def test_basic_2d_similarity(): linear_component = np.array([[2, -6], [6, 2]]) translation_component = np.array([7, -8]) h_matrix = np.eye(3, 3) h_matrix[:-1, :-1] = linear_component h_matrix[:-1, -1] = translation_component similarity = SimilarityTransform(h_matrix) x = np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]) # transform x explicitly solution = np.dot(x, linear_component.T) + translation_component # transform x using the affine transform result = similarity.apply(x) # check that both answers are equivalent assert_allclose(solution, result) # create several copies of x x_copies = np.array([x, x, x, x, x, x, x, x]) # transform all of copies at once using the affine transform results = similarity.apply(x_copies) # check that all copies have been transformed correctly for r in results: assert_allclose(solution, r)
def test_align_2d_similarity(): linear_component = np.array([[2, -6], [6, 2]]) translation_component = np.array([7, -8]) h_matrix = np.eye(3, 3) h_matrix[:-1, :-1] = linear_component h_matrix[:-1, -1] = translation_component similarity = SimilarityTransform(h_matrix) source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]])) target = similarity.apply(source) # estimate the transform from source and target estimate = SimilarityTransform.align(source, target) # check the estimates is correct assert_allclose(similarity.h_matrix, estimate.h_matrix)