This Python script uses the OpenCV library to perform real-time face detection and create an output video with rectangles drawn around detected faces. The script imports the OpenCV library, loads a pre-trained face detection classifier using the Haar Cascade classifier, opens a video capture object, retrieves video frame dimensions, defines the video codec (XVID), and creates a VideoWriter object to save processed frames as an output video.

The variable 'total_faces' is initialized to count the number of detected faces. The script enters a loop to process each frame of the video, reads a video frame, converts it to grayscale, and applies the Haar Cascade classifier to detect faces in the grayscale frame. For each detected face, a green rectangle is drawn around it using OpenCV's drawing functions.

The processed frame is then written to the output video. The processed frame is displayed in a window with the title 'Video'. The loop continues until the 'q' key is pressed, and the script releases video objects and closes OpenCV windows. The video capture object and VideoWriter object are released to free up system resources.

In summary, this script reads a video file, applies face detection to each frame, draws rectangles around detected faces, and saves the processed frames as a new video. The final output is an AVI video file with detected faces highlighted by green rectangles.