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