MRG: Merge remote-tracking branch 'remotes/origin/contingency_table' into develop

Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx

@@ -22,6 +22,7 @@
 
 // Validation
 #include "otbConfusionMatrixCalculator.h"
+#include "otbContingencyTableCalculator.h"
 
 namespace otb
 {
@@ -48,6 +49,9 @@ public:
   typedef ConfusionMatrixCalculatorType::ConfusionMatrixType ConfusionMatrixType;
   typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
   typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
+  
+  typedef ContingencyTable<ClassLabelType> ContingencyTableType;
+  typedef ContingencyTableType::Pointer    ContingencyTablePointerType;
 
 protected:
   void DoInit()
@@ -72,32 +76,57 @@ protected:
 
   void DoExecute()
   {
-    // Enforce the need of class field name in supervised mode
-    if (GetClassifierCategory() == Supervised)
+    m_FeaturesInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+
+    if( m_FeaturesInfo.m_SelectedCFieldIdx.empty() && GetClassifierCategory() == Supervised )
       {
-      m_FeaturesInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+      otbAppLogFATAL( << "No field has been selected for data labelling!" );
+      }
 
-      if( m_FeaturesInfo.m_SelectedCFieldIdx.empty() )
-        {
-        otbAppLogFATAL( << "No field has been selected for data labelling!" );
-        }
+    Superclass::DoExecute();
+
+    if (GetClassifierCategory() == Supervised)
+      {
+      ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( m_PredictedList,
+                                                                                   m_ClassificationSamplesWithLabel.labeledListSample );
+      WriteConfusionMatrix( confMatCalc );
+      }
+    else
+      {
+      ContingencyTablePointerType table = ComputeContingencyTable( m_PredictedList,
+                                                                   m_ClassificationSamplesWithLabel.labeledListSample );
+      WriteContingencyTable( table );
       }
+  }
 
-      Superclass::DoExecute();
+  ContingencyTablePointerType ComputeContingencyTable(const TargetListSampleType::Pointer &predictedListSample,
+                                                      const TargetListSampleType::Pointer &performanceLabeledListSample)
+  {
+    typedef ContingencyTableCalculator<ClassLabelType> ContigencyTableCalcutaltorType;
 
-      if (GetClassifierCategory() == Supervised)
-        {
-        ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( m_PredictedList,
-                                                                                     m_ClassificationSamplesWithLabel.labeledListSample );
-        WriteConfusionMatrix( confMatCalc );
-        }
-      else
-        {
-        // TODO Compute Contingency Table
-        }
+    ContigencyTableCalcutaltorType::Pointer contingencyTableCalculator = ContigencyTableCalcutaltorType::New();
+    contingencyTableCalculator->Compute(performanceLabeledListSample->Begin(),
+                                        performanceLabeledListSample->End(),predictedListSample->Begin(), predictedListSample->End());
+
+    otbAppLogINFO( "Training performances:" );
+
+    otbAppLogINFO(<<"Contingency table: reference labels (rows) vs. produced labels (cols)\n"<<contingencyTableCalculator->BuildContingencyTable());
+
+    return contingencyTableCalculator->BuildContingencyTable();
   }
 
 
+  void WriteContingencyTable(const ContingencyTablePointerType& table)
+  {
+    if(IsParameterEnabled("io.confmatout"))
+      {
+      // Write contingency table
+      std::ofstream outFile;
+      outFile.open( this->GetParameterString( "io.confmatout" ).c_str() );
+      outFile << table->ToCSV();
+      }
+  }
+
 
   ConfusionMatrixCalculatorType::Pointer
   ComputeConfusionMatrix(const TargetListSampleType::Pointer &predictedListSample,
@@ -111,7 +140,7 @@ protected:
     confMatCalc->SetProducedLabels( predictedListSample );
     confMatCalc->Compute();
 
-    otbAppLogINFO( "training performances" );
+    otbAppLogINFO( "Training performances:" );
     LogConfusionMatrix( confMatCalc );
 
     for( unsigned int itClasses = 0; itClasses < confMatCalc->GetNumberOfClasses(); itClasses++ )

Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx

@@ -33,6 +33,7 @@ namespace Wrapper
 template <class TInputValue, class TOutputValue>
 LearningApplicationBase<TInputValue,TOutputValue>
 ::LearningApplicationBase() : m_RegressionFlag(false)
+
 {
 }
 
@@ -59,7 +60,9 @@ LearningApplicationBase<TInputValue,TOutputValue>
 
   InitUnsupervisedClassifierParams();
   std::vector<std::string> allClassifier = GetChoiceKeys("classifier");
-  m_UnsupervisedClassifier.assign(allClassifier.begin() + m_SupervisedClassifier.size(), allClassifier.end());
+  // Check for empty unsupervised classifier
+  if( allClassifier.size() > m_UnsupervisedClassifier.size() )
+    m_UnsupervisedClassifier.assign( allClassifier.begin() + m_SupervisedClassifier.size(), allClassifier.end() );
 }
 
 template <class TInputValue, class TOutputValue>
@@ -67,10 +70,16 @@ typename LearningApplicationBase<TInputValue,TOutputValue>::ClassifierCategory
 LearningApplicationBase<TInputValue,TOutputValue>
 ::GetClassifierCategory()
 {
-  bool foundUnsupervised =
-          std::find(m_UnsupervisedClassifier.begin(), m_UnsupervisedClassifier.end(),
-                    GetParameterString("classifier")) != m_UnsupervisedClassifier.end();
-  return foundUnsupervised ? Unsupervised : Supervised;
+  if( m_UnsupervisedClassifier.empty() )
+    {
+    return Supervised;
+    }
+  else
+    {
+    bool foundUnsupervised = std::find( m_UnsupervisedClassifier.begin(), m_UnsupervisedClassifier.end(),
+                                        GetParameterString( "classifier" ) ) != m_UnsupervisedClassifier.end();
+    return foundUnsupervised ? Unsupervised : Supervised;
+    }
 }
 
 template <class TInputValue, class TOutputValue>

Modules/Applications/AppClassification/include/otbTrainVectorBase.h

@@ -125,7 +125,7 @@ protected:
     {
       m_SelectedCFieldIdx = selectedCFieldIdx;
       // Handle only one class field name, if several are provided only the first one is used.
-      m_SelectedCFieldName = cFieldNames[selectedCFieldIdx.front()];
+      m_SelectedCFieldName = selectedCFieldIdx.empty() ? cFieldNames.front() : cFieldNames[selectedCFieldIdx.front()];
     }
   };
 

Modules/Applications/AppClassification/test/CMakeLists.txt

@@ -392,6 +392,7 @@ otb_test_application(NAME apTvComputeConfusionMatrixExtraReferenceLabelsR
   OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho_C7.tif
   -ref raster
   -ref.raster.in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
+  -ref.raster.nodata 255
   -nodatalabel 255
   -out ${TEMP}/apTvComputeConfusionMatrixExtraRefLabelsROut.csv
   VALID   --compare-ascii ${NOTOL}
@@ -403,6 +404,7 @@ otb_test_application(NAME apTvComputeConfusionMatrixR
   OPTIONS -in ${OTBAPP_BASELINE}/clLabeledImageQB123_1.tif
   -ref raster
   -ref.raster.in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
+  -ref.raster.nodata 255
   -nodatalabel 255
   -out ${TEMP}/apTvComputeConfusionMatrixTconfusionROut.csv
   VALID   --compare-ascii ${NOTOL}
@@ -414,12 +416,27 @@ otb_test_application(NAME apTvComputeConfusionMatrixExtraProducedLabelsR
   OPTIONS -in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
   -ref raster
   -ref.raster.in ${INPUTDATA}/Classification/QB_1_ortho_C8.tif
+  -ref.raster.nodata 255
   -nodatalabel 255
   -out ${TEMP}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv
   VALID   --compare-ascii ${NOTOL}
   ${OTBAPP_BASELINE_FILES}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv
   ${TEMP}/apTvComputeConfusionMatrixExtraProdLabelsROut.csv)
 
+#----------- ComputeContingencyTable TESTS ----------------
+
+otb_test_application(NAME apTvComputeContingencyTableExtraProducedLabelsR
+  APP  ComputeConfusionMatrix
+  OPTIONS -in ${INPUTDATA}/Classification/clLabeledImageQB456_1_NoData_255.tif
+  -ref raster
+  -ref.raster.in ${INPUTDATA}/Classification/QB_1_ortho_C8.tif
+  -ref.raster.nodata 255
+  -nodatalabel 255
+  -format contingencytable
+  -out ${TEMP}/apTvComputeContingencyTableExtraProdLabelsROut.csv
+  VALID   --compare-ascii ${NOTOL}
+  ${OTBAPP_BASELINE_FILES}/apTvComputeContingencyTableExtraProdLabelsROut.csv
+  ${TEMP}/apTvComputeContingencyTableExtraProdLabelsROut.csv)
 
 #----------- FusionOfClassifications TESTS ----------------
 otb_test_application(NAME apTvFusionOfClassificationsDSPrecision6Inputs

Modules/Learning/Unsupervised/include/otbContingencyTable.h

+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ *     https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef otbContingencyTable_h
+#define otbContingencyTable_h
+
+#include <vector>
+#include <iostream>
+#include <iomanip>
+#include <itkObject.h>
+#include <itkObjectFactory.h>
+#include <itkVariableSizeMatrix.h>
+
+namespace otb
+{
+template<class TClassLabel>
+class ContingencyTable : public itk::Object
+{
+public:
+  /** Standard class typedefs */
+  typedef ContingencyTable              Self;
+  typedef itk::Object                   Superclass;
+  typedef itk::SmartPointer<Self>       Pointer;
+  typedef itk::SmartPointer<const Self> ConstPointer;
+
+  /** Run-time type information (and related methods). */
+  itkTypeMacro(ContingencyTableCalculator, itk::Object);
+
+  /** Method for creation through the object factory. */
+  itkNewMacro(Self);
+
+  typedef itk::VariableSizeMatrix<unsigned long> MatrixType;
+  typedef std::vector<TClassLabel>               LabelList;
+
+  MatrixType matrix;
+
+  void SetLabels(const LabelList &referenceLabels, const LabelList &producedLabels)
+  {
+    m_RefLabels = referenceLabels;
+    m_ProdLabels = producedLabels;
+    unsigned int rows = static_cast<unsigned int>(m_RefLabels.size());
+    unsigned int cols = static_cast<unsigned int>(m_ProdLabels.size());
+    matrix.SetSize( rows, cols );
+    matrix.Fill( 0 );
+  }
+
+  friend std::ostream &operator<<(std::ostream &o, const ContingencyTable<TClassLabel> &contingencyTable)
+  {
+
+    // Retrieve the maximal width from the matrix and the labels
+    size_t maxWidth = 6;
+    maxWidth = GetLabelsMaximumLength(contingencyTable.m_ProdLabels, maxWidth);
+    maxWidth = GetLabelsMaximumLength(contingencyTable.m_RefLabels, maxWidth);
+
+    for( unsigned int i = 0; i < contingencyTable.matrix.Rows(); ++i )
+      {
+      for( unsigned int j = 0; j < contingencyTable.matrix.Cols(); ++j )
+        {
+        std::ostringstream oss;
+        oss << contingencyTable.matrix( i, j );
+        size_t length = oss.str().length();
+        if( length > maxWidth )
+          maxWidth = length;
+        }
+      }
+
+    int width = static_cast<int>(maxWidth)+1;
+
+    // Write the first line of the matrix (produced labels)
+    o << std::setfill(' ') << std::setw( width ) << "labels";
+    for( size_t i = 0; i < contingencyTable.m_ProdLabels.size(); ++i )
+      {
+      o << std::setfill(' ') << std::setw( width ) << contingencyTable.m_ProdLabels[i];
+      }
+    o << std::endl;
+
+    // For each line write the reference label, then the count value
+    for( unsigned int i = 0; i < contingencyTable.matrix.Rows(); ++i )
+      {
+      o << std::setfill(' ') << std::setw( width ) << contingencyTable.m_RefLabels[i];
+      for( unsigned int j = 0; j < contingencyTable.matrix.Cols(); ++j )
+        {
+        o << std::setfill(' ') << std::setw( width ) << contingencyTable.matrix( i, j );
+        }
+      o << std::endl;
+      }
+
+    return o;
+  }
+
+  std::string ToCSV() const
+  {
+    const char separator = ',';
+
+    std::ostringstream oss;
+    oss << "labels";
+    for( size_t i = 0; i < m_ProdLabels.size(); ++i )
+      {
+      oss << separator << m_ProdLabels[i];
+      }
+    oss << std::endl;
+
+    // For each line write the reference label, then the count value
+    for( unsigned int i = 0; i < matrix.Rows(); ++i )
+      {
+      oss << m_RefLabels[i];
+      for( unsigned int j = 0; j < matrix.Cols(); ++j )
+        {
+        oss << separator << matrix( i, j );
+        }
+      oss << std::endl;
+      }
+    oss << std::endl;
+
+    return oss.str();
+  }
+
+protected:
+  ContingencyTable()
+  {
+    SetLabels(LabelList{}, LabelList{});
+  }
+  ~ContingencyTable() ITK_OVERRIDE {}
+  void PrintSelf(std::ostream& os, itk::Indent itkNotUsed(indent)) const ITK_OVERRIDE
+  {
+    os << *this;
+  }
+
+private:
+  ContingencyTable(const Self &); //purposely not implemented
+  void operator=(const Self &); //purposely not implemented
+
+  static size_t GetLabelsMaximumLength(const LabelList& labels, size_t maxWidth)
+  {
+    size_t tmpMaxWidth = maxWidth;
+    for( size_t i = 0; i < labels.size(); ++i )
+      {
+      std::ostringstream oss;
+      oss << labels[i];
+      size_t length = oss.str().length();
+      if( length > tmpMaxWidth )
+        tmpMaxWidth = length;
+      }
+    return tmpMaxWidth;
+  }
+
+  LabelList m_RefLabels;
+  LabelList m_ProdLabels;
+
+
+};
+}
+
+#endif //otbContingencyTable_h

Modules/Learning/Unsupervised/include/otbContingencyTableCalculator.h

+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ *     https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef otbContingencyTableCalculator_h
+#define otbContingencyTableCalculator_h
+
+#include "itkObject.h"
+#include "itkObjectFactory.h"
+#include "otbContingencyTable.h"
+#include <map>
+
+namespace otb
+{
+/**
+ * \brief  ContingencyTableCalculator provide facilities to compute ContingencyTable.
+ *
+ * The ContingencyTableCalculator can be used with different structure type,
+ * the size of the label list should be the same for both list.
+ *
+ * \tparam TClassLabel the label data type
+ * \ingroup OTBUnsupervised
+ */
+template<class TClassLabel>
+class ITK_EXPORT ContingencyTableCalculator : public itk::Object
+{
+public:
+  /** Standard class typedefs */
+  typedef ContingencyTableCalculator    Self;
+  typedef itk::Object                   Superclass;
+  typedef itk::SmartPointer <Self>      Pointer;
+  typedef itk::SmartPointer<const Self> ConstPointer;
+
+  /** Run-time type information (and related methods). */
+  itkTypeMacro(ContingencyTableCalculator, itk::Object);
+
+  /** Method for creation through the object factory. */
+  itkNewMacro(Self);
+
+  typedef ContingencyTable<TClassLabel>          ContingencyTableType;
+  typedef typename ContingencyTableType::Pointer ContingencyTablePointerType;
+
+  typedef typename std::map<TClassLabel, unsigned long> CountMapType;
+  typedef typename std::map<TClassLabel, CountMapType > MapOfClassesType;
+
+  /** Populate the confusion Matrix for a image iteration.
+   *  \tparam TRefListLabel data structure type which contain the reference labels.
+   *  \tparam TProdListLabel data structure type which contain the produced labels.
+   */
+  template<class TRefIterator, class TProdIterator>
+  void Compute(TRefIterator itRef, TProdIterator itProd, bool refHasNoData = false, typename TRefIterator::InternalPixelType refNoData = 0,
+               bool prodHasNoData = false, typename TProdIterator::InternalPixelType prodNoData = 0);
+
+  /** Populate the confusion Matrix with input which provide GetMeasurementVector()[0] access
+   *  \tparam TRefListLabel data structure type which contain the reference labels.
+   *  \tparam TProdListLabel data structure type which contain the produced labels.
+   */
+  template<class TRefIterator, class TProdIterator>
+  void Compute(TRefIterator refBegin, TRefIterator refEnd, TProdIterator prodBegin, TProdIterator prodEnd);
+
+  itkGetConstMacro(NumberOfRefClasses, unsigned long);
+  itkGetConstMacro(NumberOfProdClasses, unsigned long);
+  itkGetConstMacro(NumberOfSamples, unsigned long);
+
+  void Clear();
+  ContingencyTablePointerType BuildContingencyTable();
+
+protected:
+  ContingencyTableCalculator();
+  ~ContingencyTableCalculator() ITK_OVERRIDE {}
+  //void PrintSelf(std::ostream& os, itk::Indent indent) const ITK_OVERRIDE;
+
+private:
+  ContingencyTableCalculator(const Self &); //purposely not implemented
+  void operator=(const Self &); //purposely not implemented
+
+  MapOfClassesType m_LabelCount;
+  unsigned long m_NumberOfRefClasses;
+  unsigned long m_NumberOfProdClasses;
+  unsigned long m_NumberOfSamples;
+};
+}
+
+#ifndef OTB_MANUAL_INSTANTIATION
+#include "otbContingencyTableCalculator.txx"
+#endif
+
+#endif //otbContingencyTableCalculator_h

Modules/Learning/Unsupervised/include/otbContingencyTableCalculator.txx

+/*
+ * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ *     https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef otbContingencyTableCalculator_txx
+#define otbContingencyTableCalculator_txx
+
+#include "otbContingencyTableCalculator.h"
+#include "itkMacro.h"
+#include "itkVariableLengthVector.h"
+#include "itkListSample.h"
+
+#include <set>
+#include <iterator>
+
+namespace otb
+{
+template<class TClassLabel>
+ContingencyTableCalculator<TClassLabel>::ContingencyTableCalculator()
+        : m_NumberOfRefClasses(0), m_NumberOfProdClasses(0), m_NumberOfSamples(0)
+{}
+
+template<class TClassLabel>
+void
+ContingencyTableCalculator<TClassLabel>
+::Clear()
+{
+  m_LabelCount.clear();
+  m_NumberOfRefClasses = 0;
+  m_NumberOfProdClasses = 0;
+  m_NumberOfSamples = 0;
+}
+
+template<class TClassLabel>
+template<class TRefIterator, class TProdIterator>
+void
+ContingencyTableCalculator<TClassLabel>
+::Compute(TRefIterator refBegin, TRefIterator refEnd, TProdIterator prodBegin, TProdIterator prodEnd)
+{
+  while( refBegin != refEnd && prodBegin != prodEnd )
+    {
+    ++m_LabelCount[refBegin.GetMeasurementVector()[0]][prodBegin.GetMeasurementVector()[0]];
+    ++refBegin;
+    ++prodBegin;
+    ++m_NumberOfSamples;
+    }
+
+  if( refBegin != refEnd || prodBegin != prodEnd )
+    itkExceptionMacro(<< "The references and produced labels did not end simultaneously.");
+}
+
+template<class TClassLabel>
+template<class TRefIterator, class TProdIterator>
+void
+ContingencyTableCalculator<TClassLabel>
+::Compute(TRefIterator itRef, TProdIterator itProd, bool refHasNoData, typename TRefIterator::InternalPixelType refNoData,
+          bool prodHasNoData, typename TProdIterator::InternalPixelType prodNoData)
+{
+  while( !itRef.IsAtEnd() && !itProd.IsAtEnd() )
+    {
+    if((!prodHasNoData || itProd.Get()!=prodNoData)
+       &&(!refHasNoData || itRef.Get()!=refNoData))
+      {
+      ++m_LabelCount[itRef.Get()][itProd.Get()];
+      ++m_NumberOfSamples;
+      }
+    ++itRef;
+    ++itProd;
+    }
+
+  if( !itRef.IsAtEnd() || !itProd.IsAtEnd() )
+    itkExceptionMacro(<< "The references and produced labels did not end simultaneously.");
+
+}
+
+
+template<class TClassLabel>
+typename ContingencyTableCalculator<TClassLabel>::ContingencyTablePointerType
+ContingencyTableCalculator<TClassLabel>
+::BuildContingencyTable()
+{
+  std::set<TClassLabel> refLabels;
+  std::set<TClassLabel> prodLabels;
+
+  // Retrieve all labels needed to iterate over all labelCount
+  for(typename MapOfClassesType::const_iterator refIt = m_LabelCount.begin(); refIt != m_LabelCount.end(); ++refIt)
+    {
+    refLabels.insert(refIt->first);
+    CountMapType cmt = refIt->second;
+    for(typename CountMapType::const_iterator prodIt = cmt.begin(); prodIt != cmt.end(); ++prodIt)
+      {
+      prodLabels.insert(prodIt->first);
+      }
+    }
+
+  m_NumberOfRefClasses = refLabels.size();
+  m_NumberOfProdClasses = prodLabels.size();
+
+  unsigned int rows = static_cast<unsigned int>(m_NumberOfRefClasses);
+  unsigned int cols = static_cast<unsigned int>(m_NumberOfProdClasses);
+
+  std::vector<TClassLabel> referenceLabels(refLabels.begin(), refLabels.end());
+  std::vector<TClassLabel> producedLabels(prodLabels.begin(), prodLabels.end());
+
+  ContingencyTablePointerType contingencyTable = ContingencyTableType::New();
+  contingencyTable->SetLabels(referenceLabels, producedLabels);
+
+  for( unsigned int i = 0; i < rows; ++i )
+    for( unsigned int j = 0; j < cols; ++j )
+      contingencyTable->matrix(i,j) = m_LabelCount[referenceLabels[i]][producedLabels[j]];
+
+
+  return contingencyTable;
+}
+
+}
+
+#endif

Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h

@@ -20,6 +20,7 @@
 #ifndef otbSharkKMeansMachineLearningModel_h
 #define otbSharkKMeansMachineLearningModel_h
 
+#include "boost/shared_ptr.hpp"
 #include "itkLightObject.h"
 #include "otbMachineLearningModel.h"
 

Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx

@@ -21,6 +21,7 @@
 #define otbSharkKMeansMachineLearningModel_txx
 
 #include <fstream>
+#include "boost/make_shared.hpp"
 #include "itkMacro.h"
 #include "otbSharkKMeansMachineLearningModel.h"
 
@@ -54,7 +55,7 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
         m_Normalized( false ), m_K(2), m_MaximumNumberOfIterations( 10 )
 {
   // Default set HardClusteringModel
-  m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
+  m_ClusteringModel = boost::make_shared<ClusteringModelType>( &m_Centroids );
 }
 
 
@@ -81,7 +82,7 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
 
   // Use a Hard Clustering Model for classification
   shark::kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations );
-  m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
+  m_ClusteringModel = boost::make_shared<ClusteringModelType>( &m_Centroids );
 }
 
 template<class TInputValue, class TOutputValue>
@@ -153,7 +154,7 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
   shark::Data<ClusteringOutputType> clusters;
   try
     {
-     clusters = ( *m_ClusteringModel )( inputSamples );
+    clusters = ( *m_ClusteringModel )( inputSamples );
     }
   catch( ... )
     {

Modules/Learning/Unsupervised/test/CMakeLists.txt

@@ -3,10 +3,24 @@ set(OTBUnsupervisedTests
   otbUnsupervisedTestDriver.cxx
   otbMachineLearningUnsupervisedModelCanRead.cxx
   otbTrainMachineLearningUnsupervisedModel.cxx
+  otbContingencyTableCalculatorTest.cxx
   )
 
 # Tests Declaration
 
+otb_add_test(NAME leTuContingencyTableCalculatorNew COMMAND otbUnsupervisedTestDriver
+  otbContingencyTableCalculatorNew)
+