normalize.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 <QFutureSynchronizer>
#include <QtConcurrentRun>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <Eigen/Core>
#include "openbr_internal.h"
#include "openbr/core/common.h"
#include "openbr/core/opencvutils.h"
#include "openbr/core/qtutils.h"
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Histogram equalization
* \author Josh Klontz \cite jklontz
*/
class EqualizeHistTransform : public UntrainableTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
equalizeHist(src, dst);
}
};
BR_REGISTER(Transform, EqualizeHistTransform)
/*!
* \ingroup transforms
* \brief Normalize matrix to unit length
* \author Josh Klontz \cite jklontz
*/
class NormalizeTransform : public UntrainableTransform
{
Q_OBJECT
Q_ENUMS(NormType)
Q_PROPERTY(NormType normType READ get_normType WRITE set_normType RESET reset_normType STORED false)
Q_PROPERTY(bool ByRow READ get_ByRow WRITE set_ByRow RESET reset_ByRow STORED false)
BR_PROPERTY(bool, ByRow, false)
public:
/*!< */
enum NormType { Inf = NORM_INF,
L1 = NORM_L1,
L2 = NORM_L2 };
private:
BR_PROPERTY(NormType, normType, L2)
void project(const Template &src, Template &dst) const
{
if (!ByRow) normalize(src, dst, 1, 0, normType, CV_32F);
else {
dst = src;
for (int i=0; i<dst.m().rows; i++) {
Mat temp;
cv::normalize(dst.m().row(i), temp, 1, 0, normType);
temp.copyTo(dst.m().row(i));
}
}
}
};
BR_REGISTER(Transform, NormalizeTransform)
/*!
* \ingroup transforms
* \brief Normalize each dimension based on training data.
* \author Josh Klontz \cite jklontz
*/
class CenterTransform : public Transform
{
Q_OBJECT
Q_ENUMS(Method)
Q_PROPERTY(Method method READ get_method WRITE set_method RESET reset_method STORED false)
public:
/*!< */
enum Method { Mean,
Median,
Range,
Hellinger };
private:
BR_PROPERTY(Method, method, Mean)
Mat a, b; // dst = (src - b) / a
static void _train(Method method, const cv::Mat &m, const QList<int> &labels, double *ca, double *cb)
{
double A = 1, B = 0;
if (method == Mean) mean(m, &A, &B);
else if (method == Median) median(m, &A, &B);
else if (method == Range) range(m, &A, &B);
else if (method == Hellinger) hellinger(m, labels, &A, &B);
else qFatal("Invalid method.");
*ca = A;
*cb = B;
}
void train(const TemplateList &data)
{
Mat m;
OpenCVUtils::toMat(data.data()).convertTo(m, CV_64F);
const QList<int> labels = data.labels<int>();
const int dims = m.cols;
vector<Mat> mv, av, bv;
split(m, mv);
for (size_t c = 0; c < mv.size(); c++) {
av.push_back(Mat(1, dims, CV_64FC1));
bv.push_back(Mat(1, dims, CV_64FC1));
}
QFutureSynchronizer<void> futures;
const bool parallel = (data.size() > 1000) && Globals->parallelism;
for (size_t c = 0; c < mv.size(); c++) {
for (int i=0; i<dims; i++)
if (parallel) futures.addFuture(QtConcurrent::run(_train, method, mv[c].col(i), labels, &av[c].at<double>(0, i), &bv[c].at<double>(0, i)));
else _train (method, mv[c].col(i), labels, &av[c].at<double>(0, i), &bv[c].at<double>(0, i));
av[c] = av[c].reshape(1, data.first().m().rows);
bv[c] = bv[c].reshape(1, data.first().m().rows);
}
futures.waitForFinished();
merge(av, a);
merge(bv, b);
a.convertTo(a, data.first().m().type());
b.convertTo(b, data.first().m().type());
OpenCVUtils::saveImage(a, Globals->property("CENTER_TRAIN_A").toString());
OpenCVUtils::saveImage(b, Globals->property("CENTER_TRAIN_B").toString());
}
void project(const Template &src, Template &dst) const
{
subtract(src, b, dst);
divide(dst, a, dst);
}
void store(QDataStream &stream) const
{
stream << a << b;
}
void load(QDataStream &stream)
{
stream >> a >> b;
}
static void mean(const Mat &src, double *a, double *b)
{
Scalar mean, stddev;
meanStdDev(src, mean, stddev);
*a = stddev[0];
*b = mean[0];
}
static void median(const Mat &src, double *a, double *b)
{
QVector<double> vals; vals.reserve(src.rows);
for (int i=0; i<src.rows; i++)
vals.append(src.at<double>(i, 0));
double q1, q3;
*b = Common::Median(vals, &q1, &q3);
*a = q3 - q1;
}
static void range(const Mat &src, double *a, double *b)
{
double min, max;
minMaxLoc(src, &min, &max);
*a = max - min;
*b = min;
}
static void hellinger(const Mat &src, const QList<int> &labels, double *a, double *b)
{
const QList<float> vals = OpenCVUtils::matrixToVector<float>(src);
if (vals.size() != labels.size())
qFatal("Logic error.");
QVector<float> genuineScores; genuineScores.reserve(vals.size());
QVector<float> impostorScores; impostorScores.reserve(vals.size()*vals.size()/2);
for (int i=0; i<vals.size(); i++)
for (int j=i+1; j<vals.size(); j++)
if (labels[i] == labels[j]) genuineScores.append(vals[i]-vals[j]);
else impostorScores.append(vals[i]-vals[j]);
float min, max;
Common::MinMax(vals, &min, &max);
double gm, gs, im, is;
Common::MeanStdDev(genuineScores, &gm, &gs);
Common::MeanStdDev(impostorScores, &im, &is);
*a = (max-min)/sqrt(1-sqrt(2*gs*is/(gs*gs+is*is))*exp(-0.25*pow(gm-im,2.0)/(gs*gs+is*is)));
*b = min;
}
};
BR_REGISTER(Transform, CenterTransform)
/*!
* \ingroup transforms
* \brief Remove the row-wise training set average.
* \author Josh Klontz \cite jklontz
*/
class RowWiseMeanCenterTransform : public Transform
{
Q_OBJECT
Mat mean;
void train(const TemplateList &data)
{
Mat m = OpenCVUtils::toMatByRow(data.data());
mean = Mat(1, m.cols, m.type());
for (int i=0; i<m.cols; i++)
mean.col(i) = cv::mean(m.col(i));
}
void project(const Template &src, Template &dst) const
{
Mat m = src.m().clone();
for (int i=0; i<m.rows; i++)
m.row(i) -= mean;
dst = m;
}
void store(QDataStream &stream) const
{
stream << mean;
}
void load(QDataStream &stream)
{
stream >> mean;
}
};
BR_REGISTER(Transform, RowWiseMeanCenterTransform)
/*!
* \ingroup transforms
* \brief dst=sqrt(norm_L1(src)) proposed as RootSIFT in \cite Arandjelovic12
* \author Josh Klontz \cite jklontz
*/
class RootNormTransform : public UntrainableTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
const Mat &m = src;
dst.m() = Mat(m.rows, m.cols, m.type());
for (int i=0; i<m.rows; i++) {
Mat temp;
cv::normalize(m.row(i), temp, 1, 0, NORM_L1);
cv::sqrt(temp, temp);
temp.copyTo(dst.m().row(i));
}
}
};
BR_REGISTER(Transform, RootNormTransform)
} // namespace br
#include "normalize.moc"