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''' |
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Reference: https://huggingface.co/datasets/nielsr/funsd-layoutlmv3/blob/main/funsd-layoutlmv3.py |
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''' |
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import ast |
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import os |
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import random |
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import re |
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import datasets |
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import matplotlib.pyplot as plt |
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import pandas as pd |
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from pdf2image import convert_from_path |
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from PIL import Image |
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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(1000 * bbox[3] / size[1]), |
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] |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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} |
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""" |
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_DESCRIPTION = """\ |
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""" |
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class DireitoDigitalConfig(datasets.BuilderConfig): |
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"""BuilderConfig for DIREITO DIGITAL""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for DIREITODIGITAL. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(DireitoDigitalConfig, self).__init__(**kwargs) |
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class DireitoDigital(datasets.GeneratorBasedBuilder): |
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"""Conll2003 dataset.""" |
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BUILDER_CONFIGS = [ |
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DireitoDigitalConfig(name="direitodigital", version=datasets.Version("1.0.0"), description="DIREITO DIGITAL 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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"tokens": datasets.Sequence(datasets.Value("string")), |
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"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
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"segment_class": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=["O", "B-PARTES","I-PARTES", "B-EMENTA","I-EMENTA", "B-ACORDAO","I-ACORDAO", "B-RELATORIO","I-RELATORIO", "B-VOTO", "I-VOTO"] |
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) |
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), |
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"image": datasets.features.Image(), |
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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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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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downloaded_file = dl_manager.download_and_extract("http://direitodigital.ufms.br:8000/direitodigital.zip") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('trainmini_stf'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/stf"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('dev_stf'), gen_kwargs={"filepath": f"{downloaded_file}/dev/stf"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('trainmini_stj'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/stj"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('dev_stj'), gen_kwargs={"filepath": f"{downloaded_file}/dev/stj"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('trainmini_trf2'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/trf2"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('dev_trf2'), gen_kwargs={"filepath": f"{downloaded_file}/dev/trf2"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('trainmini_tjpb'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/tjpb"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('dev_tjpb'), gen_kwargs={"filepath": f"{downloaded_file}/dev/tjpb"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('trainmini_tjmg'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/tjmg"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.NamedSplit('dev_tjmg'), gen_kwargs={"filepath": f"{downloaded_file}/dev/tjmg"} |
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) |
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] |
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def get_line_bbox(self, bboxs): |
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x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)] |
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y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)] |
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x0, y0, x1, y1 = min(x), min(y), max(x), max(y) |
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assert x1 >= x0 and y1 >= y0 |
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bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))] |
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return bbox |
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def _generate_examples(self, filepath): |
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guid = 0 |
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file_paths = [ |
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os.path.join(root, filename) |
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for root, dirs, files in os.walk(filepath) |
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for filename in files |
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if filename.endswith('.tsv') |
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] |
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random.shuffle(file_paths) |
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for tsv_name in file_paths: |
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base_path = os.path.dirname(os.path.dirname(filepath)) |
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pdf_base_path = os.path.join(base_path, 'pdf') |
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pdf_name = tsv_name.replace('.tsv', '.pdf') |
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pdf_name = pdf_name.replace(base_path,pdf_base_path) |
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img_path = tsv_name.replace('.tsv','') |
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print(pdf_name) |
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pages_img = convert_from_path(pdf_name, size=(595,840),fmt="png") |
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dataframe = pd.read_csv(tsv_name ,delimiter='\t', keep_default_na=False).replace(["None","SUMULA","CERTIDAO_DE_JULGAMENTO","AUTUACAO","CERTIDAO","EXTRATO_DE_ATA"], 'OUTROS') |
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for page in dataframe['page'].unique(): |
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image, size = pages_img[page-1], pages_img[page-1].size |
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data = (dataframe[dataframe["page"] == page]) |
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form = [] |
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for index, row in data.iterrows(): |
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tokens = [] |
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for token in ast.literal_eval(row['tokens']): |
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tokens.append({ |
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'box' : |
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[token['x'], token['y'], token['x']+token['width'], token['y'] + token['height']], |
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'text' : token['text'] |
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}) |
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line_dict = { |
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'text': row['text'], |
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'box': [row['x'], row['y'], row['x']+row['width'], row['y'] + row['height']], |
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'label': row['label'], |
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'words': tokens |
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} |
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form.append(line_dict) |
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yield from self.get_form(guid, image, size, form) |
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guid += 1 |
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def get_form(self, guid, image, size, form): |
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tokens = [] |
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bboxes = [] |
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segment_class = [] |
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for item in form: |
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cur_line_bboxes = [] |
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words, label = item["words"], item["label"] |
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words = [w for w in words if w["text"].strip() != ""] |
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if len(words) == 0: |
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continue |
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if label == "OUTROS": |
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for w in words: |
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tokens.append(w["text"]) |
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segment_class.append("O") |
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cur_line_bboxes.append(normalize_bbox(w["box"], size)) |
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else: |
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tokens.append(words[0]["text"]) |
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segment_class.append("B-" + label.upper()) |
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cur_line_bboxes.append(normalize_bbox(words[0]["box"], size)) |
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for w in words[1:]: |
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tokens.append(w["text"]) |
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segment_class.append("I-" + label.upper()) |
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cur_line_bboxes.append(normalize_bbox(w["box"], size)) |
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cur_line_bboxes = self.get_line_bbox(cur_line_bboxes) |
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bboxes.extend(cur_line_bboxes) |
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yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "segment_class": segment_class, |
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"image": image} |
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def main(): |
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dataset = DireitoDigital() |
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for example in dataset._generate_examples('/home/marlon/LayoutLM_dataset/trainmini'): |
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print(example) |
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if __name__ == '__main__': |
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main() |
