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/*=========================================================================
*
* Copyright UMC Utrecht and contributors
*
* 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.
*
*=========================================================================*/
#ifndef itkImageRandomSamplerSparseMask_hxx
#define itkImageRandomSamplerSparseMask_hxx
#include "itkImageRandomSamplerSparseMask.h"
#include <itkDeref.h>
#include <cassert>
namespace itk
{
/**
* ******************* GenerateData *******************
*/
template <class TInputImage>
void
ImageRandomSamplerSparseMask<TInputImage>::GenerateData()
{
/** Get a handle to the mask. */
const MaskType * const mask = this->Superclass::GetMask();
/** Sanity check. */
if (mask == nullptr)
{
itkExceptionMacro("ERROR: do not call this function when no mask is supplied. When using the "
"ImageRandomSamplerSparseMask sampler, a mask is required. Otherwise you may consider using a "
"sampler that does not require a mask, for example, ImageRandomSampler.");
}
/** Get handles to the input image and output sample container. */
const InputImageType & inputImage = Deref(this->GetInput());
ImageSampleContainerType & sampleContainer = Deref(this->GetOutput());
// Take capacity from the output container, and clear it.
std::vector<ImageSampleType> sampleVector;
sampleContainer.swap(sampleVector);
sampleVector.clear();
/** Make sure the internal full sampler is up-to-date. */
this->m_InternalFullSampler->SetInput(&inputImage);
this->m_InternalFullSampler->SetMask(mask);
this->m_InternalFullSampler->SetInputImageRegion(this->GetCroppedInputImageRegion());
this->m_InternalFullSampler->SetUseMultiThread(Superclass::m_UseMultiThread);
/** Use try/catch, since the full sampler may crash, due to insufficient memory. */
try
{
this->m_InternalFullSampler->Update();
}
catch (const ExceptionObject & err)
{
itkExceptionMacro("ERROR: This ImageSampler internally uses the ImageFullSampler. Updating of this internal "
"sampler raised the exception:\n"
<< err.GetDescription());
}
/** Get a handle to the full sampler output. */
const ImageSampleContainerType & allValidSamples = Deref(this->m_InternalFullSampler->GetOutput());
unsigned long numberOfValidSamples = allValidSamples.Size();
/** If desired we exercise a multi-threaded version. */
if (Superclass::m_UseMultiThread)
{
m_RandomIndices.clear();
m_RandomIndices.reserve(Superclass::m_NumberOfSamples);
for (unsigned int i = 0; i < Superclass::m_NumberOfSamples; ++i)
{
m_RandomIndices.push_back(m_RandomGenerator->GetIntegerVariate(numberOfValidSamples - 1));
}
auto & samples = sampleContainer.CastToSTLContainer();
samples.resize(m_RandomIndices.size());
UserData userData{ allValidSamples.CastToSTLConstContainer(), m_RandomIndices, samples };
Deref(this->ProcessObject::GetMultiThreader()).SetSingleMethodAndExecute(&Self::ThreaderCallback, &userData);
return;
}
/** Take random samples from the allValidSamples-container. */
sampleVector.reserve(Superclass::m_NumberOfSamples);
for (unsigned int i = 0; i < Superclass::m_NumberOfSamples; ++i)
{
unsigned long randomIndex = this->m_RandomGenerator->GetIntegerVariate(numberOfValidSamples - 1);
sampleVector.push_back(allValidSamples.ElementAt(randomIndex));
}
// Move the samples from the vector into the output container.
sampleContainer.swap(sampleVector);
} // end GenerateData()
template <class TInputImage>
ITK_THREAD_RETURN_FUNCTION_CALL_CONVENTION
ImageRandomSamplerSparseMask<TInputImage>::ThreaderCallback(void * const arg)
{
assert(arg);
const auto & info = *static_cast<const MultiThreaderBase::WorkUnitInfo *>(arg);
assert(info.UserData);
auto & userData = *static_cast<UserData *>(info.UserData);
const auto & randomIndices = userData.m_RandomIndices;
auto & samples = userData.m_Samples;
const auto totalNumberOfSamples = samples.size();
assert(totalNumberOfSamples == randomIndices.size());
const auto numberOfSamplesPerWorkUnit = totalNumberOfSamples / info.NumberOfWorkUnits;
const auto remainderNumberOfSamples = totalNumberOfSamples % info.NumberOfWorkUnits;
const auto offset =
info.WorkUnitID * numberOfSamplesPerWorkUnit + std::min<size_t>(info.WorkUnitID, remainderNumberOfSamples);
const auto beginOfRandomIndices = randomIndices.data() + offset;
const auto beginOfSamples = samples.data() + offset;
const auto & allValidSamples = userData.m_AllValidSamples;
const size_t n{ numberOfSamplesPerWorkUnit + (info.WorkUnitID < remainderNumberOfSamples ? 1 : 0) };
for (size_t i = 0; i < n; ++i)
{
beginOfSamples[i] = allValidSamples[beginOfRandomIndices[i]];
}
return ITK_THREAD_RETURN_DEFAULT_VALUE;
}
/**
* ******************* PrintSelf *******************
*/
template <class TInputImage>
void
ImageRandomSamplerSparseMask<TInputImage>::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "InternalFullSampler: " << this->m_InternalFullSampler.GetPointer() << std::endl;
os << indent << "RandomGenerator: " << this->m_RandomGenerator.GetPointer() << std::endl;
} // end PrintSelf()
} // end namespace itk
#endif // end #ifndef itkImageRandomSamplerSparseMask_hxx
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