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方法1：感觉鲁棒性刚好些，但在两点拟合时出现 x1 无限接近 x2 或者 y1 无限接近 y2 时效果不好。

方法2：不存在方法1的问题，但是鲁棒性感觉不如方法1好。

FitLine1(std::vector<cv::Point2f> & SrcPts, MV_Line & DstLine)
{int nSize = SrcPts.size();if (nSize < 2)return;// 这里注意，为了提高精度，需要判断拟合的方向 X 方向还是 Y 方向double dMinX = SrcPts[0].x, dMaxX = SrcPts[0].x;double dMinY = SrcPts[0].y, dMaxY = SrcPts[0].y;for (int i = 0; i < nSize; i++){if (SrcPts[i].x < dMinX)dMinX = SrcPts[i].x;if (SrcPts[i].x > dMaxX)dMaxX = SrcPts[i].x;if (SrcPts[i].y < dMinY)dMinY = SrcPts[i].y;if (SrcPts[i].y > dMaxY)dMaxY = SrcPts[i].y;}int nType = 0;//0=X方向 1=Y方向if (abs(dMaxY - dMinY) > abs(dMaxX - dMinX))nType = 1;// 如果 X 方向直接复制，如果 Y 方向则颠倒 X Y 坐标顺序std::vector<cv::Point2f> Pts;if (nType == 0){for (int i = 0; i < nSize; i++){Pts.push_back(cv::Point2f(SrcPts[i].x, SrcPts[i].y));}}else{for (int i = 0; i < nSize; i++){Pts.push_back(cv::Point2f(SrcPts[i].y, SrcPts[i].x));}}// 最小二乘法拟合double A = 0;double B = 0;double C = 0;double D = 0;double E = 0;double F = 0;double a = 0;double b = 0;double c = 0; double dYmean = 0;for (int i = 0; i < Pts.size(); i++){A += Pts[i].x * Pts[i].x;B += Pts[i].x;C += Pts[i].x * Pts[i].y;D += Pts[i].y;}dYmean = D / nSize;E = Pts.size() * A - B * B;a = Pts.size() * C - B * D;b = (-1) * E;c = A * D - B * C;// 计算结果DstLine.A = a / b;DstLine.B = 1;DstLine.C = c / b;DstLine.Error = 0;// 调整方向if (nType == 1){DstLine.A = 1;DstLine.B = a / b;DstLine.C = c / b;}DstLine.SSR = 0;DstLine.SSE = 0;DstLine.SST = 0;DstLine.R = 1;if (DstLine.A == 0 || DstLine.B == 0)return;for (int i = 0; i < nSize; i++){double dY = DstLine.Get_k() * SrcPts[i].x + DstLine.Get_b();double dError = dY - SrcPts[i].y;DstLine.SSR += std::pow(dY - dYmean, 2);DstLine.SSE += std::pow(dError, 2);DstLine.Error += std::abs(dError);}DstLine.SST = DstLine.SSR + DstLine.SSE;DstLine.R = 1 - DstLine.SSE / DstLine.SST;
}

void FitLine2(std::vector<cv::Point2f> & SrcPts, MV_Line & DstLine)
{int size = SrcPts.size();if (size < 2)return ;double x_mean = 0;double y_mean = 0;for (int i = 0; i < size; i++){x_mean += SrcPts[i].x;y_mean += SrcPts[i].y;}x_mean /= size;y_mean /= size; //至此，计算出了 x y 的均值double Dxx = 0, Dxy = 0, Dyy = 0;for (int i = 0; i < size; i++){Dxx += (SrcPts[i].x - x_mean) * (SrcPts[i].x - x_mean);Dxy += (SrcPts[i].x - x_mean) * (SrcPts[i].y - y_mean);Dyy += (SrcPts[i].y - y_mean) * (SrcPts[i].y - y_mean);}if (Dxx == 0){DstLine.A = 1;DstLine.B = 0;DstLine.C = x_mean * (-1);}else if (Dyy == 0){DstLine.A = 0;DstLine.B = 1;DstLine.C = y_mean * (-1);}else{double lambda = ((Dxx + Dyy) - sqrt((Dxx - Dyy) * (Dxx - Dyy) + 4 * Dxy * Dxy)) / 2.0;double den = sqrt(Dxy * Dxy + (lambda - Dxx) * (lambda - Dxx));DstLine.A = Dxy / den;DstLine.B = (lambda - Dxx) / den;DstLine.C = (-1)*DstLine.A * x_mean - DstLine.B * y_mean;}DstLine.SSR = 0;DstLine.SSE = 0;DstLine.SST = 0;DstLine.R = 1;if (DstLine.A * DstLine.B == 0)return;for (int i = 0; i < size; i++){double dY = DstLine.Get_k() * SrcPts[i].x + DstLine.Get_b();double dError = dY - SrcPts[i].y;DstLine.SSR += std::pow(dY - y_mean, 2);DstLine.SSE += std::pow(dError, 2);DstLine.Error += std::abs(dError);}DstLine.SST = DstLine.SSR + DstLine.SSE;DstLine.R = 1 - DstLine.SSE / DstLine.SST;return ;
}