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Merge pull request #287 from ahojnnes/morph
ENH: Improve performance of erosion and dilation.
This commit is contained in:
Stefan van der Walt
@@ -1,3 +1,5 @@
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from .binary import (binary_erosion, binary_dilation, binary_opening,
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binary_closing)
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from .grey import *
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from .selem import *
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from .ccomp import label
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@@ -0,0 +1,133 @@
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import numpy as np
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from scipy import ndimage
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def binary_erosion(image, selem, out=None):
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"""Return fast binary morphological erosion of an image.
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This function returns the same result as greyscale erosion but performs
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faster for binary images.
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Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
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in the neighborhood centered at (i,j). Erosion shrinks bright regions and
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enlarges dark regions.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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The array to store the result of the morphology. If None is
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passed, a new array will be allocated.
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Returns
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-------
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eroded : bool array
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The result of the morphological erosion.
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"""
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out = ndimage.convolve(image > 0, selem, output=out,
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mode='constant', cval=1)
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return np.equal(out, np.sum(selem), out=out)
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def binary_dilation(image, selem, out=None):
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"""Return fast binary morphological dilation of an image.
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This function returns the same result as greyscale dilation but performs
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faster for binary images.
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Morphological dilation sets a pixel at (i,j) to the maximum over all pixels
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in the neighborhood centered at (i,j). Dilation enlarges bright regions
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and shrinks dark regions.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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The array to store the result of the morphology. If None, is
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passed, a new array will be allocated.
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Returns
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-------
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dilated : bool array
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The result of the morphological dilation.
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"""
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out = ndimage.convolve(image > 0, selem, output=out,
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mode='constant', cval=1)
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return np.not_equal(out, 0, out=out)
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def binary_opening(image, selem, out=None):
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"""Return fast binary morphological opening of an image.
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This function returns the same result as greyscale opening but performs
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faster for binary images.
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The morphological opening on an image is defined as an erosion followed by
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a dilation. Opening can remove small bright spots (i.e. "salt") and connect
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small dark cracks. This tends to "open" up (dark) gaps between (bright)
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features.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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The array to store the result of the morphology. If None
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is passed, a new array will be allocated.
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Returns
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-------
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opening : bool array
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The result of the morphological opening.
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"""
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eroded = binary_erosion(image, selem)
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out = binary_dilation(eroded, selem, out=out)
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return out
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def binary_closing(image, selem, out=None):
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"""Return fast binary morphological closing of an image.
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This function returns the same result as greyscale closing but performs
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faster for binary images.
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The morphological closing on an image is defined as a dilation followed by
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an erosion. Closing can remove small dark spots (i.e. "pepper") and connect
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small bright cracks. This tends to "close" up (dark) gaps between (bright)
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features.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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The array to store the result of the morphology. If None,
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is passed, a new array will be allocated.
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Returns
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-------
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closing : bool array
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The result of the morphological closing.
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"""
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dilated = binary_dilation(image, selem)
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out = binary_erosion(dilated, selem, out=out)
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return out
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@@ -1,116 +1,118 @@
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"""
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:author: Damian Eads, 2009
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:license: modified BSD
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"""
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#cython: cdivision=True
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#cython: boundscheck=False
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#cython: nonecheck=False
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#cython: wraparound=False
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from __future__ import division
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import numpy as np
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cimport numpy as np
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cimport cython
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from cpython cimport bool
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from libc.stdlib cimport malloc, free
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STREL_DTYPE = np.uint8
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ctypedef np.uint8_t STREL_DTYPE_t
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IMAGE_DTYPE = np.uint8
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ctypedef np.uint8_t IMAGE_DTYPE_t
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def dilate(np.ndarray[np.uint8_t, ndim=2] image,
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np.ndarray[np.uint8_t, ndim=2] selem,
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np.ndarray[np.uint8_t, ndim=2] out=None,
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char shift_x=0, char shift_y=0):
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cdef inline int int_max(int a, int b): return a if a >= b else b
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cdef inline int int_min(int a, int b): return a if a <= b else b
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cdef int rows = image.shape[0]
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cdef int cols = image.shape[1]
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cdef int srows = selem.shape[0]
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cdef int scols = selem.shape[1]
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@cython.boundscheck(False)
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def dilate(np.ndarray[IMAGE_DTYPE_t, ndim=2] image not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] selem not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] out,
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bool shift_x, bool shift_y):
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cdef int hw = selem.shape[0] // 2
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cdef int hh = selem.shape[1] // 2
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if shift_x:
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hh -= 1
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if shift_y:
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hw -= 1
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cdef int centre_r = int(selem.shape[0] / 2) - shift_y
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cdef int centre_c = int(selem.shape[1] / 2) - shift_x
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cdef int width = image.shape[0], height = image.shape[1]
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image = np.ascontiguousarray(image)
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if out is None:
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out = np.zeros([width, height], dtype=IMAGE_DTYPE)
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out = np.zeros((rows, cols), dtype=np.uint8)
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else:
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out = np.ascontiguousarray(out)
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assert out.shape[0] == image.shape[0]
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assert out.shape[1] == image.shape[1]
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cdef np.uint8_t* out_data = <np.uint8_t*>out.data
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cdef np.uint8_t* image_data = <np.uint8_t*>image.data
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cdef int x, y, ix, iy, cx, cy
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cdef IMAGE_DTYPE_t max_so_far
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cdef int r, c, rr, cc, s, value, local_max
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cdef int sw = selem.shape[0], sh = selem.shape[1]
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cdef int selem_num = np.sum(selem != 0)
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cdef int* sr = <int*>malloc(selem_num * sizeof(int))
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cdef int* sc = <int*>malloc(selem_num * sizeof(int))
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cdef np.ndarray[np.int_t, ndim=2] xinc = np.zeros([sw, sh], dtype=np.int)
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cdef np.ndarray[np.int_t, ndim=2] yinc = np.zeros([sw, sh], dtype=np.int)
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s = 0
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for r in range(srows):
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for c in range(scols):
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if selem[r, c] != 0:
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sr[s] = r - centre_r
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sc[s] = c - centre_c
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s += 1
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for x in range(sw):
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for y in range(sh):
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xinc[x, y] = (x - hw)
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yinc[x, y] = (y - hh)
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for r in range(rows):
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for c in range(cols):
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local_max = 0
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for s in range(selem_num):
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rr = r + sr[s]
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cc = c + sc[s]
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if 0 <= rr < rows and 0 <= cc < cols:
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value = image_data[rr * rows + cc]
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if value > local_max:
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local_max = value
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out_data[r * cols + c] = local_max
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for x in range(width):
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for y in range(height):
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max_so_far = 0
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for cx in range(0, sw):
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for cy in range(0, sh):
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ix = x + xinc[cx,cy]
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iy = y + yinc[cx,cy]
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if ix>=0 and iy>=0 and ix < width and iy < height \
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and selem[cx, cy] == 1 \
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and image[ix,iy] > max_so_far:
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max_so_far = image[ix,iy]
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out[x,y] = max_so_far
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free(sr)
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free(sc)
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return out
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@cython.boundscheck(False)
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def erode(np.ndarray[IMAGE_DTYPE_t, ndim=2] image not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] selem not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] out,
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bool shift_x, bool shift_y):
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cdef int hw = selem.shape[0] // 2
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cdef int hh = selem.shape[1] // 2
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if shift_x:
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hh -= 1
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if shift_y:
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hw -= 1
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def erode(np.ndarray[np.uint8_t, ndim=2] image,
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np.ndarray[np.uint8_t, ndim=2] selem,
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np.ndarray[np.uint8_t, ndim=2] out=None,
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char shift_x=0, char shift_y=0):
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cdef int width = image.shape[0], height = image.shape[1]
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cdef int rows = image.shape[0]
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cdef int cols = image.shape[1]
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cdef int srows = selem.shape[0]
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cdef int scols = selem.shape[1]
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cdef int centre_r = int(selem.shape[0] / 2) - shift_y
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cdef int centre_c = int(selem.shape[1] / 2) - shift_x
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image = np.ascontiguousarray(image)
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if out is None:
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out = np.zeros([width, height], dtype=IMAGE_DTYPE)
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out = np.zeros((rows, cols), dtype=np.uint8)
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else:
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out = np.ascontiguousarray(out)
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assert out.shape[0] == image.shape[0]
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assert out.shape[1] == image.shape[1]
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cdef np.uint8_t* out_data = <np.uint8_t*>out.data
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cdef np.uint8_t* image_data = <np.uint8_t*>image.data
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cdef int x, y, ix, iy, cx, cy
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cdef IMAGE_DTYPE_t min_so_far
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cdef int r, c, rr, cc, s, value, local_max
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cdef int sw = selem.shape[0], sh = selem.shape[1]
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cdef int selem_num = np.sum(selem != 0)
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cdef int* sr = <int*>malloc(selem_num * sizeof(int))
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cdef int* sc = <int*>malloc(selem_num * sizeof(int))
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cdef np.ndarray[np.int_t, ndim=2] xinc = np.zeros([sw, sh], dtype=np.int)
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cdef np.ndarray[np.int_t, ndim=2] yinc = np.zeros([sw, sh], dtype=np.int)
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s = 0
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for r in range(srows):
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for c in range(scols):
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if selem[r, c] != 0:
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sr[s] = r - centre_r
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sc[s] = c - centre_c
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s += 1
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for x in range(sw):
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for y in range(sh):
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xinc[x, y] = (x - hw)
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yinc[x, y] = (y - hh)
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for r in range(rows):
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for c in range(cols):
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local_min = 255
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for s in range(selem_num):
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rr = r + sr[s]
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cc = c + sc[s]
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if 0 <= rr < rows and 0 <= cc < cols:
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value = image_data[rr * rows + cc]
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if value < local_min:
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local_min = value
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for x in range(width):
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for y in range(height):
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min_so_far = 255
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for cx in range(0, sw):
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for cy in range(0, sh):
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ix = x + xinc[cx,cy]
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iy = y + yinc[cx,cy]
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if ix>=0 and iy>=0 and ix < width \
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and iy < height and selem[cx, cy] == 1 \
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and image[ix,iy] < min_so_far:
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min_so_far = image[ix,iy]
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out[x,y] = min_so_far
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out_data[r * cols + c] = local_min
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free(sr)
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free(sc)
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return out
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+42
-61
@@ -6,11 +6,11 @@
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__docformat__ = 'restructuredtext en'
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import warnings
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import numpy as np
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import skimage
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from . import cmorph
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__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat',
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'black_tophat', 'greyscale_erode', 'greyscale_dilate',
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@@ -28,15 +28,12 @@ def erosion(image, selem, out=None, shift_x=False, shift_y=False):
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Parameters
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----------
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image : ndarray
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Image array.
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|
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Image array.
|
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selem : ndarray
|
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The neighborhood expressed as a 2-D array of 1's and 0's.
|
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|
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The neighborhood expressed as a 2-D array of 1's and 0's.
|
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out : ndarray
|
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The array to store the result of the morphology. If None is
|
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passed, a new array will be allocated.
|
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|
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The array to store the result of the morphology. If None is
|
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passed, a new array will be allocated.
|
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shift_x, shift_y : bool
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shift structuring element about center point. This only affects
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eccentric structuring elements (i.e. selem with even numbered sides).
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@@ -44,7 +41,7 @@ def erosion(image, selem, out=None, shift_x=False, shift_y=False):
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Returns
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-------
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eroded : uint8 array
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The result of the morphological erosion.
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The result of the morphological erosion.
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Examples
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--------
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@@ -63,17 +60,13 @@ def erosion(image, selem, out=None, shift_x=False, shift_y=False):
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[0, 0, 0, 0, 0]], dtype='uint8')
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"""
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if image is out:
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raise NotImplementedError("In-place erosion not supported!")
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image = skimage.img_as_ubyte(image)
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try:
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import skimage.morphology.cmorph as cmorph
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out = cmorph.erode(image, selem, out=out,
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shift_x=shift_x, shift_y=shift_y)
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return out
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except ImportError:
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raise ImportError("cmorph extension not available.")
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selem = skimage.img_as_ubyte(selem)
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return cmorph.erode(image, selem, out=out,
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shift_x=shift_x, shift_y=shift_y)
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def dilation(image, selem, out=None, shift_x=False, shift_y=False):
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@@ -87,15 +80,12 @@ def dilation(image, selem, out=None, shift_x=False, shift_y=False):
|
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----------
|
||||
|
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image : ndarray
|
||||
Image array.
|
||||
|
||||
Image array.
|
||||
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.
|
||||
out : ndarray
|
||||
The array to store the result of the morphology. If None, is
|
||||
passed, a new array will be allocated.
|
||||
|
||||
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).
|
||||
@@ -103,7 +93,7 @@ def dilation(image, selem, out=None, shift_x=False, shift_y=False):
|
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Returns
|
||||
-------
|
||||
dilated : uint8 array
|
||||
The result of the morphological dilation.
|
||||
The result of the morphological dilation.
|
||||
|
||||
Examples
|
||||
--------
|
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@@ -122,17 +112,13 @@ def dilation(image, selem, out=None, shift_x=False, shift_y=False):
|
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[0, 0, 0, 0, 0]], dtype='uint8')
|
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|
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"""
|
||||
|
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if image is out:
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raise NotImplementedError("In-place dilation not supported!")
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image = skimage.img_as_ubyte(image)
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||||
|
||||
try:
|
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from . import cmorph
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||||
out = cmorph.dilate(image, selem, out=out,
|
||||
shift_x=shift_x, shift_y=shift_y)
|
||||
return out
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||||
except ImportError:
|
||||
raise ImportError("cmorph extension not available.")
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||||
selem = skimage.img_as_ubyte(selem)
|
||||
return cmorph.dilate(image, selem, out=out,
|
||||
shift_x=shift_x, shift_y=shift_y)
|
||||
|
||||
|
||||
def opening(image, selem, out=None):
|
||||
@@ -146,19 +132,17 @@ def opening(image, selem, out=None):
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray
|
||||
Image array.
|
||||
|
||||
Image array.
|
||||
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.
|
||||
out : ndarray
|
||||
The array to store the result of the morphology. If None
|
||||
is passed, a new array will be allocated.
|
||||
The array to store the result of the morphology. If None
|
||||
is passed, a new array will be allocated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
opening : uint8 array
|
||||
The result of the morphological opening.
|
||||
The result of the morphological opening.
|
||||
|
||||
Examples
|
||||
--------
|
||||
@@ -177,6 +161,7 @@ def opening(image, selem, out=None):
|
||||
[0, 0, 0, 0, 0]], dtype='uint8')
|
||||
|
||||
"""
|
||||
|
||||
h, w = selem.shape
|
||||
shift_x = True if (w % 2) == 0 else False
|
||||
shift_y = True if (h % 2) == 0 else False
|
||||
@@ -197,19 +182,17 @@ def closing(image, selem, out=None):
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray
|
||||
Image array.
|
||||
|
||||
Image array.
|
||||
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.
|
||||
out : ndarray
|
||||
The array to store the result of the morphology. If None,
|
||||
is passed, a new array will be allocated.
|
||||
The array to store the result of the morphology. If None,
|
||||
is passed, a new array will be allocated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
closing : uint8 array
|
||||
The result of the morphological closing.
|
||||
The result of the morphological closing.
|
||||
|
||||
Examples
|
||||
--------
|
||||
@@ -228,6 +211,7 @@ def closing(image, selem, out=None):
|
||||
[0, 0, 0, 0, 0]], dtype='uint8')
|
||||
|
||||
"""
|
||||
|
||||
h, w = selem.shape
|
||||
shift_x = True if (w % 2) == 0 else False
|
||||
shift_y = True if (h % 2) == 0 else False
|
||||
@@ -247,19 +231,17 @@ def white_tophat(image, selem, out=None):
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray
|
||||
Image array.
|
||||
|
||||
Image array.
|
||||
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.
|
||||
out : ndarray
|
||||
The array to store the result of the morphology. If None
|
||||
is passed, a new array will be allocated.
|
||||
The array to store the result of the morphology. If None
|
||||
is passed, a new array will be allocated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
opening : uint8 array
|
||||
The result of the morphological white top hat.
|
||||
The result of the morphological white top hat.
|
||||
|
||||
Examples
|
||||
--------
|
||||
@@ -298,14 +280,12 @@ def black_tophat(image, selem, out=None):
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray
|
||||
Image array.
|
||||
|
||||
Image array.
|
||||
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.
|
||||
out : ndarray
|
||||
The array to store the result of the morphology. If None
|
||||
is passed, a new array will be allocated.
|
||||
The array to store the result of the morphology. If None
|
||||
is passed, a new array will be allocated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
@@ -329,6 +309,7 @@ def black_tophat(image, selem, out=None):
|
||||
[0, 0, 0, 0, 0]], dtype='uint8')
|
||||
|
||||
"""
|
||||
|
||||
if image is out:
|
||||
raise NotImplementedError("Cannot perform white top hat in place.")
|
||||
image = skimage.img_as_ubyte(image)
|
||||
|
||||
@@ -5,11 +5,11 @@ from numpy import testing
|
||||
|
||||
import skimage
|
||||
from skimage import data_dir
|
||||
from skimage.morphology import grey
|
||||
from skimage.morphology import selem
|
||||
from skimage.morphology import binary, grey, selem
|
||||
|
||||
|
||||
lena = np.load(os.path.join(data_dir, 'lena_GRAY_U8.npy'))
|
||||
bw_lena = lena > 0.4
|
||||
|
||||
|
||||
class TestMorphology():
|
||||
@@ -154,5 +154,33 @@ class TestDTypes():
|
||||
self._test_image(image)
|
||||
|
||||
|
||||
def test_binary_erosion():
|
||||
strel = selem.square(3)
|
||||
binary_res = binary.binary_erosion(bw_lena, strel)
|
||||
grey_res = grey.erosion(bw_lena, strel)
|
||||
testing.assert_array_equal(binary_res, grey_res)
|
||||
|
||||
|
||||
def test_binary_dilation():
|
||||
strel = selem.square(3)
|
||||
binary_res = binary.binary_dilation(bw_lena, strel)
|
||||
grey_res = grey.dilation(bw_lena, strel)
|
||||
testing.assert_array_equal(binary_res, grey_res)
|
||||
|
||||
|
||||
def test_binary_closing():
|
||||
strel = selem.square(3)
|
||||
binary_res = binary.binary_closing(bw_lena, strel)
|
||||
grey_res = grey.closing(bw_lena, strel)
|
||||
testing.assert_array_equal(binary_res, grey_res)
|
||||
|
||||
|
||||
def test_binary_opening():
|
||||
strel = selem.square(3)
|
||||
binary_res = binary.binary_opening(bw_lena, strel)
|
||||
grey_res = grey.opening(bw_lena, strel)
|
||||
testing.assert_array_equal(binary_res, grey_res)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
testing.run_module_suite()
|
||||
|
||||
Reference in New Issue
Block a user