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cut long line, autopep8

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This commit is contained in:
Olivier Debeir
2012-10-29 18:07:50 +01:00
parent 2844db6025
commit b8d9227d85
8 changed files with 174 additions and 71 deletions
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skimage/filter/rank/_core16.pxd
+1 -1
View File
@@ -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,
skimage/filter/rank/_core16.pyx
+1 -1
View File
@@ -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,
skimage/filter/rank/_core8.pxd
+1 -1
View File
@@ -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,
skimage/filter/rank/_core8.pyx
+1 -1
View File
@@ -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,
skimage/filter/rank/_crank16_bilateral.pyx
+8 -2
View File
@@ -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:
skimage/filter/rank/_crank16_percentiles.pyx
+56 -16
View File
@@ -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)
skimage/filter/rank/_crank8.pyx
+64 -35
View File
@@ -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,
skimage/filter/rank/_crank8_percentiles.pyx
+42 -14
View File
@@ -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)