Peter M. Groen / openbr

Commit d86637aec2a1289af9f609d7cd5968b646321dff

Authored by Josh Klontz
1 parent 0d0f5bd4

implemented BayesianQuantizationDistance

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Showing 1 changed file with 61 additions and 0 deletions
openbr/plugins/quantize.cpp
  View file @d86637a
@@ -56,6 +56,67 @@ class QuantizeTransform : public Transform @@ -56,6 +56,67 @@ class QuantizeTransform : public Transform
56 BR_REGISTER(Transform, QuantizeTransform) 56 BR_REGISTER(Transform, QuantizeTransform)
57 57
58 /*! 58 /*!
  59 + * \ingroup distances
  60 + * \brief Bayesian quantization distance
  61 + * \author Josh Klontz \cite jklontz
  62 + */
  63 +class BayesianQuantizationDistance : public Distance
  64 +{
  65 + Q_OBJECT
  66 + QVector<float> loglikelihood;
  67 +
  68 + void train(const TemplateList &src)
  69 + {
  70 + if (src.first().size() > 1)
  71 + qFatal("Expected sigle matrix templates.");
  72 +
  73 + Mat data = OpenCVUtils::toMat(src.data());
  74 + QList<int> labels = src.labels<int>();
  75 +
  76 + QVector<qint64> genuines(256*256,0), impostors(256*256,0);
  77 + qint64 totalGenuines(0), totalImpostors(0);
  78 + for (int i=0; i<labels.size(); i++) {
  79 + const uchar *a = data.ptr(i);
  80 + for (int j=0; j<labels.size(); j++) {
  81 + const uchar *b = data.ptr(j);
  82 + const bool genuine = (labels[i] == labels[j]);
  83 + for (int k=0; k<data.cols; k++) {
  84 + if (genuine) { genuines[256*a[k]+b[k]]++; genuines[256*b[k]+a[k]]++; totalGenuines++; }
  85 + else { impostors[256*a[k]+b[k]]++; impostors[256*b[k]+a[k]]++; totalImpostors++; }
  86 + }
  87 + }
  88 + }
  89 +
  90 + loglikelihood = QVector<float>(256*256);
  91 + for (int i=0; i<256*256; i++)
  92 + loglikelihood[i] = log((double(genuines[i]+1)/totalGenuines)/(double(impostors[i]+1)/totalImpostors));
  93 + }
  94 +
  95 + float compare(const Template &a, const Template &b) const
  96 + {
  97 + const uchar *aData = a.m().data;
  98 + const uchar *bData = b.m().data;
  99 + const int size = a.m().rows * a.m().cols;
  100 + float likelihood = 0;
  101 + for (int i=0; i<size; i++)
  102 + likelihood += loglikelihood[256*aData[i]+bData[i]];
  103 + return likelihood;
  104 + }
  105 +
  106 + void load(QDataStream &stream)
  107 + {
  108 + stream >> loglikelihood;
  109 + }
  110 +
  111 + void store(QDataStream &stream) const
  112 + {
  113 + stream << loglikelihood;
  114 + }
  115 +};
  116 +
  117 +BR_REGISTER(Distance, BayesianQuantizationDistance)
  118 +
  119 +/*!
59 * \ingroup transforms 120 * \ingroup transforms
60 * \brief Approximate floats as signed bit. 121 * \brief Approximate floats as signed bit.
61 * \author Josh Klontz \cite jklontz 122 * \author Josh Klontz \cite jklontz