Remove whitespaces

modules/tracking/doc/tracking.bib

@@ -83,13 +83,13 @@
   year = {2012},
 }
 
-@INPROCEEDINGS{KCF_CN, 
-  author={Danelljan, M. and Khan, F.S. and Felsberg, M. and van de Weijer, J.}, 
-  booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on}, 
-  title={Adaptive Color Attributes for Real-Time Visual Tracking}, 
-  year={2014}, 
-  month={June}, 
-  pages={1090-1097}, 
-  keywords={computer vision;feature extraction;image colour analysis;image representation;image sequences;adaptive color attributes;benchmark color sequences;color features;color representations;computer vision;image description;real-time visual tracking;tracking-by-detection framework;Color;Computational modeling;Covariance matrices;Image color analysis;Kernel;Target tracking;Visualization;Adaptive Dimensionality Reduction;Appearance Model;Color Features;Visual Tracking}, 
+@INPROCEEDINGS{KCF_CN,
+  author={Danelljan, M. and Khan, F.S. and Felsberg, M. and van de Weijer, J.},
+  booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
+  title={Adaptive Color Attributes for Real-Time Visual Tracking},
+  year={2014},
+  month={June},
+  pages={1090-1097},
+  keywords={computer vision;feature extraction;image colour analysis;image representation;image sequences;adaptive color attributes;benchmark color sequences;color features;color representations;computer vision;image description;real-time visual tracking;tracking-by-detection framework;Color;Computational modeling;Covariance matrices;Image color analysis;Kernel;Target tracking;Visualization;Adaptive Dimensionality Reduction;Appearance Model;Color Features;Visual Tracking},
   doi={10.1109/CVPR.2014.143},
 }

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -1191,8 +1191,8 @@ class CV_EXPORTS_W TrackerTLD : public Tracker
 
 /** @brief KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed.
  * This tracking method is an implementation of @cite KCF_ECCV which is extended to KFC with color-names features (@cite KCF_CN).
- * The original paper of KCF is available at <http://home.isr.uc.pt/~henriques/circulant/index.html> 
- * as well as the matlab implementation. For more information about KCF with color-names features, please refer to 
+ * The original paper of KCF is available at <http://home.isr.uc.pt/~henriques/circulant/index.html>
+ * as well as the matlab implementation. For more information about KCF with color-names features, please refer to
  * <http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html>.
  */
 class CV_EXPORTS_W TrackerKCF : public Tracker
@@ -1202,21 +1202,21 @@ class CV_EXPORTS_W TrackerKCF : public Tracker
   struct CV_EXPORTS Params
   {
     Params();
-    
+
     /**
      * \brief Read parameters from file, currently unused
      */
     void read( const FileNode& /*fn*/ );
-    
+
     /**
      * \brief Read parameters from file, currently unused
      */
     void write( FileStorage& /*fs*/ ) const;
-    
+
     double sigma;                 //!<  gaussian kernel bandwidth
     double lambda;                //!<  regularization
     double interp_factor;         //!<  linear interpolation factor for adaptation
-    double output_sigma_factor;   //!<  spatial bandwidth (proportional to target) 
+    double output_sigma_factor;   //!<  spatial bandwidth (proportional to target)
     double pca_learning_rate;     //!<  compression learning rate
     bool resize;                  //!<  activate the resize feature to improve the processing speed
     bool splitCoeff;              //!<  split the training coefficients into two matrices

modules/tracking/src/trackerKCF.cpp

@@ -65,7 +65,7 @@ namespace cv{
 |  TrackerKCF
 |---------------------------*/
 namespace cv{
-  
+
   /*
  * Prototype
  */
@@ -74,23 +74,23 @@ namespace cv{
     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 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;
@@ -100,16 +100,16 @@ namespace cv{
     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) 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
@@ -119,12 +119,12 @@ namespace cv{
     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
  */
@@ -136,10 +136,10 @@ namespace cv{
   {
     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 );
   }
@@ -147,9 +147,9 @@ namespace cv{
   void TrackerKCFImpl::write( cv::FileStorage& fs ) const {
     params.write( fs );
   }
-  
+
   /*
-   * Initialization: 
+   * Initialization:
    * - creating hann window filter
    * - ROI padding
    * - creating a gaussian response for the training ground-truth
@@ -158,11 +158,11 @@ namespace cv{
   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;
@@ -170,21 +170,21 @@ namespace cv{
       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); 
+      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++){
@@ -192,19 +192,19 @@ namespace cv{
         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
    */
@@ -212,17 +212,17 @@ namespace cv{
     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
@@ -232,52 +232,52 @@ namespace cv{
         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; 
-    
+      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); 
+     */
+    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);
@@ -290,7 +290,7 @@ namespace cv{
         }
       }
     }
-    
+
     // update the RLS model
     if(frame==0){
       alphaf=new_alphaf.clone();
@@ -299,17 +299,17 @@ namespace cv{
       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 {
@@ -346,30 +346,30 @@ namespace cv{
       // 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);
     }
@@ -381,19 +381,19 @@ namespace cv{
   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
    */
@@ -403,18 +403,18 @@ namespace cv{
       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];
@@ -424,25 +424,25 @@ namespace cv{
     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();    
+    _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
    */
@@ -451,22 +451,22 @@ namespace cv{
     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 
+
+    // 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;
@@ -475,7 +475,7 @@ namespace cv{
     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;
@@ -485,7 +485,7 @@ namespace cv{
 
     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:
@@ -501,21 +501,21 @@ namespace cv{
         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);
@@ -527,10 +527,10 @@ namespace cv{
         }
       }
     }
-    
+
     cnFeatures=temp.clone();
   }
-  
+
   /*
    *  dense gauss kernel function
    */
@@ -538,24 +538,24 @@ namespace cv{
     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());
 
@@ -566,13 +566,13 @@ namespace cv{
         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
    */
@@ -601,7 +601,7 @@ namespace cv{
           shiftRows(mat);
         }
         flip(mat,mat,0);
-      }else{ 
+      }else{
         for(int _k=0; _k < n;_k++) {
           shiftRows(mat);
         }
@@ -628,14 +628,14 @@ namespace cv{
    * calculate the detection response
    */
   void TrackerKCFImpl::calcResponse(const Mat _alphaf, const Mat _k, Mat & _response) const {
-    //alpha f--> 2channels ; k --> 1 channel; 
+    //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
    */
@@ -643,11 +643,11 @@ namespace cv{
     Mat _kf;
     fft2(_k,_kf);
     Mat spec;
-    Mat spec2=Mat_<Vec2d >(_k.rows, _k.cols); 
+    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)*/);
@@ -655,11 +655,11 @@ namespace cv{
         spec2.at<Vec2d >(i,j)[1]=temp.imag();
       }
     }
-      
+
     ifft2(spec2,_response);
   }
   /*----------------------------------------------------------------------*/
-  
+
   /*
  * Parameters
  */
@@ -673,7 +673,7 @@ namespace cv{
       descriptor=CN;
       splitCoeff=true;
       wrapKernel=false;
-      
+
       //feature compression
       compressFeature=true;
       compressed_size=2;
@@ -683,5 +683,5 @@ namespace cv{
   void TrackerKCF::Params::read( const cv::FileNode& /*fn*/ ){}
 
   void TrackerKCF::Params::write( cv::FileStorage& /*fs*/ ) const{}
-  
+
 } /* namespace cv */