本文整理汇总了C++中ImageFeature::size方法的典型用法代码示例。如果您正苦于以下问题：C++ ImageFeature::size方法的具体用法？C++ ImageFeature::size怎么用？C++ ImageFeature::size使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类ImageFeature的用法示例。
在下文中一共展示了ImageFeature::size方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: toVector
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;
}示例2: histogramNormalization
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)];
}
}
}
}示例3: main
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];
//.........这里部分代码省略.........