Upload cvdataset-layoutlmv3.py
Browse files- cvdataset-layoutlmv3.py +128 -0
cvdataset-layoutlmv3.py
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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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import json
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logger = datasets.logging.get_logger(__name__)
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def load_image(image_path):
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image = Image.open(image_path).convert("RGB")
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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(100 * bbox[3] / size[1]),
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]
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def _get_drive_url(url):
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base_url = 'https://drive.google.com/uc?id='
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split_url = url.split("/")
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return base_url + split_url[5]
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_URLS = [
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_get_drive_url("https://drive.google.com/file/d/1KdDBmGP96lFc7jv2Bf4eqrO121ST-TCh/"),
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]
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_CITATION = """\
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@article{liharding-nguyen,
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title={CVDS: A Dataset for CV Form Understanding},
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author={MISA - employees},
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year={2022},
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}
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"""
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_DESCRIPTION = """\
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Dataset for key information extraction with cv form understanding
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"""
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class DatasetConfig(datasets.BuilderConfig):
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"""BuilderConfig for CV Dataset"""
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def __init__(self, **kwargs):
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"""BuilderConfig for CV 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 CVDS(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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DatasetConfig(name="CVDS", version=datasets.Version("1.0.0"), description="CV 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=['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']
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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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download_file = dl_manager.download_and_extract(_URLS)
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dest = Path(download_file[0])/"data1"
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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, file_path, dest):
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df = pd.read_csv(dest/"class_list.txt", delimiter="\s", header=None)
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id2label = dict(zip(df[0].tolist(), df[1].tolist()))
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logger.info("⏳ Generating examples from = %s", file_path)
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item_list = []
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with open(file_path, "r", encoding="utf8") 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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bboxes = [[i["box"][6], i["box"][7], i["box"][2]. i["box"][3]] for i in data["annotations"]]
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word = [i['text'] for i in data["annotations"]]
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label = [id2label[i["label"]] for i in data["annotations"]]
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bboxes = [normalize_bbox(box, size) for box in bboxes]
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flag=0
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for i in bboxes:
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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:
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print(image_path)
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yield guid, {"id": str(guid), "words": word, "bboxes": bboxes, "ner_tags": label, "image_path": image_path}
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