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);
}
ImageFeature detect(const ImageFeature& img, const PCA& pca,uint dim, uint w, uint h) {
uint imgdim=img.xsize()*img.ysize();
uint padx=img.xsize()+w; uint pady=img.ysize()+h;
uint paddim=padx*pady;
#ifdef HAVE_FFT_LIBRARY
// get memory for calculations
fftw_complex *FIMG=NULL, **FFILTER=NULL, **FMULT=NULL;
FIMG=new fftw_complex[paddim];
for(uint i=0;i<paddim;++i) {FIMG[i].re=0.0;FIMG[i].im=0.0;}
FFILTER=new fftw_complex*[dim];
FMULT=new fftw_complex*[dim];
for(uint i=0;i<dim;++i) {
FFILTER[i]=new fftw_complex[paddim];
for(uint j=0;j<imgdim;++j) {FFILTER[i][j].re=0.0;FFILTER[i][j].im=0.0;}
FMULT[i]=new fftw_complex[paddim];
for(uint j=0;j<imgdim;++j) {FMULT[i][j].re=0.0;FMULT[i][j].im=0.0;}
}
DBG(10) << "Memory allocated" << endl;
vector<double> meanTrans=pca.transform(pca.mean(),dim);
// create strategy for fft
fftwnd_plan plan = fftw2d_create_plan(padx,pady, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_IN_PLACE);
fftwnd_plan planb = fftw2d_create_plan(padx,pady, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_IN_PLACE);
DBG(10) << "Strategies for FFT created" << endl;
//copy image into fourier transform data structure
for(uint x=0;x<img.xsize();++x) { for(uint y=0;y<img.ysize();++y) { FIMG[y*padx+x].re=img(x,y,0); } }
// fourier transform the image
fftwnd_one(plan,FIMG,NULL);
DBG(10) << "Image Transformed" << endl;
// fourier transform the filters
for(uint d=0;d<dim;++d) {
for(uint x=0;x<w;++x) {
for(uint y=0;y<h;++y) {
uint i=y*padx+x;
FFILTER[d][i].re=pca.eigenvector(d)[y*w+x];
}
}
fftwnd_one(plan,FFILTER[d],NULL);
DBG(10) << "Filter " << d << " transformed." << endl;
}
// multiplication in fourier domain
for(uint d=0;d<dim;++d) {
for(uint i=0;i<paddim;++i) {
FMULT[d][i].re=FIMG[i].re*FFILTER[d][i].re-FIMG[i].im*FFILTER[d][i].im;
FMULT[d][i].im=FIMG[i].re*FFILTER[d][i].im+FIMG[i].im*FFILTER[d][i].re;
}
DBG(10) << "Filter " << d << " applied." ;
//fourier back transform
fftwnd_one(planb,FMULT[d],NULL);
BLINK(10) << "... backtransformed.." ;
// subtract transformed mean
for(uint i=0;i<paddim;++i) {
FMULT[d][i].re=FMULT[d][i].re/paddim-meanTrans[d];
}
BLINK(10) << ". Mean subtracted." << endl;
}
double energyTrans, energyImg;
ImageFeature fpm(img.xsize(),img.ysize(),1);
for(uint x=0;x<img.xsize();++x) {
for(uint y=0;y<img.ysize();++y) {
uint i=y*padx+x;
energyTrans=energTrans(FMULT,i,dim);
energyImg=energImg(img,x,y,w,h,pca.mean());
DBG(25) << VAR(x) << " " << VAR(y) << " " << VAR(energyTrans) << " " << VAR(energyImg) << endl;
fpm(x,y,0)=energyImg-energyTrans;
}
}
DBG(10) << "fpm generation." << endl;
return fpm;
#endif
}
