opencv阈值分割算法c++

Sure, I'd be happy to help you with that! The OpenCV library provides different thresholding methods for image segmentation in C language. The most common ones are binary, adaptive, and Otsu thresholding. Do you have any specific questions about these methods or their implementation in C?

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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;
}

opencv c++阈值分割otsu

Otsu阈值分割是一种图像二值化算法，它可以自适应地确定一个阈值，使得分割后的两部分图像的类内方差最小、类间方差最大。它在OpenCV中的实现函数为cv::threshold，可通过设置第三个参数为cv::THRESH_OTSU来使用Otsu阈值分割。

具体来说，Otsu阈值分割算法的步骤如下：
1. 统计图像的灰度级直方图，计算每个灰度级出现的概率；
2. 初始化总类间方差为0，对于每个可能的阈值T，计算其对应的类内方差和类间方差，并更新总类间方差；
3. 找到总类间方差最大的阈值，作为最终的阈值。