opencv/opencv_contrib
1
0
Fork 0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-25 04:26:17 +08:00
Code Issues Releases Wiki Activity

vadim 7, 8

Browse Source
This commit is contained in:
Alex Leontiev
2014-07-24 04:36:13 +09:00
parent 4b5c8412c1
commit b76a7a3d44
3 changed files with 25 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
modules/tracking/src/tld_tracker.cpp
View File

@@ -191,8 +191,8 @@ class TrackerTLDModel : public TrackerModel{
void setBoudingBox(Rect2d boundingBox){boundingBox_=boundingBox;} void setBoudingBox(Rect2d boundingBox){boundingBox_=boundingBox;}
double getOriginalVariance(){return originalVariance_;} double getOriginalVariance(){return originalVariance_;}
std::vector<TLDEnsembleClassifier>* getClassifiers(){return &classifiers;} std::vector<TLDEnsembleClassifier>* getClassifiers(){return &classifiers;}
double Sr(const Mat_<uchar> patch); double Sr(const Mat_<uchar>& patch);
double Sc(const Mat_<uchar> patch); double Sc(const Mat_<uchar>& patch);
void integrateRelabeled(Mat& img,Mat& imgBlurred,const std::vector<Rect2d>& box,const std::vector<bool>& isPositive, void integrateRelabeled(Mat& img,Mat& imgBlurred,const std::vector<Rect2d>& box,const std::vector<bool>& isPositive,
const std::vector<bool>& alsoIntoModel); const std::vector<bool>& alsoIntoModel);
void integrateAdditional(const std::vector<Mat_<uchar> >& eForModel,const std::vector<Mat_<uchar> >& eForEnsemble,bool isPositive); void integrateAdditional(const std::vector<Mat_<uchar> >& eForModel,const std::vector<Mat_<uchar> >& eForEnsemble,bool isPositive);
@@ -588,14 +588,14 @@ double TLDDetector::ensembleClassifierNum(const uchar* data,int rowstep){
return p; return p;
} }
double TrackerTLDModel::Sr(const Mat_<uchar> patch){ double TrackerTLDModel::Sr(const Mat_<uchar>& patch){
double splus=0.0; double splus=0.0;
for(int i=0;i<(int)positiveExamples.size();i++){ for(int i=0;i<(int)positiveExamples.size();i++){
splus=MAX(splus,0.5*(NCC(positiveExamples[i],patch)+1.0)); splus=std::max(splus,0.5*(NCC(positiveExamples[i],patch)+1.0));
} }
double sminus=0.0; double sminus=0.0;
for(int i=0;i<(int)negativeExamples.size();i++){ for(int i=0;i<(int)negativeExamples.size();i++){
sminus=MAX(sminus,0.5*(NCC(negativeExamples[i],patch)+1.0)); sminus=std::max(sminus,0.5*(NCC(negativeExamples[i],patch)+1.0));
} }
if(splus+sminus==0.0){ if(splus+sminus==0.0){
return 0.0; return 0.0;
@@ -603,17 +603,17 @@ double TrackerTLDModel::Sr(const Mat_<uchar> patch){
return splus/(sminus+splus); return splus/(sminus+splus);
} }
double TrackerTLDModel::Sc(const Mat_<uchar> patch){ double TrackerTLDModel::Sc(const Mat_<uchar>& patch){
double splus=0.0; double splus=0.0;
int med=getMedian(timeStampsPositive); int med=getMedian(timeStampsPositive);
for(int i=0;i<(int)positiveExamples.size();i++){ for(int i=0;i<(int)positiveExamples.size();i++){
if((int)timeStampsPositive[i]<=med){ if((int)timeStampsPositive[i]<=med){
splus=MAX(splus,0.5*(NCC(positiveExamples[i],patch)+1.0)); splus=std::max(splus,0.5*(NCC(positiveExamples[i],patch)+1.0));
} }
} }
double sminus=0.0; double sminus=0.0;
for(int i=0;i<(int)negativeExamples.size();i++){ for(int i=0;i<(int)negativeExamples.size();i++){
sminus=MAX(sminus,0.5*(NCC(negativeExamples[i],patch)+1.0)); sminus=std::max(sminus,0.5*(NCC(negativeExamples[i],patch)+1.0));
} }
if(splus+sminus==0.0){ if(splus+sminus==0.0){
return 0.0; return 0.0;
@@ -753,7 +753,7 @@ bool Nexpert::operator()(Rect2d box){
} }
Data::Data(Rect2d initBox){ Data::Data(Rect2d initBox){
double minDim=MIN(initBox.width,initBox.height); double minDim=std::min(initBox.width,initBox.height);
scale = 20.0/minDim; scale = 20.0/minDim;
minSize.width=(int)(initBox.width*20.0/minDim); minSize.width=(int)(initBox.width*20.0/minDim);
minSize.height=(int)(initBox.height*20.0/minDim); minSize.height=(int)(initBox.height*20.0/minDim);

17
modules/tracking/src/tld_tracker.hpp
View File

@@ -73,14 +73,23 @@ void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,Rect2d whiteOne
void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,std::vector<Rect2d>& whiteOnes); void drawWithRects(const Mat& img,std::vector<Rect2d>& blackOnes,std::vector<Rect2d>& whiteOnes);
//aux functions and variables //aux functions and variables
//#define CLIP(x,a,b) MIN(MAX((x),(a)),(b)) //#define CLIP(x,a,b) std::min(std::max((x),(a)),(b))
template<typename T> inline T CLIP(T x,T a,T b){return MIN(MAX(x,a),b);} template<typename T> inline T CLIP(T x,T a,T b){return std::min(std::max(x,a),b);}
/** Computes overlap between the two given rectangles. Overlap is computed as ratio of rectangles' intersection to that
* of their union.*/
double overlap(const Rect2d& r1,const Rect2d& r2); double overlap(const Rect2d& r1,const Rect2d& r2);
/** Resamples the area surrounded by r2 in img so it matches the size of samples, where it is written.*/
void resample(const Mat& img,const RotatedRect& r2,Mat_<uchar>& samples); void resample(const Mat& img,const RotatedRect& r2,Mat_<uchar>& samples);
/** Specialization of resample() for rectangles without retation for better performance and simplicity.*/
void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples); void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples);
/** Computes the variance of single given image.*/
double variance(const Mat& img); double variance(const Mat& img);
/** Computes the variance of subimage given by box, with the help of two integral
* images intImgP and intImgP2 (sum of squares), which should be also provided.*/
double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box); double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box);
double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2); /** Computes normalized corellation coefficient between the two patches (they should be
* of the same size).*/
double NCC(const Mat_<uchar>& patch1,const Mat_<uchar>& patch2);
void getClosestN(std::vector<Rect2d>& scanGrid,Rect2d bBox,int n,std::vector<Rect2d>& res); void getClosestN(std::vector<Rect2d>& scanGrid,Rect2d bBox,int n,std::vector<Rect2d>& res);
double scaleAndBlur(const Mat& originalImg,int scale,Mat& scaledImg,Mat& blurredImg,Size GaussBlurKernelSize); double scaleAndBlur(const Mat& originalImg,int scale,Mat& scaledImg,Mat& blurredImg,Size GaussBlurKernelSize);
unsigned int getMedian(const std::vector<unsigned int>& values, int size=-1); unsigned int getMedian(const std::vector<unsigned int>& values, int size=-1);
@@ -88,7 +97,7 @@ unsigned int getMedian(const std::vector<unsigned int>& values, int size=-1);
class TLDEnsembleClassifier{ class TLDEnsembleClassifier{
public: public:
TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier); TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier);
void integrate(Mat_<uchar> patch,bool isPositive); void integrate(const Mat_<uchar>& patch,bool isPositive);
double posteriorProbability(const uchar* data,int rowstep)const; double posteriorProbability(const uchar* data,int rowstep)const;
static int getMaxOrdinal(); static int getMaxOrdinal();
private: private:

6
modules/tracking/src/tld_utils.cpp
View File

@@ -199,7 +199,7 @@ double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box){
return p2-p*p; return p2-p*p;
} }
double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2){ double NCC(const Mat_<uchar>& patch1,const Mat_<uchar>& patch2){
CV_Assert(patch1.rows==patch2.rows); CV_Assert(patch1.rows==patch2.rows);
CV_Assert(patch1.cols==patch2.cols); CV_Assert(patch1.cols==patch2.cols);
@@ -207,7 +207,7 @@ double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2){
double s1=sum(patch1)(0),s2=sum(patch2)(0); double s1=sum(patch1)(0),s2=sum(patch2)(0);
double n1=norm(patch1),n2=norm(patch2); double n1=norm(patch1),n2=norm(patch2);
double prod=patch1.dot(patch2); double prod=patch1.dot(patch2);
double sq1=sqrt(MAX(0.0,n1*n1-s1*s1/N)),sq2=sqrt(MAX(0.0,n2*n2-s2*s2/N)); double sq1=sqrt(std::max(0.0,n1*n1-s1*s1/N)),sq2=sqrt(std::max(0.0,n2*n2-s2*s2/N));
double ares=(sq2==0)?sq1/abs(sq1):(prod-s1*s2/N)/sq1/sq2; double ares=(sq2==0)?sq1/abs(sq1):(prod-s1*s2/N)/sq1/sq2;
return ares; return ares;
} }
@@ -300,7 +300,7 @@ TLDEnsembleClassifier::TLDEnsembleClassifier(int ordinal,Size size,int measurePe
pos=std::vector<unsigned int>(posSize,0); pos=std::vector<unsigned int>(posSize,0);
neg=std::vector<unsigned int>(posSize,0); neg=std::vector<unsigned int>(posSize,0);
} }
void TLDEnsembleClassifier::integrate(Mat_<uchar> patch,bool isPositive){ void TLDEnsembleClassifier::integrate(const Mat_<uchar>& patch,bool isPositive){
unsigned short int position=code(patch.data,(int)patch.step[0]); unsigned short int position=code(patch.data,(int)patch.step[0]);
if(isPositive){ if(isPositive){
pos[position]++; pos[position]++;