ml: apply CV_OVERRIDE/CV_FINAL

modules/ml/src/em.cpp

@@ -48,7 +48,7 @@ namespace ml
 
 const double minEigenValue = DBL_EPSILON;
 
-class CV_EXPORTS EMImpl : public EM
+class CV_EXPORTS EMImpl CV_FINAL : public EM
 {
 public:
 
@@ -56,20 +56,21 @@ public:
     int covMatType;
     TermCriteria termCrit;
 
-    CV_IMPL_PROPERTY_S(TermCriteria, TermCriteria, termCrit)
+    inline TermCriteria getTermCriteria() const CV_OVERRIDE { return termCrit; }
+    inline void setTermCriteria(const TermCriteria& val) CV_OVERRIDE { termCrit = val; }
 
-    void setClustersNumber(int val)
+    void setClustersNumber(int val) CV_OVERRIDE
     {
         nclusters = val;
         CV_Assert(nclusters >= 1);
     }
 
-    int getClustersNumber() const
+    int getClustersNumber() const CV_OVERRIDE
     {
         return nclusters;
     }
 
-    void setCovarianceMatrixType(int val)
+    void setCovarianceMatrixType(int val) CV_OVERRIDE
     {
         covMatType = val;
         CV_Assert(covMatType == COV_MAT_SPHERICAL ||
@@ -77,7 +78,7 @@ public:
                   covMatType == COV_MAT_GENERIC);
     }
 
-    int getCovarianceMatrixType() const
+    int getCovarianceMatrixType() const CV_OVERRIDE
     {
         return covMatType;
     }
@@ -91,7 +92,7 @@ public:
 
     virtual ~EMImpl() {}
 
-    void clear()
+    void clear() CV_OVERRIDE
     {
         trainSamples.release();
         trainProbs.release();
@@ -109,7 +110,7 @@ public:
         logWeightDivDet.release();
     }
 
-    bool train(const Ptr<TrainData>& data, int)
+    bool train(const Ptr<TrainData>& data, int) CV_OVERRIDE
     {
         Mat samples = data->getTrainSamples(), labels;
         return trainEM(samples, labels, noArray(), noArray());
@@ -118,7 +119,7 @@ public:
     bool trainEM(InputArray samples,
                OutputArray logLikelihoods,
                OutputArray labels,
-               OutputArray probs)
+               OutputArray probs) CV_OVERRIDE
     {
         Mat samplesMat = samples.getMat();
         setTrainData(START_AUTO_STEP, samplesMat, 0, 0, 0, 0);
@@ -131,7 +132,7 @@ public:
                 InputArray _weights0,
                 OutputArray logLikelihoods,
                 OutputArray labels,
-                OutputArray probs)
+                OutputArray probs) CV_OVERRIDE
     {
         Mat samplesMat = samples.getMat();
         std::vector<Mat> covs0;
@@ -148,7 +149,7 @@ public:
                 InputArray _probs0,
                 OutputArray logLikelihoods,
                 OutputArray labels,
-                OutputArray probs)
+                OutputArray probs) CV_OVERRIDE
     {
         Mat samplesMat = samples.getMat();
         Mat probs0 = _probs0.getMat();
@@ -157,7 +158,7 @@ public:
         return doTrain(START_M_STEP, logLikelihoods, labels, probs);
     }
 
-    float predict(InputArray _inputs, OutputArray _outputs, int) const
+    float predict(InputArray _inputs, OutputArray _outputs, int) const CV_OVERRIDE
     {
         bool needprobs = _outputs.needed();
         Mat samples = _inputs.getMat(), probs, probsrow;
@@ -186,7 +187,7 @@ public:
         return firstres;
     }
 
-    Vec2d predict2(InputArray _sample, OutputArray _probs) const
+    Vec2d predict2(InputArray _sample, OutputArray _probs) const CV_OVERRIDE
     {
         int ptype = CV_64F;
         Mat sample = _sample.getMat();
@@ -213,22 +214,22 @@ public:
         return computeProbabilities(sample, !probs.empty() ? &probs : 0, ptype);
     }
 
-    bool isTrained() const
+    bool isTrained() const CV_OVERRIDE
     {
         return !means.empty();
     }
 
-    bool isClassifier() const
+    bool isClassifier() const CV_OVERRIDE
     {
         return true;
     }
 
-    int getVarCount() const
+    int getVarCount() const CV_OVERRIDE
     {
         return means.cols;
     }
 
-    String getDefaultName() const
+    String getDefaultName() const CV_OVERRIDE
     {
         return "opencv_ml_em";
     }
@@ -768,7 +769,7 @@ public:
         writeTermCrit(fs, termCrit);
     }
 
-    void write(FileStorage& fs) const
+    void write(FileStorage& fs) const CV_OVERRIDE
     {
         writeFormat(fs);
         fs << "training_params" << "{";
@@ -796,7 +797,7 @@ public:
         termCrit = readTermCrit(fn);
     }
 
-    void read(const FileNode& fn)
+    void read(const FileNode& fn) CV_OVERRIDE
     {
         clear();
         read_params(fn["training_params"]);
@@ -816,9 +817,9 @@ public:
         computeLogWeightDivDet();
     }
 
-    Mat getWeights() const { return weights; }
-    Mat getMeans() const { return means; }
-    void getCovs(std::vector<Mat>& _covs) const
+    Mat getWeights() const CV_OVERRIDE { return weights; }
+    Mat getMeans() const CV_OVERRIDE { return means; }
+    void getCovs(std::vector<Mat>& _covs) const CV_OVERRIDE
     {
         _covs.resize(covs.size());
         std::copy(covs.begin(), covs.end(), _covs.begin());