/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "exposure_compensate.hpp" #include "util.hpp" using namespace std; using namespace cv; using namespace cv::gpu; Ptr ExposureCompensator::createDefault(int type) { if (type == NO) return new NoExposureCompensator(); if (type == GAIN) return new GainCompensator(); if (type == GAIN_BLOCKS) return new BlocksGainCompensator(); CV_Error(CV_StsBadArg, "unsupported exposure compensation method"); return NULL; } void ExposureCompensator::feed(const vector &corners, const vector &images, const vector &masks) { vector > level_masks; for (size_t i = 0; i < masks.size(); ++i) level_masks.push_back(make_pair(masks[i], 255)); feed(corners, images, level_masks); } void GainCompensator::feed(const vector &corners, const vector &images, const vector > &masks) { CV_Assert(corners.size() == images.size() && images.size() == masks.size()); const int num_images = static_cast(images.size()); Mat_ N(num_images, num_images); N.setTo(0); Mat_ I(num_images, num_images); I.setTo(0); Rect dst_roi = resultRoi(corners, images); Mat subimg1, subimg2; Mat_ submask1, submask2, intersect; for (int i = 0; i < num_images; ++i) { for (int j = i; j < num_images; ++j) { Rect roi; if (overlapRoi(corners[i], corners[j], images[i].size(), images[j].size(), roi)) { subimg1 = images[i](Rect(roi.tl() - corners[i], roi.br() - corners[i])); subimg2 = images[j](Rect(roi.tl() - corners[j], roi.br() - corners[j])); submask1 = masks[i].first(Rect(roi.tl() - corners[i], roi.br() - corners[i])); submask2 = masks[j].first(Rect(roi.tl() - corners[j], roi.br() - corners[j])); intersect = (submask1 == masks[i].second) & (submask2 == masks[j].second); N(i, j) = N(j, i) = max(1, countNonZero(intersect)); double Isum1 = 0, Isum2 = 0; for (int y = 0; y < roi.height; ++y) { const Point3_* r1 = subimg1.ptr >(y); const Point3_* r2 = subimg2.ptr >(y); for (int x = 0; x < roi.width; ++x) { if (intersect(y, x)) { Isum1 += sqrt(static_cast(sqr(r1[x].x) + sqr(r1[x].y) + sqr(r1[x].z))); Isum2 += sqrt(static_cast(sqr(r2[x].x) + sqr(r2[x].y) + sqr(r2[x].z))); } } } I(i, j) = Isum1 / N(i, j); I(j, i) = Isum2 / N(i, j); } } } double alpha = 0.01; double beta = 100; Mat_ A(num_images, num_images); A.setTo(0); Mat_ b(num_images, 1); b.setTo(0); for (int i = 0; i < num_images; ++i) { for (int j = 0; j < num_images; ++j) { b(i, 0) += beta * N(i, j); A(i, i) += beta * N(i, j); if (j == i) continue; A(i, i) += 2 * alpha * I(i, j) * I(i, j) * N(i, j); A(i, j) -= 2 * alpha * I(i, j) * I(j, i) * N(i, j); } } solve(A, b, gains_); } void GainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/) { image *= gains_(index, 0); } vector GainCompensator::gains() const { vector gains_vec(gains_.rows); for (int i = 0; i < gains_.rows; ++i) gains_vec[i] = gains_(i, 0); return gains_vec; } void BlocksGainCompensator::feed(const vector &corners, const vector &images, const vector > &masks) { CV_Assert(corners.size() == images.size() && images.size() == masks.size()); const int num_images = static_cast(images.size()); vector bl_per_imgs(num_images); vector block_corners; vector block_images; vector > block_masks; // Construct blocks for gain compensator for (int img_idx = 0; img_idx < num_images; ++img_idx) { Size bl_per_img((images[img_idx].cols + bl_width_ - 1) / bl_width_, (images[img_idx].rows + bl_height_ - 1) / bl_height_); int bl_width = (images[img_idx].cols + bl_per_img.width - 1) / bl_per_img.width; int bl_height = (images[img_idx].rows + bl_per_img.height - 1) / bl_per_img.height; bl_per_imgs[img_idx] = bl_per_img; for (int by = 0; by < bl_per_img.height; ++by) { for (int bx = 0; bx < bl_per_img.width; ++bx) { Point bl_tl(bx * bl_width, by * bl_height); Point bl_br(min(bl_tl.x + bl_width, images[img_idx].cols), min(bl_tl.y + bl_height, images[img_idx].rows)); block_corners.push_back(corners[img_idx] + bl_tl); block_images.push_back(images[img_idx](Rect(bl_tl, bl_br))); block_masks.push_back(make_pair(masks[img_idx].first(Rect(bl_tl, bl_br)), masks[img_idx].second)); } } } GainCompensator compensator; compensator.feed(block_corners, block_images, block_masks); vector gains = compensator.gains(); gain_maps_.resize(num_images); int bl_idx = 0; for (int img_idx = 0; img_idx < num_images; ++img_idx) { Size bl_per_img = bl_per_imgs[img_idx]; gain_maps_[img_idx].create(bl_per_img); for (int by = 0; by < bl_per_img.height; ++by) for (int bx = 0; bx < bl_per_img.width; ++bx, ++bl_idx) gain_maps_[img_idx](by, bx) = static_cast(gains[bl_idx]); Mat_ ker(1, 3); ker(0,0) = 0.25; ker(0,1) = 0.5; ker(0,2) = 0.25; sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker); sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker); } } void BlocksGainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/) { CV_Assert(image.type() == CV_8UC3); Mat_ gain_map; if (gain_maps_[index].size() == image.size()) gain_map = gain_maps_[index]; else resize(gain_maps_[index], gain_map, image.size(), 0, 0, INTER_LINEAR); for (int y = 0; y < image.rows; ++y) { const float* gain_row = gain_map.ptr(y); Point3_* row = image.ptr >(y); for (int x = 0; x < image.cols; ++x) { row[x].x = saturate_cast(row[x].x * gain_row[x]); row[x].y = saturate_cast(row[x].y * gain_row[x]); row[x].z = saturate_cast(row[x].z * gain_row[x]); } } }