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)