diff --git a/Modules/Applications/AppDimensionalityReduction/app/otbDimensionalityReduction.cxx b/Modules/Applications/AppDimensionalityReduction/app/otbDimensionalityReduction.cxx
index 3e5cbf8e7a4ad2ecbac6182a3b42cf13e56c9141..1e5b2f19fc957523a9790769b3428e2c384da072 100644
--- a/Modules/Applications/AppDimensionalityReduction/app/otbDimensionalityReduction.cxx
+++ b/Modules/Applications/AppDimensionalityReduction/app/otbDimensionalityReduction.cxx
@@ -103,13 +103,13 @@ private:
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription("out", "output image. Components are ordered by decreasing eigenvalues.");
MandatoryOff("out");
-
+
AddParameter(ParameterType_Choice, "rescale", "Rescale Output");
SetParameterDescription("rescale", "Enable rescaling of the reduced output image.");
MandatoryOff("rescale");
- AddChoice("rescale.no","No rescale");
- AddChoice("rescale.minmax","rescale to min max value");
+ AddChoice("rescale.no", "No rescale");
+ AddChoice("rescale.minmax", "rescale to min max value");
AddParameter(ParameterType_Float, "rescale.minmax.outmin", "Output min value");
AddParameter(ParameterType_Float, "rescale.minmax.outmax", "Output max value");
@@ -134,7 +134,7 @@ private:
SetParameterDescription("method.pca.whiten", "Perform whitening and ensure uncorrelated outputs with unit component wise variances");
SetParameterInt("method.pca.whiten", 1);
MandatoryOff("method.pca.whiten");
-
+
AddChoice("method.napca", "NA-PCA");
SetParameterDescription("method.napca", "Noise Adjusted Principal Component Analysis.");
AddParameter(ParameterType_Int, "method.napca.radiusx", "Set the x radius of the sliding window");
@@ -146,7 +146,7 @@ private:
AddChoice("method.maf", "MAF");
SetParameterDescription("method.maf", "Maximum Autocorrelation Factor.");
-
+
AddChoice("method.ica", "ICA");
SetParameterDescription("method.ica", "Independent Component Analysis using a stabilized fixed point FastICA algorithm.");
AddParameter(ParameterType_Int, "method.ica.iter", "number of iterations");
@@ -175,8 +175,8 @@ private:
MandatoryOff("nbcomp");
SetMinimumParameterIntValue("nbcomp", 0);
- AddParameter(ParameterType_Bool, "normalize", "Normalize");
- SetParameterDescription("normalize", "Center and reduce data before Dimensionality reduction.");
+ AddParameter(ParameterType_Bool, "normalize", "Center and reduce data");
+ SetParameterDescription("normalize", "Center and reduce data before Dimensionality reduction (if this parameter is set to false, the data will be centered but not reduced.");
AddParameter(ParameterType_OutputFilename, "outmatrix", "Transformation matrix output (text format)");
SetParameterDescription("outmatrix", "Filename to store the transformation matrix (csv format)");
@@ -253,94 +253,95 @@ private:
// PCA Algorithm
case 0:
{
- otbAppLogINFO("Using the PCA Algorithm ");
- PCAForwardFilterType::Pointer filter = PCAForwardFilterType::New();
- m_ForwardFilter = filter;
- PCAInverseFilterType::Pointer invFilter = PCAInverseFilterType::New();
- m_InverseFilter = invFilter;
-
- filter->SetInput(GetParameterFloatVectorImage("in"));
- filter->SetNumberOfPrincipalComponentsRequired(nbComp);
- filter->SetWhitening(GetParameterInt("method.pca.whiten"));
-
- // Center AND reduce the input data.
- if (normalize)
- {
- filter->SetUseNormalization(true);
- filter->SetUseVarianceForNormalization(true);
- }
- // Only center the input data.
- else
- {
- filter->SetUseNormalization(true);
- filter->SetUseVarianceForNormalization(false);
- }
-
- m_ForwardFilter->GetOutput()->UpdateOutputInformation();
-
- //Write eigenvalues
- std::ofstream outFile;
- outFile.open(this->GetParameterString("method.pca.outeigenvalues"));
- if (outFile.is_open())
+ otbAppLogINFO("Using the PCA Algorithm ");
+ PCAForwardFilterType::Pointer filter = PCAForwardFilterType::New();
+ m_ForwardFilter = filter;
+ PCAInverseFilterType::Pointer invFilter = PCAInverseFilterType::New();
+ m_InverseFilter = invFilter;
+
+ filter->SetInput(GetParameterFloatVectorImage("in"));
+ filter->SetNumberOfPrincipalComponentsRequired(nbComp);
+ filter->SetWhitening(GetParameterInt("method.pca.whiten"));
+
+ // Center AND reduce the input data.
+ if (normalize)
{
- outFile << std::fixed;
- outFile.precision(10);
+ filter->SetUseNormalization(true);
+ filter->SetUseVarianceForNormalization(true);
+ }
+ // Only center the input data.
+ else
+ {
+ filter->SetUseNormalization(true);
+ filter->SetUseVarianceForNormalization(false);
+ }
- outFile << filter->GetEigenValues() << std::endl;;
- outFile.close();
+ m_ForwardFilter->GetOutput()->UpdateOutputInformation();
+
+ // Write eigenvalues
+ std::ofstream outFile;
+ outFile.open(this->GetParameterString("method.pca.outeigenvalues"));
+ if (outFile.is_open())
+ {
+ outFile << std::fixed;
+ outFile.precision(10);
+
+ outFile << filter->GetEigenValues() << std::endl;
+ ;
+ outFile.close();
}
-
- if (invTransform)
+
+ if (invTransform)
{
- invFilter->SetInput(m_ForwardFilter->GetOutput());
- // Data has been centered and reduced by the forward filter
- if (normalize)
+ invFilter->SetInput(m_ForwardFilter->GetOutput());
+ // Data has been centered and reduced by the forward filter
+ if (normalize)
{
otbAppLogINFO( << "Normalization MeanValue:"<<filter->GetMeanValues() );
invFilter->SetMeanValues(filter->GetMeanValues());
invFilter->SetStdDevValues(filter->GetStdDevValues());
}
- // Data has been centered by the forward filter
- else
+ // Data has been centered by the forward filter
+ else
{
- invFilter->SetMeanValues(filter->GetMeanValues());
+ invFilter->SetMeanValues(filter->GetMeanValues());
}
-
- invFilter->SetTransformationMatrix(filter->GetTransformationMatrix());
- m_TransformationMatrix = invFilter->GetTransformationMatrix();
+
+ invFilter->SetTransformationMatrix(filter->GetTransformationMatrix());
+ m_TransformationMatrix = invFilter->GetTransformationMatrix();
}
m_TransformationMatrix = filter->GetTransformationMatrix();
-
+
otbAppLogINFO("PCA transform has been computed.");
break;
}
case 1:
{
- otbAppLogINFO("Using the NA-PCA Algorithm ");
+ otbAppLogINFO("Using the NA-PCA Algorithm ");
- // NA-PCA
+ // NA-PCA
- unsigned int radiusX = static_cast<unsigned int> (GetParameterInt("method.napca.radiusx"));
- unsigned int radiusY = static_cast<unsigned int> (GetParameterInt("method.napca.radiusy"));
+ unsigned int radiusX = static_cast<unsigned int>(GetParameterInt("method.napca.radiusx"));
+ unsigned int radiusY = static_cast<unsigned int>(GetParameterInt("method.napca.radiusy"));
- // Noise filtering
- NoiseFilterType::RadiusType radius = { { radiusX, radiusY } };
+ // Noise filtering
+ NoiseFilterType::RadiusType radius = {{radiusX, radiusY}};
- NAPCAForwardFilterType::Pointer filter = NAPCAForwardFilterType::New();
- m_ForwardFilter = filter;
- NAPCAInverseFilterType::Pointer invFilter = NAPCAInverseFilterType::New();
- m_InverseFilter = invFilter;
+ NAPCAForwardFilterType::Pointer filter = NAPCAForwardFilterType::New();
+ m_ForwardFilter = filter;
+ NAPCAInverseFilterType::Pointer invFilter = NAPCAInverseFilterType::New();
+ m_InverseFilter = invFilter;
- filter->SetInput(GetParameterFloatVectorImage("in"));
- filter->SetNumberOfPrincipalComponentsRequired(nbComp);
- filter->SetUseNormalization(normalize);
- filter->GetNoiseImageFilter()->SetRadius(radius);
+ filter->SetInput(GetParameterFloatVectorImage("in"));
+ filter->SetNumberOfPrincipalComponentsRequired(nbComp);
+ filter->SetUseNormalization(normalize);
+ filter->GetNoiseImageFilter()->SetRadius(radius);
- m_ForwardFilter->GetOutput()->UpdateOutputInformation();
-
- if (invTransform)
+ m_ForwardFilter->GetOutput()->UpdateOutputInformation();
+
+ if (invTransform)
{
otbAppLogDEBUG( << "Compute Inverse Transform");
invFilter->SetInput(m_ForwardFilter->GetOutput());
@@ -363,32 +364,32 @@ private:
}
case 2:
{
- otbAppLogINFO("Using the MAF Algorithm ");
- MAFForwardFilterType::Pointer filter = MAFForwardFilterType::New();
- m_ForwardFilter = filter;
- filter->SetInput(GetParameterFloatVectorImage("in"));
- otbAppLogINFO( << "V :"<<std::endl<<filter->GetV()<<"Auto-Correlation :"<<std::endl <<filter->GetAutoCorrelation() );
+ otbAppLogINFO("Using the MAF Algorithm ");
+ MAFForwardFilterType::Pointer filter = MAFForwardFilterType::New();
+ m_ForwardFilter = filter;
+ filter->SetInput(GetParameterFloatVectorImage("in"));
+ otbAppLogINFO(<< "V :" << std::endl << filter->GetV() << "Auto-Correlation :" << std::endl << filter->GetAutoCorrelation());
- break;
+ break;
}
case 3:
{
- otbAppLogINFO("Using the fast ICA Algorithm ");
+ otbAppLogINFO("Using the fast ICA Algorithm ");
- unsigned int nbIterations = static_cast<unsigned int> (GetParameterInt("method.ica.iter"));
- double mu = static_cast<double> (GetParameterFloat("method.ica.mu"));
+ unsigned int nbIterations = static_cast<unsigned int>(GetParameterInt("method.ica.iter"));
+ double mu = static_cast<double>(GetParameterFloat("method.ica.mu"));
- ICAForwardFilterType::Pointer filter = ICAForwardFilterType::New();
- m_ForwardFilter = filter;
- ICAInverseFilterType::Pointer invFilter = ICAInverseFilterType::New();
- m_InverseFilter = invFilter;
+ ICAForwardFilterType::Pointer filter = ICAForwardFilterType::New();
+ m_ForwardFilter = filter;
+ ICAInverseFilterType::Pointer invFilter = ICAInverseFilterType::New();
+ m_InverseFilter = invFilter;
- filter->SetInput(GetParameterFloatVectorImage("in"));
- filter->SetNumberOfPrincipalComponentsRequired(nbComp);
- filter->SetNumberOfIterations(nbIterations);
- filter->SetMu(mu);
-
- switch (GetParameterInt("method.ica.g"))
+ filter->SetInput(GetParameterFloatVectorImage("in"));
+ filter->SetNumberOfPrincipalComponentsRequired(nbComp);
+ filter->SetNumberOfIterations(nbIterations);
+ filter->SetMu(mu);
+
+ switch (GetParameterInt("method.ica.g"))
{
// tanh
case 0:
@@ -442,10 +443,10 @@ private:
invFilter->SetPCATransformationMatrix(filter->GetPCATransformationMatrix());
invFilter->SetTransformationMatrix(filter->GetTransformationMatrix());
}
- otbAppLogINFO("ICA transform has been computed.");
- m_TransformationMatrix = filter->GetTransformationMatrix();
+ otbAppLogINFO("ICA transform has been computed.");
+ m_TransformationMatrix = filter->GetTransformationMatrix();
- break;
+ break;
}
default:
@@ -480,53 +481,53 @@ private:
std::ofstream outFile;
outFile.open(this->GetParameterString("outmatrix"));
if (outFile.is_open())
- {
+ {
outFile << std::fixed;
outFile.precision(10);
outFile << m_TransformationMatrix;
outFile.close();
- }
+ }
}
}
- if (GetParameterString("rescale")=="no")
+ if (GetParameterString("rescale") == "no")
{
SetParameterOutputImage("out", m_ForwardFilter->GetOutput());
}
else
{
- otbAppLogINFO( "Starting Min/Max computation for rescaling" );
+ otbAppLogINFO("Starting Min/Max computation for rescaling");
- m_MinMaxFilter = MinMaxFilterType::New();
- m_MinMaxFilter->SetInput(m_ForwardFilter->GetOutput());
- m_MinMaxFilter->GetStreamer()->SetAutomaticAdaptativeStreaming(GetParameterInt("ram"));
+ m_MinMaxFilter = MinMaxFilterType::New();
+ m_MinMaxFilter->SetInput(m_ForwardFilter->GetOutput());
+ m_MinMaxFilter->GetStreamer()->SetAutomaticAdaptativeStreaming(GetParameterInt("ram"));
- AddProcess(m_MinMaxFilter->GetStreamer(), "Min/Max computing");
- m_MinMaxFilter->Update();
+ AddProcess(m_MinMaxFilter->GetStreamer(), "Min/Max computing");
+ m_MinMaxFilter->Update();
- otbAppLogINFO( << "Min/Max computation done : min=" << m_MinMaxFilter->GetMinimum()
- << " max=" << m_MinMaxFilter->GetMaximum() )
+ otbAppLogINFO(<< "Min/Max computation done : min=" << m_MinMaxFilter->GetMinimum() << " max=" << m_MinMaxFilter->GetMaximum())
- FloatVectorImageType::PixelType inMin, inMax;
+ FloatVectorImageType::PixelType inMin,
+ inMax;
- m_RescaleFilter = RescaleImageFilterType::New();
- m_RescaleFilter->SetInput(m_ForwardFilter->GetOutput());
- m_RescaleFilter->SetAutomaticInputMinMaxComputation(false);
- m_RescaleFilter->SetInputMinimum(m_MinMaxFilter->GetMinimum());
- m_RescaleFilter->SetInputMaximum(m_MinMaxFilter->GetMaximum());
+ m_RescaleFilter = RescaleImageFilterType::New();
+ m_RescaleFilter->SetInput(m_ForwardFilter->GetOutput());
+ m_RescaleFilter->SetAutomaticInputMinMaxComputation(false);
+ m_RescaleFilter->SetInputMinimum(m_MinMaxFilter->GetMinimum());
+ m_RescaleFilter->SetInputMaximum(m_MinMaxFilter->GetMaximum());
- FloatVectorImageType::PixelType outMin, outMax;
- outMin.SetSize(m_ForwardFilter->GetOutput()->GetNumberOfComponentsPerPixel());
- outMax.SetSize(m_ForwardFilter->GetOutput()->GetNumberOfComponentsPerPixel());
- outMin.Fill(GetParameterFloat("rescale.minmax.outmin"));
- outMax.Fill(GetParameterFloat("rescale.minmax.outmax"));
+ FloatVectorImageType::PixelType outMin, outMax;
+ outMin.SetSize(m_ForwardFilter->GetOutput()->GetNumberOfComponentsPerPixel());
+ outMax.SetSize(m_ForwardFilter->GetOutput()->GetNumberOfComponentsPerPixel());
+ outMin.Fill(GetParameterFloat("rescale.minmax.outmin"));
+ outMax.Fill(GetParameterFloat("rescale.minmax.outmax"));
- m_RescaleFilter->SetOutputMinimum(outMin);
- m_RescaleFilter->SetOutputMaximum(outMax);
- m_RescaleFilter->UpdateOutputInformation();
+ m_RescaleFilter->SetOutputMinimum(outMin);
+ m_RescaleFilter->SetOutputMaximum(outMax);
+ m_RescaleFilter->UpdateOutputInformation();
- SetParameterOutputImage("out", m_RescaleFilter->GetOutput());
+ SetParameterOutputImage("out", m_RescaleFilter->GetOutput());
}
diff --git a/Modules/Filtering/DimensionalityReduction/include/otbPCAImageFilter.h b/Modules/Filtering/DimensionalityReduction/include/otbPCAImageFilter.h
index b90faae53679d0d806cc3886d10d72bdb465e762..3fbec43b0f57f17148931481aa6e87bcf10d249e 100644
--- a/Modules/Filtering/DimensionalityReduction/include/otbPCAImageFilter.h
+++ b/Modules/Filtering/DimensionalityReduction/include/otbPCAImageFilter.h
@@ -97,7 +97,7 @@ public:
*/
itkSetMacro(NumberOfPrincipalComponentsRequired, unsigned int);
itkGetMacro(NumberOfPrincipalComponentsRequired, unsigned int);
-
+
itkSetMacro(Whitening, bool);
itkGetMacro(Whitening, bool);