C++ Pointer::GetNumberOfParameters方法代码示例

// perform B-spline registration for 2D image
void runBspline2D(StringVector& args) {
    typedef itk::BSplineTransform<double, 2, 3> TransformType;
    typedef itk::LBFGSOptimizer OptimizerType;
    typedef itk::MeanSquaresImageToImageMetric<RealImage2, RealImage2> MetricType;
    typedef itk:: LinearInterpolateImageFunction<RealImage2, double> InterpolatorType;
    typedef itk::ImageRegistrationMethod<RealImage2, RealImage2> RegistrationType;

    MetricType::Pointer         metric        = MetricType::New();
    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();

    // The old registration framework has problems with multi-threading
    // For now, we set the number of threads to 1
    registration->SetNumberOfThreads(1);

    registration->SetMetric(        metric        );
    registration->SetOptimizer(     optimizer     );
    registration->SetInterpolator(  interpolator  );

    TransformType::Pointer  transform = TransformType::New();
    registration->SetTransform( transform );


    ImageIO<RealImage2> io;

    // Create the synthetic images
    RealImage2::Pointer  fixedImage  = io.ReadImage(args[0]);
    RealImage2::Pointer  movingImage  = io.ReadImage(args[1]);

    // Setup the registration
    registration->SetFixedImage(  fixedImage   );
    registration->SetMovingImage(   movingImage);

    RealImage2::RegionType fixedRegion = fixedImage->GetBufferedRegion();
    registration->SetFixedImageRegion( fixedRegion );

    TransformType::PhysicalDimensionsType   fixedPhysicalDimensions;
    TransformType::MeshSizeType             meshSize;
    for( unsigned int i=0; i < 2; i++ )
    {
        fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
        static_cast<double>(
                            fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
    }
    unsigned int numberOfGridNodesInOneDimension = 18;
    meshSize.Fill( numberOfGridNodesInOneDimension - 3 );
    transform->SetTransformDomainOrigin( fixedImage->GetOrigin() );
    transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
    transform->SetTransformDomainMeshSize( meshSize );
    transform->SetTransformDomainDirection( fixedImage->GetDirection() );

    typedef TransformType::ParametersType     ParametersType;

    const unsigned int numberOfParameters =
    transform->GetNumberOfParameters();

    ParametersType parameters( numberOfParameters );

    parameters.Fill( 0.0 );

    transform->SetParameters( parameters );

    //  We now pass the parameters of the current transform as the initial
    //  parameters to be used when the registration process starts.

    registration->SetInitialTransformParameters( transform->GetParameters() );

    std::cout << "Intial Parameters = " << std::endl;
    std::cout << transform->GetParameters() << std::endl;

    //  Next we set the parameters of the LBFGS Optimizer.

    optimizer->SetGradientConvergenceTolerance( 0.005 );
    optimizer->SetLineSearchAccuracy( 0.9 );
    optimizer->SetDefaultStepLength( .1 );
    optimizer->TraceOn();
    optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );

    std::cout << std::endl << "Starting Registration" << std::endl;

    try
    {
        registration->Update();
        std::cout << "Optimizer stop condition = "
        << registration->GetOptimizer()->GetStopConditionDescription()
        << std::endl;
    }
    catch( itk::ExceptionObject & err )
    {
        std::cerr << "ExceptionObject caught !" << std::endl;
        std::cerr << err << std::endl;
        return;
    }

    OptimizerType::ParametersType finalParameters =
    registration->GetLastTransformParameters();

    std::cout << "Last Transform Parameters" << std::endl;
//.........这里部分代码省略.........

    RealImage::Pointer bsplineRegistration(RealImage::Pointer srcImg, RealImage::Pointer dstImg) {

        const unsigned int SpaceDimension = ImageDimension;
        const unsigned int SplineOrder = 3;
        typedef double CoordinateRepType;

        typedef itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder> TransformType;
        typedef itk::LBFGSOptimizer OptimizerType;
        typedef itk::MeanSquaresImageToImageMetric<ImageType, ImageType> MetricType;
        typedef itk::LinearInterpolateImageFunction<ImageType, double> InterpolatorType;
        typedef itk::ImageRegistrationMethod<ImageType, ImageType> RegistrationType;

        MetricType::Pointer         metric        = MetricType::New();
        OptimizerType::Pointer      optimizer     = OptimizerType::New();
        InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
        RegistrationType::Pointer   registration  = RegistrationType::New();



        // The old registration framework has problems with multi-threading
        // For now, we set the number of threads to 1
//        registration->SetNumberOfThreads(1);
        registration->SetMetric(        metric        );
        registration->SetOptimizer(     optimizer     );
        registration->SetInterpolator(  interpolator  );

        TransformType::Pointer  transform = TransformType::New();
        registration->SetTransform( transform );

        // Setup the registration
        registration->SetFixedImage(  dstImg   );
        registration->SetMovingImage(   srcImg);

        ImageType::RegionType fixedRegion = srcImg->GetBufferedRegion();
        registration->SetFixedImageRegion( fixedRegion );

        //  Here we define the parameters of the BSplineDeformableTransform grid.  We
        //  arbitrarily decide to use a grid with $5 \times 5$ nodes within the image.
        //  The reader should note that the BSpline computation requires a
        //  finite support region ( 1 grid node at the lower borders and 2
        //  grid nodes at upper borders). Therefore in this example, we set
        //  the grid size to be $8 \times 8$ and place the grid origin such that
        //  grid node (1,1) coincides with the first pixel in the fixed image.

        TransformType::PhysicalDimensionsType   fixedPhysicalDimensions;
        TransformType::MeshSizeType             meshSize;
        for (unsigned int i=0; i < ImageDimension; i++) {
            fixedPhysicalDimensions[i] = dstImg->GetSpacing()[i] *
            static_cast<double>(dstImg->GetLargestPossibleRegion().GetSize()[i] - 1 );
            meshSize[i] = dstImg->GetLargestPossibleRegion().GetSize()[i] / 8 - SplineOrder;
        }
//        unsigned int numberOfGridNodesInOneDimension = 15;
//        meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
        transform->SetTransformDomainOrigin( dstImg->GetOrigin() );
        transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
        transform->SetTransformDomainMeshSize( meshSize );
        transform->SetTransformDomainDirection( dstImg->GetDirection() );

        typedef TransformType::ParametersType     ParametersType;

        const unsigned int numberOfParameters = transform->GetNumberOfParameters();

        ParametersType parameters( numberOfParameters );
        parameters.Fill( 0.0 );

        transform->SetParameters( parameters );

        //  We now pass the parameters of the current transform as the initial
        //  parameters to be used when the registration process starts.

        registration->SetInitialTransformParameters( transform->GetParameters() );

        std::cout << "Intial Parameters = " << std::endl;
        std::cout << transform->GetParameters() << std::endl;

        //  Next we set the parameters of the LBFGS Optimizer.
        optimizer->SetGradientConvergenceTolerance(0.1);
        optimizer->SetLineSearchAccuracy(0.09);
        optimizer->SetDefaultStepLength(.1);
        optimizer->TraceOn();
        optimizer->SetMaximumNumberOfFunctionEvaluations(1000);

        std::cout << std::endl << "Starting Registration" << std::endl;

        try {
            registration->Update();
            std::cout << "Optimizer stop condition = "
            << registration->GetOptimizer()->GetStopConditionDescription()
            << std::endl;
        } catch (itk::ExceptionObject & err) {
            std::cerr << "ExceptionObject caught !" << std::endl;
            std::cerr << err << std::endl;
            return RealImage::Pointer();
        }

        OptimizerType::ParametersType finalParameters =
        registration->GetLastTransformParameters();
        
        std::cout << "Last Transform Parameters" << std::endl;
        std::cout << finalParameters << std::endl;
//.........这里部分代码省略.........

bool mitk::NavigationDataLandmarkTransformFilter::FindCorrespondentLandmarks(LandmarkPointContainer& sources, const LandmarkPointContainer& targets) const
{
  if (sources.size() < 6 || targets.size() < 6)
    return false;
  //throw std::invalid_argument("ICP correspondence finding needs at least 6 landmarks");

  /* lots of type definitions */
  typedef itk::PointSet<mitk::ScalarType, 3> PointSetType;
  //typedef itk::BoundingBox<PointSetType::PointIdentifier, PointSetType::PointDimension> BoundingBoxType;

  typedef itk::EuclideanDistancePointMetric< PointSetType, PointSetType> MetricType;
  //typedef MetricType::TransformType TransformBaseType;
  //typedef MetricType::TransformType::ParametersType ParametersType;
  //typedef TransformBaseType::JacobianType JacobianType;
  //typedef itk::Euler3DTransform< double > TransformType;
  typedef itk::VersorRigid3DTransform< double > TransformType;
  typedef TransformType ParametersType;
  typedef itk::PointSetToPointSetRegistrationMethod< PointSetType, PointSetType > RegistrationType;

  /* copy landmarks to itk pointsets for registration */
  PointSetType::Pointer sourcePointSet = PointSetType::New();
  unsigned int i = 0;
  for (LandmarkPointContainer::const_iterator it = sources.begin(); it != sources.end(); ++it)
  {
    PointSetType::PointType doublePoint;
    mitk::itk2vtk(*it, doublePoint); // copy mitk::ScalarType point into double point as workaround to ITK 3.10 bug
    sourcePointSet->SetPoint(i++, doublePoint /**it*/);
  }

  i = 0;
  PointSetType::Pointer targetPointSet = PointSetType::New();
  for (LandmarkPointContainer::const_iterator it = targets.begin(); it != targets.end(); ++it)
  {
    PointSetType::PointType doublePoint;
    mitk::itk2vtk(*it, doublePoint); // copy mitk::ScalarType point into double point as workaround to ITK 3.10 bug
    targetPointSet->SetPoint(i++, doublePoint /**it*/);
  }

  /* get centroid and extends of our pointsets */
  //BoundingBoxType::Pointer sourceBoundingBox = BoundingBoxType::New();
  //sourceBoundingBox->SetPoints(sourcePointSet->GetPoints());
  //sourceBoundingBox->ComputeBoundingBox();
  //BoundingBoxType::Pointer targetBoundingBox = BoundingBoxType::New();
  //targetBoundingBox->SetPoints(targetPointSet->GetPoints());
  //targetBoundingBox->ComputeBoundingBox();


  TransformType::Pointer transform = TransformType::New();
  transform->SetIdentity();
  //transform->SetTranslation(targetBoundingBox->GetCenter() - sourceBoundingBox->GetCenter());

  itk::LevenbergMarquardtOptimizer::Pointer optimizer = itk::LevenbergMarquardtOptimizer::New();
  optimizer->SetUseCostFunctionGradient(false);

  RegistrationType::Pointer registration = RegistrationType::New();

  // Scale the translation components of the Transform in the Optimizer
  itk::LevenbergMarquardtOptimizer::ScalesType scales(transform->GetNumberOfParameters());
  const double translationScale = 5000; //sqrtf(targetBoundingBox->GetDiagonalLength2())  * 1000; // dynamic range of translations
  const double rotationScale = 1.0; // dynamic range of rotations
  scales[0] = 1.0 / rotationScale;
  scales[1] = 1.0 / rotationScale;
  scales[2] = 1.0 / rotationScale;
  scales[3] = 1.0 / translationScale;
  scales[4] = 1.0 / translationScale;
  scales[5] = 1.0 / translationScale;
  //scales.Fill(0.01);
  unsigned long numberOfIterations = 80000;
  double gradientTolerance = 1e-10; // convergence criterion
  double valueTolerance = 1e-10; // convergence criterion
  double epsilonFunction = 1e-10; // convergence criterion
  optimizer->SetScales( scales );
  optimizer->SetNumberOfIterations( numberOfIterations );
  optimizer->SetValueTolerance( valueTolerance );
  optimizer->SetGradientTolerance( gradientTolerance );
  optimizer->SetEpsilonFunction( epsilonFunction );


  registration->SetInitialTransformParameters( transform->GetParameters() );
  //------------------------------------------------------
  // Connect all the components required for Registration
  //------------------------------------------------------
  MetricType::Pointer metric = MetricType::New();

  registration->SetMetric( metric );
  registration->SetOptimizer( optimizer );
  registration->SetTransform( transform );
  registration->SetFixedPointSet( targetPointSet );
  registration->SetMovingPointSet( sourcePointSet );

  try
  {
    //registration->StartRegistration();
    registration->Update();
  }
  catch( itk::ExceptionObject & e )
  {
    MITK_INFO << "Exception caught during ICP optimization: " << e;
    return false;
    //throw e;
//.........这里部分代码省略.........

//.........这里部分代码省略.........
                                MovingImageType, 
                                InternalImageType 
                                              > MovingNormalizeFilterType;

    FixedNormalizeFilterType::Pointer fixedNormalizer = 
                                            FixedNormalizeFilterType::New();

    MovingNormalizeFilterType::Pointer movingNormalizer =
                                            MovingNormalizeFilterType::New();
    typedef itk::DiscreteGaussianImageFilter<
                                      InternalImageType, 
                                      InternalImageType
                                                    > GaussianFilterType;

    GaussianFilterType::Pointer fixedSmoother  = GaussianFilterType::New();
    GaussianFilterType::Pointer movingSmoother = GaussianFilterType::New();

    fixedSmoother->SetVariance(2.0);
    movingSmoother->SetVariance(2.0);
    fixedNormalizer->SetInput(extractFixedImageFilter->GetOutput());
    movingNormalizer->SetInput(extractMovingImageFilter->GetOutput());

    fixedSmoother->SetInput(fixedNormalizer->GetOutput());
    movingSmoother->SetInput(movingNormalizer->GetOutput());

    registration->SetFixedImage(fixedSmoother->GetOutput());
    registration->SetMovingImage(movingSmoother->GetOutput());

    fixedNormalizer->Update();
    registration->SetFixedImageRegion(
       fixedNormalizer->GetOutput()->GetBufferedRegion());

    typedef RegistrationType::ParametersType ParametersType;
    ParametersType initialParameters(transform->GetNumberOfParameters());

    initialParameters[0] = 0.0;  // Initial offset in mm along X
    initialParameters[1] = 0.0;  // Initial offset in mm along Y

    registration->SetInitialTransformParameters(initialParameters);

    optimizer->SetLearningRate(20.0);
    optimizer->SetNumberOfIterations(200);
    optimizer->MaximizeOn();

    try 
    { 
        registration->Update();
    } 
    catch(itk::ExceptionObject & err) 
    { 
        std::cout << "ExceptionObject caught !" << std::endl; 
        std::cout << err << std::endl; 
        return;
    } 

    ParametersType finalParameters = registration->GetLastTransformParameters();

    double TranslationAlongX = finalParameters[0];
    double TranslationAlongY = finalParameters[1];

    // Print out results
    //
    DEBUG_LOG(QString("Result = "));
    DEBUG_LOG(QString(" Translation X = %1").arg(TranslationAlongX));
    DEBUG_LOG(QString(" Translation Y = %1").arg(TranslationAlongY));
    DEBUG_LOG(QString(" Iterations    = %1").arg(optimizer->GetCurrentIteration()));