Update new_dataset_script.py

new_dataset_script.py

@@ -115,115 +115,122 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+    # 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 [
+        SplitGenerator(
+            name=Split.TRAIN,
+            gen_kwargs={
+                "filepaths": train_files,
+                "species_info": species_info,
+                "data_dir": extracted_images_path,
+                "split": "train",
+            },
+        ),
+        SplitGenerator(
+            name=Split.VALIDATION,
+            gen_kwargs={
+                "filepaths": val_files,
+                "species_info": species_info,
+                "data_dir": extracted_images_path,
+                "split": "validation",
+            },
+        ),
+        SplitGenerator(
+            name=Split.TEST,
+            gen_kwargs={
+                "filepaths": test_files,
+                "species_info": species_info,
+                "data_dir": extracted_images_path,
+                "split": "test",
+            },
+        ),
+    ]
 
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        
-        # 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"])
+    import os
+from PIL import Image
+import numpy as np
+import pandas as pd
 
-        # Get the list of all image files from the CSV
-        all_files = species_info['FileName'].tolist()
+# ... other necessary imports and class definitions
 
-        # Shuffle the list for random split
-        random.shuffle(all_files)
+def _generate_examples(self, filepaths, species_info, data_dir, split):
+    """Yields examples as (key, example) tuples."""
+    for file_name in filepaths:
+        # Extract the base name without the file extension for image_id
+        image_id = os.path.splitext(file_name)[0]
         
-        # Split the files into train/validation/test
-        num_files = len(all_files)
-        train_split_end = int(num_files * 0.7)
-        val_split_end = train_split_end + int(num_files * 0.15)
+        # Construct the full image file path and label file path
+        image_path = os.path.join(data_dir, f"{image_id}.jpg")
+        label_path = os.path.join(data_dir, f"{image_id}.txt")
         
-        train_files = all_files[:train_split_end]
-        val_files = all_files[train_split_end:val_split_end]
-        test_files = all_files[val_split_end:]
-
-        return [
-            SplitGenerator(
-                name=Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": train_files,
-                    "species_info": species_info,
-                    "data_dir": extracted_path,
-                    "split": "train",
-                },
-            ),
-            SplitGenerator(
-                name=Split.VALIDATION,
-                gen_kwargs={
-                    "filepaths": val_files,
-                    "species_info": species_info,
-                    "data_dir": extracted_path,
-                    "split": "validation",
-                },
-            ),
-            SplitGenerator(
-                name=Split.TEST,
-                gen_kwargs={
-                    "filepaths": test_files,
-                    "species_info": species_info,
-                    "data_dir": extracted_path,
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepaths, species_info, data_dir, split):
-        """Yields examples as (key, example) tuples."""
-        for file_name in filepaths:
-            # Extract the base name without the file extension
-            image_id = os.path.splitext(file_name)[0]
-            
-            # Construct the full image file path
-            image_path = os.path.join(data_dir, f"{image_id}.jpg")
-            # Construct the full label file path
-            label_path = os.path.join(data_dir, f"{image_id}.txt")
-            
-            # Open image and convert to array
-            with Image.open(image_path) as img:
-                pics_array = np.array(img)
-                width, height = img.size
-            
-            # Extract species and scientific name from CSV file
-            species_row = species_info.loc[species_info['FileName'] == image_id]
-            species = species_row['Species'].values[0]
-            scientific_name = species_row['ScientificName'].values[0]
-
-            # Parse YOLO annotations
-            annotations = self._parse_yolo_labels(label_path, width, height)
-
-            # Yield the final structured example
-            yield image_id, {
-                "image_id": image_id,
-                "species": species,
-                "scientific_name": scientific_name,
-                "pics_array": pics_array,
-                "image_resolution": {"width": width, "height": height},
-                "annotations": annotations
+        # Open image and convert to array
+        with Image.open(image_path) as img:
+            pics_array = np.array(img)
+            width, height = img.size
+        
+        # Extract species and scientific name from CSV file
+        species_row = species_info.loc[species_info['FileName'] == file_name]
+        species = species_row['Species'].values[0] if not species_row.empty else None
+        scientific_name = species_row['ScientificName'].values[0] if not species_row.empty else None
+
+        # Parse YOLO annotations
+        annotations = self._parse_yolo_labels(label_path, width, height)
+
+        # Yield the final structured example
+        yield image_id, {
+            "image_id": image_id,
+            "species": species,
+            "scientific_name": scientific_name,
+            "pics_array": pics_array,
+            "image_resolution": {"width": width, "height": height},
+            "annotations": annotations
+        }
+
+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_center_abs = x_center_rel * width
+        y_center_abs = y_center_rel * height
+        bbox_width = width_rel * width
+        bbox_height = height_rel * height
+        annotations.append({
+            "category_id": int(class_id),
+            "bounding_box": {
+                "x_center": x_center_abs,
+                "y_center": y_center_abs,
+                "width": bbox_width,
+                "height": bbox_height
             }
-
-    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())
-            annotations.append({
-                "category_id": int(class_id),
-                "bounding_box": {
-                    "x_center": x_center_rel * width,
-                    "y_center": y_center_rel * height,
-                    "width": width_rel * width,
-                    "height": height_rel * height
-                }
-            })
-        return annotatio
+        })
+    return annotations