最优阈值分割算法（迭代）

来源：互联网发布：河南统计网络直报平台编辑：程序博客网时间：2024/05/19 22:26
原理太多，不再赘述
#include<iostream>#include<opencv2/opencv.hpp>void OptimSegmentation(unsigned char*inputimage, int height, int width);using namespace cv;void OptimSegmentation(unsigned char*inputimage, int height, int width){    //1.计算直方图    int *histarray = new int[256];    int i,x,y;    for (i = 0; i < 256; i++)    {        histarray[i] = 0;    }    //中间变量    int temp,temp1,temp2;    //统计灰度直方图    for (y = 0; y < height; y++)    {        for (x = 0; x < width; x++)        {            int k = width*y + x;            temp = inputimage[k];            histarray[temp]++;        }    }    //2.计算最优阈值//  int minT = 0,maxT=0;    double MinGrayValue = 0, MaxGrayValue =0;    //float P0 = 0.0, Pt = 0.0;    for (y = 0; y < height; y++)    {        for (x = 0; x < width; x++)        {            int k = width*y + x;            temp1 = inputimage[k];            if (MinGrayValue > temp1)                MinGrayValue = temp1;//最小灰度值        }    }    for (y = 0; y < height; y++)    {        for (x = 0; x < width; x++)        {            int k = width*y + x;            temp2 = inputimage[k];            if (MaxGrayValue < temp2)                MaxGrayValue = temp2;//最大灰度值        }    }    double newT = (MinGrayValue + MaxGrayValue) / 2;    //std::cout << newT << std::endl;    double T = 0.0;    double S0 = 0.0,N1 = 0.0; //初始化大于阈值的元素的个数及其灰度总值    double S1 = 0.0,N2 = 0.0; //初始化小于阈值的元素的个数及其灰度总值    while (abs(newT - T)>0)    {        T = newT;        //std::cout << T << std::endl;        for (i = 0; i < 256; i++)        {            if (i > T)            {                S0 = S0 + histarray[i]; //记录大于阈值的元素个数及其灰度总值                //N1 = N1 + 1;                N1 += i*histarray[i];            }            else            {                S1 = S1 + histarray[i];                //N2 = N2 + 1;                N2 += i*histarray[i];            }        }        double T0 = N1/S0;  //生成新阈值        double T1 = N2/S1;        newT = (T0 + T1) / 2;    //  std::cout << newT << std::endl;        S0 = 0.0; N1 = 0.0;  //重置初值        S1 = 0.0; N2 = 0.0;    }    //3.图像二值化//  unsigned char *outimage1=new unsigned char[height*width];    for (y = 0; y<height; y++)    {        for (x = 0; x < width; x++)        {            int k = width*y + x;            //outimage1[k] = inputimage[k];            //std::cout << outimage1[k] << std::endl;        //  std::cout << T << std::endl;            if (inputimage[k] > T)   //亮点为白色                inputimage[k] = 255;            else                inputimage[k] = 0;        }    }    delete[] histarray;    //delete[] outimage1;}int main(){    Mat lena = imread("test2.png");    //imshow("1", lena);    cvtColor(lena, lena, CV_RGB2GRAY);    int height = lena.rows;    int width = lena.cols;    uchar* covertimage = new uchar[height * width];    uchar* p;    int i, j;    for (i = 0; i<height; i++)    {        p = lena.ptr<uchar>(i);        for (j = 0; j<width; j++)        {            covertimage[i*width + j] = p[j];        }    }    OptimSegmentation(covertimage, width, height);    Mat outimage(height, width, CV_8UC1, covertimage); //一维数组转Mat类    //cvNamedWindow("图像显示", CV_WINDOW_AUTOSIZE);    imshow("output", outimage);    waitKey(0);    delete[] covertimage;    return 0;}
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