Removed remaining SWIG marks from headers

modules/highgui/src/makeswig.sh

-swig -DSKIP_INCLUDES -python -small highgui.i
-gcc -I/usr/include/python2.3/ -I../../cxcore/include -D CV_NO_BACKWARD_COMPATIBILITY -c highgui_wrap.c

modules/legacy/include/opencv2/legacy/legacy.hpp

@@ -1787,7 +1787,6 @@ public:
 
     virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
 
-#ifndef SWIG
     CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
                   CvEMParams params=CvEMParams() );
 
@@ -1806,7 +1805,6 @@ public:
     CV_WRAP cv::Mat getProbs() const;
 
     CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? logLikelihood : DBL_MAX; }
-#endif
 
     CV_WRAP virtual void clear();
 

modules/ml/include/opencv2/ml/ml.hpp

@@ -201,14 +201,12 @@ public:
     virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
     CV_WRAP virtual void clear();
 
-#ifndef SWIG
     CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
                             const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
     CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
                        const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
                        bool update=false );
     CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
-#endif
 
     virtual void write( CvFileStorage* storage, const char* name ) const;
     virtual void read( CvFileStorage* storage, CvFileNode* node );
@@ -249,7 +247,6 @@ public:
     virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
         const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
 
-#ifndef SWIG
     CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
                const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
 
@@ -262,7 +259,6 @@ public:
                                 cv::Mat* dist=0 ) const;
     CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
                                         CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
-#endif
 
     virtual void clear();
     int get_max_k() const;
@@ -490,7 +486,6 @@ public:
     virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
     virtual float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
 
-#ifndef SWIG
     CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
           const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
           CvSVMParams params=CvSVMParams() );
@@ -511,7 +506,6 @@ public:
                             bool balanced=false);
     CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
     CV_WRAP_AS(predict_all) virtual void predict( cv::InputArray samples, cv::OutputArray results ) const;
-#endif
 
     CV_WRAP virtual int get_support_vector_count() const;
     virtual const float* get_support_vector(int i) const;
@@ -868,7 +862,6 @@ public:
     virtual CvDTreeNode* predict( const CvMat* sample, const CvMat* missingDataMask=0,
                                   bool preprocessedInput=false ) const;
 
-#ifndef SWIG
     CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
                        const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
                        const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@@ -878,7 +871,6 @@ public:
     CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
                                   bool preprocessedInput=false ) const;
     CV_WRAP virtual cv::Mat getVarImportance();
-#endif
 
     virtual const CvMat* get_var_importance();
     CV_WRAP virtual void clear();
@@ -1011,7 +1003,6 @@ public:
     virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;
     virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
 
-#ifndef SWIG
     CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
                        const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
                        const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@@ -1020,7 +1011,6 @@ public:
     CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
     CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
     CV_WRAP virtual cv::Mat getVarImportance();
-#endif
 
     CV_WRAP virtual void clear();
 
@@ -1107,13 +1097,11 @@ public:
                         const CvMat* sampleIdx=0, const CvMat* varType=0,
                         const CvMat* missingDataMask=0,
                         CvRTParams params=CvRTParams());
-#ifndef SWIG
     CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
                        const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
                        const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
                        const cv::Mat& missingDataMask=cv::Mat(),
                        CvRTParams params=CvRTParams());
-#endif
     virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
 protected:
     virtual std::string getName() const;
@@ -1220,7 +1208,6 @@ public:
                            CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
                            bool raw_mode=false, bool return_sum=false ) const;
 
-#ifndef SWIG
     CV_WRAP CvBoost( const cv::Mat& trainData, int tflag,
             const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
             const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@@ -1237,7 +1224,6 @@ public:
     CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
                                    const cv::Range& slice=cv::Range::all(), bool rawMode=false,
                                    bool returnSum=false ) const;
-#endif
 
     virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
 
@@ -1904,7 +1890,6 @@ public:
                        int flags=0 );
     virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
 
-#ifndef SWIG
     CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
               int activateFunc=CvANN_MLP::SIGMOID_SYM,
               double fparam1=0, double fparam2=0 );
@@ -1919,7 +1904,6 @@ public:
                       int flags=0 );
 
     CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
-#endif
 
     CV_WRAP virtual void clear();