OPENCV去除小连通区域，去除孔洞

一、对于二值图，0代表黑色，255代表白色。去除小连通区域与孔洞，小连通区域用8邻域，孔洞用4邻域。

 

  函数名字为：void RemoveSmallRegion(Mat &Src, Mat &Dst,int AreaLimit, int CheckMode, int NeihborMode)

     CheckMode: 0代表去除黑区域，1代表去除白区域; NeihborMode：0代表4邻域，1代表8邻域;  

如果去除小连通区域CheckMode=1,NeihborMode=1去除孔洞CheckMode=0，NeihborMode=0

     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 。

1.先对整个图像扫描，如果是去除小连通区域，则将黑色的背景图作为合格，像素值标记为3，如果是去除孔洞，则将白色的色素点作为合格，像素值标记为3。

2.扫面整个图像，对图像进行处理。

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 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;}

调用函数：dst是原来的二值图。

        Mat erzhi1 = Mat::zeros(srcImage.rows, srcImage.cols, CV_8UC1);
RemoveSmallRegion(dst, erzhi,100, 1, 1);
RemoveSmallRegion(erzhi, erzhi,100, 0, 0);
imshow("erzhi1", erzhi);

和之前的图像相比