Repository for OpenCV's extra modules
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/*
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* If you do not agree to this license, do not download, install,
* copy or use the software.
*
*
* License Agreement
* For Open Source Computer Vision Library
* (3 - clause BSD License)
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met :
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and / or other materials provided with the distribution.
*
* * Neither the names of the copyright holders nor the names of the contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* This software is provided by the copyright holders and contributors "as is" and
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* warranties of merchantability and fitness for a particular purpose are disclaimed.
* In no event shall copyright holders or contributors be liable for any direct,
* indirect, incidental, special, exemplary, or consequential damages
* (including, but not limited to, procurement of substitute goods or services;
* loss of use, data, or profits; or business interruption) however caused
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*/
#include "precomp.hpp"
#include <vector>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
using namespace cv;
using namespace std;
namespace
{
class ParallelDft : public ParallelLoopBody
{
private:
vector<Mat> src_;
public:
ParallelDft(vector<Mat> &s)
{
src_ = s;
}
void operator() (const Range& range) const CV_OVERRIDE
{
for (int i = range.start; i != range.end; i++)
{
dft(src_[i], src_[i]);
}
}
};
class ParallelIdft : public ParallelLoopBody
{
private:
vector<Mat> src_;
public:
ParallelIdft(vector<Mat> &s)
{
src_ = s;
}
void operator() (const Range& range) const CV_OVERRIDE
{
for (int i = range.start; i != range.end; i++)
{
idft(src_[i], src_[i],DFT_SCALE);
}
}
};
class ParallelDivComplexByReal : public ParallelLoopBody
{
private:
vector<Mat> numer_;
vector<Mat> denom_;
vector<Mat> dst_;
public:
ParallelDivComplexByReal(vector<Mat> &numer, vector<Mat> &denom, vector<Mat> &dst)
{
numer_ = numer;
denom_ = denom;
dst_ = dst;
}
void operator() (const Range& range) const CV_OVERRIDE
{
for (int i = range.start; i != range.end; i++)
{
Mat aPanels[2];
Mat bPanels[2];
split(numer_[i], aPanels);
split(denom_[i], bPanels);
Mat realPart;
Mat imaginaryPart;
divide(aPanels[0], denom_[i], realPart);
divide(aPanels[1], denom_[i], imaginaryPart);
aPanels[0] = realPart;
aPanels[1] = imaginaryPart;
merge(aPanels, 2, dst_[i]);
}
}
};
void shift(InputArray src, OutputArray dst, int shift_x, int shift_y)
{
Mat S = src.getMat();
Mat D = dst.getMat();
if(S.data == D.data)
{
S = S.clone();
}
D.create(S.size(), S.type());
Mat s0(S, Rect(0, 0, S.cols - shift_x, S.rows - shift_y));
Mat s1(S, Rect(S.cols - shift_x, 0, shift_x, S.rows - shift_y));
Mat s2(S, Rect(0, S.rows - shift_y, S.cols-shift_x, shift_y));
Mat s3(S, Rect(S.cols - shift_x, S.rows- shift_y, shift_x, shift_y));
Mat d0(D, Rect(shift_x, shift_y, S.cols - shift_x, S.rows - shift_y));
Mat d1(D, Rect(0, shift_y, shift_x, S.rows - shift_y));
Mat d2(D, Rect(shift_x, 0, S.cols-shift_x, shift_y));
Mat d3(D, Rect(0,0,shift_x, shift_y));
s0.copyTo(d0);
s1.copyTo(d1);
s2.copyTo(d2);
s3.copyTo(d3);
}
// dft after padding imaginary
void fft(InputArray src, OutputArray dst)
{
Mat S = src.getMat();
Mat planes[] = {S.clone(), Mat::zeros(S.size(), S.type())};
Mat x;
merge(planes, 2, dst);
// compute the result
dft(dst, dst);
}
void psf2otf(InputArray src, OutputArray dst, int height, int width)
{
Mat S = src.getMat();
Mat D = dst.getMat();
Mat padded;
if(S.data == D.data){
S = S.clone();
}
// add padding
copyMakeBorder(S, padded, 0, height - S.rows, 0, width - S.cols,
BORDER_CONSTANT, Scalar::all(0));
shift(padded, padded, width - S.cols / 2, height - S.rows / 2);
// convert to frequency domain
fft(padded, dst);
}
void dftMultiChannel(InputArray src, vector<Mat> &dst)
{
Mat S = src.getMat();
split(S, dst);
for(int i = 0; i < S.channels(); i++){
Mat planes[] = {dst[i].clone(), Mat::zeros(dst[i].size(), dst[i].type())};
merge(planes, 2, dst[i]);
}
parallel_for_(cv::Range(0,S.channels()), ParallelDft(dst));
}
void idftMultiChannel(const vector<Mat> &src, OutputArray dst)
{
vector<Mat> channels(src);
parallel_for_(Range(0, int(src.size())), ParallelIdft(channels));
for(int i = 0; unsigned(i) < src.size(); i++){
Mat panels[2];
split(channels[i], panels);
channels[i] = panels[0];
}
Mat D;
merge(channels, D);
D.copyTo(dst);
}
void divComplexByRealMultiChannel(vector<Mat> &numer,
vector<Mat> &denom, vector<Mat> &dst)
{
for(int i = 0; unsigned(i) < numer.size(); i++)
{
dst[i].create(numer[i].size(), numer[i].type());
}
parallel_for_(Range(0, int(numer.size())), ParallelDivComplexByReal(numer, denom, dst));
}
// power of 2 of the absolute value of the complex
Mat pow2absComplex(InputArray src)
{
Mat S = src.getMat();
Mat sPanels[2];
split(S, sPanels);
Mat mag;
magnitude(sPanels[0], sPanels[1], mag);
pow(mag, 2, mag);
return mag;
}
}
namespace cv
{
namespace ximgproc
{
void l0Smooth(InputArray src, OutputArray dst, double lambda, double kappa)
{
Mat S = src.getMat();
CV_Assert(!S.empty());
CV_Assert(S.depth() == CV_8U || S.depth() == CV_16U
|| S.depth() == CV_32F || S.depth() == CV_64F);
dst.create(src.size(), src.type());
if(S.data == dst.getMat().data)
{
S = S.clone();
}
if(S.depth() == CV_8U)
{
S.convertTo(S, CV_32F, 1/255.0f);
}
else if(S.depth() == CV_16U)
{
S.convertTo(S, CV_32F, 1/65535.0f);
}
else if(S.depth() == CV_64F)
{
S.convertTo(S, CV_32F);
}
const double betaMax = 100000;
// gradient operators in frequency domain
Mat otfFx, otfFy;
float kernel[2] = {-1, 1};
float kernel_inv[2] = {1,-1};
psf2otf(Mat(1,2,CV_32FC1, kernel_inv), otfFx, S.rows, S.cols);
psf2otf(Mat(2,1,CV_32FC1, kernel_inv), otfFy, S.rows, S.cols);
vector<Mat> denomConst;
Mat tmp = pow2absComplex(otfFx) + pow2absComplex(otfFy);
for(int i = 0; i < S.channels(); i++)
{
denomConst.push_back(tmp);
}
// input image in frequency domain
vector<Mat> numerConst;
dftMultiChannel(S, numerConst);
/*********************************
* solver
*********************************/
double beta = 2 * lambda;
while(beta < betaMax){
// h, v subproblem
Mat h, v;
filter2D(S, h, -1, Mat(1, 2, CV_32FC1, kernel), Point(0, 0),
0, BORDER_REPLICATE);
filter2D(S, v, -1, Mat(2, 1, CV_32FC1, kernel), Point(0, 0),
0, BORDER_REPLICATE);
Mat hvMag = h.mul(h) + v.mul(v);
Mat mask;
if(S.channels() == 1)
{
threshold(hvMag, mask, lambda/beta, 1, THRESH_BINARY);
}
else if(S.channels() > 1)
{
vector<Mat> channels(S.channels());
split(hvMag, channels);
hvMag = channels[0];
for(int i = 1; i < S.channels(); i++)
{
hvMag = hvMag + channels[i];
}
threshold(hvMag, mask, lambda/beta, 1, THRESH_BINARY);
Mat in[] = {mask, mask, mask};
merge(in, 3, mask);
}
h = h.mul(mask);
v = v.mul(mask);
// S subproblem
vector<Mat> denom(S.channels());
for(int i = 0; i < S.channels(); i++)
{
denom[i] = beta * denomConst[i] + 1;
}
Mat hGrad, vGrad;
filter2D(h, hGrad, -1, Mat(1, 2, CV_32FC1, kernel_inv));
filter2D(v, vGrad, -1, Mat(2, 1, CV_32FC1, kernel_inv));
vector<Mat> hvGradFreq;
dftMultiChannel(hGrad+vGrad, hvGradFreq);
vector<Mat> numer(S.channels());
for(int i = 0; i < S.channels(); i++)
{
numer[i] = numerConst[i] + hvGradFreq[i] * beta;
}
vector<Mat> sFreq(S.channels());
divComplexByRealMultiChannel(numer, denom, sFreq);
idftMultiChannel(sFreq, S);
beta = beta * kappa;
}
Mat D = dst.getMat();
if(D.depth() == CV_8U)
{
S.convertTo(D, CV_8U, 255);
}
else if(D.depth() == CV_16U)
{
S.convertTo(D, CV_16U, 65535);
}
else if(D.depth() == CV_64F)
{
S.convertTo(D, CV_64F);
}
else
{
S.copyTo(D);
}
}
}
}