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Bigraph.cpp
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Bigraph.cpp
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/*
Samsonov Dima Israel 8/2017 sdima1357@gmail.com
ported to openCV env from my old vi3dim (vi3dim.com) image registration code, toward open source...
bigraph vertices registration demo code
*/
#include <stdio.h>
#include <iomanip>
#include <iostream>
#include "options.h"
#include <opencv2/opencv.hpp>
using namespace cv;
#define PRINT(x) std::cout<<""<<std::setw(15)<<(x)<<" " #x "\n"
#define MAX_LEVEL 8
#define NUM_EDGES_AROUND_VERTEX 10
#define LEDGE_SX 4
#define LEDGE_SY 3
#define LEDGE_SIZE (LEDGE_SX*LEDGE_SY)
// LUT_BITS 13-17
#define LUT_BITS 16
#define LUT_SIZE (1<<LUT_BITS)
// NUM_VOTES_OF_EDGE 1-5
#define NUM_VOTES_OF_EDGE 1
class HiCl
{
public:
int64 e1;
string Name;
HiCl(const char*name):Name(name)
{
e1 = cv::getTickCount();
}
~HiCl()
{
int64 e2 = cv::getTickCount();
double t = 1000*double(e2 - e1)/getTickFrequency();
std::cout<<Name<<" "<<t<<"ms\n";
}
};
inline uint32_t hashLUT(const uint32_t inp)
{
return (inp*7+(inp>>LUT_BITS)*13)&(LUT_SIZE-1);
}
inline uint32_t cntBits(uint32_t x)
{
x = x - ((x >> 1u) & 0x55555555u);
x = (x & 0x33333333u) + ((x >> 2u) & 0x33333333u);
x = x + (x >> 4u);
x &= 0xF0F0F0Fu;
return (x * 0x01010101u) >> 24u;
}
void boxFilter3(Mat& image)
{
const int X = image.size().width;
const int Y = image.size().height;
const float fact = 1.0f/9.0f;
const int AX = (X+63)&~63;
float * __restrict__ tb = new float[AX*3];
memset(tb,0,AX*3*sizeof(float));
float * __restrict__ t0 = tb;
float * __restrict__ t1 = t0+AX;
float * __restrict__ t2 = t1+AX;
for(int y=0;y<Y;y++)
{
float * __restrict__ pimage = image.ptr<float>(y);
t2[0]= pimage[0]*2+pimage[1];
for(int x = 1;x<X-1;x++)
{
t2[x] = (pimage[x-1]+pimage[x]+pimage[x+1]);
}
t2[X-1]= pimage[X-1]*2+pimage[X-2];
if(y==1)
{
float *__restrict__ pimageA =image.ptr<float>(0);
for(int x = 0;x<X;x++)
{
pimageA[x] = (t2[x]+t1[x]+t1[x])*fact;
}
}
else if(y>1)
{
float *__restrict__ pimageA =image.ptr<float>(y-1);
for(int x = 0;x<X;x++)
{
pimageA[x] = (t0[x]+t1[x]+t2[x])*fact;
}
}
std::swap(t0,t1);
std::swap(t1,t2);
}
{
float * pimageA =image.ptr<float>(Y-1);
for(int x = 0;x<X;x++)
{
pimageA[x] = (t1[x]+t1[x]+t0[x])*fact;
}
}
delete [] tb;
}
struct FeatVertex
{
Vec2f coord;
int type;
int level;
int index;
};
struct FeatEdge
{
int first;
int second;
int32_t mask;
int32_t fmask;
int32_t smask;
int level;
int8_t val[LEDGE_SX*LEDGE_SY];
};
template<class T> inline T clamp( T v, const T lo,const T hi )
{
return std::max(std::min(v,hi),lo);
}
template<class AV> class FindNeig
{
typedef vector<AV*> VERT;
typedef VERT * PVERT;
deque<int> lst;
PVERT* STABLE;
Vec2f shift;
Vec2f shift0;
float dScale;
int cellX;
int cellY;
public:
FindNeig(vector<FeatVertex> & vertices,int W,int H, float numVInCell)
{
STABLE = NULL;
if(!vertices.size())
{
return ;
}
shift[0] = 0;
shift[1] = 0;
float cellSize = 64;
if(numVInCell<0.0f)
{
cellSize = -numVInCell;
}
else
{
float area = W*H;
float cells = vertices.size()/numVInCell;
cellSize = sqrt(1)*sqrt(area)/sqrt(cells);
}
cellX = ceilf(W/cellSize);
cellY = ceilf(H/cellSize);
dScale = 1.0f/cellSize;
STABLE =new PVERT[cellX*cellY];
for(int i=0;i<cellX*cellY;i++)
{
STABLE[i] =NULL;
}
for(int i=0;i<vertices.size();i++)
{
Vec2f tr = (vertices[i].coord+shift)*dScale;
int indX =tr[0];
int indY =tr[1];
int ind = clamp(indY*cellX+indX,0,cellX*cellY-1);
if(STABLE[ind]==NULL)
{
STABLE[ind] = new VERT();
lst.push_back(ind);
}
STABLE[ind]->push_back(&vertices[i]);
}
shift0 = shift;
shift-=Vec2f(0.5f,0.5f)/dScale;
}
struct mVert8
{
AV* vt;
float currMin;
float dummy;
};
inline float sdot(Vec2f a)
{
return a[0]*a[0]+a[1]*a[1];
}
inline int findNeigAn4(const Vec2f coord,mVert8* res ,const int n)
{
Vec2f tr = (coord+shift)*dScale;//-Vec2f(0.5f,0.5f);
int indX = tr[0];
int indY = tr[1];
for(int k=0;k<n+1;k++)
{
res[k].currMin = 100000000.0f;
res[k].vt = NULL;
}
mVert8 * pr = &res[n];
for(int yy=0;yy<2;yy++)
{
int indYY = indY+yy;
if(indYY>=0&&indYY<cellY)
{
for(int xx=0;xx<2;xx++)
{
int indXX = indX+xx;
if(indXX>=0&&indXX<cellX)
{
PVERT ST = STABLE[indYY*cellX+indXX];
if(ST)
{
for(auto k=ST->begin();k!=ST->end();k++)
{
pr->vt =*k;
pr->currMin = sdot(coord-pr->vt->coord);
for(int s=n;s>0;s--)
{
if(res[s].currMin<res[s-1].currMin)
{
swap(res[s],res[s-1]);
}
else break;
}
}
}
}
}
}
}
if(res[n].vt==NULL)
{
for(int yy=-1;yy<3;yy++)
{
int indYY = indY+yy;
if(indYY>=0&&indYY<cellY)
{
for(int xx=-1;xx<3;xx++)
{
if(!((yy==0||yy==1)&&(xx==0||xx==1)))
{
int indXX = indX+xx;
if(indXX>=0&&indXX<cellX)
{
PVERT ST = STABLE[indYY*cellX+indXX];
if(ST)
{
for(auto k=ST->begin();k!=ST->end();k++)
{
pr->vt =*k;
pr->currMin = sdot(coord-pr->vt->coord);
for(int s=n;s>0;s--)
{
if(res[s].currMin<res[s-1].currMin)
{
swap(res[s],res[s-1]);
}
else break;
}
}
}
}
}
}
}
}
}
int cntr = n;
while(cntr>=0&&res[cntr].vt==NULL)
{
cntr--;
}
return cntr+1;
}
~FindNeig()
{
for(deque<int>::iterator i = lst.begin();i!=lst.end();i++)
{
delete STABLE[*i];
STABLE[*i] = NULL;
}
lst.clear();
delete [] STABLE;
}
};
class Detector
{
Mat orig;
Mat featuresImage;
public:
Mat lowPassedImage;
int level;
Detector(){};
void setImage(Mat& src_image);
void setImage2(Detector& upperLevelDet);
void detect(float thresh,vector<FeatVertex>& verts,vector<FeatEdge>& edges)
{
const int W = orig.size().width;
const int H = orig.size().height;
vector<FeatVertex> vertices_local;
#define S 2
const float mscale = 1.0f/9.0f;
for(int y=S;y<H-S;y++)
{
float * pb = featuresImage.ptr<float>(y);
const float * pbs = lowPassedImage.ptr<float>(y);
for(int x=S;x<W-S;x+=2)
{
float DT = -2.0f*pbs[x];
float DT1 = -2.0f*pbs[x+1];
float DX = pbs[x+S]+pbs[x-S];
float DY = pbs[x+S*W]+pbs[x-S*W];
float DXY = pbs[x-S-S*W]+pbs[x+S+S*W] - pbs[x+S-S*W]-pbs[x-S+S*W];
float DX1 = pbs[x+S+1]+pbs[x-S+1];
float DY1 = pbs[x+S*W+1]+pbs[x-S*W+1];
float DXY1 = pbs[x-S-S*W+1]+pbs[x+S+S*W+1] - pbs[x+S-S*W+1]-pbs[x-S+S*W+1];
pb[x]=((DT+DX)*(DT+DY)-mscale*DXY*DXY);
pb[x+1]=((DT1+DX1)*(DT1+DY1)-mscale*DXY1*DXY1);
}
}
boxFilter3(featuresImage);
boxFilter3(featuresImage);
for(int y=2;y<H-2;y++)
{
const float * porig = orig.ptr<float>(y);
const float * plowPass = lowPassedImage.ptr<float>(y);
const float * pb = featuresImage.ptr<float>(y);
for(int x=2;x<W-2;x++)
{
float pbval = pb[x];
if(
pbval>thresh&&
pbval>pb[x+1]&&
pbval>pb[x-1]&&
pbval>pb[x+W]&&
pbval>pb[x-W]&&
pbval>pb[-W-1+x]&&
pbval>pb[-W+1+x]&&
pbval>pb[W-1+x]&&
pbval>pb[W+1+x]
)
{
// find 2'nd order subpixel maximum
float dx = (pb[x+1] - pb[x-1]) * 0.5f;
float dy = (pb[x+W] - pb[x-W]) * 0.5f;
float dxx = (pb[x+1] + pb[x-1] - 2.0f * pbval);
float dyy = (pb[x+W] + pb[x-W] - 2.0f * pbval);
float dxy = (pb[x+1+W] - pb[x+1-W] - pb[x-1+W] +pb[x-1-W]) *0.25f;
float det = (dxx*dyy-dxy*dxy);
det = 1.0f/det;
FeatVertex tt;
tt.coord = Vec2f(x,y)-Vec2f((dyy*dx - dxy*dy),(dxx*dy - dxy*dx))*det;
tt.type = porig[x]>plowPass[x];
tt.level = level;
vertices_local.push_back(tt);
}
}
}
// make bigraph edges
vector<FeatVertex> Red;
vector<FeatVertex> Green;
for(int k=0;k<vertices_local.size();k++)
{
FeatVertex& curr = vertices_local[k];
curr.index = k;
if(curr.type)
{
Red.push_back(curr);
}
else
{
Green.push_back(curr);
}
}
if(Green.size()>=1)
{
FindNeig<FeatVertex>* fs = new FindNeig<FeatVertex>(Green,W,H,NUM_EDGES_AROUND_VERTEX);
FindNeig<FeatVertex>::mVert8 buff[NUM_EDGES_AROUND_VERTEX+1];
float *porigS1 = lowPassedImage.ptr<float>(0);;
for(int i=0;i<Red.size();i++)
{
int ind0 = Red[i].index;
Vec2f & coord = Red[i].coord;
// find closest neig onto buffer :)
int cnt = fs->findNeigAn4(coord,buff,NUM_EDGES_AROUND_VERTEX);
for(int k=0;k<cnt;k++)
{
int ind1 = buff[k].vt->index;
Vec2f dir = (buff[k].vt->coord - coord)/2.0f;//*0.5f; // make edge direction
Vec2f dirsw = Vec2f(-dir[1],dir[0])/2.0f; // make ortodirection
Vec2f center = (buff[k].vt->coord+coord)*0.5f;
uint32_t num =0;
int n=0;
float val[LEDGE_SX*LEDGE_SY];
for(int y=0;y<LEDGE_SY;y++)
{
for(int x=0;x<LEDGE_SX;x++)
{
Vec2f coord = center+(x-(LEDGE_SX-1)*0.5f)*dir+(y-(LEDGE_SY-1)*0.5f)*dirsw;
// bilinear sample of smoothed image on coord
float X = clamp(coord[0],0.0f,float(W-1.01f));
float Y = clamp(coord[1],0.0f,float(H-1.01f));
int lx = int(X);
int ly = int(Y);
float sx = X-lx;
float sy = Y-ly;
float* pp = porigS1+ly*W+lx;
float val0 = (1.0f-sx)*pp[0]+sx*pp[1];
float val1 = (1.0f-sx)*pp[W]+sx*pp[W+1];
val[n] = ((1.0f-sy)*val0+val1*sy);//-meanY;
n++;
}
}
// make long bitfield feature ~24 bit
int rcnt =0;
for(int y=0;y<LEDGE_SY;y++)
{
for(int x=0;x<LEDGE_SX;x++)
{
num|=(val[y*LEDGE_SX+x]>val[((y+0)%LEDGE_SY)*LEDGE_SX+((x+3)%LEDGE_SX)])<<rcnt;
rcnt++;
num|=(val[y*LEDGE_SX+x]>val[((y+1)%LEDGE_SY)*LEDGE_SX+((x+0)%LEDGE_SX)])<<rcnt;
rcnt++;
}
}
// make short bitfield feature ~6 bit
int sm = 0;
int kcnt = 0;
{
int y = (LEDGE_SY-1)/2;
for(int x=0;x<LEDGE_SX;x++)
{
for(int z=x+1;z<LEDGE_SX;z++)
{
sm|=(val[y*LEDGE_SX+x]>val[y*LEDGE_SX+((z)%LEDGE_SX)])<<kcnt;
kcnt++;
}
}
}
float summ =0;
for(int k=0;k<LEDGE_SX*LEDGE_SY;k++)
{
summ+=val[k];
}
summ/=LEDGE_SX*LEDGE_SY;
FeatEdge c;
c.mask = (rcnt>LUT_BITS ? hashLUT(num) : num);
c.first = ind0;
c.second = ind1;
c.fmask = num;
c.smask = sm;
c.level = level;
for(int k=0;k<LEDGE_SX*LEDGE_SY;k++)
{
c.val[k] = (val[k]-summ)/2;
}
edges.push_back(c);
}
}
delete fs;
}
// correct of position for 0 base interpolation
for(int k=0;k<vertices_local.size();k++)
{
FeatVertex & curr = vertices_local[k];
curr.coord[0] = (curr.coord[0]+0.5f)*(1<<level)-0.5f;
curr.coord[1] = (curr.coord[1]+0.5f)*(1<<level)-0.5f;
verts.push_back(curr);
}
PRINT(vertices_local.size());
}
};
void Detector::setImage(Mat& src_image)
{
level = 0;
orig= src_image.clone();
lowPassedImage= orig.clone();
// gauss smooth approximation 3 times box3x3
boxFilter3(lowPassedImage);
boxFilter3(lowPassedImage);
boxFilter3(lowPassedImage);
featuresImage.create(lowPassedImage.size(), lowPassedImage.type());
}
void Detector::setImage2(Detector& upperLevelDet)
{
level = upperLevelDet.level+1;
Size NewSize;
NewSize.width = ((upperLevelDet.lowPassedImage.size().width)/2)&~1;
NewSize.height = ((upperLevelDet.lowPassedImage.size().height)/2)&~1;
//~ cout<<NewSize.width<<" "<<NewSize.height<<"\n";
orig.create(NewSize,upperLevelDet.lowPassedImage.type());
const int oldW = upperLevelDet.lowPassedImage.size().width;
for(int y=0;y<NewSize.height;y++)
{
float * porig = orig.ptr<float>(y);
const float * plp = upperLevelDet.lowPassedImage.ptr<float>(y*2);
for(int x=0;x<NewSize.width;x++)
{
porig[x] = (plp[x*2]+plp[x*2+1]+plp[x*2+oldW]+plp[x*2+oldW+1])*0.25f;
}
}
lowPassedImage= orig.clone();
// gauss smooth approximation 3 times box3x3
boxFilter3(lowPassedImage);
boxFilter3(lowPassedImage);
boxFilter3(lowPassedImage);
featuresImage.create(lowPassedImage.size(), lowPassedImage.type());
}
class LevelsStore
{
public:
vector<FeatVertex> levelsV[MAX_LEVEL];
vector<FeatEdge> levelsE[MAX_LEVEL];
LevelsStore(vector<FeatVertex> &verts,vector<FeatEdge>&edges,int makeOneLevel);
~LevelsStore()
{
for(int i=0;i<MAX_LEVEL;i++)
{
levelsV[i].clear();
levelsE[i].clear();
}
}
};
LevelsStore::LevelsStore(vector<FeatVertex> &verts,vector<FeatEdge>&edges,int makeOneLevel)
{
for(int i=0;i<MAX_LEVEL;i++)
{
levelsV[i].clear();
levelsE[i].clear();
}
for(int i=0;i<verts.size();i++)
{
int lev = verts[i].level;
if(lev>=0&&lev<MAX_LEVEL)
{
levelsV[lev].push_back(verts[i]);
}
//else ??
}
for(int i=0;i<edges.size();i++)
{
int leve = edges[i].level;
if(leve>=0&&leve<MAX_LEVEL)
{
levelsE[leve].push_back(edges[i]);
}
//else ??
}
if(makeOneLevel>0)
{
HiCl cl("make One Level");
int base = levelsV[0].size();
int startLevel = 0;
for(int lev = 1;lev<MAX_LEVEL;lev++)
{
for(int k=0;k<levelsV[lev].size();k++)
{
levelsV[0].push_back(levelsV[lev][k]);
}
for(int k=0;k<levelsE[lev].size();k++)
{
FeatEdge edge = levelsE[lev][k];
edge.first +=base;
edge.second+=base;
levelsE[0].push_back(edge);
}
base+=levelsV[lev].size();
}
}
if(1) // if want compare multiple times
{
HiCl cl("sort for better cache access");
#define BTS 8
for(int leve=0;leve<MAX_LEVEL;leve++)
{
int size = levelsE[leve].size();
if(size)
{
vector<vector<FeatEdge> >groups(1<<BTS);
for(int k=0;k<size;k++)
{
int ind = (levelsE[leve][k].mask>>(16-BTS))&((1<<BTS)-1);
groups[ind].push_back(levelsE[leve][k]);
}
vector<FeatEdge> ncand;
for(int g=0;g<(1<<BTS);g++)
{
for(int k=0;k<groups[g].size();k++)
{
ncand.push_back(groups[g][k]);
}
}
std::swap(levelsE[leve],ncand);
}
}
}
}
struct rMis
{
int distance;
int first;
int second;
};
int main(int argc, char** argv )
{
string inputFileNameA;
string inputFileNameB;
int makeOneLevel;
float threshold;
int paint;
int startLevel;
COptions Options(argc,argv);
Options.Parse("-a" ,inputFileNameA ," Input File A ",string("00000001.jpg"));
Options.Parse("-b" ,inputFileNameB ," Input File B ",string("00000010.jpg"));
Options.Parse("-t" ,threshold ," detection threshold ", 10.0f );
Options.Parse("-p" ,paint ," paint ", 1 );
Options.Parse("-l" ,startLevel ,"Lowest resolution level", 1 );
Options.Parse("-m" ,makeOneLevel , "make one Level", -1);
if(!Options.IsOk()){Options.Usage("Parse Error");return 0;}
//~ printf("OpenCV: %s", cv::getBuildInformation().c_str());
Mat imageA;
Mat imageB;
imageA = imread( inputFileNameA.c_str(), 1 );
if ( !imageA.data )
{
printf("No image A data \n");
return -1;
}
imageB = imread( inputFileNameB.c_str(), 1 );
if ( !imageB.data )
{
printf("No image B data \n");
return -1;
}
Mat gray;
cvtColor(imageA, gray, COLOR_BGR2GRAY);
Mat imgFloat;
Mat imgFloat1;
gray.convertTo(imgFloat, CV_32FC1);
gray.convertTo(imgFloat1, CV_32FC1);
Mat imgFloatB;
//~ imgFloatB= imgFloat.clone();
cvtColor(imageB, gray, COLOR_BGR2GRAY);
//~
gray.convertTo(imgFloatB, CV_32FC1);
startLevel = clamp(startLevel,0,MAX_LEVEL-1);
Detector* dtArr[MAX_LEVEL];
for(int k=0;k<MAX_LEVEL;k++)
{
dtArr[k] = new Detector;
}
LevelsStore* LS[2];
for(int tt=0;tt<2;tt++)
{
vector<FeatVertex> verts;
vector<FeatEdge> edges;
{
HiCl cl("set up images and detect");
for(int k=0;k<MAX_LEVEL;k++)
{
if(k==0)
{
dtArr[0]->setImage(tt==0?imgFloat:imgFloatB);
}
else
{
dtArr[k]->setImage2(*dtArr[k-1]);
}
}
//~
for(int k=startLevel;k<MAX_LEVEL;k++)
{
dtArr[k]->detect(threshold,verts,edges);
}
}
LS[tt] = new LevelsStore(verts,edges,makeOneLevel);
cout<<"verts "<<verts.size()<<"\n";
cout<<"edges "<<edges.size()<<"\n";
}
// compare
//~ struct countIndex
//~ {
//~ uint count: 10;
//~ uint index: 22;
//~ };
#define SBB 22
#define SB (1<<SBB)
#define SBM (SB-1)
vector< vector<int> > LutOfRightEdges(LUT_SIZE);
for(int LV=MAX_LEVEL-1;LV>=0;LV--)
{
vector<FeatEdge>& RightEdge = LS[1]->levelsE[LV];
vector<FeatEdge>& LeftEdge = LS[0]->levelsE[LV];
if(RightEdge.size()>=2)
{
PRINT(RightEdge.size());
PRINT(LeftEdge.size());
HiCl cl("bind Level");
int rcount = 0;
int rcountOK = 0;
int size = RightEdge.size();
const int sflg = (makeOneLevel>0)?makeOneLevel:1;
const int rmask = size>(2048*sflg)?(LUT_SIZE-1):0;
const int smaxSize = 128*sflg;
const int smaxSizeS = 128*sflg;
const int smaxDiff = rmask?(LEDGE_SIZE*4):(LEDGE_SIZE*4);
const int MIN_VOTES = rmask?(4*NUM_VOTES_OF_EDGE-1):(4*NUM_VOTES_OF_EDGE-1);
int smask = 0xff; //0 or 0xff
//~ vector<vector<countIndex > > voting_result(LS[0]->levelsV[LV].size());
vector<vector<int > > voting_result(LS[0]->levelsV[LV].size());
if(rmask) // search long mask
{
for(int i=0;i<RightEdge.size();i++)
{
LutOfRightEdges[RightEdge[i].mask].push_back(i);
}
{
for(auto curr=LeftEdge.begin();curr!=LeftEdge.end();curr++)
{
auto & bindN = LutOfRightEdges[curr->mask];
int size = bindN.size();
if(size&&size<smaxSize)
{
rcount++;
int indF = curr->first;
int indS = curr->second;
rMis TS[NUM_VOTES_OF_EDGE];
for(int k=0;k<NUM_VOTES_OF_EDGE;k++)
{
TS[k].distance = smaxDiff;
}
for(int k=0;k<size;k++)
{
const FeatEdge& cand = RightEdge[bindN[k]];
if(cand.fmask==curr->fmask)
{
int summ = 0;
for(int p=0;p<LEDGE_SIZE;p++)
{
summ+=std::abs(curr->val[p] - cand.val[p]);
}
if(summ<TS[NUM_VOTES_OF_EDGE-1].distance)
{
TS[NUM_VOTES_OF_EDGE-1].first = cand.first;
TS[NUM_VOTES_OF_EDGE-1].second = cand.second;
TS[NUM_VOTES_OF_EDGE-1].distance = summ;
for(int s=NUM_VOTES_OF_EDGE-1;s>0;s--)
{
if(TS[s].distance<TS[s-1].distance)
{
swap(TS[s],TS[s-1]);
}
else break;
}
}
}
}
for(int k=0;k<NUM_VOTES_OF_EDGE;k++)
{
if(TS[k].distance<smaxDiff)
{
int weight = NUM_VOTES_OF_EDGE-k;
rcountOK++;
int indHF = TS[k].first;
int indHS = TS[k].second;
{
int bFound = 0;
for(int s=0;s<voting_result[indF].size();s++)
{
int sflag = ((voting_result[indF][s]&SBM)==indHF);
voting_result[indF][s]+=weight*SB*sflag;
bFound |= sflag;
}
if(!bFound)
{
voting_result[indF].push_back(indHF|(weight*SB));
}
}
{
int bFound = 0;
for(int s=0;s<voting_result[indS].size();s++)
{
int sflag =((voting_result[indS][s]&SBM)==indHS);
voting_result[indS][s]+=(weight*SB)*sflag;
bFound |= sflag;
}
if(!bFound)
{
voting_result[indS].push_back(indHS|(weight*SB));
}
}
}
}
}
}
}
}
else if(smask) // search short mask
{
for(int i=0;i<RightEdge.size();i++)
{
LutOfRightEdges[RightEdge[i].smask&smask].push_back(i);
}
{
for(auto curr=LeftEdge.begin();curr!=LeftEdge.end();curr++)
{
auto & bindN = LutOfRightEdges[curr->smask&smask];
int size = bindN.size();
if(size&&size<smaxSizeS)
{
rcount++;
int indF = curr->first;
int indS = curr->second;
//~ int rmin = smaxDiff;
rMis TS[NUM_VOTES_OF_EDGE];
for(int k=0;k<NUM_VOTES_OF_EDGE;k++)
{
TS[k].distance = smaxDiff;
}
for(int k=0;k<size;k++)
{
const FeatEdge& cand = RightEdge[bindN[k]];
if(cntBits(curr->fmask^cand.fmask)<6)
{
int summ = 0;
for(int p=0;p<LEDGE_SIZE;p++)
{
summ+=std::abs(curr->val[p] - cand.val[p]);
}
if(summ<TS[NUM_VOTES_OF_EDGE-1].distance)
{
TS[NUM_VOTES_OF_EDGE-1].first = cand.first;
TS[NUM_VOTES_OF_EDGE-1].second = cand.second;
TS[NUM_VOTES_OF_EDGE-1].distance = summ;
for(int s=NUM_VOTES_OF_EDGE-1;s>0;s--)
{
if(TS[s].distance<TS[s-1].distance)
{
swap(TS[s],TS[s-1]);
}
else break;
}
}
}
}
for(int k=0;k<NUM_VOTES_OF_EDGE;k++)
{
if(TS[k].distance<smaxDiff)
{
int weight = NUM_VOTES_OF_EDGE-k;
rcountOK++;
int indHF = TS[k].first;
int indHS = TS[k].second;
{
int bFound = 0;
for(int s=0;s<voting_result[indF].size();s++)
{
int sflag = ((voting_result[indF][s]&SBM)==indHF);
voting_result[indF][s]+=weight*SB*sflag;
bFound |=sflag;
}
if(!bFound)
{
voting_result[indF].push_back(indHF|(weight*SB));
}
}
{
int bFound = 0;
for(int s=0;s<voting_result[indS].size();s++)
{
int sflag =((voting_result[indS][s]&SBM)==indHS);
voting_result[indS][s]+=(weight*SB)*sflag;
bFound |= sflag;
}
if(!bFound)
{
voting_result[indS].push_back(indHS|(weight*SB));
}
}
}
}
}
}
}
}
else // full search due small numbers
{
{
for(auto curr = LeftEdge.begin();curr!=LeftEdge.end();curr++)
{
rcount++;
int indF = curr->first;
int indS = curr->second;
rMis TS[NUM_VOTES_OF_EDGE];
for(int k=0;k<NUM_VOTES_OF_EDGE;k++)
{
TS[k].distance = smaxDiff;
}
for(auto cand = RightEdge.begin();cand != RightEdge.end();cand++)
{
if(cntBits(curr->fmask^cand->fmask)<10)
{
if(cntBits(curr->smask^cand->smask)<4)
{
int summ = 0;
for(int p=0;p<LEDGE_SIZE;p++)
{
summ+=std::abs(curr->val[p] - cand->val[p]);
}
if(summ<TS[NUM_VOTES_OF_EDGE-1].distance)
{
TS[NUM_VOTES_OF_EDGE-1].first = cand->first;
TS[NUM_VOTES_OF_EDGE-1].second = cand->second;
TS[NUM_VOTES_OF_EDGE-1].distance = summ;
for(int s=NUM_VOTES_OF_EDGE-1;s>0;s--)
{