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Do not acquire GIL for quickshift

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Johannes Schönberger
2015-05-19 22:43:46 -07:00
parent 785176b4c2
commit 2d0b4dc37a
2 changed files with 41 additions and 37 deletions
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skimage/segmentation/_quickshift.pyx
+39 -36
View File
@@ -8,6 +8,7 @@ from itertools import product
cimport numpy as cnp
from libc.math cimport exp, sqrt
from libc.float cimport DBL_MAX
from ..util import img_as_float
from ..color import rgb2lab
@@ -99,19 +100,20 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10,
= np.zeros((height, width))
# compute densities
for r in range(height):
for c in range(width):
r_min, r_max = max(r - w, 0), min(r + w + 1, height)
c_min, c_max = max(c - w, 0), min(c + w + 1, width)
for r_ in range(r_min, r_max):
for c_ in range(c_min, c_max):
dist = 0
for channel in range(channels):
dist += (current_pixel_p[channel] -
image_c[r_, c_, channel])**2
dist += (r - r_)**2 + (c - c_)**2
densities[r, c] += exp(-dist / (2 * kernel_size**2))
current_pixel_p += channels
with nogil:
for r in range(height):
for c in range(width):
r_min, r_max = max(r - w, 0), min(r + w + 1, height)
c_min, c_max = max(c - w, 0), min(c + w + 1, width)
for r_ in range(r_min, r_max):
for c_ in range(c_min, c_max):
dist = 0
for channel in range(channels):
dist += (current_pixel_p[channel] -
image_c[r_, c_, channel])**2
dist += (r - r_)**2 + (c - c_)**2
densities[r, c] += exp(-dist / (2 * kernel_size**2))
current_pixel_p += channels
# this will break ties that otherwise would give us headache
densities += random_state.normal(scale=0.00001, size=(height, width))
@@ -123,29 +125,30 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10,
= np.zeros((height, width))
# find nearest node with higher density
current_pixel_p = image_p
for r in range(height):
for c in range(width):
current_density = densities[r, c]
closest = np.inf
r_min, r_max = max(r - w, 0), min(r + w + 1, height)
c_min, c_max = max(c - w, 0), min(c + w + 1, width)
for r_ in range(r_min, r_max):
for c_ in range(c_min, c_max):
if densities[r_, c_] > current_density:
dist = 0
# We compute the distances twice since otherwise
# we get crazy memory overhead
# (width * height * windowsize**2)
for channel in range(channels):
dist += (current_pixel_p[channel] -
image_c[r_, c_, channel])**2
dist += (r - r_)**2 + (c - c_)**2
if dist < closest:
closest = dist
parent[r, c] = r_ * width + c_
dist_parent[r, c] = sqrt(closest)
current_pixel_p += channels
with nogil:
current_pixel_p = image_p
for r in range(height):
for c in range(width):
current_density = densities[r, c]
closest = DBL_MAX
r_min, r_max = max(r - w, 0), min(r + w + 1, height)
c_min, c_max = max(c - w, 0), min(c + w + 1, width)
for r_ in range(r_min, r_max):
for c_ in range(c_min, c_max):
if densities[r_, c_] > current_density:
dist = 0
# We compute the distances twice since otherwise
# we get crazy memory overhead
# (width * height * windowsize**2)
for channel in range(channels):
dist += (current_pixel_p[channel] -
image_c[r_, c_, channel])**2
dist += (r - r_)**2 + (c - c_)**2
if dist < closest:
closest = dist
parent[r, c] = r_ * width + c_
dist_parent[r, c] = sqrt(closest)
current_pixel_p += channels
dist_parent_flat = dist_parent.ravel()
flat = parent.ravel()
skimage/segmentation/tests/test_quickshift.py
+2 -1
View File
@@ -1,10 +1,11 @@
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
from nose.tools import assert_true
from skimage._shared.testing import assert_greater
from skimage._shared.testing import assert_greater, test_parallel
from skimage.segmentation import quickshift
@test_parallel()
def test_grey():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21))