gpuvideo module for video processing
This commit is contained in:
Vladislav Vinogradov
@@ -54,6 +54,7 @@
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#include "opencv2/gpufilters.hpp"
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#include "opencv2/gpuimgproc.hpp"
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#include "opencv2/gpufeatures2d.hpp"
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#include "opencv2/gpuvideo.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/objdetect.hpp"
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@@ -433,543 +434,23 @@ private:
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////////////////////////////////// Optical Flow //////////////////////////////////////////
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class CV_EXPORTS BroxOpticalFlow
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{
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public:
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BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
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alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
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inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
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{
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}
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//! Compute optical flow
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//! frame0 - source frame (supports only CV_32FC1 type)
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//! frame1 - frame to track (with the same size and type as frame0)
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//! u - flow horizontal component (along x axis)
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//! v - flow vertical component (along y axis)
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void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
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//! flow smoothness
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float alpha;
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//! gradient constancy importance
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float gamma;
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//! pyramid scale factor
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float scale_factor;
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//! number of lagged non-linearity iterations (inner loop)
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int inner_iterations;
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//! number of warping iterations (number of pyramid levels)
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int outer_iterations;
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//! number of linear system solver iterations
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int solver_iterations;
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GpuMat buf;
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};
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class CV_EXPORTS PyrLKOpticalFlow
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{
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public:
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PyrLKOpticalFlow();
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void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
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GpuMat& status, GpuMat* err = 0);
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void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
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void releaseMemory();
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Size winSize;
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int maxLevel;
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int iters;
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bool useInitialFlow;
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private:
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std::vector<GpuMat> prevPyr_;
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std::vector<GpuMat> nextPyr_;
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GpuMat buf_;
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GpuMat uPyr_[2];
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GpuMat vPyr_[2];
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};
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class CV_EXPORTS FarnebackOpticalFlow
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{
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public:
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FarnebackOpticalFlow()
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{
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numLevels = 5;
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pyrScale = 0.5;
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fastPyramids = false;
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winSize = 13;
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numIters = 10;
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polyN = 5;
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polySigma = 1.1;
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flags = 0;
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}
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int numLevels;
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double pyrScale;
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bool fastPyramids;
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int winSize;
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int numIters;
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int polyN;
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double polySigma;
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int flags;
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void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
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void releaseMemory()
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{
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frames_[0].release();
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frames_[1].release();
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pyrLevel_[0].release();
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pyrLevel_[1].release();
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M_.release();
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bufM_.release();
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R_[0].release();
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R_[1].release();
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blurredFrame_[0].release();
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blurredFrame_[1].release();
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pyramid0_.clear();
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pyramid1_.clear();
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}
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private:
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void prepareGaussian(
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int n, double sigma, float *g, float *xg, float *xxg,
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double &ig11, double &ig03, double &ig33, double &ig55);
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void setPolynomialExpansionConsts(int n, double sigma);
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void updateFlow_boxFilter(
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const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
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GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
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void updateFlow_gaussianBlur(
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const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
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GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
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GpuMat frames_[2];
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GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
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std::vector<GpuMat> pyramid0_, pyramid1_;
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};
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// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
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//
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// see reference:
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// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
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// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
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class CV_EXPORTS OpticalFlowDual_TVL1_GPU
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{
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public:
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OpticalFlowDual_TVL1_GPU();
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void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy);
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void collectGarbage();
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/**
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* Time step of the numerical scheme.
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*/
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double tau;
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/**
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* Weight parameter for the data term, attachment parameter.
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* This is the most relevant parameter, which determines the smoothness of the output.
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* The smaller this parameter is, the smoother the solutions we obtain.
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* It depends on the range of motions of the images, so its value should be adapted to each image sequence.
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*/
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double lambda;
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/**
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* Weight parameter for (u - v)^2, tightness parameter.
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* It serves as a link between the attachment and the regularization terms.
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* In theory, it should have a small value in order to maintain both parts in correspondence.
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* The method is stable for a large range of values of this parameter.
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*/
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double theta;
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/**
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* Number of scales used to create the pyramid of images.
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*/
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int nscales;
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/**
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* Number of warpings per scale.
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* Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
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* This is a parameter that assures the stability of the method.
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* It also affects the running time, so it is a compromise between speed and accuracy.
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*/
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int warps;
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/**
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* Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
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* A small value will yield more accurate solutions at the expense of a slower convergence.
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*/
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double epsilon;
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/**
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* Stopping criterion iterations number used in the numerical scheme.
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*/
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int iterations;
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double scaleStep;
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bool useInitialFlow;
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private:
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void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2);
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std::vector<GpuMat> I0s;
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std::vector<GpuMat> I1s;
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std::vector<GpuMat> u1s;
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std::vector<GpuMat> u2s;
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GpuMat I1x_buf;
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GpuMat I1y_buf;
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GpuMat I1w_buf;
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GpuMat I1wx_buf;
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GpuMat I1wy_buf;
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GpuMat grad_buf;
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GpuMat rho_c_buf;
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GpuMat p11_buf;
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GpuMat p12_buf;
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GpuMat p21_buf;
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GpuMat p22_buf;
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GpuMat diff_buf;
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GpuMat norm_buf;
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};
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//! Calculates optical flow for 2 images using block matching algorithm */
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CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
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Size block_size, Size shift_size, Size max_range, bool use_previous,
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GpuMat& velx, GpuMat& vely, GpuMat& buf,
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Stream& stream = Stream::Null());
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class CV_EXPORTS FastOpticalFlowBM
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{
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public:
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void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
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private:
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GpuMat buffer;
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GpuMat extended_I0;
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GpuMat extended_I1;
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};
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//! Interpolate frames (images) using provided optical flow (displacement field).
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//! frame0 - frame 0 (32-bit floating point images, single channel)
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//! frame1 - frame 1 (the same type and size)
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//! fu - forward horizontal displacement
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//! fv - forward vertical displacement
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//! bu - backward horizontal displacement
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//! bv - backward vertical displacement
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//! pos - new frame position
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//! newFrame - new frame
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//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
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//! occlusion masks 0, occlusion masks 1,
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//! interpolated forward flow 0, interpolated forward flow 1,
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//! interpolated backward flow 0, interpolated backward flow 1
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//!
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CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
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const GpuMat& fu, const GpuMat& fv,
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const GpuMat& bu, const GpuMat& bv,
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float pos, GpuMat& newFrame, GpuMat& buf,
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Stream& stream = Stream::Null());
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CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
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//////////////////////// Background/foreground segmentation ////////////////////////
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// Foreground Object Detection from Videos Containing Complex Background.
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// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
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// ACM MM2003 9p
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class CV_EXPORTS FGDStatModel
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{
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public:
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struct CV_EXPORTS Params
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{
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int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
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int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
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int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
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// Used to allow the first N1c vectors to adapt over time to changing background.
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int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
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int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
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int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
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// Used to allow the first N1cc vectors to adapt over time to changing background.
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bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
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int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
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// These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
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float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
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float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
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float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
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float delta; // Affects color and color co-occurrence quantization, typically set to 2.
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float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
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float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
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// default Params
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Params();
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};
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// out_cn - channels count in output result (can be 3 or 4)
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// 4-channels require more memory, but a bit faster
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explicit FGDStatModel(int out_cn = 3);
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explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
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~FGDStatModel();
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void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
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void release();
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int update(const cv::gpu::GpuMat& curFrame);
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//8UC3 or 8UC4 reference background image
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cv::gpu::GpuMat background;
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//8UC1 foreground image
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cv::gpu::GpuMat foreground;
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std::vector< std::vector<cv::Point> > foreground_regions;
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private:
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FGDStatModel(const FGDStatModel&);
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FGDStatModel& operator=(const FGDStatModel&);
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class Impl;
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std::auto_ptr<Impl> impl_;
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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_GPU
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{
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public:
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//! the default constructor
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MOG_GPU(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 GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());
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//! computes a background image which are the mean of all background gaussians
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void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) 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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GpuMat weight_;
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GpuMat sortKey_;
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GpuMat mean_;
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GpuMat 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_GPU
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{
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public:
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//! the default constructor
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MOG2_GPU(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 GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
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//! computes a background image which are the mean of all background gaussians
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void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) 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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GpuMat weight_;
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GpuMat variance_;
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GpuMat mean_;
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GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
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};
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/**
|
||||
* 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<FilterEngine_GPU> 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);
|
||||
|
||||
Reference in New Issue
Block a user