gen_script.py

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}",
                },
            )