def test_batch_support_check(self, device, dtype):
P1_batch = torch.tensor(
[
[
[9.4692e02, -9.6658e02, 6.0862e02, -2.3076e05],
[-2.1829e02, 5.4163e02, 1.3445e03, -6.4387e05],
[-6.0675e-01, -6.9807e-01, 3.8021e-01, 3.8896e02],
],
[
[9.4692e02, -9.6658e02, 6.0862e02, -2.3076e05],
[-2.1829e02, 5.4163e02, 1.3445e03, -6.4387e05],
[-6.0675e-01, -6.9807e-01, 3.8021e-01, 3.8896e02],
],
],
device=device,
dtype=dtype,
)
P1 = torch.tensor(
[
[
[9.4692e02, -9.6658e02, 6.0862e02, -2.3076e05],
[-2.1829e02, 5.4163e02, 1.3445e03, -6.4387e05],
[-6.0675e-01, -6.9807e-01, 3.8021e-01, 3.8896e02],
]
],
device=device,
dtype=dtype,
)
P2_batch = torch.tensor(
[
[
[1.1518e03, -7.5822e02, 5.4764e02, -1.9764e05],
[-2.1548e02, 5.3102e02, 1.3492e03, -6.4731e05],
[-4.3727e-01, -7.8632e-01, 4.3646e-01, 3.4515e02],
],
[
[9.9595e02, -8.6464e02, 6.7959e02, -2.7517e05],
[-8.1716e01, 7.7826e02, 1.2395e03, -5.8137e05],
[-5.7090e-01, -6.0416e-01, 5.5594e-01, 2.8111e02],
],
],
device=device,
dtype=dtype,
)
P2 = torch.tensor(
[
[
[1.1518e03, -7.5822e02, 5.4764e02, -1.9764e05],
[-2.1548e02, 5.3102e02, 1.3492e03, -6.4731e05],
[-4.3727e-01, -7.8632e-01, 4.3646e-01, 3.4515e02],
]
],
device=device,
dtype=dtype,
)
F_batch = epi.fundamental_from_projections(P1_batch, P2_batch)
F = epi.fundamental_from_projections(P1, P2)
assert (F_batch[0] == F[0]).all()
def test_from_to_projections(self, device, dtype):
P1 = torch.tensor(
[[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [1.0, 0.0, 1.0, 0.0]]], device=device, dtype=dtype
)
P2 = torch.tensor(
[[[1.0, 1.0, 1.0, 3.0], [0.0, 2.0, 0.0, 3.0], [0.0, 1.0, 1.0, 0.0]]], device=device, dtype=dtype
)
F_mat = epi.fundamental_from_projections(P1, P2)
P_mat = epi.projections_from_fundamental(F_mat)
F_hat = epi.fundamental_from_projections(P_mat[..., 0], P_mat[..., 1])
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 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_shape_large(self, device, dtype):
P1 = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
P2 = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
F_mat = epi.fundamental_from_projections(P1, P2)
assert F_mat.shape == (1, 2, 3, 3)
def test_shape(self, batch_size, device, dtype):
B: int = batch_size
P1 = torch.rand(B, 3, 4, device=device, dtype=dtype)
P2 = torch.rand(B, 3, 4, device=device, dtype=dtype)
F_mat = epi.fundamental_from_projections(P1, P2)
assert F_mat.shape == (B, 3, 3)
