.lena()) + >>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape) + >>> denoised_lena = denoise_tv(lena, weight=60) + + """ + + px = np.zeros_like(im) + py = np.zeros_like(im) + gx = np.zeros_like(im) + gy = np.zeros_like(im) + d = np.zeros_like(im) + i = 0 + while i < n_iter_max: + d = -px - py + d[1:] += px[:-1] + d[:, 1:] += py[:, :-1] + + out = im + d + E = (d**2).sum() + gx[:-1] = np.diff(out, axis=0) + gy[:, :-1] = np.diff(out, axis=1) + norm = np.sqrt(gx**2 + gy**2) + E += weight * norm.sum() + norm *= 0.5 / weight + norm += 1 + px -= 0.25 * gx + px /= norm + py -= 0.25 * gy + py /= norm + E /= float(im.size) + if i == 0: + E_init = E + E_previous = E + else: + if np.abs(E_previous - E) < eps * E_init: + break + else: + E_previous = E + i += 1 + return out + + +def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200, + multichannel=False): + """Perform total-variation denoising on 2D and 3D images. + + Parameters + ---------- + im : ndarray (2d or 3d) of ints, uints or floats + Input data to be denoised. `im` can be of any numeric type, + but it is cast into an ndarray of floats for the computation + of the denoised image. + weight : float, optional + Denoising weight. The greater `weight`, the more denoising (at + the expense of fidelity to `input`). + eps : float, optional + Relative difference of the value of the cost function that + determines the stop criterion. The algorithm stops when: + + (E_(n-1) - E_n) < eps * E_0 + + n_iter_max : int, optional + Maximal number of iterations used for the optimization. + multichannel : bool, optional + Apply total-variation denoising separately for each channel. This + option should be true for color images, otherwise the denoising is + also applied in the 3rd dimension. + + Returns + ------- + out : ndarray + Denoised image. + + Notes + ----- + The principle of total variation denoising is explained in + http://en.wikipedia.org/wiki/Total_variation_denoising + + The principle of total variation denoising is to minimize the + total variation of the image, which can be roughly described as + the integral of the norm of the image gradient. Total variation + denoising tends to produce "cartoon-like" images, that is, + piecewise-constant images. + + This code is an implementation of the algorithm of Rudin, Fatemi and Osher + that was proposed by Chambolle in [1]_. + + References + ---------- + .. [1] A. Chambolle, An algorithm for total variation minimization and + applications, Journal of Mathematical Imaging and Vision, + Springer, 2004, 20, 89-97. + + Examples + --------- + 2D example on Lena image: + + >>> from skimage import color, data + >>> lena = color.rgb2gray(data.lena()) + >>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape) + >>> denoised_lena = denoise_tv(lena, weight=60) + + 3D example on synthetic data: + + >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] + >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 + >>> mask = mask.astype(np.float) + >>> mask += 0.2*np.random.randn(*mask.shape) + >>> res = denoise_tv(mask, weight=100) + + """ + + im_type = im.dtype + if not im_type.kind == 'f': + im = img_as_float(im) + + if im.ndim == 2: + out = _denoise_tv_2d(im, weight, eps, n_iter_max) + elif im.ndim == 3: + if multichannel: + out = np.zeros_like(im) + for c in range(im.shape[2]): + out[..., c] = _denoise_tv_2d(im[..., c], weight, eps, + n_iter_max) + else: + out = _denoise_tv_3d(im, weight, eps, n_iter_max) + else: + raise ValueError('only 2-d and 3-d images may be denoised with this ' + 'function') + return out + + +tv_denoise = deprecated('skimage.filter.denoise_tv_chambolle')\ + (denoise_tv_chambolle) From b7254f225ce10c371310363f5af611d3d3ac0f48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:26:43 +0100 Subject: [PATCH 493/625] Update denoising example --- doc/examples/plot_denoise.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/examples/plot_denoise.py b/doc/examples/plot_denoise.py index cfcec5e4..debb2ea6 100644 --- a/doc/examples/plot_denoise.py +++ b/doc/examples/plot_denoise.py @@ -30,12 +30,12 @@ import numpy as np import matplotlib.pyplot as plt from skimage import data, color, img_as_float -from skimage.filter import denoise_tv, denoise_bilateral +from skimage.filter import denoise_tv_chambolle, denoise_bilateral -lena = color.rgb2gray(img_as_float(data.lena())) +lena = img_as_float(data.lena()) lena = lena[220:300, 220:320] -noisy = lena + 0.5 * lena.std() * np.random.random(lena.shape) +noisy = lena + 0.6 * lena.std() * np.random.random(lena.shape) noisy = np.clip(noisy, 0, 1) fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5)) @@ -45,17 +45,17 @@ plt.gray() ax[0, 0].imshow(noisy) ax[0, 0].axis('off') ax[0, 0].set_title('noisy') -ax[0, 1].imshow(denoise_tv(noisy, weight=40)) +ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True)) ax[0, 1].axis('off') ax[0, 1].set_title('TV') -ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.03, sigma_spatial=15)) +ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15)) ax[0, 2].axis('off') ax[0, 2].set_title('Bilateral') -ax[1, 0].imshow(denoise_tv(noisy, weight=20)) +ax[1, 0].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True)) ax[1, 0].axis('off') ax[1, 0].set_title('(more) TV') -ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.06, sigma_spatial=15)) +ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15)) ax[1, 1].axis('off') ax[1, 1].set_title('(more) Bilateral') ax[1, 2].imshow(lena) From aadc45cd5b42f5d15b6fb041197246a77bf2dd7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:27:08 +0100 Subject: [PATCH 494/625] Fix bugs caused by renaming functions --- skimage/filter/_denoise.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/filter/_denoise.py b/skimage/filter/_denoise.py index 315efc05..07f3c58f 100644 --- a/skimage/filter/_denoise.py +++ b/skimage/filter/_denoise.py @@ -242,15 +242,15 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200, im = img_as_float(im) if im.ndim == 2: - out = _denoise_tv_2d(im, weight, eps, n_iter_max) + out = _denoise_tv_chambolle_2d(im, weight, eps, n_iter_max) elif im.ndim == 3: if multichannel: out = np.zeros_like(im) for c in range(im.shape[2]): - out[..., c] = _denoise_tv_2d(im[..., c], weight, eps, + out[..., c] = _denoise_tv_chambolle_2d(im[..., c], weight, eps, n_iter_max) else: - out = _denoise_tv_3d(im, weight, eps, n_iter_max) + out = _denoise_tv_chambolle_3d(im, weight, eps, n_iter_max) else: raise ValueError('only 2-d and 3-d images may be denoised with this ' 'function') From 5078da0aed06e4b512a0bc880ad407745e128d35 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:30:21 +0100 Subject: [PATCH 495/625] Add test cases for chambolle tv denoising implementation --- skimage/filter/tests/test_denoise.py | 51 ++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index 10745b72..c8e7bb02 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -8,6 +8,57 @@ lena = img_as_float(data.lena()[:256, :256]) lena_gray = color.rgb2gray(lena) +def test_denoise_tv_chambolle_2d(): + # lena image + img = lena_gray + # add noise to lena + img += 0.5 * img.std() * np.random.random(img.shape) + # clip noise so that it does not exceed allowed range for float images. + img = np.clip(img, 0, 1) + # denoise + denoised_lena = filter.denoise_tv_chambolle(img, weight=60.0) + # which dtype? + assert denoised_lena.dtype in [np.float, np.float32, np.float64] + from scipy import ndimage + grad = ndimage.morphological_gradient(img, size=((3, 3))) + grad_denoised = ndimage.morphological_gradient( + denoised_lena, size=((3, 3))) + # test if the total variation has decreased + assert grad_denoised.dtype == np.float + assert (np.sqrt((grad_denoised**2).sum()) + < np.sqrt((grad**2).sum()) / 2) + + +def test_denoise_tv_chambolle_float_result_range(): + # lena image + img = lena_gray + int_lena = np.multiply(img, 255).astype(np.uint8) + assert np.max(int_lena) > 1 + denoised_int_lena = filter.denoise_tv_chambolle(int_lena, weight=60.0) + # test if the value range of output float data is within [0.0:1.0] + assert denoised_int_lena.dtype == np.float + assert np.max(denoised_int_lena) <= 1.0 + assert np.min(denoised_int_lena) >= 0.0 + + +def test_denoise_tv_chambolle_3d(): + """Apply the TV denoising algorithm on a 3D image representing a sphere.""" + x, y, z = np.ogrid[0:40, 0:40, 0:40] + mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 + mask = 100 * mask.astype(np.float) + mask += 60 + mask += 20 * np.random.random(mask.shape) + mask[mask < 0] = 0 + mask[mask > 255] = 255 + res = filter.denoise_tv_chambolle(mask.astype(np.uint8), weight=100) + assert res.dtype == np.float + assert res.std() * 255 < mask.std() + + # test wrong number of dimensions + assert_raises(ValueError, filter.denoise_tv_chambolle, + np.random.random((8, 8, 8, 8))) + + def test_denoise_tv_bregman_2d(): img = lena_gray # add some random noise From db5460591abecf55bd09e2d76721b66efd213557 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:33:16 +0100 Subject: [PATCH 496/625] Add test case for multi-channel chambolle denoising --- skimage/filter/tests/test_denoise.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index c8e7bb02..cb3b5da3 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -1,5 +1,5 @@ import numpy as np -from numpy.testing import run_module_suite, assert_raises +from numpy.testing import run_module_suite, assert_raises, assert_equal from skimage import filter, data, color, img_as_float @@ -29,6 +29,12 @@ def test_denoise_tv_chambolle_2d(): < np.sqrt((grad**2).sum()) / 2) +def test_denoise_tv_chambolle_multichannel(): + denoised0 = filter.denoise_tv_chambolle(lena[..., 0], weight=60.0) + denoised = filter.denoise_tv_chambolle(lena, weight=60.0, multichannel=True) + assert_equal(denoised[..., 0], denoised0) + + def test_denoise_tv_chambolle_float_result_range(): # lena image img = lena_gray From d82ae4dd95b3184cb09617c04969d7e5483ffdc2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 25 Dec 2012 11:36:48 +0100 Subject: [PATCH 497/625] Add note about multichannel parameter --- skimage/filter/_denoise.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/skimage/filter/_denoise.py b/skimage/filter/_denoise.py index 07f3c58f..e26f8387 100644 --- a/skimage/filter/_denoise.py +++ b/skimage/filter/_denoise.py @@ -200,6 +200,8 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200, Notes ----- + Make sure to set the multichannel parameter appropriately for color images. + The principle of total variation denoising is explained in http://en.wikipedia.org/wiki/Total_variation_denoising From 8da3541bff57246c80d4567bad180c3a2e4e42f9 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Wed, 26 Dec 2012 09:11:53 -0600 Subject: [PATCH 498/625] STY: Minor change to docstring to test travis build --- skimage/exposure/tests/test_exposure.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index a6323ea8..a5d72cbb 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -108,7 +108,7 @@ def test_adapthist_grayscale(): def test_adapthist_color(): - '''Test a color uint16 image + '''Test an RGB color uint16 image ''' img = skimage.img_as_uint(data.lena()) adapted = exposure.equalize_adapthist(img, clip_limit=0.01) From 81d88b8cc3a4a1b35ad3e6c29cf02ef95cd94331 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 27 Dec 2012 08:44:50 +0100 Subject: [PATCH 499/625] Include more information about reference papers --- skimage/filter/_denoise_cy.pyx | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/skimage/filter/_denoise_cy.pyx b/skimage/filter/_denoise_cy.pyx index 47e2a6d5..8ebf5c73 100644 --- a/skimage/filter/_denoise_cy.pyx +++ b/skimage/filter/_denoise_cy.pyx @@ -212,8 +212,12 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3): References ---------- .. [1] http://en.wikipedia.org/wiki/Total_variation_denoising - .. [2] ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf - .. [3] http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf + .. [2] Tom Goldstein and Stanley Osher, "The Split Bregman Method For L1 + Regularized Problems", + ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf + .. [3] Pascal Getreuer, "Rudin–Osher–Fatemi Total Variation Denoising + using Split Bregman" in Image Processing On Line on 2012–05–19, + http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf """ From 8b6b51ec04fda676de5fed7dd9b4c67809fe3aa2 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Thu, 27 Dec 2012 10:49:29 +0100 Subject: [PATCH 500/625] Add myself as contributor. --- CONTRIBUTORS.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 012fd53d..10f2a94a 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -123,3 +123,6 @@ - Luis Pedro Coelho imread plugin + +- Anders Boesen Lindbo Larsen + Dense DAISY feature description, circle perimeter drawing. From 4173d166399136201731fcbd29483bff17e3e84d Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 8 Dec 2012 18:26:13 +0100 Subject: [PATCH 501/625] Add dense DAISY feature description. --- skimage/feature/__init__.py | 1 + skimage/feature/_daisy.py | 167 ++++++++++++++++++++++++++++ skimage/feature/tests/test_daisy.py | 88 +++++++++++++++ 3 files changed, 256 insertions(+) create mode 100644 skimage/feature/_daisy.py create mode 100644 skimage/feature/tests/test_daisy.py diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py index e0756bfe..9f1e5f92 100644 --- a/skimage/feature/__init__.py +++ b/skimage/feature/__init__.py @@ -1,3 +1,4 @@ +from ._daisy import daisy from ._hog import hog from .texture import greycomatrix, greycoprops, local_binary_pattern from .peak import peak_local_max diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py new file mode 100644 index 00000000..7fb0364c --- /dev/null +++ b/skimage/feature/_daisy.py @@ -0,0 +1,167 @@ +import numpy as np +import scipy as sp +from numpy.linalg import norm +from scipy import sqrt, pi, arctan2, cos, sin, exp +from scipy.ndimage import gaussian_filter +from scipy.special import iv + +def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, + normalization='l1', sigmas=None, ring_radii=None): + '''Extract DAISY feature descriptors densely for the given image. + + DAISY is a feature descriptor similar to SIFT formulated in a way + that allows for fast dense extraction. Typically, this is practical + for bag-of-features image representations. + + The implementation follows Tola et al. [1] but deviate on the + following points: + * Histogram bin contribution are smoothed with a circular + Gaussian window over the tonal range (the angular range). + * The sigma values of the spatial Gaussian smoothing in this code + do not match the sigma values in the original code by Tola et + al. [2]. In their code, spatial smoothing is applied to both the + input image and the center histogram. However, this smoothing is + not documented in [1] and, therefore, it is omitted. + + Parameters + ---------- + img : (M, N) array + Input image (greyscale). + step : int, optional + Distance between descriptor sampling points. + radius : int, optional + Radius (in pixels) of the outermost ring. + rings : int, optional + Number of rings. + histograms : int, optional + Number of histograms sampled per ring. + orientations : int, optional + Number of orientations (bins) per histogram. + normalization : {'l1', 'l2', 'daisy', 'off'}, optional + How to normalize the descriptors: + * 'l1': L1-normalization of each descriptor. + * 'l2': L2-normalization of each descriptor. + * 'daisy': L2-normalization of individual histograms. + * 'off': Disable normalization. + sigmas : 1D array of float, optional + Standard deviation of spatial Gaussian smoothing for the center + histogram and for each ring of histograms. The array of sigmas + should be sorted from the center and out. I.e. the first sigma + value specifies the spatial smoothing of the center histogram + and the last sigma value specifies the spatial smoothing of the + outermost ring. + Specifying sigmas overrides the following parameter: + rings = len(sigmas)-1 + ring_radii : 1D array of int, optional + Radius (in pixels) for each ring. + Specifying ring_radii overrides the following two parameters: + rings = len(ring_radii) + radius = ring_radii[-1] + If both sigmas and ring_radii are given, they must satisfy + len(ring_radii) == len(sigmas)+1 + since no radius is needed for the center histogram. + + Returns + ------- + descs : array + Grid of DAISY descriptors for the given image as an array + dimensionality (P, Q, R) where + P = ceil((M-radius*2)/step) + Q = ceil((N-radius*2)/step) + R = (rings*histograms + 1)*orientations + + References + ---------- + [1] Tola et al. "Daisy: An efficient dense descriptor applied to + wide-baseline stereo." Pattern Analysis and Machine Intelligence, + IEEE Transactions on 32.5 (2010): 815-830. + [2] http://cvlab.epfl.ch/alumni/tola/daisy.html + ''' + + # Validate image format. + if img.ndim > 2: + raise ValueError('Only grey-level images are supported.') + if img.dtype.kind == 'u': + img = img.astype(float) + img = img/255. + + # Validate parameters. + if sigmas != None and ring_radii != None \ + and len(sigmas)-1 != len(ring_radii): + raise ValueError('len(sigmas)-1 != len(ring_radii)') + if ring_radii != None: + rings = len(ring_radii) + radius = ring_radii[-1] + if sigmas != None: + rings = len(sigmas)-1 + if sigmas == None: + sigmas = [radius*(i+1)/float(2*rings) for i in range(rings)] + if ring_radii == None: + ring_radii = [radius*(i+1)/float(rings) for i in range(rings)] + if normalization not in ['l1', 'l2', 'daisy', 'off']: + raise ValueError('Invalid normalization method.') + + # Compute image derivatives. + dx = np.zeros(img.shape) + dy = np.zeros(img.shape) + dx[:, :-1] = np.diff(img, n=1, axis=1) + dy[:-1, :] = np.diff(img, n=1, axis=0) + + # Compute gradient orientation and magnitude and their contribution + # to the histograms. + grad_mag = sqrt(dx**2 + dy**2) + grad_ori = arctan2(dy, dx) + hist_sigma = pi/orientations + kappa = 1./hist_sigma; + bessel = iv(0, kappa) + hist = np.empty((orientations,) + img.shape, dtype=float) + for i in range(orientations): + mu = 2*i*pi/orientations-pi + # Weigh bin contribution by the circular normal distribution + hist[i,:,:] = exp(kappa*cos(grad_ori-mu))/(2*pi*bessel) + # Weigh bin contribution by the gradient magnitude + hist[i,:,:] = np.multiply(hist[i,:,:], grad_mag) + + # Smooth orientation histograms for the center and all rings. + sigmas = [sigmas[0]] + sigmas + hist_smooth = np.empty((rings+1,)+hist.shape, dtype=float) + for i in range(rings+1): + for j in range(orientations): + hist_smooth[i,j,:,:] = \ + gaussian_filter(hist[j,:,:], sigma=sigmas[i]) + + # Assemble descriptor grid. + theta = [2*pi*j/histograms for j in range(histograms)] + desc_dims = (rings*histograms + 1)*orientations + descs = np.empty((desc_dims, img.shape[0]-2*radius, + img.shape[1]-2*radius)) + descs[:orientations,:,:] = \ + hist_smooth[0,:,radius:-radius,radius:-radius] + idx = orientations + for i in range(rings): + for j in range(histograms): + y_min = radius + int(round(ring_radii[i]*sin(theta[j]))) + y_max = descs.shape[1] + y_min + x_min = radius + int(round(ring_radii[i]*cos(theta[j]))) + x_max = descs.shape[2] + x_min + descs[idx:idx+orientations,:,:] = \ + hist_smooth[i+1,:,y_min:y_max,x_min:x_max] + idx += orientations + descs = descs[:,::step,::step] + descs = descs.swapaxes(0,1).swapaxes(1,2) + + # Normalize descriptors. + if normalization != 'off': + descs += 1e-10 + if normalization == 'l1': + descs /= np.sum(descs, axis=2)[:,:,np.newaxis] + elif normalization == 'l2': + descs /= sqrt(np.sum(descs**2, axis=2))[:,:,np.newaxis] + elif normalization == 'daisy': + for i in range(0, desc_dims, orientations): + norms = sqrt(np.sum(descs[:,:,i:i+orientations]**2, + axis=2)) + descs[:,:,i:i+orientations] /= norms[:,:,np.newaxis] + + return descs + diff --git a/skimage/feature/tests/test_daisy.py b/skimage/feature/tests/test_daisy.py new file mode 100644 index 00000000..9e4c663a --- /dev/null +++ b/skimage/feature/tests/test_daisy.py @@ -0,0 +1,88 @@ +import numpy as np +from numpy.testing import assert_raises, assert_almost_equal +from numpy import sqrt, ceil + +from skimage import data +from skimage import img_as_float +from skimage.feature import daisy + + +def test_daisy_color_image_unsupported_error(): + img = np.zeros((20, 20, 3)) + assert_raises(ValueError, daisy, img) + + +def test_daisy_desc_dims(): + img = img_as_float(data.lena()[:128, :128].mean(axis=2)) + rings = 2 + histograms = 4 + orientations = 3 + descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) + assert(descs.shape[2] == (rings*histograms + 1)*orientations) + + rings = 4 + histograms = 5 + orientations = 13 + descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) + assert(descs.shape[2] == (rings*histograms + 1)*orientations) + + +def test_descs_shape(): + img = img_as_float(data.lena()[:256, :256].mean(axis=2)) + radius = 20 + step = 8 + descs = daisy(img, radius=radius, step=step) + assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) + assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + + img = img[:-1,:-2] + radius = 5 + step = 3 + descs = daisy(img, radius=radius, step=step) + assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) + assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + + +def test_daisy_incompatible_sigmas_and_radii(): + img = img_as_float(data.lena()[:128, :128].mean(axis=2)) + sigmas = [1, 2] + radii = [1, 2] + assert_raises(ValueError, daisy, img, sigmas=sigmas, ring_radii=radii) + + +def test_daisy_normalization(): + img = img_as_float(data.lena()[:64, :64].mean(axis=2)) + + descs = daisy(img, normalization='l1') + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + assert_almost_equal(np.sum(descs[i,j,:]), 1) + descs_ = daisy(img) + assert_almost_equal(descs, descs_) + + descs = daisy(img, normalization='l2') + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + assert_almost_equal(sqrt(np.sum(descs[i,j,:]**2)), 1) + + orientations = 8 + descs = daisy(img, orientations=orientations, normalization='daisy') + desc_dims = descs.shape[2] + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + for k in range(0, desc_dims, orientations): + assert_almost_equal(sqrt(np.sum( + descs[i,j,k:k+orientations]**2)), 1) + + img = np.zeros((50, 50)) + descs = daisy(img, normalization='off') + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + assert_almost_equal(np.sum(descs[i,j,:]), 0) + + assert_raises(ValueError, daisy, img, normalization='does_not_exist') + + +if __name__ == '__main__': + from numpy import testing + testing.run_module_suite() From 24d39218c2bd8d2348e2b163a05af8f1735efaac Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 8 Dec 2012 22:37:36 +0100 Subject: [PATCH 502/625] PEP8 style corrections. --- skimage/feature/_daisy.py | 150 ++++++++++++++-------------- skimage/feature/tests/test_daisy.py | 28 +++--- 2 files changed, 89 insertions(+), 89 deletions(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 7fb0364c..afe059be 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -1,27 +1,26 @@ import numpy as np -import scipy as sp -from numpy.linalg import norm from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from scipy.special import iv + def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, - normalization='l1', sigmas=None, ring_radii=None): + normalization='l1', sigmas=None, ring_radii=None): '''Extract DAISY feature descriptors densely for the given image. - DAISY is a feature descriptor similar to SIFT formulated in a way - that allows for fast dense extraction. Typically, this is practical - for bag-of-features image representations. + DAISY is a feature descriptor similar to SIFT formulated in a way that + allows for fast dense extraction. Typically, this is practical for + bag-of-features image representations. - The implementation follows Tola et al. [1] but deviate on the - following points: - * Histogram bin contribution are smoothed with a circular - Gaussian window over the tonal range (the angular range). - * The sigma values of the spatial Gaussian smoothing in this code - do not match the sigma values in the original code by Tola et - al. [2]. In their code, spatial smoothing is applied to both the - input image and the center histogram. However, this smoothing is - not documented in [1] and, therefore, it is omitted. + The implementation follows Tola et al. [1] but deviate on the following + points: + * Histogram bin contribution are smoothed with a circular Gaussian + window over the tonal range (the angular range). + * The sigma values of the spatial Gaussian smoothing in this code do + not match the sigma values in the original code by Tola et al. [2]. + In their code, spatial smoothing is applied to both the input image + and the center histogram. However, this smoothing is not documented + in [1] and, therefore, it is omitted. Parameters ---------- @@ -39,26 +38,25 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, Number of orientations (bins) per histogram. normalization : {'l1', 'l2', 'daisy', 'off'}, optional How to normalize the descriptors: - * 'l1': L1-normalization of each descriptor. - * 'l2': L2-normalization of each descriptor. - * 'daisy': L2-normalization of individual histograms. - * 'off': Disable normalization. + * 'l1': L1-normalization of each descriptor. + * 'l2': L2-normalization of each descriptor. + * 'daisy': L2-normalization of individual histograms. + * 'off': Disable normalization. sigmas : 1D array of float, optional Standard deviation of spatial Gaussian smoothing for the center - histogram and for each ring of histograms. The array of sigmas - should be sorted from the center and out. I.e. the first sigma - value specifies the spatial smoothing of the center histogram - and the last sigma value specifies the spatial smoothing of the - outermost ring. - Specifying sigmas overrides the following parameter: - rings = len(sigmas)-1 + histogram and for each ring of histograms. The array of sigmas should + be sorted from the center and out. I.e. the first sigma value defines + the spatial smoothing of the center histogram and the last sigma value + defines the spatial smoothing of the outermost ring. Specifying sigmas + overrides the following parameter: + rings = len(sigmas)-1 ring_radii : 1D array of int, optional - Radius (in pixels) for each ring. - Specifying ring_radii overrides the following two parameters: - rings = len(ring_radii) - radius = ring_radii[-1] + Radius (in pixels) for each ring. Specifying ring_radii overrides the + following two parameters: + rings = len(ring_radii) + radius = ring_radii[-1] If both sigmas and ring_radii are given, they must satisfy - len(ring_radii) == len(sigmas)+1 + len(ring_radii) == len(sigmas)+1 since no radius is needed for the center histogram. Returns @@ -66,16 +64,16 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, descs : array Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where - P = ceil((M-radius*2)/step) - Q = ceil((N-radius*2)/step) - R = (rings*histograms + 1)*orientations + P = ceil((M-radius*2)/step) + Q = ceil((N-radius*2)/step) + R = (rings*histograms + 1)*orientations References ---------- - [1] Tola et al. "Daisy: An efficient dense descriptor applied to - wide-baseline stereo." Pattern Analysis and Machine Intelligence, - IEEE Transactions on 32.5 (2010): 815-830. - [2] http://cvlab.epfl.ch/alumni/tola/daisy.html + [1]: Tola et al. "Daisy: An efficient dense descriptor applied to + wide-baseline stereo." Pattern Analysis and Machine Intelligence, + IEEE Transactions on 32.5 (2010): 815-830. + [2]: http://cvlab.epfl.ch/alumni/tola/daisy.html ''' # Validate image format. @@ -83,21 +81,21 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, raise ValueError('Only grey-level images are supported.') if img.dtype.kind == 'u': img = img.astype(float) - img = img/255. + img = img / 255. # Validate parameters. - if sigmas != None and ring_radii != None \ - and len(sigmas)-1 != len(ring_radii): + if sigmas is not None and ring_radii is not None \ + and len(sigmas) - 1 != len(ring_radii): raise ValueError('len(sigmas)-1 != len(ring_radii)') - if ring_radii != None: + if ring_radii is not None: rings = len(ring_radii) radius = ring_radii[-1] - if sigmas != None: - rings = len(sigmas)-1 - if sigmas == None: - sigmas = [radius*(i+1)/float(2*rings) for i in range(rings)] - if ring_radii == None: - ring_radii = [radius*(i+1)/float(rings) for i in range(rings)] + if sigmas is not None: + rings = len(sigmas) - 1 + if sigmas is None: + sigmas = [radius * (i + 1) / float(2 * rings) for i in range(rings)] + if ring_radii is None: + ring_radii = [radius * (i + 1) / float(rings) for i in range(rings)] if normalization not in ['l1', 'l2', 'daisy', 'off']: raise ValueError('Invalid normalization method.') @@ -109,59 +107,59 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, # Compute gradient orientation and magnitude and their contribution # to the histograms. - grad_mag = sqrt(dx**2 + dy**2) + grad_mag = sqrt(dx ** 2 + dy ** 2) grad_ori = arctan2(dy, dx) - hist_sigma = pi/orientations - kappa = 1./hist_sigma; + hist_sigma = pi / orientations + kappa = 1. / hist_sigma bessel = iv(0, kappa) hist = np.empty((orientations,) + img.shape, dtype=float) for i in range(orientations): - mu = 2*i*pi/orientations-pi + mu = 2 * i * pi / orientations - pi # Weigh bin contribution by the circular normal distribution - hist[i,:,:] = exp(kappa*cos(grad_ori-mu))/(2*pi*bessel) + hist[i, :, :] = exp(kappa * cos(grad_ori - mu)) / (2 * pi * bessel) # Weigh bin contribution by the gradient magnitude - hist[i,:,:] = np.multiply(hist[i,:,:], grad_mag) + hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) # Smooth orientation histograms for the center and all rings. sigmas = [sigmas[0]] + sigmas - hist_smooth = np.empty((rings+1,)+hist.shape, dtype=float) - for i in range(rings+1): + hist_smooth = np.empty((rings + 1,) + hist.shape, dtype=float) + for i in range(rings + 1): for j in range(orientations): - hist_smooth[i,j,:,:] = \ - gaussian_filter(hist[j,:,:], sigma=sigmas[i]) + hist_smooth[i, j, :, :] = gaussian_filter(hist[j, :, :], + sigma=sigmas[i]) # Assemble descriptor grid. - theta = [2*pi*j/histograms for j in range(histograms)] - desc_dims = (rings*histograms + 1)*orientations - descs = np.empty((desc_dims, img.shape[0]-2*radius, - img.shape[1]-2*radius)) - descs[:orientations,:,:] = \ - hist_smooth[0,:,radius:-radius,radius:-radius] + theta = [2 * pi * j / histograms for j in range(histograms)] + desc_dims = (rings * histograms + 1) * orientations + descs = np.empty((desc_dims, img.shape[0] - 2 * radius, + img.shape[1] - 2 * radius)) + descs[:orientations, :, :] = hist_smooth[0, :, radius:-radius, + radius:-radius] idx = orientations for i in range(rings): for j in range(histograms): - y_min = radius + int(round(ring_radii[i]*sin(theta[j]))) + y_min = radius + int(round(ring_radii[i] * sin(theta[j]))) y_max = descs.shape[1] + y_min - x_min = radius + int(round(ring_radii[i]*cos(theta[j]))) + x_min = radius + int(round(ring_radii[i] * cos(theta[j]))) x_max = descs.shape[2] + x_min - descs[idx:idx+orientations,:,:] = \ - hist_smooth[i+1,:,y_min:y_max,x_min:x_max] + descs[idx:idx + orientations, :, :] = hist_smooth[i + 1, :, + y_min:y_max, + x_min:x_max] idx += orientations - descs = descs[:,::step,::step] - descs = descs.swapaxes(0,1).swapaxes(1,2) + descs = descs[:, ::step, ::step] + descs = descs.swapaxes(0, 1).swapaxes(1, 2) # Normalize descriptors. if normalization != 'off': descs += 1e-10 if normalization == 'l1': - descs /= np.sum(descs, axis=2)[:,:,np.newaxis] + descs /= np.sum(descs, axis=2)[:, :, np.newaxis] elif normalization == 'l2': - descs /= sqrt(np.sum(descs**2, axis=2))[:,:,np.newaxis] + descs /= sqrt(np.sum(descs ** 2, axis=2))[:, :, np.newaxis] elif normalization == 'daisy': for i in range(0, desc_dims, orientations): - norms = sqrt(np.sum(descs[:,:,i:i+orientations]**2, - axis=2)) - descs[:,:,i:i+orientations] /= norms[:,:,np.newaxis] + norms = sqrt(np.sum(descs[:, :, i:i + orientations] ** 2, + axis=2)) + descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] return descs - diff --git a/skimage/feature/tests/test_daisy.py b/skimage/feature/tests/test_daisy.py index 9e4c663a..b0dd28de 100644 --- a/skimage/feature/tests/test_daisy.py +++ b/skimage/feature/tests/test_daisy.py @@ -17,14 +17,16 @@ def test_daisy_desc_dims(): rings = 2 histograms = 4 orientations = 3 - descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) - assert(descs.shape[2] == (rings*histograms + 1)*orientations) + descs = daisy(img, rings=rings, histograms=histograms, + orientations=orientations) + assert(descs.shape[2] == (rings * histograms + 1) * orientations) rings = 4 histograms = 5 orientations = 13 - descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) - assert(descs.shape[2] == (rings*histograms + 1)*orientations) + descs = daisy(img, rings=rings, histograms=histograms, + orientations=orientations) + assert(descs.shape[2] == (rings * histograms + 1) * orientations) def test_descs_shape(): @@ -32,15 +34,15 @@ def test_descs_shape(): radius = 20 step = 8 descs = daisy(img, radius=radius, step=step) - assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) - assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))) + assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))) - img = img[:-1,:-2] + img = img[:-1, :-2] radius = 5 step = 3 descs = daisy(img, radius=radius, step=step) - assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step))) - assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step))) + assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))) + assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))) def test_daisy_incompatible_sigmas_and_radii(): @@ -56,14 +58,14 @@ def test_daisy_normalization(): descs = daisy(img, normalization='l1') for i in range(descs.shape[0]): for j in range(descs.shape[1]): - assert_almost_equal(np.sum(descs[i,j,:]), 1) + assert_almost_equal(np.sum(descs[i, j, :]), 1) descs_ = daisy(img) assert_almost_equal(descs, descs_) descs = daisy(img, normalization='l2') for i in range(descs.shape[0]): for j in range(descs.shape[1]): - assert_almost_equal(sqrt(np.sum(descs[i,j,:]**2)), 1) + assert_almost_equal(sqrt(np.sum(descs[i, j, :] ** 2)), 1) orientations = 8 descs = daisy(img, orientations=orientations, normalization='daisy') @@ -72,13 +74,13 @@ def test_daisy_normalization(): for j in range(descs.shape[1]): for k in range(0, desc_dims, orientations): assert_almost_equal(sqrt(np.sum( - descs[i,j,k:k+orientations]**2)), 1) + descs[i, j, k:k + orientations] ** 2)), 1) img = np.zeros((50, 50)) descs = daisy(img, normalization='off') for i in range(descs.shape[0]): for j in range(descs.shape[1]): - assert_almost_equal(np.sum(descs[i,j,:]), 0) + assert_almost_equal(np.sum(descs[i, j, :]), 0) assert_raises(ValueError, daisy, img, normalization='does_not_exist') From 633443ec4928fe64bc3908428058afd4598ab712 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 9 Dec 2012 12:53:52 +0100 Subject: [PATCH 503/625] Better Sphinx documentation. --- skimage/feature/_daisy.py | 69 +++++++++++++++++++++------------------ 1 file changed, 38 insertions(+), 31 deletions(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index afe059be..b0cc6748 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -2,6 +2,7 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from scipy.special import iv +from skimage import img_as_float def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, @@ -12,15 +13,16 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, allows for fast dense extraction. Typically, this is practical for bag-of-features image representations. - The implementation follows Tola et al. [1] but deviate on the following + The implementation follows Tola et al. [1]_ but deviate on the following points: - * Histogram bin contribution are smoothed with a circular Gaussian - window over the tonal range (the angular range). - * The sigma values of the spatial Gaussian smoothing in this code do - not match the sigma values in the original code by Tola et al. [2]. - In their code, spatial smoothing is applied to both the input image - and the center histogram. However, this smoothing is not documented - in [1] and, therefore, it is omitted. + + * Histogram bin contribution are smoothed with a circular Gaussian + window over the tonal range (the angular range). + * The sigma values of the spatial Gaussian smoothing in this code do not + match the sigma values in the original code by Tola et al. [2]_. In + their code, spatial smoothing is applied to both the input image and + the center histogram. However, this smoothing is not documented in [1]_ + and, therefore, it is omitted. Parameters ---------- @@ -36,52 +38,57 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, Number of histograms sampled per ring. orientations : int, optional Number of orientations (bins) per histogram. - normalization : {'l1', 'l2', 'daisy', 'off'}, optional - How to normalize the descriptors: - * 'l1': L1-normalization of each descriptor. - * 'l2': L2-normalization of each descriptor. - * 'daisy': L2-normalization of individual histograms. - * 'off': Disable normalization. + normalization : [ 'l1' | 'l2' | 'daisy' | 'off' ], optional + How to normalize the descriptors + + * 'l1': L1-normalization of each descriptor. + * 'l2': L2-normalization of each descriptor. + * 'daisy': L2-normalization of individual histograms. + * 'off': Disable normalization. + sigmas : 1D array of float, optional Standard deviation of spatial Gaussian smoothing for the center histogram and for each ring of histograms. The array of sigmas should be sorted from the center and out. I.e. the first sigma value defines the spatial smoothing of the center histogram and the last sigma value defines the spatial smoothing of the outermost ring. Specifying sigmas - overrides the following parameter: - rings = len(sigmas)-1 + overrides the following parameter. + ``rings = len(sigmas)-1`` + ring_radii : 1D array of int, optional Radius (in pixels) for each ring. Specifying ring_radii overrides the - following two parameters: - rings = len(ring_radii) - radius = ring_radii[-1] - If both sigmas and ring_radii are given, they must satisfy - len(ring_radii) == len(sigmas)+1 - since no radius is needed for the center histogram. + following two parameters. + | ``rings = len(ring_radii)`` + | ``radius = ring_radii[-1]`` + + If both sigmas and ring_radii are given, they must satisfy the + following predicate since no radius is needed for the center + histogram. + ``len(ring_radii) == len(sigmas)+1`` + Returns ------- descs : array Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where - P = ceil((M-radius*2)/step) - Q = ceil((N-radius*2)/step) - R = (rings*histograms + 1)*orientations + | ``P = ceil((M-radius*2)/step)`` + | ``Q = ceil((N-radius*2)/step)`` + | ``R = (rings*histograms + 1)*orientations`` References ---------- - [1]: Tola et al. "Daisy: An efficient dense descriptor applied to - wide-baseline stereo." Pattern Analysis and Machine Intelligence, - IEEE Transactions on 32.5 (2010): 815-830. - [2]: http://cvlab.epfl.ch/alumni/tola/daisy.html + .. [1] Tola et al. "Daisy\: An efficient dense descriptor applied to wide- + baseline stereo." Pattern Analysis and Machine Intelligence, IEEE + Transactions on 32.5 (2010): 815-830. + .. [2] http://cvlab.epfl.ch/alumni/tola/daisy.html ''' # Validate image format. if img.ndim > 2: raise ValueError('Only grey-level images are supported.') if img.dtype.kind == 'u': - img = img.astype(float) - img = img / 255. + img = img_as_float(img) # Validate parameters. if sigmas is not None and ring_radii is not None \ From 2313361cf95008df94e3dd832f2122d41ed7ae51 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 15 Dec 2012 18:43:33 +0100 Subject: [PATCH 504/625] Add circle perimeter to draw module. --- skimage/draw/__init__.py | 2 +- skimage/draw/_draw.pyx | 39 +++++++++++++++++++++++++++++++++ skimage/draw/tests/test_draw.py | 33 +++++++++++++++++++++++++++- 3 files changed, 72 insertions(+), 2 deletions(-) diff --git a/skimage/draw/__init__.py b/skimage/draw/__init__.py index 4eac9b63..b213f93f 100644 --- a/skimage/draw/__init__.py +++ b/skimage/draw/__init__.py @@ -1,2 +1,2 @@ -from ._draw import line, polygon, ellipse, circle +from ._draw import line, polygon, ellipse, circle, circle_perimeter bresenham = line diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index ca0c502a..b5b96a2b 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -187,3 +187,42 @@ def circle(double cy, double cx, double radius, shape=None): ``img[rr, cc] = 1``. """ return ellipse(cy, cx, radius, radius, shape) + + +def circle_perimeter(int cy, int cx, int radius): + """Generate circle perimeter coordinates. + + Parameters + ---------- + cy, cx : int + Centre coordinate of circle. + radius: int + Radius of circle. + + Returns + ------- + rr, cc : (N,) ndarray of int + Indices of pixels that belong to the circle perimeter. + May be used to directly index into an array, e.g. + ``img[rr, cc] = 1``. + + """ + + cdef list rr = list() + cdef list cc = list() + + cdef int x = 0 + cdef int y = radius + cdef int d = 3 - 2 * radius + + while y >= x: + rr.extend([y, -y, y, -y, x, -x, x, -x]) + cc.extend([x, x, -x, -x, y, y, -y, -y]) + if d < 0: + d += 4 * x + 6 + else: + d += 4 * (x - y) + 10 + y -= 1 + x += 1 + + return np.array(rr) + cy, np.array(cc) + cx diff --git a/skimage/draw/tests/test_draw.py b/skimage/draw/tests/test_draw.py index 9e6ca8e8..04cbae92 100644 --- a/skimage/draw/tests/test_draw.py +++ b/skimage/draw/tests/test_draw.py @@ -1,7 +1,7 @@ from numpy.testing import assert_array_equal import numpy as np -from skimage.draw import line, polygon, circle, ellipse +from skimage.draw import line, polygon, circle, circle_perimeter, ellipse def test_line_horizontal(): @@ -150,6 +150,37 @@ def test_circle(): assert_array_equal(img, img_) +def test_circle_perimeter(): + img = np.zeros((15, 15), 'uint8') + rr, cc = circle_perimeter(7, 7, 0) + img[rr, cc] = 1 + assert(np.sum(img) == 1) + + img = np.zeros((17, 15), 'uint8') + rr, cc = circle_perimeter(7, 7, 7) + img[rr, cc] = 1 + img_ = np.array( + [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] + ) + assert_array_equal(img, img_) + + def test_ellipse(): img = np.zeros((15, 15), 'uint8') From 67701614c1c952a2d1bf4bc4695b998f73ad864c Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sat, 15 Dec 2012 18:44:23 +0100 Subject: [PATCH 505/625] Add DAISY visualization + example plot. --- doc/examples/plot_daisy.py | 28 ++++++++++++ doc/examples/plot_shapes.py | 8 +++- skimage/feature/_daisy.py | 70 ++++++++++++++++++++++++++--- skimage/feature/tests/test_daisy.py | 5 +++ 4 files changed, 103 insertions(+), 8 deletions(-) create mode 100644 doc/examples/plot_daisy.py diff --git a/doc/examples/plot_daisy.py b/doc/examples/plot_daisy.py new file mode 100644 index 00000000..af78103d --- /dev/null +++ b/doc/examples/plot_daisy.py @@ -0,0 +1,28 @@ +""" +=============================== +Dense DAISY feature description +=============================== + +The DAISY local image descriptor is based on gradient orientation histograms +similar to the SIFT descriptor. It is formulated in a way that allows for fast +dense extraction which is useful for e.g. bag-of-features image +representations. + +In this example a limited number of DAISY descriptors are extracted at a large +scale for illustrative purposes. +""" + +from skimage.feature import daisy +from skimage import data +import matplotlib.pyplot as plt + + +img = data.camera() +descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6, + orientations=8, visualize=True) + +plt.axis('off') +plt.imshow(descs_img) +descs_num = descs.shape[0] * descs.shape[1] +plt.title('%i DAISY descriptors extracted:' % descs_num) +plt.show() diff --git a/doc/examples/plot_shapes.py b/doc/examples/plot_shapes.py index 9e586209..25a48ebe 100644 --- a/doc/examples/plot_shapes.py +++ b/doc/examples/plot_shapes.py @@ -13,7 +13,7 @@ This example shows how to fill several different shapes: import matplotlib.pyplot as plt -from skimage.draw import line, polygon, circle, ellipse +from skimage.draw import line, polygon, circle, circle_perimeter, ellipse import numpy as np @@ -42,5 +42,9 @@ img[rr,cc,:] = (255, 255, 0) rr, cc = ellipse(300, 300, 100, 200, img.shape) img[rr,cc,2] = 255 +# circle +rr, cc = circle_perimeter(120, 400, 50) +img[rr, cc, :] = (255, 0, 255) + plt.imshow(img) -plt.show() \ No newline at end of file +plt.show() diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index b0cc6748..85e259d0 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -6,7 +6,7 @@ from skimage import img_as_float def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, - normalization='l1', sigmas=None, ring_radii=None): + normalization='l1', sigmas=None, ring_radii=None, visualize=False): '''Extract DAISY feature descriptors densely for the given image. DAISY is a feature descriptor similar to SIFT formulated in a way that @@ -65,6 +65,8 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, following predicate since no radius is needed for the center histogram. ``len(ring_radii) == len(sigmas)+1`` + visualize : bool, optional + Generate a visualization of the DAISY descriptors Returns @@ -75,10 +77,12 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, | ``P = ceil((M-radius*2)/step)`` | ``Q = ceil((N-radius*2)/step)`` | ``R = (rings*histograms + 1)*orientations`` + descs_img : (M, N, 3) array (only if visualize==True) + Visualization of the DAISY descriptors. References ---------- - .. [1] Tola et al. "Daisy\: An efficient dense descriptor applied to wide- + .. [1] Tola et al. "Daisy: An efficient dense descriptor applied to wide- baseline stereo." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.5 (2010): 815-830. .. [2] http://cvlab.epfl.ch/alumni/tola/daisy.html @@ -120,10 +124,11 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, kappa = 1. / hist_sigma bessel = iv(0, kappa) hist = np.empty((orientations,) + img.shape, dtype=float) - for i in range(orientations): - mu = 2 * i * pi / orientations - pi + orientation_angles = [2 * o * pi / orientations - pi + for o in range(orientations)] + for i, o in enumerate(orientation_angles): # Weigh bin contribution by the circular normal distribution - hist[i, :, :] = exp(kappa * cos(grad_ori - mu)) / (2 * pi * bessel) + hist[i, :, :] = exp(kappa * cos(grad_ori - o)) / (2 * pi * bessel) # Weigh bin contribution by the gradient magnitude hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) @@ -169,4 +174,57 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, axis=2)) descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] - return descs + if visualize: + from skimage import draw + from skimage import color + descs_img = color.gray2rgb(img) + for i in range(descs.shape[0]): + for j in range(descs.shape[1]): + # Draw center histogram sigma + color = (1, 0, 0) + desc_y = i * step + radius + desc_x = j * step + radius + coords = draw.circle_perimeter(desc_y, desc_x, sigmas[0]) + set_color(descs_img, coords, color) + max_bin = np.max(descs[i, j, :]) + for o_num, o in enumerate(orientation_angles): + # Draw center histogram bins + bin_size = descs[i, j, o_num] / max_bin + dy = sigmas[0] * bin_size * sin(o) + dx = sigmas[0] * bin_size * cos(o) + coords = draw.line(desc_y, desc_x, desc_y + dy, + desc_x + dx) + set_color(descs_img, coords, color) + for r_num, r in enumerate(ring_radii): + color_offset = float(1 + r_num) / rings + color = (1 - color_offset, 1, color_offset) + for t_num, t in enumerate(theta): + # Draw ring histogram sigmas + hist_y = desc_y + int(round(r * sin(t))) + hist_x = desc_x + int(round(r * cos(t))) + coords = draw.circle_perimeter(hist_y, hist_x, + sigmas[r_num + 1]) + set_color(descs_img, coords, color) + for o_num, o in enumerate(orientation_angles): + # Draw histogram bins + bin_size = descs[i, j, orientations + r_num * + histograms * orientations + + t_num * orientations + o_num] + bin_size /= max_bin + dy = sigmas[r_num + 1] * bin_size * sin(o) + dx = sigmas[r_num + 1] * bin_size * cos(o) + coords = draw.line(hist_y, hist_x, hist_y + dy, + hist_x + dx) + set_color(descs_img, coords, color) + return descs, descs_img + else: + return descs + + +def set_color(img, coords, color): + ''' Set pixel color at the given coordiantes in the image.''' + coords = filter(lambda (y, x): y >= 0 and y < img.shape[0] and x >= 0 + and x < img.shape[1], zip(*coords)) + if coords: + rr, cc = zip(*coords) + img[rr, cc, :] = color diff --git a/skimage/feature/tests/test_daisy.py b/skimage/feature/tests/test_daisy.py index b0dd28de..40781a64 100644 --- a/skimage/feature/tests/test_daisy.py +++ b/skimage/feature/tests/test_daisy.py @@ -85,6 +85,11 @@ def test_daisy_normalization(): assert_raises(ValueError, daisy, img, normalization='does_not_exist') +def test_daisy_visualization(): + img = img_as_float(data.lena()[:128, :128].mean(axis=2)) + descs, descs_img = daisy(img, visualize=True) + assert(descs_img.shape == (128, 128, 3)) + if __name__ == '__main__': from numpy import testing testing.run_module_suite() From 74e6c38d6f2995d03d6522472c0a3b67b3d1f74b Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 16 Dec 2012 13:47:35 +0100 Subject: [PATCH 506/625] Handle all non-float images. --- skimage/feature/_daisy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 85e259d0..d011d693 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -91,7 +91,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, # Validate image format. if img.ndim > 2: raise ValueError('Only grey-level images are supported.') - if img.dtype.kind == 'u': + if img.dtype.kind != 'f': img = img_as_float(img) # Validate parameters. From b770578aaeae1efe767db06f402d2185203673ce Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 16 Dec 2012 16:44:56 +0100 Subject: [PATCH 507/625] Move set_color() to draw module. --- skimage/draw/__init__.py | 2 +- skimage/draw/_draw.pyx | 27 +++++++++++++++++++++++++++ skimage/feature/_daisy.py | 24 +++++++----------------- 3 files changed, 35 insertions(+), 18 deletions(-) diff --git a/skimage/draw/__init__.py b/skimage/draw/__init__.py index b213f93f..4b30ea18 100644 --- a/skimage/draw/__init__.py +++ b/skimage/draw/__init__.py @@ -1,2 +1,2 @@ -from ._draw import line, polygon, ellipse, circle, circle_perimeter +from ._draw import line, polygon, ellipse, circle, circle_perimeter, set_color bresenham = line diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index b5b96a2b..509796bb 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -226,3 +226,30 @@ def circle_perimeter(int cy, int cx, int radius): x += 1 return np.array(rr) + cy, np.array(cc) + cx + + +@cython.boundscheck(False) +@cython.wraparound(False) +def set_color(img, coords, color): + """Set pixel color in the image at the given coordiantes. Coordinates that + exceeed the shape of the image will be ignored. + + Parameters + ---------- + img : (M, N, D) ndarray + Image + coords : ((P,) ndarray, (P,) ndarray) + Coordinates of pixels to be colored. + color : (D,) ndarray + Color to be assigned to coordinates in the image. + + Returns + ------- + img : (M, N, D) ndarray + The updated image. + """ + rr, cc = coords + rr_inside = np.logical_and(rr >= 0, rr < img.shape[0]) + cc_inside = np.logical_and(cc >= 0, cc < img.shape[1]) + inside = np.logical_and(rr_inside, cc_inside) + img[rr[inside], cc[inside]] = color diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index d011d693..07caf22b 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -2,7 +2,8 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from scipy.special import iv -from skimage import img_as_float +import skimage +from skimage import img_as_float, draw def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, @@ -175,9 +176,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] if visualize: - from skimage import draw - from skimage import color - descs_img = color.gray2rgb(img) + descs_img = skimage.color.gray2rgb(img) for i in range(descs.shape[0]): for j in range(descs.shape[1]): # Draw center histogram sigma @@ -185,7 +184,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, desc_y = i * step + radius desc_x = j * step + radius coords = draw.circle_perimeter(desc_y, desc_x, sigmas[0]) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) max_bin = np.max(descs[i, j, :]) for o_num, o in enumerate(orientation_angles): # Draw center histogram bins @@ -194,7 +193,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, dx = sigmas[0] * bin_size * cos(o) coords = draw.line(desc_y, desc_x, desc_y + dy, desc_x + dx) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) for r_num, r in enumerate(ring_radii): color_offset = float(1 + r_num) / rings color = (1 - color_offset, 1, color_offset) @@ -204,7 +203,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, hist_x = desc_x + int(round(r * cos(t))) coords = draw.circle_perimeter(hist_y, hist_x, sigmas[r_num + 1]) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) for o_num, o in enumerate(orientation_angles): # Draw histogram bins bin_size = descs[i, j, orientations + r_num * @@ -215,16 +214,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, dx = sigmas[r_num + 1] * bin_size * cos(o) coords = draw.line(hist_y, hist_x, hist_y + dy, hist_x + dx) - set_color(descs_img, coords, color) + draw.set_color(descs_img, coords, color) return descs, descs_img else: return descs - - -def set_color(img, coords, color): - ''' Set pixel color at the given coordiantes in the image.''' - coords = filter(lambda (y, x): y >= 0 and y < img.shape[0] and x >= 0 - and x < img.shape[1], zip(*coords)) - if coords: - rr, cc = zip(*coords) - img[rr, cc, :] = color From 8d8b8ef4f74c00fbfb21bf1f603a3841f96a0b4a Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Sun, 16 Dec 2012 17:26:32 +0100 Subject: [PATCH 508/625] No need to normalize angular smoothing. --- skimage/feature/_daisy.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 07caf22b..f5599dd5 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -1,7 +1,6 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter -from scipy.special import iv import skimage from skimage import img_as_float, draw @@ -121,15 +120,13 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, # to the histograms. grad_mag = sqrt(dx ** 2 + dy ** 2) grad_ori = arctan2(dy, dx) - hist_sigma = pi / orientations - kappa = 1. / hist_sigma - bessel = iv(0, kappa) - hist = np.empty((orientations,) + img.shape, dtype=float) + orientation_kappa = orientations / pi orientation_angles = [2 * o * pi / orientations - pi for o in range(orientations)] + hist = np.empty((orientations,) + img.shape, dtype=float) for i, o in enumerate(orientation_angles): # Weigh bin contribution by the circular normal distribution - hist[i, :, :] = exp(kappa * cos(grad_ori - o)) / (2 * pi * bessel) + hist[i, :, :] = exp(orientation_kappa * cos(grad_ori - o)) # Weigh bin contribution by the gradient magnitude hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) From 42068041750e9ef0c7799e9177917465d8225854 Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Mon, 17 Dec 2012 11:54:37 +0100 Subject: [PATCH 509/625] Fixed submodule import. --- skimage/feature/_daisy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index f5599dd5..f83605b5 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -1,7 +1,7 @@ import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter -import skimage +import skimage.color from skimage import img_as_float, draw From 101bcc6a82920f89ace043c72b282e2aeb3a6d4d Mon Sep 17 00:00:00 2001 From: Anders Boesen Lindbo Larsen Date: Thu, 27 Dec 2012 10:49:29 +0100 Subject: [PATCH 510/625] Add myself as contributor. --- CONTRIBUTORS.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index fd3ff235..9f3f2aa7 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -126,3 +126,6 @@ - Steven Silvester, Karel Zuiderveld Adaptive Histogram Equalization + +- Anders Boesen Lindbo Larsen + Dense DAISY feature description, circle perimeter drawing. From 713e5dc734f817601ea4c68c04b918161d1a70fa Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Fri, 28 Dec 2012 00:53:23 -0600 Subject: [PATCH 511/625] BUG: Adapthist error caused by divisor operator change in py3k --- skimage/exposure/_adapthist.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 88729ce2..91787513 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -226,7 +226,7 @@ def clip_histogram(hist, clip_limit): n_excess = excess.sum() - excess.size * clip_limit # Second part: clip histogram and redistribute excess pixels in each bin - bin_incr = n_excess / hist.size # average binincrement + bin_incr = int(n_excess / hist.size) # average binincrement upper = clip_limit - bin_incr # Bins larger than upper set to cliplimit hist[excess_mask] = clip_limit From 1af3d69cde636c1f76cb9eb895ad6da6e1f085ce Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Fri, 28 Dec 2012 01:33:01 -0600 Subject: [PATCH 512/625] BUG: Fixed another division error --- skimage/exposure/_adapthist.py | 8 ++++---- skimage/exposure/tests/test_exposure.py | 14 ++++++++------ 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 91787513..c2b85d8d 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -158,11 +158,11 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): for y in range(ntiles_y + 1): xstart = 0 if y == 0: # special case: top row - ystep = y_size / 2 + ystep = int(y_size / 2) yU = 0 yB = 0 elif y == ntiles_y: # special case: bottom row - ystep = y_size / 2 + ystep = int(y_size / 2) yU = ntiles_y - 1 yB = yU else: # default values @@ -172,11 +172,11 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): for x in range(ntiles_x + 1): if x == 0: # special case: left column - xstep = x_size / 2 + xstep = int(x_size / 2) xL = 0 xR = 0 elif x == ntiles_x: # special case: right column - xstep = x_size / 2 + xstep = int(x_size / 2) xL = ntiles_x - 1 xR = xL else: # default values diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index a5d72cbb..6c7dd9a4 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -87,8 +87,9 @@ def test_adapthist_scalar(): full_scale = skimage.exposure.rescale_intensity(skimage.img_as_uint(img)) assert_almost_equal = np.testing.assert_almost_equal - assert peak_snr(full_scale, adapted) > 95.0 - assert norm_brightness_err(full_scale, adapted) < 0.05 + assert_almost_equal(peak_snr(full_scale, adapted), 101.231, 3) + assert_almost_equal(norm_brightness_err(full_scale, adapted), + 0.041, 3) return img, adapted @@ -102,8 +103,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert peak_snr(img, adapted) > 95.0 - assert norm_brightness_err(img, adapted) < 0.05 + assert_almost_equal(peak_snr(img, adapted), 97.949, 3) + assert_almost_equal(norm_brightness_err(img, adapted), 0.03077, 3) return data, adapted @@ -117,8 +118,9 @@ def test_adapthist_color(): assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) - assert peak_snr(img, adapted) > 95.0 - assert norm_brightness_err(img, adapted) < 0.05 + assert_almost_equal(peak_snr(full_scale, adapted), 102.940, 3) + assert_almost_equal(norm_brightness_err(full_scale, adapted), + 0.0110, 3) return data, adapted From f48f94f0be6ce5808314765176054c48d137f17a Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Fri, 28 Dec 2012 18:37:15 -0600 Subject: [PATCH 513/625] BUG: Adapthist interpolation grids could be improperly spaces --- skimage/exposure/_adapthist.py | 8 ++++---- skimage/exposure/tests/test_exposure.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index c2b85d8d..0cf569d4 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -158,11 +158,11 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): for y in range(ntiles_y + 1): xstart = 0 if y == 0: # special case: top row - ystep = int(y_size / 2) + ystep = y_size / 2. yU = 0 yB = 0 elif y == ntiles_y: # special case: bottom row - ystep = int(y_size / 2) + ystep = y_size / 2. yU = ntiles_y - 1 yB = yU else: # default values @@ -172,11 +172,11 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): for x in range(ntiles_x + 1): if x == 0: # special case: left column - xstep = int(x_size / 2) + xstep = x_size / 2. xL = 0 xR = 0 elif x == ntiles_x: # special case: right column - xstep = int(x_size / 2) + xstep = x_size / 2. xL = ntiles_x - 1 xR = xL else: # default values diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py index 6c7dd9a4..086739de 100644 --- a/skimage/exposure/tests/test_exposure.py +++ b/skimage/exposure/tests/test_exposure.py @@ -103,8 +103,8 @@ def test_adapthist_grayscale(): nbins=128) assert_almost_equal = np.testing.assert_almost_equal assert img.shape == adapted.shape - assert_almost_equal(peak_snr(img, adapted), 97.949, 3) - assert_almost_equal(norm_brightness_err(img, adapted), 0.03077, 3) + assert_almost_equal(peak_snr(img, adapted), 97.531, 3) + assert_almost_equal(norm_brightness_err(img, adapted), 0.0313, 3) return data, adapted From eeaf04ed59cecf5cf95272767fe387f6e8d788f6 Mon Sep 17 00:00:00 2001 From: Steven Silvester Date: Fri, 28 Dec 2012 21:59:19 -0600 Subject: [PATCH 514/625] STY: Minor change to trigger Travis build --- skimage/exposure/_adapthist.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 0cf569d4..346d8424 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -158,11 +158,11 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): for y in range(ntiles_y + 1): xstart = 0 if y == 0: # special case: top row - ystep = y_size / 2. + ystep = y_size / 2.0 yU = 0 yB = 0 elif y == ntiles_y: # special case: bottom row - ystep = y_size / 2. + ystep = y_size / 2.0 yU = ntiles_y - 1 yB = yU else: # default values @@ -172,11 +172,11 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128): for x in range(ntiles_x + 1): if x == 0: # special case: left column - xstep = x_size / 2. + xstep = x_size / 2.0 xL = 0 xR = 0 elif x == ntiles_x: # special case: right column - xstep = x_size / 2. + xstep = x_size / 2.0 xL = ntiles_x - 1 xR = xL else: # default values From 60b560ff9eec996dea872078f2cd09d102f65af0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 12 Jan 2013 10:56:52 +0100 Subject: [PATCH 515/625] add andres method for circle_perimeter --- skimage/draw/_draw.pyx | 46 +++++++++++++++++++++++++++------ skimage/draw/tests/test_draw.py | 36 +++++++++++++++++++++++--- 2 files changed, 71 insertions(+), 11 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 509796bb..ff41410c 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -189,7 +189,7 @@ def circle(double cy, double cx, double radius, shape=None): return ellipse(cy, cx, radius, radius, shape) -def circle_perimeter(int cy, int cx, int radius): +def circle_perimeter(int cy, int cx, int radius, method='bresenham'): """Generate circle perimeter coordinates. Parameters @@ -198,6 +198,10 @@ def circle_perimeter(int cy, int cx, int radius): Centre coordinate of circle. radius: int Radius of circle. + method : string + bresenham : Bresenham method + andres : Andres method + Returns ------- @@ -206,6 +210,13 @@ def circle_perimeter(int cy, int cx, int radius): May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. + Notes + ----- + Andres method presents the advantage that concentric + circles create a disc whereas Bresenham can make holes. There + is also less distortions when Andres circles are rotated. + Bresenham method is also known as midpoint circle algorithm. + """ cdef list rr = list() @@ -213,17 +224,36 @@ def circle_perimeter(int cy, int cx, int radius): cdef int x = 0 cdef int y = radius - cdef int d = 3 - 2 * radius + cdef int d = 0 + if method == 'bresenham': + d = 3 - 2 * radius + elif method == 'andres': + d = radius - 1 + else: + raise ValueError('Wrong method') while y >= x: rr.extend([y, -y, y, -y, x, -x, x, -x]) cc.extend([x, x, -x, -x, y, y, -y, -y]) - if d < 0: - d += 4 * x + 6 - else: - d += 4 * (x - y) + 10 - y -= 1 - x += 1 + + if method == 'bresenham': + if d < 0: + d += 4 * x + 6 + else: + d += 4 * (x - y) + 10 + y -= 1 + x += 1 + elif method == 'andres': + if err >= 2 * (x - 1): + err = err - 2*x + x = x + 1 + elif err <= 2*(radius - y): + err = err + 2 * y - 1 + y = y - 1 + else: + err = err + 2*(y - x - 1) + y = y - 1 + x = x + 1 return np.array(rr) + cy, np.array(cc) + cx diff --git a/skimage/draw/tests/test_draw.py b/skimage/draw/tests/test_draw.py index 04cbae92..b2325015 100644 --- a/skimage/draw/tests/test_draw.py +++ b/skimage/draw/tests/test_draw.py @@ -150,14 +150,14 @@ def test_circle(): assert_array_equal(img, img_) -def test_circle_perimeter(): +def test_circle_perimeter_bresenham(): img = np.zeros((15, 15), 'uint8') - rr, cc = circle_perimeter(7, 7, 0) + rr, cc = circle_perimeter(7, 7, 0, method='bresenham') img[rr, cc] = 1 assert(np.sum(img) == 1) img = np.zeros((17, 15), 'uint8') - rr, cc = circle_perimeter(7, 7, 7) + rr, cc = circle_perimeter(7, 7, 7, method='bresenham') img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], @@ -180,6 +180,36 @@ def test_circle_perimeter(): ) assert_array_equal(img, img_) +def test_circle_perimeter_andres(): + img = np.zeros((15, 15), 'uint8') + rr, cc = circle_perimeter(7, 7, 0, method='andres') + img[rr, cc] = 1 + assert(np.sum(img) == 1) + + img = np.zeros((17, 15), 'uint8') + rr, cc = circle_perimeter(7, 7, 7, method='andres') + img[rr, cc] = 1 + img_ = np.array( + [[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], + [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], + [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], + [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], + [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] + ) + assert_array_equal(img, img_) + def test_ellipse(): img = np.zeros((15, 15), 'uint8') From 1fb5ae1a7514d4963de5be63d78a760df9cee493 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 12 Jan 2013 11:29:18 +0100 Subject: [PATCH 516/625] fix variable --- skimage/draw/_draw.pyx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index ff41410c..e3048de3 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -244,14 +244,14 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): y -= 1 x += 1 elif method == 'andres': - if err >= 2 * (x - 1): - err = err - 2*x + if d >= 2 * (x - 1): + d = d - 2*x x = x + 1 - elif err <= 2*(radius - y): - err = err + 2 * y - 1 + elif d <= 2*(radius - y): + d = d + 2 * y - 1 y = y - 1 else: - err = err + 2*(y - x - 1) + d = d + 2*(y - x - 1) y = y - 1 x = x + 1 From 3b84744c251c4ad1855af3a6314d1e9cfecf5467 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 12 Jan 2013 11:58:33 +0100 Subject: [PATCH 517/625] fix docstring + PEP8 --- skimage/draw/_draw.pyx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index e3048de3..6c50842f 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -198,7 +198,7 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): Centre coordinate of circle. radius: int Radius of circle. - method : string + method : {'bresenham', 'andres'}, optional bresenham : Bresenham method andres : Andres method @@ -245,13 +245,13 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): x += 1 elif method == 'andres': if d >= 2 * (x - 1): - d = d - 2*x + d = d - 2 * x x = x + 1 - elif d <= 2*(radius - y): + elif d <= 2 * (radius - y): d = d + 2 * y - 1 y = y - 1 else: - d = d + 2*(y - x - 1) + d = d + 2 * (y - x - 1) y = y - 1 x = x + 1 From 7eab419cabbc6eea70b04121be3762df0315f02c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 12 Jan 2013 12:19:06 +0100 Subject: [PATCH 518/625] add references --- skimage/draw/_draw.pyx | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 6c50842f..7c77b423 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -217,6 +217,12 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): is also less distortions when Andres circles are rotated. Bresenham method is also known as midpoint circle algorithm. + References + ---------- + .. [1] J.E. Bresenham, "Algorithm for computer control of a digital + plotter", 4 (1965) 25-30. + .. [2] E. Andres, "Discrete circles, rings and spheres", 18 (1994) 695-706. + """ cdef list rr = list() From accdf3b370e7caa2b514ae6b594708e97cbe6c1e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 13 Jan 2013 11:38:36 +0100 Subject: [PATCH 519/625] Fix typo in doc string --- skimage/draw/_draw.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 7c77b423..66b41d2c 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -267,8 +267,8 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): @cython.boundscheck(False) @cython.wraparound(False) def set_color(img, coords, color): - """Set pixel color in the image at the given coordiantes. Coordinates that - exceeed the shape of the image will be ignored. + """Set pixel color in the image at the given coordinates. Coordinates that + exceed the shape of the image will be ignored. Parameters ---------- From 075c43109abeec1dfd35e1a7af7365143a665649 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 13 Jan 2013 16:39:02 +0100 Subject: [PATCH 520/625] new method: ellipse_perimeter --- skimage/draw/__init__.py | 2 +- skimage/draw/_draw.pyx | 84 +++++++++++++++++++++++++++++++++ skimage/draw/tests/test_draw.py | 47 +++++++++++++++++- 3 files changed, 130 insertions(+), 3 deletions(-) diff --git a/skimage/draw/__init__.py b/skimage/draw/__init__.py index 4b30ea18..67692f1b 100644 --- a/skimage/draw/__init__.py +++ b/skimage/draw/__init__.py @@ -1,2 +1,2 @@ -from ._draw import line, polygon, ellipse, circle, circle_perimeter, set_color +from ._draw import line, polygon, ellipse, circle, circle_perimeter, ellipse_perimeter, set_color bresenham = line diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 66b41d2c..73c9a341 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -263,6 +263,90 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): return np.array(rr) + cy, np.array(cc) + cx +def ellipse_perimeter(int cy, int cx, int yradius, int xradius): + """Generate ellipse perimeter coordinates. + + Parameters + ---------- + cy, cx : int + Centre coordinate of ellipse. + yradius, xradius: int + Main radial values. + + Returns + ------- + rr, cc : (N,) ndarray of int + Indices of pixels that belong to the circle perimeter. + May be used to directly index into an array, e.g. + ``img[rr, cc] = 1``. + + References + ---------- + .. [1] J. Kennedy "A fast Bresenham type algorithm for + drawing ellipses". + """ + # If both radii == 0, return the center + # to avoid infinite loop in 2nd set + if (xradius == 0 and yradius == 0): + return np.array(cy), np.array(cx) + + # a and b are xradius an yradius + # compute 2a^2 and 2b^2 + cdef int twoasquared = 2 * xradius * xradius + cdef int twobsquared = 2 * yradius * yradius + + # Pixels + cdef list px = list() + cdef list py = list() + + # First set of points: + # start at the top + cdef int x = xradius + cdef int y = 0 + + cdef int err = 0 + cdef int xstop = twobsquared * xradius + cdef int ystop = 0 + cdef int xchange = yradius * yradius * (1 - 2 * xradius) + cdef int ychange = xradius * xradius + + while(xstop > ystop): + px.extend([x, -x, -x, x]) + py.extend([y, y, -y, -y]) + y += 1 + ystop += twoasquared + err += ychange + ychange += twoasquared + if ((2 * err + xchange) > 0): + x -= 1 + xstop -= twobsquared + err += xchange + xchange += twobsquared + + # Second set of points: + x = 0 + y = yradius + + err = 0 + xstop = 0 + ystop = twoasquared * yradius + xchange = yradius * yradius + ychange = xradius * xradius * (1 - 2 * yradius) + + while(xstop <= ystop): + px.extend([x, -x, -x, x]) + py.extend([y, y, -y, -y]) + x += 1 + xstop += twobsquared + err += xchange + xchange += twobsquared + if ((2 * err + ychange) > 0): + y -= 1 + ystop -= twoasquared + err += ychange + ychange += twobsquared + + return np.array(py) + cy, np.array(px) + cx @cython.boundscheck(False) @cython.wraparound(False) diff --git a/skimage/draw/tests/test_draw.py b/skimage/draw/tests/test_draw.py index b2325015..7b4edbe4 100644 --- a/skimage/draw/tests/test_draw.py +++ b/skimage/draw/tests/test_draw.py @@ -1,7 +1,7 @@ from numpy.testing import assert_array_equal import numpy as np -from skimage.draw import line, polygon, circle, circle_perimeter, ellipse +from skimage.draw import line, polygon, circle, circle_perimeter, ellipse, ellipse_perimeter def test_line_horizontal(): @@ -210,7 +210,6 @@ def test_circle_perimeter_andres(): ) assert_array_equal(img, img_) - def test_ellipse(): img = np.zeros((15, 15), 'uint8') @@ -237,6 +236,50 @@ def test_ellipse(): assert_array_equal(img, img_) +def test_ellipse_perimeter(): + img = np.zeros((30, 15), 'uint8') + rr, cc = ellipse_perimeter(15, 7, 0, 0) + img[rr, cc] = 1 + assert(np.sum(img) == 1) + + img = np.zeros((30, 15), 'uint8') + rr, cc = ellipse_perimeter(15, 7, 14, 6) + img[rr, cc] = 1 + print(img) + img_ = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0]] + ) + + assert_array_equal(img, img_) if __name__ == "__main__": from numpy.testing import run_module_suite From 70825a420e35cfe72a7bde2163124b0cd2a8fd84 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 13 Jan 2013 11:44:09 +0100 Subject: [PATCH 521/625] Use char type for circle_perimeter method for speedup --- skimage/draw/_draw.pyx | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 73c9a341..1bcca5a6 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -231,10 +231,13 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): cdef int x = 0 cdef int y = radius cdef int d = 0 + cdef char cmethod if method == 'bresenham': d = 3 - 2 * radius + cmethod = 'b' elif method == 'andres': d = radius - 1 + cmethod = 'a' else: raise ValueError('Wrong method') @@ -242,14 +245,14 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): rr.extend([y, -y, y, -y, x, -x, x, -x]) cc.extend([x, x, -x, -x, y, y, -y, -y]) - if method == 'bresenham': + if cmethod == 'b': if d < 0: d += 4 * x + 6 else: d += 4 * (x - y) + 10 y -= 1 x += 1 - elif method == 'andres': + elif cmethod == 'a': if d >= 2 * (x - 1): d = d - 2 * x x = x + 1 From b1e5106029d010d052a7c07188dcfbd68e50d325 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 13 Jan 2013 11:46:42 +0100 Subject: [PATCH 522/625] Use same cython directives for whole source file --- skimage/draw/_draw.pyx | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 1bcca5a6..146197ae 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -1,3 +1,7 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np import math from libc.math cimport sqrt @@ -6,8 +10,6 @@ cimport cython from skimage._shared.geometry cimport point_in_polygon -@cython.boundscheck(False) -@cython.wraparound(False) def line(int y, int x, int y2, int x2): """Generate line pixel coordinates. @@ -66,9 +68,6 @@ def line(int y, int x, int y2, int x2): return rr, cc -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.nonecheck(False) def polygon(y, x, shape=None): """Generate coordinates of pixels within polygon. @@ -123,10 +122,6 @@ def polygon(y, x, shape=None): return np.array(rr), np.array(cc) -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.nonecheck(False) -@cython.cdivision(True) def ellipse(double cy, double cx, double b, double a, shape=None): """Generate coordinates of pixels within ellipse. @@ -351,8 +346,6 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): return np.array(py) + cy, np.array(px) + cx -@cython.boundscheck(False) -@cython.wraparound(False) def set_color(img, coords, color): """Set pixel color in the image at the given coordinates. Coordinates that exceed the shape of the image will be ignored. From 805e554a1130448e39b326b7f4fd23d63a97e965 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 13 Jan 2013 11:48:37 +0100 Subject: [PATCH 523/625] Fix PEP8 issues --- skimage/draw/_draw.pyx | 40 ++++++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 146197ae..2576c861 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -35,15 +35,19 @@ def line(int y, int x, int y2, int x2): cdef int dy = abs(y2 - y) cdef int sx, sy, d, i - if (x2 - x) > 0: sx = 1 - else: sx = -1 - if (y2 - y) > 0: sy = 1 - else: sy = -1 + if (x2 - x) > 0: + sx = 1 + else: + sx = -1 + if (y2 - y) > 0: + sy = 1 + else: + sy = -1 if dy > dx: steep = 1 - x,y = y,x - dx,dy = dy,dx - sx,sy = sy,sx + x, y = y, x + dx, dy = dy, dx + sx, sy = sy, sx d = (2 * dy) - dx rr = np.zeros(int(dx) + 1, dtype=np.int32) @@ -97,8 +101,8 @@ def polygon(y, x, shape=None): # make sure output coordinates do not exceed image size if shape is not None: - maxr = min(shape[0]-1, maxr) - maxc = min(shape[1]-1, maxc) + maxr = min(shape[0] - 1, maxr) + maxc = min(shape[1] - 1, maxc) cdef int r, c @@ -139,15 +143,15 @@ def ellipse(double cy, double cx, double b, double a, shape=None): May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. """ - cdef int minr = max(0, cy-b) - cdef int maxr = math.ceil(cy+b) - cdef int minc = max(0, cx-a) - cdef int maxc = math.ceil(cx+a) + cdef int minr = max(0, cy - b) + cdef int maxr = math.ceil(cy + b) + cdef int minc = max(0, cx - a) + cdef int maxc = math.ceil(cx + a) # make sure output coordinates do not exceed image size if shape is not None: - maxr = min(shape[0]-1, maxr) - maxc = min(shape[1]-1, maxc) + maxr = min(shape[0] - 1, maxr) + maxc = min(shape[1] - 1, maxc) cdef int r, c @@ -155,9 +159,9 @@ def ellipse(double cy, double cx, double b, double a, shape=None): cdef list rr = list() cdef list cc = list() - for r in range(minr, maxr+1): - for c in range(minc, maxc+1): - if sqrt(((r - cy)/b)**2 + ((c - cx)/a)**2) < 1: + for r in range(minr, maxr + 1): + for c in range(minc, maxc + 1): + if sqrt(((r - cy) / b)**2 + ((c - cx) / a)**2) < 1: rr.append(r) cc.append(c) From dd41c9a654555cc99b70d6684a933413d297ee3e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 13 Jan 2013 16:40:11 +0100 Subject: [PATCH 524/625] add myself to contributors --- CONTRIBUTORS.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 9f3f2aa7..d582ab63 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -129,3 +129,6 @@ - Anders Boesen Lindbo Larsen Dense DAISY feature description, circle perimeter drawing. + +- François Boulogne + Andres Method for circle perimeter, ellipse perimeter drawing. From cc061029f321578809052bbccfed89f61ab34032 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 13 Jan 2013 17:45:54 +0100 Subject: [PATCH 525/625] remove print statement --- skimage/draw/tests/test_draw.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skimage/draw/tests/test_draw.py b/skimage/draw/tests/test_draw.py index 7b4edbe4..e979c292 100644 --- a/skimage/draw/tests/test_draw.py +++ b/skimage/draw/tests/test_draw.py @@ -245,7 +245,6 @@ def test_ellipse_perimeter(): img = np.zeros((30, 15), 'uint8') rr, cc = ellipse_perimeter(15, 7, 14, 6) img[rr, cc] = 1 - print(img) img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], From 843d2d11603c0fdb21283d9c83ecce379d71de02 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 13 Jan 2013 17:47:59 +0100 Subject: [PATCH 526/625] remove brackets --- skimage/draw/_draw.pyx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 2576c861..904c3f82 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -289,7 +289,7 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): """ # If both radii == 0, return the center # to avoid infinite loop in 2nd set - if (xradius == 0 and yradius == 0): + if xradius == 0 and yradius == 0: return np.array(cy), np.array(cx) # a and b are xradius an yradius @@ -312,14 +312,14 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): cdef int xchange = yradius * yradius * (1 - 2 * xradius) cdef int ychange = xradius * xradius - while(xstop > ystop): + while xstop > ystop: px.extend([x, -x, -x, x]) py.extend([y, y, -y, -y]) y += 1 ystop += twoasquared err += ychange ychange += twoasquared - if ((2 * err + xchange) > 0): + if (2 * err + xchange) > 0: x -= 1 xstop -= twobsquared err += xchange @@ -335,14 +335,14 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): xchange = yradius * yradius ychange = xradius * xradius * (1 - 2 * yradius) - while(xstop <= ystop): + while xstop <= ystop: px.extend([x, -x, -x, x]) py.extend([y, y, -y, -y]) x += 1 xstop += twobsquared err += xchange xchange += twobsquared - if ((2 * err + ychange) > 0): + if (2 * err + ychange) > 0: y -= 1 ystop -= twoasquared err += ychange From 1400eb44b3e33784a0a583a3ab01fc5dbd73f757 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Wed, 16 Jan 2013 09:33:58 +0100 Subject: [PATCH 527/625] fix for master --- skimage/draw/_draw.pyx | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 904c3f82..c0fd798b 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -126,15 +126,15 @@ def polygon(y, x, shape=None): return np.array(rr), np.array(cc) -def ellipse(double cy, double cx, double b, double a, shape=None): +def ellipse(double cy, double cx, double yradius, double xradius, shape=None): """Generate coordinates of pixels within ellipse. Parameters ---------- cy, cx : double Centre coordinate of ellipse. - b, a: double - Minor and major semi-axes. ``(x/a)**2 + (y/b)**2 = 1``. + yradius, xradius: double + Minor and major semi-axes. ``(x/xradius)**2 + (y/yradius)**2 = 1``. Returns ------- @@ -143,10 +143,10 @@ def ellipse(double cy, double cx, double b, double a, shape=None): May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. """ - cdef int minr = max(0, cy - b) - cdef int maxr = math.ceil(cy + b) - cdef int minc = max(0, cx - a) - cdef int maxc = math.ceil(cx + a) + cdef int minr = max(0, cy-yradius) + cdef int maxr = math.ceil(cy+yradius) + cdef int minc = max(0, cx-xradius) + cdef int maxc = math.ceil(cx+xradius) # make sure output coordinates do not exceed image size if shape is not None: @@ -159,9 +159,9 @@ def ellipse(double cy, double cx, double b, double a, shape=None): cdef list rr = list() cdef list cc = list() - for r in range(minr, maxr + 1): - for c in range(minc, maxc + 1): - if sqrt(((r - cy) / b)**2 + ((c - cx) / a)**2) < 1: + for r in range(minr, maxr+1): + for c in range(minc, maxc+1): + if sqrt(((r - cy)/yradius)**2 + ((c - cx)/xradius)**2) < 1: rr.append(r) cc.append(c) From 5711cea92d65a90ddcdfdd0dfc5001dcf842122c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Tue, 15 Jan 2013 14:35:17 +0100 Subject: [PATCH 528/625] fix docstring --- skimage/draw/_draw.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index c0fd798b..551673f0 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -133,7 +133,7 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): ---------- cy, cx : double Centre coordinate of ellipse. - yradius, xradius: double + yradius, xradius : double Minor and major semi-axes. ``(x/xradius)**2 + (y/yradius)**2 = 1``. Returns From 62b359a39b963e4edbd882ded5b688818b500812 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Tue, 15 Jan 2013 14:57:20 +0100 Subject: [PATCH 529/625] add test point position circle-ellipse --- skimage/draw/tests/test_draw.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/skimage/draw/tests/test_draw.py b/skimage/draw/tests/test_draw.py index e979c292..ef09747c 100644 --- a/skimage/draw/tests/test_draw.py +++ b/skimage/draw/tests/test_draw.py @@ -155,6 +155,7 @@ def test_circle_perimeter_bresenham(): rr, cc = circle_perimeter(7, 7, 0, method='bresenham') img[rr, cc] = 1 assert(np.sum(img) == 1) + assert(img[7][7] == 1) img = np.zeros((17, 15), 'uint8') rr, cc = circle_perimeter(7, 7, 7, method='bresenham') @@ -185,6 +186,7 @@ def test_circle_perimeter_andres(): rr, cc = circle_perimeter(7, 7, 0, method='andres') img[rr, cc] = 1 assert(np.sum(img) == 1) + assert(img[7][7] == 1) img = np.zeros((17, 15), 'uint8') rr, cc = circle_perimeter(7, 7, 7, method='andres') @@ -241,6 +243,7 @@ def test_ellipse_perimeter(): rr, cc = ellipse_perimeter(15, 7, 0, 0) img[rr, cc] = 1 assert(np.sum(img) == 1) + assert(img[15][7] == 1) img = np.zeros((30, 15), 'uint8') rr, cc = ellipse_perimeter(15, 7, 14, 6) From 4b27c87385ddcee9e0f3a13f5944a0ed2d28c87c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 16 Jan 2013 17:09:04 +0100 Subject: [PATCH 530/625] Fix PEP8 issues --- skimage/draw/_draw.pyx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 551673f0..29771b28 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -143,10 +143,10 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. """ - cdef int minr = max(0, cy-yradius) - cdef int maxr = math.ceil(cy+yradius) - cdef int minc = max(0, cx-xradius) - cdef int maxc = math.ceil(cx+xradius) + cdef int minr = max(0, cy - yradius) + cdef int maxr = math.ceil(cy + yradius) + cdef int minc = max(0, cx - xradius) + cdef int maxc = math.ceil(cx + xradius) # make sure output coordinates do not exceed image size if shape is not None: @@ -161,7 +161,7 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): for r in range(minr, maxr+1): for c in range(minc, maxc+1): - if sqrt(((r - cy)/yradius)**2 + ((c - cx)/xradius)**2) < 1: + if sqrt(((r - cy) / yradius)**2 + ((c - cx) / xradius)**2) < 1: rr.append(r) cc.append(c) From 0f36316dd44e4cc1ef66e808bd0888a8e677ab5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 16 Jan 2013 17:18:12 +0100 Subject: [PATCH 531/625] Add empty lines between functions --- skimage/draw/_draw.pyx | 2 ++ 1 file changed, 2 insertions(+) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 29771b28..033de8f0 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -265,6 +265,7 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): return np.array(rr) + cy, np.array(cc) + cx + def ellipse_perimeter(int cy, int cx, int yradius, int xradius): """Generate ellipse perimeter coordinates. @@ -350,6 +351,7 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): return np.array(py) + cy, np.array(px) + cx + def set_color(img, coords, color): """Set pixel color in the image at the given coordinates. Coordinates that exceed the shape of the image will be ignored. From ebc020fe01d38c50ad4485b76adb882c25e78c93 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 16 Jan 2013 20:01:41 +0100 Subject: [PATCH 532/625] Wrap line and group imports --- skimage/draw/__init__.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/skimage/draw/__init__.py b/skimage/draw/__init__.py index 67692f1b..4aa6bf35 100644 --- a/skimage/draw/__init__.py +++ b/skimage/draw/__init__.py @@ -1,2 +1,5 @@ -from ._draw import line, polygon, ellipse, circle, circle_perimeter, ellipse_perimeter, set_color +from ._draw import line, polygon, ellipse, ellipse_perimeter, \ + circle, circle_perimeter, set_color + + bresenham = line From 8578158f1d31c7b427f01a19503c9177e4593102 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 16 Jan 2013 20:06:53 +0100 Subject: [PATCH 533/625] Make some stylistic code layout changes --- skimage/draw/_draw.pyx | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 033de8f0..e0c9231e 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -28,6 +28,7 @@ def line(int y, int x, int y2, int x2): ``img[rr, cc] = 1``. """ + cdef np.ndarray[np.int32_t, ndim=1, mode="c"] rr, cc cdef int steep = 0 @@ -92,7 +93,9 @@ def polygon(y, x, shape=None): Pixel coordinates of polygon. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. + """ + cdef int nr_verts = x.shape[0] cdef int minr = max(0, y.min()) cdef int maxr = math.ceil(y.max()) @@ -142,7 +145,9 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): Pixel coordinates of ellipse. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. + """ + cdef int minr = max(0, cy - yradius) cdef int maxr = math.ceil(cy + yradius) cdef int minc = max(0, cx - xradius) @@ -184,7 +189,9 @@ def circle(double cy, double cx, double radius, shape=None): Pixel coordinates of circle. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. + """ + return ellipse(cy, cx, radius, radius, shape) @@ -287,16 +294,16 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): ---------- .. [1] J. Kennedy "A fast Bresenham type algorithm for drawing ellipses". + """ - # If both radii == 0, return the center - # to avoid infinite loop in 2nd set + + # If both radii == 0, return the center to avoid infinite loop in 2nd set if xradius == 0 and yradius == 0: return np.array(cy), np.array(cx) - # a and b are xradius an yradius - # compute 2a^2 and 2b^2 - cdef int twoasquared = 2 * xradius * xradius - cdef int twobsquared = 2 * yradius * yradius + # a and b are xradius an yradius compute 2a^2 and 2b^2 + cdef int twoasquared = 2 * xradius**2 + cdef int twobsquared = 2 * yradius**2 # Pixels cdef list px = list() @@ -353,8 +360,9 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): def set_color(img, coords, color): - """Set pixel color in the image at the given coordinates. Coordinates that - exceed the shape of the image will be ignored. + """Set pixel color in the image at the given coordinates. + + Coordinates that exceed the shape of the image will be ignored. Parameters ---------- @@ -369,7 +377,9 @@ def set_color(img, coords, color): ------- img : (M, N, D) ndarray The updated image. + """ + rr, cc = coords rr_inside = np.logical_and(rr >= 0, rr < img.shape[0]) cc_inside = np.logical_and(cc >= 0, cc < img.shape[1]) From 697c173227110a791c6237828bb773af6661af10 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Wed, 16 Jan 2013 19:37:47 -0600 Subject: [PATCH 534/625] BUG: Update bento build to match setup files. --- bento.info | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/bento.info b/bento.info index 8e001582..57d9f714 100644 --- a/bento.info +++ b/bento.info @@ -64,9 +64,9 @@ Library: Extension: skimage.filter._ctmf Sources: skimage/filter/_ctmf.pyx - Extension: skimage.filter._denoise + Extension: skimage.filter._denoise_cy Sources: - skimage/filter/_denoise.pyx + skimage/filter/_denoise_cy.pyx Extension: skimage.morphology.ccomp Sources: skimage/morphology/ccomp.pyx @@ -91,6 +91,9 @@ Library: Extension: skimage.morphology._greyreconstruct Sources: skimage/morphology/_greyreconstruct.pyx + Extension: skimage.feature.corner_cy + Sources: + skimage/feature/corner_cy.pyx Extension: skimage.feature._texture Sources: skimage/feature/_texture.pyx From bf4709155d3c64d26553eb6614e496083af2cb22 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Thu, 17 Jan 2013 11:11:21 +0700 Subject: [PATCH 535/625] PKG: MSVC build fixes by Christoph Gohlke. --- skimage/draw/setup.py | 2 +- skimage/filter/rank/_crank16.pyx | 4 ++-- skimage/filter/rank/_crank8.pyx | 4 ++-- skimage/filter/setup.py | 20 ++++++++++---------- 4 files changed, 15 insertions(+), 15 deletions(-) diff --git a/skimage/draw/setup.py b/skimage/draw/setup.py index 5b8e237d..1414fca8 100644 --- a/skimage/draw/setup.py +++ b/skimage/draw/setup.py @@ -15,7 +15,7 @@ def configuration(parent_package='', top_path=None): cython(['_draw.pyx'], working_path=base_path) config.add_extension('_draw', sources=['_draw.c'], - include_dirs=[get_numpy_include_dirs(), '../shared']) + include_dirs=[get_numpy_include_dirs(), '../_shared']) return config diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index e81bf81f..b7aca483 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -5,7 +5,7 @@ import numpy as np cimport numpy as np -from libc.math cimport log2 +from libc.math cimport log from skimage.filter.rank._core16 cimport _core16 @@ -274,7 +274,7 @@ cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, for i in range(maxbin): p = histo[i] / pop if p > 0: - e -= p * log2(p) + e -= p * log(p) / 0.30102999566398119521373889472449 return < np.uint16_t > e * 1000 else: diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 8bff9703..d7ece4dd 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -5,7 +5,7 @@ import numpy as np cimport numpy as np -from libc.math cimport log2 +from libc.math cimport log from skimage.filter.rank._core8 cimport _core8 @@ -279,7 +279,7 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, for i in range(256): p = histo[i] / pop if p > 0: - e -= p * log2(p) + e -= p * log(p) / 0.30102999566398119521373889472449 return < np.uint8_t > e * 10 else: diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 650a26a6..934d3f2e 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -29,31 +29,31 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_denoise_cy', sources=['_denoise_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) - config.add_extension('rank/_core8', sources=['rank/_core8.c'], + config.add_extension('rank._core8', sources=['rank/_core8.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('rank/_core16', sources=['rank/_core16.c'], + config.add_extension('rank._core16', sources=['rank/_core16.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('rank/_crank8', sources=['rank/_crank8.c'], + config.add_extension('rank._crank8', sources=['rank/_crank8.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/_crank8_percentiles', sources=['rank/_crank8_percentiles.c'], + 'rank._crank8_percentiles', sources=['rank/_crank8_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('rank/_crank16', sources=['rank/_crank16.c'], + config.add_extension('rank._crank16', sources=['rank/_crank16.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/_crank16_percentiles', sources=['rank/_crank16_percentiles.c'], + 'rank._crank16_percentiles', sources=['rank/_crank16_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/_crank16_bilateral', sources=['rank/_crank16_bilateral.c'], + 'rank._crank16_bilateral', sources=['rank/_crank16_bilateral.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/rank', sources=['rank/rank.c'], + 'rank.rank', sources=['rank/rank.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/percentile_rank', sources=['rank/percentile_rank.c'], + 'rank.percentile_rank', sources=['rank/percentile_rank.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/bilateral_rank', sources=['rank/bilateral_rank.c'], + 'rank.bilateral_rank', sources=['rank/bilateral_rank.c'], include_dirs=[get_numpy_include_dirs()]) return config From 6d2aad45ab52e1a062f8f9cbc3efa62783c53d7d Mon Sep 17 00:00:00 2001 From: "Josh Warner (Mac)" Date: Wed, 16 Jan 2013 23:35:50 -0600 Subject: [PATCH 536/625] fix: set random seed in `test_noisy_square_image` --- skimage/feature/tests/test_corner.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skimage/feature/tests/test_corner.py b/skimage/feature/tests/test_corner.py index 46e5466f..a7ee01ff 100644 --- a/skimage/feature/tests/test_corner.py +++ b/skimage/feature/tests/test_corner.py @@ -31,6 +31,7 @@ def test_square_image(): def test_noisy_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. + np.random.seed(seed=1234) im = im + np.random.uniform(size=im.shape) * .2 # Moravec From 5f012cb19dc8fca1149ef581788c18a4277b471d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 17 Jan 2013 09:07:48 +0100 Subject: [PATCH 537/625] Set random seed value to ensure tests always pass --- skimage/measure/tests/test_structural_similarity.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/skimage/measure/tests/test_structural_similarity.py b/skimage/measure/tests/test_structural_similarity.py index ac45072a..ec5ce7ef 100644 --- a/skimage/measure/tests/test_structural_similarity.py +++ b/skimage/measure/tests/test_structural_similarity.py @@ -4,6 +4,9 @@ from numpy.testing import assert_equal, assert_raises from skimage.measure import structural_similarity as ssim +np.random.seed(1234) + + def test_ssim_patch_range(): N = 51 X = (np.random.random((N, N)) * 255).astype(np.uint8) From fcc7edd411ae4babff4ced75d3d6dbc63fe2c80a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Thu, 17 Jan 2013 09:11:21 +0100 Subject: [PATCH 538/625] Set random seed value for denoising tests --- skimage/filter/tests/test_denoise.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py index cb3b5da3..f81ba07b 100644 --- a/skimage/filter/tests/test_denoise.py +++ b/skimage/filter/tests/test_denoise.py @@ -4,6 +4,9 @@ from numpy.testing import run_module_suite, assert_raises, assert_equal from skimage import filter, data, color, img_as_float +np.random.seed(1234) + + lena = img_as_float(data.lena()[:256, :256]) lena_gray = color.rgb2gray(lena) From ad23f203da33b69cd20ba9044bb0b9787156afbb Mon Sep 17 00:00:00 2001 From: Matt McCormick Date: Sun, 22 Jul 2012 19:36:33 -0400 Subject: [PATCH 539/625] ENH: Add SimpleITK IO plugin. IO plugin for SimpleITK, http://simpleitk.org/ imread and imsave implemented. Tests based off the PIL tests. --- skimage/io/_plugins/simpleitk_plugin.ini | 3 + skimage/io/_plugins/simpleitk_plugin.py | 21 ++++++ skimage/io/tests/test_simpleitk.py | 93 ++++++++++++++++++++++++ 3 files changed, 117 insertions(+) create mode 100644 skimage/io/_plugins/simpleitk_plugin.ini create mode 100644 skimage/io/_plugins/simpleitk_plugin.py create mode 100644 skimage/io/tests/test_simpleitk.py diff --git a/skimage/io/_plugins/simpleitk_plugin.ini b/skimage/io/_plugins/simpleitk_plugin.ini new file mode 100644 index 00000000..75a6d995 --- /dev/null +++ b/skimage/io/_plugins/simpleitk_plugin.ini @@ -0,0 +1,3 @@ +[simpleitk] +description = Image reading and writing via SimpleITK +provides = imread, imsave diff --git a/skimage/io/_plugins/simpleitk_plugin.py b/skimage/io/_plugins/simpleitk_plugin.py new file mode 100644 index 00000000..90f7cbc6 --- /dev/null +++ b/skimage/io/_plugins/simpleitk_plugin.py @@ -0,0 +1,21 @@ +__all__ = ['imread', 'imsave'] + +try: + import SimpleITK as sitk +except ImportError: + raise ImportError("SimpleITK could not be found. " + "Please try " + " easy_install SimpleITK " + "or refer to " + " http://simpleitk.org/ " + "for further instructions.") + + +def imread(fname): + sitk_img = sitk.ReadImage(fname) + return sitk.GetArrayFromImage(sitk_img) + + +def imsave(fname, arr): + sitk_img = sitk.GetImageFromArray(arr, isVector=True) + sitk.WriteImage(sitk_img, fname) diff --git a/skimage/io/tests/test_simpleitk.py b/skimage/io/tests/test_simpleitk.py new file mode 100644 index 00000000..4bb2cc23 --- /dev/null +++ b/skimage/io/tests/test_simpleitk.py @@ -0,0 +1,93 @@ +import os.path +import numpy as np +from numpy.testing import * +from numpy.testing.decorators import skipif + +from tempfile import NamedTemporaryFile + +from skimage import data_dir +from skimage.io import imread, imsave, use_plugin, reset_plugins + +try: + import SimpleITK as sitk + use_plugin('simpleitk') +except ImportError: + sitk_available = False +else: + sitk_available = True + + +def teardown(): + reset_plugins() + + +def setup_module(self): + """The effect of the `plugin.use` call may be overridden by later imports. + Call `use_plugin` directly before the tests to ensure that sitk is used. + + """ + try: + use_plugin('simpleitk') + except ImportError: + pass + + +@skipif(not sitk_available) +def test_imread_flatten(): + # a color image is flattened + img = imread(os.path.join(data_dir, 'color.png'), flatten=True) + assert img.ndim == 2 + assert img.dtype == np.float64 + img = imread(os.path.join(data_dir, 'camera.png'), flatten=True) + # check that flattening does not occur for an image that is grey already. + assert np.sctype2char(img.dtype) in np.typecodes['AllInteger'] + + +@skipif(not sitk_available) +def test_bilevel(): + expected = np.zeros((10, 10)) + expected[::2] = 255 + + img = imread(os.path.join(data_dir, 'checker_bilevel.png')) + assert_array_equal(img, expected) + + +@skipif(not sitk_available) +def test_imread_uint16(): + expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) + img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16.tif')) + assert np.issubdtype(img.dtype, np.uint16) + assert_array_almost_equal(img, expected) + + +@skipif(not sitk_available) +def test_imread_uint16_big_endian(): + expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) + img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16B.tif')) + assert_array_almost_equal(img, expected) + + +class TestSave: + def roundtrip(self, dtype, x): + f = NamedTemporaryFile(suffix='.mha') + fname = f.name + f.close() + imsave(fname, x) + y = imread(fname) + + assert_array_almost_equal(x, y) + + @skipif(not sitk_available) + def test_imsave_roundtrip(self): + for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: + for dtype in (np.uint8, np.uint16, np.float32, np.float64): + x = np.ones(shape, dtype=dtype) * np.random.random(shape) + + if np.issubdtype(dtype, float): + yield self.roundtrip, dtype, x + else: + x = (x * 255).astype(dtype) + yield self.roundtrip, dtype, x + +if __name__ == "__main__": + run_module_suite() From f78d10c099de66686eb83357502494d266145e57 Mon Sep 17 00:00:00 2001 From: Colin Lea Date: Thu, 17 Jan 2013 11:36:59 -0500 Subject: [PATCH 540/625] Swapped the direection that the histograms point in the hog visualisation --- skimage/feature/_hog.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/feature/_hog.py b/skimage/feature/_hog.py index ce7db462..8e8ea7f2 100644 --- a/skimage/feature/_hog.py +++ b/skimage/feature/_hog.py @@ -142,8 +142,8 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8), centre = tuple([y * cy + cy // 2, x * cx + cx // 2]) dx = radius * cos(float(o) / orientations * np.pi) dy = radius * sin(float(o) / orientations * np.pi) - rr, cc = draw.bresenham(centre[0] - dy, centre[1] - dx, - centre[0] + dy, centre[1] + dx) + rr, cc = draw.bresenham(centre[0] - dx, centre[1] - dy, + centre[0] + dx, centre[1] + dy) hog_image[rr, cc] += orientation_histogram[y, x, o] """ From fe6d3d500cebdf9d0dc51d50adcd35b3918a11d6 Mon Sep 17 00:00:00 2001 From: Christoph Gohlke Date: Fri, 18 Jan 2013 19:48:30 -0800 Subject: [PATCH 541/625] Do not install the doc package --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index e1f6c0dc..0040534e 100644 --- a/setup.py +++ b/setup.py @@ -138,7 +138,7 @@ if __name__ == "__main__": configuration=configuration, - packages=setuptools.find_packages(), + packages=setuptools.find_packages(exclude=['doc']), include_package_data=True, zip_safe=False, # the package can run out of an .egg file From 11f32577ab46f4e0010f79239fc61c2486abb59b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 23 Jan 2013 14:54:33 +0100 Subject: [PATCH 542/625] Fix wrong include paths for shared subpackage --- skimage/draw/setup.py | 2 +- skimage/morphology/setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/draw/setup.py b/skimage/draw/setup.py index 5b8e237d..1414fca8 100644 --- a/skimage/draw/setup.py +++ b/skimage/draw/setup.py @@ -15,7 +15,7 @@ def configuration(parent_package='', top_path=None): cython(['_draw.pyx'], working_path=base_path) config.add_extension('_draw', sources=['_draw.c'], - include_dirs=[get_numpy_include_dirs(), '../shared']) + include_dirs=[get_numpy_include_dirs(), '../_shared']) return config diff --git a/skimage/morphology/setup.py b/skimage/morphology/setup.py index b8564ce4..1936377b 100644 --- a/skimage/morphology/setup.py +++ b/skimage/morphology/setup.py @@ -29,7 +29,7 @@ def configuration(parent_package='', top_path=None): config.add_extension('_skeletonize_cy', sources=['_skeletonize_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_pnpoly', sources=['_pnpoly.c'], - include_dirs=[get_numpy_include_dirs(), '../shared']) + include_dirs=[get_numpy_include_dirs(), '../_shared']) config.add_extension('_convex_hull', sources=['_convex_hull.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_greyreconstruct', sources=['_greyreconstruct.c'], From bdb2df89c030a16bf9d59eb7dc3602d935a96d0f Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Tue, 12 Feb 2013 14:32:08 -0500 Subject: [PATCH 543/625] use SSE to add/subtract histograms --- skimage/filter/_ctmf.pyx | 35 +++++++++++++++++++++++++++++------ 1 file changed, 29 insertions(+), 6 deletions(-) diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index c133d8d6..d24a9693 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -17,6 +17,13 @@ cimport cython from libc.stdlib cimport malloc, free from libc.string cimport memset +cdef extern from "emmintrin.h": + ctypedef long __m128i + __m128i _mm_load_si128 (__m128i *p) + __m128i _mm_adds_epu16 (__m128i a, __m128i b) + __m128i _mm_subs_epu16 (__m128i a, __m128i b) + void _mm_store_si128 (__m128i *p, __m128i a) + np.import_array() ############################################################################## @@ -342,14 +349,30 @@ cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx): # TO_DO - optimize using SIMD instructions # cdef inline void add16(np.uint16_t *dest, np.uint16_t *src): - cdef int i - for i in range(16): - dest[i] += src[i] + cdef __m128i d, s, *pd, *ps + pd = <__m128i *> dest + ps = <__m128i *> src + d = _mm_load_si128(pd) + s = _mm_load_si128(ps) + d = _mm_adds_epu16 (d, s) + _mm_store_si128(pd, d) + d = _mm_load_si128(pd + 1) + s = _mm_load_si128(ps + 1) + d = _mm_adds_epu16 (d, s) + _mm_store_si128(pd + 1, d) cdef inline void sub16(np.uint16_t *dest, np.uint16_t *src): - cdef int i - for i in range(16): - dest[i] -= src[i] + cdef __m128i d, s, *pd, *ps + pd = <__m128i *> dest + ps = <__m128i *> src + d = _mm_load_si128(pd) + s = _mm_load_si128(ps) + d = _mm_subs_epu16 (d, s) + _mm_store_si128(pd, d) + d = _mm_load_si128(pd + 1) + s = _mm_load_si128(ps + 1) + d = _mm_subs_epu16 (d, s) + _mm_store_si128(pd + 1, d) ############################################################################ # From 32c0b1ca979fcfd201a435f13851666c01247941 Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Tue, 12 Feb 2013 15:40:43 -0500 Subject: [PATCH 544/625] Rewrite histogram operations to remove GPL license on Perreault's code --- skimage/filter/_ctmf.pyx | 42 ++----------- skimage/filter/_histogram.h | 119 ++++++++++++++++++++++++++++++++++++ skimage/filter/setup.py | 2 +- 3 files changed, 124 insertions(+), 39 deletions(-) create mode 100644 skimage/filter/_histogram.h diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index d24a9693..8133efb0 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -17,12 +17,10 @@ cimport cython from libc.stdlib cimport malloc, free from libc.string cimport memset -cdef extern from "emmintrin.h": - ctypedef long __m128i - __m128i _mm_load_si128 (__m128i *p) - __m128i _mm_adds_epu16 (__m128i a, __m128i b) - __m128i _mm_subs_epu16 (__m128i a, __m128i b) - void _mm_store_si128 (__m128i *p, __m128i a) +cdef extern from "_histogram.h": + ctypedef unsigned short int uint16_t + void add16(uint16_t *dest, uint16_t *src) + void sub16(uint16_t *dest, uint16_t *src) np.import_array() @@ -341,38 +339,6 @@ cdef inline np.int32_t leading_edge_colidx(Histograms *ph, np.int32_t colidx): cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx): return (colidx + 3*ph.radius - 1) % ph.stripe_length -# -# add16 - add 16 consecutive integers -# -# Add an array of 16 16-bit integers to an accumulator of 16 16-bit integers -# -# TO_DO - optimize using SIMD instructions -# -cdef inline void add16(np.uint16_t *dest, np.uint16_t *src): - cdef __m128i d, s, *pd, *ps - pd = <__m128i *> dest - ps = <__m128i *> src - d = _mm_load_si128(pd) - s = _mm_load_si128(ps) - d = _mm_adds_epu16 (d, s) - _mm_store_si128(pd, d) - d = _mm_load_si128(pd + 1) - s = _mm_load_si128(ps + 1) - d = _mm_adds_epu16 (d, s) - _mm_store_si128(pd + 1, d) - -cdef inline void sub16(np.uint16_t *dest, np.uint16_t *src): - cdef __m128i d, s, *pd, *ps - pd = <__m128i *> dest - ps = <__m128i *> src - d = _mm_load_si128(pd) - s = _mm_load_si128(ps) - d = _mm_subs_epu16 (d, s) - _mm_store_si128(pd, d) - d = _mm_load_si128(pd + 1) - s = _mm_load_si128(ps + 1) - d = _mm_subs_epu16 (d, s) - _mm_store_si128(pd + 1, d) ############################################################################ # diff --git a/skimage/filter/_histogram.h b/skimage/filter/_histogram.h new file mode 100644 index 00000000..968b664f --- /dev/null +++ b/skimage/filter/_histogram.h @@ -0,0 +1,119 @@ +/* + * Copyright (C) 2013 Thouis "Ray" Jones + * Modified from code + * Copyright (C) 2006 Simon Perreault + * + * Reference: S. Perreault and P. Hébert, "Median Filtering in Constant Time", + * IEEE Transactions on Image Processing, September 2007. + * + */ + +/* Intrinsic declarations */ +#if defined(__SSE2__) +#include +#elif defined(__MMX__) +#include +#elif defined(__ALTIVEC__) +#include +#endif + +/* Compiler peculiarities */ +#if defined(__GNUC__) +#include +#elif defined(_MSC_VER) +#define inline __inline +#endif + +/** + * Add 16 unsigned 16-bit integers using SSE2, MMX or Altivec, if + * available. + */ +#if defined(__SSE2__) +static inline void add16(uint16_t *dest, uint16_t *src) +{ + __m128i *d, *s; + d = (__m128i *) dest; + s = (__m128i *) src; + *d = _mm_add_epi16(*d, *s); + d++; s++; + *d = _mm_add_epi16(*d, *s); +} +#elif defined(__MMX__) +static inline void add16(uint16_t *dest, uint16_t *src) +{ + __m64 *d, *s; + d = (__m64 *) dest; + s = (__m64 *) src; + *d = _mm_add_pi16(*d, *s); + d++; s++; + *d = _mm_add_pi16(*d, *s); + d++; s++; + *d = _mm_add_pi16(*d, *s); + d++; s++; + *d = _mm_add_pi16(*d, *s); +} +#elif defined(__ALTIVEC__) +static inline void add16(uint16_t *dest, uint16_t *src) +{ + vector unsigned short *d, *s; + d = (vector unsigned short *) dest; + s = (vector unsigned short *) src; + *d = vec_add(*d, *s); + d++; s++; + *d = vec_add(*d, *s); +} +#else +static inline void add16(uint16_t *dest, uint16_t *src) +{ + int i; + + for (i = 0; i < 16; i++) dest[i] += src[i]; +} +#endif + +/** + * Subtract 16 unsigned 16-bit integers using SSE2, MMX or Altivec, if + * available. + */ +#if defined(__SSE2__) +static inline void sub16(uint16_t *dest, uint16_t *src) +{ + __m128i *d, *s; + d = (__m128i *) dest; + s = (__m128i *) src; + *d = _mm_sub_epi16(*d, *s); + d++; s++; + *d = _mm_sub_epi16(*d, *s); +} +#elif defined(__MMX__) +static inline void sub16(uint16_t *dest, uint16_t *src) +{ + __m64 *d, *s; + d = (__m64 *) dest; + s = (__m64 *) src; + *d = _mm_sub_pi16(*d, *s); + d++; s++; + *d = _mm_sub_pi16(*d, *s); + d++; s++; + *d = _mm_sub_pi16(*d, *s); + d++; s++; + *d = _mm_sub_pi16(*d, *s); +} +#elif defined(__ALTIVEC__) +static inline void sub16(uint16_t *dest, uint16_t *src) +{ + vector unsigned short *d, *s; + d = (vector unsigned short *) dest; + s = (vector unsigned short *) src; + *d = vec_sub(*d, *s); + d++; s++; + *d = vec_sub(*d, *s); +} +#else +static inline void sub16(uint16_t *dest, uint16_t *src) +{ + int i; + + for (i = 0; i < 16; i++) dest[i] -= src[i]; +} +#endif diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 650a26a6..cfcb0794 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -25,7 +25,7 @@ def configuration(parent_package='', top_path=None): cython(['rank/percentile_rank.pyx'], working_path=base_path) cython(['rank/bilateral_rank.pyx'], working_path=base_path) - config.add_extension('_ctmf', sources=['_ctmf.c'], + config.add_extension('_ctmf', sources=['_ctmf.c', '_histogram.h'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_denoise_cy', sources=['_denoise_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) From db764671d43d5e57242842508ff6d6fe31e0cfd1 Mon Sep 17 00:00:00 2001 From: odebeir Date: Wed, 13 Feb 2013 16:20:32 +0100 Subject: [PATCH 545/625] fix: Otsu(8bit) and bilateral functions docstrings --- skimage/filter/rank/bilateral_rank.pyx | 37 +++++++++++++------------- skimage/filter/rank/rank.pyx | 12 +++++---- 2 files changed, 26 insertions(+), 23 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index dec07c28..04f6cb35 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -3,8 +3,8 @@ The local histogram is computed using a sliding window similar to the method described in [1]_. -Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit -images are casted in 16-bit the number of histogram bins is determined from the +Input image must be 16-bit with a value < 4096 (i.e. 12 bit), +the number of histogram bins is determined from the maximum value present in the image. The pixel neighborhood is defined by: @@ -89,9 +89,8 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the - algorithm uses max. 12bit histogram, an exception will be raised if - image has a value > 4095 + Image array (uint16). As the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray @@ -108,7 +107,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, Returns ------- - out : uint16 array (uint8 image are casted to uint16) + out : uint16 array The result of the local bilateral mean. See also @@ -118,17 +117,15 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, Notes ----- - * input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit) - - * 8-bit images are casted in 16-bit + * input image are 16-bit only Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filter.rank import bilateral_mean - >>> # Load test image - >>> ima = data.camera() + >>> # Load test image / cast to uint16 + >>> ima = data.camera().astype(np.uint16) >>> # bilateral filtering of cameraman image using a flat kernel >>> bilat_ima = bilateral_mean(ima, disk(20), s0=10,s1=10) """ @@ -146,9 +143,8 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, as the - algorithm uses max. 12bit histogram, an exception will be raised if - image has a value > 4095 + Image array (uint16). As the algorithm uses max. 12bit histogram, + an exception will be raised if image has a value > 4095 selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray @@ -165,20 +161,25 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, Returns ------- - out : uint16 array (uint8 image are casted to uint16) + out : uint16 array the local number of pixels inside the bilateral neighborhood + Notes + ----- + + * input image are 16-bit only + Examples -------- >>> # Local mean >>> from skimage.morphology import square >>> import skimage.filter.rank as rank - >>> ima8 = 255 * np.array([[0, 0, 0, 0, 0], + >>> ima16 = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.bilateral_pop(ima8, square(3), s0=10,s1=10) + ... [0, 0, 0, 0, 0]], dtype=np.uint16) + >>> rank.bilateral_pop(ima16, square(3), s0=10,s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 43f03133..261f0bb6 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -726,9 +726,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Parameters ---------- image : ndarray - Image array (uint8 array or uint16). If image is uint16, the algorithm - uses max. 12bit histogram, an exception will be raised if image has a - value > 4095. + Image array (uint8 array). selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray @@ -743,20 +741,24 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): Returns ------- - out : uint8 array or uint16 array (same as input image) + out : uint8 array Otsu's threshold values References ---------- .. [otsu] http://en.wikipedia.org/wiki/Otsu's_method + Notes + ----- + * input image are 8-bit only + Examples -------- >>> # Local entropy >>> from skimage import data >>> from skimage.filter.rank import otsu >>> from skimage.morphology import disk - >>> # defining a 8- and a 16-bit test images + >>> # defining a 8-bit test images >>> a8 = data.camera() >>> loc_otsu = otsu(a8, disk(5)) >>> thresh_image = a8 >= loc_otsu From b5b99d8872649e5fddb7d9307985d6e3cbbebab7 Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Tue, 19 Feb 2013 16:05:31 -0500 Subject: [PATCH 546/625] fix UTF-8 --- skimage/filter/_histogram.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/_histogram.h b/skimage/filter/_histogram.h index 968b664f..3df3a70d 100644 --- a/skimage/filter/_histogram.h +++ b/skimage/filter/_histogram.h @@ -3,7 +3,7 @@ * Modified from code * Copyright (C) 2006 Simon Perreault * - * Reference: S. Perreault and P. Hébert, "Median Filtering in Constant Time", + * Reference: S. Perreault and P. Hébert, "Median Filtering in Constant Time", * IEEE Transactions on Image Processing, September 2007. * */ From b41bfba22f4c92e8d5910bc73971f43238c89f30 Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Tue, 19 Feb 2013 16:41:25 -0500 Subject: [PATCH 547/625] remove typedef, use numpy type in its place --- skimage/filter/_ctmf.pyx | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index 8133efb0..f5cd5fd0 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -18,9 +18,8 @@ from libc.stdlib cimport malloc, free from libc.string cimport memset cdef extern from "_histogram.h": - ctypedef unsigned short int uint16_t - void add16(uint16_t *dest, uint16_t *src) - void sub16(uint16_t *dest, uint16_t *src) + void add16(np.uint16_t *dest, np.uint16_t *src) + void sub16(np.uint16_t *dest, np.uint16_t *src) np.import_array() From 4af05245edb0537037b4fda3dc81efa01f1f4977 Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Tue, 19 Feb 2013 16:41:54 -0500 Subject: [PATCH 548/625] add casts to suppress compiler warnings --- skimage/filter/_ctmf.pyx | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index f5cd5fd0..87e4a8b9 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -327,17 +327,17 @@ cdef void set_stride(Histograms *ph, SCoord *psc): # ############################################################################ cdef inline np.int32_t tl_br_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length + return (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length cdef inline np.int32_t tr_bl_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \ + return (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \ ph.stripe_length cdef inline np.int32_t leading_edge_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 5*ph.radius) % ph.stripe_length + return (colidx + 5*ph.radius) % ph.stripe_length cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3*ph.radius - 1) % ph.stripe_length + return (colidx + 3*ph.radius - 1) % ph.stripe_length ############################################################################ # @@ -612,7 +612,7 @@ cdef inline np.uint8_t find_median(Histograms *ph): if ph.accumulator_count == 0: return 0 - pixels_below = ((ph.accumulator_count * ph.percent + 50) + pixels_below = ((ph.accumulator_count * ph.percent + 50) / 100) # +50 for roundoff if pixels_below > 0: pixels_below -= 1 @@ -774,8 +774,8 @@ def median_filter( raise ValueError('Data shape (%d, %d) is not output shape (%d, %d)' % (data.shape[0], data.shape[1], output.shape[0], output.shape[1])) - if c_median_filter(data.shape[0], data.shape[1], - data.strides[0], data.strides[1], + if c_median_filter( data.shape[0], data.shape[1], + data.strides[0], data.strides[1], radius, percent, data.data, mask.data, From ba1d27b35761b168ef6eedfcc2c7092a498a6eb2 Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Tue, 19 Feb 2013 21:44:06 -0500 Subject: [PATCH 549/625] move _histogram.h to _shared/vectorized_ops.h, and copyright info to CONTRIBUTORS.txt --- CONTRIBUTORS.txt | 3 +++ .../{filter/_histogram.h => _shared/vectorized_ops.h} | 10 ---------- skimage/filter/_ctmf.pyx | 2 +- 3 files changed, 4 insertions(+), 11 deletions(-) rename skimage/{filter/_histogram.h => _shared/vectorized_ops.h} (90%) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index d582ab63..38e2ea32 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -132,3 +132,6 @@ - François Boulogne Andres Method for circle perimeter, ellipse perimeter drawing. + +- Thouis Jones + Vectorized operators for arrays of 16-bit ints. diff --git a/skimage/filter/_histogram.h b/skimage/_shared/vectorized_ops.h similarity index 90% rename from skimage/filter/_histogram.h rename to skimage/_shared/vectorized_ops.h index 3df3a70d..e426c86e 100644 --- a/skimage/filter/_histogram.h +++ b/skimage/_shared/vectorized_ops.h @@ -1,13 +1,3 @@ -/* - * Copyright (C) 2013 Thouis "Ray" Jones - * Modified from code - * Copyright (C) 2006 Simon Perreault - * - * Reference: S. Perreault and P. Hébert, "Median Filtering in Constant Time", - * IEEE Transactions on Image Processing, September 2007. - * - */ - /* Intrinsic declarations */ #if defined(__SSE2__) #include diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index 87e4a8b9..75c1b47a 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -17,7 +17,7 @@ cimport cython from libc.stdlib cimport malloc, free from libc.string cimport memset -cdef extern from "_histogram.h": +cdef extern from "../_shared/vectorized_ops.h": void add16(np.uint16_t *dest, np.uint16_t *src) void sub16(np.uint16_t *dest, np.uint16_t *src) From 1580c466c17409ee47d0fca1d36865256e3d6eaf Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Wed, 20 Feb 2013 10:35:51 -0500 Subject: [PATCH 550/625] pep8 --- skimage/filter/_ctmf.pyx | 108 +++++++++++++++++++-------------------- 1 file changed, 53 insertions(+), 55 deletions(-) diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index 75c1b47a..5a81cb98 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -62,11 +62,11 @@ cdef struct HistogramPiece: np.uint16_t fine[256] cdef struct Histogram: - HistogramPiece top_left # top-left corner - HistogramPiece top_right # top-right corner - HistogramPiece edge # leading/trailing edge - HistogramPiece bottom_left # bottom-left corner - HistogramPiece bottom_right # bottom-right corner + HistogramPiece top_left # top-left corner + HistogramPiece top_right # top-right corner + HistogramPiece edge # leading/trailing edge + HistogramPiece bottom_left # bottom-left corner + HistogramPiece bottom_right # bottom-right corner # The pixel count has the number of pixels histogrammed in # each of the five compartments for this position. This changes @@ -127,16 +127,16 @@ cdef struct Histograms: # # x --> # - SCoord last_top_left # (-) left side of octagon's top - 1 row - SCoord top_left # (+) -1 row from trailing edge top - SCoord last_top_right # (-) right side of octagon's top - 1 col - 1 row - SCoord top_right # (+) -1 col -1 row from leading edge top - SCoord last_leading_edge # (-) leading edge (right) top stride - 1 row - SCoord leading_edge # (+) leading edge bottom stride - SCoord last_bottom_right # (-) leading edge bottom - 1 col - SCoord bottom_right # (+) right side of octagon's bottom - 1 col - SCoord last_bottom_left # (-) trailing edge bottom - 1 col - SCoord bottom_left # (+) left side of octagon's bottom - 1 col + SCoord last_top_left # (-) left side of octagon's top - 1 row + SCoord top_left # (+) -1 row from trailing edge top + SCoord last_top_right # (-) right side of octagon's top - 1 col - 1 row + SCoord top_right # (+) -1 col -1 row from leading edge top + SCoord last_leading_edge # (-) leading edge (right) top stride - 1 row + SCoord leading_edge # (+) leading edge bottom stride + SCoord last_bottom_right # (-) leading edge bottom - 1 col + SCoord bottom_right # (+) right side of octagon's bottom - 1 col + SCoord last_bottom_left # (-) trailing edge bottom - 1 col + SCoord bottom_left # (+) left side of octagon's bottom - 1 col np.int32_t row_stride # stride between one row and the next np.int32_t col_stride # stride between one column and the next @@ -185,25 +185,25 @@ cdef Histograms *allocate_histograms(np.int32_t rows, SCoord *psc memory_size = (adjusted_stripe_length * - (sizeof(Histogram) + sizeof(PixelCount))+ - sizeof(Histograms)+32) + (sizeof(Histogram) + sizeof(PixelCount)) + + sizeof(Histograms) + 32) ptr = malloc(memory_size) memset(ptr, 0, memory_size) - ph = ptr + ph = ptr if not ptr: return ph ph.memory = ptr - ptr = (ph+1) - ph.pixel_count = ptr - ptr = (ph.pixel_count + adjusted_stripe_length) + ptr = (ph + 1) + ph.pixel_count = ptr + ptr = (ph.pixel_count + adjusted_stripe_length) # # Align histogram memory to a 32-byte boundary # - roundoff = ptr + roundoff = ptr roundoff += 31 roundoff -= roundoff % 32 - ptr = roundoff - ph.histogram = ptr + ptr = roundoff + ph.histogram = ptr # # Fill in the statistical things we keep around # @@ -232,7 +232,7 @@ cdef Histograms *allocate_histograms(np.int32_t rows, # a_2 is the offset from the center to each of the octagon # corners # - a = (radius * 2.0 / 2.414213) + a = ( radius * 2.0 / 2.414213) a_2 = a / 2 if a_2 == 0: a_2 = 1 @@ -327,17 +327,18 @@ cdef void set_stride(Histograms *ph, SCoord *psc): # ############################################################################ cdef inline np.int32_t tl_br_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length + return (colidx + 3 * ph.radius + ph.current_row) % \ + ph.stripe_length cdef inline np.int32_t tr_bl_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \ - ph.stripe_length + return (colidx + 3 * ph.radius + ph.row_count - ph.current_row) % \ + ph.stripe_length cdef inline np.int32_t leading_edge_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 5*ph.radius) % ph.stripe_length + return (colidx + 5 * ph.radius) % ph.stripe_length cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3*ph.radius - 1) % ph.stripe_length + return (colidx + 3 * ph.radius - 1) % ph.stripe_length ############################################################################ # @@ -528,9 +529,9 @@ cdef inline void update_histogram(Histograms *ph, y = last_coord.y + current_row stride = current_stride+last_coord.stride - if (x >= 0 and x < column_count and - y >= 0 and y < row_count and - ph.mask[stride]): + if (x >= 0 and x < column_count and \ + y >= 0 and y < row_count and \ + ph.mask[stride]): value = ph.data[stride] pixel_count[0] -= 1 hist_piece.fine[value] -= 1 @@ -540,9 +541,9 @@ cdef inline void update_histogram(Histograms *ph, y = coord.y + current_row stride = current_stride + coord.stride - if (x >= 0 and x < column_count and - y >= 0 and y < row_count and - ph.mask[stride]): + if (x >= 0 and x < column_count and \ + y >= 0 and y < row_count and \ + ph.mask[stride]): value = ph.data[stride] pixel_count[0] += 1 hist_piece.fine[value] += 1 @@ -613,7 +614,7 @@ cdef inline np.uint8_t find_median(Histograms *ph): if ph.accumulator_count == 0: return 0 pixels_below = ((ph.accumulator_count * ph.percent + 50) - / 100) # +50 for roundoff + / 100) # +50 for roundoff if pixels_below > 0: pixels_below -= 1 @@ -625,7 +626,7 @@ cdef inline np.uint8_t find_median(Histograms *ph): accumulator -= ph.accumulator.coarse[i] update_fine(ph, i) - for j in range(i*16,(i+1)*16): + for j in range(i * 16, (i + 1) * 16): accumulator += ph.accumulator.fine[j] if accumulator > pixels_below: return j @@ -659,9 +660,7 @@ cdef int c_median_filter(np.int32_t rows, cdef: Histograms *ph Histogram *phistogram - int row - int col - int i + int row, col, i np.int32_t top_left_off np.int32_t top_right_off np.int32_t bottom_left_off @@ -670,7 +669,7 @@ cdef int c_median_filter(np.int32_t rows, ph = allocate_histograms(rows, columns, row_stride, col_stride, radius, percent, data, mask, output) if not ph: - return 1 + return 1 for row in range(-radius, rows): # @@ -709,7 +708,7 @@ cdef int c_median_filter(np.int32_t rows, # Update locations and coarse accumulator for the octagon # for points before 0 # - for col in range(-radius, 0 if row >=0 else columns+radius): + for col in range(-radius, 0 if row >= 0 else columns+radius): ph.current_column = col ph.current_stride = row * row_stride + col * col_stride update_current_location(ph) @@ -730,16 +729,15 @@ cdef int c_median_filter(np.int32_t rows, ph.current_stride = row * row_stride + col * col_stride update_current_location(ph) - free_histograms(ph) return 0 -def median_filter( - np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] data, - np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] mask, - np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] output, - int radius, - np.int32_t percent): + +def median_filter(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] data, + np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] mask, + np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] output, + int radius, + np.int32_t percent): """Median filter with octagon shape and masking. Parameters @@ -761,10 +759,10 @@ def median_filter( """ if percent < 0: - raise ValueError('Median filter percent = %d is less than zero' % \ + raise ValueError('Median filter percent = %d is less than zero' % percent) if percent > 100: - raise ValueError('Median filter percent = %d is greater than 100' % \ + raise ValueError('Median filter percent = %d is greater than 100' % percent) if data.shape[0] != mask.shape[0] or data.shape[1] != mask.shape[1]: raise ValueError('Data shape (%d, %d) is not mask shape (%d, %d)' % @@ -777,7 +775,7 @@ def median_filter( if c_median_filter( data.shape[0], data.shape[1], data.strides[0], data.strides[1], radius, percent, - data.data, - mask.data, - output.data): + data.data, + mask.data, + output.data): raise MemoryError('Failed to allocate scratchpad memory') From d2b98a93e9d578c86d8bd7cd440f0996dbdf3821 Mon Sep 17 00:00:00 2001 From: "Thouis (Ray) Jones" Date: Thu, 21 Feb 2013 11:45:32 -0500 Subject: [PATCH 551/625] remove _histogram.h from setup.py, it's been moved to _shared --- skimage/filter/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index cfcb0794..650a26a6 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -25,7 +25,7 @@ def configuration(parent_package='', top_path=None): cython(['rank/percentile_rank.pyx'], working_path=base_path) cython(['rank/bilateral_rank.pyx'], working_path=base_path) - config.add_extension('_ctmf', sources=['_ctmf.c', '_histogram.h'], + config.add_extension('_ctmf', sources=['_ctmf.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_denoise_cy', sources=['_denoise_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) From 92f609a9486d14e1bdde85ff7296b755caf1f3b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 18:33:00 +0100 Subject: [PATCH 552/625] Change type to ssize_t for all index and size variables --- skimage/draw/_draw.pyx | 73 +++++++++++++++++++++--------------------- 1 file changed, 37 insertions(+), 36 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index e0c9231e..28103304 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -2,15 +2,15 @@ #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False -import numpy as np -import math -from libc.math cimport sqrt -cimport numpy as np cimport cython +cimport numpy as np +from libc.math cimport sqrt +import math +import numpy as np from skimage._shared.geometry cimport point_in_polygon -def line(int y, int x, int y2, int x2): +def line(ssize_t y, ssize_t x, ssize_t y2, ssize_t x2): """Generate line pixel coordinates. Parameters @@ -29,12 +29,12 @@ def line(int y, int x, int y2, int x2): """ - cdef np.ndarray[np.int32_t, ndim=1, mode="c"] rr, cc + cdef np.ndarray[ssize_t, ndim=1, mode="c"] rr, cc - cdef int steep = 0 - cdef int dx = abs(x2 - x) - cdef int dy = abs(y2 - y) - cdef int sx, sy, d, i + cdef char steep = 0 + cdef ssize_t dx = abs(x2 - x) + cdef ssize_t dy = abs(y2 - y) + cdef ssize_t sx, sy, d, i if (x2 - x) > 0: sx = 1 @@ -51,8 +51,8 @@ def line(int y, int x, int y2, int x2): sx, sy = sy, sx d = (2 * dy) - dx - rr = np.zeros(int(dx) + 1, dtype=np.int32) - cc = np.zeros(int(dx) + 1, dtype=np.int32) + rr = np.zeros(int(dx) + 1, dtype=np.intp) + cc = np.zeros(int(dx) + 1, dtype=np.intp) for i in range(dx): if steep: @@ -96,18 +96,18 @@ def polygon(y, x, shape=None): """ - cdef int nr_verts = x.shape[0] - cdef int minr = max(0, y.min()) - cdef int maxr = math.ceil(y.max()) - cdef int minc = max(0, x.min()) - cdef int maxc = math.ceil(x.max()) + cdef ssize_t nr_verts = x.shape[0] + cdef ssize_t minr = max(0, y.min()) + cdef ssize_t maxr = math.ceil(y.max()) + cdef ssize_t minc = max(0, x.min()) + cdef ssize_t maxc = math.ceil(x.max()) # make sure output coordinates do not exceed image size if shape is not None: maxr = min(shape[0] - 1, maxr) maxc = min(shape[1] - 1, maxc) - cdef int r, c + cdef ssize_t r, c #: make contigous arrays for r, c coordinates cdef np.ndarray contiguous_rdata, contiguous_cdata @@ -148,17 +148,17 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): """ - cdef int minr = max(0, cy - yradius) - cdef int maxr = math.ceil(cy + yradius) - cdef int minc = max(0, cx - xradius) - cdef int maxc = math.ceil(cx + xradius) + cdef ssize_t minr = max(0, cy - yradius) + cdef ssize_t maxr = math.ceil(cy + yradius) + cdef ssize_t minc = max(0, cx - xradius) + cdef ssize_t maxc = math.ceil(cx + xradius) # make sure output coordinates do not exceed image size if shape is not None: maxr = min(shape[0] - 1, maxr) maxc = min(shape[1] - 1, maxc) - cdef int r, c + cdef ssize_t r, c #: output coordinate arrays cdef list rr = list() @@ -195,7 +195,8 @@ def circle(double cy, double cx, double radius, shape=None): return ellipse(cy, cx, radius, radius, shape) -def circle_perimeter(int cy, int cx, int radius, method='bresenham'): +def circle_perimeter(ssize_t cy, ssize_t cx, ssize_t radius, + method='bresenham'): """Generate circle perimeter coordinates. Parameters @@ -234,9 +235,9 @@ def circle_perimeter(int cy, int cx, int radius, method='bresenham'): cdef list rr = list() cdef list cc = list() - cdef int x = 0 - cdef int y = radius - cdef int d = 0 + cdef ssize_t x = 0 + cdef ssize_t y = radius + cdef ssize_t d = 0 cdef char cmethod if method == 'bresenham': d = 3 - 2 * radius @@ -302,8 +303,8 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): return np.array(cy), np.array(cx) # a and b are xradius an yradius compute 2a^2 and 2b^2 - cdef int twoasquared = 2 * xradius**2 - cdef int twobsquared = 2 * yradius**2 + cdef ssize_t twoasquared = 2 * xradius**2 + cdef ssize_t twobsquared = 2 * yradius**2 # Pixels cdef list px = list() @@ -311,14 +312,14 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): # First set of points: # start at the top - cdef int x = xradius - cdef int y = 0 + cdef ssize_t x = xradius + cdef ssize_t y = 0 - cdef int err = 0 - cdef int xstop = twobsquared * xradius - cdef int ystop = 0 - cdef int xchange = yradius * yradius * (1 - 2 * xradius) - cdef int ychange = xradius * xradius + cdef ssize_t err = 0 + cdef ssize_t xstop = twobsquared * xradius + cdef ssize_t ystop = 0 + cdef ssize_t xchange = yradius * yradius * (1 - 2 * xradius) + cdef ssize_t ychange = xradius * xradius while xstop > ystop: px.extend([x, -x, -x, x]) From df6852646261c959c12077f7cb7bc10c818df4d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 18:49:18 +0100 Subject: [PATCH 553/625] Change type to ssize_t for all index and size variables --- skimage/_shared/geometry.pxd | 6 +-- skimage/_shared/geometry.pyx | 14 +++--- skimage/_shared/interpolation.pxd | 20 ++++---- skimage/_shared/interpolation.pyx | 78 +++++++++++++++---------------- skimage/_shared/transform.pxd | 2 +- skimage/_shared/transform.pyx | 2 +- skimage/transform/_warps_cy.pyx | 2 +- 7 files changed, 62 insertions(+), 62 deletions(-) diff --git a/skimage/_shared/geometry.pxd b/skimage/_shared/geometry.pxd index afdc6b5b..a89ce894 100644 --- a/skimage/_shared/geometry.pxd +++ b/skimage/_shared/geometry.pxd @@ -1,6 +1,6 @@ -cdef unsigned char point_in_polygon(int nr_verts, double *xp, double *yp, +cdef unsigned char point_in_polygon(ssize_t nr_verts, double *xp, double *yp, double x, double y) -cdef void points_in_polygon(int nr_verts, double *xp, double *yp, - int nr_points, double *x, double *y, +cdef void points_in_polygon(ssize_t nr_verts, double *xp, double *yp, + ssize_t nr_points, double *x, double *y, unsigned char *result) diff --git a/skimage/_shared/geometry.pyx b/skimage/_shared/geometry.pyx index 3f4850b0..5a56df7c 100644 --- a/skimage/_shared/geometry.pyx +++ b/skimage/_shared/geometry.pyx @@ -4,8 +4,8 @@ #cython: wraparound=False -cdef inline unsigned char point_in_polygon(int nr_verts, double *xp, double *yp, - double x, double y): +cdef inline unsigned char point_in_polygon(ssize_t nr_verts, double *xp, + double *yp, double x, double y): """Test whether point lies inside a polygon. Parameters @@ -17,9 +17,9 @@ cdef inline unsigned char point_in_polygon(int nr_verts, double *xp, double *yp, x, y : double Coordinates of point. """ - cdef int i + cdef ssize_t i cdef unsigned char c = 0 - cdef int j = nr_verts - 1 + cdef ssize_t j = nr_verts - 1 for i in range(nr_verts): if ( (((yp[i] <= y) and (y < yp[j])) or @@ -31,8 +31,8 @@ cdef inline unsigned char point_in_polygon(int nr_verts, double *xp, double *yp, return c -cdef void points_in_polygon(int nr_verts, double *xp, double *yp, - int nr_points, double *x, double *y, +cdef void points_in_polygon(ssize_t nr_verts, double *xp, double *yp, + ssize_t nr_points, double *x, double *y, unsigned char *result): """Test whether points lie inside a polygon. @@ -49,6 +49,6 @@ cdef void points_in_polygon(int nr_verts, double *xp, double *yp, result : unsigned char array Test results for each point. """ - cdef int n + cdef ssize_t n for n in range(nr_points): result[n] = point_in_polygon(nr_verts, xp, yp, x[n], y[n]) diff --git a/skimage/_shared/interpolation.pxd b/skimage/_shared/interpolation.pxd index 4e00dfb0..0f9123e9 100644 --- a/skimage/_shared/interpolation.pxd +++ b/skimage/_shared/interpolation.pxd @@ -1,27 +1,27 @@ -cdef double nearest_neighbour_interpolation(double* image, int rows, - int cols, double r, +cdef double nearest_neighbour_interpolation(double* image, ssize_t rows, + ssize_t cols, double r, double c, char mode, double cval) -cdef double bilinear_interpolation(double* image, int rows, int cols, +cdef double bilinear_interpolation(double* image, ssize_t rows, ssize_t cols, double r, double c, char mode, double cval) cdef double quadratic_interpolation(double x, double[3] f) -cdef double biquadratic_interpolation(double* image, int rows, int cols, +cdef double biquadratic_interpolation(double* image, ssize_t rows, ssize_t cols, double r, double c, char mode, double cval) cdef double cubic_interpolation(double x, double[4] f) -cdef double bicubic_interpolation(double* image, int rows, int cols, +cdef double bicubic_interpolation(double* image, ssize_t rows, ssize_t cols, double r, double c, char mode, double cval) -cdef double get_pixel2d(double* image, int rows, int cols, int r, int c, - char mode, double cval) +cdef double get_pixel2d(double* image, ssize_t rows, ssize_t cols, ssize_t r, + ssize_t c, char mode, double cval) -cdef double get_pixel3d(double* image, int rows, int cols, int dims, int r, - int c, int d, char mode, double cval) +cdef double get_pixel3d(double* image, ssize_t rows, ssize_t cols, ssize_t dims, + ssize_t r, ssize_t c, ssize_t d, char mode, double cval) -cdef int coord_map(int dim, int coord, char mode) +cdef ssize_t coord_map(ssize_t dim, ssize_t coord, char mode) diff --git a/skimage/_shared/interpolation.pyx b/skimage/_shared/interpolation.pyx index b34ba507..50b05a00 100644 --- a/skimage/_shared/interpolation.pyx +++ b/skimage/_shared/interpolation.pyx @@ -5,12 +5,12 @@ from libc.math cimport ceil, floor -cdef inline int round(double r): - return ((r + 0.5) if (r > 0.0) else (r - 0.5)) +cdef inline ssize_t round(double r): + return ((r + 0.5) if (r > 0.0) else (r - 0.5)) -cdef inline double nearest_neighbour_interpolation(double* image, int rows, - int cols, double r, +cdef inline double nearest_neighbour_interpolation(double* image, ssize_t rows, + ssize_t cols, double r, double c, char mode, double cval): """Nearest neighbour interpolation at a given position in the image. @@ -35,13 +35,12 @@ cdef inline double nearest_neighbour_interpolation(double* image, int rows, """ - return get_pixel2d(image, rows, cols, round(r), round(c), - mode, cval) + return get_pixel2d(image, rows, cols, round(r), round(c), mode, cval) -cdef inline double bilinear_interpolation(double* image, int rows, int cols, - double r, double c, char mode, - double cval): +cdef inline double bilinear_interpolation(double* image, ssize_t rows, + ssize_t cols, double r, double c, + char mode, double cval): """Bilinear interpolation at a given position in the image. Parameters @@ -64,12 +63,12 @@ cdef inline double bilinear_interpolation(double* image, int rows, int cols, """ cdef double dr, dc - cdef int minr, minc, maxr, maxc + cdef ssize_t minr, minc, maxr, maxc - minr = floor(r) - minc = floor(c) - maxr = ceil(r) - maxc = ceil(c) + minr = floor(r) + minc = floor(c) + maxr = ceil(r) + maxc = ceil(c) dr = r - minr dc = c - minc top = (1 - dc) * get_pixel2d(image, rows, cols, minr, minc, mode, cval) \ @@ -98,9 +97,9 @@ cdef inline double quadratic_interpolation(double x, double[3] f): return f[1] - 0.25 * (f[0] - f[2]) * x -cdef inline double biquadratic_interpolation(double* image, int rows, int cols, - double r, double c, char mode, - double cval): +cdef inline double biquadratic_interpolation(double* image, ssize_t rows, + ssize_t cols, double r, double c, + char mode, double cval): """Biquadratic interpolation at a given position in the image. Parameters @@ -123,8 +122,8 @@ cdef inline double biquadratic_interpolation(double* image, int rows, int cols, """ - cdef int r0 = round(r) - cdef int c0 = round(c) + cdef ssize_t r0 = round(r) + cdef ssize_t c0 = round(c) if r < 0: r0 -= 1 if c < 0: @@ -139,7 +138,7 @@ cdef inline double biquadratic_interpolation(double* image, int rows, int cols, cdef double fc[3], fr[3] - cdef int pr, pc + cdef ssize_t pr, pc # row-wise cubic interpolation for pr in range(r0, r0 + 3): @@ -174,9 +173,9 @@ cdef inline double cubic_interpolation(double x, double[4] f): (3.0 * (f[1] - f[2]) + f[3] - f[0]))) -cdef inline double bicubic_interpolation(double* image, int rows, int cols, - double r, double c, char mode, - double cval): +cdef inline double bicubic_interpolation(double* image, ssize_t rows, + ssize_t cols, double r, double c, + char mode, double cval): """Bicubic interpolation at a given position in the image. Parameters @@ -199,8 +198,8 @@ cdef inline double bicubic_interpolation(double* image, int rows, int cols, """ - cdef int r0 = r - 1 - cdef int c0 = c - 1 + cdef ssize_t r0 = r - 1 + cdef ssize_t c0 = c - 1 if r < 0: r0 -= 1 if c < 0: @@ -211,7 +210,7 @@ cdef inline double bicubic_interpolation(double* image, int rows, int cols, cdef double fc[4], fr[4] - cdef int pr, pc + cdef ssize_t pr, pc # row-wise cubic interpolation for pr in range(r0, r0 + 4): @@ -223,8 +222,8 @@ cdef inline double bicubic_interpolation(double* image, int rows, int cols, return cubic_interpolation(xr, fr) -cdef inline double get_pixel2d(double* image, int rows, int cols, int r, int c, - char mode, double cval): +cdef inline double get_pixel2d(double* image, ssize_t rows, ssize_t cols, + ssize_t r, ssize_t c, char mode, double cval): """Get a pixel from the image, taking wrapping mode into consideration. Parameters @@ -255,8 +254,9 @@ cdef inline double get_pixel2d(double* image, int rows, int cols, int r, int c, return image[coord_map(rows, r, mode) * cols + coord_map(cols, c, mode)] -cdef inline double get_pixel3d(double* image, int rows, int cols, int dims, int r, - int c, int d, char mode, double cval): +cdef inline double get_pixel3d(double* image, ssize_t rows, ssize_t cols, + ssize_t dims, ssize_t r, ssize_t c, ssize_t d, + char mode, double cval): """Get a pixel from the image, taking wrapping mode into consideration. Parameters @@ -289,7 +289,7 @@ cdef inline double get_pixel3d(double* image, int rows, int cols, int dims, int + d] -cdef inline int coord_map(int dim, int coord, char mode): +cdef inline ssize_t coord_map(ssize_t dim, ssize_t coord, char mode): """ Wrap a coordinate, according to a given mode. @@ -308,20 +308,20 @@ cdef inline int coord_map(int dim, int coord, char mode): if mode == 'R': # reflect if coord < 0: # How many times times does the coordinate wrap? - if (-coord / dim) % 2 != 0: - return dim - (-coord % dim) + if (-coord / dim) % 2 != 0: + return dim - (-coord % dim) else: - return (-coord % dim) + return (-coord % dim) elif coord > dim: - if (coord / dim) % 2 != 0: - return (dim - (coord % dim)) + if (coord / dim) % 2 != 0: + return (dim - (coord % dim)) else: - return (coord % dim) + return (coord % dim) elif mode == 'W': # wrap if coord < 0: - return (dim - (-coord % dim)) + return (dim - (-coord % dim)) elif coord > dim: - return (coord % dim) + return (coord % dim) elif mode == 'N': # nearest if coord < 0: return 0 diff --git a/skimage/_shared/transform.pxd b/skimage/_shared/transform.pxd index 0edc22a4..f91a1658 100644 --- a/skimage/_shared/transform.pxd +++ b/skimage/_shared/transform.pxd @@ -2,4 +2,4 @@ cimport numpy as cnp cdef float integrate(cnp.ndarray[float, ndim=2, mode="c"] sat, - int r0, int c0, int r1, int c1) + ssize_t r0, ssize_t c0, ssize_t r1, ssize_t c1) diff --git a/skimage/_shared/transform.pyx b/skimage/_shared/transform.pyx index ba0efc71..24e3150d 100644 --- a/skimage/_shared/transform.pyx +++ b/skimage/_shared/transform.pyx @@ -6,7 +6,7 @@ cimport numpy as cnp cdef float integrate(cnp.ndarray[float, ndim=2, mode="c"] sat, - int r0, int c0, int r1, int c1): + ssize_t r0, ssize_t c0, ssize_t r1, ssize_t c1): """ Using a summed area table / integral image, calculate the sum over a given window. diff --git a/skimage/transform/_warps_cy.pyx b/skimage/transform/_warps_cy.pyx index ce400ed6..a5a372d2 100644 --- a/skimage/transform/_warps_cy.pyx +++ b/skimage/transform/_warps_cy.pyx @@ -109,7 +109,7 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, cdef int rows = img.shape[0] cdef int cols = img.shape[1] - cdef double (*interp_func)(double*, int, int, double, double, + cdef double (*interp_func)(double*, ssize_t, ssize_t, double, double, char, double) if order == 0: interp_func = nearest_neighbour_interpolation From 6026ddd9a27be6457a4cf07ece73e5afa1ed587b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 18:58:48 +0100 Subject: [PATCH 554/625] Change type to ssize_t for all index and size variables --- skimage/feature/_template.pyx | 31 ++++++++++++++++++------------- skimage/feature/_texture.pyx | 12 ++++++------ skimage/feature/corner_cy.pyx | 8 ++++---- 3 files changed, 28 insertions(+), 23 deletions(-) diff --git a/skimage/feature/_template.pyx b/skimage/feature/_template.pyx index 58d48524..938e3a18 100644 --- a/skimage/feature/_template.pyx +++ b/skimage/feature/_template.pyx @@ -42,12 +42,17 @@ from skimage._shared.transform cimport integrate @cython.boundscheck(False) def match_template(np.ndarray[float, ndim=2, mode="c"] image, np.ndarray[float, ndim=2, mode="c"] template): + cdef np.ndarray[float, ndim=2, mode="c"] corr cdef np.ndarray[float, ndim=2, mode="c"] image_sat cdef np.ndarray[float, ndim=2, mode="c"] image_sqr_sat cdef float template_mean = np.mean(template) cdef float template_ssd cdef float inv_area + cdef ssize_t r, c, r_end, c_end + cdef ssize_t template_rows = template.shape[0] + cdef ssize_t template_cols = template.shape[1] + cdef float den, window_sqr_sum, window_mean_sqr, window_sum image_sat = integral.integral_image(image) image_sqr_sat = integral.integral_image(image**2) @@ -63,24 +68,24 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image, mode="valid"), dtype=np.float32) - cdef int i, j - cdef float den, window_sqr_sum, window_mean_sqr, window_sum, - # move window through convolution results, normalizing in the process - for i in range(corr.shape[0]): - for j in range(corr.shape[1]): - # subtract 1 because `i_end` and `j_end` are used for indexing into - # summed-area table, instead of slicing windows of the image. - i_end = i + template.shape[0] - 1 - j_end = j + template.shape[1] - 1 - window_sum = integrate(image_sat, i, j, i_end, j_end) + # move window through convolution results, normalizing in the process + for r in range(corr.shape[0]): + for c in range(corr.shape[1]): + # subtract 1 because `i_end` and `c_end` are used for indexing into + # summed-area table, instead of slicing windows of the image. + r_end = r + template_rows - 1 + c_end = c + template_cols - 1 + + window_sum = integrate(image_sat, r, c, r_end, c_end) window_mean_sqr = window_sum * window_sum * inv_area - window_sqr_sum = integrate(image_sqr_sat, i, j, i_end, j_end) + window_sqr_sum = integrate(image_sqr_sat, r, c, r_end, c_end) if window_sqr_sum <= window_mean_sqr: - corr[i, j] = 0 + corr[r, c] = 0 continue den = sqrt((window_sqr_sum - window_mean_sqr) * template_ssd) - corr[i, j] /= den + corr[r, c] /= den + return corr diff --git a/skimage/feature/_texture.pyx b/skimage/feature/_texture.pyx index 70a446bb..e2e3bb35 100644 --- a/skimage/feature/_texture.pyx +++ b/skimage/feature/_texture.pyx @@ -39,7 +39,7 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, """ cdef: np.int32_t a_inx, d_idx - np.int32_t r, c, rows, cols, row, col + ssize_t r, c, rows, cols, row, col np.int32_t i, j rows = image.shape[0] @@ -52,8 +52,8 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, i = image[r, c] # compute the location of the offset pixel - row = r + (sin(angle) * distance + 0.5) - col = c + (cos(angle) * distance + 0.5); + row = r + (sin(angle) * distance + 0.5) + col = c + (cos(angle) * distance + 0.5); # make sure the offset is within bounds if row >= 0 and row < rows and \ @@ -123,11 +123,11 @@ def _local_binary_pattern(np.ndarray[double, ndim=2] image, output_shape = (image.shape[0], image.shape[1]) cdef np.ndarray[double, ndim=2] output = np.zeros(output_shape, np.double) - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] + cdef ssize_t rows = image.shape[0] + cdef ssize_t cols = image.shape[1] cdef double lbp - cdef int r, c, changes, i + cdef ssize_t r, c, changes, i for r in range(image.shape[0]): for c in range(image.shape[1]): for i in range(P): diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx index 2f001ea4..34913450 100644 --- a/skimage/feature/corner_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -10,7 +10,7 @@ from skimage.color import rgb2grey from skimage.util import img_as_float -def corner_moravec(image, int window_size=1): +def corner_moravec(image, ssize_t window_size=1): """Compute Moravec corner measure response image. This is one of the simplest corner detectors and is comparatively fast but @@ -56,8 +56,8 @@ def corner_moravec(image, int window_size=1): [ 0., 0., 0., 0., 0., 0., 0.]]) """ - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] + cdef ssize_t rows = image.shape[0] + cdef ssize_t cols = image.shape[1] cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] cimage, out @@ -71,7 +71,7 @@ def corner_moravec(image, int window_size=1): cdef double* out_data = out.data cdef double msum, min_msum - cdef int r, c, br, bc, mr, mc, a, b + cdef ssize_t r, c, br, bc, mr, mc, a, b for r in range(2 * window_size, rows - 2 * window_size): for c in range(2 * window_size, cols - 2 * window_size): min_msum = DBL_MAX From 96c5f894ecee50f16104fea8ee6ef7b38cd31f12 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 19:14:49 +0100 Subject: [PATCH 555/625] Change type to ssize_t for all index and size variables --- skimage/filter/_ctmf.pyx | 196 ++++++++++++++++----------------- skimage/filter/_denoise_cy.pyx | 32 +++--- 2 files changed, 113 insertions(+), 115 deletions(-) diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index 5a81cb98..83269e46 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -84,9 +84,9 @@ cdef struct PixelCount: # relative offsets from the octagon center # cdef struct SCoord: - np.int32_t stride # add the stride to the memory location - np.int32_t x - np.int32_t y + ssize_t stride # add the stride to the memory location + ssize_t x + ssize_t y cdef struct Histograms: void *memory # pointer to the allocated memory @@ -95,16 +95,16 @@ cdef struct Histograms: np.uint8_t *data # pointer to the image data np.uint8_t *mask # pointer to the image mask np.uint8_t *output # pointer to the output array - np.int32_t column_count # number of columns represented by this + ssize_t column_count # number of columns represented by this # structure - np.int32_t stripe_length # number of columns including "radius" before + ssize_t stripe_length # number of columns including "radius" before # and after - np.int32_t row_count # number of rows available in image - np.int32_t current_column # the column being processed - np.int32_t current_row # the row being processed - np.int32_t current_stride # offset in data and mask to current location - np.int32_t radius # the "radius" of the octagon - np.int32_t a_2 # 1/2 of the length of a side of the octagon + ssize_t row_count # number of rows available in image + ssize_t current_column # the column being processed + ssize_t current_row # the row being processed + ssize_t current_stride # offset in data and mask to current location + ssize_t radius # the "radius" of the octagon + ssize_t a_2 # 1/2 of the length of a side of the octagon # # # The strides are the offsets in the array to the points that need to @@ -138,50 +138,50 @@ cdef struct Histograms: SCoord last_bottom_left # (-) trailing edge bottom - 1 col SCoord bottom_left # (+) left side of octagon's bottom - 1 col - np.int32_t row_stride # stride between one row and the next - np.int32_t col_stride # stride between one column and the next + ssize_t row_stride # stride between one row and the next + ssize_t col_stride # stride between one column and the next # The accumulator holds the running histogram # HistogramPiece accumulator # # The running count of pixels in the accumulator # - np.uint32_t accumulator_count + ssize_t accumulator_count # # The percent of pixels within the octagon whose value is # less than or equal to the median-filtered value (e.g. for # median, this is 50, for lower quartile it's 25) # - np.int32_t percent + ssize_t percent # # last_update_column keeps track of the column # of the last update # to the fine histogram accumulator. Short-term, the median # stays in one coarse block so only one fine histogram might # need to be updated # - np.int32_t last_update_column[16] + ssize_t last_update_column[16] ############################################################################ # # allocate_histograms - allocates the Histograms structure for the run # ############################################################################ -cdef Histograms *allocate_histograms(np.int32_t rows, - np.int32_t columns, - np.int32_t row_stride, - np.int32_t col_stride, - np.int32_t radius, - np.int32_t percent, +cdef Histograms *allocate_histograms(ssize_t rows, + ssize_t columns, + ssize_t row_stride, + ssize_t col_stride, + ssize_t radius, + ssize_t percent, np.uint8_t *data, np.uint8_t *mask, np.uint8_t *output): cdef: - unsigned int adjusted_stripe_length = columns + 2*radius + 1 - unsigned int memory_size + ssize_t adjusted_stripe_length = columns + 2*radius + 1 + ssize_t memory_size void *ptr Histograms *ph size_t roundoff - int a + ssize_t a SCoord *psc memory_size = (adjusted_stripe_length * @@ -199,7 +199,7 @@ cdef Histograms *allocate_histograms(np.int32_t rows, # # Align histogram memory to a 32-byte boundary # - roundoff = ptr + roundoff = ptr roundoff += 31 roundoff -= roundoff % 32 ptr = roundoff @@ -232,7 +232,7 @@ cdef Histograms *allocate_histograms(np.int32_t rows, # a_2 is the offset from the center to each of the octagon # corners # - a = ( radius * 2.0 / 2.414213) + a = (radius * 2.0 / 2.414213) a_2 = a / 2 if a_2 == 0: a_2 = 1 @@ -326,19 +326,18 @@ cdef void set_stride(Histograms *ph, SCoord *psc): # a column that is "radius" to the left. # ############################################################################ -cdef inline np.int32_t tl_br_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3 * ph.radius + ph.current_row) % \ - ph.stripe_length +cdef inline ssize_t tl_br_colidx(Histograms *ph, ssize_t colidx): + return (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length -cdef inline np.int32_t tr_bl_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3 * ph.radius + ph.row_count - ph.current_row) % \ - ph.stripe_length +cdef inline ssize_t tr_bl_colidx(Histograms *ph, ssize_t colidx): + return (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \ + ph.stripe_length -cdef inline np.int32_t leading_edge_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 5 * ph.radius) % ph.stripe_length +cdef inline ssize_t leading_edge_colidx(Histograms *ph, ssize_t colidx): + return (colidx + 5*ph.radius) % ph.stripe_length -cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx): - return (colidx + 3 * ph.radius - 1) % ph.stripe_length +cdef inline ssize_t trailing_edge_colidx(Histograms *ph, ssize_t colidx): + return (colidx + 3*ph.radius - 1) % ph.stripe_length ############################################################################ # @@ -349,9 +348,8 @@ cdef inline np.int32_t trailing_edge_colidx(Histograms *ph, np.int32_t colidx): # colidx - the index of the column to add # ############################################################################ -cdef inline void accumulate_coarse_histogram(Histograms *ph, np.int32_t colidx): - cdef: - int offset +cdef inline void accumulate_coarse_histogram(Histograms *ph, ssize_t colidx): + cdef ssize_t offset offset = tr_bl_colidx(ph, colidx) if ph.pixel_count[offset].top_right > 0: @@ -372,9 +370,8 @@ cdef inline void accumulate_coarse_histogram(Histograms *ph, np.int32_t colidx): # for a given column # ############################################################################ -cdef inline void deaccumulate_coarse_histogram(Histograms *ph, np.int32_t colidx): - cdef: - int offset +cdef inline void deaccumulate_coarse_histogram(Histograms *ph, ssize_t colidx): + cdef ssize_t offset # # The trailing diagonals don't appear until here # @@ -403,11 +400,11 @@ cdef inline void deaccumulate_coarse_histogram(Histograms *ph, np.int32_t colidx # ############################################################################ cdef inline void accumulate_fine_histogram(Histograms *ph, - np.int32_t colidx, - np.uint32_t fineidx): + ssize_t colidx, + ssize_t fineidx): cdef: - int fineoffset = fineidx * 16 - int offset + ssize_t fineoffset = fineidx * 16 + ssize_t offset offset = tr_bl_colidx(ph, colidx) add16(ph.accumulator.fine + fineoffset, @@ -427,11 +424,11 @@ cdef inline void accumulate_fine_histogram(Histograms *ph, # ############################################################################ cdef inline void deaccumulate_fine_histogram(Histograms *ph, - np.int32_t colidx, - np.uint32_t fineidx): + ssize_t colidx, + ssize_t fineidx): cdef: - int fineoffset = fineidx * 16 - int offset + ssize_t fineoffset = fineidx * 16 + ssize_t offset # # The trailing diagonals don't appear until here @@ -459,10 +456,7 @@ cdef inline void deaccumulate_fine_histogram(Histograms *ph, ############################################################################ cdef inline void accumulate(Histograms *ph): - cdef: - int i - int j - np.int32_t accumulator + cdef np.int32_t accumulator accumulate_coarse_histogram(ph, ph.current_column) deaccumulate_coarse_histogram(ph, ph.current_column) @@ -486,11 +480,11 @@ cdef inline void accumulate(Histograms *ph): # to choose remains to be done. ############################################################################ -cdef inline void update_fine(Histograms *ph, int fineidx): +cdef inline void update_fine(Histograms *ph, ssize_t fineidx): cdef: - int first_update_column = ph.last_update_column[fineidx]+1 - int update_limit = ph.current_column+1 - int i + ssize_t first_update_column = ph.last_update_column[fineidx]+1 + ssize_t update_limit = ph.current_column+1 + ssize_t i for i in range(first_update_column, update_limit): accumulate_fine_histogram(ph, i, fineidx) @@ -515,15 +509,15 @@ cdef inline void update_histogram(Histograms *ph, SCoord *last_coord, SCoord *coord): cdef: - np.int32_t current_column = ph.current_column - np.int32_t current_row = ph.current_row - np.int32_t current_stride = ph.current_stride - np.int32_t column_count = ph.column_count - np.int32_t row_count = ph.row_count + ssize_t current_column = ph.current_column + ssize_t current_row = ph.current_row + ssize_t current_stride = ph.current_stride + ssize_t column_count = ph.column_count + ssize_t row_count = ph.row_count np.uint8_t value - np.int32_t stride - np.int32_t x - np.int32_t y + ssize_t stride + ssize_t x + ssize_t y x = last_coord.x + current_column y = last_coord.y + current_row @@ -556,21 +550,21 @@ cdef inline void update_histogram(Histograms *ph, ############################################################################ cdef inline void update_current_location(Histograms *ph): cdef: - np.int32_t current_column = ph.current_column - np.int32_t radius = ph.radius - np.int32_t top_left_off = tl_br_colidx(ph, current_column) - np.int32_t top_right_off = tr_bl_colidx(ph, current_column) - np.int32_t bottom_left_off = tr_bl_colidx(ph, current_column) - np.int32_t bottom_right_off = tl_br_colidx(ph, current_column) - np.int32_t leading_edge_off = leading_edge_colidx(ph, current_column) + ssize_t current_column = ph.current_column + ssize_t radius = ph.radius + ssize_t top_left_off = tl_br_colidx(ph, current_column) + ssize_t top_right_off = tr_bl_colidx(ph, current_column) + ssize_t bottom_left_off = tr_bl_colidx(ph, current_column) + ssize_t bottom_right_off = tl_br_colidx(ph, current_column) + ssize_t leading_edge_off = leading_edge_colidx(ph, current_column) np.int32_t *coarse_histogram np.int32_t *fine_histogram - np.int32_t last_xoff - np.int32_t last_yoff - np.int32_t last_stride - np.int32_t xoff - np.int32_t yoff - np.int32_t stride + ssize_t last_xoff + ssize_t last_yoff + ssize_t last_stride + ssize_t xoff + ssize_t yoff + ssize_t stride update_histogram(ph, &ph.histogram[top_left_off].top_left, &ph.pixel_count[top_left_off].top_left, @@ -605,16 +599,18 @@ cdef inline void update_current_location(Histograms *ph): cdef inline np.uint8_t find_median(Histograms *ph): cdef: - np.uint32_t pixels_below # of pixels below the median - int i - int j - int k + ssize_t pixels_below # of pixels below the median + ssize_t i + ssize_t j + ssize_t k np.uint32_t accumulator if ph.accumulator_count == 0: return 0 - pixels_below = ((ph.accumulator_count * ph.percent + 50) - / 100) # +50 for roundoff + + # +50 for roundoff + pixels_below = (ph.accumulator_count * ph.percent + 50) / 100 + if pixels_below > 0: pixels_below -= 1 @@ -626,10 +622,10 @@ cdef inline np.uint8_t find_median(Histograms *ph): accumulator -= ph.accumulator.coarse[i] update_fine(ph, i) - for j in range(i * 16, (i + 1) * 16): + for j in range(i*16, (i + 1)*16): accumulator += ph.accumulator.fine[j] if accumulator > pixels_below: - return j + return j return 0 @@ -648,23 +644,25 @@ cdef inline np.uint8_t find_median(Histograms *ph): # output - array to be filled with filtered pixels # ############################################################################ -cdef int c_median_filter(np.int32_t rows, - np.int32_t columns, - np.int32_t row_stride, - np.int32_t col_stride, - np.int32_t radius, - np.int32_t percent, +cdef int c_median_filter(ssize_t rows, + ssize_t columns, + ssize_t row_stride, + ssize_t col_stride, + ssize_t radius, + ssize_t percent, np.uint8_t *data, np.uint8_t *mask, np.uint8_t *output): cdef: Histograms *ph Histogram *phistogram - int row, col, i - np.int32_t top_left_off - np.int32_t top_right_off - np.int32_t bottom_left_off - np.int32_t bottom_right_off + ssize_t row + ssize_t col + ssize_t i + ssize_t top_left_off + ssize_t top_right_off + ssize_t bottom_left_off + ssize_t bottom_right_off ph = allocate_histograms(rows, columns, row_stride, col_stride, radius, percent, data, mask, output) diff --git a/skimage/filter/_denoise_cy.pyx b/skimage/filter/_denoise_cy.pyx index 8ebf5c73..91c09fbe 100644 --- a/skimage/filter/_denoise_cy.pyx +++ b/skimage/filter/_denoise_cy.pyx @@ -17,7 +17,7 @@ cdef inline double _gaussian_weight(double sigma, double value): return exp(-0.5 * (value / sigma)**2) -cdef double* _compute_color_lut(int bins, double sigma, double max_value): +cdef double* _compute_color_lut(ssize_t bins, double sigma, double max_value): cdef: double* color_lut = malloc(bins * sizeof(double)) @@ -29,7 +29,7 @@ cdef double* _compute_color_lut(int bins, double sigma, double max_value): return color_lut -cdef double* _compute_range_lut(int win_size, double sigma): +cdef double* _compute_range_lut(ssize_t win_size, double sigma): cdef: double* range_lut = malloc(win_size**2 * sizeof(double)) @@ -45,9 +45,9 @@ cdef double* _compute_range_lut(int win_size, double sigma): return range_lut -def denoise_bilateral(image, int win_size=5, sigma_range=None, - double sigma_spatial=1, int bins=10000, mode='constant', - double cval=0): +def denoise_bilateral(image, ssize_t win_size=5, sigma_range=None, + double sigma_spatial=1, ssize_t bins=10000, + mode='constant', double cval=0): """Denoise image using bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages @@ -100,10 +100,10 @@ def denoise_bilateral(image, int win_size=5, sigma_range=None, image = np.atleast_3d(img_as_float(image)) cdef: - Py_ssize_t rows = image.shape[0] - Py_ssize_t cols = image.shape[1] - Py_ssize_t dims = image.shape[2] - Py_ssize_t window_ext = (win_size - 1) / 2 + ssize_t rows = image.shape[0] + ssize_t cols = image.shape[1] + ssize_t dims = image.shape[2] + ssize_t window_ext = (win_size - 1) / 2 double max_value = image.max() @@ -224,14 +224,14 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3): image = np.atleast_3d(img_as_float(image)) cdef: - Py_ssize_t rows = image.shape[0] - Py_ssize_t cols = image.shape[1] - Py_ssize_t dims = image.shape[2] - Py_ssize_t rows2 = rows + 2 - Py_ssize_t cols2 = cols + 2 - Py_ssize_t r, c, k + ssize_t rows = image.shape[0] + ssize_t cols = image.shape[1] + ssize_t dims = image.shape[2] + ssize_t rows2 = rows + 2 + ssize_t cols2 = cols + 2 + ssize_t r, c, k - Py_ssize_t total = rows * cols * dims + ssize_t total = rows * cols * dims shape_ext = (rows2, cols2, dims) From 8eb208616cd8b45ba35d0a3f855c3fea95d49f77 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 19:16:39 +0100 Subject: [PATCH 556/625] Rename Py_ssize_t to ssize_t for consistency within skimage --- skimage/filter/rank/_core16.pxd | 10 +- skimage/filter/rank/_core16.pyx | 54 ++--- skimage/filter/rank/_core8.pxd | 10 +- skimage/filter/rank/_core8.pyx | 50 ++--- skimage/filter/rank/_crank16.pyx | 212 +++++++++---------- skimage/filter/rank/_crank16_bilateral.pyx | 16 +- skimage/filter/rank/_crank16_percentiles.pyx | 84 ++++---- skimage/filter/rank/_crank8.pyx | 140 ++++++------ skimage/filter/rank/_crank8_percentiles.pyx | 48 ++--- 9 files changed, 312 insertions(+), 312 deletions(-) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index 5f7de9df..96f34121 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -6,12 +6,12 @@ cdef int int_min(int a, int b) # 16-bit core kernel receives extra information about data bitdepth -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, - Py_ssize_t, Py_ssize_t, Py_ssize_t, float, - float, Py_ssize_t, Py_ssize_t), +cdef void _core16(np.uint16_t kernel(ssize_t *, float, np.uint16_t, + ssize_t, ssize_t, ssize_t, float, + float, ssize_t, ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y, Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except * + char shift_x, char shift_y, ssize_t bitdepth, + float p0, float p1, ssize_t s0, ssize_t s1) except * diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index aa959fd0..323692ec 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -17,38 +17,38 @@ cdef inline int int_min(int a, int b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, +cdef inline void histogram_increment(ssize_t * histo, float * pop, np.uint16_t value): histo[value] += 1 pop[0] += 1 -cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, +cdef inline void histogram_decrement(ssize_t * histo, float * pop, np.uint16_t value): histo[value] -= 1 pop[0] -= 1 -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, - Py_ssize_t, Py_ssize_t, Py_ssize_t, float, - float, Py_ssize_t, Py_ssize_t), +cdef void _core16(np.uint16_t kernel(ssize_t *, float, np.uint16_t, + ssize_t, ssize_t, ssize_t, float, + float, ssize_t, ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y, Py_ssize_t bitdepth, - float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except *: + char shift_x, char shift_y, ssize_t bitdepth, + float p0, float p1, ssize_t s0, ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ - cdef Py_ssize_t rows = image.shape[0] - cdef Py_ssize_t cols = image.shape[1] - cdef Py_ssize_t srows = selem.shape[0] - cdef Py_ssize_t scols = selem.shape[1] + cdef ssize_t rows = image.shape[0] + cdef ssize_t cols = image.shape[1] + cdef ssize_t srows = selem.shape[0] + cdef ssize_t scols = selem.shape[1] - cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y - cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x + cdef ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -61,8 +61,8 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, midbin_list = [0, 0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] # set maxbin and midbin - cdef Py_ssize_t maxbin = maxbin_list[bitdepth] - cdef Py_ssize_t midbin = midbin_list[bitdepth] + cdef ssize_t maxbin = maxbin_list[bitdepth] + cdef ssize_t midbin = midbin_list[bitdepth] assert (image < maxbin).all() @@ -72,30 +72,30 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, cdef np.uint8_t * mask_data = mask.data # define local variable types - cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row + cdef ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) cdef float pop # allocate memory with malloc - cdef Py_ssize_t max_se = srows * scols + cdef ssize_t max_se = srows * scols # number of element in each attack border - cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w + cdef ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) + cdef ssize_t * histo = malloc(maxbin * sizeof(ssize_t)) # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border - cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef ssize_t * se_e_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_e_c = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_w_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_w_c = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_n_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_n_c = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_s_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_s_c = malloc(max_se * sizeof(ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 38236b87..6ee2e0a3 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -5,18 +5,18 @@ cdef np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) cdef np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) -cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t r, Py_ssize_t c, +cdef np.uint8_t is_in_mask(ssize_t rows, ssize_t cols, + ssize_t r, ssize_t c, np.uint8_t * mask) # 8-bit core kernel receives extra information about data inferior and superior # percentiles -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, - float, Py_ssize_t, Py_ssize_t), +cdef void _core8(np.uint8_t kernel(ssize_t *, float, np.uint8_t, float, + float, ssize_t, ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1) except * + ssize_t s0, ssize_t s1) except * diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 9f4bc693..7dbd4524 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -16,20 +16,20 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b -cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, +cdef inline void histogram_increment(ssize_t * histo, float * pop, np.uint8_t value): histo[value] += 1 pop[0] += 1 -cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, +cdef inline void histogram_decrement(ssize_t * histo, float * pop, np.uint8_t value): histo[value] -= 1 pop[0] -= 1 -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t r, Py_ssize_t c, +cdef inline np.uint8_t is_in_mask(ssize_t rows, ssize_t cols, + ssize_t r, ssize_t c, np.uint8_t * mask): """Check whether given coordinate is within image and mask is true.""" if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: @@ -41,25 +41,25 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, return 0 -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, - float, Py_ssize_t, Py_ssize_t), +cdef void _core8(np.uint8_t kernel(ssize_t *, float, np.uint8_t, float, + float, ssize_t, ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1) except *: + ssize_t s0, ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ - cdef Py_ssize_t rows = image.shape[0] - cdef Py_ssize_t cols = image.shape[1] - cdef Py_ssize_t srows = selem.shape[0] - cdef Py_ssize_t scols = selem.shape[1] + cdef ssize_t rows = image.shape[0] + cdef ssize_t cols = image.shape[1] + cdef ssize_t srows = selem.shape[0] + cdef ssize_t scols = selem.shape[1] - cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y - cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x + cdef ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -74,31 +74,31 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, cdef np.uint8_t * mask_data = mask.data # define local variable types - cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row + cdef ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) cdef float pop # allocate memory with malloc - cdef Py_ssize_t max_se = srows * scols + cdef ssize_t max_se = srows * scols # number of element in each attack border - cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w + cdef ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) + cdef ssize_t * histo = malloc(256 * sizeof(ssize_t)) # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border - cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) - cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef ssize_t * se_e_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_e_c = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_w_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_w_c = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_n_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_n_c = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_s_r = malloc(max_se * sizeof(ssize_t)) + cdef ssize_t * se_s_c = malloc(max_se * sizeof(ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index e81bf81f..b5dda3c2 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -14,12 +14,12 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_autolevel(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i, imin, imax, delta + ssize_t s0, ssize_t s1): + cdef ssize_t i, imin, imax, delta if pop: for i in range(maxbin - 1, -1, -1): @@ -37,12 +37,12 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, return < np.uint16_t > (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_bottomhat(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i if pop: for i in range(maxbin): @@ -53,12 +53,12 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_equalize(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float sum = 0. if pop: @@ -72,12 +72,12 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_gradient(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i, imin, imax + ssize_t s0, ssize_t s1): + cdef ssize_t i, imin, imax if pop: for i in range(maxbin - 1, -1, -1): @@ -93,12 +93,12 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_maximum(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i if pop: for i in range(maxbin - 1, -1, -1): @@ -108,12 +108,12 @@ cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float mean = 0. if pop: @@ -124,15 +124,15 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, +cdef inline np.uint16_t kernel_meansubstraction(ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, + ssize_t bitdepth, + ssize_t maxbin, + ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float mean = 0. if pop: @@ -143,12 +143,12 @@ cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_median(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float sum = pop / 2.0 if pop: @@ -161,12 +161,12 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_minimum(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i if pop: for i in range(maxbin): @@ -176,12 +176,12 @@ cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_modal(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t hmax = 0, imax = 0 + ssize_t s0, ssize_t s1): + cdef ssize_t hmax = 0, imax = 0 if pop: for i in range(maxbin): @@ -193,15 +193,15 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, +cdef inline np.uint16_t kernel_morph_contr_enh(ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, + ssize_t bitdepth, + ssize_t maxbin, + ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i, imin, imax + ssize_t s0, ssize_t s1): + cdef ssize_t i, imin, imax if pop: for i in range(maxbin - 1, -1, -1): @@ -220,20 +220,20 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): return < np.uint16_t > (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_threshold(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float mean = 0. if pop: @@ -244,12 +244,12 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_tophat(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i if pop: for i in range(maxbin - 1, -1, -1): @@ -260,12 +260,12 @@ cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_entropy(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float e, p if pop: @@ -289,132 +289,132 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def entropy(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, ssize_t bitdepth=8): _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index c71ccc5c..82236d63 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -13,11 +13,11 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, bilat_pop = 0 cdef float mean = 0. @@ -35,11 +35,11 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, bilat_pop = 0 diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 220d2386..e8836b5b 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -13,11 +13,11 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_autolevel(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, imin, imax, sum, delta @@ -46,11 +46,11 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_gradient(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, imin, imax, sum, delta @@ -74,11 +74,11 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, sum, mean, n @@ -100,14 +100,14 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, +cdef inline np.uint16_t kernel_mean_substraction(ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, + ssize_t bitdepth, + ssize_t maxbin, + ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, sum, mean, n @@ -128,14 +128,14 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, +cdef inline np.uint16_t kernel_morph_contr_enh(ssize_t * histo, float pop, np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, + ssize_t bitdepth, + ssize_t maxbin, + ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, imin, imax, sum, delta @@ -165,11 +165,11 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_percentile(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i cdef float sum = 0. @@ -185,11 +185,11 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, sum, n @@ -205,11 +205,11 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, +cdef inline np.uint16_t kernel_threshold(ssize_t * histo, float pop, + np.uint16_t g, ssize_t bitdepth, + ssize_t maxbin, ssize_t midbin, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i cdef float sum = 0. @@ -239,7 +239,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, """bottom hat """ _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -251,7 +251,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, """return p0,p1 percentile gradient """ _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -263,7 +263,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -276,7 +276,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, """ _core16( kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -288,7 +288,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, """reforce contrast using percentiles """ _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -300,7 +300,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, """return p0 percentile """ _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -312,7 +312,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -324,4 +324,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, """return (maxbin-1) if g > percentile p0 """ _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 8bff9703..582bcbb7 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -14,11 +14,11 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_autolevel(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i, imin, imax, delta + cdef ssize_t i, imin, imax, delta if pop: for i in range(255, -1, -1): @@ -38,11 +38,11 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_bottomhat(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i if pop: for i in range(256): @@ -54,11 +54,11 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_equalize(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i cdef float sum = 0. if pop: @@ -72,11 +72,11 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_gradient(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i, imin, imax + cdef ssize_t i, imin, imax if pop: for i in range(255, -1, -1): @@ -92,11 +92,11 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_maximum(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i if pop: for i in range(255, -1, -1): @@ -106,11 +106,11 @@ cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i cdef float mean = 0. if pop: @@ -121,11 +121,11 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_meansubstraction(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i cdef float mean = 0. if pop: @@ -136,11 +136,11 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_median(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i cdef float sum = pop / 2.0 if pop: @@ -153,11 +153,11 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_minimum(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i if pop: for i in range(256): @@ -167,11 +167,11 @@ cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_modal(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t hmax = 0, imax = 0 + cdef ssize_t hmax = 0, imax = 0 if pop: for i in range(256): @@ -183,11 +183,11 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_morph_contr_enh(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i, imin, imax + cdef ssize_t i, imin, imax if pop: for i in range(255, -1, -1): @@ -206,18 +206,18 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_pop(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): return < np.uint8_t > (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_threshold(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i cdef float mean = 0. if pop: @@ -228,11 +228,11 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_tophat(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i + cdef ssize_t i if pop: for i in range(255, -1, -1): @@ -243,12 +243,12 @@ cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_noise_filter(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): - cdef Py_ssize_t i - cdef Py_ssize_t min_i + cdef ssize_t i + cdef ssize_t min_i # early stop if at least one pixel of the neighborhood has the same g if histo[g] > 0: @@ -267,10 +267,10 @@ cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, return < np.uint8_t > min_i -cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_entropy(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - cdef Py_ssize_t i + ssize_t s0, ssize_t s1): + cdef ssize_t i cdef float e, p if pop: @@ -285,11 +285,11 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): - cdef Py_ssize_t i - cdef Py_ssize_t max_i +cdef inline np.uint8_t kernel_otsu(ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, ssize_t s0, + ssize_t s1): + cdef ssize_t i + cdef ssize_t max_i cdef float P, mu1, mu2, q1, new_q1, sigma_b, max_sigma_b cdef float mu = 0. @@ -334,7 +334,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -343,7 +343,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -352,7 +352,7 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -361,7 +361,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -370,7 +370,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -379,7 +379,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -388,7 +388,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -397,7 +397,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, @@ -406,7 +406,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -415,7 +415,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -424,7 +424,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -433,7 +433,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -442,7 +442,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, 0, 0, - 0, 0) + 0, 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, @@ -451,7 +451,7 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, @@ -460,7 +460,7 @@ def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def entropy(np.ndarray[np.uint8_t, ndim=2] image, @@ -469,7 +469,7 @@ def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def otsu(np.ndarray[np.uint8_t, ndim=2] image, @@ -478,4 +478,4 @@ def otsu(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 31b00742..bd48c78b 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -13,9 +13,9 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_autolevel(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -45,9 +45,9 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, return < np.uint8_t > (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_gradient(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -70,9 +70,9 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, sum, mean, n if pop: @@ -92,11 +92,11 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, +cdef inline np.uint8_t kernel_mean_substraction(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, sum, mean, n if pop: @@ -116,10 +116,10 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, +cdef inline np.uint8_t kernel_morph_contr_enh(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -148,9 +148,9 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_percentile(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i cdef float sum = 0. @@ -165,9 +165,9 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_pop(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i, sum, n if pop: @@ -182,9 +182,9 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_threshold(ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): + ssize_t s0, ssize_t s1): cdef int i cdef float sum = 0. @@ -212,7 +212,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, """autolevel """ _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -223,7 +223,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, """return p0,p1 percentile gradient """ _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -234,7 +234,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -245,7 +245,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, """return original - mean between [p0 and p1] percentiles *.5 +127 """ _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -256,7 +256,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, """reforce contrast using percentiles """ _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, 0, 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -267,7 +267,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, """return p0 percentile """ _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -278,7 +278,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -289,4 +289,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, """return 255 if g > percentile p0 """ _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) From 0b99eb68f45dee29e2d59ad77bdd861e3eb4cbca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 19:24:56 +0100 Subject: [PATCH 557/625] Change type to ssize_t for all index and size variables --- skimage/graph/heap.pxd | 15 +++++++-------- skimage/measure/_find_contours.pyx | 13 +++++-------- skimage/measure/_moments.pyx | 4 ++-- 3 files changed, 14 insertions(+), 18 deletions(-) diff --git a/skimage/graph/heap.pxd b/skimage/graph/heap.pxd index 51b4f79c..74366368 100644 --- a/skimage/graph/heap.pxd +++ b/skimage/graph/heap.pxd @@ -1,29 +1,29 @@ """ This is the definition file for heap.pyx. It contains the definitions of the heap classes, such that other cython modules can "cimport heap" and thus use the -C versions of pop(), push(), and value_of(): pop_fast(), push_fast() and +C versions of pop(), push(), and value_of(): pop_fast(), push_fast() and value_of_fast() """ # determine datatypes for heap ctypedef double VALUE_T -ctypedef Py_ssize_t REFERENCE_T +ctypedef ssize_t REFERENCE_T ctypedef REFERENCE_T INDEX_T ctypedef unsigned char BOOL_T ctypedef unsigned char LEVELS_T cdef class BinaryHeap: cdef readonly INDEX_T count - cdef readonly LEVELS_T levels, min_levels + cdef readonly LEVELS_T levels, min_levels cdef VALUE_T *_values cdef REFERENCE_T *_references cdef REFERENCE_T _popped_ref - + cdef void _add_or_remove_level(self, LEVELS_T add_or_remove) cdef void _update(self) cdef void _update_one(self, INDEX_T i) cdef void _remove(self, INDEX_T i) - + cdef INDEX_T push_fast(self, VALUE_T value, REFERENCE_T reference) cdef VALUE_T pop_fast(self) @@ -32,8 +32,7 @@ cdef class FastUpdateBinaryHeap(BinaryHeap): cdef INDEX_T *_crossref cdef BOOL_T _invalid_ref cdef BOOL_T _pushed - + cdef VALUE_T value_of_fast(self, REFERENCE_T reference) - cdef INDEX_T push_if_lower_fast(self, VALUE_T value, + cdef INDEX_T push_if_lower_fast(self, VALUE_T value, REFERENCE_T reference) - \ No newline at end of file diff --git a/skimage/measure/_find_contours.pyx b/skimage/measure/_find_contours.pyx index 4f1b3cee..30eb1250 100644 --- a/skimage/measure/_find_contours.pyx +++ b/skimage/measure/_find_contours.pyx @@ -1,10 +1,7 @@ -# -*- python -*- # cython: cdivision=True - import numpy as np cimport numpy as np -np.import_array() cdef inline double _get_fraction(double from_value, double to_value, double level): @@ -14,7 +11,7 @@ cdef inline double _get_fraction(double from_value, double to_value, def iterate_and_store(np.ndarray[double, ndim=2] array, - double level, int vertex_connect_high): + double level, ssize_t vertex_connect_high): """Iterate across the given array in a marching-squares fashion, looking for segments that cross 'level'. If such a segment is found, its coordinates are added to a growing list of segments, @@ -27,7 +24,7 @@ def iterate_and_store(np.ndarray[double, ndim=2] array, raise ValueError("Input array must be at least 2x2.") cdef list arc_list = [] - cdef int n + cdef ssize_t n # The plan is to iterate a 2x2 square across the input array. This means # that the upper-left corner of the square needs to iterate across a @@ -39,17 +36,17 @@ def iterate_and_store(np.ndarray[double, ndim=2] array, # index varies the fastest). # Current coords start at 0,0. - cdef int[2] coords + cdef ssize_t[2] coords coords[0] = 0 coords[1] = 0 # Calculate the number of iterations we'll need - cdef int num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1) + cdef ssize_t num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1) cdef unsigned char square_case = 0 cdef tuple top, bottom, left, right cdef double ul, ur, ll, lr - cdef int r0, r1, c0, c1 + cdef ssize_t r0, r1, c0, c1 for n in range(num_square_steps): # There are sixteen different possible square types, diagramed below. diff --git a/skimage/measure/_moments.pyx b/skimage/measure/_moments.pyx index f84e14dd..759e4a4f 100644 --- a/skimage/measure/_moments.pyx +++ b/skimage/measure/_moments.pyx @@ -7,7 +7,7 @@ cimport numpy as np def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc, int order): - cdef int p, q, r, c + cdef ssize_t p, q, r, c cdef np.ndarray[np.double_t, ndim=2] mu mu = np.zeros((order + 1, order + 1), 'double') for p in range(order + 1): @@ -18,7 +18,7 @@ def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc, return mu def normalized_moments(np.ndarray[np.double_t, ndim=2] mu, int order): - cdef int p, q + cdef ssize_t p, q cdef np.ndarray[np.double_t, ndim=2] nu nu = np.zeros((order + 1, order + 1), 'double') for p in range(order + 1): From c9f72e93d6f6cb3cdde2548c1fc213c7d31b7ee2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 22:14:22 +0100 Subject: [PATCH 558/625] Change type to ssize_t for all index and size variables --- skimage/morphology/_convex_hull.pyx | 57 ++++++++------- skimage/morphology/_pnpoly.pyx | 13 ++-- skimage/morphology/_skeletonize.py | 4 +- skimage/morphology/_skeletonize_cy.pyx | 36 +++++----- skimage/morphology/_watershed.pyx | 49 ++++++------- skimage/morphology/ccomp.pxd | 12 ++-- skimage/morphology/ccomp.pyx | 37 +++++----- skimage/morphology/cmorph.pyx | 38 +++++----- skimage/morphology/heap_general.pxi | 84 +++++++++++------------ skimage/morphology/heap_watershed.pxi | 7 +- skimage/morphology/watershed.py | 2 - skimage/segmentation/_felzenszwalb_cy.pyx | 14 ++-- 12 files changed, 173 insertions(+), 180 deletions(-) diff --git a/skimage/morphology/_convex_hull.pyx b/skimage/morphology/_convex_hull.pyx index dc426900..6c9d1083 100644 --- a/skimage/morphology/_convex_hull.pyx +++ b/skimage/morphology/_convex_hull.pyx @@ -13,47 +13,44 @@ def possible_hull(np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] img): Returns ------- - coords : ndarray (N, 2) + coords : ndarray (cols, 2) The ``(row, column)`` coordinates of all pixels that possibly belong to the convex hull. """ - cdef int i, j, k - cdef unsigned int M, N - - M = img.shape[0] - N = img.shape[1] + cdef ssize_t r, c + cdef ssize_t rows = img.shape[0] + cdef ssize_t cols = img.shape[1] - # Output: M storage slots for left boundary pixels - # N storage slots for top boundary pixels - # M storage slots for right boundary pixels - # N storage slots for bottom boundary pixels - cdef np.ndarray[dtype=np.int_t, ndim=2] nonzero = \ - np.ones((2 * (M + N), 2), dtype=np.int) - nonzero *= -1 + # Output: rows storage slots for left boundary pixels + # cols storage slots for top boundary pixels + # rows storage slots for right boundary pixels + # cols storage slots for bottom boundary pixels + cdef np.ndarray[dtype=ssize_t, ndim=2] nonzero = \ + np.ones((2 * (rows + cols), 2), dtype=np.int) + nonzero *= -1 - k = 0 - for i in range(M): - for j in range(N): - if img[i, j] != 0: + for r in range(rows): + for c in range(cols): + if img[r, c] != 0: # Left check - if nonzero[i, 1] == -1: - nonzero[i, 0] = i - nonzero[i, 1] = j + if nonzero[r, 1] == -1: + nonzero[r, 0] = r + nonzero[r, 1] = c # Right check - elif nonzero[M + N + i, 1] < j: - nonzero[M + N + i, 0] = i - nonzero[M + N + i, 1] = j + elif nonzero[rows + cols + r, 1] < c: + nonzero[rows + cols + r, 0] = r + nonzero[rows + cols + r, 1] = c # Top check - if nonzero[M + j, 1] == -1: - nonzero[M + j, 0] = i - nonzero[M + j, 1] = j + if nonzero[rows + c, 1] == -1: + nonzero[rows + c, 0] = r + nonzero[rows + c, 1] = c # Bottom check - elif nonzero[2 * M + N + j, 0] < i: - nonzero[2 * M + N + j, 0] = i - nonzero[2 * M + N + j, 1] = j - + elif nonzero[2 * rows + cols + c, 0] < r: + nonzero[2 * rows + cols + c, 0] = r + nonzero[2 * rows + cols + c, 1] = c + return nonzero[nonzero[:, 0] != -1] diff --git a/skimage/morphology/_pnpoly.pyx b/skimage/morphology/_pnpoly.pyx index 7deb6a50..d1413a24 100644 --- a/skimage/morphology/_pnpoly.pyx +++ b/skimage/morphology/_pnpoly.pyx @@ -31,18 +31,19 @@ def grid_points_inside_poly(shape, verts): vx = verts[:, 0].astype(np.double) vy = verts[:, 1].astype(np.double) - cdef int V = vx.shape[0] + cdef ssize_t V = vx.shape[0] - cdef int M = shape[0] - cdef int N = shape[1] - cdef int m, n + cdef ssize_t M = shape[0] + cdef ssize_t N = shape[1] + cdef ssize_t m, n cdef np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] out = \ np.zeros((M, N), dtype=np.uint8) for m in range(M): for n in range(N): - out[m, n] = point_in_polygon(V, vx.data, vy.data, m, n) + out[m, n] = point_in_polygon(V, vx.data, vy.data, + m, n) return out.view(bool) @@ -76,7 +77,7 @@ def points_inside_poly(points, verts): vy = verts[:, 1].astype(np.double) cdef np.ndarray[np.uint8_t, ndim=1] out = \ - np.zeros(x.shape[0], dtype=np.uint8) + np.zeros(x.shape[0], dtype=np.uint8) points_in_polygon(vx.shape[0], vx.data, vy.data, x.shape[0], x.data, y.data, diff --git a/skimage/morphology/_skeletonize.py b/skimage/morphology/_skeletonize.py index 04a65da6..b48beb86 100644 --- a/skimage/morphology/_skeletonize.py +++ b/skimage/morphology/_skeletonize.py @@ -277,8 +277,8 @@ def medial_axis(image, mask=None, return_distance=False): i, j = np.mgrid[0:image.shape[0], 0:image.shape[1]] result = masked_image.copy() distance = distance[result] - i = np.ascontiguousarray(i[result], np.int32) - j = np.ascontiguousarray(j[result], np.int32) + i = np.ascontiguousarray(i[result], np.intp) + j = np.ascontiguousarray(j[result], np.intp) result = np.ascontiguousarray(result, np.uint8) # Determine the order in which pixels are processed. diff --git a/skimage/morphology/_skeletonize_cy.pyx b/skimage/morphology/_skeletonize_cy.pyx index ff5fcdf2..c4aa0d48 100644 --- a/skimage/morphology/_skeletonize_cy.pyx +++ b/skimage/morphology/_skeletonize_cy.pyx @@ -15,11 +15,11 @@ cimport cython @cython.boundscheck(False) -def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, +def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] result, - np.ndarray[dtype=np.int32_t, ndim=1, + np.ndarray[dtype=np.intp_t, ndim=1, negative_indices=False, mode='c'] i, - np.ndarray[dtype=np.int32_t, ndim=1, + np.ndarray[dtype=np.intp_t, ndim=1, negative_indices=False, mode='c'] j, np.ndarray[dtype=np.int32_t, ndim=1, negative_indices=False, mode='c'] order, @@ -37,13 +37,13 @@ def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, i, j : ndarrays The coordinates of each foreground pixel in the image - + order : ndarray The index of each pixel, in the order of processing (order[0] is the first pixel to process, etc.) - + table : ndarray - The 512-element lookup table of values after transformation + The 512-element lookup table of values after transformation (whether to keep or not each configuration in a binary 3x3 array) Notes @@ -55,15 +55,15 @@ def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, the quench-line of the brushfire will be evaluated later than a point closer to the edge. - Note that the neighbourhood of a pixel may evolve before the loop - arrives at this pixel. This is why it is possible to compute the + Note that the neighbourhood of a pixel may evolve before the loop + arrives at this pixel. This is why it is possible to compute the skeleton in only one pass, thanks to an adapted ordering of the pixels. """ cdef: np.int32_t accumulator - np.int32_t index, order_index - np.int32_t ii, jj + ssize_t index, order_index + ssize_t ii, jj for index in range(order.shape[0]): accumulator = 16 @@ -110,21 +110,21 @@ def _table_lookup_index(np.ndarray[dtype=np.uint8_t, ndim=2, 256 128 64 32 16 8 4 2 1 - + but this runs about twice as fast because of inlining and the hardwired kernel. """ cdef: - np.ndarray[dtype=np.int32_t, ndim=2, + np.ndarray[dtype=np.int32_t, ndim=2, negative_indices=False, mode='c'] indexer np.int32_t *p_indexer np.uint8_t *p_image - np.int32_t i_stride - np.int32_t i_shape - np.int32_t j_shape - np.int32_t i - np.int32_t j - np.int32_t offset + ssize_t i_stride + ssize_t i_shape + ssize_t j_shape + ssize_t i + ssize_t j + ssize_t offset i_shape = image.shape[0] j_shape = image.shape[1] diff --git a/skimage/morphology/_watershed.pyx b/skimage/morphology/_watershed.pyx index c86d8744..b98ca332 100644 --- a/skimage/morphology/_watershed.pyx +++ b/skimage/morphology/_watershed.pyx @@ -9,39 +9,33 @@ All rights reserved. Original author: Lee Kamentsky """ - -cdef extern from "numpy/arrayobject.h": - cdef void import_array() -import_array() - import numpy as np cimport numpy as np cimport cython -DTYPE_INT32 = np.int32 + ctypedef np.int32_t DTYPE_INT32_t DTYPE_BOOL = np.bool ctypedef np.int8_t DTYPE_BOOL_t + include "heap_watershed.pxi" + @cython.boundscheck(False) -def watershed(np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, - mode='c'] image, - np.ndarray[DTYPE_INT32_t, ndim=2, negative_indices=False, - mode='c'] pq, - DTYPE_INT32_t age, - np.ndarray[DTYPE_INT32_t, ndim=2, negative_indices=False, - mode='c'] structure, - DTYPE_INT32_t ndim, - np.ndarray[DTYPE_BOOL_t, ndim=1, negative_indices=False, - mode='c'] mask, - np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, - mode='c'] image_shape, - np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, - mode='c'] output): +def watershed(np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, + mode='c'] image, + np.ndarray[DTYPE_INT32_t, ndim=2, negative_indices=False, + mode='c'] pq, + ssize_t age, + np.ndarray[DTYPE_INT32_t, ndim=2, negative_indices=False, + mode='c'] structure, + np.ndarray[DTYPE_BOOL_t, ndim=1, negative_indices=False, + mode='c'] mask, + np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, + mode='c'] output): """Do heavy lifting of watershed algorithm - + Parameters ---------- @@ -58,20 +52,17 @@ def watershed(np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, in a flattened array. The remaining elements are the offsets from the point to its neighbor in the various dimensions - ndim - # of dimensions in the image mask - numpy boolean (char) array indicating which pixels to consider and which to ignore. Also flattened. - image_shape - the dimensions of the image, for boundary checking, - a numpy array of np.int32 output - put the image labels in here """ cdef Heapitem elem cdef Heapitem new_elem - cdef DTYPE_INT32_t nneighbors = structure.shape[0] - cdef DTYPE_INT32_t i = 0 - cdef DTYPE_INT32_t index = 0 - cdef DTYPE_INT32_t old_index = 0 - cdef DTYPE_INT32_t max_index = image.shape[0] + cdef ssize_t nneighbors = structure.shape[0] + cdef ssize_t i = 0 + cdef ssize_t index = 0 + cdef ssize_t old_index = 0 + cdef ssize_t max_index = image.shape[0] cdef Heap *hp = heap_from_numpy2() diff --git a/skimage/morphology/ccomp.pxd b/skimage/morphology/ccomp.pxd index 0b431832..b50703e9 100644 --- a/skimage/morphology/ccomp.pxd +++ b/skimage/morphology/ccomp.pxd @@ -1,10 +1,10 @@ """Export fast union find in Cython""" cimport numpy as np -DTYPE = np.int -ctypedef np.int_t DTYPE_t +DTYPE = np.intp +ctypedef np.intp_t DTYPE_t -cdef DTYPE_t find_root(np.int_t *forest, np.int_t n) -cdef set_root(np.int_t *forest, np.int_t n, np.int_t root) -cdef join_trees(np.int_t *forest, np.int_t n, np.int_t m) -cdef link_bg(np.int_t *forest, np.int_t n, np.int_t *background_node) +cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) +cdef set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) +cdef join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m) +cdef link_bg(DTYPE_t *forest, DTYPE_t n, DTYPE_t *background_node) diff --git a/skimage/morphology/ccomp.pyx b/skimage/morphology/ccomp.pyx index 6a4fb1f2..dc85196b 100644 --- a/skimage/morphology/ccomp.pyx +++ b/skimage/morphology/ccomp.pyx @@ -23,23 +23,25 @@ See also: # Tree operations implemented by an array as described in Wu et al. # The term "forest" is used to indicate an array that stores one or more trees -DTYPE = np.int +DTYPE = np.intp -cdef DTYPE_t find_root(np.int_t *forest, np.int_t n): + +cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n): """Find the root of node n. """ - cdef np.int_t root = n + cdef DTYPE_t root = n while (forest[root] < root): root = forest[root] return root -cdef set_root(np.int_t *forest, np.int_t n, np.int_t root): + +cdef set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root): """ Set all nodes on a path to point to new_root. """ - cdef np.int_t j + cdef DTYPE_t j while (forest[n] < n): j = forest[n] forest[n] = root @@ -48,12 +50,12 @@ cdef set_root(np.int_t *forest, np.int_t n, np.int_t root): forest[n] = root -cdef join_trees(np.int_t *forest, np.int_t n, np.int_t m): +cdef join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m): """Join two trees containing nodes n and m. """ - cdef np.int_t root = find_root(forest, n) - cdef np.int_t root_m + cdef DTYPE_t root = find_root(forest, n) + cdef DTYPE_t root_m if (n != m): root_m = find_root(forest, m) @@ -64,7 +66,8 @@ cdef join_trees(np.int_t *forest, np.int_t n, np.int_t m): set_root(forest, n, root) set_root(forest, m, root) -cdef link_bg(np.int_t *forest, np.int_t n, np.int_t *background_node): + +cdef link_bg(DTYPE_t *forest, DTYPE_t n, DTYPE_t *background_node): """ Link a node to the background node. @@ -76,7 +79,7 @@ cdef link_bg(np.int_t *forest, np.int_t n, np.int_t *background_node): # Connected components search as described in Fiorio et al. -def label(input, np.int_t neighbors=8, np.int_t background=-1): +def label(input, DTYPE_t neighbors=8, DTYPE_t background=-1): """Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. @@ -134,8 +137,8 @@ def label(input, np.int_t neighbors=8, np.int_t background=-1): [-1 -1 -1]] """ - cdef np.int_t rows = input.shape[0] - cdef np.int_t cols = input.shape[1] + cdef DTYPE_t rows = input.shape[0] + cdef DTYPE_t cols = input.shape[1] cdef np.ndarray[DTYPE_t, ndim=2] data = np.array(input, copy=True, dtype=DTYPE) @@ -143,12 +146,12 @@ def label(input, np.int_t neighbors=8, np.int_t background=-1): forest = np.arange(data.size, dtype=DTYPE).reshape((rows, cols)) - cdef np.int_t *forest_p = forest.data - cdef np.int_t *data_p = data.data + cdef DTYPE_t *forest_p = forest.data + cdef DTYPE_t *data_p = data.data - cdef np.int_t i, j + cdef DTYPE_t i, j - cdef np.int_t background_node = -999 + cdef DTYPE_t background_node = -999 if neighbors != 4 and neighbors != 8: raise ValueError('Neighbors must be either 4 or 8.') @@ -197,7 +200,7 @@ def label(input, np.int_t neighbors=8, np.int_t background=-1): # Label output - cdef np.int_t ctr = 0 + cdef DTYPE_t ctr = 0 for i in range(rows): for j in range(cols): if (i*cols + j) == background_node: diff --git a/skimage/morphology/cmorph.pyx b/skimage/morphology/cmorph.pyx index 9b8b3a27..418b0571 100644 --- a/skimage/morphology/cmorph.pyx +++ b/skimage/morphology/cmorph.pyx @@ -13,13 +13,13 @@ def dilate(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] + cdef ssize_t rows = image.shape[0] + cdef ssize_t cols = image.shape[1] + cdef ssize_t srows = selem.shape[0] + cdef ssize_t scols = selem.shape[1] - cdef int centre_r = int(selem.shape[0] / 2) - shift_y - cdef int centre_c = int(selem.shape[1] / 2) - shift_x + cdef ssize_t centre_r = int(selem.shape[0] / 2) - shift_y + cdef ssize_t centre_c = int(selem.shape[1] / 2) - shift_x image = np.ascontiguousarray(image) if out is None: @@ -30,11 +30,11 @@ def dilate(np.ndarray[np.uint8_t, ndim=2] image, cdef np.uint8_t* out_data = out.data cdef np.uint8_t* image_data = image.data - cdef int r, c, rr, cc, s, value, local_max + cdef ssize_t r, c, rr, cc, s, value, local_max - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) + cdef ssize_t selem_num = np.sum(selem != 0) + cdef ssize_t* sr = malloc(selem_num * sizeof(ssize_t)) + cdef ssize_t* sc = malloc(selem_num * sizeof(ssize_t)) s = 0 for r in range(srows): @@ -68,13 +68,13 @@ def erode(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - cdef int rows = image.shape[0] - cdef int cols = image.shape[1] - cdef int srows = selem.shape[0] - cdef int scols = selem.shape[1] + cdef ssize_t rows = image.shape[0] + cdef ssize_t cols = image.shape[1] + cdef ssize_t srows = selem.shape[0] + cdef ssize_t scols = selem.shape[1] - cdef int centre_r = int(selem.shape[0] / 2) - shift_y - cdef int centre_c = int(selem.shape[1] / 2) - shift_x + cdef ssize_t centre_r = int(selem.shape[0] / 2) - shift_y + cdef ssize_t centre_c = int(selem.shape[1] / 2) - shift_x image = np.ascontiguousarray(image) if out is None: @@ -87,9 +87,9 @@ def erode(np.ndarray[np.uint8_t, ndim=2] image, cdef int r, c, rr, cc, s, value, local_min - cdef int selem_num = np.sum(selem != 0) - cdef int* sr = malloc(selem_num * sizeof(int)) - cdef int* sc = malloc(selem_num * sizeof(int)) + cdef ssize_t selem_num = np.sum(selem != 0) + cdef ssize_t* sr = malloc(selem_num * sizeof(ssize_t)) + cdef ssize_t* sc = malloc(selem_num * sizeof(ssize_t)) s = 0 for r in range(srows): diff --git a/skimage/morphology/heap_general.pxi b/skimage/morphology/heap_general.pxi index a113b98e..c60096d6 100644 --- a/skimage/morphology/heap_general.pxi +++ b/skimage/morphology/heap_general.pxi @@ -10,21 +10,18 @@ All rights reserved. Original author: Lee Kamentsky """ -cdef extern from "stdlib.h": - ctypedef unsigned long size_t - void free(void *ptr) - void *malloc(size_t size) - void *realloc(void *ptr, size_t size) +from libc.stdlib cimport free, malloc, realloc + cdef struct Heap: - unsigned int items - unsigned int space + ssize_t items + ssize_t space Heapitem *data Heapitem **ptrs cdef inline Heap *heap_from_numpy2(): - cdef unsigned int k - cdef Heap *heap + cdef ssize_t k + cdef Heap *heap heap = malloc(sizeof (Heap)) heap.items = 0 heap.space = 1000 @@ -39,7 +36,7 @@ cdef inline void heap_done(Heap *heap): free(heap.ptrs) free(heap) -cdef inline void swap(unsigned int a, unsigned int b, Heap *h): +cdef inline void swap(ssize_t a, ssize_t b, Heap *h): h.ptrs[a], h.ptrs[b] = h.ptrs[b], h.ptrs[a] @@ -47,13 +44,13 @@ cdef inline void swap(unsigned int a, unsigned int b, Heap *h): # heappop - inlined # # pop an element off the heap, maintaining heap invariant -# +# # Note: heap ordering is the same as python heapq, i.e., smallest first. ###################################################### -cdef inline void heappop(Heap *heap, - Heapitem *dest): - cdef unsigned int i, smallest, l, r # heap indices - +cdef inline void heappop(Heap *heap, Heapitem *dest): + + cdef ssize_t i, smallest, l, r # heap indices + # # Start by copying the first element to the destination # @@ -76,10 +73,10 @@ cdef inline void heappop(Heap *heap, smallest = i while True: # loop invariant here: smallest == i - + # find smallest of (i, l, r), and swap it to i's position if necessary - l = i*2+1 #__left(i) - r = i*2+2 #__right(i) + l = i * 2 + 1 #__left(i) + r = i * 2 + 2 #__right(i) if l < heap.items: if smaller(heap.ptrs[l], heap.ptrs[i]): smallest = l @@ -88,13 +85,14 @@ cdef inline void heappop(Heap *heap, else: # this is unnecessary, but trims 0.04 out of 0.85 seconds... break - # the element at i is smaller than either of its children, heap invariant restored. + # the element at i is smaller than either of its children, heap + # invariant restored. if smallest == i: break # swap swap(i, smallest, heap) i = smallest - + ################################################## # heappush - inlined # @@ -102,34 +100,36 @@ cdef inline void heappop(Heap *heap, # # Note: heap ordering is the same as python heapq, i.e., smallest first. ################################################## -cdef inline void heappush(Heap *heap, - Heapitem *new_elem): - cdef unsigned int child = heap.items - cdef unsigned int parent - cdef unsigned int k - cdef Heapitem *new_data +cdef inline void heappush(Heap *heap, Heapitem *new_elem): - # grow if necessary - if heap.items == heap.space: + cdef ssize_t child = heap.items + cdef ssize_t parent + cdef ssize_t k + cdef Heapitem *new_data + + # grow if necessary + if heap.items == heap.space: heap.space = heap.space * 2 - new_data = realloc( heap.data, (heap.space * sizeof(Heapitem))) - heap.ptrs = realloc( heap.ptrs, (heap.space * sizeof(Heapitem *))) + new_data = realloc(heap.data, + (heap.space * sizeof(Heapitem))) + heap.ptrs = realloc(heap.ptrs, + (heap.space * sizeof(Heapitem *))) for k in range(heap.items): heap.ptrs[k] = new_data + (heap.ptrs[k] - heap.data) for k in range(heap.items, heap.space): heap.ptrs[k] = new_data + k heap.data = new_data - # insert new data at child - heap.ptrs[child][0] = new_elem[0] - heap.items += 1 + # insert new data at child + heap.ptrs[child][0] = new_elem[0] + heap.items += 1 - # restore heap invariant, all parents <= children - while child>0: - parent = (child + 1) / 2 - 1 # __parent(i) - - if smaller(heap.ptrs[child], heap.ptrs[parent]): - swap(parent, child, heap) - child = parent - else: - break + # restore heap invariant, all parents <= children + while child > 0: + parent = (child + 1) / 2 - 1 # __parent(i) + + if smaller(heap.ptrs[child], heap.ptrs[parent]): + swap(parent, child, heap) + child = parent + else: + break diff --git a/skimage/morphology/heap_watershed.pxi b/skimage/morphology/heap_watershed.pxi index ea66da26..43917690 100644 --- a/skimage/morphology/heap_watershed.pxi +++ b/skimage/morphology/heap_watershed.pxi @@ -13,14 +13,17 @@ import numpy as np cimport numpy as np cimport cython + cdef struct Heapitem: np.int32_t value np.int32_t age - np.int32_t index + ssize_t index + cdef inline int smaller(Heapitem *a, Heapitem *b): if a.value <> b.value: - return a.value < b.value + return a.value < b.value return a.age < b.age + include "heap_general.pxi" diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py index b6f2a4fb..fbd63281 100644 --- a/skimage/morphology/watershed.py +++ b/skimage/morphology/watershed.py @@ -214,9 +214,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None): c_mask = c_mask.astype(np.int8).flatten() _watershed.watershed(c_image.flatten(), pq, age, c, - c_image.ndim, c_mask, - np.array(c_image.shape, np.int32), c_output) c_output = c_output.reshape(c_image.shape)[[slice(1, -1, None)] * image.ndim] diff --git a/skimage/segmentation/_felzenszwalb_cy.pyx b/skimage/segmentation/_felzenszwalb_cy.pyx index efae45a4..79131eac 100644 --- a/skimage/segmentation/_felzenszwalb_cy.pyx +++ b/skimage/segmentation/_felzenszwalb_cy.pyx @@ -54,23 +54,23 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, int min_size=20): uright_cost.ravel()]).astype(np.float) # compute edges between pixels: height, width = image.shape[:2] - cdef np.ndarray[np.int_t, ndim=2] segments \ - = np.arange(width * height, dtype=np.int).reshape(height, width) + cdef np.ndarray[np.intp_t, ndim=2] segments \ + = np.arange(width * height, dtype=np.intp).reshape(height, width) right_edges = np.c_[segments[1:, :].ravel(), segments[:-1, :].ravel()] down_edges = np.c_[segments[:, 1:].ravel(), segments[:, :-1].ravel()] dright_edges = np.c_[segments[1:, 1:].ravel(), segments[:-1, :-1].ravel()] uright_edges = np.c_[segments[:-1, 1:].ravel(), segments[1:, :-1].ravel()] - cdef np.ndarray[np.int_t, ndim=2] edges \ + cdef np.ndarray[np.intp_t, ndim=2] edges \ = np.vstack([right_edges, down_edges, dright_edges, uright_edges]) # initialize data structures for segment size # and inner cost, then start greedy iteration over edges. edge_queue = np.argsort(costs) edges = np.ascontiguousarray(edges[edge_queue]) costs = np.ascontiguousarray(costs[edge_queue]) - cdef np.int_t *segments_p = segments.data - cdef np.int_t *edges_p = edges.data + cdef np.intp_t *segments_p = segments.data + cdef np.intp_t *edges_p = edges.data cdef np.float_t *costs_p = costs.data - cdef np.ndarray[np.int_t, ndim=1] segment_size \ + cdef np.ndarray[np.intp_t, ndim=1] segment_size \ = np.ones(width * height, dtype=np.int) # inner cost of segments cdef np.ndarray[np.float_t, ndim=1] cint = np.zeros(width * height) @@ -96,7 +96,7 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, int min_size=20): cint[seg_new] = costs_p[0] # postprocessing to remove small segments - edges_p = edges.data + edges_p = edges.data for e in range(costs.size): seg0 = find_root(segments_p, edges_p[0]) seg1 = find_root(segments_p, edges_p[1]) From 9f55fa2112ab10c8112276bb15b24a4284164a7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 22:23:54 +0100 Subject: [PATCH 559/625] Change type to ssize_t for all index and size variables --- skimage/segmentation/_felzenszwalb_cy.pyx | 2 +- skimage/segmentation/_quickshift.pyx | 10 ++++++---- skimage/segmentation/_slic.pyx | 13 +++++++------ 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/skimage/segmentation/_felzenszwalb_cy.pyx b/skimage/segmentation/_felzenszwalb_cy.pyx index 79131eac..2a4ea173 100644 --- a/skimage/segmentation/_felzenszwalb_cy.pyx +++ b/skimage/segmentation/_felzenszwalb_cy.pyx @@ -10,7 +10,7 @@ from ..util import img_as_float @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) -def _felzenszwalb_grey(image, double scale=1, sigma=0.8, int min_size=20): +def _felzenszwalb_grey(image, double scale=1, sigma=0.8, ssize_t min_size=20): """Felzenszwalb's efficient graph based segmentation for a single channel. Produces an oversegmentation of a 2d image using a fast, minimum spanning diff --git a/skimage/segmentation/_quickshift.pyx b/skimage/segmentation/_quickshift.pyx index b465eb08..4362725c 100644 --- a/skimage/segmentation/_quickshift.pyx +++ b/skimage/segmentation/_quickshift.pyx @@ -85,18 +85,19 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, raise ValueError("Sigma should be >= 1") cdef int w = int(3 * kernel_size) - cdef int height = image_c.shape[0] - cdef int width = image_c.shape[1] - cdef int channels = image_c.shape[2] + cdef ssize_t height = image_c.shape[0] + cdef ssize_t width = image_c.shape[1] + cdef ssize_t channels = image_c.shape[2] cdef double current_density, closest, dist - cdef int r, c, r_, c_, channel + cdef ssize_t r, c, r_, c_, channel, r_min, c_min cdef np.float_t* image_p = image_c.data cdef np.float_t* current_pixel_p = image_p cdef np.ndarray[dtype=np.float_t, ndim=2] densities \ = np.zeros((height, width)) + # compute densities for r in range(height): for c in range(width): @@ -120,6 +121,7 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, = np.arange(width * height).reshape(height, width) cdef np.ndarray[dtype=np.float_t, ndim=2] dist_parent \ = np.zeros((height, width)) + # find nearest node with higher density current_pixel_p = image_p for r in range(height): diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index dfb37a7c..260b7689 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -1,3 +1,4 @@ +#cython: boundscheck=False import numpy as np cimport numpy as np from time import time @@ -7,7 +8,7 @@ from ..color import rgb2lab, gray2rgb def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, - convert2lab=True): + convert2lab=True): """Segments image using k-means clustering in Color-(x,y) space. Parameters @@ -62,10 +63,10 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, image = rgb2lab(image) # initialize on grid: - cdef int height, width + cdef ssize_t height, width height, width = image.shape[:2] # approximate grid size for desired n_segments - cdef int step = np.ceil(np.sqrt(height * width / n_segments)) + cdef ssize_t step = np.ceil(np.sqrt(height * width / n_segments)) grid_y, grid_x = np.mgrid[:height, :width] means_y = grid_y[::step, ::step] means_x = grid_x[::step, ::step] @@ -81,11 +82,11 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, ratio = (ratio / float(step)) ** 2 cdef np.ndarray[dtype=np.float_t, ndim=3] image_yx \ = np.dstack([grid_y, grid_x, image / ratio]).copy("C") - cdef int i, k, x, y, x_min, x_max, y_min, y_max, changes + cdef ssize_t i, k, x, y, x_min, x_max, y_min, y_max, changes cdef double dist_mean - cdef np.ndarray[dtype=np.int_t, ndim=2] nearest_mean \ - = np.zeros((height, width), dtype=np.int) + cdef np.ndarray[dtype=np.intp_t, ndim=2] nearest_mean \ + = np.zeros((height, width), dtype=np.intp) cdef np.ndarray[dtype=np.float_t, ndim=2] distance \ = np.empty((height, width)) cdef np.float_t* image_p = image_yx.data From 2957cb873ba5bb03f371847458c35be1441c88c6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 22:29:03 +0100 Subject: [PATCH 560/625] Change type to ssize_t for all index and size variables --- skimage/transform/_hough_transform.pyx | 30 +++++++++++++------------- skimage/transform/_warps_cy.pyx | 8 +++---- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index ef1cf700..016be878 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -13,8 +13,8 @@ cdef double PI_2 = 1.5707963267948966 cdef double NEG_PI_2 = -PI_2 -cdef inline int round(double r): - return ((r + 0.5) if (r > 0.0) else (r - 0.5)) +cdef inline ssize_t round(double r): + return ((r + 0.5) if (r > 0.0) else (r - 0.5)) @cython.boundscheck(False) @@ -36,21 +36,20 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): # compute the bins and allocate the accumulator array cdef np.ndarray[ndim=2, dtype=np.uint64_t] accum cdef np.ndarray[ndim=1, dtype=np.double_t] bins - cdef int max_distance, offset + cdef ssize_t max_distance, offset - max_distance = 2 * ceil((sqrt(img.shape[0] * img.shape[0] + - img.shape[1] * img.shape[1]))) + max_distance = 2 * ceil(sqrt(img.shape[0] * img.shape[0] + + img.shape[1] * img.shape[1])) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64) bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance) offset = max_distance / 2 # compute the nonzero indexes cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs - y_idxs, x_idxs = np.PyArray_Nonzero(img) - + y_idxs, x_idxs = np.nonzero(img) # finally, run the transform - cdef int nidxs, nthetas, i, j, x, y, accum_idx + cdef ssize_t nidxs, nthetas, i, j, x, y, accum_idx nidxs = y_idxs.shape[0] # x and y are the same shape nthetas = theta.shape[0] for i in range(nidxs): @@ -78,26 +77,27 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \ theta = math.pi/2-np.arange(180)/180.0* math.pi ctheta = np.cos(theta) stheta = np.sin(theta) - cdef int height = img.shape[0] - cdef int width = img.shape[1] + cdef ssize_t height = img.shape[0] + cdef ssize_t width = img.shape[1] # compute the bins and allocate the accumulator array cdef np.ndarray[ndim=2, dtype=np.int64_t] accum cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = np.zeros((height, width), dtype=np.uint8) cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = np.zeros((2, 2), dtype=np.int32) - cdef int max_distance, offset, num_indexes, index + cdef ssize_t max_distance, offset, num_indexes, index cdef double a, b - cdef int nidxs, nthetas, i, j, x, y, px, py, accum_idx, value, max_value, max_theta + cdef ssize_t nidxs, nthetas, i, j, x, y, px, py, accum_idx + cdef int value, max_value, max_theta cdef int shift = 16 # maximum line number cutoff - cdef int lines_max = 2 ** 15 - cdef int xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count + cdef ssize_t lines_max = 2 ** 15 + cdef ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count max_distance = 2 * ceil((sqrt(img.shape[0] * img.shape[0] + img.shape[1] * img.shape[1]))) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64) offset = max_distance / 2 # find the nonzero indexes cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs - y_idxs, x_idxs = np.nonzero(img) + y_idxs, x_idxs = np.nonzero(img) num_indexes = y_idxs.shape[0] # x and y are the same shape nthetas = theta.shape[0] points = [] diff --git a/skimage/transform/_warps_cy.pyx b/skimage/transform/_warps_cy.pyx index a5a372d2..28c258fd 100644 --- a/skimage/transform/_warps_cy.pyx +++ b/skimage/transform/_warps_cy.pyx @@ -93,7 +93,7 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, "`constant`, `nearest`, `wrap` or `reflect`.") cdef char mode_c = ord(mode[0].upper()) - cdef int out_r, out_c + cdef ssize_t out_r, out_c if output_shape is None: out_r = img.shape[0] out_c = img.shape[1] @@ -104,10 +104,10 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, cdef np.ndarray[dtype=np.double_t, ndim=2] out = \ np.zeros((out_r, out_c), dtype=np.double) - cdef int tfr, tfc + cdef ssize_t tfr, tfc cdef double r, c - cdef int rows = img.shape[0] - cdef int cols = img.shape[1] + cdef ssize_t rows = img.shape[0] + cdef ssize_t cols = img.shape[1] cdef double (*interp_func)(double*, ssize_t, ssize_t, double, double, char, double) From 2c90aa70e5ece6ec5d117e3c76ad6040696c26e7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 22 Jan 2013 22:36:58 +0100 Subject: [PATCH 561/625] Use np.intp rather than ssize_t for ndarrays --- skimage/draw/_draw.pyx | 2 +- skimage/morphology/_convex_hull.pyx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 28103304..50342c91 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -29,7 +29,7 @@ def line(ssize_t y, ssize_t x, ssize_t y2, ssize_t x2): """ - cdef np.ndarray[ssize_t, ndim=1, mode="c"] rr, cc + cdef np.ndarray[np.intp_t, ndim=1, mode="c"] rr, cc cdef char steep = 0 cdef ssize_t dx = abs(x2 - x) diff --git a/skimage/morphology/_convex_hull.pyx b/skimage/morphology/_convex_hull.pyx index 6c9d1083..6746fa9f 100644 --- a/skimage/morphology/_convex_hull.pyx +++ b/skimage/morphology/_convex_hull.pyx @@ -26,7 +26,7 @@ def possible_hull(np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] img): # cols storage slots for top boundary pixels # rows storage slots for right boundary pixels # cols storage slots for bottom boundary pixels - cdef np.ndarray[dtype=ssize_t, ndim=2] nonzero = \ + cdef np.ndarray[dtype=np.intp_t, ndim=2] nonzero = \ np.ones((2 * (rows + cols), 2), dtype=np.int) nonzero *= -1 From 614023b941913b4ad368a2584bf23a0630831523 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 14:58:14 +0100 Subject: [PATCH 562/625] Replace ssize_t with Py_ssize_t globally --- skimage/_shared/geometry.pxd | 6 +- skimage/_shared/geometry.pyx | 12 +- skimage/_shared/interpolation.pxd | 20 +- skimage/_shared/interpolation.pyx | 68 +++--- skimage/_shared/transform.pxd | 2 +- skimage/_shared/transform.pyx | 2 +- skimage/draw/_draw.pyx | 56 ++--- skimage/feature/_template.pyx | 6 +- skimage/feature/_texture.pyx | 12 +- skimage/feature/corner_cy.pyx | 8 +- skimage/filter/_ctmf.pyx | 170 +++++++-------- skimage/filter/_denoise_cy.pyx | 30 +-- skimage/filter/rank/_core16.pxd | 10 +- skimage/filter/rank/_core16.pyx | 54 ++--- skimage/filter/rank/_core8.pxd | 10 +- skimage/filter/rank/_core8.pyx | 50 ++--- skimage/filter/rank/_crank16.pyx | 212 +++++++++---------- skimage/filter/rank/_crank16_bilateral.pyx | 16 +- skimage/filter/rank/_crank16_percentiles.pyx | 84 ++++---- skimage/filter/rank/_crank8.pyx | 140 ++++++------ skimage/filter/rank/_crank8_percentiles.pyx | 48 ++--- skimage/graph/heap.pxd | 2 +- skimage/measure/_find_contours.pyx | 10 +- skimage/measure/_moments.pyx | 4 +- skimage/morphology/_convex_hull.pyx | 6 +- skimage/morphology/_pnpoly.pyx | 8 +- skimage/morphology/_skeletonize_cy.pyx | 16 +- skimage/morphology/_watershed.pyx | 12 +- skimage/morphology/cmorph.pyx | 38 ++-- skimage/morphology/heap_general.pxi | 20 +- skimage/morphology/heap_watershed.pxi | 2 +- skimage/segmentation/_felzenszwalb_cy.pyx | 2 +- skimage/segmentation/_quickshift.pyx | 8 +- skimage/segmentation/_slic.pyx | 6 +- skimage/transform/_hough_transform.pyx | 22 +- skimage/transform/_warps_cy.pyx | 10 +- 36 files changed, 591 insertions(+), 591 deletions(-) diff --git a/skimage/_shared/geometry.pxd b/skimage/_shared/geometry.pxd index a89ce894..3379318c 100644 --- a/skimage/_shared/geometry.pxd +++ b/skimage/_shared/geometry.pxd @@ -1,6 +1,6 @@ -cdef unsigned char point_in_polygon(ssize_t nr_verts, double *xp, double *yp, +cdef unsigned char point_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, double x, double y) -cdef void points_in_polygon(ssize_t nr_verts, double *xp, double *yp, - ssize_t nr_points, double *x, double *y, +cdef void points_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, + Py_ssize_t nr_points, double *x, double *y, unsigned char *result) diff --git a/skimage/_shared/geometry.pyx b/skimage/_shared/geometry.pyx index 5a56df7c..beb07e14 100644 --- a/skimage/_shared/geometry.pyx +++ b/skimage/_shared/geometry.pyx @@ -4,7 +4,7 @@ #cython: wraparound=False -cdef inline unsigned char point_in_polygon(ssize_t nr_verts, double *xp, +cdef inline unsigned char point_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, double x, double y): """Test whether point lies inside a polygon. @@ -17,9 +17,9 @@ cdef inline unsigned char point_in_polygon(ssize_t nr_verts, double *xp, x, y : double Coordinates of point. """ - cdef ssize_t i + cdef Py_ssize_t i cdef unsigned char c = 0 - cdef ssize_t j = nr_verts - 1 + cdef Py_ssize_t j = nr_verts - 1 for i in range(nr_verts): if ( (((yp[i] <= y) and (y < yp[j])) or @@ -31,8 +31,8 @@ cdef inline unsigned char point_in_polygon(ssize_t nr_verts, double *xp, return c -cdef void points_in_polygon(ssize_t nr_verts, double *xp, double *yp, - ssize_t nr_points, double *x, double *y, +cdef void points_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, + Py_ssize_t nr_points, double *x, double *y, unsigned char *result): """Test whether points lie inside a polygon. @@ -49,6 +49,6 @@ cdef void points_in_polygon(ssize_t nr_verts, double *xp, double *yp, result : unsigned char array Test results for each point. """ - cdef ssize_t n + cdef Py_ssize_t n for n in range(nr_points): result[n] = point_in_polygon(nr_verts, xp, yp, x[n], y[n]) diff --git a/skimage/_shared/interpolation.pxd b/skimage/_shared/interpolation.pxd index 0f9123e9..c5f32b6a 100644 --- a/skimage/_shared/interpolation.pxd +++ b/skimage/_shared/interpolation.pxd @@ -1,27 +1,27 @@ -cdef double nearest_neighbour_interpolation(double* image, ssize_t rows, - ssize_t cols, double r, +cdef double nearest_neighbour_interpolation(double* image, Py_ssize_t rows, + Py_ssize_t cols, double r, double c, char mode, double cval) -cdef double bilinear_interpolation(double* image, ssize_t rows, ssize_t cols, +cdef double bilinear_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) cdef double quadratic_interpolation(double x, double[3] f) -cdef double biquadratic_interpolation(double* image, ssize_t rows, ssize_t cols, +cdef double biquadratic_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) cdef double cubic_interpolation(double x, double[4] f) -cdef double bicubic_interpolation(double* image, ssize_t rows, ssize_t cols, +cdef double bicubic_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) -cdef double get_pixel2d(double* image, ssize_t rows, ssize_t cols, ssize_t r, - ssize_t c, char mode, double cval) +cdef double get_pixel2d(double* image, Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, + Py_ssize_t c, char mode, double cval) -cdef double get_pixel3d(double* image, ssize_t rows, ssize_t cols, ssize_t dims, - ssize_t r, ssize_t c, ssize_t d, char mode, double cval) +cdef double get_pixel3d(double* image, Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t dims, + Py_ssize_t r, Py_ssize_t c, Py_ssize_t d, char mode, double cval) -cdef ssize_t coord_map(ssize_t dim, ssize_t coord, char mode) +cdef Py_ssize_t coord_map(Py_ssize_t dim, Py_ssize_t coord, char mode) diff --git a/skimage/_shared/interpolation.pyx b/skimage/_shared/interpolation.pyx index 50b05a00..a8b96014 100644 --- a/skimage/_shared/interpolation.pyx +++ b/skimage/_shared/interpolation.pyx @@ -5,12 +5,12 @@ from libc.math cimport ceil, floor -cdef inline ssize_t round(double r): - return ((r + 0.5) if (r > 0.0) else (r - 0.5)) +cdef inline Py_ssize_t round(double r): + return ((r + 0.5) if (r > 0.0) else (r - 0.5)) -cdef inline double nearest_neighbour_interpolation(double* image, ssize_t rows, - ssize_t cols, double r, +cdef inline double nearest_neighbour_interpolation(double* image, Py_ssize_t rows, + Py_ssize_t cols, double r, double c, char mode, double cval): """Nearest neighbour interpolation at a given position in the image. @@ -38,8 +38,8 @@ cdef inline double nearest_neighbour_interpolation(double* image, ssize_t rows, return get_pixel2d(image, rows, cols, round(r), round(c), mode, cval) -cdef inline double bilinear_interpolation(double* image, ssize_t rows, - ssize_t cols, double r, double c, +cdef inline double bilinear_interpolation(double* image, Py_ssize_t rows, + Py_ssize_t cols, double r, double c, char mode, double cval): """Bilinear interpolation at a given position in the image. @@ -63,12 +63,12 @@ cdef inline double bilinear_interpolation(double* image, ssize_t rows, """ cdef double dr, dc - cdef ssize_t minr, minc, maxr, maxc + cdef Py_ssize_t minr, minc, maxr, maxc - minr = floor(r) - minc = floor(c) - maxr = ceil(r) - maxc = ceil(c) + minr = floor(r) + minc = floor(c) + maxr = ceil(r) + maxc = ceil(c) dr = r - minr dc = c - minc top = (1 - dc) * get_pixel2d(image, rows, cols, minr, minc, mode, cval) \ @@ -97,8 +97,8 @@ cdef inline double quadratic_interpolation(double x, double[3] f): return f[1] - 0.25 * (f[0] - f[2]) * x -cdef inline double biquadratic_interpolation(double* image, ssize_t rows, - ssize_t cols, double r, double c, +cdef inline double biquadratic_interpolation(double* image, Py_ssize_t rows, + Py_ssize_t cols, double r, double c, char mode, double cval): """Biquadratic interpolation at a given position in the image. @@ -122,8 +122,8 @@ cdef inline double biquadratic_interpolation(double* image, ssize_t rows, """ - cdef ssize_t r0 = round(r) - cdef ssize_t c0 = round(c) + cdef Py_ssize_t r0 = round(r) + cdef Py_ssize_t c0 = round(c) if r < 0: r0 -= 1 if c < 0: @@ -138,7 +138,7 @@ cdef inline double biquadratic_interpolation(double* image, ssize_t rows, cdef double fc[3], fr[3] - cdef ssize_t pr, pc + cdef Py_ssize_t pr, pc # row-wise cubic interpolation for pr in range(r0, r0 + 3): @@ -173,8 +173,8 @@ cdef inline double cubic_interpolation(double x, double[4] f): (3.0 * (f[1] - f[2]) + f[3] - f[0]))) -cdef inline double bicubic_interpolation(double* image, ssize_t rows, - ssize_t cols, double r, double c, +cdef inline double bicubic_interpolation(double* image, Py_ssize_t rows, + Py_ssize_t cols, double r, double c, char mode, double cval): """Bicubic interpolation at a given position in the image. @@ -198,8 +198,8 @@ cdef inline double bicubic_interpolation(double* image, ssize_t rows, """ - cdef ssize_t r0 = r - 1 - cdef ssize_t c0 = c - 1 + cdef Py_ssize_t r0 = r - 1 + cdef Py_ssize_t c0 = c - 1 if r < 0: r0 -= 1 if c < 0: @@ -210,7 +210,7 @@ cdef inline double bicubic_interpolation(double* image, ssize_t rows, cdef double fc[4], fr[4] - cdef ssize_t pr, pc + cdef Py_ssize_t pr, pc # row-wise cubic interpolation for pr in range(r0, r0 + 4): @@ -222,8 +222,8 @@ cdef inline double bicubic_interpolation(double* image, ssize_t rows, return cubic_interpolation(xr, fr) -cdef inline double get_pixel2d(double* image, ssize_t rows, ssize_t cols, - ssize_t r, ssize_t c, char mode, double cval): +cdef inline double get_pixel2d(double* image, Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, char mode, double cval): """Get a pixel from the image, taking wrapping mode into consideration. Parameters @@ -254,8 +254,8 @@ cdef inline double get_pixel2d(double* image, ssize_t rows, ssize_t cols, return image[coord_map(rows, r, mode) * cols + coord_map(cols, c, mode)] -cdef inline double get_pixel3d(double* image, ssize_t rows, ssize_t cols, - ssize_t dims, ssize_t r, ssize_t c, ssize_t d, +cdef inline double get_pixel3d(double* image, Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t dims, Py_ssize_t r, Py_ssize_t c, Py_ssize_t d, char mode, double cval): """Get a pixel from the image, taking wrapping mode into consideration. @@ -289,7 +289,7 @@ cdef inline double get_pixel3d(double* image, ssize_t rows, ssize_t cols, + d] -cdef inline ssize_t coord_map(ssize_t dim, ssize_t coord, char mode): +cdef inline Py_ssize_t coord_map(Py_ssize_t dim, Py_ssize_t coord, char mode): """ Wrap a coordinate, according to a given mode. @@ -308,20 +308,20 @@ cdef inline ssize_t coord_map(ssize_t dim, ssize_t coord, char mode): if mode == 'R': # reflect if coord < 0: # How many times times does the coordinate wrap? - if (-coord / dim) % 2 != 0: - return dim - (-coord % dim) + if (-coord / dim) % 2 != 0: + return dim - (-coord % dim) else: - return (-coord % dim) + return (-coord % dim) elif coord > dim: - if (coord / dim) % 2 != 0: - return (dim - (coord % dim)) + if (coord / dim) % 2 != 0: + return (dim - (coord % dim)) else: - return (coord % dim) + return (coord % dim) elif mode == 'W': # wrap if coord < 0: - return (dim - (-coord % dim)) + return (dim - (-coord % dim)) elif coord > dim: - return (coord % dim) + return (coord % dim) elif mode == 'N': # nearest if coord < 0: return 0 diff --git a/skimage/_shared/transform.pxd b/skimage/_shared/transform.pxd index f91a1658..ccb16ff3 100644 --- a/skimage/_shared/transform.pxd +++ b/skimage/_shared/transform.pxd @@ -2,4 +2,4 @@ cimport numpy as cnp cdef float integrate(cnp.ndarray[float, ndim=2, mode="c"] sat, - ssize_t r0, ssize_t c0, ssize_t r1, ssize_t c1) + Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1) diff --git a/skimage/_shared/transform.pyx b/skimage/_shared/transform.pyx index 24e3150d..8ce2ab67 100644 --- a/skimage/_shared/transform.pyx +++ b/skimage/_shared/transform.pyx @@ -6,7 +6,7 @@ cimport numpy as cnp cdef float integrate(cnp.ndarray[float, ndim=2, mode="c"] sat, - ssize_t r0, ssize_t c0, ssize_t r1, ssize_t c1): + Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1): """ Using a summed area table / integral image, calculate the sum over a given window. diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 50342c91..5bba8c32 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -10,7 +10,7 @@ import numpy as np from skimage._shared.geometry cimport point_in_polygon -def line(ssize_t y, ssize_t x, ssize_t y2, ssize_t x2): +def line(Py_ssize_t y, Py_ssize_t x, Py_ssize_t y2, Py_ssize_t x2): """Generate line pixel coordinates. Parameters @@ -32,9 +32,9 @@ def line(ssize_t y, ssize_t x, ssize_t y2, ssize_t x2): cdef np.ndarray[np.intp_t, ndim=1, mode="c"] rr, cc cdef char steep = 0 - cdef ssize_t dx = abs(x2 - x) - cdef ssize_t dy = abs(y2 - y) - cdef ssize_t sx, sy, d, i + cdef Py_ssize_t dx = abs(x2 - x) + cdef Py_ssize_t dy = abs(y2 - y) + cdef Py_ssize_t sx, sy, d, i if (x2 - x) > 0: sx = 1 @@ -96,18 +96,18 @@ def polygon(y, x, shape=None): """ - cdef ssize_t nr_verts = x.shape[0] - cdef ssize_t minr = max(0, y.min()) - cdef ssize_t maxr = math.ceil(y.max()) - cdef ssize_t minc = max(0, x.min()) - cdef ssize_t maxc = math.ceil(x.max()) + cdef Py_ssize_t nr_verts = x.shape[0] + cdef Py_ssize_t minr = max(0, y.min()) + cdef Py_ssize_t maxr = math.ceil(y.max()) + cdef Py_ssize_t minc = max(0, x.min()) + cdef Py_ssize_t maxc = math.ceil(x.max()) # make sure output coordinates do not exceed image size if shape is not None: maxr = min(shape[0] - 1, maxr) maxc = min(shape[1] - 1, maxc) - cdef ssize_t r, c + cdef Py_ssize_t r, c #: make contigous arrays for r, c coordinates cdef np.ndarray contiguous_rdata, contiguous_cdata @@ -148,17 +148,17 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): """ - cdef ssize_t minr = max(0, cy - yradius) - cdef ssize_t maxr = math.ceil(cy + yradius) - cdef ssize_t minc = max(0, cx - xradius) - cdef ssize_t maxc = math.ceil(cx + xradius) + cdef Py_ssize_t minr = max(0, cy - yradius) + cdef Py_ssize_t maxr = math.ceil(cy + yradius) + cdef Py_ssize_t minc = max(0, cx - xradius) + cdef Py_ssize_t maxc = math.ceil(cx + xradius) # make sure output coordinates do not exceed image size if shape is not None: maxr = min(shape[0] - 1, maxr) maxc = min(shape[1] - 1, maxc) - cdef ssize_t r, c + cdef Py_ssize_t r, c #: output coordinate arrays cdef list rr = list() @@ -195,7 +195,7 @@ def circle(double cy, double cx, double radius, shape=None): return ellipse(cy, cx, radius, radius, shape) -def circle_perimeter(ssize_t cy, ssize_t cx, ssize_t radius, +def circle_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t radius, method='bresenham'): """Generate circle perimeter coordinates. @@ -235,9 +235,9 @@ def circle_perimeter(ssize_t cy, ssize_t cx, ssize_t radius, cdef list rr = list() cdef list cc = list() - cdef ssize_t x = 0 - cdef ssize_t y = radius - cdef ssize_t d = 0 + cdef Py_ssize_t x = 0 + cdef Py_ssize_t y = radius + cdef Py_ssize_t d = 0 cdef char cmethod if method == 'bresenham': d = 3 - 2 * radius @@ -303,8 +303,8 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): return np.array(cy), np.array(cx) # a and b are xradius an yradius compute 2a^2 and 2b^2 - cdef ssize_t twoasquared = 2 * xradius**2 - cdef ssize_t twobsquared = 2 * yradius**2 + cdef Py_ssize_t twoasquared = 2 * xradius**2 + cdef Py_ssize_t twobsquared = 2 * yradius**2 # Pixels cdef list px = list() @@ -312,14 +312,14 @@ def ellipse_perimeter(int cy, int cx, int yradius, int xradius): # First set of points: # start at the top - cdef ssize_t x = xradius - cdef ssize_t y = 0 + cdef Py_ssize_t x = xradius + cdef Py_ssize_t y = 0 - cdef ssize_t err = 0 - cdef ssize_t xstop = twobsquared * xradius - cdef ssize_t ystop = 0 - cdef ssize_t xchange = yradius * yradius * (1 - 2 * xradius) - cdef ssize_t ychange = xradius * xradius + cdef Py_ssize_t err = 0 + cdef Py_ssize_t xstop = twobsquared * xradius + cdef Py_ssize_t ystop = 0 + cdef Py_ssize_t xchange = yradius * yradius * (1 - 2 * xradius) + cdef Py_ssize_t ychange = xradius * xradius while xstop > ystop: px.extend([x, -x, -x, x]) diff --git a/skimage/feature/_template.pyx b/skimage/feature/_template.pyx index 938e3a18..842e1eda 100644 --- a/skimage/feature/_template.pyx +++ b/skimage/feature/_template.pyx @@ -49,9 +49,9 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image, cdef float template_mean = np.mean(template) cdef float template_ssd cdef float inv_area - cdef ssize_t r, c, r_end, c_end - cdef ssize_t template_rows = template.shape[0] - cdef ssize_t template_cols = template.shape[1] + cdef Py_ssize_t r, c, r_end, c_end + cdef Py_ssize_t template_rows = template.shape[0] + cdef Py_ssize_t template_cols = template.shape[1] cdef float den, window_sqr_sum, window_mean_sqr, window_sum image_sat = integral.integral_image(image) diff --git a/skimage/feature/_texture.pyx b/skimage/feature/_texture.pyx index e2e3bb35..2b9a9adc 100644 --- a/skimage/feature/_texture.pyx +++ b/skimage/feature/_texture.pyx @@ -39,7 +39,7 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, """ cdef: np.int32_t a_inx, d_idx - ssize_t r, c, rows, cols, row, col + Py_ssize_t r, c, rows, cols, row, col np.int32_t i, j rows = image.shape[0] @@ -52,8 +52,8 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, i = image[r, c] # compute the location of the offset pixel - row = r + (sin(angle) * distance + 0.5) - col = c + (cos(angle) * distance + 0.5); + row = r + (sin(angle) * distance + 0.5) + col = c + (cos(angle) * distance + 0.5); # make sure the offset is within bounds if row >= 0 and row < rows and \ @@ -123,11 +123,11 @@ def _local_binary_pattern(np.ndarray[double, ndim=2] image, output_shape = (image.shape[0], image.shape[1]) cdef np.ndarray[double, ndim=2] output = np.zeros(output_shape, np.double) - cdef ssize_t rows = image.shape[0] - cdef ssize_t cols = image.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] cdef double lbp - cdef ssize_t r, c, changes, i + cdef Py_ssize_t r, c, changes, i for r in range(image.shape[0]): for c in range(image.shape[1]): for i in range(P): diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx index 34913450..407e6642 100644 --- a/skimage/feature/corner_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -10,7 +10,7 @@ from skimage.color import rgb2grey from skimage.util import img_as_float -def corner_moravec(image, ssize_t window_size=1): +def corner_moravec(image, Py_ssize_t window_size=1): """Compute Moravec corner measure response image. This is one of the simplest corner detectors and is comparatively fast but @@ -56,8 +56,8 @@ def corner_moravec(image, ssize_t window_size=1): [ 0., 0., 0., 0., 0., 0., 0.]]) """ - cdef ssize_t rows = image.shape[0] - cdef ssize_t cols = image.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] cimage, out @@ -71,7 +71,7 @@ def corner_moravec(image, ssize_t window_size=1): cdef double* out_data = out.data cdef double msum, min_msum - cdef ssize_t r, c, br, bc, mr, mc, a, b + cdef Py_ssize_t r, c, br, bc, mr, mc, a, b for r in range(2 * window_size, rows - 2 * window_size): for c in range(2 * window_size, cols - 2 * window_size): min_msum = DBL_MAX diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index 83269e46..65feb9e4 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -84,9 +84,9 @@ cdef struct PixelCount: # relative offsets from the octagon center # cdef struct SCoord: - ssize_t stride # add the stride to the memory location - ssize_t x - ssize_t y + Py_ssize_t stride # add the stride to the memory location + Py_ssize_t x + Py_ssize_t y cdef struct Histograms: void *memory # pointer to the allocated memory @@ -95,16 +95,16 @@ cdef struct Histograms: np.uint8_t *data # pointer to the image data np.uint8_t *mask # pointer to the image mask np.uint8_t *output # pointer to the output array - ssize_t column_count # number of columns represented by this + Py_ssize_t column_count # number of columns represented by this # structure - ssize_t stripe_length # number of columns including "radius" before + Py_ssize_t stripe_length # number of columns including "radius" before # and after - ssize_t row_count # number of rows available in image - ssize_t current_column # the column being processed - ssize_t current_row # the row being processed - ssize_t current_stride # offset in data and mask to current location - ssize_t radius # the "radius" of the octagon - ssize_t a_2 # 1/2 of the length of a side of the octagon + Py_ssize_t row_count # number of rows available in image + Py_ssize_t current_column # the column being processed + Py_ssize_t current_row # the row being processed + Py_ssize_t current_stride # offset in data and mask to current location + Py_ssize_t radius # the "radius" of the octagon + Py_ssize_t a_2 # 1/2 of the length of a side of the octagon # # # The strides are the offsets in the array to the points that need to @@ -138,50 +138,50 @@ cdef struct Histograms: SCoord last_bottom_left # (-) trailing edge bottom - 1 col SCoord bottom_left # (+) left side of octagon's bottom - 1 col - ssize_t row_stride # stride between one row and the next - ssize_t col_stride # stride between one column and the next + Py_ssize_t row_stride # stride between one row and the next + Py_ssize_t col_stride # stride between one column and the next # The accumulator holds the running histogram # HistogramPiece accumulator # # The running count of pixels in the accumulator # - ssize_t accumulator_count + Py_ssize_t accumulator_count # # The percent of pixels within the octagon whose value is # less than or equal to the median-filtered value (e.g. for # median, this is 50, for lower quartile it's 25) # - ssize_t percent + Py_ssize_t percent # # last_update_column keeps track of the column # of the last update # to the fine histogram accumulator. Short-term, the median # stays in one coarse block so only one fine histogram might # need to be updated # - ssize_t last_update_column[16] + Py_ssize_t last_update_column[16] ############################################################################ # # allocate_histograms - allocates the Histograms structure for the run # ############################################################################ -cdef Histograms *allocate_histograms(ssize_t rows, - ssize_t columns, - ssize_t row_stride, - ssize_t col_stride, - ssize_t radius, - ssize_t percent, +cdef Histograms *allocate_histograms(Py_ssize_t rows, + Py_ssize_t columns, + Py_ssize_t row_stride, + Py_ssize_t col_stride, + Py_ssize_t radius, + Py_ssize_t percent, np.uint8_t *data, np.uint8_t *mask, np.uint8_t *output): cdef: - ssize_t adjusted_stripe_length = columns + 2*radius + 1 - ssize_t memory_size + Py_ssize_t adjusted_stripe_length = columns + 2*radius + 1 + Py_ssize_t memory_size void *ptr Histograms *ph size_t roundoff - ssize_t a + Py_ssize_t a SCoord *psc memory_size = (adjusted_stripe_length * @@ -199,7 +199,7 @@ cdef Histograms *allocate_histograms(ssize_t rows, # # Align histogram memory to a 32-byte boundary # - roundoff = ptr + roundoff = ptr roundoff += 31 roundoff -= roundoff % 32 ptr = roundoff @@ -232,7 +232,7 @@ cdef Histograms *allocate_histograms(ssize_t rows, # a_2 is the offset from the center to each of the octagon # corners # - a = (radius * 2.0 / 2.414213) + a = (radius * 2.0 / 2.414213) a_2 = a / 2 if a_2 == 0: a_2 = 1 @@ -326,17 +326,17 @@ cdef void set_stride(Histograms *ph, SCoord *psc): # a column that is "radius" to the left. # ############################################################################ -cdef inline ssize_t tl_br_colidx(Histograms *ph, ssize_t colidx): +cdef inline Py_ssize_t tl_br_colidx(Histograms *ph, Py_ssize_t colidx): return (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length -cdef inline ssize_t tr_bl_colidx(Histograms *ph, ssize_t colidx): +cdef inline Py_ssize_t tr_bl_colidx(Histograms *ph, Py_ssize_t colidx): return (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \ ph.stripe_length -cdef inline ssize_t leading_edge_colidx(Histograms *ph, ssize_t colidx): +cdef inline Py_ssize_t leading_edge_colidx(Histograms *ph, Py_ssize_t colidx): return (colidx + 5*ph.radius) % ph.stripe_length -cdef inline ssize_t trailing_edge_colidx(Histograms *ph, ssize_t colidx): +cdef inline Py_ssize_t trailing_edge_colidx(Histograms *ph, Py_ssize_t colidx): return (colidx + 3*ph.radius - 1) % ph.stripe_length ############################################################################ @@ -348,8 +348,8 @@ cdef inline ssize_t trailing_edge_colidx(Histograms *ph, ssize_t colidx): # colidx - the index of the column to add # ############################################################################ -cdef inline void accumulate_coarse_histogram(Histograms *ph, ssize_t colidx): - cdef ssize_t offset +cdef inline void accumulate_coarse_histogram(Histograms *ph, Py_ssize_t colidx): + cdef Py_ssize_t offset offset = tr_bl_colidx(ph, colidx) if ph.pixel_count[offset].top_right > 0: @@ -370,8 +370,8 @@ cdef inline void accumulate_coarse_histogram(Histograms *ph, ssize_t colidx): # for a given column # ############################################################################ -cdef inline void deaccumulate_coarse_histogram(Histograms *ph, ssize_t colidx): - cdef ssize_t offset +cdef inline void deaccumulate_coarse_histogram(Histograms *ph, Py_ssize_t colidx): + cdef Py_ssize_t offset # # The trailing diagonals don't appear until here # @@ -400,11 +400,11 @@ cdef inline void deaccumulate_coarse_histogram(Histograms *ph, ssize_t colidx): # ############################################################################ cdef inline void accumulate_fine_histogram(Histograms *ph, - ssize_t colidx, - ssize_t fineidx): + Py_ssize_t colidx, + Py_ssize_t fineidx): cdef: - ssize_t fineoffset = fineidx * 16 - ssize_t offset + Py_ssize_t fineoffset = fineidx * 16 + Py_ssize_t offset offset = tr_bl_colidx(ph, colidx) add16(ph.accumulator.fine + fineoffset, @@ -424,11 +424,11 @@ cdef inline void accumulate_fine_histogram(Histograms *ph, # ############################################################################ cdef inline void deaccumulate_fine_histogram(Histograms *ph, - ssize_t colidx, - ssize_t fineidx): + Py_ssize_t colidx, + Py_ssize_t fineidx): cdef: - ssize_t fineoffset = fineidx * 16 - ssize_t offset + Py_ssize_t fineoffset = fineidx * 16 + Py_ssize_t offset # # The trailing diagonals don't appear until here @@ -480,11 +480,11 @@ cdef inline void accumulate(Histograms *ph): # to choose remains to be done. ############################################################################ -cdef inline void update_fine(Histograms *ph, ssize_t fineidx): +cdef inline void update_fine(Histograms *ph, Py_ssize_t fineidx): cdef: - ssize_t first_update_column = ph.last_update_column[fineidx]+1 - ssize_t update_limit = ph.current_column+1 - ssize_t i + Py_ssize_t first_update_column = ph.last_update_column[fineidx]+1 + Py_ssize_t update_limit = ph.current_column+1 + Py_ssize_t i for i in range(first_update_column, update_limit): accumulate_fine_histogram(ph, i, fineidx) @@ -509,15 +509,15 @@ cdef inline void update_histogram(Histograms *ph, SCoord *last_coord, SCoord *coord): cdef: - ssize_t current_column = ph.current_column - ssize_t current_row = ph.current_row - ssize_t current_stride = ph.current_stride - ssize_t column_count = ph.column_count - ssize_t row_count = ph.row_count + Py_ssize_t current_column = ph.current_column + Py_ssize_t current_row = ph.current_row + Py_ssize_t current_stride = ph.current_stride + Py_ssize_t column_count = ph.column_count + Py_ssize_t row_count = ph.row_count np.uint8_t value - ssize_t stride - ssize_t x - ssize_t y + Py_ssize_t stride + Py_ssize_t x + Py_ssize_t y x = last_coord.x + current_column y = last_coord.y + current_row @@ -550,21 +550,21 @@ cdef inline void update_histogram(Histograms *ph, ############################################################################ cdef inline void update_current_location(Histograms *ph): cdef: - ssize_t current_column = ph.current_column - ssize_t radius = ph.radius - ssize_t top_left_off = tl_br_colidx(ph, current_column) - ssize_t top_right_off = tr_bl_colidx(ph, current_column) - ssize_t bottom_left_off = tr_bl_colidx(ph, current_column) - ssize_t bottom_right_off = tl_br_colidx(ph, current_column) - ssize_t leading_edge_off = leading_edge_colidx(ph, current_column) + Py_ssize_t current_column = ph.current_column + Py_ssize_t radius = ph.radius + Py_ssize_t top_left_off = tl_br_colidx(ph, current_column) + Py_ssize_t top_right_off = tr_bl_colidx(ph, current_column) + Py_ssize_t bottom_left_off = tr_bl_colidx(ph, current_column) + Py_ssize_t bottom_right_off = tl_br_colidx(ph, current_column) + Py_ssize_t leading_edge_off = leading_edge_colidx(ph, current_column) np.int32_t *coarse_histogram np.int32_t *fine_histogram - ssize_t last_xoff - ssize_t last_yoff - ssize_t last_stride - ssize_t xoff - ssize_t yoff - ssize_t stride + Py_ssize_t last_xoff + Py_ssize_t last_yoff + Py_ssize_t last_stride + Py_ssize_t xoff + Py_ssize_t yoff + Py_ssize_t stride update_histogram(ph, &ph.histogram[top_left_off].top_left, &ph.pixel_count[top_left_off].top_left, @@ -599,10 +599,10 @@ cdef inline void update_current_location(Histograms *ph): cdef inline np.uint8_t find_median(Histograms *ph): cdef: - ssize_t pixels_below # of pixels below the median - ssize_t i - ssize_t j - ssize_t k + Py_ssize_t pixels_below # of pixels below the median + Py_ssize_t i + Py_ssize_t j + Py_ssize_t k np.uint32_t accumulator if ph.accumulator_count == 0: @@ -644,25 +644,25 @@ cdef inline np.uint8_t find_median(Histograms *ph): # output - array to be filled with filtered pixels # ############################################################################ -cdef int c_median_filter(ssize_t rows, - ssize_t columns, - ssize_t row_stride, - ssize_t col_stride, - ssize_t radius, - ssize_t percent, +cdef int c_median_filter(Py_ssize_t rows, + Py_ssize_t columns, + Py_ssize_t row_stride, + Py_ssize_t col_stride, + Py_ssize_t radius, + Py_ssize_t percent, np.uint8_t *data, np.uint8_t *mask, np.uint8_t *output): cdef: Histograms *ph Histogram *phistogram - ssize_t row - ssize_t col - ssize_t i - ssize_t top_left_off - ssize_t top_right_off - ssize_t bottom_left_off - ssize_t bottom_right_off + Py_ssize_t row + Py_ssize_t col + Py_ssize_t i + Py_ssize_t top_left_off + Py_ssize_t top_right_off + Py_ssize_t bottom_left_off + Py_ssize_t bottom_right_off ph = allocate_histograms(rows, columns, row_stride, col_stride, radius, percent, data, mask, output) diff --git a/skimage/filter/_denoise_cy.pyx b/skimage/filter/_denoise_cy.pyx index 91c09fbe..5a85e84a 100644 --- a/skimage/filter/_denoise_cy.pyx +++ b/skimage/filter/_denoise_cy.pyx @@ -17,7 +17,7 @@ cdef inline double _gaussian_weight(double sigma, double value): return exp(-0.5 * (value / sigma)**2) -cdef double* _compute_color_lut(ssize_t bins, double sigma, double max_value): +cdef double* _compute_color_lut(Py_ssize_t bins, double sigma, double max_value): cdef: double* color_lut = malloc(bins * sizeof(double)) @@ -29,7 +29,7 @@ cdef double* _compute_color_lut(ssize_t bins, double sigma, double max_value): return color_lut -cdef double* _compute_range_lut(ssize_t win_size, double sigma): +cdef double* _compute_range_lut(Py_ssize_t win_size, double sigma): cdef: double* range_lut = malloc(win_size**2 * sizeof(double)) @@ -45,8 +45,8 @@ cdef double* _compute_range_lut(ssize_t win_size, double sigma): return range_lut -def denoise_bilateral(image, ssize_t win_size=5, sigma_range=None, - double sigma_spatial=1, ssize_t bins=10000, +def denoise_bilateral(image, Py_ssize_t win_size=5, sigma_range=None, + double sigma_spatial=1, Py_ssize_t bins=10000, mode='constant', double cval=0): """Denoise image using bilateral filter. @@ -100,10 +100,10 @@ def denoise_bilateral(image, ssize_t win_size=5, sigma_range=None, image = np.atleast_3d(img_as_float(image)) cdef: - ssize_t rows = image.shape[0] - ssize_t cols = image.shape[1] - ssize_t dims = image.shape[2] - ssize_t window_ext = (win_size - 1) / 2 + Py_ssize_t rows = image.shape[0] + Py_ssize_t cols = image.shape[1] + Py_ssize_t dims = image.shape[2] + Py_ssize_t window_ext = (win_size - 1) / 2 double max_value = image.max() @@ -224,14 +224,14 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3): image = np.atleast_3d(img_as_float(image)) cdef: - ssize_t rows = image.shape[0] - ssize_t cols = image.shape[1] - ssize_t dims = image.shape[2] - ssize_t rows2 = rows + 2 - ssize_t cols2 = cols + 2 - ssize_t r, c, k + Py_ssize_t rows = image.shape[0] + Py_ssize_t cols = image.shape[1] + Py_ssize_t dims = image.shape[2] + Py_ssize_t rows2 = rows + 2 + Py_ssize_t cols2 = cols + 2 + Py_ssize_t r, c, k - ssize_t total = rows * cols * dims + Py_ssize_t total = rows * cols * dims shape_ext = (rows2, cols2, dims) diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index 96f34121..5f7de9df 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -6,12 +6,12 @@ cdef int int_min(int a, int b) # 16-bit core kernel receives extra information about data bitdepth -cdef void _core16(np.uint16_t kernel(ssize_t *, float, np.uint16_t, - ssize_t, ssize_t, ssize_t, float, - float, ssize_t, ssize_t), +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, + Py_ssize_t, Py_ssize_t, Py_ssize_t, float, + float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y, ssize_t bitdepth, - float p0, float p1, ssize_t s0, ssize_t s1) except * + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except * diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index 323692ec..aa959fd0 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -17,38 +17,38 @@ cdef inline int int_min(int a, int b): return a if a <= b else b -cdef inline void histogram_increment(ssize_t * histo, float * pop, +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] += 1 pop[0] += 1 -cdef inline void histogram_decrement(ssize_t * histo, float * pop, +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint16_t value): histo[value] -= 1 pop[0] -= 1 -cdef void _core16(np.uint16_t kernel(ssize_t *, float, np.uint16_t, - ssize_t, ssize_t, ssize_t, float, - float, ssize_t, ssize_t), +cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, + Py_ssize_t, Py_ssize_t, Py_ssize_t, float, + float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint16_t, ndim=2] out, - char shift_x, char shift_y, ssize_t bitdepth, - float p0, float p1, ssize_t s0, ssize_t s1) except *: + char shift_x, char shift_y, Py_ssize_t bitdepth, + float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ - cdef ssize_t rows = image.shape[0] - cdef ssize_t cols = image.shape[1] - cdef ssize_t srows = selem.shape[0] - cdef ssize_t scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef ssize_t centre_r = int(selem.shape[0] / 2) + shift_y - cdef ssize_t centre_c = int(selem.shape[1] / 2) + shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -61,8 +61,8 @@ cdef void _core16(np.uint16_t kernel(ssize_t *, float, np.uint16_t, midbin_list = [0, 0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] # set maxbin and midbin - cdef ssize_t maxbin = maxbin_list[bitdepth] - cdef ssize_t midbin = midbin_list[bitdepth] + cdef Py_ssize_t maxbin = maxbin_list[bitdepth] + cdef Py_ssize_t midbin = midbin_list[bitdepth] assert (image < maxbin).all() @@ -72,30 +72,30 @@ cdef void _core16(np.uint16_t kernel(ssize_t *, float, np.uint16_t, cdef np.uint8_t * mask_data = mask.data # define local variable types - cdef ssize_t r, c, rr, cc, s, value, local_max, i, even_row + cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) cdef float pop # allocate memory with malloc - cdef ssize_t max_se = srows * scols + cdef Py_ssize_t max_se = srows * scols # number of element in each attack border - cdef ssize_t num_se_n, num_se_s, num_se_e, num_se_w + cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef ssize_t * histo = malloc(maxbin * sizeof(ssize_t)) + cdef Py_ssize_t * histo = malloc(maxbin * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border - cdef ssize_t * se_e_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_e_c = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_w_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_w_c = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_n_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_n_c = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_s_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_s_c = malloc(max_se * sizeof(ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 6ee2e0a3..38236b87 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -5,18 +5,18 @@ cdef np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) cdef np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) -cdef np.uint8_t is_in_mask(ssize_t rows, ssize_t cols, - ssize_t r, ssize_t c, +cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask) # 8-bit core kernel receives extra information about data inferior and superior # percentiles -cdef void _core8(np.uint8_t kernel(ssize_t *, float, np.uint8_t, float, - float, ssize_t, ssize_t), +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, + float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, - ssize_t s0, ssize_t s1) except * + Py_ssize_t s0, Py_ssize_t s1) except * diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 7dbd4524..9f4bc693 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -16,20 +16,20 @@ cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): return a if a <= b else b -cdef inline void histogram_increment(ssize_t * histo, float * pop, +cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] += 1 pop[0] += 1 -cdef inline void histogram_decrement(ssize_t * histo, float * pop, +cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, np.uint8_t value): histo[value] -= 1 pop[0] -= 1 -cdef inline np.uint8_t is_in_mask(ssize_t rows, ssize_t cols, - ssize_t r, ssize_t c, +cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, np.uint8_t * mask): """Check whether given coordinate is within image and mask is true.""" if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: @@ -41,25 +41,25 @@ cdef inline np.uint8_t is_in_mask(ssize_t rows, ssize_t cols, return 0 -cdef void _core8(np.uint8_t kernel(ssize_t *, float, np.uint8_t, float, - float, ssize_t, ssize_t), +cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, + float, Py_ssize_t, Py_ssize_t), np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask, np.ndarray[np.uint8_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, - ssize_t s0, ssize_t s1) except *: + Py_ssize_t s0, Py_ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ - cdef ssize_t rows = image.shape[0] - cdef ssize_t cols = image.shape[1] - cdef ssize_t srows = selem.shape[0] - cdef ssize_t scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef ssize_t centre_r = int(selem.shape[0] / 2) + shift_y - cdef ssize_t centre_c = int(selem.shape[1] / 2) + shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 @@ -74,31 +74,31 @@ cdef void _core8(np.uint8_t kernel(ssize_t *, float, np.uint8_t, float, cdef np.uint8_t * mask_data = mask.data # define local variable types - cdef ssize_t r, c, rr, cc, s, value, local_max, i, even_row + cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (float) cdef float pop # allocate memory with malloc - cdef ssize_t max_se = srows * scols + cdef Py_ssize_t max_se = srows * scols # number of element in each attack border - cdef ssize_t num_se_n, num_se_s, num_se_e, num_se_w + cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution - cdef ssize_t * histo = malloc(256 * sizeof(ssize_t)) + cdef Py_ssize_t * histo = malloc(256 * sizeof(Py_ssize_t)) # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border - cdef ssize_t * se_e_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_e_c = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_w_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_w_c = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_n_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_n_c = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_s_r = malloc(max_se * sizeof(ssize_t)) - cdef ssize_t * se_s_c = malloc(max_se * sizeof(ssize_t)) + cdef Py_ssize_t * se_e_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_e_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_w_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_n_c = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_r = malloc(max_se * sizeof(Py_ssize_t)) + cdef Py_ssize_t * se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders # by using difference along axis diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index b5dda3c2..e81bf81f 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -14,12 +14,12 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i, imin, imax, delta + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax, delta if pop: for i in range(maxbin - 1, -1, -1): @@ -37,12 +37,12 @@ cdef inline np.uint16_t kernel_autolevel(ssize_t * histo, float pop, return < np.uint16_t > (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i if pop: for i in range(maxbin): @@ -53,12 +53,12 @@ cdef inline np.uint16_t kernel_bottomhat(ssize_t * histo, float pop, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_equalize(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float sum = 0. if pop: @@ -72,12 +72,12 @@ cdef inline np.uint16_t kernel_equalize(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i, imin, imax + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: for i in range(maxbin - 1, -1, -1): @@ -93,12 +93,12 @@ cdef inline np.uint16_t kernel_gradient(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_maximum(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i if pop: for i in range(maxbin - 1, -1, -1): @@ -108,12 +108,12 @@ cdef inline np.uint16_t kernel_maximum(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -124,15 +124,15 @@ cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_meansubstraction(ssize_t * histo, +cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, float pop, np.uint16_t g, - ssize_t bitdepth, - ssize_t maxbin, - ssize_t midbin, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -143,12 +143,12 @@ cdef inline np.uint16_t kernel_meansubstraction(ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_median(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float sum = pop / 2.0 if pop: @@ -161,12 +161,12 @@ cdef inline np.uint16_t kernel_median(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_minimum(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i if pop: for i in range(maxbin): @@ -176,12 +176,12 @@ cdef inline np.uint16_t kernel_minimum(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_modal(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t hmax = 0, imax = 0 + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(maxbin): @@ -193,15 +193,15 @@ cdef inline np.uint16_t kernel_modal(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(ssize_t * histo, +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint16_t g, - ssize_t bitdepth, - ssize_t maxbin, - ssize_t midbin, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i, imin, imax + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i, imin, imax if pop: for i in range(maxbin - 1, -1, -1): @@ -220,20 +220,20 @@ cdef inline np.uint16_t kernel_morph_contr_enh(ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): return < np.uint16_t > (pop) -cdef inline np.uint16_t kernel_threshold(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -244,12 +244,12 @@ cdef inline np.uint16_t kernel_threshold(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_tophat(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i if pop: for i in range(maxbin - 1, -1, -1): @@ -260,12 +260,12 @@ cdef inline np.uint16_t kernel_tophat(ssize_t * histo, float pop, else: return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_entropy(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float e, p if pop: @@ -289,132 +289,132 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def equalize(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def maximum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def median(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def minimum(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def modal(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def tophat(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) def entropy(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, np.ndarray[np.uint16_t, ndim=2] out=None, - char shift_x=0, char shift_y=0, ssize_t bitdepth=8): + char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - bitdepth, 0, 0, 0, 0) + bitdepth, 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index 82236d63..c71ccc5c 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -13,11 +13,11 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, bilat_pop = 0 cdef float mean = 0. @@ -35,11 +35,11 @@ cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, bilat_pop = 0 diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index e8836b5b..220d2386 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -13,11 +13,11 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -46,11 +46,11 @@ cdef inline np.uint16_t kernel_autolevel(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_gradient(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -74,11 +74,11 @@ cdef inline np.uint16_t kernel_gradient(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -100,14 +100,14 @@ cdef inline np.uint16_t kernel_mean(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_mean_substraction(ssize_t * histo, +cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint16_t g, - ssize_t bitdepth, - ssize_t maxbin, - ssize_t midbin, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -128,14 +128,14 @@ cdef inline np.uint16_t kernel_mean_substraction(ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_morph_contr_enh(ssize_t * histo, +cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint16_t g, - ssize_t bitdepth, - ssize_t maxbin, - ssize_t midbin, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -165,11 +165,11 @@ cdef inline np.uint16_t kernel_morph_contr_enh(ssize_t * histo, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_percentile(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -185,11 +185,11 @@ cdef inline np.uint16_t kernel_percentile(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n @@ -205,11 +205,11 @@ cdef inline np.uint16_t kernel_pop(ssize_t * histo, float pop, return < np.uint16_t > (0) -cdef inline np.uint16_t kernel_threshold(ssize_t * histo, float pop, - np.uint16_t g, ssize_t bitdepth, - ssize_t maxbin, ssize_t midbin, +cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, + np.uint16_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -239,7 +239,7 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, """bottom hat """ _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def gradient(np.ndarray[np.uint16_t, ndim=2] image, @@ -251,7 +251,7 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, """return p0,p1 percentile gradient """ _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def mean(np.ndarray[np.uint16_t, ndim=2] image, @@ -263,7 +263,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, @@ -276,7 +276,7 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, """ _core16( kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, @@ -288,7 +288,7 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, """reforce contrast using percentiles """ _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def percentile(np.ndarray[np.uint16_t, ndim=2] image, @@ -300,7 +300,7 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, """return p0 percentile """ _core16(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def pop(np.ndarray[np.uint16_t, ndim=2] image, @@ -312,7 +312,7 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) def threshold(np.ndarray[np.uint16_t, ndim=2] image, @@ -324,4 +324,4 @@ def threshold(np.ndarray[np.uint16_t, ndim=2] image, """return (maxbin-1) if g > percentile p0 """ _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, - bitdepth, p0, p1, 0, 0) + bitdepth, p0, p1, 0, 0) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 582bcbb7..8bff9703 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -14,11 +14,11 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i, imin, imax, delta + cdef Py_ssize_t i, imin, imax, delta if pop: for i in range(255, -1, -1): @@ -38,11 +38,11 @@ cdef inline np.uint8_t kernel_autolevel(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_bottomhat(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i if pop: for i in range(256): @@ -54,11 +54,11 @@ cdef inline np.uint8_t kernel_bottomhat(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_equalize(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i cdef float sum = 0. if pop: @@ -72,11 +72,11 @@ cdef inline np.uint8_t kernel_equalize(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_gradient(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i, imin, imax + cdef Py_ssize_t i, imin, imax if pop: for i in range(255, -1, -1): @@ -92,11 +92,11 @@ cdef inline np.uint8_t kernel_gradient(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_maximum(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i if pop: for i in range(255, -1, -1): @@ -106,11 +106,11 @@ cdef inline np.uint8_t kernel_maximum(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -121,11 +121,11 @@ cdef inline np.uint8_t kernel_mean(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_meansubstraction(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -136,11 +136,11 @@ cdef inline np.uint8_t kernel_meansubstraction(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_median(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i cdef float sum = pop / 2.0 if pop: @@ -153,11 +153,11 @@ cdef inline np.uint8_t kernel_median(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_minimum(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i if pop: for i in range(256): @@ -167,11 +167,11 @@ cdef inline np.uint8_t kernel_minimum(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_modal(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t hmax = 0, imax = 0 + cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(256): @@ -183,11 +183,11 @@ cdef inline np.uint8_t kernel_modal(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i, imin, imax + cdef Py_ssize_t i, imin, imax if pop: for i in range(255, -1, -1): @@ -206,18 +206,18 @@ cdef inline np.uint8_t kernel_morph_contr_enh(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): return < np.uint8_t > (pop) -cdef inline np.uint8_t kernel_threshold(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i cdef float mean = 0. if pop: @@ -228,11 +228,11 @@ cdef inline np.uint8_t kernel_threshold(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_tophat(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i + cdef Py_ssize_t i if pop: for i in range(255, -1, -1): @@ -243,12 +243,12 @@ cdef inline np.uint8_t kernel_tophat(ssize_t * histo, float pop, else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_noise_filter(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): - cdef ssize_t i - cdef ssize_t min_i + cdef Py_ssize_t i + cdef Py_ssize_t min_i # early stop if at least one pixel of the neighborhood has the same g if histo[g] > 0: @@ -267,10 +267,10 @@ cdef inline np.uint8_t kernel_noise_filter(ssize_t * histo, float pop, return < np.uint8_t > min_i -cdef inline np.uint8_t kernel_entropy(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): - cdef ssize_t i + Py_ssize_t s0, Py_ssize_t s1): + cdef Py_ssize_t i cdef float e, p if pop: @@ -285,11 +285,11 @@ cdef inline np.uint8_t kernel_entropy(ssize_t * histo, float pop, else: return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_otsu(ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, ssize_t s0, - ssize_t s1): - cdef ssize_t i - cdef ssize_t max_i +cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, + float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): + cdef Py_ssize_t i + cdef Py_ssize_t max_i cdef float P, mu1, mu2, q1, new_q1, sigma_b, max_sigma_b cdef float mu = 0. @@ -334,7 +334,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, @@ -343,7 +343,7 @@ def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def equalize(np.ndarray[np.uint8_t, ndim=2] image, @@ -352,7 +352,7 @@ def equalize(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -361,7 +361,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def maximum(np.ndarray[np.uint8_t, ndim=2] image, @@ -370,7 +370,7 @@ def maximum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -379,7 +379,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -388,7 +388,7 @@ def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def median(np.ndarray[np.uint8_t, ndim=2] image, @@ -397,7 +397,7 @@ def median(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def minimum(np.ndarray[np.uint8_t, ndim=2] image, @@ -406,7 +406,7 @@ def minimum(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -415,7 +415,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def modal(np.ndarray[np.uint8_t, ndim=2] image, @@ -424,7 +424,7 @@ def modal(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -433,7 +433,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -442,7 +442,7 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, 0, 0, - 0, 0) + 0, 0) def tophat(np.ndarray[np.uint8_t, ndim=2] image, @@ -451,7 +451,7 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, @@ -460,7 +460,7 @@ def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def entropy(np.ndarray[np.uint8_t, ndim=2] image, @@ -469,7 +469,7 @@ def entropy(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) def otsu(np.ndarray[np.uint8_t, ndim=2] image, @@ -478,4 +478,4 @@ def otsu(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, - 0, 0, 0, 0) + 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index bd48c78b..31b00742 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -13,9 +13,9 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -45,9 +45,9 @@ cdef inline np.uint8_t kernel_autolevel(ssize_t * histo, float pop, return < np.uint8_t > (128) -cdef inline np.uint8_t kernel_gradient(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -70,9 +70,9 @@ cdef inline np.uint8_t kernel_gradient(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -92,11 +92,11 @@ cdef inline np.uint8_t kernel_mean(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_mean_substraction(ssize_t * histo, +cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -116,10 +116,10 @@ cdef inline np.uint8_t kernel_mean_substraction(ssize_t * histo, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_morph_contr_enh(ssize_t * histo, +cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -148,9 +148,9 @@ cdef inline np.uint8_t kernel_morph_contr_enh(ssize_t * histo, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_percentile(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -165,9 +165,9 @@ cdef inline np.uint8_t kernel_percentile(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_pop(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n if pop: @@ -182,9 +182,9 @@ cdef inline np.uint8_t kernel_pop(ssize_t * histo, float pop, return < np.uint8_t > (0) -cdef inline np.uint8_t kernel_threshold(ssize_t * histo, float pop, +cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, - ssize_t s0, ssize_t s1): + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -212,7 +212,7 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, """autolevel """ _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def gradient(np.ndarray[np.uint8_t, ndim=2] image, @@ -223,7 +223,7 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, """return p0,p1 percentile gradient """ _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def mean(np.ndarray[np.uint8_t, ndim=2] image, @@ -234,7 +234,7 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, """return mean between [p0 and p1] percentiles """ _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, @@ -245,7 +245,7 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, """return original - mean between [p0 and p1] percentiles *.5 +127 """ _core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, 0, 0) def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, @@ -256,7 +256,7 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, """reforce contrast using percentiles """ _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, 0, 0) def percentile(np.ndarray[np.uint8_t, ndim=2] image, @@ -267,7 +267,7 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, """return p0 percentile """ _core8(kernel_percentile, image, selem, mask, out, shift_x, shift_y, - p0, p1, 0, 0) + p0, p1, 0, 0) def pop(np.ndarray[np.uint8_t, ndim=2] image, @@ -278,7 +278,7 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, """return nb of pixels between [p0 and p1] """ _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) def threshold(np.ndarray[np.uint8_t, ndim=2] image, @@ -289,4 +289,4 @@ def threshold(np.ndarray[np.uint8_t, ndim=2] image, """return 255 if g > percentile p0 """ _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, p0, p1, - 0, 0) + 0, 0) diff --git a/skimage/graph/heap.pxd b/skimage/graph/heap.pxd index 74366368..e31aa150 100644 --- a/skimage/graph/heap.pxd +++ b/skimage/graph/heap.pxd @@ -7,7 +7,7 @@ value_of_fast() # determine datatypes for heap ctypedef double VALUE_T -ctypedef ssize_t REFERENCE_T +ctypedef Py_ssize_t REFERENCE_T ctypedef REFERENCE_T INDEX_T ctypedef unsigned char BOOL_T ctypedef unsigned char LEVELS_T diff --git a/skimage/measure/_find_contours.pyx b/skimage/measure/_find_contours.pyx index 30eb1250..91ba318c 100644 --- a/skimage/measure/_find_contours.pyx +++ b/skimage/measure/_find_contours.pyx @@ -11,7 +11,7 @@ cdef inline double _get_fraction(double from_value, double to_value, def iterate_and_store(np.ndarray[double, ndim=2] array, - double level, ssize_t vertex_connect_high): + double level, Py_ssize_t vertex_connect_high): """Iterate across the given array in a marching-squares fashion, looking for segments that cross 'level'. If such a segment is found, its coordinates are added to a growing list of segments, @@ -24,7 +24,7 @@ def iterate_and_store(np.ndarray[double, ndim=2] array, raise ValueError("Input array must be at least 2x2.") cdef list arc_list = [] - cdef ssize_t n + cdef Py_ssize_t n # The plan is to iterate a 2x2 square across the input array. This means # that the upper-left corner of the square needs to iterate across a @@ -36,17 +36,17 @@ def iterate_and_store(np.ndarray[double, ndim=2] array, # index varies the fastest). # Current coords start at 0,0. - cdef ssize_t[2] coords + cdef Py_ssize_t[2] coords coords[0] = 0 coords[1] = 0 # Calculate the number of iterations we'll need - cdef ssize_t num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1) + cdef Py_ssize_t num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1) cdef unsigned char square_case = 0 cdef tuple top, bottom, left, right cdef double ul, ur, ll, lr - cdef ssize_t r0, r1, c0, c1 + cdef Py_ssize_t r0, r1, c0, c1 for n in range(num_square_steps): # There are sixteen different possible square types, diagramed below. diff --git a/skimage/measure/_moments.pyx b/skimage/measure/_moments.pyx index 759e4a4f..b94a7b89 100644 --- a/skimage/measure/_moments.pyx +++ b/skimage/measure/_moments.pyx @@ -7,7 +7,7 @@ cimport numpy as np def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc, int order): - cdef ssize_t p, q, r, c + cdef Py_ssize_t p, q, r, c cdef np.ndarray[np.double_t, ndim=2] mu mu = np.zeros((order + 1, order + 1), 'double') for p in range(order + 1): @@ -18,7 +18,7 @@ def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc, return mu def normalized_moments(np.ndarray[np.double_t, ndim=2] mu, int order): - cdef ssize_t p, q + cdef Py_ssize_t p, q cdef np.ndarray[np.double_t, ndim=2] nu nu = np.zeros((order + 1, order + 1), 'double') for p in range(order + 1): diff --git a/skimage/morphology/_convex_hull.pyx b/skimage/morphology/_convex_hull.pyx index 6746fa9f..9bec825b 100644 --- a/skimage/morphology/_convex_hull.pyx +++ b/skimage/morphology/_convex_hull.pyx @@ -18,9 +18,9 @@ def possible_hull(np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] img): the convex hull. """ - cdef ssize_t r, c - cdef ssize_t rows = img.shape[0] - cdef ssize_t cols = img.shape[1] + cdef Py_ssize_t r, c + cdef Py_ssize_t rows = img.shape[0] + cdef Py_ssize_t cols = img.shape[1] # Output: rows storage slots for left boundary pixels # cols storage slots for top boundary pixels diff --git a/skimage/morphology/_pnpoly.pyx b/skimage/morphology/_pnpoly.pyx index d1413a24..f6cf386f 100644 --- a/skimage/morphology/_pnpoly.pyx +++ b/skimage/morphology/_pnpoly.pyx @@ -31,11 +31,11 @@ def grid_points_inside_poly(shape, verts): vx = verts[:, 0].astype(np.double) vy = verts[:, 1].astype(np.double) - cdef ssize_t V = vx.shape[0] + cdef Py_ssize_t V = vx.shape[0] - cdef ssize_t M = shape[0] - cdef ssize_t N = shape[1] - cdef ssize_t m, n + cdef Py_ssize_t M = shape[0] + cdef Py_ssize_t N = shape[1] + cdef Py_ssize_t m, n cdef np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] out = \ np.zeros((M, N), dtype=np.uint8) diff --git a/skimage/morphology/_skeletonize_cy.pyx b/skimage/morphology/_skeletonize_cy.pyx index c4aa0d48..9bef86b9 100644 --- a/skimage/morphology/_skeletonize_cy.pyx +++ b/skimage/morphology/_skeletonize_cy.pyx @@ -62,8 +62,8 @@ def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, """ cdef: np.int32_t accumulator - ssize_t index, order_index - ssize_t ii, jj + Py_ssize_t index, order_index + Py_ssize_t ii, jj for index in range(order.shape[0]): accumulator = 16 @@ -119,12 +119,12 @@ def _table_lookup_index(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] indexer np.int32_t *p_indexer np.uint8_t *p_image - ssize_t i_stride - ssize_t i_shape - ssize_t j_shape - ssize_t i - ssize_t j - ssize_t offset + Py_ssize_t i_stride + Py_ssize_t i_shape + Py_ssize_t j_shape + Py_ssize_t i + Py_ssize_t j + Py_ssize_t offset i_shape = image.shape[0] j_shape = image.shape[1] diff --git a/skimage/morphology/_watershed.pyx b/skimage/morphology/_watershed.pyx index b98ca332..122f0262 100644 --- a/skimage/morphology/_watershed.pyx +++ b/skimage/morphology/_watershed.pyx @@ -27,7 +27,7 @@ def watershed(np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, mode='c'] image, np.ndarray[DTYPE_INT32_t, ndim=2, negative_indices=False, mode='c'] pq, - ssize_t age, + Py_ssize_t age, np.ndarray[DTYPE_INT32_t, ndim=2, negative_indices=False, mode='c'] structure, np.ndarray[DTYPE_BOOL_t, ndim=1, negative_indices=False, @@ -58,11 +58,11 @@ def watershed(np.ndarray[DTYPE_INT32_t, ndim=1, negative_indices=False, """ cdef Heapitem elem cdef Heapitem new_elem - cdef ssize_t nneighbors = structure.shape[0] - cdef ssize_t i = 0 - cdef ssize_t index = 0 - cdef ssize_t old_index = 0 - cdef ssize_t max_index = image.shape[0] + cdef Py_ssize_t nneighbors = structure.shape[0] + cdef Py_ssize_t i = 0 + cdef Py_ssize_t index = 0 + cdef Py_ssize_t old_index = 0 + cdef Py_ssize_t max_index = image.shape[0] cdef Heap *hp = heap_from_numpy2() diff --git a/skimage/morphology/cmorph.pyx b/skimage/morphology/cmorph.pyx index 418b0571..a09a39a3 100644 --- a/skimage/morphology/cmorph.pyx +++ b/skimage/morphology/cmorph.pyx @@ -13,13 +13,13 @@ def dilate(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - cdef ssize_t rows = image.shape[0] - cdef ssize_t cols = image.shape[1] - cdef ssize_t srows = selem.shape[0] - cdef ssize_t scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef ssize_t centre_r = int(selem.shape[0] / 2) - shift_y - cdef ssize_t centre_c = int(selem.shape[1] / 2) - shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) - shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) - shift_x image = np.ascontiguousarray(image) if out is None: @@ -30,11 +30,11 @@ def dilate(np.ndarray[np.uint8_t, ndim=2] image, cdef np.uint8_t* out_data = out.data cdef np.uint8_t* image_data = image.data - cdef ssize_t r, c, rr, cc, s, value, local_max + cdef Py_ssize_t r, c, rr, cc, s, value, local_max - cdef ssize_t selem_num = np.sum(selem != 0) - cdef ssize_t* sr = malloc(selem_num * sizeof(ssize_t)) - cdef ssize_t* sc = malloc(selem_num * sizeof(ssize_t)) + cdef Py_ssize_t selem_num = np.sum(selem != 0) + cdef Py_ssize_t* sr = malloc(selem_num * sizeof(Py_ssize_t)) + cdef Py_ssize_t* sc = malloc(selem_num * sizeof(Py_ssize_t)) s = 0 for r in range(srows): @@ -68,13 +68,13 @@ def erode(np.ndarray[np.uint8_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] out=None, char shift_x=0, char shift_y=0): - cdef ssize_t rows = image.shape[0] - cdef ssize_t cols = image.shape[1] - cdef ssize_t srows = selem.shape[0] - cdef ssize_t scols = selem.shape[1] + cdef Py_ssize_t rows = image.shape[0] + cdef Py_ssize_t cols = image.shape[1] + cdef Py_ssize_t srows = selem.shape[0] + cdef Py_ssize_t scols = selem.shape[1] - cdef ssize_t centre_r = int(selem.shape[0] / 2) - shift_y - cdef ssize_t centre_c = int(selem.shape[1] / 2) - shift_x + cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) - shift_y + cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) - shift_x image = np.ascontiguousarray(image) if out is None: @@ -87,9 +87,9 @@ def erode(np.ndarray[np.uint8_t, ndim=2] image, cdef int r, c, rr, cc, s, value, local_min - cdef ssize_t selem_num = np.sum(selem != 0) - cdef ssize_t* sr = malloc(selem_num * sizeof(ssize_t)) - cdef ssize_t* sc = malloc(selem_num * sizeof(ssize_t)) + cdef Py_ssize_t selem_num = np.sum(selem != 0) + cdef Py_ssize_t* sr = malloc(selem_num * sizeof(Py_ssize_t)) + cdef Py_ssize_t* sc = malloc(selem_num * sizeof(Py_ssize_t)) s = 0 for r in range(srows): diff --git a/skimage/morphology/heap_general.pxi b/skimage/morphology/heap_general.pxi index c60096d6..67b21fa6 100644 --- a/skimage/morphology/heap_general.pxi +++ b/skimage/morphology/heap_general.pxi @@ -14,13 +14,13 @@ from libc.stdlib cimport free, malloc, realloc cdef struct Heap: - ssize_t items - ssize_t space + Py_ssize_t items + Py_ssize_t space Heapitem *data Heapitem **ptrs cdef inline Heap *heap_from_numpy2(): - cdef ssize_t k + cdef Py_ssize_t k cdef Heap *heap heap = malloc(sizeof (Heap)) heap.items = 0 @@ -36,7 +36,7 @@ cdef inline void heap_done(Heap *heap): free(heap.ptrs) free(heap) -cdef inline void swap(ssize_t a, ssize_t b, Heap *h): +cdef inline void swap(Py_ssize_t a, Py_ssize_t b, Heap *h): h.ptrs[a], h.ptrs[b] = h.ptrs[b], h.ptrs[a] @@ -49,7 +49,7 @@ cdef inline void swap(ssize_t a, ssize_t b, Heap *h): ###################################################### cdef inline void heappop(Heap *heap, Heapitem *dest): - cdef ssize_t i, smallest, l, r # heap indices + cdef Py_ssize_t i, smallest, l, r # heap indices # # Start by copying the first element to the destination @@ -102,18 +102,18 @@ cdef inline void heappop(Heap *heap, Heapitem *dest): ################################################## cdef inline void heappush(Heap *heap, Heapitem *new_elem): - cdef ssize_t child = heap.items - cdef ssize_t parent - cdef ssize_t k + cdef Py_ssize_t child = heap.items + cdef Py_ssize_t parent + cdef Py_ssize_t k cdef Heapitem *new_data # grow if necessary if heap.items == heap.space: heap.space = heap.space * 2 new_data = realloc(heap.data, - (heap.space * sizeof(Heapitem))) + (heap.space * sizeof(Heapitem))) heap.ptrs = realloc(heap.ptrs, - (heap.space * sizeof(Heapitem *))) + (heap.space * sizeof(Heapitem *))) for k in range(heap.items): heap.ptrs[k] = new_data + (heap.ptrs[k] - heap.data) for k in range(heap.items, heap.space): diff --git a/skimage/morphology/heap_watershed.pxi b/skimage/morphology/heap_watershed.pxi index 43917690..573d32bb 100644 --- a/skimage/morphology/heap_watershed.pxi +++ b/skimage/morphology/heap_watershed.pxi @@ -17,7 +17,7 @@ cimport cython cdef struct Heapitem: np.int32_t value np.int32_t age - ssize_t index + Py_ssize_t index cdef inline int smaller(Heapitem *a, Heapitem *b): diff --git a/skimage/segmentation/_felzenszwalb_cy.pyx b/skimage/segmentation/_felzenszwalb_cy.pyx index 2a4ea173..5a8ac012 100644 --- a/skimage/segmentation/_felzenszwalb_cy.pyx +++ b/skimage/segmentation/_felzenszwalb_cy.pyx @@ -10,7 +10,7 @@ from ..util import img_as_float @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) -def _felzenszwalb_grey(image, double scale=1, sigma=0.8, ssize_t min_size=20): +def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20): """Felzenszwalb's efficient graph based segmentation for a single channel. Produces an oversegmentation of a 2d image using a fast, minimum spanning diff --git a/skimage/segmentation/_quickshift.pyx b/skimage/segmentation/_quickshift.pyx index 4362725c..61e4d0e0 100644 --- a/skimage/segmentation/_quickshift.pyx +++ b/skimage/segmentation/_quickshift.pyx @@ -85,12 +85,12 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, raise ValueError("Sigma should be >= 1") cdef int w = int(3 * kernel_size) - cdef ssize_t height = image_c.shape[0] - cdef ssize_t width = image_c.shape[1] - cdef ssize_t channels = image_c.shape[2] + cdef Py_ssize_t height = image_c.shape[0] + cdef Py_ssize_t width = image_c.shape[1] + cdef Py_ssize_t channels = image_c.shape[2] cdef double current_density, closest, dist - cdef ssize_t r, c, r_, c_, channel, r_min, c_min + cdef Py_ssize_t r, c, r_, c_, channel, r_min, c_min cdef np.float_t* image_p = image_c.data cdef np.float_t* current_pixel_p = image_p diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index 260b7689..2fcee46b 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -63,10 +63,10 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, image = rgb2lab(image) # initialize on grid: - cdef ssize_t height, width + cdef Py_ssize_t height, width height, width = image.shape[:2] # approximate grid size for desired n_segments - cdef ssize_t step = np.ceil(np.sqrt(height * width / n_segments)) + cdef Py_ssize_t step = np.ceil(np.sqrt(height * width / n_segments)) grid_y, grid_x = np.mgrid[:height, :width] means_y = grid_y[::step, ::step] means_x = grid_x[::step, ::step] @@ -82,7 +82,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, ratio = (ratio / float(step)) ** 2 cdef np.ndarray[dtype=np.float_t, ndim=3] image_yx \ = np.dstack([grid_y, grid_x, image / ratio]).copy("C") - cdef ssize_t i, k, x, y, x_min, x_max, y_min, y_max, changes + cdef Py_ssize_t i, k, x, y, x_min, x_max, y_min, y_max, changes cdef double dist_mean cdef np.ndarray[dtype=np.intp_t, ndim=2] nearest_mean \ diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 016be878..01a45a48 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -13,8 +13,8 @@ cdef double PI_2 = 1.5707963267948966 cdef double NEG_PI_2 = -PI_2 -cdef inline ssize_t round(double r): - return ((r + 0.5) if (r > 0.0) else (r - 0.5)) +cdef inline Py_ssize_t round(double r): + return ((r + 0.5) if (r > 0.0) else (r - 0.5)) @cython.boundscheck(False) @@ -36,9 +36,9 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): # compute the bins and allocate the accumulator array cdef np.ndarray[ndim=2, dtype=np.uint64_t] accum cdef np.ndarray[ndim=1, dtype=np.double_t] bins - cdef ssize_t max_distance, offset + cdef Py_ssize_t max_distance, offset - max_distance = 2 * ceil(sqrt(img.shape[0] * img.shape[0] + + max_distance = 2 * ceil(sqrt(img.shape[0] * img.shape[0] + img.shape[1] * img.shape[1])) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64) bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance) @@ -49,7 +49,7 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): y_idxs, x_idxs = np.nonzero(img) # finally, run the transform - cdef ssize_t nidxs, nthetas, i, j, x, y, accum_idx + cdef Py_ssize_t nidxs, nthetas, i, j, x, y, accum_idx nidxs = y_idxs.shape[0] # x and y are the same shape nthetas = theta.shape[0] for i in range(nidxs): @@ -77,20 +77,20 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \ theta = math.pi/2-np.arange(180)/180.0* math.pi ctheta = np.cos(theta) stheta = np.sin(theta) - cdef ssize_t height = img.shape[0] - cdef ssize_t width = img.shape[1] + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] # compute the bins and allocate the accumulator array cdef np.ndarray[ndim=2, dtype=np.int64_t] accum cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = np.zeros((height, width), dtype=np.uint8) cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = np.zeros((2, 2), dtype=np.int32) - cdef ssize_t max_distance, offset, num_indexes, index + cdef Py_ssize_t max_distance, offset, num_indexes, index cdef double a, b - cdef ssize_t nidxs, nthetas, i, j, x, y, px, py, accum_idx + cdef Py_ssize_t nidxs, nthetas, i, j, x, y, px, py, accum_idx cdef int value, max_value, max_theta cdef int shift = 16 # maximum line number cutoff - cdef ssize_t lines_max = 2 ** 15 - cdef ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count + cdef Py_ssize_t lines_max = 2 ** 15 + cdef Py_ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count max_distance = 2 * ceil((sqrt(img.shape[0] * img.shape[0] + img.shape[1] * img.shape[1]))) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64) diff --git a/skimage/transform/_warps_cy.pyx b/skimage/transform/_warps_cy.pyx index 28c258fd..951eb861 100644 --- a/skimage/transform/_warps_cy.pyx +++ b/skimage/transform/_warps_cy.pyx @@ -93,7 +93,7 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, "`constant`, `nearest`, `wrap` or `reflect`.") cdef char mode_c = ord(mode[0].upper()) - cdef ssize_t out_r, out_c + cdef Py_ssize_t out_r, out_c if output_shape is None: out_r = img.shape[0] out_c = img.shape[1] @@ -104,12 +104,12 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, cdef np.ndarray[dtype=np.double_t, ndim=2] out = \ np.zeros((out_r, out_c), dtype=np.double) - cdef ssize_t tfr, tfc + cdef Py_ssize_t tfr, tfc cdef double r, c - cdef ssize_t rows = img.shape[0] - cdef ssize_t cols = img.shape[1] + cdef Py_ssize_t rows = img.shape[0] + cdef Py_ssize_t cols = img.shape[1] - cdef double (*interp_func)(double*, ssize_t, ssize_t, double, double, + cdef double (*interp_func)(double*, Py_ssize_t, Py_ssize_t, double, double, char, double) if order == 0: interp_func = nearest_neighbour_interpolation From a68be842ab346aa55d1610aa2c568813c25acb00 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 15:10:08 +0100 Subject: [PATCH 563/625] Fix index type errors in draw module --- skimage/draw/_draw.pyx | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 5bba8c32..4b7fb094 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -97,10 +97,10 @@ def polygon(y, x, shape=None): """ cdef Py_ssize_t nr_verts = x.shape[0] - cdef Py_ssize_t minr = max(0, y.min()) - cdef Py_ssize_t maxr = math.ceil(y.max()) - cdef Py_ssize_t minc = max(0, x.min()) - cdef Py_ssize_t maxc = math.ceil(x.max()) + cdef Py_ssize_t minr = int(max(0, y.min())) + cdef Py_ssize_t maxr = int(math.ceil(y.max())) + cdef Py_ssize_t minc = int(max(0, x.min())) + cdef Py_ssize_t maxc = int(math.ceil(x.max())) # make sure output coordinates do not exceed image size if shape is not None: @@ -148,10 +148,10 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): """ - cdef Py_ssize_t minr = max(0, cy - yradius) - cdef Py_ssize_t maxr = math.ceil(cy + yradius) - cdef Py_ssize_t minc = max(0, cx - xradius) - cdef Py_ssize_t maxc = math.ceil(cx + xradius) + cdef Py_ssize_t minr = int(max(0, cy - yradius)) + cdef Py_ssize_t maxr = int(math.ceil(cy + yradius)) + cdef Py_ssize_t minc = int(max(0, cx - xradius)) + cdef Py_ssize_t maxc = int(math.ceil(cx + xradius)) # make sure output coordinates do not exceed image size if shape is not None: From 983cb76319856e4d7bb348c23e5bd9b26433eea7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 15:10:51 +0100 Subject: [PATCH 564/625] Remove print statement --- skimage/segmentation/tests/test_slic.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/segmentation/tests/test_slic.py b/skimage/segmentation/tests/test_slic.py index 59088fcd..89dee59b 100644 --- a/skimage/segmentation/tests/test_slic.py +++ b/skimage/segmentation/tests/test_slic.py @@ -13,8 +13,8 @@ def test_color(): img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, sigma=0, n_segments=4) - # we expect 4 segments: - print(seg) + + # we expect 4 segments assert_equal(len(np.unique(seg)), 4) assert_array_equal(seg[:10, :10], 0) assert_array_equal(seg[10:, :10], 2) @@ -31,7 +31,7 @@ def test_gray(): img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, sigma=0, n_segments=4, ratio=50.0) - print(seg) + assert_equal(len(np.unique(seg)), 4) assert_array_equal(seg[:10, :10], 0) assert_array_equal(seg[10:, :10], 2) From 7e578b5dec35ed52a33dcfed09d2ebc9df4e9b23 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 15:11:54 +0100 Subject: [PATCH 565/625] Fix index type error in SLIC function --- skimage/segmentation/_slic.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index 2fcee46b..e91ed21d 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -66,7 +66,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, cdef Py_ssize_t height, width height, width = image.shape[:2] # approximate grid size for desired n_segments - cdef Py_ssize_t step = np.ceil(np.sqrt(height * width / n_segments)) + cdef Py_ssize_t step = int(np.ceil(np.sqrt(height * width / n_segments))) grid_y, grid_x = np.mgrid[:height, :width] means_y = grid_y[::step, ::step] means_x = grid_x[::step, ::step] From cf09c582b6091b74dab414eb29e44678ca552660 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 15:18:22 +0100 Subject: [PATCH 566/625] Fix index type error in GLCM --- skimage/feature/_texture.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/feature/_texture.pyx b/skimage/feature/_texture.pyx index 2b9a9adc..174d5e38 100644 --- a/skimage/feature/_texture.pyx +++ b/skimage/feature/_texture.pyx @@ -52,8 +52,8 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, i = image[r, c] # compute the location of the offset pixel - row = r + (sin(angle) * distance + 0.5) - col = c + (cos(angle) * distance + 0.5); + row = r + (sin(angle) * distance + 0.5) + col = c + (cos(angle) * distance + 0.5) # make sure the offset is within bounds if row >= 0 and row < rows and \ From 3125e63fceda387b9938efdc1c1eda63fb09ca74 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 15:25:21 +0100 Subject: [PATCH 567/625] Improve performance and type usage for GLCM loop --- skimage/feature/_texture.pyx | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/skimage/feature/_texture.pyx b/skimage/feature/_texture.pyx index 174d5e38..ffff38d7 100644 --- a/skimage/feature/_texture.pyx +++ b/skimage/feature/_texture.pyx @@ -16,8 +16,7 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] angles, int levels, np.ndarray[dtype=np.uint32_t, ndim=4, - negative_indices=False, mode='c'] out - ): + negative_indices=False, mode='c'] out): """Perform co-occurrence matrix accumulation. Parameters @@ -37,16 +36,19 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, the results of the GLCM computation. """ + cdef: - np.int32_t a_inx, d_idx - Py_ssize_t r, c, rows, cols, row, col - np.int32_t i, j + Py_ssize_t a_idx, d_idx, r, c, rows, cols, row, col + np.uint8_t i, j + np.float64_t angle, distance rows = image.shape[0] cols = image.shape[1] - for a_idx, angle in enumerate(angles): - for d_idx, distance in enumerate(distances): + for a_idx in range(len(angles)): + angle = angles[a_idx] + for d_idx in range(len(distances)): + distance = distances[d_idx] for r in range(rows): for c in range(cols): i = image[r, c] From 52e3436f61c62b18c8f7e0fbfb8329bde302e9dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Fri, 15 Feb 2013 15:39:48 +0100 Subject: [PATCH 568/625] Fix integer type bugs in feature detection algorithms --- skimage/draw/_draw.pyx | 3 ++- skimage/feature/_daisy.py | 13 +++++++------ skimage/feature/_hog.py | 6 ++++-- skimage/feature/tests/test_hog.py | 2 +- 4 files changed, 14 insertions(+), 10 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index 4b7fb094..efd5fdc5 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -274,7 +274,8 @@ def circle_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t radius, return np.array(rr) + cy, np.array(cc) + cx -def ellipse_perimeter(int cy, int cx, int yradius, int xradius): +def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius, + Py_ssize_t xradius): """Generate ellipse perimeter coordinates. Parameters diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index f83605b5..1bb8cbf5 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -180,7 +180,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, color = (1, 0, 0) desc_y = i * step + radius desc_x = j * step + radius - coords = draw.circle_perimeter(desc_y, desc_x, sigmas[0]) + coords = draw.circle_perimeter(desc_y, desc_x, int(sigmas[0])) draw.set_color(descs_img, coords, color) max_bin = np.max(descs[i, j, :]) for o_num, o in enumerate(orientation_angles): @@ -188,8 +188,8 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, bin_size = descs[i, j, o_num] / max_bin dy = sigmas[0] * bin_size * sin(o) dx = sigmas[0] * bin_size * cos(o) - coords = draw.line(desc_y, desc_x, desc_y + dy, - desc_x + dx) + coords = draw.line(desc_y, desc_x, int(desc_y + dy), + int(desc_x + dx)) draw.set_color(descs_img, coords, color) for r_num, r in enumerate(ring_radii): color_offset = float(1 + r_num) / rings @@ -199,7 +199,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, hist_y = desc_y + int(round(r * sin(t))) hist_x = desc_x + int(round(r * cos(t))) coords = draw.circle_perimeter(hist_y, hist_x, - sigmas[r_num + 1]) + int(sigmas[r_num + 1])) draw.set_color(descs_img, coords, color) for o_num, o in enumerate(orientation_angles): # Draw histogram bins @@ -209,8 +209,9 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, bin_size /= max_bin dy = sigmas[r_num + 1] * bin_size * sin(o) dx = sigmas[r_num + 1] * bin_size * cos(o) - coords = draw.line(hist_y, hist_x, hist_y + dy, - hist_x + dx) + coords = draw.line(hist_y, hist_x, + int(hist_y + dy), + int(hist_x + dx)) draw.set_color(descs_img, coords, color) return descs, descs_img else: diff --git a/skimage/feature/_hog.py b/skimage/feature/_hog.py index 8e8ea7f2..9fa018b7 100644 --- a/skimage/feature/_hog.py +++ b/skimage/feature/_hog.py @@ -142,8 +142,10 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8), centre = tuple([y * cy + cy // 2, x * cx + cx // 2]) dx = radius * cos(float(o) / orientations * np.pi) dy = radius * sin(float(o) / orientations * np.pi) - rr, cc = draw.bresenham(centre[0] - dx, centre[1] - dy, - centre[0] + dx, centre[1] + dy) + rr, cc = draw.bresenham(int(centre[0] - dx), + int(centre[1] - dy), + int(centre[0] + dx), + int(centre[1] + dy)) hog_image[rr, cc] += orientation_histogram[y, x, o] """ diff --git a/skimage/feature/tests/test_hog.py b/skimage/feature/tests/test_hog.py index 6f2d4cdf..cfef2da0 100644 --- a/skimage/feature/tests/test_hog.py +++ b/skimage/feature/tests/test_hog.py @@ -102,7 +102,7 @@ def test_hog_orientations_circle(): width = height = 100 image = np.zeros((height, width)) - rr, cc = draw.circle(height/2, width/2, width/3) + rr, cc = draw.circle(int(height / 2), int(width / 2), int(width / 3)) image[rr, cc] = 100 image = ndimage.gaussian_filter(image, 2) From 572000f0e2908ba66d992fcdf96313caa9634827 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 16 Feb 2013 07:55:18 +0100 Subject: [PATCH 569/625] Improve code layout of probabilistic hough --- skimage/transform/_hough_transform.pyx | 76 +++++++++++++++----------- 1 file changed, 44 insertions(+), 32 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 01a45a48..f578b28c 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -1,4 +1,5 @@ cimport cython +import math import numpy as np cimport numpy as np from random import randint @@ -60,68 +61,77 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): accum[accum_idx, j] += 1 return accum, theta, bins -import math @cython.cdivision(True) @cython.boundscheck(False) -def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \ - int line_gap, np.ndarray[ndim=1, dtype=np.double_t] theta=None): +def _probabilistic_hough(np.ndarray img, int value_threshold, + int line_length, int line_gap, + np.ndarray[ndim=1, dtype=np.double_t] theta=None): + if img.ndim != 2: raise ValueError('The input image must be 2D.') - # compute the array of angles and their sine and cosine - cdef np.ndarray[ndim=1, dtype=np.double_t] ctheta - cdef np.ndarray[ndim=1, dtype=np.double_t] stheta - # calculate thetas if none specified + if theta is None: - theta = np.linspace(math.pi/2, -math.pi/2, 180) - theta = math.pi/2-np.arange(180)/180.0* math.pi - ctheta = np.cos(theta) - stheta = np.sin(theta) + theta = PI_2 - np.arange(180) / 180.0 * 2 * PI_2 + cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] + # compute the bins and allocate the accumulator array cdef np.ndarray[ndim=2, dtype=np.int64_t] accum - cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = np.zeros((height, width), dtype=np.uint8) - cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = np.zeros((2, 2), dtype=np.int32) + cdef np.ndarray[ndim=1, dtype=np.double_t] ctheta, stheta + cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = \ + np.zeros((height, width), dtype=np.uint8) + cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = \ + np.zeros((2, 2), dtype=np.int32) cdef Py_ssize_t max_distance, offset, num_indexes, index cdef double a, b - cdef Py_ssize_t nidxs, nthetas, i, j, x, y, px, py, accum_idx + cdef Py_ssize_t nidxs, i, j, x, y, px, py, accum_idx cdef int value, max_value, max_theta cdef int shift = 16 # maximum line number cutoff cdef Py_ssize_t lines_max = 2 ** 15 - cdef Py_ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count + cdef Py_ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, \ + good_line, count + cdef list lines = list() + max_distance = 2 * ceil((sqrt(img.shape[0] * img.shape[0] + img.shape[1] * img.shape[1]))) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64) offset = max_distance / 2 - # find the nonzero indexes - cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs - y_idxs, x_idxs = np.nonzero(img) - num_indexes = y_idxs.shape[0] # x and y are the same shape nthetas = theta.shape[0] - points = [] - for i in range(num_indexes): - points.append((x_idxs[i], y_idxs[i])) - lines = [] - # create mask of all non-zero indexes - for i in range(num_indexes): - mask[y_idxs[i], x_idxs[i]] = 1 + + # compute sine and cosine of angles + ctheta = np.cos(theta) + stheta = np.sin(theta) + + # find the nonzero indexes + y_idxs, x_idxs = np.nonzero(img) + points = zip(x_idxs, y_idxs) + # mask all non-zero indexes + mask[y_idxs, x_idxs] = 1 + while 1: - # select random non-zero point + + # quit if no remaining points count = len(points) if count == 0: break - index = rand() % (count) + + # select random non-zero point + index = rand() % count x = points[index][0] y = points[index][1] del points[index] + # if previously eliminated, skip if not mask[y, x]: continue + value = 0 - max_value = value_threshold-1 + max_value = value_threshold - 1 max_theta = -1 + # apply hough transform on point for j in range(nthetas): accum_idx = round((ctheta[j] * x + stheta[j] * y)) + offset @@ -132,7 +142,9 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \ max_theta = j if max_value < value_threshold: continue - # from the random point walk in opposite directions and find line beginning and end + + # from the random point walk in opposite directions and find line + # beginning and end a = -stheta[max_theta] b = ctheta[max_theta] x0 = x @@ -188,6 +200,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \ # confirm line length is sufficient good_line = abs(line_end[1, 1] - line_end[0, 1]) >= line_length or \ abs(line_end[1, 0] - line_end[0, 0]) >= line_length + # pass 2: walk the line again and reset accumulator and mask for k in range(2): px = x0 @@ -221,6 +234,5 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \ lines.append(((line_end[0, 0], line_end[0, 1]), (line_end[1, 0], line_end[1, 1]))) if len(lines) > lines_max: return lines + return lines - - From 2d9c211cf25556cc0cb75e1ea6eafc6f2830a86f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 16 Feb 2013 08:03:12 +0100 Subject: [PATCH 570/625] Remove unused CAPI numpy import and extend cython directives --- skimage/transform/_hough_transform.pyx | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index f578b28c..e63afa60 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -1,3 +1,7 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False cimport cython import math import numpy as np @@ -7,9 +11,6 @@ from libc.math cimport abs, fabs, sqrt, ceil, floor from libc.stdlib cimport rand -np.import_array() - - cdef double PI_2 = 1.5707963267948966 cdef double NEG_PI_2 = -PI_2 @@ -18,7 +19,6 @@ cdef inline Py_ssize_t round(double r): return ((r + 0.5) if (r > 0.0) else (r - 0.5)) -@cython.boundscheck(False) def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): if img.ndim != 2: @@ -62,8 +62,6 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): return accum, theta, bins -@cython.cdivision(True) -@cython.boundscheck(False) def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, int line_gap, np.ndarray[ndim=1, dtype=np.double_t] theta=None): From aedd12faebc6ca275fc40df419546e6afe054e93 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 16 Feb 2013 08:09:13 +0100 Subject: [PATCH 571/625] Remove unused imports --- skimage/transform/_hough_transform.pyx | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index e63afa60..f2a22d15 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -3,11 +3,9 @@ #cython: nonecheck=False #cython: wraparound=False cimport cython -import math import numpy as np cimport numpy as np -from random import randint -from libc.math cimport abs, fabs, sqrt, ceil, floor +from libc.math cimport abs, fabs, sqrt, ceil from libc.stdlib cimport rand @@ -40,7 +38,7 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): cdef Py_ssize_t max_distance, offset max_distance = 2 * ceil(sqrt(img.shape[0] * img.shape[0] + - img.shape[1] * img.shape[1])) + img.shape[1] * img.shape[1])) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64) bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance) offset = max_distance / 2 From 6cb47a6e3b4594273257261ce47ad844a68bdd2c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 16 Feb 2013 08:11:00 +0100 Subject: [PATCH 572/625] Make list of zip in Python 3 explicitely --- skimage/transform/_hough_transform.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index f2a22d15..d5483895 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -103,7 +103,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, # find the nonzero indexes y_idxs, x_idxs = np.nonzero(img) - points = zip(x_idxs, y_idxs) + points = list(zip(x_idxs, y_idxs)) # mask all non-zero indexes mask[y_idxs, x_idxs] = 1 From b716d8b1ed203700db9f8012ca6f1f5aca36345d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sat, 16 Feb 2013 08:22:39 +0100 Subject: [PATCH 573/625] Wrap lines --- skimage/transform/_hough_transform.pyx | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index d5483895..662b3b88 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -216,7 +216,8 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, # if non-zero point found, continue the line if mask[y1, x1]: if good_line: - accum_idx = round((ctheta[j] * x1 + stheta[j] * y1)) + offset + accum_idx = round((ctheta[j] * x1 \ + + stheta[j] * y1)) + offset accum[accum_idx, max_theta] -= 1 mask[y1, x1] = 0 # exit when the point is the line end @@ -227,7 +228,8 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, # add line to the result if good_line: - lines.append(((line_end[0, 0], line_end[0, 1]), (line_end[1, 0], line_end[1, 1]))) + lines.append(((line_end[0, 0], line_end[0, 1]), + (line_end[1, 0], line_end[1, 1]))) if len(lines) > lines_max: return lines From a9ec085cc41a95b36680f4a20a450fde18493f53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 24 Feb 2013 12:22:24 +0100 Subject: [PATCH 574/625] Add note about proper use of Py_ssize_t --- DEVELOPMENT.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/DEVELOPMENT.txt b/DEVELOPMENT.txt index 8726394b..daffe705 100644 --- a/DEVELOPMENT.txt +++ b/DEVELOPMENT.txt @@ -81,6 +81,9 @@ Stylistic Guidelines hough(canny(my_image)) + * Use `Py_ssize_t` as data type for all indexing, shape and size variables in + C/C++ and Cython code. + Test coverage ------------- From 62d83ad42cf30855ecd969104e0c0285018c09eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 24 Feb 2013 14:14:14 +0100 Subject: [PATCH 575/625] Globally change np to cnp if cimported --- skimage/draw/_draw.pyx | 14 +- skimage/feature/_template.pyx | 26 +- skimage/feature/_texture.pyx | 36 +- skimage/filter/_ctmf.pyx | 77 ++-- skimage/filter/rank/_core16.pxd | 19 +- skimage/filter/rank/_core16.pyx | 27 +- skimage/filter/rank/_core8.pxd | 27 +- skimage/filter/rank/_core8.pyx | 35 +- skimage/filter/rank/_crank16.pyx | 348 ++++++++++--------- skimage/filter/rank/_crank16_bilateral.pyx | 52 +-- skimage/filter/rank/_crank16_percentiles.pyx | 209 +++++------ skimage/filter/rank/_crank8.pyx | 316 ++++++++--------- skimage/filter/rank/_crank8_percentiles.pyx | 170 ++++----- skimage/graph/_mcp.pxd | 8 +- skimage/graph/_mcp.pyx | 48 +-- skimage/io/_plugins/_colormixer.pyx | 149 ++++---- skimage/io/_plugins/_histograms.pyx | 15 +- skimage/measure/_find_contours.pyx | 13 +- skimage/measure/_moments.pyx | 24 +- skimage/morphology/_convex_hull.pyx | 14 +- skimage/morphology/_pnpoly.pyx | 21 +- skimage/morphology/_skeletonize_cy.pyx | 51 +-- skimage/morphology/ccomp.pxd | 6 +- skimage/morphology/ccomp.pyx | 13 +- skimage/morphology/heap_watershed.pxi | 8 +- skimage/segmentation/_felzenszwalb_cy.pyx | 30 +- skimage/segmentation/_quickshift.pyx | 28 +- skimage/segmentation/_slic.pyx | 29 +- skimage/transform/_hough_transform.pyx | 28 +- skimage/transform/_warps_cy.pyx | 12 +- 30 files changed, 953 insertions(+), 900 deletions(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index efd5fdc5..2b319094 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -2,11 +2,11 @@ #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False -cimport cython -cimport numpy as np -from libc.math cimport sqrt import math import numpy as np + +cimport numpy as cnp +from libc.math cimport sqrt from skimage._shared.geometry cimport point_in_polygon @@ -29,7 +29,7 @@ def line(Py_ssize_t y, Py_ssize_t x, Py_ssize_t y2, Py_ssize_t x2): """ - cdef np.ndarray[np.intp_t, ndim=1, mode="c"] rr, cc + cdef cnp.ndarray[cnp.intp_t, ndim=1, mode="c"] rr, cc cdef char steep = 0 cdef Py_ssize_t dx = abs(x2 - x) @@ -110,11 +110,11 @@ def polygon(y, x, shape=None): cdef Py_ssize_t r, c #: make contigous arrays for r, c coordinates - cdef np.ndarray contiguous_rdata, contiguous_cdata + cdef cnp.ndarray contiguous_rdata, contiguous_cdata contiguous_rdata = np.ascontiguousarray(y, 'double') contiguous_cdata = np.ascontiguousarray(x, 'double') - cdef np.double_t* rptr = contiguous_rdata.data - cdef np.double_t* cptr = contiguous_cdata.data + cdef cnp.double_t* rptr = contiguous_rdata.data + cdef cnp.double_t* cptr = contiguous_cdata.data #: output coordinate arrays cdef list rr = list() diff --git a/skimage/feature/_template.pyx b/skimage/feature/_template.pyx index 842e1eda..03695959 100644 --- a/skimage/feature/_template.pyx +++ b/skimage/feature/_template.pyx @@ -1,3 +1,8 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + """ Template matching using normalized cross-correlation. @@ -30,22 +35,24 @@ the image window *before* squaring.) .. [2] J. P. Lewis, "Fast Normalized Cross-Correlation", Industrial Light and Magic. """ -import cython -cimport numpy as np + import numpy as np from scipy.signal import fftconvolve -from skimage.transform import integral + +cimport numpy as cnp from libc.math cimport sqrt, fabs from skimage._shared.transform cimport integrate -@cython.boundscheck(False) -def match_template(np.ndarray[float, ndim=2, mode="c"] image, - np.ndarray[float, ndim=2, mode="c"] template): +from skimage.transform import integral - cdef np.ndarray[float, ndim=2, mode="c"] corr - cdef np.ndarray[float, ndim=2, mode="c"] image_sat - cdef np.ndarray[float, ndim=2, mode="c"] image_sqr_sat + +def match_template(cnp.ndarray[float, ndim=2, mode="c"] image, + cnp.ndarray[float, ndim=2, mode="c"] template): + + cdef cnp.ndarray[float, ndim=2, mode="c"] corr + cdef cnp.ndarray[float, ndim=2, mode="c"] image_sat + cdef cnp.ndarray[float, ndim=2, mode="c"] image_sqr_sat cdef float template_mean = np.mean(template) cdef float template_ssd cdef float inv_area @@ -88,4 +95,3 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image, corr[r, c] /= den return corr - diff --git a/skimage/feature/_texture.pyx b/skimage/feature/_texture.pyx index ffff38d7..f98ed4ca 100644 --- a/skimage/feature/_texture.pyx +++ b/skimage/feature/_texture.pyx @@ -3,20 +3,20 @@ #cython: nonecheck=False #cython: wraparound=False import numpy as np -cimport numpy as np +cimport numpy as cnp from libc.math cimport sin, cos, abs from skimage._shared.interpolation cimport bilinear_interpolation -def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, - negative_indices=False, mode='c'] image, - np.ndarray[dtype=np.float64_t, ndim=1, - negative_indices=False, mode='c'] distances, - np.ndarray[dtype=np.float64_t, ndim=1, - negative_indices=False, mode='c'] angles, +def _glcm_loop(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, + negative_indices=False, mode='c'] image, + cnp.ndarray[dtype=cnp.float64_t, ndim=1, + negative_indices=False, mode='c'] distances, + cnp.ndarray[dtype=cnp.float64_t, ndim=1, + negative_indices=False, mode='c'] angles, int levels, - np.ndarray[dtype=np.uint32_t, ndim=4, - negative_indices=False, mode='c'] out): + cnp.ndarray[dtype=cnp.uint32_t, ndim=4, + negative_indices=False, mode='c'] out): """Perform co-occurrence matrix accumulation. Parameters @@ -39,8 +39,8 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2, cdef: Py_ssize_t a_idx, d_idx, r, c, rows, cols, row, col - np.uint8_t i, j - np.float64_t angle, distance + cnp.uint8_t i, j + cnp.float64_t angle, distance rows = image.shape[0] cols = image.shape[1] @@ -81,7 +81,7 @@ cdef inline int _bit_rotate_right(int value, int length): return (value >> 1) | ((value & 1) << (length - 1)) -def _local_binary_pattern(np.ndarray[double, ndim=2] image, +def _local_binary_pattern(cnp.ndarray[double, ndim=2] image, int P, float R, char method='D'): """Gray scale and rotation invariant LBP (Local Binary Patterns). @@ -111,19 +111,19 @@ def _local_binary_pattern(np.ndarray[double, ndim=2] image, """ # texture weights - cdef np.ndarray[int, ndim=1] weights = 2 ** np.arange(P, dtype=np.int32) + cdef cnp.ndarray[int, ndim=1] weights = 2 ** np.arange(P, dtype=np.int32) # local position of texture elements rp = - R * np.sin(2 * np.pi * np.arange(P, dtype=np.double) / P) cp = R * np.cos(2 * np.pi * np.arange(P, dtype=np.double) / P) - cdef np.ndarray[double, ndim=2] coords = np.round(np.vstack([rp, cp]).T, 5) + cdef cnp.ndarray[double, ndim=2] coords = np.round(np.vstack([rp, cp]).T, 5) # pre allocate arrays for computation - cdef np.ndarray[double, ndim=1] texture = np.zeros(P, np.double) - cdef np.ndarray[char, ndim=1] signed_texture = np.zeros(P, np.int8) - cdef np.ndarray[int, ndim=1] rotation_chain = np.zeros(P, np.int32) + cdef cnp.ndarray[double, ndim=1] texture = np.zeros(P, np.double) + cdef cnp.ndarray[char, ndim=1] signed_texture = np.zeros(P, np.int8) + cdef cnp.ndarray[int, ndim=1] rotation_chain = np.zeros(P, np.int32) output_shape = (image.shape[0], image.shape[1]) - cdef np.ndarray[double, ndim=2] output = np.zeros(output_shape, np.double) + cdef cnp.ndarray[double, ndim=2] output = np.zeros(output_shape, np.double) cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] diff --git a/skimage/filter/_ctmf.pyx b/skimage/filter/_ctmf.pyx index 65feb9e4..1a03ff5a 100644 --- a/skimage/filter/_ctmf.pyx +++ b/skimage/filter/_ctmf.pyx @@ -10,18 +10,20 @@ Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky ''' + import numpy as np -cimport numpy as np + +cimport numpy as cnp cimport cython from libc.stdlib cimport malloc, free from libc.string cimport memset -cdef extern from "../_shared/vectorized_ops.h": - void add16(np.uint16_t *dest, np.uint16_t *src) - void sub16(np.uint16_t *dest, np.uint16_t *src) -np.import_array() +cdef extern from "../_shared/vectorized_ops.h": + void add16(cnp.uint16_t *dest, cnp.uint16_t *src) + void sub16(cnp.uint16_t *dest, cnp.uint16_t *src) + ############################################################################## # @@ -43,7 +45,7 @@ np.import_array() DTYPE_UINT32 = np.uint32 DTYPE_BOOL = np.bool -ctypedef np.uint16_t pixel_count_t +ctypedef cnp.uint16_t pixel_count_t ########### # @@ -58,8 +60,8 @@ ctypedef np.uint16_t pixel_count_t ########### cdef struct HistogramPiece: - np.uint16_t coarse[16] - np.uint16_t fine[256] + cnp.uint16_t coarse[16] + cnp.uint16_t fine[256] cdef struct Histogram: HistogramPiece top_left # top-left corner @@ -92,9 +94,9 @@ cdef struct Histograms: void *memory # pointer to the allocated memory Histogram *histogram # pointer to the histogram memory PixelCount *pixel_count # pointer to the pixel count memory - np.uint8_t *data # pointer to the image data - np.uint8_t *mask # pointer to the image mask - np.uint8_t *output # pointer to the output array + cnp.uint8_t *data # pointer to the image data + cnp.uint8_t *mask # pointer to the image mask + cnp.uint8_t *output # pointer to the output array Py_ssize_t column_count # number of columns represented by this # structure Py_ssize_t stripe_length # number of columns including "radius" before @@ -172,15 +174,15 @@ cdef Histograms *allocate_histograms(Py_ssize_t rows, Py_ssize_t col_stride, Py_ssize_t radius, Py_ssize_t percent, - np.uint8_t *data, - np.uint8_t *mask, - np.uint8_t *output): + cnp.uint8_t *data, + cnp.uint8_t *mask, + cnp.uint8_t *output): cdef: Py_ssize_t adjusted_stripe_length = columns + 2*radius + 1 Py_ssize_t memory_size void *ptr Histograms *ph - size_t roundoff + Py_ssize_t roundoff Py_ssize_t a SCoord *psc @@ -232,7 +234,7 @@ cdef Histograms *allocate_histograms(Py_ssize_t rows, # a_2 is the offset from the center to each of the octagon # corners # - a = (radius * 2.0 / 2.414213) + a = (radius * 2.0 / 2.414213) a_2 = a / 2 if a_2 == 0: a_2 = 1 @@ -456,7 +458,7 @@ cdef inline void deaccumulate_fine_histogram(Histograms *ph, ############################################################################ cdef inline void accumulate(Histograms *ph): - cdef np.int32_t accumulator + cdef cnp.int32_t accumulator accumulate_coarse_histogram(ph, ph.current_column) deaccumulate_coarse_histogram(ph, ph.current_column) @@ -514,7 +516,7 @@ cdef inline void update_histogram(Histograms *ph, Py_ssize_t current_stride = ph.current_stride Py_ssize_t column_count = ph.column_count Py_ssize_t row_count = ph.row_count - np.uint8_t value + cnp.uint8_t value Py_ssize_t stride Py_ssize_t x Py_ssize_t y @@ -557,8 +559,8 @@ cdef inline void update_current_location(Histograms *ph): Py_ssize_t bottom_left_off = tr_bl_colidx(ph, current_column) Py_ssize_t bottom_right_off = tl_br_colidx(ph, current_column) Py_ssize_t leading_edge_off = leading_edge_colidx(ph, current_column) - np.int32_t *coarse_histogram - np.int32_t *fine_histogram + cnp.int32_t *coarse_histogram + cnp.int32_t *fine_histogram Py_ssize_t last_xoff Py_ssize_t last_yoff Py_ssize_t last_stride @@ -597,13 +599,13 @@ cdef inline void update_current_location(Histograms *ph): # ############################################################################ -cdef inline np.uint8_t find_median(Histograms *ph): +cdef inline cnp.uint8_t find_median(Histograms *ph): cdef: Py_ssize_t pixels_below # of pixels below the median Py_ssize_t i Py_ssize_t j Py_ssize_t k - np.uint32_t accumulator + cnp.uint32_t accumulator if ph.accumulator_count == 0: return 0 @@ -625,7 +627,7 @@ cdef inline np.uint8_t find_median(Histograms *ph): for j in range(i*16, (i + 1)*16): accumulator += ph.accumulator.fine[j] if accumulator > pixels_below: - return j + return j return 0 @@ -650,9 +652,9 @@ cdef int c_median_filter(Py_ssize_t rows, Py_ssize_t col_stride, Py_ssize_t radius, Py_ssize_t percent, - np.uint8_t *data, - np.uint8_t *mask, - np.uint8_t *output): + cnp.uint8_t *data, + cnp.uint8_t *mask, + cnp.uint8_t *output): cdef: Histograms *ph Histogram *phistogram @@ -731,11 +733,14 @@ cdef int c_median_filter(Py_ssize_t rows, return 0 -def median_filter(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] data, - np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] mask, - np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, mode='c'] output, +def median_filter(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, + negative_indices=False, mode='c'] data, + cnp.ndarray[dtype=cnp.uint8_t, ndim=2, + negative_indices=False, mode='c'] mask, + cnp.ndarray[dtype=cnp.uint8_t, ndim=2, + negative_indices=False, mode='c'] output, int radius, - np.int32_t percent): + cnp.int32_t percent): """Median filter with octagon shape and masking. Parameters @@ -770,10 +775,12 @@ def median_filter(np.ndarray[dtype=np.uint8_t, ndim=2, negative_indices=False, m raise ValueError('Data shape (%d, %d) is not output shape (%d, %d)' % (data.shape[0], data.shape[1], output.shape[0], output.shape[1])) - if c_median_filter( data.shape[0], data.shape[1], - data.strides[0], data.strides[1], + if c_median_filter(data.shape[0], + data.shape[1], + data.strides[0], + data.strides[1], radius, percent, - data.data, - mask.data, - output.data): + data.data, + mask.data, + output.data): raise MemoryError('Failed to allocate scratchpad memory') diff --git a/skimage/filter/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd index 5f7de9df..5586aea1 100644 --- a/skimage/filter/rank/_core16.pxd +++ b/skimage/filter/rank/_core16.pxd @@ -1,4 +1,7 @@ -cimport numpy as np +cimport numpy as cnp + + +ctypedef cnp.uint16_t dtype_t cdef int int_max(int a, int b) @@ -6,12 +9,12 @@ cdef int int_min(int a, int b) # 16-bit core kernel receives extra information about data bitdepth -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, - Py_ssize_t, Py_ssize_t, Py_ssize_t, float, - float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint16_t, ndim=2] out, +cdef void _core16(dtype_t kernel(Py_ssize_t *, float, dtype_t, + Py_ssize_t, Py_ssize_t, Py_ssize_t, float, + float, Py_ssize_t, Py_ssize_t), + cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask, + cnp.ndarray[dtype_t, ndim=2] out, char shift_x, char shift_y, Py_ssize_t bitdepth, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except * diff --git a/skimage/filter/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx index aa959fd0..36bc500d 100644 --- a/skimage/filter/rank/_core16.pyx +++ b/skimage/filter/rank/_core16.pyx @@ -4,7 +4,8 @@ #cython: wraparound=False import numpy as np -cimport numpy as np + +cimport numpy as cnp from libc.stdlib cimport malloc, free from _core8 cimport is_in_mask @@ -18,24 +19,24 @@ cdef inline int int_min(int a, int b): cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, - np.uint16_t value): + dtype_t value): histo[value] += 1 pop[0] += 1 cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, - np.uint16_t value): + dtype_t value): histo[value] -= 1 pop[0] -= 1 -cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, - Py_ssize_t, Py_ssize_t, Py_ssize_t, float, - float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint16_t, ndim=2] out, +cdef void _core16(dtype_t kernel(Py_ssize_t *, float, dtype_t, + Py_ssize_t, Py_ssize_t, Py_ssize_t, float, + float, Py_ssize_t, Py_ssize_t), + cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask, + cnp.ndarray[dtype_t, ndim=2] out, char shift_x, char shift_y, Py_ssize_t bitdepth, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and @@ -67,9 +68,9 @@ cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t, assert (image < maxbin).all() # define pointers to the data - cdef np.uint16_t * out_data = out.data - cdef np.uint16_t * image_data = image.data - cdef np.uint8_t * mask_data = mask.data + cdef dtype_t * out_data = out.data + cdef dtype_t * image_data = image.data + cdef cnp.uint8_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row diff --git a/skimage/filter/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd index 38236b87..d3b6d8c2 100644 --- a/skimage/filter/rank/_core8.pxd +++ b/skimage/filter/rank/_core8.pxd @@ -1,22 +1,25 @@ -cimport numpy as np +cimport numpy as cnp -cdef np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b) -cdef np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b) +ctypedef cnp.uint8_t dtype_t -cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t r, Py_ssize_t c, - np.uint8_t * mask) +cdef dtype_t uint8_max(dtype_t a, dtype_t b) +cdef dtype_t uint8_min(dtype_t a, dtype_t b) + + +cdef dtype_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, + dtype_t * mask) # 8-bit core kernel receives extra information about data inferior and superior # percentiles -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, - float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint8_t, ndim=2] out, +cdef void _core8(dtype_t kernel(Py_ssize_t *, float, dtype_t, float, + float, Py_ssize_t, Py_ssize_t), + cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask, + cnp.ndarray[dtype_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except * diff --git a/skimage/filter/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx index 9f4bc693..eca47891 100644 --- a/skimage/filter/rank/_core8.pyx +++ b/skimage/filter/rank/_core8.pyx @@ -4,33 +4,34 @@ #cython: wraparound=False import numpy as np -cimport numpy as np + +cimport numpy as cnp from libc.stdlib cimport malloc, free -cdef inline np.uint8_t uint8_max(np.uint8_t a, np.uint8_t b): +cdef inline dtype_t uint8_max(dtype_t a, dtype_t b): return a if a >= b else b -cdef inline np.uint8_t uint8_min(np.uint8_t a, np.uint8_t b): +cdef inline dtype_t uint8_min(dtype_t a, dtype_t b): return a if a <= b else b cdef inline void histogram_increment(Py_ssize_t * histo, float * pop, - np.uint8_t value): + dtype_t value): histo[value] += 1 pop[0] += 1 cdef inline void histogram_decrement(Py_ssize_t * histo, float * pop, - np.uint8_t value): + dtype_t value): histo[value] -= 1 pop[0] -= 1 -cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, - Py_ssize_t r, Py_ssize_t c, - np.uint8_t * mask): +cdef inline dtype_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, + Py_ssize_t r, Py_ssize_t c, + dtype_t * mask): """Check whether given coordinate is within image and mask is true.""" if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 @@ -41,12 +42,12 @@ cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols, return 0 -cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, - float, Py_ssize_t, Py_ssize_t), - np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask, - np.ndarray[np.uint8_t, ndim=2] out, +cdef void _core8(dtype_t kernel(Py_ssize_t *, float, dtype_t, float, + float, Py_ssize_t, Py_ssize_t), + cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask, + cnp.ndarray[dtype_t, ndim=2] out, char shift_x, char shift_y, float p0, float p1, Py_ssize_t s0, Py_ssize_t s1) except *: """Compute histogram for each pixel neighborhood, apply kernel function and @@ -69,9 +70,9 @@ cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float, # define pointers to the data - cdef np.uint8_t * out_data = out.data - cdef np.uint8_t * image_data = image.data - cdef np.uint8_t * mask_data = mask.data + cdef dtype_t * out_data = out.data + cdef dtype_t * image_data = image.data + cdef dtype_t * mask_data = mask.data # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index e81bf81f..87199e80 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -3,8 +3,7 @@ #cython: nonecheck=False #cython: wraparound=False -import numpy as np -cimport numpy as np +cimport numpy as cnp from libc.math cimport log2 from skimage.filter.rank._core16 cimport _core16 @@ -14,11 +13,14 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +ctypedef cnp.uint16_t dtype_t + + +cdef inline dtype_t kernel_autolevel(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax, delta if pop: @@ -32,16 +34,16 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, break delta = imax - imin if delta > 0: - return < np.uint16_t > (1. * (maxbin - 1) * (g - imin) / delta) + return (1. * (maxbin - 1) * (g - imin) / delta) else: - return < np.uint16_t > (imax - imin) + return (imax - imin) -cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_bottomhat(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -49,15 +51,15 @@ cdef inline np.uint16_t kernel_bottomhat(Py_ssize_t * histo, float pop, if histo[i]: break - return < np.uint16_t > (g - i) + return (g - i) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_equalize(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -67,16 +69,16 @@ cdef inline np.uint16_t kernel_equalize(Py_ssize_t * histo, float pop, if i >= g: break - return < np.uint16_t > (((maxbin - 1) * sum) / pop) + return (((maxbin - 1) * sum) / pop) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_gradient(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax if pop: @@ -88,66 +90,66 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, if histo[i]: imin = i break - return < np.uint16_t > (imax - imin) + return (imax - imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_maximum(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_maximum(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(maxbin - 1, -1, -1): if histo[i]: - return < np.uint16_t > (i) + return (i) - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): mean += histo[i] * i - return < np.uint16_t > (mean / pop) + return (mean / pop) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_meansubstraction(Py_ssize_t * histo, - float pop, - np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_meansubstraction(Py_ssize_t * histo, + float pop, + dtype_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): mean += histo[i] * i - return < np.uint16_t > ((g - mean / pop) / 2. + (midbin - 1)) + return ((g - mean / pop) / 2. + (midbin - 1)) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_median(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop / 2.0 @@ -156,31 +158,31 @@ cdef inline np.uint16_t kernel_median(Py_ssize_t * histo, float pop, if histo[i]: sum -= histo[i] if sum < 0: - return < np.uint16_t > (i) + return (i) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_minimum(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_minimum(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(maxbin): if histo[i]: - return < np.uint16_t > (i) + return (i) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_modal(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax = 0, imax = 0 if pop: @@ -188,19 +190,19 @@ cdef inline np.uint16_t kernel_modal(Py_ssize_t * histo, float pop, if histo[i] > hmax: hmax = histo[i] imax = i - return < np.uint16_t > (imax) + return (imax) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, - float pop, - np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_morph_contr_enh(Py_ssize_t * histo, + float pop, + dtype_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax if pop: @@ -213,42 +215,42 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, imin = i break if imax - g < g - imin: - return < np.uint16_t > (imax) + return (imax) else: - return < np.uint16_t > (imin) + return (imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): - return < np.uint16_t > (pop) +cdef inline dtype_t kernel_pop(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): + return (pop) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_threshold(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. if pop: for i in range(maxbin): mean += histo[i] * i - return < np.uint16_t > (g > (mean / pop)) + return (g > (mean / pop)) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_tophat(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: @@ -256,15 +258,15 @@ cdef inline np.uint16_t kernel_tophat(Py_ssize_t * histo, float pop, if histo[i]: break - return < np.uint16_t > (i - g) + return (i - g) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_entropy(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float e, p @@ -276,145 +278,145 @@ cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, if p > 0: e -= p * log2(p) - return < np.uint16_t > e * 1000 + return e * 1000 else: - return < np.uint16_t > (0) + return (0) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- -def autolevel(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def autolevel(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def bottomhat(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def equalize(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def equalize(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_equalize, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def gradient(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def gradient(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_gradient, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def maximum(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def maximum(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_maximum, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def mean(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_mean, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def meansubstraction(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def median(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def median(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_median, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def minimum(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def minimum(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_minimum, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def morph_contr_enh(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def modal(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def modal(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_modal, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def pop(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_pop, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def threshold(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def threshold(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_threshold, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def tophat(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def tophat(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_tophat, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) -def entropy(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def entropy(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, Py_ssize_t bitdepth=8): _core16(kernel_entropy, image, selem, mask, out, shift_x, shift_y, bitdepth, 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx index c71ccc5c..e431e42b 100644 --- a/skimage/filter/rank/_crank16_bilateral.pyx +++ b/skimage/filter/rank/_crank16_bilateral.pyx @@ -3,8 +3,7 @@ #cython: nonecheck=False #cython: wraparound=False -import numpy as np -cimport numpy as np +cimport numpy as cnp from skimage.filter.rank._core16 cimport _core16 @@ -13,11 +12,14 @@ from skimage.filter.rank._core16 cimport _core16 # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +ctypedef cnp.uint16_t dtype_t + + +cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, bilat_pop = 0 cdef float mean = 0. @@ -28,18 +30,18 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, bilat_pop += histo[i] mean += histo[i] * i if bilat_pop: - return < np.uint16_t > (mean / bilat_pop) + return (mean / bilat_pop) else: - return < np.uint16_t > (0) + return (0) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_pop(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, bilat_pop = 0 @@ -47,9 +49,9 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, for i in range(maxbin): if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] - return < np.uint16_t > (bilat_pop) + return (bilat_pop) else: - return < np.uint16_t > (0) + return (0) # ----------------------------------------------------------------- @@ -57,10 +59,10 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, # ----------------------------------------------------------------- -def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def mean(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """average greylevel (clipped on uint8) """ @@ -68,10 +70,10 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, 0., 0., s0, s1) -def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def pop(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): """returns the number of actual pixels of the structuring element inside the mask diff --git a/skimage/filter/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx index 220d2386..0ab71353 100644 --- a/skimage/filter/rank/_crank16_percentiles.pyx +++ b/skimage/filter/rank/_crank16_percentiles.pyx @@ -3,8 +3,7 @@ #cython: nonecheck=False #cython: wraparound=False -import numpy as np -cimport numpy as np +cimport numpy as cnp from skimage.filter.rank._core16 cimport _core16, int_min, int_max @@ -13,11 +12,14 @@ from skimage.filter.rank._core16 cimport _core16, int_min, int_max # ----------------------------------------------------------------- -cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +ctypedef cnp.uint16_t dtype_t + + +cdef inline dtype_t kernel_autolevel(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -38,19 +40,20 @@ cdef inline np.uint16_t kernel_autolevel(Py_ssize_t * histo, float pop, delta = imax - imin if delta > 0: - return < np.uint16_t > (1.0 * (maxbin - 1) - * (int_min(int_max(imin, g), imax) - imin) / delta) + return (1.0 * (maxbin - 1) + * (int_min(int_max(imin, g), imax) + - imin) / delta) else: - return < np.uint16_t > (imax - imin) + return (imax - imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_gradient(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -69,16 +72,16 @@ cdef inline np.uint16_t kernel_gradient(Py_ssize_t * histo, float pop, imax = i break - return < np.uint16_t > (imax - imin) + return (imax - imin) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -93,21 +96,21 @@ cdef inline np.uint16_t kernel_mean(Py_ssize_t * histo, float pop, mean += histo[i] * i if n > 0: - return < np.uint16_t > (1.0 * mean / n) + return (1.0 * mean / n) else: - return < np.uint16_t > (0) + return (0) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, - float pop, - np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_mean_substraction(Py_ssize_t * histo, + float pop, + dtype_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n @@ -121,21 +124,21 @@ cdef inline np.uint16_t kernel_mean_substraction(Py_ssize_t * histo, n += histo[i] mean += histo[i] * i if n > 0: - return < np.uint16_t > ((g - (mean / n)) * .5 + midbin) + return ((g - (mean / n)) * .5 + midbin) else: - return < np.uint16_t > (0) + return (0) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, - float pop, - np.uint16_t g, - Py_ssize_t bitdepth, - Py_ssize_t maxbin, - Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_morph_contr_enh(Py_ssize_t * histo, + float pop, + dtype_t g, + Py_ssize_t bitdepth, + Py_ssize_t maxbin, + Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta @@ -154,22 +157,22 @@ cdef inline np.uint16_t kernel_morph_contr_enh(Py_ssize_t * histo, imax = i break if g > imax: - return < np.uint16_t > imax + return imax if g < imin: - return < np.uint16_t > imin + return imin if imax - g < g - imin: - return < np.uint16_t > imax + return imax else: - return < np.uint16_t > imin + return imin else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_percentile(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -180,16 +183,16 @@ cdef inline np.uint16_t kernel_percentile(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return < np.uint16_t > (i) + return (i) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_pop(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n @@ -200,16 +203,16 @@ cdef inline np.uint16_t kernel_pop(Py_ssize_t * histo, float pop, sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return < np.uint16_t > (n) + return (n) else: - return < np.uint16_t > (0) + return (0) -cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, - np.uint16_t g, Py_ssize_t bitdepth, - Py_ssize_t maxbin, Py_ssize_t midbin, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_threshold(Py_ssize_t * histo, float pop, + dtype_t g, Py_ssize_t bitdepth, + Py_ssize_t maxbin, Py_ssize_t midbin, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -220,9 +223,9 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return < np.uint16_t > ((maxbin - 1) * (g >= i)) + return ((maxbin - 1) * (g >= i)) else: - return < np.uint16_t > (0) + return (0) # ----------------------------------------------------------------- @@ -230,10 +233,10 @@ cdef inline np.uint16_t kernel_threshold(Py_ssize_t * histo, float pop, # ----------------------------------------------------------------- -def autolevel(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def autolevel(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """bottom hat @@ -242,10 +245,10 @@ def autolevel(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def gradient(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def gradient(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0,p1 percentile gradient @@ -254,10 +257,10 @@ def gradient(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def mean(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def mean(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles @@ -266,10 +269,10 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def mean_substraction(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 @@ -279,10 +282,10 @@ def mean_substraction(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def morph_contr_enh(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """reforce contrast using percentiles @@ -291,10 +294,10 @@ def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def percentile(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def percentile(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return p0 percentile @@ -303,10 +306,10 @@ def percentile(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def pop(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def pop(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] @@ -315,10 +318,10 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, bitdepth, p0, p1, 0, 0) -def threshold(np.ndarray[np.uint16_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint16_t, ndim=2] out=None, +def threshold(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[cnp.uint8_t, ndim=2] selem, + cnp.ndarray[cnp.uint8_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, int bitdepth=8, float p0=0., float p1=0.): """return (maxbin-1) if g > percentile p0 diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index 8bff9703..c9e8b14f 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -3,8 +3,7 @@ #cython: nonecheck=False #cython: wraparound=False -import numpy as np -cimport numpy as np +cimport numpy as cnp from libc.math cimport log2 from skimage.filter.rank._core8 cimport _core8 @@ -14,9 +13,12 @@ from skimage.filter.rank._core8 cimport _core8 # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +ctypedef cnp.uint8_t dtype_t + + +cdef inline dtype_t kernel_autolevel(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax, delta @@ -31,16 +33,16 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, break delta = imax - imin if delta > 0: - return < np.uint8_t > (255. * (g - imin) / delta) + return (255. * (g - imin) / delta) else: - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_bottomhat(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -49,14 +51,14 @@ cdef inline np.uint8_t kernel_bottomhat(Py_ssize_t * histo, float pop, if histo[i]: break - return < np.uint8_t > (g - i) + return (g - i) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_equalize(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = 0. @@ -67,14 +69,14 @@ cdef inline np.uint8_t kernel_equalize(Py_ssize_t * histo, float pop, if i >= g: break - return < np.uint8_t > ((255 * sum) / pop) + return ((255 * sum) / pop) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_gradient(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax @@ -87,28 +89,28 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, if histo[i]: imin = i break - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_maximum(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_maximum(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(255, -1, -1): if histo[i]: - return < np.uint8_t > (i) + return (i) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -116,14 +118,14 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, if pop: for i in range(256): mean += histo[i] * i - return < np.uint8_t > (mean / pop) + return (mean / pop) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_meansubstraction(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -131,14 +133,14 @@ cdef inline np.uint8_t kernel_meansubstraction(Py_ssize_t * histo, float pop, if pop: for i in range(256): mean += histo[i] * i - return < np.uint8_t > ((g - mean / pop) / 2. + 127) + return ((g - mean / pop) / 2. + 127) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_median(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float sum = pop / 2.0 @@ -148,28 +150,28 @@ cdef inline np.uint8_t kernel_median(Py_ssize_t * histo, float pop, if histo[i]: sum -= histo[i] if sum < 0: - return < np.uint8_t > (i) + return (i) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_minimum(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_minimum(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i if pop: for i in range(256): if histo[i]: - return < np.uint8_t > (i) + return (i) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_modal(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t hmax = 0, imax = 0 @@ -178,14 +180,14 @@ cdef inline np.uint8_t kernel_modal(Py_ssize_t * histo, float pop, if histo[i] > hmax: hmax = histo[i] imax = i - return < np.uint8_t > (imax) + return (imax) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i, imin, imax @@ -199,23 +201,23 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, float pop, imin = i break if imax - g < g - imin: - return < np.uint8_t > (imax) + return (imax) else: - return < np.uint8_t > (imin) + return (imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_pop(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): - return < np.uint8_t > (pop) + return (pop) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_threshold(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float mean = 0. @@ -223,14 +225,14 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, if pop: for i in range(256): mean += histo[i] * i - return < np.uint8_t > (g > (mean / pop)) + return (g > (mean / pop)) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_tophat(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i @@ -239,20 +241,20 @@ cdef inline np.uint8_t kernel_tophat(Py_ssize_t * histo, float pop, if histo[i]: break - return < np.uint8_t > (i - g) + return (i - g) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_noise_filter(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef Py_ssize_t min_i # early stop if at least one pixel of the neighborhood has the same g if histo[g] > 0: - return < np.uint8_t > 0 + return 0 for i in range(g, -1, -1): if histo[i]: @@ -262,14 +264,14 @@ cdef inline np.uint8_t kernel_noise_filter(Py_ssize_t * histo, float pop, if histo[i]: break if i - g < min_i: - return < np.uint8_t > (i - g) + return (i - g) else: - return < np.uint8_t > min_i + return min_i -cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_entropy(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef Py_ssize_t i cdef float e, p @@ -281,13 +283,13 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, if p > 0: e -= p * log2(p) - return < np.uint8_t > e * 10 + return e * 10 else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, - float p0, float p1, Py_ssize_t s0, - Py_ssize_t s1): +cdef inline dtype_t kernel_otsu(Py_ssize_t * histo, float pop, dtype_t g, + float p0, float p1, Py_ssize_t s0, + Py_ssize_t s1): cdef Py_ssize_t i cdef Py_ssize_t max_i cdef float P, mu1, mu2, q1, new_q1, sigma_b, max_sigma_b @@ -299,7 +301,7 @@ cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, mu += histo[i] * i mu = (mu / pop) else: - return < np.uint8_t > (0) + return (0) # maximizing the between class variance max_i = 0 @@ -319,7 +321,7 @@ cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, max_i = i q1 = new_q1 - return < np.uint8_t > max_i + return max_i # ----------------------------------------------------------------- @@ -328,154 +330,154 @@ cdef inline np.uint8_t kernel_otsu(Py_ssize_t * histo, float pop, np.uint8_t g, # ----------------------------------------------------------------- -def autolevel(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def autolevel(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_autolevel, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def bottomhat(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def bottomhat(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_bottomhat, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def equalize(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def equalize(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_equalize, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def gradient(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def gradient(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_gradient, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def maximum(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def maximum(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_maximum, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def mean(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def mean(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_mean, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def meansubstraction(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def meansubstraction(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_meansubstraction, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def median(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def median(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_median, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def minimum(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def minimum(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_minimum, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def morph_contr_enh(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_morph_contr_enh, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def modal(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def modal(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_modal, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def pop(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def pop(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_pop, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def threshold(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def threshold(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_threshold, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def tophat(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def tophat(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def noise_filter(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def noise_filter(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def entropy(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def entropy(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_entropy, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) -def otsu(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def otsu(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0): _core8(kernel_otsu, image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0) diff --git a/skimage/filter/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx index 31b00742..d085957e 100644 --- a/skimage/filter/rank/_crank8_percentiles.pyx +++ b/skimage/filter/rank/_crank8_percentiles.pyx @@ -3,8 +3,7 @@ #cython: nonecheck=False #cython: wraparound=False -import numpy as np -cimport numpy as np +cimport numpy as cnp from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min @@ -13,9 +12,12 @@ from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min # ----------------------------------------------------------------- -cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +ctypedef cnp.uint8_t dtype_t + + +cdef inline dtype_t kernel_autolevel(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -37,17 +39,17 @@ cdef inline np.uint8_t kernel_autolevel(Py_ssize_t * histo, float pop, break delta = imax - imin if delta > 0: - return < np.uint8_t > (255 - * (uint8_min(uint8_max(imin, g), imax) - imin) / delta) + return (255 * (uint8_min(uint8_max(imin, g), imax) + - imin) / delta) else: - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (128) + return (128) -cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_gradient(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -65,14 +67,14 @@ cdef inline np.uint8_t kernel_gradient(Py_ssize_t * histo, float pop, imax = i break - return < np.uint8_t > (imax - imin) + return (imax - imin) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -85,18 +87,18 @@ cdef inline np.uint8_t kernel_mean(Py_ssize_t * histo, float pop, n += histo[i] mean += histo[i] * i if n > 0: - return < np.uint8_t > (1.0 * mean / n) + return (1.0 * mean / n) else: - return < np.uint8_t > (0) + return (0) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, - float pop, - np.uint8_t g, - float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_mean_substraction(Py_ssize_t * histo, + float pop, + dtype_t g, + float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, mean, n if pop: @@ -109,17 +111,17 @@ cdef inline np.uint8_t kernel_mean_substraction(Py_ssize_t * histo, n += histo[i] mean += histo[i] * i if n > 0: - return < np.uint8_t > ((g - (mean / n)) * .5 + 127) + return ((g - (mean / n)) * .5 + 127) else: - return < np.uint8_t > (0) + return (0) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, - float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_morph_contr_enh(Py_ssize_t * histo, + float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, imin, imax, sum, delta if pop: @@ -137,20 +139,20 @@ cdef inline np.uint8_t kernel_morph_contr_enh(Py_ssize_t * histo, imax = i break if g > imax: - return < np.uint8_t > imax + return imax if g < imin: - return < np.uint8_t > imin + return imin if imax - g < g - imin: - return < np.uint8_t > imax + return imax else: - return < np.uint8_t > imin + return imin else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_percentile(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -160,14 +162,14 @@ cdef inline np.uint8_t kernel_percentile(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return < np.uint8_t > (i) + return (i) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_pop(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i, sum, n if pop: @@ -177,14 +179,14 @@ cdef inline np.uint8_t kernel_pop(Py_ssize_t * histo, float pop, sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] - return < np.uint8_t > (n) + return (n) else: - return < np.uint8_t > (0) + return (0) -cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, - np.uint8_t g, float p0, float p1, - Py_ssize_t s0, Py_ssize_t s1): +cdef inline dtype_t kernel_threshold(Py_ssize_t * histo, float pop, + dtype_t g, float p0, float p1, + Py_ssize_t s0, Py_ssize_t s1): cdef int i cdef float sum = 0. @@ -194,9 +196,9 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, if sum >= p0 * pop: break - return < np.uint8_t > (255 * (g >= i)) + return (255 * (g >= i)) else: - return < np.uint8_t > (0) + return (0) # ----------------------------------------------------------------- @@ -204,10 +206,10 @@ cdef inline np.uint8_t kernel_threshold(Py_ssize_t * histo, float pop, # ----------------------------------------------------------------- -def autolevel(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def autolevel(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """autolevel """ @@ -215,10 +217,10 @@ def autolevel(np.ndarray[np.uint8_t, ndim=2] image, 0, 0) -def gradient(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def gradient(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0,p1 percentile gradient """ @@ -226,10 +228,10 @@ def gradient(np.ndarray[np.uint8_t, ndim=2] image, 0, 0) -def mean(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def mean(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return mean between [p0 and p1] percentiles """ @@ -237,10 +239,10 @@ def mean(np.ndarray[np.uint8_t, ndim=2] image, 0, 0) -def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def mean_substraction(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return original - mean between [p0 and p1] percentiles *.5 +127 """ @@ -248,10 +250,10 @@ def mean_substraction(np.ndarray[np.uint8_t, ndim=2] image, p0, p1, 0, 0) -def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def morph_contr_enh(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """reforce contrast using percentiles """ @@ -259,10 +261,10 @@ def morph_contr_enh(np.ndarray[np.uint8_t, ndim=2] image, p0, p1, 0, 0) -def percentile(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def percentile(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return p0 percentile """ @@ -270,10 +272,10 @@ def percentile(np.ndarray[np.uint8_t, ndim=2] image, p0, p1, 0, 0) -def pop(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def pop(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return nb of pixels between [p0 and p1] """ @@ -281,10 +283,10 @@ def pop(np.ndarray[np.uint8_t, ndim=2] image, 0, 0) -def threshold(np.ndarray[np.uint8_t, ndim=2] image, - np.ndarray[np.uint8_t, ndim=2] selem, - np.ndarray[np.uint8_t, ndim=2] mask=None, - np.ndarray[np.uint8_t, ndim=2] out=None, +def threshold(cnp.ndarray[dtype_t, ndim=2] image, + cnp.ndarray[dtype_t, ndim=2] selem, + cnp.ndarray[dtype_t, ndim=2] mask=None, + cnp.ndarray[dtype_t, ndim=2] out=None, char shift_x=0, char shift_y=0, float p0=0., float p1=0.): """return 255 if g > percentile p0 """ diff --git a/skimage/graph/_mcp.pxd b/skimage/graph/_mcp.pxd index 4222d877..b2e2a548 100644 --- a/skimage/graph/_mcp.pxd +++ b/skimage/graph/_mcp.pxd @@ -4,11 +4,11 @@ other cython modules can "cimport mcp" and subclass it. """ cimport heap -cimport numpy as np +cimport numpy as cnp ctypedef heap.BOOL_T BOOL_T -ctypedef unsigned char DIM_T -ctypedef np.float64_t FLOAT_T +ctypedef unsigned char DIM_T +ctypedef cnp.float64_t FLOAT_T cdef class MCP: cdef heap.FastUpdateBinaryHeap costs_heap @@ -23,7 +23,7 @@ cdef class MCP: cdef object flat_offsets cdef object offset_lengths cdef BOOL_T dirty - cdef BOOL_T use_start_cost + cdef BOOL_T use_start_cost # if use_start_cost is true, the cost of the starting element is added to # the cost of the path. Set to true by default in the base class... diff --git a/skimage/graph/_mcp.pyx b/skimage/graph/_mcp.pyx index 320493c2..4e5f6d52 100644 --- a/skimage/graph/_mcp.pyx +++ b/skimage/graph/_mcp.pyx @@ -1,5 +1,7 @@ -# -*- python -*- - +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False """Cython implementation of Dijkstra's minimum cost path algorithm, for use with data on a n-dimensional lattice. @@ -32,19 +34,19 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ -import cython -cimport numpy as np import numpy as np -cimport heap import heap -ctypedef np.int8_t OFFSET_T +cimport numpy as cnp +cimport heap + +ctypedef cnp.int8_t OFFSET_T OFFSET_D = np.int8 -ctypedef np.int16_t OFFSETS_INDEX_T +ctypedef cnp.int16_t OFFSETS_INDEX_T OFFSETS_INDEX_D = np.int16 -ctypedef np.int8_t EDGE_T +ctypedef cnp.int8_t EDGE_T EDGE_D = np.int8 -ctypedef np.intp_t INDEX_T +ctypedef cnp.intp_t INDEX_T INDEX_D = np.intp FLOAT_D = np.float64 @@ -317,7 +319,6 @@ cdef class MCP: FLOAT_T new_cost, FLOAT_T offset_length): return new_cost - @cython.boundscheck(False) def find_costs(self, starts, ends=None, find_all_ends=True): """ Find the minimum-cost path from the given starting points. @@ -366,7 +367,7 @@ cdef class MCP: cdef BOOL_T use_ends = 0 cdef INDEX_T num_ends cdef BOOL_T all_ends = find_all_ends - cdef np.ndarray[INDEX_T, ndim=1] flat_ends + cdef cnp.ndarray[INDEX_T, ndim=1] flat_ends starts = _normalize_indices(starts, self.costs_shape) if starts is None: raise ValueError('start points must all be within the costs array') @@ -385,18 +386,18 @@ cdef class MCP: # lookup and array-ify object attributes for fast use cdef heap.FastUpdateBinaryHeap costs_heap = self.costs_heap - cdef np.ndarray[FLOAT_T, ndim=1] flat_costs = self.flat_costs - cdef np.ndarray[FLOAT_T, ndim=1] flat_cumulative_costs = \ + cdef cnp.ndarray[FLOAT_T, ndim=1] flat_costs = self.flat_costs + cdef cnp.ndarray[FLOAT_T, ndim=1] flat_cumulative_costs = \ self.flat_cumulative_costs - cdef np.ndarray[OFFSETS_INDEX_T, ndim=1] traceback_offsets = \ + cdef cnp.ndarray[OFFSETS_INDEX_T, ndim=1] traceback_offsets = \ self.traceback_offsets - cdef np.ndarray[EDGE_T, ndim=2] flat_pos_edge_map = \ + cdef cnp.ndarray[EDGE_T, ndim=2] flat_pos_edge_map = \ self.flat_pos_edge_map - cdef np.ndarray[EDGE_T, ndim=2] flat_neg_edge_map = \ + cdef cnp.ndarray[EDGE_T, ndim=2] flat_neg_edge_map = \ self.flat_neg_edge_map - cdef np.ndarray[OFFSET_T, ndim=2] offsets = self.offsets - cdef np.ndarray[INDEX_T, ndim=1] flat_offsets = self.flat_offsets - cdef np.ndarray[FLOAT_T, ndim=1] offset_lengths = self.offset_lengths + cdef cnp.ndarray[OFFSET_T, ndim=2] offsets = self.offsets + cdef cnp.ndarray[INDEX_T, ndim=1] flat_offsets = self.flat_offsets + cdef cnp.ndarray[FLOAT_T, ndim=1] offset_lengths = self.offset_lengths cdef DIM_T dim = self.dim cdef int num_offsets = len(flat_offsets) @@ -514,7 +515,6 @@ cdef class MCP: self.dirty = 1 return cumulative_costs, traceback - @cython.boundscheck(False) def traceback(self, end): """traceback(end) @@ -555,12 +555,12 @@ cdef class MCP: raise ValueError('no minimum-cost path was found ' 'to the specified end point') - cdef np.ndarray[INDEX_T, ndim=1] position = \ + cdef cnp.ndarray[INDEX_T, ndim=1] position = \ np.array(ends[0], dtype=INDEX_D) - cdef np.ndarray[OFFSETS_INDEX_T, ndim=1] traceback_offsets = \ + cdef cnp.ndarray[OFFSETS_INDEX_T, ndim=1] traceback_offsets = \ self.traceback_offsets - cdef np.ndarray[OFFSET_T, ndim=2] offsets = self.offsets - cdef np.ndarray[INDEX_T, ndim=1] flat_offsets = self.flat_offsets + cdef cnp.ndarray[OFFSET_T, ndim=2] offsets = self.offsets + cdef cnp.ndarray[INDEX_T, ndim=1] flat_offsets = self.flat_offsets cdef OFFSETS_INDEX_T offset cdef DIM_T d diff --git a/skimage/io/_plugins/_colormixer.pyx b/skimage/io/_plugins/_colormixer.pyx index ace45c94..4fbe2f18 100644 --- a/skimage/io/_plugins/_colormixer.pyx +++ b/skimage/io/_plugins/_colormixer.pyx @@ -1,4 +1,7 @@ -# -*- python -*- +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False """Colour Mixer @@ -9,15 +12,14 @@ one. """ import cython -import numpy as np -cimport numpy as np + +cimport numpy as cnp from libc.math cimport exp, pow -@cython.boundscheck(False) -def add(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, - int channel, int amount): +def add(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, + Py_ssize_t channel, Py_ssize_t amount): """Add a given amount to a colour channel of `stateimg`, and store the result in `img`. Overflow is clipped. @@ -26,45 +28,44 @@ def add(np.ndarray[np.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Input image. + Icnput image. channel : int Channel (0 for "red", 1 for "green", 2 for "blue"). amount : int Value to add. """ - cdef int height = img.shape[0] - cdef int width = img.shape[1] - cdef int k = channel - cdef int n = amount + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] + cdef Py_ssize_t k = channel + cdef Py_ssize_t n = amount - cdef np.int16_t op_result + cdef cnp.int16_t op_result - cdef np.uint8_t lut[256] + cdef cnp.uint8_t lut[256] - cdef int i, j, l + cdef Py_ssize_t i, j, l with nogil: for l from 0 <= l < 256: - op_result = (l + n) + op_result = (l + n) if op_result > 255: op_result = 255 elif op_result < 0: op_result = 0 else: pass - lut[l] = op_result + lut[l] = op_result for i from 0 <= i < height: for j from 0 <= j < width: img[i, j, k] = lut[stateimg[i,j,k]] -@cython.boundscheck(False) -def multiply(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, - int channel, float amount): +def multiply(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, + Py_ssize_t channel, float amount): """Multiply a colour channel of `stateimg` by a certain amount, and store the result in `img`. Overflow is clipped. @@ -73,23 +74,23 @@ def multiply(np.ndarray[np.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Input image. + Icnput image. channel : int Channel (0 for "red", 1 for "green", 2 for "blue"). amount : float Multiplication factor. """ - cdef int height = img.shape[0] - cdef int width = img.shape[1] - cdef int k = channel + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] + cdef Py_ssize_t k = channel cdef float n = amount cdef float op_result - cdef np.uint8_t lut[256] + cdef cnp.uint8_t lut[256] - cdef int i, j, l + cdef Py_ssize_t i, j, l with nogil: @@ -101,17 +102,16 @@ def multiply(np.ndarray[np.uint8_t, ndim=3] img, op_result = 0 else: pass - lut[l] = op_result + lut[l] = op_result for i from 0 <= i < height: for j from 0 <= j < width: img[i,j,k] = lut[stateimg[i,j,k]] -@cython.boundscheck(False) -def brightness(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, - float factor, int offset): +def brightness(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, + float factor, Py_ssize_t offset): """Modify the brightness of an image. 'factor' is multiplied to all channels, which are then added by 'amount'. Overflow is clipped. @@ -121,7 +121,7 @@ def brightness(np.ndarray[np.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Input image. + Icnput image. factor : float Multiplication factor. offset : int @@ -129,13 +129,13 @@ def brightness(np.ndarray[np.uint8_t, ndim=3] img, """ - cdef int height = img.shape[0] - cdef int width = img.shape[1] + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] cdef float op_result - cdef np.uint8_t lut[256] + cdef cnp.uint8_t lut[256] - cdef int i, j, k + cdef Py_ssize_t i, j, k with nogil: for k from 0 <= k < 256: @@ -146,7 +146,7 @@ def brightness(np.ndarray[np.uint8_t, ndim=3] img, op_result = 0 else: pass - lut[k] = op_result + lut[k] = op_result for i from 0 <= i < height: for j from 0 <= j < width: @@ -155,27 +155,25 @@ def brightness(np.ndarray[np.uint8_t, ndim=3] img, img[i,j,2] = lut[stateimg[i,j,2]] -@cython.boundscheck(False) -@cython.cdivision(True) -def sigmoid_gamma(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, +def sigmoid_gamma(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float alpha, float beta): - cdef int height = img.shape[0] - cdef int width = img.shape[1] + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] - cdef int i, j, k + cdef Py_ssize_t i, j, k cdef float c1 = 1 / (1 + exp(beta)) cdef float c2 = 1 / (1 + exp(beta - alpha)) - c1 - cdef np.uint8_t lut[256] + cdef cnp.uint8_t lut[256] with nogil: # compute the lut for k from 0 <= k < 256: - lut[k] = (((1 / (1 + exp(beta - (k / 255.) * alpha))) + lut[k] = (((1 / (1 + exp(beta - (k / 255.) * alpha))) - c1) * 255 / c2) for i from 0 <= i < height: for j from 0 <= j < width: @@ -184,17 +182,16 @@ def sigmoid_gamma(np.ndarray[np.uint8_t, ndim=3] img, img[i,j,2] = lut[stateimg[i,j,2]] -@cython.boundscheck(False) -def gamma(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, +def gamma(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float gamma): - cdef int height = img.shape[0] - cdef int width = img.shape[1] + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] - cdef np.uint8_t lut[256] + cdef cnp.uint8_t lut[256] - cdef int i, j, k + cdef Py_ssize_t i, j, k if gamma == 0: gamma = 0.00000000000000000001 @@ -204,7 +201,7 @@ def gamma(np.ndarray[np.uint8_t, ndim=3] img, # compute the lut for k from 0 <= k < 256: - lut[k] = ((pow((k / 255.), gamma) * 255)) + lut[k] = ((pow((k / 255.), gamma) * 255)) for i from 0 <= i < height: for j from 0 <= j < width: @@ -213,7 +210,6 @@ def gamma(np.ndarray[np.uint8_t, ndim=3] img, img[i,j,2] = lut[stateimg[i,j,2]] -@cython.cdivision(True) cdef void rgb_2_hsv(float* RGB, float* HSV) nogil: cdef float R, G, B, H, S, V, MAX, MIN R = RGB[0] @@ -277,11 +273,10 @@ cdef void rgb_2_hsv(float* RGB, float* HSV) nogil: HSV[2] = V -@cython.cdivision(True) cdef void hsv_2_rgb(float* HSV, float* RGB) nogil: cdef float H, S, V cdef float f, p, q, t, r, g, b - cdef int hi + cdef Py_ssize_t hi H = HSV[0] S = HSV[1] @@ -422,9 +417,8 @@ def py_rgb_2_hsv(R, G, B): return (H, S, V) -@cython.boundscheck(False) -def hsv_add(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, +def hsv_add(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float h_amt, float s_amt, float v_amt): """Modify the image color by specifying additive HSV Values. @@ -444,7 +438,7 @@ def hsv_add(np.ndarray[np.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Input image. + Icnput image. h_amt : float Ammount to add to H channel. s_amt : float @@ -455,13 +449,13 @@ def hsv_add(np.ndarray[np.uint8_t, ndim=3] img, """ - cdef int height = img.shape[0] - cdef int width = img.shape[1] + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] cdef float HSV[3] cdef float RGB[3] - cdef int i, j + cdef Py_ssize_t i, j with nogil: for i from 0 <= i < height: @@ -483,14 +477,13 @@ def hsv_add(np.ndarray[np.uint8_t, ndim=3] img, RGB[1] *= 255 RGB[2] *= 255 - img[i, j, 0] = RGB[0] - img[i, j, 1] = RGB[1] - img[i, j, 2] = RGB[2] + img[i, j, 0] = RGB[0] + img[i, j, 1] = RGB[1] + img[i, j, 2] = RGB[2] -@cython.boundscheck(False) -def hsv_multiply(np.ndarray[np.uint8_t, ndim=3] img, - np.ndarray[np.uint8_t, ndim=3] stateimg, +def hsv_multiply(cnp.ndarray[cnp.uint8_t, ndim=3] img, + cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float h_amt, float s_amt, float v_amt): """Modify the image color by specifying multiplicative HSV Values. @@ -514,7 +507,7 @@ def hsv_multiply(np.ndarray[np.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Input image. + Icnput image. h_amt : float Ammount to add to H channel. s_amt : float @@ -525,13 +518,13 @@ def hsv_multiply(np.ndarray[np.uint8_t, ndim=3] img, """ - cdef int height = img.shape[0] - cdef int width = img.shape[1] + cdef Py_ssize_t height = img.shape[0] + cdef Py_ssize_t width = img.shape[1] cdef float HSV[3] cdef float RGB[3] - cdef int i, j + cdef Py_ssize_t i, j with nogil: for i from 0 <= i < height: @@ -553,6 +546,6 @@ def hsv_multiply(np.ndarray[np.uint8_t, ndim=3] img, RGB[1] *= 255 RGB[2] *= 255 - img[i, j, 0] = RGB[0] - img[i, j, 1] = RGB[1] - img[i, j, 2] = RGB[2] + img[i, j, 0] = RGB[0] + img[i, j, 1] = RGB[1] + img[i, j, 2] = RGB[2] diff --git a/skimage/io/_plugins/_histograms.pyx b/skimage/io/_plugins/_histograms.pyx index 41ec79a1..1f4344d1 100644 --- a/skimage/io/_plugins/_histograms.pyx +++ b/skimage/io/_plugins/_histograms.pyx @@ -1,7 +1,10 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np -import cython +cimport numpy as cnp cdef inline float tri_max(float a, float b, float c): @@ -18,8 +21,7 @@ cdef inline float tri_max(float a, float b, float c): return c -@cython.boundscheck(False) -def histograms(np.ndarray[np.uint8_t, ndim=3] img, int nbins): +def histograms(cnp.ndarray[cnp.uint8_t, ndim=3] img, int nbins): '''Calculate the channel histograms of the current image. Parameters @@ -39,10 +41,7 @@ def histograms(np.ndarray[np.uint8_t, ndim=3] img, int nbins): ''' cdef int width = img.shape[1] cdef int height = img.shape[0] - cdef np.ndarray[np.int32_t, ndim=1] r - cdef np.ndarray[np.int32_t, ndim=1] g - cdef np.ndarray[np.int32_t, ndim=1] b - cdef np.ndarray[np.int32_t, ndim=1] v + cdef cnp.ndarray[cnp.int32_t, ndim=1] r, g, b, v r = np.zeros((nbins,), dtype=np.int32) g = np.zeros((nbins,), dtype=np.int32) diff --git a/skimage/measure/_find_contours.pyx b/skimage/measure/_find_contours.pyx index 91ba318c..d05d9aa7 100644 --- a/skimage/measure/_find_contours.pyx +++ b/skimage/measure/_find_contours.pyx @@ -1,6 +1,10 @@ -# cython: cdivision=True +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np + +cimport numpy as cnp cdef inline double _get_fraction(double from_value, double to_value, @@ -10,7 +14,7 @@ cdef inline double _get_fraction(double from_value, double to_value, return ((level - from_value) / (to_value - from_value)) -def iterate_and_store(np.ndarray[double, ndim=2] array, +def iterate_and_store(cnp.ndarray[double, ndim=2] array, double level, Py_ssize_t vertex_connect_high): """Iterate across the given array in a marching-squares fashion, looking for segments that cross 'level'. If such a segment is @@ -41,7 +45,8 @@ def iterate_and_store(np.ndarray[double, ndim=2] array, coords[1] = 0 # Calculate the number of iterations we'll need - cdef Py_ssize_t num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1) + cdef Py_ssize_t num_square_steps = (array.shape[0] - 1) \ + * (array.shape[1] - 1) cdef unsigned char square_case = 0 cdef tuple top, bottom, left, right diff --git a/skimage/measure/_moments.pyx b/skimage/measure/_moments.pyx index b94a7b89..145f6052 100644 --- a/skimage/measure/_moments.pyx +++ b/skimage/measure/_moments.pyx @@ -1,14 +1,16 @@ -#cython: boundscheck=False -#cython: wraparound=False #cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np + +cimport numpy as cnp -def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc, - int order): +def central_moments(cnp.ndarray[cnp.double_t, ndim=2] array, double cr, + double cc, int order): cdef Py_ssize_t p, q, r, c - cdef np.ndarray[np.double_t, ndim=2] mu + cdef cnp.ndarray[cnp.double_t, ndim=2] mu mu = np.zeros((order + 1, order + 1), 'double') for p in range(order + 1): for q in range(order + 1): @@ -17,9 +19,10 @@ def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc, mu[p,q] += array[r,c] * (r - cr) ** q * (c - cc) ** p return mu -def normalized_moments(np.ndarray[np.double_t, ndim=2] mu, int order): + +def normalized_moments(cnp.ndarray[cnp.double_t, ndim=2] mu, int order): cdef Py_ssize_t p, q - cdef np.ndarray[np.double_t, ndim=2] nu + cdef cnp.ndarray[cnp.double_t, ndim=2] nu nu = np.zeros((order + 1, order + 1), 'double') for p in range(order + 1): for q in range(order + 1): @@ -29,8 +32,9 @@ def normalized_moments(np.ndarray[np.double_t, ndim=2] mu, int order): nu[p,q] = np.nan return nu -def hu_moments(np.ndarray[np.double_t, ndim=2] nu): - cdef np.ndarray[np.double_t, ndim=1] hu = np.zeros((7,), 'double') + +def hu_moments(cnp.ndarray[cnp.double_t, ndim=2] nu): + cdef cnp.ndarray[cnp.double_t, ndim=1] hu = np.zeros((7,), 'double') cdef double t0 = nu[3,0] + nu[1,2] cdef double t1 = nu[2,1] + nu[0,3] cdef double q0 = t0 * t0 diff --git a/skimage/morphology/_convex_hull.pyx b/skimage/morphology/_convex_hull.pyx index 9bec825b..e4b6470c 100644 --- a/skimage/morphology/_convex_hull.pyx +++ b/skimage/morphology/_convex_hull.pyx @@ -1,9 +1,13 @@ -# -*- python -*- - -cimport numpy as np +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -def possible_hull(np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] img): +cimport numpy as cnp + + +def possible_hull(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, mode="c"] img): """Return positions of pixels that possibly belong to the convex hull. Parameters @@ -26,7 +30,7 @@ def possible_hull(np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] img): # cols storage slots for top boundary pixels # rows storage slots for right boundary pixels # cols storage slots for bottom boundary pixels - cdef np.ndarray[dtype=np.intp_t, ndim=2] nonzero = \ + cdef cnp.ndarray[dtype=cnp.intp_t, ndim=2] nonzero = \ np.ones((2 * (rows + cols), 2), dtype=np.int) nonzero *= -1 diff --git a/skimage/morphology/_pnpoly.pyx b/skimage/morphology/_pnpoly.pyx index f6cf386f..f32778cc 100644 --- a/skimage/morphology/_pnpoly.pyx +++ b/skimage/morphology/_pnpoly.pyx @@ -1,7 +1,10 @@ -# -*- python -*- - -cimport numpy as np +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np + +cimport numpy as cnp from skimage._shared.geometry cimport point_in_polygon, points_in_polygon @@ -26,7 +29,7 @@ def grid_points_inside_poly(shape, verts): True where the grid falls inside the polygon. """ - cdef np.ndarray[np.double_t, ndim=1, mode="c"] vx, vy + cdef cnp.ndarray[cnp.double_t, ndim=1, mode="c"] vx, vy verts = np.asarray(verts) vx = verts[:, 0].astype(np.double) @@ -37,7 +40,7 @@ def grid_points_inside_poly(shape, verts): cdef Py_ssize_t N = shape[1] cdef Py_ssize_t m, n - cdef np.ndarray[dtype=np.uint8_t, ndim=2, mode="c"] out = \ + cdef cnp.ndarray[dtype=cnp.uint8_t, ndim=2, mode="c"] out = \ np.zeros((M, N), dtype=np.uint8) for m in range(M): @@ -65,7 +68,7 @@ def points_inside_poly(points, verts): True if corresponding point is inside the polygon. """ - cdef np.ndarray[np.double_t, ndim=1, mode="c"] x, y, vx, vy + cdef cnp.ndarray[cnp.double_t, ndim=1, mode="c"] x, y, vx, vy points = np.asarray(points) verts = np.asarray(verts) @@ -76,12 +79,12 @@ def points_inside_poly(points, verts): vx = verts[:, 0].astype(np.double) vy = verts[:, 1].astype(np.double) - cdef np.ndarray[np.uint8_t, ndim=1] out = \ + cdef cnp.ndarray[cnp.uint8_t, ndim=1] out = \ np.zeros(x.shape[0], dtype=np.uint8) points_in_polygon(vx.shape[0], vx.data, vy.data, - x.shape[0], x.data, y.data, - out.data) + x.shape[0], x.data, y.data, + out.data) return out.astype(bool) diff --git a/skimage/morphology/_skeletonize_cy.pyx b/skimage/morphology/_skeletonize_cy.pyx index 9bef86b9..13e303d4 100644 --- a/skimage/morphology/_skeletonize_cy.pyx +++ b/skimage/morphology/_skeletonize_cy.pyx @@ -1,3 +1,8 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False + ''' Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org @@ -10,21 +15,20 @@ Original author: Lee Kamentsky ''' import numpy as np -cimport numpy as np -cimport cython + +cimport numpy as cnp -@cython.boundscheck(False) -def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, - negative_indices=False, mode='c'] result, - np.ndarray[dtype=np.intp_t, ndim=1, - negative_indices=False, mode='c'] i, - np.ndarray[dtype=np.intp_t, ndim=1, - negative_indices=False, mode='c'] j, - np.ndarray[dtype=np.int32_t, ndim=1, - negative_indices=False, mode='c'] order, - np.ndarray[dtype=np.uint8_t, ndim=1, - negative_indices=False, mode='c'] table): +def _skeletonize_loop(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, + negative_indices=False, mode='c'] result, + cnp.ndarray[dtype=cnp.intp_t, ndim=1, + negative_indices=False, mode='c'] i, + cnp.ndarray[dtype=cnp.intp_t, ndim=1, + negative_indices=False, mode='c'] j, + cnp.ndarray[dtype=cnp.int32_t, ndim=1, + negative_indices=False, mode='c'] order, + cnp.ndarray[dtype=cnp.uint8_t, ndim=1, + negative_indices=False, mode='c'] table): """ Inner loop of skeletonize function @@ -61,7 +65,7 @@ def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, pixels. """ cdef: - np.int32_t accumulator + cnp.int32_t accumulator Py_ssize_t index, order_index Py_ssize_t ii, jj @@ -92,9 +96,10 @@ def _skeletonize_loop(np.ndarray[dtype=np.uint8_t, ndim=2, # Assign the value of table corresponding to the configuration result[ii, jj] = table[accumulator] -@cython.boundscheck(False) -def _table_lookup_index(np.ndarray[dtype=np.uint8_t, ndim=2, - negative_indices=False, mode='c'] image): + + +def _table_lookup_index(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, + negative_indices=False, mode='c'] image): """ Return an index into a table per pixel of a binary image @@ -115,10 +120,10 @@ def _table_lookup_index(np.ndarray[dtype=np.uint8_t, ndim=2, hardwired kernel. """ cdef: - np.ndarray[dtype=np.int32_t, ndim=2, - negative_indices=False, mode='c'] indexer - np.int32_t *p_indexer - np.uint8_t *p_image + cnp.ndarray[dtype=cnp.int32_t, ndim=2, + negative_indices=False, mode='c'] indexer + cnp.int32_t *p_indexer + cnp.uint8_t *p_image Py_ssize_t i_stride Py_ssize_t i_shape Py_ssize_t j_shape @@ -129,8 +134,8 @@ def _table_lookup_index(np.ndarray[dtype=np.uint8_t, ndim=2, i_shape = image.shape[0] j_shape = image.shape[1] indexer = np.zeros((i_shape, j_shape), np.int32) - p_indexer = indexer.data - p_image = image.data + p_indexer = indexer.data + p_image = image.data i_stride = image.strides[0] assert i_shape >= 3 and j_shape >= 3, \ "Please use the slow method for arrays < 3x3" diff --git a/skimage/morphology/ccomp.pxd b/skimage/morphology/ccomp.pxd index b50703e9..569921fa 100644 --- a/skimage/morphology/ccomp.pxd +++ b/skimage/morphology/ccomp.pxd @@ -1,8 +1,8 @@ """Export fast union find in Cython""" -cimport numpy as np +cimport numpy as cnp -DTYPE = np.intp -ctypedef np.intp_t DTYPE_t +DTYPE = cnp.intp +ctypedef cnp.intp_t DTYPE_t cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) cdef set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) diff --git a/skimage/morphology/ccomp.pyx b/skimage/morphology/ccomp.pyx index dc85196b..bbf6ed44 100644 --- a/skimage/morphology/ccomp.pyx +++ b/skimage/morphology/ccomp.pyx @@ -1,8 +1,11 @@ -# -*- python -*- #cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np + +cimport numpy as cnp """ See also: @@ -140,9 +143,9 @@ def label(input, DTYPE_t neighbors=8, DTYPE_t background=-1): cdef DTYPE_t rows = input.shape[0] cdef DTYPE_t cols = input.shape[1] - cdef np.ndarray[DTYPE_t, ndim=2] data = np.array(input, copy=True, - dtype=DTYPE) - cdef np.ndarray[DTYPE_t, ndim=2] forest + cdef cnp.ndarray[DTYPE_t, ndim=2] data = np.array(input, copy=True, + dtype=DTYPE) + cdef cnp.ndarray[DTYPE_t, ndim=2] forest forest = np.arange(data.size, dtype=DTYPE).reshape((rows, cols)) diff --git a/skimage/morphology/heap_watershed.pxi b/skimage/morphology/heap_watershed.pxi index 573d32bb..07b29f5c 100644 --- a/skimage/morphology/heap_watershed.pxi +++ b/skimage/morphology/heap_watershed.pxi @@ -9,14 +9,12 @@ All rights reserved. Original author: Lee Kamentsky """ -import numpy as np -cimport numpy as np -cimport cython +cimport numpy as cnp cdef struct Heapitem: - np.int32_t value - np.int32_t age + cnp.int32_t value + cnp.int32_t age Py_ssize_t index diff --git a/skimage/segmentation/_felzenszwalb_cy.pyx b/skimage/segmentation/_felzenszwalb_cy.pyx index 5a8ac012..f285e3db 100644 --- a/skimage/segmentation/_felzenszwalb_cy.pyx +++ b/skimage/segmentation/_felzenszwalb_cy.pyx @@ -1,15 +1,17 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np import scipy -cimport cython +cimport cython +cimport numpy as cnp from skimage.morphology.ccomp cimport find_root, join_trees from ..util import img_as_float -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) + def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20): """Felzenszwalb's efficient graph based segmentation for a single channel. @@ -49,31 +51,31 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20) down_cost = np.abs((image[:, 1:] - image[:, :-1])) dright_cost = np.abs((image[1:, 1:] - image[:-1, :-1])) uright_cost = np.abs((image[1:, :-1] - image[:-1, 1:])) - cdef np.ndarray[np.float_t, ndim=1] costs = np.hstack([right_cost.ravel(), + cdef cnp.ndarray[cnp.float_t, ndim=1] costs = np.hstack([right_cost.ravel(), down_cost.ravel(), dright_cost.ravel(), uright_cost.ravel()]).astype(np.float) # compute edges between pixels: height, width = image.shape[:2] - cdef np.ndarray[np.intp_t, ndim=2] segments \ + cdef cnp.ndarray[cnp.intp_t, ndim=2] segments \ = np.arange(width * height, dtype=np.intp).reshape(height, width) right_edges = np.c_[segments[1:, :].ravel(), segments[:-1, :].ravel()] down_edges = np.c_[segments[:, 1:].ravel(), segments[:, :-1].ravel()] dright_edges = np.c_[segments[1:, 1:].ravel(), segments[:-1, :-1].ravel()] uright_edges = np.c_[segments[:-1, 1:].ravel(), segments[1:, :-1].ravel()] - cdef np.ndarray[np.intp_t, ndim=2] edges \ + cdef cnp.ndarray[cnp.intp_t, ndim=2] edges \ = np.vstack([right_edges, down_edges, dright_edges, uright_edges]) # initialize data structures for segment size # and inner cost, then start greedy iteration over edges. edge_queue = np.argsort(costs) edges = np.ascontiguousarray(edges[edge_queue]) costs = np.ascontiguousarray(costs[edge_queue]) - cdef np.intp_t *segments_p = segments.data - cdef np.intp_t *edges_p = edges.data - cdef np.float_t *costs_p = costs.data - cdef np.ndarray[np.intp_t, ndim=1] segment_size \ + cdef cnp.intp_t *segments_p = segments.data + cdef cnp.intp_t *edges_p = edges.data + cdef cnp.float_t *costs_p = costs.data + cdef cnp.ndarray[cnp.intp_t, ndim=1] segment_size \ = np.ones(width * height, dtype=np.int) # inner cost of segments - cdef np.ndarray[np.float_t, ndim=1] cint = np.zeros(width * height) + cdef cnp.ndarray[cnp.float_t, ndim=1] cint = np.zeros(width * height) cdef int seg0, seg1, seg_new, e cdef float cost, inner_cost0, inner_cost1 # set costs_p back one. we increase it before we use it @@ -96,7 +98,7 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20) cint[seg_new] = costs_p[0] # postprocessing to remove small segments - edges_p = edges.data + edges_p = edges.data for e in range(costs.size): seg0 = find_root(segments_p, edges_p[0]) seg1 = find_root(segments_p, edges_p[1]) diff --git a/skimage/segmentation/_quickshift.pyx b/skimage/segmentation/_quickshift.pyx index 61e4d0e0..cc649e8f 100644 --- a/skimage/segmentation/_quickshift.pyx +++ b/skimage/segmentation/_quickshift.pyx @@ -1,18 +1,18 @@ +#cython: cdivision=True +#cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np -cimport cython -from libc.math cimport exp, sqrt - -from itertools import product from scipy import ndimage +from itertools import product + +cimport numpy as cnp +from libc.math cimport exp, sqrt from ..util import img_as_float from ..color import rgb2lab -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, return_tree=False, sigma=0, convert2lab=True, random_seed=None): """Segments image using quickshift clustering in Color-(x,y) space. @@ -69,7 +69,7 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, image = rgb2lab(image) image = ndimage.gaussian_filter(img_as_float(image), [sigma, sigma, 0]) - cdef np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image_c \ + cdef cnp.ndarray[dtype=cnp.float_t, ndim=3, mode="c"] image_c \ = np.ascontiguousarray(image) * ratio random_state = np.random.RandomState(random_seed) @@ -92,10 +92,10 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, cdef Py_ssize_t r, c, r_, c_, channel, r_min, c_min - cdef np.float_t* image_p = image_c.data - cdef np.float_t* current_pixel_p = image_p + cdef cnp.float_t* image_p = image_c.data + cdef cnp.float_t* current_pixel_p = image_p - cdef np.ndarray[dtype=np.float_t, ndim=2] densities \ + cdef cnp.ndarray[dtype=cnp.float_t, ndim=2] densities \ = np.zeros((height, width)) # compute densities @@ -117,9 +117,9 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, densities += random_state.normal(scale=0.00001, size=(height, width)) # default parent to self: - cdef np.ndarray[dtype=np.int_t, ndim=2] parent \ + cdef cnp.ndarray[dtype=cnp.int_t, ndim=2] parent \ = np.arange(width * height).reshape(height, width) - cdef np.ndarray[dtype=np.float_t, ndim=2] dist_parent \ + cdef cnp.ndarray[dtype=cnp.float_t, ndim=2] dist_parent \ = np.zeros((height, width)) # find nearest node with higher density diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index e91ed21d..7db072c0 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -1,8 +1,13 @@ +#cython: cdivision=True #cython: boundscheck=False +#cython: nonecheck=False +#cython: wraparound=False import numpy as np -cimport numpy as np from time import time from scipy import ndimage + +cimport numpy as cnp + from ..util import img_as_float from ..color import rgb2lab, gray2rgb @@ -72,32 +77,32 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, means_x = grid_x[::step, ::step] means_color = np.zeros((means_y.shape[0], means_y.shape[1], 3)) - cdef np.ndarray[dtype=np.float_t, ndim=2] means \ + cdef cnp.ndarray[dtype=cnp.float_t, ndim=2] means \ = np.dstack([means_y, means_x, means_color]).reshape(-1, 5) - cdef np.float_t* current_mean - cdef np.float_t* mean_entry + cdef cnp.float_t* current_mean + cdef cnp.float_t* mean_entry n_means = means.shape[0] # we do the scaling of ratio in the same way as in the SLIC paper # so the values have the same meaning ratio = (ratio / float(step)) ** 2 - cdef np.ndarray[dtype=np.float_t, ndim=3] image_yx \ + cdef cnp.ndarray[dtype=cnp.float_t, ndim=3] image_yx \ = np.dstack([grid_y, grid_x, image / ratio]).copy("C") cdef Py_ssize_t i, k, x, y, x_min, x_max, y_min, y_max, changes cdef double dist_mean - cdef np.ndarray[dtype=np.intp_t, ndim=2] nearest_mean \ + cdef cnp.ndarray[dtype=cnp.intp_t, ndim=2] nearest_mean \ = np.zeros((height, width), dtype=np.intp) - cdef np.ndarray[dtype=np.float_t, ndim=2] distance \ + cdef cnp.ndarray[dtype=cnp.float_t, ndim=2] distance \ = np.empty((height, width)) - cdef np.float_t* image_p = image_yx.data - cdef np.float_t* distance_p = distance.data - cdef np.float_t* current_distance - cdef np.float_t* current_pixel + cdef cnp.float_t* image_p = image_yx.data + cdef cnp.float_t* distance_p = distance.data + cdef cnp.float_t* current_distance + cdef cnp.float_t* current_pixel cdef double tmp for i in range(max_iter): distance.fill(np.inf) changes = 0 - current_mean = means.data + current_mean = means.data # assign pixels to means for k in range(n_means): # compute windows: diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 662b3b88..5d1f27af 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -2,9 +2,9 @@ #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False -cimport cython import numpy as np -cimport numpy as np + +cimport numpy as cnp from libc.math cimport abs, fabs, sqrt, ceil from libc.stdlib cimport rand @@ -17,14 +17,14 @@ cdef inline Py_ssize_t round(double r): return ((r + 0.5) if (r > 0.0) else (r - 0.5)) -def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): +def _hough(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None): if img.ndim != 2: raise ValueError('The input image must be 2D.') # Compute the array of angles and their sine and cosine - cdef np.ndarray[ndim=1, dtype=np.double_t] ctheta - cdef np.ndarray[ndim=1, dtype=np.double_t] stheta + cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] ctheta + cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] stheta if theta is None: theta = np.linspace(PI_2, NEG_PI_2, 180) @@ -33,8 +33,8 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): stheta = np.sin(theta) # compute the bins and allocate the accumulator array - cdef np.ndarray[ndim=2, dtype=np.uint64_t] accum - cdef np.ndarray[ndim=1, dtype=np.double_t] bins + cdef cnp.ndarray[ndim=2, dtype=cnp.uint64_t] accum + cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] bins cdef Py_ssize_t max_distance, offset max_distance = 2 * ceil(sqrt(img.shape[0] * img.shape[0] + @@ -44,7 +44,7 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): offset = max_distance / 2 # compute the nonzero indexes - cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs + cdef cnp.ndarray[ndim=1, dtype=cnp.npy_intp] x_idxs, y_idxs y_idxs, x_idxs = np.nonzero(img) # finally, run the transform @@ -60,9 +60,9 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): return accum, theta, bins -def _probabilistic_hough(np.ndarray img, int value_threshold, +def _probabilistic_hough(cnp.ndarray img, int value_threshold, int line_length, int line_gap, - np.ndarray[ndim=1, dtype=np.double_t] theta=None): + cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None): if img.ndim != 2: raise ValueError('The input image must be 2D.') @@ -74,11 +74,11 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, cdef Py_ssize_t width = img.shape[1] # compute the bins and allocate the accumulator array - cdef np.ndarray[ndim=2, dtype=np.int64_t] accum - cdef np.ndarray[ndim=1, dtype=np.double_t] ctheta, stheta - cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = \ + cdef cnp.ndarray[ndim=2, dtype=cnp.int64_t] accum + cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] ctheta, stheta + cdef cnp.ndarray[ndim=2, dtype=cnp.uint8_t] mask = \ np.zeros((height, width), dtype=np.uint8) - cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = \ + cdef cnp.ndarray[ndim=2, dtype=cnp.int32_t] line_end = \ np.zeros((2, 2), dtype=np.int32) cdef Py_ssize_t max_distance, offset, num_indexes, index cdef double a, b diff --git a/skimage/transform/_warps_cy.pyx b/skimage/transform/_warps_cy.pyx index 951eb861..968f643a 100644 --- a/skimage/transform/_warps_cy.pyx +++ b/skimage/transform/_warps_cy.pyx @@ -2,9 +2,9 @@ #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False - -cimport numpy as np import numpy as np + +cimport numpy as cnp from skimage._shared.interpolation cimport (nearest_neighbour_interpolation, bilinear_interpolation, biquadratic_interpolation, @@ -35,7 +35,7 @@ cdef inline void _matrix_transform(double x, double y, double* H, double *x_, y_[0] = yy / zz -def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, +def _warp_fast(cnp.ndarray image, cnp.ndarray H, output_shape=None, int order=1, mode='constant', double cval=0): """Projective transformation (homography). @@ -83,9 +83,9 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, """ - cdef np.ndarray[dtype=np.double_t, ndim=2, mode="c"] img = \ + cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode="c"] img = \ np.ascontiguousarray(image, dtype=np.double) - cdef np.ndarray[dtype=np.double_t, ndim=2, mode="c"] M = \ + cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode="c"] M = \ np.ascontiguousarray(H) if mode not in ('constant', 'wrap', 'reflect', 'nearest'): @@ -101,7 +101,7 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1, out_r = output_shape[0] out_c = output_shape[1] - cdef np.ndarray[dtype=np.double_t, ndim=2] out = \ + cdef cnp.ndarray[dtype=cnp.double_t, ndim=2] out = \ np.zeros((out_r, out_c), dtype=np.double) cdef Py_ssize_t tfr, tfc From 21d480c32c29798ab232614fa68883a43ea6a0d1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 24 Feb 2013 14:20:02 +0100 Subject: [PATCH 576/625] Fix accidentially changed doc strings --- skimage/io/_plugins/_colormixer.pyx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skimage/io/_plugins/_colormixer.pyx b/skimage/io/_plugins/_colormixer.pyx index 4fbe2f18..64ac62ff 100644 --- a/skimage/io/_plugins/_colormixer.pyx +++ b/skimage/io/_plugins/_colormixer.pyx @@ -28,7 +28,7 @@ def add(cnp.ndarray[cnp.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Icnput image. + Input image. channel : int Channel (0 for "red", 1 for "green", 2 for "blue"). amount : int @@ -74,7 +74,7 @@ def multiply(cnp.ndarray[cnp.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Icnput image. + Input image. channel : int Channel (0 for "red", 1 for "green", 2 for "blue"). amount : float @@ -121,7 +121,7 @@ def brightness(cnp.ndarray[cnp.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Icnput image. + Input image. factor : float Multiplication factor. offset : int @@ -438,7 +438,7 @@ def hsv_add(cnp.ndarray[cnp.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Icnput image. + Input image. h_amt : float Ammount to add to H channel. s_amt : float @@ -507,7 +507,7 @@ def hsv_multiply(cnp.ndarray[cnp.uint8_t, ndim=3] img, img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 - Icnput image. + Input image. h_amt : float Ammount to add to H channel. s_amt : float From 19e69e94c112b2f03a50ff8e570003ceeed4fc04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 24 Feb 2013 16:42:37 +0100 Subject: [PATCH 577/625] Docstring circle + ellipse --- skimage/draw/_draw.pyx | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index efd5fdc5..1a868ae5 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -138,6 +138,10 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None): Centre coordinate of ellipse. yradius, xradius : double Minor and major semi-axes. ``(x/xradius)**2 + (y/yradius)**2 = 1``. + shape : tuple, optional + image shape which is used to determine maximum extents of output pixel + coordinates. This is useful for ellipses which exceed the image size. + By default the full extents of the ellipse are used. Returns ------- @@ -182,6 +186,10 @@ def circle(double cy, double cx, double radius, shape=None): Centre coordinate of circle. radius: double Radius of circle. + shape : tuple, optional + image shape which is used to determine maximum extents of output pixel + coordinates. This is useful for circles which exceed the image size. + By default the full extents of the circle are used. Returns ------- @@ -189,7 +197,9 @@ def circle(double cy, double cx, double radius, shape=None): Pixel coordinates of circle. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. - + Notes + ----- + This function is a wrapper for skimage.draw.ellipse() """ return ellipse(cy, cx, radius, radius, shape) From 27b837b79979957879ebf027323d86f2aedf2327 Mon Sep 17 00:00:00 2001 From: Christoph Gohlke Date: Sun, 24 Feb 2013 08:57:53 -0800 Subject: [PATCH 578/625] Use log(2) instead of log10(2) --- skimage/filter/rank/_crank16.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index b7aca483..3c0e5ceb 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -274,7 +274,7 @@ cdef inline np.uint16_t kernel_entropy(Py_ssize_t * histo, float pop, for i in range(maxbin): p = histo[i] / pop if p > 0: - e -= p * log(p) / 0.30102999566398119521373889472449 + e -= p * log(p) / 0.6931471805599453 return < np.uint16_t > e * 1000 else: From 2e230c70c0c512b812e0bbd4999c2d77a42cc155 Mon Sep 17 00:00:00 2001 From: Christoph Gohlke Date: Sun, 24 Feb 2013 09:01:09 -0800 Subject: [PATCH 579/625] Use log(2) instead of log10(2) --- skimage/filter/rank/_crank8.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index d7ece4dd..c64d78ba 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -279,7 +279,7 @@ cdef inline np.uint8_t kernel_entropy(Py_ssize_t * histo, float pop, for i in range(256): p = histo[i] / pop if p > 0: - e -= p * log(p) / 0.30102999566398119521373889472449 + e -= p * log(p) / 0.6931471805599453 return < np.uint8_t > e * 10 else: From a17d8182aabfe10ba4f6629435a2bd11bcf80852 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Thu, 17 Jan 2013 11:11:21 +0700 Subject: [PATCH 580/625] PKG: MSVC build fixes by Christoph Gohlke. --- skimage/filter/rank/_crank16.pyx | 2 +- skimage/filter/rank/_crank8.pyx | 2 +- skimage/filter/setup.py | 20 ++++++++++---------- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/filter/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx index 87199e80..667c179d 100644 --- a/skimage/filter/rank/_crank16.pyx +++ b/skimage/filter/rank/_crank16.pyx @@ -276,7 +276,7 @@ cdef inline dtype_t kernel_entropy(Py_ssize_t * histo, float pop, for i in range(maxbin): p = histo[i] / pop if p > 0: - e -= p * log2(p) + e -= p * log(p) / 0.30102999566398119521373889472449 return e * 1000 else: diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx index c9e8b14f..3e06f319 100644 --- a/skimage/filter/rank/_crank8.pyx +++ b/skimage/filter/rank/_crank8.pyx @@ -281,7 +281,7 @@ cdef inline dtype_t kernel_entropy(Py_ssize_t * histo, float pop, for i in range(256): p = histo[i] / pop if p > 0: - e -= p * log2(p) + e -= p * log(p) / 0.30102999566398119521373889472449 return e * 10 else: diff --git a/skimage/filter/setup.py b/skimage/filter/setup.py index 650a26a6..934d3f2e 100644 --- a/skimage/filter/setup.py +++ b/skimage/filter/setup.py @@ -29,31 +29,31 @@ def configuration(parent_package='', top_path=None): include_dirs=[get_numpy_include_dirs()]) config.add_extension('_denoise_cy', sources=['_denoise_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) - config.add_extension('rank/_core8', sources=['rank/_core8.c'], + config.add_extension('rank._core8', sources=['rank/_core8.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('rank/_core16', sources=['rank/_core16.c'], + config.add_extension('rank._core16', sources=['rank/_core16.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('rank/_crank8', sources=['rank/_crank8.c'], + config.add_extension('rank._crank8', sources=['rank/_crank8.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/_crank8_percentiles', sources=['rank/_crank8_percentiles.c'], + 'rank._crank8_percentiles', sources=['rank/_crank8_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) - config.add_extension('rank/_crank16', sources=['rank/_crank16.c'], + config.add_extension('rank._crank16', sources=['rank/_crank16.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/_crank16_percentiles', sources=['rank/_crank16_percentiles.c'], + 'rank._crank16_percentiles', sources=['rank/_crank16_percentiles.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/_crank16_bilateral', sources=['rank/_crank16_bilateral.c'], + 'rank._crank16_bilateral', sources=['rank/_crank16_bilateral.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/rank', sources=['rank/rank.c'], + 'rank.rank', sources=['rank/rank.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/percentile_rank', sources=['rank/percentile_rank.c'], + 'rank.percentile_rank', sources=['rank/percentile_rank.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( - 'rank/bilateral_rank', sources=['rank/bilateral_rank.c'], + 'rank.bilateral_rank', sources=['rank/bilateral_rank.c'], include_dirs=[get_numpy_include_dirs()]) return config From a997195881756d04839b4eba566572cd96d59fba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 9 Feb 2013 20:00:19 +0100 Subject: [PATCH 581/625] hough transform for circles --- skimage/transform/_hough_transform.pyx | 86 +++++++++++++++++++++++++- skimage/transform/hough_transform.py | 32 +++++++++- 2 files changed, 116 insertions(+), 2 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 662b3b88..6164f304 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -7,7 +7,7 @@ import numpy as np cimport numpy as np from libc.math cimport abs, fabs, sqrt, ceil from libc.stdlib cimport rand - +from skimage.draw import circle_perimeter cdef double PI_2 = 1.5707963267948966 cdef double NEG_PI_2 = -PI_2 @@ -17,6 +17,90 @@ cdef inline Py_ssize_t round(double r): return ((r + 0.5) if (r > 0.0) else (r - 0.5)) +@cython.boundscheck(False) +def _hough_circle(np.ndarray img, \ + np.ndarray[ndim=1, dtype=np.npy_intp] radius, \ + normalize=True): + + if img.ndim != 2: + raise ValueError('The input image must be 2D.') + + # compute the nonzero indexes + cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y + y, x = np.PyArray_Nonzero(img) + + # Offset the image + max_radius = radius.max() + x = x + max_radius + y = y + max_radius + + cdef list H = list() + + for rad in radius: + # Accumulator + out = np.zeros((img.shape[0] + 2 * max_radius, img.shape[1] + 2 * max_radius)) + + # Store in memory the circle of given radius + # centered at (0,0) + circle_x, circle_y = circle_perimeter(0, 0, rad) + + # For each non zero pixel + for (px, py) in zip(x, y): + # Plug the circle at (px, py), + # its coordinates are (tx, ty) + tx = circle_x + px + ty = circle_y + py + out[tx, ty] += 1 + + if normalize: + out = out / len(circle_x) + + H.append(out) + return np.array(H) + + +@cython.boundscheck(False) +def _hough_circle(np.ndarray img, \ + np.ndarray[ndim=1, dtype=np.npy_intp] radius, \ + normalize=True): + + if img.ndim != 2: + raise ValueError('The input image must be 2D.') + + # compute the nonzero indexes + cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y + y, x = np.PyArray_Nonzero(img) + + # Offset the image + max_radius = radius.max() + x = x + max_radius + y = y + max_radius + + cdef list H = list() + + for rad in radius: + # Accumulator + out = np.zeros((img.shape[0] + 2 * max_radius, img.shape[1] + 2 * max_radius)) + + # Store in memory the circle of given radius + # centered at (0,0) + circle_x, circle_y = circle_perimeter(0, 0, rad) + + # For each non zero pixel + for (px, py) in zip(x, y): + # Plug the circle at (px, py), + # its coordinates are (tx, ty) + tx = circle_x + px + ty = circle_y + py + out[tx, ty] += 1 + + if normalize: + out = out / len(circle_x) + + H.append(out) + return np.array(H) + + def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): if img.ndim != 2: diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 05d5cfbe..88ef4b83 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -1,4 +1,4 @@ -__all__ = ['hough', 'hough_peaks', 'probabilistic_hough'] +__all__ = ['hough', 'hough_line', 'hough_circle', 'hough_peaks', 'probabilistic_hough'] from itertools import izip as zip @@ -96,8 +96,15 @@ def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10, """ return _probabilistic_hough(img, threshold, line_length, line_gap, theta) +from skimage._shared.utils import deprecated +@deprecated('hough_line') def hough(img, theta=None): + return hough_line(img, theta) + +from ._hough_transform import _hough_circle + +def hough_line(img, theta=None): """Perform a straight line Hough transform. Parameters @@ -138,6 +145,29 @@ def hough(img, theta=None): """ return _hough(img, theta) +def hough_circle(img, radius, normalize=True): + """Perform a circle Hough transform. + + Parameters + ---------- + img : (M, N) ndarray + Input image with nonzero values representing edges. + radius : ndarray + Radii at which to compute the Hough transform. + normalize : boolean + Normalize the accumulator with the number + of pixels used to draw the radius + + Returns + ------- + H : 3D ndarray (radius index, (M, N) ndarray) + Hough transform accumulator for each radius + + Examples + -------- + + """ + return _hough_circle(img, radius, normalize) def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, threshold=None, num_peaks=np.inf): From 12c1bf8883153fa2b75469210b450050f795d695 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 9 Feb 2013 20:00:41 +0100 Subject: [PATCH 582/625] add unittest for hough circle --- .../transform/tests/test_hough_transform.py | 32 +++++++++++++------ 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py index a38e6e76..f829b1bb 100644 --- a/skimage/transform/tests/test_hough_transform.py +++ b/skimage/transform/tests/test_hough_transform.py @@ -4,6 +4,7 @@ from numpy.testing import * import skimage.transform as tf import skimage.transform.hough_transform as ht from skimage.transform import probabilistic_hough +from skimage.draw import circle_perimeter def append_desc(func, description): @@ -14,8 +15,6 @@ def append_desc(func, description): return func -from skimage.transform import * - def test_hough(): # Generate a test image @@ -23,7 +22,7 @@ def test_hough(): for i in range(25, 75): img[100 - i, i] = 1 - out, angles, d = tf.hough(img) + out, angles, d = tf.hough_line(img) y, x = np.where(out == out.max()) dist = d[y[0]] @@ -37,7 +36,7 @@ def test_hough_angles(): img = np.zeros((10, 10)) img[0, 0] = 1 - out, angles, d = tf.hough(img, np.linspace(0, 360, 10)) + out, angles, d = tf.hough_line(img, np.linspace(0, 360, 10)) assert_equal(len(angles), 10) @@ -76,7 +75,7 @@ def test_hough_peaks_dist(): img = np.zeros((100, 100), dtype=np.bool_) img[:, 30] = True img[:, 40] = True - hspace, angles, dists = tf.hough(img) + hspace, angles, dists = tf.hough_line(img) assert len(tf.hough_peaks(hspace, angles, dists, min_distance=5)[0]) == 2 assert len(tf.hough_peaks(hspace, angles, dists, min_distance=15)[0]) == 1 @@ -86,17 +85,17 @@ def test_hough_peaks_angle(): img[:, 0] = True img[0, :] = True - hspace, angles, dists = tf.hough(img) + hspace, angles, dists = tf.hough_line(img) assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 theta = np.linspace(0, np.pi, 100) - hspace, angles, dists = tf.hough(img, theta) + hspace, angles, dists = tf.hough_line(img, theta) assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100) - hspace, angles, dists = tf.hough(img, theta) + hspace, angles, dists = tf.hough_line(img, theta) assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 @@ -105,10 +104,25 @@ def test_hough_peaks_num(): img = np.zeros((100, 100), dtype=np.bool_) img[:, 30] = True img[:, 40] = True - hspace, angles, dists = tf.hough(img) + hspace, angles, dists = tf.hough_line(img) assert len(tf.hough_peaks(hspace, angles, dists, min_distance=0, min_angle=0, num_peaks=1)[0]) == 1 +def test_houghcircle(): + # Prepare picture + img = np.zeros((100, 100), dtype=int) + radius = 20 + x_0, y_0 = (50, 50) + x, y = circle_perimeter(y_0, x_0, radius) + img[y, x] = 1 + + out = tf.hough_circle(img, np.array([radius])) + + y, x = np.where(out[0] == out[0].max()) + # Offset for x_0, y_0 + assert_equal(x[0], x_0 + radius) + assert_equal(y[0], y_0 + radius) + if __name__ == "__main__": run_module_suite() From ffeb64572f9f3bc432aa4b540128abd8ae256bf4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 9 Feb 2013 20:01:00 +0100 Subject: [PATCH 583/625] update contrib --- CONTRIBUTORS.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 38e2ea32..80cb92f0 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -132,6 +132,7 @@ - François Boulogne Andres Method for circle perimeter, ellipse perimeter drawing. + Hough transform for circles - Thouis Jones Vectorized operators for arrays of 16-bit ints. From 88eaaad9c797765fddac76eb5c5ae87ae4cda64f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Mon, 11 Feb 2013 17:31:23 +0100 Subject: [PATCH 584/625] add blank line --- skimage/transform/_hough_transform.pyx | 1 + 1 file changed, 1 insertion(+) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 6164f304..3c39d815 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -7,6 +7,7 @@ import numpy as np cimport numpy as np from libc.math cimport abs, fabs, sqrt, ceil from libc.stdlib cimport rand + from skimage.draw import circle_perimeter cdef double PI_2 = 1.5707963267948966 From 5161687730be0222ae9448697bd9a11dc6ef999b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 16 Feb 2013 08:11:30 +0100 Subject: [PATCH 585/625] various minor fixes --- skimage/transform/_hough_transform.pyx | 61 ++++++++------------------ skimage/transform/hough_transform.py | 7 +-- 2 files changed, 20 insertions(+), 48 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 3c39d815..c274ed0d 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -22,55 +22,30 @@ cdef inline Py_ssize_t round(double r): def _hough_circle(np.ndarray img, \ np.ndarray[ndim=1, dtype=np.npy_intp] radius, \ normalize=True): + """Perform a circular Hough transform. + Parameters + ---------- + img : (M, N) ndarray + Input image with nonzero values representing edges. + radius : ndarray + Radii at which to compute the Hough transform. + normalize : boolean, optional + Normalize the accumulator with the number + of pixels used to draw the radius + + Returns + ------- + H : 3D ndarray (radius index, (M, N) ndarray) + Hough transform accumulator for each radius + + """ if img.ndim != 2: raise ValueError('The input image must be 2D.') # compute the nonzero indexes cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y - y, x = np.PyArray_Nonzero(img) - - # Offset the image - max_radius = radius.max() - x = x + max_radius - y = y + max_radius - - cdef list H = list() - - for rad in radius: - # Accumulator - out = np.zeros((img.shape[0] + 2 * max_radius, img.shape[1] + 2 * max_radius)) - - # Store in memory the circle of given radius - # centered at (0,0) - circle_x, circle_y = circle_perimeter(0, 0, rad) - - # For each non zero pixel - for (px, py) in zip(x, y): - # Plug the circle at (px, py), - # its coordinates are (tx, ty) - tx = circle_x + px - ty = circle_y + py - out[tx, ty] += 1 - - if normalize: - out = out / len(circle_x) - - H.append(out) - return np.array(H) - - -@cython.boundscheck(False) -def _hough_circle(np.ndarray img, \ - np.ndarray[ndim=1, dtype=np.npy_intp] radius, \ - normalize=True): - - if img.ndim != 2: - raise ValueError('The input image must be 2D.') - - # compute the nonzero indexes - cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y - y, x = np.PyArray_Nonzero(img) + y, x = np.nonzero(img) # Offset the image max_radius = radius.max() diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 88ef4b83..17b76826 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -146,7 +146,7 @@ def hough_line(img, theta=None): return _hough(img, theta) def hough_circle(img, radius, normalize=True): - """Perform a circle Hough transform. + """Perform a circular Hough transform. Parameters ---------- @@ -154,7 +154,7 @@ def hough_circle(img, radius, normalize=True): Input image with nonzero values representing edges. radius : ndarray Radii at which to compute the Hough transform. - normalize : boolean + normalize : boolean, optional Normalize the accumulator with the number of pixels used to draw the radius @@ -163,9 +163,6 @@ def hough_circle(img, radius, normalize=True): H : 3D ndarray (radius index, (M, N) ndarray) Hough transform accumulator for each radius - Examples - -------- - """ return _hough_circle(img, radius, normalize) From 99e29973379b9351e1b71b8314400123ea3947fa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 16 Feb 2013 08:33:41 +0100 Subject: [PATCH 586/625] declare cython variables --- skimage/transform/_hough_transform.pyx | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index c274ed0d..98ba1bff 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -48,11 +48,13 @@ def _hough_circle(np.ndarray img, \ y, x = np.nonzero(img) # Offset the image - max_radius = radius.max() + cdef int max_radius = radius.max() x = x + max_radius y = y + max_radius cdef list H = list() + cdef int px, py + cdef np.ndarray[ndim=1, dtype=np.npy_intp] tx, ty for rad in radius: # Accumulator From de6e63de2e711af178787c4ef78f00996f30257f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 16 Feb 2013 09:30:26 +0100 Subject: [PATCH 587/625] use a single array --- skimage/transform/_hough_transform.pyx | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 98ba1bff..fb36c231 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -52,13 +52,13 @@ def _hough_circle(np.ndarray img, \ x = x + max_radius y = y + max_radius - cdef list H = list() cdef int px, py cdef np.ndarray[ndim=1, dtype=np.npy_intp] tx, ty + cdef np.ndarray acc = np.zeros((radius.size, + img.shape[0] + 2 * max_radius, + img.shape[1] + 2 * max_radius)) - for rad in radius: - # Accumulator - out = np.zeros((img.shape[0] + 2 * max_radius, img.shape[1] + 2 * max_radius)) + for i, rad in enumerate(radius): # Store in memory the circle of given radius # centered at (0,0) @@ -70,13 +70,12 @@ def _hough_circle(np.ndarray img, \ # its coordinates are (tx, ty) tx = circle_x + px ty = circle_y + py - out[tx, ty] += 1 + acc[i, tx, ty] += 1 if normalize: - out = out / len(circle_x) + acc[i] = acc[i] / len(circle_x) - H.append(out) - return np.array(H) + return acc def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None): From 928468b999df891116f3258d0058ab38b798dba1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Mon, 18 Feb 2013 19:23:08 +0100 Subject: [PATCH 588/625] variable in cdef --- skimage/transform/_hough_transform.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index fb36c231..83be34ad 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -53,7 +53,7 @@ def _hough_circle(np.ndarray img, \ y = y + max_radius cdef int px, py - cdef np.ndarray[ndim=1, dtype=np.npy_intp] tx, ty + cdef np.ndarray[ndim=1, dtype=np.npy_intp] tx, ty, circle_x, circle_y cdef np.ndarray acc = np.zeros((radius.size, img.shape[0] + 2 * max_radius, img.shape[1] + 2 * max_radius)) From ecb762c9e41ddf17a22c531235711ed2a1e435ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 23 Feb 2013 11:08:23 +0100 Subject: [PATCH 589/625] bugfix dim + unittest --- skimage/transform/_hough_transform.pyx | 2 +- skimage/transform/tests/test_hough_transform.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 83be34ad..35426cad 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -45,7 +45,7 @@ def _hough_circle(np.ndarray img, \ # compute the nonzero indexes cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y - y, x = np.nonzero(img) + x, y = np.nonzero(img) # Offset the image cdef int max_radius = radius.max() diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py index f829b1bb..00427332 100644 --- a/skimage/transform/tests/test_hough_transform.py +++ b/skimage/transform/tests/test_hough_transform.py @@ -111,15 +111,15 @@ def test_hough_peaks_num(): def test_houghcircle(): # Prepare picture - img = np.zeros((100, 100), dtype=int) + img = np.zeros((120, 100), dtype=int) radius = 20 - x_0, y_0 = (50, 50) + x_0, y_0 = (99, 50) x, y = circle_perimeter(y_0, x_0, radius) img[y, x] = 1 out = tf.hough_circle(img, np.array([radius])) - y, x = np.where(out[0] == out[0].max()) + x, y = np.where(out[0] == out[0].max()) # Offset for x_0, y_0 assert_equal(x[0], x_0 + radius) assert_equal(y[0], y_0 + radius) From 5572d16cb221f08fcbd97880447653af77f0aefd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 23 Feb 2013 13:49:21 +0100 Subject: [PATCH 590/625] add example for cicular hough transform --- doc/examples/plot_circular_hough_transform.py | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100755 doc/examples/plot_circular_hough_transform.py diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py new file mode 100755 index 00000000..0caee3c4 --- /dev/null +++ b/doc/examples/plot_circular_hough_transform.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python2 +# -*- coding: utf-8 -*- +# Author: Francois Boulogne +# License: GPL + +""" +======================== +Circular Hough Transform +======================== + + + +""" + + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches + +from skimage import data, filter +from skimage.transform import hough_circle +from skimage.feature import peak_local_max + +# Load picture and detect edges +image = data.coins()[0:95, 70:370] +edges = filter.canny(filter.sobel(image), sigma=2.8) + + +fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6)) +ax.imshow(image, cmap=plt.cm.gray) + +# Detect two radii +radii = np.array([21, 25]) +hough_res = hough_circle(edges, radii) + +for radius, h in zip(radii, hough_res): + # For each radius, keep two circles + maxima = peak_local_max(h, num_peaks=2) + for maximum in maxima: + center_x, center_y = maximum - radii.max() + circ = mpatches.Circle((center_y, center_x), radius, + fill=False, edgecolor='red', linewidth=2) + ax.add_patch(circ) + +plt.show() From de2fa28b77591bbebe7369b43c8901a39cc878f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 24 Feb 2013 10:47:12 +0100 Subject: [PATCH 591/625] Docstring + J.S. modifications --- CONTRIBUTORS.txt | 2 +- doc/examples/plot_circular_hough_transform.py | 61 +++++++++++++------ 2 files changed, 45 insertions(+), 18 deletions(-) diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 80cb92f0..fd0656db 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -132,7 +132,7 @@ - François Boulogne Andres Method for circle perimeter, ellipse perimeter drawing. - Hough transform for circles + Circular Hough Transform - Thouis Jones Vectorized operators for arrays of 16-bit ints. diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py index 0caee3c4..cc4ea664 100755 --- a/doc/examples/plot_circular_hough_transform.py +++ b/doc/examples/plot_circular_hough_transform.py @@ -1,14 +1,32 @@ -#!/usr/bin/env python2 -# -*- coding: utf-8 -*- -# Author: Francois Boulogne -# License: GPL - """ ======================== Circular Hough Transform ======================== +The Hough transform in its simplest form is a `method to detect +straight lines `__ +but it can also be used to detect circles. +In the following example, the Hough transform is used to detect +coin positions and match their edges. We provide a range of +plausible radii. For each radius, two circles are extracted and +we finally keep the five most prominent candidates. +The result shows that coin positions are well-detected. + + +Algorithm overview +------------------ + +Given a black circle on a white background, we first guess its +radius (or a range of radii) to construct a new circle. +This circle is applied on each black pixel of the original picture +and the coordinates of this circle are voting in an accumulator. +From this geometrical construction, the original circle center +position receives the highest score. + +Note that the accumulator size is built to be larger than the +original picture in order to detect centers outside the frame. +Its size is extended by two times the larger radius. """ @@ -23,23 +41,32 @@ from skimage.feature import peak_local_max # Load picture and detect edges image = data.coins()[0:95, 70:370] -edges = filter.canny(filter.sobel(image), sigma=2.8) - +edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50) fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6)) ax.imshow(image, cmap=plt.cm.gray) # Detect two radii -radii = np.array([21, 25]) -hough_res = hough_circle(edges, radii) +hough_radii = np.arange(15, 30, 2) +hough_res = hough_circle(edges, hough_radii) -for radius, h in zip(radii, hough_res): - # For each radius, keep two circles - maxima = peak_local_max(h, num_peaks=2) - for maximum in maxima: - center_x, center_y = maximum - radii.max() - circ = mpatches.Circle((center_y, center_x), radius, - fill=False, edgecolor='red', linewidth=2) - ax.add_patch(circ) +centers = [] +accums = [] +radii = [] + +for radius, h in zip(hough_radii, hough_res): + # For each radius, extract two circles + peaks = peak_local_max(h, num_peaks=2) + centers.extend(peaks - hough_radii.max()) + accums.extend(h[peaks[:, 0], peaks[:, 1]]) + radii.extend([radius, radius]) + +# Draw the most prominent 5 circles +for idx in np.argsort(accums)[::-1][:5]: + center_x, center_y = centers[idx] + radius = radii[idx] + circ = mpatches.Circle((center_y, center_x), radius, + fill=False, edgecolor='red', linewidth=2) + ax.add_patch(circ) plt.show() From 86f777ae3de257c1eca82a2edc9a079a63fd9455 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sat, 9 Feb 2013 20:00:19 +0100 Subject: [PATCH 592/625] hough transform for circles --- skimage/transform/_hough_transform.pyx | 1 - 1 file changed, 1 deletion(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 35426cad..e1c06d77 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -59,7 +59,6 @@ def _hough_circle(np.ndarray img, \ img.shape[1] + 2 * max_radius)) for i, rad in enumerate(radius): - # Store in memory the circle of given radius # centered at (0,0) circle_x, circle_y = circle_perimeter(0, 0, rad) From 0451a2e17b775183e8a547c2a1125d20d45c0331 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 24 Feb 2013 16:27:40 +0100 Subject: [PATCH 593/625] use draw instead of mpl patches --- doc/examples/plot_circular_hough_transform.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py index cc4ea664..f7490051 100755 --- a/doc/examples/plot_circular_hough_transform.py +++ b/doc/examples/plot_circular_hough_transform.py @@ -38,13 +38,13 @@ import matplotlib.patches as mpatches from skimage import data, filter from skimage.transform import hough_circle from skimage.feature import peak_local_max +from skimage.draw import circle_perimeter # Load picture and detect edges image = data.coins()[0:95, 70:370] edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50) fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6)) -ax.imshow(image, cmap=plt.cm.gray) # Detect two radii hough_radii = np.arange(15, 30, 2) @@ -65,8 +65,8 @@ for radius, h in zip(hough_radii, hough_res): for idx in np.argsort(accums)[::-1][:5]: center_x, center_y = centers[idx] radius = radii[idx] - circ = mpatches.Circle((center_y, center_x), radius, - fill=False, edgecolor='red', linewidth=2) - ax.add_patch(circ) + cx, cy = circle_perimeter(center_y, center_x, radius) + image[cy, cx] = 0 +ax.imshow(image, cmap=plt.cm.gray) plt.show() From 609ce322d9ee9f1eda9f22dbdb457fca9d7d0d9f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= Date: Sun, 24 Feb 2013 16:45:28 +0100 Subject: [PATCH 594/625] remove import mpl patches --- doc/examples/plot_circular_hough_transform.py | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py index f7490051..2574a1ac 100755 --- a/doc/examples/plot_circular_hough_transform.py +++ b/doc/examples/plot_circular_hough_transform.py @@ -33,7 +33,6 @@ Its size is extended by two times the larger radius. import numpy as np import matplotlib.pyplot as plt -import matplotlib.patches as mpatches from skimage import data, filter from skimage.transform import hough_circle From c2023e9975946fdefe88c624e745ee6c6eb8b6cb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 26 Feb 2013 12:12:02 +0100 Subject: [PATCH 595/625] Remove duplicate return statement --- skimage/transform/_warps.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index 0ace0937..5af3afa6 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -348,6 +348,3 @@ def homography(image, H, output_shape=None, order=1, tform = ProjectiveTransform(H) return warp(image, inverse_map=tform.inverse, output_shape=output_shape, order=order, mode=mode, cval=cval) - - return warp(image, inverse_map=tform.inverse, output_shape=output_shape, - order=order, mode=mode, cval=cval) From 72e1d20a2948b166b0cea311290cdbec1610e414 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 26 Feb 2013 12:22:13 +0100 Subject: [PATCH 596/625] Remove deprecated homography function --- skimage/transform/_warps.py | 95 ------------------------------------- 1 file changed, 95 deletions(-) diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index 5af3afa6..7ac3e63f 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -253,98 +253,3 @@ def swirl(image, center=None, strength=1, radius=100, rotation=0, return warp(image, _swirl_mapping, map_args=warp_args, output_shape=output_shape, order=order, mode=mode, cval=cval) - - -def homography(image, H, output_shape=None, order=1, - mode='constant', cval=0.): - """ - .. note:: Deprecated in skimage 0.7 - `homography` will be removed in skimage 0.8, it is replaced by - `warp` because the latter provides the same functionality:: - - warp(image, ProjectiveTransform(H)) - - Perform a projective transformation (homography) on an image. - - For each pixel, given its homogeneous coordinate :math:`\mathbf{x} - = [x, y, 1]^T`, its target position is calculated by multiplying - with the given matrix, :math:`H`, to give :math:`H \mathbf{x}`. - E.g., to rotate by theta degrees clockwise, the matrix should be - - :: - - [[cos(theta) -sin(theta) 0] - [sin(theta) cos(theta) 0] - [0 0 1]] - - or, to translate x by 10 and y by 20, - - :: - - [[1 0 10] - [0 1 20] - [0 0 1 ]]. - - Parameters - ---------- - image : 2-D array - Input image. - H : array of shape ``(3, 3)`` - Transformation matrix H that defines the homography. - output_shape : tuple (rows, cols) - Shape of the output image generated. - order : int - Order of splines used in interpolation. - mode : string - How to handle values outside the image borders. Passed as-is - to ndimage. - cval : string - Used in conjunction with mode 'constant', the value outside - the image boundaries. - - Examples - -------- - >>> # rotate by 90 degrees around origin and shift down by 2 - >>> x = np.arange(9, dtype=np.uint8).reshape((3, 3)) + 1 - >>> x - array([[1, 2, 3], - [4, 5, 6], - [7, 8, 9]], dtype=uint8) - >>> theta = -np.pi/2 - >>> M = np.array([[np.cos(theta),-np.sin(theta),0], - ... [np.sin(theta), np.cos(theta),2], - ... [0, 0, 1]]) - >>> x90 = homography(x, M, order=1) - >>> x90 - array([[3, 6, 9], - [2, 5, 8], - [1, 4, 7]], dtype=uint8) - >>> # translate right by 2 and down by 1 - >>> y = np.zeros((5,5), dtype=np.uint8) - >>> y[1, 1] = 255 - >>> y - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - >>> M = np.array([[ 1., 0., 2.], - ... [ 0., 1., 1.], - ... [ 0., 0., 1.]]) - >>> y21 = homography(y, M, order=1) - >>> y21 - array([[ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 255, 0], - [ 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - """ - import warnings - warnings.warn('the homography function is deprecated; ' - 'use the `warp` and `ProjectiveTransform` class instead', - category=DeprecationWarning) - - tform = ProjectiveTransform(H) - return warp(image, inverse_map=tform.inverse, output_shape=output_shape, - order=order, mode=mode, cval=cval) From f2d91d2c296e3662a1b656f0fdf5191665ff363b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 26 Feb 2013 16:57:43 +0100 Subject: [PATCH 597/625] Remove deprecated import of hompgraphy --- skimage/transform/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skimage/transform/__init__.py b/skimage/transform/__init__.py index befcc23f..089487ee 100644 --- a/skimage/transform/__init__.py +++ b/skimage/transform/__init__.py @@ -6,6 +6,6 @@ from ._geometric import (warp, warp_coords, estimate_transform, SimilarityTransform, AffineTransform, ProjectiveTransform, PolynomialTransform, PiecewiseAffineTransform) -from ._warps import swirl, homography, resize, rotate, rescale +from ._warps import swirl, resize, rotate, rescale from .pyramids import (pyramid_reduce, pyramid_expand, pyramid_gaussian, pyramid_laplacian) From dd08c8cd83293cc31a3f1722577d8f4aefb0c86e Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Tue, 26 Feb 2013 20:34:29 -0600 Subject: [PATCH 598/625] Fix return value of deprecated `equalize` function. --- doc/examples/plot_equalize.py | 1 - doc/examples/plot_local_equalize.py | 2 +- skimage/exposure/exposure.py | 2 +- 3 files changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index f57cea9c..3569c4ee 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -19,7 +19,6 @@ that fall within the 2nd and 98th percentiles [2]_. """ from skimage import data, img_as_float -from skimage.util.dtype import dtype_range from skimage import exposure import matplotlib.pyplot as plt diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py index bc458505..33b2c2e4 100644 --- a/doc/examples/plot_local_equalize.py +++ b/doc/examples/plot_local_equalize.py @@ -57,7 +57,7 @@ img = data.moon() # Contrast stretching p2 = np.percentile(img, 2) p98 = np.percentile(img, 98) -img_rescale = exposure.equalize(img) +img_rescale = exposure.equalize_hist(img) # Equalization selem = disk(30) diff --git a/skimage/exposure/exposure.py b/skimage/exposure/exposure.py index 465b9182..c7f1e712 100644 --- a/skimage/exposure/exposure.py +++ b/skimage/exposure/exposure.py @@ -81,7 +81,7 @@ def cumulative_distribution(image, nbins=256): @deprecated('equalize_hist') def equalize(image, nbins=256): - equalize_hist(image, nbins) + return equalize_hist(image, nbins) def equalize_hist(image, nbins=256): From 374a967ffc76a34971043630ad6b0221629e838c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 27 Feb 2013 08:45:45 +0100 Subject: [PATCH 599/625] Remove deprecated tests for homography --- skimage/transform/tests/test_warps.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index 2cdda001..93f87320 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -5,7 +5,7 @@ from scipy.ndimage import map_coordinates from skimage.transform import (warp, warp_coords, rotate, resize, rescale, AffineTransform, ProjectiveTransform, - SimilarityTransform, homography) + SimilarityTransform) from skimage import transform as tf, data, img_as_float from skimage.color import rgb2gray @@ -39,10 +39,6 @@ def test_homography(): assert_array_almost_equal(x90, np.rot90(x)) -def test_homography_basic(): - homography(np.random.random((25, 25)), np.eye(3)) - - def test_fast_homography(): img = rgb2gray(data.lena()).astype(np.uint8) img = img[:, :100] @@ -87,10 +83,10 @@ def test_rotate(): def test_rotate_resize(): x = np.zeros((10, 10), dtype=np.double) - + x45 = rotate(x, 45, resize=False) assert x45.shape == (10, 10) - + x45 = rotate(x, 45, resize=True) # new dimension should be d = sqrt(2 * (10/2)^2) assert x45.shape == (14, 14) From 2ff289fcec7454b2c7af16c9b79b1bf7b38274e0 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Fri, 1 Mar 2013 17:19:36 +0200 Subject: [PATCH 600/625] DOC: Use brighter color in circular Hough demo. --- doc/examples/plot_circular_hough_transform.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py index 2574a1ac..fc891d04 100755 --- a/doc/examples/plot_circular_hough_transform.py +++ b/doc/examples/plot_circular_hough_transform.py @@ -34,7 +34,7 @@ Its size is extended by two times the larger radius. import numpy as np import matplotlib.pyplot as plt -from skimage import data, filter +from skimage import data, filter, color from skimage.transform import hough_circle from skimage.feature import peak_local_max from skimage.draw import circle_perimeter @@ -61,11 +61,12 @@ for radius, h in zip(hough_radii, hough_res): radii.extend([radius, radius]) # Draw the most prominent 5 circles +image = color.gray2rgb(image) for idx in np.argsort(accums)[::-1][:5]: center_x, center_y = centers[idx] radius = radii[idx] cx, cy = circle_perimeter(center_y, center_x, radius) - image[cy, cx] = 0 + image[cy, cx] = (220, 250, 150) ax.imshow(image, cmap=plt.cm.gray) plt.show() From d6d177fec5da516d00f53fd97ab6d30f1f2acd80 Mon Sep 17 00:00:00 2001 From: Jan Margeta Date: Sat, 2 Mar 2013 15:39:20 +0100 Subject: [PATCH 601/625] Fixing MSVC build by adding appropriate typedefs for uint16_t --- skimage/_shared/vectorized_ops.h | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/skimage/_shared/vectorized_ops.h b/skimage/_shared/vectorized_ops.h index e426c86e..29026490 100644 --- a/skimage/_shared/vectorized_ops.h +++ b/skimage/_shared/vectorized_ops.h @@ -12,6 +12,11 @@ #include #elif defined(_MSC_VER) #define inline __inline +#if (_MSC_VER < 1300) +typedef unsigned short uint16_t; +#else +typedef unsigned __int16 uint16_t; +#endif #endif /** From db7a063d763135e08069b30fb5c4a292d39de987 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sat, 2 Mar 2013 17:21:14 -0600 Subject: [PATCH 602/625] ENH: If example fails, print traceback and carry on. --- doc/ext/plot2rst.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/doc/ext/plot2rst.py b/doc/ext/plot2rst.py index 65c77521..00560be4 100644 --- a/doc/ext/plot2rst.py +++ b/doc/ext/plot2rst.py @@ -69,6 +69,7 @@ import os import shutil import token import tokenize +import traceback import numpy as np import matplotlib @@ -247,7 +248,16 @@ def write_gallery(gallery_index, src_dir, rst_dir, cfg, depth=0): gallery_index.write(TOCTREE_TEMPLATE % (sub_dir + '\n '.join(ex_names))) for src_name in examples: - write_example(src_name, src_dir, rst_dir, cfg) + + try: + write_example(src_name, src_dir, rst_dir, cfg) + except Exception: + print "Exception raised while running:" + print "%s in %s" % (src_name, src_dir) + print '~' * 60 + traceback.print_exc() + print '~' * 60 + continue link_name = sub_dir.pjoin(src_name) link_name = link_name.replace(os.path.sep, '_') From 17e7ba3de7177b5afacd6b08f15f23800bcd4d36 Mon Sep 17 00:00:00 2001 From: Tony S Yu Date: Sat, 2 Mar 2013 17:22:10 -0600 Subject: [PATCH 603/625] DOC: Fix Sphinx errors. --- doc/examples/plot_corner.py | 1 - .../plot_random_walker_segmentation.py | 1 - skimage/exposure/_adapthist.py | 13 +++++----- skimage/feature/_daisy.py | 21 +++++++++------ skimage/feature/corner.py | 6 ++--- skimage/feature/corner_cy.pyx | 2 +- skimage/feature/peak.py | 7 ++--- skimage/filter/_denoise.py | 6 ++--- skimage/filter/rank/rank.pyx | 4 +-- .../random_walker_segmentation.py | 26 +++++++++++-------- 10 files changed, 48 insertions(+), 39 deletions(-) diff --git a/doc/examples/plot_corner.py b/doc/examples/plot_corner.py index 4d01d177..30f1f0cf 100644 --- a/doc/examples/plot_corner.py +++ b/doc/examples/plot_corner.py @@ -11,7 +11,6 @@ position of corners. """ -import numpy as np from matplotlib import pyplot as plt from skimage import data diff --git a/doc/examples/plot_random_walker_segmentation.py b/doc/examples/plot_random_walker_segmentation.py index 6d194293..7d9f4fa8 100644 --- a/doc/examples/plot_random_walker_segmentation.py +++ b/doc/examples/plot_random_walker_segmentation.py @@ -19,7 +19,6 @@ values, and use the random walker for the segmentation. .. [1] *Random walks for image segmentation*, Leo Grady, IEEE Trans. Pattern Anal. Mach. Intell. 2006 Nov; 28(11):1768-83 """ -print __doc__ import numpy as np from scipy import ndimage diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py index 346d8424..8a825435 100644 --- a/skimage/exposure/_adapthist.py +++ b/skimage/exposure/_adapthist.py @@ -259,6 +259,8 @@ def map_histogram(hist, min_val, max_val, n_pixels): It does so by cumulating the input histogram. + Parameters + ---------- hist : ndarray Clipped histogram. min_val : int @@ -301,12 +303,11 @@ def interpolate(image, xslice, yslice, out : ndarray Original image with the subregion replaced. - Note - ---- - This function calculates the new greylevel assignments of pixels - within a submatrix of the image. - This is done by a bilinear interpolation between four different - mappings in order to eliminate boundary artifacts. + Notes + ----- + This function calculates the new greylevel assignments of pixels within + a submatrix of the image. This is done by a bilinear interpolation between + four different mappings in order to eliminate boundary artifacts. """ norm = xslice.size * yslice.size # Normalization factor # interpolation weight matrices diff --git a/skimage/feature/_daisy.py b/skimage/feature/_daisy.py index 1bb8cbf5..1a97de8f 100644 --- a/skimage/feature/_daisy.py +++ b/skimage/feature/_daisy.py @@ -53,30 +53,35 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, the spatial smoothing of the center histogram and the last sigma value defines the spatial smoothing of the outermost ring. Specifying sigmas overrides the following parameter. - ``rings = len(sigmas)-1`` + + ``rings = len(sigmas) - 1`` ring_radii : 1D array of int, optional Radius (in pixels) for each ring. Specifying ring_radii overrides the following two parameters. - | ``rings = len(ring_radii)`` - | ``radius = ring_radii[-1]`` + + ``rings = len(ring_radii)`` + ``radius = ring_radii[-1]`` If both sigmas and ring_radii are given, they must satisfy the following predicate since no radius is needed for the center histogram. - ``len(ring_radii) == len(sigmas)+1`` + + ``len(ring_radii) == len(sigmas) + 1`` + visualize : bool, optional Generate a visualization of the DAISY descriptors - Returns ------- descs : array Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where - | ``P = ceil((M-radius*2)/step)`` - | ``Q = ceil((N-radius*2)/step)`` - | ``R = (rings*histograms + 1)*orientations`` + + ``P = ceil((M - radius*2) / step)`` + ``Q = ceil((N - radius*2) / step)`` + ``R = (rings * histograms + 1) * orientations`` + descs_img : (M, N, 3) array (only if visualize==True) Visualization of the DAISY descriptors. diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py index 867acb5d..12d3fb70 100644 --- a/skimage/feature/corner.py +++ b/skimage/feature/corner.py @@ -140,7 +140,7 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): ..[2] http://en.wikipedia.org/wiki/Corner_detection Examples - ------- + -------- >>> from skimage.feature import corner_harris, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 @@ -210,7 +210,7 @@ def corner_shi_tomasi(image, sigma=1): ..[2] http://en.wikipedia.org/wiki/Corner_detection Examples - ------- + -------- >>> from skimage.feature import corner_shi_tomasi, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 @@ -277,7 +277,7 @@ def corner_foerstner(image, sigma=1): ..[2] http://en.wikipedia.org/wiki/Corner_detection Examples - ------- + -------- >>> from skimage.feature import corner_foerstner, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx index 407e6642..71c748f8 100644 --- a/skimage/feature/corner_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -34,7 +34,7 @@ def corner_moravec(image, Py_ssize_t window_size=1): ..[2] http://en.wikipedia.org/wiki/Corner_detection Examples - ------- + -------- >>> from skimage.feature import moravec, peak_local_max >>> square = np.zeros([7, 7]) >>> square[3, 3] = 1 diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 4b0f6b4f..9c7a934f 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -50,9 +50,10 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, Returns ------- output : (N, 2) array or ndarray of bools - If `indices = True` : (row, column) coordinates of peaks. - If `indices = False` : Boolean array shaped like `image`, - with peaks represented by True values. + + * If `indices = True` : (row, column) coordinates of peaks. + * If `indices = False` : Boolean array shaped like `image`, with peaks + represented by True values. Notes ----- diff --git a/skimage/filter/_denoise.py b/skimage/filter/_denoise.py index e26f8387..87850759 100644 --- a/skimage/filter/_denoise.py +++ b/skimage/filter/_denoise.py @@ -32,7 +32,7 @@ def _denoise_tv_chambolle_3d(im, weight=100, eps=2.e-4, n_iter_max=200): Rudin, Osher and Fatemi algorithm. Examples - --------- + -------- >>> x, y, z = np.ogrid[0:40, 0:40, 0:40] >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 >>> mask = mask.astype(np.float) @@ -123,7 +123,7 @@ def _denoise_tv_chambolle_2d(im, weight=50, eps=2.e-4, n_iter_max=200): Springer, 2004, 20, 89-97. Examples - --------- + -------- >>> from skimage import color, data >>> lena = color.rgb2gray(data.lena()) >>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape) @@ -221,7 +221,7 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200, Springer, 2004, 20, 89-97. Examples - --------- + -------- 2D example on Lena image: >>> from skimage import color, data diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index 261f0bb6..e8a4c8f1 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -648,12 +648,12 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False, References ---------- .. [Hashimoto12] N. Hashimoto et al. Referenceless image quality evaluation - for whole slide imaging. J Pathol Inform 2012;3:9. + for whole slide imaging. J Pathol Inform 2012;3:9. Returns ------- out : uint8 array or uint16 array (same as input image) - The image noise . + The image noise. """ diff --git a/skimage/segmentation/random_walker_segmentation.py b/skimage/segmentation/random_walker_segmentation.py index 32baf59c..6df714d2 100644 --- a/skimage/segmentation/random_walker_segmentation.py +++ b/skimage/segmentation/random_walker_segmentation.py @@ -30,8 +30,7 @@ from ..filter import rank_order def _make_graph_edges_3d(n_x, n_y, n_z): - """ - Returns a list of edges for a 3D image. + """Returns a list of edges for a 3D image. Parameters ---------- @@ -45,9 +44,12 @@ def _make_graph_edges_3d(n_x, n_y, n_z): Returns ------- edges : (2, N) ndarray - with the total number of edges N = n_x * n_y * (nz - 1) + - n_x * (n_y - 1) * nz + - (n_x - 1) * n_y * nz + with the total number of edges:: + + N = n_x * n_y * (nz - 1) + + n_x * (n_y - 1) * nz + + (n_x - 1) * n_y * nz + Graph edges with each column describing a node-id pair. """ vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z)) @@ -200,6 +202,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, mode : {'bf', 'cg_mg', 'cg'} (default: 'bf') Mode for solving the linear system in the random walker algorithm. + - 'bf' (brute force, default): an LU factorization of the Laplacian is computed. This is fast for small images (<1024x1024), but very slow (due to the memory cost) and memory-consuming for big images (in 3-D @@ -214,6 +217,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, requires that the pyamg module (http://code.google.com/p/pyamg/) is installed. For images of size > 512x512, this is the recommended (fastest) mode. + tol : float tolerance to achieve when solving the linear system, in cg' and 'cg_mg' modes. @@ -237,12 +241,12 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, Returns ------- output : ndarray - If `return_full_prob` is False, array of ints of same shape as `data`, - in which each pixel has been labeled according to the marker that - reached the pixel first by anisotropic diffusion. - If `return_full_prob` is True, array of floats of shape - `(nlabels, data.shape)`. `output[label_nb, i, j]` is the probability - that label `label_nb` reaches the pixel `(i, j)` first. + * If `return_full_prob` is False, array of ints of same shape as + `data`, in which each pixel has been labeled according to the marker + that reached the pixel first by anisotropic diffusion. + * If `return_full_prob` is True, array of floats of shape + `(nlabels, data.shape)`. `output[label_nb, i, j]` is the probability + that label `label_nb` reaches the pixel `(i, j)` first. See also -------- From 6e0112f449fb46ea11af5a004aca7acfca2f567d Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Fri, 1 Mar 2013 18:00:58 +0200 Subject: [PATCH 604/625] BUG: Recast label output to satisfy ndimage on 32-bit platforms. --- skimage/morphology/ccomp.pyx | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/skimage/morphology/ccomp.pyx b/skimage/morphology/ccomp.pyx index bbf6ed44..1db78a6d 100644 --- a/skimage/morphology/ccomp.pyx +++ b/skimage/morphology/ccomp.pyx @@ -214,4 +214,8 @@ def label(input, DTYPE_t neighbors=8, DTYPE_t background=-1): else: data[i, j] = data_p[forest[i, j]] - return data + # Work around a bug in ndimage's type checking on 32-bit platforms + if data.dtype == np.int32: + return data.view(np.int32) + else: + return data From 5686e11c97e5855e074a677a249285f546713f63 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Thu, 21 Feb 2013 17:17:58 +1100 Subject: [PATCH 605/625] Add funct to remove small connected compononents --- skimage/morphology/__init__.py | 1 + skimage/morphology/misc.py | 40 ++++++++++++++++++++++++++++++++++ 2 files changed, 41 insertions(+) create mode 100644 skimage/morphology/misc.py diff --git a/skimage/morphology/__init__.py b/skimage/morphology/__init__.py index d4c775eb..e93d22ad 100644 --- a/skimage/morphology/__init__.py +++ b/skimage/morphology/__init__.py @@ -7,3 +7,4 @@ from .watershed import watershed, is_local_maximum from ._skeletonize import skeletonize, medial_axis from .convex_hull import convex_hull_image from .greyreconstruct import reconstruction +from .misc import remove_small_connected_components diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py new file mode 100644 index 00000000..159a3a3b --- /dev/null +++ b/skimage/morphology/misc.py @@ -0,0 +1,40 @@ +import numpy as np +import scipy.ndimage as nd + +def remove_small_connected_components(ar, min_size=64, + connectivity=1, in_place=False): + """Remove connected components smaller than the specified size. + + Parameters + ---------- + ar : ndarray (arbitrary shape, int or bool type) + The array containing the connected components of interest. + min_size : int, optional (default: 64) + The smallest allowable connected component size. + connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) + The connectivity defining the neighborhood of a pixel. + in_place : bool, optional (default: False) + If `True`, remove the connected components in the input array itself. + Otherwise, make a copy. + + Returns + ------- + out : ndarray, same shape and type as input `ar` + The input array with small connected components removed. + """ + structuring_element = nd.generate_binary_structure(ar.ndim, connectivity) + if in_place: + out = ar + else: + out = ar.copy() + if min_size == 0: # shortcut for efficiency + return out + if out.dtype == bool: + ccs = nd.label(ar, structuring_element)[0] + else: + ccs = out + component_sizes = np.bincount(ccs.ravel()) + too_small = component_sizes < min_size + too_small_mask = too_small[ccs] + out[too_small_mask] = 0 + return out From 24b14508c804446f40ba1fdf4007ce35c7c7ec77 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Sun, 3 Mar 2013 14:34:14 +1100 Subject: [PATCH 606/625] Add examples to docstring --- skimage/morphology/misc.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py index 159a3a3b..81f47e9c 100644 --- a/skimage/morphology/misc.py +++ b/skimage/morphology/misc.py @@ -21,6 +21,28 @@ def remove_small_connected_components(ar, min_size=64, ------- out : ndarray, same shape and type as input `ar` The input array with small connected components removed. + + Examples + -------- + >>> import numpy as np + >>> from skimage import morphology + >>> from scipy import ndimage as nd + >>> a = np.array([[0, 0, 0, 1, 0], + ... [1, 1, 1, 0, 0], + ... [1, 1, 1, 0, 1]], bool) + >>> b = morphology.remove_small_connected_components(a, 6) + >>> b + array([[False, False, False, False, False], + [ True, True, True, False, False], + [ True, True, True, False, False]], dtype=bool) + >>> c = morphology.remove_small_connected_components(a, 7, connectivity=2) + >>> c + array([[False, False, False, True, False], + [ True, True, True, False, False], + [ True, True, True, False, False]], dtype=bool) + >>> d = morphology.remove_small_connected_components(a, 6, in_place=True) + >>> d is a + True """ structuring_element = nd.generate_binary_structure(ar.ndim, connectivity) if in_place: From 5222fb5ff7eef0115603e99d8f266eda884fb141 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Sun, 3 Mar 2013 14:50:20 +1100 Subject: [PATCH 607/625] Add tests for remove_small_connected_components --- skimage/morphology/tests/test_misc.py | 30 +++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 skimage/morphology/tests/test_misc.py diff --git a/skimage/morphology/tests/test_misc.py b/skimage/morphology/tests/test_misc.py new file mode 100644 index 00000000..77bb20c3 --- /dev/null +++ b/skimage/morphology/tests/test_misc.py @@ -0,0 +1,30 @@ +import numpy as np +from numpy.testing import assert_array_equal, assert_equal +from skimage.morphology import remove_small_connected_components + +test_image = np.array([[0, 0, 0, 1, 0], + [1, 1, 1, 0, 0], + [1, 1, 1, 0, 1]], bool) +def test_one_connectivity(): + expected = np.array([[0, 0, 0, 0, 0], + [1, 1, 1, 0, 0], + [1, 1, 1, 0, 0]], bool) + observed = remove_small_connected_components(test_image, min_size=6) + assert_array_equal(observed, expected) + +def test_two_connectivity(): + expected = np.array([[0, 0, 0, 1, 0], + [1, 1, 1, 0, 0], + [1, 1, 1, 0, 0]], bool) + observed = remove_small_connected_components(test_image, min_size=7, + connectivity=2) + assert_array_equal(observed, expected) + +def test_in_place(): + observed = remove_small_connected_components(test_image, min_size=6, + in_place=True) + assert_equal(observed is test_image, True, + "remove_small_connected_components in_place argument failed.") + +if __name__ == "__main__": + np.testing.run_module_suite() From afed313369e332f2c7b64bcb9e65582126016a4e Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Sun, 3 Mar 2013 15:18:12 +1100 Subject: [PATCH 608/625] Modify example to use remove_small_connected_components --- doc/examples/applications/plot_coins_segmentation.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/doc/examples/applications/plot_coins_segmentation.py b/doc/examples/applications/plot_coins_segmentation.py index f46bbd33..943c57fb 100644 --- a/doc/examples/applications/plot_coins_segmentation.py +++ b/doc/examples/applications/plot_coins_segmentation.py @@ -90,12 +90,8 @@ plt.title('Filling the holes') Small spurious objects are easily removed by setting a minimum size for valid objects. """ - -label_objects, nb_labels = ndimage.label(fill_coins) -sizes = np.bincount(label_objects.ravel()) -mask_sizes = sizes > 20 -mask_sizes[0] = 0 -coins_cleaned = mask_sizes[label_objects] +from skimage import morphology +coins_cleaned = morphology.remove_small_connected_components(fill_coins, 21) plt.figure(figsize=(4, 3)) plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest') @@ -149,8 +145,7 @@ plt.title('markers') Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above: """ -from skimage.morphology import watershed -segmentation = watershed(elevation_map, markers) +segmentation = morphology.watershed(elevation_map, markers) plt.figure(figsize=(4, 3)) plt.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest') From d6c5ba38ef03bd9e3c9f82bd1c724ea06ebc7f11 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Sun, 3 Mar 2013 20:33:05 +1100 Subject: [PATCH 609/625] Improve docstring for remove_small_connected_components - Explain behavior if input is of int type. - Add `Raises` section for possible errors. - Remove explicit numpy import from example. --- skimage/morphology/misc.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py index 81f47e9c..d70e8eb8 100644 --- a/skimage/morphology/misc.py +++ b/skimage/morphology/misc.py @@ -2,13 +2,15 @@ import numpy as np import scipy.ndimage as nd def remove_small_connected_components(ar, min_size=64, - connectivity=1, in_place=False): + connectivity=1, in_place=False): """Remove connected components smaller than the specified size. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) - The array containing the connected components of interest. + The array containing the connected components of interest. If the array + type is int, it is assumed that it contains already-labeled objects. + The ints must be non-negative. min_size : int, optional (default: 64) The smallest allowable connected component size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) @@ -17,6 +19,13 @@ def remove_small_connected_components(ar, min_size=64, If `True`, remove the connected components in the input array itself. Otherwise, make a copy. + Raises + ------ + ValueError + If the input array is of int type but contains negative values. + TypeError + If the input array is of an invalid type, such as float or string. + Returns ------- out : ndarray, same shape and type as input `ar` @@ -24,7 +33,6 @@ def remove_small_connected_components(ar, min_size=64, Examples -------- - >>> import numpy as np >>> from skimage import morphology >>> from scipy import ndimage as nd >>> a = np.array([[0, 0, 0, 1, 0], From b07f7fd9bd910784ee14f08800765d3276b1e6fa Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Sun, 3 Mar 2013 20:47:02 +1100 Subject: [PATCH 610/625] Rename function to remove_small_objects --- skimage/morphology/__init__.py | 2 +- skimage/morphology/misc.py | 3 +-- skimage/morphology/tests/test_misc.py | 10 +++++----- 3 files changed, 7 insertions(+), 8 deletions(-) diff --git a/skimage/morphology/__init__.py b/skimage/morphology/__init__.py index e93d22ad..044fcf89 100644 --- a/skimage/morphology/__init__.py +++ b/skimage/morphology/__init__.py @@ -7,4 +7,4 @@ from .watershed import watershed, is_local_maximum from ._skeletonize import skeletonize, medial_axis from .convex_hull import convex_hull_image from .greyreconstruct import reconstruction -from .misc import remove_small_connected_components +from .misc import remove_small_objects diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py index d70e8eb8..719fba5a 100644 --- a/skimage/morphology/misc.py +++ b/skimage/morphology/misc.py @@ -1,8 +1,7 @@ import numpy as np import scipy.ndimage as nd -def remove_small_connected_components(ar, min_size=64, - connectivity=1, in_place=False): +def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): """Remove connected components smaller than the specified size. Parameters diff --git a/skimage/morphology/tests/test_misc.py b/skimage/morphology/tests/test_misc.py index 77bb20c3..f27b1a45 100644 --- a/skimage/morphology/tests/test_misc.py +++ b/skimage/morphology/tests/test_misc.py @@ -1,6 +1,6 @@ import numpy as np from numpy.testing import assert_array_equal, assert_equal -from skimage.morphology import remove_small_connected_components +from skimage.morphology import remove_small_objects test_image = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], @@ -9,22 +9,22 @@ def test_one_connectivity(): expected = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool) - observed = remove_small_connected_components(test_image, min_size=6) + observed = remove_small_objects(test_image, min_size=6) assert_array_equal(observed, expected) def test_two_connectivity(): expected = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool) - observed = remove_small_connected_components(test_image, min_size=7, + observed = remove_small_objects(test_image, min_size=7, connectivity=2) assert_array_equal(observed, expected) def test_in_place(): - observed = remove_small_connected_components(test_image, min_size=6, + observed = remove_small_objects(test_image, min_size=6, in_place=True) assert_equal(observed is test_image, True, - "remove_small_connected_components in_place argument failed.") + "remove_small_objects in_place argument failed.") if __name__ == "__main__": np.testing.run_module_suite() From 6b9ecd80b91ad4c2ded3536ac34d21d09ba33bc8 Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Sun, 3 Mar 2013 21:43:56 +1100 Subject: [PATCH 611/625] Add raise and labeled image tests --- skimage/morphology/tests/test_misc.py | 36 +++++++++++++++++++++++---- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/skimage/morphology/tests/test_misc.py b/skimage/morphology/tests/test_misc.py index f27b1a45..4b6e6e01 100644 --- a/skimage/morphology/tests/test_misc.py +++ b/skimage/morphology/tests/test_misc.py @@ -1,10 +1,12 @@ import numpy as np -from numpy.testing import assert_array_equal, assert_equal +from numpy.testing import assert_array_equal, assert_equal, assert_raises from skimage.morphology import remove_small_objects test_image = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 1]], bool) + + def test_one_connectivity(): expected = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 0, 0], @@ -12,19 +14,43 @@ def test_one_connectivity(): observed = remove_small_objects(test_image, min_size=6) assert_array_equal(observed, expected) + def test_two_connectivity(): expected = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool) - observed = remove_small_objects(test_image, min_size=7, - connectivity=2) + observed = remove_small_objects(test_image, min_size=7, connectivity=2) assert_array_equal(observed, expected) + def test_in_place(): - observed = remove_small_objects(test_image, min_size=6, - in_place=True) + observed = remove_small_objects(test_image, min_size=6, in_place=True) assert_equal(observed is test_image, True, "remove_small_objects in_place argument failed.") + +def test_labeled_image(): + labeled_image = np.array([[2, 2, 2, 0, 1], + [2, 2, 2, 0, 1], + [2, 0, 0, 0, 0], + [0, 0, 3, 3, 3]], dtype=int) + expected = np.array([[2, 2, 2, 0, 0], + [2, 2, 2, 0, 0], + [2, 0, 0, 0, 0], + [0, 0, 3, 3, 3]], dtype=int) + observed = remove_small_objects(labeled_image, min_size=3) + assert_array_equal(observed, expected) + + +def test_float_input(): + float_test = np.random.rand(5, 5) + assert_raises(TypeError, remove_small_objects, float_test) + + +def test_negative_input(): + negative_int = np.random.randint(-4, -1, size=(5, 5)) + assert_raises(ValueError, remove_small_objects, negative_int) + + if __name__ == "__main__": np.testing.run_module_suite() From 332469c6df8fcb0d3151b457a76abccb1c136c1b Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Mon, 4 Mar 2013 01:24:26 +1100 Subject: [PATCH 612/625] Fix missed rename in example --- doc/examples/applications/plot_coins_segmentation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/examples/applications/plot_coins_segmentation.py b/doc/examples/applications/plot_coins_segmentation.py index 943c57fb..0d1d8a34 100644 --- a/doc/examples/applications/plot_coins_segmentation.py +++ b/doc/examples/applications/plot_coins_segmentation.py @@ -91,7 +91,7 @@ Small spurious objects are easily removed by setting a minimum size for valid objects. """ from skimage import morphology -coins_cleaned = morphology.remove_small_connected_components(fill_coins, 21) +coins_cleaned = morphology.remove_small_objects(fill_coins, 21) plt.figure(figsize=(4, 3)) plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest') From dc83c64d44ca8296f1757321fad9603bad79a676 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Sun, 3 Mar 2013 15:37:45 +0100 Subject: [PATCH 613/625] Change data type of index variables --- skimage/transform/_hough_transform.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 29a38eb7..a6ce6c27 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -50,11 +50,11 @@ def _hough_circle(cnp.ndarray img, \ x, y = np.nonzero(img) # Offset the image - cdef int max_radius = radius.max() + cdef Py_ssize_t max_radius = radius.max() x = x + max_radius y = y + max_radius - cdef int px, py + cdef Py_ssize_t px, py cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] tx, ty, circle_x, circle_y cdef cnp.ndarray acc = np.zeros((radius.size, img.shape[0] + 2 * max_radius, From 3869d1ef8a10f31ba0c345f80cc76bec5e8b3bb8 Mon Sep 17 00:00:00 2001 From: Jan Margeta Date: Sun, 3 Mar 2013 22:45:03 +0100 Subject: [PATCH 614/625] Removing unnecessary typedefs for uint16_t in MSVC build --- skimage/_shared/vectorized_ops.h | 4 ---- 1 file changed, 4 deletions(-) diff --git a/skimage/_shared/vectorized_ops.h b/skimage/_shared/vectorized_ops.h index 29026490..ab9647ea 100644 --- a/skimage/_shared/vectorized_ops.h +++ b/skimage/_shared/vectorized_ops.h @@ -12,12 +12,8 @@ #include #elif defined(_MSC_VER) #define inline __inline -#if (_MSC_VER < 1300) -typedef unsigned short uint16_t; -#else typedef unsigned __int16 uint16_t; #endif -#endif /** * Add 16 unsigned 16-bit integers using SSE2, MMX or Altivec, if From 3249faf308af182435b081e28210274179fdd81e Mon Sep 17 00:00:00 2001 From: Juan Nunez-Iglesias Date: Mon, 4 Mar 2013 16:54:29 +1100 Subject: [PATCH 615/625] Add specific error messages and exceptions --- skimage/morphology/misc.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py index 719fba5a..bb0ff687 100644 --- a/skimage/morphology/misc.py +++ b/skimage/morphology/misc.py @@ -51,7 +51,11 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): >>> d is a True """ - structuring_element = nd.generate_binary_structure(ar.ndim, connectivity) + errmsg = "Only numpy.ndarrays of bool or integer type are supported. " + if type(ar) != np.ndarray: + raise TypeError(errmsg + "Got a %s." % type(ar)) + elif ar.dtype != bool and not np.issubdtype(ar.dtype, np.integer): + raise TypeError(errmsg + "Got a numpy.ndarray of type %s" % ar.dtype) if in_place: out = ar else: @@ -59,10 +63,15 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): if min_size == 0: # shortcut for efficiency return out if out.dtype == bool: - ccs = nd.label(ar, structuring_element)[0] + selem = nd.generate_binary_structure(ar.ndim, connectivity) + ccs = nd.label(ar, selem)[0] else: ccs = out - component_sizes = np.bincount(ccs.ravel()) + try: + component_sizes = np.bincount(ccs.ravel()) + except ValueError: + raise ValueError("Negative value labels are not supported. Try " + "relabeling the input with `scipy.ndimage.label`.") too_small = component_sizes < min_size too_small_mask = too_small[ccs] out[too_small_mask] = 0 From dae9b7adfdabc065c2107d19779a1bb965bff759 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 4 Mar 2013 11:30:19 +0200 Subject: [PATCH 616/625] STY: Minor cleanup. --- skimage/morphology/misc.py | 21 ++++++++++++++------- skimage/morphology/tests/test_misc.py | 4 ++-- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py index bb0ff687..5f548503 100644 --- a/skimage/morphology/misc.py +++ b/skimage/morphology/misc.py @@ -1,6 +1,7 @@ import numpy as np import scipy.ndimage as nd + def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): """Remove connected components smaller than the specified size. @@ -51,28 +52,34 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): >>> d is a True """ - errmsg = "Only numpy.ndarrays of bool or integer type are supported. " - if type(ar) != np.ndarray: - raise TypeError(errmsg + "Got a %s." % type(ar)) - elif ar.dtype != bool and not np.issubdtype(ar.dtype, np.integer): - raise TypeError(errmsg + "Got a numpy.ndarray of type %s" % ar.dtype) + # Should use `issubdtype` below, but there's a bug in numpy 1.7 + if not (ar.dtype == bool or np.issubdtype(ar.dtype, int)): + raise ValueError("Only bool or integer image types are supported. " + "Got %s." % ar.dtype) + if in_place: out = ar else: out = ar.copy() - if min_size == 0: # shortcut for efficiency + + if min_size == 0: # shortcut for efficiency return out + if out.dtype == bool: selem = nd.generate_binary_structure(ar.ndim, connectivity) ccs = nd.label(ar, selem)[0] else: ccs = out + try: component_sizes = np.bincount(ccs.ravel()) except ValueError: raise ValueError("Negative value labels are not supported. Try " - "relabeling the input with `scipy.ndimage.label`.") + "relabeling the input with `scipy.ndimage.label` or " + "`skimage.morphology.label`.") + too_small = component_sizes < min_size too_small_mask = too_small[ccs] out[too_small_mask] = 0 + return out diff --git a/skimage/morphology/tests/test_misc.py b/skimage/morphology/tests/test_misc.py index 4b6e6e01..abc718c2 100644 --- a/skimage/morphology/tests/test_misc.py +++ b/skimage/morphology/tests/test_misc.py @@ -25,7 +25,7 @@ def test_two_connectivity(): def test_in_place(): observed = remove_small_objects(test_image, min_size=6, in_place=True) - assert_equal(observed is test_image, True, + assert_equal(observed is test_image, True, "remove_small_objects in_place argument failed.") @@ -44,7 +44,7 @@ def test_labeled_image(): def test_float_input(): float_test = np.random.rand(5, 5) - assert_raises(TypeError, remove_small_objects, float_test) + assert_raises(ValueError, remove_small_objects, float_test) def test_negative_input(): From dec104e9ffb5514ea5cf6c8423d64acbe9831a39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 15:00:37 +0100 Subject: [PATCH 617/625] Improve performance and usage of data types of hough circle transformation --- skimage/transform/_hough_transform.pyx | 30 +++++++++++++++----------- 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index a6ce6c27..e0c004a3 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -20,10 +20,9 @@ cdef inline Py_ssize_t round(double r): return ((r + 0.5) if (r > 0.0) else (r - 0.5)) -@cython.boundscheck(False) def _hough_circle(cnp.ndarray img, \ cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius, \ - normalize=True): + char normalize=True): """Perform a circular Hough transform. Parameters @@ -49,32 +48,39 @@ def _hough_circle(cnp.ndarray img, \ cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] x, y x, y = np.nonzero(img) + cdef Py_ssize_t num_pixels = x.size + # Offset the image cdef Py_ssize_t max_radius = radius.max() x = x + max_radius y = y + max_radius - cdef Py_ssize_t px, py - cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] tx, ty, circle_x, circle_y - cdef cnp.ndarray acc = np.zeros((radius.size, - img.shape[0] + 2 * max_radius, - img.shape[1] + 2 * max_radius)) + cdef Py_ssize_t i, p, c, num_circle_pixels, tx, ty + cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] circle_x, circle_y + + cdef cnp.ndarray[ndim=3, dtype=cnp.double_t] acc = \ + np.zeros((radius.size, + img.shape[0] + 2 * max_radius, + img.shape[1] + 2 * max_radius), dtype=np.double) for i, rad in enumerate(radius): # Store in memory the circle of given radius # centered at (0,0) circle_x, circle_y = circle_perimeter(0, 0, rad) + num_circle_pixels = circle_x.size + # For each non zero pixel - for (px, py) in zip(x, y): + for p in range(num_pixels): # Plug the circle at (px, py), # its coordinates are (tx, ty) - tx = circle_x + px - ty = circle_y + py - acc[i, tx, ty] += 1 + for c in range(num_circle_pixels): + tx = circle_x[c] + x[p] + ty = circle_y[c] + y[p] + acc[i, tx, ty] += 1 if normalize: - acc[i] = acc[i] / len(circle_x) + acc[i, :, :] /= num_circle_pixels return acc From 2e3f82e9d9dd4ce73b3b37c6c6aa566b64588d36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 15:01:03 +0100 Subject: [PATCH 618/625] Remove unnecessary statement breaks --- skimage/transform/_hough_transform.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index e0c004a3..97ff720b 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -20,8 +20,8 @@ cdef inline Py_ssize_t round(double r): return ((r + 0.5) if (r > 0.0) else (r - 0.5)) -def _hough_circle(cnp.ndarray img, \ - cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius, \ +def _hough_circle(cnp.ndarray img, + cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius, char normalize=True): """Perform a circular Hough transform. From 2237cc1617f62618a78a8b1bc1a2b19b8106274f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 15:02:09 +0100 Subject: [PATCH 619/625] Change color of circles for better visibility --- doc/examples/plot_circular_hough_transform.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/examples/plot_circular_hough_transform.py b/doc/examples/plot_circular_hough_transform.py index fc891d04..d2f8f2ae 100755 --- a/doc/examples/plot_circular_hough_transform.py +++ b/doc/examples/plot_circular_hough_transform.py @@ -66,7 +66,7 @@ for idx in np.argsort(accums)[::-1][:5]: center_x, center_y = centers[idx] radius = radii[idx] cx, cy = circle_perimeter(center_y, center_x, radius) - image[cy, cx] = (220, 250, 150) + image[cy, cx] = (220, 20, 20) ax.imshow(image, cmap=plt.cm.gray) plt.show() From 4925205082011dda230e7690f17042763a87b281 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 16:26:06 +0100 Subject: [PATCH 620/625] Improve performance of normalization --- skimage/transform/_hough_transform.pyx | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index 97ff720b..2b1acc7c 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -56,6 +56,7 @@ def _hough_circle(cnp.ndarray img, y = y + max_radius cdef Py_ssize_t i, p, c, num_circle_pixels, tx, ty + cdef double incr cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] circle_x, circle_y cdef cnp.ndarray[ndim=3, dtype=cnp.double_t] acc = \ @@ -70,6 +71,11 @@ def _hough_circle(cnp.ndarray img, num_circle_pixels = circle_x.size + if normalize: + incr = 1.0 / num_circle_pixels + else: + incr = 1 + # For each non zero pixel for p in range(num_pixels): # Plug the circle at (px, py), @@ -77,10 +83,7 @@ def _hough_circle(cnp.ndarray img, for c in range(num_circle_pixels): tx = circle_x[c] + x[p] ty = circle_y[c] + y[p] - acc[i, tx, ty] += 1 - - if normalize: - acc[i, :, :] /= num_circle_pixels + acc[i, tx, ty] += incr return acc From f3924da90b26b8bf4274463c7abd76e08366b2d4 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Mon, 4 Mar 2013 18:36:32 +0200 Subject: [PATCH 621/625] DOC: Fix typo in remove_small_objects docstring. --- skimage/morphology/misc.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skimage/morphology/misc.py b/skimage/morphology/misc.py index 5f548503..6274df53 100644 --- a/skimage/morphology/misc.py +++ b/skimage/morphology/misc.py @@ -22,8 +22,6 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): Raises ------ ValueError - If the input array is of int type but contains negative values. - TypeError If the input array is of an invalid type, such as float or string. Returns From 4a8016fde6e0aa421d8ca90557492ac538238d00 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 20:38:41 +0100 Subject: [PATCH 622/625] Add release notes for version 0.8 --- doc/release/release_0.8.txt | 71 +++++++++++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 doc/release/release_0.8.txt diff --git a/doc/release/release_0.8.txt b/doc/release/release_0.8.txt new file mode 100644 index 00000000..d146659c --- /dev/null +++ b/doc/release/release_0.8.txt @@ -0,0 +1,71 @@ +Announcement: scikits-image 0.8.0 +================================= + +We're happy to announce the 8th version of scikit-image! + +scikit-image is an image processing toolbox for SciPy that includes algorithms +for segmentation, geometric transformations, color space manipulation, +analysis, filtering, morphology, feature detection, and more. + +For more information, examples, and documentation, please visit our website: + + http://scikit-image.org + + +New Features +------------ + +- New rank filter package with many new functions and a very fast underlying + local histogram algorithm, especially for large structuring elements + `skimage.filter.rank.*` +- New function for small object removal + `skimage.morphology.remove_small_objects` +- New circular hough transformation `skimage.transform.hough_circle` +- New function to draw circle perimeter `skimage.draw.circle_perimeter` and + ellipse perimeter `skimage.draw.ellipse_perimeter` +- New dense DAISY feature descriptor `skimage.feature.daisy` +- New bilateral filter `skimage.filter.denoise_bilateral` +- New faster TV denoising filter based on split-Bregman algorithm + `skimage.filter.denoise_tv_bregman` +- New linear hough peak detection `skimage.transform.hough_peaks` +- New Scharr edge detection `skimage.filter.scharr` +- New geometric image scaling as convenience function + `skimage.transform.rescale` +- New theme for documentation and website +- Faster median filter through vectorization `skimage.filter.median_filter` +- Grayscale images supported for SLIC segmentation +- Unified peak detection with more options `skimage.feature.peak_local_max` +- `imread` can read images via URL and knows more formats `skimage.io.imread` + +Additionally, this release adds lots of bug fixes, new examples, and +performance enhancements. + + +Contributors to this release +---------------------------- + +This release was only possible due to the efforts of many contributors, both +new and old. + +- Adam Ginsburg +- Anders Boesen Lindbo Larsen +- Andreas Mueller +- Christoph Gohlke +- Christos Psaltis +- Colin Lea +- François Boulogne +- Jan Margeta +- Johannes Schönberger +- Josh Warner (Mac) +- Juan Nunez-Iglesias +- Luis Pedro Coelho +- Marianne Corvellec +- Matt McCormick +- Nicolas Pinto +- Olivier Debeir +- Paul Ivanov +- Sergey Karayev +- Stefan van der Walt +- Steven Silvester +- Thouis (Ray) Jones +- Tony S Yu From 96e5c1b1b8b1d78881ca860aea882c3e793d5bde Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 20:41:19 +0100 Subject: [PATCH 623/625] Update version number for 0.8 --- bento.info | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/bento.info b/bento.info index 57d9f714..10ee84f2 100644 --- a/bento.info +++ b/bento.info @@ -1,5 +1,5 @@ Name: scikit-image -Version: 0.8.dev0 +Version: 0.8.0 Summary: Image processing routines for SciPy Url: http://scikit-image.org DownloadUrl: http://github.com/scikit-image/scikit-image diff --git a/setup.py b/setup.py index 0040534e..22971ba7 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,7 @@ MAINTAINER_EMAIL = 'stefan@sun.ac.za' URL = 'http://scikit-image.org' LICENSE = 'Modified BSD' DOWNLOAD_URL = 'http://github.com/scikit-image/scikit-image' -VERSION = '0.8dev' +VERSION = '0.8.0' PYTHON_VERSION = (2, 5) DEPENDENCIES = { 'numpy': (1, 6), From ade23a2f7119b2d19876dacadb68df7f62ef339c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 20:42:46 +0100 Subject: [PATCH 624/625] Update docversion for 0.8 --- doc/source/_static/docversions.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/_static/docversions.js b/doc/source/_static/docversions.js index 0b414325..fde9437b 100644 --- a/doc/source/_static/docversions.js +++ b/doc/source/_static/docversions.js @@ -1,5 +1,5 @@ function insert_version_links() { - var labels = ['dev', '0.7.0', '0.6', '0.5', '0.4', '0.3']; + var labels = ['dev', '0.8.0', '0.7.0', '0.6', '0.5', '0.4', '0.3']; for (i = 0; i < labels.length; i++){ open_list = '

        • ' From 5755ccb6129aa5fd76292ad56f550a2242246c26 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Mon, 4 Mar 2013 20:46:09 +0100 Subject: [PATCH 625/625] Fix release instruction for making docs --- RELEASE.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASE.txt b/RELEASE.txt index ce05e24e..e42d1c85 100644 --- a/RELEASE.txt +++ b/RELEASE.txt @@ -12,7 +12,7 @@ How to make a new release of ``skimage`` - Edit ``doc/source/themes/agogo/static/docversions.js`` and commit - Build a clean version of the docs. Run ``make`` in the root dir, then - ``rm build -rf; make html`` in the docs. + ``rm -rf build; make html`` in the docs. - Run ``make html`` again to copy the newly generated ``random.js`` into place. Double check ``random.js``, otherwise the skimage.org front page gets broken!