nyu_depth_v2.py

# 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 os

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 = {
    "depth_estimation": {
        "train/val": "http://datasets.lids.mit.edu/fastdepth/data/nyudepthv2.tar.gz",
    }
}

_IMG_EXTENSIONS = [".h5"]


class NYUDepthV2(datasets.GeneratorBasedBuilder):
    """NYU-Depth V2 dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="depth_estimation",
            version=VERSION,
            description="The depth estimation variant.",
        ),
    ]

    DEFAULT_CONFIG_NAME = "depth_estimation"

    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 _get_file_paths(self, dir):
        # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L31-L44
        file_paths = []
        dir = os.path.expanduser(dir)

        for target in sorted(os.listdir(dir)):
            d = os.path.join(dir, target)
            if not os.path.isdir(d):
                continue
            for root, _, fnames in sorted(os.walk(d)):
                for fname in sorted(fnames):
                    if self._is_image_file(fname):
                        path = os.path.join(root, fname)
                        file_paths.append(path)

        return file_paths

    def _h5_loader(self, path):
        # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13
        h5f = h5py.File(path, "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):
        urls = _URLS[self.config.name]
        base_path = dl_manager.download_and_extract(urls)["train/val"]

        train_data_files = self._get_file_paths(
            os.path.join(base_path, "nyudepthv2", "train")
        )
        val_data_files = self._get_file_paths(os.path.join(base_path, "nyudepthv2", "val"))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepaths": train_data_files},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepaths": val_data_files},
            ),
        ]

    def _generate_examples(self, filepaths):
        for idx, filepath in enumerate(filepaths):
            image, depth = self._h5_loader(filepath)
            yield idx, {"image": image, "depth_map": depth}