Chiebot-Mirror
/
opencv_contrib
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

commit

Browse Source
pull/29/head
Alex Leontiev 11 years ago
parent be49e4a046
commit f080b3448f
2 changed files with 33 additions and 34 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      modules/tracking/src/TLD.hpp
  2. 65
      modules/tracking/src/trackerTLD.cpp

2
modules/tracking/src/TLD.hpp
Unescape View File

@ -56,7 +56,7 @@ namespace cv
start=clock();{a} milisec=1000.0*(clock()-start)/CLOCKS_PER_SEC;\
printf("%-90s took %f milis\n",#a,milisec); }
#define HERE fprintf(stderr,"%d\n",__LINE__);fflush(stderr);
#define START_TICK(name) { clock_t start;float milisec=0.0; start=clock();
#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;\
printf("%s took %f milis\n",name,milisec); }
extern Rect2d etalon;

65
modules/tracking/src/trackerTLD.cpp
Unescape View File

@ -81,6 +81,7 @@ public:
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_;
@ -309,13 +310,15 @@ bool TrackerTLD::updateImpl(const Mat& image, Rect2d& boundingBox){
}
data->printme();
tldModel->printme(stdout);
if(!true && data->frameNum==82){
#if !1
if(data->frameNum==82){
printf("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;
@ -397,25 +400,21 @@ timeStampPositiveNext(0),timeStampNegativeNext(0),params_(params){
for(int i=0;i<(int)closest.size();i++){
for(int j=0;j<20;j++){
Mat_<uchar> standardPatch(15,15);
if(true){
center.x=closest[i].x+closest[i].width*(0.5+rng.uniform(-0.01,0.01));
center.y=closest[i].y+closest[i].height*(0.5+rng.uniform(-0.01,0.01));
size.width=closest[i].width*rng.uniform((double)0.99,(double)1.01);
size.height=closest[i].height*rng.uniform((double)0.99,(double)1.01);
float angle=rng.uniform((double)-10.0,(double)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)+=rng.gaussian(5.0);
}
}
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=rng.uniform(-10.0,10.0);
resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
}else{
resample(scaledImg,closest[i],standardPatch);
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);
}
}
resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
pushIntoModel(standardPatch,true);
resample(blurredImg,closest[i],blurredPatch);
for(int k=0;k<(int)classifiers.size();k++){
@ -502,7 +501,7 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
if(!patchVariance(intImgP,intImgP2,originalVariance,Point(dx*i,dy*j),initSize)){
continue;
}
if(!ensembleClassifier(&blurred_img.at<uchar>(dy*j,dx*i),blurred_img.step[0])){
if(!ensembleClassifier(&blurred_img.at<uchar>(dy*j,dx*i),(int)blurred_img.step[0])){
continue;
}
pass++;
@ -530,12 +529,12 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
size.height/=1.2;
scale*=1.2;
resize(img,resized_img,size);
GaussianBlur(resized_img,blurred_img,GaussBlurKernelSize,0.0);
GaussianBlur(resized_img,blurred_img,GaussBlurKernelSize,0.0f);
}while(size.width>=initSize.width && size.height>=initSize.height);
END_TICK("detector");
fprintf(stdout,"after NCC: nneg=%d npos=%d\n",nneg,npos);
if(!false){
#if !0
std::vector<Rect2d> poss,negs;
for(int i=0;i<(int)rect.size();i++){
if(isObject[i])
@ -545,8 +544,8 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
}
fprintf(stdout,"%d pos and %d neg\n",(int)poss.size(),(int)negs.size());
drawWithRects(img,negs,poss);
}
if(!true){
#endif
#if !1
std::vector<Rect2d> scanGrid;
generateScanGrid(img.rows,img.cols,initSize,scanGrid);
std::vector<double> results;
@ -561,8 +560,8 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
rectangle( image,scanGrid[it-results.begin()], 255, 1, 1 );
imshow("img",image);
waitKey();
}
if(!true){
#endif
#if !1
Mat image;
img.copyTo(image);
rectangle( image,res, 255, 1, 1 );
@ -571,7 +570,7 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
}
imshow("img",image);
waitKey();
}
#endif
fprintf(stdout,"%d after ensemble\n",pass);
if(maxSc<0){
@ -698,7 +697,7 @@ void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForM
}
double p=0;
for(int i=0;i<(int)classifiers.size();i++){
p+=classifiers[i].posteriorProbability(eForEnsemble[k].data,eForEnsemble[k].step[0]);
p+=classifiers[i].posteriorProbability(eForEnsemble[k].data,(int)eForEnsemble[k].step[0]);
}
p/=classifiers.size();
if((p>0.5)!=isPositive){
@ -739,17 +738,17 @@ int Pexpert::additionalExamples(std::vector<Mat_<uchar> >& examplesForModel,std:
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=closest[i].x+closest[i].width*(0.5+rng.uniform(-0.01,0.01));
center.y=closest[i].y+closest[i].height*(0.5+rng.uniform(-0.01,0.01));
size.width=closest[i].width*rng.uniform((double)0.99,(double)1.01);
size.height=closest[i].height*rng.uniform((double)0.99,(double)1.01);
float angle=rng.uniform((double)-5.0,(double)5.0);
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=rng.uniform(-5.0,5.0);
resample(scaledImg,RotatedRect(center,size,angle),standardPatch);
resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
for(int y=0;y<standardPatch.rows;y++){
for(int x=0;x<standardPatch.cols;x++){
standardPatch(x,y)+=rng.gaussian(5.0);
standardPatch(x,y)+=(uchar)rng.gaussian(5.0);
}
}
examplesForModel.push_back(standardPatch);

Write Preview
Loading…
Cancel
Save