def test_simple(self, device, dtype):
P = torch.tensor(
[[[308.0, 139.0, 231.0, 84.0], [481.0, 161.0, 358.0, 341.0],
[384.0, 387.0, 459.0, 102.0]]],
device=device,
dtype=dtype,
)
K_expected = torch.tensor(
[[[17.006138, 122.441254, 390.211426],
[0.0, 228.743622, 577.167480], [0.0, 0.0, 712.675232]]],
device=device,
dtype=dtype,
)
R_expected = torch.tensor(
[[[0.396559, 0.511023, -0.762625], [
0.743249, -0.666318, -0.060006
], [0.538815, 0.543024, 0.644052]]],
device=device,
dtype=dtype,
)
t_expected = torch.tensor([[[-6.477699], [1.129624], [0.143123]]],
device=device,
dtype=dtype)
K_estimated, R_estimated, t_estimated = epi.KRt_from_projection(P)
assert_allclose(K_estimated, K_expected, atol=1e-4, rtol=1e-4)
assert_allclose(R_estimated, R_expected, atol=1e-4, rtol=1e-4)
assert_allclose(t_estimated, t_expected, atol=1e-4, rtol=1e-4)
def test_krt_from_projection(self, device, dtype):
P = torch.tensor([[
[10., 0., 30., 100.],
[0., 20., 40., 160.],
[0., 0., 1., 3.],
]],
device=device,
dtype=dtype)
K_expected = torch.tensor([[
[10., 0., 30.],
[0., 20., 40.],
[0., 0., 1.],
]],
device=device,
dtype=dtype)
R_expected = torch.tensor([[
[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.],
]],
device=device,
dtype=dtype)
t_expected = torch.tensor([[[1.], [2.], [3.]]],
device=device,
dtype=dtype)
K_estimated, R_estimated, t_estimated = epi.KRt_from_projection(P)
assert_allclose(K_estimated, K_expected)
assert_allclose(R_estimated, R_expected)
assert_allclose(t_estimated, t_expected)
def test_shape(self, batch_size, device, dtype):
B: int = batch_size
P = torch.rand(B, 3, 4, device=device, dtype=dtype)
K, R, t = epi.KRt_from_projection(P)
assert K.shape == (B, 3, 3)
assert R.shape == (B, 3, 3)
assert t.shape == (B, 3, 1)
def test_krt_from_projection(self, device, dtype):
P = torch.tensor([[[10.0, 0.0, 30.0, 100.0], [0.0, 20.0, 40.0, 160.0],
[0.0, 0.0, 1.0, 3.0]]],
device=device,
dtype=dtype)
K_expected = torch.tensor(
[[[10.0, 0.0, 30.0], [0.0, 20.0, 40.0], [0.0, 0.0, 1.0]]],
device=device,
dtype=dtype)
R_expected = torch.tensor(
[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]],
device=device,
dtype=dtype)
t_expected = torch.tensor([[[1.0], [2.0], [3.0]]],
device=device,
dtype=dtype)
K_estimated, R_estimated, t_estimated = epi.KRt_from_projection(P)
assert_allclose(K_estimated, K_expected, atol=1e-4, rtol=1e-4)
assert_allclose(R_estimated, R_expected, atol=1e-4, rtol=1e-4)
assert_allclose(t_estimated, t_expected, atol=1e-4, rtol=1e-4)
def test_smoke(self, device, dtype):
P = torch.randn(1, 3, 4, device=device, dtype=dtype)
K, R, t = epi.KRt_from_projection(P)
assert K.shape == (1, 3, 3)
assert R.shape == (1, 3, 3)
assert t.shape == (1, 3, 1)