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Initial attempt at updating SLIC for memoryviews

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Juan Nunez-Iglesias
2013-06-24 22:44:50 -04:00
parent cba821d5e3
commit b1b70631bd
1 changed files with 14 additions and 30 deletions
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skimage/segmentation/_slic.pyx
+14 -30
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@@ -13,10 +13,10 @@ from ..util import img_as_float, regular_grid
from ..color import rgb2lab, gray2rgb
def _slic_cython(cnp.ndarray[dtype=cnp.float_t, ndim=4] image_zyx,
cnp.ndarray[dtype=cnp.intp_t, ndim=3] nearest_mean,
cnp.ndarray[dtype=cnp.float_t, ndim=3] distance,
cnp.ndarray[dtype=cnp.float_t, ndim=2] means,
def _slic_cython(double[:, :, :, ::1] image_zyx,
int[:, :, ::1] nearest_mean,
double[:, :, ::1] distance,
double[:, ::1] means,
float ratio, int max_iter, int n_segments):
"""Helper function for SLIC segmentation."""
@@ -30,54 +30,38 @@ def _slic_cython(cnp.ndarray[dtype=cnp.float_t, ndim=4] image_zyx,
slices = regular_grid((depth, height, width), n_segments)
step_z, step_y, step_x = [int(s.step) for s in slices]
cdef cnp.float_t* current_mean
cdef cnp.float_t* mean_entry
n_means = means.shape[0]
cdef Py_ssize_t i, k, x, y, z, x_min, x_max, y_min, y_max, z_min, z_max, \
changes
cdef double dist_mean
cdef cnp.float_t* image_p = <cnp.float_t*> image_zyx.data
cdef cnp.float_t* distance_p = <cnp.float_t*> distance.data
cdef cnp.float_t* current_distance
cdef cnp.float_t* current_pixel
cdef double tmp
for i in range(max_iter):
distance.fill(np.inf)
changes = 0
current_mean = <cnp.float_t*> means.data
# assign pixels to means
for k in range(n_means):
# compute windows:
z_min = int(max(current_mean[0] - 2 * step_z, 0))
z_max = int(min(current_mean[0] + 2 * step_z, depth))
y_min = int(max(current_mean[1] - 2 * step_y, 0))
y_max = int(min(current_mean[1] + 2 * step_y, height))
x_min = int(max(current_mean[2] - 2 * step_x, 0))
x_max = int(min(current_mean[2] + 2 * step_x, width))
z_min = int(max(means[k, 0] - 2 * step_z, 0))
z_max = int(min(means[k, 0] + 2 * step_z, depth))
y_min = int(max(means[k, 1] - 2 * step_y, 0))
y_max = int(min(means[k, 1] + 2 * step_y, height))
x_min = int(max(means[k, 2] - 2 * step_x, 0))
x_max = int(min(means[k, 2] + 2 * step_x, width))
for z in range(z_min, z_max):
for y in range(y_min, y_max):
current_pixel = \
&image_p[6 * ((z * height + y) * width + x_min)]
current_distance = \
&distance_p[(z * height + y) * width + x_min]
for x in range(x_min, x_max):
mean_entry = current_mean
dist_mean = 0
for c in range(6):
# you would think the compiler can optimize the
# squaring itself. mine can't (with O2)
tmp = current_pixel[0] - mean_entry[0]
tmp = image_zyx[z, y, x, c] - means[k, c]
dist_mean += tmp * tmp
current_pixel += 1
mean_entry += 1
# some precision issue here. Doesnt work if testing ">"
if current_distance[0] - dist_mean > 1e-10:
if distance[z, y, x] - dist_mean > 1e-10:
nearest_mean[z, y, x] = k
current_distance[0] = dist_mean
changes += 1
current_distance += 1
current_mean += 6
distance[z, y, x] = dist_mean
changes = 1
if changes == 0:
break
# recompute means: