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Append percentile, bilateral function name part

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Johannes Schönberger
2013-07-12 23:16:50 +02:00
parent ed21622caf
commit 6ee96054c9
4 changed files with 45 additions and 45 deletions
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skimage/filter/rank/__init__.py
+16 -16
View File
@@ -1,37 +1,37 @@
from .generic import (autolevel, bottomhat, equalize, gradient, maximum, mean,
subtract_mean, median, minimum, modal, enhance_contrast,
pop, threshold, tophat, noise_filter, entropy, otsu)
from .percentile import (percentile_autolevel, percentile_gradient,
percentile_mean, percentile_subtract_mean,
percentile_enhance_contrast, percentile,
percentile_pop, percentile_threshold)
from .bilateral import bilateral_mean, bilateral_pop
from .percentile import (autolevel_percentile, gradient_percentile,
mean_percentile, subtract_mean_percentile,
enhance_contrast_percentile, percentile,
pop_percentile, threshold_percentile)
from .bilateral import mean_bilateral, pop_bilateral
__all__ = ['autolevel',
'autolevel_percentile',
'bottomhat',
'equalize',
'gradient',
'gradient_percentile',
'maximum',
'mean',
'mean_percentile',
'mean_bilateral',
'subtract_mean',
'subtract_mean_percentile',
'median',
'minimum',
'modal',
'enhance_contrast',
'enhance_contrast_percentile',
'pop',
'pop_percentile',
'pop_bilateral',
'threshold',
'threshold_percentile',
'tophat',
'noise_filter',
'entropy',
'otsu',
'percentile_autolevel',
'percentile_gradient',
'percentile_mean',
'percentile_subtract_mean',
'percentile_enhance_contrast',
'percentile',
'percentile_pop',
'percentile_threshold',
'bilateral_mean',
'bilateral_pop']
'otsu'
'percentile']
skimage/filter/rank/bilateral.py
+3 -3
View File
@@ -27,7 +27,7 @@ from . import bilateral_cy
from .generic import _handle_input
__all__ = ['bilateral_mean', 'bilateral_pop']
__all__ = ['mean_bilateral', 'pop_bilateral']
def _apply(func, image, selem, out, mask, shift_x, shift_y, s0, s1):
@@ -40,7 +40,7 @@ def _apply(func, image, selem, out, mask, shift_x, shift_y, s0, s1):
return out
def bilateral_mean(image, selem, out=None, mask=None, shift_x=False,
def mean_bilateral(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, s0=10, s1=10):
"""Apply a flat kernel bilateral filter.
@@ -99,7 +99,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False,
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,
def pop_bilateral(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
skimage/filter/rank/percentile.py
+16 -16
View File
@@ -1,6 +1,6 @@
"""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]
``autolevel_percentile`` will stretch image levels between percentile [p0, p1]
instead of using [min, max]. It means that isolated bright or dark pixels will
not produce halos.
@@ -27,10 +27,10 @@ from . import percentile_cy
from .generic import _handle_input
__all__ = ['percentile_autolevel', 'percentile_gradient',
'percentile_mean', 'percentile_subtract_mean',
'percentile_enhance_contrast', 'percentile', 'percentile_pop',
'percentile_threshold']
__all__ = ['autolevel_percentile', 'gradient_percentile',
'mean_percentile', 'subtract_mean_percentile',
'enhance_contrast_percentile', 'percentile', 'pop_percentile',
'threshold_percentile']
def _apply(func, image, selem, out, mask, shift_x, shift_y, p0, p1):
@@ -43,7 +43,7 @@ def _apply(func, image, selem, out, mask, shift_x, shift_y, p0, p1):
return out
def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False,
def autolevel_percentile(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, p0=0, p1=1):
"""Return greyscale local autolevel of an image.
@@ -81,11 +81,11 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False,
shift_y=shift_y, p0=p0, p1=p1)
def percentile_gradient(image, selem, out=None, mask=None, shift_x=False,
def gradient_percentile(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, p0=0, p1=1):
"""Return greyscale local percentile_gradient of an image.
"""Return greyscale local gradient of an image.
percentile_gradient is computed on the given structuring element. Only
gradient is computed on the given structuring element. Only
levels between percentiles [p0, p1] are used.
Parameters
@@ -119,7 +119,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False,
shift_y=shift_y, p0=p0, p1=p1)
def percentile_mean(image, selem, out=None, mask=None, shift_x=False,
def mean_percentile(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, p0=0, p1=1):
"""Return greyscale local mean of an image.
@@ -157,8 +157,8 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False,
shift_y=shift_y, p0=p0, p1=p1)
def percentile_subtract_mean(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, p0=0, p1=1):
def subtract_mean_percentile(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, p0=0, p1=1):
"""Return greyscale local subtract_mean of an image.
subtract_mean is computed on the given structuring element. Only levels
@@ -195,8 +195,8 @@ def percentile_subtract_mean(image, selem, out=None, mask=None,
shift_y=shift_y, p0=p0, p1=p1)
def percentile_enhance_contrast(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, p0=0, p1=1):
def enhance_contrast_percentile(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, p0=0, p1=1):
"""Return greyscale local enhance_contrast of an image.
enhance_contrast is computed on the given structuring element. Only levels
@@ -270,7 +270,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False,
shift_y=shift_y, p0=p0, p1=0.)
def percentile_pop(image, selem, out=None, mask=None, shift_x=False,
def pop_percentile(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, p0=0, p1=1):
"""Return greyscale local pop of an image.
@@ -308,7 +308,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False,
shift_y=shift_y, p0=p0, p1=p1)
def percentile_threshold(image, selem, out=None, mask=None, shift_x=False,
def threshold_percentile(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, p0=0):
"""Return greyscale local threshold of an image.
skimage/filter/rank/tests/test_rank.py
+10 -10
View File
@@ -33,10 +33,10 @@ def test_random_sizes():
shift_x=+1, shift_y=+1)
assert_array_equal(image16.shape, out16.shape)
rank.percentile_mean(image=image16, mask=mask, out=out16,
rank.mean_percentile(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)
rank.percentile_mean(image=image16, mask=mask, out=out16,
rank.mean_percentile(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)
@@ -78,7 +78,7 @@ def test_bitdepth():
for i in range(5):
image = np.ones((100, 100), dtype=np.uint16) * 255 * 2 ** i
r = rank.percentile_mean(image=image, selem=elem, mask=mask,
r = rank.mean_percentile(image=image, selem=elem, mask=mask,
out=out, shift_x=0, shift_y=0, p0=.1, p1=.9)
@@ -156,7 +156,7 @@ def test_compare_autolevels():
selem = disk(20)
loc_autolevel = rank.autolevel(image, selem=selem)
loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem,
loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem,
p0=.0, p1=1.)
assert_array_equal(loc_autolevel, loc_perc_autolevel)
@@ -170,7 +170,7 @@ def test_compare_autolevels_16bit():
selem = disk(20)
loc_autolevel = rank.autolevel(image, selem=selem)
loc_perc_autolevel = rank.percentile_autolevel(image, selem=selem,
loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem,
p0=.0, p1=1.)
assert_array_equal(loc_autolevel, loc_perc_autolevel)
@@ -418,7 +418,7 @@ def test_selem_dtypes():
rank.mean(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_array_equal(image, out)
rank.percentile_mean(image=image, selem=elem, out=out, mask=mask,
rank.mean_percentile(image=image, selem=elem, out=out, mask=mask,
shift_x=0, shift_y=0)
assert_array_equal(image, out)
@@ -443,10 +443,10 @@ def test_bilateral():
image[10, 11] = 1010
image[10, 9] = 900
assert rank.bilateral_mean(image, selem, s0=1, s1=1)[10, 10] == 1000
assert rank.bilateral_pop(image, selem, s0=1, s1=1)[10, 10] == 1
assert rank.bilateral_mean(image, selem, s0=11, s1=11)[10, 10] == 1005
assert rank.bilateral_pop(image, selem, s0=11, s1=11)[10, 10] == 2
assert rank.mean_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1000
assert rank.pop_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1
assert rank.mean_bilateral(image, selem, s0=11, s1=11)[10, 10] == 1005
assert rank.pop_bilateral(image, selem, s0=11, s1=11)[10, 10] == 2
if __name__ == "__main__":