OpenCV自用二值化图像处理代码

梦里梦外; 2023-07-12 12:00 11阅读 0赞

## OpenCV二值化 ##

用于处理XCT断层扫描数据图，原图为32位的位图，由于我还不会处理，先转成了8位的位图，然后用以下代码二值化，并重建成三维结构，输出成为文件  
很多的没必要的代码我并没有删除，都是程序调试时，实验OpenCV函数的，自己看哪个有用，去查手册比较好  
由于绝大多数代码都是用来测试，并没有写的特别规整，请见谅  
比较重要的函数imread和imshow，以及基本数据结构Mat，阈值函数threshold和adaptiveThreshold  
代码千万不要像我写的这样，这么乱

#include <iostream>
    #include <cstdio>
    #include <fstream>
    #include <opencv2/core/core.hpp>
    #include <opencv2/highgui/highgui.hpp>
    #include <opencv2/imgproc/imgproc.hpp>
     
    int main(int argc,char *argv[])
    {
        	
    	if (argc != 5)
    	{
        
    		std::cout << "have some mistake in parameter\n";
    		std::cout << "the struct of input\n";
    		std::cout << "1. the compare block of MEAN\n";
            std::cout << "2. the parameter C of MEAN\n";
            std::cout << "3. the compare block of GAUSSIAN\n";
            std::cout << "4. the parameter C of GAUSSIAN\n";
    		exit(EXIT_FAILURE);
    	}
    
    	int MEANblock=atoi(argv[1]);
    	int MEANc=atoi(argv[2]);
    	int GAUSSIANblock=atoi(argv[3]);
    	int GAUSSIANc=atoi(argv[4]);
    
    	cv::Mat image;
    	cv::Mat result1,result2;
    
    	std::string inputName;
    	
    	std::ofstream foutMean;
        foutMean.open("Mean.dat", std::ios::ate); 
    	std::ofstream foutGaussian;
    	foutGaussian.open("Gaussian.dat", std::ios::ate); 
    	
    	int sumMean=0;
    	int sumGaussian=0;
    	int total=0;
    
    	for(int t=25;t<80;t++){
        
    		std::string str1="sli-00";
    		std::string str2=".png";
    		std::ostringstream oss;
    		oss<<str1<<t<<str2;
    		//std::cout<<oss.str()<<std::endl;
    
    		image=cv::imread(oss.str(), cv::IMREAD_GRAYSCALE);
    		oss.clear();
    		//cv::threshold(image,result,105,255,cv::THRESH_TOZERO);
    		//cv::adaptiveThreshold(image, result1, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, 9, -3);
    		//cv::adaptiveThreshold(image, result2, 255, cv::ADAPTIVE_THRESH_GAUSSIAN_C, cv::THRESH_BINARY, 11, -2);
    		cv::adaptiveThreshold(image, result1, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, MEANblock, MEANc);
    		cv::adaptiveThreshold(image, result2, 255, cv::ADAPTIVE_THRESH_GAUSSIAN_C, cv::THRESH_BINARY, GAUSSIANblock, GAUSSIANc);
    		int rows=image.rows;
    		int cols=image.cols;
    		int center=rows/2;
    		int radius=rows/2-10;
    
    		for (int i=0; i<rows ; i++) {
        
    			for (int j=0; j<cols ; j++) {
        
    				//std::cout<<(int)result1.at<uchar>(i, j)<<"\t";
    				int meanvalue=(int)result1.at<uchar>(i, j);
    				int gaussianvalue=(int)result2.at<uchar>(i, j);
    				
    				if(std::pow(i-center,2)+std::pow(j-center,2)<std::pow(radius,2)){
        
    					foutMean<<meanvalue<<"\t";
    					foutGaussian<<gaussianvalue<<"\t";
    					total++;
    					sumMean+=meanvalue/255;
    					sumGaussian+=gaussianvalue/255;
    				}else{
        
    					foutMean<<0<<"\t";
    					foutGaussian<<0<<"\t";
    				}
    			}
    		}
    	}
    	//printf("the Mean data voidage:%d,%d,%f\n",sumMean,total,1-(double)sumMean/(double)total);
    	//printf("the Gaussian data voidage:%d,%d,%f\n",sumGaussian,total,1-(double)sumGaussian/(double)total);
    	printf("the Mean data voidage:%f\n",1-(double)sumMean/(double)total);
    	printf("the Gaussian data voidage:%f\n",1-(double)sumGaussian/(double)total);
    	
    	cv::namedWindow("origin");
    	cv::imshow("origin",image);
    	cv::namedWindow("mean");
    	cv::imshow("mean",result1);
    	cv::namedWindow("gaussian");
    	cv::imshow("gaussian",result2);
    
    	cv::waitKey();
    	return 0;
    }