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fix:< Py_ssize_t > 0 and & pop

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This commit is contained in:
Olivier Debeir
2012-11-13 17:07:21 +01:00
parent 411d4686d5
commit 698d6ae13f
6 changed files with 66 additions and 66 deletions
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skimage/filter/rank/_core16.pyx
+9 -9
View File
@@ -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],
skimage/filter/rank/_core8.pyx
+9 -9
View File
@@ -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],
skimage/filter/rank/_crank16.pyx
+15 -15
View File
@@ -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, <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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 0)
bitdepth, 0, 0, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/_crank16_percentiles.pyx
+8 -8
View File
@@ -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, <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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 0)
bitdepth, p0, p1, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/_crank8.pyx
+17 -17
View File
@@ -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, <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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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,
< Py_ssize_t > 0, < Py_ssize_t > 0)
<Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>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, <Py_ssize_t>0, <Py_ssize_t>0)
skimage/filter/rank/_crank8_percentiles.pyx
+8 -8
View File
@@ -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)
<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,
< Py_ssize_t > 0, < Py_ssize_t > 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,
< Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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, < Py_ssize_t > 0, < Py_ssize_t > 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,
< Py_ssize_t > 0, < Py_ssize_t > 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,
< Py_ssize_t > 0, < Py_ssize_t > 0)
<Py_ssize_t>0, <Py_ssize_t>0)