commit

modules/tracking/src/TLD.cpp

@@ -103,7 +103,7 @@ std::string type2str(const Mat& mat){
   std::string r;
 
   uchar depth = type & CV_MAT_DEPTH_MASK;
-  uchar chans = 1 + (type >> CV_CN_SHIFT);
+  uchar chans =(uchar)( 1 + (type >> CV_CN_SHIFT));
 
   switch ( depth ) {
     case CV_8U:  r = "8U"; break;
@@ -221,7 +221,7 @@ double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2){
 }
 unsigned int getMedian(const std::vector<unsigned int>& values, int size){
     if(size==-1){
-        size=values.size();
+        size=(int)values.size();
     }
     std::vector<int> copy(values.begin(),values.begin()+size);
     std::sort(copy.begin(),copy.end());
@@ -270,7 +270,7 @@ void resample(const Mat& img,const RotatedRect& r2,Mat_<uchar>& samples){
     }
 }
 void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples){
-    if(true){
+#if 1
         double x,y,a,b,tx,ty;int ix,iy;
         for(int i=0;i<samples.rows;i++){
             y=r2.y+i*r2.height/samples.rows;
@@ -285,21 +285,21 @@ void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples){
                 samples(i,j)=(uchar)(a * (1.0 - ty) + b * ty);
             }
         }
-    }else{
+#else
         Point2f center((float)(r2.x+r2.width/2),(float)(r2.y+r2.height/2));
-        return resample(img,RotatedRect(center,Size2f(r2.width,r2.height),0.0f),samples);
-    }
+        return resample(img,RotatedRect(center,Size2f((float)r2.width,(float)r2.height),0.0f),samples);
+#endif
 }
 
 //other stuff
 void TLDEnsembleClassifier::stepPrefSuff(uchar* arr,int len){
     int gridSize=getGridSize();
-    if(false){
+#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{
+#else
         int total=len-gridSize;
         int quo=total/(gridSize-1),rem=total%(gridSize-1);
         int smallStep=quo,bigStep=quo+1;
@@ -307,20 +307,20 @@ void TLDEnsembleClassifier::stepPrefSuff(uchar* arr,int len){
         int bigOnes_front=bigOnes/2,bigOnes_back=bigOnes-bigOnes_front;
         for(int i=0;i<(int)(sizeof(x1)/sizeof(x1[0]));i++){
             if(arr[i]<bigOnes_back){
-                arr[i]=arr[i]*bigStep+arr[i];
+                arr[i]=(uchar)(arr[i]*bigStep+arr[i]);
                 continue;
             }
             if(arr[i]<(bigOnes_front+smallOnes)){
-                arr[i]=bigOnes_front*bigStep+(arr[i]-bigOnes_front)*smallStep+arr[i];
+                arr[i]=(uchar)(bigOnes_front*bigStep+(arr[i]-bigOnes_front)*smallStep+arr[i]);
                 continue;
             }
             if(arr[i]<(bigOnes_front+smallOnes+bigOnes_back)){
-                arr[i]=bigOnes_front*bigStep+smallOnes*smallStep+(arr[i]-(bigOnes_front+smallOnes))*bigStep+arr[i];
+                arr[i]=(uchar)(bigOnes_front*bigStep+smallOnes*smallStep+(arr[i]-(bigOnes_front+smallOnes))*bigStep+arr[i]);
                 continue;
             }
-            arr[i]=len-1;
-        }
+            arr[i]=(uchar)(len-1);
         }
+#endif
 }
 TLDEnsembleClassifier::TLDEnsembleClassifier(int ordinal,Size size){
     preinit(ordinal);
@@ -330,7 +330,7 @@ TLDEnsembleClassifier::TLDEnsembleClassifier(int ordinal,Size size){
     stepPrefSuff(y2,size.height);
 }
 void TLDEnsembleClassifier::integrate(Mat_<uchar> patch,bool isPositive){
-    unsigned short int position=code(patch.data,patch.step[0]);
+    unsigned short int position=code(patch.data,(int)patch.step[0]);
     if(isPositive){
         pos[position]++;
     }else{
@@ -348,18 +348,18 @@ double TLDEnsembleClassifier::posteriorProbability(const uchar* data,int rowstep
 }
 unsigned short int TLDEnsembleClassifier::code(const uchar* data,int rowstep)const{
     unsigned short int position=0;
-    char codeS[20];
+    //char codeS[20];
     for(int i=0;i<(int)(sizeof(x1)/sizeof(x1[0]));i++){
         if(*(data+rowstep*y1[i]+x1[i])<*(data+rowstep*y2[i]+x2[i])){
             position++;
-            codeS[i]='o';
+            //codeS[i]='o';
         }else{
-            codeS[i]='x';
+            //codeS[i]='x';
         }
         position=position<<1;
     }
-    codeS[13]='\0';
-    if(!true)printf("integrate with code %s\n",codeS);
+    //codeS[13]='\0';
+    //printf("integrate with code %s\n",codeS);
     return position;
 }
 

modules/tracking/src/trackerMedianFlow.cpp

@@ -163,7 +163,7 @@ std::string MedianFlowCore::type2str(int type) {
   std::string r;
 
   uchar depth = type & CV_MAT_DEPTH_MASK;
-  uchar chans = 1 + (type >> CV_CN_SHIFT);
+  uchar chans = (uchar)(1 + (type >> CV_CN_SHIFT));
 
   switch ( depth ) {
     case CV_8U:  r = "8U"; break;
@@ -192,8 +192,8 @@ bool MedianFlowCore::medianFlowImpl(Mat oldImage,Mat newImage,Rect2d& oldBox){
     for(int i=0;i<params.pointsInGrid;i++){
         for(int j=0;j<params.pointsInGrid;j++){
                 pointsToTrackOld.push_back(
-                        Point2f(oldBox.x+((1.0*oldBox.width)/params.pointsInGrid)*j+.5*oldBox.width/params.pointsInGrid,
-                        oldBox.y+((1.0*oldBox.height)/params.pointsInGrid)*i+.5*oldBox.height/params.pointsInGrid));
+                        Point2f((float)(oldBox.x+((1.0*oldBox.width)/params.pointsInGrid)*j+.5*oldBox.width/params.pointsInGrid),
+                        (float)(oldBox.y+((1.0*oldBox.height)/params.pointsInGrid)*i+.5*oldBox.height/params.pointsInGrid)));
         }
     }
 
@@ -235,7 +235,7 @@ bool MedianFlowCore::medianFlowImpl(Mat oldImage,Mat newImage,Rect2d& oldBox){
         di[i]-=mDisplacement;
         displacements.push_back(sqrt(di[i].ddot(di[i])));
     }
-    if(getMedian(displacements,displacements.size())>10){
+    if(getMedian(displacements,(int)displacements.size())>10){
         return false;
     }
 
@@ -247,7 +247,7 @@ Rect2d MedianFlowCore::vote(const std::vector<Point2f>& oldPoints,const std::vec
     static int iteration=0;//FIXME -- we don't want this static var in final release
     Rect2d newRect;
     Point2d newCenter(oldRect.x+oldRect.width/2.0,oldRect.y+oldRect.height/2.0);
-    int n=oldPoints.size();
+    int n=(int)oldPoints.size();
     std::vector<double> buf(std::max(n*(n-1)/2,3),0.0);
 
     if(oldPoints.size()==1){
@@ -265,7 +265,7 @@ Rect2d MedianFlowCore::vote(const std::vector<Point2f>& oldPoints,const std::vec
     for(int i=0;i<n;i++){  buf[i]=newPoints[i].y-oldPoints[i].y;  }
     yshift=getMedian(buf,n);
     newCenter.y+=yshift;
-    mD=Point2f(xshift,yshift);
+    mD=Point2f((float)xshift,(float)yshift);
 
     if(oldPoints.size()==1){
         newRect.x=newCenter.x-oldRect.width/2.0;
@@ -275,12 +275,12 @@ Rect2d MedianFlowCore::vote(const std::vector<Point2f>& oldPoints,const std::vec
         return newRect;
     }
 
-    float nd,od;
+    double nd,od;
     for(int i=0,ctr=0;i<n;i++){
         for(int j=0;j<i;j++){
             nd=l2distance(newPoints[i],newPoints[j]);
             od=l2distance(oldPoints[i],oldPoints[j]);
-            buf[ctr]=(od==0)?0:(nd/od);
+            buf[ctr]=(od==0.0)?0.0:(nd/od);
             ctr++;
         }
     }
@@ -304,12 +304,12 @@ Rect2d MedianFlowCore::vote(const std::vector<Point2f>& oldPoints,const std::vec
 template<typename T>
 T MedianFlowCore::getMedian(std::vector<T>& values,int size){
     if(size==-1){
-        size=values.size();
+        size=(int)values.size();
     }
     std::vector<T> copy(values.begin(),values.begin()+size);
     std::sort(copy.begin(),copy.end());
     if(size%2==0){
-        return (copy[size/2-1]+copy[size/2])/2.0;
+        return (copy[size/2-1]+copy[size/2])/((T)2.0);
     }else{
         return copy[(size-1)/2];
     }
@@ -318,7 +318,7 @@ T MedianFlowCore::getMedian(std::vector<T>& values,int size){
 void MedianFlowCore::computeStatistics(std::vector<float>& data,int size){
     int binnum=10;
     if(size==-1){
-        size=data.size();
+        size=(int)data.size();
     }
     float mini=*std::min_element(data.begin(),data.begin()+size),maxi=*std::max_element(data.begin(),data.begin()+size);
     std::vector<int> bins(binnum,(int)0);
@@ -374,7 +374,7 @@ void MedianFlowCore::check_NCC(const Mat& oldImage,const Mat& newImage,
         double sq1=sqrt(n1*n1-s1*s1/N),sq2=sqrt(n2*n2-s2*s2/N);
         double ares=(sq2==0)?sq1/abs(sq1):(prod-s1*s2/N)/sq1/sq2;
 
-		NCC[i] = ares;
+		NCC[i] = (float)ares;
 	}
 	float median = getMedian(NCC);
 	for(int i = 0; i < (int)oldPoints.size(); i++) {

modules/tracking/src/trackerTLD.cpp

@@ -63,7 +63,7 @@ using namespace cv;
  *      THETA_NN 0.5<->0.6 dramatic change vs video 6
  * TODO:
  *      schoolPC: codec, libopencv-dev
- *      fix pushbot ||video(vadim random, better?) --> debug if box size is less than 20 --> (remove ensemble self-loop) --> (try inter_area)
+ *      fix pushbot -> compare_branches ||video(vadim random, better?) --> debug if box size is less than 20 --> (remove ensemble self-loop) --> (try inter_area)
  *      perfect PN
 */
 
@@ -77,13 +77,13 @@ namespace cv
 class TLDDetector;
 class MyMouseCallbackDEBUG{
 public:
-    MyMouseCallbackDEBUG(const Mat& img,const Mat& imgBlurred,TLDDetector* detector):img_(img),imgBlurred_(imgBlurred),detector_(detector){}
+    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);
     }
 private:
     void onMouse( int event, int x, int y);
-    const Mat& img_,imgBlurred_;
+    Mat& img_,imgBlurred_;
     TLDDetector* detector_;
 };
 
@@ -248,7 +248,7 @@ bool TrackerTLD::initImpl(const Mat& image, const Rect2d& boundingBox ){
     privateInfo.push_back(Ptr<TLDDetector>(detector));
     privateInfo.push_back(Ptr<Data>(data));
 
-    if(!true){
+#if !1
         printf("here I am\n");
         Mat image_blurred;
         GaussianBlur(image_gray,image_blurred,GaussBlurKernelSize,0.0);
@@ -256,7 +256,7 @@ bool TrackerTLD::initImpl(const Mat& image, const Rect2d& boundingBox ){
         imshow("picker",image_gray);
         setMouseCallback( "picker", MyMouseCallbackDEBUG::onMouse, (void*)callback);
         waitKey();
-    }
+#endif
     return true;
 }