| # Copyright 2023-2025 Marigold Team, ETH Zürich. 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. | |
| # -------------------------------------------------------------------------- | |
| # More information about Marigold: | |
| # https://marigoldmonodepth.github.io | |
| # https://marigoldcomputervision.github.io | |
| # Efficient inference pipelines are now part of diffusers: | |
| # https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage | |
| # https://huggingface.co/docs/diffusers/api/pipelines/marigold | |
| # Examples of trained models and live demos: | |
| # https://huggingface.co/prs-eth | |
| # Related projects: | |
| # https://rollingdepth.github.io/ | |
| # https://marigolddepthcompletion.github.io/ | |
| # Citation (BibTeX): | |
| # https://github.com/prs-eth/Marigold#-citation | |
| # If you find Marigold useful, we kindly ask you to cite our papers. | |
| # -------------------------------------------------------------------------- | |
| from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode | |
| from .base_normals_dataset import BaseNormalsDataset | |
| class ScanNetDepthDataset(BaseDepthDataset): | |
| def __init__( | |
| self, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__( | |
| # ScanNet data parameter | |
| min_depth=1e-3, | |
| max_depth=10, | |
| has_filled_depth=False, | |
| name_mode=DepthFileNameMode.id, | |
| **kwargs, | |
| ) | |
| def _read_depth_file(self, rel_path): | |
| depth_in = self._read_image(rel_path) | |
| # Decode ScanNet depth | |
| depth_decoded = depth_in / 1000.0 | |
| return depth_decoded | |
| class ScanNetNormalsDataset(BaseNormalsDataset): | |
| pass | |