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Merge pull request #20564 from AleksandrPanov:update_kalman_sample

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Update kalman sample

* updated view and comments, fixed dims

* updated view and comments, added statePost
This commit is contained in:
Alexander Panov
2021-08-20 13:57:05 +03:00
committed by GitHub
parent a9817e9127
commit d6306f8ccb
2 changed files with 83 additions and 72 deletions
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samples/cpp/kalman.cpp
+34 -24
View File
@@ -1,6 +1,6 @@
#include "opencv2/video/tracking.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/cvdef.h"
#include <stdio.h>
using namespace cv;
@@ -14,15 +14,19 @@ static void help()
{
printf( "\nExample of c calls to OpenCV's Kalman filter.\n"
" Tracking of rotating point.\n"
" Rotation speed is constant.\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 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"
" 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).\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 with a different speed.\n"
" Pressing any key (except ESC) will reset the tracking.\n"
" Pressing ESC will stop the program.\n"
);
}
@@ -39,7 +43,9 @@ int main(int, char**)
for(;;)
{
randn( state, Scalar::all(0), Scalar::all(0.1) );
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);
@@ -60,36 +66,40 @@ int main(int, char**)
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
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
#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 = 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);
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 );
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 );
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);
code = (char)waitKey(1000);
if( code > 0 )
break;