Datasets:

cmarkea
/

aftdb

	import os
	import json

	import datasets
	from PIL import Image


	_DESCRIPTION = """
	The Arxiv Figure Table Database (AFTdb) facilitates the linking of documentary
	objects, such as figures and tables, with their captions. This enables a
	comprehensive description of document-oriented images (excluding images from
	cameras). For the table component, the character structure is preserved in
	addition to the image of the table and its caption. This database is ideal
	for multimodal processing of documentary images.
	"""
	_LICENSE = "apache-2.0"
	_CITATION = """
	@online{DeAFTdb,
	AUTHOR = {Cyrile Delestre},
	URL = {https://huggingface.co/datasets/cmarkea/aftdb},
	YEAR = {2024},
	KEYWORDS = {NLP ; Multimodal}
	}
	"""
	_NB_TAR_FIGURE = [158, 4] # train, test
	_NB_TAR_TABLE = [17, 1] # train, test


	def extract_files_tar(all_path, data_dir, nb_files):
	paths = [
	os.path.join(data_dir, f"train-{ii:03d}.tar")
	for ii in range(nb_files[0])
	]
	all_path['train'] += paths
	paths = [
	os.path.join(data_dir, f"test-{ii:03d}.tar")
	for ii in range(nb_files[1])
	]
	all_path['test'] += paths


	class AFTConfig(datasets.BuilderConfig):
	"""Builder Config for AFTdb"""

	def __init__(self, nb_files_figure, nb_files_table, **kwargs):
	"""BuilderConfig for AFTdb."""
	super().__init__(version=datasets.__version__, **kwargs)
	self.nb_files_figure = nb_files_figure
	self.nb_files_table = nb_files_table


	class AFT_Dataset(datasets.GeneratorBasedBuilder):
	"""Arxiv Figure Table (AFT) dataset"""

	BUILDER_CONFIGS = [
	AFTConfig(
	name="figure",
	description=(
	"Dataset containing scientific article figures associated "
	"with their caption, summary, and article title."
	),
	data_dir="./{type}",
	nb_files_figure=_NB_TAR_FIGURE,
	nb_files_table=None
	),
	AFTConfig(
	name="table",
	description=(
	"Dataset containing tables in JPG image format from "
	"scientific articles, along with the corresponding textual "
	"representation of the table, including its caption, summary, "
	"and article title."
	),
	data_dir="./{type}",
	nb_files_figure=None,
	nb_files_table=_NB_TAR_TABLE
	),
	AFTConfig(
	name="figure+table",
	description=(
	"Dataset containing figure and tables in JPG image format "
	"from scientific articles, along with the corresponding "
	"textual representation of the table, including its caption, "
	"summary, and article title."
	),
	data_dir="./{type}",
	nb_files_figure=_NB_TAR_FIGURE,
	nb_files_table=_NB_TAR_TABLE
	)
	]

	DEFAULT_CONFIG_NAME = "figure+table"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	'id': datasets.Value('string'),
	'paper_id': datasets.Value('string'),
	'type': datasets.Value('string'),
	'authors': datasets.Value('string'),
	'categories': datasets.Value('string'),
	'title': {
	'english': datasets.Value('string'),
	'french': datasets.Value('string')
	},
	'summary': {
	'english': datasets.Value('string'),
	'french': datasets.Value('string')
	},
	'caption': {
	'english': datasets.Value('string'),
	'french': datasets.Value('string')
	},
	'image': datasets.Image(),
	'data': datasets.Value('string'),
	'newcommands': datasets.Sequence(datasets.Value('string'))
	}
	),
	citation=_CITATION,
	license=_LICENSE
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager):
	all_path = dict(train=[], test=[])
	if self.config.nb_files_figure:
	extract_files_tar(
	all_path=all_path,
	data_dir=self.config.data_dir.format(type='figure'),
	nb_files=self.config.nb_files_figure
	)
	if self.config.nb_files_table:
	extract_files_tar(
	all_path=all_path,
	data_dir=self.config.data_dir.format(type='table'),
	nb_files=self.config.nb_files_table
	)
	if dl_manager.is_streaming:
	downloaded_files = dl_manager.download(all_path)
	downloaded_files['train'] = [
	dl_manager.iter_archive(ii) for ii in downloaded_files['train']
	]
	downloaded_files['test'] = [
	dl_manager.iter_archive(ii) for ii in downloaded_files['test']
	]
	else:
	downloaded_files = dl_manager.download_and_extract(all_path)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	'filepaths': downloaded_files['train'],
	'is_streaming': dl_manager.is_streaming
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepaths": downloaded_files['test'],
	'is_streaming': dl_manager.is_streaming
	}
	)
	]

	def _generate_examples(self, filepaths, is_streaming):
	if is_streaming:
	_json, _jpg = False, False
	for iter_tar in filepaths:
	for path, file_obj in iter_tar:
	if path.endswith('.json'):
	metadata = json.load(file_obj)
	_json = True
	if path.endswith('.jpg'):
	img = Image.open(file_obj)
	_jpg = True
	if _json and _jpg:
	_json, _jpg = False, False
	yield metadata['id'], {
	'id': metadata['id'],
	'paper_id': metadata['paper_id'],
	'type': metadata['type'],
	'authors': metadata['authors'],
	'categories': metadata['categories'],
	'title': metadata['title'],
	'summary': metadata['summary'],
	'caption': metadata['caption'],
	'image': img,
	'data': metadata['data'],
	'newcommands': metadata['newcommands']
	}
	else:
	for path in filepaths:
	all_file = os.listdir(path)
	all_id_obs = sorted(
	set(map(lambda x: x.split('.')[0], all_file))
	)
	for id_obs in all_id_obs:
	path_metadata = os.path.join(
	path,
	f"{id_obs}.metadata.json"
	)
	path_image = os.path.join(path, f"{id_obs}.image.jpg")
	metadata = json.load(open(path_metadata, 'r'))
	img = Image.open(path_image)
	yield id_obs, {
	'id': metadata['id'],
	'paper_id': metadata['paper_id'],
	'type': metadata['type'],
	'authors': metadata['authors'],
	'categories': metadata['categories'],
	'title': metadata['title'],
	'summary': metadata['summary'],
	'caption': metadata['caption'],
	'image': img,
	'data': metadata['data'],
	'newcommands': metadata['newcommands']
	}