first commit

cord.py

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+# coding=utf-8
+import json
+import os
+from pathlib import Path
+
+import datasets
+from PIL import Image
+# import torch
+# from detectron2.data.transforms import ResizeTransform, TransformList
+
+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/
+"""
+
+# def load_image(image_path):
+#     image = read_image(image_path, format="BGR")
+#     h = image.shape[0]
+#     w = image.shape[1]
+#     img_trans = TransformList([ResizeTransform(h=h, w=w, new_h=224, new_w=224)])
+#     image = torch.tensor(img_trans.apply_image(image).copy()).permute(2, 0, 1)  # copy to make it writeable
+#     return image, (w, h)
+
+def load_image(image_path):
+    image = Image.open(image_path)
+    w, h = image.size
+    return image, (w, h)
+
+def normalize_bbox(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(quad):
+    # test 87 is wrongly annotated
+    box = (
+        max(0, quad["x1"]),
+        max(0, quad["y1"]),
+        quad["x3"],
+        quad["y3"]
+    )
+
+    return box
+
+def _get_drive_url(url):
+    base_url = 'https://drive.google.com/uc?id='
+    split_url = url.split('/')
+    return base_url + split_url[5]
+
+_URLS = [
+    _get_drive_url("https://drive.google.com/file/d/1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI/"),
+    _get_drive_url("https://drive.google.com/file/d/1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU/")
+]
+
+class CordConfig(datasets.BuilderConfig):
+    """BuilderConfig for CORD"""
+
+    def __init__(self, **kwargs):
+        """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 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.Array3D(shape=(3, 224, 224), dtype="uint8"),
+                    "image_path": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+            homepage="https://github.com/clovaai/cord/",
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        """Uses local files located with data_dir"""
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        # move files from the second URL together with files from the first one.
+        dest = Path(downloaded_file[0])/"CORD"
+        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)
+        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 _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))):
+            words = []
+            bboxes = []
+            ner_tags = []
+
+            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)
+            image_path = image_path.replace("json", "png")
+            image, size = load_image(image_path)
+
+            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
+                if label == "other":
+                    for w in line_words:
+                        words.append(w["text"])
+                        ner_tags.append("O")
+                        bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size))
+                else:
+                    words.append(line_words[0]["text"])
+                    ner_tags.append("B-" + label.upper())
+                    bboxes.append(normalize_bbox(quad_to_box(line_words[0]["quad"]), size))
+                    for w in line_words[1:]:
+                        words.append(w["text"])
+                        ner_tags.append("I-" + label.upper())
+                        bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size))
+
+            # yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image": image}
+            yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image_path": image_path}
+