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itkLabelStatisticsImageFilter.hxx
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itkLabelStatisticsImageFilter.hxx
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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkLabelStatisticsImageFilter_hxx
#define itkLabelStatisticsImageFilter_hxx
#include "itkImageLinearConstIteratorWithIndex.h"
#include "itkImageScanlineConstIterator.h"
#include "itkTotalProgressReporter.h"
#include <algorithm> // For min and max.
namespace itk
{
template <typename TInputImage, typename TLabelImage>
LabelStatisticsImageFilter<TInputImage, TLabelImage>::LabelStatisticsImageFilter()
{
Self::AddRequiredInputName("LabelInput");
m_UseHistograms = false;
m_NumBins.SetSize(1);
m_NumBins[0] = 256;
m_LowerBound = static_cast<RealType>(NumericTraits<PixelType>::NonpositiveMin());
m_UpperBound = static_cast<RealType>(NumericTraits<PixelType>::max());
m_ValidLabelValues.clear();
}
template <typename TInputImage, typename TLabelImage>
void
LabelStatisticsImageFilter<TInputImage, TLabelImage>::SetHistogramParameters(const int numBins,
RealType lowerBound,
RealType upperBound)
{
m_NumBins[0] = numBins;
m_LowerBound = lowerBound;
m_UpperBound = upperBound;
m_UseHistograms = true;
}
template <typename TInputImage, typename TLabelImage>
void
LabelStatisticsImageFilter<TInputImage, TLabelImage>::MergeMap(MapType & m1, MapType & m2) const
{
for (auto & m2_value : m2)
{
// does this label exist in the cumulative structure yet?
auto m1It = m1.find(m2_value.first);
if (m1It == m1.end())
{
// move m2 entry into m1, this reuses the histogram if needed.
m1.emplace(m2_value.first, std::move(m2_value.second));
}
else
{
typename MapType::mapped_type & labelStats = m1It->second;
// accumulate the information from this thread
labelStats.m_Count += m2_value.second.m_Count;
labelStats.m_Sum += m2_value.second.m_Sum;
labelStats.m_SumOfSquares += m2_value.second.m_SumOfSquares;
if (labelStats.m_Minimum > m2_value.second.m_Minimum)
{
labelStats.m_Minimum = m2_value.second.m_Minimum;
}
if (labelStats.m_Maximum < m2_value.second.m_Maximum)
{
labelStats.m_Maximum = m2_value.second.m_Maximum;
}
// bounding box is min,max pairs
for (unsigned int ii = 0; ii < (ImageDimension * 2); ii += 2)
{
labelStats.m_BoundingBox[ii] = std::min(labelStats.m_BoundingBox[ii], m2_value.second.m_BoundingBox[ii]);
labelStats.m_BoundingBox[ii + 1] =
std::max(labelStats.m_BoundingBox[ii + 1], m2_value.second.m_BoundingBox[ii + 1]);
}
// if enabled, update the histogram for this label
if (m_UseHistograms)
{
typename HistogramType::IndexType index;
index.SetSize(1);
for (unsigned int bin = 0; bin < m_NumBins[0]; ++bin)
{
index[0] = bin;
labelStats.m_Histogram->IncreaseFrequency(bin, m2_value.second.m_Histogram->GetFrequency(bin));
}
}
}
}
}
template <typename TInputImage, typename TLabelImage>
void
LabelStatisticsImageFilter<TInputImage, TLabelImage>::AfterStreamedGenerateData()
{
Superclass::AfterStreamedGenerateData();
// compute the remainder of the statistics
for (auto & mapValue : m_LabelStatistics)
{
typename MapType::mapped_type & labelStats = mapValue.second;
labelStats.m_Mean = labelStats.m_Sum / static_cast<RealType>(labelStats.m_Count);
// variance
if (labelStats.m_Count > 1)
{
// unbiased estimate of variance
LabelStatistics & ls = mapValue.second;
const RealType sumSquared = ls.m_Sum * ls.m_Sum;
const auto count = static_cast<RealType>(ls.m_Count);
ls.m_Variance = (ls.m_SumOfSquares - sumSquared / count) / (count - 1.0);
}
else
{
labelStats.m_Variance = RealType{};
}
// sigma
labelStats.m_Sigma = 0.0;
if (labelStats.m_Variance >= 0.0)
{
labelStats.m_Sigma = std::sqrt(labelStats.m_Variance);
}
}
{
// Now update the cached vector of valid labels.
m_ValidLabelValues.resize(0);
m_ValidLabelValues.reserve(m_LabelStatistics.size());
for (auto & mapValue : m_LabelStatistics)
{
m_ValidLabelValues.push_back(mapValue.first);
}
}
}
template <typename TInputImage, typename TLabelImage>
void
LabelStatisticsImageFilter<TInputImage, TLabelImage>::ThreadedStreamedGenerateData(
const RegionType & outputRegionForThread)
{
MapType localStatistics;
typename HistogramType::IndexType histogramIndex(1);
typename HistogramType::MeasurementVectorType histogramMeasurement(1);
const SizeValueType size0 = outputRegionForThread.GetSize(0);
if (size0 == 0)
{
return;
}
ImageLinearConstIteratorWithIndex<TInputImage> it(this->GetInput(), outputRegionForThread);
ImageScanlineConstIterator labelIt(this->GetLabelInput(), outputRegionForThread);
auto mapIt = localStatistics.end();
// do the work
while (!it.IsAtEnd())
{
while (!it.IsAtEndOfLine())
{
const RealType & value = static_cast<RealType>(it.Get());
const LabelPixelType & label = labelIt.Get();
// is the label already in this thread?
mapIt = localStatistics.find(label);
if (mapIt == localStatistics.end())
{
// create a new statistics object
if (m_UseHistograms)
{
mapIt = localStatistics.emplace(label, LabelStatistics(m_NumBins[0], m_LowerBound, m_UpperBound)).first;
}
else
{
mapIt = localStatistics.emplace(label, LabelStatistics()).first;
}
}
typename MapType::mapped_type & labelStats = mapIt->second;
// update the values for this label and this thread
if (value < labelStats.m_Minimum)
{
labelStats.m_Minimum = value;
}
if (value > labelStats.m_Maximum)
{
labelStats.m_Maximum = value;
}
// bounding box is min,max pairs
for (unsigned int i = 0; i < (2 * TInputImage::ImageDimension); i += 2)
{
const IndexType & index = it.GetIndex();
labelStats.m_BoundingBox[i] = std::min(labelStats.m_BoundingBox[i], index[i / 2]);
labelStats.m_BoundingBox[i + 1] = std::max(labelStats.m_BoundingBox[i + 1], index[i / 2]);
}
labelStats.m_Sum += value;
labelStats.m_SumOfSquares += (value * value);
labelStats.m_Count++;
// if enabled, update the histogram for this label
if (m_UseHistograms)
{
histogramMeasurement[0] = value;
labelStats.m_Histogram->GetIndex(histogramMeasurement, histogramIndex);
labelStats.m_Histogram->IncreaseFrequencyOfIndex(histogramIndex, 1);
}
++labelIt;
++it;
}
labelIt.NextLine();
it.NextLine();
}
// Merge localStatistics and m_LabelStatistics concurrently safe in a
// local copy, this thread may do multiple merges.
while (true)
{
MapType tomerge{};
{
const std::lock_guard<std::mutex> lockGuard(m_Mutex);
if (m_LabelStatistics.empty())
{
swap(m_LabelStatistics, localStatistics);
break;
}
// Move the data of the output map to the local `tomerge` and clear the output map.
swap(m_LabelStatistics, tomerge);
} // release lock, allow other threads to merge data
// Merge tomerge into localStatistics, locally
MergeMap(localStatistics, tomerge);
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetMinimum(LabelPixelType label) const -> RealType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return NumericTraits<PixelType>::max();
}
else
{
return mapIt->second.m_Minimum;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetMaximum(LabelPixelType label) const -> RealType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return NumericTraits<PixelType>::NonpositiveMin();
}
else
{
return mapIt->second.m_Maximum;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetMean(LabelPixelType label) const -> RealType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return PixelType{};
}
else
{
return mapIt->second.m_Mean;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetSum(LabelPixelType label) const -> RealType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return PixelType{};
}
else
{
return mapIt->second.m_Sum;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetSigma(LabelPixelType label) const -> RealType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return PixelType{};
}
else
{
return mapIt->second.m_Sigma;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetVariance(LabelPixelType label) const -> RealType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return PixelType{};
}
else
{
return mapIt->second.m_Variance;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetBoundingBox(LabelPixelType label) const -> BoundingBoxType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
BoundingBoxType emptyBox;
// label does not exist, return a default value
return emptyBox;
}
else
{
return mapIt->second.m_BoundingBox;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetRegion(LabelPixelType label) const -> RegionType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
RegionType emptyRegion;
// label does not exist, return a default value
return emptyRegion;
}
else
{
BoundingBoxType bbox = this->GetBoundingBox(label);
IndexType index;
SizeType size;
for (unsigned int i = 0; i < ImageDimension; ++i)
{
index[i] = bbox[2 * i];
size[i] = bbox[2 * i + 1] - bbox[2 * i] + 1;
}
const RegionType region(index, size);
return region;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetCount(LabelPixelType label) const -> MapSizeType
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return 0;
}
else
{
return mapIt->second.m_Count;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetMedian(LabelPixelType label) const -> RealType
{
RealType median = 0.0;
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end() || !m_UseHistograms)
{
// label does not exist OR histograms not enabled, return the default value
return median;
}
else
{
typename HistogramType::SizeValueType bin = 0;
typename HistogramType::IndexType index;
index.SetSize(1);
RealType total = 0;
// count bins until just over half the distribution is counted
while (total <= (mapIt->second.m_Count / 2) && (bin < m_NumBins[0]))
{
index[0] = bin;
total += mapIt->second.m_Histogram->GetFrequency(index);
++bin;
}
--bin;
index[0] = bin;
// return center of bin range
RealType lowRange = mapIt->second.m_Histogram->GetBinMin(0, bin);
RealType highRange = mapIt->second.m_Histogram->GetBinMax(0, bin);
median = lowRange + (highRange - lowRange) / 2;
return median;
}
}
template <typename TInputImage, typename TLabelImage>
auto
LabelStatisticsImageFilter<TInputImage, TLabelImage>::GetHistogram(LabelPixelType label) const -> HistogramPointer
{
MapConstIterator mapIt;
mapIt = m_LabelStatistics.find(label);
if (mapIt == m_LabelStatistics.end())
{
// label does not exist, return a default value
return nullptr;
}
else
{
// this will be zero if histograms have not been enabled
return mapIt->second.m_Histogram;
}
}
template <typename TImage, typename TLabelImage>
void
LabelStatisticsImageFilter<TImage, TLabelImage>::PrintSelf(std::ostream & os, Indent indent) const
{
using namespace print_helper;
Superclass::PrintSelf(os, indent);
os << indent << "LabelStatistics: " << std::endl;
for (auto const & pair : m_LabelStatistics)
{
os << indent.GetNextIndent() << "{" << pair.first << ": " << pair.second << "}" << std::endl;
}
os << indent << "ValidLabelValues: " << m_ValidLabelValues << std::endl;
os << indent << "UseHistograms: " << (m_UseHistograms ? "On" : "Off") << std::endl;
os << indent << "NumBins: " << m_NumBins << std::endl;
os << indent << "LowerBound: " << static_cast<typename NumericTraits<RealType>::PrintType>(m_LowerBound) << std::endl;
os << indent << "UpperBound: " << static_cast<typename NumericTraits<RealType>::PrintType>(m_UpperBound) << std::endl;
}
} // end namespace itk
#endif