Examples/Filtering/SubsampleVolume.cxx

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 *
 *  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
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 *         https://www.apache.org/licenses/LICENSE-2.0.txt
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 *  distributed under the License is distributed on an "AS IS" BASIS,
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 *  See the License for the specific language governing permissions and
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//  Software Guide : BeginLatex
//
//  This example illustrates how to perform subsampling of a volume using ITK
//  classes.  In order to avoid aliasing artifacts, the volume must be
//  processed by a low-pass filter before resampling.  Here we use the
//  \doxygen{RecursiveGaussianImageFilter} as a low-pass filter. The image is
//  then resampled by using three different factors, one per dimension of the
//  image.
//
//  Software Guide : EndLatex
 
 
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
 
// Software Guide : BeginLatex
//
// The most important headers to include here are those corresponding to the
// resampling image filter, the transform, the interpolator and the smoothing
// filter.
//
// Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkResampleImageFilter.h"
#include "itkIdentityTransform.h"
#include "itkRecursiveGaussianImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
#include "itkCastImageFilter.h"
 
 
int
main(int argc, char * argv[])
{
  if (argc < 6)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0]
              << "  inputImageFile  outputImageFile factorX factorY factorZ"
              << std::endl;
    return EXIT_FAILURE;
  }
 
 
  // Software Guide : BeginLatex
  //
  // We explicitly instantiate the pixel type and dimension of the input
  // image, and the images that will be used internally for computing the
  // resampling.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 3;
 
  using InputPixelType = unsigned char;
 
  using InternalPixelType = float;
  using OutputPixelType = unsigned char;
 
  using InputImageType = itk::Image<InputPixelType, Dimension>;
  using InternalImageType = itk::Image<InternalPixelType, Dimension>;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  using ReaderType = itk::ImageFileReader<InputImageType>;
 
  auto reader = ReaderType::New();
 
  reader->SetFileName(argv[1]);
 
 
  // Software Guide : BeginLatex
  //
  // In this particular case we take the factors for resampling directly from
  // the command line arguments.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const double factorX = std::stod(argv[3]);
  const double factorY = std::stod(argv[4]);
  const double factorZ = std::stod(argv[5]);
  // Software Guide : EndCodeSnippet
 
 
  try
  {
    reader->Update();
  }
  catch (const itk::ExceptionObject & excep)
  {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
  }
 
 
  InputImageType::ConstPointer inputImage = reader->GetOutput();
 
 
  // Software Guide : BeginLatex
  //
  // A casting filter is instantiated in order to convert the pixel type of
  // the input image into the pixel type desired for computing the resampling.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using CastFilterType =
    itk::CastImageFilter<InputImageType, InternalImageType>;
 
  auto caster = CastFilterType::New();
 
  caster->SetInput(inputImage);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The smoothing filter of choice is the
  // \code{RecursiveGaussianImageFilter}. We create three of them in order to
  // have the freedom of performing smoothing with different sigma values
  // along each dimension.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using GaussianFilterType =
    itk::RecursiveGaussianImageFilter<InternalImageType, InternalImageType>;
 
  auto smootherX = GaussianFilterType::New();
  auto smootherY = GaussianFilterType::New();
  auto smootherZ = GaussianFilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The smoothing filters are connected in a cascade in the pipeline.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  smootherX->SetInput(caster->GetOutput());
  smootherY->SetInput(smootherX->GetOutput());
  smootherZ->SetInput(smootherY->GetOutput());
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The sigma values to use in the smoothing filters are computed based on
  // the pixel spacing of the input image and the factors provided as
  // arguments.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const InputImageType::SpacingType & inputSpacing = inputImage->GetSpacing();
 
  const double sigmaX = inputSpacing[0] * factorX;
  const double sigmaY = inputSpacing[1] * factorY;
  const double sigmaZ = inputSpacing[2] * factorZ;
 
  smootherX->SetSigma(sigmaX);
  smootherY->SetSigma(sigmaY);
  smootherZ->SetSigma(sigmaZ);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // We instruct each one of the smoothing filters to act along a particular
  // direction of the image, and set them to use normalization across scale
  // space in order to account for the reduction of intensity that accompanies
  // the diffusion process associated with the Gaussian smoothing.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  smootherX->SetDirection(0);
  smootherY->SetDirection(1);
  smootherZ->SetDirection(2);
 
  smootherX->SetNormalizeAcrossScale(false);
  smootherY->SetNormalizeAcrossScale(false);
  smootherZ->SetNormalizeAcrossScale(false);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The type of the resampling filter is instantiated using the internal
  // image type and the output image type.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using ResampleFilterType =
    itk::ResampleImageFilter<InternalImageType, OutputImageType>;
 
  auto resampler = ResampleFilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // Since the resampling is performed in the same physical extent of the
  // input image, we select the IdentityTransform as the one to be used by the
  // resampling filter.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using TransformType = itk::IdentityTransform<double, Dimension>;
 
  auto transform = TransformType::New();
  transform->SetIdentity();
  resampler->SetTransform(transform);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The Linear interpolator is selected because it provides a good run-time
  // performance.  For applications that require better precision you may want
  // to replace this interpolator with the
  // \doxygen{BSplineInterpolateImageFunction} interpolator or with the
  // \doxygen{WindowedSincInterpolateImageFunction} interpolator.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InterpolatorType =
    itk::LinearInterpolateImageFunction<InternalImageType, double>;
  auto interpolator = InterpolatorType::New();
  resampler->SetInterpolator(interpolator);
  // Software Guide : EndCodeSnippet
 
  resampler->SetDefaultPixelValue(0); // value for regions without source
 
  // Software Guide : BeginLatex
  //
  // The spacing to be used in the grid of the resampled image is computed
  // using the input image spacing and the factors provided in the command
  // line arguments.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  OutputImageType::SpacingType spacing;
 
  spacing[0] = inputSpacing[0] * factorX;
  spacing[1] = inputSpacing[1] * factorY;
  spacing[2] = inputSpacing[2] * factorZ;
 
  resampler->SetOutputSpacing(spacing);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The origin and direction of the input image are both preserved and passed
  // to the output image.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  resampler->SetOutputOrigin(inputImage->GetOrigin());
  resampler->SetOutputDirection(inputImage->GetDirection());
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The number of pixels to use along each direction on the grid of the
  // resampled image is computed using the number of pixels in the input image
  // and the sampling factors.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  InputImageType::SizeType inputSize =
    inputImage->GetLargestPossibleRegion().GetSize();
 
  using SizeValueType = InputImageType::SizeType::SizeValueType;
 
  InputImageType::SizeType size;
 
  size[0] = static_cast<SizeValueType>(inputSize[0] / factorX);
  size[1] = static_cast<SizeValueType>(inputSize[1] / factorY);
  size[2] = static_cast<SizeValueType>(inputSize[2] / factorZ);
 
  resampler->SetSize(size);
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // Finally, the input to the resampler is taken from the output of the
  // smoothing filter.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  resampler->SetInput(smootherZ->GetOutput());
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // At this point we can trigger the execution of the resampling by calling
  // the \code{Update()} method, or we can choose to pass the output of the
  // resampling filter to another section of pipeline, for example, an image
  // writer.
  //
  // Software Guide : EndLatex
 
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  auto writer = WriterType::New();
 
  writer->SetInput(resampler->GetOutput());
 
  writer->SetFileName(argv[2]);
 
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & excep)
  {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
  }
 
  std::cout << "Resampling Done !" << std::endl;
 
 
  return EXIT_SUCCESS;
}