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import json |
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import os |
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import datasets |
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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://huggingface.co/datasets/katanaml/cord |
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""" |
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def normalize_bbox(bbox, width, height): |
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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): |
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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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"""CORD dataset.""" |
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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', |
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'I-menu.cnt', |
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'I-menu.discountprice', |
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'I-menu.nm', |
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'I-menu.num', |
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'I-menu.price', |
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'I-menu.sub_cnt', |
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'I-menu.sub_nm', |
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'I-menu.sub_price', |
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'I-menu.unitprice', |
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'I-sub_total.discount_price', |
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'I-sub_total.etc', |
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'I-sub_total.service_price', |
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'I-sub_total.subtotal_price', |
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'I-sub_total.tax_price', |
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'I-total.cashprice', |
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'I-total.changeprice', |
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'I-total.creditcardprice', |
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'I-total.emoneyprice', |
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'I-total.menuqty_cnt', |
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'I-total.menutype_cnt', |
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'I-total.total_etc', |
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'I-total.total_price'] |
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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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homepage="https://huggingface.co/datasets/katanaml/cord", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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downloaded_file = dl_manager.download_and_extract( |
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"https://huggingface.co/datasets/katanaml/cord/resolve/main/dataset.zip") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/CORD/train/"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/CORD/test/"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": f"{downloaded_file}/CORD/dev/"} |
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), |
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] |
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def _generate_examples(self, filepath): |
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guid = -1 |
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replacing_labels = ['menu.etc', 'menu.itemsubtotal', 'menu.sub_etc', 'menu.sub_unitprice', 'menu.vatyn', |
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'void_menu.nm', 'void_menu.price', 'sub_total.othersvc_price'] |
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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 file in sorted(os.listdir(ann_dir)): |
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guid += 1 |
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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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width, height = data["meta"]["image_size"]["width"], data["meta"]["image_size"]["height"] |
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image_id = data["meta"]["image_id"] |
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for item in data["valid_line"]: |
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for word in item['words']: |
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txt = word['text'] |
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x1 = abs(word['quad']['x1']) |
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y1 = abs(word['quad']['y1']) |
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x3 = abs(word['quad']['x3']) |
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y3 = abs(word['quad']['y3']) |
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x1 = width if x1 > width else x1 |
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y1 = height if y1 > height else y1 |
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x3 = width if x3 > width else x3 |
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y3 = height if y3 > height else y3 |
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box = [x1, y1, x3, y3] |
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box = normalize_bbox(box, width=width, height=height) |
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if len(txt) < 1: |
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continue |
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words.append(txt) |
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bboxes.append(box) |
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if item['category'] in replacing_labels: |
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ner_tags.append('O') |
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else: |
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ner_tags.append('I-' + item['category']) |
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yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, |
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"image_path": image_path} |
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