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Merge pull request #857 from guillempalou/slic-connectivity

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Enforce SLIC superpixels connectivity
This commit is contained in:
Johannes Schönberger
2014-01-17 08:39:45 -08:00
parent 66909ea689 15c2989158
commit 25a32502e0
5 changed files with 154 additions and 8 deletions
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TODO.txt
+2 -1
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@@ -1,7 +1,8 @@
Version 0.11
------------
* Remove deprecated `reverse_map` parameter of `skimage.transform.warp`
* Change depecrated `enforce_connectivity=False`on skimage.segmentation.slic
and set it to True as default
Version 0.10
------------
skimage/_build.py
+1 -1
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@@ -68,4 +68,4 @@ def _changed(filename):
with open(filename_cache, 'wb') as cf:
cf.write(md5_new.encode('utf-8'))
return md5_cached != md5_new
return md5_cached != md5_new.encode('utf-8')
skimage/segmentation/_slic.pyx
+101 -1
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@@ -9,7 +9,6 @@ cimport numpy as cnp
from skimage.util import regular_grid
def _slic_cython(double[:, :, :, ::1] image_zyx,
double[:, ::1] segments,
Py_ssize_t max_iter,
@@ -146,3 +145,104 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
segments[k, c] /= n_segment_elems[k]
return np.asarray(nearest_segments)
def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
Py_ssize_t n_segments,
Py_ssize_t min_size,
Py_ssize_t max_size):
""" Helper function to remove small disconnected regions from the labels
Parameters
----------
segments : 3D array of int, shape (Z, Y, X)
The label field/superpixels found by SLIC.
n_segments: int
Number of specified segments
min_size: int
Minimum size of the segment
max_size: int
Maximum size of the segment. This is done for performance reasons,
to pre-allocate a sufficiently large array for the breadth first search
Returns
-------
connected_segments : 3D array of int, shape (Z, Y, X)
A label field with connected labels starting at label=1
"""
# get image dimensions
cdef Py_ssize_t depth, height, width
depth = segments.shape[0]
height = segments.shape[1]
width = segments.shape[2]
# neighborhood arrays
cdef Py_ssize_t[::1] ddx = np.array((1, -1, 0, 0, 0, 0))
cdef Py_ssize_t[::1] ddy = np.array((0, 0, 1, -1, 0, 0))
cdef Py_ssize_t[::1] ddz = np.array((0, 0, 0, 0, 1, -1))
# new object with connected segments initialized to -1
cdef Py_ssize_t[:, :, ::1] connected_segments \
= -1 * np.ones_like(segments, dtype=np.intp)
cdef Py_ssize_t current_new_label = 0
cdef Py_ssize_t label = 0
# variables for the breadth first search
cdef Py_ssize_t current_segment_size = 1
cdef Py_ssize_t bfs_visited = 0
cdef Py_ssize_t adjacent
cdef Py_ssize_t zz, yy, xx
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
#perform a breadth first search to find
# the size of the connected component
while bfs_visited != current_segment_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
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
return np.asarray(connected_segments)
skimage/segmentation/slic_superpixels.py
+25 -3
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@@ -6,12 +6,13 @@ from scipy import ndimage
import warnings
from skimage.util import img_as_float, regular_grid
from skimage.segmentation._slic import _slic_cython
from skimage.segmentation._slic import _slic_cython, _enforce_label_connectivity_cython
from skimage.color import rgb2lab
def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
spacing=None, multichannel=True, convert2lab=True, ratio=None):
spacing=None, multichannel=True, convert2lab=True, ratio=None,
enforce_connectivity=False, min_size_factor=0.5, max_size_factor=3):
"""Segments image using k-means clustering in Color-(x,y,z) space.
Parameters
@@ -47,7 +48,14 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
recommended.
ratio : float, optional
Synonym for `compactness`. This keyword is deprecated.
enforce_connectivity: bool, optional (default False)
Whether the generated segments are connected or not
min_size_factor: float, optional
Proportion of the minimum segment size to be removed with respect
to the supposed segment size ```depth*width*height/n_segments```
max_size_factor: float, optional
Proportion of the maximum connected segment size. A value of 3 works
in most of the cases.
Returns
-------
labels : 2D or 3D array
@@ -104,6 +112,11 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
'instead.')
compactness = ratio
if enforce_connectivity is None:
warnings.warn('Deprecation: enforce_connectivity will default to'
' True in future versions.')
enforce_connectivity = False
image = img_as_float(image)
is_2d = False
if image.ndim == 2:
@@ -163,6 +176,15 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
labels = _slic_cython(image, segments, max_iter, spacing)
if enforce_connectivity:
segment_size = depth * height * width / n_segments
min_size = int(min_size_factor * segment_size)
max_size = int(max_size_factor * segment_size)
labels = _enforce_label_connectivity_cython(labels,
n_segments,
min_size,
max_size)
if is_2d:
labels = labels[0]
skimage/segmentation/tests/test_slic.py
+25 -2
View File
@@ -76,7 +76,7 @@ def test_gray_3d():
midpoint = dim_size // 2
slices.append((slice(None, midpoint), slice(midpoint, None)))
slices = list(it.product(*slices))
shades = np.arange(0, 1.000001, 1.0/7)
shades = np.arange(0, 1.000001, 1.0 / 7)
for s, sh in zip(slices, shades):
img[s] = sh
img += 0.001 * rnd.normal(size=img.shape)
@@ -120,11 +120,34 @@ def test_spacing():
def test_invalid_lab_conversion():
img = np.array([[1, 1, 1, 0, 0],
[1, 1, 0, 0, 0]], np.float)
[1, 1, 0, 0, 0]], np.float) + 1
assert_raises(ValueError, slic, img, multichannel=True, convert2lab=True)
def test_enforce_connectivity():
img = np.array([[0, 0, 0, 1, 1, 1],
[1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 0]], np.float)
segments_connected = slic(img, 2, compactness=0.0001,
enforce_connectivity=True,
convert2lab=False)
segments_disconnected = slic(img, 2, compactness=0.0001,
enforce_connectivity=False,
convert2lab=False)
result_connected = np.array([[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]], np.float)
result_disconnected = np.array([[0, 0, 0, 1, 1, 1],
[1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 0]], np.float)
assert_equal(segments_connected, result_connected)
assert_equal(segments_disconnected, result_disconnected)
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()