kornia--kornia/tests/geometry/camera/test_perspective.py

# 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 torch

import kornia

from testing.base import BaseTester


class TestProjectPoints(BaseTester):
    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)
        self.assert_close(point_3d, point_3d_hat, atol=1e-4, rtol=1e-4)

    def test_gradcheck(self, device):
        # TODO: point [0, 0, 0] crashes
        points_3d = torch.ones(1, 3, device=device)
        camera_matrix = torch.eye(3, device=device).expand(1, -1, -1)

        # evaluate function gradient
        self.gradcheck(kornia.geometry.camera.project_points, (points_3d, camera_matrix))

    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)
        self.assert_close(op(points_3d, camera_matrix), op_jit(points_3d, camera_matrix))


class TestUnprojectPoints(BaseTester):
    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)
        self.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)
        self.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)
        self.assert_close(point_2d, point_2d_hat, atol=1e-4, rtol=1e-4)

    def test_gradcheck(self, device):
        points_2d = torch.zeros(1, 2, device=device, dtype=torch.float64)
        depth = torch.ones(1, 1, device=device, dtype=torch.float64)
        camera_matrix = torch.eye(3, device=device, dtype=torch.float64).expand(1, -1, -1)

        # evaluate function gradient
        self.gradcheck(kornia.geometry.camera.unproject_points, (points_2d, depth, camera_matrix))

    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)
        self.assert_close(op(*args), op_jit(*args))