Init load script

cord.py

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+"""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,
+            }