使用OpenCV去除面积较小的连通域

这是后期补充的部分，和前期的代码不太一样

效果图

处理前后图

二，实现源代码

//=======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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