OpenCV 阈值法总结

1.概述

在深度学习出现之前，图像中的阈值法处理主要有二值阈值法、自适应阈值法、Ostu阈值法。

2.理论对比

3. 参数说明

4.代码实现

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

int main(int argc, char** argv) {
    if(argc != 2) {
        std::cerr << "Usage: " << argv[0] << " <image_path>" << std::endl;
        return -1;
    }

    // Load the image
    cv::Mat image = cv::imread(argv[1], cv::IMREAD_GRAYSCALE);
    if(image.empty()) {
        std::cerr << "Error: Couldn't read the image. Check the path and try again." << std::endl;
        return -1;
    }
    cv::imshow("Original Image", image);

    // Binary Thresholding
    cv::Mat binaryThresholded;
    cv::threshold(image, binaryThresholded, 127, 255, cv::THRESH_BINARY);
    cv::imshow("Binary Thresholding", binaryThresholded);

    // Adaptive Thresholding
    cv::Mat adaptiveThresholded;
    cv::adaptiveThreshold(image, adaptiveThresholded, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, 11, 2);
    cv::imshow("Adaptive Thresholding", adaptiveThresholded);

    // Otsu's Thresholding
    cv::Mat otsuThresholded;
    cv::threshold(image, otsuThresholded, 0, 255, cv::THRESH_BINARY | cv::THRESH_OTSU);
    cv::imshow("Otsu's Thresholding", otsuThresholded);

    // Wait for a key press and then close
    cv::waitKey(0);
    
    return 0;
}