Datasets:

wkrl
/

cord

	"""CORD: A Consolidated Receipt Dataset for Post-OCR Parsing"""


	import json
	import os
	from pathlib import Path
	from typing import Any, Generator

	import datasets
	from PIL import Image

	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{park2019cord,
	title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
	author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
	booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	CORD (Consolidated Receipt Dataset) with normalized bounding boxes.
	"""

	_URLS = [
	"https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI",
	"https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU",
	]

	_LABELS = [
	"menu.cnt",
	"menu.discountprice",
	"menu.etc",
	"menu.itemsubtotal",
	"menu.nm",
	"menu.num",
	"menu.price",
	"menu.sub_cnt",
	"menu.sub_etc",
	"menu.sub_nm",
	"menu.sub_price",
	"menu.sub_unitprice",
	"menu.unitprice",
	"menu.vatyn",
	"sub_total.discount_price",
	"sub_total.etc",
	"sub_total.othersvc_price",
	"sub_total.service_price",
	"sub_total.subtotal_price",
	"sub_total.tax_price",
	"total.cashprice",
	"total.changeprice",
	"total.creditcardprice",
	"total.emoneyprice",
	"total.menuqty_cnt",
	"total.menutype_cnt",
	"total.total_etc",
	"total.total_price",
	"void_menu.nm",
	"void_menu.price",
	]


	def load_image(image_path: str) -> tuple:
	image = Image.open(image_path).convert("RGB")
	return image, image.size


	def quad_to_bbox(quad: dict) -> list:
	return [
	quad["x3"],
	quad["y1"],
	quad["x1"],
	quad["y3"],
	]


	def normalize_bbox(bbox: list, width: int, height: int) -> list:
	return [
	int(1000 * (bbox[0] / width)),
	int(1000 * (bbox[1] / height)),
	int(1000 * (bbox[2] / width)),
	int(1000 * (bbox[3] / height)),
	]


	class CORDConfig(datasets.BuilderConfig):
	"""BuilderConfig for CORD."""

	def __init__(self, **kwargs) -> None:
	"""BuilderConfig for CORD.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(CORDConfig, self).__init__(**kwargs)


	class CORD(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	CORDConfig(
	name="CORD",
	version=datasets.Version("1.0.0"),
	description="CORD (Consolidated Receipt Dataset)",
	),
	]

	def _info(self) -> datasets.DatasetInfo:
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"words": datasets.Sequence(datasets.Value("string")),
	"bboxes": datasets.Sequence(
	datasets.Sequence(datasets.Value("int64"))
	),
	"labels": datasets.Sequence(
	datasets.features.ClassLabel(names=_LABELS)
	),
	"images": datasets.features.Image(),
	}
	),
	citation=_CITATION,
	homepage="https://github.com/clovaai/cord/",
	)

	def _split_generators(self, dl_manager) -> list:
	base_dir_v1, base_dir_v2 = dl_manager.download_and_extract(_URLS)
	dest_dir = Path(base_dir_v1) / "CORD"

	for split_dir in ["train", "dev", "test"]:
	for type_dir in ["image", "json"]:
	if split_dir == "test" and type_dir == "json":
	continue
	files = (Path(base_dir_v2) / "CORD" / split_dir / type_dir).iterdir()
	for f in files:
	os.rename(f, dest_dir / split_dir / type_dir / f.name)

	return [
	datasets.SplitGenerator(
	name=str(datasets.Split.TRAIN), gen_kwargs={"filepath": dest_dir / "train"}
	),
	datasets.SplitGenerator(
	name=str(datasets.Split.VALIDATION), gen_kwargs={"filepath": dest_dir / "dev"},
	),
	datasets.SplitGenerator(
	name=str(datasets.Split.TEST), gen_kwargs={"filepath": dest_dir / "test"}
	),
	]

	def _generate_examples(self, **kwargs: Any) -> Generator:
	filepath = kwargs["filepath"]
	logger.info("generating examples from = %s", filepath)
	ann_dir = os.path.join(filepath, "json")
	img_dir = os.path.join(filepath, "image")

	for guid, file in enumerate(sorted(os.listdir(ann_dir))):
	WORDS, BBOXES, LABELS = [], [], []
	file_path = os.path.join(ann_dir, file)
	f = open(file_path)
	data = json.load(f)

	image_path = os.path.join(img_dir, file).replace("json", "png")
	image, (width, height) = load_image(image_path)

	for annotation in data["valid_line"]:
	label, words = annotation["category"], annotation["words"]
	for word in words:
	bbox = normalize_bbox(
	quad_to_bbox(word["quad"]), width=width, height=height
	)
	if min(bbox) >= 0 and max(bbox) <= 1000:
	WORDS.append(word["text"])
	BBOXES.append(bbox)
	LABELS.append(label)

	yield guid, {
	"id": str(guid),
	"images": image,
	"words": WORDS,
	"bboxes": BBOXES,
	"labels": LABELS,
	}