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Cleaning up the non-Cython version for mode=Octagon

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Ankit Agrawal
2013-08-15 14:59:54 +05:30
parent 474c7382dd
commit d15741c58c
1 changed files with 51 additions and 44 deletions
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skimage/feature/censure.py
+51 -44
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@@ -5,57 +5,64 @@ from ..transform import integral_image
from ..feature.corner import _compute_auto_correlation
from ..util import img_as_float
from .censure_cy import _censure_dob_loop, _slanted_integral_image
from .censure_cy import _censure_dob_loop
def _get_filtered_image(image, n, mode='DoB'):
def _get_filtered_image(image, no_of_scales, mode):
# TODO : Implement the STAR and Octagon mode
if mode == 'DoB':
inner_wt = (1.0 / (2 * n + 1)**2)
outer_wt = (1.0 / (12 * n**2 + 4 * n))
integral_img = integral_image(image)
filtered_image = np.zeros(image.shape)
_censure_dob_loop(image, n, integral_img, filtered_image, inner_wt, outer_wt)
return filtered_image
scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
for i in range(no_of_scales):
n = i + 1
inner_wt = (1.0 / (2 * n + 1)**2)
outer_wt = (1.0 / (12 * n**2 + 4 * n))
integral_img = integral_image(image)
filtered_image = np.zeros(image.shape)
_censure_dob_loop(image, n, integral_img, filtered_image, inner_wt, outer_wt)
scales[:, :, i] = filtered_image
return scales
elif mode == 'Octagon':
outer_shape = [(5, 2), (5, 3), (7, 3), (9, 4), (9, 7), (13, 7), (15, 10)]
inner_shape = [(3, 0), (3, 1), (3, 2), (5, 2), (5, 3), (5, 4), (5, 5)]
# Take these out of the loop. No need to compute again for different scales.
scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
integral_img = integral_image(image)
integral_img1 = _slanted_integral_image_modes(image, 1)
integral_img2 = _slanted_integral_image_modes(image, 2)
integral_img3 = _slanted_integral_image_modes(image, 3)
integral_img4 = _slanted_integral_image_modes(image, 4)
filtered_image = np.zeros(image.shape)
mo = outer_shape[n - 1][0]
no = outer_shape[n - 1][1]
mi = inner_shape[n - 1][0]
ni = inner_shape[n - 1][1]
outer_pixels = (mo + 2 * no)**2 - 2 * no * (no + 1)
inner_pixels = (mi + 2 * ni)**2 - 2 * ni * (ni + 1)
outer_wt = 1.0 / (outer_pixels - inner_pixels)
inner_wt = 1.0 / inner_pixels
o_m = (mo - 1) / 2
i_m = (mi - 1) / 2
o_set = o_m + no
i_set = i_m + ni
# Outsource to Cython
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):
outer = integral_img1[i + o_set, j + o_m] - integral_img1[i + o_m, j + o_set] - integral_img[i + o_set, j - o_m] + integral_img[i + o_m, j - o_m]
outer += integral_img[i + (mo - 3) / 2, j + (mo - 3) / 2] - integral_img[i - o_m, j + (mo - 3) / 2] - integral_img[i + (mo - 3) / 2, 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, j - o_m] - integral_img[i - o_m, j - (mo + 1) / 2] + integral_img[i - o_m, j - o_set - 1]
outer += integral_img2[i - o_set, j - o_m] - integral_img2[i - o_m, j - o_set] - integral_img[i - (mo + 1) / 2, -1] - integral_img[i - o_set - 1, j + (mo - 3) / 2] + integral_img[i - (mo + 1) / 2, j + (mo - 3) / 2] + integral_img[i - o_set - 1, -1]
outer += integral_img3[i - o_m, j + o_set] - integral_img3[i - o_set, j + o_m] - integral_img[-1, j + o_set + 1] - integral_img[i + (mo - 3) / 2, j + o_m] + integral_img[-1, j + o_m] + integral_img[i + (mo - 3) / 2, j + o_set + 1]
for k in range(no_of_scales):
n = k + 1
filtered_image = np.zeros(image.shape)
mo = outer_shape[n - 1][0]
no = outer_shape[n - 1][1]
mi = inner_shape[n - 1][0]
ni = inner_shape[n - 1][1]
outer_pixels = (mo + 2 * no)**2 - 2 * no * (no + 1)
inner_pixels = (mi + 2 * ni)**2 - 2 * ni * (ni + 1)
outer_wt = 1.0 / (outer_pixels - inner_pixels)
inner_wt = 1.0 / inner_pixels
o_m = (mo - 1) / 2
i_m = (mi - 1) / 2
o_set = o_m + no
i_set = i_m + ni
# Outsource to Cython
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):
outer = integral_img1[i + o_set, j + o_m] - integral_img1[i + o_m, j + o_set] - integral_img[i + o_set, j - o_m] + integral_img[i + o_m, j - o_m]
outer += integral_img[i + (mo - 3) / 2, j + (mo - 3) / 2] - integral_img[i - o_m, j + (mo - 3) / 2] - integral_img[i + (mo - 3) / 2, 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, j - o_m] - integral_img[i - o_m, j - (mo + 1) / 2] + integral_img[i - o_m, j - o_set - 1]
outer += integral_img2[i - o_set, j - o_m] - integral_img2[i - o_m, j - o_set] - integral_img[i - (mo + 1) / 2, -1] - integral_img[i - o_set - 1, j + (mo - 3) / 2] + integral_img[i - (mo + 1) / 2, j + (mo - 3) / 2] + integral_img[i - o_set - 1, -1]
outer += integral_img3[i - o_m, j + o_set] - integral_img3[i - o_set, j + o_m] - integral_img[-1, j + o_set + 1] - integral_img[i + (mo - 3) / 2, j + o_m] + integral_img[-1, j + o_m] + integral_img[i + (mo - 3) / 2, j + o_set + 1]
inner = integral_img1[i + i_set, j + i_m] - integral_img1[i + i_m, j + i_set] - integral_img[i + i_set, j - i_m] + integral_img[i + i_m, j - i_m]
inner += integral_img[i + (mi - 3) / 2, j + (mi - 3) / 2] - integral_img[i - i_m, j + (mi - 3) / 2] - integral_img[i + (mi - 3) / 2, 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, j - i_m] - integral_img[i - i_m, j - (mi + 1) / 2] + integral_img[i - i_m, j - i_set - 1]
inner += integral_img2[i - i_set, j - i_m] - integral_img2[i - i_m, j - i_set] - integral_img[i - (mi + 1) / 2, -1] - integral_img[i - i_set - 1, j + (mi - 3) / 2] + integral_img[i - (mi + 1) / 2, j + (mi - 3) / 2] + integral_img[i - i_set - 1, -1]
inner += integral_img3[i - i_m, j + i_set] - integral_img3[i - i_set, j + i_m] - integral_img[-1, j + i_set + 1] - integral_img[i + (mi - 3) / 2, j + i_m] + integral_img[-1, j + i_m] + integral_img[i + (mi - 3) / 2, j + i_set + 1]
inner = integral_img1[i + i_set, j + i_m] - integral_img1[i + i_m, j + i_set] - integral_img[i + i_set, j - i_m] + integral_img[i + i_m, j - i_m]
inner += integral_img[i + (mi - 3) / 2, j + (mi - 3) / 2] - integral_img[i - i_m, j + (mi - 3) / 2] - integral_img[i + (mi - 3) / 2, 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, j - i_m] - integral_img[i - i_m, j - (mi + 1) / 2] + integral_img[i - i_m, j - i_set - 1]
inner += integral_img2[i - i_set, j - i_m] - integral_img2[i - i_m, j - i_set] - integral_img[i - (mi + 1) / 2, -1] - integral_img[i - i_set - 1, j + (mi - 3) / 2] + integral_img[i - (mi + 1) / 2, j + (mi - 3) / 2] + integral_img[i - i_set - 1, -1]
inner += integral_img3[i - i_m, j + i_set] - integral_img3[i - i_set, j + i_m] - integral_img[-1, j + i_set + 1] - integral_img[i + (mi - 3) / 2, j + i_m] + integral_img[-1, j + i_m] + integral_img[i + (mi - 3) / 2, j + i_set + 1]
filtered_image[i, j] = outer_wt * outer - (outer_wt + inner_wt) * inner
return filtered_image
filtered_image[i, j] = outer_wt * outer - (outer_wt + inner_wt) * inner
scales[:, :, k] = filtered_image
return scales
# Outsource to Cython
@@ -79,14 +86,14 @@ def _slanted_integral_image(image):
def _slanted_integral_image_modes(img, mode=1):
if mode == 1:
image = np.copy(img)
mode1 = _slanted_integral_image(image, 1)
mode1 = _slanted_integral_image(image)
return mode1
elif mode == 2:
image = np.copy(img)
image = np.fliplr(image)
image = np.flipud(image)
mode2 = _slanted_integral_image(image, 2)
mode2 = _slanted_integral_image(image)
mode2 = np.fliplr(mode2)
mode2 = np.flipud(mode2)
return mode2
@@ -95,7 +102,7 @@ def _slanted_integral_image_modes(img, mode=1):
image = np.copy(img)
image = np.flipud(image)
image = image.T
mode3 = _slanted_integral_image(image, 3)
mode3 = _slanted_integral_image(image)
mode3 = np.flipud(mode3.T)
return mode3
@@ -103,7 +110,7 @@ def _slanted_integral_image_modes(img, mode=1):
image = np.copy(img)
image = np.fliplr(image)
image = image.T
mode4 = _slanted_integral_image(image, 4)
mode4 = _slanted_integral_image(image)
mode4 = np.fliplr(mode4.T)
return mode4
@@ -118,7 +125,7 @@ def _suppress_line(response, sigma, rpc_threshold):
return response
def censure_keypoints(image, mode='DoB', no_of_scales=7, threshold=0.03, rpc_threshold=10):
def censure_keypoints(image, no_of_scales=7, mode='DoB', threshold=0.03, rpc_threshold=10):
# TODO : Decide number of scales. Image-size dependent?
image = np.squeeze(image)
if image.ndim != 2:
@@ -129,13 +136,13 @@ def censure_keypoints(image, mode='DoB', no_of_scales=7, threshold=0.03, rpc_thr
# Generating all the scales
scales = np.zeros((image.shape[0], image.shape[1], no_of_scales))
for i in range(no_of_scales):
scales[:, :, i] = _get_filtered_image(image, i + 1, mode)
scales = _get_filtered_image(image, no_of_scales, mode)
# Suppressing points that are neither minima or maxima in their 3 x 3 x 3
# neighbourhood to zero
minimas = (minimum_filter(scales, (3, 3, 3)) == scales).astype(int) * scales
maximas = (maximum_filter(scales, (3, 3, 3)) == scales).astype(int) * scales
# Suppressing minimas and maximas weaker than threshold
minimas[np.abs(minimas) < threshold] = 0
maximas[np.abs(maximas) < threshold] = 0