1. Input/OutputArray optimizations;

2. Algorithm::load/save added (moved from StatModel)
3. copyrights updated; added copyright/licensing info for ffmpeg
4. some warnings from Xcode 6.x are fixed

modules/ml/src/ann_mlp.cpp

@@ -1294,7 +1294,7 @@ public:
         return layer_sizes.empty() ? 0 : layer_sizes[0];
     }
 
-    String getDefaultModelName() const
+    String getDefaultName() const
     {
         return "opencv_ml_ann_mlp";
     }

modules/ml/src/boost.cpp

@@ -465,7 +465,7 @@ public:
     CV_WRAP_SAME_PROPERTY(float, RegressionAccuracy, impl.params)
     CV_WRAP_SAME_PROPERTY_S(cv::Mat, Priors, impl.params)
 
-    String getDefaultModelName() const { return "opencv_ml_boost"; }
+    String getDefaultName() const { return "opencv_ml_boost"; }
 
     bool train( const Ptr<TrainData>& trainData, int flags )
     {

modules/ml/src/em.cpp

@@ -227,7 +227,7 @@ public:
         return means.cols;
     }
 
-    String getDefaultModelName() const
+    String getDefaultName() const
     {
         return "opencv_ml_em";
     }

modules/ml/src/inner_functions.cpp

@@ -50,7 +50,7 @@ ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
     logStep = std::max(_logStep, 1.);
 }
 
-void StatModel::clear() {}
+bool StatModel::empty() const { return !isTrained(); }
 
 int StatModel::getVarCount() const { return 0; }
 
@@ -111,15 +111,6 @@ float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArra
     return (float)(err / n * (isclassifier ? 100 : 1));
 }
 
-void StatModel::save(const String& filename) const
-{
-    FileStorage fs(filename, FileStorage::WRITE);
-    fs << getDefaultModelName() << "{";
-    fs << "format" << (int)3;
-    write(fs);
-    fs << "}";
-}
-
 /* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
 static void Cholesky( const Mat& A, Mat& S )
 {

modules/ml/src/knearest.cpp

@@ -496,7 +496,7 @@ public:
         return impl->train(data, flags);
     }
 
-    String getDefaultModelName() const { return impl->getModelName(); }
+    String getDefaultName() const { return impl->getModelName(); }
 
 protected:
     void initImpl(int algorithmType)

modules/ml/src/lr.cpp

@@ -104,7 +104,7 @@ public:
     virtual int getVarCount() const { return learnt_thetas.cols; }
     virtual bool isTrained() const { return !learnt_thetas.empty(); }
     virtual bool isClassifier() const { return true; }
-    virtual String getDefaultModelName() const { return "opencv_ml_lr"; }
+    virtual String getDefaultName() const { return "opencv_ml_lr"; }
 protected:
     Mat calc_sigmoid(const Mat& data) const;
     double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta);

modules/ml/src/nbayes.cpp

@@ -443,7 +443,7 @@ public:
     bool isTrained() const { return !avg.empty(); }
     bool isClassifier() const { return true; }
     int getVarCount() const { return nallvars; }
-    String getDefaultModelName() const { return "opencv_ml_nbayes"; }
+    String getDefaultName() const { return "opencv_ml_nbayes"; }
 
     int nallvars;
     Mat var_idx, cls_labels, c;

modules/ml/src/precomp.hpp

@@ -290,7 +290,7 @@ namespace ml
         virtual ~DTreesImpl();
         virtual void clear();
 
-        String getDefaultModelName() const { return "opencv_ml_dtree"; }
+        String getDefaultName() const { return "opencv_ml_dtree"; }
         bool isTrained() const { return !roots.empty(); }
         bool isClassifier() const { return _isClassifier; }
         int getVarCount() const { return varType.empty() ? 0 : (int)(varType.size() - 1); }

modules/ml/src/rtrees.cpp

@@ -375,7 +375,7 @@ public:
     RTreesImpl() {}
     virtual ~RTreesImpl() {}
 
-    String getDefaultModelName() const { return "opencv_ml_rtrees"; }
+    String getDefaultName() const { return "opencv_ml_rtrees"; }
 
     bool train( const Ptr<TrainData>& trainData, int flags )
     {

modules/ml/src/svm.cpp

@@ -2008,7 +2008,7 @@ public:
         return var_count;
     }
 
-    String getDefaultModelName() const
+    String getDefaultName() const
     {
         return "opencv_ml_svm";
     }