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 cv2 import os
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 shkd257 avi
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
# Video file path video_path = 'shkd257.avi' save them np.save(os.path.join(frame_dir
# 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: