vadim 12, 20, 22

modules/tracking/src/tld_tracker.hpp

@@ -99,8 +99,8 @@ public:
 private:
     TLDEnsembleClassifier(std::vector<Vec4b> meas,int beg,int end);
     static void stepPrefSuff(std::vector<Vec4b>& arr,int pos,int len,int gridSize);
-    unsigned short int code(const uchar* data,int rowstep)const;
-    std::vector<unsigned int> pos,neg;
+    int code(const uchar* data,int rowstep)const;
+    std::vector<Point2i> posAndNeg;
     std::vector<Vec4b> measurements;
 };
 

modules/tracking/src/tld_utils.cpp

@@ -136,7 +136,8 @@ void getClosestN(std::vector<Rect2d>& scanGrid,Rect2d bBox,int n,std::vector<Rec
         res.assign(scanGrid.begin(),scanGrid.end());
         return;
     }
-    std::vector<double> overlaps(n,0.0);
+    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);
@@ -183,10 +184,16 @@ double NCC(const Mat_<uchar>& patch1,const Mat_<uchar>& patch2){
     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(std::max(0.0,n1*n1-s1*s1/N)),sq2=sqrt(std::max(0.0,n2*n2-s2*s2/N));
+    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;
 }
@@ -264,29 +271,28 @@ void TLDEnsembleClassifier::stepPrefSuff(std::vector<Vec4b>& arr,int pos,int len
 TLDEnsembleClassifier::TLDEnsembleClassifier(std::vector<Vec4b> meas,int beg,int end){
     int posSize=1;
     for(int i=0,mpc=end-beg;i<mpc;i++)posSize*=2;
-    pos=std::vector<unsigned int>(posSize,0);
-    neg=std::vector<unsigned int>(posSize,0);
+    posAndNeg.assign(posSize,Point2i(0,0));
     measurements.assign(meas.begin()+beg,meas.begin()+end);
 }
 void TLDEnsembleClassifier::integrate(const Mat_<uchar>& patch,bool isPositive){
-    unsigned short int position=code(patch.data,(int)patch.step[0]);
+    int position=code(patch.data,(int)patch.step[0]);
     if(isPositive){
-        pos[position]++;
+        posAndNeg[position].x++;
     }else{
-        neg[position]++;
+        posAndNeg[position].y++;
     }
 }
 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];
+    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);
     }
 }
-unsigned short int TLDEnsembleClassifier::code(const uchar* data,int rowstep)const{
-    unsigned short int position=0;//TODO: this --> encapsule
+int TLDEnsembleClassifier::code(const uchar* data,int rowstep)const{
+    unsigned short 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])){