(
['TrackerCSRT','setInitialMask','',1,'void',['Mat','mask','',[]]],
['','CamShift','@brief Finds an object center, size, and orientation.

@param probImage Back projection of the object histogram. See calcBackProject.
@param window Initial search window.
@param 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 @cite 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
CV_WRAP orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()

See the OpenCV sample camshiftdemo.c that tracks colored objects.

@note
-   (Python) A sample explaining the camshift tracking algorithm can be found at
    opencv_source_code/samples/python/camshift.py',0,'RotatedRect',['Mat','probImage','',[]],['Rect','window','',['/IO','/Ref']],['TermCriteria','criteria','',[]]],
['','meanShift','@brief Finds an object on a back projection image.

@param probImage Back projection of the object histogram. See calcBackProject for details.
@param window Initial search window.
@param 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 pre-filter 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.',0,'int',['Mat','probImage','',[]],['Rect','window','',['/IO','/Ref']],['TermCriteria','criteria','',[]]],
['','buildOpticalFlowPyramid','@brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

@param img 8-bit input image.
@param pyramid output pyramid.
@param 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.
@param maxLevel 0-based maximal pyramid level number.
@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
@param pyrBorder the border mode for pyramid layers.
@param derivBorder the border mode for gradients.
@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
to force data copying.
@return number of levels in constructed pyramid. Can be less than maxLevel.',0,'int',['Mat','img','',[]],['vector_Mat','pyramid','',['/O']],['Size','winSize','',[]],['int','maxLevel','',[]],['bool','withDerivatives','true',[]],['int','pyrBorder','BORDER_REFLECT_101',[]],['int','derivBorder','BORDER_CONSTANT',[]],['bool','tryReuseInputImage','true',[]]],
['','calcOpticalFlowPyrLK','@brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
pyramids.

@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
@param nextImg second input image or pyramid of the same size and the same type as prevImg.
@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
single-precision floating-point numbers.
@param nextPts output vector of 2D points (with single-precision floating-point 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.
@param 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.
@param 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).
@param winSize size of the search window at each pyramid level.
@param maxLevel 0-based 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.
@param 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.
@param flags operation flags:
 -   **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
     not set, then prevPts is copied to nextPts and is considered the initial estimate.
 -   **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
     minEigThreshold description); if the flag is not set, then L1 distance between patches
     around the original and a moved point, divided by number of pixels in a window, is used as a
     error measure.
@param 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 @cite 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 Lucas-Kanade optical flow in pyramids. See
@cite Bouguet00 . The function is parallelized with the TBB library.

@note

-   An example using the Lucas-Kanade optical flow algorithm can be found at
    opencv_source_code/samples/cpp/lkdemo.cpp
-   (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
    opencv_source_code/samples/python/lk_track.py
-   (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
    opencv_source_code/samples/python/lk_homography.py',0,'void',['Mat','prevImg','',[]],['Mat','nextImg','',[]],['Mat','prevPts','',[]],['Mat','nextPts','',['/IO']],['Mat','status','',['/O']],['Mat','err','',['/O']],['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',[]]],
['','calcOpticalFlowFarneback','@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, this
     option 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 same
     level 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]

@note

-   An example using the optical flow algorithm described by Gunnar Farneback can be found at
    opencv_source_code/samples/cpp/fback.cpp
-   (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
    found at opencv_source_code/samples/python/opt_flow.py',0,'void',['Mat','prev','',[]],['Mat','next','',[]],['Mat','flow','',['/IO']],['double','pyr_scale','',[]],['int','levels','',[]],['int','winsize','',[]],['int','iterations','',[]],['int','poly_n','',[]],['double','poly_sigma','',[]],['int','flags','',[]]],
['','computeECC','@brief Computes the Enhanced Correlation Coefficient value between two images @cite EP08 .

@param templateImage single-channel template image; CV_8U or CV_32F array.
@param inputImage single-channel input image to be warped to provide an image similar to
 templateImage, same type as templateImage.
@param inputMask An optional mask to indicate valid values of inputImage.

@sa
findTransformECC',0,'double',['Mat','templateImage','',[]],['Mat','inputImage','',[]],['Mat','inputMask','Mat()',[]]],
['','findTransformECC','@brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .

@param templateImage single-channel template image; CV_8U or CV_32F array.
@param inputImage single-channel input image which should be warped with the final warpMatrix in
order to provide an image similar to templateImage, same type as templateImage.
@param warpMatrix floating-point \\f$2\\times 3\\f$ or \\f$3\\times 3\\f$ mapping matrix (warp).
@param motionType parameter, specifying the type of motion:
 -   **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \\f$2\\times 3\\f$ with
     the first \\f$2\\times 2\\f$ part being the unity matrix and the rest two parameters being
     estimated.
 -   **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
     parameters are estimated; warpMatrix is \\f$2\\times 3\\f$.
 -   **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
     warpMatrix is \\f$2\\times 3\\f$.
 -   **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
     estimated;\\`warpMatrix\\` is \\f$3\\times 3\\f$.
@param 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.
@param inputMask An optional mask to indicate valid values of inputImage.
@param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5)

The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
(@cite EP08), that is

\\f[\\texttt{warpMatrix} = \\arg\\max_{W} \\texttt{ECC}(\\texttt{templateImage}(x,y),\\texttt{inputImage}(x\',y\'))\\f]

where

\\f[\\begin{bmatrix} x\' \\\\ y\' \\end{bmatrix} = W \\cdot \\begin{bmatrix} x \\\\ y \\\\ 1 \\end{bmatrix}\\f]

(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 \\f$3\\times 3\\f$ matrix is given with motionType =0, 1 or 2, the third
row is ignored.

Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
area-based 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.

@sa
computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography',0,'double',['Mat','templateImage','',[]],['Mat','inputImage','',[]],['Mat','warpMatrix','',['/IO']],['int','motionType','',[]],['TermCriteria','criteria','',[]],['Mat','inputMask','',[]],['int','gaussFiltSize','',[]]],
['','findTransformECC','@overload',0,'double',['Mat','templateImage','',[]],['Mat','inputImage','',[]],['Mat','warpMatrix','',['/IO']],['int','motionType','MOTION_AFFINE',[]],['TermCriteria','criteria','TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001)',[]],['Mat','inputMask','Mat()',[]]],
['KalmanFilter','predict','@brief Computes a predicted state.

    @param control The optional input control',1,'Mat',['Mat','control','Mat()',['/C','/Ref']]],
['KalmanFilter','correct','@brief Updates the predicted state from the measurement.

    @param measurement The measured system parameters',1,'Mat',['Mat','measurement','',['/C','/Ref']]],
['','readOpticalFlow','@brief Read a .flo file

 @param 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 - floating-point, 2-channel. First channel corresponds to the
 flow in the horizontal direction (u), second - vertical (v).',0,'Mat',['String','path','',['/C','/Ref']]],
['','writeOpticalFlow','@brief Write a .flo to disk

 @param path Path to the file to be written
 @param 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 2-channel, floating-point matrix (CV_32FC2). First channel corresponds
 to the flow in the horizontal direction (u), second - vertical (v).',0,'bool',['String','path','',['/C','/Ref']],['Mat','flow','',[]]],
['DenseOpticalFlow','calc','@brief Calculates an optical flow.

    @param I0 first 8-bit single-channel input image.
    @param I1 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.',1,'void',['Mat','I0','',[]],['Mat','I1','',[]],['Mat','flow','',['/IO']]],
['DenseOpticalFlow','collectGarbage','@brief Releases all inner buffers.',1,'void'],
['SparseOpticalFlow','calc','@brief Calculates a sparse optical flow.

    @param prevImg First input image.
    @param nextImg Second input image of the same size and the same type as prevImg.
    @param prevPts Vector of 2D points for which the flow needs to be found.
    @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
    @param status Output status vector. 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.
    @param err Optional output vector that contains error response for each point (inverse confidence).',1,'void',['Mat','prevImg','',[]],['Mat','nextImg','',[]],['Mat','prevPts','',[]],['Mat','nextPts','',['/IO']],['Mat','status','',['/O']],['Mat','err','cv::Mat()',['/O']]],
['FarnebackOpticalFlow','getNumLevels','',1,'int'],
['FarnebackOpticalFlow','setNumLevels','',1,'void',['int','numLevels','',[]]],
['FarnebackOpticalFlow','getPyrScale','',1,'double'],
['FarnebackOpticalFlow','setPyrScale','',1,'void',['double','pyrScale','',[]]],
['FarnebackOpticalFlow','getFastPyramids','',1,'bool'],
['FarnebackOpticalFlow','setFastPyramids','',1,'void',['bool','fastPyramids','',[]]],
['FarnebackOpticalFlow','getWinSize','',1,'int'],
['FarnebackOpticalFlow','setWinSize','',1,'void',['int','winSize','',[]]],
['FarnebackOpticalFlow','getNumIters','',1,'int'],
['FarnebackOpticalFlow','setNumIters','',1,'void',['int','numIters','',[]]],
['FarnebackOpticalFlow','getPolyN','',1,'int'],
['FarnebackOpticalFlow','setPolyN','',1,'void',['int','polyN','',[]]],
['FarnebackOpticalFlow','getPolySigma','',1,'double'],
['FarnebackOpticalFlow','setPolySigma','',1,'void',['double','polySigma','',[]]],
['FarnebackOpticalFlow','getFlags','',1,'int'],
['FarnebackOpticalFlow','setFlags','',1,'void',['int','flags','',[]]],
['VariationalRefinement','calcUV','@brief @ref calc function overload to handle separate horizontal (u) and vertical (v) flow components
(to avoid extra splits/merges)',1,'void',['Mat','I0','',[]],['Mat','I1','',[]],['Mat','flow_u','',['/IO']],['Mat','flow_v','',['/IO']]],
['VariationalRefinement','getFixedPointIterations','@brief Number of outer (fixed-point) iterations in the minimization procedure.
@see setFixedPointIterations',1,'int'],
['VariationalRefinement','setFixedPointIterations','@copybrief getFixedPointIterations @see getFixedPointIterations',1,'void',['int','val','',[]]],
['VariationalRefinement','getSorIterations','@brief Number of inner successive over-relaxation (SOR) iterations
        in the minimization procedure to solve the respective linear system.
@see setSorIterations',1,'int'],
['VariationalRefinement','setSorIterations','@copybrief getSorIterations @see getSorIterations',1,'void',['int','val','',[]]],
['VariationalRefinement','getOmega','@brief Relaxation factor in SOR
@see setOmega',1,'float'],
['VariationalRefinement','setOmega','@copybrief getOmega @see getOmega',1,'void',['float','val','',[]]],
['VariationalRefinement','getAlpha','@brief Weight of the smoothness term
@see setAlpha',1,'float'],
['VariationalRefinement','setAlpha','@copybrief getAlpha @see getAlpha',1,'void',['float','val','',[]]],
['VariationalRefinement','getDelta','@brief Weight of the color constancy term
@see setDelta',1,'float'],
['VariationalRefinement','setDelta','@copybrief getDelta @see getDelta',1,'void',['float','val','',[]]],
['VariationalRefinement','getGamma','@brief Weight of the gradient constancy term
@see setGamma',1,'float'],
['VariationalRefinement','setGamma','@copybrief getGamma @see getGamma',1,'void',['float','val','',[]]],
['DISOpticalFlow','getFinestScale','@brief Finest level of the Gaussian pyramid on which the flow is computed (zero level
        corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.
@see setFinestScale',1,'int'],
['DISOpticalFlow','setFinestScale','@copybrief getFinestScale @see getFinestScale',1,'void',['int','val','',[]]],
['DISOpticalFlow','getPatchSize','@brief Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well
        enough in most cases.
@see setPatchSize',1,'int'],
['DISOpticalFlow','setPatchSize','@copybrief getPatchSize @see getPatchSize',1,'void',['int','val','',[]]],
['DISOpticalFlow','getPatchStride','@brief Stride between neighbor patches. Must be less than patch size. Lower values correspond
        to higher flow quality.
@see setPatchStride',1,'int'],
['DISOpticalFlow','setPatchStride','@copybrief getPatchStride @see getPatchStride',1,'void',['int','val','',[]]],
['DISOpticalFlow','getGradientDescentIterations','@brief Maximum number of gradient descent iterations in the patch inverse search stage. Higher values
        may improve quality in some cases.
@see setGradientDescentIterations',1,'int'],
['DISOpticalFlow','setGradientDescentIterations','@copybrief getGradientDescentIterations @see getGradientDescentIterations',1,'void',['int','val','',[]]],
['DISOpticalFlow','getVariationalRefinementIterations','@brief Number of fixed point iterations of variational refinement per scale. Set to zero to
        disable variational refinement completely. Higher values will typically result in more smooth and
        high-quality flow.
@see setGradientDescentIterations',1,'int'],
['DISOpticalFlow','setVariationalRefinementIterations','@copybrief getGradientDescentIterations @see getGradientDescentIterations',1,'void',['int','val','',[]]],
['DISOpticalFlow','getVariationalRefinementAlpha','@brief Weight of the smoothness term
@see setVariationalRefinementAlpha',1,'float'],
['DISOpticalFlow','setVariationalRefinementAlpha','@copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha',1,'void',['float','val','',[]]],
['DISOpticalFlow','getVariationalRefinementDelta','@brief Weight of the color constancy term
@see setVariationalRefinementDelta',1,'float'],
['DISOpticalFlow','setVariationalRefinementDelta','@copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta',1,'void',['float','val','',[]]],
['DISOpticalFlow','getVariationalRefinementGamma','@brief Weight of the gradient constancy term
@see setVariationalRefinementGamma',1,'float'],
['DISOpticalFlow','setVariationalRefinementGamma','@copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma',1,'void',['float','val','',[]]],
['DISOpticalFlow','getUseMeanNormalization','@brief Whether to use mean-normalization of patches when computing patch distance. It is turned on
        by default as it typically provides a noticeable quality boost because of increased robustness to
        illumination variations. Turn it off if you are certain that your sequence doesn\'t contain any changes
        in illumination.
@see setUseMeanNormalization',1,'bool'],
['DISOpticalFlow','setUseMeanNormalization','@copybrief getUseMeanNormalization @see getUseMeanNormalization',1,'void',['bool','val','',[]]],
['DISOpticalFlow','getUseSpatialPropagation','@brief Whether to use spatial propagation of good optical flow vectors. This option is turned on by
        default, as it tends to work better on average and can sometimes help recover from major errors
        introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this
        option off can make the output flow field a bit smoother, however.
@see setUseSpatialPropagation',1,'bool'],
['DISOpticalFlow','setUseSpatialPropagation','@copybrief getUseSpatialPropagation @see getUseSpatialPropagation',1,'void',['bool','val','',[]]],
['SparsePyrLKOpticalFlow','getWinSize','',1,'Size'],
['SparsePyrLKOpticalFlow','setWinSize','',1,'void',['Size','winSize','',[]]],
['SparsePyrLKOpticalFlow','getMaxLevel','',1,'int'],
['SparsePyrLKOpticalFlow','setMaxLevel','',1,'void',['int','maxLevel','',[]]],
['SparsePyrLKOpticalFlow','getTermCriteria','',1,'TermCriteria'],
['SparsePyrLKOpticalFlow','setTermCriteria','',1,'void',['TermCriteria','crit','',['/Ref']]],
['SparsePyrLKOpticalFlow','getFlags','',1,'int'],
['SparsePyrLKOpticalFlow','setFlags','',1,'void',['int','flags','',[]]],
['SparsePyrLKOpticalFlow','getMinEigThreshold','',1,'double'],
['SparsePyrLKOpticalFlow','setMinEigThreshold','',1,'void',['double','minEigThreshold','',[]]],
['Tracker','init','@brief Initialize the tracker with a known bounding box that surrounded the target
    @param image The initial frame
    @param boundingBox The initial bounding box',1,'void',['Mat','image','',[]],['Rect','boundingBox','',['/C','/Ref']]],
['Tracker','update','@brief Update the tracker, find the new most likely bounding box for the target
    @param image The current frame
    @param boundingBox The bounding box that represent the new target location, if true was returned, not
    modified otherwise

    @return True means that target was located and false means that tracker cannot locate target in
    current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
    missing from the frame (say, out of sight)',1,'bool',['Mat','image','',[]],['Rect','boundingBox','',['/O','/Ref']]],
['TrackerDaSiamRPN','getTrackingScore','@brief Return tracking score',1,'float'],
);
