Tomo_4.mp4

# Check if video file was opened successfully if not cap.isOpened(): print("Error opening video file")

plt.scatter(pca_features[:, 0], pca_features[:, 1]) plt.show() This example provides a basic framework for extracting deep features from a video and simple analysis. Depending on your specific requirements (e.g., video classification, anomaly detection), you might need to adjust the model, preprocessing, and analysis steps. Also, processing a video frame-by-frame can be computationally intensive and might not be suitable for real-time applications without optimization. tomo_4.mp4

# Read and display video frames frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break # Convert to RGB (OpenCV reads in BGR format) frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame_rgb) # Check if video file was opened successfully if not cap

cap.release() For extracting features, you can use a pre-trained model like VGG16. We'll use TensorFlow/Keras for this. # Read and display video frames frames = [] while cap

import cv2 import numpy as np

import matplotlib.pyplot as plt