wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-08-03 13:11:25 +08:00
Code Issues Packages Projects Releases Wiki Activity

MISC some simplifications, minor speedup

Browse Source
This commit is contained in:
Andreas Mueller
2012-08-03 11:37:11 +01:00
parent 501a6db8ad
commit 1c69adb817
1 changed files with 12 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/segmentation/km_segmentation.pyx
+12 -9
View File
@@ -24,19 +24,19 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
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]).copy("C")
cdef int i, k, x, y, x_min, x_max, y_min, y_max
cdef float dist_mean
cdef int i, k, x, y, x_min, x_max, y_min, y_max, changes
cdef double 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_distance
cdef np.float_t* current_pixel
cdef float tmp
cdef double tmp
for i in xrange(max_iter):
changes = 0
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):
@@ -47,6 +47,7 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
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)]
current_distance = &distance_p[y * width + x_min]
for x in xrange(x_min, x_max):
mean_entry = current_mean
dist_mean = 0
@@ -54,14 +55,16 @@ def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
# you would think the compiler can optimize this itself.
# mine can't (with O2)
tmp = current_pixel[0] - mean_entry[0]
dist_mean += tmp * tmp
current_pixel += 1
mean_entry += 1
dist_mean += tmp * tmp
if distance[y, x] > dist_mean:
# some precision issue here. Doesnt work if testing ">"
if current_distance[0] - dist_mean > 1e-10:
nearest_mean[y, x] = k
distance[y, x] = dist_mean
current_distance[0] = dist_mean
changes += 1
current_distance += 1
current_mean += 5
if (nearest_mean == nearest_mean_old).all():
break
# recompute means:
means_list = [np.bincount(nearest_mean.ravel(), image_yx[:, :, j].ravel())