Merge remote-tracking branch 'origin/2.4' into merge-2.4
Conflicts: .gitignore doc/tutorials/objdetect/cascade_classifier/cascade_classifier.rst modules/gpu/src/match_template.cpp modules/imgproc/include/opencv2/imgproc/imgproc.hpp modules/ocl/include/opencv2/ocl/ocl.hpp modules/ocl/perf/perf_precomp.hpp
This commit is contained in:
Roman Donchenko
@@ -519,7 +519,15 @@ namespace cv
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//! bilateralFilter
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// supports 8UC1 8UC4
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CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpave, int borderType=BORDER_DEFAULT);
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CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT);
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//! Applies an adaptive bilateral filter to the input image
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// This is not truly a bilateral filter. Instead of using user provided fixed parameters,
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// the function calculates a constant at each window based on local standard deviation,
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// and use this constant to do filtering.
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// supports 8UC1 8UC3
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CV_EXPORTS void adaptiveBilateralFilter(const oclMat& src, oclMat& dst, Size ksize, double sigmaSpace, Point anchor = Point(-1, -1), int borderType=BORDER_DEFAULT);
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//! computes exponent of each matrix element (b = e**a)
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// supports only CV_32FC1 type
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CV_EXPORTS void exp(const oclMat &a, oclMat &b);
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@@ -1797,6 +1805,155 @@ namespace cv
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// keys = {1, 2, 3} (CV_8UC1)
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// values = {6,2, 10,5, 4,3} (CV_8UC2)
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void CV_EXPORTS sortByKey(oclMat& keys, oclMat& values, int method, bool isGreaterThan = false);
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/*!Base class for MOG and MOG2!*/
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class CV_EXPORTS BackgroundSubtractor
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{
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public:
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//! the virtual destructor
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virtual ~BackgroundSubtractor();
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//! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
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virtual void operator()(const oclMat& image, oclMat& fgmask, float learningRate);
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//! computes a background image
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virtual void getBackgroundImage(oclMat& backgroundImage) const = 0;
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};
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/*!
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Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
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The class implements the following algorithm:
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"An improved adaptive background mixture model for real-time tracking with shadow detection"
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P. KadewTraKuPong and R. Bowden,
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Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
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http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
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*/
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class CV_EXPORTS MOG: public cv::ocl::BackgroundSubtractor
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{
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public:
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//! the default constructor
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MOG(int nmixtures = -1);
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//! re-initiaization method
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void initialize(Size frameSize, int frameType);
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//! the update operator
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void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = 0.f);
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//! computes a background image which are the mean of all background gaussians
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void getBackgroundImage(oclMat& backgroundImage) const;
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//! releases all inner buffers
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void release();
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int history;
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float varThreshold;
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float backgroundRatio;
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float noiseSigma;
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private:
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int nmixtures_;
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Size frameSize_;
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int frameType_;
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int nframes_;
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oclMat weight_;
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oclMat sortKey_;
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oclMat mean_;
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oclMat var_;
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};
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/*!
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The class implements the following algorithm:
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"Improved adaptive Gausian mixture model for background subtraction"
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Z.Zivkovic
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International Conference Pattern Recognition, UK, August, 2004.
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http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
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*/
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class CV_EXPORTS MOG2: public cv::ocl::BackgroundSubtractor
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{
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public:
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//! the default constructor
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MOG2(int nmixtures = -1);
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//! re-initiaization method
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void initialize(Size frameSize, int frameType);
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//! the update operator
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void operator()(const oclMat& frame, oclMat& fgmask, float learningRate = -1.0f);
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//! computes a background image which are the mean of all background gaussians
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void getBackgroundImage(oclMat& backgroundImage) const;
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//! releases all inner buffers
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void release();
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// parameters
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// you should call initialize after parameters changes
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int history;
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//! here it is the maximum allowed number of mixture components.
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//! Actual number is determined dynamically per pixel
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float varThreshold;
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// threshold on the squared Mahalanobis distance to decide if it is well described
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// by the background model or not. Related to Cthr from the paper.
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// This does not influence the update of the background. A typical value could be 4 sigma
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// and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
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/////////////////////////
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// less important parameters - things you might change but be carefull
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////////////////////////
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float backgroundRatio;
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// corresponds to fTB=1-cf from the paper
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// TB - threshold when the component becomes significant enough to be included into
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// the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
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// For alpha=0.001 it means that the mode should exist for approximately 105 frames before
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// it is considered foreground
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// float noiseSigma;
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float varThresholdGen;
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//correspondts to Tg - threshold on the squared Mahalan. dist. to decide
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//when a sample is close to the existing components. If it is not close
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//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
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//Smaller Tg leads to more generated components and higher Tg might make
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//lead to small number of components but they can grow too large
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float fVarInit;
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float fVarMin;
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float fVarMax;
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//initial variance for the newly generated components.
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//It will will influence the speed of adaptation. A good guess should be made.
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//A simple way is to estimate the typical standard deviation from the images.
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//I used here 10 as a reasonable value
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// min and max can be used to further control the variance
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float fCT; //CT - complexity reduction prior
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//this is related to the number of samples needed to accept that a component
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//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
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//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
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//shadow detection parameters
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bool bShadowDetection; //default 1 - do shadow detection
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unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
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float fTau;
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// Tau - shadow threshold. The shadow is detected if the pixel is darker
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//version of the background. Tau is a threshold on how much darker the shadow can be.
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//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
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//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
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private:
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int nmixtures_;
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Size frameSize_;
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int frameType_;
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int nframes_;
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oclMat weight_;
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oclMat variance_;
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oclMat mean_;
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oclMat bgmodelUsedModes_; //keep track of number of modes per pixel
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};
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}
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}
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#if defined _MSC_VER && _MSC_VER >= 1200
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