import cv2

filepath = "img/xingye-1.png"
img = cv2.imread(filepath) # 读取图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换灰色

# OpenCV人脸识别分类器
classifier = cv2.CascadeClassifier(
 "C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml"
)
color = (0, 255, 0) # 定义绘制颜色
# 调用识别人脸
faceRects = classifier.detectMultiScale(
 gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects): # 大于0则检测到人脸
 for faceRect in faceRects: # 单独框出每一张人脸
 x, y, w, h = faceRect
 # 框出人脸
 cv2.rectangle(img, (x, y), (x + h, y + w), color, 2)
 # 左眼
 cv2.circle(img, (x + w // 4, y + h // 4 + 30), min(w // 8, h // 8),
   color)
 #右眼
 cv2.circle(img, (x + 3 * w // 4, y + h // 4 + 30), min(w // 8, h // 8),
   color)
 #嘴巴
 cv2.rectangle(img, (x + 3 * w // 8, y + 3 * h // 4),
   (x + 5 * w // 8, y + 7 * h // 8), color)

cv2.imshow("image", img) # 显示图像
c = cv2.waitKey(10)

cv2.waitKey(0)
cv2.destroyAllWindows()

# -*- coding:utf-8 -*-
# OpenCV版本的视频检测
import cv2


# 图片识别方法封装
def discern(img):
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 cap = cv2.CascadeClassifier(
 "C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml"
 )
 faceRects = cap.detectMultiScale(
 gray, scaleFactor=1.2, minNeighbors=3, minSize=(50, 50))
 if len(faceRects):
 for faceRect in faceRects:
  x, y, w, h = faceRect
  cv2.rectangle(img, (x, y), (x + h, y + w), (0, 255, 0), 2) # 框出人脸
 cv2.imshow("Image", img)


# 获取摄像头0表示第一个摄像头
cap = cv2.VideoCapture(0)
while (1): # 逐帧显示
 ret, img = cap.read()
 # cv2.imshow("Image", img)
 discern(img)
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break
cap.release() # 释放摄像头
cv2.destroyAllWindows() # 释放窗口资源