cluster.cpp
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/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* Copyright 2012 The MITRE Corporation *
* *
* Licensed under the Apache License, Version 2.0 (the "License"); *
* you may not use this file except in compliance with the License. *
* You may obtain a copy of the License at *
* *
* http://www.apache.org/licenses/LICENSE-2.0 *
* *
* Unless required by applicable law or agreed to in writing, software *
* distributed under the License is distributed on an "AS IS" BASIS, *
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. *
* See the License for the specific language governing permissions and *
* limitations under the License. *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
#include <opencv2/flann/flann.hpp>
#include "openbr_internal.h"
#include "openbr/core/common.h"
#include "openbr/core/opencvutils.h"
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Wraps OpenCV kmeans and flann.
* \author Josh Klontz \cite jklontz
*/
class KMeansTransform : public Transform
{
Q_OBJECT
Q_PROPERTY(int kTrain READ get_kTrain WRITE set_kTrain RESET reset_kTrain STORED false)
Q_PROPERTY(int kSearch READ get_kSearch WRITE set_kSearch RESET reset_kSearch STORED false)
BR_PROPERTY(int, kTrain, 256)
BR_PROPERTY(int, kSearch, 1)
Mat centers;
mutable QScopedPointer<flann::Index> index;
mutable QMutex mutex;
void reindex()
{
index.reset(new flann::Index(centers, flann::LinearIndexParams()));
}
void train(const TemplateList &data)
{
Mat bestLabels;
const double compactness = kmeans(OpenCVUtils::toMatByRow(data.data()), kTrain, bestLabels, TermCriteria(TermCriteria::MAX_ITER, 10, 0), 3, KMEANS_PP_CENTERS, centers);
qDebug("KMeans compactness = %f", compactness);
reindex();
}
void project(const Template &src, Template &dst) const
{
QMutexLocker locker(&mutex);
Mat dists, indicies;
index->knnSearch(src, indicies, dists, kSearch);
dst = indicies.reshape(1, 1);
}
void load(QDataStream &stream)
{
stream >> centers;
reindex();
}
void store(QDataStream &stream) const
{
stream << centers;
}
};
BR_REGISTER(Transform, KMeansTransform)
/*!
* \ingroup transforms
* \brief K nearest neighbors classifier.
* \author Josh Klontz \cite jklontz
*/
class KNNTransform : public Transform
{
Q_OBJECT
Q_PROPERTY(int k READ get_k WRITE set_k RESET reset_k STORED false)
Q_PROPERTY(br::Distance *distance READ get_distance WRITE set_distance RESET reset_distance STORED false)
Q_PROPERTY(bool weighted READ get_weighted WRITE set_weighted RESET reset_weighted STORED false)
Q_PROPERTY(int numSubjects READ get_numSubjects WRITE set_numSubjects RESET reset_numSubjects STORED false)
Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
Q_PROPERTY(QString galleryName READ get_galleryName WRITE set_galleryName RESET reset_galleryName STORED false)
BR_PROPERTY(int, k, 1)
BR_PROPERTY(br::Distance*, distance, NULL)
BR_PROPERTY(bool, weighted, false)
BR_PROPERTY(int, numSubjects, 1)
BR_PROPERTY(QString, inputVariable, "Label")
BR_PROPERTY(QString, outputVariable, "KNN")
BR_PROPERTY(QString, galleryName, "")
TemplateList gallery;
void train(const TemplateList &data)
{
distance->train(data);
gallery = data;
}
void project(const Template &src, Template &dst) const
{
QList< QPair<float, int> > sortedScores = Common::Sort(distance->compare(gallery, src), true);
QStringList subjects;
for (int i=0; i<numSubjects; i++) {
QHash<QString, float> votes;
const int max = (k < 1) ? sortedScores.size() : std::min(k, sortedScores.size());
for (int j=0; j<max; j++)
votes[gallery[sortedScores[j].second].file.get<QString>(inputVariable)] += (weighted ? sortedScores[j].first : 1);
subjects.append(votes.keys()[votes.values().indexOf(Common::Max(votes.values()))]);
// Remove subject from consideration
if (subjects.size() < numSubjects)
for (int j=sortedScores.size()-1; j>=0; j--)
if (gallery[sortedScores[j].second].file.get<QString>(inputVariable) == subjects.last())
sortedScores.removeAt(j);
}
dst.file.set(outputVariable, subjects.size() > 1 ? "[" + subjects.join(",") + "]" : subjects.first());
dst.file.set("Nearest", gallery[sortedScores[0].second].file.name);
}
void store(QDataStream &stream) const
{
stream << gallery;
}
void load(QDataStream &stream)
{
stream >> gallery;
}
void init()
{
if (!galleryName.isEmpty())
gallery = TemplateList::fromGallery(galleryName);
}
};
BR_REGISTER(Transform, KNNTransform)
/*!
* \ingroup transforms
* \brief Chooses k random points to be centroids.
* \author Austin Blanton \cite imaus10
* \see KMeansTransform
*/
class RandomCentroidsTransform : public Transform
{
Q_OBJECT
Q_PROPERTY(int kTrain READ get_kTrain WRITE set_kTrain RESET reset_kTrain STORED false)
Q_PROPERTY(int kSearch READ get_kSearch WRITE set_kSearch RESET reset_kSearch STORED false)
BR_PROPERTY(int, kTrain, 256)
BR_PROPERTY(int, kSearch, 1)
Mat centers;
mutable QScopedPointer<flann::Index> index;
mutable QMutex mutex;
void reindex()
{
index.reset(new flann::Index(centers, flann::LinearIndexParams()));
}
void train(const TemplateList &data)
{
Mat flat = OpenCVUtils::toMatByRow(data.data());
QList<int> sample = Common::RandSample(kTrain, flat.rows, 0, true);
foreach (const int &idx, sample)
centers.push_back(flat.row(idx));
reindex();
}
void project(const Template &src, Template &dst) const
{
QMutexLocker locker(&mutex);
Mat dists, indicies;
index->knnSearch(src, indicies, dists, kSearch);
dst = indicies.reshape(1, 1);
}
void load(QDataStream &stream)
{
stream >> centers;
reindex();
}
void store(QDataStream &stream) const
{
stream << centers;
}
};
BR_REGISTER(Transform, RandomCentroidsTransform)
} // namespace br
#include "cluster.moc"