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Merge pull request #581 from ankit-maverick/subtract

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STY: Replacing every occurence of ``substract*`` with ``subtract*``.
This commit is contained in:
Stefan van der Walt
2013-06-06 21:47:41 -07:00
parent 5a041d2560 8ecadf175c
commit f20286b696
8 changed files with 30 additions and 30 deletions
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skimage/filter/rank/__init__.py
+4 -4
View File
@@ -1,8 +1,8 @@
from .rank import (autolevel, bottomhat, equalize, gradient, maximum, mean,
meansubstraction, median, minimum, modal, morph_contr_enh,
meansubtraction, median, minimum, modal, morph_contr_enh,
pop, threshold, tophat, noise_filter, entropy, otsu)
from .percentile_rank import (percentile_autolevel, percentile_gradient,
percentile_mean, percentile_mean_substraction,
percentile_mean, percentile_mean_subtraction,
percentile_morph_contr_enh, percentile,
percentile_pop, percentile_threshold)
from .bilateral_rank import bilateral_mean, bilateral_pop
@@ -14,7 +14,7 @@ __all__ = ['autolevel',
'gradient',
'maximum',
'mean',
'meansubstraction',
'meansubtraction',
'median',
'minimum',
'modal',
@@ -28,7 +28,7 @@ __all__ = ['autolevel',
'percentile_autolevel',
'percentile_gradient',
'percentile_mean',
'percentile_mean_substraction',
'percentile_mean_subtraction',
'percentile_morph_contr_enh',
'percentile',
'percentile_pop',
skimage/filter/rank/_crank16.pyx
+3 -3
View File
@@ -126,7 +126,7 @@ cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop,
return <dtype_t>(0)
cdef inline dtype_t kernel_meansubstraction(Py_ssize_t * histo,
cdef inline dtype_t kernel_meansubtraction(Py_ssize_t * histo,
float pop,
dtype_t g,
Py_ssize_t bitdepth,
@@ -341,12 +341,12 @@ def mean(cnp.ndarray[dtype_t, ndim=2] image,
bitdepth, 0, 0, <Py_ssize_t>0, <Py_ssize_t>0)
def meansubstraction(cnp.ndarray[dtype_t, ndim=2] image,
def meansubtraction(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,
_core16(kernel_meansubtraction, image, selem, mask, out, shift_x, shift_y,
bitdepth, 0, 0, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/_crank16_percentiles.pyx
+3 -3
View File
@@ -103,7 +103,7 @@ cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop,
return <dtype_t>(0)
cdef inline dtype_t kernel_mean_substraction(Py_ssize_t * histo,
cdef inline dtype_t kernel_mean_subtraction(Py_ssize_t * histo,
float pop,
dtype_t g,
Py_ssize_t bitdepth,
@@ -269,7 +269,7 @@ def mean(cnp.ndarray[dtype_t, ndim=2] image,
bitdepth, p0, p1, <Py_ssize_t>0, <Py_ssize_t>0)
def mean_substraction(cnp.ndarray[dtype_t, ndim=2] image,
def mean_subtraction(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,
@@ -278,7 +278,7 @@ def mean_substraction(cnp.ndarray[dtype_t, ndim=2] image,
"""return original - mean between [p0 and p1] percentiles *.5 +127
"""
_core16(
kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y,
kernel_mean_subtraction, image, selem, mask, out, shift_x, shift_y,
bitdepth, p0, p1, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/_crank8.pyx
+3 -3
View File
@@ -123,7 +123,7 @@ cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop,
return <dtype_t>(0)
cdef inline dtype_t kernel_meansubstraction(Py_ssize_t * histo, float pop,
cdef inline dtype_t kernel_meansubtraction(Py_ssize_t * histo, float pop,
dtype_t g, float p0, float p1,
Py_ssize_t s0, Py_ssize_t s1):
@@ -384,12 +384,12 @@ def mean(cnp.ndarray[dtype_t, ndim=2] image,
0, 0, <Py_ssize_t>0, <Py_ssize_t>0)
def meansubstraction(cnp.ndarray[dtype_t, ndim=2] image,
def meansubtraction(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,
_core8(kernel_meansubtraction, image, selem, mask, out, shift_x, shift_y,
0, 0, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/_crank8_percentiles.pyx
+3 -3
View File
@@ -94,7 +94,7 @@ cdef inline dtype_t kernel_mean(Py_ssize_t * histo, float pop,
return <dtype_t>(0)
cdef inline dtype_t kernel_mean_substraction(Py_ssize_t * histo,
cdef inline dtype_t kernel_mean_subtraction(Py_ssize_t * histo,
float pop,
dtype_t g,
float p0, float p1,
@@ -239,14 +239,14 @@ def mean(cnp.ndarray[dtype_t, ndim=2] image,
<Py_ssize_t>0, <Py_ssize_t>0)
def mean_substraction(cnp.ndarray[dtype_t, ndim=2] image,
def mean_subtraction(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
"""
_core8(kernel_mean_substraction, image, selem, mask, out, shift_x, shift_y,
_core8(kernel_mean_subtraction, image, selem, mask, out, shift_x, shift_y,
p0, p1, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/percentile_rank.pyx
+8 -8
View File
@@ -29,7 +29,7 @@ from skimage.filter.rank import _crank16_percentiles, _crank8_percentiles
__all__ = ['percentile_autolevel', 'percentile_gradient',
'percentile_mean', 'percentile_mean_substraction',
'percentile_mean', 'percentile_mean_subtraction',
'percentile_morph_contr_enh', 'percentile', 'percentile_pop',
'percentile_threshold']
@@ -191,11 +191,11 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False,
shift_y=shift_y, p0=p0, p1=p1)
def percentile_mean_substraction(image, selem, out=None, mask=None,
def percentile_mean_subtraction(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, p0=.0, p1=1.):
"""Return greyscale local mean_substraction of an image.
"""Return greyscale local mean_subtraction of an image.
mean_substraction is computed on the given structuring element. Only levels
mean_subtraction is computed on the given structuring element. Only levels
between percentiles [p0, p1] are used.
Parameters
@@ -221,13 +221,13 @@ def percentile_mean_substraction(image, selem, out=None, mask=None,
Returns
-------
local mean_substraction : uint8 array or uint16
The result of the local mean_substraction.
local mean_subtraction : uint8 array or uint16
The result of the local mean_subtraction.
"""
return _apply(_crank8_percentiles.mean_substraction,
_crank16_percentiles.mean_substraction,
return _apply(_crank8_percentiles.mean_subtraction,
_crank16_percentiles.mean_subtraction,
image, selem, out=out, mask=mask, shift_x=shift_x,
shift_y=shift_y, p0=p0, p1=p1)
skimage/filter/rank/rank.pyx
+5 -5
View File
@@ -23,7 +23,7 @@ from skimage.filter.rank.generic import find_bitdepth
__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean',
'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh',
'meansubtraction', 'median', 'minimum', 'modal', 'morph_contr_enh',
'pop', 'threshold', 'tophat', 'noise_filter', 'entropy', 'otsu']
@@ -298,9 +298,9 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
mask=mask, shift_x=shift_x, shift_y=shift_y)
def meansubstraction(image, selem, out=None, mask=None, shift_x=False,
def meansubtraction(image, selem, out=None, mask=None, shift_x=False,
shift_y=False):
"""Return image substracted from its local mean.
"""Return image subtracted from its local mean.
Parameters
----------
@@ -323,11 +323,11 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False,
Returns
-------
out : uint8 array or uint16 array (same as input image)
The result of the local meansubstraction.
The result of the local meansubtraction.
"""
return _apply(_crank8.meansubstraction, _crank16.meansubstraction, image,
return _apply(_crank8.meansubtraction, _crank16.meansubtraction, image,
selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
skimage/filter/rank/tests/test_rank.py
+1 -1
View File
@@ -195,7 +195,7 @@ def test_compare_8bit_vs_16bit():
assert_array_equal(image8, image16)
methods = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum',
'mean', 'meansubstraction', 'median', 'minimum', 'modal',
'mean', 'meansubtraction', 'median', 'minimum', 'modal',
'morph_contr_enh', 'pop', 'threshold', 'tophat']
for method in methods: