import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input
cap.release() print(f"Extracted {frame_count} frames.") Now, let's use a pre-trained VGG16 model to extract features from these frames. 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. import numpy as np from tensorflow
pip install tensorflow opencv-python numpy You'll need to extract frames from your video. Here's a simple way to do it: pip install tensorflow opencv-python numpy You'll need to
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
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