removed whitespaces

Author: sunitanyk

modules/alphamat/src/cm.cpp

@@ -84,21 +84,21 @@ void lle(my_vector_of_vectors_t& indm, my_vector_of_vectors_t& samples, float ep
 
   my_vector_of_vectors_t wcm;
   wcm.resize(n);
-  
+
   Mat C(20, 20, DataType<float>::type), rhs(20, 1, DataType<float>::type), Z(3, 20, DataType<float>::type), weights(20, 1, DataType<float>::type), pt(3, 1, DataType<float>::type);
   Mat ptDotN(20, 1, DataType<float>::type), imd(20, 1, DataType<float>::type);
     Mat Cones(20, 1, DataType<float>::type), Cinv(20, 1, DataType<float>::type);
   float alpha, beta, lagrangeMult;
   Cones += 1;
-  
+
   C = 0;
   rhs = 1;
-  
+
   int i, ind = 0;
   for(std::unordered_set<int>::iterator it = unk.begin(); it != unk.end(); it++){
       // filling values in Z
       i = *it;
-      
+
       int index_nbr;
       for(int j = 0; j < k; j++){
           index_nbr = indm[ind][j];
@@ -109,7 +109,7 @@ void lle(my_vector_of_vectors_t& indm, my_vector_of_vectors_t& samples, float ep
       pt.at<float>(0,0) = samples[i][0];
       pt.at<float>(1,0) = samples[i][1];
       pt.at<float>(2,0) = samples[i][2];
-      
+
       C = Z.t()*Z;
       for(int p=0; p<k; p++)
           C.at<float>(p,p) += eps;
@@ -126,7 +126,7 @@ void lle(my_vector_of_vectors_t& indm, my_vector_of_vectors_t& samples, float ep
       weights = weights / sum;
 
       int cMaj_i = findColMajorInd(i, img.rows, img.cols);
-      
+
       for(int j = 0; j < k; j++){
           int cMaj_ind_j = findColMajorInd(indm[ind][j], img.rows, img.cols);
           triplets.push_back(T(cMaj_i, cMaj_ind_j, weights.at<float>(j,0)));

modules/alphamat/src/intraU.cpp

@@ -16,7 +16,6 @@ int findColMajorInd(int rowMajorInd, int nRows, int nCols) {
   return (jInd*nRows+iInd);
 }
 
-      
 void generateFVectorIntraU(my_vector_of_vectors_t &samples, Mat &img, Mat& tmap)
 {
   int nRows = img.rows;
@@ -55,7 +54,7 @@ void kdtree_intraU(Mat &img, Mat& tmap, my_vector_of_vectors_t& indm, my_vector_
 {
   // Generate feature vectors for intra U:
   generateFVectorIntraU(samples, img, tmap);
-    
+
   typedef KDTreeVectorOfVectorsAdaptor< my_vector_of_vectors_t, double >  my_kd_tree_t;
   my_kd_tree_t mat_index(dim /*dim*/, samples, 10 /* max leaf */ );
   mat_index.index->buildIndex();
@@ -111,7 +110,7 @@ void intraU(Mat& img, my_vector_of_vectors_t& indm, my_vector_of_vectors_t& samp
         nbr_ind = indm[i][j];
         int cMaj_nbr_j = findColMajorInd(orig_ind[nbr_ind], img.rows, img.cols);
         weight = max(1-l1norm(samples[i], samples[j]), 0.0);
-        
+
         triplets.push_back(T(cMaj_i, cMaj_nbr_j, weight/2));
         td.push_back(T(cMaj_i, cMaj_i, weight/2));
 

modules/alphamat/src/local_info.cpp

@@ -36,7 +36,6 @@ void local_info(Mat& img, Mat& tmap, SparseMatrix<double>& Wl, SparseMatrix<doub
   int num_win = (win_size*2 + 1)*(win_size*2 + 1);  // number of pixels in window
   typedef Triplet<double> T;
     std::vector<T> triplets, td, tl;
-    
     int neighInd[9];
   int i, j;
   for (j = win_size; j < nCols-win_size; j++){
@@ -78,35 +77,33 @@ void local_info(Mat& img, Mat& tmap, SparseMatrix<double>& Wl, SparseMatrix<doub
       win_mean.at<double>(0, 0) = sum1/num_win;
       win_mean.at<double>(0, 1) = sum2/num_win;
       win_mean.at<double>(0, 2) = sum3/num_win;
-        
+
       // calculate the covariance matrix
       Mat covariance = (win.t() * win / num_win) - (win_mean.t() * win_mean);
-    
+
       Mat I = Mat::eye(img.channels(), img.channels(), CV_64F);
         Mat I1 = (covariance + (eps/num_win)*I);
       Mat I1_inv = I1.inv();
 
       Mat X = win - repeat(win_mean, num_win, 1);
       Mat vals = (1 + X * I1_inv * X.t()) / num_win;
-        
+
         for (int q = 0; q < num_win; q++){
             for (int p = 0; p < num_win; p++){
                 triplets.push_back(T(neighInd[p], neighInd[q], vals.at<double>(p, q)));
             }
         }
     }
   }
-    
+
     std::vector<T> tsp;
     SparseMatrix<double> W(N, N), Wsp(N,N);
     W.setFromTriplets(triplets.begin(), triplets.end());
 
-    int cntNZ = 0;
     SparseMatrix<double> Wt = W.transpose();
     SparseMatrix<double> Ws = Wt + W;
     W = Ws;
 
-    cntNZ = 0;
     for (int k=0; k<W.outerSize(); ++k) {
         double sumCol = 0;
         for (SparseMatrix<double>::InnerIterator it(W,k); it; ++it)
@@ -117,7 +114,7 @@ void local_info(Mat& img, Mat& tmap, SparseMatrix<double>& Wl, SparseMatrix<doub
         tsp.push_back(T(k, k, 1/sumCol));
     }
     Wsp.setFromTriplets(tsp.begin(), tsp.end());
-    
+
     Wl = Wsp * W; // For normalization
     //Wl = W; // No normalization
 

modules/alphamat/tutorials/alphamat_tutorial.markdown

@@ -27,13 +27,13 @@ cmake -DOPENCV_EXTRA_MODULES_PATH=<path to opencv_contrib modules> -Dopencv_alph
 
 make
 ```
-Please refer to OpenCV building tutorials for further details, if needed. The following target needs to be build for testing the sample code below: example_alphamat_information_flow_matting 
+Please refer to OpenCV building tutorials for further details, if needed. The following target needs to be build for testing the sample code below: example_alphamat_information_flow_matting
 
 # Testing
 
 The built target can be tested as follows:
 ```
-example_alphamat_information_flow_matting -img=<path to input image file> -tri=<path to the corresponding trimap> -out=<path to save output matte file> 
+example_alphamat_information_flow_matting -img=<path to input image file> -tri=<path to the corresponding trimap> -out=<path to save output matte file>
 ```
 # Source Code of the sample
 
@@ -51,4 +51,3 @@ example_alphamat_information_flow_matting -img=<path to input image file> -tri=<
 [4] Qifeng Chen, Dingzeyu Li, Chi-Keung Tang, "[KNN Matting](http://dingzeyu.li/files/knn-matting-tpami.pdf)", IEEE TPAMI, 2013.
 
 [5] Yagiz Aksoy, "[Affinity Based Matting Toolbox](https://github.com/yaksoy/AffinityBasedMattingToolbox)".
-