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Modify SLIC to allow uneven step sizes

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Juan Nunez-Iglesias
2013-07-02 13:22:12 +02:00
parent bb8cfea8c6
commit 620210025f
1 changed files with 14 additions and 14 deletions
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skimage/segmentation/_slic.pyx
+14 -14
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@@ -9,7 +9,7 @@ from scipy import ndimage
cimport numpy as cnp
from ..util import img_as_float
from ..util import img_as_float, regular_grid
from ..color import rgb2lab, gray2rgb
@@ -94,13 +94,13 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
cdef Py_ssize_t depth, height, width
depth, height, width = image.shape[:3]
# approximate grid size for desired n_segments
cdef Py_ssize_t step = int(np.ceil(
(depth * height * width / n_segments) **
(1.0/3)))
cdef Py_ssize_t step_z, step_y, step_x
grid_z, grid_y, grid_x = np.mgrid[:depth, :height, :width]
means_z = grid_z[::step, ::step, ::step]
means_y = grid_y[::step, ::step, ::step]
means_x = grid_x[::step, ::step, ::step]
slices = regular_grid(image.shape, n_segments)
step_z, step_y, step_x = [int(s.step) for s in slices]
means_z = grid_z[slices]
means_y = grid_y[slices]
means_x = grid_x[slices]
means_color = np.zeros(means_z.shape + (3,))
cdef cnp.ndarray[dtype=cnp.float_t, ndim=2] means = \
@@ -115,7 +115,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
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
ratio = (ratio / float(max((step_z, step_y, step_x)))) ** 2
cdef cnp.ndarray[dtype=cnp.float_t, ndim=4] image_zyx \
= np.concatenate([
grid_y[..., np.newaxis],
@@ -143,12 +143,12 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
# assign pixels to means
for k in range(n_means):
# compute windows:
z_min = int(max(current_mean[0] - 2 * step, 0))
z_max = int(min(current_mean[0] + 2 * step, depth))
y_min = int(max(current_mean[1] - 2 * step, 0))
y_max = int(min(current_mean[1] + 2 * step, height))
x_min = int(max(current_mean[2] - 2 * step, 0))
x_max = int(min(current_mean[2] + 2 * step, width))
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))
for z in range(z_min, z_max):
for y in range(y_min, y_max):
current_pixel = \