mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 02:30:48 +08:00
Ankit Agrawal
114 lines
4.8 KiB
Python
114 lines
4.8 KiB
Python
# Author: Damian Eads
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import os.path
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import numpy as np
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from numpy.testing import *
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from skimage import data_dir
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from skimage.io import *
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from skimage import data_dir
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from skimage.morphology import selem
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class TestSElem():
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def test_square_selem(self):
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for k in range(0, 5):
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actual_mask = selem.square(k)
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expected_mask = np.ones((k, k), dtype='uint8')
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assert_equal(expected_mask, actual_mask)
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def test_rectangle_selem(self):
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for i in range(0, 5):
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for j in range(0, 5):
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actual_mask = selem.rectangle(i, j)
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expected_mask = np.ones((i, j), dtype='uint8')
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assert_equal(expected_mask, actual_mask)
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def strel_worker(self, fn, func):
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matlab_masks = np.load(os.path.join(data_dir, fn))
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k = 0
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for arrname in sorted(matlab_masks):
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expected_mask = matlab_masks[arrname]
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actual_mask = func(k)
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if (expected_mask.shape == (1,)):
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expected_mask = expected_mask[:, np.newaxis]
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assert_equal(expected_mask, actual_mask)
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k = k + 1
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def strel_worker_3d(self, fn, func):
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matlab_masks = np.load(os.path.join(data_dir, fn))
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k = 0
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for arrname in sorted(matlab_masks):
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expected_mask = matlab_masks[arrname]
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actual_mask = func(k)
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if (expected_mask.shape == (1,)):
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expected_mask = expected_mask[:, np.newaxis]
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# Test center slice for each dimension. This gives a good
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# indication of validity without the need for a 3D reference
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# mask.
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c = int(expected_mask.shape[0]/2)
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assert_equal(expected_mask, actual_mask[c,:,:])
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assert_equal(expected_mask, actual_mask[:,c,:])
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assert_equal(expected_mask, actual_mask[:,:,c])
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k = k + 1
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def test_selem_disk(self):
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self.strel_worker("disk-matlab-output.npz", selem.disk)
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def test_selem_diamond(self):
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self.strel_worker("diamond-matlab-output.npz", selem.diamond)
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def test_selem_ball(self):
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self.strel_worker_3d("disk-matlab-output.npz", selem.ball)
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def test_selem_octahedron(self):
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self.strel_worker_3d("diamond-matlab-output.npz", selem.octahedron)
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def test_selem_octagon(self):
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expected_mask1 = np.array([[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0]], dtype=np.uint8)
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actual_mask1 = selem.octagon(5, 3)
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expected_mask2 = np.array([[0, 1, 0],
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[1, 1, 1],
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[0, 1, 0]], dtype=np.uint8)
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actual_mask2 = selem.octagon(1, 1)
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assert_equal(expected_mask1, actual_mask1)
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assert_equal(expected_mask2, actual_mask2)
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def test_selem_star(self):
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expected_mask1 = np.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
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actual_mask1 = selem.star(4)
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expected_mask2 = np.array([[1, 1, 1],
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[1, 1, 1],
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[1, 1, 1]], dtype=np.uint8)
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actual_mask2 = selem.star(1)
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assert_equal(expected_mask1, actual_mask1)
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assert_equal(expected_mask2, actual_mask2)
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if __name__ == '__main__':
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np.testing.run_module_suite()
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