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vadim 14

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pull/55/head
Alex Leontiev 11 years ago
parent 8c38fa7658
commit b564f1b698
4 changed files with 57 additions and 3734 deletions
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  1. 3693
      modules/tracking/src/tld_classifier.cpp
  2. 15
      modules/tracking/src/tld_tracker.cpp
  3. 12
      modules/tracking/src/tld_tracker.hpp
  4. 71
      modules/tracking/src/tld_utils.cpp

3693
modules/tracking/src/tld_classifier.cpp
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15
modules/tracking/src/tld_tracker.cpp
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@ -55,6 +55,9 @@
#define MAX_EXAMPLES_IN_MODEL 500
#define MEASURES_PER_CLASSIFIER 13
#define SCALE_STEP 1.2
#define ENSEMBLE_THRESHOLD 0.5
#define VARIANCE_THRESHOLD 0.5
#define GRIDSIZE 15
#define DOWNSCALE_MODE INTER_LINEAR
#define BLUR_AS_VADIM
#undef CLOSED_LOOP
@ -84,10 +87,8 @@ using namespace tld;
* ?10. all in one class
* 11. group decls logically, order of statements
* 12. not v=vector(n), but assign(n,0)
* -->14. TLDEnsembleClassifier
* 16. loops limits
* 17. inner scope loops
* 18. classify in TLDEnsembleClassifier
* 19. var checker
* 20. NCC using plain loops
* 21. precompute offset
@ -145,7 +146,7 @@ protected:
Ptr<TrackerModel> model;
void computeIntegralImages(const Mat& img,Mat_<double>& intImgP,Mat_<double>& intImgP2){integral(img,intImgP,intImgP2,CV_64F);}
inline bool patchVariance(Mat_<double>& intImgP,Mat_<double>& intImgP2,double originalVariance,Point pt,Size size);
bool ensembleClassifier(const uchar* data,int rowstep){return ensembleClassifierNum(data,rowstep)>0.5;}
bool ensembleClassifier(const uchar* data,int rowstep){return ensembleClassifierNum(data,rowstep)>ENSEMBLE_THRESHOLD;}
double ensembleClassifierNum(const uchar* data,int rowstep);
TrackerTLD::Params params_;
};
@ -417,9 +418,7 @@ timeStampPositiveNext(0),timeStampNegativeNext(0),params_(params){
getClosestN(scanGrid,Rect2d(boundingBox.x/scale,boundingBox.y/scale,boundingBox.width/scale,boundingBox.height/scale),10,closest);
Mat_<uchar> blurredPatch(minSize);
for(int i=0,howMany=TLDEnsembleClassifier::getMaxOrdinal();i<howMany;i++){
classifiers.push_back(TLDEnsembleClassifier(i+1,minSize,MEASURES_PER_CLASSIFIER));
}
TLDEnsembleClassifier::makeClassifiers(minSize,MEASURES_PER_CLASSIFIER,GRIDSIZE,classifiers);
positiveExamples.reserve(200);
Point2f center;
@ -586,7 +585,7 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
}
bool TLDDetector::patchVariance(Mat_<double>& intImgP,Mat_<double>& intImgP2,double originalVariance,Point pt,Size size){
return variance(intImgP,intImgP2,Rect(pt.x,pt.y,size.width,size.height)) >= 0.5*originalVariance;
return variance(intImgP,intImgP2,Rect(pt.x,pt.y,size.width,size.height)) >= VARIANCE_THRESHOLD*originalVariance;
}
double TLDDetector::ensembleClassifierNum(const uchar* data,int rowstep){
@ -694,7 +693,7 @@ void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForM
p+=classifiers[i].posteriorProbability(eForEnsemble[k].data,(int)eForEnsemble[k].step[0]);
}
p/=classifiers.size();
if((p>0.5)!=isPositive){
if((p>ENSEMBLE_THRESHOLD)!=isPositive){
if(isPositive){
positiveIntoEnsemble++;
}else{

12
modules/tracking/src/tld_tracker.hpp
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@ -61,7 +61,7 @@ namespace cv {namespace tld
clock_t start;float milisec=0.0;\
start=clock();{a} milisec=1000.0*(clock()-start)/CLOCKS_PER_SEC;\
dprintf(("%-90s took %f milis\n",#a,milisec)); }
#define HERE dprintf(("%d\n",__LINE__));fflush(stderr);
#define HERE dprintf(("line %d\n",__LINE__));fflush(stderr);
#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;\
dprintf(("%s took %f milis\n",name,milisec)); }
@ -96,17 +96,15 @@ unsigned int getMedian(const std::vector<unsigned int>& values, int size=-1);
class TLDEnsembleClassifier{
public:
TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier);
static int makeClassifiers(Size size,int measurePerClassifier,int gridSize,std::vector<TLDEnsembleClassifier>& classifiers);
void integrate(const Mat_<uchar>& patch,bool isPositive);
double posteriorProbability(const uchar* data,int rowstep)const;
static int getMaxOrdinal();
private:
static int getGridSize();
inline void stepPrefSuff(std::vector<uchar>& arr,int len);
void preinit(int ordinal);
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;
std::vector<uchar> x1,y1,x2,y2;
std::vector<Vec4b> measurements;
};
class TrackerProxy{

71
modules/tracking/src/tld_utils.cpp
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@ -252,8 +252,7 @@ void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples){
}
//other stuff
void TLDEnsembleClassifier::stepPrefSuff(std::vector<uchar>& arr,int len){
int gridSize=getGridSize();
void TLDEnsembleClassifier::stepPrefSuff(std::vector<Vec4b>& arr,int pos,int len,int gridSize){
#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++){
@ -266,39 +265,29 @@ void TLDEnsembleClassifier::stepPrefSuff(std::vector<uchar>& arr,int len){
int bigOnes=rem,smallOnes=gridSize-bigOnes-1;
int bigOnes_front=bigOnes/2,bigOnes_back=bigOnes-bigOnes_front;
for(int i=0;i<(int)arr.size();i++){
if(arr[i]<bigOnes_back){
arr[i]=(uchar)(arr[i]*bigStep+arr[i]);
if(arr[i].val[pos]<bigOnes_back){
arr[i].val[pos]=(uchar)(arr[i].val[pos]*bigStep+arr[i].val[pos]);
continue;
}
if(arr[i]<(bigOnes_front+smallOnes)){
arr[i]=(uchar)(bigOnes_front*bigStep+(arr[i]-bigOnes_front)*smallStep+arr[i]);
if(arr[i].val[pos]<(bigOnes_front+smallOnes)){
arr[i].val[pos]=(uchar)(bigOnes_front*bigStep+(arr[i].val[pos]-bigOnes_front)*smallStep+arr[i].val[pos]);
continue;
}
if(arr[i]<(bigOnes_front+smallOnes+bigOnes_back)){
arr[i]=(uchar)(bigOnes_front*bigStep+smallOnes*smallStep+(arr[i]-(bigOnes_front+smallOnes))*bigStep+arr[i]);
if(arr[i].val[pos]<(bigOnes_front+smallOnes+bigOnes_back)){
arr[i].val[pos]=
(uchar)(bigOnes_front*bigStep+smallOnes*smallStep+(arr[i].val[pos]-(bigOnes_front+smallOnes))*bigStep+arr[i].val[pos]);
continue;
}
arr[i]=(uchar)(len-1);
arr[i].val[pos]=(uchar)(len-1);
}
#endif
}
TLDEnsembleClassifier::TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier){
x1=std::vector<uchar>(measurePerClassifier,0);
x2=std::vector<uchar>(measurePerClassifier,0);
y1=std::vector<uchar>(measurePerClassifier,0);
y2=std::vector<uchar>(measurePerClassifier,0);
preinit(ordinal);
stepPrefSuff(x1,size.width);
stepPrefSuff(x2,size.width);
stepPrefSuff(y1,size.height);
stepPrefSuff(y2,size.height);
TLDEnsembleClassifier::TLDEnsembleClassifier(std::vector<Vec4b> meas,int beg,int end){
int posSize=1;
for(int i=0;i<measurePerClassifier;i++)posSize*=2;
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);
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]);
@ -318,15 +307,45 @@ double TLDEnsembleClassifier::posteriorProbability(const uchar* data,int rowstep
}
}
unsigned short int TLDEnsembleClassifier::code(const uchar* data,int rowstep)const{
unsigned short int position=0;
for(int i=0;i<(int)x1.size();i++){
unsigned short int position=0;//TODO: this --> encapsule
for(int i=0;i<(int)measurements.size();i++){
position=position<<1;
if(*(data+rowstep*y1[i]+x1[i])<*(data+rowstep*y2[i]+x2[i])){
if(*(data+rowstep*measurements[i].val[0]+measurements[i].val[1])<*(data+rowstep*measurements[i].val[2]+measurements[i].val[3])){
position++;
}else{
}
}
return position;
}
int TLDEnsembleClassifier::makeClassifiers(Size size,int measurePerClassifier,int gridSize,
std::vector<TLDEnsembleClassifier>& classifiers){
std::vector<Vec4b> measurements;
for(int i=0;i<gridSize;i++){
for(int j=0;j<gridSize;j++){
for(int k=0;k<j;k++){
Vec4b m;
m.val[0]=m.val[2]=i;
m.val[1]=j;m.val[3]=k;
measurements.push_back(m);
m.val[1]=m.val[3]=i;
m.val[0]=j;m.val[2]=k;
measurements.push_back(m);
}
}
}
random_shuffle(measurements.begin(),measurements.end());
stepPrefSuff(measurements,0,size.width,gridSize);
stepPrefSuff(measurements,1,size.width,gridSize);
stepPrefSuff(measurements,2,size.height,gridSize);
stepPrefSuff(measurements,3,size.height,gridSize);
for(int i=0,howMany=measurements.size()/measurePerClassifier;i<howMany;i++){
classifiers.push_back(TLDEnsembleClassifier(measurements,i*measurePerClassifier,(i+1)*measurePerClassifier));
}
return (int)classifiers.size();
}
}}

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