小玩OpenSURF图像识别 （C++）

 

在我的一篇文章 http://blog.csdn.net/chenyujing1234/article/details/7601800

里面讲到C#版的OpenSURF图像识别例子，它是不信赖于OpenCV的；

这里我将对C++版本进行代码分析。希望对大家有帮助。（源码可到http://code.google.com/p/opensurf1/ 下载）

链接的openCV lib文件有：

 

这些库文件的名字有点长，因为它是我自己下opencv源码用CMake编译出来的。

OpenCV 链接库的得到请参考我的文章<<OpenCV2.1.0编译详细讲解>>

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本例子分为6个小功能：

（1） 单张图征特征的提取

（2）Video特征的提取

（3）单张图片与视频的特征匹配

（4）

（5）

 

一、 单张图片的特征点识别

先看看本文实现的效果：

 

1、实现过程

1、1 加载图片

主要调用了opencv里的函数 cvLoadImage

 // Declare Ipoints and other stuff  IpVec ipts;  IplImage *img=cvLoadImage("imgs/sf.jpg");

1、2  检测和描述图片中的兴趣点

  // Detect and describe interest points in the image  clock_t start = clock();  surfDetDes(img, ipts, false, 5, 4, 2, 0.0004f);   clock_t end = clock();

以下是surflib.h里的代码

//! Library function builds vector of described interest pointsinline void surfDetDes(IplImage *img,  /* image to find Ipoints in */                       std::vector<Ipoint> &ipts, /* reference to vector of Ipoints */                       bool upright = false, /* 运行在交替不变模式? */                       int octaves = OCTAVES, /* 要计算的八度数量 */                       int intervals = INTERVALS, /* 每个八度的兴趣点数目 */                       int init_sample = INIT_SAMPLE, /* 初始化采样 step */                       float thres = THRES /* blob response threshold */){  // 创建 integral-image representation of the image  IplImage *int_img = Integral(img);    // 创建 Fast Hessian Object  FastHessian fh(int_img, ipts, octaves, intervals, init_sample, thres);   // 提取兴趣点并存在 vector ipts  fh.getIpoints();    // 创建 Surf Descriptor Object  Surf des(int_img, ipts);  // 提取  the ipts 的描述符  des.getDescriptors(upright);  // 释放整个图像  cvReleaseImage(&int_img);}

 

1、2、1  看创建图片的intergral-image表现的过程

//! Computes the integral image of image img.  Assumes source image to be a //! 32-bit floating point.  Returns IplImage of 32-bit float form.IplImage *Integral(IplImage *source){  //  转化图片到单通道 32f  IplImage *img = getGray(source);  IplImage *int_img = cvCreateImage(cvGetSize(img), IPL_DEPTH_32F, 1);  // 为数据访问建立变量  int height = img->height;  int width = img->width;  int step = img->widthStep/sizeof(float);  float *data   = (float *) img->imageData;    float *i_data = (float *) int_img->imageData;    // 首先是行  float rs = 0.0f;  for(int j=0; j<width; j++)   {    rs += data[j];     i_data[j] = rs;  }  // remaining cells are sum above and to the left  for(int i=1; i<height; ++i)   {    rs = 0.0f;    for(int j=0; j<width; ++j)     {      rs += data[i*step+j];       i_data[i*step+j] = rs + i_data[(i-1)*step+j];    }  }  // 释放 gray image  cvReleaseImage(&img);  // return the integral image  return int_img;}

 

将图片转化为单通道是调用了OpenCv里的库

// 转化图片到单通道 32FIplImage *getGray(const IplImage *img){  // 检查我们已经提供了一个非空的img指针  if (!img) error("Unable to create grayscale image.  No image supplied");  IplImage* gray8, * gray32;  gray32 = cvCreateImage( cvGetSize(img), IPL_DEPTH_32F, 1 );  if( img->nChannels == 1 )    gray8 = (IplImage *) cvClone( img );  else {    gray8 = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 );    cvCvtColor( img, gray8, CV_BGR2GRAY );  }  cvConvertScale( gray8, gray32, 1.0 / 255.0, 0 );  cvReleaseImage( &gray8 );  return gray32;}

1、2、2  提取兴趣点

前提是创建了 Fast  Hessian Object.

在getIpoins里完成了几件事：

(1)建立回复map

(2)获得回复layers

(3)判断是否是要提取的特征点

//! 找到图片的特征，并写到特征的vecto中void FastHessian::getIpoints(){  // 过滤 index map  static const int filter_map [OCTAVES][INTERVALS] = {{0,1,2,3}, {1,3,4,5}, {3,5,6,7}, {5,7,8,9}, {7,9,10,11}};  // 清除掉存在的ipts的vector  ipts.clear();  // 建立回复 map  buildResponseMap();  // 获得回复 layers  ResponseLayer *b, *m, *t;  for (int o = 0; o < octaves; ++o) for (int i = 0; i <= 1; ++i)  {    b = responseMap.at(filter_map[o][i]);    m = responseMap.at(filter_map[o][i+1]);    t = responseMap.at(filter_map[o][i+2]);    // loop over middle response layer at density of the most     // sparse layer (always top), to find maxima across scale and space    for (int r = 0; r < t->height; ++r)    {      for (int c = 0; c < t->width; ++c)      {        if (isExtremum(r, c, t, m, b))        {          interpolateExtremum(r, c, t, m, b);        }      }    }  }}

1、2、2、1  建立responses map的过程

(1) 获得图片属性
(2) 计算接近的决定性的hessian值

(3) 从图片中解析responses layer

//! Build map of DoH responsesvoid FastHessian::buildResponseMap(){  // Calculate responses for the first 4 octaves:  // Oct1: 9,  15, 21, 27  // Oct2: 15, 27, 39, 51  // Oct3: 27, 51, 75, 99  // Oct4: 51, 99, 147,195  // Oct5: 99, 195,291,387  // Deallocate memory and clear any existing response layers  for(unsigned int i = 0; i < responseMap.size(); ++i)      delete responseMap[i];  responseMap.clear();  // 获得图片的属性  int w = (i_width / init_sample);  int h = (i_height / init_sample);  int s = (init_sample);  // 计算接近的决定性的hessian 值  if (octaves >= 1)  {    responseMap.push_back(new ResponseLayer(w,   h,   s,   9));    responseMap.push_back(new ResponseLayer(w,   h,   s,   15));    responseMap.push_back(new ResponseLayer(w,   h,   s,   21));    responseMap.push_back(new ResponseLayer(w,   h,   s,   27));  }   if (octaves >= 2)  {    responseMap.push_back(new ResponseLayer(w/2, h/2, s*2, 39));    responseMap.push_back(new ResponseLayer(w/2, h/2, s*2, 51));  }  if (octaves >= 3)  {    responseMap.push_back(new ResponseLayer(w/4, h/4, s*4, 75));    responseMap.push_back(new ResponseLayer(w/4, h/4, s*4, 99));  }  if (octaves >= 4)  {    responseMap.push_back(new ResponseLayer(w/8, h/8, s*8, 147));    responseMap.push_back(new ResponseLayer(w/8, h/8, s*8, 195));  }  if (octaves >= 5)  {    responseMap.push_back(new ResponseLayer(w/16, h/16, s*16, 291));    responseMap.push_back(new ResponseLayer(w/16, h/16, s*16, 387));  }  // 从图片中解析回复  for (unsigned int i = 0; i < responseMap.size(); ++i)  {    buildResponseLayer(responseMap[i]);  }}

1、2、2、2 判断是否是要提取的特征点

//! Non Maximal Suppression functionint FastHessian::isExtremum(int r, int c, ResponseLayer *t, ResponseLayer *m, ResponseLayer *b){  // bounds check  int layerBorder = (t->filter + 1) / (2 * t->step);  if (r <= layerBorder || r >= t->height - layerBorder || c <= layerBorder || c >= t->width - layerBorder)    return 0;  // check the candidate point in the middle layer is above thresh   float candidate = m->getResponse(r, c, t);  if (candidate < thresh)     return 0;   for (int rr = -1; rr <=1; ++rr)  {    for (int cc = -1; cc <=1; ++cc)    {      // if any response in 3x3x3 is greater candidate not maximum      if (        t->getResponse(r+rr, c+cc) >= candidate ||        ((rr != 0 || cc != 0) && m->getResponse(r+rr, c+cc, t) >= candidate) ||        b->getResponse(r+rr, c+cc, t) >= candidate        )         return 0;    }  }  return 1;}

 

//! 插入标尺空间的极值到亚像素精度来形成一个图像特征.   void FastHessian::interpolateExtremum(int r, int c, ResponseLayer *t, ResponseLayer *m, ResponseLayer *b){  // 获得在filters之间的步距离  // 检查中间filter是在top与bottom的中间  int filterStep = (m->filter - b->filter);  assert(filterStep > 0 && t->filter - m->filter == m->filter - b->filter);   // 获得极限值的真实位置的偏移  double xi = 0, xr = 0, xc = 0;  interpolateStep(r, c, t, m, b, &xi, &xr, &xc );  // 如果点与真实的极限值是足够近的话,就把此点存起来  if( fabs( xi ) < 0.5f  &&  fabs( xr ) < 0.5f  &&  fabs( xc ) < 0.5f )  {    Ipoint ipt;    ipt.x = static_cast<float>((c + xc) * t->step);    ipt.y = static_cast<float>((r + xr) * t->step);    ipt.scale = static_cast<float>((0.1333f) * (m->filter + xi * filterStep));    ipt.laplacian = static_cast<int>(m->getLaplacian(r,c,t));    ipts.push_back(ipt);  }}

 

1、2、2  提取 ipts 的描述符

提取前提是 创建了 Surf Descriptor Object

对vector 每个元素做以下事情：

（1） 分配Orientations并提取旋转不变描述符
（2） 获得描述符

//! 描述在提供的vector中的所有特征void Surf::getDescriptors(bool upright){  // Check there are Ipoints to be described  if (!ipts.size()) return;  // 获得vector的大小    int ipts_size = (int)ipts.size();  if (upright)  {    // U-SURF loop just gets descriptors    for (int i = 0; i < ipts_size; ++i)    {      // Set the Ipoint to be described      index = i;      // Extract upright (i.e. not rotation invariant) descriptors      getDescriptor(true);    }  }  else  {    // Main SURF-64 loop assigns orientations 并获得描述符    for (int i = 0; i < ipts_size; ++i)    {      // 设置被描述的Ipoint      index = i;      // 分配 Orientations 并提取旋转不变描述符      getOrientation();      getDescriptor(false);    }  }}

 

//! Assign the supplied Ipoint an orientationvoid Surf::getOrientation(){  Ipoint *ipt = &ipts[index];  float gauss = 0.f, scale = ipt->scale;  const int s = fRound(scale), r = fRound(ipt->y), c = fRound(ipt->x);  std::vector<float> resX(109), resY(109), Ang(109);  const int id[] = {6,5,4,3,2,1,0,1,2,3,4,5,6};  int idx = 0;  // calculate haar responses for points within radius of 6*scale  for(int i = -6; i <= 6; ++i)   {    for(int j = -6; j <= 6; ++j)     {      if(i*i + j*j < 36)       {        gauss = static_cast<float>(gauss25[id[i+6]][id[j+6]]);  // could use abs() rather than id lookup, but this way is faster        resX[idx] = gauss * haarX(r+j*s, c+i*s, 4*s);        resY[idx] = gauss * haarY(r+j*s, c+i*s, 4*s);        Ang[idx] = getAngle(resX[idx], resY[idx]);        ++idx;      }    }  }  // calculate the dominant direction   float sumX=0.f, sumY=0.f;  float max=0.f, orientation = 0.f;  float ang1=0.f, ang2=0.f;  // loop slides pi/3 window around feature point  for(ang1 = 0; ang1 < 2*pi;  ang1+=0.15f) {    ang2 = ( ang1+pi/3.0f > 2*pi ? ang1-5.0f*pi/3.0f : ang1+pi/3.0f);    sumX = sumY = 0.f;     for(unsigned int k = 0; k < Ang.size(); ++k)     {      // get angle from the x-axis of the sample point      const float & ang = Ang[k];      // determine whether the point is within the window      if (ang1 < ang2 && ang1 < ang && ang < ang2)       {        sumX+=resX[k];          sumY+=resY[k];      }       else if (ang2 < ang1 &&         ((ang > 0 && ang < ang2) || (ang > ang1 && ang < 2*pi) ))       {        sumX+=resX[k];          sumY+=resY[k];      }    }    // if the vector produced from this window is longer than all     // previous vectors then this forms the new dominant direction    if (sumX*sumX + sumY*sumY > max)     {      // store largest orientation      max = sumX*sumX + sumY*sumY;      orientation = getAngle(sumX, sumY);    }  }  // assign orientation of the dominant response vector  ipt->orientation = orientation;}

 

//! Get the modified descriptor. See Agrawal ECCV 08//! Modified descriptor contributed by Pablo Fernandezvoid Surf::getDescriptor(bool bUpright){  int y, x, sample_x, sample_y, count=0;  int i = 0, ix = 0, j = 0, jx = 0, xs = 0, ys = 0;  float scale, *desc, dx, dy, mdx, mdy, co, si;  float gauss_s1 = 0.f, gauss_s2 = 0.f;  float rx = 0.f, ry = 0.f, rrx = 0.f, rry = 0.f, len = 0.f;  float cx = -0.5f, cy = 0.f; //Subregion centers for the 4x4 gaussian weighting  Ipoint *ipt = &ipts[index];  scale = ipt->scale;  x = fRound(ipt->x);  y = fRound(ipt->y);    desc = ipt->descriptor;  if (bUpright)  {    co = 1;    si = 0;  }  else  {    co = cos(ipt->orientation);    si = sin(ipt->orientation);  }  i = -8;  //Calculate descriptor for this interest point  while(i < 12)  {    j = -8;    i = i-4;    cx += 1.f;    cy = -0.5f;    while(j < 12)     {      dx=dy=mdx=mdy=0.f;      cy += 1.f;      j = j - 4;      ix = i + 5;      jx = j + 5;      xs = fRound(x + ( -jx*scale*si + ix*scale*co));      ys = fRound(y + ( jx*scale*co + ix*scale*si));      for (int k = i; k < i + 9; ++k)       {        for (int l = j; l < j + 9; ++l)         {          //Get coords of sample point on the rotated axis          sample_x = fRound(x + (-l*scale*si + k*scale*co));          sample_y = fRound(y + ( l*scale*co + k*scale*si));          //Get the gaussian weighted x and y responses          gauss_s1 = gaussian(xs-sample_x,ys-sample_y,2.5f*scale);          rx = haarX(sample_y, sample_x, 2*fRound(scale));          ry = haarY(sample_y, sample_x, 2*fRound(scale));          //Get the gaussian weighted x and y responses on rotated axis          rrx = gauss_s1*(-rx*si + ry*co);          rry = gauss_s1*(rx*co + ry*si);          dx += rrx;          dy += rry;          mdx += fabs(rrx);          mdy += fabs(rry);        }      }      //Add the values to the descriptor vector      gauss_s2 = gaussian(cx-2.0f,cy-2.0f,1.5f);      desc[count++] = dx*gauss_s2;      desc[count++] = dy*gauss_s2;      desc[count++] = mdx*gauss_s2;      desc[count++] = mdy*gauss_s2;      len += (dx*dx + dy*dy + mdx*mdx + mdy*mdy) * gauss_s2*gauss_s2;      j += 9;    }    i += 9;  }  //Convert to Unit Vector  len = sqrt(len);  for(int i = 0; i < 64; ++i)    desc[i] /= len;}

1、3   画出检测的点

即把IpVec ipts在img上画出来

  // Draw the detected points  drawIpoints(img, ipts);

 

//! Draw all the Ipoints in the provided vectorvoid drawIpoints(IplImage *img, vector<Ipoint> &ipts, int tailSize){  Ipoint *ipt;  float s, o;  int r1, c1, r2, c2, lap;  for(unsigned int i = 0; i < ipts.size(); i++)   {    ipt = &ipts.at(i);    s = (2.5f * ipt->scale);    o = ipt->orientation;    lap = ipt->laplacian;    r1 = fRound(ipt->y);    c1 = fRound(ipt->x);    c2 = fRound(s * cos(o)) + c1;    r2 = fRound(s * sin(o)) + r1;    if (o) // Green line indicates orientation      cvLine(img, cvPoint(c1, r1), cvPoint(c2, r2), cvScalar(0, 255, 0));    else  // Green dot if using upright version      cvCircle(img, cvPoint(c1,r1), 1, cvScalar(0, 255, 0),-1);    if (lap == 1)    { // Blue circles indicate dark blobs on light backgrounds      cvCircle(img, cvPoint(c1,r1), fRound(s), cvScalar(255, 0, 0),1);    }    else if (lap == 0)    { // Red circles indicate light blobs on dark backgrounds      cvCircle(img, cvPoint(c1,r1), fRound(s), cvScalar(0, 0, 255),1);    }    else if (lap == 9)    { // Red circles indicate light blobs on dark backgrounds      cvCircle(img, cvPoint(c1,r1), fRound(s), cvScalar(0, 255, 0),1);    }    // Draw motion from ipoint dx and dy    if (tailSize)    {      cvLine(img, cvPoint(c1,r1),        cvPoint(int(c1+ipt->dx*tailSize), int(r1+ipt->dy*tailSize)),        cvScalar(255,255,255), 1);    }  }}

 

1、4    显示图片

  // Display the result  showImage(img);

 

//! Show the provided image and wait for keypressvoid showImage(const IplImage *img){  cvNamedWindow("Surf", CV_WINDOW_AUTOSIZE);   cvShowImage("Surf", img);    cvWaitKey(0);}

 

二、 Video特征的提取

在计算机世界里，“视觉”含义非常丰富，一些情况下，我们要分析从其他地方载入的固定图像，在更多情况下，

我们想处理从某些摄像设备中实时读入的视频流。

这时我们调用的是cvCreateCameraCapture()，而不是cvCreateFileCapture().

1  效果

很多人可能会得到提示“Error: No Capture”.那是因为openCV的接口在电脑上查找不到可用的DirectShow摄像头.

所以运行此功能前提是你有摄像头接入到电脑上，且你的摄像头驱动运行DirectShow filter功能。

/** * Camera dispatching method: index is the camera number. * If given an index from 0 to 99, it tries to find the first * API that can access a given camera index. * Add multiples of 100 to select an API. */CV_IMPL CvCapture * cvCreateCameraCapture (int index){int  domains[] ={#ifdef HAVE_VIDEOINPUT        CV_CAP_DSHOW,#endifCV_CAP_IEEE1394,   // identical to CV_CAP_DC1394CV_CAP_STEREO,CV_CAP_VFW,        // identical to CV_CAP_V4LCV_CAP_MIL,CV_CAP_QT,CV_CAP_UNICAP,-1};// interpret preferred interface (0 = autodetect)int pref = (index / 100) * 100;if (pref){domains[0]=pref;index %= 100;domains[1]=-1;}// try every possibly installed camera APIfor (int i = 0; domains[i] >= 0; i++){        #if defined(HAVE_VIDEOINPUT) || defined(HAVE_TYZX) || defined(HAVE_VFW) || \    defined(HAVE_CAMV4L) || defined (HAVE_CAMV4L2) || defined(HAVE_GSTREAMER) || \    defined(HAVE_DC1394_2) || defined(HAVE_DC1394) || defined(HAVE_CMU1394) || \    defined(HAVE_GSTREAMER) || defined(HAVE_MIL) || defined(HAVE_QUICKTIME) || \    defined(HAVE_UNICAP)// local variable to memorize the captured deviceCvCapture *capture;#endifswitch (domains[i]){        #ifdef HAVE_VIDEOINPUT        case CV_CAP_DSHOW:            capture = cvCreateCameraCapture_DShow (index);            if (capture)                return capture;            break;        #endif#ifdef HAVE_TYZXcase CV_CAP_STEREO:capture = cvCreateCameraCapture_TYZX (index);if (capture)return capture;break;#endifcase CV_CAP_VFW:#ifdef HAVE_VFWcapture = cvCreateCameraCapture_VFW (index);if (capture)return capture;#endif#if defined (HAVE_CAMV4L) || defined (HAVE_CAMV4L2)capture = cvCreateCameraCapture_V4L (index);if (capture)return capture;#endif#ifdef HAVE_GSTREAMERcapture = cvCreateCapture_GStreamer(CV_CAP_GSTREAMER_V4L2, 0);if (capture)return capture;capture = cvCreateCapture_GStreamer(CV_CAP_GSTREAMER_V4L, 0);if (capture)return capture;#endifbreak;case CV_CAP_FIREWIRE:#ifdef HAVE_DC1394_2capture = cvCreateCameraCapture_DC1394_2 (index);if (capture)return capture;#endif#ifdef HAVE_DC1394capture = cvCreateCameraCapture_DC1394 (index);if (capture)return capture;#endif#ifdef HAVE_CMU1394capture = cvCreateCameraCapture_CMU (index);if (capture)return capture;#endif#ifdef HAVE_GSTREAMERcapture = cvCreateCapture_GStreamer(CV_CAP_GSTREAMER_1394, 0);if (capture)return capture;#endifbreak;#ifdef HAVE_MILcase CV_CAP_MIL:capture = cvCreateCameraCapture_MIL (index);if (capture)return capture;break;#endif#ifdef HAVE_QUICKTIMEcase CV_CAP_QT:capture = cvCreateCameraCapture_QT (index);if (capture)return capture;break;#endif#ifdef HAVE_UNICAPcase CV_CAP_UNICAP:  capture = cvCreateCameraCapture_Unicap (index);  if (capture)    return capture;  break;#endif}}// failed open a camerareturn 0;}

 

 

先让大家看一下效果吧（拍的是天花板）：

 

 

2、 实现过程

实现过程于对单张图征的特征的提取的方法类似，只是加了从摄像头获得图片的过程。

int mainVideo(void){  // 初始化capture 设备  CvCapture* capture = cvCaptureFromCAM( CV_CAP_ANY );  if(!capture) error("No Capture");  // Initialise video writer  //cv::VideoWriter vw("c:\\out.avi", CV_FOURCC('D','I','V','X'),10,cvSize(320,240),1);  //vw << img;  // 创建一个窗口Create a window   cvNamedWindow("OpenSURF", CV_WINDOW_AUTOSIZE );  // 声明 Ipoints 和其它的事务  IpVec ipts;  IplImage *img=NULL;  // 主要的捕获循环  while( 1 )   {    // 从摄像头中捕获帧    img = cvQueryFrame(capture);    // 提取 surf 点    surfDetDes(img, ipts, false, 4, 4, 2, 0.004f);        // 画检测到的点     drawIpoints(img, ipts);    // 画出FPS值     //drawFPS(img);    // 显示结果     cvShowImage("OpenSURF", img);    // If ESC key pressed exit loop    if( (cvWaitKey(10) & 255) == 27 ) break;  }  cvReleaseCapture( &capture );  cvDestroyWindow( "OpenSURF" );  return 0;}

 

3 、 单张图片与视频的特征匹配

经过我验证，这个功能效果不好，表现为不敏感，其实openCV 的主要功能就是用在匹配上，而这里的表现有点让我失望。

不管如何，我们还是讲一下它的实现：

它先取一张要比较的源图片，提取它的特征点，然后从视频中提取一帧，得到的特征点vector与源特征点的vector对比,看是否有类似的。

int mainMatch(void){  // Initialise capture device  CvCapture* capture = cvCaptureFromCAM( CV_CAP_ANY );  if(!capture) error("No Capture");  // Declare Ipoints and other stuff  IpPairVec matches;  IpVec ipts, ref_ipts;    // This is the reference object we wish to find in video frame  // Replace the line below with IplImage *img = cvLoadImage("imgs/object.jpg");   // where object.jpg is the planar object to be located in the video  IplImage *img = cvLoadImage("imgs/object.jpg");   if (img == NULL) error("Need to load reference image in order to run matching procedure");  CvPoint src_corners[4] = {{0,0}, {img->width,0}, {img->width, img->height}, {0, img->height}};  CvPoint dst_corners[4];  // Extract reference object Ipoints  surfDetDes(img, ref_ipts, false, 3, 4, 3, 0.004f);  drawIpoints(img, ref_ipts);  showImage(img);  // Create a window   cvNamedWindow("OpenSURF", CV_WINDOW_AUTOSIZE );  // Main capture loop  while( true )   {    // Grab frame from the capture source    img = cvQueryFrame(capture);         // Detect and describe interest points in the frame    surfDetDes(img, ipts, false, 3, 4, 3, 0.004f);    // Fill match vector    getMatches(ipts,ref_ipts,matches);        // This call finds where the object corners should be in the frame    if (translateCorners(matches, src_corners, dst_corners))    {      // Draw box around object      for(int i = 0; i < 4; i++ )      {        CvPoint r1 = dst_corners[i%4];        CvPoint r2 = dst_corners[(i+1)%4];        cvLine( img, cvPoint(r1.x, r1.y),          cvPoint(r2.x, r2.y), cvScalar(255,255,255), 3 );      }      for (unsigned int i = 0; i < matches.size(); ++i)        drawIpoint(img, matches[i].first);    }    // Draw the FPS figure    //drawFPS(img);    // Display the result    cvShowImage("OpenSURF", img);    // If ESC key pressed exit loop    if( (cvWaitKey(10) & 255) == 27 ) break;  }  // Release the capture device  cvReleaseCapture( &capture );  cvDestroyWindow( "OpenSURF" );  return 0;}