TID2008.py

import os

import pandas as pd
import datasets

_CITATION = """\
@article{ponomarenko_tid2008_2009,
author = {Ponomarenko, Nikolay and Lukin, Vladimir and Zelensky, Alexander and Egiazarian, Karen and Astola, Jaakko and Carli, Marco and Battisti, Federica},
title = {{TID2008} -- {A} {Database} for {Evaluation} of {Full}- {Reference} {Visual} {Quality} {Assessment} {Metrics}},
year = {2009}
}
"""


_DESCRIPTION = """\
Image Quality Assessment Dataset consisting of 25 reference images, 17 different distortions and 4 intensities per distortion. 
In total there are 1700 (reference, distortion, MOS) tuples.
"""

_HOMEPAGE = "https://www.ponomarenko.info/tid2008.htm"

# _LICENSE = ""


class TID2008(datasets.GeneratorBasedBuilder):
    """TID2008 Image Quality Dataset"""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "reference": datasets.Image(),
                "distorted": datasets.Image(),
                "mos": datasets.Value("float"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            # supervised_keys=("reference", "distorted", "mos"),
            homepage=_HOMEPAGE,
            # license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_path = dl_manager.download("data.zip")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data": dl_manager.download_and_extract(data_path),
                    "split": "train",
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, data, split):
        df = pd.read_csv(os.path.join(data, "image_pairs_mos.csv"), index_col=0)
        reference_paths = (
            df["Reference"]
            .apply(lambda x: os.path.join(data, "reference_images", x))
            .to_list()
        )
        distorted_paths = (
            df["Distorted"]
            .apply(lambda x: os.path.join(data, "distorted_images", x))
            .to_list()
        )

        for key, (ref, dist, m) in enumerate(
            zip(reference_paths, distorted_paths, df["MOS"])
        ):
            yield (
                key,
                {
                    "reference": ref,
                    "distorted": dist,
                    "mos": m,
                },
            )