OpenCV for Unity 2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.

Static Public Member Functions  
static BackgroundSubtractorMOG2  createBackgroundSubtractorMOG2 (int history, double varThreshold, bool detectShadows) 
Creates MOG2 Background Subtractor.  
static BackgroundSubtractorMOG2  createBackgroundSubtractorMOG2 (int history, double varThreshold) 
Creates MOG2 Background Subtractor.  
static BackgroundSubtractorMOG2  createBackgroundSubtractorMOG2 (int history) 
Creates MOG2 Background Subtractor.  
static BackgroundSubtractorMOG2  createBackgroundSubtractorMOG2 () 
Creates MOG2 Background Subtractor.  
static BackgroundSubtractorKNN  createBackgroundSubtractorKNN (int history, double dist2Threshold, bool detectShadows) 
Creates KNN Background Subtractor.  
static BackgroundSubtractorKNN  createBackgroundSubtractorKNN (int history, double dist2Threshold) 
Creates KNN Background Subtractor.  
static BackgroundSubtractorKNN  createBackgroundSubtractorKNN (int history) 
Creates KNN Background Subtractor.  
static BackgroundSubtractorKNN  createBackgroundSubtractorKNN () 
Creates KNN Background Subtractor.  
static RotatedRect  CamShift (Mat probImage, Rect window, TermCriteria criteria) 
Finds an object center, size, and orientation.  
static int  meanShift (Mat probImage, Rect window, TermCriteria criteria) 
Finds an object on a back projection image.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder, bool tryReuseInputImage) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowFarneback (Mat prev, Mat next, Mat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags) 
Computes a dense optical flow using the Gunnar Farneback's algorithm.  
static double  computeECC (Mat templateImage, Mat inputImage, Mat inputMask) 
Computes the Enhanced Correlation Coefficient value between two images [EP08] .  
static double  computeECC (Mat templateImage, Mat inputImage) 
Computes the Enhanced Correlation Coefficient value between two images [EP08] .  
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize) 
Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .  
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask) 
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria) 
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType) 
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix) 
static Mat  readOpticalFlow (string path) 
Read a .flo file.  
static bool  writeOpticalFlow (string path, Mat flow) 
Write a .flo to disk.  
static Vec5d  CamShiftAsVec5d (Mat probImage, ref Vec4i window, in Vec3d criteria) 
Finds an object center, size, and orientation.  
static int  meanShift (Mat probImage, ref Vec4i window, in Vec3d criteria) 
Finds an object on a back projection image.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in Vec2d winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder, bool tryReuseInputImage) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in Vec2d winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in Vec2d winSize, int maxLevel, bool withDerivatives, int pyrBorder) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in Vec2d winSize, int maxLevel, bool withDerivatives) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in Vec2d winSize, int maxLevel) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in Vec2d winSize, int maxLevel, in Vec3d criteria, int flags, double minEigThreshold) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in Vec2d winSize, int maxLevel, in Vec3d criteria, int flags) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in Vec2d winSize, int maxLevel, in Vec3d criteria) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in Vec2d winSize, int maxLevel) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in Vec2d winSize) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, in Vec3d criteria, Mat inputMask, int gaussFiltSize) 
Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .  
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, in Vec3d criteria, Mat inputMask) 
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, in Vec3d criteria) 
static double double double double double angle  CamShiftAsValueTuple (Mat probImage, ref(int x, int y, int width, int height) window, in(double type, double maxCount, double epsilon) criteria) 
static int  meanShift (Mat probImage, ref(int x, int y, int width, int height) window, in(double type, double maxCount, double epsilon) criteria) 
Finds an object on a back projection image.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in(double width, double height) winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder, bool tryReuseInputImage) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in(double width, double height) winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in(double width, double height) winSize, int maxLevel, bool withDerivatives, int pyrBorder) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in(double width, double height) winSize, int maxLevel, bool withDerivatives) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static int  buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, in(double width, double height) winSize, int maxLevel) 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in(double width, double height) winSize, int maxLevel, in(double type, double maxCount, double epsilon) criteria, int flags, double minEigThreshold) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in(double width, double height) winSize, int maxLevel, in(double type, double maxCount, double epsilon) criteria, int flags) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in(double width, double height) winSize, int maxLevel, in(double type, double maxCount, double epsilon) criteria) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in(double width, double height) winSize, int maxLevel) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static void  calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, in(double width, double height) winSize) 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.  
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, in(double type, double maxCount, double epsilon) criteria, Mat inputMask, int gaussFiltSize) 
Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .  
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, in(double type, double maxCount, double epsilon) criteria, Mat inputMask) 
static double  findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, in(double type, double maxCount, double epsilon) criteria) 
Static Public Attributes  
const int  OPTFLOW_USE_INITIAL_FLOW = 4 
const int  OPTFLOW_LK_GET_MIN_EIGENVALS = 8 
const int  OPTFLOW_FARNEBACK_GAUSSIAN = 256 
const int  MOTION_TRANSLATION = 0 
const int  MOTION_EUCLIDEAN = 1 
const int  MOTION_AFFINE = 2 
const int  MOTION_HOMOGRAPHY = 3 
const int  TrackerSamplerCSC_MODE_INIT_POS = 1 
const int  TrackerSamplerCSC_MODE_INIT_NEG = 2 
const int  TrackerSamplerCSC_MODE_TRACK_POS = 3 
const int  TrackerSamplerCSC_MODE_TRACK_NEG = 4 
const int  TrackerSamplerCSC_MODE_DETECT = 5 
static double  x 
Finds an object center, size, and orientation.  
static double double  y 
static double double double  width 
static double double double double  height 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
img  8bit input image. 
pyramid  output pyramid. 
winSize  window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. 
maxLevel  0based maximal pyramid level number. 
withDerivatives  set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. 
pyrBorder  the border mode for pyramid layers. 
derivBorder  the border mode for gradients. 
tryReuseInputImage  put ROI of input image into the pyramid if possible. You can pass false to force data copying. 

static 
Computes a dense optical flow using the Gunnar Farneback's algorithm.
prev  first 8bit singlechannel input image. 
next  second input image of the same size and the same type as prev. 
flow  computed flow image that has the same size as prev and type CV_32FC2. 
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. 
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. 
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. 
iterations  number of iterations the algorithm does at each pyramid level. 
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. 
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. 
flags  operation flags that can be a combination of the following:

The function finds an optical flow for each prev pixel using the [Farneback2003] algorithm so that
\[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\]

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Calculates an optical flow for a sparse feature set using the iterative LucasKanade method with pyramids.
prevImg  first 8bit input image or pyramid constructed by buildOpticalFlowPyramid. 
nextImg  second input image or pyramid of the same size and the same type as prevImg. 
prevPts  vector of 2D points for which the flow needs to be found; point coordinates must be singleprecision floatingpoint numbers. 
nextPts  output vector of 2D points (with singleprecision floatingpoint coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. 
status  output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0. 
err  output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases). 
winSize  size of the search window at each pyramid level. 
maxLevel  0based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel. 
criteria  parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon. 
flags  operation flags:

minEigThreshold  the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost. 
The function implements a sparse iterative version of the LucasKanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

static 
Finds an object center, size, and orientation.
probImage  Back projection of the object histogram. See calcBackProject. 
window  Initial search window. 
criteria  Stop criteria for the underlying meanShift. returns (in old interfaces) Number of iterations CAMSHIFT took to converge The function implements the CAMSHIFT object tracking algorithm [Bradski98] . First, it finds an object center using meanShift and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with RotatedRect.boundingRect() 
See the OpenCV sample camshiftdemo.c that tracks colored objects.

static 

static 
Finds an object center, size, and orientation.
probImage  Back projection of the object histogram. See calcBackProject. 
window  Initial search window. 
criteria  Stop criteria for the underlying meanShift. returns (in old interfaces) Number of iterations CAMSHIFT took to converge The function implements the CAMSHIFT object tracking algorithm [Bradski98] . First, it finds an object center using meanShift and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with RotatedRect.boundingRect() 
See the OpenCV sample camshiftdemo.c that tracks colored objects.

static 
Computes the Enhanced Correlation Coefficient value between two images [EP08] .
templateImage  singlechannel template image; CV_8U or CV_32F array. 
inputImage  singlechannel input image to be warped to provide an image similar to templateImage, same type as templateImage. 
inputMask  An optional mask to indicate valid values of inputImage. 

static 
Computes the Enhanced Correlation Coefficient value between two images [EP08] .
templateImage  singlechannel template image; CV_8U or CV_32F array. 
inputImage  singlechannel input image to be warped to provide an image similar to templateImage, same type as templateImage. 
inputMask  An optional mask to indicate valid values of inputImage. 

static 
Creates KNN Background Subtractor.
history  Length of the history. 
dist2Threshold  Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates KNN Background Subtractor.
history  Length of the history. 
dist2Threshold  Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates KNN Background Subtractor.
history  Length of the history. 
dist2Threshold  Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates KNN Background Subtractor.
history  Length of the history. 
dist2Threshold  Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates MOG2 Background Subtractor.
history  Length of the history. 
varThreshold  Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates MOG2 Background Subtractor.
history  Length of the history. 
varThreshold  Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates MOG2 Background Subtractor.
history  Length of the history. 
varThreshold  Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
Creates MOG2 Background Subtractor.
history  Length of the history. 
varThreshold  Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update. 
detectShadows  If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. 

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .
templateImage  singlechannel template image; CV_8U or CV_32F array. 
inputImage  singlechannel input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage. 
warpMatrix  floatingpoint \(2\times 3\) or \(3\times 3\) mapping matrix (warp). 
motionType  parameter, specifying the type of motion:

criteria  parameter, specifying the termination criteria of the ECC algorithm; criteria.epsilon defines the threshold of the increment in the correlation coefficient between two iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). Default values are shown in the declaration above. 
inputMask  An optional mask to indicate valid values of inputImage. 
gaussFiltSize  An optional value indicating size of gaussian blur filter; (DEFAULT: 5) 
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion ([EP08]), that is
\[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\]
where
\[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\]
(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third row is ignored.
Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an areabased alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) is used as an initialization. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .
templateImage  singlechannel template image; CV_8U or CV_32F array. 
inputImage  singlechannel input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage. 
warpMatrix  floatingpoint \(2\times 3\) or \(3\times 3\) mapping matrix (warp). 
motionType  parameter, specifying the type of motion:

criteria  parameter, specifying the termination criteria of the ECC algorithm; criteria.epsilon defines the threshold of the increment in the correlation coefficient between two iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). Default values are shown in the declaration above. 
inputMask  An optional mask to indicate valid values of inputImage. 
gaussFiltSize  An optional value indicating size of gaussian blur filter; (DEFAULT: 5) 
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion ([EP08]), that is
\[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\]
where
\[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\]
(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third row is ignored.
Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an areabased alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) is used as an initialization. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static 
Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .
templateImage  singlechannel template image; CV_8U or CV_32F array. 
inputImage  singlechannel input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage. 
warpMatrix  floatingpoint \(2\times 3\) or \(3\times 3\) mapping matrix (warp). 
motionType  parameter, specifying the type of motion:

criteria  parameter, specifying the termination criteria of the ECC algorithm; criteria.epsilon defines the threshold of the increment in the correlation coefficient between two iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). Default values are shown in the declaration above. 
inputMask  An optional mask to indicate valid values of inputImage. 
gaussFiltSize  An optional value indicating size of gaussian blur filter; (DEFAULT: 5) 
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion ([EP08]), that is
\[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\]
where
\[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\]
(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third row is ignored.
Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an areabased alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) is used as an initialization. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges.

static 
Finds an object on a back projection image.
probImage  Back projection of the object histogram. See calcBackProject for details. 
window  Initial search window. 
criteria  Stop criteria for the iterative search algorithm. returns : Number of iterations CAMSHIFT took to converge. The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations criteria.maxCount is done or until the window center shifts by less than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search window size or orientation do not change during the search. You can simply pass the output of calcBackProject to this function. But better results can be obtained if you prefilter the back projection and remove the noise. For example, you can do this by retrieving connected components with findContours , throwing away contours with small area ( contourArea ), and rendering the remaining contours with drawContours. 

static 
Finds an object on a back projection image.
probImage  Back projection of the object histogram. See calcBackProject for details. 
window  Initial search window. 
criteria  Stop criteria for the iterative search algorithm. returns : Number of iterations CAMSHIFT took to converge. The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations criteria.maxCount is done or until the window center shifts by less than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search window size or orientation do not change during the search. You can simply pass the output of calcBackProject to this function. But better results can be obtained if you prefilter the back projection and remove the noise. For example, you can do this by retrieving connected components with findContours , throwing away contours with small area ( contourArea ), and rendering the remaining contours with drawContours. 

static 
Finds an object on a back projection image.
probImage  Back projection of the object histogram. See calcBackProject for details. 
window  Initial search window. 
criteria  Stop criteria for the iterative search algorithm. returns : Number of iterations CAMSHIFT took to converge. The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations criteria.maxCount is done or until the window center shifts by less than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search window size or orientation do not change during the search. You can simply pass the output of calcBackProject to this function. But better results can be obtained if you prefilter the back projection and remove the noise. For example, you can do this by retrieving connected components with findContours , throwing away contours with small area ( contourArea ), and rendering the remaining contours with drawContours. 

static 
Read a .flo file.
path  Path to the file to be loaded 
The function readOpticalFlow loads a flow field from a file and returns it as a single matrix. Resulting Mat has a type CV_32FC2  floatingpoint, 2channel. First channel corresponds to the flow in the horizontal direction (u), second  vertical (v).

static 
Write a .flo to disk.
path  Path to the file to be written 
flow  Flow field to be stored 
The function stores a flow field in a file, returns true on success, false otherwise. The flow field must be a 2channel, floatingpoint matrix (CV_32FC2). First channel corresponds to the flow in the horizontal direction (u), second  vertical (v).

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static 
Finds an object center, size, and orientation.
probImage  Back projection of the object histogram. See calcBackProject. 
window  Initial search window. 
criteria  Stop criteria for the underlying meanShift. returns (in old interfaces) Number of iterations CAMSHIFT took to converge The function implements the CAMSHIFT object tracking algorithm [Bradski98] . First, it finds an object center using meanShift and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with RotatedRect.boundingRect() 
See the OpenCV sample camshiftdemo.c that tracks colored objects.

static 