243 lines
8.6 KiB
Python
243 lines
8.6 KiB
Python
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import numpy as np
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from skimage.morphology import skeletonize, medial_axis, thin
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from skimage.morphology._skeletonize import (_generate_thin_luts,
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G123_LUT, G123P_LUT)
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from skimage 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
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from skimage._shared import testing
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from skimage._shared.testing import assert_array_equal, fetch
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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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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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with testing.raises(ValueError):
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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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with testing.raises(ValueError):
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skeletonize(im, method='zhang')
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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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with testing.raises(ValueError):
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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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with testing.raises(ValueError):
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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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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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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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assert_array_equal(result, im)
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def test_skeletonize_output(self):
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im = imread(fetch("data/bw_text.png"), as_gray=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(fetch("data/bw_text_skeleton.npy"))
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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.line(250, 150, 10, 280)
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for i in range(10):
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image[rs + i, cs] = 1
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rs, cs = draw.line(10, 150, 250, 280)
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for i in range(20):
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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 np.any(blocks == 4)
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def test_lut_fix(self):
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im = np.zeros((6, 6), np.uint8)
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im[1, 2] = 1
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im[2, 2] = 1
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im[2, 3] = 1
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im[3, 3] = 1
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im[3, 4] = 1
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im[4, 4] = 1
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im[4, 5] = 1
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result = skeletonize(im)
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expected = np.array([[0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 0],
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[0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0]], dtype=np.uint8)
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assert np.all(result == expected)
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class TestThin():
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@property
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def input_image(self):
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"""image to test thinning with"""
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ii = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 1, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
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return ii
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def test_zeros(self):
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assert np.all(thin(np.zeros((10, 10))) == False)
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def test_iter_1(self):
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result = thin(self.input_image, 1).astype(np.uint8)
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expected = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 1, 0, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
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assert_array_equal(result, expected)
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def test_noiter(self):
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result = thin(self.input_image).astype(np.uint8)
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expected = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 1, 0, 1, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
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assert_array_equal(result, expected)
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def test_baddim(self):
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for ii in [np.zeros((3)), np.zeros((3, 3, 3))]:
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with testing.raises(ValueError):
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thin(ii)
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def test_lut_generation(self):
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g123, g123p = _generate_thin_luts()
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assert_array_equal(g123, G123_LUT)
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assert_array_equal(g123p, G123P_LUT)
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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_vertical_line(self):
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'''Test a thick vertical line, issue #3861'''
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img = np.zeros((9, 9))
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img[:, 2] = 1
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img[:, 3] = 1
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img[:, 4] = 1
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expected = np.full(img.shape, False)
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expected[:, 3] = True
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result = medial_axis(img)
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assert_array_equal(result, expected)
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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]],
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dtype=np.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]],
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dtype=np.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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