# LICENSE HEADER MANAGED BY add-license-header # # Copyright 2018 Kornia Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import pytest import torch from kornia.geometry.camera.projection_orthographic import ( dx_project_points_orthographic, project_points_orthographic, unproject_points_orthographic, ) from kornia.geometry.camera.projection_z1 import dx_project_points_z1, project_points_z1, unproject_points_z1 from testing.base import BaseTester class TestProjectionZ1(BaseTester): def test_smoke(self, device, dtype): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) assert project_points_z1(points) is not None def _test_cardinality_unproject_batch(self, device, dtype, batch_size): batch_tuple = (batch_size,) if batch_size is not None else () points = torch.rand(batch_tuple + (3,), device=device, dtype=dtype) assert project_points_z1(points).shape == batch_tuple + (2,) def _test_cardinality_project_batch(self, device, dtype, batch_size): batch_tuple = (batch_size,) if batch_size is not None else () points = torch.rand(batch_tuple + (2,), device=device, dtype=dtype) assert unproject_points_z1(points).shape == batch_tuple + (3,) @pytest.mark.parametrize("batch_size", [None, 1, 2, 3]) def test_cardinality(self, device, dtype, batch_size): self._test_cardinality_project_batch(device, dtype, batch_size) self._test_cardinality_unproject_batch(device, dtype, batch_size) def test_project_points_z1(self, device, dtype): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) expected = torch.tensor([0.3333333432674408, 0.6666666865348816], device=device, dtype=dtype) self.assert_close(project_points_z1(points), expected) def test_project_points_z1_batch(self, device, dtype): points = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], device=device, dtype=dtype) expected = torch.tensor( [ [0.3333333432674408, 0.6666666865348816], [0.6666666865348816, 0.8333333730697632], ], device=device, dtype=dtype, ) self.assert_close(project_points_z1(points), expected) def test_project_points_z1_invalid(self, device, dtype): # NOTE: this is a corner case where the depth is 0.0 and the point is at infinity # the projection is not defined and the function returns inf. The second point # is behind the camera which is not a valid point and the user should handle it. points = torch.tensor([[1.0, 2.0, 0.0], [4.0, 5.0, -1.0]], device=device, dtype=dtype) expected = torch.tensor([[float("inf"), float("inf")], [-4.0, -5.0]], device=device, dtype=dtype) self.assert_close(project_points_z1(points), expected) def test_unproject_points_z1(self, device, dtype): points = torch.tensor([1.0, 2.0], device=device, dtype=dtype) expected = torch.tensor([1.0, 2.0, 1.0], device=device, dtype=dtype) self.assert_close(unproject_points_z1(points), expected) def test_unproject_points_z1_batch(self, device, dtype): points = torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device, dtype=dtype) expected = torch.tensor([[1.0, 2.0, 1.0], [3.0, 4.0, 1.0]], device=device, dtype=dtype) self.assert_close(unproject_points_z1(points), expected) def test_project_unproject(self, device, dtype): points = torch.tensor([1.0, 2.0, 2.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) self.assert_close(unproject_points_z1(project_points_z1(points), extension), points) def test_unproject_points_z1_extension(self, device, dtype): points = torch.tensor([1.0, 2.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) expected = torch.tensor([2.0, 4.0, 2.0], device=device, dtype=dtype) self.assert_close(unproject_points_z1(points, extension), expected) def test_unproject_points_z1_batch_extension(self, device, dtype): points = torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device, dtype=dtype) extension = torch.tensor([2.0, 3.0], device=device, dtype=dtype) expected = torch.tensor([[2.0, 4.0, 2.0], [9.0, 12.0, 3.0]], device=device, dtype=dtype) self.assert_close(unproject_points_z1(points, extension), expected) def test_dx_proj_x(self, device, dtype): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) expected = torch.tensor( [ [0.3333333432674408, 0.0, -0.1111111119389534], [0.0, 0.3333333432674408, -0.2222222238779068], ], device=device, dtype=dtype, ) self.assert_close(dx_project_points_z1(points), expected) def test_exception(self, device, dtype) -> None: from kornia.core.exceptions import ShapeError points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) with pytest.raises(ShapeError): unproject_points_z1(points, extension) def _test_gradcheck_unproject(self, device): points = torch.tensor([1.0, 2.0], device=device, dtype=torch.float64) extension = torch.tensor([2.0], device=device, dtype=torch.float64) self.gradcheck(unproject_points_z1, (points, extension)) def _test_gradcheck_project(self, device): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=torch.float64) self.gradcheck(project_points_z1, (points,)) def test_gradcheck(self, device) -> None: self._test_gradcheck_project(device) self._test_gradcheck_unproject(device) def _test_jit_unproject(self, device, dtype) -> None: points = torch.tensor([1.0, 2.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) op_script = torch.jit.script(unproject_points_z1) actual = op_script(points, extension) expected = unproject_points_z1(points, extension) self.assert_close(actual, expected) def _test_jit_project(self, device, dtype) -> None: points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) op_script = torch.jit.script(project_points_z1) actual = op_script(points) expected = project_points_z1(points) self.assert_close(actual, expected) def test_jit(self, device, dtype) -> None: self._test_jit_project(device, dtype) self._test_jit_unproject(device, dtype) class TestProjectionOrthographic(BaseTester): def test_smoke(self, device, dtype): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) assert project_points_orthographic(points) is not None def _test_cardinality_unproject_batch(self, device, dtype, batch_size): batch_tuple = (batch_size,) if batch_size is not None else () points = torch.rand(batch_tuple + (3,), device=device, dtype=dtype) assert project_points_orthographic(points).shape == batch_tuple + (2,) def _test_cardinality_project_batch(self, device, dtype, batch_size): batch_tuple = (batch_size,) if batch_size is not None else () points = torch.rand(batch_tuple + (2,), device=device, dtype=dtype) extension = torch.rand(batch_tuple, device=device, dtype=dtype) assert unproject_points_orthographic(points, extension).shape == batch_tuple + (3,) @pytest.mark.parametrize("batch_size", [None, 1, 2, 3]) def test_cardinality(self, device, dtype, batch_size): self._test_cardinality_project_batch(device, dtype, batch_size) self._test_cardinality_unproject_batch(device, dtype, batch_size) def test_project_points_orthographic(self, device, dtype): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) expected = torch.tensor([1.0, 2.0], device=device, dtype=dtype) self.assert_close(project_points_orthographic(points), expected) def test_project_points_orthographic_batch(self, device, dtype): points = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], device=device, dtype=dtype) expected = torch.tensor([[1.0, 2.0], [4.0, 5.0]], device=device, dtype=dtype) self.assert_close(project_points_orthographic(points), expected) def test_unproject_points_orthographic_extension(self, device, dtype): points = torch.tensor([1.0, 2.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) expected = torch.tensor([1.0, 2.0, 2.0], device=device, dtype=dtype) self.assert_close(unproject_points_orthographic(points, extension), expected) def test_unproject_points_orthographic_batch_extension(self, device, dtype): points = torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device, dtype=dtype) extension = torch.tensor([2.0, 3.0], device=device, dtype=dtype) expected = torch.tensor([[1.0, 2.0, 2.0], [3.0, 4.0, 3.0]], device=device, dtype=dtype) self.assert_close(unproject_points_orthographic(points, extension), expected) def test_project_unproject(self, device, dtype): points = torch.tensor([1.0, 2.0, 2.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) self.assert_close(unproject_points_orthographic(project_points_orthographic(points), extension), points) def test_dx_proj_x(self, device, dtype): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) expected = torch.tensor([1.0], device=device, dtype=dtype) self.assert_close(dx_project_points_orthographic(points), expected) def test_exception(self, device, dtype) -> None: from kornia.core.exceptions import ShapeError points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) with pytest.raises(ShapeError): unproject_points_orthographic(points, extension) def _test_gradcheck_project(self, device): points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=torch.float64) self.gradcheck(project_points_orthographic, (points,)) def _test_gradcheck_unproject(self, device): points = torch.tensor([1.0, 2.0], device=device, dtype=torch.float64) extension = torch.tensor([2.0], device=device, dtype=torch.float64) self.gradcheck(unproject_points_orthographic, (points, extension)) def test_gradcheck(self, device) -> None: self._test_gradcheck_project(device) self._test_gradcheck_unproject(device) def _test_jit_project(self, device, dtype) -> None: points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype) op_script = torch.jit.script(project_points_orthographic) actual = op_script(points) expected = project_points_orthographic(points) self.assert_close(actual, expected) def _test_jit_unproject(self, device, dtype) -> None: points = torch.tensor([1.0, 2.0], device=device, dtype=dtype) extension = torch.tensor([2.0], device=device, dtype=dtype) op_script = torch.jit.script(unproject_points_orthographic) actual = op_script(points, extension) expected = unproject_points_orthographic(points, extension) self.assert_close(actual, expected) def test_jit(self, device, dtype) -> None: self._test_jit_project(device, dtype) self._test_jit_unproject(device, dtype)