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added support for boolean types in segementation

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Kshitij Saraogi
2015-12-01 16:32:26 +05:30
parent 3b525987f5
commit 428db535cd
2 changed files with 51 additions and 4 deletions
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skimage/segmentation/boundaries.py
+17 -4
View File
@@ -41,7 +41,7 @@ def _find_boundaries_subpixel(label_img):
for index in np.ndindex(label_img_expanded.shape):
if edges[index]:
values = np.unique(windows[index].ravel())
if len(values) > 2: # single value and max_label
if len(values) > 2: # single value and max_label
boundaries[index] = True
return boundaries
@@ -51,9 +51,9 @@ def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
Parameters
----------
label_img : array of int
An array in which different regions are labeled with different
integers.
label_img : array of int or bool
An array in which different regions are labeled with either different
integers or boolean values.
connectivity: int in {1, ..., `label_img.ndim`}, optional
A pixel is considered a boundary pixel if any of its neighbors
has a different label. `connectivity` controls which pixels are
@@ -144,7 +144,20 @@ def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
[0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> bool_image = np.array([[False, False, False, False, False],
... [False, False, False, False, False],
... [False, False, True, True, True],
... [False, False, True, True, True],
... [False, False, True, True, True]], dtype=np.bool)
>>> find_boundaries(bool_image)
array([[False, False, False, False, False],
[False, False, True, True, True],
[False, True, True, True, True],
[False, True, True, False, False],
[False, True, True, False, False]], dtype=bool)
"""
if label_img.dtype == 'bool':
label_img = label_img.astype(np.uint8)
ndim = label_img.ndim
selem = ndi.generate_binary_structure(ndim, connectivity)
if mode != 'subpixel':
skimage/segmentation/tests/test_boundaries.py
+34
View File
@@ -25,6 +25,19 @@ def test_find_boundaries():
assert_array_equal(result, ref)
def test_find_boundaries_bool():
image = np.zeros((5, 5), dtype=np.bool)
image[2:5, 2:5] = True
ref = np.array([[False, False, False, False, False],
[False, False, True, True, True],
[False, True, True, True, True],
[False, True, True, False, False],
[False, True, True, False, False]], dtype=np.bool)
result = find_boundaries(image)
assert_array_equal(result, ref)
def test_mark_boundaries():
image = np.zeros((10, 10))
label_image = np.zeros((10, 10), dtype=np.uint8)
@@ -61,6 +74,27 @@ def test_mark_boundaries():
assert_array_equal(result, ref)
def test_mark_boundaries_bool():
image = np.zeros((10, 10), dtype=np.bool)
label_image = np.zeros((10, 10), dtype=np.uint8)
label_image[2:7, 2:7] = 1
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
marked = mark_boundaries(image, label_image, color=white, mode='thick')
result = np.mean(marked, axis=-1)
assert_array_equal(result, ref)
def test_mark_boundaries_subpixel():
labels = np.array([[0, 0, 0, 0],
[0, 0, 5, 0],