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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{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/
"""
# def load_image(image_path):
# image = read_image(image_path, format="BGR")
# h = image.shape[0]
# w = image.shape[1]
# img_trans = TransformList([ResizeTransform(h=h, w=w, new_h=224, new_w=224)])
# image = torch.tensor(img_trans.apply_image(image).copy()).permute(2, 0, 1) # copy to make it writeable
# return image, (w, h)
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 quad_to_box(quad):
# test 87 is wrongly annotated
box = (
max(0, quad["x1"]),
max(0, quad["y1"]),
quad["x3"],
quad["y3"]
)
return box
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/1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI/"),
_get_drive_url("https://drive.google.com/file/d/1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU/")
]
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):
BUILDER_CONFIGS = [
CordConfig(name="cord", version=datasets.Version("1.0.0"), description="CORD 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-MENU.NM","B-MENU.NUM","B-MENU.UNITPRICE","B-MENU.CNT","B-MENU.DISCOUNTPRICE","B-MENU.PRICE","B-MENU.ITEMSUBTOTAL","B-MENU.VATYN","B-MENU.ETC","B-MENU.SUB_NM","B-MENU.SUB_UNITPRICE","B-MENU.SUB_CNT","B-MENU.SUB_PRICE","B-MENU.SUB_ETC","B-VOID_MENU.NM","B-VOID_MENU.PRICE","B-SUB_TOTAL.SUBTOTAL_PRICE","B-SUB_TOTAL.DISCOUNT_PRICE","B-SUB_TOTAL.SERVICE_PRICE","B-SUB_TOTAL.OTHERSVC_PRICE","B-SUB_TOTAL.TAX_PRICE","B-SUB_TOTAL.ETC","B-TOTAL.TOTAL_PRICE","B-TOTAL.TOTAL_ETC","B-TOTAL.CASHPRICE","B-TOTAL.CHANGEPRICE","B-TOTAL.CREDITCARDPRICE","B-TOTAL.EMONEYPRICE","B-TOTAL.MENUTYPE_CNT","B-TOTAL.MENUQTY_CNT","I-MENU.NM","I-MENU.NUM","I-MENU.UNITPRICE","I-MENU.CNT","I-MENU.DISCOUNTPRICE","I-MENU.PRICE","I-MENU.ITEMSUBTOTAL","I-MENU.VATYN","I-MENU.ETC","I-MENU.SUB_NM","I-MENU.SUB_UNITPRICE","I-MENU.SUB_CNT","I-MENU.SUB_PRICE","I-MENU.SUB_ETC","I-VOID_MENU.NM","I-VOID_MENU.PRICE","I-SUB_TOTAL.SUBTOTAL_PRICE","I-SUB_TOTAL.DISCOUNT_PRICE","I-SUB_TOTAL.SERVICE_PRICE","I-SUB_TOTAL.OTHERSVC_PRICE","I-SUB_TOTAL.TAX_PRICE","I-SUB_TOTAL.ETC","I-TOTAL.TOTAL_PRICE","I-TOTAL.TOTAL_ETC","I-TOTAL.CASHPRICE","I-TOTAL.CHANGEPRICE","I-TOTAL.CREDITCARDPRICE","I-TOTAL.EMONEYPRICE","I-TOTAL.MENUTYPE_CNT","I-TOTAL.MENUQTY_CNT"]
)
),
#"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
"image_path": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
homepage="https://github.com/clovaai/cord/",
)
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])/"CORD"
for split in ["train", "dev", "test"]:
for file_type in ["image", "json"]:
if split == "test" and file_type == "json":
continue
files = (Path(downloaded_file[1])/"CORD"/split/file_type).iterdir()
for f in files:
os.rename(f, dest/split/file_type/f.name)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dest/"dev"}
),
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, "json")
img_dir = os.path.join(filepath, "image")
for guid, file in enumerate(sorted(os.listdir(ann_dir))):
words = []
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")
image, size = load_image(image_path)
for item in data["valid_line"]:
line_words, label = item["words"], item["category"]
line_words = [w for w in line_words if w["text"].strip() != ""]
if len(line_words) == 0:
continue
if label == "other":
for w in line_words:
words.append(w["text"])
ner_tags.append("O")
bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size))
else:
words.append(line_words[0]["text"])
ner_tags.append("B-" + label.upper())
bboxes.append(normalize_bbox(quad_to_box(line_words[0]["quad"]), size))
for w in line_words[1:]:
words.append(w["text"])
ner_tags.append("I-" + label.upper())
bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size))
# yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image": image}
yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image_path": image_path}
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