Update new_dataset_script.py

new_dataset_script.py

@@ -106,95 +106,58 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         citation=_CITATION,
     )
 
-
     def _split_generators(self, dl_manager):
-    # Download and extract the dataset using Hugging Face's datasets library
-    data_files = dl_manager.download_and_extract({
-        "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
-        "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
-    })
-    
-    # Load the CSV file containing species and scientific names
-    species_info = pd.read_csv(data_files["csv"])
-    
-    # The directory 'Labeled Stomatal Images' is where the images and labels are stored after extraction
-    extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")
-
-    # Get the list of image filenames from the CSV
-    all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
-
-    # Shuffle the list for random split
-    random.seed(42)  # Set a random seed for reproducibility
-    random.shuffle(all_image_filenames)
-    
-    # Split the files into train/validation/test
-    num_files = len(all_image_filenames)
-    train_split_end = int(num_files * 0.7)
-    val_split_end = train_split_end + int(num_files * 0.15)
-    
-    train_files = all_image_filenames[:train_split_end]
-    val_files = all_image_filenames[train_split_end:val_split_end]
-    test_files = all_image_filenames[val_split_end:]
-
-    return [
-        datasets.SplitGenerator(
+        # 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/Labeled Stomatal Images.csv",
+            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
+        })
+        
+        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()
+        
+        # No longer need to randomize and split the dataset
+        return [datasets.SplitGenerator(
             name=datasets.Split.TRAIN,
             gen_kwargs={
-                "filepaths": train_files,
-                "species_info": species_info,
-                "data_dir": extracted_images_path,
-                "split": "train",
-            },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.VALIDATION,
-            gen_kwargs={
-                "filepaths": val_files,
+                "filepaths": all_image_filenames,
                 "species_info": species_info,
                 "data_dir": extracted_images_path,
-                "split": "validation",
             },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.TEST,
-            gen_kwargs={
-                "filepaths": test_files,
-                "species_info": species_info,
-                "data_dir": extracted_images_path,
-                "split": "test",
-            },
-        ),
-    ]
+        )]
 
- 
+    
 
 # ... other necessary imports and class definitions
     def _parse_yolo_labels(self, label_path, width, height):
-    annotations = []
-    with open(label_path, 'r') as file:
-        yolo_data = file.readlines()
-
-    for line in yolo_data:
-        class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
-        x_min = (x_center_rel - width_rel / 2) * width
-        y_min = (y_center_rel - height_rel / 2) * height
-        x_max = (x_center_rel + width_rel / 2) * width
-        y_max = (y_center_rel + height_rel / 2) * height
-        annotations.append({
-            "category_id": int(class_id),
-            "bounding_box": {
-                "x_min": x_min,
-                "y_min": y_min,
-                "x_max": x_max,
-                "y_max": y_max
-            }
-        })
-    return annotations
-
-    def _generate_examples(self, filepaths, species_info, data_dir, split):
-        """Yields examples as (key, example) tuples."""
+        annotations = []
+        with open(label_path, 'r') as file:
+            yolo_data = file.readlines()
+    
+        for line in yolo_data:
+            class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
+            x_min = (x_center_rel - width_rel / 2) * width
+            y_min = (y_center_rel - height_rel / 2) * height
+            x_max = (x_center_rel + width_rel / 2) * width
+            y_max = (y_center_rel + height_rel / 2) * height
+            annotations.append({
+                "category_id": int(class_id),
+                "bounding_box": {
+                    "x_min": x_min,
+                    "y_min": y_min,
+                    "x_max": x_max,
+                    "y_max": y_max
+                }
+            })
+        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_id = os.path.splitext(file_name)[0]
             image_path = os.path.join(data_dir, f"{image_id}.jpg")
             label_path = os.path.join(data_dir, f"{image_id}.txt")
             
@@ -207,12 +170,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             scientific_name = species_row['ScientificName'].values[0] if not species_row.empty else None
     
             annotations = self._parse_yolo_labels(label_path, width, height)
-    
-            yield image_id, {
+            
+            # Create a structured object that includes the image and its metadata
+            img_with_metadata_and_annotations = {
                 "image_id": image_id,
                 "species": species,
                 "scientific_name": scientific_name,
-                "pics_array": pics_array,
-                "image_resolution": {"width": width, "height": height},
+                "image": img,  # Assuming you want to keep the PIL Image object; otherwise use pics_array
                 "annotations": annotations
-            }
+            }
+            
+            yield image_id, img_with_metadata_and_annotations