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import json |
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import os |
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from pathlib import Path |
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import datasets |
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from PIL import Image |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{2019, |
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title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction}, |
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url={http://dx.doi.org/10.1109/ICDAR.2019.00244}, |
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DOI={10.1109/icdar.2019.00244}, |
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journal={2019 International Conference on Document Analysis and Recognition (ICDAR)}, |
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publisher={IEEE}, |
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author={Huang, Zheng and Chen, Kai and He, Jianhua and Bai, Xiang and Karatzas, Dimosthenis and Lu, Shijian and Jawahar, C. V.}, |
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year={2019}, |
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month={Sep} |
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} |
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""" |
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_DESCRIPTION = """\ |
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https://arxiv.org/abs/2103.10213 |
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""" |
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def load_image(image_path): |
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image = Image.open(image_path) |
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w, h = image.size |
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return image, (w, h) |
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def normalize_bbox(bbox, size): |
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return [ |
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int(1000 * bbox[0] / size[0]), |
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int(1000 * bbox[1] / size[1]), |
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int(1000 * bbox[2] / size[0]), |
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int(1000 * bbox[3] / size[1]), |
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] |
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def _get_drive_url(url): |
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base_url = 'https://drive.google.com/uc?id=' |
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split_url = url.split('/') |
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return base_url + split_url[5] |
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_URLS = [ |
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_get_drive_url("https://drive.google.com/file/d/1ZyxAw1d-9UvhgNLGRvsJK4gBCMf0VpGD/view?usp=sharing"), |
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] |
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class SroieConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SROIE""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for SROIE. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(SroieConfig, self).__init__(**kwargs) |
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class Sroie(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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SroieConfig(name="sroie", version=datasets.Version("1.0.0"), description="SROIE dataset"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"words": datasets.Sequence(datasets.Value("string")), |
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"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=["O","B-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"] |
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) |
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), |
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"image_path": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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homepage="https://arxiv.org/abs/2103.10213", |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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"""Uses local files located with data_dir""" |
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downloaded_file = dl_manager.download_and_extract(_URLS) |
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dest = Path(downloaded_file[0])/"sroie" |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test"} |
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), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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ann_dir = os.path.join(filepath, "tagged") |
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img_dir = os.path.join(filepath, "images") |
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for guid, fname in enumerate(sorted(os.listdir(img_dir))): |
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name, ext = os.path.splitext(fname) |
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file_path = os.path.join(ann_dir, name + ".json") |
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with open(file_path, "r", encoding="utf8") as f: |
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data = json.load(f) |
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image_path = os.path.join(img_dir, fname) |
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image, size = load_image(image_path) |
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boxes = [normalize_bbox(box, size) for box in data["bbox"]] |
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yield guid, {"id": str(guid), "words": data["words"], "bboxes": boxes, "ner_tags": data["labels"], "image_path": image_path} |
