| ''' | |
| Reference: https://huggingface.co/datasets/pierresi/cord/blob/main/cord.py | |
| ''' | |
| import json | |
| import os | |
| from pathlib import Path | |
| 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 = "https://github.com/clovaai/cord/" | |
| _URLS = [ | |
| "https://hcsun.net/assets/files/CORD-1k-001.zip", | |
| "https://hcsun.net/assets/files/CORD-1k-002.zip" | |
| ] | |
| class Cord(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="cord", | |
| version=datasets.Version("1.0.0"), | |
| description="CORD dataset" | |
| ), | |
| ] | |
| def _info(self): | |
| 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"))), | |
| "ner_tags": datasets.Sequence( | |
| datasets.features.ClassLabel( | |
| 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"] | |
| ) | |
| ), | |
| "image": datasets.features.Image(), | |
| } | |
| ), | |
| supervised_keys=None, | |
| citation=_CITATION, | |
| homepage="https://github.com/clovaai/cord/", | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_file = dl_manager.download_and_extract(_URLS) | |
| dest = Path(downloaded_file[0])/"CORD" | |
| self._move_files_to_dest(downloaded_file, dest) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dest/"dev"} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test"} | |
| ), | |
| ] | |
| def _move_files_to_dest(self, downloaded_file, dest): | |
| for split in ["train", "dev", "test"]: | |
| for file_type in ["image", "json"]: | |
| if split == "test" and file_type == "json": | |
| continue | |
| files = (Path(downloaded_file[1])/"CORD"/split/file_type).iterdir() | |
| for f in files: | |
| os.rename(f, dest/split/file_type/f.name) | |
| def _generate_examples(self, 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))): | |
| file_path = os.path.join(ann_dir, file) | |
| with open(file_path, "r", encoding="utf8") as f: | |
| data = json.load(f) | |
| image_path = os.path.join(img_dir, file).replace("json", "png") | |
| image, size = self._load_image(image_path) | |
| words, bboxes, ner_tags = self._process_data(data, size) | |
| yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image": image} | |
| def _load_image(self, image_path): | |
| image = Image.open(image_path).convert("RGB") | |
| w, h = image.size | |
| return image, (w, h) | |
| def _process_data(self, data, size): | |
| words = [] | |
| bboxes = [] | |
| ner_tags = [] | |
| for item in data["valid_line"]: | |
| line_words, label = item["words"], item["category"] | |
| line_words = [w for w in line_words if w["text"].strip() != ""] | |
| if len(line_words) == 0: | |
| continue | |
| cur_line_bboxes = self._process_line_words(line_words, label, size, words, ner_tags) | |
| bboxes.extend(cur_line_bboxes) | |
| return words, bboxes, ner_tags | |
| def _process_line_words(self, line_words, label, size, words, ner_tags): | |
| cur_line_bboxes = [] | |
| if label == "other": | |
| for w in line_words: | |
| words.append(w["text"]) | |
| ner_tags.append("O") | |
| cur_line_bboxes.append(self._normalize_bbox(self._quad_to_box(w["quad"]), size)) | |
| else: | |
| words.append(line_words[0]["text"]) | |
| ner_tags.append("B-" + label.upper()) | |
| cur_line_bboxes.append(self._normalize_bbox(self._quad_to_box(line_words[0]["quad"]), size)) | |
| for w in line_words[1:]: | |
| words.append(w["text"]) | |
| ner_tags.append("I-" + label.upper()) | |
| cur_line_bboxes.append(self._normalize_bbox(self._quad_to_box(w["quad"]), size)) | |
| cur_line_bboxes = self._get_line_bbox(cur_line_bboxes) | |
| return cur_line_bboxes | |
| def _normalize_bbox(self, bbox, size): | |
| return [ | |
| int(1000 * bbox[0] / size[0]), | |
| int(1000 * bbox[1] / size[1]), | |
| int(1000 * bbox[2] / size[0]), | |
| int(1000 * bbox[3] / size[1]), | |
| ] | |
| def _quad_to_box(self, quad): | |
| box = ( | |
| max(0, quad["x1"]), | |
| max(0, quad["y1"]), | |
| quad["x3"], | |
| quad["y3"] | |
| ) | |
| if box[3] < box[1] or box[2] < box[0]: | |
| box = self._fix_box(box) | |
| return box | |
| def _fix_box(self, box): | |
| bbox = list(box) | |
| if box[3] < box[1]: | |
| bbox[1], bbox[3] = bbox[3], bbox[1] | |
| if box[2] < box[0]: | |
| bbox[0], bbox[2] = bbox[2], bbox[0] | |
| return tuple(bbox) | |
| def _get_line_bbox(self, bboxs): | |
| x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)] | |
| y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)] | |
| x0, y0, x1, y1 = min(x), min(y), max(x), max(y) | |
| assert x1 >= x0 and y1 >= y0 | |
| return [[x0, y0, x1, y1] for _ in range(len(bboxs))] |