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# coding=utf-8
import json
import os
import datasets
import gdown
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://github.com/clovaai/cord
"""
_URL = "https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI"
def gdrive_downloader(url, path):
gdown.download(url, path, quiet=False)
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):
"""Conll2003 dataset."""
BUILDER_CONFIGS = [
CordConfig(name="cord", version=datasets.Version(
"1.0.0"), description="FUNSD 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"))),
"roi": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=['menu.cnt',
'menu.discountprice',
'menu.etc',
'menu.itemsubtotal',
'menu.nm',
'menu.num',
'menu.price',
'menu.sub_cnt',
'menu.sub_etc',
'menu.sub_nm',
'menu.sub_price',
'menu.sub_unitprice',
'menu.unitprice',
'menu.vatyn',
'sub_total.discount_price',
'sub_total.etc',
'sub_total.othersvc_price',
'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',
'void_menu.nm',
'void_menu.price']
)
),
"image_path": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/clovaai/cord",
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""Returns SplitGenerators."""
url_or_urls = ['https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI',
'https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU']
downloaded_file = dl_manager.extract(
dl_manager.download_custom(url_or_urls, gdrive_downloader))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={
"filepaths": downloaded_file, "mode": "/CORD/train"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={
"filepaths": downloaded_file, "mode": "/CORD/test"}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={
"filepaths": downloaded_file, "mode": "/CORD/dev"}
),
]
def _generate_examples(self, filepaths, mode):
guid = -1
for filepath in filepaths:
filepath_folder = filepath + mode
logger.info("⏳ Generating examples from = %s", filepath_folder)
ann_dir = os.path.join(filepath_folder, "json")
if not os.path.exists(ann_dir):
continue
img_dir = os.path.join(filepath_folder, "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")
if not os.path.exists(image_path):
other_dir_idx = int(not (filepaths.index(filepath)+2)%2)
image_path = image_path.replace(
filepath, filepaths[other_dir_idx])
roi = data["roi"]
if roi:
top_left = [roi["x1"], roi["y1"]]
bottom_right = [roi["x3"], roi["y3"]]
bottom_left = [roi["x4"], roi["y4"]]
top_right = [roi["x2"], roi["y2"]]
roi = [top_left, top_right, bottom_right, bottom_left]
else:
roi = []
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]
# ADDED
# skip empty word
if len(txt) < 1:
continue
tokens.append(txt)
bboxes.append(box)
ner_tags.append(item['category'])
yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags, "image_path": image_path, "roi":roi}
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