实现灰度图像和彩色图像的迭代阈值分割算法opencv c++代码实现

以下是基于OpenCV的灰度图像和彩色图像迭代阈值分割算法的C++代码实现：

灰度图像迭代阈值分割：

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

double thresholdValue(Mat& img) {
    double T = 0;
    double T_last = -1;
    int rows = img.rows;
    int cols = img.cols;
    int total = rows * cols;
    while(abs(T - T_last) > 1) {
        double sum1 = 0, sum2 = 0, count1 = 0, count2 = 0;
        for(int i = 0; i < rows; ++i) {
            for(int j = 0; j < cols; ++j) {
                if(img.at<uchar>(i, j) > T) {
                    sum1 += img.at<uchar>(i, j);
                    count1++;
                } else {
                    sum2 += img.at<uchar>(i, j);
                    count2++;
                }
            }
        }
        T_last = T;
        T = (sum1 / count1 + sum2 / count2) / 2;
    }
    return T;
}

int main() {
    Mat img = imread("lena.jpg", IMREAD_GRAYSCALE);
    if(img.empty()) {
        cout << "Could not read the image" << endl;
        return -1;
    }

    double T = thresholdValue(img);
    Mat dst;
    threshold(img, dst, T, 255, THRESH_BINARY);

    namedWindow("Original Image", WINDOW_NORMAL);
    namedWindow("Thresholded Image", WINDOW_NORMAL);
    imshow("Original Image", img);
    imshow("Thresholded Image", dst);
    waitKey(0);
    return 0;
}

彩色图像迭代阈值分割：

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

double thresholdValue(Mat& img) {
    double T = 0;
    double T_last = -1;
    int rows = img.rows;
    int cols = img.cols;
    int total = rows * cols;
    while(abs(T - T_last) > 1) {
        double sum1 = 0, sum2 = 0, count1 = 0, count2 = 0;
        for(int i = 0; i < rows; ++i) {
            for(int j = 0; j < cols; ++j) {
                Vec3b pixel = img.at<Vec3b>(i, j);
                int gray = (pixel[0] + pixel[1] + pixel[2]) / 3;
                if(gray > T) {
                    sum1 += gray;
                    count1++;
                } else {
                    sum2 += gray;
                    count2++;
                }
            }
        }
        T_last = T;
        T = (sum1 / count1 + sum2 / count2) / 2;
    }
    return T;
}

int main() {
    Mat img = imread("lena.jpg");
    if(img.empty()) {
        cout << "Could not read the image" << endl;
        return -1;
    }

    double T = thresholdValue(img);
    Mat dst;
    cvtColor(img, dst, COLOR_BGR2GRAY);
    threshold(dst, dst, T, 255, THRESH_BINARY);

    namedWindow("Original Image", WINDOW_NORMAL);
    namedWindow("Thresholded Image", WINDOW_NORMAL);
    imshow("Original Image", img);
    imshow("Thresholded Image", dst);
    waitKey(0);
    return 0;
}