python调用OpenCV实现人脸识别功能

import cv2
import numpy as np

cv2.namedWindow("test") # Create a window
cap = cv2.VideoCapture(0) #Open camera one
success, frame = cap.read() #Read one frame

print("Camera open operation is: ", success);
color = (255,0,0) #Config the color
classfier = cv2.CascadeClassifier("Resources\haarcascade_frontalface_alt.xml")

#Make sure this xml file is in the same directory with py file
#Otherwise change it to absolute directory. This xml file can be found in D:\My 

Documents\Downloads\opencv\sources\data\haarcascades

while success:
  success, frame = cap.read()
  size = frame.shape[:2] #
  image = np.zeros(size, dtype = np.float16) #
  image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #
  cv2.equalizeHist(image, image) #
  #Below three lines config the minimal image size
  divisor = 8
  h, w = size
  minSize = ((int)(w/divisor), (int)(h/divisor))
  faceRects = classfier.detectMultiScale(image, 1.2, 2, cv2.CASCADE_SCALE_IMAGE, minSize) #Face detect
  if len(faceRects) > 0:#If face array length > 0
    for faceRect in faceRects: #Draw a rectangle for every face
        xf, yf, wf, hf = faceRect
        x = int((float)(xf))
        y = int((float)(yf))
        w = int((float)(wf))
        h = int((float)(hf))
        cv2.rectangle(frame, (x, y), (x + w, y + h), color)
        cv2.circle(frame, ((int)(x + 1.2 * w / 4), (int)(y + h / 3)), min((int)(w / 8), (int)(h / 8)), (255, 0, 0))
        cv2.circle(frame, ((int)(x + 2.8 * w / 4), (int)(y + h / 3)), min((int)(w / 8), (int)(h / 8)), (255, 0, 0))
        #cv2.rectangle(frame, ((int)(x + 3 * w / 8, (int)(y + 3 * h / 4))), ((int)(x + 5 * w / 8), (int)(y + 7 * h / 8)), (255, 0, 0))
  cv2.imshow("test", frame) #Display image

  key = cv2.waitKey(10)
  c = chr(key & 255)
  if c in ['q', 'Q', chr(27)]:
    break

cv2.destroyWindow("test")