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| ==kMeansClustering.cxx==
| | {{warning|1=The media wiki content on this page is no longer maintained.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.}} |
| <source lang="cpp">
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| #include <itkImage.h>
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| #include <itkImageFileReader.h>
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| #include <itkImageFileWriter.h>
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| #include <itkScalarImageKmeansImageFilter.h>
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| | |
| int main( int argc, char * argv [] )
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| { | |
| //sample usage
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| //./kMeansClustering input.jpg output.jpg 1 3 0 100 200
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|
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| //verify command line arguments
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| if( argc < 5 )
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| {
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| std::cerr << "Usage: " << std::endl;
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| std::cerr << argv[0];
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| std::cerr << " inputScalarImage outputLabeledImage contiguousLabels";
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| std::cerr << " numberOfClasses mean1 mean2... meanN " << std::endl;
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| return EXIT_FAILURE;
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| }
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| | |
| //parse command line arguments
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| const char * inputImageFileName = argv[1];
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| const char * outputImageFileName = argv[2];
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| const unsigned int useNonContiguousLabels = atoi( argv[3] );
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| const unsigned int numberOfInitialClasses = atoi( argv[4] );
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|
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| const unsigned int argoffset = 5;
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| if( static_cast<unsigned int>(argc) <
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| numberOfInitialClasses + argoffset )
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| {
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| std::cerr << "Error: " << std::endl;
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| std::cerr << numberOfInitialClasses << " classes has been specified ";
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| std::cerr << "but no enough means have been provided in the command ";
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| std::cerr << "line arguments " << std::endl;
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| return EXIT_FAILURE;
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| }
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|
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| std::vector<double> userMeans;
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| for( unsigned k = 0; k < numberOfInitialClasses; k++ )
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| {
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| const double userProvidedInitialMean = atof( argv[k+argoffset] );
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| userMeans.push_back(userProvidedInitialMean);
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| }
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|
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| // Define the pixel type and dimension of the image that we intend to
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| // classify.
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|
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| typedef signed short PixelType;
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| const unsigned int Dimension = 2;
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| typedef itk::Image<PixelType, Dimension > ImageType;
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| | |
| // create a reader
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| typedef itk::ImageFileReader< ImageType > ReaderType;
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| ReaderType::Pointer reader = ReaderType::New();
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| reader->SetFileName( inputImageFileName );
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| | |
| // Instantiate the ScalarImageKmeansImageFilter
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| typedef itk::ScalarImageKmeansImageFilter< ImageType > KMeansFilterType; | |
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| KMeansFilterType::Pointer kmeansFilter = KMeansFilterType::New();
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| kmeansFilter->SetInput( reader->GetOutput() );
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| // Make the output image intellegable by expanding the range of output image values, if desired
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|
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| kmeansFilter->SetUseNonContiguousLabels( useNonContiguousLabels );
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| // initialize using the user input means
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|
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| for( unsigned k = 0; k < numberOfInitialClasses; k++ )
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| {
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| kmeansFilter->AddClassWithInitialMean( userMeans[k] );
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| }
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| | |
| // Create and setup a writer
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|
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| typedef KMeansFilterType::OutputImageType OutputImageType;
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| typedef itk::ImageFileWriter< OutputImageType > WriterType;
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| WriterType::Pointer writer = WriterType::New();
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|
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| writer->SetInput( kmeansFilter->GetOutput() );
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| writer->SetFileName( outputImageFileName );
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| // execut the pipeline
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| try
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| {
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| writer->Update();
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| }
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| catch( itk::ExceptionObject & excp )
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| {
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| std::cerr << "Problem encountered while writing ";
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| std::cerr << " image file : " << outputImageFileName << std::endl;
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| std::cerr << excp << std::endl;
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| return EXIT_FAILURE;
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| }
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| // inspect the means
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| KMeansFilterType::ParametersType estimatedMeans =
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| kmeansFilter->GetFinalMeans();
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| const unsigned int numberOfClasses = estimatedMeans.Size();
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| for ( unsigned int i = 0 ; i < numberOfClasses ; ++i )
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| {
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| std::cout << "cluster[" << i << "] ";
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| std::cout << " estimated mean : " << estimatedMeans[i] << std::endl;
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| }
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| return EXIT_SUCCESS;
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|
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| }
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| </source>
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| {{ITKCMakeLists|{{SUBPAGENAME}}}}
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