OpenCV自用二值化图像处理代码_opnecv二值化代码

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;
}
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