python

 import cv2



 # Load the pre-trained face detection classifier

 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')



 # Initialize the camera

 cap = cv2.VideoCapture(0)



 while True:

 # Capture frame-by-frame

 ret, frame = cap.read()



 # Convert the frame to grayscale

 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)



 # Detect faces in the grayscale frame

 faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))



 # Draw rectangles around the faces

 for (x, y, w, h) in faces:

 cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)



 # Display the frame

 cv2.imshow('Face Detection', frame)



 # Break the loop when 'q' is pressed

 if cv2.waitKey(1) & 0xFF == ord('q'):

 break



 # Release the camera and close all windows

 cap.release()

 cv2.destroyAllWindows()