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Merge pull request #1321 from ahojnnes/hough

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Use typed memoryviews in Hough Transform
This commit is contained in:
Juan Nunez-Iglesias
2014-12-27 00:38:38 +11:00
parent 8955bbf807 0c2773b9e4
commit 9c2807ac01
1 changed files with 6 additions and 8 deletions
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skimage/transform/_hough_transform.pyx
+6 -8
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@@ -359,12 +359,9 @@ def probabilistic_hough_line(cnp.ndarray img, int threshold=10,
cdef Py_ssize_t width = img.shape[1]
# compute the bins and allocate the accumulator array
cdef cnp.ndarray[ndim=2, dtype=cnp.int64_t] accum
cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] ctheta, stheta
cdef cnp.ndarray[ndim=2, dtype=cnp.uint8_t] mask = \
np.zeros((height, width), dtype=np.uint8)
cdef cnp.ndarray[ndim=2, dtype=cnp.int32_t] line_end = \
np.zeros((2, 2), dtype=np.int32)
cdef cnp.int32_t[:, ::1] line_end = np.zeros((2, 2), dtype=np.int32)
cdef Py_ssize_t max_distance, offset, num_indexes, index
cdef double a, b
cdef Py_ssize_t nidxs, i, j, x, y, px, py, accum_idx
@@ -378,17 +375,18 @@ def probabilistic_hough_line(cnp.ndarray img, int threshold=10,
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)
cdef cnp.int64_t[:, ::1] accum = \
np.zeros((max_distance, theta.shape[0]), dtype=np.int64)
offset = max_distance / 2
nthetas = theta.shape[0]
# compute sine and cosine of angles
ctheta = np.cos(theta)
stheta = np.sin(theta)
cdef cnp.double_t[::1] ctheta = np.cos(theta)
cdef cnp.double_t[::1] stheta = np.sin(theta)
# find the nonzero indexes
y_idxs, x_idxs = np.nonzero(img)
points = list(zip(x_idxs, y_idxs))
cdef list points = list(zip(x_idxs, y_idxs))
# mask all non-zero indexes
mask[y_idxs, x_idxs] = 1