Upload cord.py

cord.py

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+'''
+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))]