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Datasets:
eugenesiow
/
Set5

Tasks:
Other
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
Tags:
other-image-super-resolution
License:
Dataset card Viewer Files Community
1
Set5
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# coding=utf-8
# 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.
"""Set5 dataset: An evaluation dataset for the image super resolution task"""


import datasets
from pathlib import Path


_CITATION = """
@article{bevilacqua2012low,
  title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
  author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
  year={2012},
  publisher={BMVA press}
}
"""

_DESCRIPTION = """
Set5 is a evaluation dataset with 5 RGB images for the image super resolution task.
"""

_HOMEPAGE = "http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html"

_LICENSE = "UNK"

_DL_URL = "https://huggingface.co/datasets/eugenesiow/Set5/resolve/main/data/"

_DEFAULT_CONFIG = "bicubic_x2"

_DATA_OPTIONS = {
    "bicubic_x2": {
        "hr": _DL_URL + "Set5_HR.tar.gz",
        "lr": _DL_URL + "Set5_LR_x2.tar.gz",
    },
    "bicubic_x3": {
        "hr": _DL_URL + "Set5_HR.tar.gz",
        "lr": _DL_URL + "Set5_LR_x3.tar.gz",
    },
    "bicubic_x4": {
        "hr": _DL_URL + "Set5_HR.tar.gz",
        "lr": _DL_URL + "Set5_LR_x4.tar.gz",
    }
}


class Set5Config(datasets.BuilderConfig):
    """BuilderConfig for Set5."""

    def __init__(
        self,
        name,
        hr_url,
        lr_url,
        **kwargs,
    ):
        if name not in _DATA_OPTIONS:
            raise ValueError("data must be one of %s" % _DATA_OPTIONS)
        super(Set5Config, self).__init__(name=name, version=datasets.Version("1.0.0"), **kwargs)
        self.hr_url = hr_url
        self.lr_url = lr_url


class Set5(datasets.GeneratorBasedBuilder):
    """Set5 dataset for single image super resolution evaluation."""

    BUILDER_CONFIGS = [
        Set5Config(
            name=key,
            hr_url=values['hr'],
            lr_url=values['lr']
        ) for key, values in _DATA_OPTIONS.items()
    ]

    DEFAULT_CONFIG_NAME = _DEFAULT_CONFIG

    def _info(self):
        features = datasets.Features(
            {
                "hr": datasets.Value("string"),
                "lr": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        hr_data_dir = dl_manager.download_and_extract(self.config.hr_url)
        lr_data_dir = dl_manager.download_and_extract(self.config.lr_url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "lr_path": lr_data_dir,
                    "hr_path": str(Path(hr_data_dir) / 'Set5_HR')
                },
            )
        ]

    def _generate_examples(
        self, hr_path, lr_path
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.
        extensions = {'.jpg', '.jpeg', '.png'}
        for file_path in sorted(Path(lr_path).glob("**/*")):
            if file_path.suffix in extensions:
                file_path_str = str(file_path.as_posix())
                yield file_path_str, {
                    'lr': file_path_str,
                    'hr': str((Path(hr_path) / file_path.name).as_posix())
                }