Datasets:

biglam
/

early_printed_books_font_detection

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Dataset of illustrated and non illustrated 19th Century newspaper ads."""

	import zipfile
	from pathlib import Path

	import datasets
	import requests
	from PIL import Image

	# Some of the images are large so we set this to avoid Decompression bomb warnings

	Image.MAX_IMAGE_PIXELS = None

	_CITATION = """\
	@dataset{seuret_mathias_2019_3366686,
	author = {Seuret, Mathias and
	Limbach, Saskia and
	Weichselbaumer, Nikolaus and
	Maier, Andreas and
	Christlein, Vincent},
	title = {{Dataset of Pages from Early Printed Books with
	Multiple Font Groups}},
	month = aug,
	year = 2019,
	publisher = {Zenodo},
	version = {1.0.0},
	doi = {10.5281/zenodo.3366686},
	url = {https://doi.org/10.5281/zenodo.3366686}
	}
	"""

	_DESCRIPTION = """\
	This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list: Antiqua, Bastarda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.
	"""

	_HOMEPAGE = "https://doi.org/10.5281/zenodo.3366686"
	_LICENSE = "Creative Commons Attribution Non Commercial Share Alike 4.0 International"

	ZENDO_REPO_ID = 3366686
	ZENODO_API_URL = f"https://zenodo.org/api/records/{ZENDO_REPO_ID}"


	class EarlyBookFonts(datasets.GeneratorBasedBuilder):
	"""Early printed book fonts detection dataset"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"image": datasets.Image(),
	"labels": datasets.Sequence(
	datasets.ClassLabel(
	names=[
	"greek",
	"antiqua",
	"other_font",
	"not_a_font",
	"italic",
	"rotunda",
	"textura",
	"fraktur",
	"schwabacher",
	"hebrew",
	"bastarda",
	"gotico_antiqua",
	]
	)
	),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	zenodo_record = requests.get(ZENODO_API_URL).json()
	urls = sorted(
	file["links"]["self"]
	for file in zenodo_record["files"]
	if file["type"] == "zip"
	)
	*image_urls, label_url = urls
	labels = dl_manager.download(label_url)
	images = dl_manager.download_and_extract(image_urls)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"images": images, "labels": labels, "split": "training"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"images": images, "labels": labels, "split": "test"},
	),
	]

	def _generate_examples(self, images, labels, split):
	mapping = {}
	for directory in images:
	for file in Path(directory).rglob("*"):
	mapping["/".join(file.parts[-2:])] = file
	with zipfile.ZipFile(labels) as labelzip:
	with labelzip.open(f"labels-{split}.csv") as label_csv:
	for id_, row in enumerate(label_csv.readlines()):
	row = row.decode("utf-8")
	filename, *labels = row.split(",")
	labels = [label.strip("\n") for label in labels]
	labels = [label for label in labels if label != "-"]
	filename = mapping[filename]

	image = Image.open(filename)
	yield id_, {"image": image, "labels": labels}