void GAB::LearnGAB(DataSet& pos, DataSet& neg){
const Options& opt = Options::GetInstance();
timeval start, end;
timeval Tstart, Tend;
float time = 0;
int nPos = pos.size;
int nNeg = neg.size;
float _FAR=1.0;
int nFea=0;
float aveEval=0;
float *w = new float[nPos];
if(stages!=0){
int fail = 0;
#pragma omp parallel for
for (int i = 0; i < nPos; i++) {
float score = 0;
if(NPDClassify(pos.imgs[i].clone(),score)){
pos.Fx[i]=score;
}
else{
fail ++;
}
}
if(fail!=0){
printf("you should't change pos data! %d \n",fail);
return;
}
MiningNeg(nPos,neg);
if(neg.imgs.size()<pos.imgs.size()){
printf("neg not enough, change neg rate or add neg Imgs %d %d\n",pos.imgs.size(),neg.imgs.size());
return;
}
pos.CalcWeight(1,opt.maxWeight);
neg.CalcWeight(-1,opt.maxWeight);
}
Mat faceFea = pos.ExtractPixel();
pos.ImgClear();
printf("Extract pos feature finish\n");
Mat nonfaceFea = neg.ExtractPixel();
printf("Extract neg feature finish\n");
for (int t = stages;t<opt.maxNumWeaks;t++){
printf("start training %d stages \n",t);
gettimeofday(&start,NULL);
vector<int> posIndex;
vector<int> negIndex;
for(int i=0; i<nPos; i++)
posIndex.push_back(i);
for(int i=0; i<nNeg; i++)
negIndex.push_back(i);
//trim weight
memcpy(w,pos.W,nPos*sizeof(float));
std::sort(&w[0],&w[nPos]);
int k;
float wsum;
for(int i =0;i<nPos;i++){
wsum += w[i];
if (wsum>=opt.trimFrac){
k = i;
break;
}
}
vector< int >::iterator iter;
for(iter = posIndex.begin();iter!=posIndex.end();){
if(pos.W[*iter]<w[k])
iter = posIndex.erase(iter);
else
++iter;
}
wsum = 0;
memcpy(w,neg.W,nNeg*sizeof(float));
std::sort(&w[0],&w[nNeg]);
for(int i =0;i<nNeg;i++){
wsum += w[i];
if (wsum>=opt.trimFrac){
k = i;
break;
}
}
for(iter = negIndex.begin();iter!=negIndex.end();){
if(neg.W[*iter]<w[k])
iter = negIndex.erase(iter);
else
++iter;
}
int nPosSam = posIndex.size();
int nNegSam = negIndex.size();
int minLeaf_t = max( round((nPosSam+nNegSam)*opt.minLeafFrac),opt.minLeaf);
vector<int> feaId, leftChild, rightChild;
vector< vector<unsigned char> > cutpoint;
vector<float> fit;
printf("Iter %d: nPos=%d, nNeg=%d, ", t, nPosSam, nNegSam);
DQT dqt;
gettimeofday(&Tstart,NULL);
float mincost = dqt.Learn(faceFea,nonfaceFea,pos.W,neg.W,posIndex,negIndex,minLeaf_t,feaId,leftChild,rightChild,cutpoint,fit);
gettimeofday(&Tend,NULL);
float DQTtime = (Tend.tv_sec - Tstart.tv_sec);
printf("DQT time:%.3fs\n",DQTtime);
if (feaId.empty()){
printf("\n\nNo available features to satisfy the split. The AdaBoost learning terminates.\n");
break;
}
Mat posX(feaId.size(),faceFea.cols,CV_8UC1);
for(int i = 0;i<feaId.size();i++)
for(int j = 0;j<faceFea.cols;j++){
int x,y;
GetPoints(feaId[i],&x,&y);
unsigned char Fea = ppNpdTable.at<uchar>(faceFea.at<uchar>(x,j),faceFea.at<uchar>(y,j));
posX.at<uchar>(i,j) = Fea;
}
Mat negX(feaId.size(),nonfaceFea.cols,CV_8UC1);
for(int i = 0;i<feaId.size();i++)
for(int j = 0;j<nonfaceFea.cols;j++){
int x,y;
GetPoints(feaId[i],&x,&y);
unsigned char Fea = ppNpdTable.at<uchar>(nonfaceFea.at<uchar>(x,j),nonfaceFea.at<uchar>(y,j));
negX.at<uchar>(i,j) = Fea;
}
TestDQT(pos.Fx,fit,cutpoint,leftChild,rightChild,posX);
TestDQT(neg.Fx,fit,cutpoint,leftChild,rightChild,negX);
vector<int> negPassIndex;
for(int i=0; i<nNegSam; i++)
negPassIndex.push_back(i);
memcpy(w,pos.Fx,nPos*sizeof(float));
sort(w,w+nPos);
int index = max(floor(nPos*(1-opt.minDR)),0);
float threshold = w[index];
for(iter = negPassIndex.begin(); iter != negPassIndex.end();){
if(neg.Fx[*iter] < threshold)
iter = negPassIndex.erase(iter);
else
iter++;
}
float far = float(negPassIndex.size())/float(nNeg);
int depth = CalcTreeDepth(leftChild,rightChild);
if(t==1)
aveEval+=depth;
else
aveEval+=depth*_FAR;
_FAR *=far;
nFea = nFea + feaId.size();
gettimeofday(&end,NULL);
time += (end.tv_sec - start.tv_sec);
int nNegPass = negPassIndex.size();
printf("FAR(t)=%.2f%%, FAR=%.2g, depth=%d, nFea(t)=%d, nFea=%d, cost=%.3f.\n",far*100.,_FAR,depth,feaId.size(),nFea,mincost);
printf("\t\tnNegPass=%d, aveEval=%.3f, time=%.3fs, meanT=%.3fs.\n", nNegPass, aveEval, time, time/(stages+1));
if(_FAR<=opt.maxFAR){
printf("\n\nThe training is converged at iteration %d. FAR = %.2f%%\n", t, _FAR * 100);
break;
}
SaveIter(feaId,leftChild,rightChild,cutpoint,fit,threshold);
gettimeofday(&Tstart,NULL);
neg.Remove(negPassIndex);
MiningNeg(nPos,neg);
nonfaceFea = neg.ExtractPixel();
pos.CalcWeight(1,opt.maxWeight);
neg.CalcWeight(-1,opt.maxWeight);
gettimeofday(&Tend,NULL);
float Ttime = (Tend.tv_sec - Tstart.tv_sec);
printf("neg mining time:%.3fs\n",Ttime);
if(!(stages%opt.saveStep)){
Save();
printf("save the model\n");
}
}
delete []w;
}
