# coding=utf-8 ''' Reference: https://huggingface.co/datasets/nielsr/funsd-layoutlmv3/blob/main/funsd-layoutlmv3.py ''' import ast import os import random import re import datasets import matplotlib.pyplot as plt import pandas as pd from pdf2image import convert_from_path from PIL import Image def load_image(image_path): image = Image.open(image_path).convert("RGB") 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]), ] logger = datasets.logging.get_logger(__name__) _CITATION = """\ } """ _DESCRIPTION = """\ """ class DireitoDigitalConfig(datasets.BuilderConfig): """BuilderConfig for DIREITO DIGITAL""" def __init__(self, **kwargs): """BuilderConfig for DIREITODIGITAL. Args: **kwargs: keyword arguments forwarded to super. """ super(DireitoDigitalConfig, self).__init__(**kwargs) class DireitoDigital(datasets.GeneratorBasedBuilder): """Conll2003 dataset.""" BUILDER_CONFIGS = [ DireitoDigitalConfig(name="direitodigital", version=datasets.Version("1.0.0"), description="DIREITO DIGITAL dataset"), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), "segment_class": datasets.Sequence( datasets.features.ClassLabel( names=["O", "B-PARTES","I-PARTES", "B-EMENTA","I-EMENTA", "B-ACORDAO","I-ACORDAO", "B-RELATORIO","I-RELATORIO", "B-VOTO", "I-VOTO"] ) ), "image": datasets.features.Image(), } ), supervised_keys=None, #homepage="https://direitodigital.ufms.br/direitodigital/", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" downloaded_file = dl_manager.download_and_extract("http://direitodigital.ufms.br:8000/direitodigital.zip") return [ datasets.SplitGenerator( name=datasets.NamedSplit('trainmini_stf'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/stf"} ), datasets.SplitGenerator( name=datasets.NamedSplit('dev_stf'), gen_kwargs={"filepath": f"{downloaded_file}/dev/stf"} ), datasets.SplitGenerator( name=datasets.NamedSplit('trainmini_stj'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/stj"} ), datasets.SplitGenerator( name=datasets.NamedSplit('dev_stj'), gen_kwargs={"filepath": f"{downloaded_file}/dev/stj"} ), datasets.SplitGenerator( name=datasets.NamedSplit('trainmini_trf2'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/trf2"} ), datasets.SplitGenerator( name=datasets.NamedSplit('dev_trf2'), gen_kwargs={"filepath": f"{downloaded_file}/dev/trf2"} ), datasets.SplitGenerator( name=datasets.NamedSplit('trainmini_tjpb'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/tjpb"} ), datasets.SplitGenerator( name=datasets.NamedSplit('dev_tjpb'), gen_kwargs={"filepath": f"{downloaded_file}/dev/tjpb"} ), datasets.SplitGenerator( name=datasets.NamedSplit('trainmini_tjmg'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/tjmg"} ), datasets.SplitGenerator( name=datasets.NamedSplit('dev_tjmg'), gen_kwargs={"filepath": f"{downloaded_file}/dev/tjmg"} ) ] def get_line_bbox(self, bboxs): x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)] y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)] x0, y0, x1, y1 = min(x), min(y), max(x), max(y) assert x1 >= x0 and y1 >= y0 bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))] return bbox def _generate_examples(self, filepath): guid = 0 file_paths = [ os.path.join(root, filename) for root, dirs, files in os.walk(filepath) for filename in files if filename.endswith('.tsv') ] random.shuffle(file_paths) #for dir_path, _, file_names in os.walk(filepath): for tsv_name in file_paths: #for file in file_names: #tsv_name = os.path.join(dir_path, file) #print(file_paths) base_path = os.path.dirname(os.path.dirname(filepath)) pdf_base_path = os.path.join(base_path, 'pdf') pdf_name = tsv_name.replace('.tsv', '.pdf') pdf_name = pdf_name.replace(base_path,pdf_base_path) img_path = tsv_name.replace('.tsv','') print(pdf_name) pages_img = convert_from_path(pdf_name, size=(595,840),fmt="png") dataframe = pd.read_csv(tsv_name ,delimiter='\t', keep_default_na=False).replace(["None","SUMULA","CERTIDAO_DE_JULGAMENTO","AUTUACAO","CERTIDAO","EXTRATO_DE_ATA"], 'OUTROS') for page in dataframe['page'].unique(): #image, size = load_image(os.path.join(img_path, str(page-1)+'.png')) image, size = pages_img[page-1], pages_img[page-1].size data = (dataframe[dataframe["page"] == page]) form = [] for index, row in data.iterrows(): tokens = [] for token in ast.literal_eval(row['tokens']): tokens.append({ 'box' : [token['x'], token['y'], token['x']+token['width'], token['y'] + token['height']], 'text' : token['text'] }) line_dict = { 'text': row['text'], 'box': [row['x'], row['y'], row['x']+row['width'], row['y'] + row['height']], 'label': row['label'], 'words': tokens } form.append(line_dict) yield from self.get_form(guid, image, size, form) guid += 1 def get_form(self, guid, image, size, form): tokens = [] bboxes = [] segment_class = [] for item in form: cur_line_bboxes = [] words, label = item["words"], item["label"] words = [w for w in words if w["text"].strip() != ""] if len(words) == 0: continue if label == "OUTROS": for w in words: tokens.append(w["text"]) segment_class.append("O") #segment_class.append(label) cur_line_bboxes.append(normalize_bbox(w["box"], size)) else: tokens.append(words[0]["text"]) segment_class.append("B-" + label.upper()) cur_line_bboxes.append(normalize_bbox(words[0]["box"], size)) for w in words[1:]: tokens.append(w["text"]) segment_class.append("I-" + label.upper()) cur_line_bboxes.append(normalize_bbox(w["box"], size)) cur_line_bboxes = self.get_line_bbox(cur_line_bboxes) bboxes.extend(cur_line_bboxes) yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "segment_class": segment_class, "image": image} def main(): dataset = DireitoDigital() for example in dataset._generate_examples('/home/marlon/LayoutLM_dataset/trainmini'): print(example) if __name__ == '__main__': main()