parquet

TID2008.py

@@ -1,86 +0,0 @@
-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,
-                },
-            )
-

data.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c048c69418cb0146fe8363f637a35e16623ca6ce25a8b6bfcdd9fb47e85ecaf6
-size 704640392