Examples/RegistrationITKv4/IterativeClosestPoint3.cxx

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 *  you may not use this file except in compliance with the License.
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// Software Guide : BeginLatex
//
// This example illustrates how to perform Iterative Closest Point (ICP)
// registration in ITK using a DistanceMap in order to increase the
// performance. There is of course a trade-off between the time needed for
// computing the DistanceMap and the time saved by its repeated use during the
// iterative computation of the point-to-point distances. It is then necessary
// in practice to ponder both factors.
//
// \doxygen{EuclideanDistancePointMetric}.
//
// Software Guide : EndLatex
 
// Software Guide : BeginLatex
//
// The first step is to include the relevant headers.
//
// Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkTranslationTransform.h"
#include "itkEuclideanDistancePointMetric.h"
#include "itkLevenbergMarquardtOptimizer.h"
#include "itkPointSetToPointSetRegistrationMethod.h"
#include "itkDanielssonDistanceMapImageFilter.h"
#include "itkPointSetToImageFilter.h"
#include <iostream>
#include <fstream>
// Software Guide : EndCodeSnippet
 
int
main(int argc, char * argv[])
{
 
  if (argc < 3)
  {
    std::cerr << "Arguments Missing. " << std::endl;
    std::cerr
      << "Usage:  IterativeClosestPoint3   fixedPointsFile  movingPointsFile "
      << std::endl;
    return EXIT_FAILURE;
  }
 
  constexpr unsigned int Dimension = 2;
 
  // Software Guide : BeginLatex
  //
  // Next, define the necessary types for the fixed and moving point sets.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using PointSetType = itk::PointSet<float, Dimension>;
 
  PointSetType::Pointer fixedPointSet = PointSetType::New();
  PointSetType::Pointer movingPointSet = PointSetType::New();
 
  using PointType = PointSetType::PointType;
 
  using PointsContainer = PointSetType::PointsContainer;
 
  PointsContainer::Pointer fixedPointContainer = PointsContainer::New();
  PointsContainer::Pointer movingPointContainer = PointsContainer::New();
 
  PointType fixedPoint;
  PointType movingPoint;
  // Software Guide : EndCodeSnippet
 
  // Read the file containing coordinates of fixed points.
  std::ifstream fixedFile;
  fixedFile.open(argv[1]);
  if (fixedFile.fail())
  {
    std::cerr << "Error opening points file with name : " << std::endl;
    std::cerr << argv[1] << std::endl;
    return EXIT_FAILURE;
  }
 
  unsigned int pointId = 0;
  fixedFile >> fixedPoint;
  while (!fixedFile.eof())
  {
    fixedPointContainer->InsertElement(pointId, fixedPoint);
    fixedFile >> fixedPoint;
    pointId++;
  }
  fixedPointSet->SetPoints(fixedPointContainer);
  std::cout << "Number of fixed Points = "
            << fixedPointSet->GetNumberOfPoints() << std::endl;
 
  // Read the file containing coordinates of moving points.
  std::ifstream movingFile;
  movingFile.open(argv[2]);
  if (movingFile.fail())
  {
    std::cerr << "Error opening points file with name : " << std::endl;
    std::cerr << argv[2] << std::endl;
    return EXIT_FAILURE;
  }
 
  pointId = 0;
  movingFile >> movingPoint;
  while (!movingFile.eof())
  {
    movingPointContainer->InsertElement(pointId, movingPoint);
    movingFile >> movingPoint;
    pointId++;
  }
  movingPointSet->SetPoints(movingPointContainer);
  std::cout << "Number of moving Points = "
            << movingPointSet->GetNumberOfPoints() << std::endl;
 
  // Software Guide : BeginLatex
  //
  // Setup the metric, transform, optimizers and registration in a manner
  // similar to the previous two examples.
  //
  // Software Guide : EndLatex
 
  using MetricType =
    itk::EuclideanDistancePointMetric<PointSetType, PointSetType>;
 
  MetricType::Pointer metric = MetricType::New();
 
  //-----------------------------------------------------------
  // Set up a Transform
  //-----------------------------------------------------------
 
  using TransformType = itk::TranslationTransform<double, Dimension>;
 
  TransformType::Pointer transform = TransformType::New();
 
  // Optimizer Type
  using OptimizerType = itk::LevenbergMarquardtOptimizer;
 
  OptimizerType::Pointer optimizer = OptimizerType::New();
  optimizer->SetUseCostFunctionGradient(false);
 
  // Registration Method
  using RegistrationType =
    itk::PointSetToPointSetRegistrationMethod<PointSetType, PointSetType>;
 
  RegistrationType::Pointer registration = RegistrationType::New();
 
  // Scale the translation components of the Transform in the Optimizer
  OptimizerType::ScalesType scales(transform->GetNumberOfParameters());
  scales.Fill(0.01);
 
  constexpr unsigned long numberOfIterations = 100;
  const double            gradientTolerance = 1e-5; // convergence criterion
  const double            valueTolerance = 1e-5;    // convergence criterion
  const double            epsilonFunction = 1e-6;   // convergence criterion
 
  optimizer->SetScales(scales);
  optimizer->SetNumberOfIterations(numberOfIterations);
  optimizer->SetValueTolerance(valueTolerance);
  optimizer->SetGradientTolerance(gradientTolerance);
  optimizer->SetEpsilonFunction(epsilonFunction);
 
  // Start from an Identity transform (in a normal case, the user
  // can probably provide a better guess than the identity...
  transform->SetIdentity();
 
  registration->SetInitialTransformParameters(transform->GetParameters());
 
  //------------------------------------------------------
  // Connect all the components required for Registration
  //------------------------------------------------------
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
  registration->SetTransform(transform);
  registration->SetFixedPointSet(fixedPointSet);
  registration->SetMovingPointSet(movingPointSet);
 
  //------------------------------------------------------
  // Prepare the Distance Map in order to accelerate
  // distance computations.
  //------------------------------------------------------
  //
  //  First map the Fixed Points into a binary image.
  //  This is needed because the DanielssonDistance
  //  filter expects an image as input.
  //
  //-------------------------------------------------
  // Software Guide : BeginLatex
  //
  // In the preparation of the distance map, we first need to map the fixed
  // points into a binary image.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using BinaryImageType = itk::Image<unsigned char, Dimension>;
 
  using PointsToImageFilterType =
    itk::PointSetToImageFilter<PointSetType, BinaryImageType>;
 
  PointsToImageFilterType::Pointer pointsToImageFilter =
    PointsToImageFilterType::New();
 
  pointsToImageFilter->SetInput(fixedPointSet);
 
  BinaryImageType::SpacingType spacing;
  spacing.Fill(1.0);
 
  BinaryImageType::PointType origin;
  origin.Fill(0.0);
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // Continue to prepare the distance map, in order to accelerate the distance
  // computations.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  pointsToImageFilter->SetSpacing(spacing);
  pointsToImageFilter->SetOrigin(origin);
  pointsToImageFilter->Update();
  BinaryImageType::Pointer binaryImage = pointsToImageFilter->GetOutput();
 
  using DistanceImageType = itk::Image<unsigned short, Dimension>;
  using DistanceFilterType =
    itk::DanielssonDistanceMapImageFilter<BinaryImageType, DistanceImageType>;
 
  DistanceFilterType::Pointer distanceFilter = DistanceFilterType::New();
  distanceFilter->SetInput(binaryImage);
  distanceFilter->Update();
  metric->SetDistanceMap(distanceFilter->GetOutput());
  // Software Guide : EndCodeSnippet
 
  try
  {
    registration->Update();
  }
  catch (const itk::ExceptionObject & e)
  {
    std::cerr << e << std::endl;
    return EXIT_FAILURE;
  }
 
  std::cout << "Solution = " << transform->GetParameters() << std::endl;
 
  return EXIT_SUCCESS;
}