Datasets:

Livingwithmachines
/

MapReader_Data_SIGSPATIAL_2022

	# 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.
	"""TODO"""

	import csv
	import os

	import datasets
	from PIL import Image

	_CITATION = """\
	@dataset{kasra_hosseini_2022_7147906,
	author = {Kasra Hosseini and
	Daniel C.S. Wilson and
	Kaspar Beelen and
	Katherine McDonough},
	title = {MapReader_Data_SIGSPATIAL_2022},
	month = oct,
	year = 2022,
	publisher = {Zenodo},
	version = {v0.3.3},
	doi = {10.5281/zenodo.7147906},
	url = {https://doi.org/10.5281/zenodo.7147906}
	}
	"""

	_DESCRIPTION = """\
	TODO"""

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

	_URL = "https://zenodo.org/record/7147906/files/MapReader_Data_SIGSPATIAL_2022.zip?download=1"


	class RailspaceData(datasets.GeneratorBasedBuilder):
	"""National Library of Scotland Railspace dataset."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"image": datasets.Image(),
	"label": datasets.ClassLabel(
	names=[
	"no building or railspace",
	"railspace",
	"building",
	"railspace and non railspace building",
	]
	), # Labels: 0: no [building or railspace]; 1: railspace; 2: building; and 3: railspace and [non railspace] building.
	"map_sheet": datasets.Value("string"),
	}
	)

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

	def _split_generators(self, dl_manager):
	data = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"data": data, "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"data": data, "split": "valid"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"data": data, "split": "test"},
	),
	]

	def _generate_examples(self, data, split):
	with open(
	os.path.join(
	data, f"MapReader_Data_SIGSPATIAL_2022/annotations/{split}.csv"
	),
	"r",
	) as f:
	reader = csv.DictReader(f)
	for id_, row in enumerate(reader):
	label = row["label"]
	map_sheet = row["image_id"].split("#")[1]
	image_file = os.path.join(
	data,
	f"MapReader_Data_SIGSPATIAL_2022/annotations/{row['image_id']}",
	)
	image = Image.open(image_file)
	yield id_, {"image": image, "label": label, "map_sheet": map_sheet}