相机模型（三维重建-------3【任务3】代码）

int main(int argc, char *argv[]){

    /** 加载归一化后的匹配对 */
    sfm::Correspondences2D2D corr_all;
    std::ifstream in("./examples/task2/correspondences.txt");
    assert(in.is_open());

    std::string line, word;
    int n_line = 0;
    while(getline(in, line)){

        std::stringstream stream(line);
        if(n_line==0){
            int n_corrs = 0;
            stream>> n_corrs;
            corr_all.resize(n_corrs);

            n_line ++;
            continue;
        }
        if(n_line>0){

            stream>>corr_all[n_line-1].p1[0]>>corr_all[n_line-1].p1[1]
                  >>corr_all[n_line-1].p2[0]>>corr_all[n_line-1].p2[1];
        }
        n_line++;
    }

    /* 计算采用次数 */
    const float inlier_ratio =0.5;
    const int n_samples=8;
    int n_iterations = calc_ransac_iterations(inlier_ratio, n_samples);

    // 用于判读匹配对是否为内点
    const double inlier_thresh = 0.0015;

    // ransac 最终估计的内点
    std::vector<int> best_inliers;

    std::cout << "RANSAC-F: Running for " << n_iterations
              << " iterations, threshold " << inlier_thresh
              << "..." << std::endl;
    for(int i=0; i<n_iterations; i++){

        /* 1.0 随机找到8对不重复的匹配点 */
        std::set<int> indices;
        while(indices.size()<8){
            indices.insert(util::system::rand_int() % corr_all.size());
        }

        math::Matrix<double, 3, 8> pset1, pset2;
        std::set<int>::const_iterator iter = indices.cbegin();
        for(int j=0; j<8; j++, iter++){
            sfm::Correspondence2D2D const & match = corr_all[*iter];

            pset1(0, j) = match.p1[0];
            pset1(1, j) = match.p1[1];
            pset1(2, j) = 1.0;

            pset2(0, j) = match.p2[0];
            pset2(1, j) = match.p2[1];
            pset2(2, j) = 1.0;
        }

        /*2.0 8点法估计相机基础矩阵*/
        FundamentalMatrix F;
        calc_fundamental_8_point(pset1, pset2,F);

        /*3.0 统计所有的内点个数*/
        std::vector<int> inlier_indices = find_inliers(corr_all, F, inlier_thresh);

        if(inlier_indices.size()> best_inliers.size()){

//            std::cout << "RANSAC-F: Iteration " << i
//                      << ", inliers " << inlier_indices.size() << " ("
//                      << (100.0 * inlier_indices.size() / corr_all.size())
//                      << "%)" << std::endl;
            best_inliers.swap(inlier_indices);
        }
    }

    sfm::Correspondences2D2D corr_f;
    for(int i=0; i< best_inliers.size(); i++){
        corr_f.push_back(corr_all[best_inliers[i]]);
    }

    /*利用所有的内点进行最小二乘估计*/
    FundamentalMatrix F;
    calc_fundamental_least_squares(corr_f, F);

    std::cout<<"inlier number: "<< best_inliers.size()<<std::endl;
    std::cout<<"F\n: "<< F<<std::endl;

    std::cout<<"result should be: \n"
             <<"inliner number: 272\n"
             <<"F: \n"
             <<"-0.00961384 -0.0309071 0.703297\n"
             <<"0.0448265 -0.00158655 -0.0555796\n"
             <<"-0.703477 0.0648517 -0.0117791\n";

    return 0;
}