阈值分割：基本全局阀值法、上下阀值法、迭代法
/*---------------------------------------------------------------------------*/
/*基本全局阀值法*/
IplImage* imgBasicGlobalThreshold = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
cvCopyImage(srcImgGrey,imgBasicGlobalThreshold);
int pg[256],i,thre;
for (i=0;i<256;i++) pg[i]=0;
for (i=0;i<imgBasicGlobalThreshold->imageSize;i++) // 直方图统计
pg[(BYTE)imgBasicGlobalThreshold->imageData[i]]++;
thre = BasicGlobalThreshold(pg,0,256); // 确定阈值
cout<<"The Threshold of this Image in BasicGlobalThreshold is:"<<thre<<endl;//输出显示阀值
cvThreshold(imgBasicGlobalThreshold,imgBasicGlobalThreshold,thre,255,CV_THRESH_BINARY); // 二值化
cvNamedWindow("BasicGlobalThreshold", CV_WINDOW_AUTOSIZE );
cvShowImage( "BasicGlobalThreshold", imgBasicGlobalThreshold);//显示图像
cvReleaseImage(&imgBasicGlobalThreshold);
/*---------------------------------------------------------------------------*/
/*上下阀值法：利用正态分布求可信区间*/
IplImage* imgTopDown = cvCreateImage( cvGetSize(imgGrey), IPL_DEPTH_8U, 1 );
cvCopyImage(srcImgGrey,imgTopDown);
CvScalar mean ,std_dev;//平均值、 标准差
double u_threshold,d_threshold;
cvAvgSdv(imgTopDown,&mean,&std_dev,NULL);
u_threshold = mean.val[0] +2.5* std_dev.val[0];//上阀值
d_threshold = mean.val[0] -2.5* std_dev.val[0];//下阀值
//u_threshold = mean + 2.5 * std_dev; //错误
//d_threshold = mean - 2.5 * std_dev;
cout<<"The TopThreshold of this Image in TopDown is:"<<d_threshold<<endl;//输出显示阀值
cout<<"The DownThreshold of this Image in TopDown is:"<<u_threshold<<endl;
cvThreshold(imgTopDown,imgTopDown,d_threshold,u_threshold,CV_THRESH_BINARY_INV);//上下阀值
cvNamedWindow("imgTopDown", CV_WINDOW_AUTOSIZE );
cvShowImage( "imgTopDown", imgTopDown);//显示图像
cvReleaseImage(&imgTopDown);
/*---------------------------------------------------------------------------*/
/*迭代法*/
IplImage* imgIteration = cvCreateImage( cvGetSize(imgGrey), IPL_DEPTH_8U, 1 );
cvCopyImage(srcImgGrey,imgIteration);
int thre3,nDiffRec;
thre3 =DetectThreshold(imgIteration, 100, nDiffRec);
cout<<"The Threshold of this Image in imgIteration is:"<<thre3<<endl;//输出显示阀值
cvThreshold(imgIteration,imgIteration,thre3,255,CV_THRESH_BINARY_INV);//上下阀值
cvNamedWindow("imgIteration", CV_WINDOW_AUTOSIZE );
cvShowImage( "imgIteration", imgIteration);
cvReleaseImage(&imgIteration);
/*======================================================================*/
/* 迭代法*/
/*======================================================================*/
// nMaxIter：最大迭代次数；nDiffRec：使用给定阀值确定的亮区与暗区平均灰度差异值
int DetectThreshold(IplImage*img, int nMaxIter, int& iDiffRec) //阀值分割：迭代法
{
//图像信息
int height = img->height;
int width = img->width;
int step = img->widthStep/sizeof(uchar);
uchar *data = (uchar*)img->imageData;
iDiffRec =0;
int F[256]={ 0 }; //直方图数组
int iTotalGray=0;//灰度值和
int iTotalPixel =0;//像素数和
byte bt;//某点的像素值
uchar iThrehold,iNewThrehold;//阀值、新阀值
uchar iMaxGrayValue=0,iMinGrayValue=255;//原图像中的最大灰度值和最小灰度值
uchar iMeanGrayValue1,iMeanGrayValue2;
//获取(i,j)的值，存于直方图数组F
for(int i=0;i<width;i++)
{
for(int j=0;j<height;j++)
{
bt = data[i*step+j];
if(bt<iMinGrayValue)
iMinGrayValue = bt;
if(bt>iMaxGrayValue)
iMaxGrayValue = bt;
F[bt]++;
}
}
iThrehold =0;//
iNewThrehold = (iMinGrayValue+iMaxGrayValue)/2;//初始阀值
iDiffRec = iMaxGrayValue - iMinGrayValue;
for(int a=0;(abs(iThrehold-iNewThrehold)>0.5)&&a<nMaxIter;a++)//迭代中止条件
{
iThrehold = iNewThrehold;
//小于当前阀值部分的平均灰度值
for(int i=iMinGrayValue;i<iThrehold;i++)
{
iTotalGray += F[i]*i;//F[]存储图像信息
iTotalPixel += F[i];
}
iMeanGrayValue1 = (uchar)(iTotalGray/iTotalPixel);
//大于当前阀值部分的平均灰度值
iTotalPixel =0;
iTotalGray =0;
for(int j=iThrehold+1;j<iMaxGrayValue;j++)
{
iTotalGray += F[j]*j;//F[]存储图像信息
iTotalPixel += F[j];
}
iMeanGrayValue2 = (uchar)(iTotalGray/iTotalPixel);
iNewThrehold = (iMeanGrayValue2+iMeanGrayValue1)/2; //新阀值
iDiffRec = abs(iMeanGrayValue2 - iMeanGrayValue1);
}
//cout<<"The Threshold of this Image in imgIteration is:"<<iThrehold<<endl;
return iThrehold;
}
/*============================================================================
= 代码内容：基本全局阈值法
==============================================================================*/
int BasicGlobalThreshold(int*pg,int start,int end)
{ // 基本全局阈值法
int i,t,t1,t2,k1,k2;
double u,u1,u2;
t=0;
u=0;
for (i=start;i<end;i++)
{
t+=pg[i];
u+=i*pg[i];
}
k2=(int) (u/t); // 计算此范围灰度的平均值
do
{
k1=k2;
t1=0;
u1=0;
for (i=start;i<=k1;i++)
{ // 计算低灰度组的累加和
t1+=pg[i];
u1+=i*pg[i];
}
t2=t-t1;
u2=u-u1;
if (t1)
u1=u1/t1; // 计算低灰度组的平均值
else
u1=0;
if (t2)
u2=u2/t2; // 计算高灰度组的平均值
else
u2=0;
k2=(int) ((u1+u2)/2); // 得到新的阈值估计值
}
while(k1!=k2); // 数据未稳定，继续
//cout<<"The Threshold of this Image in BasicGlobalThreshold is:"<<k1<<endl;
return(k1); // 返回阈值
}