face.py

import cv2

face_classifier = cv2.CascadeClassifier(
    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)
video_capture = cv2.VideoCapture(0)

def detect_bounding_box(vid):
    gray_image = cv2.cvtColor(vid, cv2.COLOR_BGR2GRAY)
    faces = face_classifier.detectMultiScale(gray_image, 1.1, 5, minSize=(40, 40))
    if len(faces) > 1:
        # Draw warning message on the video frame
        cv2.putText(vid, "Warning: Multiple faces detected!", (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    for (x, y, w, h) in faces:
        cv2.rectangle(vid, (x, y), (x + w, y + h), (0, 255, 0), 4)
    return faces

while True:
    result, video_frame = video_capture.read()  # read frames from the video
    if result is False:
        break  # terminate the loop if the frame is not read successfully

    faces = detect_bounding_box(
        video_frame
    )  # apply the function we created to the video frame
    
    cv2.imshow(
        "My Face Detection Project", video_frame
    )  # display the processed frame in a window named "My Face Detection Project"

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

video_capture.release()
cv2.destroyAllWindows()