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/*
//
// Copyright 2010, 2012, 2013 SRI International
//
// This file is part of the Computational Morphometry Toolkit.
//
// http://www.nitrc.org/projects/cmtk/
//
// The Computational Morphometry Toolkit is free software: you can
// redistribute it and/or modify it under the terms of the GNU General Public
// License as published by the Free Software Foundation, either version 3 of
// the License, or (at your option) any later version.
//
// The Computational Morphometry Toolkit is distributed in the hope that it
// will be useful, but WITHOUT ANY WARRANTY; without even the implied
// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License along
// with the Computational Morphometry Toolkit. If not, see
// <http://www.gnu.org/licenses/>.
//
// $Revision: 5436 $
//
// $LastChangedDate: 2018-12-10 19:01:20 -0800 (Mon, 10 Dec 2018) $
//
// $LastChangedBy: torstenrohlfing $
//
*/
#include "cmtkSimpleLevelsetDevice.h"
#include <Base/cmtkGaussianKernel.h>
#include <Base/cmtkUnits.h>
#include <System/cmtkProgress.h>
#include <GPU/cmtkDeviceMemory.h>
#include <GPU/cmtkDeviceUniformVolume.h>
#include <GPU/cmtkDeviceUniformVolumeArray.h>
#include <GPU/cmtkDeviceImageConvolution_kernels.h>
#include <GPU/cmtkDeviceThresholdData_kernels.h>
#include <GPU/cmtkSimpleLevelsetDevice_kernels.h>
#include <vector>
void
cmtk::SimpleLevelsetDevice
::Evolve( const int numberOfIterations, const bool forceIterations )
{
FixedArray< 3, std::vector<float> > kernels;
for ( int dim = 0; dim < 3; ++dim )
{
kernels[dim] = GaussianKernel<float>::GetSymmetricKernel( this->m_FilterSigma / this->m_Volume->Deltas()[dim], 0.01f /*maxError*/ );
}
const size_t numberOfPixels = this->m_Levelset->GetNumberOfPixels();
DeviceUniformVolume::SmartPtr deviceVolume = DeviceUniformVolume::Create( *(this->m_Volume) );
DeviceUniformVolumeArray::SmartPtr deviceLevelset = DeviceUniformVolumeArray::Create( *(this->m_Levelset) );
DeviceMemory<float>::SmartPtr temporary = DeviceMemory<float>::Create( numberOfPixels );
int nInsideOld = 0, nInside = 1;
Progress::Begin( 0, numberOfIterations, 1, "Levelset Evolution" );
for ( int it = 0; (it < numberOfIterations) && ((nInside!=nInsideOld) || forceIterations); ++it )
{
Progress::SetProgress( it );
DeviceImageConvolution( temporary->Ptr(), this->m_Volume->GetDims().begin(), deviceLevelset->GetDeviceArrayPtr()->GetArrayOnDevice(),
kernels[0].size(), &kernels[0][0], kernels[1].size(), &kernels[1][0], kernels[2].size(), &kernels[2][0] );
float insideSum, outsideSum;
SimpleLevelsetDeviceUpdateInsideOutside( temporary->Ptr(), deviceVolume->GetDataOnDevice().Ptr(), numberOfPixels, &insideSum, &outsideSum, &nInside );
if ( nInside == 0 )
throw Self::DegenerateLevelsetException();
const int nOutside = numberOfPixels - nInside;
if ( nOutside == 0 )
throw Self::DegenerateLevelsetException();
SimpleLevelsetDeviceUpdateLevelset( temporary->Ptr(), deviceVolume->GetDataOnDevice().Ptr(), numberOfPixels, insideSum / nInside, outsideSum / nOutside, 1.0f * nInside / nOutside,
static_cast<float>( this->m_TimeDelta ), static_cast<float>( this->m_LevelsetThreshold ) );
deviceLevelset->GetDeviceArrayPtr()->CopyOnDeviceToArray( temporary->Ptr() );
}
temporary->CopyToHost( this->m_Levelset->GetData()->GetDataPtr(), numberOfPixels );
Progress::Done();
}
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