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Summary: This fixes a recently introduced circular import: the problem went unnoticed by having `pytorch3d.renderer` imported first... Reviewed By: bottler Differential Revision: D29686235 fbshipit-source-id: 4b9f2faecec2cc8347ee259cfc359dc9e4f67784
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| # Copyright (c) Facebook, Inc. and its 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. | ||
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| import logging | ||
| from typing import Tuple | ||
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| import torch | ||
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| from ..transforms import matrix_to_rotation_6d | ||
| from .cameras import PerspectiveCameras | ||
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| LOGGER = logging.getLogger(__name__) | ||
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| def _cameras_from_opencv_projection( | ||
| R: torch.Tensor, | ||
| tvec: torch.Tensor, | ||
| camera_matrix: torch.Tensor, | ||
| image_size: torch.Tensor, | ||
| ) -> PerspectiveCameras: | ||
| focal_length = torch.stack([camera_matrix[:, 0, 0], camera_matrix[:, 1, 1]], dim=-1) | ||
| principal_point = camera_matrix[:, :2, 2] | ||
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| # Retype the image_size correctly and flip to width, height. | ||
| image_size_wh = image_size.to(R).flip(dims=(1,)) | ||
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| # Get the PyTorch3D focal length and principal point. | ||
| focal_pytorch3d = focal_length / (0.5 * image_size_wh) | ||
| 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 | ||
| # orientation of screen axes). | ||
| # We also transpose R (opencv multiplies points from the opposite=left side). | ||
| R_pytorch3d = R.clone().permute(0, 2, 1) | ||
| T_pytorch3d = tvec.clone() | ||
| R_pytorch3d[:, :, :2] *= -1 | ||
| T_pytorch3d[:, :2] *= -1 | ||
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| return PerspectiveCameras( | ||
| R=R_pytorch3d, | ||
| T=T_pytorch3d, | ||
| focal_length=focal_pytorch3d, | ||
| principal_point=p0_pytorch3d, | ||
| ) | ||
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| def _opencv_from_cameras_projection( | ||
| cameras: PerspectiveCameras, | ||
| image_size: torch.Tensor, | ||
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | ||
| R_pytorch3d = cameras.R.clone() # pyre-ignore | ||
| T_pytorch3d = cameras.T.clone() # pyre-ignore | ||
| focal_pytorch3d = cameras.focal_length | ||
| p0_pytorch3d = cameras.principal_point | ||
| T_pytorch3d[:, :2] *= -1 | ||
| R_pytorch3d[:, :, :2] *= -1 | ||
| tvec = T_pytorch3d | ||
| R = R_pytorch3d.permute(0, 2, 1) | ||
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| # Retype the image_size correctly and flip to width, height. | ||
| 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 | ||
| focal_length = focal_pytorch3d * (0.5 * image_size_wh) | ||
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| camera_matrix = torch.zeros_like(R) | ||
| camera_matrix[:, :2, 2] = principal_point | ||
| camera_matrix[:, 2, 2] = 1.0 | ||
| camera_matrix[:, 0, 0] = focal_length[:, 0] | ||
| camera_matrix[:, 1, 1] = focal_length[:, 1] | ||
| return R, tvec, camera_matrix | ||
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| def _pulsar_from_opencv_projection( | ||
| R: torch.Tensor, | ||
| tvec: torch.Tensor, | ||
| camera_matrix: torch.Tensor, | ||
| image_size: torch.Tensor, | ||
| znear: float = 0.1, | ||
| ) -> torch.Tensor: | ||
| assert len(camera_matrix.size()) == 3, "This function requires batched inputs!" | ||
| assert len(R.size()) == 3, "This function requires batched inputs!" | ||
| assert len(tvec.size()) in (2, 3), "This function reuqires batched inputs!" | ||
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| # Validate parameters. | ||
| image_size_wh = image_size.to(R).flip(dims=(1,)) | ||
| assert torch.all( | ||
| image_size_wh > 0 | ||
| ), "height and width must be positive but min is: %s" % ( | ||
| str(image_size_wh.min().item()) | ||
| ) | ||
| assert ( | ||
| camera_matrix.size(1) == 3 and camera_matrix.size(2) == 3 | ||
| ), "Incorrect camera matrix shape: expected 3x3 but got %dx%d" % ( | ||
| camera_matrix.size(1), | ||
| camera_matrix.size(2), | ||
| ) | ||
| assert ( | ||
| R.size(1) == 3 and R.size(2) == 3 | ||
| ), "Incorrect R shape: expected 3x3 but got %dx%d" % ( | ||
| R.size(1), | ||
| R.size(2), | ||
| ) | ||
| if len(tvec.size()) == 2: | ||
| tvec = tvec.unsqueeze(2) | ||
| assert ( | ||
| tvec.size(1) == 3 and tvec.size(2) == 1 | ||
| ), "Incorrect tvec shape: expected 3x1 but got %dx%d" % ( | ||
| tvec.size(1), | ||
| tvec.size(2), | ||
| ) | ||
| # Check batch size. | ||
| batch_size = camera_matrix.size(0) | ||
| assert R.size(0) == batch_size, "Expected R to have batch size %d. Has size %d." % ( | ||
| batch_size, | ||
| R.size(0), | ||
| ) | ||
| assert ( | ||
| tvec.size(0) == batch_size | ||
| ), "Expected tvec to have batch size %d. Has size %d." % ( | ||
| batch_size, | ||
| tvec.size(0), | ||
| ) | ||
| # Check image sizes. | ||
| image_w = image_size_wh[0, 0] | ||
| image_h = image_size_wh[0, 1] | ||
| assert torch.all( | ||
| image_size_wh[:, 0] == image_w | ||
| ), "All images in a batch must have the same width!" | ||
| assert torch.all( | ||
| image_size_wh[:, 1] == image_h | ||
| ), "All images in a batch must have the same height!" | ||
| # Focal length. | ||
| fx = camera_matrix[:, 0, 0].unsqueeze(1) | ||
| fy = camera_matrix[:, 1, 1].unsqueeze(1) | ||
| # Check that we introduce less than 1% error by averaging the focal lengths. | ||
| fx_y = fx / fy | ||
| if torch.any(fx_y > 1.01) or torch.any(fx_y < 0.99): | ||
| LOGGER.warning( | ||
| "Pulsar only supports a single focal lengths. For converting OpenCV " | ||
| "focal lengths, we average them for x and y directions. " | ||
| "The focal lengths for x and y you provided differ by more than 1%, " | ||
| "which means this could introduce a noticeable error." | ||
| ) | ||
| f = (fx + fy) / 2 | ||
| # Normalize f into normalized device coordinates. | ||
| focal_length_px = f / image_w | ||
| # Transfer into focal_length and sensor_width. | ||
| focal_length = torch.tensor([znear - 1e-5], dtype=torch.float32, device=R.device) | ||
| focal_length = focal_length[None, :].repeat(batch_size, 1) | ||
| sensor_width = focal_length / focal_length_px | ||
| # Principal point. | ||
| cx = camera_matrix[:, 0, 2].unsqueeze(1) | ||
| cy = camera_matrix[:, 1, 2].unsqueeze(1) | ||
| # Transfer principal point offset into centered offset. | ||
| cx = -(cx - image_w / 2) | ||
| cy = cy - image_h / 2 | ||
| # Concatenate to final vector. | ||
| param = torch.cat([focal_length, sensor_width, cx, cy], dim=1) | ||
| R_trans = R.permute(0, 2, 1) | ||
| cam_pos = -torch.bmm(R_trans, tvec).squeeze(2) | ||
| cam_rot = matrix_to_rotation_6d(R_trans) | ||
| cam_params = torch.cat([cam_pos, cam_rot, param], dim=1) | ||
| return cam_params | ||
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| def _pulsar_from_cameras_projection( | ||
| cameras: PerspectiveCameras, | ||
| image_size: torch.Tensor, | ||
| ) -> torch.Tensor: | ||
| opencv_R, opencv_T, opencv_K = _opencv_from_cameras_projection(cameras, image_size) | ||
| return _pulsar_from_opencv_projection(opencv_R, opencv_T, opencv_K, image_size) |
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