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