def test_rotate_z(self, device, dtype):
R1 = torch.tensor([[
[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.],
]],
device=device,
dtype=dtype)
R2 = torch.tensor([[
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 1.],
]],
device=device,
dtype=dtype)
t1 = epi.vec_like(3, R1)
t2 = epi.vec_like(3, R2)
R_expected = R2.clone()
t_expected = t1
R, t = epi.relative_camera_motion(R1, t1, R2, t2)
assert_allclose(R_expected, R)
assert_allclose(t_expected, t)
def test_smoke(self, device, dtype):
R1 = torch.rand(1, 3, 3, device=device, dtype=dtype)
t1 = torch.rand(1, 3, 1, device=device, dtype=dtype)
R2 = torch.rand(1, 3, 3, device=device, dtype=dtype)
t2 = torch.rand(1, 3, 1, device=device, dtype=dtype)
R, t = epi.relative_camera_motion(R1, t1, R2, t2)
assert R.shape == (1, 3, 3)
assert t.shape == (1, 3, 1)
def test_shape(self, batch_size, device, dtype):
B: int = batch_size
R1 = torch.rand(B, 3, 3, device=device, dtype=dtype)
t1 = torch.rand(B, 3, 1, device=device, dtype=dtype)
R2 = torch.rand(1, 3, 3, device=device, dtype=dtype) # check broadcasting
t2 = torch.rand(B, 3, 1, device=device, dtype=dtype)
R, t = epi.relative_camera_motion(R1, t1, R2, t2)
assert R.shape == (B, 3, 3)
assert t.shape == (B, 3, 1)
def test_translation(self, device, dtype):
R1 = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
t1 = torch.tensor([[[10.0], [0.0], [0.0]]]).type_as(R1)
R2 = kornia.eye_like(3, R1)
t2 = kornia.vec_like(3, t1)
R_expected = R1.clone()
t_expected = -t1
R, t = epi.relative_camera_motion(R1, t1, R2, t2)
assert_close(R_expected, R)
assert_close(t_expected, t)
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()
