Add Noise To Binary Image

Synopsis

Add noise to a binary image.

Results

Yinyang.png

Output.png

Output:

Number of random samples: 105062

Code

C++

#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageRandomNonRepeatingIteratorWithIndex.h"
#include "itkImageFileWriter.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"

int
main(int argc, char * argv[])
{
  if (argc < 3)
  {
    std::cerr << "Usage: " << argv[0] << " inputFile outputFile [percent]" << std::endl;
    return EXIT_FAILURE;
  }
  double percent = .1;
  if (argc > 3)
  {
    percent = std::stod(argv[3]);
    if (percent >= 1.0)
    {
      percent /= 100.0;
    }
  }
  using ImageType = itk::Image<unsigned char, 2>;
  using ReaderType = itk::ImageFileReader<ImageType>;
  using IteratorType = itk::ImageRandomNonRepeatingIteratorWithIndex<ImageType>;
  using WriterType = itk::ImageFileWriter<ImageType>;

  // Read the binary file
  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName(argv[1]);
  reader->Update();

  // At x% of the pixels, add a uniform random value between 0 and 255
  IteratorType it(reader->GetOutput(), reader->GetOutput()->GetLargestPossibleRegion());
  it.SetNumberOfSamples(reader->GetOutput()->GetLargestPossibleRegion().GetNumberOfPixels() * percent);
  std::cout << "Number of random samples: " << it.GetNumberOfSamples() << std::endl;
  using GeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator;
  GeneratorType::Pointer random = GeneratorType::New();

  it.GoToBegin();
  while (!it.IsAtEnd())
  {
    it.Set(random->GetUniformVariate(0, 255));
    ++it;
  }

  // Write the file
  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName(argv[2]);
  writer->SetInput(reader->GetOutput());
  writer->Update();

  return EXIT_SUCCESS;
}

Classes demonstrated

template<typename TImage>
class ImageRandomNonRepeatingIteratorWithIndex : public itk::ImageRandomNonRepeatingConstIteratorWithIndex<TImage>

A multi-dimensional image iterator that visits image pixels within a region in a random order, without repeating.

This class was contributed by Rupert Brooks, McGill Centre for Intelligent Machines, Montreal, Canada. It is heavily based on the ImageRandomIterator class.

This iterator is a subclass of itk::ImageRandomNonRepeatingConstIteratorWithIndex that adds write-access functionality. Please see itk::ImageRandomNonRepeatingConstIteratorWithIndex for more information.

MORE INFORMATION

For a complete description of the ITK Image Iterators and their API, please see the Iterators chapter in the ITK Software Guide. The ITK Software Guide is available in print and as a free .pdf download from https://www.itk.org.

Author

Rupert Brooks, McGill Centre for Intelligent Machines. Canada

See

ImageConstIterator

See

ConditionalConstIterator

See

ConstNeighborhoodIterator

See

ConstShapedNeighborhoodIterator

See

ConstSliceIterator

See

CorrespondenceDataStructureIterator

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FloodFilledFunctionConditionalConstIterator

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FloodFilledImageFunctionConditionalConstIterator

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FloodFilledImageFunctionConditionalIterator

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FloodFilledSpatialFunctionConditionalConstIterator

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FloodFilledSpatialFunctionConditionalIterator

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ImageConstIterator

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ImageConstIteratorWithIndex

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ImageIterator

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ImageIteratorWithIndex

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ImageRandomNonRepeatingConstIteratorWithIndex

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ImageRandomNonRepeatingIteratorWithIndex

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ImageRandomConstIteratorWithIndex

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ImageRandomIteratorWithIndex

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ImageRegionConstIterator

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ImageRegionConstIteratorWithIndex

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ImageRegionExclusionConstIteratorWithIndex

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ImageRegionExclusionIteratorWithIndex

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ImageRegionIterator

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ImageRegionIteratorWithIndex

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ImageRegionReverseConstIterator

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ImageRegionReverseIterator

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ImageReverseConstIterator

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ImageReverseIterator

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ImageSliceConstIteratorWithIndex

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ImageSliceIteratorWithIndex

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NeighborhoodIterator

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PathConstIterator

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PathIterator

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ShapedNeighborhoodIterator

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SliceIterator

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ImageConstIteratorWithIndex

ITK Sphinx Examples:

• All ITK Sphinx Examples

• Add Noise To Binary Image

See itk::ImageRandomNonRepeatingIteratorWithIndex for additional documentation.