quantize.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 <openbr_plugin.h>
#include "core/opencvutils.h"
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Approximate floats as uchar.
* \author Josh Klontz \cite jklontz
*/
class QuantizeTransform : public Transform
{
Q_OBJECT
Q_PROPERTY(float a READ get_a WRITE set_a RESET reset_a)
Q_PROPERTY(float b READ get_b WRITE set_b RESET reset_b)
BR_PROPERTY(float, a, 1)
BR_PROPERTY(float, b, 0)
void train(const TemplateList &data)
{
double minVal, maxVal;
minMaxLoc(OpenCVUtils::toMat(data.data()), &minVal, &maxVal);
a = 255.0/(maxVal-minVal);
b = -a*minVal;
}
void project(const Template &src, Template &dst) const
{
src.m().convertTo(dst, CV_8U, a, b);
}
};
BR_REGISTER(Transform, QuantizeTransform)
/*!
* \ingroup transforms
* \brief Approximate floats as signed bit.
* \author Josh Klontz \cite jklontz
*/
class BinarizeTransform : public UntrainableTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
const Mat &m = src;
if ((m.cols % 8 != 0) || (m.type() != CV_32FC1))
qFatal("Expected CV_32FC1 matrix with a multiple of 8 columns.");
Mat n(m.rows, m.cols/8, CV_8UC1);
for (int i=0; i<m.rows; i++)
for (int j=0; j<m.cols-7; j+=8)
n.at<uchar>(i,j) = ((m.at<float>(i,j+0) > 0) << 0) +
((m.at<float>(i,j+1) > 0) << 1) +
((m.at<float>(i,j+2) > 0) << 2) +
((m.at<float>(i,j+3) > 0) << 3) +
((m.at<float>(i,j+4) > 0) << 4) +
((m.at<float>(i,j+5) > 0) << 5) +
((m.at<float>(i,j+6) > 0) << 6) +
((m.at<float>(i,j+7) > 0) << 7);
dst = n;
}
};
BR_REGISTER(Transform, BinarizeTransform)
/*!
* \ingroup transforms
* \brief Compress two uchar into one uchar.
* \author Josh Klontz \cite jklontz
*/
class PackTransform : public UntrainableTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
const Mat &m = src;
if ((m.cols % 2 != 0) || (m.type() != CV_8UC1))
qFatal("Invalid template format.");
Mat n(m.rows, m.cols/2, CV_8UC1);
for (int i=0; i<m.rows; i++)
for (int j=0; j<m.cols/2; j++)
n.at<uchar>(i,j) = ((m.at<uchar>(i,2*j+0) >> 4) << 4) +
((m.at<uchar>(i,2*j+1) >> 4) << 0);
dst = n;
}
};
BR_REGISTER(Transform, PackTransform)
QVector<Mat> BayesianProductQuantizationLUTs;
/*!
* \ingroup transforms
* \brief A bayesian extension to product quantization \cite jegou11
* \author Josh Klontz \cite jklontz
*/
class BayesianProductQuantizationTransform : public Transform
{
Q_OBJECT
Q_PROPERTY(int n READ get_n WRITE set_n RESET reset_n STORED false)
BR_PROPERTY(int, n, 3)
int index;
QList<Mat> centers;
public:
BayesianProductQuantizationTransform()
{
index = BayesianProductQuantizationLUTs.size();
BayesianProductQuantizationLUTs.append(Mat());
}
private:
void train(const TemplateList &src)
{
Mat data = OpenCVUtils::toMatByRow(src.data());
if (data.cols % n != 0) qFatal("Expected dimensionality to be divisible by n.");
Mat &lut = BayesianProductQuantizationLUTs[index];
lut = Mat(data.cols/n, 256*256, CV_32FC1);
for (int i=0; i<data.cols/n; i++) {
Mat labels, center;
kmeans(data.colRange(i*n,(i+1)*n), 256, labels, TermCriteria(TermCriteria::MAX_ITER, 10, 0), 3, KMEANS_PP_CENTERS, center);
for (int j=0; j<256; j++)
for (int k=0; k<256; k++)
lut.at<float>(i,j*256+k) = norm(center.row(k), center.row(k), NORM_L2);
centers.append(center);
}
}
void project(const Template &src, Template &dst) const
{
dst = Mat(1, src.m().cols/n, CV_8UC1);
for (int i=0; i<src.m().cols/n; i++) {
uchar bestIndex = -1;
double bestDistance = std::numeric_limits<double>::max();
Mat m = src.m().colRange(i*n, (i+1)*n);
for (uchar i=0; i<256; i++) {
double distance = norm(m, centers[index].row(i), NORM_L2);
if (distance < bestDistance) {
bestDistance = distance;
bestIndex = i;
}
}
dst.m().at<uchar>(0,i) = bestIndex;
}
}
void store(QDataStream &stream) const
{
stream << centers << BayesianProductQuantizationLUTs[index];
}
void load(QDataStream &stream)
{
stream >> centers >> BayesianProductQuantizationLUTs[index];
}
};
BR_REGISTER(Transform, BayesianProductQuantizationTransform)
} // namespace br
#include "quantize.moc"