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 = Similarity(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_similarity_2d_as_vector():
params = np.array([0.2, 0.1, 1.0, 2.0])
h**o = np.array([[params[0] + 1.0, -params[1], params[2]],
[params[1], params[0] + 1.0, params[3]], [0.0, 0.0, 1.0]])
vec = Similarity(h**o).as_vector()
assert_allclose(vec, params)
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 = Similarity(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 = AlignmentSimilarity(source, target)
# check the estimates is correct
assert_allclose(similarity.h_matrix, estimate.h_matrix)
def test_align_2d_similarity_set_h_matrix_raises_notimplemented_error():
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 = Similarity(h_matrix)
source = PointCloud(np.array([[0, 1], [1, 1], [-1, -5], [3, -5]]))
target = similarity.apply(source)
# estimate the transform from source to source
estimate = AlignmentSimilarity(source, source)
# and set the target
estimate.set_h_matrix(h_matrix)
def test_similarity_3d_n_parameters_raises_notimplementederror():
h**o = np.eye(4)
t = Similarity(h**o)
with raises(NotImplementedError):
t.n_parameters
def test_similarity_2d_n_parameters():
h**o = np.eye(3)
t = Similarity(h**o)
assert(t.n_parameters == 4)
def test_similarity_set_h_matrix_raises_notimplementederror():
s = Similarity(np.eye(3))
s.set_h_matrix(s.h_matrix)
def test_similarity_3d_n_parameters_raises_notimplementederror():
h**o = np.eye(4)
t = Similarity(h**o)
# Raises exception
t.n_parameters
def test_similarity_jacobian_3d_raises_dimensionalityerror():
t = Similarity(np.eye(4))
t.d_dp(np.ones([2, 3]))
