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emmanuelle
736802bad4
* medial_axis function in morphology.skeletonize * plot_medial_axis.py example for the gallery Compared to the skeletonize algorithm in morphology.skeletonize, medial_axis is faster because it processes all pixel in one pass. However, the resulting skeleton has more branches, that may be unwanted depending on the application.
154 lines
5.6 KiB
Python
154 lines
5.6 KiB
Python
import numpy as np
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from skimage.morphology import skeletonize, medial_axis
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import numpy.testing
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from skimage.draw import draw
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from scipy.ndimage import correlate
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from skimage.io import imread
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from skimage import data_dir
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import os.path
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class TestSkeletonize():
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def test_skeletonize_no_foreground(self):
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im = np.zeros((5,5))
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result = skeletonize(im)
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numpy.testing.assert_array_equal(result, np.zeros((5,5)))
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def test_skeletonize_wrong_dim1(self):
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im = np.zeros((5))
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numpy.testing.assert_raises(ValueError, skeletonize, im)
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def test_skeletonize_wrong_dim2(self):
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im = np.zeros((5, 5, 5))
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numpy.testing.assert_raises(ValueError, skeletonize, im)
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def test_skeletonize_not_binary(self):
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im = np.zeros((5, 5))
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im[0, 0] = 1
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im[0, 1] = 2
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numpy.testing.assert_raises(ValueError, skeletonize, im)
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def test_skeletonize_unexpected_value(self):
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im = np.zeros((5, 5))
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im[0, 0] = 2
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numpy.testing.assert_raises(ValueError, skeletonize, im)
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def test_skeletonize_all_foreground(self):
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im = np.ones((3,4))
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result = skeletonize(im)
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def test_skeletonize_single_point(self):
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im = np.zeros((5, 5), np.uint8)
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im[3, 3] = 1
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result = skeletonize(im)
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numpy.testing.assert_array_equal(result, im)
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def test_skeletonize_already_thinned(self):
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im = np.zeros((5, 5), np.uint8)
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im[3,1:-1] = 1
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im[2, -1] = 1
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im[4, 0] = 1
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result = skeletonize(im)
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numpy.testing.assert_array_equal(result, im)
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def test_skeletonize_output(self):
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im = imread(os.path.join(data_dir, "bw_text.png"), as_grey=True)
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# make black the foreground
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im = (im==0)
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result = skeletonize(im)
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expected = np.load(os.path.join(data_dir, "bw_text_skeleton.npy"))
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numpy.testing.assert_array_equal(result, expected)
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def test_skeletonize_num_neighbours(self):
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# an empty image
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image = np.zeros((300, 300))
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# foreground object 1
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image[10:-10, 10:100] = 1
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image[-100:-10, 10:-10] = 1
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image[10:-10, -100:-10] = 1
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# foreground object 2
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rs, cs = draw.bresenham(250, 150, 10, 280)
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for i in range(10): image[rs+i, cs] = 1
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rs, cs = draw.bresenham(10, 150, 250, 280)
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for i in range(20): image[rs+i, cs] = 1
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# foreground object 3
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ir, ic = np.indices(image.shape)
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circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2
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circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2
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image[circle1] = 1
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image[circle2] = 0
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result = skeletonize(image)
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# there should never be a 2x2 block of foreground pixels in a skeleton
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mask = np.array([[1, 1],
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[1, 1]], np.uint8)
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blocks = correlate(result, mask, mode='constant')
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assert not numpy.any(blocks == 4)
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class TestMedialAxis():
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def test_00_00_zeros(self):
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'''Test skeletonize on an array of all zeros'''
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result = medial_axis(np.zeros((10, 10), bool))
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assert np.all(result == False)
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def test_00_01_zeros_masked(self):
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'''Test skeletonize on an array that is completely masked'''
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result = medial_axis(np.zeros((10, 10), bool),
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np.zeros((10, 10), bool))
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assert np.all(result == False)
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def test_01_01_rectangle(self):
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'''Test skeletonize on a rectangle'''
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image = np.zeros((9, 15), bool)
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image[1:-1, 1:-1] = True
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#
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# The result should be four diagonals from the
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# corners, meeting in a horizontal line
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#
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expected = np.array([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
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[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
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[0,0,0,1,0,0,0,0,0,0,0,1,0,0,0],
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[0,0,0,0,1,1,1,1,1,1,1,0,0,0,0],
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[0,0,0,1,0,0,0,0,0,0,0,1,0,0,0],
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[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
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[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
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[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], bool)
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result = medial_axis(image)
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assert np.all(result == expected)
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result, distance = medial_axis(image, return_distance=True)
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assert distance.max() == 4
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def test_01_02_hole(self):
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'''Test skeletonize on a rectangle with a hole in the middle'''
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image = np.zeros((9, 15), bool)
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image[1:-1, 1:-1] = True
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image[4, 4:-4] = False
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expected = np.array([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
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[0,0,1,1,1,1,1,1,1,1,1,1,1,0,0],
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[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
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[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
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[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
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[0,0,1,1,1,1,1,1,1,1,1,1,1,0,0],
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[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
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[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],bool)
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result = medial_axis(image)
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assert np.all(result == expected)
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def test_narrow_image(self):
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"""Test skeletonize on a 1-pixel thin strip"""
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image = np.zeros((1, 5), bool)
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image[:, 1:-1] = True
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result = medial_axis(image)
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assert np.all(result == image)
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if __name__ == '__main__':
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np.testing.run_module_suite()
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