Remove unused code

include/LucasKanadeTracker.h

@@ -1,60 +0,0 @@
-//*****************************************************************
-// File:    LucasKanadeTracker.h
-// Author:  Juan José Gómez Rodríguez (jjgomez96@hotmail.com)
-// Date:    01/05/2019
-// Coms:    Header file of the Lucas-Kanade optical flow algorithm
-//*****************************************************************
-
-#ifndef KLT_H
-#define KLT_H
-
-#include<opencv2/core/core.hpp>
-#include "opencv2/opencv.hpp"
-
-class LucasKanadeTracker {
-public:
-    /*
-     * Default constructor
-     */
-    LucasKanadeTracker();
-
-    /*
-     * Constructor with parameters
-     */
-    LucasKanadeTracker(const cv::Size _winSize, const int _maxLevel, const int _maxIters,
-                       const float _epsilon, const float _minEigThreshold);
-
-    /*
-     *
-     */
-    void SetReferenceImage(cv::Mat &refIm, std::vector<cv::Point2f> &refPts);
-
-    int PRE_Track(cv::Mat &newIm,
-                   std::vector<cv::Point2f> &nextPts, std::vector<bool> &status, const bool bInitialFlow,
-                   const bool bCheckLength);
-
-    void DeleteRefPt(const int idx);
-    cv::Point2f GetPrevPoint(const int idx);
-
-private:
-    //-------------------------------
-    //        KLT parameters
-    //-------------------------------
-    cv::Size winSize;      //size of the integration window of the Lucas-Kanade algorithm
-    int maxLevel;           //max level of the image pyramids
-    int maxIters;           //max number of iterations of the optical flow algorithm
-    float epsilon;          //minimum optical flow imposed displacement. If lower, we stop computing
-    float minEigThreshold;  //min eigen threshold value for the Spatial Gradient matrix
-
-    //-------------------------------
-    //      Pre computed stuff
-    //-------------------------------
-    std::vector<cv::Point2f> prevPts;           //Original coordinates of the points to track
-    std::vector<cv::Mat> refPyr;                //Reference pyramid
-    std::vector<std::vector<float>> vMeanI;     //Reference window mean intensities
-    std::vector<std::vector<float>> vMeanI2;    //Reference window mean sqared intensities
-    std::vector<std::vector<cv::Mat>> Iref;     //Reference windows
-    std::vector<std::vector<cv::Mat>> Idref;    //Reference derivative windows
-};
-
-#endif //KLT_H

include/Tracking.h

@@ -37,7 +37,6 @@
 #include "MapDrawer.h"
 #include "System.h"
 #include "ImuTypes.h"
-#include "LucasKanadeTracker.h"
 
 #include "GeometricCamera.h"
 

src/LoopClosing.cc

@@ -566,9 +566,9 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector<KeyFrame*> &vpBowCand,
     {
         nBoWMatches = 20;
         nBoWInliers = 15;
-        nSim3Inliers = 30;
-        nProjMatches = 50;
-        nProjOptMatches = 70;
+        nSim3Inliers = 20;
+        nProjMatches = 35;
+        nProjOptMatches = 50;
     }*/
 
     set<KeyFrame*> spConnectedKeyFrames = mpCurrentKF->GetConnectedKeyFrames();

src/LucasKanadeTracker.cc

@@ -1,393 +0,0 @@
-//*****************************************************************
-// File:    LucasKanadeTracker.cpp
-// Author:  Juan José Gómez Rodríguez (jjgomez96@hotmail.com)
-// Date:    01/05/2019
-// Coms:    Implementation file of the Lucas-Kanade optical flow algorithm
-//*****************************************************************
-
-#include "LucasKanadeTracker.h"
-
-#include <float.h>
-
-using namespace std;
-using namespace cv;
-
-#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
-#define CV_DESCALE(x,n)     (((x) + (1 << ((n)-1))) >> (n))
-
-
-LucasKanadeTracker::LucasKanadeTracker() : winSize(Size(21, 21)), maxLevel(3), maxIters(30), epsilon(0.01),
-                                           minEigThreshold(1e-4) {}
-
-LucasKanadeTracker::LucasKanadeTracker(const cv::Size _winSize, const int _maxLevel, const int _maxIters,
-                                       const float _epsilon, const float _minEigThreshold) :
-        winSize(_winSize), maxLevel(_maxLevel), maxIters(_maxIters), epsilon(_epsilon),
-        minEigThreshold(_minEigThreshold) {}
-
-void LucasKanadeTracker::SetReferenceImage(Mat &refIm, vector<Point2f> &refPts) {
-    //Compute reference pyramid
-    cv::buildOpticalFlowPyramid(refIm, refPyr, winSize, maxLevel);
-
-    //Store points
-    prevPts = refPts;
-
-    vMeanI = vector<vector<float>>(maxLevel + 1, vector<float>(refPts.size()));
-    vMeanI2 = vector<vector<float>>(maxLevel + 1, vector<float>(refPts.size()));
-    Iref = vector<vector<Mat>>(maxLevel + 1, vector<Mat>(refPts.size()));
-    Idref = vector<vector<Mat>>(maxLevel + 1, vector<Mat>(refPts.size()));
-
-    //Compute reference windows (intensity and derivtives) and means of the windows
-    Point2f halfWin((winSize.width - 1) * 0.5f, (winSize.height - 1) * 0.5f);
-
-    for (int level = maxLevel; level >= 0; level--) {
-        //Get images form the pyramid
-        const Mat I = refPyr[level * 2];
-        const Mat derivI = refPyr[level * 2 + 1];
-
-        //Steps for matrix indexing
-        int dstep = (int) (derivI.step / derivI.elemSize1());
-        int stepI = (int) (I.step / I.elemSize1());
-
-        //Buffer for fast memory access
-        int cn = I.channels(), cn2 = cn * 2;   //cn should be 1 therefor cn2 should be 2
-        AutoBuffer<short> _buf(winSize.area() * (cn + cn2));
-        int derivDepth = DataType<short>::depth;
-
-        //Integration window buffers
-        Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (*_buf));
-        Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (*_buf) + winSize.area() * cn);
-
-        for (int i = 0; i < prevPts.size(); i++) {
-            //Compute image coordinates in the reference image at the current level
-            Point2f point = prevPts[i] / (1 << level);
-
-            Point2i ipoint;
-            point -= halfWin;
-            ipoint.x = cvFloor(point.x);
-            ipoint.y = cvFloor(point.y);
-
-            if (ipoint.x < -winSize.width || ipoint.x >= derivI.cols ||
-                    ipoint.y < -winSize.height || ipoint.y >= derivI.rows) {
-                continue;
-            }
-
-            //Compute weighs for sub pixel computation
-            float a = point.x - ipoint.x;
-            float b = point.y - ipoint.y;
-            const int W_BITS = 14, W_BITS1 = 14;
-            const float FLT_SCALE = 1.f / (1 << 20);
-            int iw00 = cvRound((1.f - a) * (1.f - b) * (1 << W_BITS));
-            int iw01 = cvRound(a * (1.f - b) * (1 << W_BITS));
-            int iw10 = cvRound((1.f - a) * b * (1 << W_BITS));
-            int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
-
-            //Compute sumI, sumI2, meanI, meanI2, Iwin, IdWin
-            float meanI = 0.f, meanI2 = 0.f;
-
-            int x, y;
-            for (y = 0; y < winSize.height; y++) {
-                //Get pointers to the images
-                const uchar *src = I.ptr() + (y + ipoint.y) * stepI + ipoint.x;
-                const short *dsrc = derivI.ptr<short>() + (y + ipoint.y) * dstep + ipoint.x * 2;
-
-                //Get pointers to the window buffers
-                short *Iptr = IWinBuf.ptr<short>(y);
-                short *dIptr = derivIWinBuf.ptr<short>(y);
-
-                x = 0;
-                for (; x < winSize.width * cn; x++, dsrc += 2, dIptr += 2) {
-                    //Get sub pixel values from images
-                    int ival = CV_DESCALE(src[x] * iw00 + src[x + cn] * iw01 +
-                                          src[x + stepI] * iw10 + src[x + stepI + cn] * iw11, W_BITS1 - 5);
-                    int ixval = CV_DESCALE(dsrc[0] * iw00 + dsrc[cn2] * iw01 +
-                                           dsrc[dstep] * iw10 + dsrc[dstep + cn2] * iw11, W_BITS1);
-                    int iyval = CV_DESCALE(dsrc[1] * iw00 + dsrc[cn2 + 1] * iw01 + dsrc[dstep + 1] * iw10 +
-                                           dsrc[dstep + cn2 + 1] * iw11, W_BITS1);
-
-                    //Store values to the window buffers
-                    Iptr[x] = (short) ival;
-                    dIptr[0] = (short) ixval;
-                    dIptr[1] = (short) iyval;
-
-                    //Compute accum values for later gain and bias computation
-                    meanI += (float) ival;
-                    meanI2 += (float) (ival * ival);
-                }
-            }
-            //Compute means for later gain and bias computation
-            vMeanI[level][i] = (meanI * FLT_SCALE) / winSize.area();
-            vMeanI2[level][i] = (meanI2 * FLT_SCALE) / winSize.area();
-
-            Iref[level][i] = IWinBuf.clone();
-            Idref[level][i] = derivIWinBuf.clone();
-        }
-    }
-
-}
-
-int LucasKanadeTracker::PRE_Track(Mat &newIm,
-                                   std::vector<Point2f> &nextPts, vector<bool> &status, const bool bInitialFlow,
-                                   const bool bCheckLength) {
-    //Set status of all the points to true
-    status = vector<bool>(prevPts.size(), true);
-
-    if (!bInitialFlow)
-        nextPts = vector<Point2f>(prevPts.size());
-
-    //Dimensions of half of the window
-    Point2f halfWin((winSize.width - 1) * 0.5f, (winSize.height - 1) * 0.5f);
-
-    //Compute pyramid images
-    vector<Mat> newPyr;
-    cv::buildOpticalFlowPyramid(newIm, newPyr, winSize, maxLevel);
-
-    //Start Lucas-Kanade optical flow algorithm
-    //First iterate over pyramid levels
-    for (int level = maxLevel; level >= 0; level--) {
-        //Get images and gradients
-        const Mat I = refPyr[level * 2];
-        const Mat J = newPyr[level * 2];
-        const Mat derivI = refPyr[level * 2 + 1];
-        const Mat derivJ = newPyr[level * 2 + 1];
-
-        //Buffer for fast memory access
-        int j, cn = I.channels(), cn2 = cn * 2;   //cn should be 1 therefor cn2 should be 2
-        AutoBuffer<short> _buf(winSize.area() * (cn + cn2) * 2);
-        int derivDepth = DataType<short>::depth;
-
-        //Integration window buffers
-        Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (*_buf));
-        Mat JWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (*_buf) + winSize.area());
-        Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (*_buf) + 2 * winSize.area());
-        Mat derivJWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (*_buf) + 4 * winSize.area());
-
-        //Steps for matrix indexing
-        int dstep = (int) (derivI.step / derivI.elemSize1());
-        int stepJ = (int) (J.step / J.elemSize1());
-
-        //Track each point at the current pyramid level
-        for (int i = 0; i < prevPts.size(); i++) {
-            //Compute image coordinates in the reference image at the current level
-            Point2f prevPt = prevPts[i] * (float) (1. / (1 << level));
-            //Compute image coordinates in the current frame at the current level
-            Point2f nextPt;
-            if (level == maxLevel) {
-                if (bInitialFlow) {
-                    nextPt = nextPts[i] * (float) (1. / (1 << level));
-                } else {
-                    nextPt = prevPt;
-                }
-            } else {
-                nextPt = nextPts[i] * 2.f;
-            }
-
-            nextPts[i] = nextPt;
-
-            //Check that previous point and next point is inside of the
-            //image boundaries
-            Point2i iprevPt, inextPt;
-            prevPt -= halfWin;
-            iprevPt.x = cvFloor(prevPt.x);
-            iprevPt.y = cvFloor(prevPt.y);
-
-            if (iprevPt.x < -winSize.width || iprevPt.x >= derivI.cols ||
-                iprevPt.y < -winSize.height || iprevPt.y >= derivI.rows) {
-                if (level == 0)
-                    status[i] = false;
-
-                continue;
-            }
-
-            //Compute weighs for sub pixel computation
-            const int W_BITS = 14, W_BITS1 = 14;
-            const float FLT_SCALE = 1.f / (1 << 20);
-
-            //Compute sumI, sumI2, meanI, meanI2, Iwin, IdWin
-            float meanI = 0.f, meanI2 = 0.f;
-
-            int x, y;
-            //Compute means for later gain and bias computation
-            meanI = vMeanI[level][i];
-            meanI2 = vMeanI2[level][i];
-
-            IWinBuf = Iref[level][i].clone();
-            derivIWinBuf = Idref[level][i].clone();
-
-            //Optical flow loop
-            Point2f prevDelta;
-            nextPt -= halfWin;
-            for (j = 0; j < maxIters; j++) {
-                //Compute weighs for sub pixel computation
-                inextPt.x = cvFloor(nextPt.x);
-                inextPt.y = cvFloor(nextPt.y);
-
-                //Check that the point is inside the image
-                if (inextPt.x < -winSize.width || inextPt.x >= J.cols ||
-                    inextPt.y < -winSize.height || inextPt.y >= J.rows) {
-                    if (level == 0)
-                        status[i] = false;
-                    break;
-                }
-
-                float aJ = nextPt.x - inextPt.x;
-                float bJ = nextPt.y - inextPt.y;
-                int jw00 = cvRound((1.f - aJ) * (1.f - bJ) * (1 << W_BITS));
-                int jw01 = cvRound(aJ * (1.f - bJ) * (1 << W_BITS));
-                int jw10 = cvRound((1.f - aJ) * bJ * (1 << W_BITS));
-                int jw11 = (1 << W_BITS) - jw00 - jw01 - jw10;
-
-                //Compute alpha and beta for gain and bias
-                //Compute sumI, sumI2, meanI, meanI2, Iwin, IdWin
-                float meanJ = 0.f, meanJ2 = 0.f;
-
-                for (y = 0; y < winSize.height; y++) {
-                    //Get pointers to the images
-                    const uchar *src = J.ptr() + (y + inextPt.y) * stepJ + inextPt.x * cn;
-                    const short *dsrc = derivJ.ptr<short>() + (y + inextPt.y) * dstep + inextPt.x * cn2;
-
-                    //Get pointers to the window buffers
-                    short *Jptr = JWinBuf.ptr<short>(y);
-                    short *dJptr = derivJWinBuf.ptr<short>(y);
-
-                    x = 0;
-
-                    for (; x < winSize.width * cn; x++, dsrc += 2, dJptr += 2) {
-                        //Get sub pixel values from images
-                        int jval = CV_DESCALE(src[x] * jw00 + src[x + cn] * jw01 +
-                                              src[x + stepJ] * jw10 + src[x + stepJ + cn] * jw11, W_BITS1 - 5);
-                        int jxval = CV_DESCALE(dsrc[0] * jw00 + dsrc[cn2] * jw01 +
-                                               dsrc[dstep] * jw10 + dsrc[dstep + cn2] * jw11, W_BITS1);
-                        int jyval = CV_DESCALE(dsrc[1] * jw00 + dsrc[cn2 + 1] * jw01 + dsrc[dstep + 1] * jw10 +
-                                               dsrc[dstep + cn2 + 1] * jw11, W_BITS1);
-
-                        //Store values to the window buffers
-                        Jptr[x] = (short) jval;
-                        dJptr[0] = (short) jxval;
-                        dJptr[1] = (short) jyval;
-
-                        //Compute accum values for later gain and bias computation
-                        meanJ += (float) jval;
-                        meanJ2 += (float) (jval * jval);
-                    }
-                }
-
-                //Compute means for later gain and bias computation
-                meanJ = (meanJ * FLT_SCALE) / winSize.area();
-                meanJ2 = (meanJ2 * FLT_SCALE) / winSize.area();
-
-                //Compute alpha and beta
-                float alpha = sqrt(meanI2 / meanJ2);
-                float beta = meanI - alpha * meanJ;
-
-                //Compute image gradient insensitive to ilumination changes
-                float ib1 = 0, ib2 = 0;
-                float b1, b2;
-                float iA11 = 0, iA12 = 0, iA22 = 0;
-                float A11, A12, A22;
-
-                for (y = 0; y < winSize.height; y++) {
-                    //Get pointers to the buffers
-                    const short *Iptr = IWinBuf.ptr<short>(y);
-                    const short *Jptr = JWinBuf.ptr<short>(y);
-                    const short *dIptr = derivIWinBuf.ptr<short>(y);
-                    const short *dJptr = derivJWinBuf.ptr<short>(y);
-
-                    x = 0;
-                    for (; x < winSize.width * cn; x++, dIptr += 2, dJptr += 2) {
-                        int diff = Jptr[x] * alpha - Iptr[x] - beta;
-                        float dx = (float) (dIptr[0] + dJptr[0] * alpha);
-                        float dy = (float) (dIptr[1] + dJptr[1] * alpha);
-
-                        ib1 += (float) (diff * dx);
-                        ib2 += (float) (diff * dy);
-
-                        iA11 += (float) (dx * dx);
-                        iA22 += (float) (dy * dy);
-                        iA12 += (float) (dx * dy);
-                    }
-                }
-                b1 = ib1 * FLT_SCALE;
-                b2 = ib2 * FLT_SCALE;
-
-                //Compute spatial gradient matrix
-                A11 = iA11 * FLT_SCALE;
-                A12 = iA12 * FLT_SCALE;
-                A22 = iA22 * FLT_SCALE;
-
-                float D = A11 * A22 - A12 * A12;
-                float minEig = (A22 + A11 - std::sqrt((A11 - A22) * (A11 - A22) +
-                                                      4.f * A12 * A12)) / (2 * winSize.width * winSize.height);
-
-                if (minEig < minEigThreshold || D < FLT_EPSILON) {
-                    if (level == 0)
-                        status[i] = false;
-                    continue;
-                }
-
-                D = 1.f / D;
-
-                //Compute optical flow
-                Point2f delta((float) ((A12 * b2 - A22 * b1) * D),
-                              (float) ((A12 * b1 - A11 * b2) * D));
-
-                nextPt += delta;
-                nextPts[i] = nextPt + halfWin;
-
-                if (delta.ddot(delta) <= epsilon)
-                    break;
-
-                if (j > 0 && std::abs(delta.x + prevDelta.x) < 0.01 &&
-                    std::abs(delta.y + prevDelta.y) < 0.01) {
-                    nextPts[i] -= delta * 0.5f;
-                    break;
-                }
-                prevDelta = delta;
-            }
-        }
-    }
-
-    //Track lenght check
-    if (bCheckLength) {
-        //Compute total displacement of aeach good tracked point
-        vector<float> deltas, deltasShorted;
-        for (int i = 0; i < prevPts.size(); i++) {
-            if (!status[i]) {
-                deltas.push_back(0.f);
-                continue;
-            }
-            Point2f pDelta = nextPts[i] - prevPts[i];
-            deltas.push_back(sqrt(pDelta.x * pDelta.x + pDelta.y * pDelta.y));
-            deltasShorted.push_back(sqrt(pDelta.x * pDelta.x + pDelta.y * pDelta.y));
-        }
-        sort(deltasShorted.begin(), deltasShorted.end());
-        float median = deltasShorted[deltasShorted.size() / 2];
-
-        if(median < 20.f){
-            return deltasShorted.size();
-        }
-
-        for (int i = deltas.size() - 1; i >= 0; i--) {
-            if (deltas[i] > median * 2)
-                status[i] = false;
-        }
-        return deltasShorted.size();
-    }
-}
-
-void LucasKanadeTracker::DeleteRefPt(const int idx)
-{
-    prevPts.erase(prevPts.begin()+idx);
-    for(int level = maxLevel; level >= 0; level--)
-    {
-        vMeanI[level].erase(vMeanI[level].begin()+idx);
-        vMeanI2[level].erase(vMeanI2[level].begin()+idx);
-        Iref[level].erase(Iref[level].begin()+idx);
-        Idref[level].erase(Idref[level].begin()+idx);
-
-    }
-}
-
-cv::Point2f LucasKanadeTracker::GetPrevPoint(const int idx)
-{
-    return prevPts[idx];
-}

src/Tracking.cc

@@ -49,7 +49,7 @@ Tracking::Tracking(System *pSys, ORBVocabulary* pVoc, FrameDrawer *pFrameDrawer,
     mState(NO_IMAGES_YET), mSensor(sensor), mTrackedFr(0), mbStep(false),
     mbOnlyTracking(false), mbMapUpdated(false), mbVO(false), mpORBVocabulary(pVoc), mpKeyFrameDB(pKFDB),
     mpInitializer(static_cast<Initializer*>(NULL)), mpSystem(pSys), mpViewer(NULL),
-    mpFrameDrawer(pFrameDrawer), mpMapDrawer(pMapDrawer), mpAtlas(pAtlas), mnLastRelocFrameId(0), time_recently_lost(3.0),
+    mpFrameDrawer(pFrameDrawer), mpMapDrawer(pMapDrawer), mpAtlas(pAtlas), mnLastRelocFrameId(0), time_recently_lost(5.0),
     mnInitialFrameId(0), mbCreatedMap(false), mnFirstFrameId(0), mpCamera2(nullptr)
 {
     // Load camera parameters from settings file