OpenCV  4.10.0-dev Open Source Computer Vision
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samples/cpp/kalman.cpp

An example using the standard Kalman filter

#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
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;
}
Kalman filter class.
Definition tracking.hpp:361
Template matrix class derived from Mat.
Definition mat.hpp:2230
n-dimensional dense array class
Definition mat.hpp:812
_Tp & at(int i0=0)
Returns a reference to the specified array element.
void sqrt(InputArray src, OutputArray dst)
Calculates a square root of array elements.
void setIdentity(InputOutputArray mtx, const Scalar &s=Scalar(1))
Initializes a scaled identity matrix.
void randn(InputOutputArray dst, InputArray mean, InputArray stddev)
Fills the array with normally distributed random numbers.
#define CV_32F
Definition interface.h:78
#define CV_8UC3
Definition interface.h:90
#define CV_PI
Definition cvdef.h:380
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
int waitKey(int delay=0)
Waits for a pressed key.
void drawMarker(InputOutputArray img, Point position, const Scalar &color, int markerType=MARKER_CROSS, int markerSize=20, int thickness=1, int line_type=8)
Draws a marker on a predefined position in an image.
void line(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
Draws a line segment connecting two points.
@ MARKER_SQUARE
A square marker shape.
Definition imgproc.hpp:921
@ MARKER_STAR
A star marker shape, combination of cross and tilted cross.
Definition imgproc.hpp:919
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
"black box" representation of the file storage associated with a file on disk.
Definition core.hpp:102