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137 lines
3.3 KiB
137 lines
3.3 KiB
//
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// Tonemapper.hpp
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// AwesomeAttractorND
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//
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// Created by Joshua Moerman on 10/28/11.
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// Copyright 2011 Vadovas. All rights reserved.
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//
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#ifndef AwesomeAttractorND_Tonemapper_hpp
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#define AwesomeAttractorND_Tonemapper_hpp
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#include <algorithm>
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#include <numeric>
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#include <cmath>
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#include <array>
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namespace Tonemappers {
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struct Normalizer {
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Normalizer()
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: max(0)
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{}
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template <typename C>
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void analyse(C const & canvas){
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max = *std::max_element(canvas.begin(), canvas.end());
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}
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template <typename C, typename I>
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void process(C const & canvas, I & image) const {
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for (typename C::const_iterator it = canvas.begin(); it != canvas.end(); ++it) {
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const double grayscale = get_value(*it);
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image << typename I::pixel(grayscale, grayscale, grayscale);
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}
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}
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template <typename T>
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double get_value(T const & n) const {
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return (double) n / (double) max;
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}
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protected:
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unsigned int max;
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};
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struct GammaCorrector : public Normalizer {
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GammaCorrector(double gamma = 1.0)
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: gamma(gamma)
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{}
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template <typename C>
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void analyse(C const & canvas){
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Normalizer::analyse(canvas);
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const unsigned int sum = std::accumulate(canvas.begin(), canvas.end(), 0);
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const double average = sum / (double) canvas.size();
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power = -2.5/std::log(average/max);
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}
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template <typename C, typename I>
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void process(C const & canvas, I & image) const {
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for (typename C::const_iterator it = canvas.begin(); it != canvas.end(); ++it) {
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const double grayscale = get_value(*it);
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image << typename I::pixel(grayscale, grayscale, grayscale);
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}
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}
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template <typename T>
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double get_value(T const & n, double factor = 1.0) const {
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return std::pow(Normalizer::get_value(n), factor * power / gamma);
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}
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protected:
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double power;
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double gamma;
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};
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struct Colorizer {
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Colorizer(size_t n_planes)
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: gamma_correctors(n_planes)
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, gammas()
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{
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gammas[0].push_back(1.3);
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gammas[1].push_back(2.0);
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gammas[2].push_back(3.0);
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gammas[0].push_back(3.0);
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gammas[1].push_back(1.0);
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gammas[2].push_back(1.0);
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colors[0].push_back(1.5);
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colors[1].push_back(2.0);
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colors[2].push_back(1.0);
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colors[0].push_back(-1.0);
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colors[1].push_back(-0.5);
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colors[2].push_back(0.7);
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}
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template <typename C>
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void analyse(C const & canvas){
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for (size_t i = 0; i < gamma_correctors.size(); ++i) {
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gamma_correctors[i].analyse(canvas[i]);
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}
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}
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template <typename C, typename I>
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typename std::enable_if<C::dimension == 2, void>::type process(C & canvas, I & image) const {
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for (unsigned int r = 0; r < canvas.template size<1>(); ++r){
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for (unsigned int c = 0; c < canvas.template size<0>(); ++c){
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auto const c1 = canvas[0][r][c];
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auto const c2 = canvas[1][r][c];
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std::array<decltype(c1), 2> const a = {{c1, c2}};
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image << typename I::pixel(get_value(a, 0), get_value(a, 1), get_value(a, 2));
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}
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}
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}
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protected:
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template <typename T>
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double get_value(T const & n, size_t color) const {
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double ret = 0.0;
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for (size_t i = 0; i < n.size(); ++i){
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ret += gamma_correctors[i].get_value(n[i], gammas[color][i]) * colors[color][i];
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}
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if(std::isnormal(ret))
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return ret;
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else
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return 0.0;
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}
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std::vector<GammaCorrector> gamma_correctors;
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std::array<std::vector<double>, 3> gammas;
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std::array<std::vector<double>, 3> colors;
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};
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}
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#endif
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