SphinxExamples/src/Filtering/ImageFeature/AdditiveGaussianNoiseImageFilter/Code.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
 *
 *         https://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 "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkAdditiveGaussianNoiseImageFilter.h"
 
int
main(int argc, char * argv[])
{
  // Check for proper arguments; if not, explain usage.
  if (argc != 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0];
    std::cerr << " <InputFileName> <OutputFileName> [Mean] [Standard Deviation]";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }
 
  // Initialize and assign user provided variables
  const char * inputImage = argv[1];
  const char * outputImage = argv[2];
 
  // Get floating point numbers for the mean and std dev to perform the algorithm
  const double mean = std::stod(argv[3]);
  const double deviation = std::stod(argv[4]);
 
  constexpr unsigned int Dimension = 2;
  // Use unsigned char to save to PNG format
  using PixelType = unsigned char;
  using ImageType = itk::Image<PixelType, Dimension>;
 
  const auto input = itk::ReadImage<ImageType>(inputImage);
 
  // Create the filter and apply the algorithm to the image
  using FilterType = itk::AdditiveGaussianNoiseImageFilter<ImageType, ImageType>;
  auto filter = FilterType::New();
  filter->SetInput(input);
  filter->SetMean(mean);                   // Set the mean
  filter->SetStandardDeviation(deviation); // Set the standard deviation
 
  try
  {
    itk::WriteImage(filter->GetOutput(), outputImage);
  }
  catch (const itk::ExceptionObject & error)
  {
    std::cerr << "Error: " << error << std::endl;
    return EXIT_FAILURE;
  }
 
  return EXIT_SUCCESS;
}