commit

modules/tracking/samples/benchmark.cpp

@@ -133,7 +133,7 @@ void print_table(char* videos[],int videoNum,char* algorithms[],int algNum,const
         grid[0]=std::max(grid[0],(int)strlen(videos[i]));
     }
     for(int i=0;i<algNum;i++){
-        grid[i+1]=strlen(algorithms[i]);
+        grid[i+1]=(int)strlen(algorithms[i]);
         for(int j=0;j<videoNum;j++)
             grid[i+1]=std::max(grid[i+1],(int)strlen(results[j][i]));
     }
@@ -199,11 +199,11 @@ private:
 };
 AssessmentRes::AssessmentRes(int algnum):len(0),results(algnum){
     for(int i=0;i<(int)results.size();i++){
-        if(!false){
+#if !0
             results[i].push_back(Ptr<Assessment>(new CorrectFrames(0.0)));
-        }else{
+#else
             results[i].push_back(Ptr<Assessment>(new CorrectFrames(0.5)));
-        }
+#endif
         results[i].push_back(Ptr<Assessment>(new PRF()));
     }
 }
@@ -295,7 +295,7 @@ static AssessmentRes assessment(char* video,char* gt_str, char* algorithms[],cha
   imshow( "Tracking API", image );
 
   int frameCounter = 0;
-  AssessmentRes res(trackers.size());
+  AssessmentRes res((int)trackers.size());
 
   for ( ;; ){
     if( !paused ){
@@ -330,13 +330,15 @@ static AssessmentRes assessment(char* video,char* gt_str, char* algorithms[],cha
               rectangle( image, initBoxes[i], palette[i+1], 2, 1 );
           }
 
-          if(!true && i==1){
+#if !1
+          if(i==1){
               printf("TLD\n");
               printf("boundingBox=[%f,%f,%f,%f]\n",boundingBox.x,boundingBox.y,boundingBox.width,boundingBox.height);
               printf("initBoxes[i]=[%f,%f,%f,%f]\n",initBoxes[i].x,initBoxes[i].y,initBoxes[i].width,initBoxes[i].height);
               printf("overlap=%f\n",overlap(initBoxes[i],boundingBox));
               exit(0);
           }
+#endif
 
           for(int j=0;j<(int)res.results[i].size();j++)
               res.results[i][j]->assess(boundingBox,initBoxes[i]);

modules/tracking/samples/tracker.cpp

@@ -58,7 +58,7 @@ static void onMouse( int event, int x, int y, int, void* )
           //draw the bounding box
           Mat currentFrame;
           image.copyTo( currentFrame );
-          rectangle( currentFrame, Point( boundingBox.x, boundingBox.y ), Point( x, y ), Scalar( 255, 0, 0 ), 2, 1 );
+          rectangle( currentFrame, Point((int) boundingBox.x, (int)boundingBox.y ), Point( x, y ), Scalar( 255, 0, 0 ), 2, 1 );
           imshow( "Tracking API", currentFrame );
         }
         break;
@@ -96,7 +96,7 @@ int main( int argc, char** argv ){
 
   int coords[4]={0,0,0,0};
   bool initBoxWasGivenInCommandLine=false;
-  do{
+  {
       String initBoundingBox=parser.get<String>(3);
       for(size_t npos=0,pos=0,ctr=0;ctr<4;ctr++){
         npos=initBoundingBox.find_first_of(',',pos);
@@ -119,7 +119,7 @@ int main( int argc, char** argv ){
       if(coords[0]>0 && coords[1]>0 && coords[2]>0 && coords[3]>0){
           initBoxWasGivenInCommandLine=true;
       }
-  }while(0);
+  }
 
   //open the capture
   VideoCapture cap;

modules/tracking/src/TLD.cpp

@@ -199,8 +199,8 @@ double variance(Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2,Rect bo
 }
 
 double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2){
-    CV_Assert(patch1.rows=patch2.rows);
-    CV_Assert(patch1.cols=patch2.cols);
+    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);

modules/tracking/src/trackerTLD.cpp

@@ -772,8 +772,8 @@ Data::Data(Rect2d initBox){
         printf("initial box has size %dx%d, while both dimensions should be no less than %d\n",(int)initBox.width,(int)initBox.height,20);
         exit(EXIT_FAILURE);
     }*/
-    minSize.width=initBox.width*20.0/minDim;
-    minSize.height=initBox.height*20.0/minDim;
+    minSize.width=(int)(initBox.width*20.0/minDim);
+    minSize.height=(int)(initBox.height*20.0/minDim);
     frameNum=0;
     printf("minSize= %dx%d\n",minSize.width,minSize.height);
 }
@@ -811,19 +811,19 @@ void MyMouseCallbackDEBUG::onMouse( int event, int x, int y){
         detector_->computeIntegralImages(resized_img,intImgP,intImgP2);
 
         int dx=initSize.width/10, dy=initSize.height/10,
-            i=x/scale/dx, j=y/scale/dy;
+            i=(int)(x/scale/dx), j=(int)(y/scale/dy);
 
-        fprintf(stdout,"patchVariance=%s\n",(detector_->patchVariance(intImgP,intImgP2,originalVariance,Point(dx*i,dy*j),initSize))?"true":"false");
-        fprintf(stdout,"p=%f\n",(detector_->ensembleClassifierNum(&blurred_img.at<uchar>(dy*j,dx*i),blurred_img.step[0])));
-        fprintf(stdout,"ensembleClassifier=%s\n",
-                (detector_->ensembleClassifier(&blurred_img.at<uchar>(dy*j,dx*i),blurred_img.step[0]))?"true":"false");
+        fprintf(stderr,"patchVariance=%s\n",(detector_->patchVariance(intImgP,intImgP2,originalVariance,Point(dx*i,dy*j),initSize))?"true":"false");
+        fprintf(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);
-        fprintf(stdout,"Sr=%f\n",tmp);
-        fprintf(stdout,"isObject=%s\n",(tmp>THETA_NN)?"true":"false");
-        fprintf(stdout,"shouldBeIntegrated=%s\n",(abs(tmp-THETA_NN)<0.1)?"true":"false");
-        fprintf(stdout,"Sc=%f\n",tldModel->Sc(standardPatch));
+        fprintf(stderr,"Sr=%f\n",tmp);
+        fprintf(stderr,"isObject=%s\n",(tmp>THETA_NN)?"true":"false");
+        fprintf(stderr,"shouldBeIntegrated=%s\n",(abs(tmp-THETA_NN)<0.1)?"true":"false");
+        fprintf(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);