2016-04-28 21:40:36 +02:00
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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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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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2018-02-01 21:10:10 +01:00
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#ifndef OPENCV_TRACKING_C_H
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#define OPENCV_TRACKING_C_H
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2016-04-28 21:40:36 +02:00
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#include "opencv2/imgproc/types_c.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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/** @addtogroup video_c
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@{
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*/
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/****************************************************************************************\
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* Motion Analysis *
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\****************************************************************************************/
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/************************************ optical flow ***************************************/
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#define CV_LKFLOW_PYR_A_READY 1
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#define CV_LKFLOW_PYR_B_READY 2
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#define CV_LKFLOW_INITIAL_GUESSES 4
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#define CV_LKFLOW_GET_MIN_EIGENVALS 8
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/* It is Lucas & Kanade method, modified to use pyramids.
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Also it does several iterations to get optical flow for
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every point at every pyramid level.
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Calculates optical flow between two images for certain set of points (i.e.
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it is a "sparse" optical flow, which is opposite to the previous 3 methods) */
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CVAPI(void) cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr,
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CvArr* prev_pyr, CvArr* curr_pyr,
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const CvPoint2D32f* prev_features,
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CvPoint2D32f* curr_features,
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int count,
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CvSize win_size,
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int level,
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char* status,
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float* track_error,
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CvTermCriteria criteria,
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int flags );
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/* Modification of a previous sparse optical flow algorithm to calculate
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affine flow */
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CVAPI(void) cvCalcAffineFlowPyrLK( const CvArr* prev, const CvArr* curr,
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CvArr* prev_pyr, CvArr* curr_pyr,
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const CvPoint2D32f* prev_features,
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CvPoint2D32f* curr_features,
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float* matrices, int count,
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CvSize win_size, int level,
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char* status, float* track_error,
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CvTermCriteria criteria, int flags );
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/* Estimate rigid transformation between 2 images or 2 point sets */
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CVAPI(int) cvEstimateRigidTransform( const CvArr* A, const CvArr* B,
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CvMat* M, int full_affine );
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/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */
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CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next,
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CvArr* flow, double pyr_scale, int levels,
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int winsize, int iterations, int poly_n,
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double poly_sigma, int flags );
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/********************************* motion templates *************************************/
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/****************************************************************************************\
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* All the motion template functions work only with single channel images. *
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* Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S *
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* Motion history image must have depth IPL_DEPTH_32F, *
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* Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S, *
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* Motion orientation image - IPL_DEPTH_32F *
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* Segmentation mask - IPL_DEPTH_32F *
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* All the angles are in degrees, all the times are in milliseconds *
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\****************************************************************************************/
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/* Updates motion history image given motion silhouette */
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CVAPI(void) cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi,
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double timestamp, double duration );
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/* Calculates gradient of the motion history image and fills
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a mask indicating where the gradient is valid */
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CVAPI(void) cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation,
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double delta1, double delta2,
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int aperture_size CV_DEFAULT(3));
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/* Calculates average motion direction within a selected motion region
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(region can be selected by setting ROIs and/or by composing a valid gradient mask
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with the region mask) */
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CVAPI(double) cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask,
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const CvArr* mhi, double timestamp,
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double duration );
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/* Splits a motion history image into a few parts corresponding to separate independent motions
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(e.g. left hand, right hand) */
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CVAPI(CvSeq*) cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask,
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CvMemStorage* storage,
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double timestamp, double seg_thresh );
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/****************************************************************************************\
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* Tracking *
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\****************************************************************************************/
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/* Implements CAMSHIFT algorithm - determines object position, size and orientation
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from the object histogram back project (extension of meanshift) */
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CVAPI(int) cvCamShift( const CvArr* prob_image, CvRect window,
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CvTermCriteria criteria, CvConnectedComp* comp,
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CvBox2D* box CV_DEFAULT(NULL) );
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/* Implements MeanShift algorithm - determines object position
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from the object histogram back project */
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CVAPI(int) cvMeanShift( const CvArr* prob_image, CvRect window,
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CvTermCriteria criteria, CvConnectedComp* comp );
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/*
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standard Kalman filter (in G. Welch' and G. Bishop's notation):
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x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q)
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z(k)=H*x(k)+v(k), p(v)~N(0,R)
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*/
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typedef struct CvKalman
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{
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int MP; /* number of measurement vector dimensions */
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int DP; /* number of state vector dimensions */
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int CP; /* number of control vector dimensions */
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/* backward compatibility fields */
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#if 1
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float* PosterState; /* =state_pre->data.fl */
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float* PriorState; /* =state_post->data.fl */
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float* DynamMatr; /* =transition_matrix->data.fl */
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float* MeasurementMatr; /* =measurement_matrix->data.fl */
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float* MNCovariance; /* =measurement_noise_cov->data.fl */
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float* PNCovariance; /* =process_noise_cov->data.fl */
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float* KalmGainMatr; /* =gain->data.fl */
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float* PriorErrorCovariance;/* =error_cov_pre->data.fl */
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float* PosterErrorCovariance;/* =error_cov_post->data.fl */
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float* Temp1; /* temp1->data.fl */
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float* Temp2; /* temp2->data.fl */
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#endif
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CvMat* state_pre; /* predicted state (x'(k)):
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x(k)=A*x(k-1)+B*u(k) */
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CvMat* state_post; /* corrected state (x(k)):
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x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */
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CvMat* transition_matrix; /* state transition matrix (A) */
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CvMat* control_matrix; /* control matrix (B)
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(it is not used if there is no control)*/
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CvMat* measurement_matrix; /* measurement matrix (H) */
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CvMat* process_noise_cov; /* process noise covariance matrix (Q) */
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CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */
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CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)):
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P'(k)=A*P(k-1)*At + Q)*/
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CvMat* 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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CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)):
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P(k)=(I-K(k)*H)*P'(k) */
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CvMat* temp1; /* temporary matrices */
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CvMat* temp2;
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CvMat* temp3;
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CvMat* temp4;
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CvMat* temp5;
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} CvKalman;
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/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */
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CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params,
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int control_params CV_DEFAULT(0));
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/* Releases Kalman filter state */
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CVAPI(void) cvReleaseKalman( CvKalman** kalman);
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/* Updates Kalman filter by time (predicts future state of the system) */
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CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman,
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const CvMat* control CV_DEFAULT(NULL));
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/* Updates Kalman filter by measurement
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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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/** @} video_c */
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#ifdef __cplusplus
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} // extern "C"
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#endif
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2018-02-01 21:10:10 +01:00
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#endif // OPENCV_TRACKING_C_H
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