ImageRegistrationMethod3/ImageRegistrationMethod3.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.
 *
 *=========================================================================*/
 
#include <SimpleITK.h>
#include <iostream>
#include <stdlib.h>
#include <iomanip>
 
namespace sitk = itk::simple;
 
 
class IterationUpdate : public sitk::Command
{
public:
  IterationUpdate(const sitk::ImageRegistrationMethod & m)
    : m_Method(m)
  {}
 
  void
  Execute() override
  {
    // use sitk's output operator for std::vector etc..
    using sitk::operator<<;
 
    if (m_Method.GetOptimizerIteration() == 0)
    {
      std::cout << "Estimated Scales: " << m_Method.GetOptimizerScales() << 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(7) << m_Method.GetMetricValue();
    std::cout << " : " << m_Method.GetOptimizerPosition() << std::endl;
 
    std::cout.copyfmt(state);
  }
 
private:
  const sitk::ImageRegistrationMethod & m_Method;
};
 
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cout << "Usage: " << argv[0] << " <fixedImageFile> <movingImageFile> <outputTransformFile>" << std::endl;
    return 1;
  }
 
  sitk::Image fixed = sitk::ReadImage(argv[1], sitk::sitkFloat32);
  sitk::Image moving = sitk::ReadImage(argv[2], sitk::sitkFloat32);
 
  sitk::ImageRegistrationMethod R;
 
  R.SetMetricAsCorrelation();
  R.SetOptimizerAsRegularStepGradientDescent(2.0,   // learningRate
                                             1e-4,  // minStep
                                             500,   // numberOfIterations
                                             0.5,   // relaxationFactor
                                             1e-8); // gradientMagnitudeTolerance
  R.SetOptimizerScalesFromIndexShift();
 
  sitk::Transform tx = sitk::CenteredTransformInitializer(fixed, moving, sitk::Similarity2DTransform());
  R.SetInitialTransform(tx);
  R.SetInterpolator(sitk::sitkLinear);
 
  IterationUpdate cmd(R);
  R.AddCommand(sitk::sitkIterationEvent, cmd);
 
  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]);
 
  sitk::ResampleImageFilter resampler;
  resampler.SetReferenceImage(fixed);
  resampler.SetInterpolator(sitk::sitkLinear);
  resampler.SetDefaultPixelValue(100);
  resampler.SetTransform(outTx);
 
  sitk::Image out = resampler.Execute(moving);
 
  sitk::Image simg1 = sitk::Cast(sitk::RescaleIntensity(fixed), sitk::sitkUInt8);
  sitk::Image simg2 = sitk::Cast(sitk::RescaleIntensity(out), sitk::sitkUInt8);
  sitk::Image cimg = sitk::Compose(simg1, simg2, sitk::Divide(sitk::Add(simg1, simg2), 2));
 
  if (getenv("SITK_NOSHOW") == nullptr)
  {
    sitk::Show(cimg, "ImageRegistration3 Composition");
  }
 
  return 0;
}