mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 18:09:11 +08:00
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"
|
|
#include "opencv2/highgui.hpp"
|
|
#include "opencv2/core/cvdef.h"
|
|
#include <stdio.h>
|
|
|
|
using namespace cv;
|
|
|
|
static inline Point calcPoint(Point2f center, double R, double angle)
|
|
{
|
|
return center + Point2f((float)cos(angle), (float)-sin(angle))*(float)R;
|
|
}
|
|
|
|
static void help()
|
|
{
|
|
printf( "\nExample of c calls to OpenCV's Kalman filter.\n"
|
|
" Tracking of rotating point.\n"
|
|
" Point moves in a circle and is characterized by a 1D state.\n"
|
|
" state_k+1 = state_k + speed + process_noise N(0, 1e-5)\n"
|
|
" The speed is constant.\n"
|
|
" Both state and measurements vectors are 1D (a point angle),\n"
|
|
" Measurement is the real state + gaussian noise N(0, 1e-1).\n"
|
|
" The real and the measured points are connected with red line segment,\n"
|
|
" the real and the estimated points are connected with yellow line segment,\n"
|
|
" the real and the corrected estimated points are connected with green line segment.\n"
|
|
" (if Kalman filter works correctly,\n"
|
|
" the yellow segment should be shorter than the red one and\n"
|
|
" the green segment should be shorter than the yellow one)."
|
|
"\n"
|
|
" Pressing any key (except ESC) will reset the tracking.\n"
|
|
" Pressing ESC will stop the program.\n"
|
|
);
|
|
}
|
|
|
|
int main(int, char**)
|
|
{
|
|
help();
|
|
Mat img(500, 500, CV_8UC3);
|
|
KalmanFilter KF(2, 1, 0);
|
|
Mat state(2, 1, CV_32F); /* (phi, delta_phi) */
|
|
Mat processNoise(2, 1, CV_32F);
|
|
Mat measurement = Mat::zeros(1, 1, CV_32F);
|
|
char code = (char)-1;
|
|
|
|
for(;;)
|
|
{
|
|
img = Scalar::all(0);
|
|
state.at<float>(0) = 0.0f;
|
|
state.at<float>(1) = 2.f * (float)CV_PI / 6;
|
|
KF.transitionMatrix = (Mat_<float>(2, 2) << 1, 1, 0, 1);
|
|
|
|
setIdentity(KF.measurementMatrix);
|
|
setIdentity(KF.processNoiseCov, Scalar::all(1e-5));
|
|
setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
|
|
setIdentity(KF.errorCovPost, Scalar::all(1));
|
|
|
|
randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));
|
|
|
|
for(;;)
|
|
{
|
|
Point2f center(img.cols*0.5f, img.rows*0.5f);
|
|
float R = img.cols/3.f;
|
|
double stateAngle = state.at<float>(0);
|
|
Point statePt = calcPoint(center, R, stateAngle);
|
|
|
|
Mat prediction = KF.predict();
|
|
double predictAngle = prediction.at<float>(0);
|
|
Point predictPt = calcPoint(center, R, predictAngle);
|
|
|
|
// generate measurement
|
|
randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));
|
|
measurement += KF.measurementMatrix*state;
|
|
|
|
double measAngle = measurement.at<float>(0);
|
|
Point measPt = calcPoint(center, R, measAngle);
|
|
|
|
// correct the state estimates based on measurements
|
|
// updates statePost & errorCovPost
|
|
KF.correct(measurement);
|
|
double improvedAngle = KF.statePost.at<float>(0);
|
|
Point improvedPt = calcPoint(center, R, improvedAngle);
|
|
|
|
// plot points
|
|
img = img * 0.2;
|
|
drawMarker(img, measPt, Scalar(0, 0, 255), cv::MARKER_SQUARE, 5, 2);
|
|
drawMarker(img, predictPt, Scalar(0, 255, 255), cv::MARKER_SQUARE, 5, 2);
|
|
drawMarker(img, improvedPt, Scalar(0, 255, 0), cv::MARKER_SQUARE, 5, 2);
|
|
drawMarker(img, statePt, Scalar(255, 255, 255), cv::MARKER_STAR, 10, 1);
|
|
// forecast one step
|
|
Mat test = Mat(KF.transitionMatrix*KF.statePost);
|
|
drawMarker(img, calcPoint(center, R, Mat(KF.transitionMatrix*KF.statePost).at<float>(0)),
|
|
Scalar(255, 255, 0), cv::MARKER_SQUARE, 12, 1);
|
|
|
|
line( img, statePt, measPt, Scalar(0,0,255), 1, LINE_AA, 0 );
|
|
line( img, statePt, predictPt, Scalar(0,255,255), 1, LINE_AA, 0 );
|
|
line( img, statePt, improvedPt, Scalar(0,255,0), 1, LINE_AA, 0 );
|
|
|
|
|
|
randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
|
|
state = KF.transitionMatrix*state + processNoise;
|
|
|
|
imshow( "Kalman", img );
|
|
code = (char)waitKey(1000);
|
|
|
|
if( code > 0 )
|
|
break;
|
|
}
|
|
if( code == 27 || code == 'q' || code == 'Q' )
|
|
break;
|
|
}
|
|
|
|
return 0;
|
|
}
|