:sparkles: Custom script to load the data.

tid2008.py

@@ -0,0 +1,108 @@
+import csv
+import json
+import os
+from PIL import Image
+
+import pandas as pd
+from huggingface_hub import hf_hub_download, snapshot_download
+import datasets
+import cv2
+
+# _CITATION = """\
+# @InProceedings{huggingface:dataset,
+# title = {A great new dataset},
+# author={huggingface, Inc.
+# },
+# year={2020}
+# }
+# """
+
+_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 = ""
+
+# _LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# _URLS = {
+#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+
+# _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata" # Stolen from imagenette.py
+_REPO = "https://huggingface.co/datasets/Jorgvt/TID2008/resolve/main"
+
+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)
+
+        root_path = "/".join(data_path.split("/")[:-1])
+        reference_path = os.path.join(root_path, "reference_images")
+        distorted_path = os.path.join(root_path, "distorted_images")
+        
+        data["Reference"] = data["Reference"].apply(lambda x: os.path.join(reference_path, x))
+        data["Distorted"] = data["Distorted"].apply(lambda x: os.path.join(distorted_path, x))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "reference": data["Reference"],
+                    "distorted": data["Distorted"],
+                    "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,
+            }
+        # with open(filepath, encoding="utf-8") as f:
+        #     for key, row in enumerate(f):
+        #         data = json.loads(row)
+        #         if self.config.name == "first_domain":
+        #             # Yields examples as (key, example) tuples
+        #             yield key, {
+        #                 "sentence": data["sentence"],
+        #                 "option1": data["option1"],
+        #                 "answer": "" if split == "test" else data["answer"],
+        #             }
+        #         else:
+        #             yield key, {
+        #                 "sentence": data["sentence"],
+        #                 "option2": data["option2"],
+        #                 "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
+        #             }