Datasets:
File size: 5,559 Bytes
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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() |