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sroie.py
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# coding=utf-8
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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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# import torch
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# from detectron2.data.transforms import ResizeTransform, TransformList
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@article{park2019cord,
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title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
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author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
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booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
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year={2019}
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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/1O01zsApd5z5IXYUelKhBC4idaY3Zg4KA/"),
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]
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class CordConfig(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(CordConfig, self).__init__(**kwargs)
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class SROIE(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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CordConfig(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": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
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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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# move files from the second URL together with files from the first one.
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dest = Path(downloaded_file[0])/"CORD"
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for split in ["train", "test"]:
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for file_type in ["image", "tagged"]:
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# if split == "test" and file_type == "json":
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# continue
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files = (Path(downloaded_file[1])/"sroie"/split/file_type).iterdir()
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for f in files:
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os.rename(f, dest/split/file_type/f.name)
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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, "image")
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for guid, file in enumerate(sorted(os.listdir(ann_dir))):
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file_path = os.path.join(ann_dir, file)
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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, file)
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image_path = image_path.replace("json", "png")
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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}
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