RANSC拟合最优直线，c++实现

直接上代码：

    // 基于RANSAC算法的直线拟合
    // pstData: 指向存储数据的指针
    // dataCnt: 数据点个数
    // lineParameterK: 直线的斜率
    // lineParameterB: 直线的截距
    // minCnt: 模型(直线)参数估计所需的数据点的个数
    // maxIterCnt: 最大迭代次数
    // maxErrorThreshold: 最大误差阈值
    // consensusCntThreshold: 模型一致性判断准则
    // modelMeanError: 模型误差
    // 返回值： 返回0表示获取最优模型， 否则表示未获取最优模型
    //if(!ransacLiner(dataPoints, totalCnt, 2, 50, 35, 0.1, A, B, C, meanError))

    int ransacLiner(st_Point* pstData, int dataCnt, int minCnt, double maxIterCnt, int consensusCntThreshold,
                            double maxErrorThreshold, double& A, double& B, double& C, double& modelMeanError, 
                            set &consensusIndexs)
    {
        default_random_engine rng;
        uniform_int_distribution uniform(0, dataCnt - 1);
        rng.seed(8); // 固定随机数种子
        set selectIndexs;     // 选择的点的索引
        vector selectPoints;      // 选择的点

        double temp_A = 0; 
        double temp_B = 0;
        double temp_C = 0;

        modelMeanError = 0;
        int isNonFind  = 1;
        unsigned int      bestConsensusCnt = 0; // 满足一致性估计的点的个数
        int iter = 0;
        while(iter < maxIterCnt)
        {
            selectIndexs.clear();
            selectPoints.clear();

            // Step1: 随机选择minCnt个点
            while(1)
            {
                unsigned int index = uniform(rng);
                selectIndexs.insert(index);
                if(selectIndexs.size() == minCnt)
                {
                    cout << "selectIndexs.size() == minCnt: " << endl;
                    break;
                }
            }

            // Step2: 进行模型参数估计 (y2 - y1)*x - (x2 - x1)*y + (y2 - y1)x2 - (x2 - x1)y2= 0
            set::iterator selectIter = selectIndexs.begin();
            while(selectIter != selectIndexs.end())
            {
                unsigned int index = *selectIter;
                selectPoints.push_back(pstData[index]);
                selectIter++;
            }
            double deltaY = (selectPoints[1]).y - (selectPoints[0]).y;
            double deltaX = (selectPoints[1]).x - (selectPoints[0]).x;
            temp_A = deltaY;
            temp_B = -deltaX;
            temp_C = -deltaY * (selectPoints[1]).x + deltaX * (selectPoints[1]).y;



            // Step3: 进行模型评估: 点到直线的距离
            int dataIter = 0;
            double meanError = 0;
            set tmpConsensusIndexs;
            while(dataIter < dataCnt)
            {
                double distance =  
                    (temp_A * pstData[dataIter].x + temp_B * pstData[dataIter].y + temp_C) / sqrt(temp_A*temp_A + temp_B*temp_B);
                distance = distance > 0 ? distance : -distance;
                if(distance <= maxErrorThreshold)
                {
                    tmpConsensusIndexs.insert(dataIter);
                }
                meanError += distance;
                dataIter++;
            }

            // Step4: 判断一致性: 满足一致性集合的最小元素个数条件 + 至少比上一次的好
            if(tmpConsensusIndexs.size() >= consensusCntThreshold && tmpConsensusIndexs.size() >= bestConsensusCnt )
            {
                A = temp_A;
                B = temp_B;
                C = temp_C;
                bestConsensusCnt = consensusIndexs.size();  // 更新一致性索引集合元素个数
                modelMeanError = meanError / dataCnt;
                consensusIndexs.clear();
                consensusIndexs = tmpConsensusIndexs;        // 更新一致性索引集合
                isNonFind = 0;

                cout << "bestConsensusCnt: " << bestConsensusCnt << endl;
                cout << "tmpConsensusIndexs: " << tmpConsensusIndexs.size() << endl;
            }



            iter++;
        }
    return isNonFind;
    }

参考链接：https://blog.csdn.net/hit1524468/article/details/80375495

参考链接代码有些小问题，本文中与参考链接代码稍有不同，ABC需要在函数外边定义，并将函数内ABC修改为temp_A、temp_B、temp_C，然后找到最优直线，将最优直线的参数temp_A、temp_B、temp_更新到ABC，以此来保存最优直线的参数。