/*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*/ #ifndef __OPENCV_GPU_HPP__ #define __OPENCV_GPU_HPP__ #ifndef SKIP_INCLUDES #include #include #include #endif #include "opencv2/core/gpumat.hpp" #include "opencv2/gpuarithm.hpp" #include "opencv2/gpufilters.hpp" #include "opencv2/gpuimgproc.hpp" #include "opencv2/gpufeatures2d.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/objdetect.hpp" #include "opencv2/features2d.hpp" namespace cv { namespace gpu { ////////////////////////////// Image processing ////////////////////////////// ///////////////////////////// Calibration 3D ////////////////////////////////// CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, GpuMat& dst, Stream& stream = Stream::Null()); CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst, Stream& stream = Stream::Null()); CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat, const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false, int num_iters=100, float max_dist=8.0, int min_inlier_count=100, std::vector* inliers=NULL); //////////////////////////////// Image Labeling //////////////////////////////// ////////////////////////////////// Histograms ////////////////////////////////// //////////////////////////////// StereoBM_GPU //////////////////////////////// class CV_EXPORTS StereoBM_GPU { public: enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 }; enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 }; //! the default constructor StereoBM_GPU(); //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8. StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ); //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair //! Output disparity has CV_8U type. void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null()); //! Some heuristics that tries to estmate // if current GPU will be faster than CPU in this algorithm. // It queries current active device. static bool checkIfGpuCallReasonable(); int preset; int ndisp; int winSize; // If avergeTexThreshold == 0 => post procesing is disabled // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold // i.e. input left image is low textured. float avergeTexThreshold; private: GpuMat minSSD, leBuf, riBuf; }; ////////////////////////// StereoBeliefPropagation /////////////////////////// // "Efficient Belief Propagation for Early Vision" // P.Felzenszwalb class CV_EXPORTS StereoBeliefPropagation { public: enum { DEFAULT_NDISP = 64 }; enum { DEFAULT_ITERS = 5 }; enum { DEFAULT_LEVELS = 5 }; static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels); //! the default constructor explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP, int iters = DEFAULT_ITERS, int levels = DEFAULT_LEVELS, int msg_type = CV_32F); //! the full constructor taking the number of disparities, number of BP iterations on each level, //! number of levels, truncation of data cost, data weight, //! truncation of discontinuity cost and discontinuity single jump //! DataTerm = data_weight * min(fabs(I2-I1), max_data_term) //! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term) //! please see paper for more details StereoBeliefPropagation(int ndisp, int iters, int levels, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, int msg_type = CV_32F); //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair, //! if disparity is empty output type will be CV_16S else output type will be disparity.type(). void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null()); //! version for user specified data term void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null()); int ndisp; int iters; int levels; float max_data_term; float data_weight; float max_disc_term; float disc_single_jump; int msg_type; private: GpuMat u, d, l, r, u2, d2, l2, r2; std::vector datas; GpuMat out; }; /////////////////////////// StereoConstantSpaceBP /////////////////////////// // "A Constant-Space Belief Propagation Algorithm for Stereo Matching" // Qingxiong Yang, Liang Wang, Narendra Ahuja // http://vision.ai.uiuc.edu/~qyang6/ class CV_EXPORTS StereoConstantSpaceBP { public: enum { DEFAULT_NDISP = 128 }; enum { DEFAULT_ITERS = 8 }; enum { DEFAULT_LEVELS = 4 }; enum { DEFAULT_NR_PLANE = 4 }; static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane); //! the default constructor explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP, int iters = DEFAULT_ITERS, int levels = DEFAULT_LEVELS, int nr_plane = DEFAULT_NR_PLANE, int msg_type = CV_32F); //! the full constructor taking the number of disparities, number of BP iterations on each level, //! number of levels, number of active disparity on the first level, truncation of data cost, data weight, //! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, int min_disp_th = 0, int msg_type = CV_32F); //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair, //! if disparity is empty output type will be CV_16S else output type will be disparity.type(). void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null()); int ndisp; int iters; int levels; int nr_plane; float max_data_term; float data_weight; float max_disc_term; float disc_single_jump; int min_disp_th; int msg_type; bool use_local_init_data_cost; private: GpuMat messages_buffers; GpuMat temp; GpuMat out; }; /////////////////////////// DisparityBilateralFilter /////////////////////////// // Disparity map refinement using joint bilateral filtering given a single color image. // Qingxiong Yang, Liang Wang, Narendra Ahuja // http://vision.ai.uiuc.edu/~qyang6/ class CV_EXPORTS DisparityBilateralFilter { public: enum { DEFAULT_NDISP = 64 }; enum { DEFAULT_RADIUS = 3 }; enum { DEFAULT_ITERS = 1 }; //! the default constructor explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS); //! the full constructor taking the number of disparities, filter radius, //! number of iterations, truncation of data continuity, truncation of disparity continuity //! and filter range sigma DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range); //! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image. //! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type. void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null()); private: int ndisp; int radius; int iters; float edge_threshold; float max_disc_threshold; float sigma_range; GpuMat table_color; GpuMat table_space; }; //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// struct CV_EXPORTS HOGConfidence { double scale; std::vector locations; std::vector confidences; std::vector part_scores[4]; }; struct CV_EXPORTS HOGDescriptor { enum { DEFAULT_WIN_SIGMA = -1 }; enum { DEFAULT_NLEVELS = 64 }; enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL }; HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, double threshold_L2hys=0.2, bool gamma_correction=true, int nlevels=DEFAULT_NLEVELS); size_t getDescriptorSize() const; size_t getBlockHistogramSize() const; void setSVMDetector(const std::vector& detector); static std::vector getDefaultPeopleDetector(); static std::vector getPeopleDetector48x96(); static std::vector getPeopleDetector64x128(); void detect(const GpuMat& img, std::vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size()); void detectMultiScale(const GpuMat& img, std::vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size(), double scale0=1.05, int group_threshold=2); void computeConfidence(const GpuMat& img, std::vector& hits, double hit_threshold, Size win_stride, Size padding, std::vector& locations, std::vector& confidences); void computeConfidenceMultiScale(const GpuMat& img, std::vector& found_locations, double hit_threshold, Size win_stride, Size padding, std::vector &conf_out, int group_threshold); void getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL); Size win_size; Size block_size; Size block_stride; Size cell_size; int nbins; double win_sigma; double threshold_L2hys; bool gamma_correction; int nlevels; protected: void computeBlockHistograms(const GpuMat& img); void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle); double getWinSigma() const; bool checkDetectorSize() const; static int numPartsWithin(int size, int part_size, int stride); static Size numPartsWithin(Size size, Size part_size, Size stride); // Coefficients of the separating plane float free_coef; GpuMat detector; // Results of the last classification step GpuMat labels, labels_buf; Mat labels_host; // Results of the last histogram evaluation step GpuMat block_hists, block_hists_buf; // Gradients conputation results GpuMat grad, qangle, grad_buf, qangle_buf; // returns subbuffer with required size, reallocates buffer if nessesary. static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf); static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf); std::vector image_scales; }; ////////////////////////////////// BruteForceMatcher ////////////////////////////////// template class CV_EXPORTS BruteForceMatcher_GPU; template class CV_EXPORTS BruteForceMatcher_GPU< L1 > : public BFMatcher_GPU { public: explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_L1) {} explicit BruteForceMatcher_GPU(L1 /*d*/) : BFMatcher_GPU(NORM_L1) {} }; template class CV_EXPORTS BruteForceMatcher_GPU< L2 > : public BFMatcher_GPU { public: explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_L2) {} explicit BruteForceMatcher_GPU(L2 /*d*/) : BFMatcher_GPU(NORM_L2) {} }; template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BFMatcher_GPU { public: explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_HAMMING) {} explicit BruteForceMatcher_GPU(Hamming /*d*/) : BFMatcher_GPU(NORM_HAMMING) {} }; ////////////////////////////////// CascadeClassifier_GPU ////////////////////////////////////////// // The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny. class CV_EXPORTS CascadeClassifier_GPU { public: CascadeClassifier_GPU(); CascadeClassifier_GPU(const String& filename); ~CascadeClassifier_GPU(); bool empty() const; bool load(const String& filename); void release(); /* returns number of detected objects */ int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size()); int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4); bool findLargestObject; bool visualizeInPlace; Size getClassifierSize() const; private: struct CascadeClassifierImpl; CascadeClassifierImpl* impl; struct HaarCascade; struct LbpCascade; friend class CascadeClassifier_GPU_LBP; }; ////////////////////////////////// FAST ////////////////////////////////////////// ////////////////////////////////// ORB ////////////////////////////////////////// ////////////////////////////////// Optical Flow ////////////////////////////////////////// class CV_EXPORTS BroxOpticalFlow { public: BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) : alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_), inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_) { } //! Compute optical flow //! frame0 - source frame (supports only CV_32FC1 type) //! frame1 - frame to track (with the same size and type as frame0) //! u - flow horizontal component (along x axis) //! v - flow vertical component (along y axis) void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null()); //! flow smoothness float alpha; //! gradient constancy importance float gamma; //! pyramid scale factor float scale_factor; //! number of lagged non-linearity iterations (inner loop) int inner_iterations; //! number of warping iterations (number of pyramid levels) int outer_iterations; //! number of linear system solver iterations int solver_iterations; GpuMat buf; }; class CV_EXPORTS PyrLKOpticalFlow { public: PyrLKOpticalFlow(); void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err = 0); void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0); void releaseMemory(); Size winSize; int maxLevel; int iters; bool useInitialFlow; private: std::vector prevPyr_; std::vector nextPyr_; GpuMat buf_; GpuMat uPyr_[2]; GpuMat vPyr_[2]; }; class CV_EXPORTS FarnebackOpticalFlow { public: FarnebackOpticalFlow() { numLevels = 5; pyrScale = 0.5; fastPyramids = false; winSize = 13; numIters = 10; polyN = 5; polySigma = 1.1; flags = 0; } int numLevels; double pyrScale; bool fastPyramids; int winSize; int numIters; int polyN; double polySigma; int flags; void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null()); void releaseMemory() { frames_[0].release(); frames_[1].release(); pyrLevel_[0].release(); pyrLevel_[1].release(); M_.release(); bufM_.release(); R_[0].release(); R_[1].release(); blurredFrame_[0].release(); blurredFrame_[1].release(); pyramid0_.clear(); pyramid1_.clear(); } private: void prepareGaussian( int n, double sigma, float *g, float *xg, float *xxg, double &ig11, double &ig03, double &ig33, double &ig55); void setPolynomialExpansionConsts(int n, double sigma); void updateFlow_boxFilter( const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy, GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]); void updateFlow_gaussianBlur( const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy, GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]); GpuMat frames_[2]; GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2]; std::vector pyramid0_, pyramid1_; }; // Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method // // see reference: // [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". // [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". class CV_EXPORTS OpticalFlowDual_TVL1_GPU { public: OpticalFlowDual_TVL1_GPU(); void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy); void collectGarbage(); /** * Time step of the numerical scheme. */ double tau; /** * Weight parameter for the data term, attachment parameter. * This is the most relevant parameter, which determines the smoothness of the output. * The smaller this parameter is, the smoother the solutions we obtain. * It depends on the range of motions of the images, so its value should be adapted to each image sequence. */ double lambda; /** * Weight parameter for (u - v)^2, tightness parameter. * It serves as a link between the attachment and the regularization terms. * In theory, it should have a small value in order to maintain both parts in correspondence. * The method is stable for a large range of values of this parameter. */ double theta; /** * Number of scales used to create the pyramid of images. */ int nscales; /** * Number of warpings per scale. * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale. * This is a parameter that assures the stability of the method. * It also affects the running time, so it is a compromise between speed and accuracy. */ int warps; /** * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time. * A small value will yield more accurate solutions at the expense of a slower convergence. */ double epsilon; /** * Stopping criterion iterations number used in the numerical scheme. */ int iterations; double scaleStep; bool useInitialFlow; private: void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2); std::vector I0s; std::vector I1s; std::vector u1s; std::vector u2s; GpuMat I1x_buf; GpuMat I1y_buf; GpuMat I1w_buf; GpuMat I1wx_buf; GpuMat I1wy_buf; GpuMat grad_buf; GpuMat rho_c_buf; GpuMat p11_buf; GpuMat p12_buf; GpuMat p21_buf; GpuMat p22_buf; GpuMat diff_buf; GpuMat norm_buf; }; //! Calculates optical flow for 2 images using block matching algorithm */ CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr, Size block_size, Size shift_size, Size max_range, bool use_previous, GpuMat& velx, GpuMat& vely, GpuMat& buf, Stream& stream = Stream::Null()); class CV_EXPORTS FastOpticalFlowBM { public: void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null()); private: GpuMat buffer; GpuMat extended_I0; GpuMat extended_I1; }; //! Interpolate frames (images) using provided optical flow (displacement field). //! frame0 - frame 0 (32-bit floating point images, single channel) //! frame1 - frame 1 (the same type and size) //! fu - forward horizontal displacement //! fv - forward vertical displacement //! bu - backward horizontal displacement //! bv - backward vertical displacement //! pos - new frame position //! newFrame - new frame //! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat; //! occlusion masks 0, occlusion masks 1, //! interpolated forward flow 0, interpolated forward flow 1, //! interpolated backward flow 0, interpolated backward flow 1 //! CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv, float pos, GpuMat& newFrame, GpuMat& buf, Stream& stream = Stream::Null()); CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors); //////////////////////// Background/foreground segmentation //////////////////////// // Foreground Object Detection from Videos Containing Complex Background. // Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. // ACM MM2003 9p class CV_EXPORTS FGDStatModel { public: struct CV_EXPORTS Params { int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128. int N1c; // Number of color vectors used to model normal background color variation at a given pixel. int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c. // Used to allow the first N1c vectors to adapt over time to changing background. int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64. int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel. int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc. // Used to allow the first N1cc vectors to adapt over time to changing background. bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE. int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations. // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1. float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1. float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005. float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1. float delta; // Affects color and color co-occurrence quantization, typically set to 2. float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9). float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold. // default Params Params(); }; // out_cn - channels count in output result (can be 3 or 4) // 4-channels require more memory, but a bit faster explicit FGDStatModel(int out_cn = 3); explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3); ~FGDStatModel(); void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params()); void release(); int update(const cv::gpu::GpuMat& curFrame); //8UC3 or 8UC4 reference background image cv::gpu::GpuMat background; //8UC1 foreground image cv::gpu::GpuMat foreground; std::vector< std::vector > foreground_regions; private: FGDStatModel(const FGDStatModel&); FGDStatModel& operator=(const FGDStatModel&); class Impl; std::auto_ptr impl_; }; /*! Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm The class implements the following algorithm: "An improved adaptive background mixture model for real-time tracking with shadow detection" P. KadewTraKuPong and R. Bowden, Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001." http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf */ class CV_EXPORTS MOG_GPU { public: //! the default constructor MOG_GPU(int nmixtures = -1); //! re-initiaization method void initialize(Size frameSize, int frameType); //! the update operator void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null()); //! computes a background image which are the mean of all background gaussians void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const; //! releases all inner buffers void release(); int history; float varThreshold; float backgroundRatio; float noiseSigma; private: int nmixtures_; Size frameSize_; int frameType_; int nframes_; GpuMat weight_; GpuMat sortKey_; GpuMat mean_; GpuMat var_; }; /*! The class implements the following algorithm: "Improved adaptive Gausian mixture model for background subtraction" Z.Zivkovic International Conference Pattern Recognition, UK, August, 2004. http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf */ class CV_EXPORTS MOG2_GPU { public: //! the default constructor MOG2_GPU(int nmixtures = -1); //! re-initiaization method void initialize(Size frameSize, int frameType); //! the update operator void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null()); //! computes a background image which are the mean of all background gaussians void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const; //! releases all inner buffers void release(); // parameters // you should call initialize after parameters changes int history; //! here it is the maximum allowed number of mixture components. //! Actual number is determined dynamically per pixel float 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 //////////////////////// 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; 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 //to any a new component will be generated. I use 3 sigma => Tg=3*3=9. //Smaller Tg leads to more generated components and higher Tg might make //lead to small number of components but they can grow too large float fVarInit; float fVarMin; float fVarMax; //initial variance for the newly generated components. //It will will influence the speed of adaptation. A good guess should be made. //A simple way is to estimate the typical standard deviation from the images. //I used here 10 as a reasonable value // min and max can be used to further control the variance float fCT; //CT - complexity reduction prior //this is related to the number of samples needed to accept that a component //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get //the standard Stauffer&Grimson algorithm (maybe not exact but very similar) //shadow detection parameters bool bShadowDetection; //default 1 - do shadow detection unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value float fTau; // Tau - shadow threshold. The shadow is detected if the pixel is darker //version of the background. Tau is a threshold on how much darker the shadow can be. //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003. private: int nmixtures_; Size frameSize_; int frameType_; int nframes_; GpuMat weight_; GpuMat variance_; GpuMat mean_; GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel }; /** * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1) * images of the same size, where 255 indicates Foreground and 0 represents Background. * This class implements an algorithm described in "Visual Tracking of Human Visitors under * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. */ class CV_EXPORTS GMG_GPU { public: GMG_GPU(); /** * Validate parameters and set up data structures for appropriate frame size. * @param frameSize Input frame size * @param min Minimum value taken on by pixels in image sequence. Usually 0 * @param max Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255 */ void initialize(Size frameSize, float min = 0.0f, float max = 255.0f); /** * Performs single-frame background subtraction and builds up a statistical background image * model. * @param frame Input frame * @param fgmask Output mask image representing foreground and background pixels * @param stream Stream for the asynchronous version */ void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null()); //! Releases all inner buffers void release(); //! Total number of distinct colors to maintain in histogram. int maxFeatures; //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms. float learningRate; //! Number of frames of video to use to initialize histograms. int numInitializationFrames; //! Number of discrete levels in each channel to be used in histograms. int quantizationLevels; //! Prior probability that any given pixel is a background pixel. A sensitivity parameter. float backgroundPrior; //! Value above which pixel is determined to be FG. float decisionThreshold; //! Smoothing radius, in pixels, for cleaning up FG image. int smoothingRadius; //! Perform background model update. bool updateBackgroundModel; private: float maxVal_, minVal_; Size frameSize_; int frameNum_; GpuMat nfeatures_; GpuMat colors_; GpuMat weights_; Ptr boxFilter_; GpuMat buf_; }; //! removes points (CV_32FC2, single row matrix) with zero mask value CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask); CV_EXPORTS void calcWobbleSuppressionMaps( int left, int idx, int right, Size size, const Mat &ml, const Mat &mr, GpuMat &mapx, GpuMat &mapy); } // namespace gpu } // namespace cv #endif /* __OPENCV_GPU_HPP__ */