ITK4: Change OTB API and implementation to support ITK 4.4.2

Applications/Classification/otbKMeansClassification.cxx

@@ -22,7 +22,7 @@
 #include "itkMersenneTwisterRandomVariateGenerator.h"
 #include "otbVectorImage.h"
 #include "otbImage.h"
-#include "itkEuclideanDistance.h"
+#include "itkEuclideanDistanceMetric.h"
 #include "otbStreamingTraits.h"
 #include "itkImageRegionConstIterator.h"
 #include "itkListSample.h"
@@ -99,7 +99,7 @@ public:
 
 private:
   typedef std::map<TLabel, TSample>                   CentroidMapType;
-  typedef itk::Statistics::EuclideanDistance<TSample> DistanceType;
+  typedef itk::Statistics::EuclideanDistanceMetric<TSample> DistanceType;
 
   CentroidMapType m_CentroidsMap;
   typename DistanceType::Pointer m_Distance;
@@ -292,6 +292,7 @@ private:
 
     // Training sample lists
     ListSampleType::Pointer sampleList = ListSampleType::New();
+    sampleList->SetMeasurementVectorSize(m_InImage->GetNumberOfComponentsPerPixel());
 
     //unsigned int init_means_index = 0;
 

Applications/Classification/otbSOMClassification.cxx

@@ -21,7 +21,7 @@
 #include "otbSOMMap.h"
 #include "otbSOM.h"
 #include "otbSOMImageClassificationFilter.h"
-#include "itkEuclideanDistance.h"
+#include "itkEuclideanDistanceMetric.h"
 #include "itkImageRegionSplitter.h"
 #include "otbStreamingTraits.h"
 #include "itkImageRegionConstIterator.h"
@@ -50,7 +50,7 @@ public:
   typedef UInt16ImageType LabeledImageType;
 
   typedef itk::VariableLengthVector<double> SampleType;
-  typedef itk::Statistics::EuclideanDistance<SampleType> DistanceType;
+  typedef itk::Statistics::EuclideanDistanceMetric<SampleType> DistanceType;
   typedef otb::SOMMap<SampleType, DistanceType, 2> SOMMapType;
   typedef itk::Statistics::ListSample<SampleType> ListSampleType;
   typedef otb::SOM<ListSampleType, SOMMapType> EstimatorType;
@@ -181,7 +181,7 @@ private:
   {
     // initiating random number generation
     itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer
-        randomGen = itk::Statistics::MersenneTwisterRandomVariateGenerator::New();
+        randomGen = itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance();
     
     FloatVectorImageType::Pointer input = GetParameterImage("in");
     LabeledImageType::Pointer mask;
@@ -228,6 +228,8 @@ private:
     
     // Training sample lists
     ListSampleType::Pointer sampleList = ListSampleType::New();
+    sampleList->SetMeasurementVectorSize(input->GetNumberOfComponentsPerPixel());
+
     const double trainingProb = static_cast<double>(GetParameterFloat("tp"));
     unsigned int nbsamples;
     if (HasValue("ts"))

Applications/Classification/otbTrainImagesClassifier.cxx

@@ -304,12 +304,12 @@ void TrainImagesClassifier::DoExecute()
   concatenateValidationSamples->Update();
   concatenateValidationLabels->Update();
 
-  if (concatenateTrainingSamples->GetOutputSampleList()->Size() == 0)
+  if (concatenateTrainingSamples->GetOutput()->Size() == 0)
     {
     otbAppLogFATAL("No training samples, cannot perform SVM training.");
     }
 
-  if (concatenateValidationSamples->GetOutputSampleList()->Size() == 0)
+  if (concatenateValidationSamples->GetOutput()->Size() == 0)
     {
     otbAppLogWARNING("No validation samples.");
     }
@@ -339,14 +339,14 @@ void TrainImagesClassifier::DoExecute()
   ListSampleType::Pointer validationListSample=ListSampleType::New();
 
   //Test if the validation test is empty
-  if ( concatenateValidationSamples->GetOutput()->Get()->Size() != 0 )
+  if ( concatenateValidationSamples->GetOutput()->Size() != 0 )
     {
     ShiftScaleFilterType::Pointer validationShiftScaleFilter = ShiftScaleFilterType::New();
     validationShiftScaleFilter->SetInput(concatenateValidationSamples->GetOutput());
     validationShiftScaleFilter->SetShifts(meanMeasurementVector);
     validationShiftScaleFilter->SetScales(stddevMeasurementVector);
     validationShiftScaleFilter->Update();
-    validationListSample = validationShiftScaleFilter->GetOutputSampleList();
+    validationListSample = validationShiftScaleFilter->GetOutput();
     }
 
   ListSampleType::Pointer listSample;
@@ -356,29 +356,29 @@ void TrainImagesClassifier::DoExecute()
   // if (IsParameterEnabled("sample.b"))
   //   {
   //   // Balance the list sample.
-  //   otbAppLogINFO("Number of training samples before balancing: " << concatenateTrainingSamples->GetOutputSampleList()->Size())
+  //   otbAppLogINFO("Number of training samples before balancing: " << concatenateTrainingSamples->GetOutput()->Size())
   //   BalancingListSampleFilterType::Pointer balancingFilter = BalancingListSampleFilterType::New();
-  //   balancingFilter->SetInput(trainingShiftScaleFilter->GetOutput()/*GetOutputSampleList()*/);
-  //   balancingFilter->SetInputLabel(concatenateTrainingLabels->GetOutput()/*GetOutputSampleList()*/);
+  //   balancingFilter->SetInput(trainingShiftScaleFilter->GetOutput());
+  //   balancingFilter->SetInputLabel(concatenateTrainingLabels->GetOutput());
   //   balancingFilter->SetBalancingFactor(GetParameterInt("sample.b"));
   //   balancingFilter->Update();
-  //   listSample = balancingFilter->GetOutputSampleList();
+  //   listSample = balancingFilter->GetOutput();
   //   labelListSample = balancingFilter->GetOutputLabelSampleList();
-  //   otbAppLogINFO("Number of samples after balancing: " << balancingFilter->GetOutputSampleList()->Size());
+  //   otbAppLogINFO("Number of samples after balancing: " << balancingFilter->GetOutput()->Size());
 
   //   }
   // else
   //   {
-  listSample = trainingShiftScaleFilter->GetOutputSampleList();
-  labelListSample = concatenateTrainingLabels->GetOutputSampleList();
-  otbAppLogINFO("Number of training samples: " << concatenateTrainingSamples->GetOutputSampleList()->Size());
+  listSample = trainingShiftScaleFilter->GetOutput();
+  labelListSample = concatenateTrainingLabels->GetOutput();
+  otbAppLogINFO("Number of training samples: " << concatenateTrainingSamples->GetOutput()->Size());
   //  }
   //--------------------------
   // Split the data set into training/validation set
   ListSampleType::Pointer trainingListSample = listSample;
   LabelListSampleType::Pointer trainingLabeledListSample = labelListSample;
 
-  LabelListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutputSampleList();
+  LabelListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutput();
   otbAppLogINFO("Size of training set: " << trainingListSample->Size());
   otbAppLogINFO("Size of validation set: " << validationListSample->Size());
   otbAppLogINFO("Size of labeled training set: " << trainingLabeledListSample->Size());

Applications/DisparityMap/otbFineRegistration.cxx

@@ -73,13 +73,13 @@ public:
   static const unsigned int Dimension = 2;
   typedef double     PixelType;
 
-  typedef itk::FixedArray<PixelType, Dimension>         DeformationValueType;
+  typedef itk::FixedArray<PixelType, Dimension>         DisplacementValueType;
   typedef otb::Image< PixelType,  Dimension >           ScalarImageType;
   typedef otb::VectorImage<PixelType, Dimension>        VectorImageType;
   typedef otb::ImageList<ScalarImageType>               ImageListType;
   typedef otb::ImageListToVectorImageFilter
                 <ImageListType, VectorImageType>        IL2VIFilterType;
-  typedef otb::Image<DeformationValueType, Dimension>   FieldImageType;
+  typedef otb::Image<DisplacementValueType, Dimension>   FieldImageType;
   typedef otb::FineRegistrationImageFilter
     <ScalarImageType, ScalarImageType, FieldImageType>  RegistrationFilterType;
   typedef itk::DiscreteGaussianImageFilter
@@ -362,11 +362,11 @@ private:
       }
     
     m_XExtractor = VectorImageToImageFilterType::New();
-    m_XExtractor->SetInput(m_Registration->GetOutputDeformationField());
+    m_XExtractor->SetInput(m_Registration->GetOutputDisplacementField());
     m_XExtractor->SetIndex(0);
   
     m_YExtractor = VectorImageToImageFilterType::New();
-    m_YExtractor->SetInput(m_Registration->GetOutputDeformationField());
+    m_YExtractor->SetInput(m_Registration->GetOutputDisplacementField());
     m_YExtractor->SetIndex(1);
   
     m_ImgList = ImageListType::New();
@@ -443,7 +443,7 @@ private:
       FloatVectorImageType::Pointer imageToWarp = GetParameterImage("w");
        
       m_Warp = WarpFilterType::New();
-      m_Warp->SetDeformationField(m_Cast->GetOutput());