Update new_dataset_script.py

new_dataset_script.py

@@ -60,13 +60,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.1.0")
 
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="full", version=VERSION, description="The full dataset"),
-        datasets.BuilderConfig(name="small", version=VERSION, description="A small sample of the dataset for quicker loading"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "full"
-
     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
     # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
@@ -115,104 +108,88 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
 
     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)
-
-        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:]
-    
-       
-        
-    
+    # 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"
+    })
     
-        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,
-                    "species_info": species_info,
-                    "data_dir": extracted_images_path,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": test_files,
-                    "species_info": species_info,
-                    "data_dir": extracted_images_path,
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _split_files(self, file_list):
-        num_files = len(file_list)
-        train_split_end = int(num_files * 0.7)
-        val_split_end = train_split_end + int(num_files * 0.15)
-        
-        train_files = file_list[:train_split_end]
-        val_files = file_list[train_split_end:val_split_end]
-        test_files = file_list[val_split_end:]
+    # Load the CSV file containing species and scientific names
+    species_info = pd.read_csv(data_files["csv"])
     
-        return train_files, val_files, test_files
-
+    # 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(
+            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,
+                "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
+    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."""
@@ -238,5 +215,4 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 "pics_array": pics_array,
                 "image_resolution": {"width": width, "height": height},
                 "annotations": annotations
-            }
-
+            }