1 class face_tracker{                        //face tracking class
 2 public:
 3   bool tracking;                           //are we in tracking mode?
 4   fps_timer timer;                         // 用来保存帧速率的变化
 5   vector<Point2f> points;                  //current tracked points
 6   face_detector detector;                  //detector for initialisation
 7   shape_model smodel;                      //shape model
 8   patch_models pmodel;                     //feature detectors
 9
10   face_tracker(){tracking = false;}
11
12   int                                      //0 = failure
13   track(const Mat &im,                     //image containing face
14     const face_tracker_params &p =     //fitting parameters
15     face_tracker_params());            //default tracking parameters
16
17   void
18   reset(){                                 //reset tracker
19     tracking = false; timer.reset();
20   }
21   ....
22 protected:
23   vector<Point2f>                          //points for fitted face in image
24   fit(const Mat &image,                    //image containing face
25       const vector<Point2f> &init,         //initial point estimates
26       const Size ssize = Size(21,21),      //search region size
27       const bool robust = false,           //use robust fitting?
28       const int itol = 10,                 //maximum number of iterations to try
29       const float ftol = 1e-3);            //convergence tolerance
30 };

 1 int
 2 face_tracker::
 3 track(const Mat &im,const face_tracker_params &p)
 4 {
 5   //convert image to greyscale
 6   Mat gray; if(im.channels()==1)gray = im; else cvtColor(im,gray,CV_RGB2GRAY);
 7
 8   //initialise，为第一帧或下一帧初始化人脸特征
 9   if(!tracking)
10     points = detector.detect(gray,p.scaleFactor,p.minNeighbours,p.minSize);
11   if((int)points.size() != smodel.npts())return 0;
12
13   //fit，通过迭代缩小的搜索范围，估计当前帧中的人脸特征点
14   for(int level = 0; level < int(p.ssize.size()); level++)
15     points = this->fit(gray,points,p.ssize[level],p.robust,p.itol,p.ftol);
16
17   //set tracking flag and increment timer
18   tracking = true; timer.increment();  return 1;
19 }

 1 vector<Point2f>
 2 face_tracker::
 3 fit(const Mat &image,                // 当前帧灰度图像
 4     const vector<Point2f> &init,    // 上一帧人脸特征点集（几何位置）
 5     const Size ssize,                // 搜索区域大小
 6     const bool robust,                // 标志位，决定是否采用 robustmodel fitting 流程，应对人脸特征的孤立点
 7     const int itol,                    // robustmodel fitting 迭代上限
 8     const float ftol)                // 迭代收敛判断阈值
 9 {
10   int n = smodel.npts();    // number of points int the shape model
11   assert((int(init.size())==n) && (pmodel.n_patches()==n));
12   smodel.calc_params(init); vector<Point2f> pts = smodel.calc_shape();
13
14   //find facial features in image around current estimates
15   vector<Point2f> peaks = pmodel.calc_peaks(image,pts,ssize);
16
17   //optimise
18   if(!robust){
19     smodel.calc_params(peaks); //compute shape model parameters
20     pts = smodel.calc_shape(); //update shape
21   }else{
22     Mat weight(n,1,CV_32F),weight_sort(n,1,CV_32F);
23     vector<Point2f> pts_old = pts;
24     for(int iter = 0; iter < itol; iter++){
25       //compute robust weight
26       for(int i = 0; i < n; i++)weight.fl(i) = norm(pts[i] - peaks[i]);
27       cv::sort(weight,weight_sort,CV_SORT_EVERY_COLUMN|CV_SORT_ASCENDING);
28       double var = 1.4826*weight_sort.fl(n/2); if(var < 0.1)var = 0.1;
29       pow(weight,2,weight); weight *= -0.5/(var*var); cv::exp(weight,weight);
30
31       //compute shape model parameters
32       smodel.calc_params(peaks,weight);
33
34       //update shape
35       pts = smodel.calc_shape();
36
37       //check for convergence
38       float v = 0; for(int i = 0; i < n; i++)v += norm(pts[i]-pts_old[i]);
39       if(v < ftol)break; else pts_old = pts;
40     }
41   }return pts;
42 }

1 //create face tracker model
2 face_tracker tracker;
3 tracker.smodel = load_ft<shape_model>(argv[1]);        // 加载 shape_model 对象
4 tracker.pmodel = load_ft<patch_models>(argv[2]);        // 加载 patch_model 对象
5 tracker.detector = load_ft<face_detector>(argv[3]);    // 加载 face_detector 对象
6
7 //save face tracker
8 save_ft<face_tracker>(argv[4],tracker);                // 保存 face_tracker 对象

 1 while(cam.get(CV_CAP_PROP_POS_AVI_RATIO) < 0.999999){
 2 Mat im; cam >> im;
 3 if(tracker.track(im,p))tracker.draw(im);
 4 draw_string(im,"d - redetection");
 5 tracker.timer.display_fps(im,Point(1,im.rows-1));
 6 imshow("face tracker",im);
 7 int c = waitKey(10);
 8 if(c == 'q')break;
 9 else if(c == 'd')tracker.reset();
10 }