src/LADaS.py

import glob
import os
import datasets
from PIL import Image
import csv


_VERSION = "2024-07-17"
_URL = f"https://github.com/DEFI-COLaF/LADaS/archive/refs/tags/{_VERSION}.tar.gz"
_HOMEPAGE = "https://github.com/DEFI-COLaF/LADaS"
_LICENSE = "CC BY 4.0"
_CITATION = """\
@misc{Clerice_Layout_Analysis_Dataset,
    author = {Clérice, Thibault and Janès, Juliette and Scheithauer, Hugo and Bénière, Sarah and Romary, Laurent and Sagot, Benoit and Bougrelle, Roxane},
    title = {{Layout Analysis Dataset with SegmOnto (LADaS)}},
    url = {https://github.com/DEFI-COLaF/LADaS}
}
"""

_CATEGORIES: list[str] = ["AdvertisementZone", "DigitizationArtefactZone", "DropCapitalZone", "FigureZone",
                          "FigureZone-FigDesc", "FigureZone-Head", "GraphicZone", "GraphicZone-Decoration",
                          "GraphicZone-FigDesc", "GraphicZone-Head", "GraphicZone-Maths", "GraphicZone-Part",
                          "GraphicZone-TextualContent", "MainZone-Date", "MainZone-Entry", "MainZone-Entry-Continued",
                          "MainZone-Form", "MainZone-Head", "MainZone-Lg", "MainZone-Lg-Continued", "MainZone-List",
                          "MainZone-List-Continued", "MainZone-Other", "MainZone-P", "MainZone-P-Continued",
                          "MainZone-Signature", "MainZone-Sp", "MainZone-Sp-Continued",
                          "MarginTextZone-ManuscriptAddendum", "MarginTextZone-Notes", "MarginTextZone-Notes-Continued",
                          "NumberingZone", "TitlePageZone", "TitlePageZone-Index", "QuireMarksZone", "RunningTitleZone",
                          "StampZone", "StampZone-Sticker", "TableZone", "TableZone-Continued", "TableZone-Head"]


class LadasConfig(datasets.BuilderConfig):
    """Builder Config for LADaS"""
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)


class LadasDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version(_VERSION.replace("-", "."))
    BUILDER_CONFIGS = [
        LadasConfig(
            name="full",
            description="Full version of the dataset"
        )
    ]

    def _info(self) -> datasets.DatasetInfo:
        features = datasets.Features({
            "image_path": datasets.Value("string"),
            "year": datasets.Value("int32"),
            "dating-certainty": datasets.Value("bool"),
            "set": datasets.Value("string"),
            "image": datasets.Image(),
            "width": datasets.Value("int32"),
            "height": datasets.Value("int32"),
            "objects": datasets.Sequence(
                {
                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                    "category": datasets.Value("string"),
                }
            )
        })
        return datasets.DatasetInfo(
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):
        urls_to_download = _URL
        downloaded_files = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "local_dir": downloaded_files,
                    "split": "train"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "local_dir": downloaded_files,
                    "split": "valid"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "local_dir": downloaded_files,
                    "split": "test"
                },
            ),
        ]

    def _generate_examples(self, local_dir: str, split: str):
        idx = 0

        df = {}
        for file in glob.glob(os.path.join(local_dir, "*", "metadata.csv")):
            with open(file) as f:
                reader = csv.DictReader(f)
                for line in reader:
                   df[line["file"]] = line

        for file in glob.glob(os.path.join(local_dir, "*", "data", "*", split, "labels", "*.txt")):
            objects = []
            with open(file) as f:
                for line in f:
                    cls, *bbox = line.strip().split()
                    objects.append({"category": _CATEGORIES[int(cls)], "bbox": list(map(float, bbox))})

            image_path = os.path.normpath(file).split(os.sep)
            image_path = os.path.join(*image_path[:-2], "images", image_path[-1].replace(".txt", ".jpg"))
            if file.startswith("/") and not image_path.startswith("/"):
                image_path = "/" + image_path

            with open(image_path, "rb") as f:
                image_bytes = f.read()

            with Image.open(image_path) as im:
                width, height = im.size

            filename = os.path.basename(image_path)
            line = df[filename]

            yield idx, {
                "image_path": f"{line['subset']}/{filename}",
                "image": {"path": image_path, "bytes": image_bytes},
                "year": line["year"] or None,
                "dating-certainty": line["dating-certainty"],
                "set": line["subset"],
                "width": width,
                "height": height,
                "objects": objects,
            }
            idx += 1

if __name__ == "__main__":
    LadasDataset().download_and_prepare()