def test_normalize_homography_identity(self, batch_size, device, dtype):
# create input data
input_shape = (4, 8, 5)
dst_homo_src = utils.create_eye_batch(batch_size=batch_size, eye_size=4).to(device=device, dtype=dtype)
res = torch.tensor(
[[[0.5000, 0.0, 0.0, -1.0], [0.0, 0.2857, 0.0, -1.0], [0.0, 0.0, 0.6667, -1.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
)
norm = kornia.normal_transform_pixel3d(input_shape[0], input_shape[1], input_shape[2]).to(
device=device, dtype=dtype
)
assert_close(norm, res, rtol=1e-4, atol=1e-4)
norm_homo = kornia.normalize_homography3d(dst_homo_src, input_shape, input_shape).to(device=device, dtype=dtype)
assert_close(norm_homo, dst_homo_src, rtol=1e-4, atol=1e-4)
norm_homo = kornia.normalize_homography3d(dst_homo_src, input_shape, input_shape).to(device=device, dtype=dtype)
assert_close(norm_homo, dst_homo_src, rtol=1e-4, atol=1e-4)
# change output scale
norm_homo = kornia.normalize_homography3d(
dst_homo_src, input_shape, (input_shape[0] // 2, input_shape[1] * 2, input_shape[2] // 2)
).to(device=device, dtype=dtype)
res = torch.tensor(
[[[4.0, 0.0, 0.0, 3.0], [0.0, 0.4667, 0.0, -0.5333], [0.0, 0.0, 3.0, 2.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
).repeat(batch_size, 1, 1)
assert_close(norm_homo, res, rtol=1e-4, atol=1e-4)
def test_normalize_homography_general(self, batch_size, device, dtype):
# create input data
dst_homo_src = torch.eye(4, device=device, dtype=dtype)
dst_homo_src[..., 0, 0] = 0.5
dst_homo_src[..., 1, 1] = 0.5
dst_homo_src[..., 2, 2] = 2.0
dst_homo_src[..., 0, 3] = 1.0
dst_homo_src[..., 1, 3] = 2.0
dst_homo_src[..., 2, 3] = 3.0
dst_homo_src = dst_homo_src.expand(batch_size, -1, -1)
norm_homo = kornia.normalize_homography3d(dst_homo_src, (2, 2, 5), (2, 2, 5))
res = torch.tensor([[[0.5, 0.0, 0.0, 0.0],
[0.0, 0.5, 0.0, 3.5],
[0.0, 0.0, 2.0, 7.0],
[0.0, 0.0, 0.0, 1.0]]], device=device, dtype=dtype)
assert (norm_homo == res).all()