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import json |
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import os |
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from pathlib import Path |
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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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https://github.com/clovaai/cord/ |
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""" |
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def load_image(image_path): |
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image = Image.open(image_path) |
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w, h = image.size |
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return image, (w, h) |
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def normalize_bbox(bbox, size): |
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return [ |
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int(1000 * bbox[0] / size[0]), |
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int(1000 * bbox[1] / size[1]), |
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int(1000 * bbox[2] / size[0]), |
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int(1000 * bbox[3] / size[1]), |
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] |
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def quad_to_box(quad): |
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box = ( |
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max(0, quad["x1"]), |
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max(0, quad["y1"]), |
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quad["x3"], |
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quad["y3"] |
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) |
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return box |
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def _get_drive_url(url): |
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base_url = 'https://drive.google.com/uc?id=' |
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split_url = url.split('/') |
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return base_url + split_url[5] |
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_URLS = [ |
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_get_drive_url("https://drive.google.com/file/d/1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI/"), |
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_get_drive_url("https://drive.google.com/file/d/1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU/") |
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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): |
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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(name="cord", version=datasets.Version("1.0.0"), description="CORD dataset"), |
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] |
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def _info(self): |
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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(datasets.Sequence(datasets.Value("int64"))), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=["O","B-MENU.NM","B-MENU.NUM","B-MENU.UNITPRICE","B-MENU.CNT","B-MENU.DISCOUNTPRICE","B-MENU.PRICE","B-MENU.ITEMSUBTOTAL","B-MENU.VATYN","B-MENU.ETC","B-MENU.SUB_NM","B-MENU.SUB_UNITPRICE","B-MENU.SUB_CNT","B-MENU.SUB_PRICE","B-MENU.SUB_ETC","B-VOID_MENU.NM","B-VOID_MENU.PRICE","B-SUB_TOTAL.SUBTOTAL_PRICE","B-SUB_TOTAL.DISCOUNT_PRICE","B-SUB_TOTAL.SERVICE_PRICE","B-SUB_TOTAL.OTHERSVC_PRICE","B-SUB_TOTAL.TAX_PRICE","B-SUB_TOTAL.ETC","B-TOTAL.TOTAL_PRICE","B-TOTAL.TOTAL_ETC","B-TOTAL.CASHPRICE","B-TOTAL.CHANGEPRICE","B-TOTAL.CREDITCARDPRICE","B-TOTAL.EMONEYPRICE","B-TOTAL.MENUTYPE_CNT","B-TOTAL.MENUQTY_CNT","I-MENU.NM","I-MENU.NUM","I-MENU.UNITPRICE","I-MENU.CNT","I-MENU.DISCOUNTPRICE","I-MENU.PRICE","I-MENU.ITEMSUBTOTAL","I-MENU.VATYN","I-MENU.ETC","I-MENU.SUB_NM","I-MENU.SUB_UNITPRICE","I-MENU.SUB_CNT","I-MENU.SUB_PRICE","I-MENU.SUB_ETC","I-VOID_MENU.NM","I-VOID_MENU.PRICE","I-SUB_TOTAL.SUBTOTAL_PRICE","I-SUB_TOTAL.DISCOUNT_PRICE","I-SUB_TOTAL.SERVICE_PRICE","I-SUB_TOTAL.OTHERSVC_PRICE","I-SUB_TOTAL.TAX_PRICE","I-SUB_TOTAL.ETC","I-TOTAL.TOTAL_PRICE","I-TOTAL.TOTAL_ETC","I-TOTAL.CASHPRICE","I-TOTAL.CHANGEPRICE","I-TOTAL.CREDITCARDPRICE","I-TOTAL.EMONEYPRICE","I-TOTAL.MENUTYPE_CNT","I-TOTAL.MENUQTY_CNT"] |
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) |
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), |
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"image_path": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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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): |
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"""Returns SplitGenerators.""" |
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"""Uses local files located with data_dir""" |
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downloaded_file = dl_manager.download_and_extract(_URLS) |
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dest = Path(downloaded_file[0])/"CORD" |
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for split in ["train", "dev", "test"]: |
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for file_type in ["image", "json"]: |
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if split == "test" and file_type == "json": |
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continue |
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files = (Path(downloaded_file[1])/"CORD"/split/file_type).iterdir() |
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for f in files: |
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os.rename(f, dest/split/file_type/f.name) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dest/"dev"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test"} |
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), |
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] |
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def _generate_examples(self, 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 = [] |
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bboxes = [] |
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ner_tags = [] |
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file_path = os.path.join(ann_dir, file) |
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with open(file_path, "r", encoding="utf8") as f: |
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data = json.load(f) |
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image_path = os.path.join(img_dir, file) |
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image_path = image_path.replace("json", "png") |
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image, size = load_image(image_path) |
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for item in data["valid_line"]: |
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line_words, label = item["words"], item["category"] |
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line_words = [w for w in line_words if w["text"].strip() != ""] |
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if len(line_words) == 0: |
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continue |
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if label == "other": |
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for w in line_words: |
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words.append(w["text"]) |
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ner_tags.append("O") |
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bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size)) |
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else: |
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words.append(line_words[0]["text"]) |
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ner_tags.append("B-" + label.upper()) |
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bboxes.append(normalize_bbox(quad_to_box(line_words[0]["quad"]), size)) |
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for w in line_words[1:]: |
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words.append(w["text"]) |
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ner_tags.append("I-" + label.upper()) |
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bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size)) |
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yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image_path": image_path} |
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