made everything compile and even run somehow

apps/traincascade/CMakeLists.txt

-set(OPENCV_TRAINCASCADE_DEPS opencv_core opencv_ml opencv_imgproc opencv_photo opencv_objdetect opencv_imgcodecs opencv_videoio opencv_highgui opencv_calib3d opencv_video opencv_features2d)
+set(OPENCV_TRAINCASCADE_DEPS opencv_core opencv_imgproc opencv_objdetect opencv_imgcodecs opencv_highgui opencv_calib3d opencv_features2d)
 ocv_check_dependencies(${OPENCV_TRAINCASCADE_DEPS})
 
 if(NOT OCV_DEPENDENCIES_FOUND)
@@ -10,13 +10,10 @@ project(traincascade)
 ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
 ocv_include_modules(${OPENCV_TRAINCASCADE_DEPS})
 
-set(traincascade_files traincascade.cpp
-  cascadeclassifier.cpp cascadeclassifier.h
-  boost.cpp boost.h features.cpp traincascade_features.h
-  haarfeatures.cpp haarfeatures.h
-  lbpfeatures.cpp lbpfeatures.h
-  HOGfeatures.cpp HOGfeatures.h
-  imagestorage.cpp imagestorage.h)
+file(GLOB SRCS *.cpp)
+file(GLOB HDRS *.h*)
+
+set(traincascade_files ${SRCS} ${HDRS})
 
 set(the_target opencv_traincascade)
 add_executable(${the_target} ${traincascade_files})

apps/traincascade/boost.h

@@ -2,7 +2,7 @@
 #define _OPENCV_BOOST_H_
 
 #include "traincascade_features.h"
-#include "ml.h"
+#include "old_ml.hpp"
 
 struct CvCascadeBoostParams : CvBoostParams
 {

apps/traincascade/cascadeclassifier.h

@@ -7,8 +7,6 @@
 #include "lbpfeatures.h"
 #include "HOGfeatures.h" //new
 #include "boost.h"
-#include "cv.h"
-#include "cxcore.h"
 
 #define CC_CASCADE_FILENAME "cascade.xml"
 #define CC_PARAMS_FILENAME "params.xml"

apps/traincascade/traincascade.cpp

 #include "opencv2/core.hpp"
-
-#include "cv.h"
 #include "cascadeclassifier.h"
 
 using namespace std;

apps/traincascade/traincascade_features.h

@@ -2,9 +2,6 @@
 #define _OPENCV_FEATURES_H_
 
 #include "imagestorage.h"
-#include "cxcore.h"
-#include "cv.h"
-#include "ml.h"
 #include <stdio.h>
 
 #define FEATURES "features"

modules/ml/include/opencv2/ml.hpp

@@ -135,7 +135,7 @@ public:
     virtual Mat getCatMap() const = 0;
     
     virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
-    virtual void setTrainTestSplitRatio(float ratio, bool shuffle=true) = 0;
+    virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
     virtual void shuffleTrainTest() = 0;
 
     static Mat getSubVector(const Mat& vec, const Mat& idx);
@@ -156,7 +156,6 @@ class CV_EXPORTS_W StatModel : public Algorithm
 {
 public:
     enum { UPDATE_MODEL = 1, RAW_OUTPUT=1, COMPRESSED_INPUT=2, PREPROCESSED_INPUT=4 };
-    virtual ~StatModel();
     virtual void clear();
 
     virtual int getVarCount() const = 0;
@@ -164,16 +163,30 @@ public:
     virtual bool isTrained() const = 0;
     virtual bool isClassifier() const = 0;
 
-    virtual bool train( const Ptr<TrainData>& trainData, int flags=0 ) = 0;
+    virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
+    virtual bool train( InputArray samples, int layout, InputArray responses );
     virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
     virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
 
     template<typename _Tp> static Ptr<_Tp> load(const String& filename)
     {
         FileStorage fs(filename, FileStorage::READ);
-        Ptr<_Tp> p = _Tp::create();
-        p->read(fs.getFirstTopLevelNode());
-        return p->isTrained() ? p : Ptr<_Tp>();
+        Ptr<_Tp> model = _Tp::create();
+        model->read(fs.getFirstTopLevelNode());
+        return model->isTrained() ? model : Ptr<_Tp>();
+    }
+
+    template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, const typename _Tp::Params& p, int flags=0)
+    {
+        Ptr<_Tp> model = _Tp::create(p);
+        return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
+    }
+
+    template<typename _Tp> static Ptr<_Tp> train(InputArray samples, int layout, InputArray responses,
+                                                 const typename _Tp::Params& p, int flags=0)
+    {
+        Ptr<_Tp> model = _Tp::create(p);
+        return !model.empty() && model->train(TrainData::create(samples, layout, responses), flags) ? model : Ptr<_Tp>();
     }
 
     virtual void save(const String& filename) const;
@@ -192,11 +205,17 @@ public:
 class CV_EXPORTS_W NormalBayesClassifier : public StatModel
 {
 public:
-    virtual ~NormalBayesClassifier();
+    class CV_EXPORTS_W_MAP Params
+    {
+    public:
+        Params();
+    };
     virtual float predictProb( InputArray inputs, OutputArray outputs,
                                OutputArray outputProbs, int flags=0 ) const = 0;
+    virtual void setParams(const Params& params) = 0;
+    virtual Params getParams() const = 0;
 
-    static Ptr<NormalBayesClassifier> create();
+    static Ptr<NormalBayesClassifier> create(const Params& params=Params());
 };
 
 /****************************************************************************************\
@@ -207,13 +226,21 @@ public:
 class CV_EXPORTS_W KNearest : public StatModel
 {
 public:
-    virtual void setDefaultK(int k) = 0;
-    virtual int getDefaultK() const = 0;
+    class CV_EXPORTS_W_MAP Params
+    {
+    public:
+        Params(int defaultK=10, bool isclassifier=true);
+
+        int defaultK;
+        bool isclassifier;
+    };
+    virtual void setParams(const Params& p) = 0;
+    virtual Params getParams() const = 0;
     virtual float findNearest( InputArray samples, int k,
                                OutputArray results,
                                OutputArray neighborResponses=noArray(),
                                OutputArray dist=noArray() ) const = 0;
-    static Ptr<KNearest> create(bool isclassifier=true);
+    static Ptr<KNearest> create(const Params& params=Params());
 };
 
 /****************************************************************************************\
@@ -247,7 +274,6 @@ public:
     class CV_EXPORTS Kernel : public Algorithm
     {
     public:
-        virtual ~Kernel();
         virtual int getType() const = 0;
         virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
     };
@@ -261,8 +287,6 @@ public:
     // SVM params type
     enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
 
-    virtual ~SVM();
-
     virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
                     ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
                     ParamGrid gammaGrid  = SVM::getDefaultGrid(SVM::GAMMA),
@@ -399,8 +423,6 @@ public:
         int subsetOfs;
     };
 
-    virtual ~DTrees();
-
     virtual void setDParams(const Params& p);
     virtual Params getDParams() const;
 
@@ -464,7 +486,6 @@ public:
     // Boosting type
     enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
 
-    virtual ~Boost();
     virtual Params getBParams() const = 0;
     virtual void setBParams(const Params& p) = 0;
 
@@ -491,7 +512,6 @@ public:
     };
 
     enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
-    virtual ~GBTrees();
 
     virtual void setK(int k) = 0;
 
@@ -513,10 +533,16 @@ public:
     struct CV_EXPORTS_W_MAP Params
     {
         Params();
-        Params( TermCriteria termCrit, int trainMethod, double param1, double param2=0 );
+        Params( const Mat& layerSizes, int activateFunc, double fparam1, double fparam2,
+                TermCriteria termCrit, int trainMethod, double param1, double param2=0 );
 
         enum { BACKPROP=0, RPROP=1 };
 
+        CV_PROP_RW Mat layerSizes;
+        CV_PROP_RW int activateFunc;
+        CV_PROP_RW double fparam1;
+        CV_PROP_RW double fparam2;
+
         CV_PROP_RW TermCriteria termCrit;
         CV_PROP_RW int trainMethod;
 
@@ -527,23 +553,17 @@ public:
         CV_PROP_RW double rpDW0, rpDWPlus, rpDWMinus, rpDWMin, rpDWMax;
     };
 
-    virtual ~ANN_MLP();
-
     // possible activation functions
     enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };
 
     // available training flags
     enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };
 
-    virtual Mat getLayerSizes() const = 0;
     virtual Mat getWeights(int layerIdx) const = 0;
     virtual void setParams(const Params& p) = 0;
     virtual Params getParams() const = 0;
 
-    static Ptr<ANN_MLP> create(InputArray layerSizes=noArray(),
-                               const Params& params=Params(),
-                               int activateFunc=ANN_MLP::SIGMOID_SYM,
-                               double fparam1=0, double fparam2=0);
+    static Ptr<ANN_MLP> create(const Params& params=Params());
 };
 
 /****************************************************************************************\

modules/ml/src/data.cpp

@@ -379,7 +379,7 @@ public:
                 tempCatOfs.push_back(ofs);
                 std::copy(labels.begin(), labels.end(), std::back_inserter(tempCatMap));
             }
-            else if( haveMissing )
+            else
             {
                 tempCatOfs.push_back(Vec2i(0, 0));
                 /*Mat missing_i = layout == ROW_SAMPLE ? missing.col(i) : missing.row(i);
@@ -741,9 +741,9 @@ public:
             CV_Error( CV_StsBadArg, "type of some variables is not specified" );
     }
 
-    void setTrainTestSplitRatio(float ratio, bool shuffle)
+    void setTrainTestSplitRatio(double ratio, bool shuffle)
     {
-        CV_Assert( 0 <= ratio && ratio <= 1 );
+        CV_Assert( 0. <= ratio && ratio <= 1. );
         setTrainTestSplit(cvRound(getNSamples()*ratio), shuffle);
     }
 

modules/ml/src/inner_functions.cpp

@@ -50,7 +50,6 @@ ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
     logStep = std::max(_logStep, 1.);
 }
 
-StatModel::~StatModel() {}
 void StatModel::clear() {}
 
 int StatModel::getVarCount() const { return 0; }
@@ -61,6 +60,11 @@ bool StatModel::train( const Ptr<TrainData>&, int )
     return false;
 }
 
+bool StatModel::train( InputArray samples, int layout, InputArray responses )
+{
+    return train(TrainData::create(samples, layout, responses));
+}
+
 float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArray _resp ) const
 {
     Mat samples = data->getSamples();

modules/ml/src/nbayes.cpp

@@ -43,7 +43,7 @@
 namespace cv {
 namespace ml {
 
-NormalBayesClassifier::~NormalBayesClassifier() {}
+NormalBayesClassifier::Params::Params() {}
 
 class NormalBayesClassifierImpl : public NormalBayesClassifier
 {
@@ -53,6 +53,9 @@ public:
         nallvars = 0;
     }
 
+    void setParams(const Params&) {}
+    Params getParams() const { return Params(); }
+
     bool train( const Ptr<TrainData>& trainData, int flags )
     {
         const float min_variation = FLT_EPSILON;
@@ -452,7 +455,7 @@ public:
 };
 
 
-Ptr<NormalBayesClassifier> NormalBayesClassifier::create()
+Ptr<NormalBayesClassifier> NormalBayesClassifier::create(const Params&)
 {
     Ptr<NormalBayesClassifierImpl> p = makePtr<NormalBayesClassifierImpl>();
     return p;

modules/ml/src/tree.cpp

@@ -48,8 +48,6 @@ namespace ml {
 
 using std::vector;
 
-DTrees::~DTrees() {}
-
 void DTrees::setDParams(const DTrees::Params&)
 {
     CV_Error(CV_StsNotImplemented, "");