Upload cord.py

cord.py

@@ -0,0 +1,161 @@
+import json
+import os
+
+import datasets
+
+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://huggingface.co/datasets/katanaml/cord
+"""
+
+
+def normalize_bbox(bbox, width, height):
+    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):
+        """BuilderConfig for CORD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CordConfig, self).__init__(**kwargs)
+
+
+class Cord(datasets.GeneratorBasedBuilder):
+    """CORD dataset."""
+
+    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"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=['O',
+                                   'menu.cnt',
+                                   'menu.discountprice',
+                                   'menu.nm',
+                                   'menu.num',
+                                   'menu.price',
+                                   'menu.sub_cnt',
+                                   'menu.sub_nm',
+                                   'menu.sub_price',
+                                   'menu.unitprice',
+                                   'sub_total.discount_price',
+                                   'sub_total.etc',
+                                   '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']
+                        )
+                    ),
+                    "image_path": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/katanaml/cord",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/katanaml/cord/resolve/main/dataset.zip")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/CORD/train/"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/CORD/test/"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": f"{downloaded_file}/CORD/dev/"}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        guid = -1
+
+        replacing_labels = ['menu.etc', 'menu.itemsubtotal', 'menu.sub_etc', 'menu.sub_unitprice', 'menu.vatyn',
+                            'void_menu.nm', 'void_menu.price', 'sub_total.othersvc_price']
+
+        logger.info("⏳ Generating examples from = %s", filepath)
+        ann_dir = os.path.join(filepath, "json")
+        img_dir = os.path.join(filepath, "image")
+
+        for file in sorted(os.listdir(ann_dir)):
+            guid += 1
+            tokens = []
+            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")
+
+            width, height = data["meta"]["image_size"]["width"], data["meta"]["image_size"]["height"]
+
+            for item in data["valid_line"]:
+                for word in item['words']:
+                    # get word
+                    txt = word['text']
+
+                    # get bounding box
+                    x1 = word['quad']['x1']
+                    y1 = word['quad']['y1']
+                    x3 = word['quad']['x3']
+                    y3 = word['quad']['y3']
+
+                    box = [x1, y1, x3, y3]
+                    box = normalize_bbox(box, width=width, height=height)
+
+                    # skip empty word
+                    if len(txt) < 1:
+                        continue
+
+                    tokens.append(txt)
+                    bboxes.append(box)
+
+                    if item['category'] in replacing_labels:
+                        ner_tags.append('O')
+                    else:
+                        ner_tags.append(item['category'])
+
+            yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags,
+                         "image_path": image_path}