svm.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 <QTemporaryFile>
#include <opencv2/core/core.hpp>
#include <opencv2/ml/ml.hpp>
#include <openbr_plugin.h>
#include "core/opencvutils.h"
namespace br
{
/*!
* \ingroup transforms
* \brief C. Burges. "A tutorial on support vector machines for pattern recognition,"
* Knowledge Discovery and Data Mining 2(2), 1998.
* \author Josh Klontz \cite jklontz
*/
class SVMTransform : public Transform
{
Q_OBJECT
Q_ENUMS(Kernel)
Q_ENUMS(Type)
Q_PROPERTY(Kernel kernel READ get_kernel WRITE set_kernel RESET reset_kernel STORED false)
Q_PROPERTY(Type type READ get_type WRITE set_type RESET reset_type STORED false)
Q_PROPERTY(float C READ get_C WRITE set_C RESET reset_C STORED false)
Q_PROPERTY(float gamma READ get_gamma WRITE set_gamma RESET reset_gamma STORED false)
public:
/*!
* \brief The Kernel enum
*/
enum Kernel { Linear = CvSVM::LINEAR,
Poly = CvSVM::POLY,
RBF = CvSVM::RBF,
Sigmoid = CvSVM::SIGMOID };
/*!
* \brief The Type enum
*/
enum Type { C_SVC = CvSVM::C_SVC,
NU_SVC = CvSVM::NU_SVC,
ONE_CLASS = CvSVM::ONE_CLASS,
EPS_SVR = CvSVM::EPS_SVR,
NU_SVR = CvSVM::NU_SVR};
private:
BR_PROPERTY(Kernel, kernel, Linear)
BR_PROPERTY(Type, type, C_SVC)
BR_PROPERTY(float, C, -1)
BR_PROPERTY(float, gamma, -1)
cv::SVM svm;
float a, b;
public:
SVMTransform() : a(1), b(0) {}
private:
void train(const TemplateList &_data)
{
cv::Mat data = OpenCVUtils::toMat(_data.data());
cv::Mat lab = OpenCVUtils::toMat(_data.labels<float>());
// Scale labels to [-1,1]
double min, max;
cv::minMaxLoc(lab, &min, &max);
if (max > min) {
a = 2.0/(max-min);
b = -(min*a+1);
lab = (lab * a) + b;
cv::minMaxLoc(lab, &min, &max);
}
if (data.type() != CV_32FC1)
qFatal("SVM::train expected single channel floating point training data.");
CvSVMParams params;
params.kernel_type = kernel;
params.svm_type = type;
params.p = 0.1;
params.nu = 0.5;
if ((C == -1) || ((gamma == -1) && (int(kernel) != int(CvSVM::LINEAR)))) {
try {
svm.train_auto(data, lab, cv::Mat(), cv::Mat(), params, 5);
} catch (...) {
qWarning("Some classes do not contain sufficient examples or are not discriminative enough for accurate SVM classification.");
svm.train(data, lab);
}
} else {
params.C = C;
params.gamma = gamma;
svm.train(data, lab, cv::Mat(), cv::Mat(), params);
}
CvSVMParams p = svm.get_params();
qDebug("SVM C = %f Gamma = %f Support Vectors = %d", p.C, p.gamma, svm.get_support_vector_count());
}
void project(const Template &src, Template &dst) const
{
dst = src;
dst.file.setLabel((svm.predict(src.m().reshape(0, 1)) - b)/a);
}
void store(QDataStream &stream) const
{
stream << a << b;
// Create local file
QTemporaryFile tempFile;
tempFile.open();
tempFile.close();
// Save SVM to local file
svm.save(qPrintable(tempFile.fileName()));
// Copy local file contents to stream
tempFile.open();
QByteArray data = tempFile.readAll();
tempFile.close();
stream << data;
}
void load(QDataStream &stream)
{
stream >> a >> b;
// Copy local file contents from stream
QByteArray data;
stream >> data;
// Create local file
QTemporaryFile tempFile(QDir::tempPath()+"/SVM");
tempFile.open();
tempFile.write(data);
tempFile.close();
// Load SVM from local file
svm.load(qPrintable(tempFile.fileName()));
}
};
BR_REGISTER(Transform, SVMTransform)
} // namespace br
#include "svm.moc"