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Vadim

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pull/29/head
Alex Leontiev 11 years ago
parent c019f0a844
commit 70803dff04
3 changed files with 42 additions and 56 deletions
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  1. 49
      modules/tracking/src/TLD.cpp
  2. 10
      modules/tracking/src/TLD.hpp
  3. 39
      modules/tracking/src/trackerTLD.cpp

49
modules/tracking/src/TLD.cpp
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@ -181,23 +181,23 @@ double variance(const Mat& img){
p2/=(img.cols*img.rows);
return p2-p*p;
}
double variance(Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2,Rect box){
double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box){
int x=(box.x),y=(box.y),width=(box.width),height=(box.height);
CV_Assert(0<=x && (x+width)<=intImgP.cols && (x+width)<=intImgP2.cols);
CV_Assert(0<=y && (y+height)<=intImgP.rows && (y+height)<=intImgP2.rows);
CV_Assert(0<=x && (x+width)<intImgP.cols && (x+width)<intImgP2.cols);
CV_Assert(0<=y && (y+height)<intImgP.rows && (y+height)<intImgP2.rows);
double p=0,p2=0;
unsigned int A,B,C,D;
double A,B,C,D;
A=((y>0&&x>0)?intImgP(y-1,x-1):0);
B=((y>0)?intImgP(y-1,x+width-1):0);
C=((x>0)?intImgP(y+height-1,x-1):0);
D=intImgP(y+height-1,x+width-1);
A=intImgP(y,x);
B=intImgP(y,x+width);
C=intImgP(y+height,x);
D=intImgP(y+height,x+width);
p=(0.0+A+D-B-C)/(width*height);
A=((y>0&&x>0)?intImgP2(y-1,x-1):0);
B=((y>0)?intImgP2(y-1,x+width-1):0);
C=((x>0)?intImgP2(y+height-1,x-1):0);
D=intImgP2(y+height-1,x+width-1);
A=intImgP2(y,x);
B=intImgP2(y,x+width);
C=intImgP2(y+height,x);
D=intImgP2(y+height,x+width);
p2=(0.0+(D-B)-(C-A))/(width*height);
return p2-p*p;
@ -253,11 +253,10 @@ void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples){
M(0,0)=(float)(samples.cols/r2.width); M(0,1)=0.0f; M(0,2)=(float)(-r2.x*samples.cols/r2.width);
M(1,0)=0.0f; M(1,1)=(float)(samples.rows/r2.height); M(1,2)=(float)(-r2.y*samples.rows/r2.height);
warpAffine(img,samples,M,samples.size());
}
//other stuff
void TLDEnsembleClassifier::stepPrefSuff(uchar* arr,int len){
void TLDEnsembleClassifier::stepPrefSuff(std::vector<uchar>& arr,int len){
int gridSize=getGridSize();
#if 0
int step=len/(gridSize-1), pref=(len-step*(gridSize-1))/2;
@ -270,7 +269,7 @@ void TLDEnsembleClassifier::stepPrefSuff(uchar* arr,int len){
int smallStep=quo,bigStep=quo+1;
int bigOnes=rem,smallOnes=gridSize-bigOnes-1;
int bigOnes_front=bigOnes/2,bigOnes_back=bigOnes-bigOnes_front;
for(int i=0;i<(int)(sizeof(x1)/sizeof(x1[0]));i++){
for(int i=0;i<(int)arr.size();i++){
if(arr[i]<bigOnes_back){
arr[i]=(uchar)(arr[i]*bigStep+arr[i]);
continue;
@ -287,12 +286,23 @@ void TLDEnsembleClassifier::stepPrefSuff(uchar* arr,int len){
}
#endif
}
TLDEnsembleClassifier::TLDEnsembleClassifier(int ordinal,Size size){
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);
int posSize=1;
for(int i=0;i<measurePerClassifier;i++)posSize*=2;
pos=std::vector<unsigned int>(posSize,0);
neg=std::vector<unsigned int>(posSize,0);
}
void TLDEnsembleClassifier::integrate(Mat_<uchar> patch,bool isPositive){
unsigned short int position=code(patch.data,(int)patch.step[0]);
@ -313,18 +323,13 @@ double TLDEnsembleClassifier::posteriorProbability(const uchar* data,int rowstep
}
unsigned short int TLDEnsembleClassifier::code(const uchar* data,int rowstep)const{
unsigned short int position=0;
//char codeS[20];
for(int i=0;i<(int)(sizeof(x1)/sizeof(x1[0]));i++){
for(int i=0;i<(int)x1.size();i++){
position=position<<1;
if(*(data+rowstep*y1[i]+x1[i])<*(data+rowstep*y2[i]+x2[i])){
position++;
//codeS[i]='o';
}else{
//codeS[i]='x';
}
}
//codeS[13]='\0';
//dprintf(("integrate with code %s\n",codeS));
return position;
}

10
modules/tracking/src/TLD.hpp
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@ -79,7 +79,7 @@ double overlap(const Rect2d& r1,const Rect2d& r2);
void resample(const Mat& img,const RotatedRect& r2,Mat_<uchar>& samples);
void resample(const Mat& img,const Rect2d& r2,Mat_<uchar>& samples);
double variance(const Mat& img);
double variance(Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2,Rect box);
double variance(Mat_<double>& intImgP,Mat_<double>& intImgP2,Rect box);
double NCC(Mat_<uchar> patch1,Mat_<uchar> patch2);
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);
@ -87,17 +87,17 @@ unsigned int getMedian(const std::vector<unsigned int>& values, int size=-1);
class TLDEnsembleClassifier{
public:
TLDEnsembleClassifier(int ordinal,Size size);
TLDEnsembleClassifier(int ordinal,Size size,int measurePerClassifier);
void integrate(Mat_<uchar> patch,bool isPositive);
double posteriorProbability(const uchar* data,int rowstep)const;
static int getMaxOrdinal();
private:
static int getGridSize();
inline void stepPrefSuff(uchar* arr,int len);
inline void stepPrefSuff(std::vector<uchar>& arr,int len);
void preinit(int ordinal);
unsigned short int code(const uchar* data,int rowstep)const;
unsigned int pos[8192],neg[8192];//8192=2^13
uchar x1[13],y1[13],x2[13],y2[13];
std::vector<unsigned int> pos,neg;
std::vector<uchar> x1,y1,x2,y2;
};
class TrackerProxy : public TrackerTLD::Private{

39
modules/tracking/src/trackerTLD.cpp
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@ -52,6 +52,7 @@
#define CORE_THRESHOLD 0.5
#define NEG_EXAMPLES_IN_INIT_MODEL 300
#define MAX_EXAMPLES_IN_MODEL 500
#define MEASURES_PER_CLASSIFIER 13
static const cv::Size GaussBlurKernelSize(3,3);
using namespace cv;
@ -65,19 +66,14 @@ using namespace tld;
* THETA_NN 0.5<->0.6 dramatic change vs video 6 !!
* TODO:
* schoolPC: codec, libopencv-dev
* fix pushbot ->pick commits -> compare_branches->all in 1
* ||video(0.5<->0.6) --> debug if box size is less than 20 --> (remove ensemble self-loop) --> (try inter_area)
* fix pushbot ->pick commits -> compare_branches->all in 1->resubmit
* ||video(0.5<->0.6) --> debug if box size is less than 20 --> (remove ensemble self-loop) --> (try inter_area for resize)
* perfect PN
*
* vadim:
*
* private members
*
* cv::integral
*
* 13 as enum
*
* blurred in TrackerTLDModel()
* private members
*
* warpAffine -- ?
*/
@ -129,8 +125,8 @@ public:
protected:
friend class MyMouseCallbackDEBUG;
Ptr<TrackerModel> model;
void computeIntegralImages(const Mat& img,Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2);
bool patchVariance(Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2,double originalVariance,Point pt,Size size);
inline 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;}
double ensembleClassifierNum(const uchar* data,int rowstep);
TrackerTLD::Params params_;
@ -406,7 +402,7 @@ timeStampPositiveNext(0),timeStampNegativeNext(0),params_(params){
Mat_<uchar> blurredPatch(minSize);
for(int i=0,howMany=TLDEnsembleClassifier::getMaxOrdinal();i<howMany;i++){
classifiers.push_back(TLDEnsembleClassifier(i+1,minSize));
classifiers.push_back(TLDEnsembleClassifier(i+1,minSize,MEASURES_PER_CLASSIFIER));
}
positiveExamples.reserve(200);
@ -507,7 +503,7 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
Rect2d maxScRect;
START_TICK("detector");
do{
Mat_<unsigned int> intImgP(resized_img.rows,resized_img.cols),intImgP2(resized_img.rows,resized_img.cols);
Mat_<double> intImgP,intImgP2;
computeIntegralImages(resized_img,intImgP,intImgP2);
for(int i=0,imax=cvFloor((0.0+resized_img.cols-initSize.width)/dx);i<imax;i++){
@ -595,22 +591,7 @@ bool TLDDetector::detect(const Mat& img,const Mat& imgBlurred,Rect2d& res,std::v
return true;
}
void TLDDetector::computeIntegralImages(const Mat& img,Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2){
intImgP(0,0)=img.at<uchar>(0,0);
for(int j=1;j<intImgP.cols;j++){intImgP(0,j)=intImgP(0,j-1)+img.at<uchar>(0,j);}
for(int i=1;i<intImgP.rows;i++){intImgP(i,0)=intImgP(i-1,0)+img.at<uchar>(i,0);}
for(int i=1;i<intImgP.rows;i++){for(int j=1;j<intImgP.cols;j++){
intImgP(i,j)=intImgP(i,j-1)-intImgP(i-1,j-1)+intImgP(i-1,j)+img.at<uchar>(i,j);}}
unsigned int p;
p=img.at<uchar>(0,0);intImgP2(0,0)=p*p;
for(int j=1;j<intImgP2.cols;j++){p=img.at<uchar>(0,j);intImgP2(0,j)=intImgP2(0,j-1)+p*p;}
for(int i=1;i<intImgP2.rows;i++){p=img.at<uchar>(i,0);intImgP2(i,0)=intImgP2(i-1,0)+p*p;}
for(int i=1;i<intImgP2.rows;i++){for(int j=1;j<intImgP2.cols;j++){p=img.at<uchar>(i,j);
intImgP2(i,j)=intImgP2(i,j-1)-intImgP2(i-1,j-1)+intImgP2(i-1,j)+p*p;}}
}
bool TLDDetector::patchVariance(Mat_<unsigned int>& intImgP,Mat_<unsigned int>& intImgP2,double originalVariance,Point pt,Size size){
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;
}
@ -818,7 +799,7 @@ void MyMouseCallbackDEBUG::onMouse( int event, int x, int y){
resize(img_,resized_img,size);
resize(imgBlurred_,blurred_img,size);
Mat_<unsigned int> intImgP(resized_img.rows,resized_img.cols),intImgP2(resized_img.rows,resized_img.cols);
Mat_<double> intImgP,intImgP2;
detector_->computeIntegralImages(resized_img,intImgP,intImgP2);
int dx=initSize.width/10, dy=initSize.height/10,

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