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Fixing alignments

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pull/245/head
Kurnianggoro 10 years ago
parent 6280a069f2
commit 244b7926be
1 changed files with 51 additions and 56 deletions
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  1. 107
      modules/tracking/src/trackerKCF.cpp

107
modules/tracking/src/trackerKCF.cpp
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@ -144,7 +144,7 @@ namespace cv{
params.read( fn );
}
void TrackerKCFImpl::write( cv::FileStorage& fs ) const{
void TrackerKCFImpl::write( cv::FileStorage& fs ) const {
params.write( fs );
}
@ -189,7 +189,7 @@ namespace cv{
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.at<double>(i,j)=(i-roi.height/2+1)*(i-roi.height/2+1)+(j-roi.width/2+1)*(j-roi.width/2+1);
}
}
@ -283,11 +283,11 @@ namespace cv{
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();
}
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();
}
}
}
@ -312,7 +312,7 @@ namespace cv{
/*
* hann window filter
*/
void TrackerKCFImpl::createHanningWindow(OutputArray _dst, const cv::Size winSize, const int type)const{
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);
@ -325,27 +325,22 @@ namespace cv{
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];
}
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
@ -355,7 +350,7 @@ namespace cv{
/*
* simplification of fourier transform function in opencv
*/
void inline TrackerKCFImpl::fft2(const Mat src, Mat & dest)const {
void inline TrackerKCFImpl::fft2(const Mat src, Mat & dest) const {
std::vector<Mat> layers(src.channels());
std::vector<Mat> outputs(src.channels());
@ -368,7 +363,7 @@ namespace cv{
merge(outputs,dest);
}
void inline TrackerKCFImpl::fft2(const Mat src, std::vector<Mat> & dest) const{
void inline TrackerKCFImpl::fft2(const Mat src, std::vector<Mat> & dest) const {
std::vector<Mat> layers(src.channels());
dest.clear();
dest.resize(src.channels());
@ -383,14 +378,14 @@ namespace cv{
/*
* simplification of inverse fourier transform function in opencv
*/
void inline TrackerKCFImpl::ifft2(const Mat src, Mat & dest)const {
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{
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());
@ -402,7 +397,7 @@ namespace cv{
/*
* Combines all channels in a multi-channels Mat data into a single channel
*/
void inline TrackerKCFImpl::sumChannels(std::vector<Mat> src, Mat & dest) const{
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];
@ -412,7 +407,7 @@ namespace cv{
/*
* 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{
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
@ -451,7 +446,7 @@ namespace cv{
/*
* compress the features
*/
void inline TrackerKCFImpl::compress(const Mat _proj_mtx, const Mat src, Mat & dest)const{
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();
@ -460,7 +455,7 @@ namespace cv{
/*
* obtain the patch and apply hann window filter to it
*/
bool TrackerKCFImpl::getSubWindow(const Mat img, const Rect _roi, Mat& patch) const{
bool TrackerKCFImpl::getSubWindow(const Mat img, const Rect _roi, Mat& patch) const {
Rect region=_roi;
@ -494,17 +489,17 @@ namespace cv{
// 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;
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;
CV_Assert(img.channels() == 3);
extractCN(patch,patch);
break;
case CN2:
if(patch.channels()>1)cvtColor(patch,patch, CV_BGR2GRAY);
break;
if(patch.channels()>1)cvtColor(patch,patch, CV_BGR2GRAY);
break;
}
patch=patch.mul(hann); // hann window filter
@ -523,13 +518,13 @@ namespace cv{
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];
}
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];
}
}
}
@ -539,7 +534,7 @@ namespace cv{
/*
* dense gauss kernel function
*/
void TrackerKCFImpl::denseGaussKernel(const double sigma, const Mat _x, const Mat _y, Mat & _k)const{
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;
@ -641,7 +636,7 @@ namespace cv{
/*
* calculate the detection response
*/
void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _k, Mat & _response)const {
void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _k, Mat & _response) const {
//alpha f--> 2channels ; k --> 1 channel;
Mat _kf;
fft2(_k,_kf);
@ -653,7 +648,7 @@ namespace cv{
/*
* calculate the detection response for splitted form
*/
void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _alphaf_den, const Mat _k, Mat & _response)const {
void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _alphaf_den, const Mat _k, Mat & _response) const {
Mat _kf;
fft2(_k,_kf);
Mat spec;

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