import numpy as np
# Load the VGG16 model for feature extraction model = VGG16(weights='imagenet', include_top=False, pooling='avg') shkd257 avi
# Extract features from each frame for frame_file in os.listdir(frame_dir): frame_path = os.path.join(frame_dir, frame_file) features = extract_features(frame_path) print(f"Features shape: {features.shape}") # Do something with the features, e.g., save them np.save(os.path.join(frame_dir, f'features_{frame_file}.npy'), features) If you want to aggregate these features into a single representation for the video: import numpy as np # Load the VGG16
# 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) save them np.save(os.path.join(frame_dir
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.avi'