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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/1ZyxAw1d-9UvhgNLGRvsJK4gBCMf0VpGD/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-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"]
)
),
#"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])/"sroie"
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} |