def test_motion_from_plane_homography():
R = tf.random_rotation_matrix()[:3, :3]
t = normalized(2 * np.random.rand(3) - 1)
n = normalized(2 * np.random.rand(3) - 1)
d = 2 * np.random.rand() - 1
scale = 2 * np.random.rand() - 1
H = scale * (d * R - np.outer(t, n))
motions = multiview.motion_from_plane_homography(H)
goodness = []
for Re, te, ne, de in motions:
scalee = np.linalg.norm(te)
good_R = np.allclose(R, Re)
good_t = np.allclose(normalized(te), t)
sign_n = np.sign(np.dot(ne, n))
good_n = np.allclose(sign_n * ne, n)
good_d = np.allclose(sign_n * de / scalee, d)
goodness.append(good_R and good_t and good_n and good_d)
assert any(goodness)
def test_motion_from_plane_homography():
R = tf.random_rotation_matrix()[:3, :3]
t = normalized(2 * np.random.rand(3) - 1)
n = normalized(2 * np.random.rand(3) - 1)
d = 2 * np.random.rand() - 1
scale = 2 * np.random.rand() - 1
H = scale * (d * R - np.outer(t, n))
motions = multiview.motion_from_plane_homography(H)
goodness = []
for Re, te, ne, de in motions:
scalee = np.linalg.norm(te)
good_R = np.allclose(R, Re)
good_t = np.allclose(normalized(te), t)
sign_n = np.sign(np.dot(ne, n))
good_n = np.allclose(sign_n * ne, n)
good_d = np.allclose(sign_n * de / scalee, d)
goodness.append(good_R and good_t and good_n and good_d)
assert any(goodness)