vector<double> toVector(const ImageFeature &image) {
vector<double> result(image.size());
for(uint i=0;i<image.size();++i) {
result[i]=image[i];
}
return result;
}
void histogramNormalization(ImageFeature &img) {
const uint bins=256;
vector<int> H(bins);
vector<double> T(bins);
for(uint c=0;c<img.zsize();++c) {
//DBG(10) << "c=" << c << endl;
for(uint x=0;x<img.xsize();++x) {
for(uint y=0;y<img.ysize();++y) {
++H[int(img(x,y,c)*255)];
}
}
for(uint p=1;p<bins;++p) {
H[p]+=H[p-1];
}
for(uint p=0;p<bins;++p) {
T[p]=(1.0/double(img.size()))*double(H[p]);
}
for(uint x=0;x<img.xsize();++x) {
for(uint y=0;y<img.ysize();++y) {
img(x,y,c)=T[int(img(x,y,c)*255)];
}
}
}
}
int main(int argc , char **argv) {
GetPot cl(argc,argv);
if(cl.search("-h")) {USAGE(); exit(0);}
string line;
if(cl.search("-E")) {
cout << "Estimating PCA" << endl;
ifstream filelist(cl.follow("filelist","-E"));
ImageFeature img;
getline(filelist,line);
img.load(line,true);
cout << line << endl;
PCA pca(img.size());
cout << img.size() << endl;
pca.putData(img.layerVector(0));
while(getline(filelist,line)) {
cout << line << endl;
img.load(line,true);
pca.putData(img.layerVector(0));
}
pca.dataEnd();
pca.save(cl.follow("covariance.pca","-c"));
pca.calcPCA();
pca.save(cl.follow("transformation.pca","-t"));
filelist.close();
} else if(cl.search("-T")) {
PCA pca;
pca.load(cl.follow("transformation.pca","-t"));
int dim=cl.follow(20,"-d");
ifstream filelist(cl.follow("filelist","-T"));
vector<double> tmp;
ImageFeature img;
while(getline(filelist,line)) {
cout << line << endl;
img.load(line,true);
tmp=pca.transform(img.layerVector(0),img.size());
tmp.resize(dim);
VectorFeature tmpvec(tmp);
tmpvec.save(line+".pca.vec.gz");
}
filelist.close();
} else if(cl.search("-B")) {
PCA pca;
pca.load(cl.follow("transformation.pca","-t"));
int x=cl.follow(16,"-x");
int y=cl.follow(16,"-y");
ifstream filelist(cl.follow("filelist","-B"));
vector<double> tmp;
VectorFeature tmpvec;
while(getline(filelist,line)) {
cout << line << endl;
tmpvec.load(line);
vector<double> tmp;
tmp=pca.backTransform(tmpvec.data());
ImageFeature img(tmp,x,y);
cutoff(img);
img.save(line+".backpca.png");
}
filelist.close();
} else if(cl.search("-M")) {
ImageFeature img; img.load(cl.follow("image.png","-M"),true);
PCA pca;
vector<double> backproj,vec;
pca.load(cl.follow("transformation.pca","-t"));
int w=cl.follow(16,"-x");
int h=cl.follow(16,"-y");
double scalefac=cl.follow(0.8333,"-s");
int dim=cl.follow(20,"-d");
ImageFeature scimg(img);
ImageFeature patch;
uint minX=100000, minY=100000;
uint maxX=100000, maxY=100000;
while(int(scimg.xsize())>=w and int(scimg.ysize())>=h) {
DBG(10) << VAR(scimg.xsize()) << " x " << VAR(scimg.ysize()) << endl;
ImageFeature faceprobmap(scimg.xsize(),scimg.ysize(),1);
double maxDist=0.0;
double minDist=numeric_limits<double>::max();
vector<double> tmpvec;
for(uint x=0;x<scimg.xsize();++x) {
DBG(10) << VAR(x) << endl;
for(uint y=0;y<scimg.ysize();++y) {
patch=getPatch(scimg,x,y,x+w,y+h);
vec=patch.layerVector(0);
vector<double> imgMinMean=vec;
for(uint i=0;i<imgMinMean.size();++i) {
imgMinMean[i]-=pca.mean()[i];
}
double energyImg=getEnergy(imgMinMean);
tmpvec=pca.transform(vec,dim);
double energyTrans=getEnergy(tmpvec);
backproj=pca.backTransform(tmpvec);
double energyBack=getEnergy(backproj);
double d=0;
double tmp;
for(uint i=0;i<backproj.size();++i) {
tmp=backproj[i]-vec[i];
d+=tmp*tmp;
}
faceprobmap(x,y,0)=d;
DBG(10) << VAR(energyImg) << " "
<< VAR(energyTrans) << " "
<< VAR(energyBack) << " "
<< VAR(energyImg-energyTrans) << " "
<< VAR(d) << endl;
if(minDist>d) {
minDist=d;
minX=x; minY=y;
}
if(maxDist<d) {
maxDist=d;
maxX=x; maxY=y;
}
}
}
DBG(10) << VAR(scimg.xsize()) << " " << VAR(scimg.ysize()) << endl;
DBG(10) << VAR(minDist) << " " << VAR(minX) << " " << VAR(minY) << endl;
DBG(10) << VAR(maxDist) << " " << VAR(maxX) << " " << VAR(maxY) << endl << endl;
normalize(faceprobmap);
ostringstream filenamestream;
filenamestream << cl.follow("image.png","-M") << ".fpm." << (scimg.xsize()) <<".png";
faceprobmap.save(filenamestream.str());
uint newW=int(scimg.xsize()*scalefac);
uint newH=int(scimg.ysize()*scalefac);
scimg=scale(scimg,newW,newH);
}
} else if(cl.search("-F")) {
#ifdef HAVE_FFT_LIBRARY
ImageFeature img; img.load(cl.follow("image.png","-F"),true);
PCA pca; pca.load(cl.follow("transformation.pca","-t"));
int w=cl.follow(16,"-x");
int h=cl.follow(16,"-y");
double scalefac=cl.follow(0.8333,"-s");
uint dim=cl.follow(20,"-d");
DBG(10) << "Eigenfaces loaded" << endl;
ImageFeature scimg=img;
while(int(scimg.xsize())>w and int(scimg.ysize())>h) {
ImageFeature fpm=detect(scimg,pca,dim,w,h);
pair<uint,uint> p;
p=argmax(fpm);
DBG(10) << scimg.xsize() << "x" << scimg.ysize() << " (" <<p.first<<", "<< p.second << ") " << VAR(maximum(fpm)) ;
p=argmin(fpm);
BLINK(10) << " (" <<p.first<<", "<< p.second << ") " << VAR(minimum(fpm)) << endl;
normalize(fpm);
ostringstream filenamestream;
filenamestream << cl.follow("image.png","-F") << ".fpm." << (scimg.xsize()) <<".png";
fpm.save(filenamestream.str());
scimg=scale(scimg,int(scimg.xsize()*scalefac),int(scimg.ysize()*scalefac));
}
#else
DBG(10) << "compiled without FFT lib. this does not work. use -M option" << endl;
#endif
} else {
USAGE();
exit(20);
}
}