From 0d8e89b5ce1a2f8b8870f7a8b33a303b93c57a47 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <cedric.traizet@c-s.fr>
Date: Wed, 8 Jan 2020 15:36:00 +0100
Subject: [PATCH] REFAC: remove opencv2 specific code
---
.../include/otbTrainBoost.hxx | 1 -
.../include/otbTrainDecisionTree.hxx | 19 +---
.../AppClassification/test/CMakeLists.txt | 3 -
.../include/otbBoostMachineLearningModel.h | 20 +---
.../include/otbBoostMachineLearningModel.hxx | 61 +-----------
.../Supervised/include/otbCvRTreesWrapper.h | 8 +-
.../otbDecisionTreeMachineLearningModel.h | 8 --
.../otbDecisionTreeMachineLearningModel.hxx | 48 +---------
...otbKNearestNeighborsMachineLearningModel.h | 9 +-
...bKNearestNeighborsMachineLearningModel.hxx | 54 +----------
.../otbNeuralNetworkMachineLearningModel.h | 7 +-
.../otbNeuralNetworkMachineLearningModel.hxx | 94 +------------------
.../otbNormalBayesMachineLearningModel.h | 10 +-
.../otbNormalBayesMachineLearningModel.hxx | 41 +-------
.../Supervised/include/otbOpenCVUtils.h | 3 +-
.../otbRandomForestsMachineLearningModel.h | 7 +-
.../otbRandomForestsMachineLearningModel.hxx | 57 +----------
.../include/otbSVMMachineLearningModel.h | 10 +-
.../include/otbSVMMachineLearningModel.hxx | 74 +--------------
.../Supervised/src/otbCvRTreesWrapper.cxx | 21 +----
20 files changed, 24 insertions(+), 531 deletions(-)
diff --git a/Modules/Applications/AppClassification/include/otbTrainBoost.hxx b/Modules/Applications/AppClassification/include/otbTrainBoost.hxx
index 9e551b7a94..6c831522a8 100644
--- a/Modules/Applications/AppClassification/include/otbTrainBoost.hxx
+++ b/Modules/Applications/AppClassification/include/otbTrainBoost.hxx
@@ -58,7 +58,6 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitBoostParams()
"rather than exact optimization at each step.");
SetParameterString("classifier.boost.t", "real");
SetParameterDescription("classifier.boost.t", "Type of Boosting algorithm.");
- // Do not expose SplitCriteria
// WeakCount
AddParameter(ParameterType_Int, "classifier.boost.w", "Weak count");
SetParameterInt("classifier.boost.w", 100);
diff --git a/Modules/Applications/AppClassification/include/otbTrainDecisionTree.hxx b/Modules/Applications/AppClassification/include/otbTrainDecisionTree.hxx
index c0f70c3385..e9c6416321 100644
--- a/Modules/Applications/AppClassification/include/otbTrainDecisionTree.hxx
+++ b/Modules/Applications/AppClassification/include/otbTrainDecisionTree.hxx
@@ -35,11 +35,7 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitDecisionTreeParams(
SetParameterDescription("classifier.dt", "http://docs.opencv.org/modules/ml/doc/decision_trees.html");
// MaxDepth
AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
-#ifdef OTB_OPENCV_3
SetParameterInt("classifier.dt.max", 10);
-#else
- SetParameterInt("classifier.dt.max", 65535);
-#endif
SetParameterDescription("classifier.dt.max",
"The training algorithm attempts to split each node while its depth is smaller "
"than the maximum possible depth of the tree. The actual depth may be smaller "
@@ -72,16 +68,6 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitDecisionTreeParams(
"Cluster possible values of a categorical variable into K <= cat clusters to find a "
"suboptimal split.");
-
-// CVFolds: only exposed for OPENCV 2 because it crashes in OpenCV 3
-#ifndef OTB_OPENCV_3
- AddParameter(ParameterType_Int, "classifier.dt.f", "K-fold cross-validations");
- SetParameterInt("classifier.dt.f", 10);
- SetParameterDescription("classifier.dt.f",
- "If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K "
- "is equal to cv_folds.");
-#endif
-
// Use1seRule
AddParameter(ParameterType_Bool, "classifier.dt.r", "Set Use1seRule flag to false");
SetParameterDescription("classifier.dt.r",
@@ -109,10 +95,7 @@ void LearningApplicationBase<TInputValue, TOutputValue>::TrainDecisionTree(typen
classifier->SetMinSampleCount(GetParameterInt("classifier.dt.min"));
classifier->SetRegressionAccuracy(GetParameterFloat("classifier.dt.ra"));
classifier->SetMaxCategories(GetParameterInt("classifier.dt.cat"));
-// CVFolds is only exposed for OPENCV 2 because it crashes in OpenCV 3
-#ifndef OTB_OPENCV_3
- classifier->SetCVFolds(GetParameterInt("classifier.dt.f"));
-#endif
+
if (GetParameterInt("classifier.dt.r"))
{
classifier->SetUse1seRule(false);
diff --git a/Modules/Applications/AppClassification/test/CMakeLists.txt b/Modules/Applications/AppClassification/test/CMakeLists.txt
index 9da705b64c..8e99d91e33 100644
--- a/Modules/Applications/AppClassification/test/CMakeLists.txt
+++ b/Modules/Applications/AppClassification/test/CMakeLists.txt
@@ -126,9 +126,6 @@ set(classifierList)
if(OTB_USE_OPENCV)
#list(APPEND classifierList "SVM" "BOOST" "DT" "ANN" "BAYES" "RF" "KNN")
list(APPEND classifierList "BOOST" "DT" "ANN" "BAYES" "RF" "KNN")
- if(NOT OTB_OPENCV_3)
- set(dt_output_format "cv2.dt")
- endif()
endif()
if(OTB_USE_SHARK)
list(APPEND classifierList "SHARKRF" "SHARKKM")
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
index 92d6121fd0..966b47a60f 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
@@ -27,11 +27,7 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
-#ifdef OTB_OPENCV_3
#include "otbOpenCVUtils.h"
-#else
-class CvBoost;
-#endif
namespace otb
{
@@ -65,14 +61,6 @@ public:
itkGetMacro(BoostType, int);
itkSetMacro(BoostType, int);
- /** Setters/Getters to the split criteria
- * It can be CvBoost::DEFAULT, CvBoost::GINI, CvBoost::MISCLASS, CvBoost::SQERR
- * Default is CvBoost::DEFAULT. It uses default value according to \c BoostType
- * \see http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict
- */
- itkGetMacro(SplitCrit, int);
- itkSetMacro(SplitCrit, int);
-
/** Setters/Getters to the number of weak classifiers.
* Default is 100.
* \see http://docs.opencv.org/modules/ml/doc/boosting.html#cvboostparams-cvboostparams
@@ -120,7 +108,7 @@ protected:
BoostMachineLearningModel();
/** Destructor */
- ~BoostMachineLearningModel() override;
+ ~BoostMachineLearningModel() override = default;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
@@ -132,15 +120,11 @@ private:
BoostMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
-#ifdef OTB_OPENCV_3
cv::Ptr<cv::ml::Boost> m_BoostModel;
-#else
- CvBoost* m_BoostModel;
-#endif
+
int m_BoostType;
int m_WeakCount;
double m_WeightTrimRate;
- int m_SplitCrit;
int m_MaxDepth;
};
} // end namespace otb
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.hxx
index 54a8f2aa9e..908c04b743 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.hxx
@@ -32,34 +32,15 @@ namespace otb
template <class TInputValue, class TOutputValue>
BoostMachineLearningModel<TInputValue, TOutputValue>::BoostMachineLearningModel()
- :
-#ifdef OTB_OPENCV_3
- m_BoostModel(cv::ml::Boost::create()),
-#else
- m_BoostModel(new CvBoost),
-#endif
+ : m_BoostModel(cv::ml::Boost::create()),
m_BoostType(CvBoost::REAL),
m_WeakCount(100),
m_WeightTrimRate(0.95),
-#ifdef OTB_OPENCV_3
- m_SplitCrit(0), // not used in OpenCV 3.x
-#else
- m_SplitCrit(CvBoost::DEFAULT),
-#endif
m_MaxDepth(1)
{
this->m_ConfidenceIndex = true;
}
-
-template <class TInputValue, class TOutputValue>
-BoostMachineLearningModel<TInputValue, TOutputValue>::~BoostMachineLearningModel()
-{
-#ifndef OTB_OPENCV_3
- delete m_BoostModel;
-#endif
-}
-
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void BoostMachineLearningModel<TInputValue, TOutputValue>::Train()
@@ -75,7 +56,6 @@ void BoostMachineLearningModel<TInputValue, TOutputValue>::Train()
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL)); // all inputs are numerical
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
-#ifdef OTB_OPENCV_3
m_BoostModel->setBoostType(m_BoostType);
m_BoostModel->setWeakCount(m_WeakCount);
m_BoostModel->setWeightTrimRate(m_WeightTrimRate);
@@ -83,11 +63,6 @@ void BoostMachineLearningModel<TInputValue, TOutputValue>::Train()
m_BoostModel->setUseSurrogates(false);
m_BoostModel->setPriors(cv::Mat());
m_BoostModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
-#else
- CvBoostParams params = CvBoostParams(m_BoostType, m_WeakCount, m_WeightTrimRate, m_MaxDepth, false, nullptr);
- params.split_criteria = m_SplitCrit;
- m_BoostModel->train(samples, CV_ROW_SAMPLE, labels, cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -102,23 +77,11 @@ BoostMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const InputSampl
otb::SampleToMat<InputSampleType>(input, sample);
double result = 0.;
-#ifdef OTB_OPENCV_3
result = m_BoostModel->predict(sample);
-#else
- cv::Mat missing = cv::Mat(1, input.Size(), CV_8U);
- missing.setTo(0);
- result = m_BoostModel->predict(sample, missing);
-#endif
if (quality != nullptr)
{
- (*quality) = static_cast<ConfidenceValueType>(
-#ifdef OTB_OPENCV_3
- m_BoostModel->predict(sample, cv::noArray(), cv::ml::StatModel::RAW_OUTPUT)
-#else
- m_BoostModel->predict(sample, missing, cv::Range::all(), false, true)
-#endif
- );
+ (*quality) = static_cast<ConfidenceValueType>(m_BoostModel->predict(sample, cv::noArray(), cv::ml::StatModel::RAW_OUTPUT));
}
if (proba != nullptr && !this->m_ProbaIndex)
itkExceptionMacro("Probability per class not available for this classifier !");
@@ -130,32 +93,18 @@ BoostMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const InputSampl
template <class TInputValue, class TOutputValue>
void BoostMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_BoostModel->getDefaultName() : cv::String(name)) << "{";
m_BoostModel->write(fs);
fs << "}";
fs.release();
-#else
- if (name == "")
- m_BoostModel->save(filename.c_str(), nullptr);
- else
- m_BoostModel->save(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
void BoostMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_BoostModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
-#else
- if (name == "")
- m_BoostModel->load(filename.c_str(), nullptr);
- else
- m_BoostModel->load(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -176,11 +125,7 @@ bool BoostMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(const std
std::getline(ifs, line);
// if (line.find(m_SVMModel->getName()) != std::string::npos)
- if (line.find(CV_TYPE_NAME_ML_BOOSTING) != std::string::npos
-#ifdef OTB_OPENCV_3
- || line.find(m_BoostModel->getDefaultName()) != std::string::npos
-#endif
- )
+ if (line.find(CV_TYPE_NAME_ML_BOOSTING) != std::string::npos || line.find(m_BoostModel->getDefaultName()) != std::string::npos)
{
// std::cout<<"Reading a "<<CV_TYPE_NAME_ML_BOOSTING<<" model"<<std::endl;
return true;
diff --git a/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h b/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h
index 5398430f03..6229bd0203 100644
--- a/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h
+++ b/Modules/Learning/Supervised/include/otbCvRTreesWrapper.h
@@ -33,11 +33,7 @@ namespace otb
* \ingroup OTBSupervised
*/
class OTBSupervised_EXPORT CvRTreesWrapper
-#ifdef OTB_OPENCV_3
: public cv::ml::RTrees
-#else
- : public CvRTrees
-#endif
{
public:
typedef std::vector<unsigned int> VotesVectorType;
@@ -60,7 +56,6 @@ public:
*/
float predict_margin(const cv::Mat& sample, const cv::Mat& missing = cv::Mat()) const;
-#ifdef OTB_OPENCV_3
#define OTB_CV_WRAP_PROPERTY(type, name) \
virtual type get##name() const override; \
@@ -83,6 +78,8 @@ public:
OTB_CV_WRAP_PROPERTY(int, MaxDepth)
OTB_CV_WRAP_PROPERTY(int, MinSampleCount)
OTB_CV_WRAP_PROPERTY(bool, UseSurrogates)
+
+ // warning: CV fold crash in openCV 3
OTB_CV_WRAP_PROPERTY(int, CVFolds)
OTB_CV_WRAP_PROPERTY(bool, Use1SERule)
OTB_CV_WRAP_PROPERTY(bool, TruncatePrunedTree)
@@ -125,7 +122,6 @@ public:
private:
cv::Ptr<cv::ml::RTrees> m_Impl;
-#endif // OTB_OPENCV_3
};
}
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
index e8a23d2bcd..c26a258749 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
@@ -27,11 +27,7 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
-#ifdef OTB_OPENCV_3
#include "otbOpenCVUtils.h"
-#else
-class CvDTree;
-#endif
namespace otb
{
@@ -187,11 +183,7 @@ private:
DecisionTreeMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
-#ifdef OTB_OPENCV_3
cv::Ptr<cv::ml::DTrees> m_DTreeModel;
-#else
- CvDTree* m_DTreeModel;
-#endif
int m_MaxDepth;
int m_MinSampleCount;
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.hxx
index 228c397b31..95fd46ec09 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.hxx
@@ -33,7 +33,6 @@ namespace otb
template <class TInputValue, class TOutputValue>
DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::DecisionTreeMachineLearningModel()
:
-#ifdef OTB_OPENCV_3
m_DTreeModel(cv::ml::DTrees::create()),
m_MaxDepth(10),
m_MinSampleCount(10),
@@ -41,15 +40,6 @@ DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::DecisionTreeMachine
m_UseSurrogates(false),
m_MaxCategories(10),
m_CVFolds(0),
-#else
- m_DTreeModel(new CvDTree),
- m_MaxDepth(INT_MAX),
- m_MinSampleCount(10),
- m_RegressionAccuracy(0.01),
- m_UseSurrogates(true),
- m_MaxCategories(10),
- m_CVFolds(10),
-#endif
m_Use1seRule(true),
m_TruncatePrunedTree(true)
{
@@ -59,9 +49,6 @@ DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::DecisionTreeMachine
template <class TInputValue, class TOutputValue>
DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::~DecisionTreeMachineLearningModel()
{
-#ifndef OTB_OPENCV_3
- delete m_DTreeModel;
-#endif
}
/** Train the machine learning model */
@@ -81,7 +68,6 @@ void DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::Train()
if (!this->m_RegressionMode) // Classification
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
-#ifdef OTB_OPENCV_3
m_DTreeModel->setMaxDepth(m_MaxDepth);
m_DTreeModel->setMinSampleCount(m_MinSampleCount);
m_DTreeModel->setRegressionAccuracy(m_RegressionAccuracy);
@@ -92,15 +78,6 @@ void DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::Train()
m_DTreeModel->setTruncatePrunedTree(m_TruncatePrunedTree);
m_DTreeModel->setPriors(cv::Mat(m_Priors));
m_DTreeModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
-#else
- float* priors = m_Priors.empty() ? nullptr : &m_Priors.front();
-
- CvDTreeParams params = CvDTreeParams(m_MaxDepth, m_MinSampleCount, m_RegressionAccuracy, m_UseSurrogates, m_MaxCategories, m_CVFolds, m_Use1seRule,
- m_TruncatePrunedTree, priors);
-
- // train the Decision Tree model
- m_DTreeModel->train(samples, CV_ROW_SAMPLE, labels, cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -113,11 +90,7 @@ DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const Inp
cv::Mat sample;
otb::SampleToMat<InputSampleType>(input, sample);
-#ifdef OTB_OPENCV_3
double result = m_DTreeModel->predict(sample);
-#else
- double result = m_DTreeModel->predict(sample, cv::Mat(), false)->value;
-#endif
target[0] = static_cast<TOutputValue>(result);
@@ -137,32 +110,18 @@ DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const Inp
template <class TInputValue, class TOutputValue>
void DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_DTreeModel->getDefaultName() : cv::String(name)) << "{";
m_DTreeModel->write(fs);
fs << "}";
fs.release();
-#else
- if (name == "")
- m_DTreeModel->save(filename.c_str(), nullptr);
- else
- m_DTreeModel->save(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
void DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_DTreeModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
-#else
- if (name == "")
- m_DTreeModel->load(filename.c_str(), nullptr);
- else
- m_DTreeModel->load(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -183,13 +142,8 @@ bool DecisionTreeMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(co
std::getline(ifs, line);
// if (line.find(m_SVMModel->getName()) != std::string::npos)
- if (line.find(CV_TYPE_NAME_ML_TREE) != std::string::npos
-#ifdef OTB_OPENCV_3
- || line.find(m_DTreeModel->getDefaultName()) != std::string::npos
-#endif
- )
+ if (line.find(CV_TYPE_NAME_ML_TREE) != std::string::npos || line.find(m_DTreeModel->getDefaultName()) != std::string::npos)
{
- // std::cout<<"Reading a "<<CV_TYPE_NAME_ML_TREE<<" model"<<std::endl;
return true;
}
}
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
index 887dcc659d..ed96f66a76 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
@@ -27,11 +27,7 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
-#ifdef OTB_OPENCV_3
#include "otbOpenCVUtils.h"
-#else
-class CvKNearest;
-#endif
namespace otb
{
@@ -117,11 +113,8 @@ private:
KNearestNeighborsMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
-#ifdef OTB_OPENCV_3
cv::Ptr<cv::ml::KNearest> m_KNearestModel;
-#else
- CvKNearest* m_KNearestModel;
-#endif
+
int m_K;
int m_DecisionRule;
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.hxx
index 6fefbd9f56..3b1ed213d0 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.hxx
@@ -35,11 +35,7 @@ namespace otb
template <class TInputValue, class TTargetValue>
KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::KNearestNeighborsMachineLearningModel()
:
-#ifdef OTB_OPENCV_3
m_KNearestModel(cv::ml::KNearest::create()),
-#else
- m_KNearestModel(new CvKNearest),
-#endif
m_K(32),
m_DecisionRule(KNN_VOTING)
{
@@ -51,9 +47,6 @@ KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::KNearestNeighb
template <class TInputValue, class TTargetValue>
KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::~KNearestNeighborsMachineLearningModel()
{
-#ifndef OTB_OPENCV_3
- delete m_KNearestModel;
-#endif
}
/** Train the machine learning model */
@@ -83,17 +76,12 @@ void KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::Train()
}
}
-#ifdef OTB_OPENCV_3
m_KNearestModel->setDefaultK(m_K);
// would be nice to expose KDTree mode ( maybe in a different classifier)
m_KNearestModel->setAlgorithmType(cv::ml::KNearest::BRUTE_FORCE);
m_KNearestModel->setIsClassifier(!this->m_RegressionMode);
// setEmax() ?
m_KNearestModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels));
-#else
- // train the KNN model
- m_KNearestModel->train(samples, labels, cv::Mat(), this->m_RegressionMode, m_K, false);
-#endif
}
template <class TInputValue, class TTargetValue>
@@ -109,11 +97,8 @@ KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::DoPredict(cons
float result;
cv::Mat nearest(1, m_K, CV_32FC1);
-#ifdef OTB_OPENCV_3
result = m_KNearestModel->findNearest(sample, m_K, cv::noArray(), nearest, cv::noArray());
-#else
- result = m_KNearestModel->find_nearest(sample, m_K, nullptr, nullptr, &nearest, nullptr);
-#endif
+
// compute quality if asked (only happens in classification mode)
if (quality != nullptr)
{
@@ -157,47 +142,12 @@ KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::DoPredict(cons
template <class TInputValue, class TTargetValue>
void KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_KNearestModel->getDefaultName() : cv::String(name)) << "{";
m_KNearestModel->write(fs);
fs << "DecisionRule" << m_DecisionRule;
fs << "}";
fs.release();
-#else
- (void)name;
- // there is no m_KNearestModel->save(filename.c_str(), name.c_str()).
- // We need to save the K parameter, IsRegression flag, DecisionRule and the samples.
-
- std::ofstream ofs(filename);
- // Save K parameter and IsRegression flag.
- ofs << "K=" << m_K << "\n";
- ofs << "IsRegression=" << this->m_RegressionMode << "\n";
- // Save the DecisionRule if regression
- if (this->m_RegressionMode)
- {
- ofs << "DecisionRule=" << m_DecisionRule << "\n";
- }
-
- // Save the samples. First column is the Label and other columns are the sample data.
- typename InputListSampleType::ConstIterator sampleIt = this->GetInputListSample()->Begin();
- typename TargetListSampleType::ConstIterator labelIt = this->GetTargetListSample()->Begin();
- const unsigned int sampleSize = this->GetInputListSample()->GetMeasurementVectorSize();
- for (; sampleIt != this->GetInputListSample()->End(); ++sampleIt, ++labelIt)
- {
- // Retrieve sample
- typename InputListSampleType::MeasurementVectorType sample = sampleIt.GetMeasurementVector();
- ofs << labelIt.GetMeasurementVector()[0];
-
- // Loop on sample size
- for (unsigned int i = 0; i < sampleSize; ++i)
- {
- ofs << " " << sample[i];
- }
- ofs << "\n";
- }
- ofs.close();
-#endif
}
template <class TInputValue, class TTargetValue>
@@ -208,7 +158,6 @@ void KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::Load(cons
{
itkExceptionMacro(<< "Could not read file " << filename);
}
-#ifdef OTB_OPENCV_3
// try to load with the 3.x syntax
bool isKNNv3 = false;
while (!ifs.eof())
@@ -230,7 +179,6 @@ void KNearestNeighborsMachineLearningModel<TInputValue, TTargetValue>::Load(cons
return;
}
ifs.open(filename);
-#endif
// there is no m_KNearestModel->load(filename.c_str(), name.c_str());
// first line is the K parameter of this algorithm.
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
index 0cde70c93d..32317f9ca7 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
@@ -174,7 +174,7 @@ protected:
NeuralNetworkMachineLearningModel();
/** Destructor */
- ~NeuralNetworkMachineLearningModel() override;
+ ~NeuralNetworkMachineLearningModel() override = default;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
@@ -190,12 +190,7 @@ private:
void CreateNetwork();
void SetupNetworkAndTrain(cv::Mat& labels);
-#ifdef OTB_OPENCV_3
cv::Ptr<cv::ml::ANN_MLP> m_ANNModel;
-#else
- CvANN_MLP_TrainParams SetNetworkParameters();
- CvANN_MLP* m_ANNModel;
-#endif
int m_TrainMethod;
int m_ActivateFunction;
std::vector<unsigned int> m_LayerSizes;
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.hxx
index 9389aa5ae8..dd1169141a 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.hxx
@@ -31,12 +31,7 @@ namespace otb
template <class TInputValue, class TOutputValue>
NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::NeuralNetworkMachineLearningModel()
:
-#ifdef OTB_OPENCV_3
m_ANNModel(cv::ml::ANN_MLP::create()),
-// TODO
-#else
- m_ANNModel(new CvANN_MLP),
-#endif
m_TrainMethod(CvANN_MLP_TrainParams::RPROP),
m_ActivateFunction(CvANN_MLP::SIGMOID_SYM),
m_Alpha(1.),
@@ -53,14 +48,6 @@ NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::NeuralNetworkMachi
this->m_IsRegressionSupported = true;
}
-template <class TInputValue, class TOutputValue>
-NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::~NeuralNetworkMachineLearningModel()
-{
-#ifndef OTB_OPENCV_3
- delete m_ANNModel;
-#endif
-}
-
/** Sets the topology of the NN */
template <class TInputValue, class TOutputValue>
void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetLayerSizes(const std::vector<unsigned int> layers)
@@ -148,29 +135,10 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::CreateNetwork
layers.row(i) = m_LayerSizes[i];
}
-#ifdef OTB_OPENCV_3
m_ANNModel->setLayerSizes(layers);
m_ANNModel->setActivationFunction(m_ActivateFunction, m_Alpha, m_Beta);
-#else
- m_ANNModel->create(layers, m_ActivateFunction, m_Alpha, m_Beta);
-#endif
}
-#ifndef OTB_OPENCV_3
-template <class TInputValue, class TOutputValue>
-CvANN_MLP_TrainParams NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetNetworkParameters()
-{
- CvANN_MLP_TrainParams params;
- params.train_method = m_TrainMethod;
- params.bp_dw_scale = m_BackPropDWScale;
- params.bp_moment_scale = m_BackPropMomentScale;
- params.rp_dw0 = m_RegPropDW0;
- params.rp_dw_min = m_RegPropDWMin;
- CvTermCriteria term_crit = cvTermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon);
- params.term_crit = term_crit;
- return params;
-}
-#endif
template <class TInputValue, class TOutputValue>
void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetupNetworkAndTrain(cv::Mat& labels)
@@ -179,7 +147,6 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetupNetworkA
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
this->CreateNetwork();
-#ifdef OTB_OPENCV_3
int flags = (this->m_RegressionMode ? 0 : cv::ml::ANN_MLP::NO_OUTPUT_SCALE);
m_ANNModel->setTrainMethod(m_TrainMethod);
m_ANNModel->setBackpropMomentumScale(m_BackPropMomentScale);
@@ -191,11 +158,6 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::SetupNetworkA
// m_ANNModel->setRpropDWPlus( );
m_ANNModel->setTermCriteria(cv::TermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon));
m_ANNModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels), flags);
-#else
- CvANN_MLP_TrainParams params = this->SetNetworkParameters();
- // train the Neural network model
- m_ANNModel->train(samples, labels, cv::Mat(), cv::Mat(), params);
-#endif
}
/** Train the machine learning model for classification*/
@@ -279,7 +241,6 @@ NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const In
template <class TInputValue, class TOutputValue>
void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_ANNModel->getDefaultName() : cv::String(name)) << "{";
m_ANNModel->write(fs);
@@ -290,65 +251,16 @@ void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::Save(const st
}
fs << "}";
fs.release();
-#else
- const char* lname = "my_nn";
- if (!name.empty())
- lname = name.c_str();
-
- CvFileStorage* fs = nullptr;
- fs = cvOpenFileStorage(filename.c_str(), nullptr, CV_STORAGE_WRITE);
- if (!fs)
- {
- itkExceptionMacro("Could not open the file " << filename << " for writing");
- }
-
- m_ANNModel->write(fs, lname);
- if (!m_MatrixOfLabels.empty())
- {
- // cvWrite can't write cv::Mat
- auto tmpMat = CvMat(m_MatrixOfLabels);
- cvWrite(fs, "class_labels", &tmpMat);
- }
- cvReleaseFileStorage(&fs);
-#endif
}
template <class TInputValue, class TOutputValue>
void NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::READ);
cv::FileNode model_node(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
m_ANNModel->read(model_node);
model_node["class_labels"] >> m_MatrixOfLabels;
fs.release();
-#else
- const char* lname = nullptr;
- if (!name.empty())
- lname = name.c_str();
-
- cv::FileNode model_node;
- cv::FileStorage fs(filename, cv::FileStorage::READ);
- if (!fs.isOpened())
- {
- itkExceptionMacro("Could not open the file " << filename << " for reading");
- }
-
- if (lname)
- model_node = fs[lname];
- else
- {
- cv::FileNode root = fs.root();
- if (root.size() > 0)
- {
- model_node = *(root.begin());
- }
- }
-
- m_ANNModel->read(*fs,*model_node);
- model_node["class_labels"] >> m_MatrixOfLabels;
- fs.release();
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -368,11 +280,7 @@ bool NeuralNetworkMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(c
std::string line;
std::getline(ifs, line);
- if (line.find(CV_TYPE_NAME_ML_ANN_MLP) != std::string::npos
-#ifdef OTB_OPENCV_3
- || line.find(m_ANNModel->getDefaultName()) != std::string::npos
-#endif
- )
+ if (line.find(CV_TYPE_NAME_ML_ANN_MLP) != std::string::npos || line.find(m_ANNModel->getDefaultName()) != std::string::npos)
{
return true;
}
diff --git a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
index f1d94f24ca..ed42dd2de6 100644
--- a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.h
@@ -27,11 +27,7 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
-#ifdef OTB_OPENCV_3
#include "otbOpenCVUtils.h"
-#else
-class CvNormalBayesClassifier;
-#endif
namespace otb
{
@@ -80,7 +76,7 @@ protected:
NormalBayesMachineLearningModel();
/** Destructor */
- ~NormalBayesMachineLearningModel() override;
+ ~NormalBayesMachineLearningModel() override = default;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
@@ -91,11 +87,7 @@ protected:
private:
NormalBayesMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
-#ifdef OTB_OPENCV_3
cv::Ptr<cv::ml::NormalBayesClassifier> m_NormalBayesModel;
-#else
- CvNormalBayesClassifier* m_NormalBayesModel;
-#endif
};
} // end namespace otb
diff --git a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.hxx
index df55a91b7e..16832e3028 100644
--- a/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbNormalBayesMachineLearningModel.hxx
@@ -31,24 +31,10 @@ namespace otb
template <class TInputValue, class TOutputValue>
NormalBayesMachineLearningModel<TInputValue, TOutputValue>::NormalBayesMachineLearningModel()
- :
-#ifdef OTB_OPENCV_3
- m_NormalBayesModel(cv::ml::NormalBayesClassifier::create())
-#else
- m_NormalBayesModel(new CvNormalBayesClassifier)
-#endif
+ : m_NormalBayesModel(cv::ml::NormalBayesClassifier::create())
{
}
-
-template <class TInputValue, class TOutputValue>
-NormalBayesMachineLearningModel<TInputValue, TOutputValue>::~NormalBayesMachineLearningModel()
-{
-#ifndef OTB_OPENCV_3
- delete m_NormalBayesModel;
-#endif
-}
-
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void NormalBayesMachineLearningModel<TInputValue, TOutputValue>::Train()
@@ -60,15 +46,11 @@ void NormalBayesMachineLearningModel<TInputValue, TOutputValue>::Train()
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), labels);
-#ifdef OTB_OPENCV_3
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
var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
m_NormalBayesModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
-#else
- m_NormalBayesModel->train(samples, labels, cv::Mat(), cv::Mat(), false);
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -104,32 +86,18 @@ NormalBayesMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const Inpu
template <class TInputValue, class TOutputValue>
void NormalBayesMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_NormalBayesModel->getDefaultName() : cv::String(name)) << "{";
m_NormalBayesModel->write(fs);
fs << "}";
fs.release();
-#else
- if (name == "")
- m_NormalBayesModel->save(filename.c_str(), nullptr);
- else
- m_NormalBayesModel->save(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
void NormalBayesMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_NormalBayesModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
-#else
- if (name == "")
- m_NormalBayesModel->load(filename.c_str(), nullptr);
- else
- m_NormalBayesModel->load(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -149,13 +117,8 @@ bool NormalBayesMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(con
std::string line;
std::getline(ifs, line);
- if (line.find(CV_TYPE_NAME_ML_NBAYES) != std::string::npos
-#ifdef OTB_OPENCV_3
- || line.find(m_NormalBayesModel->getDefaultName()) != std::string::npos
-#endif
- )
+ if (line.find(CV_TYPE_NAME_ML_NBAYES) != std::string::npos || line.find(m_NormalBayesModel->getDefaultName()) != std::string::npos)
{
- // std::cout<<"Reading a "<<CV_TYPE_NAME_ML_NBAYES<<" model"<<std::endl;
return true;
}
}
diff --git a/Modules/Learning/Supervised/include/otbOpenCVUtils.h b/Modules/Learning/Supervised/include/otbOpenCVUtils.h
index 2992037f13..d846b7750a 100644
--- a/Modules/Learning/Supervised/include/otbOpenCVUtils.h
+++ b/Modules/Learning/Supervised/include/otbOpenCVUtils.h
@@ -47,7 +47,6 @@
#include "itkListSample.h"
-#ifdef OTB_OPENCV_3
#define CV_TYPE_NAME_ML_SVM "opencv-ml-svm"
#define CV_TYPE_NAME_ML_RTREES "opencv-ml-random-trees"
#define CV_TYPE_NAME_ML_BOOSTING "opencv-ml-boost-tree"
@@ -62,7 +61,7 @@
#define CV_VAR_NUMERICAL cv::ml::VAR_NUMERICAL
#define CV_VAR_CATEGORICAL cv::ml::VAR_CATEGORICAL
-#endif
+
namespace otb
{
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
index ee53c3f33e..965e63397a 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
@@ -133,7 +133,7 @@ protected:
RandomForestsMachineLearningModel();
/** Destructor */
- ~RandomForestsMachineLearningModel() override;
+ ~RandomForestsMachineLearningModel() override = default;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
@@ -151,11 +151,8 @@ private:
RandomForestsMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
-#ifdef OTB_OPENCV_3
cv::Ptr<CvRTreesWrapper> m_RFModel;
-#else
- CvRTreesWrapper* m_RFModel;
-#endif
+
/** 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. */
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.hxx
index 567faa29dd..c5a2f7c48d 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.hxx
@@ -32,11 +32,7 @@ namespace otb
template <class TInputValue, class TOutputValue>
RandomForestsMachineLearningModel<TInputValue, TOutputValue>::RandomForestsMachineLearningModel()
:
-#ifdef OTB_OPENCV_3
m_RFModel(CvRTreesWrapper::create()),
-#else
- m_RFModel(new CvRTreesWrapper),
-#endif
m_MaxDepth(5),
m_MinSampleCount(10),
m_RegressionAccuracy(0.01),
@@ -54,19 +50,9 @@ RandomForestsMachineLearningModel<TInputValue, TOutputValue>::RandomForestsMachi
this->m_IsRegressionSupported = true;
}
-
-template <class TInputValue, class TOutputValue>
-RandomForestsMachineLearningModel<TInputValue, TOutputValue>::~RandomForestsMachineLearningModel()
-{
-#ifndef OTB_OPENCV_3
- delete m_RFModel;
-#endif
-}
-
template <class TInputValue, class TOutputValue>
float RandomForestsMachineLearningModel<TInputValue, TOutputValue>::GetTrainError()
{
-#ifdef OTB_OPENCV_3
// TODO
cv::Mat samples;
otb::ListSampleToMat<InputListSampleType>(this->GetInputListSample(), samples);
@@ -84,9 +70,6 @@ float RandomForestsMachineLearningModel<TInputValue, TOutputValue>::GetTrainErro
return m_RFModel->calcError(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type), false,
cv::noArray());
-#else
- return m_RFModel->get_train_error();
-#endif
}
/** Train the machine learning model */
@@ -112,7 +95,6 @@ void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Train()
// std::cout << "priors " << m_Priors[0] << std::endl;
// Define random forests paramneters
// FIXME do this in the constructor?
-#ifdef OTB_OPENCV_3
m_RFModel->setMaxDepth(m_MaxDepth);
m_RFModel->setMinSampleCount(m_MinSampleCount);
m_RFModel->setRegressionAccuracy(m_RegressionAccuracy);
@@ -123,24 +105,6 @@ void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Train()
m_RFModel->setActiveVarCount(m_MaxNumberOfVariables);
m_RFModel->setTermCriteria(cv::TermCriteria(m_TerminationCriteria, m_MaxNumberOfTrees, m_ForestAccuracy));
m_RFModel->train(cv::ml::TrainData::create(samples, cv::ml::ROW_SAMPLE, labels, cv::noArray(), cv::noArray(), cv::noArray(), var_type));
-#else
- float* priors = m_Priors.empty() ? nullptr : &m_Priors.front();
-
- CvRTParams params = CvRTParams(m_MaxDepth, // max depth
- m_MinSampleCount, // min sample count
- m_RegressionAccuracy, // regression accuracy: N/A here
- m_ComputeSurrogateSplit, // compute surrogate split, no missing data
- m_MaxNumberOfCategories, // max number of categories (use sub-optimal algorithm for larger numbers)
- 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, // forest accuracy
- m_TerminationCriteria // termination criteria
- );
- // train the RT model
- m_RFModel->train(samples, CV_ROW_SAMPLE, labels, cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -176,32 +140,18 @@ RandomForestsMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const In
template <class TInputValue, class TOutputValue>
void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_RFModel->getDefaultName() : cv::String(name)) << "{";
m_RFModel->write(fs);
fs << "}";
fs.release();
-#else
- if (name == "")
- m_RFModel->save(filename.c_str(), nullptr);
- else
- m_RFModel->save(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
void RandomForestsMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_RFModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
-#else
- if (name == "")
- m_RFModel->load(filename.c_str(), nullptr);
- else
- m_RFModel->load(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -223,13 +173,8 @@ bool RandomForestsMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(c
std::getline(ifs, line);
// if (line.find(m_RFModel->getName()) != std::string::npos)
- if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos
-#ifdef OTB_OPENCV_3
- || line.find(m_RFModel->getDefaultName()) != std::string::npos
-#endif
- )
+ if (line.find(CV_TYPE_NAME_ML_RTREES) != std::string::npos || line.find(m_RFModel->getDefaultName()) != std::string::npos)
{
- // std::cout<<"Reading a "<<CV_TYPE_NAME_ML_RTREES<<" model"<<std::endl;
return true;
}
}
diff --git a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
index f24a5d564c..8642380290 100644
--- a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.h
@@ -26,11 +26,7 @@
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"
-#ifdef OTB_OPENCV_3
#include "otbOpenCVUtils.h"
-#else
-class CvSVM;
-#endif
namespace otb
{
@@ -137,7 +133,7 @@ protected:
SVMMachineLearningModel();
/** Destructor */
- ~SVMMachineLearningModel() override;
+ ~SVMMachineLearningModel() override = default;
/** Predict values using the model */
TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;
@@ -148,11 +144,7 @@ protected:
private:
SVMMachineLearningModel(const Self&) = delete;
void operator=(const Self&) = delete;
-#ifdef OTB_OPENCV_3
cv::Ptr<cv::ml::SVM> m_SVMModel;
-#else
- CvSVM* m_SVMModel;
-#endif
int m_SVMType;
int m_KernelType;
double m_Degree;
diff --git a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.hxx b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.hxx
index 97f933668e..20eb00e7f5 100644
--- a/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.hxx
+++ b/Modules/Learning/Supervised/include/otbSVMMachineLearningModel.hxx
@@ -32,11 +32,7 @@ namespace otb
template <class TInputValue, class TOutputValue>
SVMMachineLearningModel<TInputValue, TOutputValue>::SVMMachineLearningModel()
:
-#ifdef OTB_OPENCV_3
m_SVMModel(cv::ml::SVM::create()),
-#else
- m_SVMModel(new CvSVM),
-#endif
m_SVMType(CvSVM::C_SVC),
m_KernelType(CvSVM::RBF),
m_Degree(0),
@@ -60,15 +56,6 @@ SVMMachineLearningModel<TInputValue, TOutputValue>::SVMMachineLearningModel()
this->m_IsRegressionSupported = true;
}
-
-template <class TInputValue, class TOutputValue>
-SVMMachineLearningModel<TInputValue, TOutputValue>::~SVMMachineLearningModel()
-{
-#ifndef OTB_OPENCV_3
- delete m_SVMModel;
-#endif
-}
-
/** Train the machine learning model */
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::Train()
@@ -89,7 +76,6 @@ void SVMMachineLearningModel<TInputValue, TOutputValue>::Train()
cv::Mat labels;
otb::ListSampleToMat<TargetListSampleType>(this->GetTargetListSample(), labels);
-#ifdef OTB_OPENCV_3
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
@@ -121,37 +107,6 @@ void SVMMachineLearningModel<TInputValue, TOutputValue>::Train()
m_OutputC = m_SVMModel->getC();
m_OutputNu = m_SVMModel->getNu();
m_OutputP = m_SVMModel->getP();
-#else
- // Set up SVM's parameters
- CvTermCriteria term_crit = cvTermCriteria(m_TermCriteriaType, m_MaxIter, m_Epsilon);
- CvSVMParams params(m_SVMType, m_KernelType, m_Degree, m_Gamma, m_Coef0, m_C, m_Nu, m_P, nullptr, term_crit);
-
- // Train the SVM
- if (!m_ParameterOptimization)
- {
- m_SVMModel->train(samples, labels, cv::Mat(), cv::Mat(), params);
- }
- else
- {
- // Trains SVM with optimal parameters.
- // train_auto(const Mat& trainData, const Mat& responses, const Mat& varIdx, const Mat& sampleIdx,
- // CvSVMParams params, int k_fold=10, CvParamGrid Cgrid=CvSVM::get_default_grid(CvSVM::C),
- // CvParamGrid gammaGrid=CvSVM::get_default_grid(CvSVM::GAMMA),
- // CvParamGrid pGrid=CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid=CvSVM::get_default_grid(CvSVM::NU),
- // CvParamGrid coeffGrid=CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid=CvSVM::get_default_grid(CvSVM::DEGREE),
- // bool balanced=false)
- // We used default parameters grid. If not enough, those grids should be expose to the user.
- m_SVMModel->train_auto(samples, labels, cv::Mat(), cv::Mat(), params);
- }
-
- // Export of the SVM parameters into the class SVMMachineLearningModel
- m_OutputDegree = m_SVMModel->get_params().degree;
- m_OutputGamma = m_SVMModel->get_params().gamma;
- m_OutputCoef0 = m_SVMModel->get_params().coef0;
- m_OutputC = m_SVMModel->get_params().C;
- m_OutputNu = m_SVMModel->get_params().nu;
- m_OutputP = m_SVMModel->get_params().p;
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -164,21 +119,13 @@ SVMMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const InputSampleT
otb::SampleToMat<InputSampleType>(input, sample);
-#ifdef OTB_OPENCV_3
double result = m_SVMModel->predict(sample);
-#else
- double result = m_SVMModel->predict(sample, false);
-#endif
target[0] = static_cast<TOutputValue>(result);
if (quality != nullptr)
{
-#ifdef OTB_OPENCV_3
(*quality) = m_SVMModel->predict(sample, cv::noArray(), cv::ml::StatModel::RAW_OUTPUT);
-#else
- (*quality) = m_SVMModel->predict(sample, true);
-#endif
}
if (proba != nullptr && !this->m_ProbaIndex)
itkExceptionMacro("Probability per class not available for this classifier !");
@@ -189,32 +136,18 @@ SVMMachineLearningModel<TInputValue, TOutputValue>::DoPredict(const InputSampleT
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::Save(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::WRITE);
fs << (name.empty() ? m_SVMModel->getDefaultName() : cv::String(name)) << "{";
m_SVMModel->write(fs);
fs << "}";
fs.release();
-#else
- if (name == "")
- m_SVMModel->save(filename.c_str(), nullptr);
- else
- m_SVMModel->save(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
void SVMMachineLearningModel<TInputValue, TOutputValue>::Load(const std::string& filename, const std::string& name)
{
-#ifdef OTB_OPENCV_3
cv::FileStorage fs(filename, cv::FileStorage::READ);
m_SVMModel->read(name.empty() ? fs.getFirstTopLevelNode() : fs[name]);
-#else
- if (name == "")
- m_SVMModel->load(filename.c_str(), nullptr);
- else
- m_SVMModel->load(filename.c_str(), name.c_str());
-#endif
}
template <class TInputValue, class TOutputValue>
@@ -235,13 +168,8 @@ bool SVMMachineLearningModel<TInputValue, TOutputValue>::CanReadFile(const std::
std::getline(ifs, line);
// if (line.find(m_SVMModel->getName()) != std::string::npos)
- if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos
-#ifdef OTB_OPENCV_3
- || line.find(m_SVMModel->getDefaultName()) != std::string::npos
-#endif
- )
+ if (line.find(CV_TYPE_NAME_ML_SVM) != std::string::npos || line.find(m_SVMModel->getDefaultName()) != std::string::npos)
{
- // std::cout<<"Reading a "<<CV_TYPE_NAME_ML_SVM<<" model"<<std::endl;
return true;
}
}
diff --git a/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx b/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx
index ce59504901..e1b0f756e6 100644
--- a/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx
+++ b/Modules/Learning/Supervised/src/otbCvRTreesWrapper.cxx
@@ -27,9 +27,7 @@ namespace otb
CvRTreesWrapper::CvRTreesWrapper()
{
-#ifdef OTB_OPENCV_3
m_Impl = cv::ml::RTrees::create();
-#endif
}
CvRTreesWrapper::~CvRTreesWrapper()
@@ -38,7 +36,6 @@ CvRTreesWrapper::~CvRTreesWrapper()
void CvRTreesWrapper::get_votes(const cv::Mat& sample, const cv::Mat& missing, CvRTreesWrapper::VotesVectorType& vote_count) const
{
-#ifdef OTB_OPENCV_3
// missing samples not implemented yet
(void)missing;
@@ -86,23 +83,12 @@ void CvRTreesWrapper::get_votes(const cv::Mat& sample, const cv::Mat& missing, C
// give at least 2 classes
vote_count.push_back(0);
}
-#else
- 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];
- }
-#endif
}
float CvRTreesWrapper::predict_margin(const cv::Mat& sample, const cv::Mat& missing) const
{
-#ifdef OTB_OPENCV_3
int ntrees = m_Impl->getRoots().size();
-#endif
+
// Sanity check (division by ntrees later on)
if (ntrees == 0)
{
@@ -118,9 +104,8 @@ float CvRTreesWrapper::predict_margin(const cv::Mat& sample, const cv::Mat& miss
float CvRTreesWrapper::predict_confidence(const cv::Mat& sample, const cv::Mat& missing) const
{
-#ifdef OTB_OPENCV_3
int ntrees = m_Impl->getRoots().size();
-#endif
+
// Sanity check (division by ntrees later on)
if (ntrees == 0)
{
@@ -133,7 +118,6 @@ float CvRTreesWrapper::predict_confidence(const cv::Mat& sample, const cv::Mat&
return confidence;
}
-#ifdef OTB_OPENCV_3
#define OTB_CV_WRAP_IMPL(type, name) \
type CvRTreesWrapper::get##name() const \
{ \
@@ -250,5 +234,4 @@ cv::Ptr<CvRTreesWrapper> CvRTreesWrapper::create()
#undef OTB_CV_WRAP_IMPL
#undef OTB_CV_WRAP_IMPL_REF
#undef OTB_CV_WRAP_IMPL_CSTREF_GET
-#endif
}
--
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