def test_divide_by_zero3d(self, height, width, depth, device, dtype):
output = kornia.normal_transform_pixel3d(depth,
height,
width,
device=device,
dtype=dtype)
assert torch.isinf(output).sum().item() == 0
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_transform3d_apply(self, device, dtype):
depth, height, width = 3, 2, 5
input = torch.tensor([[0.0, 0.0, 0.0], [width - 1, height - 1, depth - 1]], device=device, dtype=dtype)
expected = torch.tensor([[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]], device=device, dtype=dtype)
transform = kornia.normal_transform_pixel3d(depth, height, width, device=device, dtype=dtype)
output = kornia.transform_points(transform, input)
assert_close(output, expected.to(device=device, dtype=dtype), atol=1e-4, rtol=1e-4)
def test_transform3d_apply(self):
depth, height, width = 3, 2, 5
input = torch.tensor([[0., 0., 0.], [width - 1, height - 1, depth - 1]])
expected = torch.tensor([[-1., -1., -1.], [1., 1., 1.]])
transform = kornia.normal_transform_pixel3d(depth, height, width)
output = kornia.transform_points(transform, input)
assert_allclose(output, expected)
def test_transform3d(self, height, width, depth, expected, device, dtype):
output = kornia.normal_transform_pixel3d(depth,
height,
width,
device=device,
dtype=dtype)
assert_allclose(output,
expected.to(device=device, dtype=dtype),
atol=1e-4,
rtol=1e-4)
def test_transform3d(self):
height, width, depth = 2, 6, 4
output = kornia.normal_transform_pixel3d(depth, height, width)
expected = torch.tensor([[
[0.4, 0.0, 0.0, -1.],
[0.0, 2.0, 0.0, -1.],
[0.0, 0.0, 0.6667, -1.],
[0.0, 0.0, 0.0, 1.],
]])
assert_allclose(output, expected)
