def generate_two_view_random_scene(device: torch.device = torch.device("cpu"), dtype: torch.dtype = torch.float32 ) -> Dict[str, torch.Tensor]: num_views: int = 2 num_points: int = 30 scene: Dict[str, torch.Tensor] = epi.generate_scene(num_views, num_points) # internal parameters (same K) K1 = scene['K'].to(device, dtype) K2 = K1.clone() # rotation R1 = scene['R'][0:1].to(device, dtype) R2 = scene['R'][1:2].to(device, dtype) # translation t1 = scene['t'][0:1].to(device, dtype) t2 = scene['t'][1:2].to(device, dtype) # projection matrix, P = K(R|t) P1 = scene['P'][0:1].to(device, dtype) P2 = scene['P'][1:2].to(device, dtype) # fundamental matrix F_mat = epi.fundamental_from_projections(P1[..., :3, :], P2[..., :3, :]) F_mat = epi.normalize_transformation(F_mat) # points 3d X = scene['points3d'].to(device, dtype) # projected points x1 = scene['points2d'][0:1].to(device, dtype) x2 = scene['points2d'][1:2].to(device, dtype) return dict(K1=K1, K2=K2, R1=R1, R2=R2, t1=t1, t2=t2, P1=P1, P2=P2, F=F_mat, X=X, x1=x1, x2=x2)
def test_two_view(self, device, dtype): num_views: int = 2 num_points: int = 10 scene: Dict[str, torch.Tensor] = epi.generate_scene(num_views, num_points) P1 = scene['P'][0:1] P2 = scene['P'][1:2] x1 = scene['points2d'][0:1] x2 = scene['points2d'][1:2] X = epi.triangulate_points(P1, P2, x1, x2) x_reprojected = kornia.transform_points(scene['P'], X.expand(num_views, -1, -1)) assert_allclose(scene['points3d'], X, rtol=1e-4, atol=1e-4) assert_allclose(scene['points2d'], x_reprojected)