ITK/Examples/Statistics/KdTreeBasedKMeansClustering1D: Difference between revisions

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(Created page with "==KdTreeBasedKMeansClustering_1D.cxx== <source lang="cpp"> #include "itkDecisionRule.h" #include "itkVector.h" #include "itkListSample.h" #include "itkKdTree.h" #include "itkWeig...")
 
(Removed renderer->Render(). the renderwindow should be rendered.)
 
(11 intermediate revisions by 2 users not shown)
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==KdTreeBasedKMeansClustering_1D.cxx==
<div class="floatcenter">[[File:ITK_Examples_Baseline_Statistics_TestKdTreeBasedKMeansClustering1D.png]]</div>
Cluster a collection of measurements using the KMeans algorithm. The name "KdTreeBased" indicates that this is an efficient implementation which uses a KdTree.
 
==KdTreeBasedKMeansClustering1D.cxx==
<source lang="cpp">
<source lang="cpp">
#include "itkDecisionRule.h"
#include "itkDecisionRule.h"
Line 7: Line 10:
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#if ITK_VERSION_MAJOR < 4
#include "itkMinimumDecisionRule2.h"
#include "itkMinimumDecisionRule2.h"
#else
#include "itkMinimumDecisionRule.h"
#endif
#include "itkEuclideanDistanceMetric.h"
#include "itkEuclideanDistanceMetric.h"
#include "itkDistanceToCentroidMembershipFunction.h"
#include "itkDistanceToCentroidMembershipFunction.h"
Line 13: Line 20:
#include "itkNormalVariateGenerator.h"
#include "itkNormalVariateGenerator.h"


#include "vtkVersion.h"
#include "vtkActor.h"
#include "vtkActor.h"
#include "vtkInteractorStyleTrackballCamera.h"
#include "vtkInteractorStyleTrackballCamera.h"
Line 24: Line 32:
#include "vtkVertexGlyphFilter.h"
#include "vtkVertexGlyphFilter.h"


int main()
int main(int, char *[])
{
{
   typedef itk::Vector< double, 1 > MeasurementVectorType;
   typedef itk::Vector< double, 1 > MeasurementVectorType;
Line 88: Line 96:
   typedef MembershipFunctionType::Pointer                      MembershipFunctionPointer;
   typedef MembershipFunctionType::Pointer                      MembershipFunctionPointer;
    
    
#if ITK_VERSION_MAJOR < 4
   typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType;
   typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType;
#else
  typedef itk::Statistics::MinimumDecisionRule DecisionRuleType;
#endif
   DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
   DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();


Line 112: Line 124:
    
    
   MembershipFunctionVectorObjectType::Pointer membershipFunctionsObject =
   MembershipFunctionVectorObjectType::Pointer membershipFunctionsObject =
                                        MembershipFunctionVectorObjectType::New();
    MembershipFunctionVectorObjectType::New();
   classifier->SetMembershipFunctions( membershipFunctionsObject );
   classifier->SetMembershipFunctions( membershipFunctionsObject );


Line 168: Line 180:
   vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter1 =
   vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter1 =
     vtkSmartPointer<vtkVertexGlyphFilter>::New();
     vtkSmartPointer<vtkVertexGlyphFilter>::New();
#if VTK_MAJOR_VERSION <= 5
   glyphFilter1->SetInputConnection(polyData1->GetProducerPort());
   glyphFilter1->SetInputConnection(polyData1->GetProducerPort());
#else
  glyphFilter1->SetInputData(polyData1);
#endif
   glyphFilter1->Update();
   glyphFilter1->Update();
   vtkSmartPointer<vtkPolyDataMapper> mapper1 =
   vtkSmartPointer<vtkPolyDataMapper> mapper1 =
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   vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter2 =
   vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter2 =
     vtkSmartPointer<vtkVertexGlyphFilter>::New();
     vtkSmartPointer<vtkVertexGlyphFilter>::New();
#if VTK_MAJOR_VERSION <= 5
   glyphFilter2->SetInputConnection(polyData2->GetProducerPort());
   glyphFilter2->SetInputConnection(polyData2->GetProducerPort());
#else
  glyphFilter2->SetInputData(polyData2);
#endif
   glyphFilter2->Update();
   glyphFilter2->Update();
   vtkSmartPointer<vtkPolyDataMapper> mapper2 =
   vtkSmartPointer<vtkPolyDataMapper> mapper2 =
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   renderer->AddActor(actor2);
   renderer->AddActor(actor2);
   renderer->ResetCamera();
   renderer->ResetCamera();
  renderer->Render();


   vtkSmartPointer<vtkRenderWindowInteractor> renderWindowInteractor =
   vtkSmartPointer<vtkRenderWindowInteractor> renderWindowInteractor =
Line 214: Line 233:


   renderWindowInteractor->SetInteractorStyle(style);
   renderWindowInteractor->SetInteractorStyle(style);
   renderWindowInteractor->SetRenderWindow(renderWindow);
   renderWindowInteractor->SetRenderWindow(renderWindow);
  renderWindowInteractor->Initialize();


   renderWindowInteractor->Start();
   renderWindowInteractor->Start();
Line 224: Line 241:
</source>
</source>


==CMakeLists.txt==
<source lang="cmake">
cmake_minimum_required(VERSION 2.6)
PROJECT(KdTreeBasedKMeansClustering)


FIND_PACKAGE(VTK REQUIRED)
{{ITKVTKCMakeLists|{{SUBPAGENAME}}|}}
INCLUDE(${VTK_USE_FILE})
 
FIND_PACKAGE(ITK REQUIRED)
INCLUDE(${ITK_USE_FILE})
 
ADD_EXECUTABLE(KdTreeBasedKMeansClustering KdTreeBasedKMeansClustering.cxx)
TARGET_LINK_LIBRARIES(KdTreeBasedKMeansClustering
ITKBasicFilters ITKCommon ITKIO ITKStatistics
vtkHybrid)
 
</source>

Latest revision as of 15:50, 24 August 2013

ITK Examples Baseline Statistics TestKdTreeBasedKMeansClustering1D.png

Cluster a collection of measurements using the KMeans algorithm. The name "KdTreeBased" indicates that this is an efficient implementation which uses a KdTree.

KdTreeBasedKMeansClustering1D.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. if ITK_VERSION_MAJOR < 4
  8. include "itkMinimumDecisionRule2.h"
  9. else
  10. include "itkMinimumDecisionRule.h"
  11. endif
  12. include "itkEuclideanDistanceMetric.h"
  13. include "itkDistanceToCentroidMembershipFunction.h"
  14. include "itkSampleClassifierFilter.h"
  15. include "itkNormalVariateGenerator.h"
  1. include "vtkVersion.h"
  2. include "vtkActor.h"
  3. include "vtkInteractorStyleTrackballCamera.h"
  4. include "vtkPolyData.h"
  5. include "vtkPolyDataMapper.h"
  6. include "vtkProperty.h"
  7. include "vtkRenderer.h"
  8. include "vtkRenderWindow.h"
  9. include "vtkRenderWindowInteractor.h"
  10. include "vtkSmartPointer.h"
  11. include "vtkVertexGlyphFilter.h"

int main(int, char *[]) {

 typedef itk::Vector< double, 1 > MeasurementVectorType;
 typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
 SampleType::Pointer sample = SampleType::New();
 sample->SetMeasurementVectorSize( 1 );
 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;
   sample->PushBack( mv );
   }
 normalGenerator->Initialize( 3024 );
 mean = 200;
 standardDeviation = 30;
 for ( unsigned int i = 0 ; i < 100 ; ++i )
   {
   mv[0] = ( 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(2);
 initialMeans[0] = 0.0;
 initialMeans[1] = 0.0;
 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 < 2 ; ++i )
   {
   std::cout << "cluster[" << i << "] " << std::endl;
   std::cout << "    estimated mean : " << estimatedMeans[i] << std::endl;
   }
 typedef itk::Statistics::DistanceToCentroidMembershipFunction< MeasurementVectorType >
   MembershipFunctionType;
 typedef MembershipFunctionType::Pointer                      MembershipFunctionPointer;
 
  1. if ITK_VERSION_MAJOR < 4
 typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType;
  1. else
 typedef itk::Statistics::MinimumDecisionRule DecisionRuleType;
  1. 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], 0, 0);
     }
   else
     {
     points2->InsertNextPoint(iter.GetMeasurementVector()[0], 0, 0);
     }
   ++iter;
   }
 vtkSmartPointer<vtkPolyData> polyData1 =
   vtkSmartPointer<vtkPolyData>::New();
 polyData1->SetPoints(points1);
 vtkSmartPointer<vtkVertexGlyphFilter> glyphFilter1 =
   vtkSmartPointer<vtkVertexGlyphFilter>::New();
  1. if VTK_MAJOR_VERSION <= 5
 glyphFilter1->SetInputConnection(polyData1->GetProducerPort());
  1. else
 glyphFilter1->SetInputData(polyData1);
  1. 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();
  1. if VTK_MAJOR_VERSION <= 5
 glyphFilter2->SetInputConnection(polyData2->GetProducerPort());
  1. else
 glyphFilter2->SetInputData(polyData2);
  1. 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();
 vtkSmartPointer<vtkRenderWindowInteractor> renderWindowInteractor =
   vtkSmartPointer<vtkRenderWindowInteractor>::New();
 vtkSmartPointer<vtkInteractorStyleTrackballCamera> style =
   vtkSmartPointer<vtkInteractorStyleTrackballCamera>::New();
 renderWindowInteractor->SetInteractorStyle(style);
 renderWindowInteractor->SetRenderWindow(renderWindow);
 renderWindowInteractor->Start();
 return EXIT_SUCCESS;

} </source>


CMakeLists.txt

<syntaxhighlight lang="cmake"> cmake_minimum_required(VERSION 3.9.5)

project(KdTreeBasedKMeansClustering1D)

find_package(ITK REQUIRED) include(${ITK_USE_FILE}) if (ITKVtkGlue_LOADED)

 find_package(VTK REQUIRED)
 include(${VTK_USE_FILE})

else()

 find_package(ItkVtkGlue REQUIRED)
 include(${ItkVtkGlue_USE_FILE})
 set(Glue ItkVtkGlue)

endif()

add_executable(KdTreeBasedKMeansClustering1D MACOSX_BUNDLE KdTreeBasedKMeansClustering1D.cxx) target_link_libraries(KdTreeBasedKMeansClustering1D

 ${Glue}  ${VTK_LIBRARIES} ${ITK_LIBRARIES})

</syntaxhighlight>

Download and Build KdTreeBasedKMeansClustering1D

Click here to download KdTreeBasedKMeansClustering1D. and its CMakeLists.txt file. Once the tarball KdTreeBasedKMeansClustering1D.tar has been downloaded and extracted,

cd KdTreeBasedKMeansClustering1D/build 
  • If ITK is installed:
cmake ..
  • If ITK is not installed but compiled on your system, you will need to specify the path to your ITK build:
cmake -DITK_DIR:PATH=/home/me/itk_build ..

Build the project,

make

and run it:

./KdTreeBasedKMeansClustering1D

WINDOWS USERS PLEASE NOTE: Be sure to add the VTK and ITK bin directories to your path. This will resolve the VTK and ITK dll's at run time.

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

ITK >= 4

For examples that use QuickView (which depends on VTK), you must have built ITK with Module_ITKVtkGlue=ON.

ITK < 4

Some of the ITK Examples require VTK to display the images. 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.