295 lines
12 KiB
Python
295 lines
12 KiB
Python
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import numpy as np
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from skimage.feature import (greycomatrix,
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greycoprops,
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local_binary_pattern,
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multiblock_lbp)
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from skimage._shared.testing import test_parallel
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from skimage.transform import integral_image
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from skimage._shared import testing
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class TestGLCM():
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def setup(self):
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self.image = np.array([[0, 0, 1, 1],
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[0, 0, 1, 1],
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[0, 2, 2, 2],
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[2, 2, 3, 3]], dtype=np.uint8)
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@test_parallel()
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def test_output_angles(self):
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result = greycomatrix(self.image, [1], [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4], 4)
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assert result.shape == (4, 4, 1, 4)
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expected1 = np.array([[2, 2, 1, 0],
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[0, 2, 0, 0],
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[0, 0, 3, 1],
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[0, 0, 0, 1]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 0], expected1)
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expected2 = np.array([[1, 1, 3, 0],
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[0, 1, 1, 0],
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[0, 0, 0, 2],
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[0, 0, 0, 0]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 1], expected2)
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expected3 = np.array([[3, 0, 2, 0],
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[0, 2, 2, 0],
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[0, 0, 1, 2],
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[0, 0, 0, 0]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 2], expected3)
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expected4 = np.array([[2, 0, 0, 0],
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[1, 1, 2, 0],
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[0, 0, 2, 1],
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[0, 0, 0, 0]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 3], expected4)
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def test_output_symmetric_1(self):
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result = greycomatrix(self.image, [1], [np.pi / 2], 4,
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symmetric=True)
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assert result.shape == (4, 4, 1, 1)
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expected = np.array([[6, 0, 2, 0],
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[0, 4, 2, 0],
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[2, 2, 2, 2],
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[0, 0, 2, 0]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 0], expected)
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def test_error_raise_float(self):
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for dtype in [np.float, np.double, np.float16, np.float32, np.float64]:
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with testing.raises(ValueError):
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greycomatrix(self.image.astype(dtype), [1], [np.pi], 4)
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def test_error_raise_int_types(self):
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for dtype in [np.int16, np.int32, np.int64, np.uint16, np.uint32, np.uint64]:
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with testing.raises(ValueError):
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greycomatrix(self.image.astype(dtype), [1], [np.pi])
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def test_error_raise_negative(self):
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with testing.raises(ValueError):
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greycomatrix(self.image.astype(np.int16) - 1, [1], [np.pi], 4)
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def test_error_raise_levels_smaller_max(self):
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with testing.raises(ValueError):
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greycomatrix(self.image - 1, [1], [np.pi], 3)
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def test_image_data_types(self):
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for dtype in [np.uint16, np.uint32, np.uint64, np.int16, np.int32, np.int64]:
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img = self.image.astype(dtype)
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result = greycomatrix(img, [1], [np.pi / 2], 4,
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symmetric=True)
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assert result.shape == (4, 4, 1, 1)
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expected = np.array([[6, 0, 2, 0],
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[0, 4, 2, 0],
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[2, 2, 2, 2],
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[0, 0, 2, 0]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 0], expected)
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return
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def test_output_distance(self):
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im = np.array([[0, 0, 0, 0],
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[1, 0, 0, 1],
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[2, 0, 0, 2],
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[3, 0, 0, 3]], dtype=np.uint8)
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result = greycomatrix(im, [3], [0], 4, symmetric=False)
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expected = np.array([[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 0],
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[0, 0, 0, 1]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 0], expected)
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def test_output_combo(self):
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im = np.array([[0],
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[1],
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[2],
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[3]], dtype=np.uint8)
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result = greycomatrix(im, [1, 2], [0, np.pi / 2], 4)
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assert result.shape == (4, 4, 2, 2)
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z = np.zeros((4, 4), dtype=np.uint32)
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e1 = np.array([[0, 1, 0, 0],
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[0, 0, 1, 0],
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[0, 0, 0, 1],
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[0, 0, 0, 0]], dtype=np.uint32)
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e2 = np.array([[0, 0, 1, 0],
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[0, 0, 0, 1],
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[0, 0, 0, 0],
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[0, 0, 0, 0]], dtype=np.uint32)
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np.testing.assert_array_equal(result[:, :, 0, 0], z)
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np.testing.assert_array_equal(result[:, :, 1, 0], z)
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np.testing.assert_array_equal(result[:, :, 0, 1], e1)
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np.testing.assert_array_equal(result[:, :, 1, 1], e2)
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def test_output_empty(self):
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result = greycomatrix(self.image, [10], [0], 4)
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np.testing.assert_array_equal(result[:, :, 0, 0],
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np.zeros((4, 4), dtype=np.uint32))
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result = greycomatrix(self.image, [10], [0], 4, normed=True)
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np.testing.assert_array_equal(result[:, :, 0, 0],
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np.zeros((4, 4), dtype=np.uint32))
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def test_normed_symmetric(self):
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result = greycomatrix(self.image, [1, 2, 3],
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[0, np.pi / 2, np.pi], 4,
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normed=True, symmetric=True)
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for d in range(result.shape[2]):
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for a in range(result.shape[3]):
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np.testing.assert_almost_equal(result[:, :, d, a].sum(),
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1.0)
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np.testing.assert_array_equal(result[:, :, d, a],
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result[:, :, d, a].transpose())
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def test_contrast(self):
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result = greycomatrix(self.image, [1, 2], [0], 4,
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normed=True, symmetric=True)
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result = np.round(result, 3)
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contrast = greycoprops(result, 'contrast')
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np.testing.assert_almost_equal(contrast[0, 0], 0.585, decimal=3)
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def test_dissimilarity(self):
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result = greycomatrix(self.image, [1], [0, np.pi / 2], 4,
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normed=True, symmetric=True)
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result = np.round(result, 3)
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dissimilarity = greycoprops(result, 'dissimilarity')
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np.testing.assert_almost_equal(dissimilarity[0, 0], 0.418, decimal=3)
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def test_dissimilarity_2(self):
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result = greycomatrix(self.image, [1, 3], [np.pi / 2], 4,
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normed=True, symmetric=True)
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result = np.round(result, 3)
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dissimilarity = greycoprops(result, 'dissimilarity')[0, 0]
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np.testing.assert_almost_equal(dissimilarity, 0.665, decimal=3)
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def test_non_normalized_glcm(self):
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img = (np.random.random((100, 100)) * 8).astype(np.uint8)
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p = greycomatrix(img, [1, 2, 4, 5], [0, 0.25, 1, 1.5], levels=8)
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np.testing.assert_(np.max(greycoprops(p, 'correlation')) < 1.0)
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def test_invalid_property(self):
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result = greycomatrix(self.image, [1], [0], 4)
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with testing.raises(ValueError):
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greycoprops(result, 'ABC')
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def test_homogeneity(self):
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result = greycomatrix(self.image, [1], [0, 6], 4, normed=True,
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symmetric=True)
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homogeneity = greycoprops(result, 'homogeneity')[0, 0]
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np.testing.assert_almost_equal(homogeneity, 0.80833333)
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def test_energy(self):
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result = greycomatrix(self.image, [1], [0, 4], 4, normed=True,
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symmetric=True)
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energy = greycoprops(result, 'energy')[0, 0]
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np.testing.assert_almost_equal(energy, 0.38188131)
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def test_correlation(self):
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result = greycomatrix(self.image, [1, 2], [0], 4, normed=True,
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symmetric=True)
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energy = greycoprops(result, 'correlation')
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np.testing.assert_almost_equal(energy[0, 0], 0.71953255)
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np.testing.assert_almost_equal(energy[1, 0], 0.41176470)
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def test_uniform_properties(self):
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im = np.ones((4, 4), dtype=np.uint8)
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result = greycomatrix(im, [1, 2, 8], [0, np.pi / 2], 4, normed=True,
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symmetric=True)
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for prop in ['contrast', 'dissimilarity', 'homogeneity',
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'energy', 'correlation', 'ASM']:
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greycoprops(result, prop)
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class TestLBP():
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def setup(self):
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self.image = np.array([[255, 6, 255, 0, 141, 0],
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[ 48, 250, 204, 166, 223, 63],
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[ 8, 0, 159, 50, 255, 30],
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[167, 255, 63, 40, 128, 255],
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[ 0, 255, 30, 34, 255, 24],
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[146, 241, 255, 0, 189, 126]], dtype='double')
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@test_parallel()
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def test_default(self):
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lbp = local_binary_pattern(self.image, 8, 1, 'default')
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ref = np.array([[ 0, 251, 0, 255, 96, 255],
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[143, 0, 20, 153, 64, 56],
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[238, 255, 12, 191, 0, 252],
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[129, 64., 62, 159, 199, 0],
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[255, 4, 255, 175, 0, 254],
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[ 3, 5, 0, 255, 4, 24]])
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np.testing.assert_array_equal(lbp, ref)
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def test_ror(self):
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lbp = local_binary_pattern(self.image, 8, 1, 'ror')
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ref = np.array([[ 0, 127, 0, 255, 3, 255],
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[ 31, 0, 5, 51, 1, 7],
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[119, 255, 3, 127, 0, 63],
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[ 3, 1, 31, 63, 31, 0],
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[255, 1, 255, 95, 0, 127],
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[ 3, 5, 0, 255, 1, 3]])
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np.testing.assert_array_equal(lbp, ref)
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def test_uniform(self):
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lbp = local_binary_pattern(self.image, 8, 1, 'uniform')
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ref = np.array([[0, 7, 0, 8, 2, 8],
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[5, 0, 9, 9, 1, 3],
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[9, 8, 2, 7, 0, 6],
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[2, 1, 5, 6, 5, 0],
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[8, 1, 8, 9, 0, 7],
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[2, 9, 0, 8, 1, 2]])
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np.testing.assert_array_equal(lbp, ref)
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def test_var(self):
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# Test idea: mean of variance is estimate of overall variance.
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# Fix random seed for test stability.
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np.random.seed(13141516)
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# Create random image with known variance.
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image = np.random.rand(500, 500)
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target_std = 0.3
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image = image / image.std() * target_std
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# Use P=4 to avoid interpolation effects
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P, R = 4, 1
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lbp = local_binary_pattern(image, P, R, 'var')
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# Take central part to avoid border effect.
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lbp = lbp[5:-5, 5:-5]
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# The LBP variance is biased (ddof=0), correct for that.
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expected = target_std**2 * (P-1)/P
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np.testing.assert_almost_equal(lbp.mean(), expected, 4)
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def test_nri_uniform(self):
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lbp = local_binary_pattern(self.image, 8, 1, 'nri_uniform')
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ref = np.array([[ 0, 54, 0, 57, 12, 57],
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[34, 0, 58, 58, 3, 22],
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[58, 57, 15, 50, 0, 47],
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[10, 3, 40, 42, 35, 0],
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[57, 7, 57, 58, 0, 56],
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[ 9, 58, 0, 57, 7, 14]])
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np.testing.assert_array_almost_equal(lbp, ref)
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class TestMBLBP():
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def test_single_mblbp(self):
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# Create dummy matrix where first and fifth rectangles have greater
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# value than the central one. Therefore, the following bits
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# should be 1.
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test_img = np.zeros((9, 9), dtype='uint8')
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test_img[3:6, 3:6] = 1
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test_img[:3, :3] = 255
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test_img[6:, 6:] = 255
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# MB-LBP is filled in reverse order. So the first and fifth bits from
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# the end should be filled.
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correct_answer = 0b10001000
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int_img = integral_image(test_img)
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lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3)
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np.testing.assert_equal(lbp_code, correct_answer)
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