Merge pull request #55 from nailbiter/luca

TLD

modules/tracking/doc/tracker_algorithms.rst

@@ -9,6 +9,10 @@ The following algorithms are implemented at the moment.
 
 .. [OLB] H Grabner, M Grabner, and H Bischof, Real-time tracking via on-line boosting, In Proc. BMVC, volume 1, pages 47– 56, 2006
 
+.. [MedianFlow] Z. Kalal, K. Mikolajczyk, and J. Matas, “Forward-Backward Error: Automatic Detection of Tracking Failures,” International Conference on Pattern Recognition, 2010, pp. 23-26. 
+
+.. [TLD] Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-Learning-Detection,” Pattern Analysis and Machine Intelligence 2011.
+
 TrackerBoosting
 ---------------
 
@@ -63,7 +67,7 @@ Constructor
     :param parameters: BOOSTING parameters :ocv:struct:`TrackerBoosting::Params`
 
 TrackerMIL
-----------
+----------------------
 
 The MIL algorithm trains a classifier in an online manner to separate the object from the background. Multiple Instance Learning avoids the drift problem for a robust tracking. The implementation is based on [MIL]_.
 
@@ -118,3 +122,105 @@ Constructor
 .. ocv:function:: Ptr<trackerMIL> TrackerMIL::createTracker(const trackerMIL::Params &parameters=trackerMIL::Params())
 
     :param parameters: MIL parameters :ocv:struct:`TrackerMIL::Params`
+
+TrackerMedianFlow
+----------------------
+
+Implementation of a paper "Forward-Backward Error: Automatic Detection of Tracking Failures" by Z. Kalal, K. Mikolajczyk 
+and Jiri Matas. The implementation is based on [MedianFlow]_.
+
+The tracker is suitable for very smooth and predictable movements when object is visible throughout the whole sequence. It's quite and
+accurate for this type of problems (in particular, it was shown by authors to outperform MIL). During the implementation period the code at
+http://www.aonsquared.co.uk/node/5, the courtesy of the author Arthur Amarra, was used for the reference purpose.
+
+.. ocv:class:: TrackerMedianFlow
+
+Implementation of TrackerMedianFlow from :ocv:class:`Tracker`::
+
+    class CV_EXPORTS_W TrackerMedianFlow : public Tracker
+    {
+     public:
+      void read( const FileNode& fn );
+      void write( FileStorage& fs ) const;
+      static Ptr<trackerMedianFlow> createTracker(const trackerMedianFlow::Params &parameters=trackerMedianFlow::Params());
+      virtual ~trackerMedianFlow(){};
+
+     protected:
+      bool initImpl( const Mat& image, const Rect2d& boundingBox );
+      bool updateImpl( const Mat& image, Rect2d& boundingBox );
+    };
+
+TrackerMedianFlow::Params
+------------------------------------
+
+.. ocv:struct:: TrackerMedianFlow::Params
+
+List of MedianFlow parameters::
+
+   struct CV_EXPORTS Params
+   {
+    Params();
+    int pointsInGrid; //square root of number of keypoints used; increase it to trade
+                      //accurateness for speed; default value is sensible and recommended
+                      
+    void read( const FileNode& fn );
+    void write( FileStorage& fs ) const;
+   };
+
+TrackerMedianFlow::createTracker
+-----------------------------------
+
+Constructor
+
+.. ocv:function:: Ptr<trackerMedianFlow> TrackerMedianFlow::createTracker(const trackerMedianFlow::Params &parameters=trackerMedianFlow::Params())
+
+    :param parameters: Median Flow parameters :ocv:struct:`TrackerMedianFlow::Params`
+
+TrackerTLD
+----------------------
+
+TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector’s errors and updates it to avoid these errors in the future. The implementation is based on [TLD]_.
+
+The Median Flow algorithm (see above) was chosen as a tracking component in this implementation, following authors. Tracker is supposed to be able
+to handle rapid motions, partial occlusions, object absence etc.
+
+.. ocv:class:: TrackerTLD
+
+Implementation of TrackerTLD from :ocv:class:`Tracker`::
+
+    class CV_EXPORTS_W TrackerTLD : public Tracker
+    {
+     public:
+      void read( const FileNode& fn );
+      void write( FileStorage& fs ) const;
+      static Ptr<trackerTLD> createTracker(const trackerTLD::Params &parameters=trackerTLD::Params());
+      virtual ~trackerTLD(){};
+
+     protected:
+      bool initImpl( const Mat& image, const Rect2d& boundingBox );
+      bool updateImpl( const Mat& image, Rect2d& boundingBox );
+    };
+
+TrackerTLD::Params
+------------------------
+
+.. ocv:struct:: TrackerTLD::Params
+
+List of TLD parameters::
+
+   struct CV_EXPORTS Params
+   {
+    Params();
+
+    void read( const FileNode& fn );
+    void write( FileStorage& fs ) const;
+   };
+
+TrackerTLD::createTracker
+-------------------------------
+
+Constructor
+
+.. ocv:function:: Ptr<trackerTLD> TrackerTLD::createTracker(const trackerTLD::Params &parameters=trackerTLD::Params())
+
+    :param parameters: TLD parameters :ocv:struct:`TrackerTLD::Params`

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -1017,7 +1017,8 @@ class CV_EXPORTS_W TrackerMedianFlow : public Tracker
   struct CV_EXPORTS Params
   {
     Params();
-    int pointsInGrid;
+    int pointsInGrid; //square root of number of keypoints used; increase it to trade
+                      //accurateness for speed; default value is sensible and recommended
     void read( const FileNode& /*fn*/ );
     void write( FileStorage& /*fs*/ ) const;
   };

modules/tracking/samples/benchmark.cpp

@@ -7,9 +7,9 @@
 #include <cstring>
 #include <climits>
 
-#define CMDLINEMAX 10
-#define ASSESS_TILL 100
-#define LINEMAX 40
+const int CMDLINEMAX = 30;
+const int ASSESS_TILL = 100;
+const int LINEMAX = 40;
 
 using namespace std;
 using namespace cv;
@@ -20,7 +20,8 @@ using namespace cv;
 
 static Mat image;
 static bool paused;
-vector<Scalar> palette;
+static bool saveImageKey;
+static vector<Scalar> palette;
 
 void print_table(char* videos[],int videoNum,char* algorithms[],int algNum,const vector<vector<char*> >& results,char* tableName);
 
@@ -67,20 +68,15 @@ static void help(){
   exit(EXIT_SUCCESS);
 }
 static void parseCommandLineArgs(int argc, char** argv,char* videos[],char* gts[],
-        int* vc,char* algorithms[],char* initBoxes[][CMDLINEMAX],int* ac){
+        int* vc,char* algorithms[],char* initBoxes[][CMDLINEMAX],int* ac,char keys[CMDLINEMAX][LINEMAX]){
 
     *ac=*vc=0;
     for(int i=1;i<argc;i++){
         if(argv[i][0]=='-'){
-            char *key=(argv[i]+1),*argument=NULL;
-            if(std::strcmp("h",key)==0||std::strcmp("help",key)==0){
-                help();
-            }
-            if((argument=strchr(argv[i],'='))==NULL){
-                i++;
-                argument=argv[i];
-            }else{
-                argument++;
+            for(int j=0;j<CMDLINEMAX;j++){
+                if(!strcmp(argv[i],keys[j])){
+                    keys[j][0]='\0';
+                }
             }
             continue;
         }
@@ -193,6 +189,8 @@ static AssessmentRes assessment(char* video,char* gt_str, char* algorithms[],cha
   int linecount=0;
   Rect2d boundingBox;
   vector<double> averageMillisPerFrame(algnum,0.0);
+  static int videoNum=0;
+  videoNum++;
 
   FILE* gt=fopen(gt_str,"r");
   if(gt==NULL){
@@ -312,6 +310,11 @@ static AssessmentRes assessment(char* video,char* gt_str, char* algorithms[],cha
               res.results[i][j]->assess(boundingBox,initBoxes[i]);
       }
       imshow( "Tracking API", image );
+      if(saveImageKey){
+          char inbuf[LINEMAX];
+          sprintf(inbuf,"image%d_%d.jpg",videoNum,frameCounter);
+          imwrite(inbuf,image);
+      }
 
       if((frameCounter+1)>=ASSESS_TILL){
           break;
@@ -342,7 +345,11 @@ int main( int argc, char** argv ){
   palette.push_back(Scalar(0,255,255));
   int vcount=0,acount=0;
   char* videos[CMDLINEMAX],*gts[CMDLINEMAX],*algorithms[CMDLINEMAX],*initBoxes[CMDLINEMAX][CMDLINEMAX];
-  parseCommandLineArgs(argc,argv,videos,gts,&vcount,algorithms,initBoxes,&acount);
+  char keys[CMDLINEMAX][LINEMAX];
+  strcpy(keys[0],"-s");
+  parseCommandLineArgs(argc,argv,videos,gts,&vcount,algorithms,initBoxes,&acount,keys);
+  saveImageKey=(keys[0][0]=='\0');
+
   CV_Assert(acount<CMDLINEMAX && vcount<CMDLINEMAX);
   printf("videos and gts\n");
   for(int i=0;i<vcount;i++){
@@ -361,7 +368,7 @@ int main( int argc, char** argv ){
   for(int i=0;i<vcount;i++){
       results.push_back(assessment(videos[i],gts[i],algorithms,((char**)initBoxes)+i,acount));
   }
-  CV_Assert(results[0].results[0].size()<CMDLINEMAX);
+  CV_Assert( (int)results[0].results[0].size() < CMDLINEMAX );
   printf("\n\n");
 
   char buf[CMDLINEMAX*CMDLINEMAX*LINEMAX], buf2[CMDLINEMAX*40];

modules/tracking/src/TLD.cpp

-/*///////////////////////////////////////////////////////////////////////////////////////
- //
- //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
- //
- //  By downloading, copying, installing or using the software you agree to this license.
- //  If you do not agree to this license, do not download, install,
- //  copy or use the software.
- //
- //
- //                           License Agreement
- //                For Open Source Computer Vision Library
- //
- // Copyright (C) 2013, OpenCV Foundation, all rights reserved.
- // Third party copyrights are property of their respective owners.
- //
- // Redistribution and use in source and binary forms, with or without modification,
- // are permitted provided that the following conditions are met:
- //
- //   * Redistribution's of source code must retain the above copyright notice,
- //     this list of conditions and the following disclaimer.
- //
- //   * Redistribution's in binary form must reproduce the above copyright notice,
- //     this list of conditions and the following disclaimer in the documentation
- //     and/or other materials provided with the distribution.
- //
- //   * The name of the copyright holders may not be used to endorse or promote products
- //     derived from this software without specific prior written permission.
- //
- // This software is provided by the copyright holders and contributors "as is" and
- // any express or implied warranties, including, but not limited to, the implied
- // warranties of merchantability and fitness for a particular purpose are disclaimed.
- // In no event shall the Intel Corporation or contributors be liable for any direct,
- // indirect, incidental, special, exemplary, or consequential damages
- // (including, but not limited to, procurement of substitute goods or services;
- // loss of use, data, or profits; or business interruption) however caused
- // and on any theory of liability, whether in contract, strict liability,
- // or tort (including negligence or otherwise) arising in any way out of
- // the use of this software, even if advised of the possibility of such damage.
- //
- //M*/
-
-#include "precomp.hpp"
-#include "opencv2/video/tracking.hpp"
-#include "opencv2/imgproc.hpp"
-#include "time.h"
-#include <algorithm>
-#include <limits.h>
-#include <math.h>
-#include <opencv2/highgui.hpp>
-#include "TLD.hpp"
-
-namespace cv {namespace tld
-{
-
-//debug functions and variables
-Rect2d etalon(14.0,110.0,20.0,20.0);
-void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,Rect2d whiteOne){
-    Mat image;
-    img.copyTo(image);
-    if(whiteOne.width>=0){
-        rectangle( image,whiteOne, 255, 1, 1 );
-    }
-    for(int i=0;i<(int)blackOnes.size();i++){
-        rectangle( image,blackOnes[i], 0, 1, 1 );
-    }
-    imshow("img",image);
-}
-void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,std::vector<Rect2d>& whiteOnes){
-    Mat image;
-    img.copyTo(image);
-    for(int i=0;i<(int)whiteOnes.size();i++){
-        rectangle( image,whiteOnes[i], 255, 1, 1 );
-    }
-    for(int i=0;i<(int)blackOnes.size();i++){
-        rectangle( image,blackOnes[i], 0, 1, 1 );
-    }
-    imshow("img",image);
-}
-void myassert(const Mat& img){
-    int count=0;
-    for(int i=0;i<img.rows;i++){
-        for(int j=0;j<img.cols;j++){
-            if(img.at<uchar>(i,j)==0){
-                count++;
-            }
-        }
-    }
-    dprintf(("black: %d out of %d (%f)\n",count,img.rows*img.cols,1.0*count/img.rows/img.cols));
-}
-
-void printPatch(const Mat_<uchar>& standardPatch){
-    for(int i=0;i<standardPatch.rows;i++){
-        for(int j=0;j<standardPatch.cols;j++){
-            dprintf(("%5.2f, ",(double)standardPatch(i,j)));
-        }
-        dprintf(("\n"));
-    }
-}
-
-std::string type2str(const Mat& mat){
-  int type=mat.type();
-  std::string r;
-
-  uchar depth = type & CV_MAT_DEPTH_MASK;
-  uchar chans =(uchar)( 1 + (type >> CV_CN_SHIFT));
-
-  switch ( depth ) {
-    case CV_8U:  r = "8U"; break;
-    case CV_8S:  r = "8S"; break;
-    case CV_16U: r = "16U"; break;
-    case CV_16S: r = "16S"; break;
-    case CV_32S: r = "32S"; break;
-    case CV_32F: r = "32F"; break;
-    case CV_64F: r = "64F"; break;
-    default:     r = "User"; break;
-  }
-
-  r += "C";
-  r += (chans+'0');
-
-  return r;
-}
-
-//generic functions
-double scaleAndBlur(const Mat& originalImg,int scale,Mat& scaledImg,Mat& blurredImg,Size GaussBlurKernelSize){
-    double dScale=1.0;
-    for(int i=0;i<scale;i++,dScale*=1.2);
-    Size2d size=originalImg.size();
-    size.height/=dScale;size.width/=dScale;
-    resize(originalImg,scaledImg,size);
-    GaussianBlur(scaledImg,blurredImg,GaussBlurKernelSize,0.0);
-    return dScale;
-}
-void getClosestN(std::vector<Rect2d>& scanGrid,Rect2d bBox,int n,std::vector<Rect2d>& res){
-    if(n>=(int)scanGrid.size()){
-        res.assign(scanGrid.begin(),scanGrid.end());
-        return;
-    }
-    std::vector<double> overlaps(n,0.0);
-    res.assign(scanGrid.begin(),scanGrid.begin()+n);
-    for(int i=0;i<n;i++){
-        overlaps[i]=overlap(res[i],bBox);
-    }
-    double otmp;
-    Rect2d rtmp;
-    for (int i = 1; i < n; i++){
-        int j = i;
-        while (j > 0 && overlaps[j - 1] > overlaps[j]) {
-            otmp = overlaps[j];overlaps[j] = overlaps[j - 1];overlaps[j - 1] = otmp;
-            rtmp = res[j];res[j] = res[j - 1];res[j - 1] = rtmp;
-            j--;
-        }
-    }
-
-    double o=0.0;
-    for(int i=n;i<(int)scanGrid.size();i++){
-        if((o=overlap(scanGrid[i],bBox))<=overlaps[0]){
-            continue;
-        }
-        int j=0;
-        for(j=0;j<n && overlaps[j]<o;j++);
-        j--;
-        for(int k=0;k<j;overlaps[k]=overlaps[k+1],res[k]=res[k+1],k++);
-        overlaps[j]=o;res[j]=scanGrid[i];
-    }
-}
-
-double variance(const Mat& img){
-    double p=0,p2=0;
-    for(int i=0;i<img.rows;i++){
-        for(int j=0;j<img.cols;j++){
-            p+=img.at<uchar>(i,j);
-            p2+=img.at<uchar>(i,j)*img.at<uchar>(i,j);
-        }
-    }
-    p/=(img.cols*img.rows);
-    p2/=(img.cols*img.rows);
-    return p2-p*p;
-}
-double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box){
-    int x=(box.x),y=(box.y),width=(box.width),height=(box.height);
-    CV_Assert(0<=x && (x+width)<intImgP.cols && (x+width)<intImgP2.cols);
-    CV_Assert(0<=y && (y+height)<intImgP.rows && (y+height)<intImgP2.rows);
-    double p=0,p2=0;
-    double A,B,C,D;
-
-    A=intImgP(y,x);
-    B=intImgP(y,x+width);
-    C=intImgP(y+height,x);
-    D=intImgP(y+height,x+width);
-    p=(0.0+A+D-B-C)/(width*height);
-
-    A=intImgP2(y,x);
-    B=intImgP2(y,x+width);
-    C=intImgP2(y+height,x);
-    D=intImgP2(y+height,x+width);
-    p2=(0.0+(D-B)-(C-A))/(width*height);
-
-    return p2-p*p;
-}
-
-double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2){
-    CV_Assert(patch1.rows==patch2.rows);
-    CV_Assert(patch1.cols==patch2.cols);
-
-    int N=patch1.rows*patch1.cols;
-    double s1=sum(patch1)(0),s2=sum(patch2)(0);
-    double n1=norm(patch1),n2=norm(patch2);
-    double prod=patch1.dot(patch2);
-    double sq1=sqrt(MAX(0.0,n1*n1-s1*s1/N)),sq2=sqrt(MAX(0.0,n2*n2-s2*s2/N));
-    double ares=(sq2==0)?sq1/abs(sq1):(prod-s1*s2/N)/sq1/sq2;
-    return ares;
-}
-unsigned int getMedian(const std::vector<unsigned int>& values, int size){
-    if(size==-1){
-        size=(int)values.size();
-    }
-    std::vector<int> copy(values.begin(),values.begin()+size);
-    std::sort(copy.begin(),copy.end());
-    if(size%2==0){
-        return (copy[size/2-1]+copy[size/2])/2;
-    }else{
-        return copy[(size-1)/2];
-    }
-}
-
-double overlap(const Rect2d& r1,const Rect2d& r2){
-    double a1=r1.area(), a2=r2.area(), a0=(r1&r2).area();
-    return a0/(a1+a2-a0);
-}
-
-void resample(const Mat& img,const RotatedRect& r2,Mat_<uchar>& samples){
-    Mat_<float> M(2,3),R(2,2),Si(2,2),s(2,1),o(2,1);
-    R(0,0)=(float)cos(r2.angle*CV_PI/180);R(0,1)=(float)(-sin(r2.angle*CV_PI/180));
-    R(1,0)=(float)sin(r2.angle*CV_PI/180);R(1,1)=(float)cos(r2.angle*CV_PI/180);
-    Si(0,0)=(float)(samples.cols/r2.size.width); Si(0,1)=0.0f;
-    Si(1,0)=0.0f; Si(1,1)=(float)(samples.rows/r2.size.height);
-    s(0,0)=(float)samples.cols; s(1,0)=(float)samples.rows;
-    o(0,0)=r2.center.x;o(1,0)=r2.center.y;
-    Mat_<float> A(2,2),b(2,1);
-    A=Si*R;
-    b=s/2.0-Si*R*o;
-    A.copyTo(M.colRange(Range(0,2)));
-    b.copyTo(M.colRange(Range(2,3)));
-    warpAffine(img,samples,M,samples.size());
-}
-void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples){
-    Mat_<float> M(2,3);
-    M(0,0)=(float)(samples.cols/r2.width); M(0,1)=0.0f; M(0,2)=(float)(-r2.x*samples.cols/r2.width);
-    M(1,0)=0.0f; M(1,1)=(float)(samples.rows/r2.height); M(1,2)=(float)(-r2.y*samples.rows/r2.height);
-    warpAffine(img,samples,M,samples.size());
-}
-
-//other stuff
-void TLDEnsembleClassifier::stepPrefSuff(std::vector<uchar>& arr,int len){
-    int gridSize=getGridSize();
-#if 0
-        int step=len/(gridSize-1), pref=(len-step*(gridSize-1))/2;
-        for(int i=0;i<(int)(sizeof(x1)/sizeof(x1[0]));i++){
-            arr[i]=pref+arr[i]*step;
-        }
-#else
-        int total=len-gridSize;
-        int quo=total/(gridSize-1),rem=total%(gridSize-1);
-        int smallStep=quo,bigStep=quo+1;
-        int bigOnes=rem,smallOnes=gridSize-bigOnes-1;
-        int bigOnes_front=bigOnes/2,bigOnes_back=bigOnes-bigOnes_front;
-        for(int i=0;i<(int)arr.size();i++){
-            if(arr[i]<bigOnes_back){
-                arr[i]=(uchar)(arr[i]*bigStep+arr[i]);
-                continue;
-            }
-            if(arr[i]<(bigOnes_front+smallOnes)){
-                arr[i]=(uchar)(bigOnes_front*bigStep+(arr[i]-bigOnes_front)*smallStep+arr[i]);
-                continue;
-            }
-            if(arr[i]<(bigOnes_front+smallOnes+bigOnes_back)){
-                arr[i]=(uchar)(bigOnes_front*bigStep+smallOnes*smallStep+(arr[i]-(bigOnes_front+smallOnes))*bigStep+arr[i]);
-                continue;
-            }
-            arr[i]=(uchar)(len-1);
-        }
-#endif
-}
-TLDEnsembleClassifier::TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier){
-    x1=std::vector<uchar>(measurePerClassifier,0);
-    x2=std::vector<uchar>(measurePerClassifier,0);
-    y1=std::vector<uchar>(measurePerClassifier,0);
-    y2=std::vector<uchar>(measurePerClassifier,0);
-
-    preinit(ordinal);
-
-    stepPrefSuff(x1,size.width);
-    stepPrefSuff(x2,size.width);
-    stepPrefSuff(y1,size.height);
-    stepPrefSuff(y2,size.height);
-
-    int posSize=1;
-    for(int i=0;i<measurePerClassifier;i++)posSize*=2;
-    pos=std::vector<unsigned int>(posSize,0);
-    neg=std::vector<unsigned int>(posSize,0);
-}
-void TLDEnsembleClassifier::integrate(Mat_<uchar> patch,bool isPositive){
-    unsigned short int position=code(patch.data,(int)patch.step[0]);
-    if(isPositive){
-        pos[position]++;
-    }else{
-        neg[position]++;
-    }
-}
-double TLDEnsembleClassifier::posteriorProbability(const uchar* data,int rowstep)const{
-    unsigned short int position=code(data,rowstep);
-    double posNum=(double)pos[position], negNum=(double)neg[position];
-    if(posNum==0.0 && negNum==0.0){
-        return 0.0;
-    }else{
-        return posNum/(posNum+negNum);
-    }
-}
-unsigned short int TLDEnsembleClassifier::code(const uchar* data,int rowstep)const{
-    unsigned short int position=0;
-    for(int i=0;i<(int)x1.size();i++){
-        position=position<<1;
-        if(*(data+rowstep*y1[i]+x1[i])<*(data+rowstep*y2[i]+x2[i])){
-            position++;
-        }else{
-        }
-    }
-    return position;
-}
-
-}}

modules/tracking/src/tld_tracker.cpp

+/*///////////////////////////////////////////////////////////////////////////////////////
+ //
+ //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ //
+ //  By downloading, copying, installing or using the software you agree to this license.
+ //  If you do not agree to this license, do not download, install,
+ //  copy or use the software.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+ // Third party copyrights are property of their respective owners.
+ //
+ // Redistribution and use in source and binary forms, with or without modification,
+ // are permitted provided that the following conditions are met:
+ //
+ //   * Redistribution's of source code must retain the above copyright notice,
+ //     this list of conditions and the following disclaimer.
+ //
+ //   * Redistribution's in binary form must reproduce the above copyright notice,
+ //     this list of conditions and the following disclaimer in the documentation
+ //     and/or other materials provided with the distribution.
+ //
+ //   * The name of the copyright holders may not be used to endorse or promote products
+ //     derived from this software without specific prior written permission.
+ //
+ // This software is provided by the copyright holders and contributors "as is" and
+ // any express or implied warranties, including, but not limited to, the implied
+ // warranties of merchantability and fitness for a particular purpose are disclaimed.
+ // In no event shall the Intel Corporation or contributors be liable for any direct,
+ // indirect, incidental, special, exemplary, or consequential damages
+ // (including, but not limited to, procurement of substitute goods or services;
+ // loss of use, data, or profits; or business interruption) however caused
+ // and on any theory of liability, whether in contract, strict liability,
+ // or tort (including negligence or otherwise) arising in any way out of
+ // the use of this software, even if advised of the possibility of such damage.
+ //
+ //M*/
+
+#include "precomp.hpp"
+#include "opencv2/video/tracking.hpp"
+#include "opencv2/imgproc.hpp"
+#include "time.h"
+#include<algorithm>
+#include<limits.h>
+#include "tld_tracker.hpp"
+#include "opencv2/highgui.hpp"
+
+/*
+ * FIXME(optimize):
+ *      no median
+ *      direct formula in resamples
+ * FIXME(issues)
+ *      THETA_NN 0.5<->0.6 dramatic change vs video 6 !!
+ * TODO(features)
+ *      benchmark: two streams of photos -->better video
+ *      (try inter_area for resize)
+ * TODO:
+ *      fix pushbot->pick commits->compare_branches->all in 1->resubmit
+ *      || video(0.5<->0.6) -->debug if box size is less than 20
+ *      perfect PN
+ *
+*      vadim:
+*      ?3. comment each function/method
+*      5. empty lines to separate logical...
+*      6. comment logical sections
+*      11. group decls logically, order of statements
+*
+*      ?10. all in one class
+*      todo: 
+*           initializer lists; 
+*/
+
+/* design decisions:
+ */
+
+namespace cv
+{
+
+namespace tld
+{
+
+const int STANDARD_PATCH_SIZE = 15;
+const int NEG_EXAMPLES_IN_INIT_MODEL = 300;
+const int MAX_EXAMPLES_IN_MODEL = 500;
+const int MEASURES_PER_CLASSIFIER = 13;
+const int GRIDSIZE = 15;
+const int DOWNSCALE_MODE = cv::INTER_LINEAR;
+const double THETA_NN = 0.50;
+const double CORE_THRESHOLD = 0.5;
+const double SCALE_STEP = 1.2;
+const double ENSEMBLE_THRESHOLD = 0.5;
+const double VARIANCE_THRESHOLD = 0.5;
+const double NEXPERT_THRESHOLD = 0.2;
+#define BLUR_AS_VADIM
+#undef CLOSED_LOOP
+static const cv::Size GaussBlurKernelSize(3, 3);
+
+class TLDDetector;
+class MyMouseCallbackDEBUG
+{
+public:
+    MyMouseCallbackDEBUG(Mat& img, Mat& imgBlurred, TLDDetector* detector):img_(img), imgBlurred_(imgBlurred), detector_(detector){}
+    static void onMouse(int event, int x, int y, int, void* obj){ ((MyMouseCallbackDEBUG*)obj)->onMouse(event, x, y); }
+    MyMouseCallbackDEBUG& operator = (const MyMouseCallbackDEBUG& /*other*/){ return *this; }
+private:
+    void onMouse(int event, int x, int y);
+    Mat& img_, imgBlurred_;
+    TLDDetector* detector_;
+};
+
+class Data 
+{
+public:
+    Data(Rect2d initBox);
+    Size getMinSize(){ return minSize; }
+    double getScale(){ return scale; }
+    bool confident;
+    bool failedLastTime;
+    int frameNum;
+    void printme(FILE*  port = stdout);
+private:
+    double scale;
+    Size minSize;
+};
+
+class TLDDetector 
+{
+public:
+    TLDDetector(const TrackerTLD::Params& params, Ptr<TrackerModel> model_in):model(model_in), params_(params){}
+    ~TLDDetector(){}
+    static void generateScanGrid(int rows, int cols, Size initBox, std::vector<Rect2d>& res, bool withScaling = false);
+    struct LabeledPatch
+    {
+        Rect2d rect;
+        bool isObject, shouldBeIntegrated;
+    };
+    bool detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches);
+protected:
+    friend class MyMouseCallbackDEBUG;
+    Ptr<TrackerModel> model;
+    void computeIntegralImages(const Mat& img, Mat_<double>& intImgP, Mat_<double>& intImgP2){ integral(img, intImgP, intImgP2, CV_64F); }
+    inline bool patchVariance(Mat_<double>& intImgP, Mat_<double>& intImgP2, double originalVariance, Point pt, Size size);
+    TrackerTLD::Params params_;
+};
+
+template<class T, class Tparams>
+class TrackerProxyImpl : public TrackerProxy
+{
+public:
+    TrackerProxyImpl(Tparams params = Tparams()):params_(params){}
+    bool init(const Mat& image, const Rect2d& boundingBox)
+    {
+        trackerPtr = T::createTracker();
+        return trackerPtr->init(image, boundingBox);
+    }
+    bool update(const Mat& image, Rect2d& boundingBox)
+    {
+        return trackerPtr->update(image, boundingBox);
+    }
+private:
+    Ptr<T> trackerPtr;
+    Tparams params_;
+    Rect2d boundingBox_;
+};
+
+class TrackerTLDModel : public TrackerModel
+{
+public:
+  TrackerTLDModel(TrackerTLD::Params params, const Mat& image, const Rect2d& boundingBox, Size minSize);
+  Rect2d getBoundingBox(){ return boundingBox_; }
+  void setBoudingBox(Rect2d boundingBox){ boundingBox_ = boundingBox; }
+  double getOriginalVariance(){ return originalVariance_; }
+  inline double ensembleClassifierNum(const uchar* data);
+  inline void prepareClassifiers(int rowstep);
+  double Sr(const Mat_<uchar>& patch);
+  double Sc(const Mat_<uchar>& patch);
+  void integrateRelabeled(Mat& img, Mat& imgBlurred, const std::vector<TLDDetector::LabeledPatch>& patches);
+  void integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive);
+  Size getMinSize(){ return minSize_; }
+  void printme(FILE* port = stdout);
+
+protected:
+  Size minSize_;
+  int timeStampPositiveNext, timeStampNegativeNext;
+  TrackerTLD::Params params_;
+  void pushIntoModel(const Mat_<uchar>& example, bool positive);
+  void modelEstimationImpl( const std::vector<Mat>& /*responses*/ ){}
+  void modelUpdateImpl(){}
+  Rect2d boundingBox_;
+  double originalVariance_;
+  std::vector<Mat_<uchar> > positiveExamples, negativeExamples;
+  std::vector<int> timeStampsPositive, timeStampsNegative;
+  RNG rng;
+  std::vector<TLDEnsembleClassifier> classifiers;
+};
+
+class TrackerTLDImpl : public TrackerTLD
+{
+public:
+  TrackerTLDImpl(const TrackerTLD::Params &parameters = TrackerTLD::Params());
+  void read(const FileNode& fn);
+  void write(FileStorage& fs) const;
+
+protected:
+  class Pexpert
+  {
+  public:
+      Pexpert(const Mat& img_in, const Mat& imgBlurred_in, Rect2d& resultBox_in, 
+              const TLDDetector* detector_in, TrackerTLD::Params params_in, Size initSize_in):
+            img_(img_in), imgBlurred_(imgBlurred_in), resultBox_(resultBox_in), detector_(detector_in), params_(params_in), initSize_(initSize_in){}
+      bool operator()(Rect2d /*box*/){ return false; }
+      int additionalExamples(std::vector<Mat_<uchar> >& examplesForModel, std::vector<Mat_<uchar> >& examplesForEnsemble);
+  protected:
+      Pexpert(){}
+      Mat img_, imgBlurred_;
+      Rect2d resultBox_;
+      const TLDDetector* detector_;
+      TrackerTLD::Params params_;
+      RNG rng;
+      Size initSize_;
+  };
+
+  class Nexpert : public Pexpert
+  {
+  public:
+      Nexpert(const Mat& img_in, Rect2d& resultBox_in, const TLDDetector* detector_in, TrackerTLD::Params params_in)
+      {
+          img_ = img_in; resultBox_ = resultBox_in; detector_ = detector_in; params_ = params_in;
+      }
+      bool operator()(Rect2d box);
+      int additionalExamples(std::vector<Mat_<uchar> >& examplesForModel, std::vector<Mat_<uchar> >& examplesForEnsemble)
+      {
+          examplesForModel.clear(); examplesForEnsemble.clear(); return 0; 
+      }
+  };
+
+  bool initImpl(const Mat& image, const Rect2d& boundingBox);
+  bool updateImpl(const Mat& image, Rect2d& boundingBox);
+
+  TrackerTLD::Params params;
+  Ptr<Data> data;
+  Ptr<TrackerProxy> trackerProxy;
+  Ptr<TLDDetector> detector;
+};
+
+}
+
+TrackerTLD::Params::Params(){}
+
+void TrackerTLD::Params::read(const cv::FileNode& /*fn*/){}
+
+void TrackerTLD::Params::write(cv::FileStorage& /*fs*/) const {}
+
+Ptr<TrackerTLD> TrackerTLD::createTracker(const TrackerTLD::Params &parameters)
+{
+    return Ptr<tld::TrackerTLDImpl>(new tld::TrackerTLDImpl(parameters));
+}
+
+namespace tld
+{
+
+TrackerTLDImpl::TrackerTLDImpl(const TrackerTLD::Params &parameters) :
+    params( parameters )
+{
+  isInit = false;
+  trackerProxy = Ptr<TrackerProxyImpl<TrackerMedianFlow, TrackerMedianFlow::Params> >
+      (new TrackerProxyImpl<TrackerMedianFlow, TrackerMedianFlow::Params>());
+}
+
+void TrackerTLDImpl::read(const cv::FileNode& fn)
+{
+  params.read( fn );
+}
+
+void TrackerTLDImpl::write(cv::FileStorage& fs) const
+{
+  params.write( fs );
+}
+
+bool TrackerTLDImpl::initImpl(const Mat& image, const Rect2d& boundingBox)
+{
+    Mat image_gray;
+    trackerProxy->init(image, boundingBox);
+    cvtColor( image, image_gray, COLOR_BGR2GRAY );
+    data = Ptr<Data>(new Data(boundingBox));
+    double scale = data->getScale();
+    Rect2d myBoundingBox = boundingBox;
+    if( scale > 1.0 )
+    {
+        Mat image_proxy;
+        resize(image_gray, image_proxy, Size(cvRound(image.cols * scale), cvRound(image.rows * scale)), 0, 0, DOWNSCALE_MODE);
+        image_proxy.copyTo(image_gray);
+        myBoundingBox.x *= scale;
+        myBoundingBox.y *= scale;
+        myBoundingBox.width *= scale;
+        myBoundingBox.height *= scale;
+    }
+    model = Ptr<TrackerTLDModel>(new TrackerTLDModel(params, image_gray, myBoundingBox, data->getMinSize()));
+    detector = Ptr<TLDDetector>(new TLDDetector(params, model));
+    data->confident = false;
+    data->failedLastTime = false;
+
+    return true;
+}
+
+bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
+{
+    Mat image_gray, image_blurred, imageForDetector;
+    cvtColor( image, image_gray, COLOR_BGR2GRAY );
+    double scale = data->getScale();
+    if( scale > 1.0 )
+        resize(image_gray, imageForDetector, Size(cvRound(image.cols*scale), cvRound(image.rows*scale)), 0, 0, DOWNSCALE_MODE);
+    else
+        imageForDetector = image_gray;
+    GaussianBlur(imageForDetector, image_blurred, GaussBlurKernelSize, 0.0);
+    TrackerTLDModel* tldModel = ((TrackerTLDModel*)static_cast<TrackerModel*>(model));
+    data->frameNum++;
+    Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
+    std::vector<TLDDetector::LabeledPatch> detectorResults;
+    //best overlap around 92%
+
+    std::vector<Rect2d> candidates;
+    std::vector<double> candidatesRes;
+    bool trackerNeedsReInit = false;
+    for( int i = 0; i < 2; i++ )
+    {
+        Rect2d tmpCandid = boundingBox;
+        if( ( (i == 0) && !data->failedLastTime && trackerProxy->update(image, tmpCandid) ) || 
+                ( (i == 1) && detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults) ) )
+        {
+            candidates.push_back(tmpCandid);
+            if( i == 0 )
+                resample(image_gray, tmpCandid, standardPatch);
+            else
+                resample(imageForDetector, tmpCandid, standardPatch);
+            candidatesRes.push_back(tldModel->Sc(standardPatch));
+        }
+        else
+        {
+            if( i == 0 )
+                trackerNeedsReInit = true;
+        }
+    }
+
+    std::vector<double>::iterator it = std::max_element(candidatesRes.begin(), candidatesRes.end());
+
+    dfprintf((stdout, "scale = %f\n", log(1.0 * boundingBox.width / (data->getMinSize()).width) / log(SCALE_STEP)));
+    for( int i = 0; i < (int)candidatesRes.size(); i++ )
+        dprintf(("\tcandidatesRes[%d] = %f\n", i, candidatesRes[i]));
+    data->printme();
+    tldModel->printme(stdout);
+
+    if( it == candidatesRes.end() )
+    {
+        data->confident = false;
+        data->failedLastTime = true;
+        return false;
+    }
+    else
+    {
+        boundingBox = candidates[it - candidatesRes.begin()];
+        data->failedLastTime = false;
+        if( trackerNeedsReInit || it != candidatesRes.begin() )
+            trackerProxy->init(image, boundingBox);
+    }
+
+#if 1
+    if( it != candidatesRes.end() )
+    {
+        resample(imageForDetector, candidates[it - candidatesRes.begin()], standardPatch);
+        dfprintf((stderr, "%d %f %f\n", data->frameNum, tldModel->Sc(standardPatch), tldModel->Sr(standardPatch)));
+        if( candidatesRes.size() == 2 &&  it == (candidatesRes.begin() + 1) )
+            dfprintf((stderr, "detector WON\n"));
+    }
+    else
+    {
+        dfprintf((stderr, "%d x x\n", data->frameNum));
+    }
+#endif
+
+    if( *it > CORE_THRESHOLD )
+        data->confident = true;
+
+    if( data->confident )
+    {
+        Pexpert pExpert(imageForDetector, image_blurred, boundingBox, detector, params, data->getMinSize());
+        Nexpert nExpert(imageForDetector, boundingBox, detector, params);
+        std::vector<Mat_<uchar> > examplesForModel, examplesForEnsemble;
+        examplesForModel.reserve(100); examplesForEnsemble.reserve(100);
+        int negRelabeled = 0;
+        for( int i = 0; i < (int)detectorResults.size(); i++ )
+        {
+            bool expertResult;
+            if( detectorResults[i].isObject )
+            {
+                expertResult = nExpert(detectorResults[i].rect);
+                if( expertResult != detectorResults[i].isObject )
+                    negRelabeled++;
+            }
+            else
+            {
+                expertResult = pExpert(detectorResults[i].rect);
+            }
+
+            detectorResults[i].shouldBeIntegrated = detectorResults[i].shouldBeIntegrated || (detectorResults[i].isObject != expertResult);
+            detectorResults[i].isObject = expertResult;
+        }
+        tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
+        dprintf(("%d relabeled by nExpert\n", negRelabeled));
+        pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
+        tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, true);
+        examplesForModel.clear(); examplesForEnsemble.clear();
+        nExpert.additionalExamples(examplesForModel, examplesForEnsemble);
+        tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, false);
+    }
+    else
+    {
+#ifdef CLOSED_LOOP
+        tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
+#endif
+    }
+
+    return true;
+}
+
+TrackerTLDModel::TrackerTLDModel(TrackerTLD::Params params, const Mat& image, const Rect2d& boundingBox, Size minSize):minSize_(minSize),
+timeStampPositiveNext(0), timeStampNegativeNext(0), params_(params), boundingBox_(boundingBox)
+{
+    originalVariance_ = variance(image(boundingBox));
+    std::vector<Rect2d> closest, scanGrid;
+    Mat scaledImg, blurredImg, image_blurred;
+
+    double scale = scaleAndBlur(image, cvRound(log(1.0 * boundingBox.width / (minSize.width)) / log(SCALE_STEP)),
+            scaledImg, blurredImg, GaussBlurKernelSize, SCALE_STEP);
+    GaussianBlur(image, image_blurred, GaussBlurKernelSize, 0.0);
+    TLDDetector::generateScanGrid(image.rows, image.cols, minSize, scanGrid);
+    getClosestN(scanGrid, Rect2d(boundingBox.x / scale, boundingBox.y / scale, boundingBox.width / scale, boundingBox.height / scale), 10, closest);
+
+    Mat_<uchar> blurredPatch(minSize);
+    TLDEnsembleClassifier::makeClassifiers(minSize, MEASURES_PER_CLASSIFIER, GRIDSIZE, classifiers);
+
+    positiveExamples.reserve(200);
+    for( int i = 0; i < (int)closest.size(); i++ )
+    {
+        for( int j = 0; j < 20; j++ )
+        {
+            Point2f center;
+            Size2f size;
+            Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
+            center.x = (float)(closest[i].x + closest[i].width * (0.5 + rng.uniform(-0.01, 0.01)));
+            center.y = (float)(closest[i].y + closest[i].height * (0.5 + rng.uniform(-0.01, 0.01)));
+            size.width = (float)(closest[i].width * rng.uniform((double)0.99, (double)1.01));
+            size.height = (float)(closest[i].height * rng.uniform((double)0.99, (double)1.01));
+            float angle = (float)rng.uniform(-10.0, 10.0);
+
+            resample(scaledImg, RotatedRect(center, size, angle), standardPatch);
+            
+            for( int y = 0; y < standardPatch.rows; y++ )
+            {
+                for( int x = 0; x < standardPatch.cols; x++ )
+                {
+                    standardPatch(x, y) += (uchar)rng.gaussian(5.0);
+                }
+            }
+
+#ifdef BLUR_AS_VADIM
+            GaussianBlur(standardPatch, blurredPatch, GaussBlurKernelSize, 0.0);
+            resize(blurredPatch, blurredPatch, minSize);
+#else
+            resample(blurredImg, RotatedRect(center, size, angle), blurredPatch);
+#endif
+            pushIntoModel(standardPatch, true);
+            for( int k = 0; k < (int)classifiers.size(); k++ )
+                classifiers[k].integrate(blurredPatch, true);
+        }
+    }
+
+    TLDDetector::generateScanGrid(image.rows, image.cols, minSize, scanGrid, true);
+    negativeExamples.clear();
+    negativeExamples.reserve(NEG_EXAMPLES_IN_INIT_MODEL);
+    std::vector<int> indices;
+    indices.reserve(NEG_EXAMPLES_IN_INIT_MODEL);
+    while( (int)negativeExamples.size() < NEG_EXAMPLES_IN_INIT_MODEL )
+    {
+        int i = rng.uniform((int)0, (int)scanGrid.size());
+        if( std::find(indices.begin(), indices.end(), i) == indices.end() && overlap(boundingBox, scanGrid[i]) < NEXPERT_THRESHOLD )
+        {
+            Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
+            resample(image, scanGrid[i], standardPatch);
+            pushIntoModel(standardPatch, false);
+
+            resample(image_blurred, scanGrid[i], blurredPatch);
+            for( int k = 0; k < (int)classifiers.size(); k++ )
+                classifiers[k].integrate(blurredPatch, false);
+        }
+    }
+    dprintf(("positive patches: %d\nnegative patches: %d\n", (int)positiveExamples.size(), (int)negativeExamples.size()));
+}
+
+void TLDDetector::generateScanGrid(int rows, int cols, Size initBox, std::vector<Rect2d>& res, bool withScaling)
+{
+    res.clear();
+    //scales step: SCALE_STEP; hor step: 10% of width; verstep: 10% of height; minsize: 20pix
+    for( double h = initBox.height, w = initBox.width; h < cols && w < rows; )
+    {
+        for( double x = 0; (x + w + 1.0) <= cols; x += (0.1 * w) )
+        {
+            for( double y = 0; (y + h + 1.0) <= rows; y += (0.1 * h) )
+                res.push_back(Rect2d(x, y, w, h));
+        }
+        if( withScaling )
+        {
+            if( h <= initBox.height )
+            {
+                h /= SCALE_STEP; w /= SCALE_STEP;
+                if( h < 20 || w < 20 )
+                {
+                    h = initBox.height * SCALE_STEP; w = initBox.width * SCALE_STEP;
+                    CV_Assert( h > initBox.height || w > initBox.width);
+                }
+            }
+            else
+            {
+                h *= SCALE_STEP; w *= SCALE_STEP;
+            }
+        }
+        else
+        {
+            break;
+        }
+    }
+    dprintf(("%d rects in res\n", (int)res.size()));
+}
+
+bool TLDDetector::detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches)
+{
+    TrackerTLDModel* tldModel = ((TrackerTLDModel*)static_cast<TrackerModel*>(model));
+    Size initSize = tldModel->getMinSize();
+    patches.clear();
+
+    Mat resized_img, blurred_img;
+    Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
+    img.copyTo(resized_img);
+    imgBlurred.copyTo(blurred_img);
+    double originalVariance = tldModel->getOriginalVariance(); ;
+    int dx = initSize.width / 10, dy = initSize.height / 10;
+    Size2d size = img.size();
+    double scale = 1.0;
+    int total = 0, pass = 0;
+    int npos = 0, nneg = 0;
+    double tmp = 0, maxSc = -5.0;
+    Rect2d maxScRect;
+
+    START_TICK("detector");
+    do
+    {
+        Mat_<double> intImgP, intImgP2;
+        computeIntegralImages(resized_img, intImgP, intImgP2);
+
+        tldModel->prepareClassifiers((int)blurred_img.step[0]);
+        for( int i = 0, imax = cvFloor((0.0 + resized_img.cols - initSize.width) / dx); i < imax; i++ )
+        {
+            for( int j = 0, jmax = cvFloor((0.0 + resized_img.rows - initSize.height) / dy); j < jmax; j++ )
+            {
+                LabeledPatch labPatch;
+                total++;
+                if( !patchVariance(intImgP, intImgP2, originalVariance, Point(dx * i, dy * j), initSize) )
+                    continue;
+                if( tldModel->ensembleClassifierNum(&blurred_img.at<uchar>(dy * j, dx * i)) <= ENSEMBLE_THRESHOLD )
+                    continue;
+                pass++;
+
+                labPatch.rect = Rect2d(dx * i * scale, dy * j * scale, initSize.width * scale, initSize.height * scale);
+                resample(resized_img, Rect2d(Point(dx * i, dy * j), initSize), standardPatch);
+                tmp = tldModel->Sr(standardPatch);
+                labPatch.isObject = tmp > THETA_NN;
+                labPatch.shouldBeIntegrated = abs(tmp - THETA_NN) < 0.1;
+                patches.push_back(labPatch);
+
+                if( !labPatch.isObject )
+                {
+                    nneg++;
+                    continue;
+                }
+                else
+                {
+                    npos++;
+                }
+                tmp = tldModel->Sc(standardPatch);
+                if( tmp > maxSc )
+                {
+                    maxSc = tmp;
+                    maxScRect = labPatch.rect;
+                }
+            }
+        }
+
+        size.width /= SCALE_STEP;
+        size.height /= SCALE_STEP;
+        scale *= SCALE_STEP;
+        resize(img, resized_img, size, 0, 0, DOWNSCALE_MODE);
+        GaussianBlur(resized_img, blurred_img, GaussBlurKernelSize, 0.0f);
+    }
+    while( size.width >= initSize.width && size.height >= initSize.height );
+    END_TICK("detector");
+
+    dfprintf((stdout, "after NCC: nneg = %d npos = %d\n", nneg, npos));
+#if !0
+        std::vector<Rect2d> poss, negs;
+
+        for( int i = 0; i < (int)patches.size(); i++ )
+        {
+            if( patches[i].isObject )
+                poss.push_back(patches[i].rect);
+            else
+                negs.push_back(patches[i].rect);
+        }
+        dfprintf((stdout, "%d pos and %d neg\n", (int)poss.size(), (int)negs.size()));
+        drawWithRects(img, negs, poss, "tech");
+#endif
+
+    dfprintf((stdout, "%d after ensemble\n", pass));
+    if( maxSc < 0 )
+        return false;
+    res = maxScRect;
+    return true;
+}
+
+/** Computes the variance of subimage given by box, with the help of two integral 
+ * images intImgP and intImgP2 (sum of squares), which should be also provided.*/
+bool TLDDetector::patchVariance(Mat_<double>& intImgP, Mat_<double>& intImgP2, double originalVariance, Point pt, Size size)
+{
+    int x = (pt.x), y = (pt.y), width = (size.width), height = (size.height);
+    CV_Assert( 0 <= x && (x + width) < intImgP.cols && (x + width) < intImgP2.cols );
+    CV_Assert( 0 <= y && (y + height) < intImgP.rows && (y + height) < intImgP2.rows );
+    double p = 0, p2 = 0;
+    double A, B, C, D;
+
+    A = intImgP(y, x);
+    B = intImgP(y, x + width);
+    C = intImgP(y + height, x);
+    D = intImgP(y + height, x + width);
+    p = (A + D - B - C) / (width * height);
+
+    A = intImgP2(y, x);
+    B = intImgP2(y, x + width);
+    C = intImgP2(y + height, x);
+    D = intImgP2(y + height, x + width);
+    p2 = (A + D - B - C) / (width * height);
+
+    return ((p2 - p * p) > VARIANCE_THRESHOLD * originalVariance);
+}
+
+double TrackerTLDModel::ensembleClassifierNum(const uchar* data)
+{
+    double p = 0;
+    for( int k = 0; k < (int)classifiers.size(); k++ )
+        p += classifiers[k].posteriorProbabilityFast(data);
+    p /= classifiers.size();
+    return p;
+}
+
+double TrackerTLDModel::Sr(const Mat_<uchar>& patch)
+{
+    double splus = 0.0, sminus = 0.0;
+    for( int i = 0; i < (int)positiveExamples.size(); i++ )
+        splus = std::max(splus, 0.5 * (NCC(positiveExamples[i], patch) + 1.0));
+    for( int i = 0; i < (int)negativeExamples.size(); i++ )
+        sminus = std::max(sminus, 0.5 * (NCC(negativeExamples[i], patch) + 1.0));
+    if( splus + sminus == 0.0)
+        return 0.0;
+    return splus / (sminus + splus);
+}
+
+double TrackerTLDModel::Sc(const Mat_<uchar>& patch)
+{
+    double splus = 0.0, sminus = 0.0;
+    int med = getMedian(timeStampsPositive);
+    for( int i = 0; i < (int)positiveExamples.size(); i++ )
+    {
+        if( (int)timeStampsPositive[i] <= med )
+            splus = std::max(splus, 0.5 * (NCC(positiveExamples[i], patch) + 1.0));
+    }
+    for( int i = 0; i < (int)negativeExamples.size(); i++ )
+        sminus = std::max(sminus, 0.5 * (NCC(negativeExamples[i], patch) + 1.0));
+    if( splus + sminus == 0.0 )
+        return 0.0;
+    return splus / (sminus + splus);
+}
+
+void TrackerTLDModel::integrateRelabeled(Mat& img, Mat& imgBlurred, const std::vector<TLDDetector::LabeledPatch>& patches)
+{
+    Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE), blurredPatch(minSize_);
+    int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
+    for( int k = 0; k < (int)patches.size(); k++ )
+    {
+        if( patches[k].shouldBeIntegrated )
+        {
+            resample(img, patches[k].rect, standardPatch);
+            if( patches[k].isObject )
+            {
+                positiveIntoModel++;
+                pushIntoModel(standardPatch, true);
+            }
+            else
+            {
+                negativeIntoModel++;
+                pushIntoModel(standardPatch, false);
+            }
+        }
+
+#ifdef CLOSED_LOOP
+        if( patches[k].shouldBeIntegrated || !patches[k].isPositive )
+#else
+        if( patches[k].shouldBeIntegrated )
+#endif
+        {
+            resample(imgBlurred, patches[k].rect, blurredPatch);
+            if( patches[k].isObject )
+                positiveIntoEnsemble++;
+            else
+                negativeIntoEnsemble++;
+            for( int i = 0; i < (int)classifiers.size(); i++ )
+                classifiers[i].integrate(blurredPatch, patches[k].isObject);
+        }
+    }
+    if( negativeIntoModel > 0 )
+        dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
+    if( positiveIntoModel > 0)
+        dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
+    if( negativeIntoEnsemble > 0 )
+        dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
+    if( positiveIntoEnsemble > 0 )
+        dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
+    dfprintf((stdout, "\n"));
+}
+
+void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
+{
+    int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
+    for( int k = 0; k < (int)eForModel.size(); k++ )
+    {
+        double sr = Sr(eForModel[k]);
+        if( ( sr > THETA_NN ) != isPositive )
+        {
+            if( isPositive )
+            {
+                positiveIntoModel++;
+                pushIntoModel(eForModel[k], true);
+            }
+            else
+            {
+                negativeIntoModel++;
+                pushIntoModel(eForModel[k], false);
+            }
+        }
+        double p = 0;
+        for( int i = 0; i < (int)classifiers.size(); i++ )
+            p += classifiers[i].posteriorProbability(eForEnsemble[k].data, (int)eForEnsemble[k].step[0]);
+        p /= classifiers.size();
+        if( ( p > ENSEMBLE_THRESHOLD ) != isPositive )
+        {
+            if( isPositive )
+                positiveIntoEnsemble++;
+            else
+                negativeIntoEnsemble++;
+            for( int i = 0; i < (int)classifiers.size(); i++ )
+                classifiers[i].integrate(eForEnsemble[k], isPositive);
+        }
+    }
+    if( negativeIntoModel > 0 )
+        dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
+    if( positiveIntoModel > 0 )
+        dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
+    if( negativeIntoEnsemble > 0 )
+        dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
+    if( positiveIntoEnsemble > 0 )
+        dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
+    dfprintf((stdout, "\n"));
+}
+
+int TrackerTLDImpl::Pexpert::additionalExamples(std::vector<Mat_<uchar> >& examplesForModel, std::vector<Mat_<uchar> >& examplesForEnsemble)
+{
+    examplesForModel.clear(); examplesForEnsemble.clear();
+    examplesForModel.reserve(100); examplesForEnsemble.reserve(100);
+
+    std::vector<Rect2d> closest, scanGrid;
+    Mat scaledImg, blurredImg;
+
+    double scale = scaleAndBlur(img_, cvRound(log(1.0 * resultBox_.width / (initSize_.width)) / log(SCALE_STEP)),
+            scaledImg, blurredImg, GaussBlurKernelSize, SCALE_STEP);
+    TLDDetector::generateScanGrid(img_.rows, img_.cols, initSize_, scanGrid);
+    getClosestN(scanGrid, Rect2d(resultBox_.x / scale, resultBox_.y / scale, resultBox_.width / scale, resultBox_.height / scale), 10, closest);
+
+    for( int i = 0; i < (int)closest.size(); i++ )
+    {
+        for( int j = 0; j < 10; j++ )
+        {
+            Point2f center;
+            Size2f size;
+            Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE), blurredPatch(initSize_);
+            center.x = (float)(closest[i].x + closest[i].width * (0.5 + rng.uniform(-0.01, 0.01)));
+            center.y = (float)(closest[i].y + closest[i].height * (0.5 + rng.uniform(-0.01, 0.01)));
+            size.width = (float)(closest[i].width * rng.uniform((double)0.99, (double)1.01));
+            size.height = (float)(closest[i].height * rng.uniform((double)0.99, (double)1.01));
+            float angle = (float)rng.uniform(-5.0, 5.0);
+
+            for( int y = 0; y < standardPatch.rows; y++ )
+            {
+                for( int x = 0; x < standardPatch.cols; x++ )
+                {
+                    standardPatch(x, y) += (uchar)rng.gaussian(5.0);
+                }
+            }
+#ifdef BLUR_AS_VADIM
+            GaussianBlur(standardPatch, blurredPatch, GaussBlurKernelSize, 0.0);
+            resize(blurredPatch, blurredPatch, initSize_);
+#else
+            resample(blurredImg, RotatedRect(center, size, angle), blurredPatch);
+#endif
+            resample(scaledImg, RotatedRect(center, size, angle), standardPatch);
+            examplesForModel.push_back(standardPatch);
+            examplesForEnsemble.push_back(blurredPatch);
+        }
+    }
+    return 0;
+}
+
+bool TrackerTLDImpl::Nexpert::operator()(Rect2d box)
+{
+    if( overlap(resultBox_, box) < NEXPERT_THRESHOLD )
+        return false;
+    else
+        return true;
+}
+
+Data::Data(Rect2d initBox)
+{
+    double minDim = std::min(initBox.width, initBox.height);
+    scale = 20.0 / minDim;
+    minSize.width = (int)(initBox.width * 20.0 / minDim);
+    minSize.height = (int)(initBox.height * 20.0 / minDim);
+    frameNum = 0;
+    dprintf(("minSize = %dx%d\n", minSize.width, minSize.height));
+}
+
+void Data::printme(FILE*  port)
+{
+    dfprintf((port, "Data:\n"));
+    dfprintf((port, "\tframeNum = %d\n", frameNum));
+    dfprintf((port, "\tconfident = %s\n", confident?"true":"false"));
+    dfprintf((port, "\tfailedLastTime = %s\n", failedLastTime?"true":"false"));
+    dfprintf((port, "\tminSize = %dx%d\n", minSize.width, minSize.height));
+}
+
+void TrackerTLDModel::printme(FILE*  port)
+{
+    dfprintf((port, "TrackerTLDModel:\n"));
+    dfprintf((port, "\tpositiveExamples.size() = %d\n", (int)positiveExamples.size()));
+    dfprintf((port, "\tnegativeExamples.size() = %d\n", (int)negativeExamples.size()));
+}
+
+void MyMouseCallbackDEBUG::onMouse(int event, int x, int y)
+{
+    if( event == EVENT_LBUTTONDOWN )
+    {
+        Mat imgCanvas;
+        img_.copyTo(imgCanvas);
+        TrackerTLDModel* tldModel = ((TrackerTLDModel*)static_cast<TrackerModel*>(detector_->model));
+        Size initSize = tldModel->getMinSize();
+        Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
+        double originalVariance = tldModel->getOriginalVariance();
+        double tmp;
+
+        Mat resized_img, blurred_img;
+        double scale = SCALE_STEP;
+        //double scale = SCALE_STEP * SCALE_STEP * SCALE_STEP * SCALE_STEP;
+        Size2d size(img_.cols / scale, img_.rows / scale);
+        resize(img_, resized_img, size);
+        resize(imgBlurred_, blurred_img, size);
+
+        Mat_<double> intImgP, intImgP2;
+        detector_->computeIntegralImages(resized_img, intImgP, intImgP2);
+
+        int dx = initSize.width / 10, dy = initSize.height / 10,
+            i = (int)(x / scale / dx), j = (int)(y / scale / dy);
+
+        dfprintf((stderr, "patchVariance = %s\n", (detector_->patchVariance(intImgP, intImgP2, originalVariance,
+                            Point(dx * i, dy * j), initSize))?"true":"false"));
+        tldModel->prepareClassifiers((int)blurred_img.step[0]);
+        dfprintf((stderr, "p = %f\n", (tldModel->ensembleClassifierNum(&blurred_img.at<uchar>(dy * j, dx * i)))));
+        fprintf(stderr, "ensembleClassifier = %s\n",
+                (!(tldModel->ensembleClassifierNum(&blurred_img.at<uchar>(dy * j, dx * i)) > ENSEMBLE_THRESHOLD))?"true":"false");
+
+        resample(resized_img, Rect2d(Point(dx * i, dy * j), initSize), standardPatch);
+        tmp = tldModel->Sr(standardPatch);
+        dfprintf((stderr, "Sr = %f\n", tmp));
+        dfprintf((stderr, "isObject = %s\n", (tmp > THETA_NN)?"true":"false"));
+        dfprintf((stderr, "shouldBeIntegrated = %s\n", (abs(tmp - THETA_NN) < 0.1)?"true":"false"));
+        dfprintf((stderr, "Sc = %f\n", tldModel->Sc(standardPatch)));
+
+        rectangle(imgCanvas, Rect2d(Point2d(scale * dx * i, scale * dy * j), Size2d(initSize.width * scale, initSize.height * scale)), 0, 2, 1 );
+        imshow("picker", imgCanvas);
+        waitKey();
+    }
+}
+
+void TrackerTLDModel::pushIntoModel(const Mat_<uchar>& example, bool positive)
+{
+    std::vector<Mat_<uchar> >* proxyV;
+    int* proxyN;
+    std::vector<int>* proxyT;
+    if( positive )
+    {
+        proxyV = &positiveExamples;
+        proxyN = &timeStampPositiveNext;
+        proxyT = &timeStampsPositive;
+    }
+    else
+    {
+        proxyV = &negativeExamples;
+        proxyN = &timeStampNegativeNext;
+        proxyT = &timeStampsNegative;
+    }
+    if( (int)proxyV->size() < MAX_EXAMPLES_IN_MODEL )
+    {
+        proxyV->push_back(example);
+        proxyT->push_back(*proxyN);
+    }
+    else
+    {
+        int index = rng.uniform((int)0, (int)proxyV->size());
+        (*proxyV)[index] = example;
+        (*proxyT)[index] = (*proxyN);
+    }
+    (*proxyN)++;
+}
+void TrackerTLDModel::prepareClassifiers(int rowstep)
+{
+  for( int i = 0; i < (int)classifiers.size(); i++ ) 
+      classifiers[i].prepareClassifier(rowstep); 
+}
+
+} /* namespace tld */
+
+} /* namespace cv */

modules/tracking/src/TLD.hpp → modules/tracking/src/tld_tracker.hpp

@@ -42,8 +42,8 @@
 #include "precomp.hpp"
 #include "opencv2/video/tracking.hpp"
 #include "opencv2/imgproc.hpp"
-#include <algorithm>
-#include <limits.h>
+#include<algorithm>
+#include<limits.h>
 
 namespace cv {namespace tld
 {
@@ -57,53 +57,68 @@ namespace cv {namespace tld
 #define dfprintf(x)
 #define dprintf(x)
 #endif
-#define MEASURE_TIME(a) {\
-    clock_t start;float milisec=0.0;\
-    start=clock();{a} milisec=1000.0*(clock()-start)/CLOCKS_PER_SEC;\
-    dprintf(("%-90s took %f milis\n",#a,milisec)); }
-#define HERE dprintf(("%d\n",__LINE__));fflush(stderr);
-#define START_TICK(name) { clock_t start;double milisec=0.0; start=clock();
-#define END_TICK(name) milisec=1000.0*(clock()-start)/CLOCKS_PER_SEC;\
-    dprintf(("%s took %f milis\n",name,milisec)); }
+#define MEASURE_TIME(a)\
+{\
+    clock_t start; float milisec = 0.0; \
+    start = clock(); {a} milisec = 1000.0 * (clock() - start) / CLOCKS_PER_SEC; \
+    dprintf(("%-90s took %f milis\n", #a, milisec));\
+}
+#define HERE dprintf(("line %d\n", __LINE__)); fflush(stderr);
+#define START_TICK(name)\
+{ \
+    clock_t start; double milisec = 0.0; start = clock();
+#define END_TICK(name) milisec = 1000.0 * (clock() - start) / CLOCKS_PER_SEC; \
+    dprintf(("%s took %f milis\n", name, milisec)); \
+}
 extern Rect2d etalon;
 void myassert(const Mat& img);
 void printPatch(const Mat_<uchar>& standardPatch);
 std::string type2str(const Mat& mat);
-void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,Rect2d whiteOne=Rect2d(-1.0,-1.0,-1.0,-1.0));
-void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,std::vector<Rect2d>& whiteOnes);
+void drawWithRects(const Mat& img, std::vector<Rect2d>& blackOnes, Rect2d whiteOne = Rect2d(-1.0, -1.0, -1.0, -1.0));
+void drawWithRects(const Mat& img, std::vector<Rect2d>& blackOnes, std::vector<Rect2d>& whiteOnes, String fileName = "");
 
 //aux functions and variables
-//#define CLIP(x,a,b) MIN(MAX((x),(a)),(b))
-template<typename T> inline T CLIP(T x,T a,T b){return MIN(MAX(x,a),b);}
-double overlap(const Rect2d& r1,const Rect2d& r2);
-void resample(const Mat& img,const RotatedRect& r2,Mat_<uchar>& samples);
-void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples);
+template<typename T> inline T CLIP(T x, T a, T b){ return std::min(std::max(x, a), b); }
+/** Computes overlap between the two given rectangles. Overlap is computed as ratio of rectangles' intersection to that
+ * of their union.*/
+double overlap(const Rect2d& r1, const Rect2d& r2);
+/** Resamples the area surrounded by r2 in img so it matches the size of samples, where it is written.*/
+void resample(const Mat& img, const RotatedRect& r2, Mat_<uchar>& samples);
+/** Specialization of resample() for rectangles without retation for better performance and simplicity.*/
+void resample(const Mat& img, const Rect2d& r2, Mat_<uchar>& samples);
+/** Computes the variance of single given image.*/
 double variance(const Mat& img);
-double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box);
-double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2);
-void getClosestN(std::vector<Rect2d>& scanGrid,Rect2d bBox,int n,std::vector<Rect2d>& res);
-double scaleAndBlur(const Mat& originalImg,int scale,Mat& scaledImg,Mat& blurredImg,Size GaussBlurKernelSize);
-unsigned int getMedian(const std::vector<unsigned int>& values, int size=-1);
+/** Computes normalized corellation coefficient between the two patches (they should be
+ * of the same size).*/
+double NCC(const Mat_<uchar>& patch1, const Mat_<uchar>& patch2);
+void getClosestN(std::vector<Rect2d>& scanGrid, Rect2d bBox, int n, std::vector<Rect2d>& res);
+double scaleAndBlur(const Mat& originalImg, int scale, Mat& scaledImg, Mat& blurredImg, Size GaussBlurKernelSize, double scaleStep);
+int getMedian(const std::vector<int>& values, int size = -1);
 
-class TLDEnsembleClassifier{
+class TLDEnsembleClassifier
+{
 public:
-    TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier);
-    void integrate(Mat_<uchar> patch,bool isPositive);
-    double posteriorProbability(const uchar* data,int rowstep)const;
-    static int getMaxOrdinal();
+    static int makeClassifiers(Size size, int measurePerClassifier, int gridSize, std::vector<TLDEnsembleClassifier>& classifiers);
+    void integrate(const Mat_<uchar>& patch, bool isPositive);
+    double posteriorProbability(const uchar* data, int rowstep) const;
+    double posteriorProbabilityFast(const uchar* data) const;
+    void prepareClassifier(int rowstep);
 private:
-    static int getGridSize();
-    inline void stepPrefSuff(std::vector<uchar>& arr,int len);
-    void preinit(int ordinal);
-    unsigned short int code(const uchar* data,int rowstep)const;
-    std::vector<unsigned int> pos,neg;
-    std::vector<uchar> x1,y1,x2,y2;
+    TLDEnsembleClassifier(const std::vector<Vec4b>& meas, int beg, int end);
+    static void stepPrefSuff(std::vector<Vec4b> & arr, int pos, int len, int gridSize);
+    int code(const uchar* data, int rowstep) const;
+    int codeFast(const uchar* data) const;
+    std::vector<Point2i> posAndNeg;
+    std::vector<Vec4b> measurements;
+    std::vector<Point2i> offset;
+    int lastStep_;
 };
 
-class TrackerProxy{
+class TrackerProxy
+{
 public:
-    virtual bool init( const Mat& image, const Rect2d& boundingBox)=0;
-    virtual bool update(const Mat& image, Rect2d& boundingBox)=0;
+    virtual bool init(const Mat& image, const Rect2d& boundingBox) = 0;
+    virtual bool update(const Mat& image, Rect2d& boundingBox) = 0;
     virtual ~TrackerProxy(){}
 };
 

modules/tracking/src/tld_utils.cpp

+/*///////////////////////////////////////////////////////////////////////////////////////
+ //
+ //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ //
+ //  By downloading, copying, installing or using the software you agree to this license.
+ //  If you do not agree to this license, do not download, install,
+ //  copy or use the software.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+ // Third party copyrights are property of their respective owners.
+ //
+ // Redistribution and use in source and binary forms, with or without modification,
+ // are permitted provided that the following conditions are met:
+ //
+ //   * Redistribution's of source code must retain the above copyright notice,
+ //     this list of conditions and the following disclaimer.
+ //
+ //   * Redistribution's in binary form must reproduce the above copyright notice,
+ //     this list of conditions and the following disclaimer in the documentation
+ //     and/or other materials provided with the distribution.
+ //
+ //   * The name of the copyright holders may not be used to endorse or promote products
+ //     derived from this software without specific prior written permission.
+ //
+ // This software is provided by the copyright holders and contributors "as is" and
+ // any express or implied warranties, including, but not limited to, the implied
+ // warranties of merchantability and fitness for a particular purpose are disclaimed.
+ // In no event shall the Intel Corporation or contributors be liable for any direct,
+ // indirect, incidental, special, exemplary, or consequential damages
+ // (including, but not limited to, procurement of substitute goods or services;
+ // loss of use, data, or profits; or business interruption) however caused
+ // and on any theory of liability, whether in contract, strict liability,
+ // or tort (including negligence or otherwise) arising in any way out of
+ // the use of this software, even if advised of the possibility of such damage.
+ //
+ //M*/
+
+#include "precomp.hpp"
+#include "opencv2/video/tracking.hpp"
+#include "opencv2/imgproc.hpp"
+#include "time.h"
+#include<algorithm>
+#include<limits.h>
+#include<math.h>
+#include<opencv2/highgui.hpp>
+#include "tld_tracker.hpp"
+
+namespace cv {namespace tld
+{
+
+//debug functions and variables
+Rect2d etalon(14.0, 110.0, 20.0, 20.0);
+void drawWithRects(const Mat& img, std::vector<Rect2d>& blackOnes, Rect2d whiteOne)
+{
+    Mat image;
+    img.copyTo(image);
+    if( whiteOne.width >= 0 )
+        rectangle( image, whiteOne, 255, 1, 1 );
+    for( int i = 0; i < (int)blackOnes.size(); i++ )
+        rectangle( image, blackOnes[i], 0, 1, 1 );
+    imshow("img", image);
+}
+void drawWithRects(const Mat& img, std::vector<Rect2d>& blackOnes, std::vector<Rect2d>& whiteOnes, String filename)
+{
+    Mat image;
+    static int frameCounter = 1;
+    img.copyTo(image);
+    for( int i = 0; i < (int)whiteOnes.size(); i++ )
+        rectangle( image, whiteOnes[i], 255, 1, 1 );
+    for( int i = 0; i < (int)blackOnes.size(); i++ )
+        rectangle( image, blackOnes[i], 0, 1, 1 );
+    imshow("img", image);
+    if( filename.length() > 0 )
+    {
+        char inbuf[100];
+        sprintf(inbuf, "%s%d.jpg", filename.c_str(), frameCounter);
+        imwrite(inbuf, image);
+        frameCounter++;
+    }
+}
+void myassert(const Mat& img)
+{
+    int count = 0;
+    for( int i = 0; i < img.rows; i++ )
+    {
+        for( int j = 0; j < img.cols; j++ )
+        {
+            if( img.at<uchar>(i, j) == 0 )
+                count++;
+        }
+    }
+    dprintf(("black: %d out of %d (%f)\n", count, img.rows * img.cols, 1.0 * count / img.rows / img.cols));
+}
+
+void printPatch(const Mat_<uchar>& standardPatch)
+{
+    for( int i = 0; i < standardPatch.rows; i++ )
+    {
+        for( int j = 0; j < standardPatch.cols; j++ )
+            dprintf(("%5.2f, ", (double)standardPatch(i, j)));
+        dprintf(("\n"));
+    }
+}
+
+std::string type2str(const Mat& mat)
+{
+  int type = mat.type();
+  std::string r;
+
+  uchar depth = type & CV_MAT_DEPTH_MASK;
+  uchar chans = (uchar)(1 + (type >> CV_CN_SHIFT));
+
+  switch ( depth ) {
+    case CV_8U:  r = "8U"; break;
+    case CV_8S:  r = "8S"; break;
+    case CV_16U: r = "16U"; break;
+    case CV_16S: r = "16S"; break;
+    case CV_32S: r = "32S"; break;
+    case CV_32F: r = "32F"; break;
+    case CV_64F: r = "64F"; break;
+    default:     r = "User"; break;
+  }
+
+  r += "C";
+  r += (chans + '0');
+
+  return r;
+}
+
+//generic functions
+double scaleAndBlur(const Mat& originalImg, int scale, Mat& scaledImg, Mat& blurredImg, Size GaussBlurKernelSize, double scaleStep)
+{
+    double dScale = 1.0;
+    for( int i = 0; i < scale; i++, dScale *= scaleStep );
+    Size2d size = originalImg.size();
+    size.height /= dScale; size.width /= dScale;
+    resize(originalImg, scaledImg, size);
+    GaussianBlur(scaledImg, blurredImg, GaussBlurKernelSize, 0.0);
+    return dScale;
+}
+void getClosestN(std::vector<Rect2d>& scanGrid, Rect2d bBox, int n, std::vector<Rect2d>& res)
+{
+    if( n >= (int)scanGrid.size() )
+    {
+        res.assign(scanGrid.begin(), scanGrid.end());
+        return;
+    }
+    std::vector<double> overlaps;
+    overlaps.assign(n, 0.0);
+    res.assign(scanGrid.begin(), scanGrid.begin() + n);
+    for( int i = 0; i < n; i++ )
+        overlaps[i] = overlap(res[i], bBox);
+    double otmp;
+    Rect2d rtmp;
+    for (int i = 1; i < n; i++)
+    {
+        int j = i;
+        while (j > 0 && overlaps[j - 1] > overlaps[j]) {
+            otmp = overlaps[j]; overlaps[j] = overlaps[j - 1]; overlaps[j - 1] = otmp;
+            rtmp = res[j]; res[j] = res[j - 1]; res[j - 1] = rtmp;
+            j--;
+        }
+    }
+
+    for( int i = n; i < (int)scanGrid.size(); i++ )
+    {
+        double o = 0.0;
+        if( (o = overlap(scanGrid[i], bBox)) <= overlaps[0] )
+            continue;
+        int j = 0;
+        while( j < n && overlaps[j] < o )
+            j++;
+        j--;
+        for( int k = 0; k < j; overlaps[k] = overlaps[k + 1], res[k] = res[k + 1], k++ );
+        overlaps[j] = o; res[j] = scanGrid[i];
+    }
+}
+
+double variance(const Mat& img)
+{
+    double p = 0, p2 = 0;
+    for( int i = 0; i < img.rows; i++ )
+    {
+        for( int j = 0; j < img.cols; j++ )
+        {
+            p += img.at<uchar>(i, j);
+            p2 += img.at<uchar>(i, j) * img.at<uchar>(i, j);
+        }
+    }
+    p /= (img.cols * img.rows);
+    p2 /= (img.cols * img.rows);
+    return p2 - p * p;
+}
+
+double NCC(const Mat_<uchar>& patch1, const Mat_<uchar>& patch2)
+{
+    CV_Assert( patch1.rows == patch2.rows );
+    CV_Assert( patch1.cols == patch2.cols );
+
+    int N = patch1.rows * patch1.cols;
+    int s1 = 0, s2 = 0, n1 = 0, n2 = 0, prod = 0;
+    for( int i = 0; i < patch1.rows; i++ )
+    {
+        for( int j = 0; j < patch1.cols; j++ )
+        {
+            int p1 = patch1(i, j), p2 = patch2(i, j);
+            s1 += p1; s2 += p2;
+            n1 += (p1 * p1); n2 += (p2 * p2);
+            prod += (p1 * p2);
+        }
+    }
+    double sq1 = sqrt(std::max(0.0, n1 - 1.0 * s1 * s1 / N)), sq2 = sqrt(std::max(0.0, n2 - 1.0 * s2 * s2 / N));
+    double ares = (sq2 == 0) ? sq1 / abs(sq1) : (prod - s1 * s2 / N) / sq1 / sq2;
+    return ares;
+}
+int getMedian(const std::vector<int>& values, int size)
+{
+    if( size == -1 )
+        size = (int)values.size();
+    std::vector<int> copy(values.begin(), values.begin() + size);
+    std::sort(copy.begin(), copy.end());
+    if( size % 2 == 0 )
+        return (copy[size / 2 - 1] + copy[size / 2]) / 2;
+    else
+        return copy[(size - 1) / 2];
+}
+
+double overlap(const Rect2d& r1, const Rect2d& r2)
+{
+    double a1 = r1.area(), a2 = r2.area(), a0 = (r1&r2).area();
+    return a0 / (a1 + a2 - a0);
+}
+
+void resample(const Mat& img, const RotatedRect& r2, Mat_<uchar>& samples)
+{
+    Mat_<float> M(2, 3), R(2, 2), Si(2, 2), s(2, 1), o(2, 1);
+    R(0, 0) = (float)cos(r2.angle * CV_PI / 180); R(0, 1) = (float)(-sin(r2.angle * CV_PI / 180));
+    R(1, 0) = (float)sin(r2.angle * CV_PI / 180); R(1, 1) = (float)cos(r2.angle * CV_PI / 180);
+    Si(0, 0) = (float)(samples.cols / r2.size.width); Si(0, 1) = 0.0f;
+    Si(1, 0) = 0.0f; Si(1, 1) = (float)(samples.rows / r2.size.height);
+    s(0, 0) = (float)samples.cols; s(1, 0) = (float)samples.rows;
+    o(0, 0) = r2.center.x; o(1, 0) = r2.center.y;
+    Mat_<float> A(2, 2), b(2, 1);
+    A = Si * R;
+    b = s / 2.0 - Si * R * o;
+    A.copyTo(M.colRange(Range(0, 2)));
+    b.copyTo(M.colRange(Range(2, 3)));
+    warpAffine(img, samples, M, samples.size());
+}
+void resample(const Mat& img, const Rect2d& r2, Mat_<uchar>& samples)
+{
+    Mat_<float> M(2, 3);
+    M(0, 0) = (float)(samples.cols / r2.width); M(0, 1) = 0.0f; M(0, 2) = (float)(-r2.x * samples.cols / r2.width);
+    M(1, 0) = 0.0f; M(1, 1) = (float)(samples.rows / r2.height); M(1, 2) = (float)(-r2.y * samples.rows / r2.height);
+    warpAffine(img, samples, M, samples.size());
+}
+
+//other stuff
+void TLDEnsembleClassifier::stepPrefSuff(std::vector<Vec4b>& arr, int pos, int len, int gridSize)
+{
+#if 0
+        int step = len / (gridSize - 1), pref = (len - step * (gridSize - 1)) / 2;
+        for( int i = 0; i < (int)(sizeof(x1) / sizeof(x1[0])); i++ )
+            arr[i] = pref + arr[i] * step;
+#else
+        int total = len - gridSize;
+        int quo = total / (gridSize - 1), rem = total % (gridSize - 1);
+        int smallStep = quo, bigStep = quo + 1;
+        int bigOnes = rem, smallOnes = gridSize - bigOnes - 1;
+        int bigOnes_front = bigOnes / 2, bigOnes_back = bigOnes - bigOnes_front;
+        for( int i = 0; i < (int)arr.size(); i++ )
+        {
+            if( arr[i].val[pos] < bigOnes_back )
+            {
+                arr[i].val[pos] = (uchar)(arr[i].val[pos] * bigStep + arr[i].val[pos]);
+                continue;
+            }
+            if( arr[i].val[pos] < (bigOnes_front + smallOnes) )
+            {
+                arr[i].val[pos] = (uchar)(bigOnes_front * bigStep + (arr[i].val[pos] - bigOnes_front) * smallStep + arr[i].val[pos]);
+                continue;
+            }
+            if( arr[i].val[pos] < (bigOnes_front + smallOnes + bigOnes_back) )
+            {
+                arr[i].val[pos] =
+                    (uchar)(bigOnes_front * bigStep + smallOnes * smallStep + 
+                            (arr[i].val[pos] - (bigOnes_front + smallOnes)) * bigStep + arr[i].val[pos]);
+                continue;
+            }
+            arr[i].val[pos] = (uchar)(len - 1);
+        }
+#endif
+}
+void TLDEnsembleClassifier::prepareClassifier(int rowstep)
+{
+    if( lastStep_ != rowstep )
+    {
+        lastStep_ = rowstep;
+        for( int i = 0; i < (int)offset.size(); i++ )
+        {
+            offset[i].x = rowstep * measurements[i].val[0] + measurements[i].val[1];
+            offset[i].y = rowstep * measurements[i].val[2] + measurements[i].val[3];
+        }
+    }
+}
+TLDEnsembleClassifier::TLDEnsembleClassifier(const std::vector<Vec4b>& meas, int beg, int end):lastStep_(-1)
+{
+    int posSize = 1, mpc = end - beg;
+    for( int i = 0; i < mpc; i++ )
+        posSize *= 2;
+    posAndNeg.assign(posSize, Point2i(0, 0));
+    measurements.assign(meas.begin() + beg, meas.begin() + end);
+    offset.assign(mpc, Point2i(0, 0));
+}
+void TLDEnsembleClassifier::integrate(const Mat_<uchar>& patch, bool isPositive)
+{
+    int position = code(patch.data, (int)patch.step[0]);
+    if( isPositive )
+        posAndNeg[position].x++;
+    else
+        posAndNeg[position].y++;
+}
+double TLDEnsembleClassifier::posteriorProbability(const uchar* data, int rowstep) const
+{
+    int position = code(data, rowstep);
+    double posNum = (double)posAndNeg[position].x, negNum = (double)posAndNeg[position].y;
+    if( posNum == 0.0 && negNum == 0.0 )
+        return 0.0;
+    else
+        return posNum / (posNum + negNum);
+}
+double TLDEnsembleClassifier::posteriorProbabilityFast(const uchar* data) const
+{
+    int position = codeFast(data);
+    double posNum = (double)posAndNeg[position].x, negNum = (double)posAndNeg[position].y;
+    if( posNum == 0.0 && negNum == 0.0 )
+        return 0.0;
+    else
+        return posNum / (posNum + negNum);
+}
+int TLDEnsembleClassifier::codeFast(const uchar* data) const
+{
+    int position = 0;
+    for( int i = 0; i < (int)measurements.size(); i++ )
+    {
+        position = position << 1;
+        if( data[offset[i].x] < data[offset[i].y] )
+            position++;
+    }
+    return position;
+}
+int TLDEnsembleClassifier::code(const uchar* data, int rowstep) const
+{
+    int position = 0;
+    for( int i = 0; i < (int)measurements.size(); i++ )
+    {
+        position = position << 1;
+        if( *(data + rowstep * measurements[i].val[0] + measurements[i].val[1]) <
+                *(data + rowstep * measurements[i].val[2] + measurements[i].val[3]) )
+        {
+            position++;
+        }
+    }
+    return position;
+}
+int TLDEnsembleClassifier::makeClassifiers(Size size, int measurePerClassifier, int gridSize,
+        std::vector<TLDEnsembleClassifier>& classifiers)
+{
+
+    std::vector<Vec4b> measurements;
+
+    for( int i = 0; i < gridSize; i++ )
+    {
+        for( int j = 0; j < gridSize; j++ )
+        {
+            for( int k = 0; k < j; k++ )
+            {
+                Vec4b m;
+                m.val[0] = m.val[2] = (uchar)i;
+                m.val[1] = (uchar)j; m.val[3] = (uchar)k;
+                measurements.push_back(m);
+                m.val[1] = m.val[3] = (uchar)i;
+                m.val[0] = (uchar)j; m.val[2] = (uchar)k;
+                measurements.push_back(m);
+            }
+        }
+    }
+    random_shuffle(measurements.begin(), measurements.end());
+
+    stepPrefSuff(measurements, 0, size.width, gridSize);
+    stepPrefSuff(measurements, 1, size.width, gridSize);
+    stepPrefSuff(measurements, 2, size.height, gridSize);
+    stepPrefSuff(measurements, 3, size.height, gridSize);
+
+    for( int i = 0, howMany = (int)measurements.size() / measurePerClassifier; i < howMany; i++ )
+        classifiers.push_back(TLDEnsembleClassifier(measurements, i * measurePerClassifier, (i + 1) * measurePerClassifier));
+    return (int)classifiers.size();
+}
+
+}}

modules/tracking/src/trackerTLD.cpp

-/*///////////////////////////////////////////////////////////////////////////////////////
- //
- //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
- //
- //  By downloading, copying, installing or using the software you agree to this license.
- //  If you do not agree to this license, do not download, install,
- //  copy or use the software.
- //
- //
- //                           License Agreement
- //                For Open Source Computer Vision Library
- //
- // Copyright (C) 2013, OpenCV Foundation, all rights reserved.
- // Third party copyrights are property of their respective owners.
- //
- // Redistribution and use in source and binary forms, with or without modification,
- // are permitted provided that the following conditions are met:
- //
- //   * Redistribution's of source code must retain the above copyright notice,
- //     this list of conditions and the following disclaimer.
- //
- //   * Redistribution's in binary form must reproduce the above copyright notice,
- //     this list of conditions and the following disclaimer in the documentation
- //     and/or other materials provided with the distribution.
- //
- //   * The name of the copyright holders may not be used to endorse or promote products
- //     derived from this software without specific prior written permission.
- //
- // This software is provided by the copyright holders and contributors "as is" and
- // any express or implied warranties, including, but not limited to, the implied
- // warranties of merchantability and fitness for a particular purpose are disclaimed.
- // In no event shall the Intel Corporation or contributors be liable for any direct,
- // indirect, incidental, special, exemplary, or consequential damages
- // (including, but not limited to, procurement of substitute goods or services;
- // loss of use, data, or profits; or business interruption) however caused
- // and on any theory of liability, whether in contract, strict liability,
- // or tort (including negligence or otherwise) arising in any way out of
- // the use of this software, even if advised of the possibility of such damage.
- //
- //M*/
-
-#include "precomp.hpp"
-#include "opencv2/video/tracking.hpp"
-#include "opencv2/imgproc.hpp"
-#include "time.h"
-#include <algorithm>
-#include <limits.h>
-#include "TLD.hpp"
-#include "opencv2/highgui.hpp"
-
-#define THETA_NN 0.5
-#define CORE_THRESHOLD 0.5
-#define NEG_EXAMPLES_IN_INIT_MODEL 300
-#define MAX_EXAMPLES_IN_MODEL 500
-#define MEASURES_PER_CLASSIFIER 13
-#undef BLUR_AS_VADIM
-#undef CLOSED_LOOP
-static const cv::Size GaussBlurKernelSize(3,3);
-
-using namespace cv;
-using namespace tld;
-
-/*
- * FIXME(optimize):
- *      no median
- *      direct formula in resamples
- * FIXME(issues)
- *      THETA_NN 0.5<->0.6 dramatic change vs video 6 !!
- * FIXME(features)
- *      benchmark: save photos --> two streams of photos --> better video
- * TODO:
- *      schoolPC: codec, libopencv-dev
- *      fix pushbot ->pick commits -> compare_branches->all in 1->resubmit
- *      ||video(0.5<->0.6) --> debug if box size is less than 20 --> (remove ensemble self-loop) --> (try inter_area for resize)
- *      perfect PN
- *
- *      vadim:
- *
- *      blurred in TrackerTLDModel()
- *
- *      warpAffine -- ?
-*/
-
-/* design decisions:
- * blur --> resize (vs. resize-->blur) in detect(), ensembleClassifier stage
- * no random gauss noise, when making examples for ensembleClassifier
- */
-
-namespace cv
-{
-
-class TLDDetector;
-class MyMouseCallbackDEBUG{
-public:
-    MyMouseCallbackDEBUG( Mat& img, Mat& imgBlurred,TLDDetector* detector):img_(img),imgBlurred_(imgBlurred),detector_(detector){}
-    static void onMouse( int event, int x, int y, int, void* obj){
-        ((MyMouseCallbackDEBUG*)obj)->onMouse(event,x,y);
-    }
-    MyMouseCallbackDEBUG& operator= (const MyMouseCallbackDEBUG& /*other*/){return *this;}
-private:
-    void onMouse( int event, int x, int y);
-    Mat& img_,imgBlurred_;
-    TLDDetector* detector_;
-};
-
-class Data {
-public:
-    Data(Rect2d initBox);
-    Size getMinSize(){return minSize;}
-    double getScale(){return scale;}
-    bool confident;
-    bool failedLastTime;
-    int frameNum;
-    void printme(FILE*  port=stdout);
-private:
-    double scale;
-    Size minSize;
-};
-
-class TrackerTLDModel;
-
-class TLDDetector {
-public:
-    TLDDetector(const TrackerTLD::Params& params,Ptr<TrackerModel>model_in):model(model_in),params_(params){}
-    ~TLDDetector(){}
-    static void generateScanGrid(int rows,int cols,Size initBox,std::vector<Rect2d>& res,bool withScaling=false);
-    bool detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::vector<Rect2d>& rect,std::vector<bool>& isObject,
-            std::vector<bool>& shouldBeIntegrated);
-protected:
-    friend class MyMouseCallbackDEBUG;
-    Ptr<TrackerModel> model;
-    void computeIntegralImages(const Mat& img,Mat_<double>& intImgP,Mat_<double>& intImgP2){integral(img,intImgP,intImgP2,CV_64F);}
-    inline bool patchVariance(Mat_<double>& intImgP,Mat_<double>& intImgP2,double originalVariance,Point pt,Size size);
-    bool ensembleClassifier(const uchar* data,int rowstep){return ensembleClassifierNum(data,rowstep)>0.5;}
-    double ensembleClassifierNum(const uchar* data,int rowstep);
-    TrackerTLD::Params params_;
-};
-
-class Pexpert{
-public:
-    Pexpert(const Mat& img,const Mat& imgBlurred,Rect2d& resultBox,const TLDDetector* detector,TrackerTLD::Params params,Size initSize):
-        img_(img),imgBlurred_(imgBlurred),resultBox_(resultBox),detector_(detector),params_(params),initSize_(initSize){}
-    bool operator()(Rect2d /*box*/){return false;}
-    int additionalExamples(std::vector<Mat_<uchar> >& examplesForModel,std::vector<Mat_<uchar> >& examplesForEnsemble);
-protected:
-    Mat img_,imgBlurred_;
-    Rect2d resultBox_;
-    const TLDDetector* detector_;
-    TrackerTLD::Params params_;
-    RNG rng;
-    Size initSize_;
-};
-
-class Nexpert{
-public:
-    Nexpert(const Mat& img,Rect2d& resultBox,const TLDDetector* detector,TrackerTLD::Params params):img_(img),resultBox_(resultBox),
-        detector_(detector),params_(params){}
-    bool operator()(Rect2d box);
-    int additionalExamples(std::vector<Mat_<uchar> >& examplesForModel,std::vector<Mat_<uchar> >& examplesForEnsemble){
-        examplesForModel.clear();examplesForEnsemble.clear();return 0;}
-protected:
-    Mat img_;
-    Rect2d resultBox_;
-    const TLDDetector* detector_;
-    TrackerTLD::Params params_;
-};
-
-template <class T,class Tparams>
-class TrackerProxyImpl : public TrackerProxy{
-public:
-    TrackerProxyImpl(Tparams params=Tparams()):params_(params){}
-    bool init( const Mat& image, const Rect2d& boundingBox ){
-        trackerPtr=T::createTracker();
-        return trackerPtr->init(image,boundingBox);
-    }
-    bool update( const Mat& image,Rect2d& boundingBox){
-        return trackerPtr->update(image,boundingBox);
-    }
-private:
-    Ptr<T> trackerPtr;
-    Tparams params_;
-    Rect2d boundingBox_;
-};
-
-class TrackerTLDModel : public TrackerModel{
- public:
-  TrackerTLDModel(TrackerTLD::Params params,const Mat& image, const Rect2d& boundingBox,Size minSize);
-  Rect2d getBoundingBox(){return boundingBox_;}
-  void setBoudingBox(Rect2d boundingBox){boundingBox_=boundingBox;}
-  double getOriginalVariance(){return originalVariance_;}
-  std::vector<TLDEnsembleClassifier>* getClassifiers(){return &classifiers;}
-  double Sr(const Mat_<uchar> patch);
-  double Sc(const Mat_<uchar> patch);
-  void integrateRelabeled(Mat& img,Mat& imgBlurred,const std::vector<Rect2d>& box,const std::vector<bool>& isPositive,
-          const std::vector<bool>& alsoIntoModel);
-  void integrateAdditional(const std::vector<Mat_<uchar> >& eForModel,const std::vector<Mat_<uchar> >& eForEnsemble,bool isPositive);
-  Size getMinSize(){return minSize_;}
-  void printme(FILE*  port=stdout);
- protected:
-  Size minSize_;
-  unsigned int timeStampPositiveNext,timeStampNegativeNext;
-  TrackerTLD::Params params_;
-  void pushIntoModel(const Mat_<uchar>& example,bool positive);
-  void modelEstimationImpl( const std::vector<Mat>& /*responses*/ ){}
-  void modelUpdateImpl(){}
-  Rect2d boundingBox_;
-  double originalVariance_;
-  std::vector<Mat_<uchar> > positiveExamples,negativeExamples;
-  std::vector<unsigned int> timeStampsPositive,timeStampsNegative;
-  RNG rng;
-  std::vector<TLDEnsembleClassifier> classifiers;
-};
-
-class TrackerTLDImpl : public TrackerTLD
-{
- public:
-  TrackerTLDImpl( const TrackerTLD::Params &parameters = TrackerTLD::Params() );
-  void read( const FileNode& fn );
-  void write( FileStorage& fs ) const;
-
- protected:
-
-  bool initImpl( const Mat& image, const Rect2d& boundingBox );
-  bool updateImpl( const Mat& image, Rect2d& boundingBox );
-
-  TrackerTLD::Params params;
-  Ptr<Data> data;
-  Ptr<TrackerProxy> trackerProxy;
-  Ptr<TLDDetector> detector;
-};
-
-
-TrackerTLD::Params::Params(){
-}
-
-void TrackerTLD::Params::read( const cv::FileNode& /*fn*/ ){
-}
-
-void TrackerTLD::Params::write( cv::FileStorage& /*fs*/ ) const{
-}
-
-Ptr<TrackerTLD> TrackerTLD::createTracker(const TrackerTLD::Params &parameters){
-    return Ptr<TrackerTLDImpl>(new TrackerTLDImpl(parameters));
-}
-
-TrackerTLDImpl::TrackerTLDImpl( const TrackerTLD::Params &parameters) :
-    params( parameters ){
-  isInit = false;
-  trackerProxy=Ptr<TrackerProxyImpl<TrackerMedianFlow,TrackerMedianFlow::Params> >(
-              new TrackerProxyImpl<TrackerMedianFlow,TrackerMedianFlow::Params>());
-}
-
-void TrackerTLDImpl::read( const cv::FileNode& fn )
-{
-  params.read( fn );
-}
-
-void TrackerTLDImpl::write( cv::FileStorage& fs ) const
-{
-  params.write( fs );
-}
-
-bool TrackerTLDImpl::initImpl(const Mat& image, const Rect2d& boundingBox ){
-    Mat image_gray;
-    trackerProxy->init(image,boundingBox);
-    cvtColor( image, image_gray, COLOR_BGR2GRAY );
-    data=Ptr<Data>(new Data(boundingBox));
-    double scale=data->getScale();
-    Rect2d myBoundingBox=boundingBox;
-    if(scale>1.0){
-        Mat image_proxy;
-        resize(image_gray,image_proxy,Size(cvRound(image.cols*scale),cvRound(image.rows*scale)));
-        image_proxy.copyTo(image_gray);
-        myBoundingBox.x*=scale;
-        myBoundingBox.y*=scale;
-        myBoundingBox.width*=scale;
-        myBoundingBox.height*=scale;
-    }
-    model=Ptr<TrackerTLDModel>(new TrackerTLDModel(params,image_gray,myBoundingBox,data->getMinSize()));
-    detector=Ptr<TLDDetector>(new TLDDetector(params,model));
-    data->confident=false;
-    data->failedLastTime=false;
-
-#if !1
-        dprintf(("here I am\n"));
-        Mat image_blurred;
-        GaussianBlur(image_gray,image_blurred,GaussBlurKernelSize,0.0);
-        MyMouseCallbackDEBUG* callback=new MyMouseCallbackDEBUG(image_gray,image_blurred,detector);
-        imshow("picker",image_gray);
-        setMouseCallback( "picker", MyMouseCallbackDEBUG::onMouse, (void*)callback);
-        waitKey();
-#endif
-    return true;
-}
-
-bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox){
-    Mat image_gray,image_blurred,imageForDetector;
-    cvtColor( image, image_gray, COLOR_BGR2GRAY );
-    double scale=data->getScale();
-    if(scale>1.0){
-        resize(image_gray,imageForDetector,Size(cvRound(image.cols*scale),cvRound(image.rows*scale)));
-    }else{
-        imageForDetector=image_gray;
-    }
-    GaussianBlur(imageForDetector,image_blurred,GaussBlurKernelSize,0.0);
-    TrackerTLDModel* tldModel=((TrackerTLDModel*)static_cast<TrackerModel*>(model));
-    data->frameNum++;
-    Mat_<uchar> standardPatch(15,15);
-    std::vector<Rect2d> detectorResults;
-    std::vector<bool> isObject,shouldBeIntegrated;
-    //best overlap around 92%
-
-    Rect2d tmpCandid=boundingBox;
-    std::vector<Rect2d> candidates;
-    std::vector<double> candidatesRes;
-    bool trackerNeedsReInit=false;
-    for(int i=0;i<2;i++){
-        if(((i==0)&&!(data->failedLastTime)&&trackerProxy->update(image,tmpCandid)) || 
-                ((i==1)&&(detector->detect(imageForDetector,image_blurred,tmpCandid,detectorResults,isObject,shouldBeIntegrated)))){
-            candidates.push_back(tmpCandid);
-            if(i==0){
-                resample(image_gray,tmpCandid,standardPatch);
-            }else{
-                resample(imageForDetector,tmpCandid,standardPatch);
-            }
-            candidatesRes.push_back(tldModel->Sc(standardPatch));
-        }else{
-            if(i==0){
-                trackerNeedsReInit=true;
-            }
-        }
-    }
-
-    std::vector<double>::iterator it=std::max_element(candidatesRes.begin(),candidatesRes.end());
-
-    dfprintf((stdout,"scale=%f\n",log(1.0*boundingBox.width/(data->getMinSize()).width)/log(1.2)));
-    for(int i=0;i<(int)candidatesRes.size();i++){
-        dprintf(("\tcandidatesRes[%d]=%f\n",i,candidatesRes[i]));
-    }
-    data->printme();
-    tldModel->printme(stdout);
-#if !1
-    if(data->frameNum==82){
-        dprintf(("here I am\n"));
-        MyMouseCallbackDEBUG* callback=new MyMouseCallbackDEBUG(imageForDetector,image_blurred,detector);
-        imshow("picker",imageForDetector);
-        setMouseCallback( "picker", MyMouseCallbackDEBUG::onMouse, (void*)callback);
-        waitKey();
-    }
-#endif
-
-    if(it==candidatesRes.end()){
-        data->confident=false;
-        data->failedLastTime=true;
-        return false;
-    }else{
-        boundingBox=candidates[it-candidatesRes.begin()];
-        data->failedLastTime=false;
-        if(trackerNeedsReInit || it!=candidatesRes.begin()){
-            trackerProxy->init(image,boundingBox);
-        }
-    }
-
-    if(!false && it!=candidatesRes.end()){
-        resample(imageForDetector,candidates[it-candidatesRes.begin()],standardPatch);
-        dfprintf((stderr,"%d %f %f\n",data->frameNum,tldModel->Sc(standardPatch),tldModel->Sr(standardPatch)));
-        if(candidatesRes.size()==2 &&  it==(candidatesRes.begin()+1))
-            dfprintf((stderr,"detector WON\n"));
-    }else{
-        dfprintf((stderr,"%d x x\n",data->frameNum));
-    }
-
-    if(*it > CORE_THRESHOLD){
-        data->confident=true;
-    }
-
-    if(data->confident){
-        Pexpert pExpert(imageForDetector,image_blurred,boundingBox,detector,params,data->getMinSize());
-        Nexpert nExpert(imageForDetector,boundingBox,detector,params);
-        bool expertResult;
-        std::vector<Mat_<uchar> > examplesForModel,examplesForEnsemble;
-        examplesForModel.reserve(100);examplesForEnsemble.reserve(100);
-        int negRelabeled=0;
-        for(int i=0;i<(int)detectorResults.size();i++){
-            if(isObject[i]){
-                expertResult=nExpert(detectorResults[i]);
-                if(expertResult!=isObject[i]){negRelabeled++;}
-            }else{
-                expertResult=pExpert(detectorResults[i]);
-            }
-
-            shouldBeIntegrated[i]=shouldBeIntegrated[i] || (isObject[i]!=expertResult);
-            isObject[i]=expertResult;
-        }
-        tldModel->integrateRelabeled(imageForDetector,image_blurred,detectorResults,isObject,shouldBeIntegrated);
-        dprintf(("%d relabeled by nExpert\n",negRelabeled));
-        pExpert.additionalExamples(examplesForModel,examplesForEnsemble);
-        tldModel->integrateAdditional(examplesForModel,examplesForEnsemble,true);
-        examplesForModel.clear();examplesForEnsemble.clear();
-        nExpert.additionalExamples(examplesForModel,examplesForEnsemble);
-        tldModel->integrateAdditional(examplesForModel,examplesForEnsemble,false);
-    }else{
-#ifdef CLOSED_LOOP
-        tldModel->integrateRelabeled(imageForDetector,image_blurred,detectorResults,isObject,shouldBeIntegrated);
-#endif
-    }
-
-    return true;
-}
-
-TrackerTLDModel::TrackerTLDModel(TrackerTLD::Params params,const Mat& image, const Rect2d& boundingBox,Size minSize):minSize_(minSize),
-timeStampPositiveNext(0),timeStampNegativeNext(0),params_(params){
-    boundingBox_=boundingBox;
-    originalVariance_=variance(image(boundingBox));
-    std::vector<Rect2d> closest(10),scanGrid;
-    Mat scaledImg,blurredImg,image_blurred;
-
-    double scale=scaleAndBlur(image,cvRound(log(1.0*boundingBox.width/(minSize.width))/log(1.2)),scaledImg,blurredImg,GaussBlurKernelSize);
-    GaussianBlur(image,image_blurred,GaussBlurKernelSize,0.0);
-    TLDDetector::generateScanGrid(image.rows,image.cols,minSize,scanGrid);
-    getClosestN(scanGrid,Rect2d(boundingBox.x/scale,boundingBox.y/scale,boundingBox.width/scale,boundingBox.height/scale),10,closest);
-
-    Mat_<uchar> blurredPatch(minSize);
-    for(int i=0,howMany=TLDEnsembleClassifier::getMaxOrdinal();i<howMany;i++){
-        classifiers.push_back(TLDEnsembleClassifier(i+1,minSize,MEASURES_PER_CLASSIFIER));
-    }
-
-    positiveExamples.reserve(200);
-    Point2f center;
-    Size2f size;
-    for(int i=0;i<(int)closest.size();i++){
-        for(int j=0;j<20;j++){
-            Mat_<uchar> standardPatch(15,15);
-            center.x=(float)(closest[i].x+closest[i].width*(0.5+rng.uniform(-0.01,0.01)));
-            center.y=(float)(closest[i].y+closest[i].height*(0.5+rng.uniform(-0.01,0.01)));
-            size.width=(float)(closest[i].width*rng.uniform((double)0.99,(double)1.01));
-            size.height=(float)(closest[i].height*rng.uniform((double)0.99,(double)1.01));
-            float angle=(float)rng.uniform(-10.0,10.0);
-
-            resample(scaledImg,RotatedRect(center,size,angle),standardPatch);
-            
-            for(int y=0;y<standardPatch.rows;y++){
-                for(int x=0;x<standardPatch.cols;x++){
-                    standardPatch(x,y)+=(uchar)rng.gaussian(5.0);
-                }
-            }
-
-#ifdef BLUR_AS_VADIM
-            GaussianBlur(standardPatch,blurredPatch,GaussBlurKernelSize,0.0);
-#else
-            resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
-#endif
-            pushIntoModel(standardPatch,true);
-            for(int k=0;k<(int)classifiers.size();k++){
-                classifiers[k].integrate(blurredPatch,true);
-            }
-        }
-    }
-
-    TLDDetector::generateScanGrid(image.rows,image.cols,minSize,scanGrid,true);
-    negativeExamples.clear();
-    negativeExamples.reserve(NEG_EXAMPLES_IN_INIT_MODEL);
-    std::vector<int> indices;
-    indices.reserve(NEG_EXAMPLES_IN_INIT_MODEL);
-    while(negativeExamples.size()<NEG_EXAMPLES_IN_INIT_MODEL){
-        int i=rng.uniform((int)0,(int)scanGrid.size());
-        if(std::find(indices.begin(),indices.end(),i)==indices.end() && overlap(boundingBox,scanGrid[i])<0.2){
-            Mat_<uchar> standardPatch(15,15);
-            resample(image,scanGrid[i],standardPatch);
-            pushIntoModel(standardPatch,false);
-
-            resample(image_blurred,scanGrid[i],blurredPatch);
-            for(int k=0;k<(int)classifiers.size();k++){
-                classifiers[k].integrate(blurredPatch,false);
-            }
-        }
-    }
-    dprintf(("positive patches: %d\nnegative patches: %d\n",(int)positiveExamples.size(),(int)negativeExamples.size()));
-}
-
-void TLDDetector::generateScanGrid(int rows,int cols,Size initBox,std::vector<Rect2d>& res,bool withScaling){
-    res.clear();
-    //scales step: 1.2; hor step: 10% of width; verstep: 10% of height; minsize: 20pix
-    for(double h=initBox.height, w=initBox.width;h<cols && w<rows;){
-        for(double x=0;(x+w)<=(cols-1.0);x+=(0.1*w)){
-            for(double y=0;(y+h)<=(rows-1.0);y+=(0.1*h)){
-                res.push_back(Rect2d(x,y,w,h));
-            }
-        }
-        if(withScaling){
-            if(h<=initBox.height){
-                h/=1.2; w/=1.2;
-                if(h<20 || w<20){
-                    h=initBox.height*1.2; w=initBox.width*1.2;
-                    CV_Assert(h>initBox.height || w>initBox.width);
-                }
-            }else{
-                h*=1.2; w*=1.2;
-            }
-        }else{
-            break;
-        }
-    }
-    dprintf(("%d rects in res\n",(int)res.size()));
-}
-
-bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::vector<Rect2d>& rect,std::vector<bool>& isObject,
-        std::vector<bool>& shouldBeIntegrated){
-    TrackerTLDModel* tldModel=((TrackerTLDModel*)static_cast<TrackerModel*>(model));
-    Size initSize=tldModel->getMinSize();
-    rect.clear();
-    isObject.clear();
-    shouldBeIntegrated.clear();
-
-    Mat resized_img,blurred_img;
-    Mat_<uchar> standardPatch(15,15);
-    img.copyTo(resized_img);
-    imgBlurred.copyTo(blurred_img);
-    double originalVariance=tldModel->getOriginalVariance();;
-    int dx=initSize.width/10,dy=initSize.height/10;
-    Size2d size=img.size();
-    double scale=1.0;
-    int total=0,pass=0;
-    int npos=0,nneg=0;
-    double tmp=0,maxSc=-5.0;
-    Rect2d maxScRect;
-    START_TICK("detector");
-    do{
-        Mat_<double> intImgP,intImgP2;
-        computeIntegralImages(resized_img,intImgP,intImgP2);
-
-        for(int i=0,imax=cvFloor((0.0+resized_img.cols-initSize.width)/dx);i<imax;i++){
-            for(int j=0,jmax=cvFloor((0.0+resized_img.rows-initSize.height)/dy);j<jmax;j++){
-                total++;
-                if(!patchVariance(intImgP,intImgP2,originalVariance,Point(dx*i,dy*j),initSize)){
-                    continue;
-                }
-                if(!ensembleClassifier(&blurred_img.at<uchar>(dy*j,dx*i),(int)blurred_img.step[0])){
-                    continue;
-                }
-                pass++;
-
-                rect.push_back(Rect2d(dx*i*scale,dy*j*scale,initSize.width*scale,initSize.height*scale));
-                resample(resized_img,Rect2d(Point(dx*i,dy*j),initSize),standardPatch);
-                tmp=tldModel->Sr(standardPatch);
-                isObject.push_back(tmp>THETA_NN);
-                shouldBeIntegrated.push_back(abs(tmp-THETA_NN)<0.1);
-                if(!isObject[isObject.size()-1]){
-                    nneg++;
-                    continue;
-                }else{
-                    npos++;
-                }
-                tmp=tldModel->Sc(standardPatch);
-                if(tmp>maxSc){
-                    maxSc=tmp;
-                    maxScRect=rect[rect.size()-1];
-                }
-            }
-        }
-
-        size.width/=1.2;
-        size.height/=1.2;
-        scale*=1.2;
-        resize(img,resized_img,size);
-        GaussianBlur(resized_img,blurred_img,GaussBlurKernelSize,0.0f);
-    }while(size.width>=initSize.width && size.height>=initSize.height);
-    END_TICK("detector");
-
-    dfprintf((stdout,"after NCC: nneg=%d npos=%d\n",nneg,npos));
-#if !0
-        std::vector<Rect2d> poss,negs;
-        for(int i=0;i<(int)rect.size();i++){
-            if(isObject[i])
-                poss.push_back(rect[i]);
-            else
-                negs.push_back(rect[i]);
-        }
-        dfprintf((stdout,"%d pos and %d neg\n",(int)poss.size(),(int)negs.size()));
-        drawWithRects(img,negs,poss);
-#endif
-#if !1
-        std::vector<Rect2d> scanGrid;
-        generateScanGrid(img.rows,img.cols,initSize,scanGrid);
-        std::vector<double> results;
-        Mat_<uchar> standardPatch_inner(15,15);
-        for(int i=0;i<(int)scanGrid.size();i++){
-            resample(img,scanGrid[i],standardPatch_inner);
-            results.push_back(tldModel->Sr(standardPatch_inner));
-        }
-        std::vector<double>::iterator it=std::max_element(results.begin(),results.end());
-        Mat image;
-        img.copyTo(image);
-        rectangle( image,scanGrid[it-results.begin()], 255, 1, 1 );
-        imshow("img",image);
-        waitKey();
-#endif
-#if !1
-        Mat image;
-        img.copyTo(image);
-        rectangle( image,res, 255, 1, 1 );
-        for(int i=0;i<(int)rect.size();i++){
-          rectangle( image,rect[i], 0, 1, 1 );
-        }
-        imshow("img",image);
-        waitKey();
-#endif
-
-    dfprintf((stdout,"%d after ensemble\n",pass));
-    if(maxSc<0){
-        return false;
-    }
-    res=maxScRect;
-    return true;
-}
-
-bool TLDDetector::patchVariance(Mat_<double>& intImgP,Mat_<double>& intImgP2,double originalVariance,Point pt,Size size){
-    return variance(intImgP,intImgP2,Rect(pt.x,pt.y,size.width,size.height)) >= 0.5*originalVariance;
-}
-
-double TLDDetector::ensembleClassifierNum(const uchar* data,int rowstep){
-    TrackerTLDModel* tldModel=((TrackerTLDModel*)static_cast<TrackerModel*>(model));
-    std::vector<TLDEnsembleClassifier>* classifiers=tldModel->getClassifiers();
-    double p=0;
-    for(int k=0;k<(int)classifiers->size();k++){
-        p+=(*classifiers)[k].posteriorProbability(data,rowstep);
-    }
-    p/=classifiers->size();
-    return p;
-}
-
-double TrackerTLDModel::Sr(const Mat_<uchar> patch){
-    double splus=0.0;
-    for(int i=0;i<(int)positiveExamples.size();i++){
-        splus=MAX(splus,0.5*(NCC(positiveExamples[i],patch)+1.0));
-    }
-    double sminus=0.0;
-    for(int i=0;i<(int)negativeExamples.size();i++){
-        sminus=MAX(sminus,0.5*(NCC(negativeExamples[i],patch)+1.0));
-    }
-    if(splus+sminus==0.0){
-        return 0.0;
-    }
-    return splus/(sminus+splus);
-}
-
-double TrackerTLDModel::Sc(const Mat_<uchar> patch){
-    double splus=0.0;
-    int med=getMedian(timeStampsPositive);
-    for(int i=0;i<(int)positiveExamples.size();i++){
-        if((int)timeStampsPositive[i]<=med){
-            splus=MAX(splus,0.5*(NCC(positiveExamples[i],patch)+1.0));
-        }
-    }
-    double sminus=0.0;
-    for(int i=0;i<(int)negativeExamples.size();i++){
-        sminus=MAX(sminus,0.5*(NCC(negativeExamples[i],patch)+1.0));
-    }
-    if(splus+sminus==0.0){
-        return 0.0;
-    }
-    return splus/(sminus+splus);
-}
-
-void TrackerTLDModel::integrateRelabeled(Mat& img,Mat& imgBlurred,const std::vector<Rect2d>& box,const std::vector<bool>& isPositive,
-          const std::vector<bool>& alsoIntoModel){
-    Mat_<uchar> standardPatch(15,15),blurredPatch(minSize_);
-    int positiveIntoModel=0,negativeIntoModel=0,positiveIntoEnsemble=0,negativeIntoEnsemble=0;
-    for(int k=0;k<(int)box.size();k++){
-        if(alsoIntoModel[k]){
-            resample(img,box[k],standardPatch);
-            if(isPositive[k]){
-                positiveIntoModel++;
-                pushIntoModel(standardPatch,true);
-            }else{
-                negativeIntoModel++;
-                pushIntoModel(standardPatch,false);
-            }
-        }
-
-#ifdef CLOSED_LOOP
-        if(alsoIntoModel[k] || (isPositive[k]==false)){
-#else
-        if(alsoIntoModel[k]){
-#endif
-            resample(imgBlurred,box[k],blurredPatch);
-            if(isPositive[k]){
-                positiveIntoEnsemble++;
-            }else{
-                negativeIntoEnsemble++;
-            }
-            for(int i=0;i<(int)classifiers.size();i++){
-                classifiers[i].integrate(blurredPatch,isPositive[k]);
-            }
-        }
-    }
-    if(negativeIntoModel>0)
-        dfprintf((stdout,"negativeIntoModel=%d ",negativeIntoModel));
-    if(positiveIntoModel>0)
-        dfprintf((stdout,"positiveIntoModel=%d ",positiveIntoModel));
-    if(negativeIntoEnsemble>0)
-        dfprintf((stdout,"negativeIntoEnsemble=%d ",negativeIntoEnsemble));
-    if(positiveIntoEnsemble>0)
-        dfprintf((stdout,"positiveIntoEnsemble=%d ",positiveIntoEnsemble));
-    dfprintf((stdout,"\n"));
-}
-
-void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForModel,const std::vector<Mat_<uchar> >& eForEnsemble,bool isPositive){
-    int positiveIntoModel=0,negativeIntoModel=0,positiveIntoEnsemble=0,negativeIntoEnsemble=0;
-    for(int k=0;k<(int)eForModel.size();k++){
-        double sr=Sr(eForModel[k]);
-        if((sr>THETA_NN)!=isPositive){
-            if(isPositive){
-                positiveIntoModel++;
-                pushIntoModel(eForModel[k],true);
-            }else{
-                negativeIntoModel++;
-                pushIntoModel(eForModel[k],false);
-            }
-        }
-        double p=0;
-        for(int i=0;i<(int)classifiers.size();i++){
-            p+=classifiers[i].posteriorProbability(eForEnsemble[k].data,(int)eForEnsemble[k].step[0]);
-        }
-        p/=classifiers.size();
-        if((p>0.5)!=isPositive){
-            if(isPositive){
-                positiveIntoEnsemble++;
-            }else{
-                negativeIntoEnsemble++;
-            }
-            for(int i=0;i<(int)classifiers.size();i++){
-                classifiers[i].integrate(eForEnsemble[k],isPositive);
-            }
-        }
-    }
-    if(negativeIntoModel>0)
-        dfprintf((stdout,"negativeIntoModel=%d ",negativeIntoModel));
-    if(positiveIntoModel>0)
-        dfprintf((stdout,"positiveIntoModel=%d ",positiveIntoModel));
-    if(negativeIntoEnsemble>0)
-        dfprintf((stdout,"negativeIntoEnsemble=%d ",negativeIntoEnsemble));
-    if(positiveIntoEnsemble>0)
-        dfprintf((stdout,"positiveIntoEnsemble=%d ",positiveIntoEnsemble));
-    dfprintf((stdout,"\n"));
-}
-
-int Pexpert::additionalExamples(std::vector<Mat_<uchar> >& examplesForModel,std::vector<Mat_<uchar> >& examplesForEnsemble){
-    examplesForModel.clear();examplesForEnsemble.clear();
-    examplesForModel.reserve(100);examplesForEnsemble.reserve(100);
-
-    std::vector<Rect2d> closest(10),scanGrid;
-    Mat scaledImg,blurredImg;
-
-    double scale=scaleAndBlur(img_,cvRound(log(1.0*resultBox_.width/(initSize_.width))/log(1.2)),scaledImg,blurredImg,GaussBlurKernelSize);
-    TLDDetector::generateScanGrid(img_.rows,img_.cols,initSize_,scanGrid);
-    getClosestN(scanGrid,Rect2d(resultBox_.x/scale,resultBox_.y/scale,resultBox_.width/scale,resultBox_.height/scale),10,closest);
-
-    Point2f center;
-    Size2f size;
-    for(int i=0;i<(int)closest.size();i++){
-        for(int j=0;j<10;j++){
-            Mat_<uchar> standardPatch(15,15),blurredPatch(initSize_);
-            center.x=(float)(closest[i].x+closest[i].width*(0.5+rng.uniform(-0.01,0.01)));
-            center.y=(float)(closest[i].y+closest[i].height*(0.5+rng.uniform(-0.01,0.01)));
-            size.width=(float)(closest[i].width*rng.uniform((double)0.99,(double)1.01));
-            size.height=(float)(closest[i].height*rng.uniform((double)0.99,(double)1.01));
-            float angle=(float)rng.uniform(-5.0,5.0);
-
-#ifdef BLUR_AS_VADIM
-            GaussianBlur(standardPatch,blurredPatch,GaussBlurKernelSize,0.0);
-#else
-            resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
-#endif
-            resample(scaledImg,RotatedRect(center,size,angle),standardPatch);
-            for(int y=0;y<standardPatch.rows;y++){
-                for(int x=0;x<standardPatch.cols;x++){
-                    standardPatch(x,y)+=(uchar)rng.gaussian(5.0);
-                }
-            }
-            examplesForModel.push_back(standardPatch);
-            examplesForEnsemble.push_back(blurredPatch);
-        }
-    }
-    return 0;
-}
-
-bool Nexpert::operator()(Rect2d box){
-    if(overlap(resultBox_,box)<0.2){
-        return false;
-    }
-    return true;
-}
-
-Data::Data(Rect2d initBox){
-    double minDim=MIN(initBox.width,initBox.height);
-    scale = 20.0/minDim;
-    minSize.width=(int)(initBox.width*20.0/minDim);
-    minSize.height=(int)(initBox.height*20.0/minDim);
-    frameNum=0;
-    dprintf(("minSize= %dx%d\n",minSize.width,minSize.height));
-}
-
-void Data::printme(FILE*  port){
-    dfprintf((port,"Data:\n"));
-    dfprintf((port,"\tframeNum=%d\n",frameNum));
-    dfprintf((port,"\tconfident=%s\n",confident?"true":"false"));
-    dfprintf((port,"\tfailedLastTime=%s\n",failedLastTime?"true":"false"));
-    dfprintf((port,"\tminSize=%dx%d\n",minSize.width,minSize.height));
-}
-void TrackerTLDModel::printme(FILE*  port){
-    dfprintf((port,"TrackerTLDModel:\n"));
-    dfprintf((port,"\tpositiveExamples.size()=%d\n",(int)positiveExamples.size()));
-    dfprintf((port,"\tnegativeExamples.size()=%d\n",(int)negativeExamples.size()));
-}
-void MyMouseCallbackDEBUG::onMouse( int event, int x, int y){
-    if(event== EVENT_LBUTTONDOWN){
-        Mat imgCanvas;
-        img_.copyTo(imgCanvas);
-        TrackerTLDModel* tldModel=((TrackerTLDModel*)static_cast<TrackerModel*>(detector_->model));
-        Size initSize=tldModel->getMinSize();
-        Mat_<uchar> standardPatch(15,15);
-        double originalVariance=tldModel->getOriginalVariance();;
-        double tmp;
-
-        Mat resized_img,blurred_img;
-        double scale=1.2;
-        //double scale=1.2*1.2*1.2*1.2;
-        Size2d size(img_.cols/scale,img_.rows/scale);
-        resize(img_,resized_img,size);
-        resize(imgBlurred_,blurred_img,size);
-
-        Mat_<double> intImgP,intImgP2;
-        detector_->computeIntegralImages(resized_img,intImgP,intImgP2);
-
-        int dx=initSize.width/10, dy=initSize.height/10,
-            i=(int)(x/scale/dx), j=(int)(y/scale/dy);
-
-        dfprintf((stderr,"patchVariance=%s\n",(detector_->patchVariance(intImgP,intImgP2,originalVariance,Point(dx*i,dy*j),initSize))?"true":"false"));
-        dfprintf((stderr,"p=%f\n",(detector_->ensembleClassifierNum(&blurred_img.at<uchar>(dy*j,dx*i),(int)blurred_img.step[0]))));
-        fprintf(stderr,"ensembleClassifier=%s\n",
-                (detector_->ensembleClassifier(&blurred_img.at<uchar>(dy*j,dx*i),(int)blurred_img.step[0]))?"true":"false");
-
-        resample(resized_img,Rect2d(Point(dx*i,dy*j),initSize),standardPatch);
-        tmp=tldModel->Sr(standardPatch);
-        dfprintf((stderr,"Sr=%f\n",tmp));
-        dfprintf((stderr,"isObject=%s\n",(tmp>THETA_NN)?"true":"false"));
-        dfprintf((stderr,"shouldBeIntegrated=%s\n",(abs(tmp-THETA_NN)<0.1)?"true":"false"));
-        dfprintf((stderr,"Sc=%f\n",tldModel->Sc(standardPatch)));
-
-        rectangle(imgCanvas,Rect2d(Point2d(scale*dx*i,scale*dy*j),Size2d(initSize.width*scale,initSize.height*scale)), 0, 2, 1 );
-        imshow("picker",imgCanvas);
-        waitKey();
-    }
-}
-void TrackerTLDModel::pushIntoModel(const Mat_<uchar>& example,bool positive){
-    std::vector<Mat_<uchar> >* proxyV;
-    unsigned int* proxyN;
-    std::vector<unsigned int>* proxyT;
-    if(positive){
-        proxyV=&positiveExamples;
-        proxyN=&timeStampPositiveNext;
-        proxyT=&timeStampsPositive;
-    }else{
-        proxyV=&negativeExamples;
-        proxyN=&timeStampNegativeNext;
-        proxyT=&timeStampsNegative;
-    }
-    if(proxyV->size()<MAX_EXAMPLES_IN_MODEL){
-        proxyV->push_back(example);
-        proxyT->push_back(*proxyN);
-    }else{
-        int index=rng.uniform((int)0,(int)proxyV->size());
-        (*proxyV)[index]=example;
-        (*proxyT)[index]=(*proxyN);
-    }
-    (*proxyN)++;
-}
-
-} /* namespace cv */