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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.
*
*=========================================================================*/
// Disable warning for long symbol names in this file only
#include <itkRecursiveGaussianImageFilter.h>
#include <itkImageRegionIterator.h>
namespace
{
bool NormalizeSineWave( double frequencyPerImage, unsigned int order, double pixelSpacing = 1.0 )
{
// for an image f(x) = sin ( w*x ), where w is a measure of
// frequency, this methods verifies that the normalized scale-scale
// is with in reasonable tolerance of the theoretical value.
const unsigned int ImageDimension = 1;
const unsigned int imageSize = 1024;
const double tol = std::pow( .000001, 1.0 / order );
double frequency = frequencyPerImage * 2.0 * itk::Math::pi / ( imageSize * pixelSpacing );
// The theoretical maximal value should occur at this sigma
double sigma_max = std::sqrt( double( order ) ) / frequency;
// the theoreical maximal value of the derivative, obtained at sigma_max
double expected_max = std::pow( double(order), order *0.5 ) * std::exp( - 0.5 * order );
typedef itk::Image< double, ImageDimension > ImageType;
ImageType::Pointer image = ImageType::New();
ImageType::SizeType size;
size.Fill( imageSize );
image->SetRegions( ImageType::RegionType( size ) );
image->Allocate();
ImageType::SpacingType spacing;
spacing.Fill( pixelSpacing );
image->SetSpacing( spacing );
itk::ImageRegionIterator< ImageType > iter( image, image->GetBufferedRegion() );
// create sine wave image
while( !iter.IsAtEnd() )
{
ImageType::PointType p;
image->TransformIndexToPhysicalPoint( iter.GetIndex(), p );
const double x = p[0];
double value = std::sin( x * frequency );
iter.Set( value );
++iter;
}
typedef itk::RecursiveGaussianImageFilter<ImageType, ImageType> GaussianFilterType;
GaussianFilterType::Pointer filter = GaussianFilterType::New();
filter->SetInput( image );
filter->SetDirection( 0 );
filter->SetSigma( sigma_max );
switch( order)
{
case 1:
filter->SetOrder( GaussianFilterType::FirstOrder );
break;
case 2:
filter->SetOrder( GaussianFilterType::SecondOrder );
break;
default:
std::cerr << " only support order 1 and 2" << std::endl;
return false;
}
filter->SetNormalizeAcrossScale( true );
// The derivative need to be scaled
//
// All .Get() methods should be multiplied by this
const double scaleFactor = std::pow( 1.0/pixelSpacing, double(order) );
ImageType::Pointer outputImage = filter->GetOutput();
outputImage->Update();
// maximal value of the first derivative
double maxLx = itk::NumericTraits<double>::NonpositiveMin();
itk::ImageRegionConstIterator< ImageType > oiter( outputImage, outputImage->GetBufferedRegion() );
while ( !oiter.IsAtEnd() )
{
maxLx = std::max( maxLx, oiter.Get()*scaleFactor );
++oiter;
}
// check if the maximal is obtained with a little bit smaller Gaussian
filter->SetSigma( sigma_max*0.95 );
outputImage->Update();
oiter.GoToBegin();
while ( !oiter.IsAtEnd() )
{
if ( maxLx < oiter.Get()*scaleFactor &&
std::abs( maxLx - oiter.Get()*scaleFactor ) > tol )
{
std::cout << "FAIL: For period: " << 1.0/frequency
<< " maxLx: " << maxLx
<< " tolerance exceeded by: " << std::abs( maxLx - oiter.Get()*scaleFactor ) << std::endl;
return false;
}
++oiter;
}
// check if the maximal is obtained with a little bit bigger Gaussian
filter->SetSigma( sigma_max*1.05 );
outputImage->Update();
oiter.GoToBegin();
while ( !oiter.IsAtEnd() )
{
if ( maxLx < oiter.Get()*scaleFactor &&
std::abs( maxLx - oiter.Get()*scaleFactor ) > tol )
{
std::cout << "FAIL: For period: " << 1.0/frequency
<< " maxLx: " << maxLx
<< " tolerance exceeded by: " << std::abs( maxLx - oiter.Get()*scaleFactor ) << std::endl;
return false;
}
++oiter;
}
std::cout << "f: " << frequencyPerImage << " max: " << maxLx << " expected max: " << expected_max << std::endl;
if ( std::abs( maxLx - expected_max ) > .01 )
{
std::cout << "FAIL: tolerance of expected max exceeded!" << std::endl;
}
return true;
}
}
int itkRecursiveGaussianScaleSpaceTest1(int, char* [] )
{
bool pass = true;
std::cout << " Testing First Order Gaussian" << std::endl;
pass &= NormalizeSineWave( 1.5, 1);
pass &= NormalizeSineWave( 2.5, 1);
pass &= NormalizeSineWave( 5, 1 );
pass &= NormalizeSineWave( 10, 1 );
pass &= NormalizeSineWave( 25, 1 );
std::cout << " Testing Second Order Gaussian" << std::endl;
pass &= NormalizeSineWave( 1.5, 2);
pass &= NormalizeSineWave( 2.5, 2);
pass &= NormalizeSineWave( 5, 2 );
pass &= NormalizeSineWave( 10, 2 );
pass &= NormalizeSineWave( 25, 2 );
std::cout << " Testing Spacing Invariance" << std::endl;
pass &= NormalizeSineWave( 5, 2, 0.01 );
pass &= NormalizeSineWave( 5, 2, 100 );
if ( !pass )
{
std::cout << "Test Failed!" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
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