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ENH speedup, means and image use pointers

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This commit is contained in:
Andreas Mueller
2012-08-03 11:37:11 +01:00
parent 8f24366790
commit 501a6db8ad
1 changed files with 25 additions and 9 deletions
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skimage/segmentation/km_segmentation.pyx
+25 -9
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@@ -16,40 +16,56 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
means_color = image[means_y, means_x, :]
cdef np.ndarray[dtype=np.float_t, ndim=2] means = np.dstack([means_y, means_x, means_color]).reshape(-1, 5)
cdef np.float_t* current_mean
cdef np.float_t* mean_entry
n_means = means.shape[0]
# we do the scaling of ratio in the same way as in the SLIC paper
# so the values have the same meaning
ratio = (ratio / float(step)) ** 2
print(ratio)
cdef np.ndarray[dtype=np.float_t, ndim=3] image_yx = np.dstack([grid_y, grid_x, image / ratio])
cdef np.ndarray[dtype=np.float_t, ndim=3] image_yx = np.dstack([grid_y, grid_x, image / ratio]).copy("C")
cdef int i, k, x, y, x_min, x_max, y_min, y_max
cdef float dist_mean
cdef np.ndarray[dtype=np.int_t, ndim=2] nearest_mean = np.zeros((height, width), dtype=np.int)
cdef np.ndarray[dtype=np.float_t, ndim=2] distance = np.ones((height, width), dtype=np.float) * np.inf
cdef np.float_t* image_p = <np.float_t*> image_yx.data
cdef np.float_t* distance_p = <np.float_t*> distance.data
cdef np.float_t* current_pixel
cdef float tmp
for i in xrange(max_iter):
print("iteration %d" % i)
nearest_mean_old = nearest_mean.copy()
# we construct a new means every iteration, adjust pointer
current_mean = <np.float_t*> means.data
# assign pixels to means
for k in xrange(n_means):
# compute windows:
y_min = int(max(means[k, 0] - 2 * step, 0))
y_max = int(min(means[k, 0] + 2 * step, height))
x_min = int(max(means[k, 1] - 2 * step, 0))
x_max = int(min(means[k, 1] + 2 * step, height))
for x in xrange(x_min, x_max):
for y in xrange(y_min, y_max):
y_min = int(max(current_mean[0] - 2 * step, 0))
y_max = int(min(current_mean[0] + 2 * step, height))
x_min = int(max(current_mean[1] - 2 * step, 0))
x_max = int(min(current_mean[1] + 2 * step, height))
for y in xrange(y_min, y_max):
current_pixel = &image_p[5 * (y * width + x_min)]
for x in xrange(x_min, x_max):
mean_entry = current_mean
dist_mean = 0
for c in range(5):
dist_mean += (image_yx[y, x, c] - means[k, c]) ** 2
# you would think the compiler can optimize this itself.
# mine can't (with O2)
tmp = current_pixel[0] - mean_entry[0]
current_pixel += 1
mean_entry += 1
dist_mean += tmp * tmp
if distance[y, x] > dist_mean:
nearest_mean[y, x] = k
distance[y, x] = dist_mean
current_mean += 5
if (nearest_mean == nearest_mean_old).all():
break
# recompute means:
means_list = [np.bincount(nearest_mean.ravel(), image_yx[:, :, j].ravel())
for j in xrange(5)]
in_mean = np.bincount(nearest_mean.ravel())
means = (np.vstack(means_list) / in_mean).T
means = (np.vstack(means_list) / in_mean).T.copy("C")
return nearest_mean