Upload databases_illuminantchanges.py

databases_illuminantchanges.py

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+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="TID2013",
+            description="TID2013 data",
+            data_url=f"./TID2013.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("\d+", 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,
+            }