nvidia-vfx-prototyping/samples/external/opencv/include/opencv2/video/tracking.hpp

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#ifndef OPENCV_TRACKING_HPP
#define OPENCV_TRACKING_HPP

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"

namespace cv
{

//! @addtogroup video_track
//! @{

enum { OPTFLOW_USE_INITIAL_FLOW     = 4,
       OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
       OPTFLOW_FARNEBACK_GAUSSIAN   = 256
     };

/** @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
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
 */
CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
                                   TermCriteria criteria );
/** @example samples/cpp/camshiftdemo.cpp
An example using the mean-shift tracking algorithm
*/

/** @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.

 */
CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria );

/** @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.
 */
CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays pyramid,
                                          Size winSize, int maxLevel, bool withDerivatives = true,
                                          int pyrBorder = BORDER_REFLECT_101,
                                          int derivBorder = BORDER_CONSTANT,
                                          bool tryReuseInputImage = true );

/** @example samples/cpp/lkdemo.cpp
An example using the Lucas-Kanade optical flow algorithm
*/

/** @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
 */
CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
                                        InputArray prevPts, InputOutputArray nextPts,
                                        OutputArray status, OutputArray err,
                                        Size winSize = Size(21,21), int maxLevel = 3,
                                        TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
                                        int flags = 0, double minEigThreshold = 1e-4 );

/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm.

@param prev first 8-bit single-channel input image.
@param next second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.
@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image;
pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
one.
@param levels number of pyramid layers including the initial image; levels=1 means that no extra
layers are created and only the original images are used.
@param winsize averaging window size; larger values increase the algorithm robustness to image
noise and give more chances for fast motion detection, but yield more blurred motion field.
@param iterations number of iterations the algorithm does at each pyramid level.
@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
larger values mean that the image will be approximated with smoother surfaces, yielding more
robust algorithm and more blurred motion field, typically poly_n =5 or 7.
@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
good value would be poly_sigma=1.5.
@param flags operation flags that can be a combination of the following:
 -   **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation.
 -   **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$
     filter instead of a box filter of the same size for optical flow estimation; usually, 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
 */
CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow,
                                            double pyr_scale, int levels, int winsize,
                                            int iterations, int poly_n, double poly_sigma,
                                            int flags );

/** @brief Computes an optimal affine transformation between two 2D point sets.

@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat.
@param dst Second input 2D point set of the same size and the same type as A, or another image.
@param fullAffine If true, the function finds an optimal affine transformation with no additional
restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is
limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).

The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that
approximates best the affine transformation between:

*   Two point sets
*   Two raster images. In this case, the function first finds some features in the src image and
    finds the corresponding features in dst image. After that, the problem is reduced to the first
    case.
In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and
2x1 vector *b* so that:

\f[[A^*|b^*] = arg  \min _{[A|b]}  \sum _i  \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b  \| ^2\f]
where src[i] and dst[i] are the i-th points in src and dst, respectively
\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of
\f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ -a_{12} & a_{11} & b_2  \end{bmatrix}\f]
when fullAffine=false.

@sa
estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography
 */
CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine);
CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio,
                                         int ransacSize0);


enum
{
    MOTION_TRANSLATION = 0,
    MOTION_EUCLIDEAN   = 1,
    MOTION_AFFINE      = 2,
    MOTION_HOMOGRAPHY  = 3
};

/** @example samples/cpp/image_alignment.cpp
An example using the image alignment ECC algorithm
*/

/** @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 temlateImage.
@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.

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

\f[\texttt{warpMatrix} = \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
estimateAffine2D, estimateAffinePartial2D, findHomography
 */
CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage,
                                      InputOutputArray warpMatrix, int motionType = MOTION_AFFINE,
                                      TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
                                      InputArray inputMask = noArray());

/** @example samples/cpp/kalman.cpp
An example using the standard Kalman filter
*/

/** @brief Kalman filter class.

The class implements a standard Kalman filter <http://en.wikipedia.org/wiki/Kalman_filter>,
@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get
an extended Kalman filter functionality.
@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released
with cvReleaseKalman(&kalmanFilter)
 */
class CV_EXPORTS_W KalmanFilter
{
public:
    CV_WRAP KalmanFilter();
    /** @overload
    @param dynamParams Dimensionality of the state.
    @param measureParams Dimensionality of the measurement.
    @param controlParams Dimensionality of the control vector.
    @param type Type of the created matrices that should be CV_32F or CV_64F.
    */
    CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );

    /** @brief Re-initializes Kalman filter. The previous content is destroyed.

    @param dynamParams Dimensionality of the state.
    @param measureParams Dimensionality of the measurement.
    @param controlParams Dimensionality of the control vector.
    @param type Type of the created matrices that should be CV_32F or CV_64F.
     */
    void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );

    /** @brief Computes a predicted state.

    @param control The optional input control
     */
    CV_WRAP const Mat& predict( const Mat& control = Mat() );

    /** @brief Updates the predicted state from the measurement.

    @param measurement The measured system parameters
     */
    CV_WRAP const Mat& correct( const Mat& measurement );

    CV_PROP_RW Mat statePre;           //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
    CV_PROP_RW Mat statePost;          //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
    CV_PROP_RW Mat transitionMatrix;   //!< state transition matrix (A)
    CV_PROP_RW Mat controlMatrix;      //!< control matrix (B) (not used if there is no control)
    CV_PROP_RW Mat measurementMatrix;  //!< measurement matrix (H)
    CV_PROP_RW Mat processNoiseCov;    //!< process noise covariance matrix (Q)
    CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R)
    CV_PROP_RW Mat errorCovPre;        //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
    CV_PROP_RW Mat gain;               //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
    CV_PROP_RW Mat errorCovPost;       //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)

    // temporary matrices
    Mat temp1;
    Mat temp2;
    Mat temp3;
    Mat temp4;
    Mat temp5;
};


class CV_EXPORTS_W DenseOpticalFlow : public Algorithm
{
public:
    /** @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.
     */
    CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0;
    /** @brief Releases all inner buffers.
    */
    CV_WRAP virtual void collectGarbage() = 0;
};

/** @brief Base interface for sparse optical flow algorithms.
 */
class CV_EXPORTS_W SparseOpticalFlow : public Algorithm
{
public:
    /** @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).
     */
    CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg,
                      InputArray prevPts, InputOutputArray nextPts,
                      OutputArray status,
                      OutputArray err = cv::noArray()) = 0;
};

/** @brief "Dual TV L1" Optical Flow Algorithm.

The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
@cite Javier2012 .
Here are important members of the class that control the algorithm, which you can set after
constructing the class instance:

-   member double tau
    Time step of the numerical scheme.

-   member double lambda
    Weight parameter for the data term, attachment parameter. This is the most relevant
    parameter, which determines the smoothness of the output. The smaller this parameter is,
    the smoother the solutions we obtain. It depends on the range of motions of the images, so
    its value should be adapted to each image sequence.

-   member double theta
    Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the
    attachment and the regularization terms. In theory, it should have a small value in order
    to maintain both parts in correspondence. The method is stable for a large range of values
    of this parameter.

-   member int nscales
    Number of scales used to create the pyramid of images.

-   member int warps
    Number of warpings per scale. Represents the number of times that I1(x+u0) and grad(
    I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the
    method. It also affects the running time, so it is a compromise between speed and
    accuracy.

-   member double epsilon
    Stopping criterion threshold used in the numerical scheme, which is a trade-off between
    precision and running time. A small value will yield more accurate solutions at the
    expense of a slower convergence.

-   member int iterations
    Stopping criterion iterations number used in the numerical scheme.

C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
*/
class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow
{
public:
    //! @brief Time step of the numerical scheme
    /** @see setTau */
    CV_WRAP virtual double getTau() const = 0;
    /** @copybrief getTau @see getTau */
    CV_WRAP virtual void setTau(double val) = 0;
    //! @brief Weight parameter for the data term, attachment parameter
    /** @see setLambda */
    CV_WRAP virtual double getLambda() const = 0;
    /** @copybrief getLambda @see getLambda */
    CV_WRAP virtual void setLambda(double val) = 0;
    //! @brief Weight parameter for (u - v)^2, tightness parameter
    /** @see setTheta */
    CV_WRAP virtual double getTheta() const = 0;
    /** @copybrief getTheta @see getTheta */
    CV_WRAP virtual void setTheta(double val) = 0;
    //! @brief coefficient for additional illumination variation term
    /** @see setGamma */
    CV_WRAP virtual double getGamma() const = 0;
    /** @copybrief getGamma @see getGamma */
    CV_WRAP virtual void setGamma(double val) = 0;
    //! @brief Number of scales used to create the pyramid of images
    /** @see setScalesNumber */
    CV_WRAP virtual int getScalesNumber() const = 0;
    /** @copybrief getScalesNumber @see getScalesNumber */
    CV_WRAP virtual void setScalesNumber(int val) = 0;
    //! @brief Number of warpings per scale
    /** @see setWarpingsNumber */
    CV_WRAP virtual int getWarpingsNumber() const = 0;
    /** @copybrief getWarpingsNumber @see getWarpingsNumber */
    CV_WRAP virtual void setWarpingsNumber(int val) = 0;
    //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time
    /** @see setEpsilon */
    CV_WRAP virtual double getEpsilon() const = 0;
    /** @copybrief getEpsilon @see getEpsilon */
    CV_WRAP virtual void setEpsilon(double val) = 0;
    //! @brief Inner iterations (between outlier filtering) used in the numerical scheme
    /** @see setInnerIterations */
    CV_WRAP virtual int getInnerIterations() const = 0;
    /** @copybrief getInnerIterations @see getInnerIterations */
    CV_WRAP virtual void setInnerIterations(int val) = 0;
    //! @brief Outer iterations (number of inner loops) used in the numerical scheme
    /** @see setOuterIterations */
    CV_WRAP virtual int getOuterIterations() const = 0;
    /** @copybrief getOuterIterations @see getOuterIterations */
    CV_WRAP virtual void setOuterIterations(int val) = 0;
    //! @brief Use initial flow
    /** @see setUseInitialFlow */
    CV_WRAP virtual bool getUseInitialFlow() const = 0;
    /** @copybrief getUseInitialFlow @see getUseInitialFlow */
    CV_WRAP virtual void setUseInitialFlow(bool val) = 0;
    //! @brief Step between scales (<1)
    /** @see setScaleStep */
    CV_WRAP virtual double getScaleStep() const = 0;
    /** @copybrief getScaleStep @see getScaleStep */
    CV_WRAP virtual void setScaleStep(double val) = 0;
    //! @brief Median filter kernel size (1 = no filter) (3 or 5)
    /** @see setMedianFiltering */
    CV_WRAP virtual int getMedianFiltering() const = 0;
    /** @copybrief getMedianFiltering @see getMedianFiltering */
    CV_WRAP virtual void setMedianFiltering(int val) = 0;

    /** @brief Creates instance of cv::DualTVL1OpticalFlow*/
    CV_WRAP static Ptr<DualTVL1OpticalFlow> create(
                                            double tau = 0.25,
                                            double lambda = 0.15,
                                            double theta = 0.3,
                                            int nscales = 5,
                                            int warps = 5,
                                            double epsilon = 0.01,
                                            int innnerIterations = 30,
                                            int outerIterations = 10,
                                            double scaleStep = 0.8,
                                            double gamma = 0.0,
                                            int medianFiltering = 5,
                                            bool useInitialFlow = false);
};

/** @brief Creates instance of cv::DenseOpticalFlow
*/
CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();

/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm.
 */
class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow
{
public:
    CV_WRAP virtual int getNumLevels() const = 0;
    CV_WRAP virtual void setNumLevels(int numLevels) = 0;

    CV_WRAP virtual double getPyrScale() const = 0;
    CV_WRAP virtual void setPyrScale(double pyrScale) = 0;

    CV_WRAP virtual bool getFastPyramids() const = 0;
    CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0;

    CV_WRAP virtual int getWinSize() const = 0;
    CV_WRAP virtual void setWinSize(int winSize) = 0;

    CV_WRAP virtual int getNumIters() const = 0;
    CV_WRAP virtual void setNumIters(int numIters) = 0;

    CV_WRAP virtual int getPolyN() const = 0;
    CV_WRAP virtual void setPolyN(int polyN) = 0;

    CV_WRAP virtual double getPolySigma() const = 0;
    CV_WRAP virtual void setPolySigma(double polySigma) = 0;

    CV_WRAP virtual int getFlags() const = 0;
    CV_WRAP virtual void setFlags(int flags) = 0;

    CV_WRAP static Ptr<FarnebackOpticalFlow> create(
            int numLevels = 5,
            double pyrScale = 0.5,
            bool fastPyramids = false,
            int winSize = 13,
            int numIters = 10,
            int polyN = 5,
            double polySigma = 1.1,
            int flags = 0);
};


/** @brief Class used for calculating a sparse optical flow.

The class can calculate an optical flow for a sparse feature set using the
iterative Lucas-Kanade method with pyramids.

@sa calcOpticalFlowPyrLK

*/
class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow
{
public:
    CV_WRAP virtual Size getWinSize() const = 0;
    CV_WRAP virtual void setWinSize(Size winSize) = 0;

    CV_WRAP virtual int getMaxLevel() const = 0;
    CV_WRAP virtual void setMaxLevel(int maxLevel) = 0;

    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
    CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0;

    CV_WRAP virtual int getFlags() const = 0;
    CV_WRAP virtual void setFlags(int flags) = 0;

    CV_WRAP virtual double getMinEigThreshold() const = 0;
    CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0;

    CV_WRAP static Ptr<SparsePyrLKOpticalFlow> create(
            Size winSize = Size(21, 21),
            int maxLevel = 3, TermCriteria crit =
            TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
            int flags = 0,
            double minEigThreshold = 1e-4);
};

//! @} video_track

} // cv

#endif