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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""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}