opencv中自适应阈值函数的实现（c++）

根据《面向飞机蒙皮接缝的线结构光检测技术研究_张卡》论文中的原理，编写了自适应阈值函数

原理：

 //计算灰度最大最小值

 void MaxGrayValue(Mat image,int &max,int &min)

 {

     max = ;

     min = ;

     Mat *im = reinterpret_cast<Mat*>((void*)&image);

     int height = image.rows;

     int width = image.cols;

     int N = height * width;

     //cout << "125" << " " << "125" << " " << static_cast<int>(im->at<uchar>(Point(182, 190))) << endl;

     for (int i = ; i < height; i++)

     {

         for (int j = ; j < width; j++)

         {

             if ((static_cast<int>(im->at<uchar>(Point(j, i))))>max)

                 max = static_cast<int>(im->at<uchar>(Point(j, i)));

             else if ((static_cast<int>(im->at<uchar>(Point(j, i))))<min)

                 min = static_cast<int>(im->at<uchar>(Point(j, i)));

         }

     }

 }

 //自适应阈值 注：传入的只能是灰度图像

 void adapthreshold(Mat &image)

 {

     int max, min;    //保存最大和最小灰度值

     MaxGrayValue(image, max, min);   //计算出最大最小灰度值

     int grayvalue;    //用于暂时存取每个点的灰度值

     int Threshold = (max + min)/;  //初始阈值

     int Temp_Threshold = Threshold; //用于存取每次迭代后的阈值

     int big_than_th;       //用于累加大于阈值的灰度值

     int bigcount;           //用于存放大于阈值灰度值的像素点数量

     int sma_than_th;       //用于累加小于阈值的灰度值

     int smacount;           //用于存放小于阈值灰度值的像素点数量

     Mat *im = reinterpret_cast<Mat*>((void*)&image);    //获取像素点信息

     //Mat *im = &image;

     int height = image.rows;   //获取图像高度

     int width = image.cols;       //获取图像宽度

     for (int k = ; k < ; k++)   //计算阈值，最大迭代50次

     {

         big_than_th = ;

         bigcount = ;

         sma_than_th = ;

         smacount = ;

         for (int i = ; i < height; i++)

         {

             for (int j = ; j < width; j++)

             {

                 grayvalue = static_cast<int>(im->at<uchar>(Point(j, i)));  //获取指定点灰度值

                 if (grayvalue>Threshold)   //如果灰度值大于阈值，加到总灰度值，数量+1

                 {

                     big_than_th += grayvalue;

                     bigcount++;

                 }

                 else if (grayvalue < Threshold) //如果灰度值小于阈值，加到总灰度值，数量+1

                 {

                     sma_than_th += grayvalue;

                     smacount++;

                 }

             }

         }

         Temp_Threshold = ((big_than_th*1.0 / bigcount) + (sma_than_th*1.0 / smacount)) / ;  //计算新的阈值

         if (abs(Temp_Threshold - Threshold) < )   //如果新的阈值和旧的阈值差值的绝对值小于10，则退出

             break;

         else

             Threshold = Temp_Threshold;    //否则旧阈值等于新阈值

     }

     for (int i = ; i < height; i++)     //对图像进行二值化处理

     {

         uchar* data = image.ptr<uchar>(i);

         for (int j = ; j < width; j++)

         {

             grayvalue = static_cast<int>(im->at<uchar>(Point(j, i)));

             if (grayvalue>Threshold)

                 data[j] = ;

             else if (grayvalue < Threshold)

                 data[j] = ;

         }

     }

 }