ITK/ExamplesBoneyard/KdTreeBasedKMeansClustering 2D: Difference between revisions

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(Created page with "==KdTreeBasedKMeansClustering_2D.cxx== <source lang="cpp"> #include "itkDecisionRule.h" #include "itkVector.h" #include "itkListSample.h" #include "itkKdTree.h" #include "itkWeig...")
 
(Wrong signature for main.)
Line 24: Line 24:
#include "vtkVertexGlyphFilter.h"
#include "vtkVertexGlyphFilter.h"


int main()
int main(int, char *[])
{
{
   typedef itk::Vector< double, 2 > MeasurementVectorType;
   typedef itk::Vector< double, 2 > MeasurementVectorType;
Line 115: Line 115:


   MembershipFunctionVectorObjectType::Pointer membershipFunctionsObject =
   MembershipFunctionVectorObjectType::Pointer membershipFunctionsObject =
                                        MembershipFunctionVectorObjectType::New();
    MembershipFunctionVectorObjectType::New();
   classifier->SetMembershipFunctions( membershipFunctionsObject );
   classifier->SetMembershipFunctions( membershipFunctionsObject );



Revision as of 13:06, 19 November 2010

KdTreeBasedKMeansClustering_2D.cxx

<source lang="cpp">

  1. include "itkDecisionRule.h"
  2. include "itkVector.h"
  3. include "itkListSample.h"
  4. include "itkKdTree.h"
  5. include "itkWeightedCentroidKdTreeGenerator.h"
  6. include "itkKdTreeBasedKmeansEstimator.h"
  7. include "itkMinimumDecisionRule2.h"
  8. include "itkEuclideanDistanceMetric.h"
  9. include "itkDistanceToCentroidMembershipFunction.h"
  10. include "itkSampleClassifierFilter.h"
  11. include "itkNormalVariateGenerator.h"
  1. include "vtkActor.h"
  2. include "vtkInteractorStyleTrackballCamera.h"
  3. include "vtkPolyData.h"
  4. include "vtkPolyDataMapper.h"
  5. include "vtkProperty.h"
  6. include "vtkRenderer.h"
  7. include "vtkRenderWindow.h"
  8. include "vtkRenderWindowInteractor.h"
  9. include "vtkSmartPointer.h"
  10. include "vtkVertexGlyphFilter.h"

int main(int, char *[]) {

 typedef itk::Vector< double, 2 > MeasurementVectorType;
 typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
 SampleType::Pointer sample = SampleType::New();
 typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
 NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
 normalGenerator->Initialize( 101 );
 MeasurementVectorType mv;
 double mean = 100;
 double standardDeviation = 30;
 for ( unsigned int i = 0 ; i < 100 ; ++i )
   {
   mv[0] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
   mv[1] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
   sample->PushBack( mv );
   }
 normalGenerator->Initialize( 3024 );
 mean = 200;
 standardDeviation = 30;
 for ( unsigned int i = 0 ; i < 100 ; ++i )
   {
   mv[0] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
   mv[1] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
   sample->PushBack( mv );
   }
 typedef itk::Statistics::WeightedCentroidKdTreeGenerator< SampleType >
   TreeGeneratorType;
 TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New();
 treeGenerator->SetSample( sample );
 treeGenerator->SetBucketSize( 16 );
 treeGenerator->Update();
 typedef TreeGeneratorType::KdTreeType TreeType;
 typedef itk::Statistics::KdTreeBasedKmeansEstimator<TreeType> EstimatorType;
 EstimatorType::Pointer estimator = EstimatorType::New();
 EstimatorType::ParametersType initialMeans(4);
 initialMeans[0] = 0.0; // Cluster 1, mean[0]
 initialMeans[1] = 0.0; // Cluster 1, mean[1]
 initialMeans[2] = 5.0; // Cluster 2, mean[0]
 initialMeans[3] = 5.0; // Cluster 2, mean[1]
 estimator->SetParameters( initialMeans );
 estimator->SetKdTree( treeGenerator->GetOutput() );
 estimator->SetMaximumIteration( 200 );
 estimator->SetCentroidPositionChangesThreshold(0.0);
 estimator->StartOptimization();
 EstimatorType::ParametersType estimatedMeans = estimator->GetParameters();
 for ( unsigned int i = 0 ; i < 4 ; i+=2 )
   {
   std::cout << "cluster[" << i << "] " << std::endl;
   std::cout << "    estimated mean : " << estimatedMeans[i] << " , " << estimatedMeans[i+1] << std::endl;
   }
 typedef itk::Statistics::DistanceToCentroidMembershipFunction< MeasurementVectorType >
   MembershipFunctionType;
 typedef MembershipFunctionType::Pointer                      MembershipFunctionPointer;
 typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType;
 DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
 typedef itk::Statistics::SampleClassifierFilter< SampleType > ClassifierType;
 ClassifierType::Pointer classifier = ClassifierType::New();
 classifier->SetDecisionRule(decisionRule);
 classifier->SetInput( sample );
 classifier->SetNumberOfClasses( 2 );
 typedef ClassifierType::ClassLabelVectorObjectType               ClassLabelVectorObjectType;
 typedef ClassifierType::ClassLabelVectorType                     ClassLabelVectorType;
 typedef ClassifierType::MembershipFunctionVectorObjectType       MembershipFunctionVectorObjectType;
 typedef ClassifierType::MembershipFunctionVectorType             MembershipFunctionVectorType;
 ClassLabelVectorObjectType::Pointer  classLabelsObject = ClassLabelVectorObjectType::New();
 classifier->SetClassLabels( classLabelsObject );
 ClassLabelVectorType &  classLabelsVector = classLabelsObject->Get();
 classLabelsVector.push_back( 100 );
 classLabelsVector.push_back( 200 );


 MembershipFunctionVectorObjectType::Pointer membershipFunctionsObject =
   MembershipFunctionVectorObjectType::New();
 classifier->SetMembershipFunctions( membershipFunctionsObject );
 MembershipFunctionVectorType &  membershipFunctionsVector = membershipFunctionsObject->Get();
 MembershipFunctionType::CentroidType origin( sample->GetMeasurementVectorSize() );
 int index = 0;
 for ( unsigned int i = 0 ; i < 2 ; i++ )
   {
   MembershipFunctionPointer membershipFunction = MembershipFunctionType::New();
   for ( unsigned int j = 0 ; j < sample->GetMeasurementVectorSize(); j++ )
     {
     origin[j] = estimatedMeans[index++];
     }
   membershipFunction->SetCentroid( origin );
   membershipFunctionsVector.push_back( membershipFunction.GetPointer() );
   }
 classifier->Update();
 const ClassifierType::MembershipSampleType* membershipSample = classifier->GetOutput();
 ClassifierType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();
 while ( iter != membershipSample->End() )
   {
   std::cout << "measurement vector = " << iter.GetMeasurementVector()
             << "class label = " << iter.GetClassLabel()
             << std::endl;
   ++iter;
   }
 // Visualize
 vtkSmartPointer<vtkPoints> points1 =
   vtkSmartPointer<vtkPoints>::New();
 vtkSmartPointer<vtkPoints> points2 =
   vtkSmartPointer<vtkPoints>::New();
 iter = membershipSample->Begin();
 while ( iter != membershipSample->End() )
   {
   if(iter.GetClassLabel() == 100)
     {
     points1->InsertNextPoint(iter.GetMeasurementVector()[0], iter.GetMeasurementVector()[1], 0);
     }
   else
     {
     points2->InsertNextPoint(iter.GetMeasurementVector()[0], iter.GetMeasurementVector()[1], 0);
     }
   ++iter;
   }
 vtkSmartPointer<vtkPolyData> polyData1 =
   vtkSmartPointer<vtkPolyData>::New();
 polyData1->SetPoints(points1);
 vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter1 =
   vtkSmartPointer<vtkVertexGlyphFilter>::New();
 glyphFilter1->SetInputConnection(polyData1->GetProducerPort());
 glyphFilter1->Update();
 vtkSmartPointer<vtkPolyDataMapper> mapper1 =
   vtkSmartPointer<vtkPolyDataMapper>::New();
 mapper1->SetInputConnection(glyphFilter1->GetOutputPort());
 vtkSmartPointer<vtkActor> actor1 =
   vtkSmartPointer<vtkActor>::New();
 actor1->GetProperty()->SetColor(0,1,0);
 actor1->GetProperty()->SetPointSize(3);
 actor1->SetMapper(mapper1);
 vtkSmartPointer<vtkPolyData> polyData2 =
   vtkSmartPointer<vtkPolyData>::New();
 polyData2->SetPoints(points2);
 vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter2 =
   vtkSmartPointer<vtkVertexGlyphFilter>::New();
 glyphFilter2->SetInputConnection(polyData2->GetProducerPort());
 glyphFilter2->Update();
 vtkSmartPointer<vtkPolyDataMapper> mapper2 =
   vtkSmartPointer<vtkPolyDataMapper>::New();
 mapper2->SetInputConnection(glyphFilter2->GetOutputPort());
 vtkSmartPointer<vtkActor> actor2 =
   vtkSmartPointer<vtkActor>::New();
 actor2->GetProperty()->SetColor(1,0,0);
 actor2->GetProperty()->SetPointSize(3);
 actor2->SetMapper(mapper2);
 vtkSmartPointer<vtkRenderWindow> renderWindow =
   vtkSmartPointer<vtkRenderWindow>::New();
 renderWindow->SetSize(300,300);
 vtkSmartPointer<vtkRenderer> renderer =
   vtkSmartPointer<vtkRenderer>::New();
 renderWindow->AddRenderer(renderer);
 renderer->AddActor(actor1);
 renderer->AddActor(actor2);
 renderer->ResetCamera();
 renderer->Render();
 vtkSmartPointer<vtkRenderWindowInteractor> renderWindowInteractor =
   vtkSmartPointer<vtkRenderWindowInteractor>::New();
 vtkSmartPointer<vtkInteractorStyleTrackballCamera> style =
   vtkSmartPointer<vtkInteractorStyleTrackballCamera>::New();
 renderWindowInteractor->SetInteractorStyle(style);
 renderWindowInteractor->SetRenderWindow(renderWindow);
 renderWindowInteractor->Initialize();
 renderWindowInteractor->Start();
 return EXIT_SUCCESS;

} </source>

CMakeLists.txt

<source lang="cmake"> cmake_minimum_required(VERSION 2.6)

PROJECT(KdTreeBasedKMeansClustering_2D)

FIND_PACKAGE(VTK REQUIRED) INCLUDE(${VTK_USE_FILE})

FIND_PACKAGE(ITK REQUIRED) INCLUDE(${ITK_USE_FILE})

ADD_EXECUTABLE(KdTreeBasedKMeansClustering_2D KdTreeBasedKMeansClustering_2D.cxx) TARGET_LINK_LIBRARIES(KdTreeBasedKMeansClustering_2D ITKBasicFilters ITKCommon ITKIO ITKStatistics vtkHybrid)

</source>