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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 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_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={"data": train_data_files, "split": "training"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data": val_data_files, "split": "validation"},
+            ),
+        ]
+
+    def _generate_examples(self, filepaths):
+        for idx, filepath in enumerate(filepaths):
+            image, depth = self._h5_loader(filepath)
+            yield idx, {"image": image, "depth_map": depth}