Shkd257 Avi -

# Video file path video_path = 'shkd257.avi'

video_features = aggregate_features(frame_dir) print(f"Aggregated video features shape: {video_features.shape}") np.save('video_features.npy', video_features) This example demonstrates a basic pipeline. Depending on your specific requirements, you might want to adjust the preprocessing, the model used for feature extraction, or how you aggregate features from multiple frames. shkd257 avi

cap.release() print(f"Extracted {frame_count} frames.") Now, let's use a pre-trained VGG16 model to extract features from these frames. # Video file path video_path = 'shkd257

# Create a directory to store frames if it doesn't exist frame_dir = 'frames' if not os.path.exists(frame_dir): os.makedirs(frame_dir) the model used for feature extraction