File size: 5,716 Bytes
14190bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
"""CORD: A Consolidated Receipt Dataset for Post-OCR Parsing"""
import json
import os
from pathlib import Path
from typing import Any, Generator
import datasets
from PIL import Image
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 = """\
CORD (Consolidated Receipt Dataset) with normalized bounding boxes.
"""
_URLS = [
"https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI",
"https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU",
]
_LABELS = [
"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",
]
def load_image(image_path: str) -> tuple:
image = Image.open(image_path).convert("RGB")
return image, image.size
def quad_to_bbox(quad: dict) -> list:
return [
quad["x3"],
quad["y1"],
quad["x1"],
quad["y3"],
]
def normalize_bbox(bbox: list, width: int, height: int) -> list:
return [
int(1000 * (bbox[0] / width)),
int(1000 * (bbox[1] / height)),
int(1000 * (bbox[2] / width)),
int(1000 * (bbox[3] / height)),
]
class CORDConfig(datasets.BuilderConfig):
"""BuilderConfig for CORD."""
def __init__(self, **kwargs) -> None:
"""BuilderConfig for CORD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CORDConfig, self).__init__(**kwargs)
class CORD(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
CORDConfig(
name="CORD",
version=datasets.Version("1.0.0"),
description="CORD (Consolidated Receipt Dataset)",
),
]
def _info(self) -> datasets.DatasetInfo:
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"))
),
"labels": datasets.Sequence(
datasets.features.ClassLabel(names=_LABELS)
),
"images": datasets.features.Image(),
}
),
citation=_CITATION,
homepage="https://github.com/clovaai/cord/",
)
def _split_generators(self, dl_manager) -> list:
base_dir_v1, base_dir_v2 = dl_manager.download_and_extract(_URLS)
dest_dir = Path(base_dir_v1) / "CORD"
for split_dir in ["train", "dev", "test"]:
for type_dir in ["image", "json"]:
if split_dir == "test" and type_dir == "json":
continue
files = (Path(base_dir_v2) / "CORD" / split_dir / type_dir).iterdir()
for f in files:
os.rename(f, dest_dir / split_dir / type_dir / f.name)
return [
datasets.SplitGenerator(
name=str(datasets.Split.TRAIN), gen_kwargs={"filepath": dest_dir / "train"}
),
datasets.SplitGenerator(
name=str(datasets.Split.VALIDATION), gen_kwargs={"filepath": dest_dir / "dev"},
),
datasets.SplitGenerator(
name=str(datasets.Split.TEST), gen_kwargs={"filepath": dest_dir / "test"}
),
]
def _generate_examples(self, **kwargs: Any) -> Generator:
filepath = kwargs["filepath"]
logger.info("generating examples from = %s", filepath)
ann_dir = os.path.join(filepath, "json")
img_dir = os.path.join(filepath, "image")
for guid, file in enumerate(sorted(os.listdir(ann_dir))):
WORDS, BBOXES, LABELS = [], [], []
file_path = os.path.join(ann_dir, file)
f = open(file_path)
data = json.load(f)
image_path = os.path.join(img_dir, file).replace("json", "png")
image, (width, height) = load_image(image_path)
for annotation in data["valid_line"]:
label, words = annotation["category"], annotation["words"]
for word in words:
bbox = normalize_bbox(
quad_to_bbox(word["quad"]), width=width, height=height
)
if min(bbox) >= 0 and max(bbox) <= 1000:
WORDS.append(word["text"])
BBOXES.append(bbox)
LABELS.append(label)
yield guid, {
"id": str(guid),
"images": image,
"words": WORDS,
"bboxes": BBOXES,
"labels": LABELS,
}
|