一.OpenCV中的類定義
KalmanFilter類的定義
class CV_EXPORTS_W KalmanFilter
{
public:
CV_WRAP KalmanFilter(); //構造默認KalmanFilter對象
CV_WRAP KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F); //完整構造KalmanFilter對象方法
void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F); //初始化KalmanFilter對象,會替換原來的KF對象
CV_WRAP const Mat& predict(const Mat& control=Mat()); //計算預測的狀態值
CV_WRAP const Mat& correct(const Mat& measurement); //根據測量值更新狀態值
Mat statePre; //預測值 (x'(k)): x(k)=A*x(k-1)+B*u(k)
Mat statePost; //狀態值 (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat transitionMatrix; //狀態轉移矩陣 (A)
Mat controlMatrix; //控制矩陣 B
Mat measurementMatrix; //測量矩陣 H
Mat processNoiseCov; //系統誤差 Q
Mat measurementNoiseCov; //測量誤差 R
Mat errorCovPre; //最小均方誤差 (P'(k)): P'(k)=A*P(k-1)*At + Q)
Mat gain; //卡爾曼增益 (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Mat errorCovPost; //修正的最小均方誤差 (P(k)): P(k)=(I-K(k)*H)*P'(k)
// 臨時矩陣
Mat temp1;
Mat temp2;
Mat temp3;
Mat temp4;
Mat temp5;
};
相關函數的實現方法,即opencv/modules/video/src/kalman.cpp
#include "precomp.hpp"
namespace cv
{
KalmanFilter::KalmanFilter() {}
KalmanFilter::KalmanFilter(int dynamParams, int measureParams, int controlParams, int type)
{
init(dynamParams, measureParams, controlParams, type);
}
void KalmanFilter::init(int DP, int MP, int CP, int type)
{
CV_Assert( DP > 0 && MP > 0 );
CV_Assert( type == CV_32F || type == CV_64F );
CP = std::max(CP, 0);
statePre = Mat::zeros(DP, 1, type); //預測值 x(k)=A*x(k-1)+B*u(k)
statePost = Mat::zeros(DP, 1, type); //修正的狀態值 x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
transitionMatrix = Mat::eye(DP, DP, type); //狀態轉移矩陣
processNoiseCov = Mat::eye(DP, DP, type); //系統誤差Q
measurementMatrix = Mat::zeros(MP, DP, type); //測量矩陣
measurementNoiseCov = Mat::eye(MP, MP, type); //測量誤差
errorCovPre = Mat::zeros(DP, DP, type); //最小均方誤差 (P'(k)): P'(k)=A*P(k-1)*At + Q)
errorCovPost = Mat::zeros(DP, DP, type); //修正的最小均方誤差 (P(k)): P(k)=(I-K(k)*H)*P'(k)
gain = Mat::zeros(DP, MP, type); //卡爾曼增益
if( CP > 0 )
controlMatrix = Mat::zeros(DP, CP, type); //控制矩陣
else
controlMatrix.release();
temp1.create(DP, DP, type);
temp2.create(MP, DP, type);
temp3.create(MP, MP, type);
temp4.create(MP, DP, type);
temp5.create(MP, 1, type);
}
const Mat& KalmanFilter::predict(const Mat& control)
{
CV_INSTRUMENT_REGION();
// update the state: x'(k) = A*x(k)
statePre = transitionMatrix*statePost;
if( !control.empty() )
// x'(k) = x'(k) + B*u(k)
statePre += controlMatrix*control;
// update error covariance matrices: temp1 = A*P(k)
temp1 = transitionMatrix*errorCovPost;
// P'(k) = temp1*At + Q
gemm(temp1, transitionMatrix, 1, processNoiseCov, 1, errorCovPre, GEMM_2_T);//GEMM_2_T表示對第2個參數轉置。
// handle the case when there will be measurement before the next predict.
statePre.copyTo(statePost);
errorCovPre.copyTo(errorCovPost);
return statePre;
}
const Mat& KalmanFilter::correct(const Mat& measurement)
{
CV_INSTRUMENT_REGION();
// temp2 = H*P'(k)
temp2 = measurementMatrix * errorCovPre;
// temp3 = temp2*Ht + R
gemm(temp2, measurementMatrix, 1, measurementNoiseCov, 1, temp3, GEMM_2_T);//計算測量協方差
// temp4 = inv(temp3)*temp2 = Kt(k)
solve(temp3, temp2, temp4, DECOMP_SVD);//solve函數,用來解線性方程 temp3*temp4=temp2
// K(k)
gain = temp4.t();
// temp5 = z(k) - H*x'(k)
temp5 = measurement - measurementMatrix*statePre; //測量誤差
// x(k) = x'(k) + K(k)*temp5
statePost = statePre + gain*temp5;
// P(k) = P'(k) - K(k)*temp2
errorCovPost = errorCovPre - gain*temp2;
return statePost;
}
}
二.需要說明的地方
卡爾曼濾波的相關公式就不貼出來了,上面的更新與預測函數可以對照着那些公式,下面對幾個關鍵的地方進行說明。
(1)gemm()函數
gemm( )是矩陣的廣義乘法
void gemm(const GpuMat& src1, constGpuMat& src2, double alpha, const GpuMat& src3, double beta,GpuMat& dst, int flags=0, Stream& stream=Stream::Null())
對應着:
dst = alpha*src1*src2 +beta* src3
需要注意的一點是,程序裏面給出了最後一個參數是GEMM_2_T表示對第2個參數轉置。
(2)solve()函數
bool solve(InputArray src1, InputArray src2, OutputArray dst, int flags=DECOMP_LU)
用來解線性方程 A*X=B,src1 線性系統的左側(相當於上面的A),src2 線性系統的右側(相當於上面的B),dst 輸出的解決方案(相當於要求解的X),flag爲使用的方法。
(3)爲什麼可以用solve()函數求解卡爾曼增益
卡爾曼增益K的意義是使後驗估計誤差協方差最小,將K帶入後驗估計誤差協方差的表達式,
通過求導,可以計算出最優的K值。一般的表達式:
採用SOLVE()函數的依據就是上面的紅線部分,相當於直接進行線性方程的求解。
具體推導可以參考:
https://wenku.baidu.com/view/a5a6068619e8b8f67c1cb98b.html
(4)一個典型的例子-- 跟蹤鼠標位置
#include "opencv2/video/tracking.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdio.h>
using namespace cv;
using namespace std;
const int winHeight=600;
const int winWidth=800;
Point mousePosition= Point(winWidth>>1,winHeight>>1);
//mouse event callback
void mouseEvent(int event, int x, int y, int flags, void *param )
{
if (event==CV_EVENT_MOUSEMOVE) {
mousePosition = Point(x,y);
}
}
int main (void)
{
RNG rng;
//1.kalman filter setup
const int stateNum=4; //狀態值4×1向量(x,y,△x,△y)
const int measureNum=2; //測量值2×1向量(x,y)
KalmanFilter KF(stateNum, measureNum, 0);
KF.transitionMatrix = *(Mat_<float>(4, 4) <<1,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1); //轉移矩陣A
setIdentity(KF.measurementMatrix); //測量矩陣H
setIdentity(KF.processNoiseCov, Scalar::all(1e-5)); //系統噪聲方差矩陣Q
setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1)); //測量噪聲方差矩陣R
setIdentity(KF.errorCovPost, Scalar::all(1)); //後驗錯誤估計協方差矩陣P
rng.fill(KF.statePost,RNG::UNIFORM,0,winHeight>winWidth?winWidth:winHeight); //初始狀態值x(0)
Mat measurement = Mat::zeros(measureNum, 1, CV_32F); //初始測量值x'(0),因爲後面要更新這個值,所以必須先定義
namedWindow("kalman");
setMouseCallback("kalman",mouseEvent);
Mat image(winHeight,winWidth,CV_8UC3,Scalar(0));
while (1)
{
//2.kalman prediction
Mat prediction = KF.predict();
Point predict_pt = Point(prediction.at<float>(0),prediction.at<float>(1) ); //預測值(x',y')
//3.update measurement
measurement.at<float>(0) = (float)mousePosition.x;
measurement.at<float>(1) = (float)mousePosition.y;
//4.update
KF.correct(measurement);
//draw
image.setTo(Scalar(255,255,255,0));
circle(image,predict_pt,5,Scalar(0,255,0),3); //predicted point with green
circle(image,mousePosition,5,Scalar(255,0,0),3); //current position with red
char buf[256];
sprintf_s(buf,256,"predicted position:(%3d,%3d)",predict_pt.x,predict_pt.y);
putText(image,buf,Point(10,30),CV_FONT_HERSHEY_SCRIPT_COMPLEX,1,Scalar(0,0,0),1,8);
sprintf_s(buf,256,"current position :(%3d,%3d)",mousePosition.x,mousePosition.y);
putText(image,buf,cvPoint(10,60),CV_FONT_HERSHEY_SCRIPT_COMPLEX,1,Scalar(0,0,0),1,8);
imshow("kalman", image);
int key=waitKey(3);
if (key==27){//esc
break;
}
}
}
其他例子可以參考:
https://blog.csdn.net/haima1998/article/details/80641628