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import json
import os
from pathlib import Path
import datasets
from PIL import Image
import pandas as pd
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{Sun2021SpatialDG,
title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction},
author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang},
journal={ArXiv},
year={2021},
volume={abs/2103.14470}
}
"""
_DESCRIPTION = """\
WildReceipt is a collection of receipts. It contains, for each photo, a list of OCRs - with the bounding box, text, and class. It contains 1765 photos, with 25 classes, and 50000 text boxes. The goal is to benchmark "key information extraction" - extracting key information from documents
https://arxiv.org/abs/2103.14470
"""
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]),
]
_URLS = ["https://download.openmmlab.com/mmocr/data/wildreceipt.tar"]
class DatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for WildReceipt Dataset"""
def __init__(self, **kwargs):
"""BuilderConfig for WildReceipt Dataset.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(DatasetConfig, self).__init__(**kwargs)
class WildReceipt(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
DatasetConfig(name="WildReceipt", version=datasets.Version("1.0.0"), description="WildReceipt 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 = ['Ignore', 'Store_name_value', 'Store_name_key', 'Store_addr_value', 'Store_addr_key', 'Tel_value', 'Tel_key', 'Date_value', 'Date_key', 'Time_value', 'Time_key', 'Prod_item_value', 'Prod_item_key', 'Prod_quantity_value', 'Prod_quantity_key', 'Prod_price_value', 'Prod_price_key', 'Subtotal_value', 'Subtotal_key', 'Tax_value', 'Tax_key', 'Tips_value', 'Tips_key', 'Total_value', 'Total_key', 'Others']
)
),
"image_path": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
homepage="",
)
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])/'wildreceipt'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train.txt", "dest": dest}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test.txt", "dest": dest}
),
]
def _generate_examples(self, filepath, dest):
df = pd.read_csv(dest/'class_list.txt', delimiter='\s', header=None)
id2labels = dict(zip(df[0].tolist(), df[1].tolist()))
logger.info("⏳ Generating examples from = %s", filepath)
item_list = []
with open(filepath, 'r') as f:
for line in f:
item_list.append(line.rstrip('\n\r'))
for guid, fname in enumerate(item_list):
data = json.loads(fname)
image_path = dest/data['file_name']
image, size = load_image(image_path)
boxes = [[i['box'][6], i['box'][7], i['box'][2], i['box'][3]] for i in data['annotations']]
text = [i['text'] for i in data['annotations']]
label = [id2labels[i['label']] for i in data['annotations']]
#print(boxes)
#for i in boxes:
# print(i)
boxes = [normalize_bbox(box, size) for box in boxes]
flag=0
#print(image_path)
for i in boxes:
#print(i)
for j in i:
if j>1000:
flag+=1
#print(j)
pass
if flag>0: print(image_path)
yield guid, {"id": str(guid), "words": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path} |