使用OpenCV去除面积较小的连通域

时间:2022-01-02 03:37:55

这是后期补充的部分,和前期的代码不太一样

效果图

使用OpenCV去除面积较小的连通域

源代码

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//测试
void CCutImageVS2013Dlg::OnBnClickedTestButton1()
{
    vector<vector<Point> > contours;  //轮廓数组
    vector<Point2d> centers;    //轮廓质心坐标
    vector<vector<Point> >::iterator itr; //轮廓迭代器
    vector<Point2d>::iterator itrc;  //质心坐标迭代器
    vector<vector<Point> > con;   //当前轮廓
 
    double area;
    double minarea = 1000;
    double maxarea = 0;
    Moments mom;       // 轮廓矩
    Mat image, gray, edge, dst;
    image = imread("D:\\66.png");
    cvtColor(image, gray, COLOR_BGR2GRAY);
    Mat rgbImg(gray.size(), CV_8UC3); //创建三通道图
    blur(gray, edge, Size(3, 3));       //模糊去噪
    threshold(edge, edge, 200, 255, THRESH_BINARY_INV); //二值化处理,黑底白字
    //--------去除较小轮廓,并寻找最大轮廓--------------------------
    findContours(edge, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); //寻找轮廓
    itr = contours.begin();    //使用迭代器去除噪声轮廓
    while (itr != contours.end())
    {
        area = contourArea(*itr);  //获得轮廓面积
        if (area<minarea)    //删除较小面积的轮廓
        {
            itr = contours.erase(itr); //itr一旦erase,需要重新赋值
        }
        else
        {
            itr++;
        }
        if (area>maxarea)    //寻找最大轮廓
        {
            maxarea = area;
        }
    }
    dst = Mat::zeros(image.rows, image.cols, CV_8UC3);
    /*绘制连通区域轮廓,计算质心坐标*/
    Point2d center;
    itr = contours.begin();
    while (itr != contours.end())
    {
        area = contourArea(*itr);      
        con.push_back(*itr);   //获取当前轮廓
        if (area == maxarea)
        {
            vector<Rect> boundRect(1); //定义外接矩形集合
            boundRect[0] = boundingRect(Mat(*itr));
            cvtColor(gray, rgbImg, COLOR_GRAY2BGR);
            Rect select;
            select.x = boundRect[0].x;
            select.y = boundRect[0].y;
            select.width = boundRect[0].width;
            select.height = boundRect[0].height;
            rectangle(rgbImg, select, Scalar(0, 255, 0), 3, 2); //用矩形画矩形窗
            drawContours(dst, con, -1, Scalar(0, 0, 255), 2); //最大面积红色绘制
        }
        else
            drawContours(dst, con, -1, Scalar(255, 0, 0), 2); //其它面积蓝色绘制
        con.pop_back();
        //计算质心
        mom = moments(*itr);
        center.x = (int)(mom.m10 / mom.m00);
        center.y = (int)(mom.m01 / mom.m00);
        centers.push_back(center);
        itr++;
    }
    imshow("rgbImg", rgbImg);
    //imshow("gray", gray);
    //imshow("edge", edge);
    imshow("origin", image);
    imshow("connected_region", dst);
    waitKey(0);
    return;
}

前期做的,方法可能不太一样

一,先看效果图

原图

使用OpenCV去除面积较小的连通域

处理前后图

使用OpenCV去除面积较小的连通域

二,实现源代码

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//=======函数实现=====================================================================
void RemoveSmallRegion(Mat &Src, Mat &Dst, int AreaLimit, int CheckMode, int NeihborMode)
{
    int RemoveCount = 0;
    //新建一幅标签图像初始化为0像素点,为了记录每个像素点检验状态的标签,0代表未检查,1代表正在检查,2代表检查不合格(需要反转颜色),3代表检查合格或不需检查
    //初始化的图像全部为0,未检查
    Mat PointLabel = Mat::zeros(Src.size(), CV_8UC1);
    if (CheckMode == 1)//去除小连通区域的白色点
    {
        //cout << "去除小连通域.";
        for (int i = 0; i < Src.rows; i++)
        {
            for (int j = 0; j < Src.cols; j++)
            {
                if (Src.at<uchar>(i, j) < 10)
                {
                    PointLabel.at<uchar>(i, j) = 3;//将背景黑色点标记为合格,像素为3
                }
            }
        }
    }
    else//去除孔洞,黑色点像素
    {
        //cout << "去除孔洞";
        for (int i = 0; i < Src.rows; i++)
        {
            for (int j = 0; j < Src.cols; j++)
            {
                if (Src.at<uchar>(i, j) > 10)
                {
                    PointLabel.at<uchar>(i, j) = 3;//如果原图是白色区域,标记为合格,像素为3
                }
            }
        }
    }
    vector<Point2i>NeihborPos;//将邻域压进容器
    NeihborPos.push_back(Point2i(-1, 0));
    NeihborPos.push_back(Point2i(1, 0));
    NeihborPos.push_back(Point2i(0, -1));
    NeihborPos.push_back(Point2i(0, 1));
    if (NeihborMode == 1)
    {
        //cout << "Neighbor mode: 8邻域." << endl;
        NeihborPos.push_back(Point2i(-1, -1));
        NeihborPos.push_back(Point2i(-1, 1));
        NeihborPos.push_back(Point2i(1, -1));
        NeihborPos.push_back(Point2i(1, 1));
    }
    else int a = 0;//cout << "Neighbor mode: 4邻域." << endl;
    int NeihborCount = 4 + 4 * NeihborMode;
    int CurrX = 0, CurrY = 0;
    //开始检测
    for (int i = 0; i < Src.rows; i++)
    {
        for (int j = 0; j < Src.cols; j++)
        {
            if (PointLabel.at<uchar>(i, j) == 0)//标签图像像素点为0,表示还未检查的不合格点
            { //开始检查
                vector<Point2i>GrowBuffer;//记录检查像素点的个数
                GrowBuffer.push_back(Point2i(j, i));
                PointLabel.at<uchar>(i, j) = 1;//标记为正在检查
                int CheckResult = 0;
                for (int z = 0; z < GrowBuffer.size(); z++)
                {
                    for (int q = 0; q < NeihborCount; q++)
                    {
                        CurrX = GrowBuffer.at(z).x + NeihborPos.at(q).x;
                        CurrY = GrowBuffer.at(z).y + NeihborPos.at(q).y;
                        if (CurrX >= 0 && CurrX<Src.cols&&CurrY >= 0 && CurrY<Src.rows) //防止越界
                        {
                            if (PointLabel.at<uchar>(CurrY, CurrX) == 0)
                            {
                                GrowBuffer.push_back(Point2i(CurrX, CurrY)); //邻域点加入buffer
                                PointLabel.at<uchar>(CurrY, CurrX) = 1;   //更新邻域点的检查标签,避免重复检查
                            }
                        }
                    }
                }
                if (GrowBuffer.size()>AreaLimit) //判断结果(是否超出限定的大小),1为未超出,2为超出
                    CheckResult = 2;
                else
                {
                    CheckResult = 1;
                    RemoveCount++;//记录有多少区域被去除
                }
                for (int z = 0; z < GrowBuffer.size(); z++)
                {
                    CurrX = GrowBuffer.at(z).x;
                    CurrY = GrowBuffer.at(z).y;
                    PointLabel.at<uchar>(CurrY, CurrX) += CheckResult;//标记不合格的像素点,像素值为2
                }
                //********结束该点处的检查**********
            }
        }
    }
    CheckMode = 255 * (1 - CheckMode);
    //开始反转面积过小的区域
    for (int i = 0; i < Src.rows; ++i)
    {
        for (int j = 0; j < Src.cols; ++j)
        {
            if (PointLabel.at<uchar>(i, j) == 2)
            {
                Dst.at<uchar>(i, j) = CheckMode;
            }
            else if (PointLabel.at<uchar>(i, j) == 3)
            {
                Dst.at<uchar>(i, j) = Src.at<uchar>(i, j);
            }
        }
    }
    //cout << RemoveCount << " objects removed." << endl;
}
//=======函数实现=====================================================================
//=======调用函数=====================================================================
    Mat img;
    img = imread("D:\\1_1.jpg", 0);//读取图片
    threshold(img, img, 128, 255, CV_THRESH_BINARY_INV);
    imshow("去除前", img);
    Mat img1;
    RemoveSmallRegion(img, img, 200, 0, 1);
    imshow("去除后", img);
    waitKey(0);
//=======调用函数=====================================================================

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原文链接:https://blog.csdn.net/sxlsxl119/article/details/80493655