Shkd257 Avi Apr 2026

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

# Video file path video_path = 'shkd257.avi' 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. def extract_features(frame_path): img = image

import numpy as np

import numpy as np from tensorflow.keras.applications import VGG16 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input the model used for feature extraction