def test_rigid_no_noise():
# Generate some synthetic data.
n_frames = 40
n_basis = 1
n_points = 20
# Take a randomly generated model.
gt_model = model.generate_synthetic_model(n_frames, n_basis, n_points)
# Force scale factor to 1.
gt_model.C = np.ones((n_frames, 1))
W = gt_model.W
# Recover the model
inf_model = rigid.factor(W)
# Register with ground truth.
inf_model.register(gt_model)
delta = util.norm(inf_model.Ps - gt_model.Ps, axis=1)
# Ensure the average error is low.
npt.assert_allclose(delta.mean(), 0, atol=1e-10)
def test_recover_Gk_no_noise():
# Generate some synthetic data.
n_frames = 30
n_basis = 2
n_points = 15
gt_model = model.generate_synthetic_model(n_frames, n_basis, n_points)
# Factor the matrix.
from rigid import factor_matrix
W = gt_model.W.copy()
W -= W.mean(axis=-1)[:, np.newaxis]
M_hat, B_hat = factor_matrix(W, J=n_basis * 3)
# Try to find a G_k
G_k = shape.find_G_k(M_hat)
# Get the k'th column triple of M_k
M_k = np.dot(M_hat, G_k)
M_kx = M_k[0::2]
M_ky = M_k[1::2]
# Ensure they are orthogonal.
npt.assert_allclose((M_kx * M_ky).sum(), 0, atol=1e-3)
# And that the length of x and y are equal.
npt.assert_allclose((M_kx * M_kx).sum() - (M_ky * M_ky).sum(),
0,
atol=1e-3)
def test_shape_no_noise():
np.random.seed(100)
# Generate some synthetic data.
n_frames = 50
n_basis = 2
n_points = 15
gt_model = model.generate_synthetic_model(n_frames, n_basis, n_points)
W = gt_model.W
# Recover the model
inf_model = shape.factor(W, n_basis=n_basis)
# Register with ground truth.
inf_model.register(gt_model)
delta = util.norm(inf_model.Ps - gt_model.Ps, axis=1)
# Ensure the average error is low.
npt.assert_allclose(delta.mean(), 0, atol=0.1)
