| # Last modified: 2024-02-08 | |
| # | |
| # 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 torch | |
| import tarfile | |
| import os | |
| import numpy as np | |
| from .eval_base_dataset import DepthFileNameMode, EvaluateBaseDataset | |
| class ETH3DDataset(EvaluateBaseDataset): | |
| HEIGHT, WIDTH = 4032, 6048 | |
| def __init__( | |
| self, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__( | |
| # ETH3D data parameter | |
| min_depth=1e-5, | |
| max_depth=torch.inf, | |
| has_filled_depth=False, | |
| name_mode=DepthFileNameMode.id, | |
| **kwargs, | |
| ) | |
| def _read_depth_file(self, rel_path): | |
| # Read special binary data: https://www.eth3d.net/documentation#format-of-multi-view-data-image-formats | |
| if self.is_tar: | |
| if self.tar_obj is None: | |
| self.tar_obj = tarfile.open(self.dataset_dir) | |
| binary_data = self.tar_obj.extractfile("./" + rel_path) | |
| binary_data = binary_data.read() | |
| else: | |
| depth_path = os.path.join(self.dataset_dir, rel_path) | |
| with open(depth_path, "rb") as file: | |
| binary_data = file.read() | |
| # Convert the binary data to a numpy array of 32-bit floats | |
| depth_decoded = np.frombuffer(binary_data, dtype=np.float32).copy() | |
| depth_decoded[depth_decoded == torch.inf] = 0.0 | |
| depth_decoded = depth_decoded.reshape((self.HEIGHT, self.WIDTH)) | |
| return depth_decoded | |