Datasets:
Size Categories:
1K<n<10K
Tags:
image-quality
| 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("image_pairs_mos.csv") | |
| data = pd.read_csv(data_path, index_col=0) | |
| reference_paths = data["Reference"].apply(lambda x: os.path.join("reference_images", x)).to_list() | |
| distorted_paths = data["Distorted"].apply(lambda x: os.path.join("distorted_images", x)).to_list() | |
| reference_paths = dl_manager.download(reference_paths) | |
| distorted_paths = dl_manager.download(distorted_paths) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "reference": reference_paths, | |
| "distorted": distorted_paths, | |
| "mos": data["MOS"], | |
| "split": "train", | |
| }, | |
| ) | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, reference, distorted, mos, split): | |
| for key, (ref, dist, m) in enumerate(zip(reference, distorted, mos)): | |
| yield key, { | |
| "reference": ref, | |
| "distorted": dist, | |
| "mos": m, | |
| } |