diff --git a/Modules/Applications/AppClassification/app/otbImageClassifier.cxx b/Modules/Applications/AppClassification/app/otbImageClassifier.cxx
index a47a006c2acd93642036f558be763c70ba57f119..ba23a0d564104525d8f173136dc46f979fff87e7 100644
--- a/Modules/Applications/AppClassification/app/otbImageClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbImageClassifier.cxx
@@ -105,7 +105,7 @@ private:
" * 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"
+ " * RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.\n"
" * SVM : distance to margin (only works for 2-class models)\n");
SetDefaultOutputPixelType( "confmap", ImagePixelType_double);
MandatoryOff("confmap");
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index 63a5c0918f5df9138ab25e78968b8305743f2409..58c5d6ca1cdaa6c8acf11d1a5bed0c4fc87cbe9f 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -103,7 +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")
+set(classifier_with_confmap "LIBSVM" "BOOST" "KNN" "ANN" "RF")
# Loop on classifiers
foreach(classifier ${classifierList})
diff --git a/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h b/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h
new file mode 100644
index 0000000000000000000000000000000000000000..536a7608a9720fdfa1e14e07f2d2e84707cb8f1c
--- /dev/null
+++ b/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h
@@ -0,0 +1,64 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#ifndef __otbCvRTreesWrapper_h
+#define __otbCvRTreesWrapper_h
+
+#include "otbOpenCVUtils.h"
+#include <vector>
+
+namespace otb
+{
+
+/** \class CvRTreesWrapper
+ * \brief Wrapper for OpenCV Random Trees
+ *
+ * \ingroup OTBSupervised
+ */
+class CV_EXPORTS_W CvRTreesWrapper : public CvRTrees
+{
+public:
+ typedef std::vector<unsigned int> VotesVectorType;
+ CvRTreesWrapper();
+ virtual ~CvRTreesWrapper();
+
+ /** Compute the number of votes for each class. */
+ void get_votes(const cv::Mat& sample,
+ const cv::Mat& missing,
+ VotesVectorType& vote_count) const;
+
+ /** Predict the confidence of the classifcation by computing the proportion
+ of trees which voted for the majority class.
+ */
+ float predict_confidence(const cv::Mat& sample,
+ const cv::Mat& missing =
+ cv::Mat()) const;
+
+ /** Predict the confidence margin of the classifcation by computing the
+ difference in votes between the first and second most voted classes.
+ This measure is preferred to the proportion of votes of the majority
+ class, since it provides information about the conflict between the
+ most likely classes.
+ */
+ float predict_margin(const cv::Mat& sample,
+ const cv::Mat& missing =
+ cv::Mat()) const;
+};
+
+}
+
+#endif
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
index ec62b761358634daa8e268c1488770bb801d3543..942e3de986956154529dbda14622de898c9d5b3e 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
@@ -24,8 +24,9 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
#include "itkVariableSizeMatrix.h"
+#include "otbCvRTreesWrapper.h"
-class CvRTrees;
+class CvRTreesWrapper;
namespace otb
{
@@ -53,7 +54,7 @@ public:
//opencv typedef
- typedef CvRTrees RFType;
+ typedef CvRTreesWrapper RFType;
/** Run-time type information (and related methods). */
itkNewMacro(Self);
@@ -120,6 +121,9 @@ public:
itkGetMacro(TerminationCriteria, int);
itkSetMacro(TerminationCriteria, int);
+ itkGetMacro(ComputeMargin, bool);
+ itkSetMacro(ComputeMargin, bool);
+
/** Returns a matrix containing variable importance */
VariableImportanceMatrixType GetVariableImportance();
@@ -145,7 +149,7 @@ private:
RandomForestsMachineLearningModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
- CvRTrees * m_RFModel;
+ CvRTreesWrapper * m_RFModel;
/** The depth of the tree. A low value will likely underfit and conversely a
* high value will likely overfit. The optimal value can be obtained using cross
* validation or other suitable methods. */
@@ -189,7 +193,7 @@ private:
* first category. */
std::vector<float> m_Priors;
/** If true then variable importance will be calculated and then it can be
- * retrieved by CvRTrees::get_var_importance(). */
+ * retrieved by CvRTreesWrapper::get_var_importance(). */
bool m_CalculateVariableImportance;
/** The size of the randomly selected subset of features at each tree node and
* that are used to find the best split(s). If you set it to 0 then the size will
@@ -205,6 +209,10 @@ private:
float m_ForestAccuracy;
/** The type of the termination criteria */
int m_TerminationCriteria;
+ /** Wether to compute margin (difference in probability between the
+ * 2 most voted classes) instead of confidence (probability of the most
+ * voted class) in prediction*/
+ bool m_ComputeMargin;
};
} // end namespace otb
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
index 78642f1212ac9d75dc75d3bd9e9482d73bc948dc..aa0f054aa7a13940a2cd710e2adf326f01b9f3f5 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.txx
@@ -29,17 +29,18 @@ namespace otb
template <class TInputValue, class TOutputValue>
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
::RandomForestsMachineLearningModel() :
- m_RFModel (new CvRTrees),
- m_MaxDepth(5),
- m_MinSampleCount(10),
- m_RegressionAccuracy(0.01),
- m_ComputeSurrogateSplit(false),
- m_MaxNumberOfCategories(10),
- m_CalculateVariableImportance(false),
- m_MaxNumberOfVariables(0),
- m_MaxNumberOfTrees(100),
- m_ForestAccuracy(0.01),
- m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS)
+ m_RFModel (new CvRTreesWrapper),
+ m_MaxDepth(5),
+ m_MinSampleCount(10),
+ m_RegressionAccuracy(0.01),
+ m_ComputeSurrogateSplit(false),
+ m_MaxNumberOfCategories(10),
+ m_CalculateVariableImportance(false),
+ m_MaxNumberOfVariables(0),
+ m_MaxNumberOfTrees(100),
+ m_ForestAccuracy(0.01),
+ m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS),
+ m_ComputeMargin(false)
{
this->m_ConfidenceIndex = true;
this->m_IsRegressionSupported = true;
@@ -91,7 +92,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
m_MaxNumberOfTrees, // max number of trees in the forest
m_ForestAccuracy, // forest accuracy
m_TerminationCriteria // termination criteria
- );
+ );
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
@@ -125,7 +126,10 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
if (quality != NULL)
{
- (*quality) = m_RFModel->predict_prob(sample);
+ if(m_ComputeMargin)
+ (*quality) = m_RFModel->predict_margin(sample);
+ else
+ (*quality) = m_RFModel->predict_confidence(sample);
}
return target[0];
@@ -158,30 +162,30 @@ bool
RandomForestsMachineLearningModel<TInputValue,TOutputValue>
::CanReadFile(const std::string & file)
{
- std::ifstream ifs;
- ifs.open(file.c_str());
+ std::ifstream ifs;
+ ifs.open(file.c_str());
- if(!ifs)
- {
- std::cerr<<"Could not read file "<<file<<std::endl;
- return false;
- }
+ if(!ifs)
+ {
+ std::cerr<<"Could not read file "<<file<<std::endl;
+ return false;
+ }
- while (!ifs.eof())
- {
- std::string line;
- std::getline(ifs, line);
+ while (!ifs.eof())
+ {
+ std::string line;
+ std::getline(ifs, line);
- //if (line.find(m_RFModel->getName()) != std::string::npos)
- if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos)
+ //if (line.find(m_RFModel->getName()) != std::string::npos)
+ if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos)
{
- //std::cout<<"Reading a "<<CV_TYPE_NAME_ML_RTREES<<" model"<<std::endl;
- return true;
+ //std::cout<<"Reading a "<<CV_TYPE_NAME_ML_RTREES<<" model"<<std::endl;
+ return true;
}
- }
- ifs.close();
- return false;
+ }
+ ifs.close();
+ return false;
}
template <class TInputValue, class TOutputValue>
diff --git a/Modules/Learning/Supervised/src/CMakeLists.txt b/Modules/Learning/Supervised/src/CMakeLists.txt
index 67598c94a143f5b6f16327889e648994b4e928db..bab85f52ac967624f58f55953f756bcffc83fcb0 100644
--- a/Modules/Learning/Supervised/src/CMakeLists.txt
+++ b/Modules/Learning/Supervised/src/CMakeLists.txt
@@ -1,4 +1,5 @@
set(OTBSupervised_SRC
+ otbCvRTreesWrapper.cxx
otbMachineLearningModelFactoryBase.cxx
otbMachineLearningUtils.cxx
)
diff --git a/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx b/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..e4ac2d9f403aeb18814c1ceebc3f2bdbffec810c
--- /dev/null
+++ b/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx
@@ -0,0 +1,76 @@
+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#include "otbCvRTreesWrapper.h"
+#include <algorithm>
+
+
+namespace otb
+{
+
+CvRTreesWrapper::CvRTreesWrapper(){}
+
+CvRTreesWrapper::~CvRTreesWrapper(){}
+
+void CvRTreesWrapper::get_votes(const cv::Mat& sample,
+ const cv::Mat& missing,
+ CvRTreesWrapper::VotesVectorType& vote_count) const
+{
+ vote_count.resize(nclasses);
+ for( int k = 0; k < ntrees; k++ )
+ {
+ CvDTreeNode* predicted_node = trees[k]->predict( sample, missing );
+ int class_idx = predicted_node->class_idx;
+ CV_Assert( 0 <= class_idx && class_idx < nclasses );
+ ++vote_count[class_idx];
+ }
+}
+
+float CvRTreesWrapper::predict_margin(const cv::Mat& sample,
+ const cv::Mat& missing) const
+{
+ // Sanity check (division by ntrees later on)
+ if(ntrees == 0)
+ {
+ return 0.;
+ }
+ std::vector<unsigned int> classVotes;
+ this->get_votes(sample, missing, classVotes);
+// We only sort the 2 greatest elements
+ std::nth_element(classVotes.begin(), classVotes.begin()+1,
+ classVotes.end(), std::greater<unsigned int>());
+ float margin = static_cast<float>(classVotes[0]-classVotes[1])/ntrees;
+ return margin;
+}
+
+float CvRTreesWrapper::predict_confidence(const cv::Mat& sample,
+ const cv::Mat& missing) const
+{
+ // Sanity check (division by ntrees later on)
+ if(ntrees == 0)
+ {
+ return 0.;
+ }
+ std::vector<unsigned int> classVotes;
+ this->get_votes(sample, missing, classVotes);
+ unsigned int max_votes = *(std::max_element(classVotes.begin(),
+ classVotes.end()));
+ float confidence = static_cast<float>(max_votes)/ntrees;
+ return confidence;
+}
+
+}