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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkGaussianDerivativeImageFunctionTest.cxx
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkGaussianDerivativeImageFunction.h"
#include "itkGaussianDerivativeSpatialFunction.h"
#include "itkImage.h"
int itkGaussianDerivativeImageFunctionTest(int, char* [] )
{
const unsigned int Dimension = 2;
typedef float PixelType;
typedef itk::Image< PixelType, Dimension > ImageType;
typedef itk::GaussianDerivativeImageFunction< ImageType > DoGFunctionType;
// Create and allocate the image
ImageType::Pointer image = ImageType::New();
ImageType::SizeType size;
ImageType::IndexType start;
ImageType::RegionType region;
size[0] = 50;
size[1] = 50;
start.Fill( 0 );
region.SetIndex( start );
region.SetSize( size );
image->SetRegions( region );
image->Allocate();
ImageType::PixelType initialValue = 0;
image->FillBuffer( initialValue );
// Fill the image with a straight line
for(unsigned int i=0;i<50;i++)
{
ImageType::IndexType ind;
ind[0]=i;
ind[1]=25;
image->SetPixel(ind,1);
ind[1]=26;
image->SetPixel(ind,1);
}
// Test the derivative of Gaussian image function
DoGFunctionType::Pointer DoG = DoGFunctionType::New();
DoG->SetInputImage( image );
std::cout << "Testing Set/GetSigma(): ";
DoG->SetSigma(2.0);
const double* sigma = DoG->GetSigma();
for(unsigned int i=0;i<Dimension;i++)
{
if( sigma[i] != 2.0)
{
std::cerr << "[FAILED]" << std::endl;
return EXIT_FAILURE;
}
}
std::cout << "[PASSED] " << std::endl;
std::cout << "Testing Set/GetExtent(): ";
DoG->SetExtent(4.0);
const double* ext = DoG->GetExtent();
for(unsigned int i=0;i<Dimension;i++)
{
if( ext[i] != 4.0)
{
std::cerr << "[FAILED]" << std::endl;
return EXIT_FAILURE;
}
}
std::cout << "[PASSED] " << std::endl;
std::cout << "Testing consistency within Index/Point/ContinuousIndex: ";
itk::Index<2> index;
index.Fill(25);
DoGFunctionType::OutputType gradient_index;
gradient_index = DoG->EvaluateAtIndex( index );
DoGFunctionType::PointType pt;
pt[0]=25.0;
pt[1]=25.0;
DoGFunctionType::OutputType gradient_point;
gradient_point = DoG->Evaluate( pt );
DoGFunctionType::ContinuousIndexType continuousIndex;
continuousIndex.Fill(25);
DoGFunctionType::OutputType gradient_continuousIndex;
gradient_continuousIndex = DoG->EvaluateAtContinuousIndex( continuousIndex );
if( gradient_index != gradient_point
|| gradient_index != gradient_continuousIndex)
{
std::cerr << "[FAILED] : " << gradient_index << " : "
<< gradient_point << std::endl;
return EXIT_FAILURE;
}
std::cout << "[PASSED] " << std::endl;
gradient_point.Normalize(); // normalize the vector;
std::cout << "Testing Evaluate() : ";
if( (gradient_point[0] > 0.1) ||
(vcl_fabs(gradient_point[1]+1.0)> 10e-4)
)
{
std::cerr << "[FAILED]" << std::endl;
return EXIT_FAILURE;
}
std::cout << "[PASSED] " << std::endl;
pt[0]=25.0;
pt[1]=26.0;
gradient_point = DoG->Evaluate( pt );
gradient_point.Normalize(); // normalize the vector;
std::cout << "Testing Evaluate() : ";
if( (gradient_point[0] > 0.1) ||
(vcl_fabs(gradient_point[1]-1.0)> 10e-4)
)
{
std::cerr << "[FAILED]" << std::endl;
return EXIT_FAILURE;
}
std::cout << "[PASSED] " << std::endl;
std::cout << "Testing Gaussian Derivative Spatial Function:";
typedef itk::GaussianDerivativeSpatialFunction<double,1> GaussianDerivativeFunctionType;
GaussianDerivativeFunctionType::Pointer f = GaussianDerivativeFunctionType::New();
f->SetScale(1.0);
if(f->GetScale() != 1.0)
{
std::cerr << "Get Scale : [FAILED]" << std::endl;
return EXIT_FAILURE;
}
f->SetNormalized(true);
if(!f->GetNormalized())
{
std::cerr << "GetNormalized : [FAILED]" << std::endl;
return EXIT_FAILURE;
}
GaussianDerivativeFunctionType::ArrayType s;
s[0] = 1.0;
f->SetSigma(s);
if(f->GetSigma()[0] != 1.0)
{
std::cerr << "GetSigma : [FAILED]" << std::endl;
return EXIT_FAILURE;
}
GaussianDerivativeFunctionType::ArrayType m;
m[0] = 0.0;
f->SetMean(m);
if(f->GetMean()[0] != 0.0)
{
std::cerr << "GetMean : [FAILED]" << std::endl;
return EXIT_FAILURE;
}
f->SetDirection(0);
if(f->GetDirection() != 0)
{
std::cerr << "GetDirection : [FAILED]" << std::endl;
return EXIT_FAILURE;
}
GaussianDerivativeFunctionType::InputType point;
point[0] = 0.0;
if(f->Evaluate(point) != 0.0)
{
std::cerr << "Evaluate: [FAILED]" << std::endl;
return EXIT_FAILURE;
}
std::cout << f << std::endl;
std::cout << "[PASSED] " << std::endl;
std::cout << "GaussianDerivativeImageFunctionTest: [DONE] " << std::endl;
return EXIT_SUCCESS;
}
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