opencv例子里没有提供cvsnakeimage的使用方法，在此整理一个例子，可以形象的看看snake算法的结果，大致做法是：
首先设定域值分割，把基本的轮廓找出来，见图中蓝色轮廓线，再将轮廓点传入cvSnakeImage函数，计算出绿色的snake轮廓线。
其中参数alpha代表点相互靠拢的权值(0-1.0)，beta表示弯曲能量（越小越容易弯曲）(0-1.0)，gamma表示整体能量(0-1.0)。其中参数我自己也不确定具体的范围，最好自己更改不同的范围试试.
// TrainingTools.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include <iostream>
#include <string.h>
#include <cxcore.h>
#include <cv.h>
#include <highgui.h>
#include <fstream>
IplImage *image2 = 0 ; //原始图像copy
using namespace std;
int Thresholdness = 141;
int ialpha = 20;
int ibeta=20;
int igamma=20; void onChange(int pos)
{
if(image2) cvReleaseImage(&image2);
if(image) cvReleaseImage(&image);
image= cvLoadImage("grey.bmp",0);
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contours = 0;
CV_RETR_EXTERNAL , CV_CHAIN_APPROX_SIMPLE );
int length = contours->total;
if(length<10) return ;
CvPoint* point = new CvPoint[length]; //分配轮廓点
CvSeqReader reader;
CvPoint pt= cvPoint(0,0);;
CvSeq *contour2=contours;
for (int i = 0; i < length; i++)
{
CV_READ_SEQ_ELEM(pt, reader);
point[i]=pt;
}
cvReleaseMemStorage(&storage);
for(int i=0;i<length;i++)
{
int j = (i+1)%length;
cvLine( image2, point[i],point[j],CV_RGB( 0, 0, 255 ),1,8,0 );
}
float beta=ibeta/100.0f;
float gamma=igamma/100.0f;
size.height=3;
CvTermCriteria criteria;
criteria.type=CV_TERMCRIT_ITER;
criteria.max_iter=1000;
criteria.epsilon=0.1;
cvSnakeImage( image, point,length,&alpha,&beta,&gamma,CV_VALUE,size,criteria,0 );
for(int i=0;i<length;i++)
{
int j = (i+1)%length;
cvLine( image2, point[i],point[j],CV_RGB( 0, 255, 0 ),1,8,0 );
}
delete []point;
{
cvCreateTrackbar("Thd", "win1", &Thresholdness, 255, onChange);
cvCreateTrackbar("alpha", "win1", &ialpha, 100, onChange);
cvCreateTrackbar("beta", "win1", &ibeta, 100, onChange);
cvCreateTrackbar("gamma", "win1", &igamma, 100, onChange);
cvResizeWindow("win1",300,500);
onChange(0);
{
if(cvWaitKey(40)==27) break;
cvShowImage("win1",image2);
}
return 0;
}
//
#include "stdafx.h"
#include <iostream>
#include <string.h>
#include <cxcore.h>
#include <cv.h>
#include <highgui.h>
#include <fstream>
IplImage
*image = 0 ; //原始图像IplImage *image2 = 0 ; //原始图像copy
using namespace std;
int Thresholdness = 141;
int ialpha = 20;
int ibeta=20;
int igamma=20; void onChange(int pos)
{
if(image2) cvReleaseImage(&image2);
if(image) cvReleaseImage(&image);
image2
= cvLoadImage("grey.bmp",1); //显示图片image= cvLoadImage("grey.bmp",0);
cvThreshold(image,image,Thresholdness,
255,CV_THRESH_BINARY); //分割域值CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contours = 0;
cvFindContours( image, storage,
&contours, sizeof(CvContour), //寻找初始化轮廓CV_RETR_EXTERNAL , CV_CHAIN_APPROX_SIMPLE );
if(!contours) return ;
int length = contours->total;
if(length<10) return ;
CvPoint* point = new CvPoint[length]; //分配轮廓点
CvSeqReader reader;
CvPoint pt= cvPoint(0,0);;
CvSeq *contour2=contours;
cvStartReadSeq(contour2,
&reader);for (int i = 0; i < length; i++)
{
CV_READ_SEQ_ELEM(pt, reader);
point[i]=pt;
}
cvReleaseMemStorage(&storage);
//显示轮廓曲线
for(int i=0;i<length;i++)
{
int j = (i+1)%length;
cvLine( image2, point[i],point[j],CV_RGB( 0, 0, 255 ),1,8,0 );
}
float alpha=ialpha/100.0f;
float beta=ibeta/100.0f;
float gamma=igamma/100.0f;
CvSize size;
size.width
size.height=3;
CvTermCriteria criteria;
criteria.type=CV_TERMCRIT_ITER;
criteria.max_iter=1000;
criteria.epsilon=0.1;
cvSnakeImage( image, point,length,&alpha,&beta,&gamma,CV_VALUE,size,criteria,0 );
//显示曲线
for(int i=0;i<length;i++)
{
int j = (i+1)%length;
cvLine( image2, point[i],point[j],CV_RGB( 0, 255, 0 ),1,8,0 );
}
delete []point;
}
int main(int argc, char* argv[]){
cvNamedWindow(
cvCreateTrackbar("Thd", "win1", &Thresholdness, 255, onChange);
cvCreateTrackbar("alpha", "win1", &ialpha, 100, onChange);
cvCreateTrackbar("beta", "win1", &ibeta, 100, onChange);
cvCreateTrackbar("gamma", "win1", &igamma, 100, onChange);
cvResizeWindow("win1",300,500);
onChange(0);
for(;;)
{
if(cvWaitKey(40)==27) break;
cvShowImage("win1",image2);
}
return 0;
}