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ORBmatcher.cc
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ORBmatcher.cc
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/**
* This file is part of ORB-SLAM.
* It is based on the file orb.cpp from the OpenCV library (see BSD license below)
*
* Copyright (C) 2014 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <http://webdiis.unizar.es/~raulmur/orbslam/>
*
* ORB-SLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM. If not, see <http://www.gnu.org/licenses/>.
*/
#include "ORBmatcher.h"
#include<limits.h>
//#include<ros/ros.h>
#include<opencv2/core/core.hpp>
#include<opencv2/features2d/features2d.hpp>
#include "ORBextractor.h" //for findMatchDirect()
#include "Thirdparty/DBoW2/DBoW2/FeatureVector.h"
#include<stdint-gcc.h>
using namespace std;
namespace ORB_SLAM
{
const int ORBmatcher::TH_HIGH = 100;
const int ORBmatcher::TH_LOW = 50;
const int ORBmatcher::HISTO_LENGTH = 30;
ORBmatcher::ORBmatcher(float nnratio, bool checkOri): mfNNratio(nnratio), mbCheckOrientation(checkOri)
{
}
int ORBmatcher::SearchByProjection(Frame &F, const vector<MapPoint*> &vpMapPoints, const float th)
{
int nmatches=0;
const bool bFactor = th!=1.0;
for(size_t iMP=0; iMP<vpMapPoints.size(); iMP++)
{
MapPoint* pMP = vpMapPoints[iMP];
if(!pMP->mbTrackInView)
continue;
if(pMP->isBad())
continue;
const int &nPredictedLevel = pMP->mnTrackScaleLevel;
// The size of the window will depend on the viewing direction
float r = RadiusByViewingCos(pMP->mTrackViewCos);
if(bFactor)
r*=th;
vector<size_t> vNearIndices =
F.GetFeaturesInArea(pMP->mTrackProjX,pMP->mTrackProjY,r*F.GetScaleFactor(nPredictedLevel),nPredictedLevel-1,nPredictedLevel);
if(vNearIndices.empty())
continue;
cv::Mat MPdescriptor = pMP->GetDescriptor();
int bestDist=INT_MAX;
int bestLevel= -1;
int bestDist2=INT_MAX;
int bestLevel2 = -1;
int bestIdx =-1 ;
// Get best and second matches with near keypoints
for(vector<size_t>::iterator vit=vNearIndices.begin(), vend=vNearIndices.end(); vit!=vend; vit++)
{
size_t idx = *vit;
if(F.mvpMapPoints[idx])
continue;
cv::Mat d=F.mDescriptors.row(idx);
const int dist = DescriptorDistance(MPdescriptor,d);
if(dist<bestDist)
{
bestDist2=bestDist;
bestDist=dist;
bestLevel2 = bestLevel;
bestLevel = F.mvKeysUn[idx].octave;
bestIdx=idx;
}
else if(dist<bestDist2)
{
bestLevel2 = F.mvKeysUn[idx].octave;
bestDist2=dist;
}
}
// Apply ratio to second match (only if best and second are in the same scale level)
if(bestDist<=TH_HIGH)
{
if(bestLevel==bestLevel2 && bestDist>mfNNratio*bestDist2)
continue;
F.mvpMapPoints[bestIdx]=pMP;
// ++pMP->mnObservationsInDoubleWindow; //Huai: count in the observation of current frame
nmatches++;
}
}
return nmatches;
}
/// Find a match by finding feature matches in a small neighborhood of px_cur
/// IMPORTANT! This function assumes that px_cur is already set to an estimate that is within ~2-3 pixel of the final result!
bool ORBmatcher::findMatchDirect(const cv::KeyPoint & pt, const cv::Mat &descriptor,
const ORBextractor* pORBextractor, cv::KeyPoint & pt_cur, cv::Mat & descriptor_kp)
{
int search_level_=pt.octave;// TODO: extend to lower and upper one level
cv::Point2f & px_cur=pt_cur.pt;
cv::Point2f px_scaled(px_cur*(1/pORBextractor->GetScaleFactor(search_level_)));
const int nBorder=15;
int minX= (int)std::max(0.f, round(px_scaled.x)-nBorder);
int maxX=(int)std::min(pORBextractor->mvImagePyramid[search_level_].cols, (int)(round(px_scaled.x)+nBorder+1));
int minY= (int)std::max(0.f, round(px_scaled.y)-nBorder);
int maxY=(int)std::min(pORBextractor->mvImagePyramid[search_level_].rows, (int)(round(px_scaled.y)+nBorder+1));
cv::Mat cellImage = pORBextractor->mvImagePyramid[search_level_].rowRange(minY,maxY).colRange(minX,maxX);
vector<cv::KeyPoint> vCellKeyPoints;
vCellKeyPoints.reserve(10);
cv::FAST(cellImage,vCellKeyPoints,7,true);
if(vCellKeyPoints.size()==0)
return false;
for(vector<cv::KeyPoint>::iterator it=vCellKeyPoints.begin(),
ite=vCellKeyPoints.end(); it!=ite; ++it)
{
it->pt.x+= minX;
it->pt.y+= minY;
}
computeOrientation(pORBextractor->mvImagePyramid[search_level_],
vCellKeyPoints, pORBextractor->umax);
const cv::Mat& workingMat = pORBextractor->mvBlurredImagePyramid[search_level_];
cv::Mat descriptor_cur= cv::Mat(vCellKeyPoints.size(), 32, CV_8U);
computeDescriptors(workingMat, vCellKeyPoints, descriptor_cur, pORBextractor->pattern);
int bestDist = INT_MAX;
int bestDist2 = INT_MAX;
int bestIdx2 = -1;
int i2=0;
for(vector<cv::KeyPoint>::iterator vit=vCellKeyPoints.begin(),
vend=vCellKeyPoints.end(); vit!=vend; ++vit, ++i2)
{
cv::Mat d2 = descriptor_cur.row(i2);
int dist = DescriptorDistance(descriptor,d2);
if(dist<bestDist)
{
bestDist2=bestDist;
bestDist=dist;
bestIdx2=i2;
} else if(dist<bestDist2)
{
bestDist2=dist;
}
}
if(bestDist<=TH_LOW || (bestDist<=bestDist2*mfNNratio && bestDist<=TH_HIGH ))
{
pt_cur = vCellKeyPoints[bestIdx2];
pt_cur.octave= pt.octave;
pt_cur.pt*= pORBextractor->GetScaleFactor(search_level_);
descriptor_cur.row(bestIdx2).copyTo(descriptor_kp);
return true;
}
else
return false;
}
// assume left and right image already have stereo matches, to accept a match,
// we need to match a map point to both left and right image
int ORBmatcher::SearchByProjectionStereo(Frame &F, const vector<MapPoint*> &vpMapPoints, const float th)
{
int nmatches=0;
const bool bFactor = th!=1.0;
for(size_t iMP=0; iMP<vpMapPoints.size(); iMP++)
{
MapPoint* pMP = vpMapPoints[iMP];
if(!pMP->mbTrackInView)
continue;
if(pMP->isBad())
continue;
const int &nPredictedLevel = pMP->mnTrackScaleLevel;
// The size of the window will depend on the viewing direction
float r = RadiusByViewingCos(pMP->mTrackViewCos);
if(bFactor)
r*=th;
vector<size_t> vNearIndices =
F.GetFeaturesInArea(pMP->mTrackProjX,pMP->mTrackProjY,r*F.GetScaleFactor(nPredictedLevel),nPredictedLevel-1,nPredictedLevel);
if(vNearIndices.empty())
continue;
cv::Mat MPdescriptor = pMP->GetDescriptor();
int bestDist=INT_MAX;
int bestLevel= -1;
int bestDist2=INT_MAX;
int bestLevel2 = -1;
int bestIdx =-1 ;
// Get best and second matches with near keypoints for the left image
for(vector<size_t>::iterator vit=vNearIndices.begin(), vend=vNearIndices.end(); vit!=vend; vit++)
{
size_t idx = *vit;
if(F.mvpMapPoints[idx])
continue;
cv::Mat d=F.mDescriptors.row(idx);
const int dist = DescriptorDistance(MPdescriptor,d);
if(dist<bestDist)
{
bestDist2=bestDist;
bestDist=dist;
bestLevel2 = bestLevel;
bestLevel = F.mvKeysUn[idx].octave;
bestIdx=idx;
}
else if(dist<bestDist2)
{
bestLevel2 = F.mvKeysUn[idx].octave;
bestDist2=dist;
}
}
int bestRightDist=INT_MAX;
int bestRightLevel= -1;
int bestRightDist2=INT_MAX;
int bestRightLevel2 = -1;
int bestRightIdx =-1 ;
// Get best and second matches with near keypoints for the right image,
// here we assume features in the right image is already matched to the left image
for(vector<size_t>::iterator vit=vNearIndices.begin(), vend=vNearIndices.end(); vit!=vend; vit++)
{
size_t idx = *vit;
if(F.mvpMapPoints[idx])
continue;
cv::Mat d=F.mRightDescriptors.row(idx);
const int dist = DescriptorDistance(MPdescriptor,d);
if(dist<bestRightDist)
{
bestRightDist2=bestRightDist;
bestRightDist=dist;
bestRightLevel2 = bestRightLevel;
bestRightLevel = F.mvKeysUn[idx].octave;
bestRightIdx=idx;
}
else if(dist<bestRightDist2)
{
bestRightLevel2 = F.mvKeysUn[idx].octave;
bestRightDist2=dist;
}
}
// Apply ratio to second match (only if best and second are in the same scale level)
if(bestIdx==bestRightIdx && (bestDist<=TH_HIGH || bestRightDist<=TH_HIGH))
{// if bestIdx==bestRightIdx, loose the requirements
if((bestLevel==bestLevel2 && bestDist>mfNNratio*bestDist2) &&
(bestRightLevel==bestRightLevel2 && bestRightDist>mfNNratio*bestRightDist2))
continue;
assert(F.mvpMapPoints[bestIdx]==NULL);
F.mvpMapPoints[bestIdx]=pMP;
// ++pMP->mnObservationsInDoubleWindow; //Huai: count in the observation of current frame
nmatches++;
}
}
return nmatches;
}
float ORBmatcher::RadiusByViewingCos(const float &viewCos)
{
if(viewCos>0.998)
return 2.5;
else
return 4.0;
}
bool CheckDistEpipolarLine(const cv::KeyPoint &kp1,const cv::KeyPoint &kp2,
const Eigen::Matrix3d &F12,const Frame* pF2)
{
// Epipolar line in second image l = x1'F12 = [a b c]
const float a = kp1.pt.x*F12(0,0)+kp1.pt.y*F12(1,0)+F12(2,0);
const float b = kp1.pt.x*F12(0,1)+kp1.pt.y*F12(1,1)+F12(2,1);
const float c = kp1.pt.x*F12(0,2)+kp1.pt.y*F12(1,2)+F12(2,2);
const float num = a*kp2.pt.x+b*kp2.pt.y+c;
const float den = a*a+b*b;
if(den==0)
return false;
const float dsqr = num*num/den;
return dsqr<3.84*pF2->GetSigma2(kp2.octave);
}
int ORBmatcher::SearchByBoW(KeyFrame* pKF,Frame &F, vector<MapPoint*> &vpMapPointMatches)
{
vector<MapPoint*> vpMapPointsKF = pKF->GetMapPointMatches();
vpMapPointMatches = vector<MapPoint*>(F.mvpMapPoints.size(),static_cast<MapPoint*>(NULL));
DBoW2::FeatureVector vFeatVecKF = pKF->GetFeatureVector();
int nmatches=0;
vector<int> rotHist[HISTO_LENGTH];
for(int i=0;i<HISTO_LENGTH;i++)
rotHist[i].reserve(500);
const float factor = 1.0f/HISTO_LENGTH;
// We perform the matching over ORB that belong to the same vocabulary node (at a certain level)
DBoW2::FeatureVector::iterator KFit = vFeatVecKF.begin();
DBoW2::FeatureVector::iterator Fit = F.mFeatVec.begin();
DBoW2::FeatureVector::iterator KFend = vFeatVecKF.end();
DBoW2::FeatureVector::iterator Fend = F.mFeatVec.end();
while(KFit != KFend && Fit != Fend)
{
if(KFit->first == Fit->first)
{
vector<unsigned int> vIndicesKF = KFit->second;
vector<unsigned int> vIndicesF = Fit->second;
for(size_t iKF=0, iendKF=vIndicesKF.size(); iKF<iendKF; iKF++)
{
const unsigned int realIdxKF = vIndicesKF[iKF];
MapPoint* pMP = vpMapPointsKF[realIdxKF];
if(!pMP)
continue;
if(pMP->isBad())
continue;
cv::Mat dKF= pKF->GetDescriptor(realIdxKF);
int bestDist1=INT_MAX;
int bestIdxF =-1 ;
int bestDist2=INT_MAX;
for(size_t iF=0, iendF=vIndicesF.size(); iF<iendF; iF++)
{
const unsigned int realIdxF = vIndicesF[iF];
if(vpMapPointMatches[realIdxF])
continue;
cv::Mat dF = F.mDescriptors.row(realIdxF).clone();
const int dist = DescriptorDistance(dKF,dF);
if(dist<bestDist1)
{
bestDist2=bestDist1;
bestDist1=dist;
bestIdxF=realIdxF;
}
else if(dist<bestDist2)
{
bestDist2=dist;
}
}
if(bestDist1<=TH_LOW)
{
if(static_cast<float>(bestDist1)<mfNNratio*static_cast<float>(bestDist2))
{
vpMapPointMatches[bestIdxF]=pMP;
cv::KeyPoint kp = pKF->GetKeyPointUn(realIdxKF);
if(mbCheckOrientation)
{
float rot = kp.angle-F.mvKeysUn[bestIdxF].angle;
if(rot<0.0)
rot+=360.0f;
int bin = round(rot*factor);
if(bin==HISTO_LENGTH)
bin=0;
assert(bin>=0 && bin<HISTO_LENGTH);
rotHist[bin].push_back(bestIdxF);
}
nmatches++;
}
}
}
KFit++;
Fit++;
}
else if(KFit->first < Fit->first)
{
KFit = vFeatVecKF.lower_bound(Fit->first);
}
else
{
Fit = F.mFeatVec.lower_bound(KFit->first);
}
}
if(mbCheckOrientation)
{
int ind1=-1;
int ind2=-1;
int ind3=-1;
ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);
for(int i=0; i<HISTO_LENGTH; i++)
{
if(i==ind1 || i==ind2 || i==ind3)
continue;
for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
{
vpMapPointMatches[rotHist[i][j]]=NULL;
nmatches--;
}
}
}
return nmatches;
}
int ORBmatcher::SearchByProjection(KeyFrame* pKF, g2o::Sim3 Scw, const vector<MapPoint*> &vpPoints, vector<MapPoint*> &vpMatched, int th)
{
// Get Calibration Parameters for later projection
const float fx = pKF->cam_.fx();
const float fy = pKF->cam_.fy();
const float cx = pKF->cam_.cx();
const float cy = pKF->cam_.cy();
const int nMaxLevel = pKF->GetScaleLevels()-1;
vector<float> vfScaleFactors = pKF->GetScaleFactors();
// Decompose Scw
const float scw = Scw.scale();
Eigen::Matrix3d Rcw =Scw.rotation().toRotationMatrix();
Eigen::Vector3d tcw = Scw.translation()/scw;
Eigen::Vector3d Ow = -Rcw.transpose()*tcw;
// Set of MapPoints already found in the KeyFrame
set<MapPoint*> spAlreadyFound(vpMatched.begin(), vpMatched.end());
spAlreadyFound.erase(NULL);
int nmatches=0;
// For each Candidate MapPoint Project and Match
for(int iMP=0, iendMP=vpPoints.size(); iMP<iendMP; iMP++)
{
MapPoint* pMP = vpPoints[iMP];
// Discard Bad MapPoints and already found
if(pMP->isBad() || spAlreadyFound.count(pMP))
continue;
// Get 3D Coords.
Eigen::Vector3d p3Dw = pMP->GetWorldPos();
// Transform into Camera Coords.
Eigen::Vector3d p3Dc = Rcw*p3Dw+tcw;
// Depth must be positive
if(p3Dc(2)<0.0)
continue;
// Project into Image
const float invz = 1/p3Dc(2);
const float x = p3Dc(0)*invz;
const float y = p3Dc(1)*invz;
const float u = fx*x+cx;
const float v = fy*y+cy;
// Point must be inside the image
if(!pKF->IsInImage(u,v))
continue;
// Depth must be inside the scale invariance region of the point
const float maxDistance = pMP->GetMaxDistanceInvariance();
const float minDistance = pMP->GetMinDistanceInvariance();
Eigen::Vector3d PO = p3Dw-Ow;
const float dist = PO.norm();
if(dist<minDistance || dist>maxDistance)
continue;
// Viewing angle must be less than 60 deg
Eigen::Vector3d Pn = pMP->GetNormal();
if(PO.dot(Pn)<0.5*dist)
continue;
// Compute predicted scale level
const float ratio = dist/minDistance;
vector<float>::iterator it = lower_bound(vfScaleFactors.begin(), vfScaleFactors.end(), ratio);
const int nPredictedLevel = min(static_cast<int>(it-vfScaleFactors.begin()),nMaxLevel);
// Search in a radius
const float radius = th*pKF->GetScaleFactor(nPredictedLevel);
vector<size_t> vIndices = pKF->GetFeaturesInArea(u,v,radius);
if(vIndices.empty())
continue;
// Match to the most similar keypoint in the radius
cv::Mat dMP = pMP->GetDescriptor();
int bestDist = INT_MAX;
int bestIdx = -1;
for(vector<size_t>::iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++)
{
size_t idx = *vit;
if(vpMatched[idx])
continue;
const int kpLevel= pKF->GetKeyPointScaleLevel(idx);
if(kpLevel<nPredictedLevel-1 || kpLevel>nPredictedLevel)
continue;
cv::Mat dKF = pKF->GetDescriptor(idx);
const int dist = DescriptorDistance(dMP,dKF);
if(dist<bestDist)
{
bestDist = dist;
bestIdx = idx;
}
}
if(bestDist<=TH_LOW)
{
vpMatched[bestIdx]=pMP;
nmatches++;
}
}
return nmatches;
}
int ORBmatcher::WindowSearch(Frame &F1, Frame &F2, int windowSize, vector<MapPoint *> &vpMapPointMatches2, int minScaleLevel, int maxScaleLevel)
{
int nmatches=0;
vpMapPointMatches2 = vector<MapPoint*>(F2.mvpMapPoints.size(),static_cast<MapPoint*>(NULL));
vector<int> vnMatches21 = vector<int>(F2.mvKeysUn.size(),-1);
vector<int> rotHist[HISTO_LENGTH];
for(int i=0;i<HISTO_LENGTH;i++)
rotHist[i].reserve(500);
const float factor = 1.0f/HISTO_LENGTH;
const bool bMinLevel = minScaleLevel>0;
const bool bMaxLevel= maxScaleLevel<INT_MAX;
for(size_t i1=0, iend1=F1.mvpMapPoints.size(); i1<iend1; i1++)
{
MapPoint* pMP1 = F1.mvpMapPoints[i1];
if(!pMP1)
continue;
if(pMP1->isBad())
continue;
const cv::KeyPoint &kp1 = F1.mvKeysUn[i1];
int level1 = kp1.octave;
if(bMinLevel)
if(level1<minScaleLevel)
continue;
if(bMaxLevel)
if(level1>maxScaleLevel)
continue;
vector<size_t> vIndices2 = F2.GetFeaturesInArea(kp1.pt.x,kp1.pt.y, windowSize, level1, level1);
if(vIndices2.empty())
continue;
cv::Mat d1 = F1.mDescriptors.row(i1);
int bestDist = INT_MAX;
int bestDist2 = INT_MAX;
int bestIdx2 = -1;
for(vector<size_t>::iterator vit=vIndices2.begin(), vend=vIndices2.end(); vit!=vend; vit++)
{
size_t i2 = *vit;
if(vpMapPointMatches2[i2])
continue;
cv::Mat d2 = F2.mDescriptors.row(i2);
int dist = DescriptorDistance(d1,d2);
if(dist<bestDist)
{
bestDist2=bestDist;
bestDist=dist;
bestIdx2=i2;
} else if(dist<bestDist2)
{
bestDist2=dist;
}
}
if(bestDist<=bestDist2*mfNNratio && bestDist<=TH_HIGH)
{
vpMapPointMatches2[bestIdx2]=pMP1;
vnMatches21[bestIdx2]=i1;
nmatches++;
float rot = F1.mvKeysUn[i1].angle-F2.mvKeysUn[bestIdx2].angle;
if(rot<0.0)
rot+=360.0f;
int bin = round(rot*factor);
if(bin==HISTO_LENGTH)
bin=0;
assert(bin>=0 && bin<HISTO_LENGTH);
rotHist[bin].push_back(bestIdx2);
}
}
if(mbCheckOrientation)
{
int ind1=-1;
int ind2=-1;
int ind3=-1;
ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);
for(int i=0; i<HISTO_LENGTH; i++)
{
if(i!=ind1 && i!=ind2 && i!=ind3)
{
for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
{
vpMapPointMatches2[rotHist[i][j]]=NULL;
vnMatches21[rotHist[i][j]]=-1;
nmatches--;
}
}
}
}
return nmatches;
}
// for each mappoint in F1.mvpMapPoints, search for keypoint matches in F2, and add them to F2.mvpMapPoints
int ORBmatcher::SearchByProjection(Frame &F1, Frame &F2, int windowSize, vector<MapPoint *> &vpMapPointMatches2)
{
vpMapPointMatches2 = F2.mvpMapPoints;
set<MapPoint*> spMapPointsAlreadyFound(vpMapPointMatches2.begin(),vpMapPointMatches2.end());
int nmatches = 0;
const Eigen::Matrix3d Rc2w = F2.mTcw.rotationMatrix();
const Eigen::Vector3d tc2w = F2.mTcw.translation();
for(size_t i1=0, iend1=F1.mvpMapPoints.size(); i1<iend1; i1++)
{
MapPoint* pMP1 = F1.mvpMapPoints[i1];
if(!pMP1)
continue;
if(pMP1->isBad() || spMapPointsAlreadyFound.count(pMP1))
continue;
cv::KeyPoint kp1 = F1.mvKeysUn[i1];
int level1 = kp1.octave;
Eigen::Vector3d x3Dw = pMP1->GetWorldPos();
Eigen::Vector3d x3Dc2 = Rc2w*x3Dw+tc2w;
const float xc2 = x3Dc2(0);
const float yc2 = x3Dc2(1);
const float invzc2 = 1.0/x3Dc2(2);
float u2 = F2.cam_.fx()*xc2*invzc2+F2.cam_.cx();
float v2 = F2.cam_.fy()*yc2*invzc2+F2.cam_.cy();
vector<size_t> vIndices2 = F2.GetFeaturesInArea(u2,v2, windowSize, level1, level1);
if(vIndices2.empty())
continue;
cv::Mat d1 = F1.mDescriptors.row(i1);
int bestDist = INT_MAX;
int bestDist2 = INT_MAX;
int bestIdx2 = -1;
for(vector<size_t>::iterator vit=vIndices2.begin(), vend=vIndices2.end(); vit!=vend; vit++)
{
size_t i2 = *vit;
if(vpMapPointMatches2[i2])
continue;
cv::Mat d2 = F2.mDescriptors.row(i2);
int dist = DescriptorDistance(d1,d2);
if(dist<bestDist)
{
bestDist2=bestDist;
bestDist=dist;
bestIdx2=i2;
} else if(dist<bestDist2)
{
bestDist2=dist;
}
}
if(static_cast<float>(bestDist)<=static_cast<float>(bestDist2)*mfNNratio && bestDist<=TH_HIGH)
{
vpMapPointMatches2[bestIdx2]=pMP1;
nmatches++;
}
}
return nmatches;
}
int ORBmatcher::SearchForInitialization(Frame &F1, Frame &F2,
vector<cv::Point2f> &vbPrevMatched, vector<int> &vnMatches12, int windowSize)
{
int nmatches=0;
vnMatches12 = vector<int>(F1.mvKeysUn.size(),-1);
vector<int> rotHist[HISTO_LENGTH];
for(int i=0;i<HISTO_LENGTH;i++)
rotHist[i].reserve(500);
const float factor = 1.0f/HISTO_LENGTH;
vector<int> vMatchedDistance(F2.mvKeysUn.size(),INT_MAX);
vector<int> vnMatches21(F2.mvKeysUn.size(),-1);
for(size_t i1=0, iend1=F1.mvKeysUn.size(); i1<iend1; i1++)
{
cv::KeyPoint kp1 = F1.mvKeysUn[i1];
int level1 = kp1.octave;
if(level1>0)
continue;
vector<size_t> vIndices2 = F2.GetFeaturesInArea(vbPrevMatched[i1].x,vbPrevMatched[i1].y, windowSize,level1,level1);
if(vIndices2.empty())
continue;
cv::Mat d1 = F1.mDescriptors.row(i1);
int bestDist = INT_MAX;
int bestDist2 = INT_MAX;
int bestIdx2 = -1;
for(vector<size_t>::iterator vit=vIndices2.begin(); vit!=vIndices2.end(); vit++)
{
size_t i2 = *vit;
cv::Mat d2 = F2.mDescriptors.row(i2);
int dist = DescriptorDistance(d1,d2);
if(vMatchedDistance[i2]<=dist)
continue;
if(dist<bestDist)
{
bestDist2=bestDist;
bestDist=dist;
bestIdx2=i2;
}
else if(dist<bestDist2)
{
bestDist2=dist;
}
}
if(bestDist<=TH_LOW)
{
if(bestDist<(float)bestDist2*mfNNratio)
{
if(vnMatches21[bestIdx2]>=0)
{
vnMatches12[vnMatches21[bestIdx2]]=-1;
nmatches--;
}
vnMatches12[i1]=bestIdx2;
vnMatches21[bestIdx2]=i1;
vMatchedDistance[bestIdx2]=bestDist;
nmatches++;
if(mbCheckOrientation)
{
float rot = F1.mvKeysUn[i1].angle-F2.mvKeysUn[bestIdx2].angle;
if(rot<0.0)
rot+=360.0f;
int bin = round(rot*factor);
if(bin==HISTO_LENGTH)
bin=0;
assert(bin>=0 && bin<HISTO_LENGTH);
rotHist[bin].push_back(i1);
}
}
}
}
if(mbCheckOrientation)
{
int ind1=-1;
int ind2=-1;
int ind3=-1;
ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);
for(int i=0; i<HISTO_LENGTH; i++)
{
if(i==ind1 || i==ind2 || i==ind3)
continue;
for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
{
int idx1 = rotHist[i][j];
if(vnMatches12[idx1]>=0)
{
vnMatches12[idx1]=-1;
nmatches--;
}
}
}
}
//Update prev matched
for(size_t i1=0, iend1=vnMatches12.size(); i1<iend1; i1++)
if(vnMatches12[i1]>=0)
vbPrevMatched[i1]=F2.mvKeysUn[vnMatches12[i1]].pt;
return nmatches;
}
// match features in KF1 to map points in KF2
int ORBmatcher::SearchByBoW(KeyFrame *pKF1, KeyFrame *pKF2, vector<MapPoint *> &vpMatches12)
{
vector<cv::KeyPoint> vKeysUn1 = pKF1->GetKeyPointsUn();
DBoW2::FeatureVector vFeatVec1 = pKF1->GetFeatureVector();
vector<MapPoint*> vpMapPoints1 = pKF1->GetMapPointMatches();
cv::Mat Descriptors1 = pKF1->GetDescriptors();
vector<cv::KeyPoint> vKeysUn2 = pKF2->GetKeyPointsUn();
DBoW2::FeatureVector vFeatVec2 = pKF2->GetFeatureVector();
vector<MapPoint*> vpMapPoints2 = pKF2->GetMapPointMatches();
cv::Mat Descriptors2 = pKF2->GetDescriptors();
vpMatches12 = vector<MapPoint*>(vpMapPoints1.size(),static_cast<MapPoint*>(NULL));
vector<bool> vbMatched2(vpMapPoints2.size(),false);
vector<int> rotHist[HISTO_LENGTH];
for(int i=0;i<HISTO_LENGTH;i++)
rotHist[i].reserve(500);
const float factor = 1.0f/HISTO_LENGTH;
int nmatches = 0;
DBoW2::FeatureVector::iterator f1it = vFeatVec1.begin();
DBoW2::FeatureVector::iterator f2it = vFeatVec2.begin();
DBoW2::FeatureVector::iterator f1end = vFeatVec1.end();
DBoW2::FeatureVector::iterator f2end = vFeatVec2.end();
while(f1it != f1end && f2it != f2end)
{
if(f1it->first == f2it->first)
{
for(size_t i1=0, iend1=f1it->second.size(); i1<iend1; i1++)
{
size_t idx1 = f1it->second[i1];
MapPoint* pMP1 = vpMapPoints1[idx1];
if(!pMP1)
continue;
if(pMP1->isBad())
continue;
cv::Mat d1 = Descriptors1.row(idx1);
int bestDist1=INT_MAX;
int bestIdx2 =-1 ;
int bestDist2=INT_MAX;
for(size_t i2=0, iend2=f2it->second.size(); i2<iend2; i2++)
{
size_t idx2 = f2it->second[i2];
MapPoint* pMP2 = vpMapPoints2[idx2];
if(vbMatched2[idx2] || !pMP2)
continue;
if(pMP2->isBad())
continue;
cv::Mat d2 = Descriptors2.row(idx2);
int dist = DescriptorDistance(d1,d2);
if(dist<bestDist1)
{
bestDist2=bestDist1;
bestDist1=dist;
bestIdx2=idx2;
}
else if(dist<bestDist2)
{
bestDist2=dist;
}
}
if(bestDist1<TH_LOW)
{
if(static_cast<float>(bestDist1)<mfNNratio*static_cast<float>(bestDist2))
{
vpMatches12[idx1]=vpMapPoints2[bestIdx2];
vbMatched2[bestIdx2]=true;
if(mbCheckOrientation)
{
float rot = vKeysUn1[idx1].angle-vKeysUn2[bestIdx2].angle;
if(rot<0.0)
rot+=360.0f;
int bin = round(rot*factor);
if(bin==HISTO_LENGTH)
bin=0;
assert(bin>=0 && bin<HISTO_LENGTH);
rotHist[bin].push_back(idx1);
}
nmatches++;
}
}
}
f1it++;
f2it++;
}
else if(f1it->first < f2it->first)
{
f1it = vFeatVec1.lower_bound(f2it->first);
}
else
{
f2it = vFeatVec2.lower_bound(f1it->first);