人脸识别(2)--Python3.6+OpenCV3.2识别实例

这里提供两个简单实例：

调用摄像头获取图片进行实时检测

本地获取图片进行人脸检测并保存图片

前提条件：

确保python+opencv环境已经搭建成功。搭建步骤可以参考上一篇博客：http://blog.csdn.net/u012842255/article/details/70171617

调用摄像头获取图片进行实时检测

调用摄像头，可以看到头像用方框框起来，并实时根据情况调整位置。

import cv2

import numpy as np

 

cv2.namedWindow(“test”)

cap=cv2.VideoCapture(0)

success, frame = cap.read()

color = (0,0,0)

classfier=cv2.CascadeClassifier(“/Users/liuqi/opencv/data/haarcascades/haarcascade_frontalface_alt.xml”)

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)

 divisor=8

 h, w = size

 minSize =(w//divisor, h//divisor)

 faceRects = classfier.detectMultiScale(image, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,minSize)

 if len(faceRects)>0:

  for faceRect in faceRects:

   x, y, w, h = faceRect

   cv2.rectangle(frame, (x, y), (x+w, y+h), color)

 cv2.imshow(“test”, frame)

 key=cv2.waitKey(10)

 c = chr(key & 255)

 if c in [‘q’, ‘Q’, chr(27)]:

  break

cv2.destroyWindow(“test”)

识别结果实例：

本地获取图片进行人脸检测并保存图片

从本地路径获取图片，识别人脸后将结果图片再保存在本地。

#!/usr/bin/env python

#coding=utf-8

 

import os

import sys

from PIL import Image, ImageDraw

import numpy as np

import cv2

#import dlib

 

def detect_object(infile, save_path):

 image = cv2.imread(infile)

 ”’检测图片，获取人脸在图片中的坐标”’

 size=image.shape[:2]#获得当前桢彩色图像的大小

 #image_set=np.zeros(size,dtype=np.float16)#定义一个与当前桢图像大小相同的的灰度图像矩阵

 image_grey = np.zeros(size, np.uint8)#创建一个空白图片

 #image_grey = Image.new(mode= “RGBA”,size = size, color = (117,255,0))

 #image_grey = cv2.imread(img_grey)

 grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)#将当前桢图像转换成灰度图像

 #img_binary = cv2.threshold(image,127,255,0)#将灰度图片转化为二进制图片

 color = (135,206,250) #设置人脸框的颜色

 #eye_cascade = cv2.CascadeClassifier(‘/Users/liuqi/opencv/data/haarcascades/haarcascade_eye.xml’)

 classfier=cv2.CascadeClassifier(“/Users/liuqi/opencv/data/haarcascades/haarcascade_frontalface_alt.xml”)

 

 #人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数

 faceRects = classfier.detectMultiScale(grey, scaleFactor = 1.3, minNeighbors = 4, minSize = (32, 32))

 result = []

 

 im = Image.open(infile)

 if len(faceRects) > 0: #大于0则检测到人脸

  draw = ImageDraw.Draw(im)

  num = 0

  for faceRect in faceRects:

   num += 1

   #单独框出每一张人脸

   x, y, w, h = faceRect

   #画出矩形框

   #cv2.rectangle(image_grey, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)

   cv2.rectangle(grey, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)

   a = im.crop(faceRect)

   file_name = os.path.join(save_path,str(num)+”.jpg”)

   #a.save(file_name)

 ”’保存新生成的图片”’

  #将当前帧保存为图片

  #cv2.imwrite(file_name,image_grey)

  cv2.imwrite(file_name,grey)

 

  drow_save_path = os.path.join(save_path,”out.jpg”)

  im.save(drow_save_path, “JPEG”, quality=80)

 else:

  print (“Error: cannot detect faces on %s” % infile)

 return 0

 

def process(infile):

 #获取图片，进行检测

 #image = cv2.imread(infile)

 ”’在原图上框出头像并且截取每个头像到单独文件夹”’

 #创建输出图片的路径

 #im = Image.open(infile)

 path = os.path.abspath(infile)

 save_path = os.path.splitext(path)[0]+”_face”

 try:

  os.mkdir(save_path)

 except:

  pass

 

 faces = detect_object(infile, save_path)

 

if name == “main“:

 process(“/Users/liuqi/Desktop/2.jpg”)

实现的效果如下：

选取的图片与识别后的图片：