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.gitattributes

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generated.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{2019,
-   title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction},
-   url={http://dx.doi.org/10.1109/ICDAR.2019.00244},
-   DOI={10.1109/icdar.2019.00244},
-   journal={2019 International Conference on Document Analysis and Recognition (ICDAR)},
-   publisher={IEEE},
-   author={Huang, Zheng and Chen, Kai and He, Jianhua and Bai, Xiang and Karatzas, Dimosthenis and Lu, Shijian and Jawahar, C. V.},
-   year={2019},
-   month={Sep}
-}
-"""
-_DESCRIPTION = """\
-https://arxiv.org/abs/2103.10213
-"""
-
-
-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 _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/1FFNNKBzBXgGc8h8Du_hxkJblgQJO3Foe/view?usp=sharing"),
-]
-
-
-class SroieConfig(datasets.BuilderConfig):
-    """BuilderConfig for SROIE"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for SROIE.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(SroieConfig, self).__init__(**kwargs)
-
-
-class Sroie(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        SroieConfig(name="sroie", version=datasets.Version(
-            "1.0.0"), description="SROIE 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-ABN', 'B-BILLER', 'B-BILLER_ADDRESS', 'B-BILLER_POST_CODE', 'B-DUE_DATE',
-                                   'B-GST', 'B-INVOICE_DATE', 'B-INVOICE_NUMBER', 'B-SUBTOTAL', 'B-TOTAL', 'I-BILLER_ADDRESS']
-                        )
-                    ),
-                    # "image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
-                    "image_path": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            homepage="https://arxiv.org/abs/2103.10213",
-        )
-
-    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])/"generated"
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={
-                    "filepath": dest/"train"}
-            ),
-            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, "tagged")
-        img_dir = os.path.join(filepath, "images")
-        for guid, fname in enumerate(sorted(os.listdir(img_dir))):
-            name, ext = os.path.splitext(fname)
-            file_path = os.path.join(ann_dir, name + ".json")
-            with open(file_path, "r", encoding="utf8") as f:
-                data = json.load(f)
-            image_path = os.path.join(img_dir, fname)
-
-            image, size = load_image(image_path)
-
-            boxes = [normalize_bbox(box, size) for box in data["bbox"]]
-
-            yield guid, {"id": str(guid), "words": data["words"], "bboxes": boxes, "ner_tags": data["labels"], "image_path": image_path}

sroie/generated-test.parquet

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sroie/generated-train.parquet

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