File size: 4,593 Bytes
d79b683 e9d5257 d79b683 e9d5257 d79b683 |
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 |
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{LayoutLmv3 for CV extractions,
title={LayoutLmv3for Key Information Extraction},
author={MisaR&D Team},
year={2022},
}
"""
_DESCRIPTION = """\
CV is a collection of receipts. It contains, for each photo about cv personal, a list of OCRs - with the bounding box, text, and class. 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]),
]
def _get_drive_url(url):
base_url = 'https://drive.google.com/uc?id='
split_url = url.split('/')
return base_url + split_url[7]
_URLS = [
_get_drive_url("https://drive.google.com/drive/u/1/folders/1BBW2WicUqfZ5ck50N1AYY0krJoSJJQly"),
]
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="CV Extractions", version=datasets.Version("1.0.0"), description="CV 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=['person_name', 'dob_key', 'dob_value', 'gender_key', 'gender_value', 'phonenumber_key', 'phonenumber_value', 'email_key', 'email_value', 'address_key', 'address_value', 'socical_address_value', 'education', 'education_name', 'education_time', 'experience', 'experience_name', 'experience_time', 'information', 'undefined']
)
),
"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])/'data1'
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']]
boxes = [normalize_bbox(box, size) for box in boxes]
flag=0
for i in boxes:
for j in i:
if j>1000:
flag+=1
pass
if flag>0: print(image_path)
yield guid, {"id": str(guid), "words": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path} |