Merge pull request #8099 from chrizandr:ML_Wrappers

Wrappers for load methods of EM, LR, SVMSGD and Normal Bayes Classifier

modules/ml/include/opencv2/ml.hpp

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

modules/ml/src/em.cpp

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

modules/ml/src/lr.cpp

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

modules/ml/src/nbayes.cpp

@@ -458,6 +458,11 @@ Ptr<NormalBayesClassifier> NormalBayesClassifier::create()
     return p;
 }
 
+Ptr<NormalBayesClassifier> NormalBayesClassifier::load(const String& filepath, const String& nodeName)
+{
+    return Algorithm::load<NormalBayesClassifier>(filepath, nodeName);
+}
+
 }
 }
 

modules/ml/src/svmsgd.cpp

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