def test_normalize_homography_identity(self, batch_size, device, dtype):
# create input data
height, width = 2, 5
dst_homo_src = utils.create_eye_batch(batch_size=batch_size,
eye_size=3,
device=device,
dtype=dtype)
res = torch.tensor(
[[[0.5, 0.0, -1.0], [0.0, 2.0, -1.0], [0.0, 0.0, 1.0]]],
device=device,
dtype=dtype)
assert (kornia.normal_transform_pixel(height,
width,
device=device,
dtype=dtype) == res).all()
norm_homo = kornia.normalize_homography(dst_homo_src, (height, width),
(height, width))
assert (norm_homo == dst_homo_src).all()
norm_homo = kornia.normalize_homography(dst_homo_src, (height, width),
(height, width))
assert (norm_homo == dst_homo_src).all()
# change output scale
norm_homo = kornia.normalize_homography(dst_homo_src, (height, width),
(height * 2, width // 2))
res = torch.tensor(
[[[4.0, 0.0, 3.0], [0.0, 1 / 3, -2 / 3], [0.0, 0.0, 1.0]]],
device=device,
dtype=dtype).repeat(batch_size, 1, 1)
assert_allclose(norm_homo, res, atol=1e-4, rtol=1e-4)
def test_consistency(self, batch_size, device, dtype):
# create input data
height, width = 2, 5
dst_homo_src = torch.eye(3, device=device, dtype=dtype)
dst_homo_src[..., 0, 0] = 0.5
dst_homo_src[..., 1, 1] = 2.0
dst_homo_src[..., 0, 2] = 1.0
dst_homo_src[..., 1, 2] = 2.0
dst_homo_src = dst_homo_src.expand(batch_size, -1, -1)
denorm_homo = kornia.denormalize_homography(dst_homo_src, (height, width), (height, width))
norm_denorm_homo = kornia.normalize_homography(denorm_homo, (height, width), (height, width))
assert (dst_homo_src == norm_denorm_homo).all()
norm_homo = kornia.normalize_homography(dst_homo_src, (height, width), (height, width))
denorm_norm_homo = kornia.denormalize_homography(norm_homo, (height, width), (height, width))
assert (dst_homo_src == denorm_norm_homo).all()
def test_normalize_homography_general(self, batch_size, device, dtype):
# create input data
height, width = 2, 5
dst_homo_src = torch.eye(3, device=device, dtype=dtype)
dst_homo_src[..., 0, 0] = 0.5
dst_homo_src[..., 1, 1] = 2.0
dst_homo_src[..., 0, 2] = 1.0
dst_homo_src[..., 1, 2] = 2.0
dst_homo_src = dst_homo_src.expand(batch_size, -1, -1)
norm_homo = kornia.normalize_homography(dst_homo_src, (height, width), (height, width))
res = torch.tensor([[[0.5, 0.0, 0.0], [0.0, 2.0, 5.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
assert (norm_homo == res).all()