fasthik anpassungen

semseg/SemSegCsurka.cpp

@@ -13,6 +13,7 @@ using namespace NICE;
 using namespace OBJREC;
 
 #undef DEBUG_CSURK
+#define UNCERTAINTY
 
 SemSegCsurka::SemSegCsurka ( const Config *conf,
                              const MultiDataset *md )
@@ -570,9 +571,6 @@ void SemSegCsurka::doPCA ( Examples &ex )
   cout << "converting features using pca finished" << endl;
 }
 
-
-
-
 void SemSegCsurka::train ( const MultiDataset *md )
 {
 
@@ -1484,6 +1482,11 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
       }
     }
   }
+  
+#ifdef UNCERTAINTY
+  FloatImage uncert(xsize, ysize);
+#endif
+  
   if ( classifier != NULL )
   {
     clog << "[log] SemSegCsruka::classifyregions: Wahrscheinlichkeitskarten erstellen: classifier != NULL" << endl;
@@ -1502,6 +1505,11 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
           fV[j] += r.scores[j];
           preMap.set ( pce[i].second.x, pce[i].second.y, r.scores[j], j + s*klassen );
         }
+        
+#ifdef UNCERTAINTY
+        uncert(pce[i].second.x, pce[i].second.y) = r.uncertainty;
+#endif
+        
         /*if(s == 0 && i == pce.size()/2)
         {
           cout << "scores: ";
@@ -1533,10 +1541,13 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
           fV[j] += r.scores[j];
           preMap.set ( pce[i].second.x, pce[i].second.y, r.scores[j], j + s*klassen );
         }
+#ifdef UNCERTAINTY
+        uncert(pce[i].second.x, pce[i].second.y) = r.uncertainty;
+#endif
       }
     }
   }
-  cout << 1 << endl;
+  
   vector<double> scalesVec;
   for ( set<double>::const_iterator iter = scales.begin();
         iter != scales.end();
@@ -1544,7 +1555,6 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
   {
     scalesVec.push_back ( *iter );
   }
-  cout << 2 << endl;
 
 #undef VISSEMSEG
 #ifdef VISSEMSEG
@@ -1629,6 +1639,12 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
     }
 
   }
+  
+#ifdef UNCERTAINTY
+  double sigma = sigmaweight * 16.0 * 4.0;
+  FloatImage gaussUncert(xsize,ysize);
+  filterGaussSigmaApproximate<float, float, float> ( uncert, sigma, &gaussUncert );
+#endif
 
 
   // Zusammenfassen und auswerten
@@ -1713,10 +1729,12 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
 
     int regionsize = seg->segRegions ( img, mask );
 
-
-
     Regionen.clear();
     vector<vector <double> > regionprob;
+    
+#ifdef UNCERTAINTY
+    vector<double> regionUncert;
+#endif
 
     // Wahrscheinlichkeiten für jede Region initialisieren
     for ( int i = 0; i < regionsize; i++ )
@@ -1728,6 +1746,9 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
       }
       regionprob.push_back ( tmp );
       Regionen.push_back ( pair<int, Example> ( 0, Example() ) );
+#ifdef UNCERTAINTY
+      regionUncert.push_back(0.0);
+#endif
     }
 
     // Wahrscheinlichkeiten für Regionen bestimmen
@@ -1735,15 +1756,18 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
     {
       for ( int y = 0; y < ysize; y++ )
       {
+        int pos = mask ( x, y );
+        Regionen[pos].second.weight += 1.0;
+        Regionen[pos].second.x += x;
+        Regionen[pos].second.y += y;
         for ( int j = 0 ; j < ( int ) probabilities.numChannels; j++ )
         {
           double val = probabilities.get ( x, y, j );
-          int pos = mask ( x, y );
-          Regionen[pos].second.weight += 1.0;
-          Regionen[pos].second.x += x;
-          Regionen[pos].second.y += y;
           regionprob[pos][j] += val;
         }
+#ifdef UNCERTAINTY
+        regionUncert[pos] += gaussUncert(x,y);
+#endif
       }
     }
 
@@ -1789,6 +1813,9 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
       }
 
       Regionen[i].first = maxpos;
+#ifdef UNCERTAINTY
+      regionUncert[i] /= Regionen[i].second.weight;
+#endif
     }
 
     // Pixel jeder Region labeln
@@ -1798,8 +1825,22 @@ void SemSegCsurka::classifyregions ( CachedExample *ce, NICE::Image & segresult,
       {
         int pos = mask ( x, y );
         segresult.setPixel ( x, y, Regionen[pos].first );
+#ifdef UNCERTAINTY
+        uncert(x,y) = regionUncert[pos];
+#endif
       }
     }
+#ifdef UNCERTAINTY
+    std::string s;
+    std::stringstream out;
+    std::vector< std::string > list;
+    StringTools::split ( Globals::getCurrentImgFN (), '/', list );
+    out << "uncertainty/" << list.back();
+    ColorImage imgrgb(xsize,ysize);
+    ICETools::convertToRGB ( uncert, imgrgb );
+    imgrgb.write(out.str());
+#endif
+    
 #undef WRITEREGIONS
 #ifdef WRITEREGIONS
     RegionGraph rg;
@@ -1904,5 +1945,3 @@ void SemSegCsurka::semanticseg ( CachedExample *ce, NICE::Image & segresult, NIC
 #endif
 
 }
-
-