def test_procrustes_no_target():
# square
src_1 = PointCloud(np.array([[1.0, 1.0], [1.0, -1.0],
[-1.0, -1.0], [-1.0, 1.0]]))
# rhombus
src_2 = PointCloud(np.array([[2.0, 0.0], [4.0, 2.0],
[6.0, 0.0], [4.0, -2.0]]))
# trapezoid
src_3 = PointCloud(np.array([[-0.5, -1.5], [2.5, -1.5],
[2.8, -2.5], [-0.2, -2.5]]))
gpa = GeneralizedProcrustesAnalysis([src_1, src_2, src_3],
allow_mirror=True)
aligned_1 = gpa.transforms[0].apply(gpa.sources[0])
aligned_2 = gpa.transforms[1].apply(gpa.sources[1])
aligned_3 = gpa.transforms[2].apply(gpa.sources[2])
assert(gpa.converged is True)
assert(gpa.n_iterations == 5)
assert(gpa.n_sources == 3)
assert(np.round(gpa.initial_target_scale * 100) == 195.)
assert(np.round(gpa.mean_alignment_error() * 10) == 4.)
assert_allclose(np.around(aligned_1.points, decimals=1),
np.around(aligned_2.points, decimals=1))
res_3 = np.array([[0.7, -0.3], [2.6, -0.4], [2.7, -1.0], [0.9, -0.9]])
assert_allclose(np.around(aligned_3.points, decimals=1), res_3)
assert_allclose(gpa.target.points, gpa.mean_aligned_shape().points)
def test_procrustes_with_target():
# square
src_1 = PointCloud(np.array([[1.0, 1.0], [1.0, -1.0],
[-1.0, -1.0], [-1.0, 1.0]]))
# trapezoid
src_2 = PointCloud(np.array([[-0.5, -1.5], [2.5, -1.5],
[2.8, -2.5], [-0.2, -2.5]]))
# rhombus as target
src_trg = PointCloud(np.array([[2.0, 0.0], [4.0, 2.0],
[6.0, 0.0], [4.0, -2.0]]))
gpa = GeneralizedProcrustesAnalysis([src_1, src_2], target=src_trg,
allow_mirror=True)
aligned_1 = gpa.transforms[0].apply(gpa.sources[0])
aligned_2 = gpa.transforms[1].apply(gpa.sources[1])
assert(gpa.converged is True)
assert(gpa.n_iterations == 2)
assert(gpa.n_sources == 2)
assert(gpa.initial_target_scale == 4.)
assert(np.round(gpa.mean_alignment_error() * 100) == 93.)
assert_allclose(np.around(aligned_1.points, decimals=1),
np.around(src_trg.points, decimals=1))
res_2 = np.array([[2.0, -0.9], [4.9, 1.6], [6.0, 0.9], [3.1, -1.6]])
assert_allclose(np.around(aligned_2.points, decimals=1), res_2)
mean = np.array([[2.0, -0.5], [4.5, 1.8], [6.0, 0.5], [3.5, -1.8]])
assert_allclose(np.around(gpa.mean_aligned_shape().points, decimals=1),
mean)
