def test_identity_parameters():
for transform in regtransforms.values():
dim = transform.get_dim()
theta = transform.get_identity_parameters()
expected = np.eye(dim + 1)
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
def test_identity_parameters():
for transform in regtransforms.values():
dim = transform.get_dim()
theta = transform.get_identity_parameters()
expected = np.eye(dim + 1)
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
def test_param_to_matrix_2d():
rng = np.random.RandomState()
# Test translation matrix 2D
transform = regtransforms[('TRANSLATION', 2)]
dx, dy = rng.uniform(size=(2, ))
theta = np.array([dx, dy])
expected = np.array([[1, 0, dx], [0, 1, dy], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_equal(actual, expected)
# Test rotation matrix 2D
transform = regtransforms[('ROTATION', 2)]
angle = rng.uniform()
theta = np.array([angle])
ct = np.cos(angle)
st = np.sin(angle)
expected = np.array([[ct, -st, 0], [st, ct, 0], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test rigid matrix 2D
transform = regtransforms[('RIGID', 2)]
angle, dx, dy = rng.uniform(size=(3, ))
theta = np.array([angle, dx, dy])
ct = np.cos(angle)
st = np.sin(angle)
expected = np.array([[ct, -st, dx], [st, ct, dy], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test rigid matrix 2D
transform = regtransforms[('SCALING', 2)]
factor = rng.uniform()
theta = np.array([factor])
expected = np.array([[factor, 0, 0], [0, factor, 0], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test affine 2D
transform = regtransforms[('AFFINE', 2)]
theta = rng.uniform(size=(6, ))
expected = np.eye(3)
expected[0, :] = theta[:3]
expected[1, :] = theta[3:6]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Verify that ValueError is raised if incorrect number of parameters
for transform in regtransforms.values():
n = transform.get_number_of_parameters()
# Set incorrect number of parameters
theta = np.zeros(n + 1, dtype=np.float64)
assert_raises(ValueError, transform.param_to_matrix, theta)
def test_param_to_matrix_2d():
rng = np.random.RandomState()
# Test translation matrix 2D
transform = regtransforms[('TRANSLATION', 2)]
dx, dy = rng.uniform(size=(2,))
theta = np.array([dx, dy])
expected = np.array([[1, 0, dx], [0, 1, dy], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_equal(actual, expected)
# Test rotation matrix 2D
transform = regtransforms[('ROTATION', 2)]
angle = rng.uniform()
theta = np.array([angle])
ct = np.cos(angle)
st = np.sin(angle)
expected = np.array([[ct, -st, 0], [st, ct, 0], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test rigid matrix 2D
transform = regtransforms[('RIGID', 2)]
angle, dx, dy = rng.uniform(size=(3,))
theta = np.array([angle, dx, dy])
ct = np.cos(angle)
st = np.sin(angle)
expected = np.array([[ct, -st, dx], [st, ct, dy], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test rigid matrix 2D
transform = regtransforms[('SCALING', 2)]
factor = rng.uniform()
theta = np.array([factor])
expected = np.array([[factor, 0, 0], [0, factor, 0], [0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test affine 2D
transform = regtransforms[('AFFINE', 2)]
theta = rng.uniform(size=(6,))
expected = np.eye(3)
expected[0,:] = theta[:3]
expected[1,:] = theta[3:6]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Verify that ValueError is raised if incorrect number of parameters
for transform in regtransforms.values():
n = transform.get_number_of_parameters()
# Set incorrect number of parameters
theta = np.zeros(n + 1, dtype=np.float64)
assert_raises(ValueError, transform.param_to_matrix, theta)
def test_jacobian_functions():
rng = np.random.RandomState()
# Compare the analytical Jacobians with their numerical approximations
h = 1e-8
nsamples = 50
for transform in regtransforms.values():
n = transform.get_number_of_parameters()
dim = transform.get_dim()
expected = np.empty((dim, n))
theta = rng.uniform(size=(n,))
T = transform.param_to_matrix(theta)
for j in range(nsamples):
x = 255 * (rng.uniform(size=(dim,)) - 0.5)
actual = transform.jacobian(theta, x)
# Approximate with finite differences
x_hom = np.ones(dim + 1)
x_hom[:dim] = x[:]
for i in range(n):
dtheta = theta.copy()
dtheta[i] += h
dT = np.array(transform.param_to_matrix(dtheta))
g = (dT - T).dot(x_hom) / h
expected[:, i] = g[:dim]
assert_array_almost_equal(actual, expected, decimal=5)
# Test ValueError is raised when theta parameter doesn't have the right
# length
for transform in regtransforms.values():
n = transform.get_number_of_parameters()
# Wrong number of parameters
theta = np.zeros(n + 1)
x = np.zeros(dim)
assert_raises(ValueError, transform.jacobian, theta, x)
def test_param_to_matrix_3d():
rng = np.random.RandomState()
# Test translation matrix 3D
transform = regtransforms[('TRANSLATION', 3)]
dx, dy, dz = rng.uniform(size=(3, ))
theta = np.array([dx, dy, dz])
expected = np.array([[1, 0, 0, dx], [0, 1, 0, dy], [0, 0, 1, dz],
[0, 0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_equal(actual, expected)
# Test rotation matrix 3D
transform = regtransforms[('ROTATION', 3)]
theta = rng.uniform(size=(3, ))
ca = np.cos(theta[0])
sa = np.sin(theta[0])
cb = np.cos(theta[1])
sb = np.sin(theta[1])
cc = np.cos(theta[2])
sc = np.sin(theta[2])
X = np.array([[1, 0, 0], [0, ca, -sa], [0, sa, ca]])
Y = np.array([[cb, 0, sb], [0, 1, 0], [-sb, 0, cb]])
Z = np.array([[cc, -sc, 0], [sc, cc, 0], [0, 0, 1]])
R = Z.dot(X.dot(Y)) # Apply in order: Y, X, Z (Y goes to the right)
expected = np.eye(4)
expected[:3, :3] = R[:3, :3]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test rigid matrix 3D
transform = regtransforms[('RIGID', 3)]
theta = rng.uniform(size=(6, ))
ca = np.cos(theta[0])
sa = np.sin(theta[0])
cb = np.cos(theta[1])
sb = np.sin(theta[1])
cc = np.cos(theta[2])
sc = np.sin(theta[2])
X = np.array([[1, 0, 0], [0, ca, -sa], [0, sa, ca]])
Y = np.array([[cb, 0, sb], [0, 1, 0], [-sb, 0, cb]])
Z = np.array([[cc, -sc, 0], [sc, cc, 0], [0, 0, 1]])
R = Z.dot(X.dot(Y)) # Apply in order: Y, X, Z (Y goes to the right)
expected = np.eye(4)
expected[:3, :3] = R[:3, :3]
expected[:3, 3] = theta[3:6]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test scaling matrix 3D
transform = regtransforms[('SCALING', 3)]
factor = rng.uniform()
theta = np.array([factor])
expected = np.array([[factor, 0, 0, 0], [0, factor, 0, 0],
[0, 0, factor, 0], [0, 0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test affine 3D
transform = regtransforms[('AFFINE', 3)]
theta = rng.uniform(size=(12, ))
expected = np.eye(4)
expected[0, :] = theta[:4]
expected[1, :] = theta[4:8]
expected[2, :] = theta[8:12]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Verify that ValueError is raised if incorrect number of parameters
for transform in regtransforms.values():
n = transform.get_number_of_parameters()
# Set incorrect number of parameters
theta = np.zeros(n + 1, dtype=np.float64)
assert_raises(ValueError, transform.param_to_matrix, theta)
def test_param_to_matrix_3d():
rng = np.random.RandomState()
# Test translation matrix 3D
transform = regtransforms[('TRANSLATION', 3)]
dx, dy, dz = rng.uniform(size=(3,))
theta = np.array([dx, dy, dz])
expected = np.array([[1, 0, 0, dx],
[0, 1, 0, dy],
[0, 0, 1, dz],
[0, 0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_equal(actual, expected)
# Test rotation matrix 3D
transform = regtransforms[('ROTATION', 3)]
theta = rng.uniform(size=(3,))
ca = np.cos(theta[0])
sa = np.sin(theta[0])
cb = np.cos(theta[1])
sb = np.sin(theta[1])
cc = np.cos(theta[2])
sc = np.sin(theta[2])
X = np.array([[1, 0, 0],
[0, ca, -sa],
[0, sa, ca]])
Y = np.array([[cb, 0, sb],
[0, 1, 0],
[-sb, 0, cb]])
Z = np.array([[cc, -sc, 0],
[sc, cc, 0],
[0, 0, 1]])
R = Z.dot(X.dot(Y)) # Apply in order: Y, X, Z (Y goes to the right)
expected = np.eye(4)
expected[:3, :3] = R[:3, :3]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test rigid matrix 3D
transform = regtransforms[('RIGID', 3)]
theta = rng.uniform(size=(6,))
ca = np.cos(theta[0])
sa = np.sin(theta[0])
cb = np.cos(theta[1])
sb = np.sin(theta[1])
cc = np.cos(theta[2])
sc = np.sin(theta[2])
X = np.array([[1, 0, 0],
[0, ca, -sa],
[0, sa, ca]])
Y = np.array([[cb, 0, sb],
[0, 1, 0],
[-sb, 0, cb]])
Z = np.array([[cc, -sc, 0],
[sc, cc, 0],
[0, 0, 1]])
R = Z.dot(X.dot(Y)) # Apply in order: Y, X, Z (Y goes to the right)
expected = np.eye(4)
expected[:3, :3] = R[:3, :3]
expected[:3, 3] = theta[3:6]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test scaling matrix 3D
transform = regtransforms[('SCALING', 3)]
factor = rng.uniform()
theta = np.array([factor])
expected = np.array([[factor, 0, 0, 0],
[0, factor, 0, 0],
[0, 0, factor, 0],
[0, 0, 0, 1]])
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Test affine 3D
transform = regtransforms[('AFFINE', 3)]
theta = rng.uniform(size=(12,))
expected = np.eye(4)
expected[0, :] = theta[:4]
expected[1, :] = theta[4:8]
expected[2, :] = theta[8:12]
actual = transform.param_to_matrix(theta)
assert_array_almost_equal(actual, expected)
# Verify that ValueError is raised if incorrect number of parameters
for transform in regtransforms.values():
n = transform.get_number_of_parameters()
# Set incorrect number of parameters
theta = np.zeros(n + 1, dtype=np.float64)
assert_raises(ValueError, transform.param_to_matrix, theta)
