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#include "precomp.hpp"
#include <complex>
/*---------------------------
| TrackerKCFModel
|---------------------------*/
namespace cv{
/**
* \brief Implementation of TrackerModel for MIL algorithm
*/
class TrackerKCFModel : public TrackerModel{
public:
TrackerKCFModel(TrackerKCF::Params /*params*/){}
~TrackerKCFModel(){}
protected:
void modelEstimationImpl( const std::vector<Mat>& /*responses*/ ){}
void modelUpdateImpl(){}
};
} /* namespace cv */
/*---------------------------
| TrackerKCF
|---------------------------*/
namespace cv{
/*
* Prototype
*/
class TrackerKCFImpl : public TrackerKCF {
public:
TrackerKCFImpl( const TrackerKCF::Params ¶meters = TrackerKCF::Params() );
void read( const FileNode& /*fn*/ );
void write( FileStorage& /*fs*/ ) const;
void setFeatureExtractor(void (*f)(const Mat, const Rect, Mat&), bool pca_func = false);
protected:
/*
* basic functions and vars
*/
bool initImpl( const Mat& /*image*/, const Rect2d& boundingBox );
bool updateImpl( const Mat& image, Rect2d& boundingBox );
TrackerKCF::Params params;
/*
* KCF functions and vars
*/
void createHanningWindow(OutputArray dest, const cv::Size winSize, const int type) const;
void inline fft2(const Mat src, std::vector<Mat> & dest, std::vector<Mat> & layers_data) const;
void inline fft2(const Mat src, Mat & dest) const;
void inline ifft2(const Mat src, Mat & dest) const;
void inline pixelWiseMult(const std::vector<Mat> src1, const std::vector<Mat> src2, std::vector<Mat> & dest, const int flags, const bool conjB=false) const;
void inline sumChannels(std::vector<Mat> src, Mat & dest) const;
void inline updateProjectionMatrix(const Mat src, Mat & old_cov,Mat & proj_matrix,double pca_rate, int compressed_sz,
std::vector<Mat> & layers_pca,std::vector<Scalar> & average, Mat pca_data, Mat new_cov, Mat w, Mat u, Mat v) const;
void inline compress(const Mat proj_matrix, const Mat src, Mat & dest, Mat & data, Mat & compressed) const;
bool getSubWindow(const Mat img, const Rect roi, Mat& feat, Mat& patch, TrackerKCF::MODE desc = GRAY) const;
bool getSubWindow(const Mat img, const Rect roi, Mat& feat, void (*f)(const Mat, const Rect, Mat& )) const;
void extractCN(Mat patch_data, Mat & cnFeatures) const;
void denseGaussKernel(const double sigma, const Mat , const Mat y_data, Mat & k_data,
std::vector<Mat> & layers_data,std::vector<Mat> & xf_data,std::vector<Mat> & yf_data, std::vector<Mat> xyf_v, Mat xy, Mat xyf ) const;
void calcResponse(const Mat alphaf_data, const Mat kf_data, Mat & response_data, Mat & spec_data) const;
void calcResponse(const Mat alphaf_data, const Mat alphaf_den_data, const Mat kf_data, Mat & response_data, Mat & spec_data, Mat & spec2_data) const;
void shiftRows(Mat& mat) const;
void shiftRows(Mat& mat, int n) const;
void shiftCols(Mat& mat, int n) const;
private:
double output_sigma;
Rect2d roi;
Mat hann; //hann window filter
Mat hann_cn; //10 dimensional hann-window filter for CN features,
Mat y,yf; // training response and its FFT
Mat x; // observation and its FFT
Mat k,kf; // dense gaussian kernel and its FFT
Mat kf_lambda; // kf+lambda
Mat new_alphaf, alphaf; // training coefficients
Mat new_alphaf_den, alphaf_den; // for splitted training coefficients
Mat z; // model
Mat response; // detection result
Mat old_cov_mtx, proj_mtx; // for feature compression
// pre-defined Mat variables for optimization of private functions
Mat spec, spec2;
std::vector<Mat> layers;
std::vector<Mat> vxf,vyf,vxyf;
Mat xy_data,xyf_data;
Mat data_temp, compress_data;
std::vector<Mat> layers_pca_data;
std::vector<Scalar> average_data;
Mat img_Patch;
// storage for the extracted features, KRLS model, KRLS compressed model
Mat X[2],Z[2],Zc[2];
// storage of the extracted features
std::vector<Mat> features_pca;
std::vector<Mat> features_npca;
std::vector<MODE> descriptors_pca;
std::vector<MODE> descriptors_npca;
// optimization variables for updateProjectionMatrix
Mat data_pca, new_covar,w_data,u_data,vt_data;
// custom feature extractor
bool use_custom_extractor_pca;
bool use_custom_extractor_npca;
std::vector<void(*)(const Mat img, const Rect roi, Mat& output)> extractor_pca;
std::vector<void(*)(const Mat img, const Rect roi, Mat& output)> extractor_npca;
bool resizeImage; // resize the image whenever needed and the patch size is large
int frame;
};
/*
* Constructor
*/
Ptr<TrackerKCF> TrackerKCF::createTracker(const TrackerKCF::Params ¶meters){
return Ptr<TrackerKCFImpl>(new TrackerKCFImpl(parameters));
}
TrackerKCFImpl::TrackerKCFImpl( const TrackerKCF::Params ¶meters ) :
params( parameters )
{
isInit = false;
resizeImage = false;
use_custom_extractor_pca = false;
use_custom_extractor_npca = false;
}
void TrackerKCFImpl::read( const cv::FileNode& fn ){
params.read( fn );
}
void TrackerKCFImpl::write( cv::FileStorage& fs ) const {
params.write( fs );
}
/*
* Initialization:
* - creating hann window filter
* - ROI padding
* - creating a gaussian response for the training ground-truth
* - perform FFT to the gaussian response
*/
bool TrackerKCFImpl::initImpl( const Mat& /*image*/, const Rect2d& boundingBox ){
frame=0;
roi = boundingBox;
//calclulate output sigma
output_sigma=sqrt(roi.width*roi.height)*params.output_sigma_factor;
output_sigma=-0.5/(output_sigma*output_sigma);
//resize the ROI whenever needed
if(params.resize && roi.width*roi.height>params.max_patch_size){
resizeImage=true;
roi.x/=2.0;
roi.y/=2.0;
roi.width/=2.0;
roi.height/=2.0;
}
// add padding to the roi
roi.x-=roi.width/2;
roi.y-=roi.height/2;
roi.width*=2;
roi.height*=2;
// initialize the hann window filter
createHanningWindow(hann, roi.size(), CV_64F);
// hann window filter for CN feature
Mat _layer[] = {hann, hann, hann, hann, hann, hann, hann, hann, hann, hann};
merge(_layer, 10, hann_cn);
// create gaussian response
y=Mat::zeros((int)roi.height,(int)roi.width,CV_64F);
for(unsigned i=0;i<roi.height;i++){
for(unsigned j=0;j<roi.width;j++){
y.at<double>(i,j)=(i-roi.height/2+1)*(i-roi.height/2+1)+(j-roi.width/2+1)*(j-roi.width/2+1);
}
}
y*=(double)output_sigma;
cv::exp(y,y);
// perform fourier transfor to the gaussian response
fft2(y,yf);
model=Ptr<TrackerKCFModel>(new TrackerKCFModel(params));
// record the non-compressed descriptors
if((params.desc_npca & GRAY) == GRAY)descriptors_npca.push_back(GRAY);
if((params.desc_npca & CN) == CN)descriptors_npca.push_back(CN);
if(use_custom_extractor_npca)descriptors_npca.push_back(CUSTOM);
features_npca.resize(descriptors_npca.size());
// record the compressed descriptors
if((params.desc_pca & GRAY) == GRAY)descriptors_pca.push_back(GRAY);
if((params.desc_pca & CN) == CN)descriptors_pca.push_back(CN);
if(use_custom_extractor_pca)descriptors_pca.push_back(CUSTOM);
features_pca.resize(descriptors_pca.size());
// accept only the available descriptor modes
CV_Assert(
(params.desc_pca & GRAY) == GRAY
|| (params.desc_npca & GRAY) == GRAY
|| (params.desc_pca & CN) == CN
|| (params.desc_npca & CN) == CN
|| use_custom_extractor_pca
|| use_custom_extractor_npca
);
// TODO: return true only if roi inside the image
return true;
}
/*
* Main part of the KCF algorithm
*/
bool TrackerKCFImpl::updateImpl( const Mat& image, Rect2d& boundingBox ){
double minVal, maxVal; // min-max response
Point minLoc,maxLoc; // min-max location
Mat img=image.clone();
// check the channels of the input image, grayscale is preferred
CV_Assert(img.channels() == 1 || img.channels() == 3);
// resize the image whenever needed
if(resizeImage)resize(img,img,Size(img.cols/2,img.rows/2));
// detection part
if(frame>0){
// extract and pre-process the patch
// get non compressed descriptors
for(unsigned i=0;i<descriptors_npca.size()-extractor_npca.size();i++){
if(!getSubWindow(img,roi, features_npca[i], img_Patch, descriptors_npca[i]))return false;
}
//get non-compressed custom descriptors
for(unsigned i=0,j=(unsigned)(descriptors_npca.size()-extractor_npca.size());i<extractor_npca.size();i++,j++){
if(!getSubWindow(img,roi, features_npca[j], extractor_npca[i]))return false;
}
if(features_npca.size()>0)merge(features_npca,X[1]);
// get compressed descriptors
for(unsigned i=0;i<descriptors_pca.size()-extractor_pca.size();i++){
if(!getSubWindow(img,roi, features_pca[i], img_Patch, descriptors_pca[i]))return false;
}
//get compressed custom descriptors
for(unsigned i=0,j=(unsigned)(descriptors_pca.size()-extractor_pca.size());i<extractor_pca.size();i++,j++){
if(!getSubWindow(img,roi, features_pca[j], extractor_pca[i]))return false;
}
if(features_pca.size()>0)merge(features_pca,X[0]);
//compress the features and the KRSL model
if(params.desc_pca !=0){
compress(proj_mtx,X[0],X[0],data_temp,compress_data);
compress(proj_mtx,Z[0],Zc[0],data_temp,compress_data);
}
// copy the compressed KRLS model
Zc[1] = Z[1];
// merge all features
if(features_npca.size()==0){
x = X[0];
z = Zc[0];
}else if(features_pca.size()==0){
x = X[1];
z = Z[1];
}else{
merge(X,2,x);
merge(Zc,2,z);
}
//compute the gaussian kernel
denseGaussKernel(params.sigma,x,z,k,layers,vxf,vyf,vxyf,xy_data,xyf_data);
// compute the fourier transform of the kernel
fft2(k,kf);
if(frame==1)spec2=Mat_<Vec2d >(kf.rows, kf.cols);
// calculate filter response
if(params.split_coeff)
calcResponse(alphaf,alphaf_den,kf,response, spec, spec2);
else
calcResponse(alphaf,kf,response, spec);
// extract the maximum response
minMaxLoc( response, &minVal, &maxVal, &minLoc, &maxLoc );
roi.x+=(maxLoc.x-roi.width/2+1);
roi.y+=(maxLoc.y-roi.height/2+1);
// update the bounding box
boundingBox.x=(resizeImage?roi.x*2:roi.x)+boundingBox.width/2;
boundingBox.y=(resizeImage?roi.y*2:roi.y)+boundingBox.height/2;
}
// extract the patch for learning purpose
// get non compressed descriptors
for(unsigned i=0;i<descriptors_npca.size()-extractor_npca.size();i++){
if(!getSubWindow(img,roi, features_npca[i], img_Patch, descriptors_npca[i]))return false;
}
//get non-compressed custom descriptors
for(unsigned i=0,j=(unsigned)(descriptors_npca.size()-extractor_npca.size());i<extractor_npca.size();i++,j++){
if(!getSubWindow(img,roi, features_npca[j], extractor_npca[i]))return false;
}
if(features_npca.size()>0)merge(features_npca,X[1]);
// get compressed descriptors
for(unsigned i=0;i<descriptors_pca.size()-extractor_pca.size();i++){
if(!getSubWindow(img,roi, features_pca[i], img_Patch, descriptors_pca[i]))return false;
}
//get compressed custom descriptors
for(unsigned i=0,j=(unsigned)(descriptors_pca.size()-extractor_pca.size());i<extractor_pca.size();i++,j++){
if(!getSubWindow(img,roi, features_pca[j], extractor_pca[i]))return false;
}
if(features_pca.size()>0)merge(features_pca,X[0]);
//update the training data
if(frame==0){
Z[0] = X[0].clone();
Z[1] = X[1].clone();
}else{
Z[0]=(1.0-params.interp_factor)*Z[0]+params.interp_factor*X[0];
Z[1]=(1.0-params.interp_factor)*Z[1]+params.interp_factor*X[1];
}
if(params.desc_pca !=0 || use_custom_extractor_pca){
// initialize the vector of Mat variables
if(frame==0){
layers_pca_data.resize(Z[0].channels());
average_data.resize(Z[0].channels());
}
// feature compression
updateProjectionMatrix(Z[0],old_cov_mtx,proj_mtx,params.pca_learning_rate,params.compressed_size,layers_pca_data,average_data,data_pca, new_covar,w_data,u_data,vt_data);
compress(proj_mtx,X[0],X[0],data_temp,compress_data);
}
// merge all features
if(features_npca.size()==0)
x = X[0];
else if(features_pca.size()==0)
x = X[1];
else
merge(X,2,x);
// initialize some required Mat variables
if(frame==0){
layers.resize(x.channels());
vxf.resize(x.channels());
vyf.resize(x.channels());
vxyf.resize(vyf.size());
new_alphaf=Mat_<Vec2d >(yf.rows, yf.cols);
}
// Kernel Regularized Least-Squares, calculate alphas
denseGaussKernel(params.sigma,x,x,k,layers,vxf,vyf,vxyf,xy_data,xyf_data);
// compute the fourier transform of the kernel and add a small value
fft2(k,kf);
kf_lambda=kf+params.lambda;
double den;
if(params.split_coeff){
mulSpectrums(yf,kf,new_alphaf,0);
mulSpectrums(kf,kf_lambda,new_alphaf_den,0);
}else{
for(int i=0;i<yf.rows;i++){
for(int j=0;j<yf.cols;j++){
den = 1.0/(kf_lambda.at<Vec2d>(i,j)[0]*kf_lambda.at<Vec2d>(i,j)[0]+kf_lambda.at<Vec2d>(i,j)[1]*kf_lambda.at<Vec2d>(i,j)[1]);
new_alphaf.at<Vec2d>(i,j)[0]=
(yf.at<Vec2d>(i,j)[0]*kf_lambda.at<Vec2d>(i,j)[0]+yf.at<Vec2d>(i,j)[1]*kf_lambda.at<Vec2d>(i,j)[1])*den;
new_alphaf.at<Vec2d>(i,j)[1]=
(yf.at<Vec2d>(i,j)[1]*kf_lambda.at<Vec2d>(i,j)[0]-yf.at<Vec2d>(i,j)[0]*kf_lambda.at<Vec2d>(i,j)[1])*den;
}
}
}
// update the RLS model
if(frame==0){
alphaf=new_alphaf.clone();
if(params.split_coeff)alphaf_den=new_alphaf_den.clone();
}else{
alphaf=(1.0-params.interp_factor)*alphaf+params.interp_factor*new_alphaf;
if(params.split_coeff)alphaf_den=(1.0-params.interp_factor)*alphaf_den+params.interp_factor*new_alphaf_den;
}
frame++;
return true;
}
/*-------------------------------------
| implementation of the KCF functions
|-------------------------------------*/
/*
* hann window filter
*/
void TrackerKCFImpl::createHanningWindow(OutputArray dest, const cv::Size winSize, const int type) const {
CV_Assert( type == CV_32FC1 || type == CV_64FC1 );
dest.create(winSize, type);
Mat dst = dest.getMat();
int rows = dst.rows, cols = dst.cols;
AutoBuffer<double> _wc(cols);
double * const wc = (double *)_wc;
double coeff0 = 2.0 * CV_PI / (double)(cols - 1), coeff1 = 2.0f * CV_PI / (double)(rows - 1);
for(int j = 0; j < cols; j++)
wc[j] = 0.5 * (1.0 - cos(coeff0 * j));
if(dst.depth() == CV_32F){
for(int i = 0; i < rows; i++){
float* dstData = dst.ptr<float>(i);
double wr = 0.5 * (1.0 - cos(coeff1 * i));
for(int j = 0; j < cols; j++)
dstData[j] = (float)(wr * wc[j]);
}
}else{
for(int i = 0; i < rows; i++){
double* dstData = dst.ptr<double>(i);
double wr = 0.5 * (1.0 - cos(coeff1 * i));
for(int j = 0; j < cols; j++)
dstData[j] = wr * wc[j];
}
}
// perform batch sqrt for SSE performance gains
//cv::sqrt(dst, dst); //matlab do not use the square rooted version
}
/*
* simplification of fourier transform function in opencv
*/
void inline TrackerKCFImpl::fft2(const Mat src, Mat & dest) const {
dft(src,dest,DFT_COMPLEX_OUTPUT);
}
void inline TrackerKCFImpl::fft2(const Mat src, std::vector<Mat> & dest, std::vector<Mat> & layers_data) const {
split(src, layers_data);
for(int i=0;i<src.channels();i++){
dft(layers_data[i],dest[i],DFT_COMPLEX_OUTPUT);
}
}
/*
* simplification of inverse fourier transform function in opencv
*/
void inline TrackerKCFImpl::ifft2(const Mat src, Mat & dest) const {
idft(src,dest,DFT_SCALE+DFT_REAL_OUTPUT);
}
/*
* Point-wise multiplication of two Multichannel Mat data
*/
void inline TrackerKCFImpl::pixelWiseMult(const std::vector<Mat> src1, const std::vector<Mat> src2, std::vector<Mat> & dest, const int flags, const bool conjB) const {
for(unsigned i=0;i<src1.size();i++){
mulSpectrums(src1[i], src2[i], dest[i],flags,conjB);
}
}
/*
* Combines all channels in a multi-channels Mat data into a single channel
*/
void inline TrackerKCFImpl::sumChannels(std::vector<Mat> src, Mat & dest) const {
dest=src[0].clone();
for(unsigned i=1;i<src.size();i++){
dest+=src[i];
}
}
/*
* obtains the projection matrix using PCA
*/
void inline TrackerKCFImpl::updateProjectionMatrix(const Mat src, Mat & old_cov,Mat & proj_matrix, double pca_rate, int compressed_sz,
std::vector<Mat> & layers_pca,std::vector<Scalar> & average, Mat pca_data, Mat new_cov, Mat w, Mat u, Mat vt) const {
CV_Assert(compressed_sz<=src.channels());
split(src,layers_pca);
for (int i=0;i<src.channels();i++){
average[i]=mean(layers_pca[i]);
layers_pca[i]-=average[i];
}
// calc covariance matrix
merge(layers_pca,pca_data);
pca_data=pca_data.reshape(1,src.rows*src.cols);
new_cov=1.0/(double)(src.rows*src.cols-1)*(pca_data.t()*pca_data);
if(old_cov.rows==0)old_cov=new_cov.clone();
// calc PCA
SVD::compute((1.0-pca_rate)*old_cov+pca_rate*new_cov, w, u, vt);
// extract the projection matrix
proj_matrix=u(Rect(0,0,compressed_sz,src.channels())).clone();
Mat proj_vars=Mat::eye(compressed_sz,compressed_sz,proj_matrix.type());
for(int i=0;i<compressed_sz;i++){
proj_vars.at<double>(i,i)=w.at<double>(i);
}
// update the covariance matrix
old_cov=(1.0-pca_rate)*old_cov+pca_rate*proj_matrix*proj_vars*proj_matrix.t();
}
/*
* compress the features
*/
void inline TrackerKCFImpl::compress(const Mat proj_matrix, const Mat src, Mat & dest, Mat & data, Mat & compressed) const {
data=src.reshape(1,src.rows*src.cols);
compressed=data*proj_matrix;
dest=compressed.reshape(proj_matrix.cols,src.rows).clone();
}
/*
* obtain the patch and apply hann window filter to it
*/
bool TrackerKCFImpl::getSubWindow(const Mat img, const Rect _roi, Mat& feat, Mat& patch, TrackerKCF::MODE desc) const {
Rect region=_roi;
// return false if roi is outside the image
if((_roi.x+_roi.width<0)
||(_roi.y+_roi.height<0)
||(_roi.x>=img.cols)
||(_roi.y>=img.rows)
)return false;
// extract patch inside the image
if(_roi.x<0){region.x=0;region.width+=_roi.x;}
if(_roi.y<0){region.y=0;region.height+=_roi.y;}
if(_roi.x+_roi.width>img.cols)region.width=img.cols-_roi.x;
if(_roi.y+_roi.height>img.rows)region.height=img.rows-_roi.y;
if(region.width>img.cols)region.width=img.cols;
if(region.height>img.rows)region.height=img.rows;
patch=img(region).clone();
// add some padding to compensate when the patch is outside image border
int addTop,addBottom, addLeft, addRight;
addTop=region.y-_roi.y;
addBottom=(_roi.height+_roi.y>img.rows?_roi.height+_roi.y-img.rows:0);
addLeft=region.x-_roi.x;
addRight=(_roi.width+_roi.x>img.cols?_roi.width+_roi.x-img.cols:0);
copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
if(patch.rows==0 || patch.cols==0)return false;
// extract the desired descriptors
switch(desc){
case CN:
CV_Assert(img.channels() == 3);
extractCN(patch,feat);
feat=feat.mul(hann_cn); // hann window filter
break;
default: // GRAY
if(img.channels()>1)
cvtColor(patch,feat, CV_BGR2GRAY);
else
feat=patch;
feat.convertTo(feat,CV_64F);
feat=feat/255.0-0.5; // normalize to range -0.5 .. 0.5
feat=feat.mul(hann); // hann window filter
break;
}
return true;
}
/*
* get feature using external function
*/
bool TrackerKCFImpl::getSubWindow(const Mat img, const Rect _roi, Mat& feat, void (*f)(const Mat, const Rect, Mat& )) const{
// return false if roi is outside the image
if((_roi.x+_roi.width<0)
||(_roi.y+_roi.height<0)
||(_roi.x>=img.cols)
||(_roi.y>=img.rows)
)return false;
f(img, _roi, feat);
if(_roi.width != feat.cols || _roi.height != feat.rows){
printf("error in customized function of features extractor!\n");
printf("Rules: roi.width==feat.cols && roi.height = feat.rows \n");
}
Mat hann_win;
std::vector<Mat> _layers;
for(int i=0;i<feat.channels();i++)
_layers.push_back(hann);
merge(_layers, hann_win);
feat=feat.mul(hann_win); // hann window filter
return true;
}
/* Convert BGR to ColorNames
*/
void TrackerKCFImpl::extractCN(Mat patch_data, Mat & cnFeatures) const {
Vec3b & pixel = patch_data.at<Vec3b>(0,0);
unsigned index;
if(cnFeatures.type() != CV_64FC(10))
cnFeatures = Mat::zeros(patch_data.rows,patch_data.cols,CV_64FC(10));
for(int i=0;i<patch_data.rows;i++){
for(int j=0;j<patch_data.cols;j++){
pixel=patch_data.at<Vec3b>(i,j);
index=(unsigned)(floor((float)pixel[2]/8)+32*floor((float)pixel[1]/8)+32*32*floor((float)pixel[0]/8));
//copy the values
for(int _k=0;_k<10;_k++){
cnFeatures.at<Vec<double,10> >(i,j)[_k]=ColorNames[index][_k];
}
}
}
}
/*
* dense gauss kernel function
*/
void TrackerKCFImpl::denseGaussKernel(const double sigma, const Mat x_data, const Mat y_data, Mat & k_data,
std::vector<Mat> & layers_data,std::vector<Mat> & xf_data,std::vector<Mat> & yf_data, std::vector<Mat> xyf_v, Mat xy, Mat xyf ) const {
double normX, normY;
fft2(x_data,xf_data,layers_data);
fft2(y_data,yf_data,layers_data);
normX=norm(x_data);
normX*=normX;
normY=norm(y_data);
normY*=normY;
pixelWiseMult(xf_data,yf_data,xyf_v,0,true);
sumChannels(xyf_v,xyf);
ifft2(xyf,xyf);
if(params.wrap_kernel){
shiftRows(xyf, x_data.rows/2);
shiftCols(xyf, x_data.cols/2);
}
//(xx + yy - 2 * xy) / numel(x)
xy=(normX+normY-2*xyf)/(x_data.rows*x_data.cols*x_data.channels());
// TODO: check wether we really need thresholding or not
//threshold(xy,xy,0.0,0.0,THRESH_TOZERO);//max(0, (xx + yy - 2 * xy) / numel(x))
for(int i=0;i<xy.rows;i++){
for(int j=0;j<xy.cols;j++){
if(xy.at<double>(i,j)<0.0)xy.at<double>(i,j)=0.0;
}
}
double sig=-1.0/(sigma*sigma);
xy=sig*xy;
exp(xy,k_data);
}
/* CIRCULAR SHIFT Function
* http://stackoverflow.com/questions/10420454/shift-like-matlab-function-rows-or-columns-of-a-matrix-in-opencv
*/
// circular shift one row from up to down
void TrackerKCFImpl::shiftRows(Mat& mat) const {
Mat temp;
Mat m;
int _k = (mat.rows-1);
mat.row(_k).copyTo(temp);
for(; _k > 0 ; _k-- ) {
m = mat.row(_k);
mat.row(_k-1).copyTo(m);
}
m = mat.row(0);
temp.copyTo(m);
}
// circular shift n rows from up to down if n > 0, -n rows from down to up if n < 0
void TrackerKCFImpl::shiftRows(Mat& mat, int n) const {
if( n < 0 ) {
n = -n;
flip(mat,mat,0);
for(int _k=0; _k < n;_k++) {
shiftRows(mat);
}
flip(mat,mat,0);
}else{
for(int _k=0; _k < n;_k++) {
shiftRows(mat);
}
}
}
//circular shift n columns from left to right if n > 0, -n columns from right to left if n < 0
void TrackerKCFImpl::shiftCols(Mat& mat, int n) const {
if(n < 0){
n = -n;
flip(mat,mat,1);
transpose(mat,mat);
shiftRows(mat,n);
transpose(mat,mat);
flip(mat,mat,1);
}else{
transpose(mat,mat);
shiftRows(mat,n);
transpose(mat,mat);
}
}
/*
* calculate the detection response
*/
void TrackerKCFImpl::calcResponse(const Mat alphaf_data, const Mat kf_data, Mat & response_data, Mat & spec_data) const {
//alpha f--> 2channels ; k --> 1 channel;
mulSpectrums(alphaf_data,kf_data,spec_data,0,false);
ifft2(spec_data,response_data);
}
/*
* calculate the detection response for splitted form
*/
void TrackerKCFImpl::calcResponse(const Mat alphaf_data, const Mat _alphaf_den, const Mat kf_data, Mat & response_data, Mat & spec_data, Mat & spec2_data) const {
mulSpectrums(alphaf_data,kf_data,spec_data,0,false);
//z=(a+bi)/(c+di)=[(ac+bd)+i(bc-ad)]/(c^2+d^2)
double den;
for(int i=0;i<kf_data.rows;i++){
for(int j=0;j<kf_data.cols;j++){
den=1.0/(_alphaf_den.at<Vec2d>(i,j)[0]*_alphaf_den.at<Vec2d>(i,j)[0]+_alphaf_den.at<Vec2d>(i,j)[1]*_alphaf_den.at<Vec2d>(i,j)[1]);
spec2_data.at<Vec2d>(i,j)[0]=
(spec_data.at<Vec2d>(i,j)[0]*_alphaf_den.at<Vec2d>(i,j)[0]+spec_data.at<Vec2d>(i,j)[1]*_alphaf_den.at<Vec2d>(i,j)[1])*den;
spec2_data.at<Vec2d>(i,j)[1]=
(spec_data.at<Vec2d>(i,j)[1]*_alphaf_den.at<Vec2d>(i,j)[0]-spec_data.at<Vec2d>(i,j)[0]*_alphaf_den.at<Vec2d>(i,j)[1])*den;
}
}
ifft2(spec2_data,response_data);
}
void TrackerKCFImpl::setFeatureExtractor(void (*f)(const Mat, const Rect, Mat&), bool pca_func){
if(pca_func){
extractor_pca.push_back(f);
use_custom_extractor_pca = true;
}else{
extractor_npca.push_back(f);
use_custom_extractor_npca = true;
}
}
/*----------------------------------------------------------------------*/
/*
* Parameters
*/
TrackerKCF::Params::Params(){
sigma=0.2;
lambda=0.01;
interp_factor=0.075;
output_sigma_factor=1.0/16.0;
resize=true;
max_patch_size=80*80;
split_coeff=true;
wrap_kernel=false;
desc_npca = GRAY;
desc_pca = CN;
//feature compression
compress_feature=true;
compressed_size=2;
pca_learning_rate=0.15;
}
void TrackerKCF::Params::read( const cv::FileNode& /*fn*/ ){}
void TrackerKCF::Params::write( cv::FileStorage& /*fs*/ ) const{}
void TrackerKCF::setFeatureExtractor(void (*)(const Mat, const Rect, Mat&), bool ){};
} /* namespace cv */