added helper macros to the function declarations

modules/core/include/opencv2/core/core.hpp

@@ -212,7 +212,7 @@ CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback,
 #define CV_DbgAssert(expr)
 #endif
 
-CV_EXPORTS void setNumThreads(int);
+CV_EXPORTS void setNumThreads(int nthreads);
 CV_EXPORTS int getNumThreads();
 CV_EXPORTS int getThreadNum();
 
@@ -330,7 +330,7 @@ static inline size_t alignSize(size_t sz, int n)
   \note{Since optimization may imply using special data structures, it may be unsafe
   to call this function anywhere in the code. Instead, call it somewhere at the top level.}
 */  
-CV_EXPORTS void setUseOptimized(bool);
+CV_EXPORTS void setUseOptimized(bool onoff);
 
 /*!
   Returns the current optimization status

modules/core/include/opencv2/core/types_c.h

@@ -158,9 +158,12 @@ typedef unsigned short ushort;
 typedef signed char schar;
 
 /* special informative macros for wrapper generators */
-#define CV_OUT
 #define CV_CARRAY(counter)
+#define CV_CUSTOM_CARRAY(args)
 #define CV_METHOD
+#define CV_NO_WRAP
+#define CV_OUT
+#define CV_WRAP_AS(synonym)
 
 /* CvArr* is used to pass arbitrary
  * array-like data structures

modules/features2d/include/opencv2/features2d/features2d.hpp

@@ -232,10 +232,12 @@ public:
             : pt(x, y), size(_size), angle(_angle),
             response(_response), octave(_octave), class_id(_class_id) {}
     //! converts vector of keypoints to vector of points
-    static void convert(const std::vector<KeyPoint>& keypoints, std::vector<Point2f>& points2f,
+    static void convert(const std::vector<KeyPoint>& keypoints,
+                        CV_OUT std::vector<Point2f>& points2f,
                         const std::vector<int>& keypointIndexes=std::vector<int>());
     //! converts vector of points to the vector of keypoints, where each keypoint is assigned the same size and the same orientation
-    static void convert(const std::vector<Point2f>& points2f, std::vector<KeyPoint>& keypoints,
+    static void convert(const std::vector<Point2f>& points2f,
+                        CV_OUT std::vector<KeyPoint>& keypoints,
                         float size=1, float response=1, int octave=0, int class_id=-1);
 
     //! computes overlap for pair of keypoints;
@@ -254,7 +256,7 @@ public:
 //! writes vector of keypoints to the file storage
 CV_EXPORTS void write(FileStorage& fs, const string& name, const vector<KeyPoint>& keypoints);
 //! reads vector of keypoints from the specified file storage node
-CV_EXPORTS void read(const FileNode& node, vector<KeyPoint>& keypoints);    
+CV_EXPORTS void read(const FileNode& node, CV_OUT vector<KeyPoint>& keypoints);    
 
 /*!
  SIFT implementation.
@@ -357,12 +359,12 @@ public:
     //! returns the descriptor size in float's (64 or 128)
     int descriptorSize() const;
     //! finds the keypoints using fast hessian detector used in SURF
-    void operator()(const Mat& img, const Mat& mask,
-                    vector<KeyPoint>& keypoints) const;
+    CV_WRAP_AS(detect) void operator()(const Mat& img, const Mat& mask,
+                    CV_OUT vector<KeyPoint>& keypoints) const;
     //! finds the keypoints and computes their descriptors. Optionally it can compute descriptors for the user-provided keypoints
-    void operator()(const Mat& img, const Mat& mask,
-                    vector<KeyPoint>& keypoints,
-                    vector<float>& descriptors,
+    CV_WRAP_AS(detect) void operator()(const Mat& img, const Mat& mask,
+                    CV_OUT vector<KeyPoint>& keypoints,
+                    CV_OUT vector<float>& descriptors,
                     bool useProvidedKeypoints=false) const;
 };
 
@@ -386,7 +388,8 @@ public:
           int _max_evolution, double _area_threshold,
           double _min_margin, int _edge_blur_size );
     //! the operator that extracts the MSERs from the image or the specific part of it
-    void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
+    CV_WRAP_AS(detect) void operator()( const Mat& image,
+        CV_OUT vector<vector<Point> >& msers, const Mat& mask ) const;
 };
 
 /*!
@@ -405,11 +408,13 @@ public:
                  int _lineThresholdBinarized,
                  int _suppressNonmaxSize);
     //! finds the keypoints in the image
-    void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
+    CV_WRAP_AS(detect) void operator()(const Mat& image,
+                CV_OUT vector<KeyPoint>& keypoints) const;
 };
 
 //! detects corners using FAST algorithm by E. Rosten
-CV_EXPORTS void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true );
+CV_EXPORTS void FAST( const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
+                      int threshold, bool nonmaxSupression=true );
 
 /*!
  The Patch Generator class 
@@ -423,13 +428,14 @@ public:
                    double _lambdaMin=0.6, double _lambdaMax=1.5,
                    double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
                    double _phiMin=-CV_PI, double _phiMax=CV_PI );
-    void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
-    void operator()(const Mat& image, const Mat& transform, Mat& patch,
+    CV_WRAP_AS(generate) void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
+    CV_WRAP_AS(generate) void operator()(const Mat& image, const Mat& transform, Mat& patch,
                     Size patchSize, RNG& rng) const;
     void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
-                        Mat& warped, int border, RNG& rng) const;
+                        CV_OUT Mat& warped, int border, RNG& rng) const;
     void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
-                                 Mat& transform, RNG& rng, bool inverse=false) const;
+                                 CV_OUT Mat& transform, RNG& rng,
+                                 bool inverse=false) const;
     void setAffineParam(double lambda, double theta, double phi);
     
     double backgroundMin, backgroundMax;
@@ -447,9 +453,13 @@ public:
     LDetector();
     LDetector(int _radius, int _threshold, int _nOctaves,
               int _nViews, double _baseFeatureSize, double _clusteringDistance);
-    void operator()(const Mat& image, vector<KeyPoint>& keypoints, int maxCount=0, bool scaleCoords=true) const;
-    void operator()(const vector<Mat>& pyr, vector<KeyPoint>& keypoints, int maxCount=0, bool scaleCoords=true) const;
-    void getMostStable2D(const Mat& image, vector<KeyPoint>& keypoints,
+    CV_WRAP_AS(detect) void operator()(const Mat& image,
+                    CV_OUT vector<KeyPoint>& keypoints,
+                    int maxCount=0, bool scaleCoords=true) const;
+    CV_WRAP_AS(detect) void operator()(const vector<Mat>& pyr,
+                    CV_OUT vector<KeyPoint>& keypoints,
+                    int maxCount=0, bool scaleCoords=true) const;
+    void getMostStable2D(const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
                          int maxCount, const PatchGenerator& patchGenerator) const;
     void setVerbose(bool verbose);
     
@@ -561,6 +571,7 @@ protected:
     vector<float> posteriors;
 };
 
+
 class CV_EXPORTS PlanarObjectDetector
 {
 public:
@@ -596,9 +607,10 @@ public:
     
     void read(const FileNode& node);
     void write(FileStorage& fs, const String& name=String()) const;
-    bool operator()(const Mat& image, Mat& H, vector<Point2f>& corners) const;
-    bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
-                    Mat& H, vector<Point2f>& corners, vector<int>* pairs=0) const;
+    CV_WRAP_AS(detect) bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT vector<Point2f>& corners) const;
+    CV_WRAP_AS(detect) bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
+                                       CV_OUT Mat& H, CV_OUT vector<Point2f>& corners,
+                                       CV_OUT vector<int>* pairs=0) const;
     
 protected:
     bool verbose;
@@ -735,7 +747,6 @@ struct CV_EXPORTS RTreeNode
   short offset1, offset2;
 
   RTreeNode() {}
-
   RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
     : offset1(y1*RandomizedTree::PATCH_SIZE + x1),
       offset2(y2*RandomizedTree::PATCH_SIZE + x2)
@@ -755,7 +766,6 @@ public:
   static const size_t DEFAULT_NUM_QUANT_BITS = 4;
 
   RTreeClassifier();
-
   void train(std::vector<BaseKeypoint> const& base_set,
              RNG &rng,
              int num_trees = RTreeClassifier::DEFAULT_TREES,

modules/highgui/include/opencv2/highgui/highgui.hpp

@@ -106,7 +106,7 @@ CV_EXPORTS bool imwrite( const string& filename, const Mat& img,
               const vector<int>& params=vector<int>());
 CV_EXPORTS Mat imdecode( const Mat& buf, int flags );
 CV_EXPORTS bool imencode( const string& ext, const Mat& img,
-                          vector<uchar>& buf,
+                          CV_OUT vector<uchar>& buf,
                           const vector<int>& params=vector<int>());
 
 CV_EXPORTS int waitKey(int delay=0);
@@ -130,8 +130,8 @@ public:
     virtual void release();
     
     virtual bool grab();
-    virtual bool retrieve(Mat& image, int channel=0);
-    virtual VideoCapture& operator >> (Mat& image);
+    virtual bool retrieve(CV_OUT Mat& image, int channel=0);
+    virtual CV_WRAP_AS(query) VideoCapture& operator >> (Mat& image);
     
     virtual bool set(int propId, double value);
     virtual double get(int propId);
@@ -145,12 +145,14 @@ class CV_EXPORTS VideoWriter
 {
 public:    
     VideoWriter();
-    VideoWriter(const string& filename, int fourcc, double fps, Size frameSize, bool isColor=true);
+    VideoWriter(const string& filename, int fourcc, double fps,
+                Size frameSize, bool isColor=true);
     
     virtual ~VideoWriter();
-    virtual bool open(const string& filename, int fourcc, double fps, Size frameSize, bool isColor=true);
+    virtual bool open(const string& filename, int fourcc, double fps,
+                      Size frameSize, bool isColor=true);
     virtual bool isOpened() const;
-    virtual VideoWriter& operator << (const Mat& image);
+    virtual CV_WRAP_AS(write) VideoWriter& operator << (const Mat& image);
     
 protected:
     Ptr<CvVideoWriter> writer;

modules/imgproc/include/opencv2/imgproc/imgproc.hpp

@@ -327,7 +327,8 @@ CV_EXPORTS Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
                                     double sigma1, double sigma2=0,
                                     int borderType=BORDER_DEFAULT);
 //! initializes kernels of the generalized Sobel operator
-CV_EXPORTS void getDerivKernels( Mat& kx, Mat& ky, int dx, int dy, int ksize,
+CV_EXPORTS void getDerivKernels( CV_OUT Mat& kx, CV_OUT Mat& ky,
+                                 int dx, int dy, int ksize,
                                  bool normalize=false, int ktype=CV_32F );
 //! returns filter engine for the generalized Sobel operator
 CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
@@ -337,7 +338,7 @@ CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
 CV_EXPORTS Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
                                               int ksize, int anchor=-1);
 //! returns vertical 1D box filter
-CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType,
+CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
                                                      int ksize, int anchor=-1,
                                                      double scale=1);
 //! returns box filter engine
@@ -374,27 +375,27 @@ CV_EXPORTS Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-
 template<> CV_EXPORTS void Ptr<IplConvKernel>::delete_obj();
 
 //! copies 2D array to a larger destination array with extrapolation of the outer part of src using the specified border mode 
-CV_EXPORTS void copyMakeBorder( const Mat& src, Mat& dst,
+CV_EXPORTS void copyMakeBorder( const Mat& src, CV_OUT Mat& dst,
                                 int top, int bottom, int left, int right,
                                 int borderType, const Scalar& value=Scalar() );
 
 //! smooths the image using median filter.
-CV_EXPORTS void medianBlur( const Mat& src, Mat& dst, int ksize );
+CV_EXPORTS void medianBlur( const Mat& src, CV_OUT Mat& dst, int ksize );
 //! smooths the image using Gaussian filter.
-CV_EXPORTS void GaussianBlur( const Mat& src, Mat& dst, Size ksize,
+CV_EXPORTS void GaussianBlur( const Mat& src, CV_OUT Mat& dst, Size ksize,
                               double sigma1, double sigma2=0,
                               int borderType=BORDER_DEFAULT );
 //! smooths the image using bilateral filter
-CV_EXPORTS void bilateralFilter( const Mat& src, Mat& dst, int d,
+CV_EXPORTS void bilateralFilter( const Mat& src, CV_OUT Mat& dst, int d,
                                  double sigmaColor, double sigmaSpace,
                                  int borderType=BORDER_DEFAULT );
 //! smooths the image using the box filter. Each pixel is processed in O(1) time
-CV_EXPORTS void boxFilter( const Mat& src, Mat& dst, int ddepth,
+CV_EXPORTS void boxFilter( const Mat& src, CV_OUT Mat& dst, int ddepth,
                            Size ksize, Point anchor=Point(-1,-1),
                            bool normalize=true,
                            int borderType=BORDER_DEFAULT );
 //! a synonym for normalized box filter
-static inline void blur( const Mat& src, Mat& dst,
+static inline void blur( const Mat& src, CV_OUT Mat& dst,
                          Size ksize, Point anchor=Point(-1,-1),
                          int borderType=BORDER_DEFAULT )
 {
@@ -402,54 +403,54 @@ static inline void blur( const Mat& src, Mat& dst,
 }
 
 //! applies non-separable 2D linear filter to the image
-CV_EXPORTS void filter2D( const Mat& src, Mat& dst, int ddepth,
+CV_EXPORTS void filter2D( const Mat& src, CV_OUT Mat& dst, int ddepth,
                           const Mat& kernel, Point anchor=Point(-1,-1),
                           double delta=0, int borderType=BORDER_DEFAULT );
 
 //! applies separable 2D linear filter to the image
-CV_EXPORTS void sepFilter2D( const Mat& src, Mat& dst, int ddepth,
+CV_EXPORTS void sepFilter2D( const Mat& src, CV_OUT Mat& dst, int ddepth,
                              const Mat& kernelX, const Mat& kernelY,
                              Point anchor=Point(-1,-1),
                              double delta=0, int borderType=BORDER_DEFAULT );
     
 //! applies generalized Sobel operator to the image
-CV_EXPORTS void Sobel( const Mat& src, Mat& dst, int ddepth,
+CV_EXPORTS void Sobel( const Mat& src, CV_OUT Mat& dst, int ddepth,
                        int dx, int dy, int ksize=3,
                        double scale=1, double delta=0,
                        int borderType=BORDER_DEFAULT );
 
 //! applies the vertical or horizontal Scharr operator to the image
-CV_EXPORTS void Scharr( const Mat& src, Mat& dst, int ddepth,
+CV_EXPORTS void Scharr( const Mat& src, CV_OUT Mat& dst, int ddepth,
                         int dx, int dy, double scale=1, double delta=0,
                         int borderType=BORDER_DEFAULT );
 
 //! applies Laplacian operator to the image
-CV_EXPORTS void Laplacian( const Mat& src, Mat& dst, int ddepth,
+CV_EXPORTS void Laplacian( const Mat& src, CV_OUT Mat& dst, int ddepth,
                            int ksize=1, double scale=1, double delta=0,
                            int borderType=BORDER_DEFAULT );
 
 //! applies Canny edge detector and produces the edge map.
-CV_EXPORTS void Canny( const Mat& image, Mat& edges,
+CV_EXPORTS void Canny( const Mat& image, CV_OUT Mat& edges,
                        double threshold1, double threshold2,
                        int apertureSize=3, bool L2gradient=false );
 
 //! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
-CV_EXPORTS void cornerMinEigenVal( const Mat& src, Mat& dst,
+CV_EXPORTS void cornerMinEigenVal( const Mat& src, CV_OUT Mat& dst,
                                    int blockSize, int ksize=3,
                                    int borderType=BORDER_DEFAULT );
 
 //! computes Harris cornerness criteria at each image pixel
-CV_EXPORTS void cornerHarris( const Mat& src, Mat& dst, int blockSize,
+CV_EXPORTS void cornerHarris( const Mat& src, CV_OUT Mat& dst, int blockSize,
                               int ksize, double k,
                               int borderType=BORDER_DEFAULT );
 
 //! computes both eigenvalues and the eigenvectors of 2x2 derivative covariation matrix  at each pixel. The output is stored as 6-channel matrix.
-CV_EXPORTS void cornerEigenValsAndVecs( const Mat& src, Mat& dst,
+CV_EXPORTS void cornerEigenValsAndVecs( const Mat& src, CV_OUT Mat& dst,
                                         int blockSize, int ksize,
                                         int borderType=BORDER_DEFAULT );
 
 //! computes another complex cornerness criteria at each pixel
-CV_EXPORTS void preCornerDetect( const Mat& src, Mat& dst, int ksize,
+CV_EXPORTS void preCornerDetect( const Mat& src, CV_OUT Mat& dst, int ksize,
                                  int borderType=BORDER_DEFAULT );
 
 //! adjusts the corner locations with sub-pixel accuracy to maximize the certain cornerness criteria
@@ -458,41 +459,42 @@ CV_EXPORTS void cornerSubPix( const Mat& image, vector<Point2f>& corners,
                               TermCriteria criteria );
 
 //! finds the strong enough corners where the cornerMinEigenVal() or cornerHarris() report the local maxima
-CV_EXPORTS void goodFeaturesToTrack( const Mat& image, vector<Point2f>& corners,
+CV_EXPORTS void goodFeaturesToTrack( const Mat& image, CV_OUT vector<Point2f>& corners,
                                      int maxCorners, double qualityLevel, double minDistance,
                                      const Mat& mask=Mat(), int blockSize=3,
                                      bool useHarrisDetector=false, double k=0.04 );
 
 //! finds lines in the black-n-white image using the standard or pyramid Hough transform
-CV_EXPORTS void HoughLines( const Mat& image, vector<Vec2f>& lines,
+CV_EXPORTS void HoughLines( const Mat& image, CV_OUT vector<Vec2f>& lines,
                             double rho, double theta, int threshold,
                             double srn=0, double stn=0 );
 
 //! finds line segments in the black-n-white image using probabalistic Hough transform
-CV_EXPORTS void HoughLinesP( Mat& image, vector<Vec4i>& lines,
+CV_EXPORTS void HoughLinesP( Mat& image, CV_OUT vector<Vec4i>& lines,
                              double rho, double theta, int threshold,
                              double minLineLength=0, double maxLineGap=0 );
 
 //! finds circles in the grayscale image using 2+1 gradient Hough transform 
-CV_EXPORTS void HoughCircles( const Mat& image, vector<Vec3f>& circles,
+CV_EXPORTS void HoughCircles( const Mat& image, CV_OUT vector<Vec3f>& circles,
                               int method, double dp, double minDist,
                               double param1=100, double param2=100,
                               int minRadius=0, int maxRadius=0 );
 
 //! erodes the image (applies the local minimum operator)
-CV_EXPORTS void erode( const Mat& src, Mat& dst, const Mat& kernel,
+CV_EXPORTS void erode( const Mat& src, CV_OUT Mat& dst, const Mat& kernel,
                        Point anchor=Point(-1,-1), int iterations=1,
                        int borderType=BORDER_CONSTANT,
                        const Scalar& borderValue=morphologyDefaultBorderValue() );
     
 //! dilates the image (applies the local maximum operator)
-CV_EXPORTS void dilate( const Mat& src, Mat& dst, const Mat& kernel,
+CV_EXPORTS void dilate( const Mat& src, CV_OUT Mat& dst, const Mat& kernel,
                         Point anchor=Point(-1,-1), int iterations=1,
                         int borderType=BORDER_CONSTANT,
                         const Scalar& borderValue=morphologyDefaultBorderValue() );
     
 //! applies an advanced morphological operation to the image
-CV_EXPORTS void morphologyEx( const Mat& src, Mat& dst, int op, const Mat& kernel,
+CV_EXPORTS void morphologyEx( const Mat& src, CV_OUT Mat& dst,
+                              int op, const Mat& kernel,
                               Point anchor=Point(-1,-1), int iterations=1,
                               int borderType=BORDER_CONSTANT,
                               const Scalar& borderValue=morphologyDefaultBorderValue() );
@@ -510,19 +512,19 @@ enum
 };
 
 //! resizes the image
-CV_EXPORTS void resize( const Mat& src, Mat& dst,
+CV_EXPORTS void resize( const Mat& src, CV_OUT Mat& dst,
                         Size dsize, double fx=0, double fy=0,
                         int interpolation=INTER_LINEAR );
 
 //! warps the image using affine transformation
-CV_EXPORTS void warpAffine( const Mat& src, Mat& dst,
+CV_EXPORTS void warpAffine( const Mat& src, CV_OUT Mat& dst,
                             const Mat& M, Size dsize,
                             int flags=INTER_LINEAR,
                             int borderMode=BORDER_CONSTANT,
                             const Scalar& borderValue=Scalar());
     
 //! warps the image using perspective transformation
-CV_EXPORTS void warpPerspective( const Mat& src, Mat& dst,
+CV_EXPORTS void warpPerspective( const Mat& src, CV_OUT Mat& dst,
                                  const Mat& M, Size dsize,
                                  int flags=INTER_LINEAR,
                                  int borderMode=BORDER_CONSTANT,
@@ -533,12 +535,13 @@ enum { INTER_BITS=5, INTER_BITS2=INTER_BITS*2,
     INTER_TAB_SIZE2=INTER_TAB_SIZE*INTER_TAB_SIZE };    
 
 //! warps the image using the precomputed maps. The maps are stored in either floating-point or integer fixed-point format
-CV_EXPORTS void remap( const Mat& src, Mat& dst, const Mat& map1, const Mat& map2,
+CV_EXPORTS void remap( const Mat& src, CV_OUT Mat& dst, const Mat& map1, const Mat& map2,
                        int interpolation, int borderMode=BORDER_CONSTANT,
                        const Scalar& borderValue=Scalar());
 
 //! converts maps for remap from floating-point to fixed-point format or backwards
-CV_EXPORTS void convertMaps( const Mat& map1, const Mat& map2, Mat& dstmap1, Mat& dstmap2,
+CV_EXPORTS void convertMaps( const Mat& map1, const Mat& map2,
+                             CV_OUT Mat& dstmap1, CV_OUT Mat& dstmap2,
                              int dstmap1type, bool nninterpolation=false );
                              
 //! returns 2x3 affine transformation matrix for the planar rotation.
@@ -548,28 +551,28 @@ CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[]
 //! returns 2x3 affine transformation for the corresponding 3 point pairs.
 CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
 //! computes 2x3 affine transformation matrix that is inverse to the specified 2x3 affine transformation.
-CV_EXPORTS void invertAffineTransform(const Mat& M, Mat& iM);
+CV_EXPORTS void invertAffineTransform( const Mat& M, CV_OUT Mat& iM );
 
 //! extracts rectangle from the image at sub-pixel location
 CV_EXPORTS void getRectSubPix( const Mat& image, Size patchSize,
-                               Point2f center, Mat& patch, int patchType=-1 );
+                               Point2f center, CV_OUT Mat& patch, int patchType=-1 );
 
 //! computes the integral image
-CV_EXPORTS void integral( const Mat& src, Mat& sum, int sdepth=-1 );
+CV_EXPORTS void integral( const Mat& src, CV_OUT Mat& sum, int sdepth=-1 );
 //! computes the integral image and integral for the squared image
-CV_EXPORTS void integral( const Mat& src, Mat& sum, Mat& sqsum, int sdepth=-1 );
+CV_EXPORTS void integral( const Mat& src, CV_OUT Mat& sum, CV_OUT Mat& sqsum, int sdepth=-1 );
 //! computes the integral image, integral for the squared image and the tilted integral image
-CV_EXPORTS void integral( const Mat& src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth=-1 );
+CV_EXPORTS void integral( const Mat& src, CV_OUT Mat& sum, CV_OUT Mat& sqsum, CV_OUT Mat& tilted, int sdepth=-1 );
 
 //! adds image to the accumulator (dst += src). Unlike cv::add, dst and src can have different types.
-CV_EXPORTS void accumulate( const Mat& src, Mat& dst, const Mat& mask=Mat() );
+CV_EXPORTS void accumulate( const Mat& src, CV_OUT Mat& dst, const Mat& mask=Mat() );
 //! adds squared src image to the accumulator (dst += src*src).
-CV_EXPORTS void accumulateSquare( const Mat& src, Mat& dst, const Mat& mask=Mat() );
+CV_EXPORTS void accumulateSquare( const Mat& src, CV_OUT Mat& dst, const Mat& mask=Mat() );
 //! adds product of the 2 images to the accumulator (dst += src1*src2).
 CV_EXPORTS void accumulateProduct( const Mat& src1, const Mat& src2,
-                                   Mat& dst, const Mat& mask=Mat() );
+                                   CV_OUT Mat& dst, const Mat& mask=Mat() );
 //! updates the running average (dst = dst*(1-alpha) + src*alpha)
-CV_EXPORTS void accumulateWeighted( const Mat& src, Mat& dst,
+CV_EXPORTS void accumulateWeighted( const Mat& src, CV_OUT Mat& dst,
                                     double alpha, const Mat& mask=Mat() );
     
 //! type of the threshold operation
@@ -577,30 +580,30 @@ enum { THRESH_BINARY=0, THRESH_BINARY_INV=1, THRESH_TRUNC=2, THRESH_TOZERO=3,
        THRESH_TOZERO_INV=4, THRESH_MASK=7, THRESH_OTSU=8 };
 
 //! applies fixed threshold to the image
-CV_EXPORTS double threshold( const Mat& src, Mat& dst, double thresh, double maxval, int type );
+CV_EXPORTS double threshold( const Mat& src, CV_OUT Mat& dst, double thresh, double maxval, int type );
 
 //! adaptive threshold algorithm
 enum { ADAPTIVE_THRESH_MEAN_C=0, ADAPTIVE_THRESH_GAUSSIAN_C=1 };
 
 //! applies variable (adaptive) threshold to the image
-CV_EXPORTS void adaptiveThreshold( const Mat& src, Mat& dst, double maxValue,
+CV_EXPORTS void adaptiveThreshold( const Mat& src, CV_OUT Mat& dst, double maxValue,
                                    int adaptiveMethod, int thresholdType,
                                    int blockSize, double C );
 
 //! smooths and downsamples the image
-CV_EXPORTS void pyrDown( const Mat& src, Mat& dst, const Size& dstsize=Size());
+CV_EXPORTS void pyrDown( const Mat& src, CV_OUT Mat& dst, const Size& dstsize=Size());
 //! upsamples and smoothes the image
-CV_EXPORTS void pyrUp( const Mat& src, Mat& dst, const Size& dstsize=Size());
+CV_EXPORTS void pyrUp( const Mat& src, CV_OUT Mat& dst, const Size& dstsize=Size());
 //! builds the gaussian pyramid using pyrDown() as a basic operation
-CV_EXPORTS void buildPyramid( const Mat& src, vector<Mat>& dst, int maxlevel );
+CV_EXPORTS void buildPyramid( const Mat& src, CV_OUT vector<Mat>& dst, int maxlevel );
 
 //! corrects lens distortion for the given camera matrix and distortion coefficients
-CV_EXPORTS void undistort( const Mat& src, Mat& dst, const Mat& cameraMatrix,
+CV_EXPORTS void undistort( const Mat& src, CV_OUT Mat& dst, const Mat& cameraMatrix,
                            const Mat& distCoeffs, const Mat& newCameraMatrix=Mat() );
 //! initializes maps for cv::remap() to correct lens distortion and optionally rectify the image
 CV_EXPORTS void initUndistortRectifyMap( const Mat& cameraMatrix, const Mat& distCoeffs,
                            const Mat& R, const Mat& newCameraMatrix,
-                           Size size, int m1type, Mat& map1, Mat& map2 );
+                           Size size, int m1type, CV_OUT Mat& map1, CV_OUT Mat& map2 );
 
 enum
 {
@@ -611,63 +614,65 @@ enum
 //! initializes maps for cv::remap() for wide-angle
 CV_EXPORTS float initWideAngleProjMap( const Mat& cameraMatrix, const Mat& distCoeffs,
                                        Size imageSize, int destImageWidth,
-                                       int m1type, Mat& map1, Mat& map2,
+                                       int m1type, CV_OUT Mat& map1, CV_OUT Mat& map2,
                                        int projType=PROJ_SPHERICAL_EQRECT, double alpha=0);
     
 //! returns the default new camera matrix (by default it is the same as cameraMatrix unless centerPricipalPoint=true)
 CV_EXPORTS Mat getDefaultNewCameraMatrix( const Mat& cameraMatrix, Size imgsize=Size(),
                                           bool centerPrincipalPoint=false );
 //! returns points' coordinates after lens distortion correction
-CV_EXPORTS void undistortPoints( const Mat& src, vector<Point2f>& dst,
+CV_EXPORTS void undistortPoints( const Mat& src, CV_OUT vector<Point2f>& dst,
                                  const Mat& cameraMatrix, const Mat& distCoeffs,
                                  const Mat& R=Mat(), const Mat& P=Mat());
 //! returns points' coordinates after lens distortion correction
-CV_EXPORTS void undistortPoints( const Mat& src, Mat& dst,
+CV_EXPORTS void undistortPoints( const Mat& src, CV_OUT Mat& dst,
                                  const Mat& cameraMatrix, const Mat& distCoeffs,
                                  const Mat& R=Mat(), const Mat& P=Mat());
 
 template<> CV_EXPORTS void Ptr<CvHistogram>::delete_obj();
     
 //! computes the joint dense histogram for a set of images.
-CV_EXPORTS void calcHist( const Mat* images, int nimages,
-                          const int* channels, const Mat& mask,
-                          MatND& hist, int dims, const int* histSize,
-                          const float** ranges, bool uniform=true,
-                          bool accumulate=false );
+CV_EXPORTS void calcHist( CV_CARRAY(nimages) const Mat* images, int nimages,
+                          CV_CARRAY(dims) const int* channels, const Mat& mask,
+                          CV_OUT Mat& hist, int dims, CV_CARRAY(dims) const int* histSize,
+                          CV_CUSTOM_CARRAY((dims,histSize,uniform)) const float** ranges,
+                          bool uniform=true, bool accumulate=false );
 
 //! computes the joint sparse histogram for a set of images.
-CV_EXPORTS void calcHist( const Mat* images, int nimages,
-                          const int* channels, const Mat& mask,
-                          SparseMat& hist, int dims, const int* histSize,
-                          const float** ranges, bool uniform=true,
-                          bool accumulate=false );
+CV_EXPORTS void calcHist( CV_CARRAY(nimages) const Mat* images, int nimages,
+                          CV_CARRAY(dims) const int* channels, const Mat& mask,
+                          CV_OUT SparseMat& hist, int dims, CV_CARRAY(dims) const int* histSize,
+                          CV_CUSTOM_CARRAY((dims,histSize,uniform)) const float** ranges,
+                          bool uniform=true, bool accumulate=false );
 
 //! computes back projection for the set of images
-CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
-                                 const int* channels, const MatND& hist,
-                                 Mat& backProject, const float** ranges,
+CV_EXPORTS void calcBackProject( CV_CARRAY(nimages) const Mat* images, int nimages,
+                                 CV_CARRAY(hist.dims) const int* channels, const Mat& hist,
+                                 CV_OUT Mat& backProject,
+                                 CV_CUSTOM_CARRAY(hist) const float** ranges,
                                  double scale=1, bool uniform=true );
 
 //! computes back projection for the set of images
-CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
-                                 const int* channels, const SparseMat& hist,
-                                 Mat& backProject, const float** ranges,
+CV_EXPORTS void calcBackProject( CV_CARRAY(nimages) const Mat* images, int nimages,
+                                 CV_CARRAY(hist.dims()) const int* channels,
+                                 const SparseMat& hist, CV_OUT Mat& backProject,
+                                 CV_CUSTOM_CARRAY(hist) const float** ranges,
                                  double scale=1, bool uniform=true );
 
 //! compares two histograms stored in dense arrays
-CV_EXPORTS double compareHist( const MatND& H1, const MatND& H2, int method );
+CV_EXPORTS double compareHist( const Mat& H1, const Mat& H2, int method );
 
 //! compares two histograms stored in sparse arrays
 CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
 
 //! normalizes the grayscale image brightness and contrast by normalizing its histogram
-CV_EXPORTS void equalizeHist( const Mat& src, Mat& dst );
+CV_EXPORTS void equalizeHist( const Mat& src, CV_OUT Mat& dst );
 
 //! segments the image using watershed algorithm
 CV_EXPORTS void watershed( const Mat& image, Mat& markers );
 
 //! filters image using meanshift algorithm
-CV_EXPORTS void pyrMeanShiftFiltering( const Mat& src, Mat& dst,
+CV_EXPORTS void pyrMeanShiftFiltering( const Mat& src, CV_OUT Mat& dst,
                     double sp, double sr, int maxLevel=1,
                     TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
 
@@ -698,14 +703,14 @@ enum
 
 //! restores the damaged image areas using one of the available intpainting algorithms
 CV_EXPORTS void inpaint( const Mat& src, const Mat& inpaintMask,
-                         Mat& dst, double inpaintRange, int flags );
+                         CV_OUT Mat& dst, double inpaintRange, int flags );
 
 //! builds the discrete Voronoi diagram
-CV_EXPORTS void distanceTransform( const Mat& src, Mat& dst, Mat& labels,
+CV_EXPORTS void distanceTransform( const Mat& src, CV_OUT Mat& dst, Mat& labels,
                                    int distanceType, int maskSize );
 
 //! computes the distance transform map
-CV_EXPORTS void distanceTransform( const Mat& src, Mat& dst,
+CV_EXPORTS void distanceTransform( const Mat& src, CV_OUT Mat& dst,
                                    int distanceType, int maskSize );
 
 enum { FLOODFILL_FIXED_RANGE = 1 << 16,
@@ -724,7 +729,7 @@ CV_EXPORTS int floodFill( Mat& image, Mat& mask,
                           int flags=4 );
 
 //! converts image from one color space to another
-CV_EXPORTS void cvtColor( const Mat& src, Mat& dst, int code, int dstCn=0 );
+CV_EXPORTS void cvtColor( const Mat& src, CV_OUT Mat& dst, int code, int dstCn=0 );
 
 //! raster image moments
 class CV_EXPORTS Moments
@@ -758,7 +763,7 @@ CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
 enum { TM_SQDIFF=0, TM_SQDIFF_NORMED=1, TM_CCORR=2, TM_CCORR_NORMED=3, TM_CCOEFF=4, TM_CCOEFF_NORMED=5 };
 
 //! computes the proximity map for the raster template and the image where the template is searched for
-CV_EXPORTS void matchTemplate( const Mat& image, const Mat& templ, Mat& result, int method );
+CV_EXPORTS void matchTemplate( const Mat& image, const Mat& templ, CV_OUT Mat& result, int method );
 
 //! mode of the contour retrieval algorithm
 enum
@@ -779,12 +784,12 @@ enum
 };
 
 //! retrieves contours and the hierarchical information from black-n-white image.
-CV_EXPORTS void findContours( Mat& image, vector<vector<Point> >& contours,
+CV_EXPORTS void findContours( Mat& image, CV_OUT vector<vector<Point> >& contours,
                               vector<Vec4i>& hierarchy, int mode,
                               int method, Point offset=Point());
 
 //! retrieves contours from black-n-white image.
-CV_EXPORTS void findContours( Mat& image, vector<vector<Point> >& contours,
+CV_EXPORTS void findContours( Mat& image, CV_OUT vector<vector<Point> >& contours,
                               int mode, int method, Point offset=Point());
 
 //! draws contours in the image
@@ -796,11 +801,11 @@ CV_EXPORTS void drawContours( Mat& image, const vector<vector<Point> >& contours
 
 //! approximates contour or a curve using Douglas-Peucker algorithm
 CV_EXPORTS void approxPolyDP( const Mat& curve,
-                              vector<Point>& approxCurve,
+                              CV_OUT vector<Point>& approxCurve,
                               double epsilon, bool closed );
 //! approximates contour or a curve using Douglas-Peucker algorithm
 CV_EXPORTS void approxPolyDP( const Mat& curve,
-                              vector<Point2f>& approxCurve,
+                              CV_OUT vector<Point2f>& approxCurve,
                               double epsilon, bool closed );
 //! computes the contour perimeter (closed=true) or a curve length
 CV_EXPORTS double arcLength( const Mat& curve, bool closed );
@@ -818,11 +823,11 @@ CV_EXPORTS double matchShapes( const Mat& contour1,
                                const Mat& contour2,
                                int method, double parameter );
 //! computes convex hull for a set of 2D points.
-CV_EXPORTS void convexHull( const Mat& points, vector<int>& hull, bool clockwise=false );
+CV_EXPORTS void convexHull( const Mat& points, CV_OUT vector<int>& hull, bool clockwise=false );
 //! computes convex hull for a set of 2D points.
-CV_EXPORTS void convexHull( const Mat& points, vector<Point>& hull, bool clockwise=false );
+CV_EXPORTS void convexHull( const Mat& points, CV_OUT vector<Point>& hull, bool clockwise=false );
 //! computes convex hull for a set of 2D points.
-CV_EXPORTS void convexHull( const Mat& points, vector<Point2f>& hull, bool clockwise=false );
+CV_EXPORTS void convexHull( const Mat& points, CV_OUT vector<Point2f>& hull, bool clockwise=false );
 
 //! returns true iff the contour is convex. Does not support contours with self-intersection
 CV_EXPORTS bool isContourConvex( const Mat& contour );
@@ -831,10 +836,10 @@ CV_EXPORTS bool isContourConvex( const Mat& contour );
 CV_EXPORTS RotatedRect fitEllipse( const Mat& points );
 
 //! fits line to the set of 2D points using M-estimator algorithm
-CV_EXPORTS void fitLine( const Mat& points, Vec4f& line, int distType,
+CV_EXPORTS void fitLine( const Mat& points, CV_OUT Vec4f& line, int distType,
                          double param, double reps, double aeps );
 //! fits line to the set of 3D points using M-estimator algorithm
-CV_EXPORTS void fitLine( const Mat& points, Vec6f& line, int distType,
+CV_EXPORTS void fitLine( const Mat& points, CV_OUT Vec6f& line, int distType,
                          double param, double reps, double aeps );
 //! checks if the point is inside the contour. Optionally computes the signed distance from the point to the contour boundary
 CV_EXPORTS double pointPolygonTest( const Mat& contour,
@@ -845,7 +850,7 @@ CV_EXPORTS Mat estimateRigidTransform( const Mat& A, const Mat& B,
                                        bool fullAffine );
 
 //! computes the best-fit affine transformation that maps one 3D point set to another (RANSAC algorithm is used)
-CV_EXPORTS int estimateAffine3D(const Mat& from, const Mat& to, Mat& out,
+CV_EXPORTS int estimateAffine3D(const Mat& from, const Mat& to, CV_OUT Mat& dst,
                                 vector<uchar>& outliers,
                                 double param1 = 3.0, double param2 = 0.99);
     

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

@@ -247,23 +247,21 @@ public:
     CvNormalBayesClassifier();
     virtual ~CvNormalBayesClassifier();
 
-    CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
+    CV_NO_WRAP CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
         const CvMat* _var_idx=0, const CvMat* _sample_idx=0 );
     
-    virtual bool train( const CvMat* _train_data, const CvMat* _responses,
+    CV_NO_WRAP virtual bool train( const CvMat* _train_data, const CvMat* _responses,
         const CvMat* _var_idx = 0, const CvMat* _sample_idx=0, bool update=false );
    
-    virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
+    CV_NO_WRAP virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
     virtual void clear();
 
-#ifndef SWIG
     CvNormalBayesClassifier( const cv::Mat& _train_data, const cv::Mat& _responses,
                             const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat() );
     virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
                        const cv::Mat& _var_idx = cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
                        bool update=false );
     virtual float predict( const cv::Mat& _samples, cv::Mat* results=0 ) const;
-#endif
     
     virtual void write( CvFileStorage* storage, const char* name ) const;
     virtual void read( CvFileStorage* storage, CvFileNode* node );

modules/objdetect/include/opencv2/objdetect/objdetect.hpp

@@ -271,7 +271,7 @@ namespace cv
 ///////////////////////////// Object Detection ////////////////////////////
 
 CV_EXPORTS void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
-CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& weights, int groupThreshold, double eps=0.2);
+CV_EXPORTS void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
         
 class CV_EXPORTS FeatureEvaluator
 {
@@ -328,7 +328,7 @@ public:
     bool load(const string& filename);
     bool read(const FileNode& node);
     void detectMultiScale( const Mat& image,
-                           vector<Rect>& objects,
+                           CV_OUT vector<Rect>& objects,
                            double scaleFactor=1.1,
                            int minNeighbors=3, int flags=0,
                            Size minSize=Size());
@@ -401,18 +401,18 @@ public:
     virtual void copyTo(HOGDescriptor& c) const;
     
     virtual void compute(const Mat& img,
-                         vector<float>& descriptors,
+                         CV_OUT vector<float>& descriptors,
                          Size winStride=Size(), Size padding=Size(),
                          const vector<Point>& locations=vector<Point>()) const;
-    virtual void detect(const Mat& img, vector<Point>& foundLocations,
+    virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,
                         double hitThreshold=0, Size winStride=Size(),
                         Size padding=Size(),
                         const vector<Point>& searchLocations=vector<Point>()) const;
-    virtual void detectMultiScale(const Mat& img, vector<Rect>& foundLocations,
+    virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,
                                   double hitThreshold=0, Size winStride=Size(),
                                   Size padding=Size(), double scale=1.05,
                                   int groupThreshold=2) const;
-    virtual void computeGradient(const Mat& img, Mat& grad, Mat& angleOfs,
+    virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
                                  Size paddingTL=Size(), Size paddingBR=Size()) const;
     
     static vector<float> getDefaultPeopleDetector();

modules/video/include/opencv2/video/background_segm.hpp

@@ -112,17 +112,11 @@ typedef struct CvBGStatModel
 // 
 
 // Releases memory used by BGStatModel
-CV_INLINE void cvReleaseBGStatModel( CvBGStatModel** bg_model )
-{
-    if( bg_model && *bg_model && (*bg_model)->release )
-        (*bg_model)->release( bg_model );
-}
+CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model );
 
 // Updates statistical model and returns number of found foreground regions
-CV_INLINE int cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel*  bg_model, double learningRate CV_DEFAULT(-1))
-{
-    return bg_model && bg_model->update ? bg_model->update( current_frame, bg_model, learningRate ) : 0;
-}
+CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel*  bg_model,
+                                double learningRate CV_DEFAULT(-1));
 
 // Performs FG post-processing using segmentation
 // (all pixels of a region will be classified as foreground if majority of pixels of the region are FG).
@@ -365,7 +359,8 @@ public:
     //! the virtual destructor
     virtual ~BackgroundSubtractor();
     //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
-    virtual void operator()(const Mat& image, Mat& fgmask, double learningRate=0);
+    virtual CV_WRAP_AS(apply) void operator()(const Mat& image, CV_OUT Mat& fgmask,
+                                              double learningRate=0);
 };
 
 

modules/video/include/opencv2/video/tracking.hpp

@@ -248,8 +248,8 @@ CV_EXPORTS void updateMotionHistory( const Mat& silhouette, Mat& mhi,
                                      double timestamp, double duration );
 
 //! computes the motion gradient orientation image from the motion history image
-CV_EXPORTS void calcMotionGradient( const Mat& mhi, Mat& mask,
-                                    Mat& orientation,
+CV_EXPORTS void calcMotionGradient( const Mat& mhi, CV_OUT Mat& mask,
+                                    CV_OUT Mat& orientation,
                                     double delta1, double delta2,
                                     int apertureSize=3 );
 
@@ -260,11 +260,11 @@ CV_EXPORTS double calcGlobalOrientation( const Mat& orientation, const Mat& mask
 // TODO: need good API for cvSegmentMotion
 
 //! updates the object tracking window using CAMSHIFT algorithm
-CV_EXPORTS RotatedRect CamShift( const Mat& probImage, Rect& window,
+CV_EXPORTS RotatedRect CamShift( const Mat& probImage, CV_OUT Rect& window,
                                  TermCriteria criteria );
 
 //! updates the object tracking window using meanshift algorithm
-CV_EXPORTS int meanShift( const Mat& probImage, Rect& window,
+CV_EXPORTS int meanShift( const Mat& probImage, CV_OUT Rect& window,
                           TermCriteria criteria );
 
 /*!
@@ -313,8 +313,8 @@ enum { OPTFLOW_USE_INITIAL_FLOW=4, OPTFLOW_FARNEBACK_GAUSSIAN=256 };
 
 //! computes sparse optical flow using multi-scale Lucas-Kanade algorithm
 CV_EXPORTS void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
-                           const vector<Point2f>& prevPts, vector<Point2f>& nextPts,
-                           vector<uchar>& status, vector<float>& err,
+                           const vector<Point2f>& prevPts, CV_OUT vector<Point2f>& nextPts,
+                           CV_OUT vector<uchar>& status, CV_OUT vector<float>& err,
                            Size winSize=Size(15,15), int maxLevel=3,
                            TermCriteria criteria=TermCriteria(
                             TermCriteria::COUNT+TermCriteria::EPS,
@@ -323,8 +323,8 @@ CV_EXPORTS void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
                            int flags=0 );
 
 //! computes dense optical flow using Farneback algorithm
-CV_EXPORTS void calcOpticalFlowFarneback( const Mat& prev0, const Mat& next0,
-                           Mat& flow0, double pyr_scale, int levels, int winsize,
+CV_EXPORTS void calcOpticalFlowFarneback( const Mat& prev, const Mat& next,
+                           CV_OUT Mat& flow, double pyr_scale, int levels, int winsize,
                            int iterations, int poly_n, double poly_sigma, int flags );
 
 }

modules/video/src/bgfg_estimation.cpp

@@ -56,9 +56,22 @@ CvBGStatModel* cvCreateBGStatModel( IplImage* first_frame, int model_type, void*
     return bg_model;
 }
 
+void cvReleaseBGStatModel( CvBGStatModel** bg_model )
+{
+    if( bg_model && *bg_model && (*bg_model)->release )
+        (*bg_model)->release( bg_model );
+}
+
+int cvUpdateBGStatModel( IplImage* current_frame,
+                         CvBGStatModel*  bg_model,
+                         double learningRate )
+{
+    return bg_model && bg_model->update ? bg_model->update( current_frame, bg_model, learningRate ) : 0;
+}
+
 
 /* FOREGROUND DETECTOR INTERFACE */
-class CvFGDetectorBase:public CvFGDetector
+class CvFGDetectorBase : public CvFGDetector
 {
 protected:
     CvBGStatModel*  m_pFG;