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Removing slanted_integral_image Cython functions for mode=Octagon

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Ankit Agrawal
2013-07-31 23:15:45 +05:30
parent 3318886ff3
commit 344374e290
1 changed files with 0 additions and 84 deletions
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skimage/feature/censure_cy.pyx
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@@ -20,87 +20,3 @@ def _censure_dob_loop(double[:, ::1] image, Py_ssize_t n,
inner = integral_img[i + n, j + n] + integral_img[i - n - 1, j - n - 1] - integral_img[i + n, j - n - 1] - integral_img[i - n - 1, j + n]
outer = integral_img[i + 2 * n, j + 2 * n] + integral_img[i - 2 * n - 1, j - 2 * n - 1] - integral_img[i + 2 * n, j - 2 * n - 1] - integral_img[i - 2 * n - 1, j + 2 * n]
filtered_image[i, j] = outer_weight * outer - (inner_weight + outer_weight) * inner
def _slanted_integral_image(double[:, :] image,
double[:, :] integral_img):
cdef Py_ssize_t i, j
cdef double[:] left_sum = np.zeros(image.shape[0], dtype=np.float)
flipped_lr = np.asarray(image[:, ::-1])
for i in range(image.shape[1] - image.shape[0], image.shape[1]):
left_sum[image.shape[1] - 1 - i] = np.sum(flipped_lr.diagonal(i))
left_sum_np = np.asarray(left_sum)
# Initializing the leftmost column of the slanted integral image
left_sum_np = left_sum_np.cumsum(0)
# Initializing the rightmost column of the slanted integral image
right_sum_np = np.sum(image, 1).cumsum(0)
for i in range(image.shape[0]):
image[i, 0] = left_sum_np[i]
image[i, -1] = right_sum_np[i]
for i in range(1, integral_img.shape[0]):
for j in range(integral_img.shape[1]):
integral_img[i, j] = image[i - 1, j]
for i in range(1, integral_img.shape[0]):
for j in range(1, integral_img.shape[1] - 1):
integral_img[i, j] += integral_img[i, j - 1] + integral_img[i - 1, j + 1] - integral_img[i - 1, j]
def _censure_octagon_loop(double[:, :] image, double[:, :] integral_img,
double[:, :] integral_img1,
double[:, :] integral_img2,
double[:, :] integral_img3,
double[:, :] integral_img4,
double[:, :] filtered_image,
double outer_weight, double inner_weight,
int mo, int no, int mi, int ni):
cdef Py_ssize_t i, j, o_m, i_m, o_set, i_set
"""
For a (5, 2) octagon, i.e. mo = 5 and no = 2,
|---o_set---|
[0, 0, 1, 1, 1, 1, 1, 0, 0]
[0, 1, 1, 1, 1, 1, 1, 1, 0]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
[0, 1, 1, 1, 1, 1, 1, 1, 0]
[0, 0, 1, 1, 1, 1, 1, 0, 0]
|-o_m-|
"""
o_m = (mo - 1) / 2
i_m = (mi - 1) / 2
# o_set and i_set are the distances of the center of the octagon
# from the horizontal or vertical sides of the octagon,
# for outer and inner octagon respectively
o_set = o_m + no
i_set = i_m + ni
for i in range(o_set + 1, image.shape[0] - o_set - 1):
for j in range(o_set + 1, image.shape[1] - o_set - 1):
# Calculating the sum of pixels in the outer octagon
outer = integral_img1[i + o_set, j + o_m] - integral_img1[i + o_m - 1, j + o_set + 1] - integral_img[i + o_set, j - o_m] + integral_img[i + o_m - 1, j - o_m]
outer += integral_img[i + o_m - 1, j + o_m - 1] - integral_img[i - o_m, j + o_m - 1] - integral_img[i + o_m - 1, j - o_m] + integral_img[i - o_m, j - o_m]
outer += integral_img4[i + o_m, j - o_set] - integral_img4[i + o_set + 1, j - o_m + 1] - integral_img[i - o_m, j - o_m] + integral_img[i - o_m, j - o_set - 1]
outer += integral_img2[i - o_set, j - o_m] - integral_img2[i - o_m + 1, j - o_set - 1] - integral_img[i - o_m, -1] - integral_img[i - o_set - 1, j + o_m - 1] + integral_img[i - o_m, j + o_m - 1] + integral_img[i - o_set - 1, -1]
outer += integral_img3[i - o_m, j + o_set] - integral_img3[i - o_set - 1, j + o_m - 1] - integral_img[-1, j + o_set] - integral_img[i + o_m - 1, j + o_m - 1] + integral_img[-1, j + o_m - 1] + integral_img[i + o_m - 1, j + o_set]
# Calculating the sum of pixels in the inner octagon
inner = integral_img1[i + i_set, j + i_m] - integral_img1[i + i_m - 1, j + i_set + 1] - integral_img[i + i_set, j - i_m] + integral_img[i + i_m - 1, j - i_m]
inner += integral_img[i + i_m - 1, j + i_m - 1] - integral_img[i - i_m, j + i_m - 1] - integral_img[i + i_m - 1, j - i_m] + integral_img[i - i_m, j - i_m]
inner += integral_img4[i + i_m, j - i_set] - integral_img4[i + i_set + 1, j - i_m + 1] - integral_img[i - i_m, j - i_m] + integral_img[i - i_m, j - i_set - 1]
inner += integral_img2[i - i_set, j - i_m] - integral_img2[i - i_m + 1, j - i_set - 1] - integral_img[i - i_m, -1] - integral_img[i - i_set - 1, j + i_m - 1] + integral_img[i - i_m, j + i_m - 1] + integral_img[i - i_set - 1, -1]
inner += integral_img3[i - i_m, j + i_set] - integral_img3[i - i_set - 1, j + i_m - 1] - integral_img[-1, j + i_set] - integral_img[i + i_m - 1, j + i_m - 1] + integral_img[-1, j + i_m - 1] + integral_img[i + i_m - 1, j + i_set]
filtered_image[i, j] = outer_weight * outer - (outer_weight + inner_weight) * inner