Shkd257 Avi

# 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)

import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input shkd257 avi

import numpy as np

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

# Video file path video_path = 'shkd257.avi' axis=0) return aggregated_features

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