ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx

/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
 
// This one header will include all SimpleITK filters and external
// objects.
#include <SimpleITK.h>
#include <iostream>
#include <stdlib.h>
#include <iomanip>
 
namespace sitk = itk::simple;
 
// use sitk's output operator for std::vector etc..
using sitk::operator<<;
 
 
class IterationUpdate : public sitk::Command
{
public:
  IterationUpdate(const sitk::ImageRegistrationMethod & m, const sitk::BSplineTransform & tx)
    : m_Method(m)
    , m_BSplineTransform(tx)
  {}
 
  // Override method from parent which is called at for the requested event
  void
  Execute() override
  {
    if (m_Method.GetOptimizerIteration() == 0)
    {
      // The BSpline is resized before the first optimizer
      // iteration is completed per level. Print the transform object
      // to show the adapted BSpline transform.
      std::cout << m_BSplineTransform.ToString() << std::endl;
    }
 
    // stash the stream state
    std::ios state(NULL);
    state.copyfmt(std::cout);
    std::cout << std::fixed << std::setfill(' ') << std::setprecision(5);
    std::cout << std::setw(3) << m_Method.GetOptimizerIteration();
    std::cout << " = " << std::setw(10) << m_Method.GetMetricValue() << std::endl;
    std::cout.copyfmt(state);
  }
 
private:
  const sitk::ImageRegistrationMethod & m_Method;
  const sitk::BSplineTransform &        m_BSplineTransform;
};
 
 
class MultiResolutionIterationUpdate : public sitk::Command
{
public:
  MultiResolutionIterationUpdate(const sitk::ImageRegistrationMethod & m)
    : m_Method(m)
  {}
 
  // Override method from parent which is called at for the requested event
  void
  Execute() override
  {
    // The sitkMultiResolutionIterationEvent occurs before the
    // resolution of the transform. This event is used here to print
    // the status of the optimizer from the previous registration level.
    if (m_Method.GetCurrentLevel() > 0)
    {
      std::cout << "Optimizer stop condition: " << m_Method.GetOptimizerStopConditionDescription() << std::endl;
      std::cout << " Iteration: " << m_Method.GetOptimizerIteration() << std::endl;
      std::cout << " Metric value: " << m_Method.GetMetricValue() << std::endl;
    }
 
    std::cout << "--------- Resolution Changing ---------" << std::endl;
  }
 
private:
  const sitk::ImageRegistrationMethod & m_Method;
};
 
 
int
main(int argc, char * argv[])
{
 
  if (argc < 4)
  {
    std::cerr << "Usage: " << argv[0] << " <fixedImageFilter> <movingImageFile> <outputTransformFile>" << std::endl;
    return 1;
  }
 
  sitk::Image fixed = sitk::ReadImage(argv[1], sitk::sitkFloat32);
 
  sitk::Image moving = sitk::ReadImage(argv[2], sitk::sitkFloat32);
 
  std::vector<unsigned int> transformDomainMeshSize(fixed.GetDimension(), 2);
 
  sitk::BSplineTransform tx = sitk::BSplineTransformInitializer(fixed, transformDomainMeshSize);
 
  std::cout << "Initial Number of Parameters:" << tx.GetNumberOfParameters() << std::endl;
 
 
  sitk::ImageRegistrationMethod R;
  R.SetMetricAsJointHistogramMutualInformation();
 
 
  const double       learningRate = 5.0;
  const unsigned int numberOfIterations = 100u;
  const double       convergenceMinimumValue = 1e-4;
  const unsigned int convergenceWindowSize = 5;
  R.SetOptimizerAsGradientDescentLineSearch(
    learningRate, numberOfIterations, convergenceMinimumValue, convergenceWindowSize);
 
  R.SetInterpolator(sitk::sitkLinear);
 
  std::vector<unsigned int> scaleFactors = { 1, 2, 5 };
  const bool                inPlace = true;
  R.SetInitialTransformAsBSpline(tx, inPlace, scaleFactors);
 
  std::vector<unsigned int> shrinkFactors = { 4, 2, 1 };
  R.SetShrinkFactorsPerLevel(shrinkFactors);
 
  std::vector<double> smoothingSigmas = { 4.0, 2.0, 1.0 };
  R.SetSmoothingSigmasPerLevel(smoothingSigmas);
 
  IterationUpdate cmd1(R, tx);
  R.AddCommand(sitk::sitkIterationEvent, cmd1);
 
  MultiResolutionIterationUpdate cmd2(R);
  R.AddCommand(sitk::sitkMultiResolutionIterationEvent, cmd2);
 
  sitk::Transform outTx = R.Execute(fixed, moving);
 
  std::cout << "-------" << std::endl;
  std::cout << outTx.ToString() << std::endl;
  std::cout << "Optimizer stop condition: " << R.GetOptimizerStopConditionDescription() << std::endl;
  std::cout << " Iteration: " << R.GetOptimizerIteration() << std::endl;
  std::cout << " Metric value: " << R.GetMetricValue() << std::endl;
 
  sitk::WriteTransform(outTx, argv[3]);
 
  return 0;
}