|
''' |
|
Reference: https://huggingface.co/datasets/pierresi/cord/blob/main/cord.py |
|
''' |
|
|
|
|
|
import json |
|
import os |
|
from pathlib import Path |
|
|
|
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 = """\ |
|
https://github.com/clovaai/cord/ |
|
""" |
|
|
|
def load_image(image_path): |
|
image = Image.open(image_path).convert("RGB") |
|
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): |
|
|
|
box = ( |
|
max(0, quad["x1"]), |
|
max(0, quad["y1"]), |
|
quad["x3"], |
|
quad["y3"] |
|
) |
|
if box[3] < box[1]: |
|
bbox = list(box) |
|
tmp = bbox[3] |
|
bbox[3] = bbox[1] |
|
bbox[1] = tmp |
|
box = tuple(bbox) |
|
if box[2] < box[0]: |
|
bbox = list(box) |
|
tmp = bbox[2] |
|
bbox[2] = bbox[0] |
|
bbox[0] = tmp |
|
box = tuple(bbox) |
|
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.features.Image(), |
|
} |
|
), |
|
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) |
|
|
|
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 get_line_bbox(self, bboxs): |
|
x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)] |
|
y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)] |
|
|
|
x0, y0, x1, y1 = min(x), min(y), max(x), max(y) |
|
|
|
assert x1 >= x0 and y1 >= y0 |
|
bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))] |
|
return bbox |
|
|
|
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"]: |
|
cur_line_bboxes = [] |
|
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") |
|
cur_line_bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size)) |
|
else: |
|
words.append(line_words[0]["text"]) |
|
ner_tags.append("B-" + label.upper()) |
|
cur_line_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()) |
|
cur_line_bboxes.append(normalize_bbox(quad_to_box(w["quad"]), size)) |
|
|
|
|
|
cur_line_bboxes = self.get_line_bbox(cur_line_bboxes) |
|
bboxes.extend(cur_line_bboxes) |
|
|
|
yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, |
|
"image": image} |
