bayesianquantization.cpp
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#include <QtConcurrent>
#include <openbr/plugins/openbr_internal.h>
#include <openbr/core/opencvutils.h>
using namespace cv;
namespace br
{
/*!
* \ingroup distances
* \brief Bayesian quantization distance
* \author Josh Klontz \cite jklontz
*/
class BayesianQuantizationDistance : public Distance
{
Q_OBJECT
Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
BR_PROPERTY(QString, inputVariable, "Label")
QVector<float> loglikelihoods;
static void computeLogLikelihood(const Mat &data, const QList<int> &labels, float *loglikelihood)
{
const QList<uchar> vals = OpenCVUtils::matrixToVector<uchar>(data);
if (vals.size() != labels.size())
qFatal("Logic error.");
QVector<quint64> genuines(256, 0), impostors(256,0);
for (int i=0; i<vals.size(); i++)
for (int j=i+1; j<vals.size(); j++)
if (labels[i] == labels[j]) genuines[abs(vals[i]-vals[j])]++;
else impostors[abs(vals[i]-vals[j])]++;
quint64 totalGenuines(0), totalImpostors(0);
for (int i=0; i<256; i++) {
totalGenuines += genuines[i];
totalImpostors += impostors[i];
}
for (int i=0; i<256; i++)
loglikelihood[i] = log((float(genuines[i]+1)/totalGenuines)/(float(impostors[i]+1)/totalImpostors));
}
void train(const TemplateList &src)
{
if ((src.first().size() > 1) || (src.first().m().type() != CV_8UC1))
qFatal("Expected sigle matrix templates of type CV_8UC1!");
const Mat data = OpenCVUtils::toMat(src.data());
const QList<int> templateLabels = src.indexProperty(inputVariable);
loglikelihoods = QVector<float>(data.cols*256, 0);
QFutureSynchronizer<void> futures;
for (int i=0; i<data.cols; i++)
futures.addFuture(QtConcurrent::run(&BayesianQuantizationDistance::computeLogLikelihood, data.col(i), templateLabels, &loglikelihoods.data()[i*256]));
futures.waitForFinished();
}
float compare(const cv::Mat &a, const cv::Mat &b) const
{
const uchar *aData = a.data;
const uchar *bData = b.data;
const int size = a.rows * a.cols;
float likelihood = 0;
for (int i=0; i<size; i++)
likelihood += loglikelihoods[i*256+abs(aData[i]-bData[i])];
return likelihood;
}
void store(QDataStream &stream) const
{
stream << loglikelihoods;
}
void load(QDataStream &stream)
{
stream >> loglikelihoods;
}
};
BR_REGISTER(Distance, BayesianQuantizationDistance)
} // namespace br
#include "distance/bayesianquantization.moc"