# Simple example: visualize the feature space using PCA from sklearn.decomposition import PCA
import cv2 import numpy as np
pca = PCA(n_components=2) pca_features = pca.fit_transform(features) tomo_4.mp4
# Load the video cap = cv2.VideoCapture('tomo_4.mp4') # Simple example: visualize the feature space using
# Check if video file was opened successfully if not cap.isOpened(): print("Error opening video file") tomo_4.mp4
# Define a function to extract features from frames def extract_features(frames): # Convert frames to batch frames_batch = np.array(frames) # Preprocess for VGG16 frames_batch = preprocess_input(frames_batch) # Extract features features = model.predict(frames_batch) return features