Datasets:
Tasks:
Depth Estimation
Modalities:
Image
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
depth-estimation
License:
loading of multiple shards
#5
by
sayakpaul
HF staff
- opened
- README.md +16 -1
- nyu_depth_v2.py +25 -46
README.md
CHANGED
@@ -1,3 +1,18 @@
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---
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license: apache-2.0
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: image
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dtype: image
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- name: depth_map
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dtype: image
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splits:
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- name: train
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num_bytes: 20212097551
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num_examples: 47584
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- name: validation
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num_bytes: 240785762
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num_examples: 654
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download_size: 35151124480
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dataset_size: 20452883313
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---
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nyu_depth_v2.py
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"""NYU-Depth V2."""
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import
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import datasets
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import h5py
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@@ -44,9 +44,8 @@ _HOMEPAGE = "https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html"
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_LICENSE = "Apace 2.0 License"
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_URLS = {
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"
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}
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}
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_IMG_EXTENSIONS = [".h5"]
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@@ -57,16 +56,6 @@ class NYUDepthV2(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="depth_estimation",
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version=VERSION,
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description="The depth estimation variant.",
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),
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]
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DEFAULT_CONFIG_NAME = "depth_estimation"
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def _info(self):
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features = datasets.Features(
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{"image": datasets.Image(), "depth_map": datasets.Image()}
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# Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L21-L23
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return any(filename.endswith(extension) for extension in _IMG_EXTENSIONS)
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def
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# Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L31-L44
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file_paths = []
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dir = os.path.expanduser(dir)
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for target in sorted(os.listdir(dir)):
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d = os.path.join(dir, target)
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if not os.path.isdir(d):
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continue
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for root, _, fnames in sorted(os.walk(d)):
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for fname in sorted(fnames):
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if self._is_image_file(fname):
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path = os.path.join(root, fname)
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file_paths.append(path)
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return file_paths
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def _h5_loader(self, path):
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# Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13
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rgb = np.array(h5f["rgb"])
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rgb = np.transpose(rgb, (1, 2, 0))
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depth = np.array(h5f["depth"])
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return rgb, depth
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def _split_generators(self, dl_manager):
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base_path = dl_manager.download_and_extract(urls)["train/val"]
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train_data_files = self._get_file_paths(
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os.path.join(base_path, "nyudepthv2", "train")
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)
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val_data_files = self._get_file_paths(os.path.join(base_path, "nyudepthv2", "val"))
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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),
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]
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def _generate_examples(self,
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"""NYU-Depth V2."""
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import io
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import datasets
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import h5py
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_LICENSE = "Apace 2.0 License"
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_URLS = {
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"train": [f"data/train-{i:06d}.tar" for i in range(12)],
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"val": [f"data/val-{i:06d}.tar" for i in range(2)],
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}
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_IMG_EXTENSIONS = [".h5"]
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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features = datasets.Features(
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{"image": datasets.Image(), "depth_map": datasets.Image()}
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# Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L21-L23
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return any(filename.endswith(extension) for extension in _IMG_EXTENSIONS)
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def _h5_loader(self, bytes_stream):
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# Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13
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f = io.BytesIO(bytes_stream)
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h5f = h5py.File(f, "r")
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rgb = np.array(h5f["rgb"])
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rgb = np.transpose(rgb, (1, 2, 0))
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depth = np.array(h5f["depth"])
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return rgb, depth
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def _split_generators(self, dl_manager):
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archives = dl_manager.download(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"archives": [
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dl_manager.iter_archive(archive) for archive in archives["train"]
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]
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"archives": [
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dl_manager.iter_archive(archive) for archive in archives["val"]
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]
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},
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),
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]
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def _generate_examples(self, archives):
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idx = 0
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for archive in archives:
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for path, file in archive:
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if self._is_image_file(path):
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image, depth = self._h5_loader(file.read())
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yield idx, {"image": image, "depth_map": depth}
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idx += 1
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