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_transform2d_apply(self, device, dtype):
height, width = 2, 5
input = torch.tensor([[0.0, 0.0], [width - 1, height - 1]], device=device, dtype=dtype)
expected = torch.tensor([[-1.0, -1.0], [1.0, 1.0]], device=device, dtype=dtype)
transform = kornia.normal_transform_pixel(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_transform2d_apply(self):
height, width = 2, 5
input = torch.tensor([[0., 0.], [width - 1, height - 1]])
expected = torch.tensor([[-1., -1.], [1., 1.]])
transform = kornia.normal_transform_pixel(height, width)
output = kornia.transform_points(transform, input)
assert_allclose(output, expected)
def test_normalize_pixel_grid(device, dtype):
if device.type == 'cuda' and dtype == torch.float16:
pytest.skip('"inverse_cuda" not implemented for "Half"')
# generate input data
batch_size = 1
height, width = 2, 4
# create points grid
grid_norm = kornia.utils.create_meshgrid(height, width, normalized_coordinates=True, device=device, dtype=dtype)
assert grid_norm.device == device
assert grid_norm.dtype == dtype
grid_norm = torch.unsqueeze(grid_norm, dim=0)
grid_pix = kornia.utils.create_meshgrid(height, width, normalized_coordinates=False, device=device, dtype=dtype)
assert grid_pix.device == device
assert grid_pix.dtype == dtype
grid_pix = torch.unsqueeze(grid_pix, dim=0)
# grid from pixel space to normalized
norm_trans_pix = kornia.normal_transform_pixel(height, width, device=device, dtype=dtype) # 1x3x3
pix_trans_norm = torch.inverse(norm_trans_pix) # 1x3x3
# transform grids
grid_pix_to_norm = kornia.transform_points(norm_trans_pix, grid_pix)
grid_norm_to_pix = kornia.transform_points(pix_trans_norm, grid_norm)
assert_close(grid_pix, grid_norm_to_pix)
assert_close(grid_norm, grid_pix_to_norm)
def test_transform2d(self):
height, width = 2, 5
output = kornia.normal_transform_pixel(height, width)
expected = torch.tensor([[
[0.5, 0.0, -1.],
[0.0, 2.0, -1.],
[0.0, 0.0, 1.]]])
assert_allclose(output, expected)
def test_transform2d(self, height, width, expected, device, dtype):
output = kornia.normal_transform_pixel(height,
width,
device=device,
dtype=dtype)
assert_allclose(output,
expected.to(device=device, dtype=dtype),
atol=1e-4,
rtol=1e-4)
def test_normalize_pixel_grid():
# generate input data
batch_size = 1
height, width = 2, 4
# create points grid
grid_norm = kornia.utils.create_meshgrid(
height, width, normalized_coordinates=True)
grid_norm = torch.unsqueeze(grid_norm, dim=0)
grid_pix = kornia.utils.create_meshgrid(
height, width, normalized_coordinates=False)
grid_pix = torch.unsqueeze(grid_pix, dim=0)
# grid from pixel space to normalized
norm_trans_pix = kornia.normal_transform_pixel(height, width) # 1x3x3
pix_trans_norm = torch.inverse(norm_trans_pix) # 1x3x3
# transform grids
grid_pix_to_norm = kornia.transform_points(norm_trans_pix, grid_pix)
grid_norm_to_pix = kornia.transform_points(pix_trans_norm, grid_norm)
assert_allclose(grid_pix, grid_norm_to_pix)
assert_allclose(grid_norm, grid_pix_to_norm)
def test_divide_by_zero2d(self, height, width, device, dtype):
output = kornia.normal_transform_pixel(height,
width,
device=device,
dtype=dtype)
assert torch.isinf(output).sum().item() == 0
