opencv 2.4.9+vs2013 人脸识别环境搭建，眼睛，鼻子，嘴巴等 摄像头读取和显示

一 ，环境设置  

      工具： 

      opencv2.4.9地址：https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download
      VS2013自行安装
      步骤：

     1.   安装opencv2.4.9，解压，请务必记住自己解压的路径。以我自己的路径为例D:\

       


     2.  配置环境变量

         （1）系统变量 Path：添加 D:\opencv2.4.9\opencv\build\x86\vc12\bin

      （2）用户变量： 添加opencv变量值
D:\opencv2.4.9\opencv\build

 
         添加PATH变量（有就不需要添加，但是值需要添加）值D:\opencv2.4.9\opencv\build\x86\vc12\bin

 
    说明：不管你系统是32位还是64位，路径目录均选择X86，因为编译都是使用32位编译；如果选用X64，则程序运行时候会出错。

 
   


3.
 新建visual C项目

 
  新建 visual C++项目，如下图所示，项目选项注意：如下图。

 
  


 
 


 
  

   4.  工程目录的配置（Debug）

      找到属性管理器     视图---其他窗口----属性管理器
        如果找不到，请安装下图方法找到。双击Debug|Win32打开如下窗口，

    


   设置如下：（下图红框项为设置项）

   1、包含目录：（VC++目录）

        D:\opencv2.4.9\opencv\build\include

        D:\opencv2.4.9\opencv\build\include\opencv

        D:\opencv2.4.9\opencv\build\include\opencv2

  2、库目录：（VC++目录）D:\opencv2.4.9\opencv\build\x86\vc12\lib

  3、连接器->输入->附加依赖项：

  

opencv_ml249d.lib

opencv_calib3d249d.lib

opencv_contrib249d.lib

opencv_core249d.lib

opencv_features2d249d.lib

opencv_flann249d.lib

opencv_gpu249d.lib

opencv_highgui249d.lib

opencv_imgproc249d.lib

opencv_legacy249d.lib

opencv_objdetect249d.lib

opencv_ts249d.lib

opencv_video249d.lib

opencv_nonfree249d.lib

opencv_ocl249d.lib

opencv_photo249d.lib

opencv_stitching249d.lib

opencv_superres249d.lib

opencv_videostab249d.lib

其实以上都是D:\Program Files\opencv\build\x86\vc12\lib下所有的lib文件，你会发现，有的后面带上d，有的没有d，这是因为Debug的就有d，Release则没有d。

 




5.  工程目录的配置（Release）

    其他与Debug一样，只是连接器->输入->附加依赖项不一样，设置如下：

opencv_objdetect249.lib

opencv_ts249.lib

opencv_video249.lib

opencv_nonfree249.lib

opencv_ocl249.lib

opencv_photo249.lib

opencv_stitching249.lib

opencv_superres249.lib

opencv_videostab249.lib

opencv_calib3d249.lib

opencv_contrib249.lib

opencv_core249.lib

opencv_features2d249.lib

opencv_flann249.lib

opencv_gpu249.lib

opencv_highgui249.lib

opencv_imgproc249.lib

opencv_legacy249.lib

opencv_ml249.lib

6.  测试代码

   解决方案资源管理器-----源文件-----右键-----添加---新建项---写代码

二 人脸识别，

   以下包含三个部分： 摄像头读取和显示，人脸识别单张图像，人脸识别视频形式

1.   读取摄像头和显示：

int main( int argc, char** argv ) {
//int i=0;
cvNamedWindow( "Example2_9", CV_WINDOW_AUTOSIZE );
CvCapture* capture;
capture = cvCreateCameraCapture(0);
assert( capture != NULL );
IplImage* frame;
//frame = cvQueryFrame( capture );  //先读一次规避掉第一帧
while(1) {
frame = cvQueryFrame( capture );
if( !frame ) break;         //如果程序不能读取摄像头，那么将此句删除或加个判断即采用注释掉的i语句又或者在while前读一次
//if( !frame&i>0 ) break;
//if(i>0)
cvShowImage( "Example2_9", frame );
char c = cvWaitKey(10);
if( c == 27 ) break;
//i++;
}
cvReleaseCapture( &capture );
cvDestroyWindow( "Example2_9" );
return 0;
}
//在运行书上第2章练习2运动跟踪时，删掉掉if语句不能运行,加个判断可以

2.  人脸识别

OpenCV_人脸检测

利用OpenCV自带的人脸识别库haarcascade_frontalface_alt.xml进行人脸识别测试

Opencv自带了几个训练好的分类器，我们可以直接调用测试，分类器的目录在opencv的安装目录opencv246\opencv\sources\data\haarcascades\文件夹下

关于人脸检测有四个分类器



这里我们采用haarcascade_frontalface_alt.xml进行人脸识别测试。

调用代码如下：

#include "cv.h"
#include "highgui.h"

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <math.h>
#include <float.h>
#include <limits.h>
#include <time.h>
#include <ctype.h>
using namespace std;

static CvMemStorage* storage = 0;
static CvHaarClassifierCascade* cascade = 0;

void detect_and_draw( IplImage* image );

const char* cascade_name ="D:/opencv2.4.9/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml"; //opencv自带人脸识别训练结果
/* "haarcascade_profileface.xml";*/

int main()
{
CvCapture* capture = 0;

cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );//加载opencv自带人脸识别训练结果

if( !cascade )
{
fprintf( stderr, "ERROR: Could not load classifier cascade/n" );
//fprintf( stderr,
//"Usage: facedetect --cascade=/"<cascade_path>"/[filename|camera_index]/n" );
return -1;
}
storage = cvCreateMemStorage(0);

cvNamedWindow( "result", 1 );

const char* filename = "people.jpg";
IplImage* image = cvLoadImage(filename ); //加载图像
if( image )
{
detect_and_draw( image );
cvWaitKey(0);
cvReleaseImage( &image );
}

cvDestroyWindow("result");
cvWaitKey(0);
return 0;
}

void detect_and_draw( IplImage* img ) //检测人脸并画出区域
{
static CvScalar colors[] =   //随机生成颜色序列
{
{{0,0,255}},
{{0,128,255}},
{{0,255,255}},
{{0,255,0}},
{{255,128,0}},
{{255,255,0}},
{{255,0,0}},
{{255,0,255}}
};

double scale = 1.3;
IplImage* gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 );
IplImage* small_img = cvCreateImage( cvSize( cvRound (img->width/scale),
cvRound (img->height/scale)),8, 1 );
int i;

cvCvtColor( img, gray, CV_BGR2GRAY );//彩色图转化为灰度图
cvResize( gray, small_img, CV_INTER_LINEAR ); //利用线性插值算法归一化图像
cvEqualizeHist( small_img, small_img ); //直方图均衡化
cvClearMemStorage( storage );

if( cascade )
{
double t = (double)cvGetTickCount();
CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage,
1.1, 2, 0/*CV_HAAR_DO_CANNY_PRUNING*/,
cvSize(30, 30) );
t = (double)cvGetTickCount() - t;
printf( "detection time = %gms/n", t/((double)cvGetTickFrequency()*1000.) ); //统计人脸定位所用时间
for( i = 0; i < (faces ? faces->total : 0); i++ )
{
CvRect* r = (CvRect*)cvGetSeqElem( faces, i );
CvPoint center;
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale); //圆心
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale); //半径
cvCircle( img, center, radius, colors[i%8], 3, 8, 0 ); //用圆形圈出人脸区域
}
}

cvShowImage( "result", img );
cvReleaseImage( &gray );
cvReleaseImage( &small_img );
}

   2.  人脸识别 ———视频

#include "cv.h"
#include "highgui.h"

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <math.h>
#include <float.h>
#include <limits.h>
#include <time.h>
#include <ctype.h>
using namespace std;

static CvMemStorage* storage = 0;
static CvHaarClassifierCascade* cascade = 0;

void detect_and_draw(IplImage* image);

const char* cascade_name = "D:/opencv2.4.9/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml"; //opencv×Ô´øÈËÁ³Ê¶±ðѵÁ·½á¹û
/* "haarcascade_profileface.xml";*/

int main()
{
CvCapture* capture;
capture = cvCreateCameraCapture(0);
cvNamedWindow("face", 1);

cascade = (CvHaarClassifierCascade*)cvLoad(cascade_name, 0, 0, 0);//¼ÓÔØopencv×Ô´øÈËÁ³Ê¶±ðѵÁ·½á¹û

if (!cascade)
{
fprintf(stderr, "ERROR: Could not load classifier cascade/n");
//fprintf( stderr,
//"Usage: facedetect --cascade=/"<cascade_path>"/[filename|camera_index]/n" );
return -1;
}
storage = cvCreateMemStorage(0);
assert(capture != NULL);

IplImage* frame;
frame = cvQueryFrame( capture );  //ÏȶÁÒ»´Î¹æ±ÜµôµÚÒ»Ö¡
while (1) {
frame = cvQueryFrame(capture);
detect_and_draw(frame);
//if (!frame) break;         //Èç¹û³ÌÐò²»ÄܶÁÈ¡ÉãÏñÍ·£¬ÄÇô½«´Ë¾äɾ³ý»ò¼Ó¸öÅжϼ´²ÉÓÃ×¢Ê͵ôµÄiÓï¾äÓÖ»òÕßÔÚwhileÇ°¶ÁÒ»´Î
//if( !frame&i>0 ) break;
//if(i>0)
cvShowImage("face", frame);
char c = cvWaitKey(10);
if (c == 27) break;
//i++;
}
cvReleaseCapture(&capture);
cvDestroyWindow("face");
cvWaitKey(0);
return 0;

}

void detect_and_draw(IplImage* img) //¼ì²âÈËÁ³²¢»­³öÇøÓò
{
static CvScalar colors[] =   //Ëæ»úÉú³ÉÑÕÉ«ÐòÁÐ
{
{ { 0, 0, 255 } },
{ { 0, 128, 255 } },
{ { 0, 255, 255 } },
{ { 0, 255, 0 } },
{ { 255, 128, 0 } },
{ { 255, 255, 0 } },
{ { 255, 0, 0 } },
{ { 255, 0, 255 } }
};

double scale = 1.3;
IplImage* gray = cvCreateImage(cvSize(img->width, img->height), 8, 1);
IplImage* small_img = cvCreateImage(cvSize(cvRound(img->width / scale),
cvRound(img->height / scale)), 8, 1);
int i;

cvCvtColor(img, gray, CV_BGR2GRAY);//²Êɫͼת»¯Îª»Ò¶Èͼ
cvResize(gray, small_img, CV_INTER_LINEAR); //ÀûÓÃÏßÐÔ²åÖµËã·¨¹éÒ»»¯Í¼Ïñ
cvEqualizeHist(small_img, small_img); //Ö±·½Í¼¾ùºâ»¯
cvClearMemStorage(storage);

if (cascade)
{
double t = (double)cvGetTickCount();
CvSeq* faces = cvHaarDetectObjects(small_img, cascade, storage,
1.1, 2, 0/*CV_HAAR_DO_CANNY_PRUNING*/,
cvSize(30, 30));
t = (double)cvGetTickCount() - t;
printf("detection time = %gms/n", t / ((double)cvGetTickFrequency()*1000.)); //ͳ¼ÆÈËÁ³¶¨Î»ËùÓÃʱ¼ä
for (i = 0; i < (faces ? faces->total : 0); i++)
{
CvRect* r = (CvRect*)cvGetSeqElem(faces, i);
CvPoint center;
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale); //Ô²ÐÄ
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale); //°ë¾¶
cvCircle(img, center, radius, colors[i % 8], 3, 8, 0); //ÓÃÔ²ÐÎȦ³öÈËÁ³ÇøÓò
}
}

//cvShowImage("result", img);
cvReleaseImage(&gray);
cvReleaseImage(&small_img);
}

注释：

const char* cascade_name="haarcascade_frontalface_alt2.xml";//分类器的名称

const char* cascade_name1="haarcascade_eye_tree_eyeglasses.xml";//分类器的名称

const char* cascade_name2="haarcascade_frontalface_alt_tree.xml";//分类器的名称

const char* cascade_name3="haarcascade_mcs_mouth.xml";//分类器的名称

const char* cascade_name4="haarcascade_mcs_nose.xml";//分类器的名称

这是不同的分类器，你可以在你安装的OpenCV中找到。如D:\Program Files\OpenCV2.0\vs2008\data\haarcascades

不同分类器能够帮助你识别不同的部分，如眼睛，鼻子和嘴，更多的需要自己去探索吧。
注释：

  把圆形转换成矩形：

pt1.x = r->x*scale; 
pt2.x = (r->x+r->width)*scale; 
pt1.y = r->y*scale; 
pt2.y = (r->y+r->height)*scale; 
cvRectangle( img, pt1, pt2, CV_RGB(255,0,0), 3, 8, 0 ); 

pt1 : 矩形左上角

pt2:  矩形右下角

只需把  

cvCircle(img, center, radius, colors[i % 8], 3, 8, 0); //用圆形圈出人脸区域
换成

cvRectangle(img, cvPoint(r->x*scale, r->y*scale), cvPoint((r->x + r->width)*scale, (r->x + r->height)*scale), cvScalar(0, 0, 255), 2, 8, 0);