reduce.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>
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Subtract two matrices.
* \author Josh Klontz \cite jklontz
*/
class SubtractTransform : public UntrainableMetaTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
if (src.size() != 2) qFatal("Expected exactly two source images, got %d.", src.size());
dst.file = src.file;
subtract(src[0], src[1], dst);
}
};
BR_REGISTER(Transform, SubtractTransform)
/*!
* \ingroup transforms
* \brief Take the absolute difference of two matrices.
* \author Josh Klontz \cite jklontz
*/
class AbsDiffTransform : public UntrainableMetaTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
if (src.size() != 2) qFatal("Expected exactly two source images, got %d.", src.size());
dst.file = src.file;
absdiff(src[0], src[1], dst);
}
};
BR_REGISTER(Transform, AbsDiffTransform)
/*!
* \ingroup transforms
* \brief Logical AND of two matrices.
* \author Josh Klontz \cite jklontz
*/
class AndTransform : public UntrainableMetaTransform
{
Q_OBJECT
void project(const Template &src, Template &dst) const
{
dst.file = src.file;
dst.m() = src.first();
for (int i=1; i<src.size(); i++)
bitwise_and(src[i], dst, dst);
}
};
BR_REGISTER(Transform, AndTransform)
/*!
* \ingroup transforms
* \brief Statistics
* \author Josh Klontz \cite jklontz
*/
class StatTransform : public UntrainableTransform
{
Q_OBJECT
Q_ENUMS(Statistic)
Q_PROPERTY(Statistic statistic READ get_statistic WRITE set_statistic RESET reset_statistic STORED false)
public:
/*!
* \brief Available statistics
*/
enum Statistic { Min, Max, Mean, StdDev };
private:
BR_PROPERTY(Statistic, statistic, Mean)
void project(const Template &src, Template &dst) const
{
if (src.m().channels() != 1)
qFatal("Expected 1 channel matrix.");
Mat m(1, 1, CV_32FC1);
if ((statistic == Min) || (statistic == Max)) {
double min, max;
minMaxLoc(src, &min, &max);
m.at<float>(1, 1) = (statistic == Min ? min : max);
} else {
Scalar mean, stddev;
meanStdDev(src, mean, stddev);
m.at<float>(1,1) = (statistic == Mean ? mean[0] : stddev[0]);
}
dst = m;
}
};
BR_REGISTER(Transform, StatTransform)
/*!
* \ingroup transforms
* \brief Downsample the rows and columns of a matrix.
* \author Lacey Best-Rowden \cite lbestrowden
*/
class DownsampleTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(int k READ get_k WRITE set_k RESET reset_k STORED false)
BR_PROPERTY(int, k, 1)
void project(const Template &src, Template &dst) const
{
if (src.m().channels() != 1)
qFatal("Expected 1 channel matrix.");
Mat input = src.m();
Mat output(ceil((double)input.rows/k), ceil((double)input.cols/k), CV_32FC1);
for (int r=0; r<output.rows; r++) {
for (int c=0; c<output.cols; c++) {
output.at<float>(r,c) = input.at<float>(r*k,c*k);
}
}
dst.m() = output;
}
};
BR_REGISTER(Transform, DownsampleTransform)
} // namespace br
#include "reduce.moc"