Examples/RegistrationITKv4/IterativeClosestPoint3.cxx

/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  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.
 *
 *=========================================================================*/

// 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( itk::ExceptionObject & e )
    {
    std::cerr << e << std::endl;
    return EXIT_FAILURE;
    }

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

  return EXIT_SUCCESS;
}