erzhihua.rar_opencv二值化

// erzhihua.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.h" #include <stdio.h> #include <math.h> #include <cxcore.h> #include <cv.h> #include "highgui.h" #include <iostream> using namespace std; int Multi_threshold(IplImage *src) ; IplImage* ImageBinarization(IplImage* img); //IplImage* zhijian(IplImage*I,IplImage*Iin) //{ // IplImage *W=Iin; // int th=Multi_threshold(I); // IplImage *mask=ImageBinarization(I); // // //[th mask]=Multi_threshold(I); // int m=I->height; // int n=I->width; // for (int i=0;i<m;i++) // { // for(int j=0;j<n;j++) // if (cvGetReal2D(I,i,j)>th) // cvSetReal2D(W,i,j,cvGetReal2D(I,i,j)); // } // return W; //} int Multi_threshold(IplImage *src) //求阈值 { /*对灰度图像二值化，自适应门限threshold*/ int i,j,width,height,step,chanel,threshold; /*size是图像尺寸，svg是灰度直方图均值，va是方差*/ float size,avg,va,maxVa,p,a,s; unsigned char *dataSrc; float histogram[256]; double value1,min1,min2; int n1,n2; double max1=0; int index1=0; double max2=0; int index2=0; width = src->width; height = src->height; dataSrc = (unsigned char *)src->imageData; step = src->widthStep/sizeof(char); chanel = src->nChannels; /*计算直方图并归一化histogram*/ for(i=0; i<256; i++) histogram[i] = 0; for(i=0; i<height; i++) { for(j=0; j<width*chanel; j++) { histogram[dataSrc[i*step+j]-'0'+48]++; //需看懂这一行，么意思？应该是进行统计，但“+48”是什么意思，如果是彩色图像怎么处理的呢？ } } size = width * height; for(i=0; i<256; i++) histogram[i] /=size;//这一行的作用是归一化 /***********第一个循环求第一个直方图峰值***********/ for (i=0; i<255; i++) { if (max1<=histogram[i]) { if ((histogram[i+1]-histogram[i])<0) { if (i==0)//处理第一个峰值在索引为0时的情况 { max1=histogram[i]; index1=i; } else if((histogram[i]-histogram[i-1])>0) { max1=histogram[i]; index1=i; } } } } /***********第二个循环求第二个直方图峰值***********/ for (i=1; i<255; i++) { if (max2<=histogram[i]) { if((histogram[i+1]-histogram[i])<0) { if ((histogram[i]-histogram[i-1])>0) { if (i!=index1)//如果第二个峰值不等于第一个峰值，就赋值给max2及index2 { max2=histogram[i]; index2=i; } } } } } n1=min(index1,index2);//n1是记录两个峰值中较小的索引，n2记录两个峰值中较大的索引 n2=max(index1,index2); min1=1000; value1=0; for(i=n1;i<n2+1;i++) { if (min1>=histogram[i]) { if((histogram[i+1]-histogram[i])>=0) { if ((histogram[i]-histogram[i-1])<=0) { min1=histogram[i]; value1=i; } } } } threshold=value1; return threshold; } IplImage* ImageBinarization(IplImage* img)//二值化 { IplImage* biimg = cvCreateImage(cvGetSize(img),img->depth,img->nChannels); CvScalar s; int i,j,sum=0; int t=Multi_threshold(img); for (i=0;i<img->height;i++) { for (j=0;j<img->width;j++) { s = cvGet2D(img,i,j); sum = (s.val[0]+s.val[1]+s.val[2])/3; if (sum >t) { s.val[0]=s.val[1]=s.val[2]=0; cvSet2D(img,i,j,s); } else { s.val[0]=s.val[1]=s.val[2]=255; cvSet2D(img,i,j,s); } } } cvCopy(img,biimg); cvReleaseImage(&img); return biimg; } int _tmain(int argc, _TCHAR* argv[]) { IplImage* img = cvLoadImage("C:\\E\\13110862186.jpg",1); int t=Multi_threshold(img); cout<<t<<endl; //显示阈值 IplImage *biimg=ImageBinarization(img); cvNamedWindow("imagebinary"); cvShowImage("imagebinary",biimg); cvWaitKey(0); cvDestroyWindow("imagebinary"); cvReleaseImage(&biimg); return 0; }