import numpy as np from skimage.feature import greycomatrix, greycoprops, local_binary_pattern class TestGLCM(): def setup(self): self.image = np.array([[0, 0, 1, 1], [0, 0, 1, 1], [0, 2, 2, 2], [2, 2, 3, 3]], dtype=np.uint8) def test_output_angles(self): result = greycomatrix(self.image, [1], [0, np.pi / 2], 4) assert result.shape == (4, 4, 1, 2) expected1 = np.array([[2, 2, 1, 0], [0, 2, 0, 0], [0, 0, 3, 1], [0, 0, 0, 1]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected1) expected2 = np.array([[3, 0, 2, 0], [0, 2, 2, 0], [0, 0, 1, 2], [0, 0, 0, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 1], expected2) def test_output_symmetric_1(self): result = greycomatrix(self.image, [1], [np.pi / 2], 4, symmetric=True) assert result.shape == (4, 4, 1, 1) expected = np.array([[6, 0, 2, 0], [0, 4, 2, 0], [2, 2, 2, 2], [0, 0, 2, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected) def test_output_distance(self): im = np.array([[0, 0, 0, 0], [1, 0, 0, 1], [2, 0, 0, 2], [3, 0, 0, 3]], dtype=np.uint8) result = greycomatrix(im, [3], [0], 4, symmetric=False) expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected) def test_output_combo(self): im = np.array([[0], [1], [2], [3]], dtype=np.uint8) result = greycomatrix(im, [1, 2], [0, np.pi / 2], 4) assert result.shape == (4, 4, 2, 2) z = np.zeros((4, 4), dtype=np.uint32) e1 = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]], dtype=np.uint32) e2 = np.array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], z) np.testing.assert_array_equal(result[:, :, 1, 0], z) np.testing.assert_array_equal(result[:, :, 0, 1], e1) np.testing.assert_array_equal(result[:, :, 1, 1], e2) def test_output_empty(self): result = greycomatrix(self.image, [10], [0], 4) np.testing.assert_array_equal(result[:, :, 0, 0], np.zeros((4, 4), dtype=np.uint32)) result = greycomatrix(self.image, [10], [0], 4, normed=True) np.testing.assert_array_equal(result[:, :, 0, 0], np.zeros((4, 4), dtype=np.uint32)) def test_normed_symmetric(self): result = greycomatrix(self.image, [1, 2, 3], [0, np.pi / 2, np.pi], 4, normed=True, symmetric=True) for d in range(result.shape[2]): for a in range(result.shape[3]): np.testing.assert_almost_equal(result[:, :, d, a].sum(), 1.0) np.testing.assert_array_equal(result[:, :, d, a], result[:, :, d, a].transpose()) def test_contrast(self): result = greycomatrix(self.image, [1, 2], [0], 4, normed=True, symmetric=True) result = np.round(result, 3) contrast = greycoprops(result, 'contrast') np.testing.assert_almost_equal(contrast[0, 0], 0.586) def test_dissimilarity(self): result = greycomatrix(self.image, [1], [0, np.pi / 2], 4, normed=True, symmetric=True) result = np.round(result, 3) dissimilarity = greycoprops(result, 'dissimilarity') np.testing.assert_almost_equal(dissimilarity[0, 0], 0.418) def test_dissimilarity_2(self): result = greycomatrix(self.image, [1, 3], [np.pi / 2], 4, normed=True, symmetric=True) result = np.round(result, 3) dissimilarity = greycoprops(result, 'dissimilarity')[0, 0] np.testing.assert_almost_equal(dissimilarity, 0.664) def test_invalid_property(self): result = greycomatrix(self.image, [1], [0], 4) np.testing.assert_raises(ValueError, greycoprops, result, 'ABC') def test_homogeneity(self): result = greycomatrix(self.image, [1], [0, 6], 4, normed=True, symmetric=True) homogeneity = greycoprops(result, 'homogeneity')[0, 0] np.testing.assert_almost_equal(homogeneity, 0.80833333) def test_energy(self): result = greycomatrix(self.image, [1], [0, 4], 4, normed=True, symmetric=True) energy = greycoprops(result, 'energy')[0, 0] np.testing.assert_almost_equal(energy, 0.38188131) def test_correlation(self): result = greycomatrix(self.image, [1, 2], [0], 4, normed=True, symmetric=True) energy = greycoprops(result, 'correlation') np.testing.assert_almost_equal(energy[0, 0], 0.71953255) np.testing.assert_almost_equal(energy[1, 0], 0.41176470) def test_uniform_properties(self): im = np.ones((4, 4), dtype=np.uint8) result = greycomatrix(im, [1, 2, 8], [0, np.pi / 2], 4, normed=True, symmetric=True) for prop in ['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM']: greycoprops(result, prop) class TestLBP(): def setup(self): self.image = np.array([[255, 6, 255, 0, 141, 0], [ 48, 250, 204, 166, 223, 63], [ 8, 0, 159, 50, 255, 30], [167, 255, 63, 40, 128, 255], [ 0, 255, 30, 34, 255, 24], [146, 241, 255, 0, 189, 126]], dtype='double') def test_default(self): lbp = local_binary_pattern(self.image, 8, 1, 'default') ref = np.array([[ 0, 251, 0, 255, 96, 255], [143, 0, 20, 153, 64, 56], [238, 255, 12, 191, 0, 252], [129, 64., 62, 159, 199, 0], [255, 4, 255, 175, 0, 254], [ 3, 5, 0, 255, 4, 24]]) np.testing.assert_array_equal(lbp, ref) def test_ror(self): lbp = local_binary_pattern(self.image, 8, 1, 'ror') ref = np.array([[ 0, 127, 0, 255, 3, 255], [ 31, 0, 5, 51, 1, 7], [119, 255, 3, 127, 0, 63], [ 3, 1, 31, 63, 31, 0], [255, 1, 255, 95, 0, 127], [ 3, 5, 0, 255, 1, 3]]) np.testing.assert_array_equal(lbp, ref) def test_uniform(self): lbp = local_binary_pattern(self.image, 8, 1, 'uniform') ref = np.array([[0, 7, 0, 8, 2, 8], [5, 0, 9, 9, 1, 3], [9, 8, 2, 7, 0, 6], [2, 1, 5, 6, 5, 0], [8, 1, 8, 9, 0, 7], [2, 9, 0, 8, 1, 2]]) np.testing.assert_array_equal(lbp, ref) def test_var(self): lbp = local_binary_pattern(self.image, 8, 1, 'var') ref = np.array([[0. , 0.00072786, 0. , 0.00115377, 0.00032355, 0.00224467], [0.00051758, 0. , 0.0026383 , 0.00163246, 0.00027414, 0.00041124], [0.00192834, 0.00130368, 0.00042095, 0.00171894, 0. , 0.00063726], [0.00023048, 0.00019464 , 0.00082291, 0.00225386, 0.00076696, 0. ], [0.00097253, 0.00013236, 0.0009134 , 0.0014467 , 0. , 0.00082472], [0.00024701, 0.0012277 , 0. , 0.00109869, 0.00015445, 0.00035881]]) np.testing.assert_array_almost_equal(lbp, ref) if __name__ == '__main__': np.testing.run_module_suite()