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)
2024-04-28 20:05:51
主程序(核心部分)
代码
1
2
3
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
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
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
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
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
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
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);
2
3
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
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
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
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
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
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
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
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:"<<iThrehold<<endl;
66 return iThrehold;
67 }
68
2
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:"<<iThrehold<<endl;
66 return iThrehold;
67 }
68
Otsu代码一
代码
1
2
3
4
5
11
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 ++ ;
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];
53 n += ihist[k];
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
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 }
2
3
4
5
11
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 ++ ;
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];
53 n += ihist[k];
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
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
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];
42 n += ihist[k];
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
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:"<<thresholdValue<<endl;
87 return(thresholdValue);
88 }
最大熵阀值
代码
1
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
34 cur_entropy += - percentage * logf(percentage);
35
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 }
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
34 cur_entropy += - percentage * logf(percentage);
35
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
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:"<<k1<<endl;
40 return (k1); // 返回阈值
41 }
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:"<<k1<<endl;
40 return (k1); // 返回阈值
41 }
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)相关推荐
- 【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:zgzhaobo@gmail.com QQ:452728574 Latest Modified Da ...
- 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)...
转自:http://blog.csdn.net/xw20084898/article/details/17564957 一.工具:VC+OpenCV 二.语言:C++ 三.原理 otsu法(最大类间方 ...
- 【转】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)...
http://blog.csdn.net/xw20084898/article/details/17564957 一.工具:VC+OpenCV 二.语言:C++ 三.原理 otsu法(最大类间方差法, ...
- 【千律】OpenCV基础：图像阈值分割 -- 自适应阈值分割 -- 代码实现
环境:Python3.8 和 OpenCV 内容:自适应阈值分割代码实现 import cv2 as cv import numpy as np import matplotlib.pyplot as ...
- 图像处理合集：图像基础操作（图像翻转、图像锐化、图像平滑等）、图像阈值分割（边缘检测、迭代法、OSTU、区域增长法等）、图像特征提取（图像分割、灰度共生矩阵、PCA图像压缩）
文章目录 说明 一.图像锐化或增强相关 1. 图像点处理 1.1 图像翻转 1.2 幂运算和对数运算 2. 直方图处理 3. 图像平滑 4. 图像锐化 5. 图像增强 二.图像阈值分割 1. 边缘检测 ...
- opencv中几种阈值分割
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) 一.工具:VC+OpenCV 二.语言:C++ 三.原理 otsu法(最大类间方差法,有时也称之为大津算法) ...
- Halcon算子学习：图像阈值分割-threshold、binary_threshold、dyn_threshold算子
Halcon算子学习:图像阈值分割算子 前言 1.threshold-全局固定阈值分割 2.Binary Threshold-自动全局阈值分割 3.dyn_threshold-局部动态阈值分割 小结: ...
- 图像二值化方法及适用场景分析（OTSU Trangle 自适应阈值分割）
图像二值化 应用场景 二值图像定义 阈值获取的方法 手动阈值法 自动阈值法 灰度均值法 基于直方图均值法 OTSU Triangle 自适应均值阈值分割方法 总结 参考文献 应用场景 二值图像处理与分 ...
- OpenCV —— 阈值分割（直方图技术法，熵算法，Otsu，自适应阈值算法）
阈值分割 1. 全局阈值分割 直方图技术法 熵算法 Otsu算法 2. 局部阈值分割 自适应阈值 阈值的分割的核心就是如何选取阈值,选取正确的阈值时分割成功的关键.可以使用手动设置阈值,也可以采用直方 ...
最新文章
- 学好Linux必备知识
- query什么意思php,关于 QueryPHP
- IntelliJ IDEA for Mac在MacOS模式下的动态代码模板快捷键（Live Templates Shortcut）
- vue this.$set 给data对象新增属性，并触发视图更新
- 自动化运维环境搭建过程
- img标签中alt和title属性的正确使用
- 在谈判中别无选择的时候，怎么办？
- idea 设置代码的颜色
- android中自定义 toast,Android自定义Toast
- 蓝桥杯 基础练习 特殊回文数 JAVA
- HashMap常见面试题
- 错误1406.无法将数值写入键/Software/Classess/.htm/OpenWithList/devenv.exer的解决方案
- 思科2960交换机 - 配置命令大全
- Imperfect C++读书笔记（二）：前置条件、后置条件和不变式
- rockchip rk3566 android11 网口log报错： DMA engine initialization failed
- VOT（Visual Object Tracking）评价指标综述
- js制作12进制的时钟特效
- 华硕h110m主板怎么样_华硕台式机主板怎么样 华硕台式机主板多少钱【详细介绍】...
- win10在编写python程序时,问题出现权限不足的问题
- LeetCode 开心字符串