Update new_dataset_script.py

new_dataset_script.py

@@ -18,7 +18,8 @@
 import csv
 import json
 import os
-
+from PIL import Image
+import numpy as np
 import datasets
 
 
@@ -129,55 +130,60 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             },
         )]
 
-    
-
-# ... 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):
-    """Yields examples as (key, example) tuples."""
-        for file_name in filepaths:
-            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")
-            
-            with Image.open(image_path) as img:
-                pics_array = np.array(img)
-                width, height = img.size
-            
-            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
-    
-            annotations = self._parse_yolo_labels(label_path, width, height)
-            
-            # 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,
-                "image": img,  # Assuming you want to keep the PIL Image object; otherwise use pics_array
-                "annotations": annotations
+ 
+def save_metadata_as_json(image_id, annotations, species, scientific_name, json_path):
+    metadata = {
+        "image_id": image_id,
+        "species": species,
+        "scientific_name": scientific_name,
+        "annotations": annotations
+    }
+    with open(json_path, 'w') as json_file:
+        json.dump(metadata, json_file)
+
+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
             }
-            
-            yield image_id, img_with_metadata_and_annotations
+        })
+    return annotations
+
+def _generate_examples(self, filepaths, species_info, data_dir, split):
+    """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")
+        label_path = os.path.join(data_dir, f"{image_id}.txt")
+        json_path = os.path.join(data_dir, f"{image_id}.json")  # JSON file path
+        
+        with Image.open(image_path) as img:
+            width, height = img.size
+        
+        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
+
+        annotations = self._parse_yolo_labels(label_path, width, height)
+        
+        # Save metadata to JSON
+        save_metadata_as_json(image_id, annotations, species, scientific_name, json_path)
+        
+        yield image_id, {
+            "image": img,  # Return the PIL image
+            "metadata_json": json_path  # Return the path to the JSON file
+        }