Databases_IlluminantChanges.py

import os
import re

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 = """"""

_HOMEPAGE = ""
_REPO = ""

# _LICENSE = ""
class IlluminantConfig(datasets.BuilderConfig):
    """BuilderConfig for IlluminantChanges."""

    def __init__(self, data_url, **kwargs):
        """BuilderConfig for Imagette.
        Args:
          data_url: `string`, url to download the zip file from.
          matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
          **kwargs: keyword arguments forwarded to super.
        """
        super(IlluminantConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_url = data_url

class Databases_IlluminantChanges(datasets.GeneratorBasedBuilder):
    """"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        IlluminantConfig(
            name="MNIST",
            description="MNIST data",
            data_url=f"./MNIST.zip",
        ),
        IlluminantConfig(
            name="CIFAR",
            description="CIFAR data",
            data_url=f"./CIFAR.zip",
        ),
        IlluminantConfig(
            name="Imagenet",
            description="Imagenet data",
            data_url=f"./Imagenet.zip",
        ),
        IlluminantConfig(
            name="TID13",
            description="TID13 data",
            data_url=f"./TID13.zip",
        ),
    ]

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                # "images": datasets.Image(),
                "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):

        archive_path = dl_manager.download(self.config.data_url)
        # print(f"Data url: {self.config.data_url} | {archive_path}")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.download_and_extract(archive_path),
                    "split": "train",
                },
            )
        ]

    def _generate_examples(self, images, split):
        desat_path = os.path.join(images, self.config.name, "Desat")
        illum_path = os.path.join(images, self.config.name, "Illum")

        illum_paths = [os.path.join(illum_path, p) for p in os.listdir(illum_path)]

        ## Get the correct desat image for each illum image
        img_numbers = [re.findall("im_(\d+)_tono_\d+_sat_\d+.png", p)[0] for p in illum_paths]
        desat_paths = [os.path.join(desat_path, f"im_orig_desat{n}.png") for n in img_numbers]

        # print(desat_paths[0], illum_paths[0])
        for key, (desat, illum) in enumerate(zip(desat_paths, illum_paths)):
            yield key, {
                "reference": desat,
                "distorted": illum,
            }