宜配屋

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);