def test_two_view(self, device, dtype):
scene = utils.generate_two_view_random_scene(device, dtype)
R1, t1 = scene['R1'], scene['t1']
R2, t2 = scene['R2'], scene['t2']
E_mat = epi.essential_from_Rt(R1, t1, R2, t2)
R, t = epi.relative_camera_motion(R1, t1, R2, t2)
t = torch.nn.functional.normalize(t, dim=1)
Rs, ts = epi.motion_from_essential(E_mat)
rot_error = (Rs - R).abs().sum((-2, -1))
vec_error = (ts - t).abs().sum((-1))
rtol: float = 1e-4
assert (rot_error < rtol).any() & (vec_error < rtol).any()
def eval_vec(input):
return epi.motion_from_essential(input)[1]
def eval_rot(input):
return epi.motion_from_essential(input)[0]
def test_shape(self, batch_shape, device, dtype):
E_mat = torch.rand(batch_shape, device=device, dtype=dtype)
Rs, Ts = epi.motion_from_essential(E_mat)
assert Rs.shape == batch_shape[:-2] + (4, 3, 3)
assert Ts.shape == batch_shape[:-2] + (4, 3, 1)
def test_smoke(self, device, dtype):
E_mat = torch.rand(1, 3, 3, device=device, dtype=dtype)
Rs, Ts = epi.motion_from_essential(E_mat)
assert Rs.shape == (1, 4, 3, 3)
assert Ts.shape == (1, 4, 3, 1)
