TermCriteria模板类

学习写vo过程中遇到的　　　类功能：模板类，作为迭代算法的终止条件。

构造函数：

TermCriteria(int type,int maxCount,double epsilon);

参数说明：

type 迭代终止条件类型

type=TermCriteria::MAX_ITER/TermCriteria::COUNT 迭代到最大迭代次数终止

type= TermCriteria::EPS 迭代到阈值终止

type= TermCriteria::MAX_ITER+ TermCriteria::EPS 上述两者都作为迭代终止条件

maxCount 迭代的最大次数

epsilon 阈值(中心位移值)

调用参考：

cv::TermCriteria criteria(cv::TermCriteria::MAX_ITER,10,0.01);

TermCriteria termcrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,0.01);

在opencv type.hpp中兼有此类魔板

/** @brief The class defining termination criteria for iterative algorithms.You can initialize it by default constructor and then override any parameters, or the structure may
be fully initialized using the advanced variant of the constructor.
*/
class CV_EXPORTS TermCriteria
{
public:/**Criteria type, can be one of: COUNT, EPS or COUNT + EPS*/enum Type{COUNT=1, //!< the maximum number of iterations or elements to computeMAX_ITER=COUNT, //!< dittoEPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops};//! default constructorTermCriteria();/**@param type The type of termination criteria, one of TermCriteria::Type@param maxCount The maximum number of iterations or elements to compute.@param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops.*/TermCriteria(int type, int maxCount, double epsilon);int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPSint maxCount; // the maximum number of iterations/elementsdouble epsilon; // the desired accuracy
};

光流法：调用模板类TermCriteria对象termcrit

 calcOpticalFlowPyrLK(img_1,img_2,points1,points2,
status,err,winSize,3,termcrit,0,0.001);

@note-   An example using the Lucas-Kanade optical flow algorithm can be found atopencv_source_code/samples/cpp/lkdemo.cpp
-   (Python) An example using the Lucas-Kanade optical flow algorithm can be found atopencv_source_code/samples/python/lk_track.py
-   (Python) An example using the Lucas-Kanade tracker for homography matching can be found atopencv_source_code/samples/python/lk_homography.py*/
CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,InputArray prevPts, InputOutputArray nextPts,OutputArray status, OutputArray err,Size winSize = Size(21,21), int maxLevel = 3,TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),int flags = 0, double minEigThreshold = 1e-4 );/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm.@param prev first 8-bit single-channel input image.
@param next second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.
@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image;
pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
one.
@param levels number of pyramid layers including the initial image; levels=1 means that no extra
layers are created and only the original images are used.
@param winsize averaging window size; larger values increase the algorithm robustness to image
noise and give more chances for fast motion detection, but yield more blurred motion field.
@param iterations number of iterations the algorithm does at each pyramid level.
@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
larger values mean that the image will be approximated with smoother surfaces, yielding more
robust algorithm and more blurred motion field, typically poly_n =5 or 7.
@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
good value would be poly_sigma=1.5.
@param flags operation flags that can be a combination of the following:-   **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation.-   **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$filter instead of a box filter of the same size for optical flow estimation; usually, thisoption gives z more accurate flow than with a box filter, at the cost of lower speed;normally, winsize for a Gaussian window should be set to a larger value to achieve the samelevel of robustness.The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that\f[\texttt{prev} (y,x)  \sim \texttt{next} ( y + \texttt{flow} (y,x)[1],  x + \texttt{flow} (y,x)[0])\f]

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