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README.md

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----
-license: apache-2.0
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: depth_map
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 20212097551
-    num_examples: 47584
-  - name: validation
-    num_bytes: 240785762
-    num_examples: 654
-  download_size: 35151124480
-  dataset_size: 20452883313
----

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nyu_depth_v2.py

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-# 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