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Wrappers for load methods of EM, LR, SVMSGD and Normal Bayes Classifier
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‎modules/ml/include/opencv2/ml.hpp

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@@ -393,6 +393,17 @@ class CV_EXPORTS_W NormalBayesClassifier : public StatModel
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/** Creates empty model
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Use StatModel::train to train the model after creation. */
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CV_WRAP static Ptr<NormalBayesClassifier> create();
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/** @brief Loads and creates a serialized NormalBayesClassifier from a file
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*
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* Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
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* Load the NormalBayesClassifier from this file again, by calling this function with the path to the file.
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* Optionally specify the node for the file containing the classifier
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*
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* @param filepath path to serialized NormalBayesClassifier
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* @param nodeName name of node containing the classifier
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*/
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CV_WRAP static Ptr<NormalBayesClassifier> load(const String& filepath , const String& nodeName = String());
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};
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/****************************************************************************************\
@@ -927,6 +938,17 @@ class CV_EXPORTS_W EM : public StatModel
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can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
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*/
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CV_WRAP static Ptr<EM> create();
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/** @brief Loads and creates a serialized EM from a file
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*
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* Use EM::save to serialize and store an EM to disk.
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* Load the EM from this file again, by calling this function with the path to the file.
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* Optionally specify the node for the file containing the classifier
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*
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* @param filepath path to serialized EM
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* @param nodeName name of node containing the classifier
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*/
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CV_WRAP static Ptr<EM> load(const String& filepath , const String& nodeName = String());
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};
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/****************************************************************************************\
@@ -1512,6 +1534,17 @@ class CV_EXPORTS_W LogisticRegression : public StatModel
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Creates Logistic Regression model with parameters given.
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*/
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CV_WRAP static Ptr<LogisticRegression> create();
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/** @brief Loads and creates a serialized LogisticRegression from a file
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*
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* Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
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* Load the LogisticRegression from this file again, by calling this function with the path to the file.
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* Optionally specify the node for the file containing the classifier
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*
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* @param filepath path to serialized LogisticRegression
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* @param nodeName name of node containing the classifier
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*/
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CV_WRAP static Ptr<LogisticRegression> load(const String& filepath , const String& nodeName = String());
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};
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@@ -1627,6 +1660,17 @@ class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel
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*/
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CV_WRAP static Ptr<SVMSGD> create();
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/** @brief Loads and creates a serialized SVMSGD from a file
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*
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* Use SVMSGD::save to serialize and store an SVMSGD to disk.
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* Load the SVMSGD from this file again, by calling this function with the path to the file.
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* Optionally specify the node for the file containing the classifier
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*
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* @param filepath path to serialized SVMSGD
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* @param nodeName name of node containing the classifier
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*/
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CV_WRAP static Ptr<SVMSGD> load(const String& filepath , const String& nodeName = String());
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/** @brief Function sets optimal parameters values for chosen SVM SGD model.
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* @param svmsgdType is the type of SVMSGD classifier.
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* @param marginType is the type of margin constraint.

‎modules/ml/src/em.cpp

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@@ -845,6 +845,11 @@ Ptr<EM> EM::create()
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return makePtr<EMImpl>();
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}
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Ptr<EM> EM::load(const String& filepath, const String& nodeName)
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{
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return Algorithm::load<EM>(filepath, nodeName);
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}
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}
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} // namespace cv
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‎modules/ml/src/lr.cpp

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@@ -127,6 +127,12 @@ Ptr<LogisticRegression> LogisticRegression::create()
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return makePtr<LogisticRegressionImpl>();
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}
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Ptr<LogisticRegression> LogisticRegression::load(const String& filepath, const String& nodeName)
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{
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return Algorithm::load<LogisticRegression>(filepath, nodeName);
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}
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bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
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{
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// return value

‎modules/ml/src/nbayes.cpp

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@@ -458,6 +458,11 @@ Ptr<NormalBayesClassifier> NormalBayesClassifier::create()
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return p;
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}
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Ptr<NormalBayesClassifier> NormalBayesClassifier::load(const String& filepath, const String& nodeName)
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{
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return Algorithm::load<NormalBayesClassifier>(filepath, nodeName);
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}
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}
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}
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‎modules/ml/src/svmsgd.cpp

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@@ -134,6 +134,12 @@ Ptr<SVMSGD> SVMSGD::create()
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return makePtr<SVMSGDImpl>();
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}
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Ptr<SVMSGD> SVMSGD::load(const String& filepath, const String& nodeName)
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{
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return Algorithm::load<SVMSGD>(filepath, nodeName);
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}
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void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
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{
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int featuresCount = samples.cols;

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