Update code.txt

code.txt

@@ -259,4 +259,69 @@ function ObjectDetector() {
 
 export default ObjectDetector;
 
+
+
+import json
+import random
+import os
+
+# Load the COCO annotations file
+coco_file = 'annotations.json'  # Path to your COCO annotations file
+output_dir = 'output_dir/'  # Directory to save the split files
+train_ratio = 0.8  # 80% for training, 20% for validation
+
+# Create output directory if it doesn't exist
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Load COCO annotations
+with open(coco_file, 'r') as f:
+    coco_data = json.load(f)
+
+# Extract images and annotations
+images = coco_data['images']
+annotations = coco_data['annotations']
+
+# Shuffle images to ensure random split
+random.shuffle(images)
+
+# Split images into training and validation sets
+train_size = int(len(images) * train_ratio)
+train_images = images[:train_size]
+val_images = images[train_size:]
+
+# Create dictionaries to store image IDs for filtering annotations
+train_image_ids = {img['id'] for img in train_images}
+val_image_ids = {img['id'] for img in val_images}
+
+# Split annotations based on image IDs
+train_annotations = [ann for ann in annotations if ann['image_id'] in train_image_ids]
+val_annotations = [ann for ann in annotations if ann['image_id'] in val_image_ids]
+
+# Create train and validation splits for COCO format
+train_data = {
+    'images': train_images,
+    'annotations': train_annotations,
+    'categories': coco_data['categories'],  # Keep categories the same
+}
+
+val_data = {
+    'images': val_images,
+    'annotations': val_annotations,
+    'categories': coco_data['categories'],  # Keep categories the same
+}
+
+# Save the new train and validation annotation files
+train_file = os.path.join(output_dir, 'train_annotations.json')
+val_file = os.path.join(output_dir, 'val_annotations.json')
+
+with open(train_file, 'w') as f:
+    json.dump(train_data, f)
+
+with open(val_file, 'w') as f:
+    json.dump(val_data, f)
+
+print(f"Train annotations saved to: {train_file}")
+print(f"Validation annotations saved to: {val_file}")
+