Shkd257 Avi May 2026

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

# Load the VGG16 model for feature extraction model = VGG16(weights='imagenet', include_top=False, pooling='avg') shkd257 avi

def aggregate_features(frame_dir): features_list = [] for file in os.listdir(frame_dir): if file.startswith('features'): features = np.load(os.path.join(frame_dir, file)) features_list.append(features.squeeze()) aggregated_features = np.mean(features_list, axis=0) return aggregated_features pip install tensorflow opencv-python numpy You'll need to

def extract_features(frame_path): img = image.load_img(frame_path, target_size=(224, 224)) img_data = image.img_to_array(img) img_data = np.expand_dims(img_data, axis=0) img_data = preprocess_input(img_data) features = model.predict(img_data) return features cap.release() print(f"Extracted {frame_count} frames.") Now

while cap.isOpened(): ret, frame = cap.read() if not ret: break # Save frame cv2.imwrite(os.path.join(frame_dir, f'frame_{frame_count}.jpg'), frame) frame_count += 1

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.

pip install tensorflow opencv-python numpy You'll need to extract frames from your video. Here's a simple way to do it: