Update Populus_Stomatal_Images_Datasets.py

Populus_Stomatal_Images_Datasets.py

@@ -109,36 +109,39 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Download the CSV file and both ZIP files
+        # Download and extract the data files
         data_files = dl_manager.download_and_extract({
             "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.csv",
-            "zip_all": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip",
             "zip_config": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images_config.zip"
         })
         
-        # Load the CSV file containing species and scientific names
-        species_info_full = pd.read_csv(data_files["csv"])
+        # The path to the CSV file
+        csv_path = data_files["csv"]
         
-        # Assume the config ZIP is extracted to 'Labeled Stomatal Images_config' folder
-        extracted_config_path = os.path.join(dl_manager._cache_dir, "Labeled Stomatal Images_config")
-    
-        # List all image filenames in the extracted config directory
-        all_image_filenames_config = [f for f in os.listdir(extracted_config_path) if f.endswith('.jpg')]
-    
-        # Filter the species_info dataframe to only include the rows with filenames that are in the new configuration
-        # Here, we're extracting the filename without the extension to match with the 'FileName' column in the CSV
-        species_info = species_info_full[species_info_full['FileName'].isin([os.path.splitext(f)[0] for f in all_image_filenames_config])]
+        # The path to the folder where the config ZIP was extracted
+        extracted_config_path = data_files["zip_config"]
+        
+        # Read the CSV file to get the species information
+        species_info = pd.read_csv(csv_path)
+        
+        # Get the list of image filenames from the CSV that are part of the config
+        image_filenames_config = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
+        
+        # Filter the list to include only the images present in the config directory
+        image_filenames = [f for f in image_filenames_config if os.path.exists(os.path.join(extracted_config_path, f))]
     
+        # Use the filtered list of filenames to create the dataset split
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepaths": all_image_filenames_config,
+                    "filepaths": image_filenames,
                     "species_info": species_info,
                     "data_dir": extracted_config_path
                 },
             )
         ]
+