Update Populus_Stomatal_Images_Datasets.py

Populus_Stomatal_Images_Datasets.py

@@ -109,38 +109,27 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Download and extract the data files
+        # Only download data, no need to split
         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_config": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images_config.zip"
+            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip"
         })
         
-        # The path to the CSV file
-        csv_path = data_files["csv"]
-        
-        # 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()
+        species_info = pd.read_csv(data_files["csv"])
+        extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")
+
+        # Get all image filenames
+        all_image_filenames = 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": image_filenames,
-                    "species_info": species_info,
-                    "data_dir": extracted_config_path
-                },
-            )
-        ]
+        # No longer need to randomize and split the dataset
+        return [datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                "filepaths": all_image_filenames,
+                "species_info": species_info,
+                "data_dir": extracted_images_path
+            },
+        )]
     
 
 
@@ -168,6 +157,7 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         return annotations
     
     def _generate_examples(self, filepaths, species_info, data_dir):
+        """Yields examples as (key, example) tuples."""
         for file_name in filepaths:
             image_id = os.path.splitext(file_name)[0]  # Extract the base name without the file extension
             image_path = os.path.join(data_dir, f"{image_id}.jpg")
@@ -178,15 +168,19 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             if not species_row.empty:
                 species = species_row['Species'].values[0]
                 scientific_name = species_row['ScientificName'].values[0]
-                width = species_row['Width'].values[0]
-                height = species_row['Height'].values[0]
+                width = species_row['Witdh'].values[0]
+                height = species_row['Heigth'].values[0]
             else:
-                # If the species info is not found, continue to the next file
-                continue
-        
+                # Default values if not found
+                species = None
+                scientific_name = None
+                width = 1024  # Default value
+                height = 768   # Default value
+    
+            pics_array = None
             with Image.open(image_path) as img:
                 pics_array = np.array(img).tolist()  # Convert the PIL image to a numpy array and then to a list
-        
+    
             annotations = self._parse_yolo_labels(label_path, width, height)
             
             # Yield the dataset example
@@ -199,3 +193,4 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 "annotations": annotations
             }
 
+