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import json |
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import os |
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from pathlib import Path |
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import datasets |
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from PIL import Image |
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import pandas as pd |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{Sun2021SpatialDG, |
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title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction}, |
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author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang}, |
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journal={ArXiv}, |
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year={2021}, |
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volume={abs/2103.14470} |
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} |
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""" |
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_DESCRIPTION = """\ |
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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 |
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https://arxiv.org/abs/2103.14470 |
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""" |
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def load_image(image_path): |
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image = Image.open(image_path) |
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w, h = image.size |
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return image, (w,h) |
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def normalize_bbox(bbox, size): |
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return [ |
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int(1000 * bbox[0] / size[0]), |
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int(1000 * bbox[1] / size[1]), |
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int(1000 * bbox[2] / size[0]), |
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int(1000 * bbox[3] / size[1]), |
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] |
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_URLS = ["https://download.openmmlab.com/mmocr/data/wildreceipt.tar"] |
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class DatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for WildReceipt Dataset""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for WildReceipt Dataset. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(DatasetConfig, self).__init__(**kwargs) |
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class WildReceipt(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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DatasetConfig(name="WildReceipt", version=datasets.Version("1.0.0"), description="WildReceipt dataset"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"words": datasets.Sequence(datasets.Value("string")), |
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"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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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'] |
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) |
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), |
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"image_path": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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homepage="", |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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"""Uses local files located with data_dir""" |
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downloaded_file = dl_manager.download_and_extract(_URLS) |
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dest = Path(downloaded_file[0])/'wildreceipt' |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train.txt", "dest": dest} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test.txt", "dest": dest} |
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), |
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] |
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def _generate_examples(self, filepath, dest): |
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df = pd.read_csv(dest/'class_list.txt', delimiter='\s', header=None) |
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id2labels = dict(zip(df[0].tolist(), df[1].tolist())) |
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logger.info("⏳ Generating examples from = %s", filepath) |
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item_list = [] |
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with open(filepath, 'r') as f: |
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for line in f: |
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item_list.append(line.rstrip('\n\r')) |
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for guid, fname in enumerate(item_list): |
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data = json.loads(fname) |
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image_path = dest/data['file_name'] |
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image, size = load_image(image_path) |
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boxes = [[i['box'][6], i['box'][7], i['box'][2], i['box'][3]] for i in data['annotations']] |
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text = [i['text'] for i in data['annotations']] |
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label = [id2labels[i['label']] for i in data['annotations']] |
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boxes = [normalize_bbox(box, size) for box in boxes] |
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flag=0 |
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for i in boxes: |
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for j in i: |
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if j>1000: |
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flag+=1 |
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pass |
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if flag>0: print(image_path) |
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yield guid, {"id": str(guid), "words": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path} |
