diff --git a/Code/Learning/otbSVMClassifier.txx b/Code/Learning/otbSVMClassifier.txx
index 2e534e74ed04acddf7959fdfa4f5ab22e68c81cf..fe8e2bf773ae77fc6762484a2e20667ceeb26bf2 100644
--- a/Code/Learning/otbSVMClassifier.txx
+++ b/Code/Learning/otbSVMClassifier.txx
@@ -180,6 +180,7 @@ SVMClassifier< TSample, TLabel >
typename OutputType::ConstIterator endO = m_Output->End() ;
typename TSample::MeasurementVectorType measurements ;
+
int numberOfComponentsPerSample = iter.GetMeasurementVector().Size() ;//this->GetSample().GetMeasurementVectorSize();//
int max_line_len = 1024;
@@ -187,13 +188,16 @@ SVMClassifier< TSample, TLabel >
int max_nr_attr = 64;
bool predict_probability = 1;
+
const struct svm_model* model = m_Model->GetModel();
// char* line = (char *) malloc(max_line_len*sizeof(char));
// x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct
// svm_node));
- m_Model->AllocateProblem(1, numberOfComponentsPerSample);
- x = m_Model->GetXSpace();
+
+/* m_Model->AllocateProblem(1, numberOfComponentsPerSample);*/
+
+ x = new svm_node[numberOfComponentsPerSample+1];//m_Model->GetXSpace();
//std::cout << "XSpace Allocated" << std::endl;
if(svm_check_probability_model(model)==0)
@@ -208,11 +212,14 @@ SVMClassifier< TSample, TLabel >
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
-
+
+
int svm_type=svm_get_svm_type(model);
//std::cout << "SVM Type = " << svm_type << std::endl;
+
int nr_class=svm_get_nr_class(model);
//std::cout << "SVM nr_class = " << nr_class << std::endl;
+
int *labels=(int *) malloc(nr_class*sizeof(int));
double *prob_estimates=NULL;
int j;
@@ -223,17 +230,19 @@ SVMClassifier< TSample, TLabel >
printf("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));
else
{
+
svm_get_labels(model,labels);
+
prob_estimates = (double *) malloc(nr_class*sizeof(double));
+ }
/*fprintf(output,"labels");
for(j=0;j<nr_class;j++)
fprintf(output," %d",labels[j]);
fprintf(output,"\n");*/
- }
}
// while(1)
- //std::cout << "Starting iterations " << std::endl;
+// std::cout << "Starting iterations " << std::endl;
while (iter != end && iterO != endO)
{
@@ -329,7 +338,8 @@ if(predict_probability)
//std::cout << "End of iterations and free" << std::endl;
// free(x);
-
+
+delete [] x;
}
} // end of namespace itk
diff --git a/Code/Learning/otbSVMModel.h b/Code/Learning/otbSVMModel.h
index ea37fc27ae5a519c62cc3875496604ac33576c8c..d4fc25084be21eb6af02a9d54b6cbf71c202719a 100644
--- a/Code/Learning/otbSVMModel.h
+++ b/Code/Learning/otbSVMModel.h
@@ -145,6 +145,7 @@ public:
/** Allocates the problem */
void AllocateProblem(int l, long int elements);
+
/** Sets the model */
void SetModel(struct svm_model* aModel);
diff --git a/Examples/Data/ROI_mask_multi.png b/Examples/Data/ROI_mask_multi.png
index 7322cfc2294637951d68ae78986acd066f814175..3a3de297566e508c8eb9162bd71cc70724c242e1 100644
Binary files a/Examples/Data/ROI_mask_multi.png and b/Examples/Data/ROI_mask_multi.png differ
diff --git a/Examples/Learning/GenerateTrainingImageExample.cxx b/Examples/Learning/GenerateTrainingImageExample.cxx
index 525c497cf9ff22266f1a00bade4e95e16c9388e8..81175718553de4da93a19b6bc69769dbb1e932ac 100644
--- a/Examples/Learning/GenerateTrainingImageExample.cxx
+++ b/Examples/Learning/GenerateTrainingImageExample.cxx
@@ -116,14 +116,15 @@ int main( int argc, char ** argv )
--nbRois;
OutputPixelType label = 0;
- unsigned long xUL, yUL, xBR, yBR = 0;
+ unsigned long xUL, yUL, xBR, yBR, tmp_label = 0;
- roisFile >> label;
+ roisFile >> tmp_label;
roisFile >> xUL;
roisFile >> yUL;
roisFile >> xBR;
roisFile >> yBR;
+ label = static_cast<OutputPixelType>(tmp_label);
std::cout << "Label : " << int(label) << std::endl;
std::cout << "( " << xUL << " , " << yUL << " )" << std::endl;
@@ -151,6 +152,7 @@ int main( int argc, char ** argv )
while(!it.IsAtEnd())
{
+
it.Set(static_cast<OutputPixelType>(label));
//std::cout << static_cast<OutputPixelType>(label) << " -- ";
diff --git a/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx b/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx
index 9384a997010e756c1f4649de2143cc6993b3157f..268dce5913f8a5e7805662dc49eae5ee1cffcce6 100644
--- a/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx
+++ b/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx
@@ -152,6 +152,7 @@ int main( int argc, char* argv[] )
// Software Guide : BeginCodeSnippet
svmEstimator->SaveModel(outputModelFileName);
+
// Software Guide : EndCodeSnippet
@@ -176,11 +177,9 @@ int main( int argc, char* argv[] )
ClassifyReaderType::Pointer cReader = ClassifyReaderType::New();
-
cReader->SetFileName( inputImageFileName );
cReader->Update();
-
// Software Guide : BeginLatex
//
@@ -197,7 +196,6 @@ int main( int argc, char* argv[] )
typedef itk::Statistics::ImageToListAdaptor< ClassifyImageType > SampleType;
SampleType::Pointer sample = SampleType::New();
-
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
@@ -211,7 +209,6 @@ int main( int argc, char* argv[] )
sample->SetImage(cReader->GetOutput());
-
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
@@ -230,7 +227,7 @@ int main( int argc, char* argv[] )
typedef otb::SVMModel< InputPixelType, LabelPixelType > ModelType;
ModelType::Pointer model = svmEstimator->GetModel();
-
+ //model->LoadModel(outputModelFileName);
// Software Guide : EndCodeSnippet
@@ -260,13 +257,17 @@ int main( int argc, char* argv[] )
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
-
- int numberOfClasses = model->GetNumberOfClasses();
+ std::cout << "GNC" << std::endl;
+ int numberOfClasses = model->GetNumberOfClasses();
+ std::cout << "SNC = "<< numberOfClasses << std::endl;
classifier->SetNumberOfClasses(numberOfClasses) ;
+ std::cout << "SM" << std::endl;
classifier->SetModel( model );
+ std::cout << "SS" << std::endl;
classifier->SetSample(sample.GetPointer()) ;
+ std::cout << "Up" << std::endl;
classifier->Update() ;
-
+ std::cout << "---" << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
@@ -288,7 +289,7 @@ int main( int argc, char* argv[] )
typedef otb::Image< OutputPixelType, Dimension > OutputImageType;
OutputImageType::Pointer outputImage = OutputImageType::New();
-
+ std::cout << "---" << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
@@ -320,7 +321,7 @@ int main( int argc, char* argv[] )
outputImage->SetRegions( region );
outputImage->Allocate();
-
+ std::cout << "---" << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
@@ -360,14 +361,14 @@ int main( int argc, char* argv[] )
// Software Guide : BeginCodeSnippet
-
+ std::cout << "---" << std::endl;
while (m_iter != m_last && !outIt.IsAtEnd())
{
outIt.Set(m_iter.GetClassLabel());
++m_iter ;
++outIt;
}
-
+ std::cout << "---" << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
@@ -388,12 +389,12 @@ int main( int argc, char* argv[] )
FileImageType > RescalerType;
RescalerType::Pointer rescaler = RescalerType::New();
-
+ std::cout << "---" << std::endl;
rescaler->SetOutputMinimum( itk::NumericTraits< unsigned char >::min());
rescaler->SetOutputMaximum( itk::NumericTraits< unsigned char >::max());
rescaler->SetInput( outputImage );
-
+ std::cout << "---" << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex