opencv中自適應閾值函數的實現（c++）

根據《面向飛機蒙皮接縫的線結構光檢測技術研究_張卡》論文中的原理，編寫了自適應閾值函數

原理：

 

//計算灰度最大最小值
void MaxGrayValue(Mat image,int &max,int &min)
{
    max = 0;
    min = 255;
    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 = 0; i < height; i++)
    {
        for (int j = 0; 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)));
        }
    }
}

 

//自適應閾值 注：傳入的只能是灰度圖像
void adapthreshold(Mat &image)
{
    int max, min;    //保存最大和最小灰度值
    MaxGrayValue(image, max, min);   //計算出最大最小灰度值
    int grayvalue;    //用於暫時存取每個點的灰度值
    int Threshold = (max + min)/2;  //初始閾值
    int Temp_Threshold = Threshold; //用於存取每次迭代后的閾值
    int big_than_th;       //用於累加大於閾值的灰度值
    int bigcount;           //用於存放大於閾值灰度值的像素點數量
    int sma_than_th;       //用於累加小於閾值的灰度值
    int smacount;           //用於存放小於閾值灰度值的像素點數量
    Mat *im = reinterpret_cast<Mat*>((void*)&image);    //獲取像素點信息
    //Mat *im = &image;
    int height = image.rows;   //獲取圖像高度
    int width = image.cols;       //獲取圖像寬度

    
    for (int k = 0; k < 50; k++)   //計算閾值，最大迭代50次
    {
        big_than_th = 0;    
        bigcount = 0;
        sma_than_th = 0;
        smacount = 0;
        for (int i = 0; i < height; i++)
        {
            for (int j = 0; j < width; j++)
            {
                grayvalue = static_cast<int>(im->at<uchar>(Point(j, i)));  //獲取指定點灰度值
                if (grayvalue>Threshold)   //如果灰度值大於閾值，加到總灰度值，數量+1
                {
                    big_than_th += grayvalue;
                    bigcount++;
                }
                else if (grayvalue < Threshold) //如果灰度值小於閾值，加到總灰度值，數量+1
                {
                    sma_than_th += grayvalue;
                    smacount++;
                }
            }
        }
        Temp_Threshold = ((big_than_th*1.0 / bigcount) + (sma_than_th*1.0 / smacount)) / 2;  //計算新的閾值
        if (abs(Temp_Threshold - Threshold) < 2)   //如果新的閾值和舊的閾值差值的絕對值小於10，則退出
            break;
        else
            Threshold = Temp_Threshold;    //否則舊閾值等於新閾值
    }
    for (int i = 0; i < height; i++)     //對圖像進行二值化處理
    {
        uchar* data = image.ptr<uchar>(i);
        for (int j = 0; j < width; j++)
        {
            grayvalue = static_cast<int>(im->at<uchar>(Point(j, i)));
            if (grayvalue>Threshold)
                data[j] = 255;
            else if (grayvalue < Threshold)
                data[j] = 0;
        }
    }
}