diff --git a/Examples/Projections/SensorModelExample.cxx b/Examples/Projections/SensorModelExample.cxx
index d8753bcb4b31f7c526e472860f4cd8aa0dec940b..b162762e692b9c20bbb32b17085ff19d64ed4de2 100644
--- a/Examples/Projections/SensorModelExample.cxx
+++ b/Examples/Projections/SensorModelExample.cxx
@@ -472,8 +472,8 @@ int main(int argc, char* argv[])
outputImage->SetPixel(currentIndexBis, pixelValue);
}
- delete pixelIndexArray;
- delete currentIndexArray;
+ delete[] pixelIndexArray;
+ delete[] currentIndexArray;
}
// Software Guide : EndCodeSnippet
diff --git a/Examples/Simulation/LAIAndPROSAILToSensorResponse.cxx b/Examples/Simulation/LAIAndPROSAILToSensorResponse.cxx
index f49bb64095440abcc2fe87c69129be25abb736eb..38c27e81336d53c44fa8c6fc124f15aeb73dbd0e 100644
--- a/Examples/Simulation/LAIAndPROSAILToSensorResponse.cxx
+++ b/Examples/Simulation/LAIAndPROSAILToSensorResponse.cxx
@@ -102,16 +102,15 @@ public:
ArrayType GetUniqueValues() const
{
+ ArrayType uniqueValues;
if( !m_Image )
{
- itkExceptionMacro(<<"GetUniqueValues(): Null input image pointer.");
+ return uniqueValues;
}
itk::ImageRegionConstIterator< ImageType > it( m_Image,
m_Image->GetRequestedRegion() );
- ArrayType uniqueValues;
-
uniqueValues.push_back(it.Get());
++it;
while( !it.IsAtEnd() )
@@ -758,6 +757,11 @@ int main(int argc, char *argv[])
UniqueCalculatorType::ArrayType uniqueVals =
uniqueCalculator->GetUniqueValues();
+ if (uniqueVals.empty())
+ {
+ std::cerr << "No label value found!"<< std::endl;
+ return EXIT_FAILURE;
+ }
std::cout << "Labels are " << std::endl;
UniqueCalculatorType::ArrayType::const_iterator uvIt = uniqueVals.begin();
diff --git a/Modules/Applications/AppClassification/app/otbDSFuzzyModelEstimation.cxx b/Modules/Applications/AppClassification/app/otbDSFuzzyModelEstimation.cxx
index 2f8a942ea8c98e973b0ef050df8b2a62576c30e7..cd7c9883616e5514719ca1242b7089c357992e73 100644
--- a/Modules/Applications/AppClassification/app/otbDSFuzzyModelEstimation.cxx
+++ b/Modules/Applications/AppClassification/app/otbDSFuzzyModelEstimation.cxx
@@ -261,6 +261,10 @@ private:
accNbElemPS++;
}
}
+ if (accNbElemPS == 0)
+ {
+ otbAppLogFATAL(<< "Error : no element found in positive vector data!");
+ }
TreeIteratorType itVectorNS(nsVectorData->GetDataTree());
std::vector<double> accFirstOrderNS, accSecondOrderNS, minNS, maxNS;
@@ -300,6 +304,10 @@ private:
accNbElemNS++;
}
}
+ if (accNbElemNS == 0)
+ {
+ otbAppLogFATAL(<< "Error : no element found in negative vector data!");
+ }
otbAppLogINFO( << "Descriptors Stats : ");
otbAppLogINFO( << "Positive Samples");
for (unsigned int i = 0; i < descList.size(); ++i)
@@ -406,11 +414,9 @@ private:
{
// An error has occurred in the optimization.
// Update the parameters
- otbAppLogFATAL("ERROR: Exception Catched!" << std::endl);
- otbAppLogFATAL(<< err.GetDescription() << std::endl);
- const unsigned int numberOfIterations = m_Optimizer->GetOptimizer()->get_num_evaluations();
- otbAppLogFATAL("numberOfIterations : " << numberOfIterations << std::endl);
- otbAppLogFATAL("Results : " << m_Optimizer->GetCurrentPosition() << std::endl);
+ otbAppLogFATAL("ERROR: Exception Catched : "<< err.GetDescription() << std::endl
+ << "numberOfIterations : " << m_Optimizer->GetOptimizer()->get_num_evaluations() << std::endl
+ << "Results : " << m_Optimizer->GetCurrentPosition() << std::endl);
}
// get the results
const unsigned int numberOfIterations = m_Optimizer->GetOptimizer()->get_num_evaluations();
diff --git a/Modules/Applications/AppSegmentation/app/otbLSMSSmallRegionsMerging.cxx b/Modules/Applications/AppSegmentation/app/otbLSMSSmallRegionsMerging.cxx
index c2122b50ba7f088baeaa6f5a33f5165113ec840b..64156f9eda640859db4c3bbeab02b0fe950f61b1 100644
--- a/Modules/Applications/AppSegmentation/app/otbLSMSSmallRegionsMerging.cxx
+++ b/Modules/Applications/AppSegmentation/app/otbLSMSSmallRegionsMerging.cxx
@@ -325,8 +325,8 @@ private:
if(nbPixels[curLabel]==size)
{
edgeLabel.insert(curLabel);
- for(std::set<int>::iterator itAdjLabel=adjMap[curLabel].begin();
- itAdjLabel!=adjMap[curLabel].end(); ++itAdjLabel)
+ for(std::set<int>::iterator itAdjLabel=(adjMap[curLabel]).begin();
+ itAdjLabel!=(adjMap[curLabel]).end(); ++itAdjLabel)
{
double tmpError = 0;
LabelImagePixelType tmpLabel = *itAdjLabel;
diff --git a/Modules/Filtering/MathParser/test/otbBandMathImageFilter.cxx b/Modules/Filtering/MathParser/test/otbBandMathImageFilter.cxx
index 499e5b864e84edba146045d76e8862003c3c74bd..f18f325a573d6047b86a4d77381e69a589362cc9 100644
--- a/Modules/Filtering/MathParser/test/otbBandMathImageFilter.cxx
+++ b/Modules/Filtering/MathParser/test/otbBandMathImageFilter.cxx
@@ -138,22 +138,17 @@ int otbBandMathImageFilter( int itkNotUsed(argc), char* itkNotUsed(argv) [])
error = (result - expected) * (result - expected) / (result + expected);
if ( error > 1E-9 )
{
- itkGenericExceptionMacro( <<std::endl
+ std::cout
<< "Error = " << error << " > 1E-9 -> TEST FAILLED" << std::endl
<< "Pixel_1 = " << it1.Get()
<< " Pixel_2 = " << it2.Get()
<< " Pixel_3 = " << it3.Get()
<< " Result = " << it.Get()
- << " Expected = " << expected << std::endl );
+ << " Expected = " << expected << std::endl;
FAIL_FLAG++;
+ break;
}
- }
- if(!FAIL_FLAG)
- std::cout << "[PASSED]" << std::endl;
- else
- std::cout << "[FAILLED]" << std::endl;
- FAIL_FLAG = 0;
-
+ }
/** Edge Effect Handling */
@@ -166,14 +161,23 @@ int otbBandMathImageFilter( int itkNotUsed(argc), char* itkNotUsed(argv) [])
it1.GoToBegin(); it2.GoToBegin(); it.GoToBegin();
for(i=1; i<=50; ++i , ++it1, ++it2, ++it){}
if(vnl_math_isnan(it.Get()))
- std::cout << "Pixel_1 = " << it1.Get() << " Pixel_2 = " << it2.Get() << " Result = " << it.Get() << " Expected = nan\n";
+ std::cout << "Pixel_1 = " << it1.Get() << " Pixel_2 = " << it2.Get() << " Result = " << it.Get() << " Expected = nan\n" << std::endl;
else
- itkGenericExceptionMacro(
+ {
+ std::cout
<< "\nError > Bad Edge Effect Handling -> Test Failled\n"
<< "Pixel_1 = " << it1.Get() << " Pixel_2 = " << it2.Get()
- << " Result = " << it.Get() << " Expected = nan\n" );
- std::cout << std::endl;
+ << " Result = " << it.Get() << " Expected = nan\n" << std::endl;
+ FAIL_FLAG++;
+ }
+
+ if (FAIL_FLAG)
+ {
+ std::cout << "[FAILLED]" << std::endl;
+ return EXIT_FAILURE;
+ }
+ std::cout << "[PASSED]" << std::endl;
return EXIT_SUCCESS;
}
diff --git a/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.h b/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.h
index 3c8bb76d2d82e232ad2b61e83dd113a8291337f4..ed0d020562cb47b20b14643ccaad873042ba2661 100644
--- a/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.h
+++ b/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.h
@@ -68,7 +68,7 @@ public:
* \param imageList The analysed image at each level of the pyramid.
*/
using Superclass::SetInput;
- void SetInput(InputImageListType * imageList);
+ void SetInput(const InputImageListType * imageList);
/**
* Set The SupFilter details
* \param imageList The brighter details extracted from the filtering operation.
diff --git a/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.txx b/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.txx
index 33bdc365764245cac76fd7f97943e41739dfcd9f..3c26e6f695104cbc46d9b0f74da03fb88b1be17c 100644
--- a/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.txx
+++ b/Modules/Filtering/MorphologicalPyramid/include/otbMorphologicalPyramidMRToMSConverter.txx
@@ -118,7 +118,7 @@ MRToMSConverter<TInputImage, TOutputImage>
template <class TInputImage, class TOutputImage>
void
MRToMSConverter<TInputImage, TOutputImage>
-::SetInput(InputImageListType * imageList)
+::SetInput(const InputImageListType * imageList)
{
this->SetNthInput(2, const_cast<InputImageListType *>(imageList));
}
diff --git a/Modules/Filtering/Statistics/include/otbStreamingStatisticsImageFilter.txx b/Modules/Filtering/Statistics/include/otbStreamingStatisticsImageFilter.txx
index 5a873618bef4fe6c0eef3d379a06d801673c78ba..328f517d8a944749e78867d3d14168b114503b06 100644
--- a/Modules/Filtering/Statistics/include/otbStreamingStatisticsImageFilter.txx
+++ b/Modules/Filtering/Statistics/include/otbStreamingStatisticsImageFilter.txx
@@ -240,9 +240,9 @@ PersistentStatisticsImageFilter<TInputImage>
PixelType minimum;
PixelType maximum;
- RealType mean;
- RealType sigma;
- RealType variance;
+ RealType mean = itk::NumericTraits<RealType>::Zero;
+ RealType sigma = itk::NumericTraits<RealType>::Zero;
+ RealType variance = itk::NumericTraits<RealType>::Zero;
RealType sum;
sum = sumOfSquares = itk::NumericTraits<RealType>::Zero;
@@ -267,13 +267,23 @@ PersistentStatisticsImageFilter<TInputImage>
maximum = m_ThreadMax[i];
}
}
- // compute statistics
- mean = sum / static_cast<RealType>(count);
+ if (count > 0)
+ {
+ // compute statistics
+ mean = sum / static_cast<RealType>(count);
- // unbiased estimate
- variance = (sumOfSquares - (sum * sum / static_cast<RealType>(count)))
- / (static_cast<RealType>(count) - 1);
- sigma = vcl_sqrt(variance);
+ if (count > 1)
+ {
+ // unbiased estimate
+ variance = (sumOfSquares - (sum * sum / static_cast<RealType>(count)))
+ / static_cast<RealType>(count - 1);
+ sigma = vcl_sqrt(variance);
+ }
+ }
+ else
+ {
+ itkWarningMacro(<<"No pixel found to compute statistics!");
+ }
// Set the outputs
this->GetMinimumOutput()->Set(minimum);
diff --git a/Modules/Learning/Supervised/include/otbConfusionMatrixMeasurements.txx b/Modules/Learning/Supervised/include/otbConfusionMatrixMeasurements.txx
index 3cdf66f91de2332db3563e2d5409e988db921432..da4dcb4940682d4acba7aa56d2f62641aa65b29d 100644
--- a/Modules/Learning/Supervised/include/otbConfusionMatrixMeasurements.txx
+++ b/Modules/Learning/Supervised/include/otbConfusionMatrixMeasurements.txx
@@ -55,6 +55,7 @@ ConfusionMatrixMeasurements<TConfusionMatrix, TLabel>
m_TruePositiveValues.Fill(0);
m_TrueNegativeValues.Fill(0);
+ const double epsilon = 0.0000000001;
double luckyRate = 0.;
this->m_NumberOfSamples = 0;
this->m_OverallAccuracy = 0.;
@@ -83,8 +84,6 @@ ConfusionMatrixMeasurements<TConfusionMatrix, TLabel>
luckyRate += sum_along_row_i * sum_along_column_i;
}
- this->m_OverallAccuracy /= static_cast<double>(m_NumberOfSamples);
-
this->m_TrueNegativeValues.Fill(m_NumberOfSamples);
this->m_TrueNegativeValues -= this->m_FalseNegativeValues
+ this->m_FalsePositiveValues
@@ -116,7 +115,27 @@ ConfusionMatrixMeasurements<TConfusionMatrix, TLabel>
m_Recalls.Fill(0.);
m_FScores.Fill(0.);
- const double epsilon = 0.0000000001;
+ if (m_NumberOfSamples > 0)
+ {
+ this->m_OverallAccuracy /= static_cast<double>(m_NumberOfSamples);
+ luckyRate /= vcl_pow(m_NumberOfSamples, 2.0);
+
+ if (vcl_abs(1 - luckyRate) > epsilon)
+ {
+ m_KappaIndex = (m_OverallAccuracy - luckyRate) / (1 - luckyRate);
+ }
+ else
+ {
+ this->m_KappaIndex = 1.;
+ }
+ }
+ else
+ {
+ this->m_OverallAccuracy = 0.;
+ this->m_KappaIndex = 0.;
+ // no samples, no need to continue
+ return;
+ }
for (unsigned int i = 0; i < m_NumberOfClasses; ++i)
{
@@ -155,13 +174,6 @@ ConfusionMatrixMeasurements<TConfusionMatrix, TLabel>
this->m_FScore = 2 * this->m_Recall * this->m_Precision / (this->m_Recall + this->m_Precision);
}
}
-
- luckyRate /= vcl_pow(m_NumberOfSamples, 2.0);
-
- if (vcl_abs(1 - luckyRate) > epsilon)
- {
- m_KappaIndex = (m_OverallAccuracy - luckyRate) / (1 - luckyRate);
- }
}
/*
diff --git a/Modules/Radiometry/Simulation/src/otbSailModel.cxx b/Modules/Radiometry/Simulation/src/otbSailModel.cxx
index 76dfd3c08f7a8d40cf087aee507e8381aa94e7ca..2d6b66fae20177039eeb8e5a56f847832fc331bb 100644
--- a/Modules/Radiometry/Simulation/src/otbSailModel.cxx
+++ b/Modules/Radiometry/Simulation/src/otbSailModel.cxx
@@ -337,7 +337,7 @@ SailModel
// Outputs for the case LAI = 0
if (m_LAI<0)
{
- tss = 1;
+ //tss = 1;
too = 1;
tsstoo = 1;
rdd = 0;
@@ -355,7 +355,7 @@ SailModel
rdot = rsoil0;
rsodt = 0;
rsost = rsoil0;
- rsot = rsoil0;
+ //rsot = rsoil0;
}
// Other cases (LAI > 0)