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# 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() |