本文整理汇总了C++中MetaData::containsLabel方法的典型用法代码示例。如果您正苦于以下问题：C++ MetaData::containsLabel方法的具体用法？C++ MetaData::containsLabel怎么用？C++ MetaData::containsLabel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MetaData的用法示例。
在下文中一共展示了MetaData::containsLabel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: run
void run()
{
// Get angles ==============================================================
MetaData angles;
angles.read(fnIn);
size_t AngleNo = angles.size();
if (AngleNo == 0 || !angles.containsLabel(MDL_ANGLE_ROT))
REPORT_ERROR(ERR_MD_BADLABEL, "Input file doesn't contain angular information");
double maxWeight = -99.e99;
MultidimArray<double> weight;
weight.initZeros(AngleNo);
if (angles.containsLabel(MDL_WEIGHT))
{
// Find maximum weight
int i=0;
FOR_ALL_OBJECTS_IN_METADATA(angles)
{
double w;
angles.getValue(MDL_WEIGHT,w,__iter.objId);
DIRECT_A1D_ELEM(weight,i++)=w;
maxWeight=XMIPP_MAX(w,maxWeight);
}
}示例2: processInprocessInputPrepareSPTH
//majorAxis and minorAxis is the estimated particle size in px
void ProgSortByStatistics::processInprocessInputPrepareSPTH(MetaData &SF, bool trained)
{
//#define DEBUG
PCAMahalanobisAnalyzer tempPcaAnalyzer0;
PCAMahalanobisAnalyzer tempPcaAnalyzer1;
PCAMahalanobisAnalyzer tempPcaAnalyzer2;
PCAMahalanobisAnalyzer tempPcaAnalyzer3;
PCAMahalanobisAnalyzer tempPcaAnalyzer4;
//Morphology
tempPcaAnalyzer0.clear();
//Signal to noise ratio
tempPcaAnalyzer1.clear();
tempPcaAnalyzer2.clear();
tempPcaAnalyzer3.clear();
//Histogram analysis, to detect black points and saturated parts
tempPcaAnalyzer4.clear();
double sign = 1;//;-1;
int numNorm = 3;
int numDescriptors0=numNorm;
int numDescriptors2=4;
int numDescriptors3=11;
int numDescriptors4 = 10;
MultidimArray<float> v0(numDescriptors0);
MultidimArray<float> v2(numDescriptors2);
MultidimArray<float> v3(numDescriptors3);
MultidimArray<float> v4(numDescriptors4);
if (verbose>0)
{
std::cout << " Sorting particle set by new xmipp method..." << std::endl;
}
int nr_imgs = SF.size();
if (verbose>0)
init_progress_bar(nr_imgs);
int c = XMIPP_MAX(1, nr_imgs / 60);
int imgno = 0, imgnoPCA=0;
bool thereIsEnable=SF.containsLabel(MDL_ENABLED);
bool first=true;
// We assume that at least there is one particle
size_t Xdim, Ydim, Zdim, Ndim;
getImageSize(SF,Xdim,Ydim,Zdim,Ndim);
//Initialization:
MultidimArray<double> nI, modI, tempI, tempM, ROI;
MultidimArray<bool> mask;
nI.resizeNoCopy(Ydim,Xdim);
modI.resizeNoCopy(Ydim,Xdim);
tempI.resizeNoCopy(Ydim,Xdim);
tempM.resizeNoCopy(Ydim,Xdim);
mask.resizeNoCopy(Ydim,Xdim);
mask.initConstant(true);
MultidimArray<double> autoCorr(2*Ydim,2*Xdim);
MultidimArray<double> smallAutoCorr;
Histogram1D hist;
Matrix2D<double> U,V,temp;
Matrix1D<double> D;
MultidimArray<int> radial_count;
MultidimArray<double> radial_avg;
Matrix1D<int> center(2);
MultidimArray<int> distance;
int dim;
center.initZeros();
v0.initZeros(numDescriptors0);
v2.initZeros(numDescriptors2);
v3.initZeros(numDescriptors3);
v4.initZeros(numDescriptors4);
ROI.resizeNoCopy(Ydim,Xdim);
ROI.setXmippOrigin();
FOR_ALL_ELEMENTS_IN_ARRAY2D(ROI)
{
double temp = std::sqrt(i*i+j*j);
if ( temp < (Xdim/2))
A2D_ELEM(ROI,i,j)= 1;
else
A2D_ELEM(ROI,i,j)= 0;
}
Image<double> img;
FourierTransformer transformer(FFTW_BACKWARD);
FOR_ALL_OBJECTS_IN_METADATA(SF)
{
if (thereIsEnable)
{
int enabled;
SF.getValue(MDL_ENABLED,enabled,__iter.objId);
if ( (enabled==-1) )
//.........这里部分代码省略.........
示例3: processInputPrepare
void ProgSortByStatistics::processInputPrepare(MetaData &SF)
{
PCAMahalanobisAnalyzer tempPcaAnalyzer;
tempPcaAnalyzer.clear();
Image<double> img;
MultidimArray<double> img2;
MultidimArray<int> radial_count;
MultidimArray<double> radial_avg;
Matrix1D<int> center(2);
center.initZeros();
if (verbose>0)
std::cout << " Processing training set ..." << std::endl;
int nr_imgs = SF.size();
if (verbose>0)
init_progress_bar(nr_imgs);
int c = XMIPP_MAX(1, nr_imgs / 60);
int imgno = 0, imgnoPCA=0;
MultidimArray<float> v;
MultidimArray<int> distance;
int dim;
bool thereIsEnable=SF.containsLabel(MDL_ENABLED);
bool first=true;
FOR_ALL_OBJECTS_IN_METADATA(SF)
{
if (thereIsEnable)
{
int enabled;
SF.getValue(MDL_ENABLED,enabled,__iter.objId);
if (enabled==-1)
continue;
}
img.readApplyGeo(SF,__iter.objId);
if (targetXdim!=-1 && targetXdim!=XSIZE(img()))
selfScaleToSize(LINEAR,img(),targetXdim,targetXdim,1);
MultidimArray<double> &mI=img();
mI.setXmippOrigin();
mI.statisticsAdjust(0,1);
// Overall statistics
Histogram1D hist;
compute_hist(mI,hist,-4,4,31);
// Radial profile
img2.resizeNoCopy(mI);
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(img2)
{
double val=DIRECT_MULTIDIM_ELEM(mI,n);
DIRECT_MULTIDIM_ELEM(img2,n)=val*val;
}
if (first)
{
radialAveragePrecomputeDistance(img2, center, distance, dim);
first=false;
}
fastRadialAverage(img2, distance, dim, radial_avg, radial_count);
// Build vector
v.initZeros(XSIZE(hist)+XSIZE(img2)/2);
int idx=0;
FOR_ALL_DIRECT_ELEMENTS_IN_ARRAY1D(hist)
v(idx++)=(float)DIRECT_A1D_ELEM(hist,i);
for (size_t i=0; i<XSIZE(img2)/2; i++)
v(idx++)=(float)DIRECT_A1D_ELEM(radial_avg,i);
tempPcaAnalyzer.addVector(v);
if (imgno % c == 0 && verbose>0)
progress_bar(imgno);
imgno++;
imgnoPCA++;
}
if (verbose>0)
progress_bar(nr_imgs);
MultidimArray<double> vavg,vstddev;
tempPcaAnalyzer.computeStatistics(vavg,vstddev);
tempPcaAnalyzer.evaluateZScore(2,20,false);
pcaAnalyzer.insert(pcaAnalyzer.begin(), tempPcaAnalyzer);
}