filter.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/imgproc/imgproc.hpp>
#include <openbr_plugin.h>
#include "core/tanh_sse.h"
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Gamma correction
* \author Josh Klontz \cite jklontz
*/
class GammaTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(float gamma READ get_gamma WRITE set_gamma RESET reset_gamma)
BR_PROPERTY(float, gamma, 0.2)
Mat lut;
void init()
{
lut.create(256, 1, CV_32FC1);
if (gamma == 0) for (int i=0; i<256; i++) lut.at<float>(i,0) = log((float)i);
else for (int i=0; i<256; i++) lut.at<float>(i,0) = pow(i, gamma);
}
void project(const Template &src, Template &dst) const
{
LUT(src, lut, dst);
}
};
BR_REGISTER(Transform, GammaTransform)
/*!
* \ingroup transforms
* \brief Gaussian blur
* \author Josh Klontz \cite jklontz
*/
class BlurTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(float sigma READ get_sigma WRITE set_sigma RESET reset_sigma STORED false)
BR_PROPERTY(float, sigma, 1)
void project(const Template &src, Template &dst) const
{
GaussianBlur(src, dst, Size(0,0), sigma);
}
};
BR_REGISTER(Transform, BlurTransform)
/*!
* \ingroup transforms
* \brief Difference of gaussians
* \author Josh Klontz \cite jklontz
*/
class DoGTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(float sigma0 READ get_sigma0 WRITE set_sigma0 RESET reset_sigma0 STORED false)
Q_PROPERTY(float sigma1 READ get_sigma1 WRITE set_sigma1 RESET reset_sigma1 STORED false)
BR_PROPERTY(float, sigma0, 1)
BR_PROPERTY(float, sigma1, 2)
Size ksize0, ksize1;
static Size getKernelSize(double sigma)
{
// Inverts OpenCV's conversion from kernel size to sigma:
// sigma = ((ksize-1)*0.5 - 1)*0.3 + 0.8
// See documentation for cv::getGaussianKernel()
int ksize = ((sigma - 0.8) / 0.3 + 1) * 2 + 1;
if (ksize % 2 == 0) ksize++;
return Size(ksize, ksize);
}
void init()
{
ksize0 = getKernelSize(sigma0);
ksize1 = getKernelSize(sigma1);
}
void project(const Template &src, Template &dst) const
{
Mat g0, g1;
GaussianBlur(src, g0, ksize0, 0);
GaussianBlur(src, g1, ksize1, 0);
subtract(g0, g1, dst);
}
};
BR_REGISTER(Transform, DoGTransform)
/*!
* \ingroup transforms
* \brief Meyers, E.; Wolf, L.
* “Using biologically inspired features for face processing,”
* Int. Journal of Computer Vision, vol. 76, no. 1, pp 93–104, 2008.
* \author Scott Klum \cite sklum
*/
class CSDNTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(float s READ get_s WRITE set_s RESET reset_s STORED false)
BR_PROPERTY(int, s, 16)
void project(const Template &src, Template &dst) const
{
if (src.m().channels() != 1) qFatal("ContrastEq::project expected single channel source matrix.");
const int nRows = src.m().rows;
const int nCols = src.m().cols;
Mat m;
src.m().convertTo(m, CV_32FC1);
const int surround = s/2;
for ( int i = 0; i < nRows; i++ )
{
for ( int j = 0; j < nCols; j++ )
{
int width = min( j+surround, nCols ) - max( 0, j-surround );
int height = min( i+surround, nRows ) - max( 0, i-surround );
Rect_<int> ROI(max(0, j-surround), max(0, i-surround), width, height);
Scalar_<float> avg = mean(m(ROI));
m.at<float>(i,j) = m.at<float>(i,j) - avg[0];
}
}
m.convertTo(m, CV_8UC1);
dst = m;
}
};
BR_REGISTER(Transform, CSDNTransform)
/*!
* \ingroup transforms
* \brief Xiaoyang Tan; Triggs, B.;
* "Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions,"
* Image Processing, IEEE Transactions on , vol.19, no.6, pp.1635-1650, June 2010
* \author Josh Klontz \cite jklontz
*/
class ContrastEqTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(float a READ get_a WRITE set_a RESET reset_a STORED false)
Q_PROPERTY(float t READ get_t WRITE set_t RESET reset_t STORED false)
BR_PROPERTY(float, a, 1)
BR_PROPERTY(float, t, 0.1)
void project(const Template &src, Template &dst) const
{
if (src.m().channels() != 1) qFatal("ContrastEq::project expected single channel source matrix.");
// Stage 1
Mat stage1;
{
Mat abs_dst;
absdiff(src, Scalar(0), abs_dst);
Mat pow_dst;
pow(abs_dst, a, pow_dst);
float denominator = pow((float)mean(pow_dst)[0], 1.f/a);
src.m().convertTo(stage1, CV_32F, 1/denominator);
}
// Stage 2
Mat stage2;
{
Mat abs_dst;
absdiff(stage1, Scalar(0), abs_dst);
Mat min_dst;
min(abs_dst, t, min_dst);
Mat pow_dst;
pow(min_dst, a, pow_dst);
float denominator = pow((float)mean(pow_dst)[0], 1.f/a);
stage1.convertTo(stage2, CV_32F, 1/denominator);
}
// Hyperbolic tangent
const int nRows = src.m().rows;
const int nCols = src.m().cols;
const float* p = (const float*)stage2.ptr();
Mat m(nRows, nCols, CV_32FC1);
for (int i=0; i<nRows; i++)
for (int j=0; j<nCols; j++)
m.at<float>(i, j) = fast_tanh(p[i*nCols+j]);
dst = m;
}
};
BR_REGISTER(Transform, ContrastEqTransform)
/*!
* \ingroup transforms
* \brief Raise each element to the specified power.
* \author Josh Klontz \cite jklontz
*/
class PowTransform : public UntrainableTransform
{
Q_OBJECT
Q_PROPERTY(float power READ get_power WRITE set_power RESET reset_power STORED false)
Q_PROPERTY(bool preserveSign READ get_preserveSign WRITE set_preserveSign RESET reset_preserveSign STORED false)
BR_PROPERTY(float, power, 2)
BR_PROPERTY(bool, preserveSign, false)
void project(const Template &src, Template &dst) const
{
pow(src, power, dst);
if (preserveSign) subtract(Scalar::all(0), dst, dst, src.m() < 0);
}
};
BR_REGISTER(Transform, PowTransform)
} // namespace br
#include "filter.moc"