facerecognitions.py

import cv2

# load the required trained XML classifiers
face_cascade = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('./haarcascade_eye.xml')

# define a video capture object
vid = cv2.VideoCapture(0)

while True:
    # detect if the camera is working or not
    if not vid.isOpened():
        print("Please check your camera")
        exit()

    # Capture the video frame
    # by frame
    ret, frame = vid.read()

    # Convert to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect the faces
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

    # Draw the rectangle around each face
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)

        # Detect the eyes
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]
        eyes = eye_cascade.detectMultiScale(roi_gray)
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)

    # Display the output
    cv2.imshow('Simple Face Recognitions', frame)

    # Stop if escape key is pressed
    k = cv2.waitKey(30) & 0xff
    if k==27:
        break

# Release the VideoCapture object
vid.release()