merged 2.4 into trunk

modules/video/doc/motion_analysis_and_object_tracking.rst

@@ -404,11 +404,11 @@ Updates the predicted state from the measurement.
 BackgroundSubtractor
 --------------------
 
-.. ocv:class:: BackgroundSubtractor
+.. ocv:class:: BackgroundSubtractor : public Algorithm
 
 Base class for background/foreground segmentation. ::
 
-    class BackgroundSubtractor
+    class BackgroundSubtractor : public Algorithm
     {
     public:
         virtual ~BackgroundSubtractor();

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

@@ -0,0 +1,119 @@
+/*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;
+}
+    
+}