Tomo_4.mp4 May 2026

# Load the video cap = cv2.VideoCapture('tomo_4.mp4')

import matplotlib.pyplot as plt

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

import cv2 import numpy as np

# Check if video file was opened successfully if not cap.isOpened(): print("Error opening video file") # Load the video cap = cv2

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. pip install tensorflow opencv-python numpy You'll need to

pip install tensorflow opencv-python numpy You'll need to load the video, extract frames, and then feed these frames into a deep learning model to extract features.