Tomo_4.mp4 -

# Load the VGG16 model for feature extraction model = VGG16(weights='imagenet', include_top=False, pooling='avg')

# Simple example: visualize the feature space using PCA from sklearn.decomposition import PCA tomo_4.mp4

# Load the video cap = cv2.VideoCapture('tomo_4.mp4') # Load the VGG16 model for feature extraction

import cv2 import numpy as np

import matplotlib.pyplot as plt

pca = PCA(n_components=2) pca_features = pca.fit_transform(features) and analysis steps. Also

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.