def test_from_to_essential(self, device, dtype): scene = utils.generate_two_view_random_scene(device, dtype) F_mat = scene['F'] E_mat = epi.essential_from_fundamental(F_mat, scene['K1'], scene['K2']) F_hat = epi.fundamental_from_essential(E_mat, scene['K1'], scene['K2']) F_mat_norm = epi.normalize_transformation(F_mat) F_hat_norm = epi.normalize_transformation(F_hat) assert_close(F_mat_norm, F_hat_norm, atol=1e-4, rtol=1e-4)
def test_from_fundamental_Rt(self, device, dtype): scene = utils.generate_two_view_random_scene(device, dtype) E_from_Rt = epi.essential_from_Rt(scene['R1'], scene['t1'], scene['R2'], scene['t2']) E_from_F = epi.essential_from_fundamental(scene['F'], scene['K1'], scene['K2']) E_from_Rt_norm = epi.normalize_transformation(E_from_Rt) E_from_F_norm = epi.normalize_transformation(E_from_F) # TODO: occasionally failed with error > 0.04 assert_close(E_from_Rt_norm, E_from_F_norm, rtol=1e-3, atol=1e-3)
def test_synthetic_sampson(self, device, dtype): scene: Dict[str, torch.Tensor] = utils.generate_two_view_random_scene(device, dtype) x1 = scene['x1'] x2 = scene['x2'] weights = torch.ones_like(x1)[..., 0] F_est = epi.find_fundamental(x1, x2, weights) error = epi.sampson_epipolar_distance(x1, x2, F_est) assert_close(error, torch.tensor(0.0, device=device, dtype=dtype), atol=1e-4, rtol=1e-4)
def test_from_fundamental(self, device, dtype): scene = utils.generate_two_view_random_scene(device, dtype) F_mat = scene['F'] K1 = scene['K1'] K2 = scene['K2'] E_mat = epi.essential_from_fundamental(F_mat, K1, K2) F_hat = epi.fundamental_from_essential(E_mat, K1, K2) F_mat_norm = epi.normalize_transformation(F_mat) F_hat_norm = epi.normalize_transformation(F_hat) assert_allclose(F_mat_norm, F_hat_norm)
def test_two_view(self, device, dtype): scene = utils.generate_two_view_random_scene(device, dtype) E_mat = epi.essential_from_Rt(scene['R1'], scene['t1'], scene['R2'], scene['t2']) R, t = epi.relative_camera_motion(scene['R1'], scene['t1'], scene['R2'], scene['t2']) t = torch.nn.functional.normalize(t, dim=1) R_hat, t_hat, _ = epi.motion_from_essential_choose_solution( E_mat, scene['K1'], scene['K2'], scene['x1'], scene['x2'] ) assert_close(t, t_hat) assert_close(R, R_hat, rtol=1e-4, atol=1e-4)
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()