quality.cpp
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#include <openbr_plugin.h>
#include "core/common.h"
using namespace br;
namespace br
{
/*!
* \ingroup transforms
* \brief Impostor Uniqueness Measure \cite klare12
* \author Josh Klontz \cite jklontz
*/
class IUMTransform : public Transform
{
Q_OBJECT
Q_PROPERTY(br::Distance* distance READ get_distance WRITE set_distance RESET reset_distance STORED false)
Q_PROPERTY(double mean READ get_mean WRITE set_mean RESET reset_mean)
Q_PROPERTY(double stddev READ get_stddev WRITE set_stddev RESET reset_stddev)
BR_PROPERTY(br::Distance*, distance, Distance::make("Dist(L2)", this))
BR_PROPERTY(double, mean, 0)
BR_PROPERTY(double, stddev, 1)
br::TemplateList impostors;
float calculateIUM(const Template &probe, const TemplateList &gallery) const
{
const int probeLabel = probe.file.label();
TemplateList subset = gallery;
for (int j=subset.size()-1; j>=0; j--)
if (subset[j].file.label() == probeLabel)
subset.removeAt(j);
QList<float> scores = distance->compare(subset, probe);
float min, max;
Common::MinMax(scores, &min, &max);
double mean;
Common::Mean(scores, &mean);
return (max-mean)/(max-min);
}
void train(const TemplateList &data)
{
distance->train(data);
impostors = data;
QList<float> iums; iums.reserve(impostors.size());
for (int i=0; i<data.size(); i++)
iums.append(calculateIUM(impostors[i], impostors));
Common::MeanStdDev(iums, &mean, &stddev);
}
void project(const Template &src, Template &dst) const
{
dst = src;
float ium = calculateIUM(src, impostors);
dst.file.insert("IUM", ium);
dst.file.insert("IUM_Bin", ium < mean-stddev ? 0 : (ium < mean+stddev ? 1 : 2));
}
void store(QDataStream &stream) const
{
distance->store(stream);
stream << mean << stddev << impostors;
}
void load(QDataStream &stream)
{
distance->load(stream);
stream >> mean >> stddev >> impostors;
}
};
BR_REGISTER(Transform, IUMTransform)
/* Kernel Density Estimator */
struct KDE
{
float min, max;
double mean, stddev;
QList<float> bins;
KDE() : min(0), max(1) {}
KDE(const QList<float> &scores)
{
Common::MinMax(scores, &min, &max);
Common::MeanStdDev(scores, &mean, &stddev);
double h = Common::KernelDensityBandwidth(scores);
const int size = 255;
bins.reserve(size);
for (int i=0; i<size; i++)
bins.append(Common::KernelDensityEstimation(scores, min + (max-min)*i/(size-1), h));
}
float operator()(float score, bool gaussian = false) const
{
if (gaussian) return 1/(stddev*sqrt(2*CV_PI))*exp(-0.5*pow((score-mean)/stddev, 2));
if (score <= min) return bins.first();
if (score >= max) return bins.last();
const float x = (score-min)/(max-min)*bins.size();
const float y1 = bins[floor(x)];
const float y2 = bins[ceil(x)];
return y1 + (y2-y1)*(x-floor(x));
}
};
QDataStream &operator<<(QDataStream &stream, const KDE &kde)
{
return stream << kde.min << kde.max << kde.mean << kde.stddev << kde.bins;
}
QDataStream &operator>>(QDataStream &stream, KDE &kde)
{
return stream >> kde.min >> kde.max >> kde.mean >> kde.stddev >> kde.bins;
}
/* Match Probability */
struct MP
{
KDE genuine, impostor;
MP() {}
MP(const QList<float> &genuineScores, const QList<float> &impostorScores)
: genuine(genuineScores), impostor(impostorScores) {}
float operator()(float score, bool gaussian = false, bool log = false) const
{
const float g = genuine(score, gaussian);
const float s = g / (impostor(score, gaussian) + g);
if (log) return (std::max(std::log10(s), -10.f) + 10)/10;
else return s;
}
};
QDataStream &operator<<(QDataStream &stream, const MP &nmp)
{
return stream << nmp.genuine << nmp.impostor;
}
QDataStream &operator>>(QDataStream &stream, MP &nmp)
{
return stream >> nmp.genuine >> nmp.impostor;
}
/*!
* \ingroup distances
* \brief Match Probability \cite klare12
* \author Josh Klontz \cite jklontz
*/
class MPDistance : public Distance
{
Q_OBJECT
Q_PROPERTY(br::Distance* distance READ get_distance WRITE set_distance RESET reset_distance STORED false)
Q_PROPERTY(QString binKey READ get_binKey WRITE set_binKey RESET reset_binKey STORED false)
Q_PROPERTY(bool gaussian READ get_gaussian WRITE set_gaussian RESET reset_gaussian STORED false)
Q_PROPERTY(bool log READ get_log WRITE set_log RESET reset_log STORED false)
BR_PROPERTY(br::Distance*, distance, make("Dist(L2)"))
BR_PROPERTY(QString, binKey, "")
BR_PROPERTY(bool, gaussian, false)
BR_PROPERTY(bool, log, false)
QHash<QString, MP> mps;
void train(const TemplateList &src)
{
distance->train(src);
const QList<int> labels = src.labels<int>();
QScopedPointer<MatrixOutput> memoryOutput(dynamic_cast<MatrixOutput*>(Output::make(".Matrix", FileList(src.size()), FileList(src.size()))));
distance->compare(src, src, memoryOutput.data());
QHash< QString, QList<float> > genuineScores, impostorScores;
for (int i=0; i<src.size(); i++)
for (int j=0; j<i; j++) {
const float score = memoryOutput.data()->data.at<float>(i, j);
const QString bin = src[i].file.getString(binKey, "");
if (labels[i] == labels[j]) genuineScores[bin].append(score);
else impostorScores[bin].append(score);
}
foreach (const QString &key, genuineScores.keys())
mps.insert(key, MP(genuineScores[key], impostorScores[key]));
}
float _compare(const Template &target, const Template &query) const
{
return mps[query.file.getString(binKey, "")](distance->compare(target, query), gaussian, log);
}
void store(QDataStream &stream) const
{
distance->store(stream);
stream << mps;
}
void load(QDataStream &stream)
{
distance->load(stream);
stream >> mps;
}
};
BR_REGISTER(Distance, MPDistance)
} // namespace br
#include "quality.moc"