【图像算法】七种常见阈值分割代码(Otsu、最大熵、迭代法、自适应阀值、手动、迭代法、基本全局阈值法)...

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

更多精彩请直接访问SkySeraph个人站点：www.skyseraph.com 

 图像算法系列： http://skyseraph.com/2011/08/27/CV/图像算法专题/ 

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

1 /* ====================================================================== */
2 /* 迭代法 */
3 /* ====================================================================== */
4 // 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
5 int DetectThreshold(IplImage * img, int nMaxIter, int & iDiffRec) // 阀值分割：迭代法
6 {
7 // 图像信息
8 int height = img -> height;
9 int width = img -> width;
10 int step = img -> widthStep / sizeof (uchar);
11 uchar * data = (uchar * )img -> imageData;
12
13 iDiffRec = 0 ;
14 int F[ 256 ] = { 0 }; // 直方图数组
15 int iTotalGray = 0 ; // 灰度值和
16 int iTotalPixel = 0 ; // 像素数和
17 byte bt; // 某点的像素值
18
19 uchar iThrehold,iNewThrehold; // 阀值、新阀值
20 uchar iMaxGrayValue = 0 ,iMinGrayValue = 255 ; // 原图像中的最大灰度值和最小灰度值
21 uchar iMeanGrayValue1,iMeanGrayValue2;
22
23 // 获取(i,j)的值，存于直方图数组F
24 for ( int i = 0 ;i < width;i ++ )
25 {
26 for ( int j = 0 ;j < height;j ++ )
27 {
28 bt = data[i * step + j];
29 if (bt < iMinGrayValue)
30 iMinGrayValue = bt;
31 if (bt > iMaxGrayValue)
32 iMaxGrayValue = bt;
33 F[bt] ++ ;
34 }
35 }
36
37 iThrehold = 0 ; //
38 iNewThrehold = (iMinGrayValue + iMaxGrayValue) / 2 ; // 初始阀值
39 iDiffRec = iMaxGrayValue - iMinGrayValue;
40
41 for ( int a = 0 ;(abs(iThrehold - iNewThrehold) > 0.5 ) && a < nMaxIter;a ++ ) // 迭代中止条件
42 {
43 iThrehold = iNewThrehold;
44 // 小于当前阀值部分的平均灰度值
45 for ( int i = iMinGrayValue;i < iThrehold;i ++ )
46 {
47 iTotalGray += F[i] * i; // F[]存储图像信息
48 iTotalPixel += F[i];
49 }
50 iMeanGrayValue1 = (uchar)(iTotalGray / iTotalPixel);
51 // 大于当前阀值部分的平均灰度值
52 iTotalPixel = 0 ;
53 iTotalGray = 0 ;
54 for ( int j = iThrehold + 1 ;j < iMaxGrayValue;j ++ )
55 {
56 iTotalGray += F[j] * j; // F[]存储图像信息
57 iTotalPixel += F[j];
58 }
59 iMeanGrayValue2 = (uchar)(iTotalGray / iTotalPixel);
60
61 iNewThrehold = (iMeanGrayValue2 + iMeanGrayValue1) / 2 ; // 新阀值
62 iDiffRec = abs(iMeanGrayValue2 - iMeanGrayValue1);
63 }
64
65 // cout<<"The Threshold of this Image in imgIteration is:"<
66 return iThrehold;
67 }
68

  

Otsu代码一 

代码

1 /* ====================================================================== */
2 /* OTSU global thresholding routine */
3 /* takes a 2D unsigned char array pointer, number of rows, and */
4 /* number of cols in the array. returns the value of the threshold */
5 /* parameter:
6 *image --- buffer for image
7 rows, cols --- size of image
8 x0, y0, dx, dy --- region of vector used for computing threshold
9 vvv --- debug option, is 0, no debug information outputed
10 */
11 /*
12 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。
13 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。
14 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。
15 划分点就是求得的阈值。
16 */
17 /* ====================================================================== */
18 int otsu (unsigned char * image, int rows, int cols, int x0, int y0, int dx, int dy, int vvv)
19 {
20
21 unsigned char * np; // 图像指针
22 int thresholdValue = 1 ; // 阈值
23 int ihist[ 256 ]; // 图像直方图，256个点
24
25 int i, j, k; // various counters
26 int n, n1, n2, gmin, gmax;
27 double m1, m2, sum, csum, fmax, sb;
28
29 // 对直方图置零
30 memset(ihist, 0 , sizeof (ihist));
31
32 gmin = 255 ; gmax = 0 ;
33 // 生成直方图
34 for (i = y0 + 1 ; i < y0 + dy - 1 ; i ++ )
35 {
36 np = (unsigned char * )image[i * cols + x0 + 1 ];
37 for (j = x0 + 1 ; j < x0 + dx - 1 ; j ++ )
38 {
39 ihist[ * np] ++ ;
40 if ( * np > gmax) gmax =* np;
41 if ( * np < gmin) gmin =* np;
42 np ++ ; /* next pixel */
43 }
44 }
45
46 // set up everything
47 sum = csum = 0.0 ;
48 n = 0 ;
49
50 for (k = 0 ; k <= 255 ; k ++ )
51 {
52 sum += ( double ) k * ( double ) ihist[k]; /* x*f(x) 质量矩 */
53 n += ihist[k]; /* f(x) 质量 */
54 }
55
56 if ( ! n)
57 {
58 // if n has no value, there is problems...
59 fprintf (stderr, " NOT NORMAL thresholdValue = 160\n " );
60 return ( 160 );
61 }
62
63 // do the otsu global thresholding method
64 fmax = - 1.0 ;
65 n1 = 0 ;
66 for (k = 0 ; k < 255 ; k ++ )
67 {
68 n1 += ihist[k];
69 if ( ! n1)
70 {
71 continue ;
72 }
73 n2 = n - n1;
74 if (n2 == 0 )
75 {
76 break ;
77 }
78 csum += ( double ) k * ihist[k];
79 m1 = csum / n1;
80 m2 = (sum - csum) / n2;
81 sb = ( double ) n1 * ( double ) n2 * (m1 - m2) * (m1 - m2);
82 /* bbg: note: can be optimized. */
83 if (sb > fmax)
84 {
85 fmax = sb;
86 thresholdValue = k;
87 }
88 }
89
90 // at this point we have our thresholding value
91
92 // debug code to display thresholding values
93 if ( vvv & 1 )
94 fprintf(stderr, " # OTSU: thresholdValue = %d gmin=%d gmax=%d\n " ,
95 thresholdValue, gmin, gmax);
96
97 return (thresholdValue);
98 }

Otsu代码二 

代码

1 /* ====================================================================== */
2 /* OTSU global thresholding routine */
3 /* ====================================================================== */
4 int otsu2 (IplImage * image)
5 {
6 int w = image -> width;
7 int h = image -> height;
8
9 unsigned char * np; // 图像指针
10 unsigned char pixel;
11 int thresholdValue = 1 ; // 阈值
12 int ihist[ 256 ]; // 图像直方图，256个点
13
14 int i, j, k; // various counters
15 int n, n1, n2, gmin, gmax;
16 double m1, m2, sum, csum, fmax, sb;
17
18 // 对直方图置零...
19 memset(ihist, 0 , sizeof (ihist));
20
21 gmin = 255 ; gmax = 0 ;
22 // 生成直方图
23 for (i = 0 ; i < h; i ++ )
24 {
25 np = (unsigned char * )(image -> imageData + image -> widthStep * i);
26 for (j = 0 ; j < w; j ++ )
27 {
28 pixel = np[j];
29 ihist[ pixel] ++ ;
30 if (pixel > gmax) gmax = pixel;
31 if (pixel < gmin) gmin = pixel;
32 }
33 }
34
35 // set up everything
36 sum = csum = 0.0 ;
37 n = 0 ;
38
39 for (k = 0 ; k <= 255 ; k ++ )
40 {
41 sum += k * ihist[k]; /* x*f(x) 质量矩 */
42 n += ihist[k]; /* f(x) 质量 */
43 }
44
45 if ( ! n)
46 {
47 // if n has no value, there is problems...
48 // fprintf (stderr, "NOT NORMAL thresholdValue = 160\n");
49 thresholdValue = 160 ;
50 goto L;
51 }
52
53 // do the otsu global thresholding method
54 fmax = - 1.0 ;
55 n1 = 0 ;
56 for (k = 0 ; k < 255 ; k ++ )
57 {
58 n1 += ihist[k];
59 if ( ! n1) { continue ; }
60 n2 = n - n1;
61 if (n2 == 0 ) { break ; }
62 csum += k * ihist[k];
63 m1 = csum / n1;
64 m2 = (sum - csum) / n2;
65 sb = n1 * n2 * (m1 - m2) * (m1 - m2);
66 /* bbg: note: can be optimized. */
67 if (sb > fmax)
68 {
69 fmax = sb;
70 thresholdValue = k;
71 }
72 }
73
74 L:
75 for (i = 0 ; i < h; i ++ )
76 {
77 np = (unsigned char * )(image -> imageData + image -> widthStep * i);
78 for (j = 0 ; j < w; j ++ )
79 {
80 if (np[j] >= thresholdValue)
81 np[j] = 255 ;
82 else np[j] = 0 ;
83 }
84 }
85
86 // cout<<"The Threshold of this Image in Otsu is:"<
87 return (thresholdValue);
88 }

 

最大熵阀值 

代码

1 /* ============================================================================
2 = 代码内容：最大熵阈值分割
3 = 修改日期:2009-3-3
4 = 作者:crond123
5 = 博客: http://blog.csdn.net/crond123/
6 = E_Mail:[email protected]
7 =============================================================================== */
8 // 计算当前位置的能量熵
9 double caculateCurrentEntropy(CvHistogram * Histogram1, int cur_threshold,entropy_state state)
10 {
11 int start,end;
12 int total = 0 ;
13 double cur_entropy = 0.0 ;
14 if (state == back)
15 {
16 start = 0 ;
17 end = cur_threshold;
18 }
19 else
20 {
21 start = cur_threshold;
22 end = 256 ;
23 }
24 for ( int i = start;i < end;i ++ )
25 {
26 total += ( int )cvQueryHistValue_1D(Histogram1,i); // 查询直方块的值 P304
27 }
28 for ( int j = start;j < end;j ++ )
29 {
30 if (( int )cvQueryHistValue_1D(Histogram1,j) == 0 )
31 continue ;
32 double percentage = cvQueryHistValue_1D(Histogram1,j) / total;
33 /* 熵的定义公式 */
34 cur_entropy += - percentage * logf(percentage);
35 /* 根据泰勒展式去掉高次项得到的熵的近似计算公式
36 cur_entropy += percentage*percentage; */
37 }
38 return cur_entropy;
39 // return (1-cur_entropy);
40 }
41
42 // 寻找最大熵阈值并分割
43 void MaxEntropy(IplImage * src,IplImage * dst)
44 {
45 assert(src != NULL);
46 assert(src -> depth == 8 && dst -> depth == 8 );
47 assert(src -> nChannels == 1 );
48 CvHistogram * hist = cvCreateHist( 1 , & HistogramBins,CV_HIST_ARRAY,HistogramRange); // 创建一个指定尺寸的直方图
49 // 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
50 cvCalcHist( & src,hist); // 计算直方图
51 double maxentropy = - 1.0 ;
52 int max_index = - 1 ;
53 // 循环测试每个分割点，寻找到最大的阈值分割点
54 for ( int i = 0 ;i < HistogramBins;i ++ )
55 {
56 double cur_entropy = caculateCurrentEntropy(hist,i, object ) + caculateCurrentEntropy(hist,i,back);
57 if (cur_entropy > maxentropy)
58 {
59 maxentropy = cur_entropy;
60 max_index = i;
61 }
62 }
63 cout << " The Threshold of this Image in MaxEntropy is: " << max_index << endl;
64 cvThreshold(src, dst, ( double )max_index, 255 , CV_THRESH_BINARY);
65 cvReleaseHist( & hist);
66 }

基本全局阀值法 

代码

1 /* ============================================================================
2 = 代码内容：基本全局阈值法
3 ============================================================================== */
4 int BasicGlobalThreshold( int * pg, int start, int end)
5 { // 基本全局阈值法
6 int i,t,t1,t2,k1,k2;
7 double u,u1,u2;
8 t = 0 ;
9 u = 0 ;
10 for (i = start;i < end;i ++ )
11 {
12 t += pg[i];
13 u += i * pg[i];
14 }
15 k2 = ( int ) (u / t); // 计算此范围灰度的平均值
16 do
17 {
18 k1 = k2;
19 t1 = 0 ;
20 u1 = 0 ;
21 for (i = start;i <= k1;i ++ )
22 { // 计算低灰度组的累加和
23 t1 += pg[i];
24 u1 += i * pg[i];
25 }
26 t2 = t - t1;
27 u2 = u - u1;
28 if (t1)
29 u1 = u1 / t1; // 计算低灰度组的平均值
30 else
31 u1 = 0 ;
32 if (t2)
33 u2 = u2 / t2; // 计算高灰度组的平均值
34 else
35 u2 = 0 ;
36 k2 = ( int ) ((u1 + u2) / 2 ); // 得到新的阈值估计值
37 }
38 while (k1 != k2); // 数据未稳定，继续
39 // cout<<"The Threshold of this Image in BasicGlobalThreshold is:"<
40 return (k1); // 返回阈值
41 }

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。