高翔《视觉SLAM十四讲》第8讲ch8视觉里程计2实践代码报错解决方法-不用安装opencv4

 一、终端报错

 在运行14讲第二版的第8讲的程序时，出现了以下错误：

原因是 parallel_for_函数出现错误，这是因为opencv3不支持std::bind函数，作者写书的时候忘提这件事了，后来在勘误里加上了，可以自己安装opencv 4，或者自行改写下parallel_for_函数。(高翔回答https://github.com/gaoxiang12/slambook2/issues/57)

二、解决方法

有两种解决方法：

1.安装opencv4

2.修改optical_flow.cpp与direct_method.cpp程序（本人采用第二种方法）

     继续深入查询发现已经有人分享能使用opencv3运行的direct_method.cpp（https://github.com/gaoxiang12/slambook2/issues/32），但没有找到修改好的optical_flow.cpp。本人对optical_flow.cpp进行修改，下面分享修改好的两个文件：     

     direct_method.cpp（转自https://github.com/rancheng/slambook2/blob/opencv3_2_0/ch8/direct_method.cpp）：

#include 
#include 
#include 
#include 

using namespace std;

typedef vector> VecVector2d;

// Camera intrinsics
double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157;
// baseline
double baseline = 0.573;
// paths
string left_file = "./left.png";
string disparity_file = "./disparity.png";
boost::format fmt_others("./%06d.png");    // other files

// useful typedefs
typedef Eigen::Matrix Matrix6d;
typedef Eigen::Matrix Matrix26d;
typedef Eigen::Matrix Vector6d;

/**
 * pose estimation using direct method
 * @param img1
 * @param img2
 * @param px_ref
 * @param depth_ref
 * @param T21
 */
void DirectPoseEstimationMultiLayer(
        const cv::Mat &img1,
        const cv::Mat &img2,
        const VecVector2d &px_ref,
        const vector depth_ref,
        Sophus::SE3d &T21
);

/**
 * pose estimation using direct method
 * @param img1
 * @param img2
 * @param px_ref
 * @param depth_ref
 * @param T21
 */
void DirectPoseEstimationSingleLayer(
        const cv::Mat &img1,
        const cv::Mat &img2,
        const VecVector2d &px_ref,
        const vector depth_ref,
        Sophus::SE3d &T21
);

// bilinear interpolation
inline float GetPixelValue(const cv::Mat &img, float x, float y) {
    // boundary check
    if (x < 0) x = 0;
    if (y < 0) y = 0;
    if (x >= img.cols) x = img.cols - 1;
    if (y >= img.rows) y = img.rows - 1;
    uchar *data = &img.data[int(y) * img.step + int(x)];
    float xx = x - floor(x);
    float yy = y - floor(y);
    return float(
            (1 - xx) * (1 - yy) * data[0] +
            xx * (1 - yy) * data[1] +
            (1 - xx) * yy * data[img.step] +
            xx * yy * data[img.step + 1]
    );
}

/// class for accumulator jacobians in parallel
class JacobianAccumulator: public cv::ParallelLoopBody {
private:
    const cv::Mat &img1;
    const cv::Mat &img2;
    const VecVector2d &px_ref;
    const vector depth_ref;
    Sophus::SE3d &T21;
    mutable VecVector2d projection; // projected points

    mutable std::mutex hessian_mutex;
    mutable Matrix6d H = Matrix6d::Zero();
    mutable Vector6d b = Vector6d::Zero();
    mutable double cost = 0;

public:
    JacobianAccumulator(
        const cv::Mat &img1_,
        const cv::Mat &img2_,
        const VecVector2d &px_ref_,
        const vector depth_ref_,
        Sophus::SE3d &T21_) :
        img1(img1_), img2(img2_), px_ref(px_ref_), depth_ref(depth_ref_), T21(T21_) {
        projection = VecVector2d(px_ref.size(), Eigen::Vector2d(0, 0));
    }

    /// accumulate jacobians in a range
//    void accumulate_jacobian(const cv::Range &range);


    /// get hessian matrix
    Matrix6d hessian() const { return H; }

    /// get bias
    Vector6d bias() const { return b; }

    /// get total cost
    double cost_func() const { return cost; }

    /// get projected points
    VecVector2d projected_points() const { return projection; }

    /// reset h, b, cost to zero
    void reset() {
        H = Matrix6d::Zero();
        b = Vector6d::Zero();
        cost = 0;
    }

    virtual void operator()(const cv::Range& range) const {

        // parameters
        const int half_patch_size = 1;
        int cnt_good = 0;
        Matrix6d hessian = Matrix6d::Zero();
        Vector6d bias = Vector6d::Zero();
        double cost_tmp = 0;

        for (size_t i = range.start; i < range.end; i++) {

            // compute the projection in the second image
            Eigen::Vector3d point_ref =
                    depth_ref[i] * Eigen::Vector3d((px_ref[i][0] - cx) / fx, (px_ref[i][1] - cy) / fy, 1);
            Eigen::Vector3d point_cur = T21 * point_ref;
            if (point_cur[2] < 0)   // depth invalid
                continue;

            float u = fx * point_cur[0] / point_cur[2] + cx, v = fy * point_cur[1] / point_cur[2] + cy;
            if (u < half_patch_size || u > img2.cols - half_patch_size || v < half_patch_size ||
                v > img2.rows - half_patch_size)
                continue;

            projection[i] = Eigen::Vector2d(u, v);
            double X = point_cur[0], Y = point_cur[1], Z = point_cur[2],
                    Z2 = Z * Z, Z_inv = 1.0 / Z, Z2_inv = Z_inv * Z_inv;
            cnt_good++;

            // and compute error and jacobian
            for (int x = -half_patch_size; x <= half_patch_size; x++)
                for (int y = -half_patch_size; y <= half_patch_size; y++) {

                    double error = GetPixelValue(img1, px_ref[i][0] + x, px_ref[i][1] + y) -
                                   GetPixelValue(img2, u + x, v + y);
                    Matrix26d J_pixel_xi;
                    Eigen::Vector2d J_img_pixel;

                    J_pixel_xi(0, 0) = fx * Z_inv;
                    J_pixel_xi(0, 1) = 0;
                    J_pixel_xi(0, 2) = -fx * X * Z2_inv;
                    J_pixel_xi(0, 3) = -fx * X * Y * Z2_inv;
                    J_pixel_xi(0, 4) = fx + fx * X * X * Z2_inv;
                    J_pixel_xi(0, 5) = -fx * Y * Z_inv;

                    J_pixel_xi(1, 0) = 0;
                    J_pixel_xi(1, 1) = fy * Z_inv;
                    J_pixel_xi(1, 2) = -fy * Y * Z2_inv;
                    J_pixel_xi(1, 3) = -fy - fy * Y * Y * Z2_inv;
                    J_pixel_xi(1, 4) = fy * X * Y * Z2_inv;
                    J_pixel_xi(1, 5) = fy * X * Z_inv;

                    J_img_pixel = Eigen::Vector2d(
                            0.5 * (GetPixelValue(img2, u + 1 + x, v + y) - GetPixelValue(img2, u - 1 + x, v + y)),
                            0.5 * (GetPixelValue(img2, u + x, v + 1 + y) - GetPixelValue(img2, u + x, v - 1 + y))
                    );

                    // total jacobian
                    Vector6d J = -1.0 * (J_img_pixel.transpose() * J_pixel_xi).transpose();

                    hessian += J * J.transpose();
                    bias += -error * J;
                    cost_tmp += error * error;
                }
        }

        if (cnt_good) {
            // set hessian, bias and cost
            unique_lock lck(hessian_mutex);
            H += hessian;
            b += bias;
            cost += cost_tmp / cnt_good;
        }
    }


};



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

    cv::Mat left_img = cv::imread(left_file, 0);
    cv::Mat disparity_img = cv::imread(disparity_file, 0);
    if (left_img.empty() || disparity_img.empty())
    {
        std::cout << "!!! Failed imread(): image not found" << std::endl;
        return 1;
    }
    // let's randomly pick pixels in the first image and generate some 3d points in the first image's frame
    cv::RNG rng;
    int nPoints = 2000;
    int boarder = 20;
    VecVector2d pixels_ref;
    vector depth_ref;
    cout << "left_img.cols" << left_img.cols << endl;
    cout << "left_img: " << left_img << endl;
    // generate pixels in ref and load depth data
    for (int i = 0; i < nPoints; i++) {
        int x = rng.uniform(boarder, left_img.cols - boarder);  // don't pick pixels close to boarder
        int y = rng.uniform(boarder, left_img.rows - boarder);  // don't pick pixels close to boarder
        int disparity = disparity_img.at(y, x);
        double depth = fx * baseline / disparity; // you know this is disparity to depth
        depth_ref.push_back(depth);
        pixels_ref.push_back(Eigen::Vector2d(x, y));
    }

    // estimates 01~05.png's pose using this information
    Sophus::SE3d T_cur_ref;

    for (int i = 1; i < 6; i++) {  // 1~10
        cv::Mat img = cv::imread((fmt_others % i).str(), 0);
        // try single layer by uncomment this line
        // DirectPoseEstimationSingleLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref);
        DirectPoseEstimationMultiLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref);
    }
    return 0;
}

void DirectPoseEstimationSingleLayer(
    const cv::Mat &img1,
    const cv::Mat &img2,
    const VecVector2d &px_ref,
    const vector depth_ref,
    Sophus::SE3d &T21) {

    const int iterations = 10;
    double cost = 0, lastCost = 0;
    auto t1 = chrono::steady_clock::now();
    JacobianAccumulator jaco_accu(img1, img2, px_ref, depth_ref, T21);

    for (int iter = 0; iter < iterations; iter++) {
        jaco_accu.reset();
        cv::parallel_for_(cv::Range(0, px_ref.size()), jaco_accu);
        Matrix6d H = jaco_accu.hessian();
        Vector6d b = jaco_accu.bias();

        // solve update and put it into estimation
        Vector6d update = H.ldlt().solve(b);;
        T21 = Sophus::SE3d::exp(update) * T21;
        cost = jaco_accu.cost_func();

        if (std::isnan(update[0])) {
            // sometimes occurred when we have a black or white patch and H is irreversible
            cout << "update is nan" << endl;
            break;
        }
        if (iter > 0 && cost > lastCost) {
            cout << "cost increased: " << cost << ", " << lastCost << endl;
            break;
        }
        if (update.norm() < 1e-3) {
            // converge
            break;
        }

        lastCost = cost;
        cout << "iteration: " << iter << ", cost: " << cost << endl;
    }

    cout << "T21 = \n" << T21.matrix() << endl;
    auto t2 = chrono::steady_clock::now();
    auto time_used = chrono::duration_cast>(t2 - t1);
    cout << "direct method for single layer: " << time_used.count() << endl;

    // plot the projected pixels here
    cv::Mat img2_show;
    cv::cvtColor(img2, img2_show, CV_GRAY2BGR);
    VecVector2d projection = jaco_accu.projected_points();
    for (size_t i = 0; i < px_ref.size(); ++i) {
        auto p_ref = px_ref[i];
        auto p_cur = projection[i];
        if (p_cur[0] > 0 && p_cur[1] > 0) {
            cv::circle(img2_show, cv::Point2f(p_cur[0], p_cur[1]), 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_show, cv::Point2f(p_ref[0], p_ref[1]), cv::Point2f(p_cur[0], p_cur[1]),
                     cv::Scalar(0, 250, 0));
        }
    }
    cv::imshow("current", img2_show);
    cv::waitKey();
}


void DirectPoseEstimationMultiLayer(
    const cv::Mat &img1,
    const cv::Mat &img2,
    const VecVector2d &px_ref,
    const vector depth_ref,
    Sophus::SE3d &T21) {

    // parameters
    int pyramids = 4;
    double pyramid_scale = 0.5;
    double scales[] = {1.0, 0.5, 0.25, 0.125};

    // create pyramids
    vector pyr1, pyr2; // image pyramids
    for (int i = 0; i < pyramids; i++) {
        if (i == 0) {
            pyr1.push_back(img1);
            pyr2.push_back(img2);
        } else {
            cv::Mat img1_pyr, img2_pyr;
            cv::resize(pyr1[i - 1], img1_pyr,
                       cv::Size(pyr1[i - 1].cols * pyramid_scale, pyr1[i - 1].rows * pyramid_scale));
            cv::resize(pyr2[i - 1], img2_pyr,
                       cv::Size(pyr2[i - 1].cols * pyramid_scale, pyr2[i - 1].rows * pyramid_scale));
            pyr1.push_back(img1_pyr);
            pyr2.push_back(img2_pyr);
        }
    }

    double fxG = fx, fyG = fy, cxG = cx, cyG = cy;  // backup the old values
    for (int level = pyramids - 1; level >= 0; level--) {
        VecVector2d px_ref_pyr; // set the keypoints in this pyramid level
        for (auto &px: px_ref) {
            px_ref_pyr.push_back(scales[level] * px);
        }

        // scale fx, fy, cx, cy in different pyramid levels
        fx = fxG * scales[level];
        fy = fyG * scales[level];
        cx = cxG * scales[level];
        cy = cyG * scales[level];
        DirectPoseEstimationSingleLayer(pyr1[level], pyr2[level], px_ref_pyr, depth_ref, T21);
    }

}

     optical_flow.cpp(修改后)

#include 
#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;

string file_1 = "./LK1.png";  // first image
string file_2 = "./LK2.png";  // second image

/**
 * single level optical flow
 * @param [in] img1 the first image
 * @param [in] img2 the second image
 * @param [in] kp1 keypoints in img1
 * @param [in|out] kp2 keypoints in img2, if empty, use initial guess in kp1
 * @param [out] success true if a keypoint is tracked successfully
 * @param [in] inverse use inverse formulation?
 */
void OpticalFlowSingleLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse = false,
    bool has_initial_guess = false
);

/**
 * multi level optical flow, scale of pyramid is set to 2 by default
 * the image pyramid will be create inside the function
 * @param [in] img1 the first pyramid
 * @param [in] img2 the second pyramid
 * @param [in] kp1 keypoints in img1
 * @param [out] kp2 keypoints in img2
 * @param [out] success true if a keypoint is tracked successfully
 * @param [in] inverse set true to enable inverse formulation
 */
void OpticalFlowMultiLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse = false
);

/**
 * get a gray scale value from reference image (bi-linear interpolated)
 * @param img
 * @param x
 * @param y
 * @return the interpolated value of this pixel
 */
inline float GetPixelValue(const cv::Mat &img, float x, float y) {
    // boundary check
    if (x < 0) x = 0;
    if (y < 0) y = 0;
    if (x >= img.cols) x = img.cols - 1;
    if (y >= img.rows) y = img.rows - 1;
    uchar *data = &img.data[int(y) * img.step + int(x)];
    float xx = x - floor(x);
    float yy = y - floor(y);
    return float(
        (1 - xx) * (1 - yy) * data[0] +
        xx * (1 - yy) * data[1] +
        (1 - xx) * yy * data[img.step] +
        xx * yy * data[img.step + 1]
    );
}

/// Optical flow tracker and interface
class OpticalFlowTracker: public cv::ParallelLoopBody  {
private:
    const Mat &img1;
    const Mat &img2;
    const vector &kp1;
    vector &kp2;
    vector &success;
    bool inverse = true;
    bool has_initial = false;

public:
    OpticalFlowTracker(
        const Mat &img1_,
        const Mat &img2_,
        const vector &kp1_,
        vector &kp2_,
        vector &success_,
        bool inverse_ = true, bool has_initial_ = false) :
        img1(img1_), img2(img2_), kp1(kp1_), kp2(kp2_), success(success_), inverse(inverse_),
        has_initial(has_initial_) {}

    //  void calculateOpticalFlow(const Range &range);

    virtual void operator()(const Range &range) const {
        // parameters
        int half_patch_size = 4;
        int iterations = 10;
        for (size_t i = range.start; i < range.end; i++) {
            auto kp = kp1[i];
            double dx = 0, dy = 0; // dx,dy need to be estimated
            if (has_initial) {
                dx = kp2[i].pt.x - kp.pt.x;
                dy = kp2[i].pt.y - kp.pt.y;
            }

            double cost = 0, lastCost = 0;
            bool succ = true; // indicate if this point succeeded

            // Gauss-Newton iterations
            Eigen::Matrix2d H = Eigen::Matrix2d::Zero();    // hessian
            Eigen::Vector2d b = Eigen::Vector2d::Zero();    // bias
            Eigen::Vector2d J;  // jacobian
            for (int iter = 0; iter < iterations; iter++) {
                if (inverse == false) {
                    H = Eigen::Matrix2d::Zero();
                    b = Eigen::Vector2d::Zero();
                } else {
                    // only reset b
                    b = Eigen::Vector2d::Zero();
                }

                cost = 0;

                // compute cost and jacobian
                for (int x = -half_patch_size; x < half_patch_size; x++)
                    for (int y = -half_patch_size; y < half_patch_size; y++) {
                        double error = GetPixelValue(img1, kp.pt.x + x, kp.pt.y + y) -
                                    GetPixelValue(img2, kp.pt.x + x + dx, kp.pt.y + y + dy);;  // Jacobian
                        if (inverse == false) {
                            J = -1.0 * Eigen::Vector2d(
                                0.5 * (GetPixelValue(img2, kp.pt.x + dx + x + 1, kp.pt.y + dy + y) -
                                    GetPixelValue(img2, kp.pt.x + dx + x - 1, kp.pt.y + dy + y)),
                                0.5 * (GetPixelValue(img2, kp.pt.x + dx + x, kp.pt.y + dy + y + 1) -
                                    GetPixelValue(img2, kp.pt.x + dx + x, kp.pt.y + dy + y - 1))
                            );
                        } else if (iter == 0) {
                            // in inverse mode, J keeps same for all iterations
                            // NOTE this J does not change when dx, dy is updated, so we can store it and only compute error
                            J = -1.0 * Eigen::Vector2d(
                                0.5 * (GetPixelValue(img1, kp.pt.x + x + 1, kp.pt.y + y) -
                                    GetPixelValue(img1, kp.pt.x + x - 1, kp.pt.y + y)),
                                0.5 * (GetPixelValue(img1, kp.pt.x + x, kp.pt.y + y + 1) -
                                    GetPixelValue(img1, kp.pt.x + x, kp.pt.y + y - 1))
                            );
                        }
                        // compute H, b and set cost;
                        b += -error * J;
                        cost += error * error;
                        if (inverse == false || iter == 0) {
                            // also update H
                            H += J * J.transpose();
                        }
                    }

                // compute update
                Eigen::Vector2d update = H.ldlt().solve(b);

                if (std::isnan(update[0])) {
                    // sometimes occurred when we have a black or white patch and H is irreversible
                    cout << "update is nan" << endl;
                    succ = false;
                    break;
                }

                if (iter > 0 && cost > lastCost) {
                    break;
                }

                // update dx, dy
                dx += update[0];
                dy += update[1];
                lastCost = cost;
                succ = true;

                if (update.norm() < 1e-2) {
                    // converge
                    break;
                }
            }

            success[i] = succ;

            // set kp2
            kp2[i].pt = kp.pt + Point2f(dx, dy);
        }
    }
};


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

    // images, note they are CV_8UC1, not CV_8UC3
    Mat img1 = imread(file_1, 0);
    Mat img2 = imread(file_2, 0);

    // key points, using GFTT here.
    vector kp1;
    Ptr detector = GFTTDetector::create(500, 0.01, 20); // maximum 500 keypoints
    detector->detect(img1, kp1);

    // now lets track these key points in the second image
    // first use single level LK in the validation picture
    vector kp2_single;
    vector success_single;
    OpticalFlowSingleLevel(img1, img2, kp1, kp2_single, success_single);

    // then test multi-level LK
    vector kp2_multi;
    vector success_multi;
    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    OpticalFlowMultiLevel(img1, img2, kp1, kp2_multi, success_multi, true);
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    auto time_used = chrono::duration_cast>(t2 - t1);
    cout << "optical flow by gauss-newton: " << time_used.count() << endl;

    // use opencv's flow for validation
    vector pt1, pt2;
    for (auto &kp: kp1) pt1.push_back(kp.pt);
    vector status;
    vector error;
    t1 = chrono::steady_clock::now();
    cv::calcOpticalFlowPyrLK(img1, img2, pt1, pt2, status, error);
    t2 = chrono::steady_clock::now();
    time_used = chrono::duration_cast>(t2 - t1);
    cout << "optical flow by opencv: " << time_used.count() << endl;

    // plot the differences of those functions
    Mat img2_single;
    cv::cvtColor(img2, img2_single, CV_GRAY2BGR);
    for (int i = 0; i < kp2_single.size(); i++) {
        if (success_single[i]) {
            cv::circle(img2_single, kp2_single[i].pt, 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_single, kp1[i].pt, kp2_single[i].pt, cv::Scalar(0, 250, 0));
        }
    }

    Mat img2_multi;
    cv::cvtColor(img2, img2_multi, CV_GRAY2BGR);
    for (int i = 0; i < kp2_multi.size(); i++) {
        if (success_multi[i]) {
            cv::circle(img2_multi, kp2_multi[i].pt, 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_multi, kp1[i].pt, kp2_multi[i].pt, cv::Scalar(0, 250, 0));
        }
    }

    Mat img2_CV;
    cv::cvtColor(img2, img2_CV, CV_GRAY2BGR);
    for (int i = 0; i < pt2.size(); i++) {
        if (status[i]) {
            cv::circle(img2_CV, pt2[i], 2, cv::Scalar(0, 250, 0), 2);
            cv::line(img2_CV, pt1[i], pt2[i], cv::Scalar(0, 250, 0));
        }
    }

    cv::imshow("tracked single level", img2_single);
    cv::imshow("tracked multi level", img2_multi);
    cv::imshow("tracked by opencv", img2_CV);
    cv::waitKey(0);

    return 0;
}

void OpticalFlowSingleLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse, bool has_initial) {
    kp2.resize(kp1.size());
    success.resize(kp1.size());
    OpticalFlowTracker tracker(img1, img2, kp1, kp2, success, inverse, has_initial);
    parallel_for_(Range(0, kp1.size()), tracker);
}



void OpticalFlowMultiLevel(
    const Mat &img1,
    const Mat &img2,
    const vector &kp1,
    vector &kp2,
    vector &success,
    bool inverse) {

    // parameters
    int pyramids = 4;
    double pyramid_scale = 0.5;
    double scales[] = {1.0, 0.5, 0.25, 0.125};

    // create pyramids
    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    vector pyr1, pyr2; // image pyramids
    for (int i = 0; i < pyramids; i++) {
        if (i == 0) {
            pyr1.push_back(img1);
            pyr2.push_back(img2);
        } else {
            Mat img1_pyr, img2_pyr;
            cv::resize(pyr1[i - 1], img1_pyr,
                       cv::Size(pyr1[i - 1].cols * pyramid_scale, pyr1[i - 1].rows * pyramid_scale));
            cv::resize(pyr2[i - 1], img2_pyr,
                       cv::Size(pyr2[i - 1].cols * pyramid_scale, pyr2[i - 1].rows * pyramid_scale));
            pyr1.push_back(img1_pyr);
            pyr2.push_back(img2_pyr);
        }
    }
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    auto time_used = chrono::duration_cast>(t2 - t1);
    cout << "build pyramid time: " << time_used.count() << endl;

    // coarse-to-fine LK tracking in pyramids
    vector kp1_pyr, kp2_pyr;
    for (auto &kp:kp1) {
        auto kp_top = kp;
        kp_top.pt *= scales[pyramids - 1];
        kp1_pyr.push_back(kp_top);
        kp2_pyr.push_back(kp_top);
    }

    for (int level = pyramids - 1; level >= 0; level--) {
        // from coarse to fine
        success.clear();
        t1 = chrono::steady_clock::now();
        OpticalFlowSingleLevel(pyr1[level], pyr2[level], kp1_pyr, kp2_pyr, success, inverse, true);
        t2 = chrono::steady_clock::now();
        auto time_used = chrono::duration_cast>(t2 - t1);
        cout << "track pyr " << level << " cost time: " << time_used.count() << endl;

        if (level > 0) {
            for (auto &kp: kp1_pyr)
                kp.pt /= pyramid_scale;
            for (auto &kp: kp2_pyr)
                kp.pt /= pyramid_scale;
        }
    }

    for (auto &kp: kp2_pyr)
        kp2.push_back(kp);
}

将两个文件修改后可以在不安装opencv4的情况下编译通过。

三、结果

编译：

 运行：optical_flow

./build/optical_flow 

  运行：direct_method

./build/direct_method