wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-08 23:27:18 +08:00
Code Issues Packages Projects Releases Wiki Activity

Do not acquire GIL for slic

Browse Source
This commit is contained in:
Johannes Schönberger
2015-05-19 22:40:19 -07:00
parent 7337a3e14b
commit 785176b4c2
2 changed files with 118 additions and 115 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/segmentation/_slic.pyx
+116 -115
View File
@@ -101,88 +101,89 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
# The reference implementation (Achanta et al.) calls this invxywt
cdef double spatial_weight = float(1) / (step ** 2)
for i in range(max_iter):
change = 0
distance[:, :, :] = DBL_MAX
with nogil:
for i in range(max_iter):
change = 0
distance[:, :, :] = DBL_MAX
# assign pixels to segments
for k in range(n_segments):
# assign pixels to segments
for k in range(n_segments):
# segment coordinate centers
cz = segments[k, 0]
cy = segments[k, 1]
cx = segments[k, 2]
# segment coordinate centers
cz = segments[k, 0]
cy = segments[k, 1]
cx = segments[k, 2]
# compute windows
z_min = <Py_ssize_t>max(cz - 2 * step_z, 0)
z_max = <Py_ssize_t>min(cz + 2 * step_z + 1, depth)
y_min = <Py_ssize_t>max(cy - 2 * step_y, 0)
y_max = <Py_ssize_t>min(cy + 2 * step_y + 1, height)
x_min = <Py_ssize_t>max(cx - 2 * step_x, 0)
x_max = <Py_ssize_t>min(cx + 2 * step_x + 1, width)
# compute windows
z_min = <Py_ssize_t>max(cz - 2 * step_z, 0)
z_max = <Py_ssize_t>min(cz + 2 * step_z + 1, depth)
y_min = <Py_ssize_t>max(cy - 2 * step_y, 0)
y_max = <Py_ssize_t>min(cy + 2 * step_y + 1, height)
x_min = <Py_ssize_t>max(cx - 2 * step_x, 0)
x_max = <Py_ssize_t>min(cx + 2 * step_x + 1, width)
for z in range(z_min, z_max):
dz = (sz * (cz - z)) ** 2
for y in range(y_min, y_max):
dy = (sy * (cy - y)) ** 2
for x in range(x_min, x_max):
dist_center = (dz + dy + (sx * (cx - x)) ** 2) * spatial_weight
dist_color = 0
for c in range(3, n_features):
dist_color += (image_zyx[z, y, x, c - 3]
- segments[k, c]) ** 2
if slic_zero:
dist_center += dist_color / max_dist_color[k]
else:
dist_center += dist_color
for z in range(z_min, z_max):
dz = (sz * (cz - z)) ** 2
for y in range(y_min, y_max):
dy = (sy * (cy - y)) ** 2
for x in range(x_min, x_max):
dist_center = (dz + dy + (sx * (cx - x)) ** 2) * spatial_weight
dist_color = 0
for c in range(3, n_features):
dist_color += (image_zyx[z, y, x, c - 3]
- segments[k, c]) ** 2
if slic_zero:
dist_center += dist_color / max_dist_color[k]
else:
dist_center += dist_color
if distance[z, y, x] > dist_center:
nearest_segments[z, y, x] = k
distance[z, y, x] = dist_center
change = 1
if distance[z, y, x] > dist_center:
nearest_segments[z, y, x] = k
distance[z, y, x] = dist_center
change = 1
# stop if no pixel changed its segment
if change == 0:
break
# stop if no pixel changed its segment
if change == 0:
break
# recompute segment centers
# recompute segment centers
# sum features for all segments
n_segment_elems[:] = 0
segments[:, :] = 0
for z in range(depth):
for y in range(height):
for x in range(width):
k = nearest_segments[z, y, x]
n_segment_elems[k] += 1
segments[k, 0] += z
segments[k, 1] += y
segments[k, 2] += x
for c in range(3, n_features):
segments[k, c] += image_zyx[z, y, x, c - 3]
# divide by number of elements per segment to obtain mean
for k in range(n_segments):
for c in range(n_features):
segments[k, c] /= n_segment_elems[k]
# If in SLICO mode, update the color distance maxima
if slic_zero:
# sum features for all segments
n_segment_elems[:] = 0
segments[:, :] = 0
for z in range(depth):
for y in range(height):
for x in range(width):
k = nearest_segments[z, y, x]
dist_color = 0
n_segment_elems[k] += 1
segments[k, 0] += z
segments[k, 1] += y
segments[k, 2] += x
for c in range(3, n_features):
dist_color += (image_zyx[z, y, x, c - 3] -
segments[k, c]) ** 2
segments[k, c] += image_zyx[z, y, x, c - 3]
# The reference implementation seems to only change
# the color if it increases from previous iteration
if max_dist_color[k] < dist_color:
max_dist_color[k] = dist_color
# divide by number of elements per segment to obtain mean
for k in range(n_segments):
for c in range(n_features):
segments[k, c] /= n_segment_elems[k]
# If in SLICO mode, update the color distance maxima
if slic_zero:
for z in range(depth):
for y in range(height):
for x in range(width):
k = nearest_segments[z, y, x]
dist_color = 0
for c in range(3, n_features):
dist_color += (image_zyx[z, y, x, c - 3] -
segments[k, c]) ** 2
# The reference implementation seems to only change
# the color if it increases from previous iteration
if max_dist_color[k] < dist_color:
max_dist_color[k] = dist_color
return np.asarray(nearest_segments)
@@ -237,54 +238,54 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
cdef Py_ssize_t[:, ::1] coord_list = np.zeros((max_size, 3), dtype=np.intp)
# loop through all image
for z in range(depth):
for y in range(height):
for x in range(width):
if connected_segments[z, y, x] >= 0:
continue
# find the component size
adjacent = 0
label = segments[z, y, x]
connected_segments[z, y, x] = current_new_label
current_segment_size = 1
bfs_visited = 0
coord_list[bfs_visited, 0] = z
coord_list[bfs_visited, 1] = y
coord_list[bfs_visited, 2] = x
with nogil:
for z in range(depth):
for y in range(height):
for x in range(width):
if connected_segments[z, y, x] >= 0:
continue
# find the component size
adjacent = 0
label = segments[z, y, x]
connected_segments[z, y, x] = current_new_label
current_segment_size = 1
bfs_visited = 0
coord_list[bfs_visited, 0] = z
coord_list[bfs_visited, 1] = y
coord_list[bfs_visited, 2] = x
#perform a breadth first search to find
# the size of the connected component
while bfs_visited < current_segment_size < max_size:
for i in range(6):
zz = coord_list[bfs_visited, 0] + ddz[i]
yy = coord_list[bfs_visited, 1] + ddy[i]
xx = coord_list[bfs_visited, 2] + ddx[i]
if (0 <= xx < width and
0 <= yy < height and
0 <= zz < depth):
if (segments[zz, yy, xx] == label and
connected_segments[zz, yy, xx] == -1):
connected_segments[zz, yy, xx] = \
current_new_label
coord_list[current_segment_size, 0] = zz
coord_list[current_segment_size, 1] = yy
coord_list[current_segment_size, 2] = xx
current_segment_size += 1
if current_segment_size >= max_size:
break
elif (connected_segments[zz, yy, xx] >= 0 and
connected_segments[zz, yy, xx] != current_new_label):
adjacent = connected_segments[zz, yy, xx]
bfs_visited += 1
#perform a breadth first search to find
# the size of the connected component
while bfs_visited < current_segment_size < max_size:
for i in range(6):
zz = coord_list[bfs_visited, 0] + ddz[i]
yy = coord_list[bfs_visited, 1] + ddy[i]
xx = coord_list[bfs_visited, 2] + ddx[i]
if (0 <= xx < width and
0 <= yy < height and
0 <= zz < depth):
if (segments[zz, yy, xx] == label and
connected_segments[zz, yy, xx] == -1):
connected_segments[zz, yy, xx] = \
current_new_label
coord_list[current_segment_size, 0] = zz
coord_list[current_segment_size, 1] = yy
coord_list[current_segment_size, 2] = xx
current_segment_size += 1
if current_segment_size >= max_size:
break
elif (connected_segments[zz, yy, xx] >= 0 and
connected_segments[zz, yy, xx] != current_new_label):
adjacent = connected_segments[zz, yy, xx]
bfs_visited += 1
# change to an adjacent one, like in the original paper
if current_segment_size < min_size:
for i in range(current_segment_size):
connected_segments[coord_list[i, 0],
coord_list[i, 1],
coord_list[i, 2]] = adjacent
else:
current_new_label += 1
# change to an adjacent one, like in the original paper
if current_segment_size < min_size:
for i in range(current_segment_size):
connected_segments[coord_list[i, 0],
coord_list[i, 1],
coord_list[i, 2]] = adjacent
else:
current_new_label += 1
return np.asarray(connected_segments)
skimage/segmentation/tests/test_slic.py
+2
View File
@@ -2,8 +2,10 @@ import itertools as it
import numpy as np
from numpy.testing import assert_equal, assert_raises
from skimage.segmentation import slic
from skimage._shared.testing import test_parallel
@test_parallel()
def test_color_2d():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21, 3))