added dedicated <modname>_init.cpp files with initModule_<modname>() functions...

added dedicated <modname>_init.cpp files with initModule_<modname>() functions and all the relevant structures; made BackgroundSubtractorMOG/MOG2 derivatives from Algorithm; cleaned up MOG2 implementation and made it multi-threaded.

modules/contrib/src/contrib_init.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+

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

@@ -2020,6 +2020,15 @@ public:
 };
 
     
+typedef void (*BinaryFunc)(const uchar* src1, size_t step1,
+                           const uchar* src2, size_t step2,
+                           uchar* dst, size_t step, Size sz,
+                           void*);
+
+CV_EXPORTS BinaryFunc getConvertFunc(int sdepth, int ddepth);
+CV_EXPORTS BinaryFunc getConvertScaleFunc(int sdepth, int ddepth);
+CV_EXPORTS BinaryFunc getCopyMaskFunc(size_t esz);    
+    
 //! swaps two matrices
 CV_EXPORTS void swap(Mat& a, Mat& b);
     

modules/core/src/precomp.hpp

@@ -176,15 +176,6 @@ struct NoVec
 
 extern volatile bool USE_SSE2;
 
-typedef void (*BinaryFunc)(const uchar* src1, size_t step1,
-                           const uchar* src2, size_t step2,
-                           uchar* dst, size_t step, Size sz,
-                           void*);
-
-BinaryFunc getConvertFunc(int sdepth, int ddepth);
-BinaryFunc getConvertScaleFunc(int sdepth, int ddepth);
-BinaryFunc getCopyMaskFunc(size_t esz);
-
 enum { BLOCK_SIZE = 1024 };
 
 #ifdef HAVE_IPP

modules/features2d/src/detectors.cpp

@@ -144,40 +144,6 @@ void GFTTDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, co
     }
 }
 
-static Algorithm* createGFTT() { return new GFTTDetector; }
-static Algorithm* createHarris()
-{
-    GFTTDetector* d = new GFTTDetector;
-    d->set("useHarris", true);
-    return d;
-}
-
-static AlgorithmInfo gftt_info("Feature2D.GFTT", createGFTT);
-static AlgorithmInfo harris_info("Feature2D.HARRIS", createHarris);
-    
-AlgorithmInfo* GFTTDetector::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        GFTTDetector obj;
-        gftt_info.addParam(obj, "nfeatures", obj.nfeatures);
-        gftt_info.addParam(obj, "qualityLevel", obj.qualityLevel);
-        gftt_info.addParam(obj, "minDistance", obj.minDistance);
-        gftt_info.addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
-        gftt_info.addParam(obj, "k", obj.k);
-        
-        harris_info.addParam(obj, "nfeatures", obj.nfeatures);
-        harris_info.addParam(obj, "qualityLevel", obj.qualityLevel);
-        harris_info.addParam(obj, "minDistance", obj.minDistance);
-        harris_info.addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
-        harris_info.addParam(obj, "k", obj.k);
-        
-        initialized = true;
-    }
-    return &gftt_info;
-}
-
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
 /*
@@ -215,29 +181,6 @@ void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypo
 
     KeyPointsFilter::runByPixelsMask( keypoints, mask );
 }
-    
-
-static Algorithm* createDense() { return new DenseFeatureDetector; }
-static AlgorithmInfo dense_info("Feature2D.Dense", createDense);
-    
-AlgorithmInfo* DenseFeatureDetector::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        DenseFeatureDetector obj;
-        dense_info.addParam(obj, "initFeatureScale", obj.initFeatureScale);
-        dense_info.addParam(obj, "featureScaleLevels", obj.featureScaleLevels);
-        dense_info.addParam(obj, "featureScaleMul", obj.featureScaleMul);
-        dense_info.addParam(obj, "initXyStep", obj.initXyStep);
-        dense_info.addParam(obj, "initImgBound", obj.initImgBound);
-        dense_info.addParam(obj, "varyXyStepWithScale", obj.varyXyStepWithScale);
-        dense_info.addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale);
-        
-        initialized = true;
-    }
-    return &dense_info;
-}    
 
 /*
  *  GridAdaptedFeatureDetector
@@ -359,161 +302,6 @@ void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoin
     if( !mask.empty() )
         KeyPointsFilter::runByPixelsMask( keypoints, mask );
 }
-    
-    
-/////////////////////// AlgorithmInfo for various detector & descriptors ////////////////////////////
-
-/* NOTE!!!
-   All the AlgorithmInfo-related stuff should be in the same file as initModule_features2d().
-   Otherwise, linker may throw away some seemingly unused stuff.
-*/
-    
-static Algorithm* createBRIEF() { return new BriefDescriptorExtractor; }
-static AlgorithmInfo& brief_info()
-{
-    static AlgorithmInfo brief_info_var("Feature2D.BRIEF", createBRIEF);
-    return brief_info_var;
-}
-
-static AlgorithmInfo& brief_info_auto = brief_info();
-
-AlgorithmInfo* BriefDescriptorExtractor::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        BriefDescriptorExtractor brief;
-        brief_info().addParam(brief, "bytes", brief.bytes_);
-        
-        initialized = true;
-    }
-    return &brief_info();
-}
-    
-///////////////////////////////////////////////////////////////////////////////////////////////////////////
-    
-static Algorithm* createFAST() { return new FastFeatureDetector; }
-static AlgorithmInfo& fast_info()
-{
-    static AlgorithmInfo fast_info_var("Feature2D.FAST", createFAST);
-    return fast_info_var;
-}
-
-static AlgorithmInfo& fast_info_auto = fast_info();
-
-AlgorithmInfo* FastFeatureDetector::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        FastFeatureDetector obj;
-        fast_info().addParam(obj, "threshold", obj.threshold);
-        fast_info().addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
-        
-        initialized = true;
-    }
-    return &fast_info();
-}
-    
-
-///////////////////////////////////////////////////////////////////////////////////////////////////////////
-    
-static Algorithm* createStarDetector() { return new StarDetector; }
-static AlgorithmInfo& star_info()
-{
-    static AlgorithmInfo star_info_var("Feature2D.STAR", createStarDetector);
-    return star_info_var;
-}
-
-static AlgorithmInfo& star_info_auto = star_info();
-
-AlgorithmInfo* StarDetector::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        StarDetector obj;
-        star_info().addParam(obj, "maxSize", obj.maxSize);
-        star_info().addParam(obj, "responseThreshold", obj.responseThreshold);
-        star_info().addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
-        star_info().addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
-        star_info().addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize);
-        
-        initialized = true;
-    }
-    return &star_info();
-}    
-    
-///////////////////////////////////////////////////////////////////////////////////////////////////////////
-    
-static Algorithm* createMSER() { return new MSER; }
-static AlgorithmInfo& mser_info()
-{
-    static AlgorithmInfo mser_info_var("Feature2D.MSER", createMSER);
-    return mser_info_var;
-}
 
-static AlgorithmInfo& mser_info_auto = mser_info();
 
-AlgorithmInfo* MSER::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        MSER obj;
-        mser_info().addParam(obj, "delta", obj.delta);
-        mser_info().addParam(obj, "minArea", obj.minArea);
-        mser_info().addParam(obj, "maxArea", obj.maxArea);
-        mser_info().addParam(obj, "maxVariation", obj.maxVariation);
-        mser_info().addParam(obj, "minDiversity", obj.minDiversity);
-        mser_info().addParam(obj, "maxEvolution", obj.maxEvolution);
-        mser_info().addParam(obj, "areaThreshold", obj.areaThreshold);
-        mser_info().addParam(obj, "minMargin", obj.minMargin);
-        mser_info().addParam(obj, "edgeBlurSize", obj.edgeBlurSize);
-        
-        initialized = true;
-    }
-    return &mser_info();
-}
-    
-    
-///////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-static Algorithm* createORB() { return new ORB; }
-static AlgorithmInfo& orb_info()
-{
-    static AlgorithmInfo orb_info_var("Feature2D.ORB", createORB);
-    return orb_info_var;
-}
-
-static AlgorithmInfo& orb_info_auto = orb_info();
-
-AlgorithmInfo* ORB::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        ORB obj;
-        orb_info().addParam(obj, "nFeatures", obj.nfeatures);
-        orb_info().addParam(obj, "scaleFactor", obj.scaleFactor);
-        orb_info().addParam(obj, "nLevels", obj.nlevels);
-        orb_info().addParam(obj, "firstLevel", obj.firstLevel);
-        orb_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
-        orb_info().addParam(obj, "patchSize", obj.patchSize);
-        orb_info().addParam(obj, "WTA_K", obj.WTA_K);
-        orb_info().addParam(obj, "scoreType", obj.scoreType);
-        
-        initialized = true;
-    }
-    return &orb_info();
-}
-    
-bool initModule_features2d(void)
-{
-    Ptr<Algorithm> brief = createBRIEF(), orb = createORB(),
-        star = createStarDetector(), fastd = createFAST(), mser = createMSER();
-    return brief->info() != 0 && orb->info() != 0 && star->info() != 0 &&
-        fastd->info() != 0 && mser->info() != 0;
-}
-    
 }

modules/features2d/src/features2d_init.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+namespace cv
+{
+
+/////////////////////// AlgorithmInfo for various detector & descriptors ////////////////////////////
+
+/* NOTE!!!
+   All the AlgorithmInfo-related stuff should be in the same file as initModule_features2d().
+   Otherwise, linker may throw away some seemingly unused stuff.
+*/
+    
+static Algorithm* createBRIEF() { return new BriefDescriptorExtractor; }
+static AlgorithmInfo& brief_info()
+{
+    static AlgorithmInfo brief_info_var("Feature2D.BRIEF", createBRIEF);
+    return brief_info_var;
+}
+
+static AlgorithmInfo& brief_info_auto = brief_info();
+
+AlgorithmInfo* BriefDescriptorExtractor::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        BriefDescriptorExtractor brief;
+        brief_info().addParam(brief, "bytes", brief.bytes_);
+        
+        initialized = true;
+    }
+    return &brief_info();
+}
+    
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+    
+static Algorithm* createFAST() { return new FastFeatureDetector; }
+static AlgorithmInfo& fast_info()
+{
+    static AlgorithmInfo fast_info_var("Feature2D.FAST", createFAST);
+    return fast_info_var;
+}
+
+static AlgorithmInfo& fast_info_auto = fast_info();
+
+AlgorithmInfo* FastFeatureDetector::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        FastFeatureDetector obj;
+        fast_info().addParam(obj, "threshold", obj.threshold);
+        fast_info().addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
+        
+        initialized = true;
+    }
+    return &fast_info();
+}
+    
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+    
+static Algorithm* createStarDetector() { return new StarDetector; }
+static AlgorithmInfo& star_info()
+{
+    static AlgorithmInfo star_info_var("Feature2D.STAR", createStarDetector);
+    return star_info_var;
+}
+
+static AlgorithmInfo& star_info_auto = star_info();
+
+AlgorithmInfo* StarDetector::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        StarDetector obj;
+        star_info().addParam(obj, "maxSize", obj.maxSize);
+        star_info().addParam(obj, "responseThreshold", obj.responseThreshold);
+        star_info().addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
+        star_info().addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
+        star_info().addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize);
+        
+        initialized = true;
+    }
+    return &star_info();
+}    
+    
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+    
+static Algorithm* createMSER() { return new MSER; }
+static AlgorithmInfo& mser_info()
+{
+    static AlgorithmInfo mser_info_var("Feature2D.MSER", createMSER);
+    return mser_info_var;
+}
+
+static AlgorithmInfo& mser_info_auto = mser_info();
+
+AlgorithmInfo* MSER::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        MSER obj;
+        mser_info().addParam(obj, "delta", obj.delta);
+        mser_info().addParam(obj, "minArea", obj.minArea);
+        mser_info().addParam(obj, "maxArea", obj.maxArea);
+        mser_info().addParam(obj, "maxVariation", obj.maxVariation);
+        mser_info().addParam(obj, "minDiversity", obj.minDiversity);
+        mser_info().addParam(obj, "maxEvolution", obj.maxEvolution);
+        mser_info().addParam(obj, "areaThreshold", obj.areaThreshold);
+        mser_info().addParam(obj, "minMargin", obj.minMargin);
+        mser_info().addParam(obj, "edgeBlurSize", obj.edgeBlurSize);
+        
+        initialized = true;
+    }
+    return &mser_info();
+}
+    
+    
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+static Algorithm* createORB() { return new ORB; }
+static AlgorithmInfo& orb_info()
+{
+    static AlgorithmInfo orb_info_var("Feature2D.ORB", createORB);
+    return orb_info_var;
+}
+
+static AlgorithmInfo& orb_info_auto = orb_info();
+
+AlgorithmInfo* ORB::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        ORB obj;
+        orb_info().addParam(obj, "nFeatures", obj.nfeatures);
+        orb_info().addParam(obj, "scaleFactor", obj.scaleFactor);
+        orb_info().addParam(obj, "nLevels", obj.nlevels);
+        orb_info().addParam(obj, "firstLevel", obj.firstLevel);
+        orb_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
+        orb_info().addParam(obj, "patchSize", obj.patchSize);
+        orb_info().addParam(obj, "WTA_K", obj.WTA_K);
+        orb_info().addParam(obj, "scoreType", obj.scoreType);
+        
+        initialized = true;
+    }
+    return &orb_info();
+}
+
+static Algorithm* createGFTT() { return new GFTTDetector; }
+static Algorithm* createHarris()
+{
+    GFTTDetector* d = new GFTTDetector;
+    d->set("useHarris", true);
+    return d;
+}
+
+static AlgorithmInfo gftt_info("Feature2D.GFTT", createGFTT);
+static AlgorithmInfo harris_info("Feature2D.HARRIS", createHarris);
+    
+AlgorithmInfo* GFTTDetector::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        GFTTDetector obj;
+        gftt_info.addParam(obj, "nfeatures", obj.nfeatures);
+        gftt_info.addParam(obj, "qualityLevel", obj.qualityLevel);
+        gftt_info.addParam(obj, "minDistance", obj.minDistance);
+        gftt_info.addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
+        gftt_info.addParam(obj, "k", obj.k);
+        
+        harris_info.addParam(obj, "nfeatures", obj.nfeatures);
+        harris_info.addParam(obj, "qualityLevel", obj.qualityLevel);
+        harris_info.addParam(obj, "minDistance", obj.minDistance);
+        harris_info.addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
+        harris_info.addParam(obj, "k", obj.k);
+        
+        initialized = true;
+    }
+    return &gftt_info;
+}
+
+static Algorithm* createDense() { return new DenseFeatureDetector; }
+static AlgorithmInfo dense_info("Feature2D.Dense", createDense);
+    
+AlgorithmInfo* DenseFeatureDetector::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        DenseFeatureDetector obj;
+        dense_info.addParam(obj, "initFeatureScale", obj.initFeatureScale);
+        dense_info.addParam(obj, "featureScaleLevels", obj.featureScaleLevels);
+        dense_info.addParam(obj, "featureScaleMul", obj.featureScaleMul);
+        dense_info.addParam(obj, "initXyStep", obj.initXyStep);
+        dense_info.addParam(obj, "initImgBound", obj.initImgBound);
+        dense_info.addParam(obj, "varyXyStepWithScale", obj.varyXyStepWithScale);
+        dense_info.addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale);
+        
+        initialized = true;
+    }
+    return &dense_info;
+}    
+
+bool initModule_features2d(void)
+{
+    Ptr<Algorithm> brief = createBRIEF(), orb = createORB(),
+        star = createStarDetector(), fastd = createFAST(), mser = createMSER(),
+        dense = createDense(), gftt = createGFTT(), harris = createHarris();
+        
+    return brief->info() != 0 && orb->info() != 0 && star->info() != 0 &&
+        fastd->info() != 0 && mser->info() != 0 && dense->info() != 0 &&
+        gftt->info() != 0 && harris->info() != 0;
+}
+
+}
+

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

@@ -3365,6 +3365,7 @@ typedef struct CvGaussBGModel
     CvGaussBGStatModelParams   params;    
     CvGaussBGPoint*            g_point;    
     int                        countFrames;
+    void*                      mog;
 } CvGaussBGModel;
 
 

modules/legacy/src/bgfg_gaussmix.cpp

@@ -50,10 +50,9 @@ icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
     
     if( *bg_model )
     {
-        delete (cv::Mat*)((*bg_model)->g_point);
+        delete (cv::BackgroundSubtractorMOG*)((*bg_model)->mog);
         cvReleaseImage( &(*bg_model)->background );
         cvReleaseImage( &(*bg_model)->foreground );
-        cvReleaseMemStorage(&(*bg_model)->storage);
         memset( *bg_model, 0, sizeof(**bg_model) );
         delete *bg_model;
         *bg_model = 0;
@@ -64,70 +63,15 @@ icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
 static int CV_CDECL
 icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel*  bg_model, double learningRate )
 {
-    int region_count = 0;
-    
     cv::Mat image = cv::cvarrToMat(curr_frame), mask = cv::cvarrToMat(bg_model->foreground);
     
-    cv::BackgroundSubtractorMOG mog;
-    mog.bgmodel = *(cv::Mat*)bg_model->g_point;
-    mog.frameSize = mog.bgmodel.data ? cv::Size(cvGetSize(curr_frame)) : cv::Size();
-    mog.frameType = image.type();
-    
-    mog.nframes = bg_model->countFrames;
-    mog.history = bg_model->params.win_size;
-    mog.nmixtures = bg_model->params.n_gauss;
-    mog.varThreshold = bg_model->params.std_threshold*bg_model->params.std_threshold;
-    mog.backgroundRatio = bg_model->params.bg_threshold;
-    
-    mog(image, mask, learningRate);
-    
-    bg_model->countFrames = mog.nframes;
-    if( ((cv::Mat*)bg_model->g_point)->data != mog.bgmodel.data )
-        *((cv::Mat*)bg_model->g_point) = mog.bgmodel;
-    
-    //foreground filtering
-    
-    //filter small regions
-    cvClearMemStorage(bg_model->storage);
-    
-    //cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_OPEN, 1 );
-    //cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_CLOSE, 1 );
-    
-#if 0
-    CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
-    cvFindContours( bg_model->foreground, bg_model->storage, &first_seq, sizeof(CvContour), CV_RETR_LIST );
-    for( seq = first_seq; seq; seq = seq->h_next )
-    {
-        CvContour* cnt = (CvContour*)seq;
-        if( cnt->rect.width * cnt->rect.height < bg_model->params.minArea )
-        {
-            //delete small contour
-            prev_seq = seq->h_prev;
-            if( prev_seq )
-            {
-                prev_seq->h_next = seq->h_next;
-                if( seq->h_next ) seq->h_next->h_prev = prev_seq;
-            }
-            else
-            {
-                first_seq = seq->h_next;
-                if( seq->h_next ) seq->h_next->h_prev = NULL;
-            }
-        }
-        else
-        {
-            region_count++;
-        }
-    }
-    bg_model->foreground_regions = first_seq;
-    cvZero(bg_model->foreground);
-    cvDrawContours(bg_model->foreground, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1);
-#endif
+    cv::BackgroundSubtractorMOG* mog = (cv::BackgroundSubtractorMOG*)(bg_model->mog);
+    CV_Assert(mog != 0);
     
-    CvMat _mask = mask;
-    cvCopy(&_mask, bg_model->foreground);
+    (*mog)(image, mask, learningRate);    
+    bg_model->countFrames++;
     
-    return region_count;
+    return 0;
 }
 
 CV_IMPL CvBGStatModel*
@@ -161,15 +105,17 @@ cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parame
     
     bg_model->params = params;
     
-    //prepare storages
-    bg_model->g_point = (CvGaussBGPoint*)new cv::Mat();
+    cv::BackgroundSubtractorMOG* mog =
+        new cv::BackgroundSubtractorMOG(params.win_size,
+                                        params.n_gauss,
+                                        params.bg_threshold,
+                                        params.variance_init);
     
-    bg_model->background = cvCreateImage(cvSize(first_frame->width,
-                                                first_frame->height), IPL_DEPTH_8U, first_frame->nChannels);
-    bg_model->foreground = cvCreateImage(cvSize(first_frame->width,
-                                                first_frame->height), IPL_DEPTH_8U, 1);
+    bg_model->mog = mog;
     
-    bg_model->storage = cvCreateMemStorage();
+    CvSize sz = cvGetSize(first_frame);
+    bg_model->background = cvCreateImage(sz, IPL_DEPTH_8U, first_frame->nChannels);
+    bg_model->foreground = cvCreateImage(sz, IPL_DEPTH_8U, 1);
     
     bg_model->countFrames = 0;
     

modules/ml/src/em.cpp

@@ -671,29 +671,6 @@ void EM::read(const FileNode& fn)
     computeLogWeightDivDet();
 }
 
-static Algorithm* createEM()
-{
-    return new EM;
-}
-static AlgorithmInfo em_info("StatModel.EM", createEM);
-
-AlgorithmInfo* EM::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        EM obj;
-        em_info.addParam(obj, "nclusters", obj.nclusters, true);
-        em_info.addParam(obj, "covMatType", obj.covMatType, true);
-
-        em_info.addParam(obj, "weights", obj.weights, true);
-        em_info.addParam(obj, "means", obj.means, true);
-        em_info.addParam(obj, "covs", obj.covs, true);
-
-        initialized = true;
-    }
-    return &em_info;
-}
 } // namespace cv
 
 /* End of file. */

modules/ml/src/ml_init.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+namespace cv
+{
+
+static Algorithm* createEM()
+{
+    return new EM;
+}
+static AlgorithmInfo em_info("StatModel.EM", createEM);
+
+AlgorithmInfo* EM::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        EM obj;
+        em_info.addParam(obj, "nclusters", obj.nclusters);
+        em_info.addParam(obj, "covMatType", obj.covMatType);
+        em_info.addParam(obj, "maxIters", obj.maxIters);
+        em_info.addParam(obj, "epsilon", obj.epsilon);
+
+        em_info.addParam(obj, "weights", obj.weights, true);
+        em_info.addParam(obj, "means", obj.means, true);
+        em_info.addParam(obj, "covs", obj.covs, true);
+
+        initialized = true;
+    }
+    return &em_info;
+}
+
+bool initModule_ml(void)
+{
+    Ptr<Algorithm> em = createEM();
+    return em->info() != 0;
+}
+
+}

modules/nonfree/src/nonfree_init.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+namespace cv
+{
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+    
+static Algorithm* createSURF()
+{
+    return new SURF;
+}
+
+static AlgorithmInfo& surf_info()
+{
+    static AlgorithmInfo surf_info_var("Feature2D.SURF", createSURF);
+    return surf_info_var;
+}
+
+static AlgorithmInfo& surf_info_auto = surf_info();
+
+AlgorithmInfo* SURF::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        SURF obj;
+        surf_info().addParam(obj, "hessianThreshold", obj.hessianThreshold);
+        surf_info().addParam(obj, "nOctaves", obj.nOctaves);
+        surf_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
+        surf_info().addParam(obj, "extended", obj.extended);
+        surf_info().addParam(obj, "upright", obj.upright);
+        
+        initialized = true;
+    }
+    return &surf_info();
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+static Algorithm* createSIFT() { return new SIFT; }
+
+static AlgorithmInfo& sift_info()
+{
+    static AlgorithmInfo sift_info_var("Feature2D.SIFT", createSIFT);
+    return sift_info_var;
+}
+
+static AlgorithmInfo& sift_info_auto = sift_info();
+
+AlgorithmInfo* SIFT::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        SIFT obj;
+        sift_info().addParam(obj, "nFeatures", obj.nfeatures);
+        sift_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
+        sift_info().addParam(obj, "contrastThreshold", obj.contrastThreshold);
+        sift_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
+        sift_info().addParam(obj, "sigma", obj.sigma);
+        
+        initialized = true;
+    }
+    return &sift_info();
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////    
+    
+bool initModule_nonfree(void)
+{
+    Ptr<Algorithm> sift = createSIFT(), surf = createSURF();
+    return sift->info() != 0 && surf->info() != 0;
+}
+
+}
+    
\ No newline at end of file

modules/nonfree/src/precomp.cpp

@@ -7,10 +7,11 @@
 //  copy or use the software.
 //
 //
-//                        Intel License Agreement
+//                          License Agreement
 //                For Open Source Computer Vision Library
 //
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
 // Third party copyrights are property of their respective owners.
 //
 // Redistribution and use in source and binary forms, with or without modification,
@@ -23,7 +24,7 @@
 //     this list of conditions and the following disclaimer in the documentation
 //     and/or other materials provided with the distribution.
 //
-//   * The name of Intel Corporation may not be used to endorse or promote products
+//   * The name of the copyright holders may not be used to endorse or promote products
 //     derived from this software without specific prior written permission.
 //
 // This software is provided by the copyright holders and contributors "as is" and

modules/nonfree/src/sift.cpp

@@ -7,10 +7,11 @@
 //  copy or use the software.
 //
 //
-//                        Intel License Agreement
+//                          License Agreement
 //                For Open Source Computer Vision Library
 //
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
 // Third party copyrights are property of their respective owners.
 //
 // Redistribution and use in source and binary forms, with or without modification,
@@ -23,7 +24,7 @@
 //     this list of conditions and the following disclaimer in the documentation
 //     and/or other materials provided with the distribution.
 //
-//   * The name of Intel Corporation may not be used to endorse or promote products
+//   * The name of the copyright holders may not be used to endorse or promote products
 //     derived from this software without specific prior written permission.
 //
 // This software is provided by the copyright holders and contributors "as is" and

modules/nonfree/src/surf.cpp

@@ -938,76 +938,6 @@ void SURF::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat&
 void SURF::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
 {
     (*this)(image, Mat(), keypoints, descriptors, true);
-}    
-
-    
-///////////////////////////////////////////////////////////////////////////////////////////////////////////
-    
-static Algorithm* createSURF()
-{
-    return new SURF;
-}
-
-static AlgorithmInfo& surf_info()
-{
-    static AlgorithmInfo surf_info_var("Feature2D.SURF", createSURF);
-    return surf_info_var;
-}
-
-static AlgorithmInfo& surf_info_auto = surf_info();
-
-AlgorithmInfo* SURF::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        SURF obj;
-        surf_info().addParam(obj, "hessianThreshold", obj.hessianThreshold);
-        surf_info().addParam(obj, "nOctaves", obj.nOctaves);
-        surf_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
-        surf_info().addParam(obj, "extended", obj.extended);
-        surf_info().addParam(obj, "upright", obj.upright);
-        
-        initialized = true;
-    }
-    return &surf_info();
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-static Algorithm* createSIFT() { return new SIFT; }
-
-static AlgorithmInfo& sift_info()
-{
-    static AlgorithmInfo sift_info_var("Feature2D.SIFT", createSIFT);
-    return sift_info_var;
-}
-
-static AlgorithmInfo& sift_info_auto = sift_info();
-
-AlgorithmInfo* SIFT::info() const
-{
-    static volatile bool initialized = false;
-    if( !initialized )
-    {
-        SIFT obj;
-        sift_info().addParam(obj, "nFeatures", obj.nfeatures);
-        sift_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
-        sift_info().addParam(obj, "contrastThreshold", obj.contrastThreshold);
-        sift_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
-        sift_info().addParam(obj, "sigma", obj.sigma);
-        
-        initialized = true;
-    }
-    return &sift_info();
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////////////////    
-    
-bool initModule_nonfree(void)
-{
-    Ptr<Algorithm> sift = createSIFT(), surf = createSURF();
-    return sift->info() != 0 && surf->info() != 0;
 }
 
 }

modules/objdetect/src/objdetect_init.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"

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

@@ -54,7 +54,7 @@ namespace cv
  The class is only used to define the common interface for
  the whole family of background/foreground segmentation algorithms.
 */
-class CV_EXPORTS_W BackgroundSubtractor
+class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
 {
 public:
     //! the virtual destructor
@@ -93,6 +93,9 @@ public:
     //! re-initiaization method
     virtual void initialize(Size frameSize, int frameType);
     
+    virtual AlgorithmInfo* info() const;
+
+protected:    
     Size frameSize;
     int frameType;
     Mat bgmodel;
@@ -130,6 +133,9 @@ public:
     //! re-initiaization method
     virtual void initialize(Size frameSize, int frameType);
     
+    virtual AlgorithmInfo* info() const;
+    
+protected:    
     Size frameSize;
     int frameType;
     Mat bgmodel;
@@ -137,24 +143,24 @@ public:
     int nframes;
     int history;
     int nmixtures;
-    //! here it is the maximum allowed number of mixture comonents.
+    //! here it is the maximum allowed number of mixture components.
     //! Actual number is determined dynamically per pixel
-    float varThreshold;
-    // threshold on the squared Mahalan. dist. to decide if it is well described
-    //by the background model or not. Related to Cthr from the paper.
-    //This does not influence the update of the background. A typical value could be 4 sigma
-    //and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
+    double varThreshold;
+    // threshold on the squared Mahalanobis distance to decide if it is well described
+    // by the background model or not. Related to Cthr from the paper.
+    // This does not influence the update of the background. A typical value could be 4 sigma
+    // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
     
     /////////////////////////
-    //less important parameters - things you might change but be carefull
+    // less important parameters - things you might change but be carefull
     ////////////////////////
     float backgroundRatio;
-    //corresponds to fTB=1-cf from the paper
-    //TB - threshold when the component becomes significant enough to be included into
-    //the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
-    //For alpha=0.001 it means that the mode should exist for approximately 105 frames before
-    //it is considered foreground
-    //float noiseSigma;
+    // corresponds to fTB=1-cf from the paper
+    // TB - threshold when the component becomes significant enough to be included into
+    // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
+    // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
+    // it is considered foreground
+    // float noiseSigma;
     float varThresholdGen;
     //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
     //when a sample is close to the existing components. If it is not close

modules/video/src/bgfg_gaussmix.cpp

@@ -134,17 +134,19 @@ template<typename VT> struct MixData
 };
 
     
-static void process8uC1( BackgroundSubtractorMOG& obj, const Mat& image, Mat& fgmask, double learningRate )
+static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
+                         Mat& bgmodel, int nmixtures, double backgroundRatio,
+                         double varThreshold, double noiseSigma )
 {
     int x, y, k, k1, rows = image.rows, cols = image.cols;
-    float alpha = (float)learningRate, T = (float)obj.backgroundRatio, vT = (float)obj.varThreshold;
-    int K = obj.nmixtures;
-    MixData<float>* mptr = (MixData<float>*)obj.bgmodel.data;
+    float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
+    int K = nmixtures;
+    MixData<float>* mptr = (MixData<float>*)bgmodel.data;
     
     const float w0 = (float)defaultInitialWeight;
     const float sk0 = (float)(w0/(defaultNoiseSigma*2));
     const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
-    const float minVar = (float)(obj.noiseSigma*obj.noiseSigma);
+    const float minVar = (float)(noiseSigma*noiseSigma);
     
     for( y = 0; y < rows; y++ )
     {
@@ -259,17 +261,20 @@ static void process8uC1( BackgroundSubtractorMOG& obj, const Mat& image, Mat& fg
     }
 }
 
-static void process8uC3( BackgroundSubtractorMOG& obj, const Mat& image, Mat& fgmask, double learningRate )
+    
+static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
+                         Mat& bgmodel, int nmixtures, double backgroundRatio,
+                         double varThreshold, double noiseSigma )
 {
     int x, y, k, k1, rows = image.rows, cols = image.cols;
-    float alpha = (float)learningRate, T = (float)obj.backgroundRatio, vT = (float)obj.varThreshold;
-    int K = obj.nmixtures;
+    float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
+    int K = nmixtures;
     
     const float w0 = (float)defaultInitialWeight;
     const float sk0 = (float)(w0/(defaultNoiseSigma*2*sqrt(3.)));
     const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
-    const float minVar = (float)(obj.noiseSigma*obj.noiseSigma);
-    MixData<Vec3f>* mptr = (MixData<Vec3f>*)obj.bgmodel.data;
+    const float minVar = (float)(noiseSigma*noiseSigma);
+    MixData<Vec3f>* mptr = (MixData<Vec3f>*)bgmodel.data;
     
     for( y = 0; y < rows; y++ )
     {
@@ -403,9 +408,9 @@ void BackgroundSubtractorMOG::operator()(InputArray _image, OutputArray _fgmask,
     CV_Assert(learningRate >= 0);
     
     if( image.type() == CV_8UC1 )
-        process8uC1( *this, image, fgmask, learningRate );
+        process8uC1( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
     else if( image.type() == CV_8UC3 )
-        process8uC3( *this, image, fgmask, learningRate );
+        process8uC3( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
     else
         CV_Error( CV_StsUnsupportedFormat, "Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOG" );
 }

modules/video/src/video_init.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+namespace cv
+{
+    
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+static Algorithm* createMOG()
+{
+    return new BackgroundSubtractorMOG;
+}
+
+static AlgorithmInfo& mog_info()
+{
+    static AlgorithmInfo mog_info_var("BackgroundSubtractor.MOG", createMOG);
+    return mog_info_var;
+}
+
+static AlgorithmInfo& mog_info_auto = mog_info();
+
+AlgorithmInfo* BackgroundSubtractorMOG::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        BackgroundSubtractorMOG obj;
+        
+        mog_info().addParam(obj, "history", obj.history);
+        mog_info().addParam(obj, "nmixtures", obj.nmixtures);
+        mog_info().addParam(obj, "backgroundRatio", obj.backgroundRatio);
+        mog_info().addParam(obj, "noiseSigma", obj.noiseSigma);
+        
+        initialized = true;
+    }
+    return &mog_info();
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+static Algorithm* createMOG2()
+{
+    return new BackgroundSubtractorMOG2;
+}
+
+static AlgorithmInfo& mog2_info()
+{
+    static AlgorithmInfo mog2_info_var("BackgroundSubtractor.MOG2", createMOG2);
+    return mog2_info_var;
+}
+
+static AlgorithmInfo& mog2_info_auto = mog2_info();
+
+AlgorithmInfo* BackgroundSubtractorMOG2::info() const
+{
+    static volatile bool initialized = false;
+    if( !initialized )
+    {
+        BackgroundSubtractorMOG2 obj;
+        
+        mog2_info().addParam(obj, "history", obj.history);
+        mog2_info().addParam(obj, "varThreshold", obj.varThreshold);
+        mog2_info().addParam(obj, "detectShadows", obj.bShadowDetection);
+        
+        initialized = true;
+    }
+    return &mog2_info();
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////    
+
+bool initModule_video(void)
+{
+    Ptr<Algorithm> mog = createMOG(), mog2 = createMOG2();
+    return mog->info() != 0 && mog2->info() != 0;
+}
+    
+}

samples/cpp/bgfg_segm.cpp

 #include "opencv2/core/core.hpp"
+#include "opencv2/imgproc/imgproc.hpp"
 #include "opencv2/video/background_segm.hpp"
 #include "opencv2/highgui/highgui.hpp"
 #include <stdio.h>
@@ -17,7 +18,7 @@ void help()
 
 const char* keys = 
 {
-	"{c |camera   |false    | use camera or not}"
+	"{c |camera   |true    | use camera or not}"
 	"{fn|file_name|tree.avi | movie file             }"
 };
 
@@ -49,7 +50,8 @@ int main(int argc, const char** argv)
     namedWindow("foreground image", CV_WINDOW_NORMAL);
     namedWindow("mean background image", CV_WINDOW_NORMAL);
 
-    BackgroundSubtractorMOG2 bg_model;
+    BackgroundSubtractorMOG2 bg_model;//(100, 3, 0.3, 5);
+                                     
     Mat img, fgmask, fgimg;
 
     for(;;)
@@ -59,6 +61,8 @@ int main(int argc, const char** argv)
         if( img.empty() )
             break;
         
+        //cvtColor(_img, img, COLOR_BGR2GRAY);
+        
         if( fgimg.empty() )
           fgimg.create(img.size(), img.type());