def test_rotate_z(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)
R2 = torch.tensor([[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
t1 = kornia.vec_like(3, R1)
t2 = kornia.vec_like(3, R2)
R_expected = R2.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_shape(self, batch_size, eye_size, device, dtype):
B, N = batch_size, eye_size
image = torch.rand(B, 3, 4, 4, device=device, dtype=dtype)
vec = kornia.vec_like(N, image)
assert vec.shape == (B, N, 1)
assert vec.device == image.device
assert vec.dtype == image.dtype
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_smoke(self, device, dtype):
image = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
vec = kornia.vec_like(3, image)
assert vec.shape == (1, 3, 1)
assert vec.device == image.device
assert vec.dtype == image.dtype