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/*=========================================================================
*
* 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.
*
*=========================================================================*/
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"
// Software Guide : BeginLatex
//
// In this example, the Sobel edge-detection routine is rewritten using
// convolution filtering. Convolution filtering is a standard image processing
// technique that can be implemented numerically as the inner product of all
// image neighborhoods with a convolution kernel \cite{Gonzalez1993}
// \cite{Castleman1996}. In ITK, we use a class of objects called
// \emph{neighborhood operators} as convolution kernels and a special function
// object called \doxygen{NeighborhoodInnerProduct} to calculate inner
// products.
//
// The basic ITK convolution filtering routine is to step through the image
// with a neighborhood iterator and use NeighborhoodInnerProduct to
// find the inner product of each neighborhood with the desired kernel. The
// resulting values are written to an output image. This example uses a
// neighborhood operator called the \doxygen{SobelOperator}, but all
// neighborhood operators can be convolved with images using this basic
// routine. Other examples of neighborhood operators include derivative
// kernels, Gaussian kernels, and morphological
// operators. \doxygen{NeighborhoodOperatorImageFilter} is a generalization of
// the code in this section to ND images and arbitrary convolution kernels.
//
// We start writing this example by including the header files for the Sobel
// kernel and the inner product function.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkSobelOperator.h"
#include "itkNeighborhoodInnerProduct.h"
// Software Guide : EndCodeSnippet
int main( int argc, char ** argv )
{
if ( argc < 4 )
{
std::cerr << "Missing parameters. " << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0]
<< " inputImageFile outputImageFile direction"
<< std::endl;
return EXIT_FAILURE;
}
typedef float PixelType;
typedef itk::Image< PixelType, 2 > ImageType;
typedef itk::ImageFileReader< ImageType > ReaderType;
typedef itk::ConstNeighborhoodIterator< ImageType > NeighborhoodIteratorType;
typedef itk::ImageRegionIterator< ImageType> IteratorType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
try
{
reader->Update();
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
ImageType::Pointer output = ImageType::New();
output->SetRegions(reader->GetOutput()->GetRequestedRegion());
output->Allocate();
IteratorType out(output, reader->GetOutput()->GetRequestedRegion());
// Software Guide : BeginLatex
//
// \index{convolution!kernels}
// \index{convolution!operators}
// \index{iterators!neighborhood!and convolution}
//
// Refer to the previous example for a description of reading the input image and
// setting up the output image and iterator.
//
// The following code creates a Sobel operator. The Sobel operator requires
// a direction for its partial derivatives. This direction is read from the command line.
// Changing the direction of the derivatives changes the bias of the edge
// detection, i.e. maximally vertical or maximally horizontal.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
itk::SobelOperator<PixelType, 2> sobelOperator;
sobelOperator.SetDirection( ::atoi(argv[3]) );
sobelOperator.CreateDirectional();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The neighborhood iterator is initialized as before, except that now it takes
// its radius directly from the radius of the Sobel operator. The inner
// product function object is templated over image type and requires no
// initialization.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
NeighborhoodIteratorType::RadiusType radius = sobelOperator.GetRadius();
NeighborhoodIteratorType it( radius, reader->GetOutput(),
reader->GetOutput()->GetRequestedRegion() );
itk::NeighborhoodInnerProduct<ImageType> innerProduct;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Using the Sobel operator, inner product, and neighborhood iterator objects,
// we can now write a very simple \code{for} loop for performing convolution
// filtering. As before, out-of-bounds pixel values are supplied automatically
// by the iterator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
{
out.Set( innerProduct( it, sobelOperator ) );
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output is rescaled and written as in the previous example. Applying
// this example in the $x$ and $y$ directions produces the images at the center
// and right of Figure~\ref{fig:NeighborhoodExamples1}. Note that x-direction
// operator produces the same output image as in the previous example.
//
// Software Guide : EndLatex
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
typedef itk::ImageFileWriter< WriteImageType > WriterType;
typedef itk::RescaleIntensityImageFilter<
ImageType, WriteImageType > RescaleFilterType;
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
rescaler->SetInput(output);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[2] );
writer->SetInput(rescaler->GetOutput());
try
{
writer->Update();
}
catch ( itk::ExceptionObject &err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
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