mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 20:02:49 +08:00
5284de6e97
Summary: Deprecate the `so3_exponential_map()` function in favor of its alias `so3_exp_map()`: this aligns with the naming of `so3_log_map()` and the recently introduced `se3_exp_map()` / `se3_log_map()` pair. Reviewed By: bottler Differential Revision: D29329966 fbshipit-source-id: b6f60b9e86b2995f70b1fbeb16f9feea05c55de9
127 lines
4.7 KiB
Python
127 lines
4.7 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import Tuple
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import torch
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from ..renderer import PerspectiveCameras
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from ..transforms import so3_exp_map, so3_log_map
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def cameras_from_opencv_projection(
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rvec: torch.Tensor,
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tvec: torch.Tensor,
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camera_matrix: torch.Tensor,
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image_size: torch.Tensor,
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) -> PerspectiveCameras:
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"""
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Converts a batch of OpenCV-conventioned cameras parametrized with the
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axis-angle rotation vectors `rvec`, translation vectors `tvec`, and the camera
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calibration matrices `camera_matrix` to `PerspectiveCameras` in PyTorch3D
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convention.
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More specifically, the conversion is carried out such that a projection
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of a 3D shape to the OpenCV-conventioned screen of size `image_size` results
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in the same image as a projection with the corresponding PyTorch3D camera
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to the NDC screen convention of PyTorch3D.
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More specifically, the OpenCV convention projects points to the OpenCV screen
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space as follows:
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```
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x_screen_opencv = camera_matrix @ (exp(rvec) @ x_world + tvec)
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```
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followed by the homogenization of `x_screen_opencv`.
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Note:
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The parameters `rvec, tvec, camera_matrix` correspond, e.g., to the inputs
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of `cv2.projectPoints`, or to the ouputs of `cv2.calibrateCamera`.
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Args:
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rvec: A batch of axis-angle rotation vectors of shape `(N, 3)`.
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tvec: A batch of translation vectors of shape `(N, 3)`.
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camera_matrix: A batch of camera calibration matrices of shape `(N, 3, 3)`.
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image_size: A tensor of shape `(N, 2)` containing the sizes of the images
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(height, width) attached to each camera.
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Returns:
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cameras_pytorch3d: A batch of `N` cameras in the PyTorch3D convention.
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"""
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R = so3_exp_map(rvec)
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focal_length = torch.stack([camera_matrix[:, 0, 0], camera_matrix[:, 1, 1]], dim=-1)
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principal_point = camera_matrix[:, :2, 2]
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# Retype the image_size correctly and flip to width, height.
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image_size_wh = image_size.to(R).flip(dims=(1,))
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# Get the PyTorch3D focal length and principal point.
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focal_pytorch3d = focal_length / (0.5 * image_size_wh)
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p0_pytorch3d = -(principal_point / (0.5 * image_size_wh) - 1)
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# For R, T we flip x, y axes (opencv screen space has an opposite
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# orientation of screen axes).
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# We also transpose R (opencv multiplies points from the opposite=left side).
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R_pytorch3d = R.permute(0, 2, 1)
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T_pytorch3d = tvec.clone()
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R_pytorch3d[:, :, :2] *= -1
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T_pytorch3d[:, :2] *= -1
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return PerspectiveCameras(
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R=R_pytorch3d,
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T=T_pytorch3d,
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focal_length=focal_pytorch3d,
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principal_point=p0_pytorch3d,
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)
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def opencv_from_cameras_projection(
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cameras: PerspectiveCameras,
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image_size: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Converts a batch of `PerspectiveCameras` into OpenCV-convention
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axis-angle rotation vectors `rvec`, translation vectors `tvec`, and the camera
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calibration matrices `camera_matrix`. This operation is exactly the inverse
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of `cameras_from_opencv_projection`.
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Note:
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The parameters `rvec, tvec, camera_matrix` correspond, e.g., to the inputs
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of `cv2.projectPoints`, or to the ouputs of `cv2.calibrateCamera`.
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Args:
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cameras: A batch of `N` cameras in the PyTorch3D convention.
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image_size: A tensor of shape `(N, 2)` containing the sizes of the images
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(height, width) attached to each camera.
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Returns:
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rvec: A batch of axis-angle rotation vectors of shape `(N, 3)`.
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tvec: A batch of translation vectors of shape `(N, 3)`.
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camera_matrix: A batch of camera calibration matrices of shape `(N, 3, 3)`.
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"""
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R_pytorch3d = cameras.R
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T_pytorch3d = cameras.T
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focal_pytorch3d = cameras.focal_length
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p0_pytorch3d = cameras.principal_point
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T_pytorch3d[:, :2] *= -1 # pyre-ignore
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R_pytorch3d[:, :, :2] *= -1 # pyre-ignore
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tvec = T_pytorch3d.clone() # pyre-ignore
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R = R_pytorch3d.permute(0, 2, 1) # pyre-ignore
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# Retype the image_size correctly and flip to width, height.
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image_size_wh = image_size.to(R).flip(dims=(1,))
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principal_point = (-p0_pytorch3d + 1.0) * (0.5 * image_size_wh) # pyre-ignore
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focal_length = focal_pytorch3d * (0.5 * image_size_wh)
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camera_matrix = torch.zeros_like(R)
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camera_matrix[:, :2, 2] = principal_point
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camera_matrix[:, 2, 2] = 1.0
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camera_matrix[:, 0, 0] = focal_length[:, 0]
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camera_matrix[:, 1, 1] = focal_length[:, 1]
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rvec = so3_log_map(R)
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return rvec, tvec, camera_matrix
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