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@@ -5,6 +5,7 @@
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#include "vislearning/cbaselib/CachedExample.h"
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#include "vislearning/cbaselib/PascalResults.h"
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+#include "vislearning/baselib/ColorSpace.h"
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#include "objrec/segmentation/RSMeanShift.h"
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#include "objrec/segmentation/RSGraphBased.h"
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#include "core/basics/numerictools.h"
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@@ -717,7 +718,7 @@ double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<dou
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if ( featcounter < minFeats )
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{
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- cout << "only " << featcounter << " feats in current node -> it's a leaf" << endl;
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+ //cout << "only " << featcounter << " feats in current node -> it's a leaf" << endl;
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return 0.0;
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}
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@@ -784,7 +785,7 @@ double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<dou
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if ( globent < 0.5 )
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{
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- cout << "globent to small: " << globent << endl;
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+ //cout << "globent to small: " << globent << endl;
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return 0.0;
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}
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@@ -1092,7 +1093,8 @@ void SemSegContextTree::train( const MultiDataset *md )
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int imgcounter = 0;
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- /*MultiChannelImageT<int> ttmp2(0,0,0);
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+ /*
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+ MultiChannelImageT<int> ttmp2(0,0,0);
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MultiChannelImageT<double> ttmp1(100,100,1);
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MultiChannelImageT<double> tint(100,100,1);
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ttmp1.setAll(1.0);
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@@ -1178,6 +1180,7 @@ void SemSegContextTree::train( const MultiDataset *md )
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}
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}
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+ allfeats[imgcounter] = ColorSpace::rgbtolab(allfeats[imgcounter]);
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#endif
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// getting groundtruth
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@@ -1546,7 +1549,8 @@ void SemSegContextTree::semanticseg( CachedExample *ce, NICE::Image & segresult,
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}
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}
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}
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-
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+
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+ feats = ColorSpace::rgbtolab(feats);
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#endif
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bool allleaf = false;
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