File size: 2,687 Bytes
9e2121f d3c41ec 9e2121f d3c41ec 9e2121f fc2a476 a10df7a 9e2121f a10df7a 9e2121f 5802ac9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
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,
} |