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| # 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 typing | |
| import torch | |
| from pytorch3d.common.datatypes import Device | |
| from pytorch3d.renderer.cameras import ( | |
| CamerasBase, | |
| FoVOrthographicCameras, | |
| FoVPerspectiveCameras, | |
| OpenGLOrthographicCameras, | |
| OpenGLPerspectiveCameras, | |
| OrthographicCameras, | |
| PerspectiveCameras, | |
| SfMOrthographicCameras, | |
| SfMPerspectiveCameras, | |
| ) | |
| from pytorch3d.renderer.fisheyecameras import FishEyeCameras | |
| from pytorch3d.transforms.so3 import so3_exp_map | |
| def init_random_cameras( | |
| cam_type: typing.Type[CamerasBase], | |
| batch_size: int, | |
| random_z: bool = False, | |
| device: Device = "cpu", | |
| ): | |
| cam_params = {} | |
| T = torch.randn(batch_size, 3) * 0.03 | |
| if not random_z: | |
| T[:, 2] = 4 | |
| R = so3_exp_map(torch.randn(batch_size, 3) * 3.0) | |
| cam_params = {"R": R, "T": T, "device": device} | |
| if cam_type in (OpenGLPerspectiveCameras, OpenGLOrthographicCameras): | |
| cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1 | |
| cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"] | |
| if cam_type == OpenGLPerspectiveCameras: | |
| cam_params["fov"] = torch.rand(batch_size) * 60 + 30 | |
| cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5 | |
| else: | |
| cam_params["top"] = torch.rand(batch_size) * 0.2 + 0.9 | |
| cam_params["bottom"] = -(torch.rand(batch_size)) * 0.2 - 0.9 | |
| cam_params["left"] = -(torch.rand(batch_size)) * 0.2 - 0.9 | |
| cam_params["right"] = torch.rand(batch_size) * 0.2 + 0.9 | |
| elif cam_type in (FoVPerspectiveCameras, FoVOrthographicCameras): | |
| cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1 | |
| cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"] | |
| if cam_type == FoVPerspectiveCameras: | |
| cam_params["fov"] = torch.rand(batch_size) * 60 + 30 | |
| cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5 | |
| else: | |
| cam_params["max_y"] = torch.rand(batch_size) * 0.2 + 0.9 | |
| cam_params["min_y"] = -(torch.rand(batch_size)) * 0.2 - 0.9 | |
| cam_params["min_x"] = -(torch.rand(batch_size)) * 0.2 - 0.9 | |
| cam_params["max_x"] = torch.rand(batch_size) * 0.2 + 0.9 | |
| elif cam_type in ( | |
| SfMOrthographicCameras, | |
| SfMPerspectiveCameras, | |
| OrthographicCameras, | |
| PerspectiveCameras, | |
| ): | |
| cam_params["focal_length"] = torch.rand(batch_size) * 10 + 0.1 | |
| cam_params["principal_point"] = torch.randn((batch_size, 2)) | |
| elif cam_type == FishEyeCameras: | |
| cam_params["focal_length"] = torch.rand(batch_size, 1) * 10 + 0.1 | |
| cam_params["principal_point"] = torch.randn((batch_size, 2)) | |
| cam_params["radial_params"] = torch.randn((batch_size, 6)) | |
| cam_params["tangential_params"] = torch.randn((batch_size, 2)) | |
| cam_params["thin_prism_params"] = torch.randn((batch_size, 4)) | |
| else: | |
| raise ValueError(str(cam_type)) | |
| return cam_type(**cam_params) | |