Fedor Morozov
12 years ago
parent
258b98d15b
commit
4d2ea847fa
5 changed files with 465 additions and 735 deletions
@ -1,161 +0,0 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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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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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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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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// * Redistribution's 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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// * Redistribution's 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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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software 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 the Intel Corporation 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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//M*/
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#include "precomp.hpp" |
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#include "opencv2/photo.hpp" |
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#include "opencv2/imgproc.hpp" |
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namespace cv |
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{ |
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static void downsample(Mat& src, Mat& dst) |
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{ |
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dst = Mat(src.rows / 2, src.cols / 2, CV_8UC1); |
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int offset = src.cols * 2; |
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uchar *src_ptr = src.ptr(); |
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uchar *dst_ptr = dst.ptr(); |
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for(int y = 0; y < dst.rows; y ++) { |
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uchar *ptr = src_ptr; |
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for(int x = 0; x < dst.cols; x++) { |
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dst_ptr[0] = ptr[0]; |
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dst_ptr++; |
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ptr += 2; |
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} |
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src_ptr += offset; |
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} |
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} |
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static void buildPyr(Mat& img, std::vector<Mat>& pyr, int maxlevel)
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{ |
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pyr.resize(maxlevel + 1); |
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pyr[0] = img.clone(); |
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for(int level = 0; level < maxlevel; level++) { |
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downsample(pyr[level], pyr[level + 1]); |
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} |
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} |
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static int getMedian(Mat& img) |
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{ |
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int channels = 0; |
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Mat hist;
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int hist_size = 256; |
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float range[] = {0, 256} ; |
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const float* ranges[] = {range}; |
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calcHist(&img, 1, &channels, Mat(), hist, 1, &hist_size, ranges); |
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float *ptr = hist.ptr<float>(); |
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int median = 0, sum = 0; |
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int thresh = img.total() / 2; |
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while(sum < thresh && median < 256) { |
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sum += (int)ptr[median]; |
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median++; |
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} |
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return median; |
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} |
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static void computeBitmaps(Mat& img, Mat& tb, Mat& eb, int exclude_range) |
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{ |
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int median = getMedian(img); |
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compare(img, median, tb, CMP_GT); |
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compare(abs(img - median), exclude_range, eb, CMP_GT); |
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} |
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void shiftMat(InputArray _src, Point shift, OutputArray _dst)
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{ |
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Mat src = _src.getMat(); |
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_dst.create(src.size(), src.type()); |
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Mat dst = _dst.getMat(); |
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dst = Mat::zeros(src.size(), src.type()); |
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int width = src.cols - abs(shift.x); |
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int height = src.rows - abs(shift.y); |
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Rect dst_rect(max(shift.x, 0), max(shift.y, 0), width, height); |
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Rect src_rect(max(-shift.x, 0), max(-shift.y, 0), width, height); |
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src(src_rect).copyTo(dst(dst_rect)); |
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} |
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Point getExpShift(InputArray _img0, InputArray _img1, int max_bits, int exclude_range) |
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{ |
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Mat img0 = _img0.getMat(); |
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Mat img1 = _img1.getMat(); |
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CV_Assert(img0.type() == CV_8UC1 && img1.type() == CV_8UC1); |
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CV_Assert(img0.size() == img0.size()); |
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int maxlevel = (int)(log((double)max(img0.rows, img0.cols)) / log(2.0)) - 1; |
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maxlevel = min(maxlevel, max_bits - 1); |
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std::vector<Mat> pyr0; |
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std::vector<Mat> pyr1; |
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buildPyr(img0, pyr0, maxlevel); |
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buildPyr(img1, pyr1, maxlevel);
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Point shift(0, 0); |
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for(int level = maxlevel; level >= 0; level--) { |
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shift *= 2; |
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Mat tb1, tb2, eb1, eb2; |
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computeBitmaps(pyr0[level], tb1, eb1, exclude_range); |
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computeBitmaps(pyr1[level], tb2, eb2, exclude_range); |
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int min_err = pyr0[level].total(); |
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Point new_shift(shift); |
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for(int i = -1; i <= 1; i++) { |
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for(int j = -1; j <= 1; j++) { |
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Point test_shift = shift + Point(i, j); |
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Mat shifted_tb2, shifted_eb2, diff; |
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shiftMat(tb2, test_shift, shifted_tb2); |
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shiftMat(eb2, test_shift, shifted_eb2); |
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bitwise_xor(tb1, shifted_tb2, diff); |
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bitwise_and(diff, eb1, diff); |
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bitwise_and(diff, shifted_eb2, diff); |
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int err = countNonZero(diff); |
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if(err < min_err) { |
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new_shift = test_shift; |
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min_err = err; |
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}
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} |
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} |
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shift = new_shift; |
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} |
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return shift; |
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} |
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}; |
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@ -1,294 +0,0 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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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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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
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// Third party copyrights are property of their respective owners.
|
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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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// * Redistribution's 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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// * Redistribution's 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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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software 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 the Intel Corporation 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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//M*/
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#include "opencv2/photo.hpp" |
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#include "opencv2/imgproc.hpp" |
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#include <iostream> |
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namespace cv |
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{ |
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static void triangleWeights(float weights[]) |
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{ |
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for(int i = 0; i < 128; i++) { |
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weights[i] = i + 1.0f; |
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} |
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for(int i = 128; i < 256; i++) { |
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weights[i] = 256.0f - i; |
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} |
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} |
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static Mat linearResponse() |
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{ |
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Mat response(256, 1, CV_32F); |
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for(int i = 1; i < 256; i++) { |
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response.at<float>(i) = logf((float)i); |
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} |
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response.at<float>(0) = response.at<float>(1); |
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return response; |
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} |
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static void modifyCheckResponse(Mat &response) |
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{ |
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if(response.empty()) { |
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response = linearResponse(); |
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} |
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CV_Assert(response.rows == 256 && (response.cols == 1 || response.cols == 3)); |
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response.convertTo(response, CV_32F); |
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if(response.cols == 1) { |
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Mat result(256, 3, CV_32F); |
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for(int i = 0; i < 3; i++) { |
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response.copyTo(result.col(i)); |
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} |
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response = result; |
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} |
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} |
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static void checkImages(const std::vector<Mat>& images, bool hdr, const std::vector<float>& _exp_times = std::vector<float>()) |
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{ |
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CV_Assert(!images.empty()); |
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CV_Assert(!hdr || images.size() == _exp_times.size()); |
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int width = images[0].cols; |
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int height = images[0].rows; |
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int channels = images[0].channels(); |
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for(size_t i = 0; i < images.size(); i++) { |
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CV_Assert(images[i].cols == width && images[i].rows == height); |
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CV_Assert(images[i].channels() == channels && images[i].depth() == CV_8U); |
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} |
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} |
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void alignImages(InputArrayOfArrays _src, std::vector<Mat>& dst) |
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{ |
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std::vector<Mat> src; |
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_src.getMatVector(src); |
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checkImages(src, false); |
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dst.resize(src.size()); |
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size_t pivot = src.size() / 2; |
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dst[pivot] = src[pivot]; |
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Mat gray_base; |
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cvtColor(src[pivot], gray_base, COLOR_RGB2GRAY); |
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for(size_t i = 0; i < src.size(); i++) { |
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if(i == pivot) { |
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continue; |
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} |
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Mat gray; |
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cvtColor(src[i], gray, COLOR_RGB2GRAY); |
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Point shift = getExpShift(gray_base, gray); |
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shiftMat(src[i], shift, dst[i]); |
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} |
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} |
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void makeHDR(InputArrayOfArrays _images, const std::vector<float>& _exp_times, OutputArray _dst, Mat response) |
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{ |
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std::vector<Mat> images; |
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_images.getMatVector(images); |
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checkImages(images, true, _exp_times); |
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modifyCheckResponse(response); |
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_dst.create(images[0].size(), CV_MAKETYPE(CV_32F, images[0].channels())); |
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Mat result = _dst.getMat(); |
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std::vector<float> exp_times(_exp_times.size()); |
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for(size_t i = 0; i < exp_times.size(); i++) { |
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exp_times[i] = logf(_exp_times[i]); |
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} |
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float weights[256]; |
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triangleWeights(weights); |
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int channels = images[0].channels(); |
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float *res_ptr = result.ptr<float>(); |
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for(size_t pos = 0; pos < result.total(); pos++, res_ptr += channels) { |
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std::vector<float> sum(channels, 0); |
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float weight_sum = 0; |
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for(size_t im = 0; im < images.size(); im++) { |
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uchar *img_ptr = images[im].ptr() + channels * pos; |
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float w = 0; |
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for(int channel = 0; channel < channels; channel++) { |
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w += weights[img_ptr[channel]]; |
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} |
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w /= channels; |
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weight_sum += w; |
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for(int channel = 0; channel < channels; channel++) { |
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sum[channel] += w * (response.at<float>(img_ptr[channel], channel) - exp_times[im]); |
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} |
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} |
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for(int channel = 0; channel < channels; channel++) { |
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res_ptr[channel] = exp(sum[channel] / weight_sum); |
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} |
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} |
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} |
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void exposureFusion(InputArrayOfArrays _images, OutputArray _dst, float wc, float ws, float we) |
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{ |
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std::vector<Mat> images; |
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_images.getMatVector(images); |
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checkImages(images, false); |
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std::vector<Mat> weights(images.size()); |
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Mat weight_sum = Mat::zeros(images[0].size(), CV_32FC1); |
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for(size_t im = 0; im < images.size(); im++) { |
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Mat img, gray, contrast, saturation, wellexp; |
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std::vector<Mat> channels(3); |
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images[im].convertTo(img, CV_32FC3, 1.0/255.0); |
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cvtColor(img, gray, COLOR_RGB2GRAY); |
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split(img, channels); |
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Laplacian(gray, contrast, CV_32F); |
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contrast = abs(contrast); |
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Mat mean = (channels[0] + channels[1] + channels[2]) / 3.0f; |
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saturation = Mat::zeros(channels[0].size(), CV_32FC1); |
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for(int i = 0; i < 3; i++) { |
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Mat deviation = channels[i] - mean; |
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pow(deviation, 2.0, deviation); |
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saturation += deviation; |
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} |
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sqrt(saturation, saturation); |
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wellexp = Mat::ones(gray.size(), CV_32FC1); |
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for(int i = 0; i < 3; i++) { |
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Mat exp = channels[i] - 0.5f; |
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pow(exp, 2, exp); |
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exp = -exp / 0.08; |
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wellexp = wellexp.mul(exp); |
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} |
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pow(contrast, wc, contrast); |
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pow(saturation, ws, saturation); |
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pow(wellexp, we, wellexp); |
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weights[im] = contrast; |
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weights[im] = weights[im].mul(saturation); |
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weights[im] = weights[im].mul(wellexp); |
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weight_sum += weights[im]; |
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} |
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int maxlevel = static_cast<int>(logf(static_cast<float>(max(images[0].rows, images[0].cols))) / logf(2.0)) - 1; |
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std::vector<Mat> res_pyr(maxlevel + 1); |
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for(size_t im = 0; im < images.size(); im++) { |
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weights[im] /= weight_sum; |
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Mat img; |
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images[im].convertTo(img, CV_32FC3, 1/255.0); |
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std::vector<Mat> img_pyr, weight_pyr; |
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buildPyramid(img, img_pyr, maxlevel); |
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buildPyramid(weights[im], weight_pyr, maxlevel); |
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for(int lvl = 0; lvl < maxlevel; lvl++) { |
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Mat up; |
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pyrUp(img_pyr[lvl + 1], up, img_pyr[lvl].size()); |
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img_pyr[lvl] -= up; |
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} |
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for(int lvl = 0; lvl <= maxlevel; lvl++) { |
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std::vector<Mat> channels(3); |
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split(img_pyr[lvl], channels); |
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for(int i = 0; i < 3; i++) { |
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channels[i] = channels[i].mul(weight_pyr[lvl]); |
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} |
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merge(channels, img_pyr[lvl]); |
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if(res_pyr[lvl].empty()) { |
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res_pyr[lvl] = img_pyr[lvl]; |
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} else { |
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res_pyr[lvl] += img_pyr[lvl]; |
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} |
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} |
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} |
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for(int lvl = maxlevel; lvl > 0; lvl--) { |
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Mat up; |
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pyrUp(res_pyr[lvl], up, res_pyr[lvl - 1].size()); |
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res_pyr[lvl - 1] += up; |
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} |
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_dst.create(images[0].size(), CV_32FC3); |
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Mat result = _dst.getMat(); |
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res_pyr[0].copyTo(result); |
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} |
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void estimateResponse(InputArrayOfArrays _images, const std::vector<float>& exp_times, OutputArray _dst, int samples, float lambda) |
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{ |
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std::vector<Mat> images; |
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_images.getMatVector(images); |
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checkImages(images, true, exp_times); |
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_dst.create(256, images[0].channels(), CV_32F); |
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Mat response = _dst.getMat(); |
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float w[256]; |
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triangleWeights(w); |
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for(int channel = 0; channel < images[0].channels(); channel++) { |
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Mat A = Mat::zeros(samples * images.size() + 257, 256 + samples, CV_32F); |
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Mat B = Mat::zeros(A.rows, 1, CV_32F); |
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int eq = 0; |
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for(int i = 0; i < samples; i++) { |
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int pos = 3 * (rand() % images[0].total()) + channel; |
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for(size_t j = 0; j < images.size(); j++) { |
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int val = (images[j].ptr() + pos)[0]; |
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A.at<float>(eq, val) = w[val]; |
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A.at<float>(eq, 256 + i) = -w[val]; |
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B.at<float>(eq, 0) = w[val] * log(exp_times[j]);
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eq++; |
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} |
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} |
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A.at<float>(eq, 128) = 1; |
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eq++; |
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for(int i = 0; i < 254; i++) { |
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A.at<float>(eq, i) = lambda * w[i + 1]; |
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A.at<float>(eq, i + 1) = -2 * lambda * w[i + 1]; |
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A.at<float>(eq, i + 2) = lambda * w[i + 1]; |
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eq++; |
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} |
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Mat solution; |
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solve(A, B, solution, DECOMP_SVD); |
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solution.rowRange(0, 256).copyTo(response.col(channel)); |
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} |
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} |
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}; |
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