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)
