opencv/opencv_contrib
1
0
Fork 0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-21 14:41:58 +08:00
Code Issues Releases Wiki Activity

commit

Browse Source
This commit is contained in:
Alex Leontiev
2014-06-27 17:35:17 +09:00
parent be49e4a046
commit f080b3448f
2 changed files with 32 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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

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

63
modules/tracking/src/trackerTLD.cpp
View File

@@ -81,6 +81,7 @@ public:
static void onMouse( int event, int x, int y, int, void* obj){ static void onMouse( int event, int x, int y, int, void* obj){
((MyMouseCallbackDEBUG*)obj)->onMouse(event,x,y); ((MyMouseCallbackDEBUG*)obj)->onMouse(event,x,y);
} }
MyMouseCallbackDEBUG& operator= (const MyMouseCallbackDEBUG& /*other*/){return *this;}
private: private:
void onMouse( int event, int x, int y); void onMouse( int event, int x, int y);
Mat& img_,imgBlurred_; Mat& img_,imgBlurred_;
@@ -309,13 +310,15 @@ bool TrackerTLD::updateImpl(const Mat& image, Rect2d& boundingBox){
} }
data->printme(); data->printme();
tldModel->printme(stdout); tldModel->printme(stdout);
if(!true && data->frameNum==82){ #if !1
if(data->frameNum==82){
printf("here I am\n"); printf("here I am\n");
MyMouseCallbackDEBUG* callback=new MyMouseCallbackDEBUG(imageForDetector,image_blurred,detector); MyMouseCallbackDEBUG* callback=new MyMouseCallbackDEBUG(imageForDetector,image_blurred,detector);
imshow("picker",imageForDetector); imshow("picker",imageForDetector);
setMouseCallback( "picker", MyMouseCallbackDEBUG::onMouse, (void*)callback); setMouseCallback( "picker", MyMouseCallbackDEBUG::onMouse, (void*)callback);
waitKey(); waitKey();
} }
#endif
if(it==candidatesRes.end()){ if(it==candidatesRes.end()){
data->confident=false; data->confident=false;
@@ -397,25 +400,21 @@ timeStampPositiveNext(0),timeStampNegativeNext(0),params_(params){
for(int i=0;i<(int)closest.size();i++){ for(int i=0;i<(int)closest.size();i++){
for(int j=0;j<20;j++){ for(int j=0;j<20;j++){
Mat_<uchar> standardPatch(15,15); Mat_<uchar> standardPatch(15,15);
if(true){ center.x=(float)(closest[i].x+closest[i].width*(0.5+rng.uniform(-0.01,0.01)));
center.x=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)));
center.y=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.width=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));
size.height=closest[i].height*rng.uniform((double)0.99,(double)1.01); float angle=rng.uniform(-10.0,10.0);
float angle=rng.uniform((double)-10.0,(double)10.0);
resample(scaledImg,RotatedRect(center,size,angle),standardPatch); resample(scaledImg,RotatedRect(center,size,angle),standardPatch);
for(int y=0;y<standardPatch.rows;y++){ for(int y=0;y<standardPatch.rows;y++){
for(int x=0;x<standardPatch.cols;x++){ for(int x=0;x<standardPatch.cols;x++){
standardPatch(x,y)+=rng.gaussian(5.0); standardPatch(x,y)+=(uchar)rng.gaussian(5.0);
}
} }
resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
}else{
resample(scaledImg,closest[i],standardPatch);
} }
resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
pushIntoModel(standardPatch,true); pushIntoModel(standardPatch,true);
resample(blurredImg,closest[i],blurredPatch); resample(blurredImg,closest[i],blurredPatch);
for(int k=0;k<(int)classifiers.size();k++){ 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)){ if(!patchVariance(intImgP,intImgP2,originalVariance,Point(dx*i,dy*j),initSize)){
continue; 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; continue;
} }
pass++; pass++;
@@ -530,12 +529,12 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
size.height/=1.2; size.height/=1.2;
scale*=1.2; scale*=1.2;
resize(img,resized_img,size); 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); }while(size.width>=initSize.width && size.height>=initSize.height);
END_TICK("detector"); END_TICK("detector");
fprintf(stdout,"after NCC: nneg=%d npos=%d\n",nneg,npos); fprintf(stdout,"after NCC: nneg=%d npos=%d\n",nneg,npos);
if(!false){ #if !0
std::vector<Rect2d> poss,negs; std::vector<Rect2d> poss,negs;
for(int i=0;i<(int)rect.size();i++){ for(int i=0;i<(int)rect.size();i++){
if(isObject[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()); fprintf(stdout,"%d pos and %d neg\n",(int)poss.size(),(int)negs.size());
drawWithRects(img,negs,poss); drawWithRects(img,negs,poss);
} #endif
if(!true){ #if !1
std::vector<Rect2d> scanGrid; std::vector<Rect2d> scanGrid;
generateScanGrid(img.rows,img.cols,initSize,scanGrid); generateScanGrid(img.rows,img.cols,initSize,scanGrid);
std::vector<double> results; 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 ); rectangle( image,scanGrid[it-results.begin()], 255, 1, 1 );
imshow("img",image); imshow("img",image);
waitKey(); waitKey();
} #endif
if(!true){ #if !1
Mat image; Mat image;
img.copyTo(image); img.copyTo(image);
rectangle( image,res, 255, 1, 1 ); 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); imshow("img",image);
waitKey(); waitKey();
} #endif
fprintf(stdout,"%d after ensemble\n",pass); fprintf(stdout,"%d after ensemble\n",pass);
if(maxSc<0){ if(maxSc<0){
@@ -698,7 +697,7 @@ void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForM
} }
double p=0; double p=0;
for(int i=0;i<(int)classifiers.size();i++){ 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(); p/=classifiers.size();
if((p>0.5)!=isPositive){ 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 i=0;i<(int)closest.size();i++){
for(int j=0;j<10;j++){ for(int j=0;j<10;j++){
Mat_<uchar> standardPatch(15,15),blurredPatch(initSize_); Mat_<uchar> standardPatch(15,15),blurredPatch(initSize_);
center.x=closest[i].x+closest[i].width*(0.5+rng.uniform(-0.01,0.01)); center.x=(float)(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)); center.y=(float)(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.width=(float)(closest[i].width*rng.uniform((double)0.99,(double)1.01));
size.height=closest[i].height*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((double)-5.0,(double)5.0); float angle=rng.uniform(-5.0,5.0);
resample(scaledImg,RotatedRect(center,size,angle),standardPatch); resample(scaledImg,RotatedRect(center,size,angle),standardPatch);
resample(blurredImg,RotatedRect(center,size,angle),blurredPatch); resample(blurredImg,RotatedRect(center,size,angle),blurredPatch);
for(int y=0;y<standardPatch.rows;y++){ for(int y=0;y<standardPatch.rows;y++){
for(int x=0;x<standardPatch.cols;x++){ 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); examplesForModel.push_back(standardPatch);