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"""NYU Depth Dataset V2""" |
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import numpy as np |
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import h5py |
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import datasets |
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from datasets import BuilderConfig, Features, Value, SplitGenerator, Array2D, Image, Sequence |
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import hashlib |
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_CITATION = """\ |
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@inproceedings{Silberman:ECCV12, |
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author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus}, |
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title = {Indoor Segmentation and Support Inference from RGBD Images}, |
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booktitle = {Proceedings of the European Conference on Computer Vision}, |
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year = {2012} |
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} |
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""" |
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_DESCRIPTION = """\ |
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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. It features: |
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* 1449 densely labeled pairs of aligned RGB and depth images |
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* 464 new scenes taken from 3 cities |
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* 407,024 new unlabeled frames |
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* Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc) |
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This dataset is useful for various computer vision tasks, including depth estimation, semantic segmentation, and instance segmentation. |
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This Hugging Face version of the dataset is unofficial. It downloads the data from the original source and converts it to the Hugging Face format. |
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""" |
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_HOMEPAGE = "https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html" |
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_LICENSE = "Unknown" |
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_URL = "http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat" |
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_FILE_HASH = "520609c519fba3ba5ac58c8fefcc3530" |
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class NYUv2(datasets.GeneratorBasedBuilder): |
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"""NYU Depth Dataset V2""" |
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VERSION = datasets.Version("1.2.1") |
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BUILDER_CONFIGS = [ |
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BuilderConfig(name="default", version=VERSION, description="Default configuration for NYUv2 dataset"), |
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] |
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DEFAULT_CONFIG_NAME = "default" |
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def _info(self): |
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features = Features({ |
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"image": Image(decode=True), |
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"depth": Array2D(dtype="float32", shape=(480, 640)), |
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"label": Array2D(dtype="int32", shape=(480, 640)), |
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"scene": Value("string"), |
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"scene_type": Value("string"), |
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'accelData': Sequence(Value("float32"), length=4), |
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}) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_path = dl_manager.download(_URL) |
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with open(data_path, 'rb') as f: |
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file_hash = hashlib.md5(f.read()).hexdigest() |
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if file_hash != _FILE_HASH: |
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raise ValueError( |
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f"Downloaded file hash '{file_hash}' does not match expected hash '{_FILE_HASH}'. " |
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"The downloaded dataset file might be corrupted or modified." |
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) |
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return [ |
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SplitGenerator( |
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name="train", |
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gen_kwargs={ |
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"filepath": data_path, |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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with h5py.File(filepath, 'r') as f: |
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images = np.array(f['images']) |
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depths = np.array(f['depths']) |
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instances = np.array(f['instances']) |
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labels = np.array(f['labels']) |
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scenes = [''.join(chr(int(x)) for x in f[y]) for y in f.get('scenes')[0]] |
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scene_types = [''.join(chr(int(x)) for x in f[y]) for y in f.get('sceneTypes')[0]] |
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accelData = np.array(f['accelData']).T |
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for idx in range(images.shape[0]): |
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yield idx, { |
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"image": np.rot90(images[idx].transpose(1, 2, 0), -1), |
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"depth": np.rot90(depths[idx], -1), |
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"instance": instances[idx], |
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"label": np.rot90(labels[idx], -1), |
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"scene": scenes[idx], |
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"scene_type": scene_types[idx], |
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"accelData": accelData[idx], |
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} |
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