OpenCV：使用Kalman滤波器跟踪一个旋转的点

标签：opencv

<span style="font-size:14px;">#include <opencv\cv.h>
#include <opencv\highgui.h>
#include <math.h>

int main(int argc, char** argv){

    const float A[] = { 1, 1, 0, 1 };

    IplImage* img = cvCreateImage( cvSize(500,500), 8, 3 );
    CvKalman* kalman = cvCreateKalman( 2, 1, 0 );
    // 状态是角度和角度增量
    CvMat* state = cvCreateMat( 2, 1, CV_32FC1 );
    CvMat* process_noise = cvCreateMat( 2, 1, CV_32FC1 );
    // 只有角度被测量
    CvMat* measurement = cvCreateMat( 1, 1, CV_32FC1 );
    CvRandState rng;
    int code = -1;

    cvRandInit( &rng, 0, 1, -1, CV_RAND_UNI );

    cvZero( measurement );
    cvNamedWindow( "Kalman", 1 );

    for(;;){

        cvRandSetRange( &rng, 0, 0.1, 0 );
        rng.disttype = CV_RAND_NORMAL;

        cvRand( &rng, state );

        memcpy( kalman->transition_matrix->data.fl, A, sizeof(A));
        cvSetIdentity( kalman->measurement_matrix, cvRealScalar(1) );
        cvSetIdentity( kalman->process_noise_cov, cvRealScalar(1e-5) );
        cvSetIdentity( kalman->measurement_noise_cov, cvRealScalar(1e-1) );
        cvSetIdentity( kalman->error_cov_post, cvRealScalar(1));
        // 选择随机的开始状态
        cvRand( &rng, kalman->state_post );

        rng.disttype = CV_RAND_NORMAL;

        for(;;){

#define calc_point(angle)                                                  cvPoint( cvRound(img->width/2 + img->width/3*cos(angle)),              cvRound(img->height/2 - img->width/3*sin(angle)))

            float state_angle = state->data.fl[0];
            CvPoint state_pt = calc_point(state_angle);

            // 预测点的方向
            const CvMat* prediction = cvKalmanPredict( kalman, 0 );
            float predict_angle = prediction->data.fl[0];
            CvPoint predict_pt = calc_point(predict_angle);
            float measurement_angle;
            CvPoint measurement_pt;

            cvRandSetRange( &rng, 0,
                            sqrt(kalman->measurement_noise_cov->data.fl[0]), 0 );
            cvRand( &rng, measurement );


            cvMatMulAdd( kalman->measurement_matrix,
                         state, measurement, measurement );

            measurement_angle = measurement->data.fl[0];
            measurement_pt = calc_point(measurement_angle);


#define draw_cross( center, color, d )                                             cvLine( img, cvPoint( center.x - d, center.y - d ),                            cvPoint( center.x + d, center.y + d ), color, 1, 0 );             cvLine( img, cvPoint( center.x + d, center.y - d ),                            cvPoint( center.x - d, center.y + d ), color, 1, 0 )

            cvZero( img );
            draw_cross( state_pt, CV_RGB(255,255,255), 3 );
            draw_cross( measurement_pt, CV_RGB(255,0,0), 3 );
            draw_cross( predict_pt, CV_RGB(0,255,0), 3 );
            cvLine( img, state_pt, predict_pt, CV_RGB(255,255,0), 3, 0 );

            // 调整Kalman滤波器的状态
            cvKalmanCorrect( kalman, measurement );

            cvRandSetRange( &rng, 0, sqrt(kalman->process_noise_cov->data.fl[0]), 0 );
            cvRand( &rng, process_noise );
            cvMatMulAdd( kalman->transition_matrix, state, process_noise, state );

            cvShowImage( "Kalman", img );
            code = cvWaitKey( 100 );

            if( code > 0 )
                break;
        }
        if( code == 27 )
            break;
    }

    return 0;
}
</span>

运行结果截图：

OpenCV：使用Kalman滤波器跟踪一个旋转的点

标签：opencv

原文地址：http://blog.csdn.net/u010002704/article/details/43734833