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+/*! \file tracking.hpp
+ \brief The Object and Feature Tracking
+ */
+
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_TRACKING_HPP__
+#define __OPENCV_TRACKING_HPP__
+
+#include "opencv2/core/core.hpp"
+#include "opencv2/imgproc/imgproc.hpp"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/****************************************************************************************\
+*                                  Motion Analysis                                       *
+\****************************************************************************************/
+
+/************************************ optical flow ***************************************/
+
+#define  CV_LKFLOW_PYR_A_READY       1
+#define  CV_LKFLOW_PYR_B_READY       2
+#define  CV_LKFLOW_INITIAL_GUESSES   4
+#define  CV_LKFLOW_GET_MIN_EIGENVALS 8
+
+/* It is Lucas & Kanade method, modified to use pyramids.
+   Also it does several iterations to get optical flow for
+   every point at every pyramid level.
+   Calculates optical flow between two images for certain set of points (i.e.
+   it is a "sparse" optical flow, which is opposite to the previous 3 methods) */
+CVAPI(void)  cvCalcOpticalFlowPyrLK( const CvArr*  prev, const CvArr*  curr,
+                                     CvArr*  prev_pyr, CvArr*  curr_pyr,
+                                     const CvPoint2D32f* prev_features,
+                                     CvPoint2D32f* curr_features,
+                                     int       count,
+                                     CvSize    win_size,
+                                     int       level,
+                                     char*     status,
+                                     float*    track_error,
+                                     CvTermCriteria criteria,
+                                     int       flags );
+
+
+/* Modification of a previous sparse optical flow algorithm to calculate
+   affine flow */
+CVAPI(void)  cvCalcAffineFlowPyrLK( const CvArr*  prev, const CvArr*  curr,
+                                    CvArr*  prev_pyr, CvArr*  curr_pyr,
+                                    const CvPoint2D32f* prev_features,
+                                    CvPoint2D32f* curr_features,
+                                    float* matrices, int  count,
+                                    CvSize win_size, int  level,
+                                    char* status, float* track_error,
+                                    CvTermCriteria criteria, int flags );
+
+/* Estimate rigid transformation between 2 images or 2 point sets */
+CVAPI(int)  cvEstimateRigidTransform( const CvArr* A, const CvArr* B,
+                                      CvMat* M, int full_affine );
+
+/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */
+CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next,
+                                        CvArr* flow, double pyr_scale, int levels,
+                                        int winsize, int iterations, int poly_n,
+                                        double poly_sigma, int flags );
+
+/********************************* motion templates *************************************/
+
+/****************************************************************************************\
+*        All the motion template functions work only with single channel images.         *
+*        Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S                   *
+*        Motion history image must have depth IPL_DEPTH_32F,                             *
+*        Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S,                                   *
+*        Motion orientation image - IPL_DEPTH_32F                                        *
+*        Segmentation mask - IPL_DEPTH_32F                                               *
+*        All the angles are in degrees, all the times are in milliseconds                *
+\****************************************************************************************/
+
+/* Updates motion history image given motion silhouette */
+CVAPI(void)    cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi,
+                                      double timestamp, double duration );
+
+/* Calculates gradient of the motion history image and fills
+   a mask indicating where the gradient is valid */
+CVAPI(void)    cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation,
+                                     double delta1, double delta2,
+                                     int aperture_size CV_DEFAULT(3));
+
+/* Calculates average motion direction within a selected motion region
+   (region can be selected by setting ROIs and/or by composing a valid gradient mask
+   with the region mask) */
+CVAPI(double)  cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask,
+                                        const CvArr* mhi, double timestamp,
+                                        double duration );
+
+/* Splits a motion history image into a few parts corresponding to separate independent motions
+   (e.g. left hand, right hand) */
+CVAPI(CvSeq*)  cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask,
+                                CvMemStorage* storage,
+                                double timestamp, double seg_thresh );
+
+/****************************************************************************************\
+*                                       Tracking                                         *
+\****************************************************************************************/
+
+/* Implements CAMSHIFT algorithm - determines object position, size and orientation
+   from the object histogram back project (extension of meanshift) */
+CVAPI(int)  cvCamShift( const CvArr* prob_image, CvRect  window,
+                        CvTermCriteria criteria, CvConnectedComp* comp,
+                        CvBox2D* box CV_DEFAULT(NULL) );
+
+/* Implements MeanShift algorithm - determines object position
+   from the object histogram back project */
+CVAPI(int)  cvMeanShift( const CvArr* prob_image, CvRect  window,
+                         CvTermCriteria criteria, CvConnectedComp* comp );
+
+/*
+standard Kalman filter (in G. Welch' and G. Bishop's notation):
+
+  x(k)=A*x(k-1)+B*u(k)+w(k)  p(w)~N(0,Q)
+  z(k)=H*x(k)+v(k),   p(v)~N(0,R)
+*/
+typedef struct CvKalman
+{
+    int MP;                     /* number of measurement vector dimensions */
+    int DP;                     /* number of state vector dimensions */
+    int CP;                     /* number of control vector dimensions */
+
+    /* backward compatibility fields */
+#if 1
+    float* PosterState;         /* =state_pre->data.fl */
+    float* PriorState;          /* =state_post->data.fl */
+    float* DynamMatr;           /* =transition_matrix->data.fl */
+    float* MeasurementMatr;     /* =measurement_matrix->data.fl */
+    float* MNCovariance;        /* =measurement_noise_cov->data.fl */
+    float* PNCovariance;        /* =process_noise_cov->data.fl */
+    float* KalmGainMatr;        /* =gain->data.fl */
+    float* PriorErrorCovariance;/* =error_cov_pre->data.fl */
+    float* PosterErrorCovariance;/* =error_cov_post->data.fl */
+    float* Temp1;               /* temp1->data.fl */
+    float* Temp2;               /* temp2->data.fl */
+#endif
+
+    CvMat* state_pre;           /* predicted state (x'(k)):
+                                    x(k)=A*x(k-1)+B*u(k) */
+    CvMat* state_post;          /* corrected state (x(k)):
+                                    x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */
+    CvMat* transition_matrix;   /* state transition matrix (A) */
+    CvMat* control_matrix;      /* control matrix (B)
+                                   (it is not used if there is no control)*/
+    CvMat* measurement_matrix;  /* measurement matrix (H) */
+    CvMat* process_noise_cov;   /* process noise covariance matrix (Q) */
+    CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */
+    CvMat* error_cov_pre;       /* priori error estimate covariance matrix (P'(k)):
+                                    P'(k)=A*P(k-1)*At + Q)*/
+    CvMat* gain;                /* Kalman gain matrix (K(k)):
+                                    K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/
+    CvMat* error_cov_post;      /* posteriori error estimate covariance matrix (P(k)):
+                                    P(k)=(I-K(k)*H)*P'(k) */
+    CvMat* temp1;               /* temporary matrices */
+    CvMat* temp2;
+    CvMat* temp3;
+    CvMat* temp4;
+    CvMat* temp5;
+} CvKalman;
+
+/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */
+CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params,
+                                 int control_params CV_DEFAULT(0));
+
+/* Releases Kalman filter state */
+CVAPI(void)  cvReleaseKalman( CvKalman** kalman);
+
+/* Updates Kalman filter by time (predicts future state of the system) */
+CVAPI(const CvMat*)  cvKalmanPredict( CvKalman* kalman,
+                                      const CvMat* control CV_DEFAULT(NULL));
+
+/* Updates Kalman filter by measurement
+   (corrects state of the system and internal matrices) */
+CVAPI(const CvMat*)  cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement );
+
+#define cvKalmanUpdateByTime  cvKalmanPredict
+#define cvKalmanUpdateByMeasurement cvKalmanCorrect
+
+#ifdef __cplusplus
+}
+
+namespace cv
+{
+
+//! updates motion history image using the current silhouette
+CV_EXPORTS_W void updateMotionHistory( InputArray silhouette, InputOutputArray mhi,
+                                       double timestamp, double duration );
+
+//! computes the motion gradient orientation image from the motion history image
+CV_EXPORTS_W void calcMotionGradient( InputArray mhi, OutputArray mask,
+                                      OutputArray orientation,
+                                      double delta1, double delta2,
+                                      int apertureSize=3 );
+
+//! computes the global orientation of the selected motion history image part
+CV_EXPORTS_W double calcGlobalOrientation( InputArray orientation, InputArray mask,
+                                           InputArray mhi, double timestamp,
+                                           double duration );
+
+CV_EXPORTS_W void segmentMotion(InputArray mhi, OutputArray segmask,
+                                CV_OUT vector<Rect>& boundingRects,
+                                double timestamp, double segThresh);
+
+//! updates the object tracking window using CAMSHIFT algorithm
+CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_OUT CV_IN_OUT Rect& window,
+                                   TermCriteria criteria );
+
+//! updates the object tracking window using meanshift algorithm
+CV_EXPORTS_W int meanShift( InputArray probImage, CV_OUT CV_IN_OUT Rect& window,
+                            TermCriteria criteria );
+
+/*!
+ Kalman filter.
+
+ The class implements standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter.
+ However, you can modify KalmanFilter::transitionMatrix, KalmanFilter::controlMatrix and
+ KalmanFilter::measurementMatrix to get the extended Kalman filter functionality.
+*/
+class CV_EXPORTS_W KalmanFilter
+{
+public:
+    //! the default constructor
+    CV_WRAP KalmanFilter();
+    //! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
+    CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
+    //! re-initializes Kalman filter. The previous content is destroyed.
+    void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
+
+    //! computes predicted state
+    CV_WRAP const Mat& predict(const Mat& control=Mat());
+    //! updates the predicted state from the measurement
+    CV_WRAP const Mat& correct(const Mat& measurement);
+
+    Mat statePre;           //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
+    Mat statePost;          //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
+    Mat transitionMatrix;   //!< state transition matrix (A)
+    Mat controlMatrix;      //!< control matrix (B) (not used if there is no control)
+    Mat measurementMatrix;  //!< measurement matrix (H)
+    Mat processNoiseCov;    //!< process noise covariance matrix (Q)
+    Mat measurementNoiseCov;//!< measurement noise covariance matrix (R)
+    Mat errorCovPre;        //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
+    Mat gain;               //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
+    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;
+};
+
+enum
+{
+    OPTFLOW_USE_INITIAL_FLOW = CV_LKFLOW_INITIAL_GUESSES,
+    OPTFLOW_LK_GET_MIN_EIGENVALS = CV_LKFLOW_GET_MIN_EIGENVALS,
+    OPTFLOW_FARNEBACK_GAUSSIAN = 256
+};
+
+//! constructs a pyramid which can be used as input for calcOpticalFlowPyrLK
+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);
+
+//! computes sparse optical flow using multi-scale Lucas-Kanade algorithm
+CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
+                           InputArray prevPts, CV_OUT 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);
+
+//! computes dense optical flow using Farneback algorithm
+CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next,
+                           CV_OUT InputOutputArray flow, double pyr_scale, int levels, int winsize,
+                           int iterations, int poly_n, double poly_sigma, int flags );
+
+//! estimates the best-fit Euqcidean, similarity, affine or perspective transformation
+// that maps one 2D point set to another or one image to another.
+CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst,
+                                         bool fullAffine);
+
+//! computes dense optical flow using Simple Flow algorithm
+CV_EXPORTS_W void calcOpticalFlowSF(Mat& from,
+                                    Mat& to,
+                                    Mat& flow,
+                                    int layers,
+                                    int averaging_block_size,
+                                    int max_flow);
+
+CV_EXPORTS_W void calcOpticalFlowSF(Mat& from,
+                                    Mat& to,
+                                    Mat& flow,
+                                    int layers,
+                                    int averaging_block_size,
+                                    int max_flow,
+                                    double sigma_dist,
+                                    double sigma_color,
+                                    int postprocess_window,
+                                    double sigma_dist_fix,
+                                    double sigma_color_fix,
+                                    double occ_thr,
+                                    int upscale_averaging_radius,
+                                    double upscale_sigma_dist,
+                                    double upscale_sigma_color,
+                                    double speed_up_thr);
+
+class CV_EXPORTS DenseOpticalFlow : public Algorithm
+{
+public:
+    virtual void calc(InputArray I0, InputArray I1, InputOutputArray flow) = 0;
+    virtual void collectGarbage() = 0;
+};
+
+// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
+//
+// see reference:
+//   [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
+//   [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
+CV_EXPORTS Ptr<DenseOpticalFlow> createOptFlow_DualTVL1();
+
+}
+
+#endif
+
+#endif