Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K<n<100K
License:
Commit
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Parent(s):
34bf1f6
Create MapReader_Data_SIGSPATIAL_2022
Browse files- MapReader_Data_SIGSPATIAL_2022 +110 -0
MapReader_Data_SIGSPATIAL_2022
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""TODO"""
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import csv
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import os
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import datasets
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from PIL import Image
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_CITATION = """\
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@dataset{kasra_hosseini_2022_7147906,
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author = {Kasra Hosseini and
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Daniel C.S. Wilson and
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Kaspar Beelen and
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Katherine McDonough},
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title = {MapReader_Data_SIGSPATIAL_2022},
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month = oct,
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year = 2022,
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publisher = {Zenodo},
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version = {v0.3.3},
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doi = {10.5281/zenodo.7147906},
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url = {https://doi.org/10.5281/zenodo.7147906}
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}
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"""
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_DESCRIPTION = """\
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TODO"""
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_HOMEPAGE = "https://doi.org/10.5281/zenodo.3366686"
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_LICENSE = "Creative Commons Attribution Non Commercial Share Alike 4.0 International"
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_URL = "https://zenodo.org/record/7147906/files/MapReader_Data_SIGSPATIAL_2022.zip?download=1"
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class RailspaceData(datasets.GeneratorBasedBuilder):
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"""National Library of Scotland Railspace dataset."""
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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features = datasets.Features(
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{
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"image": datasets.Image(),
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"label": datasets.ClassLabel(
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names=[
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"no building or railspace",
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"railspace",
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"building",
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"railspace and non railspace building",
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]
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), # Labels: 0: no [building or railspace]; 1: railspace; 2: building; and 3: railspace and [non railspace] building.
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"map_sheet": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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data = dl_manager.download_and_extract(_URL)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"data": data, "split": "train"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"data": data, "split": "valid"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"data": data, "split": "test"},
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),
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]
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def _generate_examples(self, data, split):
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with open(
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os.path.join(
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data, f"MapReader_Data_SIGSPATIAL_2022/annotations/{split}.csv"
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),
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"r",
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) as f:
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reader = csv.DictReader(f)
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for id_, row in enumerate(reader):
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label = row["label"]
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map_sheet = row["image_id"].split("#")[1]
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image_file = os.path.join(
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data,
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f"MapReader_Data_SIGSPATIAL_2022/annotations/{row['image_id']}",
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)
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image = Image.open(image_file)
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yield id_, {"image": image, "label": label, "map_sheet": map_sheet}
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