class cv::cuda::OpticalFlowDual_TVL1
Overview
Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method. More…
#include <cudaoptflow.hpp> class OpticalFlowDual_TVL1: public cv::cuda::DenseOpticalFlow { public: // methods virtual double getEpsilon() const = 0; virtual double getGamma() const = 0; virtual double getLambda() const = 0; virtual int getNumIterations() const = 0; virtual int getNumScales() const = 0; virtual int getNumWarps() const = 0; virtual double getScaleStep() const = 0; virtual double getTau() const = 0; virtual double getTheta() const = 0; virtual bool getUseInitialFlow() const = 0; virtual void setEpsilon(double epsilon) = 0; virtual void setGamma(double gamma) = 0; virtual void setLambda(double lambda) = 0; virtual void setNumIterations(int iterations) = 0; virtual void setNumScales(int nscales) = 0; virtual void setNumWarps(int warps) = 0; virtual void setScaleStep(double scaleStep) = 0; virtual void setTau(double tau) = 0; virtual void setTheta(double theta) = 0; virtual void setUseInitialFlow(bool useInitialFlow) = 0; static Ptr<OpticalFlowDual_TVL1> create( double tau = 0.25, double lambda = 0.15, double theta = 0.3, int nscales = 5, int warps = 5, double epsilon = 0.01, int iterations = 300, double scaleStep = 0.8, double gamma = 0.0, bool useInitialFlow = false ); };
Inherited Members
public: // methods virtual void clear(); virtual bool empty() const; virtual String getDefaultName() const; virtual void read(const FileNode& fn); virtual void save(const String& filename) const; virtual void write(FileStorage& fs) const; template <typename _Tp> static Ptr<_Tp> load( const String& filename, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> loadFromString( const String& strModel, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> read(const FileNode& fn); virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow, Stream& stream = Stream::Null() ) = 0; protected: // methods void writeFormat(FileStorage& fs) const;
Detailed Documentation
Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method.
See also:
- Zach, T. Pock and H. Bischof, “A Duality Based Approach for Realtime TV-L1 Optical Flow”.
Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. “TV-L1 Optical Flow Estimation”.
Methods
virtual double getEpsilon() const = 0
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.
virtual double getGamma() const = 0
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.
virtual double getLambda() const = 0
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.
virtual int getNumIterations() const = 0
Stopping criterion iterations number used in the numerical scheme.
virtual int getNumScales() const = 0
Number of scales used to create the pyramid of images.
virtual int getNumWarps() const = 0
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.
virtual double getTau() const = 0
Time step of the numerical scheme.
virtual double getTheta() const = 0
parameter used for motion estimation. It adds a variable allowing for illumination variations Set this parameter to 1. if you have varying illumination. See: Chambolle et al, A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging Journal of Mathematical imaging and vision, may 2011 Vol 40 issue 1, pp 120-145