OpenCV实现图像中相邻区域的连接并进行合并-Toy模板网

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问题描述：
在进行图像分割时，由于分割效果不够理想，使得两相邻区域间存在未分割的情况。如果使用合并boundingBox的方式来处理，势必又会引入更多无关的区域，从而增加了分割的难度。因此本文考虑对两区域进行连接并寻找连接后的凸包，从而实现相邻区域合并，以便于后续处理。
opencv 轮廓合并,OpenCV,C++学习,opencv,计算机视觉,图像处理,c++

图1 原始图像以及其boundingBox

算法思路：
提取轮廓->计算轮廓间的距离->连接小于阈值T的两个轮廓->寻找凸包并进行填充
opencv 轮廓合并,OpenCV,C++学习,opencv,计算机视觉,图像处理,c++

图2 连接相邻区域（蓝线）和相应的凸包

由图2显而易见，该方法相比与合并boundingBox的方式减小无关的区域。

完整实现代码如下：文章来源地址https://www.toymoban.com/news/detail-526196.html

vector<Point> GetPoint(vector<Point> contour)
{
	cv::Rect rect = cv::boundingRect(cv::Mat(contour));
	Point up, down, left, right;
	up.x = rect.x + rect.width / 2;
	up.y = rect.y;
	vector<Point> p1;
	p1.push_back(up);
	down.x = rect.x + rect.width / 2;
	down.y = rect.y + rect.height;
	p1.push_back(down);
	left.x = rect.x;
	left.y = rect.y + rect.height / 2;
	p1.push_back(left);
	right.x = rect.x + rect.width;
	right.y = rect.y + rect.height / 2;
	p1.push_back(right);
	return p1;
}
void ConnectNearRegion()
{
	Mat src = imread("D:\\PyProject01\\images\\metal.png");
	Mat gray, imageBw;
	cvtColor(src, gray, CV_BGR2GRAY);
	threshold(gray, imageBw, 0, 255, CV_THRESH_BINARY);
	//腐蚀
	/*cv::Mat element = cv::getStructuringElement(MORPH_RECT, Size(3, 3));
	Mat metalMask;
	cv::erode(imageBw, metalMask, element);*/
	vector<vector<Point>> boundingPoints;
	vector<vector<Point>> contours;
	vector<Vec4i> hierarchy;
	findContours(imageBw, contours, hierarchy, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());
	//每个轮廓都只提取四个点
	for (int i = 0; i < contours.size(); ++i)
	{
		vector<Point> p1 = GetPoint(contours[i]);
		boundingPoints.push_back(p1);
	}
	//计算轮廓间的距离
	for (int i = 0; i < contours.size(); ++i)
	{
		cv::Rect rect = cv::boundingRect(cv::Mat(contours[i]));
		//rectangle(imageBw, rect, Scalar(200,0,255), 1);
		vector<Point> p1 = contours[i]; // boundingPoints[i];
		for (int i1 = i+1; i1 < contours.size(); ++i1)
		{
			vector<Point> p2 = contours[i1];// boundingPoints[i1];
			//分别求p1和p2的四个点距离
			float T = 10;
			int flag = 0;
			for (int i2 = 0; i2 < p1.size(); i2++)
			{
				for (int i3 = 0; i3 < p2.size(); i3++)
				{
					float d = sqrt(pow(float(p1[i2].x - p2[i3].x), 2) + pow(float(p1[i2].y - p2[i3].y), 2));
					if (d < T)
					{
						line(imageBw, p1[i2], p2[i3], Scalar(255, 0, 0), 1, LINE_AA, 0);
						flag = 1;
						break;
					}
				}
				if (flag)
					break;
			}
		}
	}
	imshow("", imageBw);
	waitKey();
	//find convex
	vector<vector<Point>> contours2;
	vector<Vec4i> hierarchy2;
	//find image contours
	findContours(imageBw, contours2, hierarchy2, RETR_TREE, CHAIN_APPROX_SIMPLE, Point());
	vector<vector<Point>>hull2(contours2.size());
	//get convex
	Mat filling = Mat::zeros(imageBw.size(), CV_8UC1);
	for (int i = 0; i < contours2.size(); i++)
	{
		convexHull(Mat(contours2[i]), hull2[i], false);
		fillPoly(filling, hull2[i], Scalar(255));
	}
	imshow("", filling);
	waitKey();
}