diff --git a/Modules/Applications/AppClassification/app/otbImageClassifier.cxx b/Modules/Applications/AppClassification/app/otbImageClassifier.cxx
index 07a8a352d7622e29bed60592a69fed040ad53a8c..8cd4c5b030159f38a69b20e571dae27e149a5e2c 100644
--- a/Modules/Applications/AppClassification/app/otbImageClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbImageClassifier.cxx
@@ -59,6 +59,7 @@ public:
typedef ClassificationFilterType::ValueType ValueType;
typedef ClassificationFilterType::LabelType LabelType;
typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
+ typedef ClassificationFilterType::ConfidenceImageType ConfidenceImageType;
private:
void DoInit()
@@ -94,6 +95,21 @@ private:
SetParameterDescription( "out", "Output image containing class labels");
SetParameterOutputImagePixelType( "out", ImagePixelType_uint8);
+ AddParameter(ParameterType_OutputImage, "confmap", "Confidence map");
+ SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model : \n"
+ " - LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)\n"
+ " - OpenCV\n"
+ " * Boost : sum of votes\n"
+ " * DecisionTree : (not supported)\n"
+ " * GradientBoostedTree : (not supported)\n"
+ " * KNearestNeighbors : number of neighbors with the same label\n"
+ " * NeuralNetwork : difference between the two highest responses\n"
+ " * NormalBayes : (not supported)\n"
+ " * RandomForest : proportion of decision trees that classified the sample to the second class (only works for 2-class models)\n"
+ " * SVM : distance to margin (only works for 2-class models)\n");
+ SetParameterOutputImagePixelType( "confmap", ImagePixelType_double);
+ MandatoryOff("confmap");
+
AddRAMParameter();
// Doc example parameter settings
@@ -171,6 +187,21 @@ private:
}
SetParameterOutputImage<OutputImageType>("out", m_ClassificationFilter->GetOutput());
+
+ // output confidence map
+ if (IsParameterEnabled("confmap") && HasValue("confmap"))
+ {
+ m_ClassificationFilter->SetUseConfidenceMap(true);
+ if (m_Model->HasConfidenceIndex())
+ {
+ SetParameterOutputImage<ConfidenceImageType>("confmap",m_ClassificationFilter->GetOutputConfidence());
+ }
+ else
+ {
+ otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
+ this->DisableParameter("confmap");
+ }
+ }
}
ClassificationFilterType::Pointer m_ClassificationFilter;
diff --git a/Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx b/Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx
index 458e5b8487b8f6e11c337c3ffc0026941c8ca627..e117337708d3effd267a462a1d24f8b2f6736e18 100644
--- a/Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainLibSVM.cxx
@@ -42,6 +42,9 @@ namespace Wrapper
AddParameter(ParameterType_Empty, "classifier.libsvm.opt", "Parameters optimization");
MandatoryOff("classifier.libsvm.opt");
SetParameterDescription("classifier.libsvm.opt", "SVM parameters optimization flag.");
+ AddParameter(ParameterType_Empty, "classifier.libsvm.prob", "Probability estimation");
+ MandatoryOff("classifier.libsvm.prob");
+ SetParameterDescription("classifier.libsvm.prob", "Probability estimation flag.");
}
@@ -56,6 +59,10 @@ namespace Wrapper
{
libSVMClassifier->SetParameterOptimization(true);
}
+ if (IsParameterEnabled("classifier.libsvm.prob"))
+ {
+ libSVMClassifier->SetDoProbabilityEstimates(true);
+ }
libSVMClassifier->SetC(GetParameterFloat("classifier.libsvm.c"));
switch (GetParameterInt("classifier.libsvm.k"))
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index 77e4a3636495955d2488546da105d7dc2c97c379..c4e275fe5a33fc8e629a13c9016a5584c8a4b0a3 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -77,7 +77,7 @@ set(rf_output_format ".rf")
set(knn_output_format ".knn")
# Training algorithms parameters
-set(libsvm_parameters "-classifier.libsvm.opt" "true")
+set(libsvm_parameters "-classifier.libsvm.opt" "true" "-classifier.libsvm.prob" "true")
set(svm_parameters "-classifier.svm.opt" "true")
set(boost_parameters "")
set(dt_parameters "")
@@ -90,6 +90,7 @@ set(knn_parameters "")
# Validation depending on mode
set(ascii_comparison --compare-ascii ${NOTOL})
set(raster_comparison --compare-image ${NOTOL})
+set(raster_comparison_two --compare-n-images ${NOTOL} 2)
# Reference ffiles depending on modes
set(ascii_ref_path ${OTBAPP_BASELINE_FILES})
@@ -102,6 +103,7 @@ endif()
if(OTB_USE_OPENCV)
list(APPEND classifierList "SVM" "BOOST" "DT" "GBT" "ANN" "BAYES" "RF" "KNN")
endif()
+set(classifier_with_confmap "LIBSVM" "BOOST" "KNN" "ANN")
# Loop on classifiers
foreach(classifier ${classifierList})
@@ -110,6 +112,7 @@ foreach(classifier ${classifierList})
# Derive output file name
set(OUTMODELFILE cl${classifier}_ModelQB1${${lclassifier}_output_format})
set(OUTRASTER cl${classifier}LabeledImageQB1${raster_output_format})
+ set(OUTCONFMAP cl${classifier}ConfidenceMapQB1${raster_output_format})
otb_test_application(
NAME apTvClTrainMethod${classifier}ImagesClassifierQB1
@@ -160,7 +163,9 @@ foreach(classifier ${classifierList})
#set_tests_properties(apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1 PROPERTIES DEPENDS apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1)
- otb_test_application(
+ list(FIND classifier_with_confmap ${classifier} _classifier_has_confmap)
+ if(${_classifier_has_confmap} EQUAL -1)
+ otb_test_application(
NAME apTvClMethod${classifier}ImageClassifierQB1
APP ImageClassifier
OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
@@ -172,6 +177,23 @@ foreach(classifier ${classifierList})
${raster_ref_path}/${OUTRASTER}
${TEMP}/${OUTRASTER}
)
+ else()
+ otb_test_application(
+ NAME apTvClMethod${classifier}ImageClassifierQB1
+ APP ImageClassifier
+ OPTIONS -in ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
+ -model ${ascii_ref_path}/${OUTMODELFILE}
+ -imstat ${ascii_ref_path}/clImageStatisticsQB1${stat_input_format}
+ -out ${TEMP}/${OUTRASTER} ${raster_output_option}
+ -confmap ${TEMP}/${OUTCONFMAP}
+
+ VALID ${raster_comparison_two}
+ ${raster_ref_path}/${OUTRASTER}
+ ${TEMP}/${OUTRASTER}
+ ${raster_ref_path}/${OUTCONFMAP}
+ ${TEMP}/${OUTCONFMAP}
+ )
+ endif()
endforeach()
diff --git a/Modules/Learning/SVMLearning/include/otbSVMModel.h b/Modules/Learning/SVMLearning/include/otbSVMModel.h
index 682f3d7bb6552aba3d92435da9f81e38ae7dfc3e..f64af6c8dd751048e3db01a74d539cbdbd467cd3 100644
--- a/Modules/Learning/SVMLearning/include/otbSVMModel.h
+++ b/Modules/Learning/SVMLearning/include/otbSVMModel.h
@@ -307,6 +307,12 @@ public:
return static_cast<bool>(m_Parameters.probability);
}
+ /** Test if the model has probabilities */
+ bool HasProbabilities(void) const
+ {
+ return static_cast<bool>(svm_check_probability_model(m_Model));
+ }
+
/** Return number of support vectors */
int GetNumberOfSupportVectors(void) const
{
diff --git a/Modules/Learning/SVMLearning/include/otbSVMModel.txx b/Modules/Learning/SVMLearning/include/otbSVMModel.txx
index 07bc7eef9248266fc94eacfd43f5294a3bdb1d16..a527b2eca7656efd37b52ec66e7d39937cb23dd8 100644
--- a/Modules/Learning/SVMLearning/include/otbSVMModel.txx
+++ b/Modules/Learning/SVMLearning/include/otbSVMModel.txx
@@ -464,7 +464,7 @@ SVMModel<TValue, TLabel>::EvaluateProbabilities(const MeasurementType& measure)
itkExceptionMacro(<< "Model is not up-to-date, can not predict probabilities");
}
- if (svm_check_probability_model(m_Model) == 0)
+ if (!this->HasProbabilities())
{
throw itk::ExceptionObject(__FILE__, __LINE__,
"Model does not support probability estimates", ITK_LOCATION);
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
index 510717d4ea36312a89bc2c17459bef9d23af605a..185d10ba44bf26db0a9444c5eb61025ef4f2899a 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
@@ -40,19 +40,17 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(BoostMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(BoostMachineLearningModel, MachineLearningModel);
/** Setters/Getters to the Boost type
* It can be CvBoost::DISCRETE, CvBoost::REAL, CvBoost::LOGIT, CvBoost::GENTLE
@@ -122,7 +120,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
BoostMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx
index 825b119fc6d00f5c19d0c04ffb6c525521fc7640..493107f9ff2b8da6c652fc67bc31376664eed1cc 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.txx
@@ -37,6 +37,7 @@ BoostMachineLearningModel<TInputValue,TOutputValue>
m_SplitCrit(CvBoost::DEFAULT),
m_MaxDepth(1)
{
+ this->m_ConfidenceIndex = true;
}
@@ -76,7 +77,7 @@ template <class TInputValue, class TOutputValue>
typename BoostMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
BoostMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -91,6 +92,12 @@ BoostMachineLearningModel<TInputValue,TOutputValue>
target[0] = static_cast<TOutputValue>(result);
+ if (quality != NULL)
+ {
+ (*quality) = static_cast<ConfidenceValueType>(
+ m_BoostModel->predict(sample,missing,cv::Range::all(),false,true));
+ }
+
return target;
}
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
index 75fe1401bf431cb9e827c314f1187e0e06456c0f..6415fa940e273e5d9074a130ba95a8d8105a4978 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
@@ -40,19 +40,17 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(DecisionTreeMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(DecisionTreeMachineLearningModel, MachineLearningModel);
/** Setters/Getters to the maximum depth of the tree.
* The maximum possible depth of the tree. That is the training algorithms attempts to split a node
@@ -183,7 +181,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
DecisionTreeMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx
index ef98de235bd7a01114d5bd07db45bb3f3e7a9adf..ada456e94c35ff1467e97b8301a5e4a315fa35f6 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.txx
@@ -83,7 +83,7 @@ template <class TInputValue, class TOutputValue>
typename DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -96,6 +96,14 @@ DecisionTreeMachineLearningModel<TInputValue,TOutputValue>
target[0] = static_cast<TOutputValue>(result);
+ if (quality != NULL)
+ {
+ if (!this->m_ConfidenceIndex)
+ {
+ itkExceptionMacro("Confidence index not available for this classifier !");
+ }
+ }
+
return target;
}
diff --git a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h
index 00901e68d4b93f513c810c3e9d7fd86919e6fcf8..3126f44c0ff7073def41f025df615e74013b590b 100644
--- a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.h
@@ -40,19 +40,17 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(GradientBoostedTreeMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(GradientBoostedTreeMachineLearningModel, MachineLearningModel);
/** Type of the loss function used for training.
* It must be one of the following types: CvGBTrees::SQUARED_LOSS, CvGBTrees::ABSOLUTE_LOSS,
@@ -130,7 +128,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
GradientBoostedTreeMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx
index c7421a55b199bbe47b130d59a995508c4980746c..595f1bb060b8ffbcb533352915f41b69c2bd1458 100644
--- a/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbGradientBoostedTreeMachineLearningModel.txx
@@ -80,7 +80,7 @@ template <class TInputValue, class TOutputValue>
typename GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -93,6 +93,14 @@ GradientBoostedTreeMachineLearningModel<TInputValue,TOutputValue>
target[0] = static_cast<TOutputValue>(result);
+ if (quality != NULL)
+ {
+ if (!this->m_ConfidenceIndex)
+ {
+ itkExceptionMacro("Confidence index not available for this classifier !");
+ }
+ }
+
return target;
}
diff --git a/Modules/Learning/Supervised/include/otbImageClassificationFilter.h b/Modules/Learning/Supervised/include/otbImageClassificationFilter.h
index 2c79315b3d5ca1e127d3c7a0ead88688fe14c5e4..f6ed68aa1db6cec92aa37bdc870f0e19cbd59651 100644
--- a/Modules/Learning/Supervised/include/otbImageClassificationFilter.h
+++ b/Modules/Learning/Supervised/include/otbImageClassificationFilter.h
@@ -20,6 +20,7 @@
#include "itkImageToImageFilter.h"
#include "otbMachineLearningModel.h"
+#include "otbImage.h"
namespace otb
{
@@ -68,6 +69,9 @@ public:
typedef MachineLearningModel<ValueType, LabelType> ModelType;
typedef typename ModelType::Pointer ModelPointerType;
+ typedef otb::Image<double> ConfidenceImageType;
+ typedef typename ConfidenceImageType::Pointer ConfidenceImagePointerType;
+
/** Set/Get the model */
itkSetObjectMacro(Model, ModelType);
itkGetObjectMacro(Model, ModelType);
@@ -76,6 +80,10 @@ public:
itkSetMacro(DefaultLabel, LabelType);
itkGetMacro(DefaultLabel, LabelType);
+ /** Set/Get the confidence map flag */
+ itkSetMacro(UseConfidenceMap, bool);
+ itkGetMacro(UseConfidenceMap, bool);
+
/**
* If set, only pixels within the mask will be classified.
* All pixels with a value greater than 0 in the mask, will be classified.
@@ -89,6 +97,11 @@ public:
*/
const MaskImageType * GetInputMask(void);
+ /**
+ * Get the output confidence map
+ */
+ ConfidenceImageType * GetOutputConfidence(void);
+
protected:
/** Constructor */
ImageClassificationFilter();
@@ -110,7 +123,8 @@ private:
ModelPointerType m_Model;
/** Default label for invalid pixels (when using a mask) */
LabelType m_DefaultLabel;
-
+ /** Flag to produce the confidence map (if the model supports it) */
+ bool m_UseConfidenceMap;
};
} // End namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
diff --git a/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx b/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
index 06225af19e37ce45feb5e6008121f8c06f0bd05a..88d2ad012441455cc2c2d221e79e175731b3e31f 100644
--- a/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
+++ b/Modules/Learning/Supervised/include/otbImageClassificationFilter.txx
@@ -34,6 +34,11 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
this->SetNumberOfIndexedInputs(2);
this->SetNumberOfRequiredInputs(1);
m_DefaultLabel = itk::NumericTraits<LabelType>::ZeroValue();
+
+ this->SetNumberOfRequiredOutputs(2);
+ this->SetNthOutput(0,TOutputImage::New());
+ this->SetNthOutput(1,ConfidenceImageType::New());
+ m_UseConfidenceMap = false;
}
template <class TInputImage, class TOutputImage, class TMaskImage>
@@ -57,6 +62,19 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
return static_cast<const MaskImageType *>(this->itk::ProcessObject::GetInput(1));
}
+template <class TInputImage, class TOutputImage, class TMaskImage>
+typename ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
+::ConfidenceImageType *
+ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
+::GetOutputConfidence()
+{
+ if (this->GetNumberOfOutputs() < 2)
+ {
+ return 0;
+ }
+ return static_cast<ConfidenceImageType *>(this->itk::ProcessObject::GetOutput(1));
+}
+
template <class TInputImage, class TOutputImage, class TMaskImage>
void
ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
@@ -77,6 +95,7 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
InputImageConstPointerType inputPtr = this->GetInput();
MaskImageConstPointerType inputMaskPtr = this->GetInputMask();
OutputImagePointerType outputPtr = this->GetOutput();
+ ConfidenceImagePointerType confidencePtr = this->GetOutputConfidence();
// Progress reporting
itk::ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels());
@@ -85,6 +104,7 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
typedef itk::ImageRegionConstIterator<InputImageType> InputIteratorType;
typedef itk::ImageRegionConstIterator<MaskImageType> MaskIteratorType;
typedef itk::ImageRegionIterator<OutputImageType> OutputIteratorType;
+ typedef itk::ImageRegionIterator<ConfidenceImageType> ConfidenceMapIteratorType;
InputIteratorType inIt(inputPtr, outputRegionForThread);
OutputIteratorType outIt(outputPtr, outputRegionForThread);
@@ -97,7 +117,17 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
maskIt.GoToBegin();
}
+ // setup iterator for confidence map
+ bool computeConfidenceMap(m_UseConfidenceMap && m_Model->HasConfidenceIndex());
+ ConfidenceMapIteratorType confidenceIt;
+ if (computeConfidenceMap)
+ {
+ confidenceIt = ConfidenceMapIteratorType(confidencePtr,outputRegionForThread);
+ confidenceIt.GoToBegin();
+ }
+
bool validPoint = true;
+ double confidenceIndex = 0.0;
// Walk the part of the image
for (inIt.GoToBegin(), outIt.GoToBegin(); !inIt.IsAtEnd() && !outIt.IsAtEnd(); ++inIt, ++outIt)
@@ -112,12 +142,25 @@ ImageClassificationFilter<TInputImage, TOutputImage, TMaskImage>
if (validPoint)
{
// Classifify
- outIt.Set(m_Model->Predict(inIt.Get())[0]);
+ if (computeConfidenceMap)
+ {
+ outIt.Set(m_Model->Predict(inIt.Get(),&confidenceIndex)[0]);
+ }
+ else
+ {
+ outIt.Set(m_Model->Predict(inIt.Get())[0]);
+ }
}
else
{
// else, set default value
outIt.Set(m_DefaultLabel);
+ confidenceIndex = 0.0;
+ }
+ if (computeConfidenceMap)
+ {
+ confidenceIt.Set(confidenceIndex);
+ ++confidenceIt;
}
progress.CompletedPixel();
}
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
index d3548c9c4064a5a8388e12b35861d85ea9ba9174..b8524b7fb677aff35b706a54b87b6a917251754b 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
@@ -39,19 +39,17 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(KNearestNeighborsMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(KNearestNeighborsMachineLearningModel, MachineLearningModel);
/** Setters/Getters to the number of neighbors to use
* Default is 32
@@ -95,7 +93,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
KNearestNeighborsMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
index c1048f63604b87a48ef2c84fe58b6a6f347f1d9d..e890b9137ac284848eda3c10885bfd736a4c28ac 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.txx
@@ -35,6 +35,7 @@ KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
m_K(32),
m_IsRegression(false)
{
+ this->m_ConfidenceIndex = true;
}
@@ -66,18 +67,36 @@ template <class TInputValue, class TTargetValue>
typename KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
::TargetSampleType
KNearestNeighborsMachineLearningModel<TInputValue,TTargetValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
otb::SampleToMat<InputSampleType>(input, sample);
- double result = m_KNearestModel->find_nearest(sample, m_K);
+ float result;
+
+ if (quality != NULL)
+ {
+ cv::Mat nearest(1,m_K,CV_32FC1);
+ result = m_KNearestModel->find_nearest(sample, m_K,0,0,&nearest,0);
+ unsigned int accuracy = 0;
+ for (int k=0 ; k < m_K ; ++k)
+ {
+ if (nearest.at<float>(0,k) == result)
+ {
+ accuracy++;
+ }
+ }
+ (*quality) = static_cast<ConfidenceValueType>(accuracy);
+ }
+ else
+ {
+ result = m_KNearestModel->find_nearest(sample, m_K);
+ }
TargetSampleType target;
target[0] = static_cast<TTargetValue>(result);
-
return target;
}
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
index 5c408173bd8a22b38f48a9818d54c50c331703bc..18eba7fbb28d336967ac669d05cda51bc5ebdc4c 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.h
@@ -40,15 +40,13 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
// LibSVM related typedefs
typedef otb::Functor::VariableLengthVectorToMeasurementVectorFunctor<InputSampleType> MeasurementVectorFunctorType;
@@ -59,7 +57,7 @@ public:
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(SVMMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(SVMMachineLearningModel, MachineLearningModel);
/** Save the model to file */
virtual void Save(const std::string &filename, const std::string & name="");
@@ -108,7 +106,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
LibSVMMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
index 0cf15f4110ffaaa18d27443003ad0778a8de0fdc..24e91e54a25406d100a8cae8f4b09741f124e80f 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
@@ -74,13 +74,15 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
m_SVMestimator->SetTrainingSampleList(this->GetTargetListSample());
m_SVMestimator->Update();
+
+ this->m_ConfidenceIndex = m_DoProbabilityEstimates;
}
template <class TInputValue, class TOutputValue>
typename LibSVMMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
LibSVMMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
TargetSampleType target;
@@ -88,6 +90,31 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
target = m_SVMestimator->GetModel()->EvaluateLabel(mfunctor(input));
+ if (quality != NULL)
+ {
+ if (!this->m_ConfidenceIndex)
+ {
+ itkExceptionMacro("Confidence index not available for this classifier !");
+ }
+ typename SVMEstimatorType::ModelType::ProbabilitiesVectorType probaVector =
+ m_SVMestimator->GetModel()->EvaluateProbabilities(mfunctor(input));
+ double maxProb = 0.0;
+ double secProb = 0.0;
+ for (unsigned int i=0 ; i<probaVector.Size() ; ++i)
+ {
+ if (maxProb < probaVector[i])
+ {
+ secProb = maxProb;
+ maxProb = probaVector[i];
+ }
+ else if (secProb < probaVector[i])
+ {
+ secProb = probaVector[i];
+ }
+ }
+ (*quality) = static_cast<ConfidenceValueType>(maxProb - secProb);
+ }
+
return target;
}
@@ -105,6 +132,8 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & itkNotUsed(name))
{
m_SVMestimator->GetModel()->LoadModel(filename.c_str());
+
+ this->m_ConfidenceIndex = m_SVMestimator->GetModel()->HasProbabilities();
}
template <class TInputValue, class TOutputValue>
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModel.h b/Modules/Learning/Supervised/include/otbMachineLearningModel.h
index 7d750ed6c4047e63f65cbf578b6d7d1f2828e514..8a6d55ce07652951779fd2f0ed05c09433182fb4 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbMachineLearningModel.h
@@ -60,7 +60,7 @@ namespace otb
*
* \ingroup OTBSupervised
*/
-template <class TInputValue, class TTargetValue>
+template <class TInputValue, class TTargetValue, class TConfidenceValue = double >
class ITK_EXPORT MachineLearningModel
: public itk::Object
{
@@ -87,6 +87,9 @@ public:
typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
//@}
+ /**\name Confidence value typedef */
+ typedef TConfidenceValue ConfidenceValueType;
+
/**\name Standard macros */
//@{
/** Run-time type information (and related methods). */
@@ -97,7 +100,7 @@ public:
void Train();
/** Predict values using the model */
- TargetSampleType Predict(const InputSampleType& input) const;
+ TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality = NULL) const;
/** Classify all samples in InputListSample and fill TargetListSample with the associated label */
void PredictAll();
@@ -120,6 +123,9 @@ public:
virtual bool CanWriteFile(const std::string &) = 0;
//@}
+ /** Query capacity to produce a confidence index */
+ bool HasConfidenceIndex() const {return m_ConfidenceIndex;}
+
/**\name Input list of samples accessors */
//@{
itkSetObjectMacro(InputListSample,InputListSampleType);
@@ -163,9 +169,12 @@ protected:
(void)input;
}
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const = 0;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality = NULL) const = 0;
bool m_RegressionMode;
+
+ /** flag that tells if the model support confidence index output */
+ bool m_ConfidenceIndex;
private:
MachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbMachineLearningModel.txx
index 5164fec3e2440e30f4c2a541d45cd7cadd261676..b4b6127c11c252c3d5a73d0a50716d48da6e7a49 100644
--- a/Modules/Learning/Supervised/include/otbMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbMachineLearningModel.txx
@@ -23,20 +23,20 @@
namespace otb
{
-template <class TInputValue, class TOutputValue>
-MachineLearningModel<TInputValue,TOutputValue>
-::MachineLearningModel() : m_RegressionMode(false)
+template <class TInputValue, class TOutputValue, class TConfidenceValue>
+MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
+::MachineLearningModel() : m_RegressionMode(false),m_ConfidenceIndex(false)
{}
-template <class TInputValue, class TOutputValue>
-MachineLearningModel<TInputValue,TOutputValue>
+template <class TInputValue, class TOutputValue, class TConfidenceValue>
+MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
::~MachineLearningModel()
{}
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TOutputValue, class TConfidenceValue>
void
-MachineLearningModel<TInputValue,TOutputValue>
+MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
::Train()
{
if(m_RegressionMode)
@@ -45,20 +45,20 @@ MachineLearningModel<TInputValue,TOutputValue>
return this->TrainClassification();
}
-template <class TInputValue, class TOutputValue>
-typename MachineLearningModel<TInputValue,TOutputValue>::TargetSampleType
-MachineLearningModel<TInputValue,TOutputValue>
-::Predict(const typename MachineLearningModel<TInputValue,TOutputValue>::InputSampleType& input) const
+template <class TInputValue, class TOutputValue, class TConfidenceValue>
+typename MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>::TargetSampleType
+MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
+::Predict(const InputSampleType& input, ConfidenceValueType *quality) const
{
if(m_RegressionMode)
return this->PredictRegression(input);
else
- return this->PredictClassification(input);
+ return this->PredictClassification(input,quality);
}
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TOutputValue, class TConfidenceValue>
void
-MachineLearningModel<TInputValue,TOutputValue>
+MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
::PredictAll()
{
TargetListSampleType * targets = this->GetTargetListSample();
@@ -71,9 +71,9 @@ MachineLearningModel<TInputValue,TOutputValue>
}
}
-template <class TInputValue, class TOutputValue>
+template <class TInputValue, class TOutputValue, class TConfidenceValue>
void
-MachineLearningModel<TInputValue,TOutputValue>
+MachineLearningModel<TInputValue,TOutputValue,TConfidenceValue>
::PrintSelf(std::ostream& os, itk::Indent indent) const
{
// Call superclass implementation
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
index 3890830682222f94b8421a0d44540a25a4cd4784..2eab72042d9ff5b3228df2d628ee0618127b01f4 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
@@ -40,21 +40,19 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
typedef std::map<TargetValueType, unsigned int> MapOfLabelsType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(NeuralNetworkMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(NeuralNetworkMachineLearningModel, MachineLearningModel);
/** Setters/Getters to the train method
* 2 methods are available:
@@ -183,7 +181,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
NeuralNetworkMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx
index 95b4c1502fe457bc19d68b0def8f4fd43b98ac4c..01c31811d7c793630dfcfe5f0026cc6b7be73356 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.txx
@@ -42,6 +42,7 @@ NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::NeuralNetworkMachi
m_Epsilon(0.01),
m_CvMatOfLabels(0)
{
+ this->m_ConfidenceIndex = true;
}
template<class TInputValue, class TOutputValue>
@@ -169,7 +170,7 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TrainClassifi
template<class TInputValue, class TOutputValue>
typename NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TargetSampleType NeuralNetworkMachineLearningModel<
- TInputValue, TOutputValue>::PredictClassification(const InputSampleType & input) const
+ TInputValue, TOutputValue>::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -182,6 +183,7 @@ typename NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TargetSam
TargetSampleType target;
float currentResponse = 0;
float maxResponse = response.at<float> (0, 0);
+ float secondResponse = -1e10;
target[0] = m_CvMatOfLabels->data.i[0];
unsigned int nbClasses = m_CvMatOfLabels->cols;
@@ -190,9 +192,22 @@ typename NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::TargetSam
currentResponse = response.at<float> (0, itLabel);
if (currentResponse > maxResponse)
{
+ secondResponse = maxResponse;
maxResponse = currentResponse;
target[0] = m_CvMatOfLabels->data.i[itLabel];
}
+ else
+ {
+ if (currentResponse > secondResponse)
+ {
+ secondResponse = currentResponse;
+ }
+ }
+ }
+
+ if (quality != NULL)
+ {
+ (*quality) = static_cast<ConfidenceValueType>(maxResponse) - static_cast<ConfidenceValueType>(secondResponse);
}
return target;
diff --git a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
index 7650666fb8eea7315aadc2a96975f999d89e21cb..31b5373ce16510512aa90065e151408a764898ed 100644
--- a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
@@ -40,19 +40,17 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(NormalBayesMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(NormalBayesMachineLearningModel, MachineLearningModel);
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -82,7 +80,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
NormalBayesMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx
index 1e9d8f17216531d0c6a3916ebad8ef17c706e148..bfc163553ddb58eb7e3e9f2150fc9cb63dbfaca2 100644
--- a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.txx
@@ -61,7 +61,7 @@ template <class TInputValue, class TOutputValue>
typename NormalBayesMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
NormalBayesMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -76,6 +76,14 @@ NormalBayesMachineLearningModel<TInputValue,TOutputValue>
target[0] = static_cast<TOutputValue>(result);
+ if (quality != NULL)
+ {
+ if (!this->HasConfidenceIndex())
+ {
+ itkExceptionMacro("Confidence index not available for this classifier !");
+ }
+ }
+
return target;
}
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
index b132ffefabf05a2af1e2b3b9fbc0b8d6da23b65e..20aff52a7e200f47f9127005addc442d25fd5799 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
@@ -40,15 +40,13 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
// Other
typedef itk::VariableSizeMatrix<float> VariableImportanceMatrixType;
@@ -59,7 +57,7 @@ public:
/** Run-time type information (and related methods). */
itkNewMacro(Self);
- itkTypeMacro(RandomForestsMachineLearningModel, itk::MachineLearningModel);
+ itkTypeMacro(RandomForestsMachineLearningModel, MachineLearningModel);
/** Save the model to file */
virtual void Save(const std::string & filename, const std::string & name="");
@@ -144,7 +142,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
RandomForestsMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
index 9c06e330a25690125366f813789c519fadaffc97..0797c2fabf18077612470b70604d8a3492a64446 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
@@ -42,6 +42,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS),
m_RegressionMode(false)
{
+ this->m_ConfidenceIndex = true;
}
@@ -109,7 +110,7 @@ template <class TInputValue, class TOutputValue>
typename RandomForestsMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & value) const
+::PredictClassification(const InputSampleType & value, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -122,6 +123,11 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
target[0] = static_cast<TOutputValue>(result);
+ if (quality != NULL)
+ {
+ (*quality) = m_RFModel->predict_prob(sample);
+ }
+
return target[0];
}
diff --git a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
index 4ff5ce670315e59789076d03360fc9d202ff2859..406bdd10e6b04a808fbd2be168e0f7504aa79578 100644
--- a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
@@ -39,15 +39,13 @@ public:
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
- // Input related typedefs
- typedef TInputValue InputValueType;
- typedef itk::VariableLengthVector<InputValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> InputListSampleType;
-
- // Target related typedefs
- typedef TTargetValue TargetValueType;
- typedef itk::FixedArray<TargetValueType,1> TargetSampleType;
- typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
+ typedef typename Superclass::InputValueType InputValueType;
+ typedef typename Superclass::InputSampleType InputSampleType;
+ typedef typename Superclass::InputListSampleType InputListSampleType;
+ typedef typename Superclass::TargetValueType TargetValueType;
+ typedef typename Superclass::TargetSampleType TargetSampleType;
+ typedef typename Superclass::TargetListSampleType TargetListSampleType;
+ typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
@@ -131,7 +129,7 @@ protected:
/** Train the machine learning model */
virtual void TrainClassification();
/** Predict values using the model */
- virtual TargetSampleType PredictClassification(const InputSampleType& input) const;
+ virtual TargetSampleType PredictClassification(const InputSampleType& input, ConfidenceValueType *quality=NULL) const;
private:
SVMMachineLearningModel(const Self &); //purposely not implemented
diff --git a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx
index beca36102ae5bb865bed47514e250bd1637d7ae6..064db060d1670146d4b618d94fa529017475758e 100644
--- a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.txx
@@ -49,6 +49,7 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
m_OutputNu(0),
m_OutputP(0)
{
+ this->m_ConfidenceIndex = true;
}
@@ -108,7 +109,7 @@ template <class TInputValue, class TOutputValue>
typename SVMMachineLearningModel<TInputValue,TOutputValue>
::TargetSampleType
SVMMachineLearningModel<TInputValue,TOutputValue>
-::PredictClassification(const InputSampleType & input) const
+::PredictClassification(const InputSampleType & input, ConfidenceValueType *quality) const
{
//convert listsample to Mat
cv::Mat sample;
@@ -121,6 +122,11 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
target[0] = static_cast<TOutputValue>(result);
+ if (quality != NULL)
+ {
+ (*quality) = m_SVMModel->predict(sample,true);
+ }
+
return target;
}