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| | {{warning|1=The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.}} |
| | |
| ==ImageKmeansModelEstimator.cxx==
| |
| <source lang="cpp">
| |
| #include "itkImage.h"
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| #include "itkListSample.h"
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| #include "itkVector.h"
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| #include "itkImageKmeansModelEstimator.h"
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| #include "itkImageRegionIteratorWithIndex.h"
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| #include "itkImageToListSampleAdaptor.h"
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| #include "itkDistanceToCentroidMembershipFunction.h"
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| #include "itkSampleClassifierFilter.h"
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| #include "itkMinimumDecisionRule.h"
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| #include "itkImageFileWriter.h"
| |
| | |
| typedef itk::Vector<unsigned char,3> MeasurementVectorType;
| |
| typedef itk::Image<MeasurementVectorType,2> ColorImageType;
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| typedef itk::Image<unsigned char,2> ScalarImageType;
| |
| | |
| static void CreateImage(ColorImageType::Pointer image);
| |
| | |
| int main(int, char* [] )
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| {
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| // Create a demo image
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| ColorImageType::Pointer image = ColorImageType::New();
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| CreateImage(image);
| |
| | |
| // Compute pixel clusters using KMeans
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| typedef itk::Statistics::DistanceToCentroidMembershipFunction< MeasurementVectorType > MembershipFunctionType ;
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| typedef MembershipFunctionType::Pointer MembershipFunctionPointer ;
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| typedef std::vector< MembershipFunctionPointer > MembershipFunctionPointerVector;
| |
| | |
| typedef itk::ImageKmeansModelEstimator<ColorImageType, MembershipFunctionType> ImageKmeansModelEstimatorType;
| |
| | |
| ImageKmeansModelEstimatorType::Pointer kmeansEstimator = ImageKmeansModelEstimatorType::New();
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| kmeansEstimator->SetInputImage(image);
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| kmeansEstimator->SetNumberOfModels(3);
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| kmeansEstimator->SetThreshold(0.01 );
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| kmeansEstimator->SetOffsetAdd( 0.01 );
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| kmeansEstimator->SetOffsetMultiply( 0.01 );
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| kmeansEstimator->SetMaxSplitAttempts( 10 );
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| kmeansEstimator->Update();
| |
| | |
| // Classify each pixel
| |
| typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType ;
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| typedef itk::Statistics::SampleClassifierFilter< SampleType > ClassifierType;
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| ClassifierType::Pointer classifier = ClassifierType::New();
| |
| | |
| typedef itk::Statistics::MinimumDecisionRule DecisionRuleType;
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| DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
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|
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| classifier->SetDecisionRule(decisionRule);
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| classifier->SetNumberOfClasses(3);
| |
| | |
| typedef ClassifierType::ClassLabelVectorObjectType ClassLabelVectorObjectType;
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| typedef ClassifierType::ClassLabelVectorType ClassLabelVectorType;
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| typedef ClassifierType::MembershipFunctionVectorObjectType MembershipFunctionVectorObjectType;
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| typedef ClassifierType::MembershipFunctionVectorType MembershipFunctionVectorType;
| |
| | |
| // Setup membership functions
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| MembershipFunctionPointerVector kmeansMembershipFunctions = kmeansEstimator->GetMembershipFunctions();
| |
| | |
| MembershipFunctionVectorObjectType::Pointer membershipFunctionsVectorObject = MembershipFunctionVectorObjectType::New();
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| classifier->SetMembershipFunctions(membershipFunctionsVectorObject);
| |
| | |
| MembershipFunctionVectorType & membershipFunctionsVector = membershipFunctionsVectorObject->Get();
| |
| | |
| for(unsigned int i = 0; i < kmeansMembershipFunctions.size(); i++)
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| {
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| membershipFunctionsVector.push_back(kmeansMembershipFunctions[i].GetPointer());
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| }
| |
| | |
| // Setup class labels
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| ClassLabelVectorObjectType::Pointer classLabelsObject = ClassLabelVectorObjectType::New();
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| classifier->SetClassLabels( classLabelsObject );
| |
| | |
| ClassLabelVectorType & classLabelsVector = classLabelsObject->Get();
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| classLabelsVector.push_back( 50 );
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| classLabelsVector.push_back( 150 );
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| classLabelsVector.push_back( 250 ); | |
| | |
| // Perform the classification
| |
| typedef itk::Statistics::ImageToListSampleAdaptor< ColorImageType > SampleAdaptorType;
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| SampleAdaptorType::Pointer sample = SampleAdaptorType::New();
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| sample->SetImage(image);
| |
| | |
| classifier->SetInput(sample);
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| classifier->Update();
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|
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| // Prepare the output image
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| ScalarImageType::Pointer outputImage = ScalarImageType::New();
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| outputImage->SetRegions(image->GetLargestPossibleRegion());
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| outputImage->Allocate();
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| outputImage->FillBuffer(0);
| |
| | |
| // Setup the membership iterator
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| const ClassifierType::MembershipSampleType* membershipSample = classifier->GetOutput();
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| ClassifierType::MembershipSampleType::ConstIterator membershipIterator = membershipSample->Begin();
| |
| | |
| // Setup the output image iterator - this is automatically synchronized with the membership iterator since the sample is an adaptor
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| itk::ImageRegionIteratorWithIndex<ScalarImageType> outputIterator(outputImage,outputImage->GetLargestPossibleRegion());
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| outputIterator.GoToBegin();
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|
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| while(membershipIterator != membershipSample->End())
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| {
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| int classLabel = membershipIterator.GetClassLabel();
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| //std::cout << "Class label: " << classLabel << std::endl;
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| outputIterator.Set(classLabel);
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| ++membershipIterator;
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| ++outputIterator;
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| }
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|
| |
| typedef itk::ImageFileWriter< ColorImageType > WriterType;
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| WriterType::Pointer inputWriter = WriterType::New();
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| inputWriter->SetFileName("input.mha");
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| inputWriter->SetInput(image);
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| inputWriter->Update();
| |
| | |
| typedef itk::ImageFileWriter< ScalarImageType > ScalarWriterType;
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| ScalarWriterType::Pointer outputWriter = ScalarWriterType::New();
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| outputWriter->SetFileName("output.mha");
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| outputWriter->SetInput(outputImage);
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| outputWriter->Update();
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|
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|
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| return EXIT_SUCCESS;
| |
| }
| |
| | |
| void CreateImage(ColorImageType::Pointer image)
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| {
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| // Create a black image with a red square and a green square
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| ColorImageType::RegionType region;
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| ColorImageType::IndexType start;
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| start[0] = 0;
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| start[1] = 0;
| |
| | |
| ColorImageType::SizeType size;
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| size[0] = 200;
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| size[1] = 300;
| |
| | |
| region.SetSize(size);
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| region.SetIndex(start);
| |
| | |
| image->SetRegions(region);
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| image->Allocate();
| |
| | |
| itk::ImageRegionIterator<ColorImageType> imageIterator(image,region);
| |
| | |
| itk::Vector<unsigned char, 3> redPixel;
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| redPixel[0] = 255;
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| redPixel[1] = 0;
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| redPixel[2] = 0;
| |
| | |
| itk::Vector<unsigned char, 3> greenPixel;
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| greenPixel[0] = 0;
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| greenPixel[1] = 255;
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| greenPixel[2] = 0;
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|
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| itk::Vector<unsigned char, 3> blackPixel;
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| blackPixel[0] = 0;
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| blackPixel[1] = 0;
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| blackPixel[2] = 0;
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|
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| while(!imageIterator.IsAtEnd())
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| {
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| if(imageIterator.GetIndex()[0] > 100 &&
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| imageIterator.GetIndex()[0] < 150 &&
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| imageIterator.GetIndex()[1] > 100 &&
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| imageIterator.GetIndex()[1] < 150)
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| {
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| imageIterator.Set(redPixel);
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| }
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| else if(imageIterator.GetIndex()[0] > 50 &&
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| imageIterator.GetIndex()[0] < 70 &&
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| imageIterator.GetIndex()[1] > 50 &&
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| imageIterator.GetIndex()[1] < 70)
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| {
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| imageIterator.Set(greenPixel);
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| }
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| else
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| {
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| imageIterator.Set(blackPixel);
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| }
| |
| | |
| ++imageIterator;
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| }
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| }
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| | |
| </source>
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| | |
| {{ITKCMakeLists|ImageKmeansModelEstimator|}}
| |
