doxygenated opencv_video & opencv_calib3d modules (C++ part only)
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Vadim Pisarevsky
@@ -353,22 +353,45 @@ CVAPI(CvSeq*) cvSegmentFGMask( CvArr *fgmask, int poly1Hull0 CV_DEFAULT(1),
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namespace cv
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{
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/*!
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The Base Class for Background/Foreground Segmentation
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The class is only used to define the common interface for
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the whole family of background/foreground segmentation algorithms.
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*/
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class CV_EXPORTS BackgroundSubtractor
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{
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public:
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//! the virtual destructor
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virtual ~BackgroundSubtractor();
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//! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
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virtual void operator()(const Mat& image, Mat& fgmask, double learningRate=0);
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};
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/*!
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Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
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The class implements the following algorithm:
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"An improved adaptive background mixture model for real-time tracking with shadow detection"
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P. KadewTraKuPong and R. Bowden,
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Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
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http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
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*/
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class CV_EXPORTS BackgroundSubtractorMOG : public BackgroundSubtractor
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{
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public:
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//! the default constructor
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BackgroundSubtractorMOG();
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//! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength
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BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0);
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//! the destructor
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virtual ~BackgroundSubtractorMOG();
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//! the update operator
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virtual void operator()(const Mat& image, Mat& fgmask, double learningRate=0);
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//! re-initiaization method
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virtual void initialize(Size frameSize, int frameType);
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Size frameSize;
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@@ -1,3 +1,7 @@
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/*! \file tracking.hpp
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\brief The Object and Feature Tracking
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*/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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@@ -230,58 +234,74 @@ CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman,
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(corrects state of the system and internal matrices) */
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CVAPI(const CvMat*) cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement );
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#define cvKalmanUpdateByTime cvKalmanPredict
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#define cvKalmanUpdateByMeasurement cvKalmanCorrect
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#ifdef __cplusplus
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}
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namespace cv
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{
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//! updates motion history image using the current silhouette
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CV_EXPORTS void updateMotionHistory( const Mat& silhouette, Mat& mhi,
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double timestamp, double duration );
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//! computes the motion gradient orientation image from the motion history image
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CV_EXPORTS void calcMotionGradient( const Mat& mhi, Mat& mask,
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Mat& orientation,
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double delta1, double delta2,
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int apertureSize=3 );
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//! computes the global orientation of the selected motion history image part
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CV_EXPORTS double calcGlobalOrientation( const Mat& orientation, const Mat& mask,
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const Mat& mhi, double timestamp,
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double duration );
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// TODO: need good API for cvSegmentMotion
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//! updates the object tracking window using CAMSHIFT algorithm
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CV_EXPORTS RotatedRect CamShift( const Mat& probImage, Rect& window,
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TermCriteria criteria );
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//! updates the object tracking window using meanshift algorithm
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CV_EXPORTS int meanShift( const Mat& probImage, Rect& window,
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TermCriteria criteria );
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/*!
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Kalman filter.
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The class implements standard Kalman filter \url{http://en.wikipedia.org/wiki/Kalman_filter}.
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However, you can modify KalmanFilter::transitionMatrix, KalmanFilter::controlMatrix and
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KalmanFilter::measurementMatrix to get the extended Kalman filter functionality.
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*/
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class CV_EXPORTS KalmanFilter
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{
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public:
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//! the default constructor
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KalmanFilter();
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//! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
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KalmanFilter(int dynamParams, int measureParams, int controlParams=0);
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//! re-initializes Kalman filter. The previous content is destroyed.
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void init(int dynamParams, int measureParams, int controlParams=0);
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//! computes predicted state
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const Mat& predict(const Mat& control=Mat());
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//! updates the predicted state from the measurement
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const Mat& correct(const Mat& measurement);
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Mat statePre; // predicted state (x'(k)):
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// x(k)=A*x(k-1)+B*u(k)
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Mat statePost; // corrected state (x(k)):
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// x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
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Mat transitionMatrix; // state transition matrix (A)
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Mat controlMatrix; // control matrix (B)
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// (it is not used if there is no control)
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Mat measurementMatrix; // measurement matrix (H)
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Mat processNoiseCov; // process noise covariance matrix (Q)
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Mat measurementNoiseCov;// measurement noise covariance matrix (R)
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Mat errorCovPre; // priori error estimate covariance matrix (P'(k)):
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// P'(k)=A*P(k-1)*At + Q)*/
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Mat gain; // Kalman gain matrix (K(k)):
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// K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
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Mat errorCovPost; // posteriori error estimate covariance matrix (P(k)):
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// P(k)=(I-K(k)*H)*P'(k)
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Mat temp1; // temporary matrices
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Mat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
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Mat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
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Mat transitionMatrix; //!< state transition matrix (A)
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Mat controlMatrix; //!< control matrix (B) (not used if there is no control)
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Mat measurementMatrix; //!< measurement matrix (H)
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Mat processNoiseCov; //!< process noise covariance matrix (Q)
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Mat measurementNoiseCov;//!< measurement noise covariance matrix (R)
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Mat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
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Mat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
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Mat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
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// temporary matrices
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Mat temp1;
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Mat temp2;
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Mat temp3;
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Mat temp4;
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@@ -289,12 +309,9 @@ public:
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};
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#define cvKalmanUpdateByTime cvKalmanPredict
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#define cvKalmanUpdateByMeasurement cvKalmanCorrect
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enum { OPTFLOW_USE_INITIAL_FLOW=4, OPTFLOW_FARNEBACK_GAUSSIAN=256 };
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//! computes sparse optical flow using multi-scale Lucas-Kanade algorithm
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CV_EXPORTS void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
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const vector<Point2f>& prevPts, vector<Point2f>& nextPts,
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vector<uchar>& status, vector<float>& err,
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@@ -305,6 +322,7 @@ CV_EXPORTS void calcOpticalFlowPyrLK( const Mat& prevImg, const Mat& nextImg,
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double derivLambda=0.5,
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int flags=0 );
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//! computes dense optical flow using Farneback algorithm
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CV_EXPORTS void calcOpticalFlowFarneback( const Mat& prev0, const Mat& next0,
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Mat& flow0, double pyr_scale, int levels, int winsize,
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int iterations, int poly_n, double poly_sigma, int flags );
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