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"""CORD: A Consolidated Receipt Dataset for Post-OCR Parsing""" |
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import json |
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import os |
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from pathlib import Path |
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from typing import Any, Generator |
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import datasets |
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from PIL import Image |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{park2019cord, |
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title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing}, |
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author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk} |
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booktitle={Document Intelligence Workshop at Neural Information Processing Systems} |
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year={2019} |
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} |
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""" |
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_DESCRIPTION = """\ |
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CORD (Consolidated Receipt Dataset) with normalized bounding boxes. |
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""" |
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_URLS = [ |
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"https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI", |
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"https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU", |
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] |
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_LABELS = [ |
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"menu.cnt", |
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"menu.discountprice", |
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"menu.etc", |
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"menu.itemsubtotal", |
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"menu.nm", |
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"menu.num", |
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"menu.price", |
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"menu.sub_cnt", |
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"menu.sub_etc", |
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"menu.sub_nm", |
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"menu.sub_price", |
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"menu.sub_unitprice", |
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"menu.unitprice", |
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"menu.vatyn", |
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"sub_total.discount_price", |
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"sub_total.etc", |
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"sub_total.othersvc_price", |
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"sub_total.service_price", |
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"sub_total.subtotal_price", |
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"sub_total.tax_price", |
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"total.cashprice", |
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"total.changeprice", |
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"total.creditcardprice", |
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"total.emoneyprice", |
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"total.menuqty_cnt", |
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"total.menutype_cnt", |
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"total.total_etc", |
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"total.total_price", |
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"void_menu.nm", |
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"void_menu.price", |
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] |
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def load_image(image_path: str) -> tuple: |
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image = Image.open(image_path).convert("RGB") |
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return image, image.size |
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def quad_to_bbox(quad: dict) -> list: |
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return [ |
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quad["x3"], |
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quad["y1"], |
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quad["x1"], |
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quad["y3"], |
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] |
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def normalize_bbox(bbox: list, width: int, height: int) -> list: |
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return [ |
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int(1000 * (bbox[0] / width)), |
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int(1000 * (bbox[1] / height)), |
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int(1000 * (bbox[2] / width)), |
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int(1000 * (bbox[3] / height)), |
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] |
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class CORDConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CORD.""" |
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def __init__(self, **kwargs) -> None: |
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"""BuilderConfig for CORD. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CORDConfig, self).__init__(**kwargs) |
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class CORD(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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CORDConfig( |
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name="CORD", |
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version=datasets.Version("1.0.0"), |
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description="CORD (Consolidated Receipt Dataset)", |
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), |
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] |
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def _info(self) -> datasets.DatasetInfo: |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"words": datasets.Sequence(datasets.Value("string")), |
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"bboxes": datasets.Sequence( |
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datasets.Sequence(datasets.Value("int64")) |
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), |
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"labels": datasets.Sequence( |
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datasets.features.ClassLabel(names=_LABELS) |
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), |
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"images": datasets.features.Image(), |
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} |
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), |
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citation=_CITATION, |
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homepage="https://github.com/clovaai/cord/", |
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) |
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def _split_generators(self, dl_manager) -> list: |
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base_dir_v1, base_dir_v2 = dl_manager.download_and_extract(_URLS) |
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dest_dir = Path(base_dir_v1) / "CORD" |
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for split_dir in ["train", "dev", "test"]: |
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for type_dir in ["image", "json"]: |
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if split_dir == "test" and type_dir == "json": |
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continue |
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files = (Path(base_dir_v2) / "CORD" / split_dir / type_dir).iterdir() |
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for f in files: |
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os.rename(f, dest_dir / split_dir / type_dir / f.name) |
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return [ |
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datasets.SplitGenerator( |
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name=str(datasets.Split.TRAIN), gen_kwargs={"filepath": dest_dir / "train"} |
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), |
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datasets.SplitGenerator( |
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name=str(datasets.Split.VALIDATION), gen_kwargs={"filepath": dest_dir / "dev"}, |
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), |
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datasets.SplitGenerator( |
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name=str(datasets.Split.TEST), gen_kwargs={"filepath": dest_dir / "test"} |
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), |
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] |
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def _generate_examples(self, **kwargs: Any) -> Generator: |
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filepath = kwargs["filepath"] |
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logger.info("generating examples from = %s", filepath) |
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ann_dir = os.path.join(filepath, "json") |
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img_dir = os.path.join(filepath, "image") |
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for guid, file in enumerate(sorted(os.listdir(ann_dir))): |
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WORDS, BBOXES, LABELS = [], [], [] |
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file_path = os.path.join(ann_dir, file) |
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f = open(file_path) |
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data = json.load(f) |
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image_path = os.path.join(img_dir, file).replace("json", "png") |
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image, (width, height) = load_image(image_path) |
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for annotation in data["valid_line"]: |
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label, words = annotation["category"], annotation["words"] |
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for word in words: |
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bbox = normalize_bbox( |
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quad_to_bbox(word["quad"]), width=width, height=height |
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) |
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if min(bbox) >= 0 and max(bbox) <= 1000: |
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WORDS.append(word["text"]) |
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BBOXES.append(bbox) |
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LABELS.append(label) |
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yield guid, { |
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"id": str(guid), |
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"images": image, |
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"words": WORDS, |
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"bboxes": BBOXES, |
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"labels": LABELS, |
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} |
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