原理参考：直线拟合——最小二乘法、hough变换
/*** @description:直线拟合* @param points输入点集* @param line_para直线参数*/void fitline(std::vector<cv::Point>& points, cv::Vec4f& line_para){int N = points.size();if (N < 2)return; double sum_x = 0, sum_y = 0, sum_xx = 0, sum_xy = 0, sum_yy = 0;for (int i = 0; i < N; i++){sum_x += points[i].x;sum_y += points[i].y;sum_xx += points[i].x * points[i].x;sum_xy += points[i].x * points[i].y;sum_yy += points[i].y * points[i].y;}//least squares: y=kx+bfloat k = (N*sum_xy-sum_x*sum_y) / (N*sum_xx - sum_x*sum_x);float b = (sum_xx*sum_y - sum_x*sum_xy) / (N*sum_xx - sum_x*sum_x);std::cout << "k = " << k << ", b = " << b << std::endl;//total least squares: ax+by+c=0 float A, B, C;float mean_x = sum_x / N, mean_y = sum_y / N, mean_xx = sum_xx / N, mean_xy = sum_xy / N, mean_yy = sum_yy / N;cv::Mat m = (cv::Mat_<float>(2, 2) << mean_xx - mean_x*mean_x, mean_xy - mean_x*mean_y, mean_xy - mean_x*mean_y, mean_yy - mean_y*mean_y);cv::Mat eigenvalue, eigenvector;cv::eigen(m * N, eigenvalue, eigenvector);float v0 = eigenvalue.at<float>(0, 0), v1 = eigenvalue.at<float>(0, 1);if (abs(v0) < abs(v1)){A = eigenvector.at<float>(0, 0);B = eigenvector.at<float>(0, 1);}else {A = eigenvector.at<float>(1, 0);B = eigenvector.at<float>(1, 1);}C = -(A*mean_x + B*mean_y);std::cout << "k = " << -A / B << ", b = " << -C / B << std::endl;line_para[0] = B;line_para[1] = -A;line_para[2] = mean_x;line_para[3] = mean_y;}
代码传送门：/taifyang/OpenCV-algorithm
如果觉得《fitLine实现》对你有帮助，请点赞、收藏，并留下你的观点哦！
