| from functools import cached_property |
| from pathlib import Path |
|
|
| import datasets |
|
|
| _VERSION = "0.1.0" |
|
|
| _CITATION = """ |
| @inproceedings{5539970, |
| title = {SUN database: Large-scale scene recognition from abbey to zoo}, |
| author = {Xiao, Jianxiong and Hays, James and Ehinger, Krista A. and Oliva, Aude and Torralba, Antonio}, |
| year = 2010, |
| booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, |
| volume = {}, |
| number = {}, |
| pages = {3485--3492}, |
| doi = {10.1109/CVPR.2010.5539970}, |
| keywords = {Sun;Large-scale systems;Layout;Humans;Image databases;Computer vision;Anthropometry;Bridges;Legged locomotion;Spatial databases} |
| } |
| @article{Xiao2014SUNDE, |
| title = {SUN Database: Exploring a Large Collection of Scene Categories}, |
| author = {Jianxiong Xiao and Krista A. Ehinger and James Hays and Antonio Torralba and Aude Oliva}, |
| year = 2014, |
| journal = {International Journal of Computer Vision}, |
| volume = 119, |
| pages = {3--22}, |
| url = {https://api.semanticscholar.org/CorpusID:10224573} |
| } |
| """ |
|
|
| _DESCRIPTION = """ |
| Scene categorization is a fundamental problem in computer vision. |
| However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. |
| Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. |
| In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. |
| We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. |
| We measure human scene classification performance on the SUN database and compare this with computational methods. |
| """ |
|
|
| _HOMEPAGE = "https://vision.princeton.edu/projects/2010/SUN/" |
|
|
| _LICENSE = "" |
|
|
| _URL = "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz" |
|
|
|
|
| class SUN397(datasets.GeneratorBasedBuilder): |
| DEFAULT_WRITER_BATCH_SIZE = 1000 |
|
|
| @cached_property |
| def archive_path(self): |
| dl_manager = datasets.DownloadManager() |
| return Path(dl_manager.download_and_extract(_URL)) / "SUN397" |
|
|
| @property |
| def features(self): |
| return datasets.Features( |
| { |
| "image": datasets.Image(mode="RGB"), |
| "label": datasets.ClassLabel(names_file=self.archive_path / "ClassName.txt"), |
| } |
| ) |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| features=self.features, |
| supervised_keys=None, |
| description=_DESCRIPTION, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| version=_VERSION, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager: datasets.DownloadManager): |
| images = sorted(list(self.archive_path.rglob("*.jpg"))) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"images": images}, |
| ), |
| ] |
|
|
| def _generate_examples(self, images: list[Path]): |
| for i, image in enumerate(images): |
| yield ( |
| i, |
| { |
| "image": str(image), |
| "label": f"/{image.relative_to(self.archive_path).parent}", |
| }, |
| ) |
|
|
