Tomo_4.mp4 Apr 2026

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

# Extract features from all frames features = extract_features(frames) print(features.shape) The analysis depends on your specific goals, such as clustering, classification, or visualization. tomo_4.mp4

cap.release() For extracting features, you can use a pre-trained model like VGG16. We'll use TensorFlow/Keras for this. # 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.