粒子滤波 演示与opencv代码

转载自：http://blog.csdn.net/onezeros/article/details/6319180

 

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

 

#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：