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;
}