# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. import json import unittest import numpy as np import torch from pytorch3d.ops import eyes from pytorch3d.renderer.points.pulsar import Renderer as PulsarRenderer from pytorch3d.transforms import so3_exp_map, so3_log_map from pytorch3d.utils import ( cameras_from_opencv_projection, opencv_from_cameras_projection, pulsar_from_opencv_projection, ) from .common_testing import get_tests_dir, TestCaseMixin DATA_DIR = get_tests_dir() / "data" def cv2_project_points(pts, rvec, tvec, camera_matrix): """ Reproduces the `cv2.projectPoints` function from OpenCV using PyTorch. """ R = so3_exp_map(rvec) pts_proj_3d = ( camera_matrix.bmm(R.bmm(pts.permute(0, 2, 1)) + tvec[:, :, None]) ).permute(0, 2, 1) depth = pts_proj_3d[..., 2:] pts_proj_2d = pts_proj_3d[..., :2] / depth return pts_proj_2d class TestCameraConversions(TestCaseMixin, unittest.TestCase): def setUp(self) -> None: super().setUp() torch.manual_seed(42) np.random.seed(42) def test_cv2_project_points(self): """ Tests that the local implementation of cv2_project_points gives the same restults OpenCV's `cv2.projectPoints`. The check is done against a set of precomputed results `cv_project_points_precomputed`. """ with open(DATA_DIR / "cv_project_points_precomputed.json", "r") as f: cv_project_points_precomputed = json.load(f) for test_case in cv_project_points_precomputed: _pts_proj = cv2_project_points( **{ k: torch.tensor(test_case[k])[None] for k in ("pts", "rvec", "tvec", "camera_matrix") } ) pts_proj = torch.tensor(test_case["pts_proj"])[None] self.assertClose(_pts_proj, pts_proj, atol=1e-4) def test_opencv_conversion(self): """ Tests that the cameras converted from opencv to pytorch3d convention return correct projections of random 3D points. The check is done against a set of results precomuted using `cv2.projectPoints` function. """ device = torch.device("cuda:0") image_size = [[480, 640]] * 4 R = [ [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ], [ [1.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0], ], [ [0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], ], [ [0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], ], ] tvec = [ [0.0, 0.0, 3.0], [0.3, -0.3, 3.0], [-0.15, 0.1, 4.0], [0.0, 0.0, 4.0], ] focal_length = [ [100.0, 100.0], [115.0, 115.0], [105.0, 105.0], [120.0, 120.0], ] # These values are in y, x format, but they should be in x, y format. # The tests work like this because they only test for consistency, # but this format is misleading. principal_point = [ [240, 320], [240.5, 320.3], [241, 318], [242, 322], ] principal_point, focal_length, R, tvec, image_size = [ torch.tensor(x, device=device) for x in (principal_point, focal_length, R, tvec, image_size) ] camera_matrix = eyes(dim=3, N=4, device=device) camera_matrix[:, 0, 0], camera_matrix[:, 1, 1] = ( focal_length[:, 0], focal_length[:, 1], ) camera_matrix[:, :2, 2] = principal_point pts = torch.nn.functional.normalize( torch.randn(4, 1000, 3, device=device), dim=-1 ) # project the 3D points with the opencv projection function rvec = so3_log_map(R) pts_proj_opencv = cv2_project_points(pts, rvec, tvec, camera_matrix) # make the pytorch3d cameras cameras_opencv_to_pytorch3d = cameras_from_opencv_projection( R, tvec, camera_matrix, image_size ) self.assertEqual(cameras_opencv_to_pytorch3d.device, device) # project the 3D points with converted cameras to screen space. pts_proj_pytorch3d_screen = cameras_opencv_to_pytorch3d.transform_points_screen( pts )[..., :2] # compare to the cached projected points self.assertClose(pts_proj_opencv, pts_proj_pytorch3d_screen, atol=1e-5) # Check the inverse. R_i, tvec_i, camera_matrix_i = opencv_from_cameras_projection( cameras_opencv_to_pytorch3d, image_size ) self.assertClose(R, R_i) self.assertClose(tvec, tvec_i) self.assertClose(camera_matrix, camera_matrix_i) def test_pulsar_conversion(self): """ Tests that the cameras converted from opencv to pulsar convention return correct projections of random 3D points. The check is done against a set of results precomputed using `cv2.projectPoints` function. """ image_size = [[480, 640]] R = [ [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ], [ [0.1968, -0.6663, -0.7192], [0.7138, -0.4055, 0.5710], [-0.6721, -0.6258, 0.3959], ], ] tvec = [ [10.0, 10.0, 3.0], [-0.0, -0.0, 20.0], ] focal_length = [ [100.0, 100.0], [10.0, 10.0], ] principal_point = [ [320, 240], [320, 240], ] principal_point, focal_length, R, tvec, image_size = [ torch.FloatTensor(x) for x in (principal_point, focal_length, R, tvec, image_size) ] camera_matrix = eyes(dim=3, N=2) camera_matrix[:, 0, 0] = focal_length[:, 0] camera_matrix[:, 1, 1] = focal_length[:, 1] camera_matrix[:, :2, 2] = principal_point rvec = so3_log_map(R) pts = torch.tensor( [[[0.0, 0.0, 120.0]], [[0.0, 0.0, 120.0]]], dtype=torch.float32 ) radii = torch.tensor([[1e-5], [1e-5]], dtype=torch.float32) col = torch.zeros((2, 1, 1), dtype=torch.float32) # project the 3D points with the opencv projection function pts_proj_opencv = cv2_project_points(pts, rvec, tvec, camera_matrix) pulsar_cam = pulsar_from_opencv_projection( R, tvec, camera_matrix, image_size, znear=100.0 ) pulsar_rend = PulsarRenderer( 640, 480, 1, right_handed_system=False, n_channels=1 ) rendered = torch.flip( pulsar_rend( pts, col, radii, pulsar_cam, 1e-5, max_depth=150.0, min_depth=100.0, ), dims=(1,), ) for batch_id in range(2): point_pos = torch.where(rendered[batch_id] == rendered[batch_id].min()) point_pos = point_pos[1][0], point_pos[0][0] self.assertLess( torch.abs(point_pos[0] - pts_proj_opencv[batch_id, 0, 0]), 2 ) self.assertLess( torch.abs(point_pos[1] - pts_proj_opencv[batch_id, 0, 1]), 2 )