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# -*- coding: utf-8 -*-
"""erukaLabels.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1p0VRh0b-OtHjNNLIcNUPb2BaoiE9Mh7O
"""
# coding=utf-8
import json
import os
import datasets
from PIL import Image
import numpy as np
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[4] / size[0]),
int(1000 * bbox[5] / size[1]),
]
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
}
"""
_DESCRIPTION = """\
https://guillaumejaume.github.io/FUNSD/
"""
class FunsdConfig(datasets.BuilderConfig):
"""BuilderConfig for FUNSD"""
def __init__(self, **kwargs):
"""BuilderConfig for FUNSD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FunsdConfig, self).__init__(**kwargs)
class Funsd(datasets.GeneratorBasedBuilder):
"""Conll2003 dataset."""
BUILDER_CONFIGS = [
FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD 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"))),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=["O", "R0C0", "R1C0", "R2C0", "R3C0", "R4C0", "R5C0", "R6C0", "R7C0", "R8C0", "R9C0", "R10C0",
"R0C1", "R1C1", "R2C1", "R3C1", "R4C1", "R5C1", "R6C1", "R7C1", "R8C1", "R9C1", "R10C1",
"R0C2", "R1C2", "R2C2", "R3C2", "R4C2", "R5C2", "R6C2", "R7C2", "R8C2", "R9C2", "R10C2",
"VALUATIONS", "LAND", "BUILDINGS", "TOTAL"]
)
),
"image": datasets.features.Image(),
}
),
supervised_keys=None,
homepage="https://guillaumejaume.github.io/FUNSD/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("dataset_eruka2.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset_eruka/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset_eruka/testing_data/"}
),
]
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):
logger.info("⏳ Generating examples from = %s", filepath)
ann_dir = os.path.join(filepath, "annotations")
img_dir = os.path.join(filepath, "images")
for guid, file in enumerate(sorted(os.listdir(ann_dir))):
tokens = []
bboxes = []
ner_tags = []
if file == ".DS_Store":
continue
file_path = os.path.join(ann_dir, file)
print(file_path)
with open(file_path, "r", encoding="utf-8") as f:
data = json.load(f)
image_path = os.path.join(img_dir, file)
# changed
image_path = image_path.replace("json", "jpg")
image, size = load_image(image_path)
#new
ddata_path = data["analyzeResult"]["pages"][0]["words"]
for item in ddata_path:
cur_line_bboxes = []
words, label = [item["content"]], item["confidence"]
if len(words) == 0:
continue
tokens.append(words[0])
print(label)
print(item)
if isinstance(label, float):
ner_tags.append("O")
else:
print(label)
ner_tags.append(label.upper())
cur_line_bboxes.append(normalize_bbox(item["polygon"], 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, "ner_tags": ner_tags,
"image": image}
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