| # Last modified: 2025-01-14 | |
| # | |
| # Copyright 2025 Ziyang Song, USTC. All rights reserved. | |
| # | |
| # This file has been modified from the original version. | |
| # Original copyright (c) 2023 Bingxin Ke, ETH Zurich. All rights reserved. | |
| # | |
| # 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. | |
| # -------------------------------------------------------------------------- | |
| # If you find this code useful, we kindly ask you to cite our paper in your work. | |
| # Please find bibtex at: https://github.com/indu1ge/DepthMaster#-citation | |
| # More information about the method can be found at https://indu1ge.github.io/DepthMaster_page | |
| # -------------------------------------------------------------------------- | |
| import torch | |
| from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode | |
| class NYUDataset(BaseDepthDataset): | |
| def __init__( | |
| self, | |
| eigen_valid_mask: bool, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__( | |
| # NYUv2 dataset parameter | |
| min_depth=1e-3, | |
| max_depth=10.0, | |
| has_filled_depth=True, | |
| has_egde_mask=False, | |
| name_mode=DepthFileNameMode.rgb_id, | |
| **kwargs, | |
| ) | |
| self.eigen_valid_mask = eigen_valid_mask | |
| def _read_depth_file(self, rel_path): | |
| depth_in = self._read_image(rel_path) | |
| # Decode NYU depth | |
| depth_decoded = depth_in / 1000.0 | |
| return depth_decoded | |
| def _get_valid_mask(self, depth: torch.Tensor): | |
| valid_mask = super()._get_valid_mask(depth) | |
| # Eigen crop for evaluation | |
| if self.eigen_valid_mask: | |
| eval_mask = torch.zeros_like(valid_mask.squeeze()).bool() | |
| eval_mask[45:471, 41:601] = 1 | |
| eval_mask.reshape(valid_mask.shape) | |
| valid_mask = torch.logical_and(valid_mask, eval_mask) | |
| return valid_mask | |