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

源代码

//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

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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