SLAM之光流法代碼

單層光流

void OpticalFlowSingleLevel(
    const Mat &img1,
    const Mat &img2,
    const vector<KeyPoint> &kp1,
    vector<KeyPoint> &kp2,
    vector<bool> &success,
    bool inverse, bool has_initial) {
    kp2.resize(kp1.size());
    success.resize(kp1.size());
    OpticalFlowTracker tracker(img1, img2, kp1, kp2, success, inverse, has_initial);
    //並行
    parallel_for_(Range(0, kp1.size()),
                  std::bind(&OpticalFlowTracker::calculateOpticalFlow, &tracker, placeholders::_1));
}
void OpticalFlowTracker::calculateOpticalFlow(const Range &range){
    //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-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,kp.pt.y+dy+y-1))
                        );
                }else if(iter==0){
                    // in inverse mode, J keeps same for all iterations
                    // NOTE this 如果誤差函數e的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);
    }
    
}

calculateOpticalFlow是運用高斯牛頓法對$$min\sum_{i=1}^{N}(I_1(x_i,y_i)-I_2(x_i+dx,y_i+dy))$$優化的對象是\((dx,dy)\)。這就用光流法代替了描述子進行關鍵點之間的匹配。

多層光流

void OpticalFlowMultiLevel(
    const Mat &img1,
    const Mat &img2,
    const vector<KeyPoint> &kp1,
    vector<KeyPoint> &kp2,
    vector<bool> &success,
    bool inverse){
        //paramaters
        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<Mat>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<chrono::duration<double>>(t2 - t1);
        cout << "build pyramid time: " << time_used.count() << endl;
        //coarse-to-fine LK tracking in pyramids
        vector<KeyPoint>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<chrono::duration<double>>(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);
}

多層光流法，是爲了解決相機運動過慢或過快導致兩幀圖像差異在像素上的體現太不明顯或者太明顯這一問題，它的基礎思路是圖像金字塔。

整體代碼

#include <opencv2/opencv.hpp>
#include <string>
#include <chrono>
#include <Eigen/Core>
#include <Eigen/Dense>

using namespace std;
using namespace cv;

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

///Optical flow tracker and interface
class OpticalFlowTracker{
public:
    OpticalFlowTracker(
        const Mat &img1_,
        const Mat &img2_,
        const vector<KeyPoint>&kp1_,
        vector<KeyPoint>&kp2_,
        vector<bool>&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);
private:
    const Mat &img1;
    const Mat &img2;
    const vector<KeyPoint>&kp1;
    vector<KeyPoint>&kp2;
    vector<bool>&success;
    bool inverse=true;
    bool has_initial=false;
};
/**
 * 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<KeyPoint> &kp1,
    vector<KeyPoint> &kp2,
    vector<bool> &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<KeyPoint> &kp1,
    vector<KeyPoint> &kp2,
    vector<bool> &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 - 1) x = img.cols - 2;
    if (y >= img.rows - 1) y = img.rows - 2;
    
    float xx = x - floor(x);
    float yy = y - floor(y);
    int x_a1 = std::min(img.cols - 1, int(x) + 1);
    int y_a1 = std::min(img.rows - 1, int(y) + 1);
    
    return (1 - xx) * (1 - yy) * img.at<uchar>(y, x)
    + xx * (1 - yy) * img.at<uchar>(y, x_a1)
    + (1 - xx) * yy * img.at<uchar>(y_a1, x)
    + xx * yy * img.at<uchar>(y_a1, x_a1);
}
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<KeyPoint> kp1;
    Ptr<GFTTDetector> 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<KeyPoint> kp2_single;
    vector<bool> success_single;
    OpticalFlowSingleLevel(img1, img2, kp1, kp2_single, success_single);

    // then test multi-level LK
    vector<KeyPoint> kp2_multi;
    vector<bool> 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<chrono::duration<double>>(t2 - t1);
    cout << "optical flow by gauss-newton: " << time_used.count() << endl;

    // use opencv's flow for validation
    vector<Point2f> pt1, pt2;
    for (auto &kp: kp1) pt1.push_back(kp.pt);
    vector<uchar> status;
    vector<float> error;
    t1 = chrono::steady_clock::now();
    cv::calcOpticalFlowPyrLK(img1, img2, pt1, pt2, status, error);
    t2 = chrono::steady_clock::now();
    time_used = chrono::duration_cast<chrono::duration<double>>(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<KeyPoint> &kp1,
    vector<KeyPoint> &kp2,
    vector<bool> &success,
    bool inverse, bool has_initial) {
    kp2.resize(kp1.size());
    success.resize(kp1.size());
    OpticalFlowTracker tracker(img1, img2, kp1, kp2, success, inverse, has_initial);
    //並行
    parallel_for_(Range(0, kp1.size()),
                  std::bind(&OpticalFlowTracker::calculateOpticalFlow, &tracker, placeholders::_1));
}
void OpticalFlowTracker::calculateOpticalFlow(const Range &range){
    //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-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,kp.pt.y+dy+y-1))
                        );
                }else if(iter==0){
                    // in inverse mode, J keeps same for all iterations
                    // NOTE this 如果誤差函數e的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);
    }
    
}
void OpticalFlowMultiLevel(
    const Mat &img1,
    const Mat &img2,
    const vector<KeyPoint> &kp1,
    vector<KeyPoint> &kp2,
    vector<bool> &success,
    bool inverse){
        //paramaters
        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<Mat>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<chrono::duration<double>>(t2 - t1);
        cout << "build pyramid time: " << time_used.count() << endl;
        //coarse-to-fine LK tracking in pyramids
        vector<KeyPoint>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<chrono::duration<double>>(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);
}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)
project(ch8)

set(CMAKE_BUILD_TYPE "Release")
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS}
"/usr/include/eigen3")

add_executable(optical_flow optical_flow.cpp)
target_link_libraries(optical_flow ${OpenCV_LIBS})