Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

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.

Browse Source
pull/13383/head
Vadim Pisarevsky 13 years ago
parent 3d108958e7
commit 5b6b30ba0e
19 changed files with 1041 additions and 1198 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 44
      modules/contrib/src/contrib_init.cpp
  2. 9
      modules/core/include/opencv2/core/core.hpp
  3. 9
      modules/core/src/precomp.hpp
  4. 212
      modules/features2d/src/detectors.cpp
  5. 263
      modules/features2d/src/features2d_init.cpp
  6. 1
      modules/legacy/include/opencv2/legacy/legacy.hpp
  7. 84
      modules/legacy/src/bgfg_gaussmix.cpp
  8. 23
      modules/ml/src/em.cpp
  9. 80
      modules/ml/src/ml_init.cpp
  10. 118
      modules/nonfree/src/nonfree_init.cpp
  11. 7
      modules/nonfree/src/precomp.cpp
  12. 7
      modules/nonfree/src/sift.cpp
  13. 70
      modules/nonfree/src/surf.cpp
  14. 43
      modules/objdetect/src/objdetect_init.cpp
  15. 34
      modules/video/include/opencv2/video/background_segm.hpp
  16. 29
      modules/video/src/bgfg_gaussmix.cpp
  17. 1047
      modules/video/src/bgfg_gaussmix2.cpp
  18. 119
      modules/video/src/video_init.cpp
  19. 8
      samples/cpp/bgfg_segm.cpp

44
modules/contrib/src/contrib_init.cpp
Unescape View File

@ -0,0 +1,44 @@
/*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"

9
modules/core/include/opencv2/core/core.hpp
Unescape View File

@ -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 //! swaps two matrices
CV_EXPORTS void swap(Mat& a, Mat& b); CV_EXPORTS void swap(Mat& a, Mat& b);

9
modules/core/src/precomp.hpp
Unescape View File

@ -176,15 +176,6 @@ struct NoVec
extern volatile bool USE_SSE2; 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 }; enum { BLOCK_SIZE = 1024 };
#ifdef HAVE_IPP #ifdef HAVE_IPP

212
modules/features2d/src/detectors.cpp
Unescape View File

@ -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;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/* /*
@ -216,29 +182,6 @@ void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypo
KeyPointsFilter::runByPixelsMask( keypoints, mask ); 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 * GridAdaptedFeatureDetector
*/ */
@ -361,159 +304,4 @@ void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoin
} }
/////////////////////// 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;
}
} }

263
modules/features2d/src/features2d_init.cpp
Unescape View File

@ -0,0 +1,263 @@
/*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;
}
}

1
modules/legacy/include/opencv2/legacy/legacy.hpp
Unescape View File

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

84
modules/legacy/src/bgfg_gaussmix.cpp
Unescape View File

@ -50,10 +50,9 @@ icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
if( *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)->background );
cvReleaseImage( &(*bg_model)->foreground ); cvReleaseImage( &(*bg_model)->foreground );
cvReleaseMemStorage(&(*bg_model)->storage);
memset( *bg_model, 0, sizeof(**bg_model) ); memset( *bg_model, 0, sizeof(**bg_model) );
delete *bg_model; delete *bg_model;
*bg_model = 0; *bg_model = 0;
@ -64,70 +63,15 @@ icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
static int CV_CDECL static int CV_CDECL
icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel* bg_model, double learningRate ) 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::Mat image = cv::cvarrToMat(curr_frame), mask = cv::cvarrToMat(bg_model->foreground);
cv::BackgroundSubtractorMOG mog; cv::BackgroundSubtractorMOG* mog = (cv::BackgroundSubtractorMOG*)(bg_model->mog);
mog.bgmodel = *(cv::Mat*)bg_model->g_point; CV_Assert(mog != 0);
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 ); (*mog)(image, mask, learningRate);
//cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_CLOSE, 1 ); bg_model->countFrames++;
#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
CvMat _mask = mask;
cvCopy(&_mask, bg_model->foreground);
return region_count; return 0;
} }
CV_IMPL CvBGStatModel* CV_IMPL CvBGStatModel*
@ -161,15 +105,17 @@ cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parame
bg_model->params = params; bg_model->params = params;
//prepare storages cv::BackgroundSubtractorMOG* mog =
bg_model->g_point = (CvGaussBGPoint*)new cv::Mat(); new cv::BackgroundSubtractorMOG(params.win_size,
params.n_gauss,
params.bg_threshold,
params.variance_init);
bg_model->background = cvCreateImage(cvSize(first_frame->width, bg_model->mog = mog;
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->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; bg_model->countFrames = 0;

23
modules/ml/src/em.cpp
Unescape View File

@ -671,29 +671,6 @@ void EM::read(const FileNode& fn)
computeLogWeightDivDet(); 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 } // namespace cv
/* End of file. */ /* End of file. */

80
modules/ml/src/ml_init.cpp
Unescape View File

@ -0,0 +1,80 @@
/*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;
}
}

118
modules/nonfree/src/nonfree_init.cpp
Unescape View File

@ -0,0 +1,118 @@
/*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;
}
}

7
modules/nonfree/src/precomp.cpp
Unescape View File

@ -7,10 +7,11 @@
// copy or use the software. // copy or use the software.
// //
// //
// Intel License Agreement // License Agreement
// For Open Source Computer Vision Library // 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. // Third party copyrights are property of their respective owners.
// //
// Redistribution and use in source and binary forms, with or without modification, // 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 // this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution. // 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. // derived from this software without specific prior written permission.
// //
// This software is provided by the copyright holders and contributors "as is" and // This software is provided by the copyright holders and contributors "as is" and

7
modules/nonfree/src/sift.cpp
Unescape View File

@ -7,10 +7,11 @@
// copy or use the software. // copy or use the software.
// //
// //
// Intel License Agreement // License Agreement
// For Open Source Computer Vision Library // 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. // Third party copyrights are property of their respective owners.
// //
// Redistribution and use in source and binary forms, with or without modification, // 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 // this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution. // 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. // derived from this software without specific prior written permission.
// //
// This software is provided by the copyright holders and contributors "as is" and // This software is provided by the copyright holders and contributors "as is" and

70
modules/nonfree/src/surf.cpp
Unescape View File

@ -940,74 +940,4 @@ void SURF::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& desc
(*this)(image, Mat(), keypoints, descriptors, true); (*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;
}
} }

43
modules/objdetect/src/objdetect_init.cpp
Unescape View File

@ -0,0 +1,43 @@
/*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"

34
modules/video/include/opencv2/video/background_segm.hpp
Unescape View File

@ -54,7 +54,7 @@ namespace cv
The class is only used to define the common interface for The class is only used to define the common interface for
the whole family of background/foreground segmentation algorithms. the whole family of background/foreground segmentation algorithms.
*/ */
class CV_EXPORTS_W BackgroundSubtractor class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
{ {
public: public:
//! the virtual destructor //! the virtual destructor
@ -93,6 +93,9 @@ public:
//! re-initiaization method //! re-initiaization method
virtual void initialize(Size frameSize, int frameType); virtual void initialize(Size frameSize, int frameType);
virtual AlgorithmInfo* info() const;
protected:
Size frameSize; Size frameSize;
int frameType; int frameType;
Mat bgmodel; Mat bgmodel;
@ -130,6 +133,9 @@ public:
//! re-initiaization method //! re-initiaization method
virtual void initialize(Size frameSize, int frameType); virtual void initialize(Size frameSize, int frameType);
virtual AlgorithmInfo* info() const;
protected:
Size frameSize; Size frameSize;
int frameType; int frameType;
Mat bgmodel; Mat bgmodel;
@ -137,24 +143,24 @@ public:
int nframes; int nframes;
int history; int history;
int nmixtures; 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 //! Actual number is determined dynamically per pixel
float varThreshold; double varThreshold;
// threshold on the squared Mahalan. dist. to decide if it is well described // 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. // 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 // 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. // 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; float backgroundRatio;
//corresponds to fTB=1-cf from the paper // corresponds to fTB=1-cf from the paper
//TB - threshold when the component becomes significant enough to be included into // 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. // 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 // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
//it is considered foreground // it is considered foreground
//float noiseSigma; // float noiseSigma;
float varThresholdGen; float varThresholdGen;
//correspondts to Tg - threshold on the squared Mahalan. dist. to decide //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 //when a sample is close to the existing components. If it is not close

29
modules/video/src/bgfg_gaussmix.cpp
Unescape View File

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

1047
modules/video/src/bgfg_gaussmix2.cpp
View File

File diff suppressed because it is too large Load Diff

119
modules/video/src/video_init.cpp
Unescape View File

@ -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;
}
}

8
samples/cpp/bgfg_segm.cpp
Unescape View File

@ -1,4 +1,5 @@
#include "opencv2/core/core.hpp" #include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/background_segm.hpp" #include "opencv2/video/background_segm.hpp"
#include "opencv2/highgui/highgui.hpp" #include "opencv2/highgui/highgui.hpp"
#include <stdio.h> #include <stdio.h>
@ -17,7 +18,7 @@ void help()
const char* keys = const char* keys =
{ {
"{c |camera |false | use camera or not}" "{c |camera |true | use camera or not}"
"{fn|file_name|tree.avi | movie file }" "{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("foreground image", CV_WINDOW_NORMAL);
namedWindow("mean background 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; Mat img, fgmask, fgimg;
for(;;) for(;;)
@ -59,6 +61,8 @@ int main(int argc, const char** argv)
if( img.empty() ) if( img.empty() )
break; break;
//cvtColor(_img, img, COLOR_BGR2GRAY);
if( fgimg.empty() ) if( fgimg.empty() )
fgimg.create(img.size(), img.type()); fgimg.create(img.size(), img.type());

Write Preview
Loading…
Cancel
Save