import cv2
image = cv2.imread(imagepath)

import cv2
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

import cv2
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

import cv2
cv2.imshow("Image Title",image)

import cv2
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')

import cv2

# 探测图片中的人脸

faces = face_cascade.detectMultiScale(
  gray,
  scaleFactor = 1.15,
  minNeighbors = 5,
  minSize = (5,5),
  flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)

import cv2

print "发现{0}个人脸!".format(len(faces))

for(x,y,w,h) in faces:
  cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

# coding:utf-8

import sys

 

reload(sys)

sys.setdefaultencoding('utf8')

#  __author__ = '郭 璞'

#  __date__ = '2016/9/5'

#  __Desc__ = 人脸检测小例子，以圆圈圈出人脸

import cv2

# 待检测的图片路径

imagepath = r'./heat.jpg'

 

# 获取训练好的人脸的参数数据，这里直接从GitHub上使用默认值

face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')

 

# 读取图片

image = cv2.imread(imagepath)

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

 

# 探测图片中的人脸

faces = face_cascade.detectMultiScale(

  gray,

  scaleFactor = 1.15,

  minNeighbors = 5,

  minSize = (5,5),

  flags = cv2.cv.CV_HAAR_SCALE_IMAGE

)

 

print "发现{0}个人脸!".format(len(faces))

 

for(x,y,w,h) in faces:

  # cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)

  cv2.circle(image,((x+x+w)/2,(y+y+h)/2),w/2,(0,255,0),2)

 

cv2.imshow("Find Faces!",image)

cv2.waitKey(0)