算法步骤：

 

注意点：1.2步骤中采用百分比阈值 ，阈值的百分数应设置较大值（如99.7%），可以排除背景中的干扰点

99.97%时候的二值图（为了可视化，灰度值范围设置为0-255）

90%的二值图

2.最后一步中，通过otsu计算阈值时候，灰度值为0的像素点不计入计算。

核心算法：

void edge_threshold(Mat &src) { Mat src1 = src.clone(); Mat dst, thre, dst_x, dst_y; medianBlur(src1, src1, 3); Mat kernel_x = (Mat_<int>(3, 3) << -1, -2, -1, 0, 0, 0, 1, 2, 1); Mat kernel_y = (Mat_<int>(3, 3) << -1, 0, 1, -2, 0, 2, -1, 0, 1); filter2D(src1, dst_x, CV_16SC1, kernel_x); filter2D(src1, dst_y, CV_16SC1, kernel_y); convertScaleAbs(dst_x, dst_x); convertScaleAbs(dst_y, dst_y); dst = dst_x + dst_y; int hist[256] = { 0 }; uchar * d_data = dst.data; for (int j = 0; j < dst.rows; j++) { for (int i = 0; i < dst.cols; i++) { if (d_data[j*dst.step + i] != 0) { hist[d_data[j*dst.step + i]]++; } } } long sum = 0, Amount = 0; double percent; int T = 0; for (int k = 0; k < 256; k++) { Amount += hist[k]; } for (int i = 0; i < 256; i++) { sum += hist[i]; if (sum >= Amount*0.9) { T = i; break; } } threshold(dst, thre, T, 1, THRESH_BINARY); Mat src2; src2 = thre.mul(src1);//取thre 中的像素值为1的像素点。 Mat src3; threshold(src1, src3, OtsuThreshold(src2), 255, THRESH_BINARY); imshow("result", src3); }

otsu算法：

int OtsuThreshold(Mat src) { int threshold; int height = src.rows; int width = src.cols; //histogram float histogram[256] = { 0 }; uchar * s_data = src.data; for (int i = 0; i < height; i++) { for (int j = 0; j < width; j++) { if (s_data[i*src.step + j] != 0)//排除直方图中的0像素值 histogram[s_data[i*src.step + j]]++; } } int m = 0, n = 0; //m:第一个有像素的灰度值 n:最后一个有像素的灰度值 long sum_value = 0;//非零像素个数 bool singel = true; for (int i = 1; i < 256; i++) { if (histogram[i] != 0 && singel) { m = i; singel = false; } if (histogram[i] != 0) { sum_value++; } } for (int j = 255; j >= 0; j--) { if (histogram[j] != 0) { n = j; break; } } float sum0, sum1, cnt0, cnt1, w0, w1, u0, u1, u, variance; float maxVariance = 0; for (int i = m+1; i <= n; i++) { sum0 = 0; sum1 = 0; cnt0 = 0; cnt1 = 0; w0 = 0; w1 = 0; if (histogram[i] != 0) { for (int j = m; j < i; j++) { cnt0 += histogram[j]; sum0 += j * histogram[j]; } u0 = (double)sum0 / cnt0; w0 = (double)cnt0 / sum_value; for (int j = i; j <= n; j++) { cnt1 += histogram[j]; sum1 += j * histogram[j]; } u1 = (double)sum1 / cnt1; w1 = (double)cnt1 / sum_value; u = u0 * w0 + u1 * w1; //图像的平均灰度 variance = w0 * w1 * (u0 - u1) * (u0 - u1); if (variance > maxVariance) { maxVariance = variance; threshold = i; } } } return threshold; }