| import torch |
| from torch.autograd import gradcheck |
|
|
| import kornia |
| from kornia.testing import assert_close, tensor_to_gradcheck_var |
|
|
|
|
| class TestProjectPoints: |
| def test_smoke(self, device, dtype): |
| point_3d = torch.zeros(1, 3, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(1, -1, -1) |
| point_2d = kornia.geometry.camera.project_points(point_3d, camera_matrix) |
| assert point_2d.shape == (1, 2) |
|
|
| def test_smoke_batch(self, device, dtype): |
| point_3d = torch.zeros(2, 3, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(2, -1, -1) |
| point_2d = kornia.geometry.camera.project_points(point_3d, camera_matrix) |
| assert point_2d.shape == (2, 2) |
|
|
| def test_smoke_batch_multi(self, device, dtype): |
| point_3d = torch.zeros(2, 4, 3, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(2, 4, -1, -1) |
| point_2d = kornia.geometry.camera.project_points(point_3d, camera_matrix) |
| assert point_2d.shape == (2, 4, 2) |
|
|
| def test_project_and_unproject(self, device, dtype): |
| point_3d = torch.tensor([[10.0, 2.0, 30.0]], device=device, dtype=dtype) |
| depth = point_3d[..., -1:] |
| camera_matrix = torch.tensor( |
| [[[2746.0, 0.0, 991.0], [0.0, 2748.0, 619.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype |
| ) |
| point_2d = kornia.geometry.camera.project_points(point_3d, camera_matrix) |
| point_3d_hat = kornia.geometry.camera.unproject_points(point_2d, depth, camera_matrix) |
| assert_close(point_3d, point_3d_hat, atol=1e-4, rtol=1e-4) |
|
|
| def test_gradcheck(self, device, dtype): |
| |
| points_3d = torch.ones(1, 3, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(1, -1, -1) |
|
|
| |
| points_3d = tensor_to_gradcheck_var(points_3d) |
| camera_matrix = tensor_to_gradcheck_var(camera_matrix) |
| assert gradcheck(kornia.geometry.camera.project_points, (points_3d, camera_matrix), raise_exception=True) |
|
|
| def test_jit(self, device, dtype): |
| points_3d = torch.zeros(1, 3, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(1, -1, -1) |
| op = kornia.geometry.camera.project_points |
| op_jit = torch.jit.script(op) |
| assert_close(op(points_3d, camera_matrix), op_jit(points_3d, camera_matrix)) |
|
|
|
|
| class TestUnprojectPoints: |
| def test_smoke(self, device, dtype): |
| points_2d = torch.zeros(1, 2, device=device, dtype=dtype) |
| depth = torch.ones(1, 1, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(1, -1, -1) |
| point_3d = kornia.geometry.camera.unproject_points(points_2d, depth, camera_matrix) |
| assert point_3d.shape == (1, 3) |
|
|
| def test_smoke_batch(self, device, dtype): |
| points_2d = torch.zeros(2, 2, device=device, dtype=dtype) |
| depth = torch.ones(2, 1, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(2, -1, -1) |
| point_3d = kornia.geometry.camera.unproject_points(points_2d, depth, camera_matrix) |
| assert point_3d.shape == (2, 3) |
|
|
| def test_smoke_multi_batch(self, device, dtype): |
| points_2d = torch.zeros(2, 3, 2, device=device, dtype=dtype) |
| depth = torch.ones(2, 3, 1, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(2, 3, -1, -1) |
| point_3d = kornia.geometry.camera.unproject_points(points_2d, depth, camera_matrix) |
| assert point_3d.shape == (2, 3, 3) |
|
|
| def test_unproject_center(self, device, dtype): |
| point_2d = torch.tensor([[0.0, 0.0]], device=device, dtype=dtype) |
| depth = torch.tensor([[2.0]], 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) |
| expected = torch.tensor([[0.0, 0.0, 2.0]], device=device, dtype=dtype) |
| actual = kornia.geometry.camera.unproject_points(point_2d, depth, camera_matrix) |
| assert_close(actual, expected, atol=1e-4, rtol=1e-4) |
|
|
| def test_unproject_center_normalize(self, device, dtype): |
| point_2d = torch.tensor([[0.0, 0.0]], device=device, dtype=dtype) |
| depth = torch.tensor([[2.0]], 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) |
| expected = torch.tensor([[0.0, 0.0, 2.0]], device=device, dtype=dtype) |
| actual = kornia.geometry.camera.unproject_points(point_2d, depth, camera_matrix, True) |
| assert_close(actual, expected, atol=1e-4, rtol=1e-4) |
|
|
| def test_unproject_and_project(self, device, dtype): |
| point_2d = torch.tensor([[0.0, 0.0]], device=device, dtype=dtype) |
| depth = torch.tensor([[2.0]], 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) |
| point_3d = kornia.geometry.camera.unproject_points(point_2d, depth, camera_matrix) |
| point_2d_hat = kornia.geometry.camera.project_points(point_3d, camera_matrix) |
| assert_close(point_2d, point_2d_hat, atol=1e-4, rtol=1e-4) |
|
|
| def test_gradcheck(self, device, dtype): |
| points_2d = torch.zeros(1, 2, device=device, dtype=dtype) |
| depth = torch.ones(1, 1, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(1, -1, -1) |
|
|
| |
| points_2d = tensor_to_gradcheck_var(points_2d) |
| depth = tensor_to_gradcheck_var(depth) |
| camera_matrix = tensor_to_gradcheck_var(camera_matrix) |
| assert gradcheck( |
| kornia.geometry.camera.unproject_points, (points_2d, depth, camera_matrix), raise_exception=True |
| ) |
|
|
| def test_jit(self, device, dtype): |
| points_2d = torch.zeros(1, 2, device=device, dtype=dtype) |
| depth = torch.ones(1, 1, device=device, dtype=dtype) |
| camera_matrix = torch.eye(3, device=device, dtype=dtype).expand(1, -1, -1) |
| args = (points_2d, depth, camera_matrix) |
| op = kornia.geometry.camera.unproject_points |
| op_jit = torch.jit.script(op) |
| assert_close(op(*args), op_jit(*args)) |
|
|
