diff --git a/Modules/Applications/AppClassification/app/otbClassificationMapRegularization.cxx b/Modules/Applications/AppClassification/app/otbClassificationMapRegularization.cxx
index 4652d5556255407d811c591fb0729add8e462b90..1219414c9821766e8204898df8edba1383b41250 100644
--- a/Modules/Applications/AppClassification/app/otbClassificationMapRegularization.cxx
+++ b/Modules/Applications/AppClassification/app/otbClassificationMapRegularization.cxx
@@ -99,6 +99,13 @@ private:
SetParameterDescription("ip.undecidedlabel", "Label for the Undecided class. By default, 'ip.undecidedlabel = 0'.");
SetDefaultParameterInt("ip.undecidedlabel", 0.0);
+ AddParameter(ParameterType_Empty, "ip.onlyisolatedpixels", "Process isolated pixels only");
+ SetParameterDescription("ip.onlyisolatedpixels", "Only pixels whose label is unique in the neighbordhood will be processed. By default, 'ip.onlyisolatedpixels = false'.");
+
+ AddParameter(ParameterType_Int, "ip.isolatedthreshold", "Threshold for isolated pixels");
+ SetParameterDescription("ip.isolatedthreshold", "Maximum number of neighbours with the same label as the center pixel to consider that it is an isolated pixel. By default, 'ip.isolatedthreshold = 1'.");
+ SetDefaultParameterInt("ip.isolatedthreshold", 1);
+
AddRAMParameter();
@@ -107,6 +114,7 @@ private:
SetDocExampleParameterValue("io.out", "clLabeledImageQB123_1_CMR_r2_nodl_10_undl_7.tif");
SetDocExampleParameterValue("ip.radius", "2");
SetDocExampleParameterValue("ip.suvbool", "true");
+ SetDocExampleParameterValue("ip.onlyisolatedpixels", "true");
SetDocExampleParameterValue("ip.nodatalabel", "10");
SetDocExampleParameterValue("ip.undecidedlabel", "7");
}
@@ -149,6 +157,17 @@ private:
m_NeighMajVotingFilter->SetKeepOriginalLabelBool(true);
}
+ // Process isolated pixels only
+ if (IsParameterEnabled("ip.onlyisolatedpixels"))
+ {
+ m_NeighMajVotingFilter->SetOnlyIsolatedPixels(true);
+ m_NeighMajVotingFilter->SetIsolatedThreshold(GetParameterInt("ip.isolatedthreshold"));
+ }
+ else
+ {
+ m_NeighMajVotingFilter->SetOnlyIsolatedPixels(false);
+ }
+
/** REGULARIZATION OF CLASSIFICATION */
SetParameterOutputImage<IOLabelImageType>("io.out", m_NeighMajVotingFilter->GetOutput());
diff --git a/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.h b/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.h
index f7ce6b84e5e25ebbf74c5c3d1cd517dc8a43fce5..009462af47680c6516d562bc1cd3bb15eb1f2d93 100644
--- a/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.h
+++ b/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.h
@@ -149,6 +149,10 @@ public:
//Creates a SetKeepOriginalLabelBool method
itkSetMacro(KeepOriginalLabelBool, bool);
+ //Process only isolated pixels
+ itkSetMacro(OnlyIsolatedPixels, bool);
+ itkSetMacro(IsolatedThreshold, unsigned int);
+
protected:
NeighborhoodMajorityVotingImageFilter();
@@ -166,6 +170,31 @@ protected:
void GenerateOutputInformation() ITK_OVERRIDE;
+
+ //Type to store the useful information from the label histogram
+ struct HistoSummary
+ {
+ unsigned int freqCenterLabel;
+ PixelType majorityLabel;
+ bool majorityUnique;
+ };
+
+ struct CompareHistoFequencies
+ {
+ typedef std::pair<PixelType, unsigned int> HistoValueType;
+ bool operator()(const HistoValueType& a, const HistoValueType& b)
+ {
+ return a.second > b.second;
+ }
+ };
+
+ //Get a histogram of frequencies of labels with the 2 highest
+ // frequencies sorted in decreasing order and return the frequency
+ // of the label of the center pixel
+ const HistoSummary ComputeNeighborhoodHistogramSummary(const NeighborhoodIteratorType &nit,
+ const KernelIteratorType kernelBegin,
+ const KernelIteratorType kernelEnd) const;
+
private:
NeighborhoodMajorityVotingImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
@@ -177,6 +206,12 @@ private:
PixelType m_LabelForUndecidedPixels;
bool m_KeepOriginalLabelBool;
+ //Choose to filter only isolated pixels
+ bool m_OnlyIsolatedPixels;
+ //The center pixel is isolated if there are fewer neighbours than
+ //this threshold with the same label
+ unsigned int m_IsolatedThreshold;
+
}; // end of class
} // end namespace otb
diff --git a/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.txx b/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.txx
index 5fab37c3fe7a31395541cf91684ec5ef78419835..9c4de263463dfb2ace173423bc4bc19dd5c05253 100644
--- a/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.txx
+++ b/Modules/Fusion/MajorityVoting/include/otbNeighborhoodMajorityVotingImageFilter.txx
@@ -38,87 +38,93 @@ NeighborhoodMajorityVotingImageFilter<TInputImage, TOutputImage, TKernel>::Neigh
this->SetLabelForNoDataPixels(itk::NumericTraits<PixelType>::NonpositiveMin()); //m_LabelForNoDataPixels = 0
this->SetLabelForUndecidedPixels(itk::NumericTraits<PixelType>::NonpositiveMin()); //m_LabelForUndecidedPixels = 0
this->SetKeepOriginalLabelBool(true); //m_KeepOriginalLabelBool = true
+ this->SetOnlyIsolatedPixels(false); //process all pixels
+ this->SetIsolatedThreshold(1);
}
template<class TInputImage, class TOutputImage, class TKernel>
typename NeighborhoodMajorityVotingImageFilter<TInputImage, TOutputImage,
-TKernel>::PixelType NeighborhoodMajorityVotingImageFilter<TInputImage,
-TOutputImage, TKernel>::Evaluate(const NeighborhoodIteratorType &nit,
-const KernelIteratorType kernelBegin,
-const KernelIteratorType kernelEnd)
+ TKernel>::PixelType
+NeighborhoodMajorityVotingImageFilter<TInputImage,
+ TOutputImage, TKernel>::Evaluate(const NeighborhoodIteratorType &nit,
+ const KernelIteratorType kernelBegin,
+ const KernelIteratorType kernelEnd)
{
- PixelType majorityLabel = 0; //Value of the more representative pixels in the neighborhood
- unsigned int majorityFreq = 0; //Number of pixels with the more representative value (majorityLabel) in the neighborhood
-
- unsigned int i;
- KernelIteratorType kernel_it;
-
- std::map<PixelType, unsigned int> histoNeigh;
-
- PixelType centerPixel = nit.GetCenterPixel();
-
- if (centerPixel != m_LabelForNoDataPixels)
- {
- for (i = 0, kernel_it = kernelBegin; kernel_it < kernelEnd; ++kernel_it, ++i)
+ const PixelType centerPixel = nit.GetCenterPixel();
+ if (centerPixel == m_LabelForNoDataPixels)
{
- // if structuring element is positive, use the pixel under that element
- // in the image
-
- PixelType label = nit.GetPixel(i);
- if ((*kernel_it > itk::NumericTraits<KernelPixelType>::Zero) && (label != m_LabelForNoDataPixels))
+ return m_LabelForNoDataPixels;
+ }
+ else
+ {
+ //Get a histogram of label frequencies where the 2 highest are at the beginning and sorted
+ const HistoSummary histoSummary =
+ this->ComputeNeighborhoodHistogramSummary(nit, kernelBegin, kernelEnd);
+ if(m_OnlyIsolatedPixels &&
+ histoSummary.freqCenterLabel > m_IsolatedThreshold)
{
- // note we use GetPixel() on the SmartNeighborhoodIterator to
- // respect boundary conditions
-
- //If the current label has already been added to the histogram histoNeigh
- if (histoNeigh.count(label) > 0)
+ //If we want to filter only isolated pixels, keep the label if
+ //there are enough pixels with the center label to consider that
+ //it is not isolated
+ return centerPixel;
+ }
+ else
+ {
+ //If the majorityLabel is NOT unique in the neighborhood
+ if(!histoSummary.majorityUnique)
{
- histoNeigh[label]++;
- }
+ if (m_KeepOriginalLabelBool == true)
+ {
+ return centerPixel;
+ }
else
- {
- histoNeigh[label] = 1;
+ {
+ return m_LabelForUndecidedPixels;
+ }
}
+ //Extraction of the more representative Label in the neighborhood (majorityLabel)
+ return histoSummary.majorityLabel;
}
- }
-
- //Extraction of the more representative Label in the neighborhood (majorityLabel) and its Frequency (majorityFreq)
- typename std::map<PixelType, unsigned int>::iterator histoIt;
- for (histoIt = histoNeigh.begin(); histoIt != histoNeigh.end(); ++histoIt)
- {
- if (histoIt->second > majorityFreq)
- {
- majorityFreq = histoIt->second; //Frequency
- majorityLabel = histoIt->first; //Label
- }
- }
+ }//END if (centerPixel != m_LabelForNoDataPixels)
+}
- //If the majorityLabel is NOT unique in the neighborhood
- for (histoIt = histoNeigh.begin(); histoIt != histoNeigh.end(); ++histoIt)
+template<class TInputImage, class TOutputImage, class TKernel>
+const typename NeighborhoodMajorityVotingImageFilter<TInputImage, TOutputImage,
+ TKernel>::HistoSummary
+NeighborhoodMajorityVotingImageFilter<TInputImage, TOutputImage,
+ TKernel>::ComputeNeighborhoodHistogramSummary(const NeighborhoodIteratorType &nit,
+ const KernelIteratorType kernelBegin,
+ const KernelIteratorType kernelEnd) const
+{
+ typedef std::map<PixelType, unsigned int> HistogramType;
+ typedef std::vector<std::pair<PixelType, unsigned int> > HistoAsVectorType;
+ HistogramType histoNeigh;
+ unsigned int i = 0;
+ for (KernelIteratorType kernel_it = kernelBegin;
+ kernel_it < kernelEnd; ++kernel_it, ++i)
{
- if ( (histoIt->second == majorityFreq) && (histoIt->first != majorityLabel) )
+ // if structuring element is positive, use the pixel under that element
+ // in the image
+ // note we use GetPixel() on the SmartNeighborhoodIterator to
+ // respect boundary conditions
+ const PixelType label = nit.GetPixel(i);
+ if ((*kernel_it > itk::NumericTraits<KernelPixelType>::Zero) &&
+ (label != m_LabelForNoDataPixels))
{
- if (m_KeepOriginalLabelBool == true)
- {
- majorityLabel = centerPixel;
- }
- else
- {
- majorityLabel = m_LabelForUndecidedPixels;
- }
- break;
+ histoNeigh[label] += 1;
}
}
- }//END if (centerPixel != m_LabelForNoDataPixels)
-
- //If (centerPixel == m_LabelForNoDataPixels)
- else
- {
- majorityLabel = m_LabelForNoDataPixels;
- }
-
- return majorityLabel;
+ HistoAsVectorType histoNeighVec(histoNeigh.begin(), histoNeigh.end());
+ //Sort the 2 max elements to the beginning
+ std::nth_element(histoNeighVec.begin(), histoNeighVec.begin()+1,
+ histoNeighVec.end(), CompareHistoFequencies());
+ typename NeighborhoodMajorityVotingImageFilter<TInputImage, TOutputImage,
+ TKernel>::HistoSummary result;
+ result.freqCenterLabel = histoNeigh[nit.GetCenterPixel()];
+ result.majorityLabel = histoNeighVec[0].first;
+ result.majorityUnique = (histoNeighVec[0].second != histoNeighVec[1].second);
+ return result;
}
template<class TInputImage, class TOutputImage, class TKernel>
diff --git a/Modules/Fusion/MajorityVoting/test/CMakeLists.txt b/Modules/Fusion/MajorityVoting/test/CMakeLists.txt
index a32c7664d64ed6f6a7b5134697852a1f6a5d56b4..5547d89d23e1b9aaaf172d0fb48433b301b284e4 100644
--- a/Modules/Fusion/MajorityVoting/test/CMakeLists.txt
+++ b/Modules/Fusion/MajorityVoting/test/CMakeLists.txt
@@ -27,6 +27,11 @@ otb_add_test(NAME leTvNeighborhoodMajorityVotingImageFilterTest COMMAND otbMajor
5 #yRadius
10 #LabelForNoDataPixels
7 #LabelForUndecidedPixels
+ 0 #OnlyIsolatedPixels
+ )
+
+otb_add_test(NAME leTvNeighborhoodMajorityVotingIsolThresPixTest COMMAND otbMajorityVotingTestDriver
+ otbNeighborhoodMajorityVotingImageFilterIsolatedTest
)
otb_add_test(NAME leTvSVMImageClassificationFilterWithNeighborhoodMajorityVoting COMMAND otbMajorityVotingTestDriver
diff --git a/Modules/Fusion/MajorityVoting/test/otbMajorityVotingTestDriver.cxx b/Modules/Fusion/MajorityVoting/test/otbMajorityVotingTestDriver.cxx
index 3a961ec71e79efcfcd093dd8b4d72c45a08b3552..a3a3809011a04089e7ba5efff515975ae4fd416f 100644
--- a/Modules/Fusion/MajorityVoting/test/otbMajorityVotingTestDriver.cxx
+++ b/Modules/Fusion/MajorityVoting/test/otbMajorityVotingTestDriver.cxx
@@ -3,4 +3,5 @@ void RegisterTests()
{
REGISTER_TEST(otbNeighborhoodMajorityVotingImageFilterNew);
REGISTER_TEST(otbNeighborhoodMajorityVotingImageFilterTest);
+ REGISTER_TEST(otbNeighborhoodMajorityVotingImageFilterIsolatedTest);
}
diff --git a/Modules/Fusion/MajorityVoting/test/otbNeighborhoodMajorityVotingImageFilterTest.cxx b/Modules/Fusion/MajorityVoting/test/otbNeighborhoodMajorityVotingImageFilterTest.cxx
index b6a0e1e92376c1889f3acee7693618dcc12d78eb..9a9de6918b9d61e3b1935455c210374e0d1a6d77 100644
--- a/Modules/Fusion/MajorityVoting/test/otbNeighborhoodMajorityVotingImageFilterTest.cxx
+++ b/Modules/Fusion/MajorityVoting/test/otbNeighborhoodMajorityVotingImageFilterTest.cxx
@@ -100,6 +100,11 @@ int otbNeighborhoodMajorityVotingImageFilterTest(int argc, char* argv[])
seBall.CreateStructuringElement();
NeighMajVotingFilter->SetKernel(seBall);
+ if(argc==9)
+ {
+ NeighMajVotingFilter->SetOnlyIsolatedPixels(static_cast<bool>(atoi(argv[8])));
+ }
+
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(outputFileName);
writer->SetInput(NeighMajVotingFilter->GetOutput());
@@ -107,3 +112,96 @@ int otbNeighborhoodMajorityVotingImageFilterTest(int argc, char* argv[])
return EXIT_SUCCESS;
}
+
+int otbNeighborhoodMajorityVotingImageFilterIsolatedTest(int itkNotUsed(argc), char* itkNotUsed(argv)[])
+{
+ typedef unsigned char PixelType; // 8 bits
+ const unsigned int Dimension = 2;
+
+ typedef otb::Image<PixelType, Dimension> ImageType;
+
+ ImageType::Pointer image = ImageType::New();
+ ImageType::IndexType start;
+
+ start[0] = 0;
+ start[1] = 0;
+
+ ImageType::SizeType size;
+ size[0] = 100;
+ size[1] = 100;
+
+ ImageType::RegionType region;
+
+ region.SetSize(size);
+ region.SetIndex(start);
+ image->SetRegions(region);
+ image->Allocate();
+ image->FillBuffer(itk::NumericTraits<PixelType>::Zero);
+
+ // Neighborhood majority voting filter type
+ typedef otb::NeighborhoodMajorityVotingImageFilter<ImageType> NeighborhoodMajorityVotingFilterType;
+
+ // Binary ball Structuring Element type
+ typedef NeighborhoodMajorityVotingFilterType::KernelType StructuringType;
+ typedef StructuringType::RadiusType RadiusType;
+
+
+ // Neighborhood majority voting filter
+ NeighborhoodMajorityVotingFilterType::Pointer NeighMajVotingFilter;
+ NeighMajVotingFilter = NeighborhoodMajorityVotingFilterType::New();
+
+ NeighMajVotingFilter->SetInput(image);
+
+ StructuringType seBall;
+ RadiusType rad;
+
+
+ NeighMajVotingFilter->SetKeepOriginalLabelBool(true);
+
+ rad[0] = 1;
+ rad[1] = 1;
+ NeighMajVotingFilter->SetLabelForNoDataPixels(10);
+ NeighMajVotingFilter->SetLabelForUndecidedPixels(7);
+ seBall.SetRadius(rad);
+ seBall.CreateStructuringElement();
+ NeighMajVotingFilter->SetKernel(seBall);
+ NeighMajVotingFilter->SetOnlyIsolatedPixels(true);
+ PixelType value = 255;
+ ImageType::IndexType coordinate;
+ coordinate[0] = 10;
+ coordinate[1] = 10;
+ image->SetPixel(coordinate, value);
+ image->Update();
+ NeighMajVotingFilter->SetIsolatedThreshold(1);
+ NeighMajVotingFilter->Update();
+ PixelType result = NeighMajVotingFilter->GetOutput()->GetPixel(coordinate);
+//Should be filtered
+ if(result == value)
+ {
+ std::cout << "one pixel\n";
+ return EXIT_FAILURE;
+ }
+ coordinate[0] = 10;
+ coordinate[1] = 11;
+ image->SetPixel(coordinate, value);
+ image->Update();
+ NeighMajVotingFilter->Modified(); //needed for the filter to be updated
+ NeighMajVotingFilter->Update();
+ result = NeighMajVotingFilter->GetOutput()->GetPixel(coordinate);
+ // Should not be filtered
+ if( result != value)
+ {
+ std::cout << "2 pixels thres = 1 result = " << int(result) << '\n';
+ return EXIT_FAILURE;
+ }
+ NeighMajVotingFilter->SetIsolatedThreshold(3);
+ NeighMajVotingFilter->Update();
+ result = NeighMajVotingFilter->GetOutput()->GetPixel(coordinate);
+//Should be filtered
+ if(result == value)
+ {
+ std::cout << "2 pixels thres = 2" << '\n';
+ return EXIT_FAILURE;
+ }
+ return EXIT_SUCCESS;
+}