ITK/Examples/Statistics/KdTreeBasedKmeansEstimator 3D
From KitwarePublic
Contents |
Description
Cluster a collection of measurements using the KMeans algorithm. The name "KdTreeBased" indicates that this is an efficient implementation which uses a KdTree.
ITK Classes Demonstrated
Output
The input is shown on the left. It consists of a single collection of 3D points that lend themselves to easy clustering into 2 clusters. The output clusters are shown on the right. Points belonging to the same cluster as shown in the same color.
KdTreeBasedKMeansClustering_3D.cxx
#include "itkDecisionRule.h" #include "itkVector.h" #include "itkListSample.h" #include "itkKdTree.h" #include "itkWeightedCentroidKdTreeGenerator.h" #include "itkKdTreeBasedKmeansEstimator.h" #if ITK_VERSION_MAJOR < 4 #include "itkMinimumDecisionRule2.h" #else #include "itkMinimumDecisionRule.h" #endif #include "itkEuclideanDistanceMetric.h" #include "itkDistanceToCentroidMembershipFunction.h" #include "itkSampleClassifierFilter.h" #include "itkNormalVariateGenerator.h" #include "vtkVersion.h" #include "vtkActor.h" #include "vtkInteractorStyleTrackballCamera.h" #include "vtkPolyData.h" #include "vtkPolyDataMapper.h" #include "vtkProperty.h" #include "vtkRenderer.h" #include "vtkRenderWindow.h" #include "vtkRenderWindowInteractor.h" #include "vtkSmartPointer.h" #include "vtkVertexGlyphFilter.h" int main(int, char *[]) { typedef itk::Vector< double, 3 > 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; mv[2] = ( 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; mv[2] = ( 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(6); initialMeans[0] = 0.0; // Cluster 1, mean[0] initialMeans[1] = 0.0; // Cluster 1, mean[1] initialMeans[2] = 0.0; // Cluster 1, mean[2] initialMeans[3] = 5.0; // Cluster 2, mean[0] initialMeans[4] = 5.0; // Cluster 2, mean[1] initialMeans[5] = 5.0; // Cluster 2, mean[2] 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 < 6 ; 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; #if ITK_VERSION_MAJOR < 4 typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType; #else typedef itk::Statistics::MinimumDecisionRule DecisionRuleType; #endif 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], iter.GetMeasurementVector()[2]); } else { points2->InsertNextPoint( iter.GetMeasurementVector()[0], iter.GetMeasurementVector()[1], iter.GetMeasurementVector()[2]); } ++iter; } vtkSmartPointer<vtkPolyData> polyData1 = vtkSmartPointer<vtkPolyData>::New(); polyData1->SetPoints(points1); vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter1 = vtkSmartPointer<vtkVertexGlyphFilter>::New(); #if VTK_MAJOR_VERSION <= 5 glyphFilter1->SetInputConnection(polyData1->GetProducerPort()); #else glyphFilter1->SetInputData(polyData1); #endif 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(); #if VTK_MAJOR_VERSION <= 5 glyphFilter2->SetInputConnection(polyData2->GetProducerPort()); #else glyphFilter2->SetInputData(polyData2); #endif 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; }
CMakeLists.txt
cmake_minimum_required(VERSION 2.8) project(KdTreeBasedKMeansClustering_3D) find_package(ItkVtkGlue REQUIRED) include(${ItkVtkGlue_USE_FILE}) add_executable(KdTreeBasedKMeansClustering_3D KdTreeBasedKMeansClustering_3D.cxx) target_link_libraries(KdTreeBasedKMeansClustering_3D ItkVtkGlue ${VTK_LIBRARIES} ${ITK_LIBRARIES})
Building All of the Examples
Many of the examples in the ITK Wiki Examples Collection require VTK. You can build all of the the examples by following these instructions. If you are a new VTK user, you may want to try the Superbuild which will build a proper ITK and VTK.
ItkVtkGlue
If you download the entire ITK Wiki Examples Collection, the ItkVtkGlue directory will be included and configured. If you wish to just build a few examples, then you will need to download ItkVtkGlue and build it. When you run cmake it will ask you to specify the location of the ItkVtkGlue binary directory.
