write globfeats

semseg/SemSegContextTree.cpp

@@ -76,6 +76,7 @@ class SparseImageAcess: public ValueAccess
     {
       //MultiChannelImageT<SparseVectorInt> textonMap;
       //TODO: implement access
+      return -1.0;
     }
 
     virtual string writeInfos()
@@ -1732,12 +1733,12 @@ void SemSegContextTree::train ( const MultiDataset *md )
 #ifdef WRITEGLOB
   ofstream outstream("globtrain.feat");
   
-  for(uint i = 0; i < textonMap.size(); i++)
+  for(int i = 0; i < textonMap.size(); i++)
   {
     set<int> usedclasses;
-    for ( int x = 0; x < labels[i].rows(); x++ )
+    for ( uint x = 0; x < labels[i].rows(); x++ )
     {
-      for ( int y = 0; y < labels[i].cols(); y++ )
+      for ( uint y = 0; y < labels[i].cols(); y++ )
       {
         int classno = labels[i] ( x, y );
 
@@ -1759,7 +1760,6 @@ void SemSegContextTree::train ( const MultiDataset *md )
   }
   
   outstream.close();
-  
 #endif
     cout << "uniquenumber " << uniquenumber << endl;
     //getchar();
@@ -1767,7 +1767,7 @@ void SemSegContextTree::train ( const MultiDataset *md )
   for ( int tree = 0; tree < nbTrees; tree++ )
   {
     int t = ( int ) forest[tree].size();
-
+    
     for ( int i = 0; i < t; i++ )
     {
       printf ( "tree[%i]: left: %i, right: %i", i, forest[tree][i].left, forest[tree][i].right );
@@ -1956,6 +1956,42 @@ void SemSegContextTree::semanticseg ( CachedExample *ce, NICE::Image & segresult
 
   cout << forest[0][currentfeats.get ( xpos, ypos, 0 ) ].dist << endl;
 
+#ifdef WRITEGLOB
+  ofstream outstream("globtest.feat",ofstream::app);
+  outstream << 0 << endl;
+  integralTexton(integralTexton.width()-1, integralTexton.height()-1).store(outstream);
+  outstream.close();
+#endif
+  
+  string cndir = conf->gS ( "SSContextTree", "cndir", "" );
+  int classes = ( int ) probabilities.numChannels;
+  vector<int> useclass ( classes, 1 );
+
+  std::vector< std::string > list;
+  StringTools::split ( currentFile, '/', list );
+
+  string orgname = list.back();
+  if ( cndir != "" )
+  {
+    useclass = vector<int> ( classes, 0 );
+    ifstream infile ( ( cndir + "/" + orgname + ".dat" ).c_str() );
+    while ( !infile.eof() && infile.good() )
+    {
+      int tmp;
+      infile >> tmp;
+      assert(tmp >= 0 && tmp < classes);
+      useclass[tmp] = 1;
+    }
+    
+    for(int c = 0; c < classes; c++)
+    {
+      if(useclass[c] == 0)
+      {
+        probabilities.set(-numeric_limits< double >::max(), c);
+      }
+    }
+  }
+  
   if ( pixelWiseLabeling )
   {
     //finales labeln:
@@ -1971,11 +2007,12 @@ void SemSegContextTree::semanticseg ( CachedExample *ce, NICE::Image & segresult
 
         for ( uint i = 0; i < s; i++ )
         {
-          probabilities.data[labelmapback[i]][offset] = getMeanProb ( x, y, i, currentfeats );
+          int currentclass = labelmapback[i];
+          probabilities.data[currentclass][offset] = getMeanProb ( x, y, i, currentfeats );
 
-          if ( probabilities.data[labelmapback[i]][offset] > maxvalue )
+          if ( probabilities.data[currentclass][offset] > maxvalue )
           {
-            maxvalue = probabilities.data[labelmapback[i]][offset];
+            maxvalue = probabilities.data[currentclass][offset];
             maxindex = labelmapback[i];
           }
 
@@ -2158,7 +2195,7 @@ void SemSegContextTree::restore ( std::istream & is, int format )
       forest[t][n].feat = NULL;
       if ( feattype >= 0 )
       {
-        for ( int o = 0; o < ops.size(); o++ )
+        for ( uint o = 0; o < ops.size(); o++ )
         {
           if ( ops[o]->getOps() == feattype )
           {
@@ -2169,7 +2206,7 @@ void SemSegContextTree::restore ( std::istream & is, int format )
 
         if ( forest[t][n].feat == NULL )
         {
-          for ( int o = 0; o < cops.size(); o++ )
+          for ( uint o = 0; o < cops.size(); o++ )
           {
             if ( cops[o]->getOps() == feattype )
             {

semseg/SemSegCsurka.cpp

@@ -1843,11 +1843,8 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
   getFeats = NULL;
 }
 
-void SemSegCsurka::semanticseg ( CachedExample *ce,
-                                 NICE::Image & segresult,
-                                 NICE::MultiChannelImageT<double> & probabilities )
+void SemSegCsurka::semanticseg ( CachedExample *ce, NICE::Image & segresult, NICE::MultiChannelImageT<double> & probabilities )
 {
-
   Examples regions;
   NICE::Matrix regionmask;
   classifyregions ( ce, segresult, probabilities, regions, regionmask );