def test_simple(self, device):
# this is for default normalize_points=False
depth = 2 * torch.tensor([[[
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
]]]).to(device)
camera_matrix = torch.tensor([[
[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.],
]]).to(device)
normals_expected = torch.tensor([[[
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
], [
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
], [
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
]]]).to(device)
normals = kornia.depth_to_normals(
depth, camera_matrix) # default is normalize_points=False
assert_allclose(normals, normals_expected, 1e-3, 1e-3)
def test_simple(self, device):
depth = 2 * torch.tensor([[[
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
]]]).to(device)
camera_matrix = torch.tensor([[
[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.],
]]).to(device)
normals_expected = torch.tensor([[[
[0.3432, 0.4861, 0.7628],
[0.2873, 0.4260, 0.6672],
[0.2284, 0.3683, 0.5596],
[0.1695, 0.2980, 0.4496],
],
[
[0.3432, 0.2873, 0.2363],
[0.4861, 0.4260, 0.3785],
[0.8079, 0.7261, 0.6529],
[0.8948, 0.8237, 0.7543],
],
[
[0.8743, 0.8253, 0.6019],
[0.8253, 0.7981, 0.6415],
[0.5432, 0.5807, 0.5105],
[0.4129, 0.4824, 0.4784],
]]]).to(device)
normals = kornia.depth_to_normals(depth, camera_matrix)
assert_allclose(normals, normals_expected, 1e-3, 1e-3)
def test_shapes(self, batch_size, device, dtype):
depth = torch.rand(batch_size, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(batch_size,
3,
3,
device=device,
dtype=dtype)
points3d = kornia.depth_to_normals(depth, camera_matrix)
assert points3d.shape == (batch_size, 3, 3, 4)
def test_simple_normalized(self, device, dtype):
# this is for default normalize_points=False
depth = 2 * torch.tensor([[[
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
]]],
device=device,
dtype=dtype)
camera_matrix = torch.tensor([[
[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.],
]],
device=device,
dtype=dtype)
normals_expected = torch.tensor([[[
[0.3432, 0.4861, 0.7628],
[0.2873, 0.4260, 0.6672],
[0.2284, 0.3683, 0.5596],
[0.1695, 0.2980, 0.4496],
],
[
[0.3432, 0.2873, 0.2363],
[0.4861, 0.4260, 0.3785],
[0.8079, 0.7261, 0.6529],
[0.8948, 0.8237, 0.7543],
],
[
[0.8743, 0.8253, 0.6019],
[0.8253, 0.7981, 0.6415],
[0.5432, 0.5807, 0.5105],
[0.4129, 0.4824, 0.4784],
]]],
device=device,
dtype=dtype)
normals = kornia.depth_to_normals(depth,
camera_matrix,
normalize_points=True)
assert_allclose(normals, normals_expected, 1e-3, 1e-3)
def test_simple(self, device, dtype):
# this is for default normalize_points=False
depth = 2 * torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
camera_matrix = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
normals_expected = torch.tensor(
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
],
device=device,
dtype=dtype,
)
normals = kornia.depth_to_normals(depth, camera_matrix) # default is normalize_points=False
assert_close(normals, normals_expected, rtol=1e-3, atol=1e-3)
def test_shapes_broadcast(self, device, batch_size):
depth = torch.rand(batch_size, 1, 3, 4).to(device)
camera_matrix = torch.rand(1, 3, 3).to(device)
points3d = kornia.depth_to_normals(depth, camera_matrix)
assert points3d.shape == (batch_size, 3, 3, 4)
def test_smoke(self, device):
depth = torch.rand(1, 1, 3, 4).to(device)
camera_matrix = torch.rand(1, 3, 3).to(device)
points3d = kornia.depth_to_normals(depth, camera_matrix)
assert points3d.shape == (1, 3, 3, 4)
