Delete load_script.py

load_script.py

@@ -1,186 +0,0 @@
-"""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,
-            }