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opencv_contrib/modules/tracking/src/trackerKCF.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "featureColorName.cpp"
#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 &parameters = TrackerKCF::Params() );
void read( const FileNode& /*fn*/ );
void write( FileStorage& /*fs*/ ) const;
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 _dst, const cv::Size winSize, const int type) const;
void inline fft2(const Mat src, std::vector<Mat> & dest) 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_mtx,double pca_rate, int compressed_sz) const;
void inline compress(const Mat _proj_mtx, const Mat src, Mat & dest) const;
bool getSubWindow(const Mat img, const Rect roi, Mat& patch) const;
void extractCN(Mat _patch, Mat & cnFeatures) const;
void denseGaussKernel(const double sigma, const Mat _x, const Mat _y, Mat & _k) const;
void calcResponse(const Mat _alphaf, const Mat _k, Mat & _response) const;
void calcResponse(const Mat _alphaf, const Mat _alphaf_den, const Mat _k, Mat & _response) 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 y,yf; // training response and its FFT
Mat x,xf; // 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, new_z; // model
Mat response; // detection result
Mat old_cov_mtx, proj_mtx; // for feature compression
bool resizeImage; // resize the image whenever needed and the patch size is large
int frame;
};
/*
* Constructor
*/
Ptr<TrackerKCF> TrackerKCF::createTracker(const TrackerKCF::Params &parameters){
return Ptr<TrackerKCFImpl>(new TrackerKCFImpl(parameters));
}
TrackerKCFImpl::TrackerKCFImpl( const TrackerKCF::Params &parameters ) :
params( parameters )
{
isInit = false;
resizeImage = false;
CV_Assert(params.descriptor == GRAY || params.descriptor == CN /*|| params.descriptor == CN2*/);
}
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);
if(params.descriptor==CN){
Mat layers[] = {hann, hann, hann, hann, hann, hann, hann, hann, hann, hann};
merge(layers, 10, hann);
}
// 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));
// 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 zc;
Mat img=image.clone();
// check the channels of the input image, grayscale is preferred
CV_Assert(image.channels() == 1 || image.channels() == 3);
// resize the image whenever needed
if(resizeImage)resize(img,img,Size(img.cols/2,img.rows/2));
// extract and pre-process the patch
if(!getSubWindow(img,roi, x))return false;
// detection part
if(frame>0){
//compute the gaussian kernel
if(params.compressFeature){
compress(proj_mtx,x,x);
compress(proj_mtx,z,zc);
denseGaussKernel(params.sigma,x,zc,k);
}else{
denseGaussKernel(params.sigma,x,z,k);
}
// calculate filter response
if(params.splitCoeff){
calcResponse(alphaf,alphaf_den,k,response);
}else{
calcResponse(alphaf,k,response);
}
// 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
if(!getSubWindow(img,roi, x))return false;
//update the training data
new_z=x.clone();
if(frame==0){
z=x.clone();
}else{
z=(1.0-params.interp_factor)*z+params.interp_factor*new_z;
}
if(params.compressFeature){
// feature compression
updateProjectionMatrix(z,old_cov_mtx,proj_mtx,params.pca_learning_rate,params.compressed_size);
compress(proj_mtx,x,x);
}
// Kernel Regularized Least-Squares, calculate alphas
denseGaussKernel(params.sigma,x,x,k);
fft2(k,kf);
kf_lambda=kf+params.lambda;
/* TODO: optimize this element-wise division
* new_alphaf=yf./kf
* z=(a+bi)/(c+di)[(ac+bd)+i(bc-ad)]/(c^2+d^2)
*/
new_alphaf=Mat_<Vec2d >(yf.rows, yf.cols);
std::complex<double> temp;
if(params.splitCoeff){
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++){
temp=std::complex<double>(yf.at<Vec2d>(i,j)[0],yf.at<Vec2d>(i,j)[1])/(std::complex<double>(kf_lambda.at<Vec2d>(i,j)[0],kf_lambda.at<Vec2d>(i,j)[1])/*+std::complex<double>(0.0000000001,0.0000000001)*/);
new_alphaf.at<Vec2d >(i,j)[0]=temp.real();
new_alphaf.at<Vec2d >(i,j)[1]=temp.imag();
}
}
}
// update the RLS model
if(frame==0){
alphaf=new_alphaf.clone();
if(params.splitCoeff)alphaf_den=new_alphaf_den.clone();
}else{
alphaf=(1.0-params.interp_factor)*alphaf+params.interp_factor*new_alphaf;
if(params.splitCoeff)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 _dst, const cv::Size winSize, const int type)const{
CV_Assert( type == CV_32FC1 || type == CV_64FC1 );
_dst.create(winSize, type);
Mat dst = _dst.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 {
std::vector<Mat> layers(src.channels());
std::vector<Mat> outputs(src.channels());
split(src, layers);
for(int i=0;i<src.channels();i++){
dft(layers[i],outputs[i],DFT_COMPLEX_OUTPUT);
}
merge(outputs,dest);
}
void inline TrackerKCFImpl::fft2(const Mat src, std::vector<Mat> & dest) const{
std::vector<Mat> layers(src.channels());
dest.clear();
dest.resize(src.channels());
split(src, layers);
for(int i=0;i<src.channels();i++){
dft(layers[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{
dest.clear();
dest.resize(src1.size());
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_mtx, double pca_rate, int compressed_sz)const{
CV_Assert(compressed_sz<=src.channels());
// compute average
std::vector<Mat> layers(src.channels());
std::vector<Scalar> average(src.channels());
split(src,layers);
for (int i=0;i<src.channels();i++){
average[i]=mean(layers[i]);
layers[i]-=average[i];
}
// calc covariance matrix
Mat data,new_cov;
merge(layers,data);
data=data.reshape(1,src.rows*src.cols);
new_cov=1.0/(double)(src.rows*src.cols-1)*(data.t()*data);
if(old_cov.rows==0)old_cov=new_cov.clone();
// calc PCA
Mat w, u, vt;
SVD::compute((1.0-pca_rate)*old_cov+pca_rate*new_cov, w, u, vt);
// extract the projection matrix
_proj_mtx=u(Rect(0,0,compressed_sz,src.channels())).clone();
Mat proj_vars=Mat::eye(compressed_sz,compressed_sz,_proj_mtx.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_mtx*proj_vars*_proj_mtx.t();
}
/*
* compress the features
*/
void inline TrackerKCFImpl::compress(const Mat _proj_mtx, const Mat src, Mat & dest)const{
Mat data=src.reshape(1,src.rows*src.cols);
Mat compressed=data*_proj_mtx;
dest=compressed.reshape(_proj_mtx.cols,src.rows).clone();
}
/*
* obtain the patch and apply hann window filter to it
*/
bool TrackerKCFImpl::getSubWindow(const Mat img, const Rect _roi, Mat& patch) 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(params.descriptor){
case GRAY:
if(img.channels()>1)cvtColor(patch,patch, CV_BGR2GRAY);
patch.convertTo(patch,CV_64F);
patch=patch/255.0-0.5; // normalize to range -0.5 .. 0.5
break;
case CN:
CV_Assert(img.channels() == 3);
extractCN(patch,patch);
break;
case CN2:
if(patch.channels()>1)cvtColor(patch,patch, CV_BGR2GRAY);
break;
}
patch=patch.mul(hann); // hann window filter
return true;
}
/* Convert BGR to ColorNames
*/
void TrackerKCFImpl::extractCN(Mat _patch, Mat & cnFeatures) const {
Vec3b & pixel = _patch.at<Vec3b>(0,0);
unsigned index;
Mat temp = Mat::zeros(_patch.rows,_patch.cols,CV_64FC(10));
for(int i=0;i<_patch.rows;i++){
for(int j=0;j<_patch.cols;j++){
pixel=_patch.at<Vec3b>(i,j);
index=(unsigned)(floor(pixel[2]/8)+32*floor(pixel[1]/8)+32*32*floor(pixel[0]/8));
//copy the values
for(int _k=0;_k<10;_k++){
temp.at<Vec<double,10> >(i,j)[_k]=ColorNames[index][_k];
}
}
}
cnFeatures=temp.clone();
}
/*
* dense gauss kernel function
*/
void TrackerKCFImpl::denseGaussKernel(const double sigma, const Mat _x, const Mat _y, Mat & _k)const{
std::vector<Mat> _xf,_yf,xyf_v;
Mat xy,xyf;
double normX, normY;
fft2(_x,_xf);
fft2(_y,_yf);
normX=norm(_x);
normX*=normX;
normY=norm(_y);
normY*=normY;
pixelWiseMult(_xf,_yf,xyf_v,0,true);
sumChannels(xyf_v,xyf);
ifft2(xyf,xyf);
if(params.wrapKernel){
shiftRows(xyf, _x.rows/2);
shiftCols(xyf, _x.cols/2);
}
//(xx + yy - 2 * xy) / numel(x)
xy=(normX+normY-2*xyf)/(_x.rows*_x.cols*_x.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);
}
/* 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, const Mat _k, Mat & _response)const {
//alpha f--> 2channels ; k --> 1 channel;
Mat _kf;
fft2(_k,_kf);
Mat spec;
mulSpectrums(_alphaf,_kf,spec,0,false);
ifft2(spec,_response);
}
/*
* calculate the detection response for splitted form
*/
void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _alphaf_den, const Mat _k, Mat & _response)const {
Mat _kf;
fft2(_k,_kf);
Mat spec;
Mat spec2=Mat_<Vec2d >(_k.rows, _k.cols);
std::complex<double> temp;
mulSpectrums(_alphaf,_kf,spec,0,false);
for(int i=0;i<_k.rows;i++){
for(int j=0;j<_k.cols;j++){
temp=std::complex<double>(spec.at<Vec2d>(i,j)[0],spec.at<Vec2d>(i,j)[1])/(std::complex<double>(_alphaf_den.at<Vec2d>(i,j)[0],_alphaf_den.at<Vec2d>(i,j)[1])/*+std::complex<double>(0.0000000001,0.0000000001)*/);
spec2.at<Vec2d >(i,j)[0]=temp.real();
spec2.at<Vec2d >(i,j)[1]=temp.imag();
}
}
ifft2(spec2,_response);
}
/*----------------------------------------------------------------------*/
/*
* 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;
descriptor=CN;
splitCoeff=true;
wrapKernel=false;
//feature compression
compressFeature=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{}
} /* namespace cv */