粒子滤波 演示与opencv代码

粒子滤波的理论实在是太美妙了，用一组不同权重的随机状态来逼近复杂的概率密度函数。其再非线性、非高斯系统中具有优良的特性。opencv给出了一个实现，但是没有给出范例，学习过程中发现网络上也找不到。learning opencv一书中有介绍，但距离直接使用还是有些距离。在经过一番坎坷后，终于可以用了，希望对你有帮助。

 

 本文中给出的例子跟 我的另一篇博文是同一个应用例子，都是对二维坐标进行平滑、预测

使用方法：

1.创建并初始化

const int stateNum=4;//状态数
 const int measureNum=2;//测量变量数
 const int sampleNum=2000;//粒子数

 CvConDensation* condens = cvCreateConDensation(stateNum,measureNum,sampleNum);

在不影响性能的情况下，粒子数量越大，系统表现的越稳定

其他初始化内容请参考learning opencv

2.预测
3.更新例子可信度，也就是权重。本例中更新方法与learning opencv中有所不同，想看代码
4.更新CvConDensation

 

代码：

#include <cv.h>#include <cxcore.h>#include <highgui.h>#include <cvaux.h>#include <cmath>#include <vector>#include <iostream>using namespace std;const int winHeight=600;const int winWidth=800;CvPoint mousePosition=cvPoint(winWidth>>1,winHeight>>1);//mouse event callbackvoid mouseEvent(int event,int x,int y,int flags,void *param ){if (event==CV_EVENT_MOUSEMOVE) {mousePosition=cvPoint(x,y);}}int main (void){//1.condensation setupconst int stateNum=4;const int measureNum=2;const int sampleNum=2000;CvConDensation* condens = cvCreateConDensation(stateNum,measureNum,sampleNum);CvMat* lowerBound;CvMat* upperBound;lowerBound = cvCreateMat(stateNum, 1, CV_32F);upperBound = cvCreateMat(stateNum, 1, CV_32F);cvmSet(lowerBound,0,0,0.0 ); cvmSet(upperBound,0,0,winWidth );cvmSet(lowerBound,1,0,0.0 ); cvmSet(upperBound,1,0,winHeight );cvmSet(lowerBound,2,0,0.0 ); cvmSet(upperBound,2,0,0.0 );cvmSet(lowerBound,3,0,0.0 ); cvmSet(upperBound,3,0,0.0 );float A[stateNum][stateNum] ={1,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1};memcpy(condens->DynamMatr,A,sizeof(A));cvConDensInitSampleSet(condens, lowerBound, upperBound);CvRNG rng_state = cvRNG(0xffffffff);for(int i=0; i < sampleNum; i++){condens->flSamples[i][0] = float(cvRandInt( &rng_state ) % winWidth); //widthcondens->flSamples[i][1] = float(cvRandInt( &rng_state ) % winHeight);//height}CvFont font;cvInitFont(&font,CV_FONT_HERSHEY_SCRIPT_COMPLEX,1,1);char* winName="condensation";cvNamedWindow(winName);cvSetMouseCallback(winName,mouseEvent);IplImage* img=cvCreateImage(cvSize(winWidth,winHeight),8,3);bool isPredictOnly=false;//trigger for prediction only,press SPACEBARwhile (1){//2.condensation predictionCvPoint predict_pt=cvPoint((int)condens->State[0],(int)condens->State[1]);float variance[measureNum]={0};//get variance/standard deviation of each statefor (int i=0;i<measureNum;i++) {//sumfloat sumState=0;for (int j=0;j<condens->SamplesNum;j++) {sumState+=condens->flSamples[i][j];}//averagesumState/=sampleNum;//variancefor (int j=0;j<condens->SamplesNum;j++) {variance[i]+=(condens->flSamples[i][j]-sumState)*(condens->flSamples[i][j]-sumState);}variance[i]/=sampleNum-1;}//3.update particals confidenceCvPoint pt;if (isPredictOnly) {pt=predict_pt;}else{pt=mousePosition;}for (int i=0;i<condens->SamplesNum;i++) {float probX=(float)exp(-1*(pt.x-condens->flSamples[i][0])*(pt.x-condens->flSamples[i][0])/(2*variance[0]));float probY=(float)exp(-1*(pt.y-condens->flSamples[i][1])*(pt.y-condens->flSamples[i][1])/(2*variance[1]));condens->flConfidence[i]=probX*probY;}//4.update condensationcvConDensUpdateByTime(condens);//draw cvSet(img,cvScalar(255,255,255,0));cvCircle(img,predict_pt,5,CV_RGB(0,255,0),3);//predicted point with greenchar buf[256];sprintf_s(buf,256,"predicted position:(%3d,%3d)",predict_pt.x,predict_pt.y);cvPutText(img,buf,cvPoint(10,30),&font,CV_RGB(0,0,0));if (!isPredictOnly) {cvCircle(img,mousePosition,5,CV_RGB(255,0,0),3);//current position with redsprintf_s(buf,256,"real position :(%3d,%3d)",mousePosition.x,mousePosition.y);cvPutText(img,buf,cvPoint(10,60),&font,CV_RGB(0,0,0));}cvShowImage(winName, img);int key=cvWaitKey(30);if (key==27){//esc break;}else if (key==' ') {//trigger for prediction//isPredict=!isPredict;if (isPredictOnly) {isPredictOnly=false;}else{isPredictOnly=true;}}} cvReleaseImage(&img);cvReleaseConDensation(&condens);return 0;}

 

kalman filter 视频演示：

演示中粒子数分别为100,200,2000

请仔细观测效果

http://v.youku.com/v_show/id_XMjU4MzE0ODgw.html

demo snapshot：