From 88488484e305dbd29fbf9b7adeca0f9f5cabc8ad Mon Sep 17 00:00:00 2001
From: OTB Bot <otbbot@orfeo-toolbox.org>
Date: Wed, 20 Mar 2013 19:57:48 +0100
Subject: [PATCH] STYLE
---
...tbTrainMachineLearningImagesClassifier.cxx | 296 +++++++++---------
.../OpenCV/otbBoostMachineLearningModel.h | 2 +-
...otbKNearestNeighborsMachineLearningModel.h | 2 +-
.../OpenCV/otbLibSVMMachineLearningModel.h | 2 +-
.../OpenCV/otbMachineLearningModel.txx | 4 +-
.../OpenCV/otbMachineLearningUtils.cxx | 2 +-
.../UtilitiesAdapters/OpenCV/otbOpenCVUtils.h | 80 ++---
.../otbRandomForestsMachineLearningModel.h | 2 +-
.../otbRandomForestsMachineLearningModel.txx | 6 +-
.../OpenCV/otbSVMMachineLearningModel.h | 2 +-
.../OpenCV/otbSVMMachineLearningModel.txx | 12 +-
11 files changed, 205 insertions(+), 205 deletions(-)
diff --git a/Applications/Classification/otbTrainMachineLearningImagesClassifier.cxx b/Applications/Classification/otbTrainMachineLearningImagesClassifier.cxx
index a2fb527152..c03515a87d 100644
--- a/Applications/Classification/otbTrainMachineLearningImagesClassifier.cxx
+++ b/Applications/Classification/otbTrainMachineLearningImagesClassifier.cxx
@@ -185,10 +185,10 @@ private:
AddParameter(ParameterType_Choice, "classifier", "Classifier to used.");
- SetParameterDescription("classifier", "Choice of the classifier to used.");
+ SetParameterDescription("classifier", "Choice of the classifier to used.");
- //Group LibSVM
- AddChoice("classifier.libsvm", "LibSVM classifier");
+ //Group LibSVM
+ AddChoice("classifier.libsvm", "LibSVM classifier");
//AddParameter(ParameterType_Group,"libsvm","LibSVM classifier parameters");
SetParameterDescription("classifier.libsvm","This group of parameters allows to set SVM classifier parameters.");
AddParameter(ParameterType_Choice, "classifier.libsvm.k", "SVM Kernel Type");
@@ -207,41 +207,41 @@ private:
//Group SVM (openCV)
AddChoice("classifier.svm", "SVM classifier (OpenCV)");
- //AddParameter(ParameterType_Group,"svm","SVM classifier parameters (OpenCV)");
- SetParameterDescription("classifier.svm","This group of parameters allows to set SVM classifier parameters.");
- AddParameter(ParameterType_Choice, "classifier.svm.m", "SVM Model Type");
- AddChoice("classifier.svm.m.csvc", "C support vector classification");
- AddChoice("classifier.svm.m.nusvc", "Nu support vector classification");
- AddChoice("classifier.svm.m.oneclass", "Distribution estimation (One Class SVM)");
- AddChoice("classifier.svm.m.epssvr", "Epsilon Support Vector Regression");
- AddChoice("classifier.svm.m.nusvr", "Nu Support Vector Regression");
- SetParameterString("classifier.svm.m", "csvc");
- SetParameterDescription("classifier.svm.m", "Type of SVM formulation.");
- AddParameter(ParameterType_Choice, "classifier.svm.k", "SVM Kernel Type");
- AddChoice("classifier.svm.k.linear", "Linear");
- AddChoice("classifier.svm.k.rbf", "Gaussian radial basis function");
- AddChoice("classifier.svm.k.poly", "Polynomial");
- AddChoice("classifier.svm.k.sigmoid", "Sigmoid");
- SetParameterString("classifier.svm.k", "linear");
- SetParameterDescription("classifier.svm.k", "SVM Kernel Type.");
- AddParameter(ParameterType_Float, "classifier.svm.c", "Cost parameter C.");
- SetParameterFloat("classifier.svm.c", 1.0);
- SetParameterDescription("classifier.svm.c", "SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.");
- AddParameter(ParameterType_Float, "classifier.svm.nu", "Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS / NU_SVR).");
- SetParameterFloat("classifier.svm.nu", 0.0);
- SetParameterDescription("classifier.svm.nu", "Parameter nu of a SVM optimization problem.");
- AddParameter(ParameterType_Float, "classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
- SetParameterFloat("classifier.svm.p", 0.0);
- SetParameterDescription("classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
- AddParameter(ParameterType_Float, "classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
- SetParameterFloat("classifier.svm.coef0", 0.0);
- SetParameterDescription("classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
- AddParameter(ParameterType_Float, "classifier.svm.gamma", "Parameter gamma of a kernel function (POLY / RBF / SIGMOID).");
- SetParameterFloat("classifier.svm.gamma", 1.0);
- SetParameterDescription("classifier.svm.gamma", "Parameter gamma of a kernel function (POLY / RBF / SIGMOID).");
- AddParameter(ParameterType_Float, "classifier.svm.degree", "Parameter degree of a kernel function (POLY).");
- SetParameterFloat("classifier.svm.degree", 0.0);
- SetParameterDescription("classifier.svm.degree", "Parameter degree of a kernel function (POLY).");
+ //AddParameter(ParameterType_Group,"svm","SVM classifier parameters (OpenCV)");
+ SetParameterDescription("classifier.svm","This group of parameters allows to set SVM classifier parameters.");
+ AddParameter(ParameterType_Choice, "classifier.svm.m", "SVM Model Type");
+ AddChoice("classifier.svm.m.csvc", "C support vector classification");
+ AddChoice("classifier.svm.m.nusvc", "Nu support vector classification");
+ AddChoice("classifier.svm.m.oneclass", "Distribution estimation (One Class SVM)");
+ AddChoice("classifier.svm.m.epssvr", "Epsilon Support Vector Regression");
+ AddChoice("classifier.svm.m.nusvr", "Nu Support Vector Regression");
+ SetParameterString("classifier.svm.m", "csvc");
+ SetParameterDescription("classifier.svm.m", "Type of SVM formulation.");
+ AddParameter(ParameterType_Choice, "classifier.svm.k", "SVM Kernel Type");
+ AddChoice("classifier.svm.k.linear", "Linear");
+ AddChoice("classifier.svm.k.rbf", "Gaussian radial basis function");
+ AddChoice("classifier.svm.k.poly", "Polynomial");
+ AddChoice("classifier.svm.k.sigmoid", "Sigmoid");
+ SetParameterString("classifier.svm.k", "linear");
+ SetParameterDescription("classifier.svm.k", "SVM Kernel Type.");
+ AddParameter(ParameterType_Float, "classifier.svm.c", "Cost parameter C.");
+ SetParameterFloat("classifier.svm.c", 1.0);
+ SetParameterDescription("classifier.svm.c", "SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.");
+ AddParameter(ParameterType_Float, "classifier.svm.nu", "Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS / NU_SVR).");
+ SetParameterFloat("classifier.svm.nu", 0.0);
+ SetParameterDescription("classifier.svm.nu", "Parameter nu of a SVM optimization problem.");
+ AddParameter(ParameterType_Float, "classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
+ SetParameterFloat("classifier.svm.p", 0.0);
+ SetParameterDescription("classifier.svm.p", "Parameter epsilon of a SVM optimization problem (EPS_SVR).");
+ AddParameter(ParameterType_Float, "classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
+ SetParameterFloat("classifier.svm.coef0", 0.0);
+ SetParameterDescription("classifier.svm.coef0", "Parameter coef0 of a kernel function (POLY / SIGMOID).");
+ AddParameter(ParameterType_Float, "classifier.svm.gamma", "Parameter gamma of a kernel function (POLY / RBF / SIGMOID).");
+ SetParameterFloat("classifier.svm.gamma", 1.0);
+ SetParameterDescription("classifier.svm.gamma", "Parameter gamma of a kernel function (POLY / RBF / SIGMOID).");
+ AddParameter(ParameterType_Float, "classifier.svm.degree", "Parameter degree of a kernel function (POLY).");
+ SetParameterFloat("classifier.svm.degree", 0.0);
+ SetParameterDescription("classifier.svm.degree", "Parameter degree of a kernel function (POLY).");
AddRANDParameter();
// Doc example parameter settings
@@ -336,124 +336,124 @@ private:
void TrainLibSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
{
- LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
- libSVMClassifier->SetInputListSample(trainingListSample);
- libSVMClassifier->SetTargetListSample(trainingLabeledListSample);
- //SVM Option
- //TODO : Add other options ?
- if (IsParameterEnabled("classifier.libsvm.opt"))
- {
- libSVMClassifier->SetParameterOptimization(true);
- }
- libSVMClassifier->SetC(GetParameterFloat("classifier.libsvm.c"));
-
- switch (GetParameterInt("classifier.libsvm.k"))
- {
- case 0: // LINEAR
- libSVMClassifier->SetKernelType(LINEAR);
- break;
- case 1: // RBF
- libSVMClassifier->SetKernelType(RBF);
- break;
- case 2: // POLY
- libSVMClassifier->SetKernelType(POLY);
- break;
- case 3: // SIGMOID
- libSVMClassifier->SetKernelType(SIGMOID);
- break;
- default: // DEFAULT = LINEAR
- libSVMClassifier->SetKernelType(LINEAR);
- break;
- }
- libSVMClassifier->Train();
- libSVMClassifier->Save(GetParameterString("io.out"));
- //otbAppLogINFO( "Learning done -> Final SVM accuracy: " << libSVMClassifier->GetFinalCrossValidationAccuracy() << std::endl);
+ LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
+ libSVMClassifier->SetInputListSample(trainingListSample);
+ libSVMClassifier->SetTargetListSample(trainingLabeledListSample);
+ //SVM Option
+ //TODO : Add other options ?
+ if (IsParameterEnabled("classifier.libsvm.opt"))
+ {
+ libSVMClassifier->SetParameterOptimization(true);
+ }
+ libSVMClassifier->SetC(GetParameterFloat("classifier.libsvm.c"));
+
+ switch (GetParameterInt("classifier.libsvm.k"))
+ {
+ case 0: // LINEAR
+ libSVMClassifier->SetKernelType(LINEAR);
+ break;
+ case 1: // RBF
+ libSVMClassifier->SetKernelType(RBF);
+ break;
+ case 2: // POLY
+ libSVMClassifier->SetKernelType(POLY);
+ break;
+ case 3: // SIGMOID
+ libSVMClassifier->SetKernelType(SIGMOID);
+ break;
+ default: // DEFAULT = LINEAR
+ libSVMClassifier->SetKernelType(LINEAR);
+ break;
+ }
+ libSVMClassifier->Train();
+ libSVMClassifier->Save(GetParameterString("io.out"));
+ //otbAppLogINFO( "Learning done -> Final SVM accuracy: " << libSVMClassifier->GetFinalCrossValidationAccuracy() << std::endl);
}
void ClassifyLibSVM(ListSampleType::Pointer validationListSample, LabelListSampleType::Pointer predictedList)
{
- //Classification
- LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
- libSVMClassifier->Load(GetParameterString("io.out"));
- libSVMClassifier->SetInputListSample(validationListSample);
- libSVMClassifier->SetTargetListSample(predictedList);
- libSVMClassifier->PredictAll();
+ //Classification
+ LibSVMType::Pointer libSVMClassifier = LibSVMType::New();
+ libSVMClassifier->Load(GetParameterString("io.out"));
+ libSVMClassifier->SetInputListSample(validationListSample);
+ libSVMClassifier->SetTargetListSample(predictedList);
+ libSVMClassifier->PredictAll();
}
void TrainSVM(ListSampleType::Pointer trainingListSample, LabelListSampleType::Pointer trainingLabeledListSample)
{
- std::cout<<"svm open CV"<<std::endl;
- SVMType::Pointer SVMClassifier = SVMType::New();
- SVMClassifier->SetInputListSample(trainingListSample);
- SVMClassifier->SetTargetListSample(trainingLabeledListSample);
- switch (GetParameterInt("classifier.svm.k"))
- {
- case 0: // LINEAR
- SVMClassifier->SetKernelType(CvSVM::LINEAR);
- std::cout<<"CvSVM::LINEAR = "<<CvSVM::LINEAR<<std::endl;
- break;
- case 1: // RBF
- SVMClassifier->SetKernelType(CvSVM::RBF);
- std::cout<<"CvSVM::RBF = "<<CvSVM::RBF<<std::endl;
- break;
- case 2: // POLY
- SVMClassifier->SetKernelType(CvSVM::POLY);
- std::cout<<"CvSVM::POLY = "<<CvSVM::POLY<<std::endl;
- break;
- case 3: // SIGMOID
- SVMClassifier->SetKernelType(CvSVM::SIGMOID);
- std::cout<<"CvSVM::SIGMOID = "<<CvSVM::SIGMOID<<std::endl;
- break;
- default: // DEFAULT = LINEAR
- SVMClassifier->SetKernelType(CvSVM::LINEAR);
- std::cout<<"CvSVM::LINEAR = "<<CvSVM::LINEAR<<std::endl;
- break;
- }
- switch (GetParameterInt("classifier.svm.m"))
- {
- case 0: // C_SVC
- SVMClassifier->SetSVMType(CvSVM::C_SVC);
- std::cout<<"CvSVM::C_SVC = "<<CvSVM::C_SVC<<std::endl;
- break;
- case 1: // NU_SVC
- SVMClassifier->SetSVMType(CvSVM::NU_SVC);
- std::cout<<"CvSVM::NU_SVC = "<<CvSVM::NU_SVC<<std::endl;
- break;
- case 2: // ONE_CLASS
- SVMClassifier->SetSVMType(CvSVM::ONE_CLASS);
- std::cout<<"CvSVM::ONE_CLASS = "<<CvSVM::ONE_CLASS<<std::endl;
- break;
- case 3: // EPS_SVR
- SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
- std::cout<<"CvSVM::EPS_SVR = "<<CvSVM::EPS_SVR<<std::endl;
- break;
- case 4: // NU_SVR
- SVMClassifier->SetSVMType(CvSVM::NU_SVR);
- std::cout<<"CvSVM::NU_SVR = "<<CvSVM::NU_SVR<<std::endl;
- break;
- default: // DEFAULT = C_SVC
- SVMClassifier->SetSVMType(CvSVM::C_SVC);
- std::cout<<"CvSVM::C_SVC = "<<CvSVM::C_SVC<<std::endl;
- break;
- }
- SVMClassifier->SetC(GetParameterFloat("classifier.svm.c"));
- SVMClassifier->SetNu(GetParameterFloat("classifier.svm.nu"));
- SVMClassifier->SetP(GetParameterFloat("classifier.svm.p"));
- SVMClassifier->SetCoef0(GetParameterFloat("classifier.svm.coef0"));
- SVMClassifier->SetGamma(GetParameterFloat("classifier.svm.gamma"));
- SVMClassifier->SetDegree(GetParameterFloat("classifier.svm.degree"));
- SVMClassifier->Train();
- SVMClassifier->Save(GetParameterString("io.out"));
+ std::cout<<"svm open CV"<<std::endl;
+ SVMType::Pointer SVMClassifier = SVMType::New();
+ SVMClassifier->SetInputListSample(trainingListSample);
+ SVMClassifier->SetTargetListSample(trainingLabeledListSample);
+ switch (GetParameterInt("classifier.svm.k"))
+ {
+ case 0: // LINEAR
+ SVMClassifier->SetKernelType(CvSVM::LINEAR);
+ std::cout<<"CvSVM::LINEAR = "<<CvSVM::LINEAR<<std::endl;
+ break;
+ case 1: // RBF
+ SVMClassifier->SetKernelType(CvSVM::RBF);
+ std::cout<<"CvSVM::RBF = "<<CvSVM::RBF<<std::endl;
+ break;
+ case 2: // POLY
+ SVMClassifier->SetKernelType(CvSVM::POLY);
+ std::cout<<"CvSVM::POLY = "<<CvSVM::POLY<<std::endl;
+ break;
+ case 3: // SIGMOID
+ SVMClassifier->SetKernelType(CvSVM::SIGMOID);
+ std::cout<<"CvSVM::SIGMOID = "<<CvSVM::SIGMOID<<std::endl;
+ break;
+ default: // DEFAULT = LINEAR
+ SVMClassifier->SetKernelType(CvSVM::LINEAR);
+ std::cout<<"CvSVM::LINEAR = "<<CvSVM::LINEAR<<std::endl;
+ break;
+ }
+ switch (GetParameterInt("classifier.svm.m"))
+ {
+ case 0: // C_SVC
+ SVMClassifier->SetSVMType(CvSVM::C_SVC);
+ std::cout<<"CvSVM::C_SVC = "<<CvSVM::C_SVC<<std::endl;
+ break;
+ case 1: // NU_SVC
+ SVMClassifier->SetSVMType(CvSVM::NU_SVC);
+ std::cout<<"CvSVM::NU_SVC = "<<CvSVM::NU_SVC<<std::endl;
+ break;
+ case 2: // ONE_CLASS
+ SVMClassifier->SetSVMType(CvSVM::ONE_CLASS);
+ std::cout<<"CvSVM::ONE_CLASS = "<<CvSVM::ONE_CLASS<<std::endl;
+ break;
+ case 3: // EPS_SVR
+ SVMClassifier->SetSVMType(CvSVM::EPS_SVR);
+ std::cout<<"CvSVM::EPS_SVR = "<<CvSVM::EPS_SVR<<std::endl;
+ break;
+ case 4: // NU_SVR
+ SVMClassifier->SetSVMType(CvSVM::NU_SVR);
+ std::cout<<"CvSVM::NU_SVR = "<<CvSVM::NU_SVR<<std::endl;
+ break;
+ default: // DEFAULT = C_SVC
+ SVMClassifier->SetSVMType(CvSVM::C_SVC);
+ std::cout<<"CvSVM::C_SVC = "<<CvSVM::C_SVC<<std::endl;
+ break;
+ }
+ SVMClassifier->SetC(GetParameterFloat("classifier.svm.c"));
+ SVMClassifier->SetNu(GetParameterFloat("classifier.svm.nu"));
+ SVMClassifier->SetP(GetParameterFloat("classifier.svm.p"));
+ SVMClassifier->SetCoef0(GetParameterFloat("classifier.svm.coef0"));
+ SVMClassifier->SetGamma(GetParameterFloat("classifier.svm.gamma"));
+ SVMClassifier->SetDegree(GetParameterFloat("classifier.svm.degree"));
+ SVMClassifier->Train();
+ SVMClassifier->Save(GetParameterString("io.out"));
}
void ClassifySVM(ListSampleType::Pointer validationListSample, LabelListSampleType::Pointer predictedList)
{
- //Classification
- SVMType::Pointer SVMClassifier = SVMType::New();
- SVMClassifier->Load(GetParameterString("io.out"));
- SVMClassifier->SetInputListSample(validationListSample);
- SVMClassifier->SetTargetListSample(predictedList);
- SVMClassifier->PredictAll();
+ //Classification
+ SVMType::Pointer SVMClassifier = SVMType::New();
+ SVMClassifier->Load(GetParameterString("io.out"));
+ SVMClassifier->SetInputListSample(validationListSample);
+ SVMClassifier->SetTargetListSample(predictedList);
+ SVMClassifier->PredictAll();
}
void DoExecute()
@@ -616,13 +616,13 @@ private:
const std::string classifierType = GetParameterString("classifier");
if (classifierType == "libsvm")
{
- TrainLibSVM(trainingListSample, trainingLabeledListSample);
- ClassifyLibSVM(validationListSample, predictedList);
+ TrainLibSVM(trainingListSample, trainingLabeledListSample);
+ ClassifyLibSVM(validationListSample, predictedList);
}
else if (classifierType == "svm")
{
- TrainSVM(trainingListSample, trainingLabeledListSample);
- ClassifySVM(validationListSample, predictedList);
+ TrainSVM(trainingListSample, trainingLabeledListSample);
+ ClassifySVM(validationListSample, predictedList);
}
//--------------------------
diff --git a/Code/UtilitiesAdapters/OpenCV/otbBoostMachineLearningModel.h b/Code/UtilitiesAdapters/OpenCV/otbBoostMachineLearningModel.h
index de9244b25b..dfa7b12eed 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbBoostMachineLearningModel.h
+++ b/Code/UtilitiesAdapters/OpenCV/otbBoostMachineLearningModel.h
@@ -26,7 +26,7 @@
//include opencv
#include <cv.h> // opencv general include file
-#include <ml.h> // opencv machine learning include file
+#include <ml.h> // opencv machine learning include file
namespace otb
{
diff --git a/Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.h b/Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.h
index c383677faa..3b618d5dc4 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.h
+++ b/Code/UtilitiesAdapters/OpenCV/otbKNearestNeighborsMachineLearningModel.h
@@ -26,7 +26,7 @@
//include opencv
#include <opencv.hpp> // opencv general include file
-#include <ml/ml.hpp> // opencv machine learning include file
+#include <ml/ml.hpp> // opencv machine learning include file
namespace otb
{
diff --git a/Code/UtilitiesAdapters/OpenCV/otbLibSVMMachineLearningModel.h b/Code/UtilitiesAdapters/OpenCV/otbLibSVMMachineLearningModel.h
index ae90aa2203..75239759a6 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbLibSVMMachineLearningModel.h
+++ b/Code/UtilitiesAdapters/OpenCV/otbLibSVMMachineLearningModel.h
@@ -26,7 +26,7 @@
//include opencv
//#include <opencv.hpp> // opencv general include file
-//#include <ml/ml.hpp> // opencv machine learning include file
+//#include <ml/ml.hpp> // opencv machine learning include file
// SVM estimator
#include "otbSVMSampleListModelEstimator.h"
diff --git a/Code/UtilitiesAdapters/OpenCV/otbMachineLearningModel.txx b/Code/UtilitiesAdapters/OpenCV/otbMachineLearningModel.txx
index 4eef56e258..9281aa2653 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbMachineLearningModel.txx
+++ b/Code/UtilitiesAdapters/OpenCV/otbMachineLearningModel.txx
@@ -20,7 +20,7 @@
#include "otbMachineLearningModel.h"
-namespace otb
+namespace otb
{
template <class TInputValue, class TOutputValue>
@@ -43,7 +43,7 @@ MachineLearningModel<TInputValue,TOutputValue>
targets->Clear();
for(typename InputListSampleType::ConstIterator sIt = this->GetInputListSample()->Begin();
- sIt!=this->GetInputListSample()->End();++sIt)
+ sIt!=this->GetInputListSample()->End(); ++sIt)
{
targets->PushBack(this->Predict(sIt.GetMeasurementVector()));
}
diff --git a/Code/UtilitiesAdapters/OpenCV/otbMachineLearningUtils.cxx b/Code/UtilitiesAdapters/OpenCV/otbMachineLearningUtils.cxx
index 3b3e67bd9b..d1e4aa4d45 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbMachineLearningUtils.cxx
+++ b/Code/UtilitiesAdapters/OpenCV/otbMachineLearningUtils.cxx
@@ -79,7 +79,7 @@ bool ReadDataFile(const char * infname, InputListSampleType * samples, TargetLis
sample[id] = atof(feature.substr(semicolonpos+1,feature.size()-semicolonpos).c_str());
pos = nextpos;
}
- }
+ }
samples->PushBack(sample);
labels->PushBack(label);
}
diff --git a/Code/UtilitiesAdapters/OpenCV/otbOpenCVUtils.h b/Code/UtilitiesAdapters/OpenCV/otbOpenCVUtils.h
index 849d60a2e3..12c829d5de 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbOpenCVUtils.h
+++ b/Code/UtilitiesAdapters/OpenCV/otbOpenCVUtils.h
@@ -28,7 +28,7 @@ namespace otb
output.create(1,sample.Size(),CV_32FC1);
// Loop on sample size
- for(unsigned int i = 0; i < sample.Size();++i)
+ for(unsigned int i = 0; i < sample.Size(); ++i)
{
output.at<float>(0,i) = sample[i];
}
@@ -47,30 +47,30 @@ namespace otb
// Check for valid listSample
if(listSample != NULL && listSample->Size() > 0)
{
- // Retrieve samples count
- unsigned int sampleCount = listSample->Size();
+ // Retrieve samples count
+ unsigned int sampleCount = listSample->Size();
- // Build an iterator
- typename T::ConstIterator sampleIt = listSample->Begin();
-
- // Retrieve samples size alike
- const unsigned int sampleSize = listSample->GetMeasurementVectorSize();
-
- // Allocate CvMat
- output.create(sampleCount,sampleSize,CV_32FC1);
-
- // Fill the cv matrix
- for(;sampleIt!=listSample->End();++sampleIt,++sampleIdx)
- {
- // Retrieve sample
- typename T::MeasurementVectorType sample = sampleIt.GetMeasurementVector();
-
- // Loop on sample size
- for(unsigned int i = 0; i < sampleSize;++i)
- {
- output.at<float>(sampleIdx,i) = sample[i];
- }
- }
+ // Build an iterator
+ typename T::ConstIterator sampleIt = listSample->Begin();
+
+ // Retrieve samples size alike
+ const unsigned int sampleSize = listSample->GetMeasurementVectorSize();
+
+ // Allocate CvMat
+ output.create(sampleCount,sampleSize,CV_32FC1);
+
+ // Fill the cv matrix
+ for(; sampleIt!=listSample->End(); ++sampleIt,++sampleIdx)
+ {
+ // Retrieve sample
+ typename T::MeasurementVectorType sample = sampleIt.GetMeasurementVector();
+
+ // Loop on sample size
+ for(unsigned int i = 0; i < sampleSize; ++i)
+ {
+ output.at<float>(sampleIdx,i) = sample[i];
+ }
+ }
}
}
@@ -94,22 +94,22 @@ namespace otb
unsigned int sampleSize = cvmat.cols;
// Loop on samples
- for(unsigned int i = 0; i < sampleCount;++i)
- {
- typename T::MeasurementVectorType sample;
- itk::PixelBuilder<typename T::MeasurementVectorType>::Zero(sample,sampleSize);
-
- unsigned int realSampleSize = sample.Size();
-
- for(unsigned int j = 0; j < realSampleSize;++j)
- {
- // Don't forget to cast
- sample[j] = static_cast<typename T::MeasurementVectorType
- ::ValueType>(cvmat.at<float>(i,j));
- }
- // PushBack the new sample
- output->PushBack(sample);
- }
+ for(unsigned int i = 0; i < sampleCount; ++i)
+ {
+ typename T::MeasurementVectorType sample;
+ itk::PixelBuilder<typename T::MeasurementVectorType>::Zero(sample,sampleSize);
+
+ unsigned int realSampleSize = sample.Size();
+
+ for(unsigned int j = 0; j < realSampleSize; ++j)
+ {
+ // Don't forget to cast
+ sample[j] = static_cast<typename T::MeasurementVectorType
+ ::ValueType>(cvmat.at<float>(i,j));
+ }
+ // PushBack the new sample
+ output->PushBack(sample);
+ }
// return the output
return output;
}
diff --git a/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.h b/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.h
index 9b4e7850fc..4e9c0dfd6e 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.h
+++ b/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.h
@@ -26,7 +26,7 @@
//include opencv
#include <opencv.hpp> // opencv general include file
-#include <ml/ml.hpp> // opencv machine learning include file
+#include <ml/ml.hpp> // opencv machine learning include file
namespace otb
{
diff --git a/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.txx b/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.txx
index 25454bf0d8..3e3990f68a 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.txx
+++ b/Code/UtilitiesAdapters/OpenCV/otbRandomForestsMachineLearningModel.txx
@@ -78,8 +78,8 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
priors, // the array of priors
m_CalculateVariableImportance, // calculate variable importance
m_MaxNumberOfVariables, // number of variables randomly selected at node and used to find the best split(s).
- m_MaxNumberOfTrees, // max number of trees in the forest
- m_ForestAccuracy, // forrest accuracy
+ m_MaxNumberOfTrees, // max number of trees in the forest
+ m_ForestAccuracy, // forrest accuracy
m_TerminationCriteria // termination cirteria
);
@@ -90,7 +90,7 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
//train the RT model
m_RFModel->train(samples, CV_ROW_SAMPLE, labels,
- cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
+ cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
}
template <class TInputValue, class TOutputValue>
diff --git a/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.h b/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.h
index eafed3a419..e0c0101c94 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.h
+++ b/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.h
@@ -27,7 +27,7 @@
//include opencv
#include <opencv.hpp> // opencv general include file
-#include <ml/ml.hpp> // opencv machine learning include file
+#include <ml/ml.hpp> // opencv machine learning include file
namespace otb
{
diff --git a/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.txx b/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.txx
index a72cd285de..a532767646 100644
--- a/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.txx
+++ b/Code/UtilitiesAdapters/OpenCV/otbSVMMachineLearningModel.txx
@@ -90,10 +90,10 @@ void
SVMMachineLearningModel<TInputValue,TOutputValue>
::Save(const std::string & filename, const std::string & name)
{
- if (name == "")
- m_SVMModel->save(filename.c_str(), 0);
- else
- m_SVMModel->save(filename.c_str(), name.c_str());
+ if (name == "")
+ m_SVMModel->save(filename.c_str(), 0);
+ else
+ m_SVMModel->save(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
@@ -102,9 +102,9 @@ SVMMachineLearningModel<TInputValue,TOutputValue>
::Load(const std::string & filename, const std::string & name)
{
if (name == "")
- m_SVMModel->load(filename.c_str(), 0);
+ m_SVMModel->load(filename.c_str(), 0);
else
- m_SVMModel->load(filename.c_str(), name.c_str());
+ m_SVMModel->load(filename.c_str(), name.c_str());
}
template <class TInputValue, class TOutputValue>
--
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