二值化的前提是灰度化，手動二值化需要不斷進行調(diào)整，找到合適的閾值，大于閾值為白色。
大津二值化又叫最大類間方差法，可以自動確定二值化的閾值。基本思想是遍歷閾值，當(dāng)某個閾值下分割的兩類像素點之間灰度方差最大的時候，就是最優(yōu)的閾值。（因為類間方差越大，兩類的差別越大，不管是前景錯分到背景還是背景錯分到前景都會使類間方差變小）。數(shù)學(xué)推導(dǎo)如下：

#include <opencv2/core.hpp> #include <opencv2/highgui.hpp> #include <iostream>cv::Mat BGR2GRAY(cv::Mat img) {int width = img.cols;int height = img.rows;cv::Mat out = cv::Mat::zeros(height, width, CV_8UC1);//8位無符號灰度圖像for (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {//RGB分量取不同的權(quán)重out.at<uchar>(y, x) = 0.2126 *img.at<cv::Vec3b>(y, x)[2] \+ 0.7152 * img.at<cv::Vec3b>(y, x)[1] \+ 0.0722 * img.at<cv::Vec3b>(y, x)[0];}}return out; }cv::Mat Binarize_Otsu(cv::Mat gray) {int width = gray.cols;int height = gray.rows;double w0 = 0, w1 = 0;//像素的比例double m0 = 0, m1 = 0;//像素的均值double max_sb = 0, sb = 0;int th = 0;int val;for (int t = 0; t < 255; t++) {w0 = 0;w1 = 0;m0 = 0;m1 = 0;//獲取各類像素的個數(shù) 計算均值for (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {val = (int)(gray.at<uchar>(y, x));if (val < t) {w0++;m0 += val;}else {w1++;m1 += val;}}}m0 /= w0;m1 /= w1;w0 /= (height * width);w1 /= (height * width);sb = w0 * w1 * pow((m0 - m1), 2);//計算類間方差if (sb > max_sb) {max_sb = sb;th = t;}}std::cout << "大津算法閾值:" << th << std::endl;cv::Mat out = cv::Mat::zeros(height, width, CV_8UC1);for (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {// Binarizeif (gray.at<uchar>(y, x) > th) {out.at<uchar>(y, x) = 255;}else {out.at<uchar>(y, x) = 0;}}}return out; }int main(int argc, const char* argv[]) {cv::Mat img = cv::imread("C:/Users/zxdn/Desktop/girl.jpg", cv::IMREAD_COLOR);cv::Mat gray = BGR2GRAY(img);cv::Mat out = Binarize_Otsu(gray);cv::imshow("原圖", img);cv::imshow("大津二值化", out);cv::waitKey(0);cv::destroyAllWindows();return 0; }

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