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d6306f8ccb
Update kalman sample * updated view and comments, fixed dims * updated view and comments, added statePost
113 lines
4.2 KiB
C++
113 lines
4.2 KiB
C++
#include "opencv2/video/tracking.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/core/cvdef.h"
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#include <stdio.h>
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using namespace cv;
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static inline Point calcPoint(Point2f center, double R, double angle)
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{
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return center + Point2f((float)cos(angle), (float)-sin(angle))*(float)R;
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}
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static void help()
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{
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printf( "\nExample of c calls to OpenCV's Kalman filter.\n"
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" Tracking of rotating point.\n"
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" Point moves in a circle and is characterized by a 1D state.\n"
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" state_k+1 = state_k + speed + process_noise N(0, 1e-5)\n"
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" The speed is constant.\n"
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" Both state and measurements vectors are 1D (a point angle),\n"
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" Measurement is the real state + gaussian noise N(0, 1e-1).\n"
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" The real and the measured points are connected with red line segment,\n"
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" the real and the estimated points are connected with yellow line segment,\n"
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" the real and the corrected estimated points are connected with green line segment.\n"
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" (if Kalman filter works correctly,\n"
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" the yellow segment should be shorter than the red one and\n"
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" the green segment should be shorter than the yellow one)."
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"\n"
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" Pressing any key (except ESC) will reset the tracking.\n"
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" Pressing ESC will stop the program.\n"
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);
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}
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int main(int, char**)
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{
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help();
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Mat img(500, 500, CV_8UC3);
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KalmanFilter KF(2, 1, 0);
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Mat state(2, 1, CV_32F); /* (phi, delta_phi) */
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Mat processNoise(2, 1, CV_32F);
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Mat measurement = Mat::zeros(1, 1, CV_32F);
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char code = (char)-1;
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for(;;)
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{
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img = Scalar::all(0);
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state.at<float>(0) = 0.0f;
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state.at<float>(1) = 2.f * (float)CV_PI / 6;
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KF.transitionMatrix = (Mat_<float>(2, 2) << 1, 1, 0, 1);
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setIdentity(KF.measurementMatrix);
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setIdentity(KF.processNoiseCov, Scalar::all(1e-5));
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setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
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setIdentity(KF.errorCovPost, Scalar::all(1));
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randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));
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for(;;)
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{
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Point2f center(img.cols*0.5f, img.rows*0.5f);
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float R = img.cols/3.f;
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double stateAngle = state.at<float>(0);
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Point statePt = calcPoint(center, R, stateAngle);
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Mat prediction = KF.predict();
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double predictAngle = prediction.at<float>(0);
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Point predictPt = calcPoint(center, R, predictAngle);
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// generate measurement
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randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));
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measurement += KF.measurementMatrix*state;
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double measAngle = measurement.at<float>(0);
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Point measPt = calcPoint(center, R, measAngle);
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// correct the state estimates based on measurements
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// updates statePost & errorCovPost
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KF.correct(measurement);
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double improvedAngle = KF.statePost.at<float>(0);
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Point improvedPt = calcPoint(center, R, improvedAngle);
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// plot points
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img = img * 0.2;
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drawMarker(img, measPt, Scalar(0, 0, 255), cv::MARKER_SQUARE, 5, 2);
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drawMarker(img, predictPt, Scalar(0, 255, 255), cv::MARKER_SQUARE, 5, 2);
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drawMarker(img, improvedPt, Scalar(0, 255, 0), cv::MARKER_SQUARE, 5, 2);
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drawMarker(img, statePt, Scalar(255, 255, 255), cv::MARKER_STAR, 10, 1);
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// forecast one step
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Mat test = Mat(KF.transitionMatrix*KF.statePost);
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drawMarker(img, calcPoint(center, R, Mat(KF.transitionMatrix*KF.statePost).at<float>(0)),
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Scalar(255, 255, 0), cv::MARKER_SQUARE, 12, 1);
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line( img, statePt, measPt, Scalar(0,0,255), 1, LINE_AA, 0 );
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line( img, statePt, predictPt, Scalar(0,255,255), 1, LINE_AA, 0 );
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line( img, statePt, improvedPt, Scalar(0,255,0), 1, LINE_AA, 0 );
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randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
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state = KF.transitionMatrix*state + processNoise;
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imshow( "Kalman", img );
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code = (char)waitKey(1000);
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if( code > 0 )
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break;
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}
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if( code == 27 || code == 'q' || code == 'Q' )
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break;
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}
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return 0;
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}
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