图像算法：图像阈值分割
SkySeraph Dec 21st 2010 HQU
Email：[email protected] QQ：452728574
Latest Modified Date：Dec.21st 2010 HQU
一、工具：VC+OpenCV
二、语言：C++
三、原理(略)
四、程序
主程序(核心部分)
代码 1 /*===============================图像分割=====================================*/
2 /*---------------------------------------------------------------------------*/
3 /*手动设置阀值*/
4 IplImage* binaryImg = cvCreateImage(cvSize(w, h),IPL_DEPTH_8U, 1);
5 cvThreshold(smoothImgGauss,binaryImg,71,255,CV_THRESH_BINARY);
6 cvNamedWindow("cvThreshold", CV_WINDOW_AUTOSIZE );
7 cvShowImage( "cvThreshold", binaryImg );
8 //cvReleaseImage(&binaryImg);
9 /*---------------------------------------------------------------------------*/
10 /*自适应阀值 //计算像域邻域的平均灰度，来决定二值化的值*/
11 IplImage* adThresImg = cvCreateImage(cvSize(w, h),IPL_DEPTH_8U, 1);
12 double max_value=255;
13 int adpative_method=CV_ADAPTIVE_THRESH_GAUSSIAN_C;//CV_ADAPTIVE_THRESH_MEAN_C
14 int threshold_type=CV_THRESH_BINARY;
15 int block_size=3;//阈值的象素邻域大小
16 int offset=5;//窗口尺寸
17 cvAdaptiveThreshold(smoothImgGauss,adThresImg,max_value,adpative_method,threshold_type,block_size,offset);
18 cvNamedWindow("cvAdaptiveThreshold", CV_WINDOW_AUTOSIZE );
19 cvShowImage( "cvAdaptiveThreshold", adThresImg );
20 cvReleaseImage(&adThresImg);
21 /*---------------------------------------------------------------------------*/
22 /*最大熵阀值分割法*/
23 IplImage* imgMaxEntropy = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
24 MaxEntropy(smoothImgGauss,imgMaxEntropy);
25 cvNamedWindow("MaxEntroyThreshold", CV_WINDOW_AUTOSIZE );
26 cvShowImage( "MaxEntroyThreshold", imgMaxEntropy );//显示图像
27 cvReleaseImage(&imgMaxEntropy );
28 /*---------------------------------------------------------------------------*/
29 /*基本全局阀值法*/
30 IplImage* imgBasicGlobalThreshold = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
31 cvCopyImage(srcImgGrey,imgBasicGlobalThreshold);
32 int pg[256],i,thre;
33 for (i=0;i<256;i++) pg[i]=0;
34 for (i=0;i<imgBasicGlobalThreshold->imageSize;i++) // 直方图统计
35 pg[(BYTE)imgBasicGlobalThreshold->imageData[i]]++;
36 thre = BasicGlobalThreshold(pg,0,256); // 确定阈值
37 cout<<"The Threshold of this Image in BasicGlobalThreshold is:"<<thre<<endl;//输出显示阀值
38 cvThreshold(imgBasicGlobalThreshold,imgBasicGlobalThreshold,thre,255,CV_THRESH_BINARY); // 二值化
39 cvNamedWindow("BasicGlobalThreshold", CV_WINDOW_AUTOSIZE );
40 cvShowImage( "BasicGlobalThreshold", imgBasicGlobalThreshold);//显示图像
41 cvReleaseImage(&imgBasicGlobalThreshold);
42 /*---------------------------------------------------------------------------*/
43 /*OTSU*/
44 IplImage* imgOtsu = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
45 cvCopyImage(srcImgGrey,imgOtsu);
46 int thre2;
47 thre2 = otsu2(imgOtsu);
48 cout<<"The Threshold of this Image in Otsu is:"<<thre2<<endl;//输出显示阀值
49 cvThreshold(imgOtsu,imgOtsu,thre2,255,CV_THRESH_BINARY); // 二值化
50 cvNamedWindow("imgOtsu", CV_WINDOW_AUTOSIZE );
51 cvShowImage( "imgOtsu", imgOtsu);//显示图像
52 cvReleaseImage(&imgOtsu);
53 /*---------------------------------------------------------------------------*/
54 /*上下阀值法：利用正态分布求可信区间*/
55 IplImage* imgTopDown = cvCreateImage( cvGetSize(imgGrey), IPL_DEPTH_8U, 1 );
56 cvCopyImage(srcImgGrey,imgTopDown);
57 CvScalar mean ,std_dev;//平均值、 标准差
58 double u_threshold,d_threshold;
59 cvAvgSdv(imgTopDown,&mean,&std_dev,NULL);
60 u_threshold = mean.val[0] +2.5* std_dev.val[0];//上阀值
61 d_threshold = mean.val[0] -2.5* std_dev.val[0];//下阀值
62 //u_threshold = mean + 2.5 * std_dev; //错误
63 //d_threshold = mean - 2.5 * std_dev;
64 cout<<"The TopThreshold of this Image in TopDown is:"<<d_threshold<<endl;//输出显示阀值
65 cout<<"The DownThreshold of this Image in TopDown is:"<<u_threshold<<endl;
66 cvThreshold(imgTopDown,imgTopDown,d_threshold,u_threshold,CV_THRESH_BINARY_INV);//上下阀值
67 cvNamedWindow("imgTopDown", CV_WINDOW_AUTOSIZE );
68 cvShowImage( "imgTopDown", imgTopDown);//显示图像
69 cvReleaseImage(&imgTopDown);
70 /*---------------------------------------------------------------------------*/
71 /*迭代法*/
72 IplImage* imgIteration = cvCreateImage( cvGetSize(imgGrey), IPL_DEPTH_8U, 1 );
73 cvCopyImage(srcImgGrey,imgIteration);
74 int thre3,nDiffRec;
75 thre3 =DetectThreshold(imgIteration, 100, nDiffRec);
76 cout<<"The Threshold of this Image in imgIteration is:"<<thre3<<endl;//输出显示阀值
77 cvThreshold(imgIteration,imgIteration,thre3,255,CV_THRESH_BINARY_INV);//上下阀值
78 cvNamedWindow("imgIteration", CV_WINDOW_AUTOSIZE );
79 cvShowImage( "imgIteration", imgIteration);
80 cvReleaseImage(&imgIteration);
模块程序
迭代法
代码
/*======================================================================*/
/* 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 =;
int F[]={ }; //直方图数组
int iTotalGray=;//灰度值和
int iTotalPixel =;//像素数和
byte bt;//某点的像素值 uchar iThrehold,iNewThrehold;//阀值、新阀值
uchar iMaxGrayValue=,iMinGrayValue=;//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=;i<width;i++)
{
for(int j=;j<height;j++)
{
bt = data[i*step+j];
if(bt<iMinGrayValue)
iMinGrayValue = bt;
if(bt>iMaxGrayValue)
iMaxGrayValue = bt;
F[bt]++;
}
} iThrehold =;//
iNewThrehold = (iMinGrayValue+iMaxGrayValue)/;//初始阀值
iDiffRec = iMaxGrayValue - iMinGrayValue; for(int a=;(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 =;
iTotalGray =;
for(int j=iThrehold+;j<iMaxGrayValue;j++)
{
iTotalGray += F[j]*j;//F[]存储图像信息
iTotalPixel += F[j];
}
iMeanGrayValue2 = (uchar)(iTotalGray/iTotalPixel); iNewThrehold = (iMeanGrayValue2+iMeanGrayValue1)/; //新阀值
iDiffRec = abs(iMeanGrayValue2 - iMeanGrayValue1);
} //cout<<"The Threshold of this Image in imgIteration is:"<<iThrehold<<endl;
return iThrehold;
}
Otsu代码一
代码
/*======================================================================*/
/* OTSU global thresholding routine */
/* takes a 2D unsigned char array pointer, number of rows, and */
/* number of cols in the array. returns the value of the threshold */
/*parameter:
*image --- buffer for image
rows, cols --- size of image
x0, y0, dx, dy --- region of vector used for computing threshold
vvv --- debug option, is 0, no debug information outputed
*/
/*
OTSU 算法可以说是自适应计算单阈值（用来转换灰度图像为二值图像）的简单高效方法。
下面的代码最早由 Ryan Dibble提供，此后经过多人Joerg.Schulenburg, R.Z.Liu 等修改，补正。
算法对输入的灰度图像的直方图进行分析，将直方图分成两个部分，使得两部分之间的距离最大。
划分点就是求得的阈值。
*/
/*======================================================================*/
int otsu (unsigned char*image, int rows, int cols, int x0, int y0, int dx, int dy, int vvv)
{ unsigned char*np; // 图像指针
int thresholdValue=; // 阈值
int ihist[]; // 图像直方图，256个点 int i, j, k; // various counters
int n, n1, n2, gmin, gmax;
double m1, m2, sum, csum, fmax, sb; // 对直方图置零
memset(ihist, , sizeof(ihist)); gmin=; gmax=;
// 生成直方图
for (i = y0 +; i < y0 + dy -; i++)
{
np = (unsigned char*)image[i*cols+x0+];
for (j = x0 +; j < x0 + dx -; j++)
{
ihist[*np]++;
if(*np > gmax) gmax=*np;
if(*np < gmin) gmin=*np;
np++; /* next pixel */
}
} // set up everything
sum = csum =0.0;
n =; for (k =; k <=; k++)
{
sum += (double) k * (double) ihist[k]; /* x*f(x) 质量矩*/
n += ihist[k]; /* f(x) 质量 */
} if (!n)
{
// if n has no value, there is problems...
fprintf (stderr, "NOT NORMAL thresholdValue = 160\n");
return ();
} // do the otsu global thresholding method
fmax =-1.0;
n1 =;
for (k =; k <; k++)
{
n1 += ihist[k];
if (!n1)
{
continue;
}
n2 = n - n1;
if (n2 ==)
{
break;
}
csum += (double) k *ihist[k];
m1 = csum / n1;
m2 = (sum - csum) / n2;
sb = (double) n1 *(double) n2 *(m1 - m2) * (m1 - m2);
/* bbg: note: can be optimized. */
if (sb > fmax)
{
fmax = sb;
thresholdValue = k;
}
} // at this point we have our thresholding value // debug code to display thresholding values
if ( vvv & )
fprintf(stderr,"# OTSU: thresholdValue = %d gmin=%d gmax=%d\n",
thresholdValue, gmin, gmax); return(thresholdValue);
}
Otsu代码二
代码
/*======================================================================*/
/* OTSU global thresholding routine */
/*======================================================================*/
int otsu2 (IplImage *image)
{
int w = image->width;
int h = image->height; unsigned char*np; // 图像指针
unsigned char pixel;
int thresholdValue=; // 阈值
int ihist[]; // 图像直方图，256个点 int i, j, k; // various counters
int n, n1, n2, gmin, gmax;
double m1, m2, sum, csum, fmax, sb; // 对直方图置零...
memset(ihist, , sizeof(ihist)); gmin=; gmax=;
// 生成直方图
for (i =; i < h; i++)
{
np = (unsigned char*)(image->imageData + image->widthStep*i);
for (j =; j < w; j++)
{
pixel = np[j];
ihist[ pixel]++;
if(pixel > gmax) gmax= pixel;
if(pixel < gmin) gmin= pixel;
}
} // set up everything
sum = csum =0.0;
n =; for (k =; k <=; k++)
{
sum += k * ihist[k]; /* x*f(x) 质量矩*/
n += ihist[k]; /* f(x) 质量 */
} if (!n)
{
// if n has no value, there is problems...
//fprintf (stderr, "NOT NORMAL thresholdValue = 160\n");
thresholdValue =;
goto L;
} // do the otsu global thresholding method
fmax =-1.0;
n1 =;
for (k =; k <; k++)
{
n1 += ihist[k];
if (!n1) { continue; }
n2 = n - n1;
if (n2 ==) { break; }
csum += k *ihist[k];
m1 = csum / n1;
m2 = (sum - csum) / n2;
sb = n1 * n2 *(m1 - m2) * (m1 - m2);
/* bbg: note: can be optimized. */
if (sb > fmax)
{
fmax = sb;
thresholdValue = k;
}
} L:
for (i =; i < h; i++)
{
np = (unsigned char*)(image->imageData + image->widthStep*i);
for (j =; j < w; j++)
{
if(np[j] >= thresholdValue)
np[j] =;
else np[j] =;
}
} //cout<<"The Threshold of this Image in Otsu is:"<<thresholdValue<<endl;
return(thresholdValue);
}
最大熵阀值
代码
/*============================================================================
= 代码内容：最大熵阈值分割
= 修改日期:2009-3-3
= 作者:crond123
= 博客:http://blog.csdn.net/crond123/
= E_Mail:[email protected]
===============================================================================*/
// 计算当前位置的能量熵
double caculateCurrentEntropy(CvHistogram * Histogram1,int cur_threshold,entropy_state state)
{
int start,end;
int total =;
double cur_entropy =0.0;
if(state == back)
{
start =;
end = cur_threshold;
}
else
{
start = cur_threshold;
end =;
}
for(int i=start;i<end;i++)
{
total += (int)cvQueryHistValue_1D(Histogram1,i);//查询直方块的值 P304
}
for(int j=start;j<end;j++)
{
if((int)cvQueryHistValue_1D(Histogram1,j)==)
continue;
double percentage = cvQueryHistValue_1D(Histogram1,j)/total;
/*熵的定义公式*/
cur_entropy +=-percentage*logf(percentage);
/*根据泰勒展式去掉高次项得到的熵的近似计算公式
cur_entropy += percentage*percentage;*/
}
return cur_entropy;
// return (1-cur_entropy);
} //寻找最大熵阈值并分割
void MaxEntropy(IplImage *src,IplImage *dst)
{
assert(src != NULL);
assert(src->depth ==&& dst->depth ==);
assert(src->nChannels ==);
CvHistogram * hist = cvCreateHist(,&HistogramBins,CV_HIST_ARRAY,HistogramRange);//创建一个指定尺寸的直方图
//参数含义：直方图包含的维数、直方图维数尺寸的数组、直方图的表示格式、方块范围数组、归一化标志
cvCalcHist(&src,hist);//计算直方图
double maxentropy =-1.0;
int max_index =-;
// 循环测试每个分割点，寻找到最大的阈值分割点
for(int i=;i<HistogramBins;i++)
{
double cur_entropy = caculateCurrentEntropy(hist,i,object)+caculateCurrentEntropy(hist,i,back);
if(cur_entropy>maxentropy)
{
maxentropy = cur_entropy;
max_index = i;
}
}
cout<<"The Threshold of this Image in MaxEntropy is:"<<max_index<<endl;
cvThreshold(src, dst, (double)max_index,, CV_THRESH_BINARY);
cvReleaseHist(&hist);
}
基本全局阀值法
代码
/*============================================================================
= 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=;
u=;
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=;
u1=;
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=;
if (t2)
u2=u2/t2; // 计算高灰度组的平均值
else
u2=;
k2=(int) ((u1+u2)/); // 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
}
五 效果(略)
Author: SKySeraph
Email/GTalk: [email protected] QQ：452728574
From： http://www.cnblogs.com/skyseraph/
本文版权归作者和博客园共有，欢迎转载，但未经作者同意必须保留此段声明，且在文章页面明显位置给出原文连接，请尊重作者的劳动成果。