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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
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