本文共 3122 字，大约阅读时间需要 10 分钟。

大津法（OTSU）图像阈值分割算法详解

大津法（OTSU）是一种经典的图像二值化阈值分割算法，由日本学者大津在1979年提出。其核心思想是通过最大化图像两部分之间的类间方差来确定最优的阈值。这种方法被广泛认为是图像分割中阈值选取的最佳方案，因其计算简单且不受图像亮度和对比度的影响。

算法原理

大津法的核心在于计算图像中灰度级的类间方差，并找出使类间方差最大的阈值。具体步骤如下：

OpenCV实现

OpenCV提供了OTSU算法的接口，函数定义如下：

double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type)

代码实现

#include 
   
    #include 
    
     #include 
     
      #include 
      
       using namespace cv;int Otsu(Mat& src, Mat& dst, int thresh) {    const int Grayscale = 256;    int graynum[Grayscale] = {0};        int r = src.rows;    int c = src.cols;        for (int i = 0; i < r; ++i) {        const uchar* ptr = src.ptr(i);        for (int j = 0; j < c; ++j) {            graynum[ptr[j]]++;        }    }        double P[Grayscale] = {0};    double PK[Grayscale] = {0};    double MK[Grayscale] = {0};        double sumtmpPK = 0, sumtmpMK = 0;        for (int i = 0; i < Grayscale; ++i) {        P[i] = graynum[i] / (r * c);        PK[i] = sumtmpPK + P[i];        sumtmpPK = PK[i];        MK[i] = sumtmpMK + i * P[i];        sumtmpMK = MK[i];    }        double Var = 0;    int best_thresh = thresh;        for (int k = 0; k < Grayscale; ++k) {        double numerator = (MK[Grayscale-1] * PK[k] - MK[k]);        double denominator = PK[k] * (1 - PK[k]);        if (denominator == 0) continue;        double current_var = numerator * numerator / denominator;        if (current_var > Var) {            Var = current_var;            best_thresh = k;        }    }        if (best_thresh != thresh) {        src.copyTo(dst);        for (int i = 0; i < r; ++i) {            uchar* ptr = dst.ptr(i);            for (int j = 0; j < c; ++j) {                if (ptr[j] > best_thresh) ptr[j] = 255;                else ptr[j] = 0;            }        }    }    return best_thresh;}int main() {    Mat src = cv::imread("I:\\Learning-and-Practice\\Img\\Fig1039(a)(polymersomes).tif");    if (src.empty()) {        return -1;    }    if (src.channels() > 1) {        cvtColor(src, src, CV_RGB2GRAY);    }        Mat dst, dst2;    int thresh = 0;        // 通过OTSU算法计算阈值    thresh = Otsu(src, dst, thresh);        // 使用OpenCV自带函数计算阈值    double Otsu_thresh = threshold(src, dst2, thresh, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);        cout << "Mythresh=" << thresh << endl;    cout << "OpenCVthresh=" << Otsu_thresh << endl;        namedWindow("src", CV_WINDOW_NORMAL);    imshow("src", src);    namedWindow("dst", CV_WINDOW_NORMAL);    imshow("dst", dst);    namedWindow("dst2", CV_WINDOW_NORMAL);    imshow("dst2", dst2);        waitKey(0);    return 0;}
      
     
    
   

功能说明

实施效果

阈值分割效果

阈值准确性

• 计算时间：约1ms，运行效率较高
• 阈值精度：与OpenCV自带函数一致

参考资料

• OpenCV官方文档
• GitHub：欢迎Star、Fork

转载地址：http://xrrfk.baihongyu.com/