cleaned file

TID2008.py

@@ -1,12 +1,7 @@
-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,
@@ -26,17 +21,6 @@ In total there are 1700 (reference, distortion, MOS) tuples.
 
 # _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"""
 
@@ -63,31 +47,17 @@ class TID2008(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         data_path = dl_manager.download("image_pairs_mos.csv")
         data = pd.read_csv(data_path, index_col=0)
-
-        # kk = dl_manager.download("distorted_images")
-        # print(kk)
-
-        root_path = "/".join(data_path.split("/")[:-1])
-        # reference_path = dl_manager.download("reference_images")
-        # distorted_path = dl_manager.download("distorted_images")
         
         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)
-        
-        # dl_manager.download(data["Reference"])
-
-        # 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"],
                     "reference": reference_paths,
                     "distorted": distorted_paths,
                     "mos": data["MOS"],
@@ -103,20 +73,4 @@ class TID2008(datasets.GeneratorBasedBuilder):
                 "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"],
-        #             }
+            }