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.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 imagecv::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 Thresholdingcv::Mat binaryThresholded;cv::threshold(image, binaryThresholded, 127, 255, cv::THRESH_BINARY);cv::imshow("Binary Thresholding", binaryThresholded);// Adaptive Thresholdingcv::Mat adaptiveThresholded;cv::adaptiveThreshold(image, adaptiveThresholded, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, 11, 2);cv::imshow("Adaptive Thresholding", adaptiveThresholded);// Otsu's Thresholdingcv::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 closecv::waitKey(0);return 0;
}
)
