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Make find_boundaries symmetric

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Fixes #738
This commit is contained in:
Juan Nunez-Iglesias
2015-01-22 11:38:35 +11:00
parent f321fefb8b
commit b1891dc24e
2 changed files with 48 additions and 28 deletions
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skimage/segmentation/boundaries.py
+26 -7
View File
@@ -1,15 +1,34 @@
import numpy as np
from ..morphology import dilation, square
from scipy import ndimage as nd
from ..morphology import dilation, erosion, square
from ..util import img_as_float
from ..color import gray2rgb
from .._shared.utils import deprecated
def find_boundaries(label_img):
"""Return bool array where boundaries between labeled regions are True."""
boundaries = np.zeros(label_img.shape, dtype=np.bool)
boundaries[1:, :] += label_img[1:, :] != label_img[:-1, :]
boundaries[:, 1:] += label_img[:, 1:] != label_img[:, :-1]
def find_boundaries(label_img, connectivity=1):
"""Return bool array where boundaries between labeled regions are True.
Parameters
----------
label_img : array of int
An array in which different regions are labeled with different
integers.
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
considered neighbors. A connectivity of 1 (default) means
pixels sharing an edge (in 2D) or a face (in 3D) will be
considered neighbors. A connectivity of `label_img.ndim` means
pixels sharing a corner will be considered neighbors.
Returns
-------
boundaries : array of bool, same shape as `label_img`
A bool image where `True` represents a boundary pixel.
"""
selem = nd.generate_binary_structure(label_img.ndim, connectivity)
boundaries = dilation(label_img, selem) != erosion(label_img, selem)
return boundaries
@@ -35,7 +54,7 @@ def mark_boundaries(image, label_img, color=(1, 1, 0),
boundaries = find_boundaries(label_img)
if outline_color is not None:
outer_boundaries = dilation(boundaries.astype(np.uint8), square(2))
outer_boundaries = dilation(boundaries.astype(np.uint8), square(3))
image[outer_boundaries != 0, :] = np.array(outline_color)
image[boundaries, :] = np.array(color)
return image
skimage/segmentation/tests/test_boundaries.py
+22 -21
View File
@@ -8,12 +8,12 @@ def test_find_boundaries():
image[2:7, 2:7] = 1
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 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]])
@@ -28,27 +28,28 @@ def test_mark_boundaries():
label_image[2:7, 2:7] = 1
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 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]])
result = mark_boundaries(image, label_image, color=(1, 1, 1)).mean(axis=2)
assert_array_equal(result, ref)
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 2, 0],
[0, 0, 1, 2, 2, 2, 2, 1, 2, 0],
[0, 0, 1, 2, 0, 0, 0, 1, 2, 0],
[0, 0, 1, 2, 0, 0, 0, 1, 2, 0],
[0, 0, 1, 2, 0, 0, 0, 1, 2, 0],
[0, 0, 1, 1, 1, 1, 1, 2, 2, 0],
[0, 0, 2, 2, 2, 2, 2, 2, 0, 0],
ref = np.array([[0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
[2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
[2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
[2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
[2, 1, 1, 2, 0, 2, 1, 1, 2, 0],
[2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
[2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
[2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
[0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
result = mark_boundaries(image, label_image, color=(1, 1, 1),
outline_color=(2, 2, 2)).mean(axis=2)