current version

semseg/SemSegContextTree.cpp

@@ -428,10 +428,6 @@ double SemSegContextTree::getBestSplit ( std::vector<NICE::MultiChannelImageT<do
 
 #pragma omp critical
     {
-      //cout << "globent: " << globent <<  " bestig " << bestig << " splitfeat: " << splitfeat << " splitval: " << splitval << endl;
-      //cout << "globent: " << globent <<  " l_bestig " << l_bestig << " f: " << p << " l_splitval: " << l_splitval << endl;
-      //cout << "p: " << featsubset[f] << endl;
-
       if ( l_bestig > bestig )
       {
         bestig = l_bestig;
@@ -441,9 +437,6 @@ double SemSegContextTree::getBestSplit ( std::vector<NICE::MultiChannelImageT<do
     }
   }
 
-  //getchar();
-  //splitop->writeInfos();
-  //cout<< "ig: " << bestig << endl;
   //FIXME: delete all features!
   /*for(int i = 0; i < featsPerSplit; i++)
   {

semseg/SemSegCsurka.cpp

@@ -1513,45 +1513,33 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
         {
           if ( useclass[j] == 0 )
             continue;
+
           fV[j] += r.scores[j];
           preMap.set ( pce[i].second.x, pce[i].second.y, r.scores[j], j + s*klassen );
         }
 
-        if(r.uncertainty < 0.0)
+        /*if(r.uncertainty < 0.0)
         {
           cerr << "uncertainty: " << r.uncertainty << endl;
           pce[i].second.svec->store(cerr);
           cerr << endl;
           exit(-1);
-        }
+        }*/
 #ifdef UNCERTAINTY
         uncert[s] ( pce[i].second.x, pce[i].second.y ) = r.uncertainty;
         maxu = std::max ( r.uncertainty, maxu );
         minu = std::min ( r.uncertainty, minu );
 #endif
-
-        /*if(s == 0 && i == pce.size()/2)
-        {
-          cout << "scores: ";
-          for ( int j = 0 ; j < r.scores.size(); j++ )
-          {
-            if ( useclass[j] == 0 )
-              continue;
-            cout << r.scores[j] << " ";
-          }
-          cout << endl;
-        }*/
-
       }
     }
   }
   else
   {
     
-//#pragma omp parallel for
+#pragma omp parallel for
     for ( int s = 0; s < scalesize; s++ )
     {
-//#pragma omp parallel for
+#pragma omp parallel for
       for ( int i = s; i < ( int ) pce.size(); i += scalesize )
       {
         ClassificationResult r = vclassifier->classify ( * ( pce[i].second.vec ) );
@@ -1627,11 +1615,13 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
   {
     double sigma = sigmaweight * 16.0 * scalesVec[s];
     cerr << "sigma: " << sigma << endl;
-//#pragma omp parallel for
+#pragma omp parallel for
     for ( int i = 0; i < klassen; i++ )
     {
       if ( forbidden_classes.find ( i ) != forbidden_classes.end() )
+      {
         continue;
+      }
 
       int pos = i + s * klassen;