Difference between revisions of "ITK/Examples/WishList/Statistics/ImageKmeansModelEstimator"

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(Created page with "==ImageKmeansModelEstimator.cxx== <source lang="cpp"> #include "itkImage.h" #include "itkListSample.h" #include "itkVector.h" #include "itkImageKmeansModelEstimator.h" #include "...")
 
m (moved ITK/Examples/Statistics/ImageKmeansModelEstimator to ITK/Examples/WishList/Statistics/ImageKmeansModelEstimator: Segfault on line 102 (int classLabel = membershipIterator.GetClassLabel();))
(No difference)

Revision as of 09:26, 28 October 2012

ImageKmeansModelEstimator.cxx

#include "itkImage.h"
#include "itkListSample.h"
#include "itkVector.h"
#include "itkImageKmeansModelEstimator.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "itkImageToListSampleAdaptor.h"
#include "itkDistanceToCentroidMembershipFunction.h"
#include "itkSampleClassifierFilter.h"
#include "itkMinimumDecisionRule.h"
#include "itkImageFileWriter.h"

typedef itk::Vector<unsigned char,3> MeasurementVectorType;
typedef itk::Image<MeasurementVectorType,2> ColorImageType;
typedef itk::Image<unsigned char,2> ScalarImageType;

static void CreateImage(ColorImageType::Pointer image);

int main(int, char* [] )
{
  // Create a demo image
  ColorImageType::Pointer image = ColorImageType::New();
  CreateImage(image);

  // Compute pixel clusters using KMeans
  typedef itk::Statistics::DistanceToCentroidMembershipFunction< MeasurementVectorType >  MembershipFunctionType ;
  typedef MembershipFunctionType::Pointer MembershipFunctionPointer ;
  typedef std::vector< MembershipFunctionPointer >  MembershipFunctionPointerVector;

  typedef itk::ImageKmeansModelEstimator<ColorImageType, MembershipFunctionType>  ImageKmeansModelEstimatorType;

  ImageKmeansModelEstimatorType::Pointer kmeansEstimator = ImageKmeansModelEstimatorType::New();
  kmeansEstimator->SetInputImage(image);
  kmeansEstimator->SetNumberOfModels(3);
  kmeansEstimator->SetThreshold(0.01 );
  kmeansEstimator->SetOffsetAdd( 0.01 );
  kmeansEstimator->SetOffsetMultiply( 0.01 );
  kmeansEstimator->SetMaxSplitAttempts( 10 );
  kmeansEstimator->Update();

  // Classify each pixel
  typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType ;
  typedef itk::Statistics::SampleClassifierFilter< SampleType > ClassifierType;
  ClassifierType::Pointer classifier = ClassifierType::New();

  typedef itk::Statistics::MinimumDecisionRule DecisionRuleType;
  DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
  
  classifier->SetDecisionRule(decisionRule);
  classifier->SetNumberOfClasses(3);

  typedef ClassifierType::ClassLabelVectorObjectType               ClassLabelVectorObjectType;
  typedef ClassifierType::ClassLabelVectorType                     ClassLabelVectorType;
  typedef ClassifierType::MembershipFunctionVectorObjectType       MembershipFunctionVectorObjectType;
  typedef ClassifierType::MembershipFunctionVectorType             MembershipFunctionVectorType;

  // Setup membership functions
  MembershipFunctionPointerVector kmeansMembershipFunctions = kmeansEstimator->GetMembershipFunctions();

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

  MembershipFunctionVectorType &  membershipFunctionsVector = membershipFunctionsVectorObject->Get();

  for(unsigned int i = 0; i < kmeansMembershipFunctions.size(); i++)
    {
    membershipFunctionsVector.push_back(kmeansMembershipFunctions[i].GetPointer());
    }

  // Setup class labels
  ClassLabelVectorObjectType::Pointer  classLabelsObject = ClassLabelVectorObjectType::New();
  classifier->SetClassLabels( classLabelsObject );

  ClassLabelVectorType &  classLabelsVector = classLabelsObject->Get();
  classLabelsVector.push_back( 50 );
  classLabelsVector.push_back( 150 );
  classLabelsVector.push_back( 250 );

  // Perform the classification
  typedef itk::Statistics::ImageToListSampleAdaptor< ColorImageType > SampleAdaptorType;
  SampleAdaptorType::Pointer sample = SampleAdaptorType::New();
  sample->SetImage(image);

  classifier->SetInput(sample);
  classifier->Update();
  
  // Prepare the output image
  ScalarImageType::Pointer outputImage = ScalarImageType::New();
  outputImage->SetRegions(image->GetLargestPossibleRegion());
  outputImage->Allocate();
  outputImage->FillBuffer(0);

  // Setup the membership iterator
  const ClassifierType::MembershipSampleType* membershipSample = classifier->GetOutput();
  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
  itk::ImageRegionIteratorWithIndex<ScalarImageType> outputIterator(outputImage,outputImage->GetLargestPossibleRegion());
  outputIterator.GoToBegin();
  
  while(membershipIterator != membershipSample->End())
    {
    int classLabel = membershipIterator.GetClassLabel();
    //std::cout << "Class label: " << classLabel << std::endl;
    outputIterator.Set(classLabel);
    ++membershipIterator;
    ++outputIterator;
    }
    
  typedef  itk::ImageFileWriter< ColorImageType  > WriterType;
  WriterType::Pointer inputWriter = WriterType::New();
  inputWriter->SetFileName("input.mha");
  inputWriter->SetInput(image);
  inputWriter->Update();

  typedef  itk::ImageFileWriter< ScalarImageType  > ScalarWriterType;
  ScalarWriterType::Pointer outputWriter = ScalarWriterType::New();
  outputWriter->SetFileName("output.mha");
  outputWriter->SetInput(outputImage);
  outputWriter->Update();
  
  
  return EXIT_SUCCESS;
}

void CreateImage(ColorImageType::Pointer image)
{
  // Create a black image with a red square and a green square
  ColorImageType::RegionType region;
  ColorImageType::IndexType start;
  start[0] = 0;
  start[1] = 0;

  ColorImageType::SizeType size;
  size[0] = 200;
  size[1] = 300;

  region.SetSize(size);
  region.SetIndex(start);

  image->SetRegions(region);
  image->Allocate();

  itk::ImageRegionIterator<ColorImageType> imageIterator(image,region);

  itk::Vector<unsigned char, 3> redPixel;
  redPixel[0] = 255;
  redPixel[1] = 0;
  redPixel[2] = 0;

  itk::Vector<unsigned char, 3> greenPixel;
  greenPixel[0] = 0;
  greenPixel[1] = 255;
  greenPixel[2] = 0;
  
  itk::Vector<unsigned char, 3> blackPixel;
  blackPixel[0] = 0;
  blackPixel[1] = 0;
  blackPixel[2] = 0;
  
  while(!imageIterator.IsAtEnd())
    {
    if(imageIterator.GetIndex()[0] > 100 &&
      imageIterator.GetIndex()[0] < 150 &&
      imageIterator.GetIndex()[1] > 100 &&
      imageIterator.GetIndex()[1] < 150)
      {
      imageIterator.Set(redPixel);
      }
    else if(imageIterator.GetIndex()[0] > 50 &&
      imageIterator.GetIndex()[0] < 70 &&
      imageIterator.GetIndex()[1] > 50 &&
      imageIterator.GetIndex()[1] < 70)
      {
      imageIterator.Set(greenPixel);
      }
    else
      {
      imageIterator.Set(blackPixel);
      }

    ++imageIterator;
  }
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.9.5)
 
project(ImageKmeansModelEstimator)
 
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(ImageKmeansModelEstimator MACOSX_BUNDLE ImageKmeansModelEstimator.cxx)
target_link_libraries(ImageKmeansModelEstimator
  ${Glue}  ${VTK_LIBRARIES} ${ITK_LIBRARIES})

Download and Build ImageKmeansModelEstimator

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

cd ImageKmeansModelEstimator/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:

./ImageKmeansModelEstimator

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.