mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
102 lines
3.6 KiB
102 lines
3.6 KiB
#include "opencv2/video/tracking.hpp" |
|
#include "opencv2/highgui/highgui.hpp" |
|
|
|
#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( "\nExamle of c calls to OpenCV's Kalman filter.\n" |
|
" Tracking of rotating point.\n" |
|
" Rotation speed is constant.\n" |
|
" Both state and measurements vectors are 1D (a point angle),\n" |
|
" Measurement is the real point angle + gaussian noise.\n" |
|
" The real and the estimated points are connected with yellow line segment,\n" |
|
" the real and the measured points are connected with red line segment.\n" |
|
" (if Kalman filter works correctly,\n" |
|
" the yellow segment should be shorter than the red one).\n" |
|
"\n" |
|
" Pressing any key (except ESC) will reset the tracking with a different speed.\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(;;) |
|
{ |
|
randn( state, Scalar::all(0), Scalar::all(0.1) ); |
|
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); |
|
|
|
randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0))); |
|
|
|
// generate measurement |
|
measurement += KF.measurementMatrix*state; |
|
|
|
double measAngle = measurement.at<float>(0); |
|
Point measPt = calcPoint(center, R, measAngle); |
|
|
|
// plot points |
|
#define drawCross( center, color, d ) \ |
|
line( img, Point( center.x - d, center.y - d ), \ |
|
Point( center.x + d, center.y + d ), color, 1, LINE_AA, 0); \ |
|
line( img, Point( center.x + d, center.y - d ), \ |
|
Point( center.x - d, center.y + d ), color, 1, LINE_AA, 0 ) |
|
|
|
img = Scalar::all(0); |
|
drawCross( statePt, Scalar(255,255,255), 3 ); |
|
drawCross( measPt, Scalar(0,0,255), 3 ); |
|
drawCross( predictPt, Scalar(0,255,0), 3 ); |
|
line( img, statePt, measPt, Scalar(0,0,255), 3, LINE_AA, 0 ); |
|
line( img, statePt, predictPt, Scalar(0,255,255), 3, LINE_AA, 0 ); |
|
|
|
if(theRNG().uniform(0,4) != 0) |
|
KF.correct(measurement); |
|
|
|
randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0)))); |
|
state = KF.transitionMatrix*state + processNoise; |
|
|
|
imshow( "Kalman", img ); |
|
code = (char)waitKey(100); |
|
|
|
if( code > 0 ) |
|
break; |
|
} |
|
if( code == 27 || code == 'q' || code == 'Q' ) |
|
break; |
|
} |
|
|
|
return 0; |
|
}
|
|
|