Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size Categories:
10K<n<100K
Annotations Creators:
expert-generated
License:
File size: 3,729 Bytes
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# 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}
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