OpenCV实现图像中相邻区域的连接并进行合并 |
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OpenCV实现图像中相邻区域的连接并进行合并
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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。 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 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： vector GetPoint(vector 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 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 boundingPoints; vector contours; vector 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 cv::Rect rect = cv::boundingRect(cv::Mat(contours[i])); //rectangle(imageBw, rect, Scalar(200,0,255), 1); vector p1 = contours[i]; // boundingPoints[i]; for (int i1 = i+1; i1 for (int i3 = 0; i3 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 contours2; vector hierarchy2; //find image contours findContours(imageBw, contours2, hierarchy2, RETR_TREE, CHAIN_APPROX_SIMPLE, Point()); vectorhull2(contours2.size()); //get convex Mat filling = Mat::zeros(imageBw.size(), CV_8UC1); for (int i = 0; i |
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