240 lines
9.2 KiB
C++
240 lines
9.2 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 "exposure_compensate.hpp"
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#include "util.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::gpu;
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Ptr<ExposureCompensator> ExposureCompensator::createDefault(int type)
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{
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if (type == NO)
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return new NoExposureCompensator();
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if (type == GAIN)
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return new GainCompensator();
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if (type == GAIN_BLOCKS)
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return new BlocksGainCompensator();
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CV_Error(CV_StsBadArg, "unsupported exposure compensation method");
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return NULL;
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}
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void ExposureCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
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const vector<Mat> &masks)
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{
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vector<pair<Mat,uchar> > level_masks;
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for (size_t i = 0; i < masks.size(); ++i)
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level_masks.push_back(make_pair(masks[i], 255));
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feed(corners, images, level_masks);
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}
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void GainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
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const vector<pair<Mat,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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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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Rect dst_roi = resultRoi(corners, images);
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Mat subimg1, subimg2;
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Mat_<uchar> submask1, submask2, intersect;
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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]));
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subimg2 = images[j](Rect(roi.tl() - corners[j], roi.br() - corners[j]));
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submask1 = masks[i].first(Rect(roi.tl() - corners[i], roi.br() - corners[i]));
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submask2 = masks[j].first(Rect(roi.tl() - corners[j], roi.br() - corners[j]));
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intersect = (submask1 == masks[i].second) & (submask2 == masks[j].second);
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N(i, j) = N(j, i) = max(1, countNonZero(intersect));
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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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const Point3_<uchar>* r1 = subimg1.ptr<Point3_<uchar> >(y);
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const Point3_<uchar>* r2 = subimg2.ptr<Point3_<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 += sqrt(static_cast<double>(sqr(r1[x].x) + sqr(r1[x].y) + sqr(r1[x].z)));
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Isum2 += sqrt(static_cast<double>(sqr(r2[x].x) + sqr(r2[x].y) + sqr(r2[x].z)));
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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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double alpha = 0.01;
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double beta = 100;
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Mat_<double> A(num_images, num_images); A.setTo(0);
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Mat_<double> b(num_images, 1); b.setTo(0);
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for (int i = 0; i < num_images; ++i)
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{
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for (int j = 0; j < num_images; ++j)
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{
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b(i, 0) += beta * N(i, j);
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A(i, i) += beta * N(i, j);
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if (j == i) continue;
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A(i, i) += 2 * alpha * I(i, j) * I(i, j) * N(i, j);
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A(i, j) -= 2 * alpha * I(i, j) * I(j, i) * N(i, j);
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}
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}
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solve(A, b, gains_);
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}
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void GainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
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{
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image *= gains_(index, 0);
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}
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vector<double> GainCompensator::gains() const
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{
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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 BlocksGainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
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const vector<pair<Mat,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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vector<Size> bl_per_imgs(num_images);
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vector<Point> block_corners;
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vector<Mat> block_images;
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vector<pair<Mat,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(min(bl_tl.x + bl_width, images[img_idx].cols),
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min(bl_tl.y + bl_height, images[img_idx].rows));
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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(make_pair(masks[img_idx].first(Rect(bl_tl, bl_br)),
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masks[img_idx].second));
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}
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}
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}
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GainCompensator compensator;
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compensator.feed(block_corners, block_images, block_masks);
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vector<double> gains = compensator.gains();
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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;
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int bl_idx = 0;
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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 = bl_per_imgs[img_idx];
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gain_maps_[img_idx].create(bl_per_img);
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for (int by = 0; by < bl_per_img.height; ++by)
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for (int bx = 0; bx < bl_per_img.width; ++bx, ++bl_idx)
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gain_maps_[img_idx](by, bx) = static_cast<float>(gains[bl_idx]);
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sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker);
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sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker);
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}
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}
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void BlocksGainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
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{
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CV_Assert(image.type() == CV_8UC3);
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Mat_<float> gain_map;
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if (gain_maps_[index].size() == image.size())
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gain_map = gain_maps_[index];
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else
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resize(gain_maps_[index], gain_map, image.size(), 0, 0, INTER_LINEAR);
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for (int y = 0; y < image.rows; ++y)
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{
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const float* gain_row = gain_map.ptr<float>(y);
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Point3_<uchar>* row = image.ptr<Point3_<uchar> >(y);
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for (int x = 0; x < image.cols; ++x)
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{
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row[x].x = saturate_cast<uchar>(row[x].x * gain_row[x]);
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row[x].y = saturate_cast<uchar>(row[x].y * gain_row[x]);
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row[x].z = saturate_cast<uchar>(row[x].z * gain_row[x]);
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}
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}
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} |