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620 lines
21 KiB
C++
620 lines
21 KiB
C++
/*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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#ifdef HAVE_EIGEN
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#include <Eigen/Core>
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#include <Eigen/Dense>
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#endif
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namespace cv {
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namespace detail {
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Ptr<ExposureCompensator> ExposureCompensator::createDefault(int type)
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{
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Ptr<ExposureCompensator> e;
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if (type == NO)
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e = makePtr<NoExposureCompensator>();
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else if (type == GAIN)
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e = makePtr<GainCompensator>();
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else if (type == GAIN_BLOCKS)
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e = makePtr<BlocksGainCompensator>();
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else if (type == CHANNELS)
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e = makePtr<ChannelsCompensator>();
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else if (type == CHANNELS_BLOCKS)
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e = makePtr<BlocksChannelsCompensator>();
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if (e.get() != nullptr)
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return e;
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CV_Error(Error::StsBadArg, "unsupported exposure compensation method");
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}
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void ExposureCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
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const std::vector<UMat> &masks)
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{
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std::vector<std::pair<UMat,uchar> > level_masks;
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for (size_t i = 0; i < masks.size(); ++i)
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level_masks.push_back(std::make_pair(masks[i], (uchar)255));
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feed(corners, images, level_masks);
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}
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void GainCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
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const std::vector<std::pair<UMat,uchar> > &masks)
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{
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LOGLN("Exposure compensation...");
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#if ENABLE_LOG
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int64 t = getTickCount();
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#endif
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const int num_images = static_cast<int>(images.size());
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Mat accumulated_gains;
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prepareSimilarityMask(corners, images);
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for (int n = 0; n < nr_feeds_; ++n)
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{
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if (n > 0)
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{
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// Apply previous iteration gains
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for (int i = 0; i < num_images; ++i)
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apply(i, corners[i], images[i], masks[i].first);
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}
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singleFeed(corners, images, masks);
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if (n == 0)
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accumulated_gains = gains_.clone();
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else
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multiply(accumulated_gains, gains_, accumulated_gains);
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}
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gains_ = accumulated_gains;
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LOGLN("Exposure compensation, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
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}
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void GainCompensator::singleFeed(const std::vector<Point> &corners, const std::vector<UMat> &images,
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const std::vector<std::pair<UMat,uchar> > &masks)
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{
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CV_Assert(corners.size() == images.size() && images.size() == masks.size());
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if (images.size() == 0)
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return;
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const int num_channels = images[0].channels();
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CV_Assert(std::all_of(images.begin(), images.end(),
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[num_channels](const UMat& image) { return image.channels() == num_channels; }));
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CV_Assert(num_channels == 1 || num_channels == 3);
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const int num_images = static_cast<int>(images.size());
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Mat_<int> N(num_images, num_images); N.setTo(0);
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Mat_<double> I(num_images, num_images); I.setTo(0);
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Mat_<bool> skip(num_images, 1); skip.setTo(true);
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Mat subimg1, subimg2;
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Mat_<uchar> submask1, submask2, intersect;
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std::vector<UMat>::iterator similarity_it = similarities_.begin();
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for (int i = 0; i < num_images; ++i)
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{
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for (int j = i; j < num_images; ++j)
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{
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Rect roi;
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if (overlapRoi(corners[i], corners[j], images[i].size(), images[j].size(), roi))
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{
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subimg1 = images[i](Rect(roi.tl() - corners[i], roi.br() - corners[i])).getMat(ACCESS_READ);
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subimg2 = images[j](Rect(roi.tl() - corners[j], roi.br() - corners[j])).getMat(ACCESS_READ);
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submask1 = masks[i].first(Rect(roi.tl() - corners[i], roi.br() - corners[i])).getMat(ACCESS_READ);
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submask2 = masks[j].first(Rect(roi.tl() - corners[j], roi.br() - corners[j])).getMat(ACCESS_READ);
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intersect = (submask1 == masks[i].second) & (submask2 == masks[j].second);
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if (!similarities_.empty())
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{
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CV_Assert(similarity_it != similarities_.end());
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UMat similarity = *similarity_it++;
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// in-place operation has an issue. don't remove the swap
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// detail https://github.com/opencv/opencv/issues/19184
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Mat_<uchar> intersect_updated;
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bitwise_and(intersect, similarity, intersect_updated);
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std::swap(intersect, intersect_updated);
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}
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int intersect_count = countNonZero(intersect);
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N(i, j) = N(j, i) = std::max(1, intersect_count);
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// Don't compute Isums if subimages do not intersect anyway
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if (intersect_count == 0)
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continue;
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// Don't skip images that intersect with at least one other image
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if (i != j)
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{
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skip(i, 0) = false;
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skip(j, 0) = false;
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}
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double Isum1 = 0, Isum2 = 0;
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for (int y = 0; y < roi.height; ++y)
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{
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if (num_channels == 3)
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{
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const Vec<uchar, 3>* r1 = subimg1.ptr<Vec<uchar, 3> >(y);
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const Vec<uchar, 3>* r2 = subimg2.ptr<Vec<uchar, 3> >(y);
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for (int x = 0; x < roi.width; ++x)
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{
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if (intersect(y, x))
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{
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Isum1 += norm(r1[x]);
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Isum2 += norm(r2[x]);
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}
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}
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}
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else // if (num_channels == 1)
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{
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const uchar* r1 = subimg1.ptr<uchar>(y);
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const uchar* r2 = subimg2.ptr<uchar>(y);
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for (int x = 0; x < roi.width; ++x)
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{
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if (intersect(y, x))
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{
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Isum1 += r1[x];
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Isum2 += r2[x];
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}
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}
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}
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}
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I(i, j) = Isum1 / N(i, j);
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I(j, i) = Isum2 / N(i, j);
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}
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}
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}
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if (getUpdateGain() || gains_.rows != num_images)
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{
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double alpha = 0.01;
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double beta = 100;
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int num_eq = num_images - countNonZero(skip);
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gains_.create(num_images, 1);
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gains_.setTo(1);
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// No image process, gains are all set to one, stop here
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if (num_eq == 0)
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return;
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Mat_<double> A(num_eq, num_eq); A.setTo(0);
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Mat_<double> b(num_eq, 1); b.setTo(0);
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for (int i = 0, ki = 0; i < num_images; ++i)
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{
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if (skip(i, 0))
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continue;
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for (int j = 0, kj = 0; j < num_images; ++j)
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{
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if (skip(j, 0))
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continue;
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b(ki, 0) += beta * N(i, j);
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A(ki, ki) += beta * N(i, j);
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if (j != i)
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{
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A(ki, ki) += 2 * alpha * I(i, j) * I(i, j) * N(i, j);
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A(ki, kj) -= 2 * alpha * I(i, j) * I(j, i) * N(i, j);
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}
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++kj;
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}
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++ki;
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}
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Mat_<double> l_gains;
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#ifdef HAVE_EIGEN
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Eigen::MatrixXf eigen_A, eigen_b, eigen_x;
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cv2eigen(A, eigen_A);
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cv2eigen(b, eigen_b);
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Eigen::LLT<Eigen::MatrixXf> solver(eigen_A);
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#if ENABLE_LOG
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if (solver.info() != Eigen::ComputationInfo::Success)
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LOGLN("Failed to solve exposure compensation system");
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#endif
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eigen_x = solver.solve(eigen_b);
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Mat_<float> l_gains_float;
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eigen2cv(eigen_x, l_gains_float);
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l_gains_float.convertTo(l_gains, CV_64FC1);
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#else
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solve(A, b, l_gains);
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#endif
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CV_CheckTypeEQ(l_gains.type(), CV_64FC1, "");
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for (int i = 0, j = 0; i < num_images; ++i)
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{
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// Only assign non-skipped gains. Other gains are already set to 1
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if (!skip(i, 0))
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gains_.at<double>(i, 0) = l_gains(j++, 0);
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}
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}
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}
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void GainCompensator::apply(int index, Point /*corner*/, InputOutputArray image, InputArray /*mask*/)
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{
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CV_INSTRUMENT_REGION();
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multiply(image, gains_(index, 0), image);
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}
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std::vector<double> GainCompensator::gains() const
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{
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std::vector<double> gains_vec(gains_.rows);
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for (int i = 0; i < gains_.rows; ++i)
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gains_vec[i] = gains_(i, 0);
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return gains_vec;
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}
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void GainCompensator::getMatGains(std::vector<Mat>& umv)
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{
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umv.clear();
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for (int i = 0; i < gains_.rows; ++i)
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umv.push_back(Mat(1,1,CV_64FC1,Scalar(gains_(i, 0))));
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}
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void GainCompensator::setMatGains(std::vector<Mat>& umv)
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{
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gains_=Mat_<double>(static_cast<int>(umv.size()),1);
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for (int i = 0; i < static_cast<int>(umv.size()); i++)
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{
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int type = umv[i].type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
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CV_CheckType(type, depth == CV_64F && cn == 1, "Only double images are supported for gain");
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CV_Assert(umv[i].rows == 1 && umv[i].cols == 1);
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gains_(i, 0) = umv[i].at<double>(0, 0);
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}
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}
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void GainCompensator::prepareSimilarityMask(
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const std::vector<Point> &corners, const std::vector<UMat> &images)
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{
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if (similarity_threshold_ >= 1)
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{
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LOGLN(" skipping similarity mask: disabled");
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return;
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}
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if (!similarities_.empty())
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{
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LOGLN(" skipping similarity mask: already set");
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return;
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}
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LOGLN(" calculating similarity mask");
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const int num_images = static_cast<int>(images.size());
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for (int i = 0; i < num_images; ++i)
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{
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for (int j = i; j < num_images; ++j)
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{
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Rect roi;
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if (overlapRoi(corners[i], corners[j], images[i].size(), images[j].size(), roi))
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{
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UMat subimg1 = images[i](Rect(roi.tl() - corners[i], roi.br() - corners[i]));
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UMat subimg2 = images[j](Rect(roi.tl() - corners[j], roi.br() - corners[j]));
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UMat similarity = buildSimilarityMask(subimg1, subimg2);
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similarities_.push_back(similarity);
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}
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}
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}
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}
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UMat GainCompensator::buildSimilarityMask(InputArray src_array1, InputArray src_array2)
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{
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CV_Assert(src_array1.rows() == src_array2.rows() && src_array1.cols() == src_array2.cols());
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CV_Assert(src_array1.type() == src_array2.type());
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CV_Assert(src_array1.type() == CV_8UC3 || src_array1.type() == CV_8UC1);
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Mat src1 = src_array1.getMat();
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Mat src2 = src_array2.getMat();
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UMat umat_similarity(src1.rows, src1.cols, CV_8UC1);
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Mat similarity = umat_similarity.getMat(ACCESS_WRITE);
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if (src1.channels() == 3)
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{
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for (int y = 0; y < similarity.rows; ++y)
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{
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for (int x = 0; x < similarity.cols; ++x)
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{
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Vec<float, 3> vec_diff =
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Vec<float, 3>(*src1.ptr<Vec<uchar, 3>>(y, x))
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- Vec<float, 3>(*src2.ptr<Vec<uchar, 3>>(y, x));
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double diff = norm(vec_diff * (1.f / 255.f));
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*similarity.ptr<uchar>(y, x) = diff <= similarity_threshold_ ? 255 : 0;
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}
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}
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}
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else // if (src1.channels() == 1)
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{
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for (int y = 0; y < similarity.rows; ++y)
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{
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for (int x = 0; x < similarity.cols; ++x)
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{
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float diff = std::abs(static_cast<int>(*src1.ptr<uchar>(y, x))
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- static_cast<int>(*src2.ptr<uchar>(y, x))) / 255.f;
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*similarity.ptr<uchar>(y, x) = diff <= similarity_threshold_ ? 255 : 0;
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}
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}
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}
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similarity.release();
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Mat kernel = getStructuringElement(MORPH_RECT, Size(3,3));
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UMat umat_erode;
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erode(umat_similarity, umat_erode, kernel);
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dilate(umat_erode, umat_similarity, kernel);
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return umat_similarity;
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}
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void ChannelsCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
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const std::vector<std::pair<UMat,uchar> > &masks)
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{
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std::array<std::vector<UMat>, 3> images_channels;
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// Split channels of each input image
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for (const UMat& image: images)
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{
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std::vector<UMat> image_channels;
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image_channels.resize(3);
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split(image, image_channels);
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for (int i = 0; i < int(images_channels.size()); ++i)
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images_channels[i].emplace_back(std::move(image_channels[i]));
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}
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// For each channel, feed the channel of each image in a GainCompensator
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gains_.clear();
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gains_.resize(images.size());
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GainCompensator compensator(getNrFeeds());
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compensator.setSimilarityThreshold(getSimilarityThreshold());
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compensator.prepareSimilarityMask(corners, images);
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for (int c = 0; c < 3; ++c)
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{
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const std::vector<UMat>& channels = images_channels[c];
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compensator.feed(corners, channels, masks);
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std::vector<double> gains = compensator.gains();
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for (int i = 0; i < int(gains.size()); ++i)
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gains_.at(i)[c] = gains[i];
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}
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}
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void ChannelsCompensator::apply(int index, Point /*corner*/, InputOutputArray image, InputArray /*mask*/)
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{
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CV_INSTRUMENT_REGION();
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multiply(image, gains_.at(index), image);
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}
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void ChannelsCompensator::getMatGains(std::vector<Mat>& umv)
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{
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umv.clear();
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for (int i = 0; i < static_cast<int>(gains_.size()); ++i)
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{
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Mat m;
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Mat(gains_[i]).copyTo(m);
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umv.push_back(m);
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}
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}
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void ChannelsCompensator::setMatGains(std::vector<Mat>& umv)
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{
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for (int i = 0; i < static_cast<int>(umv.size()); i++)
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{
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Scalar s;
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umv[i].copyTo(s);
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gains_.push_back(s);
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}
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}
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template<class Compensator>
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void BlocksCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
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const std::vector<std::pair<UMat,uchar> > &masks)
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{
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CV_Assert(corners.size() == images.size() && images.size() == masks.size());
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const int num_images = static_cast<int>(images.size());
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std::vector<Size> bl_per_imgs(num_images);
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std::vector<Point> block_corners;
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std::vector<UMat> block_images;
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std::vector<std::pair<UMat,uchar> > block_masks;
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// Construct blocks for gain compensator
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for (int img_idx = 0; img_idx < num_images; ++img_idx)
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{
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Size bl_per_img((images[img_idx].cols + bl_width_ - 1) / bl_width_,
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(images[img_idx].rows + bl_height_ - 1) / bl_height_);
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int bl_width = (images[img_idx].cols + bl_per_img.width - 1) / bl_per_img.width;
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int bl_height = (images[img_idx].rows + bl_per_img.height - 1) / bl_per_img.height;
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bl_per_imgs[img_idx] = bl_per_img;
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for (int by = 0; by < bl_per_img.height; ++by)
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{
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for (int bx = 0; bx < bl_per_img.width; ++bx)
|
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{
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Point bl_tl(bx * bl_width, by * bl_height);
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Point bl_br(std::min(bl_tl.x + bl_width, images[img_idx].cols),
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std::min(bl_tl.y + bl_height, images[img_idx].rows));
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|
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block_corners.push_back(corners[img_idx] + bl_tl);
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block_images.push_back(images[img_idx](Rect(bl_tl, bl_br)));
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block_masks.push_back(std::make_pair(masks[img_idx].first(Rect(bl_tl, bl_br)),
|
|
masks[img_idx].second));
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}
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}
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|
}
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|
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if (getUpdateGain() || int(gain_maps_.size()) != num_images)
|
|
{
|
|
Compensator compensator;
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compensator.setNrFeeds(getNrFeeds());
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compensator.setSimilarityThreshold(getSimilarityThreshold());
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compensator.feed(block_corners, block_images, block_masks);
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|
|
|
gain_maps_.clear();
|
|
gain_maps_.resize(num_images);
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|
|
|
Mat_<float> ker(1, 3);
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ker(0, 0) = 0.25; ker(0, 1) = 0.5; ker(0, 2) = 0.25;
|
|
|
|
int bl_idx = 0;
|
|
for (int img_idx = 0; img_idx < num_images; ++img_idx)
|
|
{
|
|
Size bl_per_img = bl_per_imgs[img_idx];
|
|
UMat gain_map = getGainMap(compensator, bl_idx, bl_per_img);
|
|
bl_idx += bl_per_img.width*bl_per_img.height;
|
|
|
|
for (int i=0; i<nr_gain_filtering_iterations_; ++i)
|
|
{
|
|
UMat tmp;
|
|
sepFilter2D(gain_map, tmp, CV_32F, ker, ker);
|
|
swap(gain_map, tmp);
|
|
}
|
|
|
|
gain_maps_[img_idx] = gain_map;
|
|
}
|
|
}
|
|
}
|
|
|
|
UMat BlocksCompensator::getGainMap(const GainCompensator& compensator, int bl_idx, Size bl_per_img)
|
|
{
|
|
std::vector<double> gains = compensator.gains();
|
|
|
|
UMat u_gain_map(bl_per_img, CV_32F);
|
|
Mat_<float> gain_map = u_gain_map.getMat(ACCESS_WRITE);
|
|
|
|
for (int by = 0; by < bl_per_img.height; ++by)
|
|
for (int bx = 0; bx < bl_per_img.width; ++bx, ++bl_idx)
|
|
gain_map(by, bx) = static_cast<float>(gains[bl_idx]);
|
|
|
|
return u_gain_map;
|
|
}
|
|
|
|
UMat BlocksCompensator::getGainMap(const ChannelsCompensator& compensator, int bl_idx, Size bl_per_img)
|
|
{
|
|
std::vector<Scalar> gains = compensator.gains();
|
|
|
|
UMat u_gain_map(bl_per_img, CV_32FC3);
|
|
Mat_<Vec3f> gain_map = u_gain_map.getMat(ACCESS_WRITE);
|
|
|
|
for (int by = 0; by < bl_per_img.height; ++by)
|
|
for (int bx = 0; bx < bl_per_img.width; ++bx, ++bl_idx)
|
|
for (int c = 0; c < 3; ++c)
|
|
gain_map(by, bx)[c] = static_cast<float>(gains[bl_idx][c]);
|
|
|
|
return u_gain_map;
|
|
}
|
|
|
|
void BlocksCompensator::apply(int index, Point /*corner*/, InputOutputArray _image, InputArray /*mask*/)
|
|
{
|
|
CV_INSTRUMENT_REGION();
|
|
|
|
CV_Assert(_image.type() == CV_8UC3);
|
|
|
|
UMat u_gain_map;
|
|
if (gain_maps_.at(index).size() == _image.size())
|
|
u_gain_map = gain_maps_.at(index);
|
|
else
|
|
resize(gain_maps_.at(index), u_gain_map, _image.size(), 0, 0, INTER_LINEAR);
|
|
|
|
if (u_gain_map.channels() != 3)
|
|
{
|
|
std::vector<UMat> gains_channels;
|
|
gains_channels.push_back(u_gain_map);
|
|
gains_channels.push_back(u_gain_map);
|
|
gains_channels.push_back(u_gain_map);
|
|
merge(gains_channels, u_gain_map);
|
|
}
|
|
|
|
multiply(_image, u_gain_map, _image, 1, _image.type());
|
|
}
|
|
|
|
void BlocksCompensator::getMatGains(std::vector<Mat>& umv)
|
|
{
|
|
umv.clear();
|
|
for (int i = 0; i < static_cast<int>(gain_maps_.size()); ++i)
|
|
{
|
|
Mat m;
|
|
gain_maps_[i].copyTo(m);
|
|
umv.push_back(m);
|
|
}
|
|
}
|
|
|
|
void BlocksCompensator::setMatGains(std::vector<Mat>& umv)
|
|
{
|
|
for (int i = 0; i < static_cast<int>(umv.size()); i++)
|
|
{
|
|
UMat m;
|
|
umv[i].copyTo(m);
|
|
gain_maps_.push_back(m);
|
|
}
|
|
}
|
|
|
|
void BlocksGainCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
|
|
const std::vector<std::pair<UMat,uchar> > &masks)
|
|
{
|
|
BlocksCompensator::feed<GainCompensator>(corners, images, masks);
|
|
}
|
|
|
|
void BlocksChannelsCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
|
|
const std::vector<std::pair<UMat,uchar> > &masks)
|
|
{
|
|
BlocksCompensator::feed<ChannelsCompensator>(corners, images, masks);
|
|
}
|
|
|
|
|
|
} // namespace detail
|
|
} // namespace cv
|