Apply Kernel to Every Pixel

Synopsis

Apply a kernel to every pixel in an image.

Results

input.png

output.png

Code

Python

#!/usr/bin/env python

import sys
import itk

dimension = 2
float_image_type = itk.Image[itk.F, dimension]
unsigned_char_image_type = itk.Image[itk.UC, dimension]

start = itk.Index[dimension]()
start.Fill(0)

size = itk.Size[dimension]()
size.Fill(100)

region = itk.ImageRegion[dimension]()
region.SetIndex(start)
region.SetSize(size)

image = float_image_type.New(Regions=region)
image.Allocate()
image.FillBuffer(0)

image[20:80, 20:80] = 15

input_image = itk.rescale_intensity_image_filter(
    image,
    output_minimum=0,
    output_maximum=255,
    ttype=(float_image_type, unsigned_char_image_type),
)

itk.imwrite(input_image, "inputPython.png")

sobel_operator = itk.SobelOperator[itk.F, dimension]()
radius = itk.Size[dimension]()
radius.Fill(1)  # a radius of 1x1 creates a 3x3 operator
sobel_operator.SetDirection(0)  # Create the operator for the X axis derivative
sobel_operator.CreateToRadius(radius)

image = itk.neighborhood_operator_image_filter(image, operator=sobel_operator)

output_image = itk.rescale_intensity_image_filter(
    image,
    output_minimum=0,
    output_maximum=255,
    ttype=(float_image_type, unsigned_char_image_type),
)

itk.imwrite(output_image, "outputPython.png")

C++

#include "itkImage.h"
#include "itkNeighborhoodOperatorImageFilter.h"
#include "itkSobelOperator.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"

using FloatImageType = itk::Image<float, 2>;

void
CreateImage(FloatImageType::Pointer image);
void
CastRescaleAndWrite(FloatImageType::Pointer image, const std::string & filename);

int
main(int, char *[])
{
  FloatImageType::Pointer image = FloatImageType::New();
  CreateImage(image);
  CastRescaleAndWrite(image, "input.png");

  using SobelOperatorType = itk::SobelOperator<float, 2>;
  SobelOperatorType sobelOperator;
  itk::Size<2>      radius;
  radius.Fill(1);                // a radius of 1x1 creates a 3x3 operator
  sobelOperator.SetDirection(0); // Create the operator for the X axis derivative
  sobelOperator.CreateToRadius(radius);

  using NeighborhoodOperatorImageFilterType = itk::NeighborhoodOperatorImageFilter<FloatImageType, FloatImageType>;
  NeighborhoodOperatorImageFilterType::Pointer filter = NeighborhoodOperatorImageFilterType::New();
  filter->SetOperator(sobelOperator);
  filter->SetInput(image);
  filter->Update();

  CastRescaleAndWrite(filter->GetOutput(), "output.png");

  return EXIT_SUCCESS;
}

void
CreateImage(FloatImageType::Pointer image)
{
  FloatImageType::IndexType start;
  start.Fill(0);

  FloatImageType::SizeType size;
  size.Fill(100);

  FloatImageType::RegionType region(start, size);

  image->SetRegions(region);
  image->Allocate();
  image->FillBuffer(0);

  // Make a square
  for (unsigned int r = 20; r < 80; r++)
  {
    for (unsigned int c = 20; c < 80; c++)
    {
      FloatImageType::IndexType pixelIndex;
      pixelIndex[0] = r;
      pixelIndex[1] = c;

      image->SetPixel(pixelIndex, 15);
    }
  }
}

void
CastRescaleAndWrite(FloatImageType::Pointer image, const std::string & filename)
{
  using UnsignedCharImageType = itk::Image<unsigned char, 2>;
  using RescaleFilterType = itk::RescaleIntensityImageFilter<FloatImageType, UnsignedCharImageType>;
  RescaleFilterType::Pointer rescaleFilter = RescaleFilterType::New();
  rescaleFilter->SetInput(image);
  rescaleFilter->SetOutputMinimum(0);
  rescaleFilter->SetOutputMaximum(255);
  rescaleFilter->Update();

  using WriterType = itk::ImageFileWriter<UnsignedCharImageType>;
  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName(filename);
  writer->SetInput(rescaleFilter->GetOutput());
  writer->Update();
}

Classes demonstrated

template<typename TInputImage, typename TOutputImage, typename TOperatorValueType = typename TOutputImage::PixelType>
class NeighborhoodOperatorImageFilter : public itk::ImageToImageFilter<TInputImage, TOutputImage>

Applies a single NeighborhoodOperator to an image region.

This filter calculates successive inner products between a single NeighborhoodOperator and a NeighborhoodIterator, which is swept across every pixel in an image region. For operators that are symmetric across their axes, the result is a fast convolution with the image region. Apply the mirror()’d operator for non-symmetric NeighborhoodOperators.

See

Image

See

Neighborhood

See

NeighborhoodOperator

See

NeighborhoodIterator

ITK Sphinx Examples:

• All ITK Sphinx Examples

• Apply Kernel To Every Pixel

Subclassed by itk::MaskNeighborhoodOperatorImageFilter< TInputImage, TMaskImage, TOutputImage, TOperatorValueType >, itk::NormalizedCorrelationImageFilter< TInputImage, TMaskImage, TOutputImage, TOperatorValueType >

See itk::NeighborhoodOperatorImageFilter for additional documentation.