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| # Last modified: 2024-02-26 | |
| # | |
| # Copyright 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/prs-eth/Marigold#-citation | |
| # If you use or adapt this code, please attribute to https://github.com/prs-eth/marigold. | |
| # More information about the method can be found at https://marigoldmonodepth.github.io | |
| # -------------------------------------------------------------------------- | |
| import os | |
| import tarfile | |
| from io import BytesIO | |
| import numpy as np | |
| import torch | |
| from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode, DatasetMode | |
| class DIODEDataset(BaseDepthDataset): | |
| def __init__( | |
| self, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__( | |
| # DIODE data parameter | |
| min_depth=0.6, | |
| max_depth=350, | |
| has_filled_depth=False, | |
| name_mode=DepthFileNameMode.id, | |
| **kwargs, | |
| ) | |
| def _read_npy_file(self, rel_path): | |
| if self.is_tar: | |
| if self.tar_obj is None: | |
| self.tar_obj = tarfile.open(self.dataset_dir) | |
| fileobj = self.tar_obj.extractfile("./" + rel_path) | |
| npy_path_or_content = BytesIO(fileobj.read()) | |
| else: | |
| npy_path_or_content = os.path.join(self.dataset_dir, rel_path) | |
| data = np.load(npy_path_or_content).squeeze()[np.newaxis, :, :] | |
| return data | |
| def _read_depth_file(self, rel_path): | |
| depth = self._read_npy_file(rel_path) | |
| return depth | |
| def _get_data_path(self, index): | |
| return self.filenames[index] | |
| def _get_data_item(self, index): | |
| # Special: depth mask is read from data | |
| rgb_rel_path, depth_rel_path, mask_rel_path = self._get_data_path(index=index) | |
| rasters = {} | |
| # RGB data | |
| rasters.update(self._load_rgb_data(rgb_rel_path=rgb_rel_path)) | |
| # Depth data | |
| if DatasetMode.RGB_ONLY != self.mode: | |
| # load data | |
| depth_data = self._load_depth_data( | |
| depth_rel_path=depth_rel_path, filled_rel_path=None | |
| ) | |
| rasters.update(depth_data) | |
| # valid mask | |
| mask = self._read_npy_file(mask_rel_path).astype(bool) | |
| mask = torch.from_numpy(mask).bool() | |
| rasters["valid_mask_raw"] = mask.clone() | |
| rasters["valid_mask_filled"] = mask.clone() | |
| other = {"index": index, "rgb_relative_path": rgb_rel_path} | |
| return rasters, other | |