图像自适应二值化方法---迭代阈值法

通过迭代方法选择阈值， 计算方法如下：

（1）选择灰度图的平均值作为初始阈值T0 ;

（2）计算小于等于T0的平均值T1, 和大于T0的平均值T2;

（3）新的阈值为T = （T1 + T2）/ 2;

（4）比较T和T0,若相等，则返回T，即为迭代阈值; 否则 T0 = T,重复（1）-（3）

1. #include'cv.h'
2. #include 'highgui.h'
4. int IterationThreshold(CvMat* gray);
5. int main()
6. {
7. IplImage *src = cvLoadImage('flower.jpg',1);
9. const int width = src->width;
10. const int height = src->height;
12. CvMat *gray = cvCreateMat(height, width, CV_8UC1);
13. cvCvtColor(src, gray, CV_BGR2GRAY);
15. int thres = IterationThreshold(gray);
16. cvThreshold(gray, gray, thres, 255, CV_THRESH_BINARY);
18. cvShowImage('SRC', src);
19. cvShowImage('GRAY', gray);
20. cvWaitKey(0);
22. cvCvtColor(gray, src, CV_GRAY2BGR);
23. cvSaveImage('DST.bmp', src);
25. cvReleaseMat(&gray);
26. return 0;
27. }
28. int IterationThreshold(CvMat* gray)
29. {
30. int width = gray->width;
31. int height = gray->height;
33. //直方图统计
34. int histData[256] = {0};
35. for(int j = 0; j < height; j ++)
36. {
37. uchar*data = (uchar*)(gray->data.ptr + j * gray->step);
38. for (int i = 0; i < width; i ++)
39. {
40. histData[data[i]]++;
41. }
42. }
44. //求图像的平均灰度值作为初始阈值
45. int T0 = 0;
46. for (int i = 0; i < 256; i ++)
47. {
48. T0 += i * histData[i];
49. }
50. T0 /= width * height;
52. //迭代
53. int T1 = 0, T2 = 0;
54. int num1 = 0, num2 = 0;
55. int T = 0;
56. while (1)
57. {
58. for ( int i = 0; i < T0+1; i ++)
59. {
60. T1 += i * histData[i];
61. num1 += histData[i];
62. }
64. if (num1 == 0)
65. continue;
67. for ( int i = T0 + 1; i < 256; i ++)
68. {
69. T2 += i * histData[i];
70. num2 += histData[i];
71. }
73. if (num2 == 0)
74. continue;
76. T = (T1 / num1 + T2 / num2) / 2;
78. if ( T == T0 )
79. break;
80. else
81. T0 = T;
82. }
84. return T;
85. }

源图和效果图如下：