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279 lines
9.6 KiB
C++
279 lines
9.6 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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#include "opencv2/photo.hpp"
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#include "opencv2/imgproc.hpp"
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//#include "opencv2/highgui.hpp"
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#include "hdr_common.hpp"
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namespace cv
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{
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class CalibrateDebevecImpl : public CalibrateDebevec
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{
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public:
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CalibrateDebevecImpl(int _samples, float _lambda, bool _random) :
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name("CalibrateDebevec"),
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samples(_samples),
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lambda(_lambda),
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random(_random),
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w(tringleWeights())
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{
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}
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void process(InputArrayOfArrays src, OutputArray dst, InputArray _times)
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{
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std::vector<Mat> images;
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src.getMatVector(images);
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Mat times = _times.getMat();
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CV_Assert(images.size() == times.total());
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checkImageDimensions(images);
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CV_Assert(images[0].depth() == CV_8U);
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int channels = images[0].channels();
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int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
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dst.create(LDR_SIZE, 1, CV_32FCC);
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Mat result = dst.getMat();
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std::vector<Point> sample_points;
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if(random) {
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for(int i = 0; i < samples; i++) {
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sample_points.push_back(Point(rand() % images[0].cols, rand() % images[0].rows));
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}
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} else {
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int x_points = static_cast<int>(sqrt(static_cast<double>(samples) * images[0].cols / images[0].rows));
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int y_points = samples / x_points;
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int step_x = images[0].cols / x_points;
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int step_y = images[0].rows / y_points;
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for(int i = 0, x = step_x / 2; i < x_points; i++, x += step_x) {
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for(int j = 0, y = step_y / 2; j < y_points; j++, y += step_y) {
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if( 0 <= x && x < images[0].cols &&
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0 <= y && y < images[0].rows )
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sample_points.push_back(Point(x, y));
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}
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}
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}
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std::vector<Mat> result_split(channels);
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for(int channel = 0; channel < channels; channel++) {
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Mat A = Mat::zeros((int)sample_points.size() * (int)images.size() + LDR_SIZE + 1, LDR_SIZE + (int)sample_points.size(), 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(size_t i = 0; i < sample_points.size(); i++) {
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for(size_t j = 0; j < images.size(); j++) {
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int val = images[j].ptr()[3*(sample_points[i].y * images[j].cols + sample_points[i].x) + channel];
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A.at<float>(eq, val) = w.at<float>(val);
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A.at<float>(eq, LDR_SIZE + (int)i) = -w.at<float>(val);
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B.at<float>(eq, 0) = w.at<float>(val) * log(times.at<float>((int)j));
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eq++;
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}
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}
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A.at<float>(eq, LDR_SIZE / 2) = 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.at<float>(i + 1);
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A.at<float>(eq, i + 1) = -2 * lambda * w.at<float>(i + 1);
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A.at<float>(eq, i + 2) = lambda * w.at<float>(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, LDR_SIZE).copyTo(result_split[channel]);
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}
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merge(result_split, result);
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exp(result, result);
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}
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int getSamples() const { return samples; }
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void setSamples(int val) { samples = val; }
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float getLambda() const { return lambda; }
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void setLambda(float val) { lambda = val; }
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bool getRandom() const { return random; }
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void setRandom(bool val) { random = val; }
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void write(FileStorage& fs) const
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{
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fs << "name" << name
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<< "samples" << samples
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<< "lambda" << lambda
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<< "random" << static_cast<int>(random);
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}
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void read(const FileNode& fn)
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{
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FileNode n = fn["name"];
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CV_Assert(n.isString() && String(n) == name);
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samples = fn["samples"];
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lambda = fn["lambda"];
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int random_val = fn["random"];
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random = (random_val != 0);
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}
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protected:
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String name;
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int samples;
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float lambda;
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bool random;
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Mat w;
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};
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Ptr<CalibrateDebevec> createCalibrateDebevec(int samples, float lambda, bool random)
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{
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return makePtr<CalibrateDebevecImpl>(samples, lambda, random);
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}
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class CalibrateRobertsonImpl : public CalibrateRobertson
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{
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public:
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CalibrateRobertsonImpl(int _max_iter, float _threshold) :
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name("CalibrateRobertson"),
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max_iter(_max_iter),
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threshold(_threshold),
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weight(RobertsonWeights())
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{
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}
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void process(InputArrayOfArrays src, OutputArray dst, InputArray _times)
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{
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std::vector<Mat> images;
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src.getMatVector(images);
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Mat times = _times.getMat();
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CV_Assert(images.size() == times.total());
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checkImageDimensions(images);
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CV_Assert(images[0].depth() == CV_8U);
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int channels = images[0].channels();
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int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
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dst.create(LDR_SIZE, 1, CV_32FCC);
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Mat response = dst.getMat();
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response = linearResponse(3) / (LDR_SIZE / 2.0f);
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Mat card = Mat::zeros(LDR_SIZE, 1, CV_32FCC);
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for(size_t i = 0; i < images.size(); i++) {
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uchar *ptr = images[i].ptr();
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for(size_t pos = 0; pos < images[i].total(); pos++) {
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for(int c = 0; c < channels; c++, ptr++) {
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card.at<Vec3f>(*ptr)[c] += 1;
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}
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}
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}
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card = 1.0 / card;
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Ptr<MergeRobertson> merge = createMergeRobertson();
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for(int iter = 0; iter < max_iter; iter++) {
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radiance = Mat::zeros(images[0].size(), CV_32FCC);
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merge->process(images, radiance, times, response);
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Mat new_response = Mat::zeros(LDR_SIZE, 1, CV_32FC3);
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for(size_t i = 0; i < images.size(); i++) {
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uchar *ptr = images[i].ptr();
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float* rad_ptr = radiance.ptr<float>();
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for(size_t pos = 0; pos < images[i].total(); pos++) {
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for(int c = 0; c < channels; c++, ptr++, rad_ptr++) {
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new_response.at<Vec3f>(*ptr)[c] += times.at<float>((int)i) * *rad_ptr;
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}
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}
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}
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new_response = new_response.mul(card);
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for(int c = 0; c < 3; c++) {
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float middle = new_response.at<Vec3f>(LDR_SIZE / 2)[c];
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for(int i = 0; i < LDR_SIZE; i++) {
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new_response.at<Vec3f>(i)[c] /= middle;
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}
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}
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float diff = static_cast<float>(sum(sum(abs(new_response - response)))[0] / channels);
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new_response.copyTo(response);
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if(diff < threshold) {
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break;
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}
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}
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}
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int getMaxIter() const { return max_iter; }
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void setMaxIter(int val) { max_iter = val; }
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float getThreshold() const { return threshold; }
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void setThreshold(float val) { threshold = val; }
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Mat getRadiance() const { return radiance; }
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void write(FileStorage& fs) const
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{
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fs << "name" << name
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<< "max_iter" << max_iter
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<< "threshold" << threshold;
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}
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void read(const FileNode& fn)
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{
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FileNode n = fn["name"];
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CV_Assert(n.isString() && String(n) == name);
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max_iter = fn["max_iter"];
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threshold = fn["threshold"];
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}
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protected:
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String name;
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int max_iter;
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float threshold;
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Mat weight, radiance;
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};
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Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter, float threshold)
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{
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return makePtr<CalibrateRobertsonImpl>(max_iter, threshold);
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}
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}
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