灰度图像阈值分割C语言

Ⅰ 如何用C语言实现对图像的二值化

/*************************************************************************
* 该函数用于对图像进行阈值分割运算
* 参数:
* LPSTR lpDIBBits - 指向源DIB图像指针
* LONG lWidth - 源图像宽度（象素数）
* LONG lHeight - 源图像高度（象素数）
************************************************************************/

BOOL ImageChangeProc::ThresholdDIB(LPSTR lpDIBBits,LONG lWidth, LONG lHeight)
{

// 指向源图像的指针
LPSTR lpSrc;

// 指向缓存图像的指针
LPSTR lpDst;

// 指向缓存DIB图像的指针
LPSTR lpNewDIBBits;
HLOCAL hNewDIBBits;

//循环变量
long i;
long j;

unsigned char pixel;
long lHistogram[256];

//阈值，最大灰度值与最小灰度值，两个区域的平均灰度值
unsigned char Threshold,NewThreshold,MaxGrayValue,MinGrayValue,Temp1GrayValue,Temp2GrayValue;

//用于计算区域灰度平均值的中间变量
long lP1,lP2,lS1,lS2;

//迭代次数
int IterationTimes;

LONG lLineBytes;
hNewDIBBits = LocalAlloc(LHND, lWidth * lHeight);

if (hNewDIBBits == NULL)
{
// 分配内存失败
return FALSE;
}

// 锁定内存
lpNewDIBBits = (char * )LocalLock(hNewDIBBits);

// 初始化新分配的内存
lpDst = (char *)lpNewDIBBits;
memset(lpDst, (BYTE)255, lWidth * lHeight);

lLineBytes = WIDTHBYTES(lWidth * 8);

for (i = 0; i < 256;i++)
{
lHistogram[i]=0;
}

//获得直方图
MaxGrayValue = 0;
MinGrayValue = 255;
for (i = 0;i < lWidth ;i++)
{
for(j = 0;j < lHeight ;j++)
{
lpSrc = (char *)lpDIBBits + lLineBytes * j + i;

pixel = (unsigned char)*lpSrc;

lHistogram[pixel]++;
//修改最大，最小灰度值
if(MinGrayValue > pixel)
{
MinGrayValue = pixel;
}
if(MaxGrayValue < pixel)
{
MaxGrayValue = pixel;
}
}
}

//迭代求最佳阈值
NewThreshold = (MinGrayValue + MaxGrayValue)/2;
Threshold = 0;

for(IterationTimes = 0; Threshold != NewThreshold && IterationTimes < 1000;IterationTimes ++)
{
Threshold = NewThreshold;
lP1 =0;
lP2 =0;
lS1 = 0;
lS2 = 0;
//求两个区域的灰度平均值
for (i = MinGrayValue;i <=Threshold;i++)
{
lP1 += lHistogram[i]*i;
lS1 += lHistogram[i];
}

for (i = Threshold+1;i<MaxGrayValue;i++)
{
lP2 += lHistogram[i]*i;
lS2 += lHistogram[i];
}
if(lS1==0||lS2==0)
{
// 释放内存
LocalUnlock(hNewDIBBits);
LocalFree(hNewDIBBits);
return FALSE;
}
Temp1GrayValue = (unsigned char)(lP1 / lS1);
Temp2GrayValue = (unsigned char)(lP2 / lS2);
NewThreshold = (Temp1GrayValue + Temp2GrayValue)/2;
}

//根据阈值将图像二值化
for (i = 0;i < lWidth ;i++)
{
for(j = 0;j < lHeight ;j++)
{
lpSrc = (char *)lpDIBBits + lLineBytes * j + i;
lpDst = (char *)lpNewDIBBits + lLineBytes * j + i;
pixel = (unsigned char)*lpSrc;

if(pixel <= Threshold)
{
*lpDst = (unsigned char)0;
}
else
{
*lpDst = (unsigned char)255;
}
}
}

// 复制图像
memcpy(lpDIBBits, lpNewDIBBits, lWidth * lHeight);

// 释放内存
LocalUnlock(hNewDIBBits);
LocalFree(hNewDIBBits);

// 返回
return TRUE;
}

参考：http://topic.csdn.net/t/20030909/13/2240079.html