Datasets:
Tasks:
Depth Estimation
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
ArXiv:
License:
| # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # 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. | |
| """NYU-Depth V2.""" | |
| import io | |
| import datasets | |
| import h5py | |
| import numpy as np | |
| _CITATION = """\ | |
| @inproceedings{Silberman:ECCV12, | |
| author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus}, | |
| title = {Indoor Segmentation and Support Inference from RGBD Images}, | |
| booktitle = {ECCV}, | |
| year = {2012} | |
| } | |
| @inproceedings{icra_2019_fastdepth, | |
| author = {Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne}, | |
| title = {FastDepth: Fast Monocular Depth Estimation on Embedded Systems}, | |
| booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, | |
| year = {2019} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. | |
| """ | |
| _HOMEPAGE = "https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html" | |
| _LICENSE = "Apace 2.0 License" | |
| _URLS = { | |
| "train": [f"data/train-{i:06d}.tar" for i in range(12)], | |
| "val": [f"data/val-{i:06d}.tar" for i in range(2)], | |
| } | |
| _IMG_EXTENSIONS = [".h5"] | |
| class NYUDepthV2(datasets.GeneratorBasedBuilder): | |
| """NYU-Depth V2 dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| features = datasets.Features( | |
| {"image": datasets.Image(), "depth_map": datasets.Image()} | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _is_image_file(self, filename): | |
| # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L21-L23 | |
| return any(filename.endswith(extension) for extension in _IMG_EXTENSIONS) | |
| def _h5_loader(self, bytes_stream): | |
| # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13 | |
| f = io.BytesIO(bytes_stream) | |
| h5f = h5py.File(f, "r") | |
| rgb = np.array(h5f["rgb"]) | |
| rgb = np.transpose(rgb, (1, 2, 0)) | |
| depth = np.array(h5f["depth"]) | |
| return rgb, depth | |
| def _split_generators(self, dl_manager): | |
| archives = dl_manager.download(_URLS) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "archives": [ | |
| dl_manager.iter_archive(archive) for archive in archives["train"] | |
| ] | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "archives": [ | |
| dl_manager.iter_archive(archive) for archive in archives["val"] | |
| ] | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, archives): | |
| idx = 0 | |
| for archive in archives: | |
| for path, file in archive: | |
| if self._is_image_file(path): | |
| image, depth = self._h5_loader(file.read()) | |
| yield idx, {"image": image, "depth_map": depth} | |
| idx += 1 | |