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remplace int by Py_ssize_t and fix some doctests

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This commit is contained in:
Olivier Debeir
2012-10-15 13:21:33 +02:00
parent b9df8405c1
commit ed82b35fc0
2 changed files with 61 additions and 47 deletions
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skimage/rank/_core8.pyx
+19 -19
View File
@@ -29,13 +29,13 @@ char shift_x, char shift_y):
- result is uint8 casted
"""
cdef int rows = image.shape[0]
cdef int cols = image.shape[1]
cdef int srows = selem.shape[0]
cdef int scols = selem.shape[1]
cdef Py_ssize_t rows = image.shape[0]
cdef Py_ssize_t cols = image.shape[1]
cdef Py_ssize_t srows = selem.shape[0]
cdef Py_ssize_t scols = selem.shape[1]
cdef int centre_r = int(selem.shape[0] / 2) + shift_y
cdef int centre_c = int(selem.shape[1] / 2) + shift_x
cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y
cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x
# check that structuring element center is inside the element bounding box
assert centre_r >= 0
@@ -56,8 +56,8 @@ char shift_x, char shift_y):
out = np.ascontiguousarray(out)
# create extended image and mask
cdef int erows = rows+srows-1
cdef int ecols = cols+scols-1
cdef Py_ssize_t erows = rows+srows-1
cdef Py_ssize_t ecols = cols+scols-1
cdef np.ndarray emask = np.zeros((erows, ecols), dtype=np.uint8)
cdef np.ndarray eimage = np.zeros((erows, ecols), dtype=np.uint8)
@@ -76,14 +76,14 @@ char shift_x, char shift_y):
cdef np.uint8_t* mask_data = <np.uint8_t*>mask.data
# define local variable types
cdef int r, c, rr, cc, s, value, local_max, i, even_row
cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row
cdef float pop # number of pixels actually inside the neighborhood (float)
# allocate memory with malloc
cdef int max_se = srows*scols
cdef Py_ssize_t max_se = srows*scols
# number of element in each attack border
cdef int num_se_n, num_se_s, num_se_e, num_se_w
cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w
# the current local histogram distribution
cdef int* histo = <int*>malloc(256 * sizeof(int))
@@ -92,14 +92,14 @@ char shift_x, char shift_y):
# east, west, north and south
# e.g. se_e_r lists the rows of the east structuring element border
cdef int* se_e_r = <int*>malloc(max_se * sizeof(int))
cdef int* se_e_c = <int*>malloc(max_se * sizeof(int))
cdef int* se_w_r = <int*>malloc(max_se * sizeof(int))
cdef int* se_w_c = <int*>malloc(max_se * sizeof(int))
cdef int* se_n_r = <int*>malloc(max_se * sizeof(int))
cdef int* se_n_c = <int*>malloc(max_se * sizeof(int))
cdef int* se_s_r = <int*>malloc(max_se * sizeof(int))
cdef int* se_s_c = <int*>malloc(max_se * sizeof(int))
cdef Py_ssize_t* se_e_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_e_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_w_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_w_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_n_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_n_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_s_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
cdef Py_ssize_t* se_s_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
# build attack and release borders
# by using difference along axis
skimage/rank/rank.py
+42 -28
View File
@@ -59,12 +59,13 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> autolevel(ima8, square(3))
>>> rank.autolevel(ima8, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 255, 255, 255, 0],
[ 0, 255, 0, 255, 0],
@@ -76,7 +77,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> autolevel(ima16, square(3))
>>> rank.autolevel(ima16, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 4095, 0, 4095, 0],
@@ -130,12 +131,13 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> bottomhat(ima8, square(3))
>>> rank.bottomhat(ima8, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 255, 255, 255, 0],
[ 0, 255, 0, 255, 0],
@@ -147,7 +149,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> bottomhat(ima16, square(3))
>>> rank.bottomhat(ima16, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 4095, 0, 4095, 0],
@@ -200,12 +202,13 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> equalize(ima8, square(3))
>>> rank.equalize(ima8, square(3))
array([[191, 170, 127, 170, 191],
[170, 255, 255, 255, 170],
[127, 255, 255, 255, 127],
@@ -217,7 +220,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> equalize(ima16, square(3))
>>> rank.equalize(ima16, square(3))
array([[3071, 2730, 2047, 2730, 3071],
[2730, 4095, 4095, 4095, 2730],
[2047, 4095, 4095, 4095, 2047],
@@ -270,12 +273,13 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local gradient
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> gradient(ima8, square(3))
>>> rank.gradient(ima8, square(3))
array([[255, 255, 255, 255, 255],
[255, 255, 255, 255, 255],
[255, 255, 0, 255, 255],
@@ -287,7 +291,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> gradient(ima16, square(3))
>>> rank.gradient(ima16, square(3))
array([[4095, 4095, 4095, 4095, 4095],
[4095, 4095, 4095, 4095, 4095],
[4095, 4095, 0, 4095, 4095],
@@ -342,12 +346,13 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local maximum
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0],
... [0, 0, 1, 0, 0],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> maximum(ima8, square(3))
>>> rank.maximum(ima8, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 255, 255, 255, 0],
[ 0, 255, 255, 255, 0],
@@ -359,7 +364,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 0, 1, 0, 0],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> maximum(ima16, square(3))
>>> rank.maximum(ima16, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 4095, 4095, 4095, 0],
@@ -413,12 +418,13 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> mean(ima8, square(3))
>>> rank.mean(ima8, square(3))
array([[ 63, 85, 127, 85, 63],
[ 85, 113, 170, 113, 85],
[127, 170, 255, 170, 127],
@@ -430,7 +436,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> mean(ima16, square(3))
>>> rank.mean(ima16, square(3))
array([[1023, 1365, 2047, 1365, 1023],
[1365, 1820, 2730, 1820, 1365],
[2047, 2730, 4095, 2730, 2047],
@@ -484,12 +490,13 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F
to be updated
>>> # Local meansubstraction
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> meansubstraction(ima8, square(3))
>>> rank.meansubstraction(ima8, square(3))
array([[ 95, 84, 63, 84, 95],
[ 84, 197, 169, 197, 84],
[ 63, 169, 127, 169, 63],
@@ -501,7 +508,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> meansubstraction(ima16, square(3))
>>> rank.meansubstraction(ima16, square(3))
array([[1535, 1364, 1023, 1364, 1535],
[1364, 3184, 2729, 3184, 1364],
[1023, 2729, 2047, 2729, 1023],
@@ -555,12 +562,13 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local median
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> median(ima8, square(3))
>>> rank.median(ima8, square(3))
array([[ 0, 0, 255, 0, 0],
[ 0, 0, 255, 0, 0],
[255, 255, 255, 255, 255],
@@ -572,7 +580,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 0, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> median(ima16, square(3))
>>> rank.median(ima16, square(3))
array([[ 0, 0, 4095, 0, 0],
[ 0, 0, 4095, 0, 0],
[4095, 4095, 4095, 4095, 4095],
@@ -626,12 +634,13 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local minimum
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> minimum(ima8, square(3))
>>> rank.minimum(ima8, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 255, 0, 0],
@@ -644,7 +653,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> minimum(ima16, square(3))
>>> rank.minimum(ima16, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 4095, 0, 0],
@@ -698,12 +707,13 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local modal
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 5, 6, 0],
... [0, 1, 5, 5, 0],
... [0, 0, 0, 5, 0]], dtype=np.uint8)
>>> modal(ima8, square(3))
>>> rank.modal(ima8, square(3))
array([[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 1, 1, 0, 0],
@@ -716,7 +726,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 5, 6, 0],
... [0, 1, 5, 5, 0],
... [0, 0, 0, 5, 0]], dtype=np.uint16)
>>> modal(ima16, square(3))
>>> rank.modal(ima16, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 0, 100, 0, 0],
[ 0, 100, 100, 0, 0],
@@ -770,12 +780,13 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> morph_contr_enh(ima8, square(3))
>>> rank.morph_contr_enh(ima8, square(3))
array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
@@ -787,7 +798,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> morph_contr_enh(ima16, square(3))
>>> rank.morph_contr_enh(ima16, square(3))
array([[ 0, 0, 0, 0, 0],
[ 0, 4095, 4095, 4095, 0],
[ 0, 4095, 4095, 4095, 0],
@@ -841,12 +852,13 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> pop(ima8, square(3))
>>> rank.pop(ima8, square(3))
array([[4, 6, 6, 6, 4],
[6, 9, 9, 9, 6],
[6, 9, 9, 9, 6],
@@ -858,7 +870,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> pop(ima16, square(3))
>>> rank.pop(ima16, square(3))
array([[4, 6, 6, 6, 4],
[6, 9, 9, 9, 6],
[6, 9, 9, 9, 6],
@@ -912,12 +924,13 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> threshold(ima8, square(3))
>>> rank.threshold(ima8, square(3))
array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
@@ -929,7 +942,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> threshold(ima16, square(3))
>>> rank.threshold(ima16, square(3))
array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
@@ -984,12 +997,13 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
to be updated
>>> # Local mean
>>> from skimage.morphology import square
>>> import skimage.rank as rank
>>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> tophat(ima8, square(3))
>>> rank.tophat(ima8, square(3))
array([[255, 255, 255, 255, 255],
[255, 0, 0, 0, 255],
[255, 0, 0, 0, 255],
@@ -1001,7 +1015,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> tophat(ima16, square(3))
>>> rank.tophat(ima16, square(3))
array([[4095, 4095, 4095, 4095, 4095],
[4095, 0, 0, 0, 4095],
[4095, 0, 0, 0, 4095],