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);
}
}
int main(int argc, char**argv) {
GetPot cl(argc, argv);
if(cl.search(2,"-h","--help")) {USAGE(); exit(0);}
string pcafile=cl.follow("pca.pca",2,"-p","--pca");
string imagefile=cl.follow("image",2,"-i","--img");
VectorFeature vec;
PCA pca; pca.load(pcafile);
uint width, height, depth;
if(cl.search(2,"-c","--color")) {
depth=3;
} else {
depth=1;
}
if(!cl.search(2,"-w","--width") && !cl.search(2,"-h","--height")) {
width=uint(sqrt(double(pca.dim())));
height=width;
if(int(height*width)!=pca.dim()) {
ERR << "pca not for squared images, specify width or height" << endl
<< "height=" << height << "* width=" << width << "!= size=" << pca.dim() << endl;
exit(20);
}
} else {
if(cl.search(2,"-w","--width") && !cl.search(2,"-h","--height")) {
width=cl.follow(10,2,"-w","--width");
height=pca.dim()/width;
if(int(height*width)!=pca.dim()) {
ERR << "pca images of this width, specify valid values" << endl
<< "height=" << height << "* width=" << width << "!= size=" << pca.dim() << endl;
exit(20);
}
} else if(!cl.search(2,"-w","--width") && cl.search(2,"-h","--height")) {
height=cl.follow(10,2,"-j","--height");
width=pca.dim()/height;
if(int(height*width)!=pca.dim()) {
ERR << "pca images of this height, specify valid values" << endl
<< "height=" << height << "* width=" << width << "!= size=" << pca.dim() << endl;
exit(20);
}
} else {
height=cl.follow(10,2,"-j","--height");
width=cl.follow(10,2,"-w","--width");
if(int(height*width)!=pca.dim()) {
ERR << "pca images of this height and width, specify valid values" << endl
<< "height=" << height << "* width=" << width << "!= size=" << pca.dim() << endl;
exit(20);
}
}
}
vector<double> backtransformed;
if(cl.search(2,"-v","--vec") && !cl.search(2,"-b","--base") && !cl.search(2,"-M","--mean")) {
string vecfile=cl.follow("vec.vec",2,"-v","--vec");
DBG(10) << "Loading Vectorfile " << vecfile << endl;
vec.load(vecfile);
DBG(10) << "Vector to be backtransformed" ;
for(uint i=0;i<vec.size();++i) {
BLINK(10) << " "<< vec[i];
}
BLINK(10) << endl;
if(cl.search("-n1st")) vec[0]=0;
if(cl.search("--nopca")) {
backtransformed=vec.data();
} else {
backtransformed=pca.backTransform(vec.data());
}
DBG(10) << "Backtransformed Vector" ;
for(uint i=0;i<backtransformed.size();++i) {
BLINK(10) << " " <<backtransformed[i];
}
BLINK(10) << endl;
} else if(cl.search(2,"-b","--base") && !cl.search(2,"-v","--vec") && !cl.search(2,"-M","--mean")) {
uint base=cl.follow(0,2,"-b","--base");
backtransformed=pca.eigenvector(base);
} else if(!cl.search(2,"-b","--base") && !cl.search(2,"-v","--vec") && cl.search(2,"-M","--mean")) {
backtransformed=pca.mean();
} else {
USAGE();
exit(20);
}
ImageFeature image(width,height,depth);
if(cl.search(2,"-m","--minMean")) {
DBG(10) << "Subtracting mean" << endl;
for(uint i=0;i<backtransformed.size();++i) {
backtransformed[i]-pca.mean()[i];
}
}
for(uint i=0;i<backtransformed.size();++i) {
image[i]=backtransformed[i];
}
if(cl.search(2,"-n","--norm")) {
DBG(10) << "normalization" << endl;
normalize(image);
}
// //make values positive
// shift(image,-minimum(image,0));
//
cutoff(image);
//
DBG(10) << "Going to save:" ;
for(uint i=0;i<image.size();++i) {
BLINK(10) << " " <<image[i];
}
BLINK(10) << endl;
image.save(imagefile);
DBG(10) << "cmdline was: "; printCmdline(argc,argv);
}
int main(int argc, char**argv) {
GetPot cl(argc, argv);
if(cl.search(2,"-h","--help") or !cl.search("-lf")) {USAGE(); exit(0);}
bool nonorm = cl.search(1, "-nonorm");
LocalFeatures lf; lf.load(cl.follow("test.lf.gz","-lf"));
uint sc = cl.follow(1, "-scale");
uint w=uint(lf.winsize()*2+1);
ImageFeature img(w,w,lf.zsize());
string savefilename = cl.follow("", "-save");
bool backtransform = false;
PCA pca;
if(cl.search("-unpca"))
{
if( !pca.load(cl.follow("", "-unpca")) )
{
ERR << "Error loading PCA file!" << endl;
abort();
}
backtransform = true;
}
bool dontshow = cl.search("-dontshow");
for(uint i=0;i<lf.size();++i) {
DBG(10) << "lf " << i << endl;
if(!backtransform) {
for(uint j=0;j<lf.dim();++j) {
img[j] = lf.getData()[i][j];
}
} else {
vector<double> backtransformed = pca.backTransform(lf.getData()[i]);
for(uint j=0;j<(uint)pca.dim();++j) {
img[j] = backtransformed[j];
}
}
if (sc != 1) {
img = scale(img, w * sc, w * sc);
}
if (!nonorm) {
normalize(img);
}
if(!dontshow) {
if(cl.search("-layerwise")) {
for(uint c=0;c<img.zsize();++c) {
BLINK(10) << " " << c;
img.display(c,c,c);
}
BLINK(10) << endl;
} else {
BLINK(10) << "all layers" << endl;;
img.display();
}
}
if (savefilename != "") {
std::ostringstream filenamewithnr;
filenamewithnr << savefilename << "_" << setw(3) << setfill('0') << i << ".png";
img.save(filenamewithnr.str());
}
}
}
