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# coding=utf-8
'''
Reference: https://huggingface.co/datasets/nielsr/funsd/blob/main/funsd.py
'''
import json
import os
from PIL import Image
import datasets
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(bbox[0]),
int(bbox[1]),
int(bbox[2]),
int(bbox[3]),
]
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 LayoutLMConfig(datasets.BuilderConfig):
"""BuilderConfig for FUNSD"""
def __init__(self, **kwargs):
"""BuilderConfig for FUNSD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(LayoutLMConfig, self).__init__(**kwargs)
class LayoutLM(datasets.GeneratorBasedBuilder):
"""Conll2003 dataset."""
BUILDER_CONFIGS = [
LayoutLMConfig(name="dataset_layoutlm", version=datasets.Version("1.0.0"), description="FUNSD dataset probe"),
]
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", "B-HEADER", "I-HEADER","B-SUBHEADER", "I-SUBHEADER","B-TEXTO", "I-TEXTO","B-NUMERAL", "I-NUMERAL", "B-RESUMEN", "I-RESUMEN"]
)
),
"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("https://huggingface.co/datasets/SickBoy/layout_documents/resolve/main/dataset.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_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 = []
file_path = os.path.join(ann_dir, file)
with open(file_path, "r", encoding="utf8") as f:
data = json.load(f)
image_path = os.path.join(img_dir, file)
image_path = image_path.replace("json", "png")
image, size = load_image(image_path)
for item in data["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 == "otro":
for w in words:
tokens.append(w["text"])
ner_tags.append("O")
cur_line_bboxes.append(normalize_bbox(w["box"], size))
else:
tokens.append(words[0]["text"])
ner_tags.append("B-" + label.upper())
cur_line_bboxes.append(normalize_bbox(words[0]["box"], size))
for w in words[1:]:
tokens.append(w["text"])
ner_tags.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, "ner_tags": ner_tags,
"image": image} |