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Change type to ssize_t for all index and size variables

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This commit is contained in:
Johannes Schönberger
2013-02-22 17:05:11 +01:00
parent 9f55fa2112
commit 2957cb873b
2 changed files with 19 additions and 19 deletions
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skimage/transform/_hough_transform.pyx
+15 -15
View File
@@ -13,8 +13,8 @@ cdef double PI_2 = 1.5707963267948966
cdef double NEG_PI_2 = -PI_2
cdef inline int round(double r):
return <int>((r + 0.5) if (r > 0.0) else (r - 0.5))
cdef inline ssize_t round(double r):
return <ssize_t>((r + 0.5) if (r > 0.0) else (r - 0.5))
@cython.boundscheck(False)
@@ -36,21 +36,20 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
# compute the bins and allocate the accumulator array
cdef np.ndarray[ndim=2, dtype=np.uint64_t] accum
cdef np.ndarray[ndim=1, dtype=np.double_t] bins
cdef int max_distance, offset
cdef ssize_t max_distance, offset
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
img.shape[1] * img.shape[1])))
max_distance = 2 * <ssize_t>ceil(sqrt(img.shape[0] * img.shape[0] +
img.shape[1] * img.shape[1]))
accum = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64)
bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance)
offset = max_distance / 2
# compute the nonzero indexes
cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs
y_idxs, x_idxs = np.PyArray_Nonzero(img)
y_idxs, x_idxs = np.nonzero(img)
# finally, run the transform
cdef int nidxs, nthetas, i, j, x, y, accum_idx
cdef ssize_t nidxs, nthetas, i, j, x, y, accum_idx
nidxs = y_idxs.shape[0] # x and y are the same shape
nthetas = theta.shape[0]
for i in range(nidxs):
@@ -78,26 +77,27 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
theta = math.pi/2-np.arange(180)/180.0* math.pi
ctheta = np.cos(theta)
stheta = np.sin(theta)
cdef int height = img.shape[0]
cdef int width = img.shape[1]
cdef ssize_t height = img.shape[0]
cdef ssize_t width = img.shape[1]
# compute the bins and allocate the accumulator array
cdef np.ndarray[ndim=2, dtype=np.int64_t] accum
cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = np.zeros((height, width), dtype=np.uint8)
cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = np.zeros((2, 2), dtype=np.int32)
cdef int max_distance, offset, num_indexes, index
cdef ssize_t max_distance, offset, num_indexes, index
cdef double a, b
cdef int nidxs, nthetas, i, j, x, y, px, py, accum_idx, value, max_value, max_theta
cdef ssize_t nidxs, nthetas, i, j, x, y, px, py, accum_idx
cdef int value, max_value, max_theta
cdef int shift = 16
# maximum line number cutoff
cdef int lines_max = 2 ** 15
cdef int xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count
cdef ssize_t lines_max = 2 ** 15
cdef ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
img.shape[1] * img.shape[1])))
accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64)
offset = max_distance / 2
# find the nonzero indexes
cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs
y_idxs, x_idxs = np.nonzero(img)
y_idxs, x_idxs = np.nonzero(img)
num_indexes = y_idxs.shape[0] # x and y are the same shape
nthetas = theta.shape[0]
points = []
skimage/transform/_warps_cy.pyx
+4 -4
View File
@@ -93,7 +93,7 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1,
"`constant`, `nearest`, `wrap` or `reflect`.")
cdef char mode_c = ord(mode[0].upper())
cdef int out_r, out_c
cdef ssize_t out_r, out_c
if output_shape is None:
out_r = img.shape[0]
out_c = img.shape[1]
@@ -104,10 +104,10 @@ def _warp_fast(np.ndarray image, np.ndarray H, output_shape=None, int order=1,
cdef np.ndarray[dtype=np.double_t, ndim=2] out = \
np.zeros((out_r, out_c), dtype=np.double)
cdef int tfr, tfc
cdef ssize_t tfr, tfc
cdef double r, c
cdef int rows = img.shape[0]
cdef int cols = img.shape[1]
cdef ssize_t rows = img.shape[0]
cdef ssize_t cols = img.shape[1]
cdef double (*interp_func)(double*, ssize_t, ssize_t, double, double,
char, double)