学习OpenCV——粒子滤波（网上两篇文章总结）

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：

[cpp] view plain copy

print?

#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 callback

void 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 setup

const 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); //width

condens->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 SPACEBAR

while (1){

//2.condensation prediction

CvPoint predict_pt=cvPoint((int)condens->State[0],(int)condens->State[1]);

float variance[measureNum]={0};

//get variance/standard deviation of each state

for (int i=0;i<measureNum;i++) {

//sum

float sumState=0;

for (int j=0;j<condens->SamplesNum;j++) {

sumState+=condens->flSamples[i][j];

}

//average

sumState/=sampleNum;

//variance

for (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 confidence

CvPoint 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 condensation

cvConDensUpdateByTime(condens);

//draw

cvSet(img,cvScalar(255,255,255,0));

cvCircle(img,predict_pt,5,CV_RGB(0,255,0),3);//predicted point with green

char 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 red

sprintf_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：



//上面这一篇是演示点跟踪，原文http://blog.csdn.net/onezeros/article/details/6319180

//这一篇是上交的一哥们演示的窗口跟踪！（有code）
http://www.cnblogs.com/yangyangcv/archive/2010/05/23/1742263.html

from: http://blog.csdn.net/yangtrees/article/details/7616483