diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
index c71885ded4440670f0dc511e104629f14581c7f5..3a7accaf5f0e1890a522c1a0c97de5a2d33e714c 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
@@ -29,11 +29,11 @@ namespace otb
namespace Wrapper
{
-class TrainVectorClassifier : public TrainVectorBase
+class TrainVectorClassifier : public TrainVectorBase<float, int>
{
public:
typedef TrainVectorClassifier Self;
- typedef TrainVectorBase Superclass;
+ typedef TrainVectorBase<float, int> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro( Self )
diff --git a/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx
index c94cd63262905f2d5e8cb9da59ceb234a6292e5f..91ec818be1321ec783381b3d0dad31e461ca099c 100644
--- a/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainVectorRegression.cxx
@@ -25,11 +25,11 @@ namespace otb
namespace Wrapper
{
-class TrainVectorRegression : public TrainVectorBase
+class TrainVectorRegression : public TrainVectorBase<float, int>
{
public:
typedef TrainVectorRegression Self;
- typedef TrainVectorBase Superclass;
+ typedef TrainVectorBase<float, int> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
@@ -83,6 +83,13 @@ protected:
}
Superclass::DoExecute();
+
+ /*
+ std::cout << m_PredictedList << std::endl;
+ std::cout << m_ClassificationSamplesWithLabel.labeledListSample << std::endl;
+ */
+
+
}
private:
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
index 37869068157660e37b5063fd69303be97023df2f..477b798d58b550cba0d80347042c57a68cd01a2d 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.h
@@ -49,21 +49,22 @@ bool IsNotAlphaNum(char c)
return !std::isalnum( c );
}
-class TrainVectorBase : public LearningApplicationBase<float, int>
+template <class TInputValue, class TOutputValue>
+class TrainVectorBase : public LearningApplicationBase<TInputValue, TOutputValue>
{
public:
/** Standard class typedefs. */
typedef TrainVectorBase Self;
- typedef LearningApplicationBase<float, int> Superclass;
+ typedef LearningApplicationBase<TInputValue, TOutputValue> Superclass;
typedef itk::SmartPointer <Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Standard macro */
itkTypeMacro(Self, Superclass);
- typedef Superclass::SampleType SampleType;
- typedef Superclass::ListSampleType ListSampleType;
- typedef Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::SampleType SampleType;
+ typedef typename Superclass::ListSampleType ListSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
typedef double ValueType;
typedef itk::VariableLengthVector <ValueType> MeasurementType;
@@ -86,8 +87,8 @@ protected:
class SamplesWithLabel
{
public:
- ListSampleType::Pointer listSample;
- TargetListSampleType::Pointer labeledListSample;
+ typename ListSampleType::Pointer listSample;
+ typename TargetListSampleType::Pointer labeledListSample;
SamplesWithLabel()
{
listSample = ListSampleType::New();
@@ -178,7 +179,7 @@ protected:
SamplesWithLabel m_TrainingSamplesWithLabel;
SamplesWithLabel m_ClassificationSamplesWithLabel;
- TargetListSampleType::Pointer m_PredictedList;
+ typename TargetListSampleType::Pointer m_PredictedList;
FeaturesInfo m_FeaturesInfo;
void DoInit() override;
diff --git a/Modules/Applications/AppClassification/include/otbTrainVectorBase.hxx b/Modules/Applications/AppClassification/include/otbTrainVectorBase.hxx
index fb5e582dae4dc6073b77a5a2f1f2659831e1b3e9..447608d0c58057cf73592c8023462ae78e4c6cc0 100644
--- a/Modules/Applications/AppClassification/include/otbTrainVectorBase.hxx
+++ b/Modules/Applications/AppClassification/include/otbTrainVectorBase.hxx
@@ -27,100 +27,106 @@ namespace otb
namespace Wrapper
{
-void TrainVectorBase::DoInit()
+template <class TInputValue, class TOutputValue>
+void
+TrainVectorBase<TInputValue, TOutputValue>
+::DoInit()
{
// Common Parameters for all Learning Application
- AddParameter( ParameterType_Group, "io", "Input and output data" );
- SetParameterDescription( "io",
+ this->AddParameter( ParameterType_Group, "io", "Input and output data" );
+ this->SetParameterDescription( "io",
"This group of parameters allows setting input and output data." );
- AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data" );
- SetParameterDescription( "io.vd",
+ this->AddParameter( ParameterType_InputVectorDataList, "io.vd", "Input Vector Data" );
+ this->SetParameterDescription( "io.vd",
"Input geometries used for training (note: all geometries from the layer will be used)" );
- AddParameter( ParameterType_InputFilename, "io.stats", "Input XML image statistics file" );
- MandatoryOff( "io.stats" );
- SetParameterDescription( "io.stats",
+ this->AddParameter( ParameterType_InputFilename, "io.stats", "Input XML image statistics file" );
+ this->MandatoryOff( "io.stats" );
+ this->SetParameterDescription( "io.stats",
"XML file containing mean and variance of each feature." );
- AddParameter( ParameterType_OutputFilename, "io.out", "Output model" );
- SetParameterDescription( "io.out",
+ this->AddParameter( ParameterType_OutputFilename, "io.out", "Output model" );
+ this->SetParameterDescription( "io.out",
"Output file containing the model estimated (.txt format)." );
- AddParameter( ParameterType_Int, "layer", "Layer Index" );
- SetParameterDescription( "layer",
+ this->AddParameter( ParameterType_Int, "layer", "Layer Index" );
+ this->SetParameterDescription( "layer",
"Index of the layer to use in the input vector file." );
- MandatoryOff( "layer" );
- SetDefaultParameterInt( "layer", 0 );
+ this->MandatoryOff( "layer" );
+ this->SetDefaultParameterInt( "layer", 0 );
- AddParameter(ParameterType_ListView, "feat", "Field names for training features");
- SetParameterDescription("feat",
+ this->AddParameter(ParameterType_ListView, "feat", "Field names for training features");
+ this->SetParameterDescription("feat",
"List of field names in the input vector data to be used as features for training.");
// Add validation data used to compute confusion matrix or contingency table
- AddParameter( ParameterType_Group, "valid", "Validation data" );
- SetParameterDescription( "valid",
+ this->AddParameter( ParameterType_Group, "valid", "Validation data" );
+ this->SetParameterDescription( "valid",
"This group of parameters defines validation data." );
- AddParameter( ParameterType_InputVectorDataList, "valid.vd",
+ this->AddParameter( ParameterType_InputVectorDataList, "valid.vd",
"Validation Vector Data" );
- SetParameterDescription( "valid.vd", "Geometries used for validation "
+ this->SetParameterDescription( "valid.vd", "Geometries used for validation "
"(must contain the same fields used for training, all geometries from the layer will be used)" );
- MandatoryOff( "valid.vd" );
+ this->MandatoryOff( "valid.vd" );
- AddParameter( ParameterType_Int, "valid.layer", "Layer Index" );
- SetParameterDescription( "valid.layer",
+ this->AddParameter( ParameterType_Int, "valid.layer", "Layer Index" );
+ this->SetParameterDescription( "valid.layer",
"Index of the layer to use in the validation vector file." );
- MandatoryOff( "valid.layer" );
- SetDefaultParameterInt( "valid.layer", 0 );
+ this->MandatoryOff( "valid.layer" );
+ this->SetDefaultParameterInt( "valid.layer", 0 );
// Add class field if we used validation
- AddParameter( ParameterType_ListView, "cfield",
+ this->AddParameter( ParameterType_ListView, "cfield",
"Field containing the class integer label for supervision" );
- SetParameterDescription( "cfield",
+ this->SetParameterDescription( "cfield",
"Field containing the class id for supervision. "
"The values in this field shall be cast into integers. "
"Only geometries with this field available will be taken into account." );
- SetListViewSingleSelectionMode( "cfield", true );
+ this->SetListViewSingleSelectionMode( "cfield", true );
// Add a new parameter to compute confusion matrix / contingency table
- AddParameter( ParameterType_OutputFilename, "io.confmatout",
+ this->AddParameter( ParameterType_OutputFilename, "io.confmatout",
"Output confusion matrix or contingency table" );
- SetParameterDescription( "io.confmatout",
+ this->SetParameterDescription( "io.confmatout",
"Output file containing the confusion matrix or contingency table (.csv format)."
"The contingency table is output when we unsupervised algorithms is used otherwise the confusion matrix is output." );
- MandatoryOff( "io.confmatout" );
+ this->MandatoryOff( "io.confmatout" );
- AddParameter(ParameterType_Bool, "v", "Verbose mode");
- SetParameterDescription("v", "Verbose mode, display the contingency table result.");
- SetParameterInt("v", 1);
+ this->AddParameter(ParameterType_Bool, "v", "Verbose mode");
+ this->SetParameterDescription("v", "Verbose mode, display the contingency table result.");
+ this->SetParameterInt("v", 1);
// Doc example parameter settings
- SetDocExampleParameterValue( "io.vd", "vectorData.shp" );
- SetDocExampleParameterValue( "io.stats", "meanVar.xml" );
- SetDocExampleParameterValue( "io.out", "svmModel.svm" );
- SetDocExampleParameterValue( "feat", "perimeter area width" );
- SetDocExampleParameterValue( "cfield", "predicted" );
+ this->SetDocExampleParameterValue( "io.vd", "vectorData.shp" );
+ this->SetDocExampleParameterValue( "io.stats", "meanVar.xml" );
+ this->SetDocExampleParameterValue( "io.out", "svmModel.svm" );
+ this->SetDocExampleParameterValue( "feat", "perimeter area width" );
+ this->SetDocExampleParameterValue( "cfield", "predicted" );
// Add parameters for the classifier choice
Superclass::DoInit();
- AddRANDParameter();
+ this->AddRANDParameter();
}
-void TrainVectorBase::DoUpdateParameters()
+template <class TInputValue, class TOutputValue>
+void
+TrainVectorBase<TInputValue, TOutputValue>
+::DoUpdateParameters()
{
// if vector data is present and updated then reload fields
- if( HasValue( "io.vd" ) )
+ if( this->HasValue( "io.vd" ) )
{
- std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+ std::vector<std::string> vectorFileList = this->GetParameterStringList( "io.vd" );
ogr::DataSource::Pointer ogrDS = ogr::DataSource::New( vectorFileList[0], ogr::DataSource::Modes::Read );
ogr::Layer layer = ogrDS->GetLayer( static_cast<size_t>( this->GetParameterInt( "layer" ) ) );
ogr::Feature feature = layer.ogr().GetNextFeature();
- ClearChoices( "feat" );
- ClearChoices( "cfield" );
+ this->ClearChoices( "feat" );
+ this->ClearChoices( "cfield" );
for( int iField = 0; iField < feature.ogr().GetFieldCount(); iField++ )
{
@@ -134,20 +140,23 @@ void TrainVectorBase::DoUpdateParameters()
if( fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal )
{
std::string tmpKey = "feat." + key.substr( 0, static_cast<unsigned long>( end - key.begin() ) );
- AddChoice( tmpKey, item );
+ this->AddChoice( tmpKey, item );
}
if( fieldType == OFTString || fieldType == OFTInteger || fieldType == OFTInteger64 )
{
std::string tmpKey = "cfield." + key.substr( 0, static_cast<unsigned long>( end - key.begin() ) );
- AddChoice( tmpKey, item );
+ this->AddChoice( tmpKey, item );
}
}
}
}
-void TrainVectorBase::DoExecute()
+template <class TInputValue, class TOutputValue>
+void
+TrainVectorBase<TInputValue, TOutputValue>
+::DoExecute()
{
- m_FeaturesInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
+ m_FeaturesInfo.SetFieldNames( this->GetChoiceNames( "feat" ), this->GetSelectedItems( "feat" ));
// Check input parameters
if( m_FeaturesInfo.m_SelectedIdx.empty() )
@@ -158,29 +167,35 @@ void TrainVectorBase::DoExecute()
ShiftScaleParameters measurement = GetStatistics( m_FeaturesInfo.m_NbFeatures );
ExtractAllSamples( measurement );
- this->Train( m_TrainingSamplesWithLabel.listSample, m_TrainingSamplesWithLabel.labeledListSample, GetParameterString( "io.out" ) );
+ this->Train( m_TrainingSamplesWithLabel.listSample, m_TrainingSamplesWithLabel.labeledListSample, this->GetParameterString( "io.out" ) );
m_PredictedList =
- this->Classify( m_ClassificationSamplesWithLabel.listSample, GetParameterString( "io.out" ) );
+ this->Classify( m_ClassificationSamplesWithLabel.listSample, this->GetParameterString( "io.out" ) );
}
-
-void TrainVectorBase::ExtractAllSamples(const ShiftScaleParameters &measurement)
+template <class TInputValue, class TOutputValue>
+void
+TrainVectorBase<TInputValue, TOutputValue>
+::ExtractAllSamples(const ShiftScaleParameters &measurement)
{
m_TrainingSamplesWithLabel = ExtractTrainingSamplesWithLabel(measurement);
m_ClassificationSamplesWithLabel = ExtractClassificationSamplesWithLabel(measurement);
}
-TrainVectorBase::SamplesWithLabel
-TrainVectorBase::ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement)
+template <class TInputValue, class TOutputValue>
+typename TrainVectorBase<TInputValue, TOutputValue>::SamplesWithLabel
+TrainVectorBase<TInputValue, TOutputValue>
+::ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement)
{
return ExtractSamplesWithLabel( "io.vd", "layer", measurement);
}
-TrainVectorBase::SamplesWithLabel
-TrainVectorBase::ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement)
+template <class TInputValue, class TOutputValue>
+typename TrainVectorBase<TInputValue, TOutputValue>::SamplesWithLabel
+TrainVectorBase<TInputValue, TOutputValue>
+::ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement)
{
- if(GetClassifierCategory() == Supervised)
+ if(this->GetClassifierCategory() == Superclass::Supervised)
{
SamplesWithLabel tmpSamplesWithLabel;
SamplesWithLabel validationSamplesWithLabel = ExtractSamplesWithLabel( "valid.vd", "valid.layer", measurement );
@@ -206,15 +221,16 @@ TrainVectorBase::ExtractClassificationSamplesWithLabel(const ShiftScaleParameter
}
}
-
-TrainVectorBase::ShiftScaleParameters
-TrainVectorBase::GetStatistics(unsigned int nbFeatures)
+template <class TInputValue, class TOutputValue>
+typename TrainVectorBase<TInputValue, TOutputValue>::ShiftScaleParameters
+TrainVectorBase<TInputValue, TOutputValue>
+::GetStatistics(unsigned int nbFeatures)
{
ShiftScaleParameters measurement = ShiftScaleParameters();
- if( HasValue( "io.stats" ) && IsParameterEnabled( "io.stats" ) )
+ if( this->HasValue( "io.stats" ) && this->IsParameterEnabled( "io.stats" ) )
{
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- std::string XMLfile = GetParameterString( "io.stats" );
+ typename StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
+ std::string XMLfile = this->GetParameterString( "io.stats" );
statisticsReader->SetFileName( XMLfile );
measurement.meanMeasurementVector = statisticsReader->GetStatisticVectorByName( "mean" );
measurement.stddevMeasurementVector = statisticsReader->GetStatisticVectorByName( "stddev" );
@@ -229,16 +245,17 @@ TrainVectorBase::GetStatistics(unsigned int nbFeatures)
return measurement;
}
-
-TrainVectorBase::SamplesWithLabel
-TrainVectorBase::ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer,
+template <class TInputValue, class TOutputValue>
+typename TrainVectorBase<TInputValue, TOutputValue>::SamplesWithLabel
+TrainVectorBase<TInputValue, TOutputValue>
+::ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer,
const ShiftScaleParameters &measurement)
{
SamplesWithLabel samplesWithLabel;
- if( HasValue( parameterName ) && IsParameterEnabled( parameterName ) )
+ if( this->HasValue( parameterName ) && this->IsParameterEnabled( parameterName ) )
{
- ListSampleType::Pointer input = ListSampleType::New();
- TargetListSampleType::Pointer target = TargetListSampleType::New();
+ typename ListSampleType::Pointer input = ListSampleType::New();
+ typename TargetListSampleType::Pointer target = TargetListSampleType::New();
input->SetMeasurementVectorSize( m_FeaturesInfo.m_NbFeatures );
std::vector<std::string> fileList = this->GetParameterStringList( parameterName );
@@ -251,7 +268,7 @@ TrainVectorBase::ExtractSamplesWithLabel(std::string parameterName, std::string
bool goesOn = feature.addr() != 0;
if( !goesOn )
{
- otbAppLogWARNING( "The layer " << GetParameterInt( parameterLayer ) << " of " << fileList[k]
+ otbAppLogWARNING( "The layer " << this->GetParameterInt( parameterLayer ) << " of " << fileList[k]
<< " is empty, input is skipped." );
continue;
}
@@ -300,7 +317,7 @@ TrainVectorBase::ExtractSamplesWithLabel(std::string parameterName, std::string
- ShiftScaleFilterType::Pointer shiftScaleFilter = ShiftScaleFilterType::New();
+ typename ShiftScaleFilterType::Pointer shiftScaleFilter = ShiftScaleFilterType::New();
shiftScaleFilter->SetInput( input );
shiftScaleFilter->SetShifts( measurement.meanMeasurementVector );
shiftScaleFilter->SetScales( measurement.stddevMeasurementVector );