399 lines
13 KiB
C++
399 lines
13 KiB
C++
/* -*- C++ -*-
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*
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* This file is part of RawTherapee.
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*
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* Copyright (c) 2018 Alberto Griggio <alberto.griggio@gmail.com>
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*
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* RawTherapee is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* RawTherapee is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with RawTherapee. If not, see <https://www.gnu.org/licenses/>.
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*/
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#include "rawimagesource.h"
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#include "rtthumbnail.h"
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#include "curves.h"
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#include "color.h"
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#include "rt_math.h"
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#include "iccstore.h"
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#include "procparams.h"
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#include "../rtgui/mydiagonalcurve.h"
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#include "improcfun.h"
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//#define BENCHMARK
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#include "StopWatch.h"
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#include <iostream>
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namespace rtengine {
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extern const Settings *settings;
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namespace {
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struct CdfInfo {
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std::vector<int> cdf;
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int min_val;
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int max_val;
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CdfInfo(): cdf(256), min_val(-1), max_val(-1) {}
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};
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CdfInfo getCdf(const IImage8 &img)
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{
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CdfInfo ret;
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for (int y = 0; y < img.getHeight(); ++y) {
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for (int x = 0; x < img.getWidth(); ++x) {
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int lum = LIM(int(Color::rgbLuminance(float(img.r(y, x)), float(img.g(y, x)), float(img.b(y, x)))), 0, 255);
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++ret.cdf[lum];
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}
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}
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int sum = 0;
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for (size_t i = 0; i < ret.cdf.size(); ++i) {
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if (ret.cdf[i] > 0) {
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if (ret.min_val < 0) {
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ret.min_val = i;
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}
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ret.max_val = i;
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}
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sum += ret.cdf[i];
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ret.cdf[i] = sum;
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}
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return ret;
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}
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int findMatch(int val, const std::vector<int> &cdf, int j)
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{
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if (cdf[j] <= val) {
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for (; j < int(cdf.size()); ++j) {
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if (cdf[j] == val) {
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return j;
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} else if (cdf[j] > val) {
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return (cdf[j] - val <= val - cdf[j-1] ? j : j-1);
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}
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}
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return 255;
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} else {
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for (; j >= 0; --j) {
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if (cdf[j] == val) {
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return j;
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} else if (cdf[j] < val) {
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return (val - cdf[j] <= cdf[j+1] - val ? j : j+1);
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}
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}
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return 0;
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}
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}
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void mappingToCurve(const std::vector<int> &mapping, std::vector<double> &curve)
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{
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curve.clear();
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int idx = 15;
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for (; idx < int(mapping.size()); ++idx) {
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if (mapping[idx] >= idx) {
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break;
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}
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}
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if (idx == int(mapping.size())) {
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for (idx = 1; idx < int(mapping.size())-1; ++idx) {
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if (mapping[idx] >= idx) {
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break;
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}
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}
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}
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auto coord = [](int v) -> double { return double(v)/255.0; };
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auto doit =
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[&](int start, int stop, int step, bool addstart, int maxdelta=0) -> void
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{
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if (!maxdelta) maxdelta = step * 2;
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int prev = start;
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if (addstart && mapping[start] >= 0) {
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curve.push_back(coord(start));
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curve.push_back(coord(mapping[start]));
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}
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for (int i = start; i < stop; ++i) {
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int v = mapping[i];
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if (v < 0) {
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continue;
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}
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bool change = i > 0 && v != mapping[i-1];
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int diff = i - prev;
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if ((change && std::abs(diff - step) <= 1) || diff > maxdelta) {
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curve.push_back(coord(i));
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curve.push_back(coord(v));
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prev = i;
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}
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}
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};
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curve.push_back(0.0);
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curve.push_back(0.0);
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int start = 0;
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while (start < idx && (mapping[start] < 0 || start < idx / 2)) {
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++start;
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}
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const int npoints = 8;
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int step = std::max(int(mapping.size())/npoints, 1);
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int end = mapping.size();
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if (idx <= end / 3) {
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doit(start, idx, idx / 2, true);
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step = (end - idx) / 4;
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doit(idx, end, step, false, step);
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} else {
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doit(start, idx, idx > step ? step : idx / 2, true);
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doit(idx, end, step, idx - step > step / 2 && std::abs(curve[curve.size()-2] - coord(idx)) > 0.01);
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}
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if (curve.size() > 2 && (1 - curve[curve.size()-2] <= coord(step) / 3)) {
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curve.pop_back();
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curve.pop_back();
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}
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curve.push_back(1.0);
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curve.push_back(1.0);
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// we assume we are matching an S-shaped curve, so try to avoid
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// concavities in the upper part of the S
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const auto getpos =
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[](float x, float xa, float ya, float xb, float yb)
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{
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// line equation:
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// (x - xa) / (xb - xa) = (y - ya) / (yb - ya)
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return (x - xa) / (xb - xa) * (yb - ya) + ya;
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};
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idx = -1;
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for (ssize_t i = curve.size()-1; i > 0; i -= 2) {
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if (curve[i] <= 0.f) {
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idx = i+1;
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break;
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}
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}
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if (idx >= 0 && size_t(idx) < curve.size()) {
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// idx is the position of the first point in the upper part of the S
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// for each 3 consecutive points (xa, ya), (x, y), (xb, yb) we check
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// that y is above the point at x of the line between the other two
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// if this is not the case, we remove (x, y) from the curve
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while (size_t(idx+5) < curve.size()) {
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float xa = curve[idx];
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float ya = curve[idx+1];
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float x = curve[idx+2];
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float y = curve[idx+3];
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float xb = curve[idx+4];
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float yb = curve[idx+5];
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float yy = getpos(x, xa, ya, xb, yb);
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if (yy > y) {
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// we have to remove (x, y) from the curve
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curve.erase(curve.begin()+(idx+2), curve.begin()+(idx+4));
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} else {
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// move on to the next point
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idx += 2;
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}
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}
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}
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if (curve.size() < 4) {
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curve = { DCT_Linear }; // not enough points, fall back to linear
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} else {
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curve.insert(curve.begin(), DCT_Spline);
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DiagonalCurve c(curve);
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double gap = 0.05;
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double x = 0.0;
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curve = { DCT_CatumullRom };
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while (x < 1.0) {
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curve.push_back(x);
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curve.push_back(c.getVal(x));
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x += gap;
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gap *= 1.4;
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}
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curve.push_back(1.0);
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curve.push_back(c.getVal(1.0));
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}
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}
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} // namespace
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void RawImageSource::getAutoMatchedToneCurve(const ColorManagementParams &cp, std::vector<double> &outCurve)
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{
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BENCHFUN
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if (settings->verbose) {
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std::cout << "performing histogram matching for " << getFileName() << " on the embedded thumbnail" << std::endl;
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}
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const auto same_profile =
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[](const ColorManagementParams &a, const ColorManagementParams &b) -> bool
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{
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return (a.inputProfile == b.inputProfile
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&& a.toneCurve == b.toneCurve
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&& a.applyLookTable == b.applyLookTable
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&& a.applyBaselineExposureOffset == b.applyBaselineExposureOffset
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&& a.applyHueSatMap == b.applyHueSatMap
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&& a.dcpIlluminant == b.dcpIlluminant);
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};
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if (!histMatchingCache.empty() && same_profile(*histMatchingParams, cp)) {
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if (settings->verbose) {
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std::cout << "tone curve found in cache" << std::endl;
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}
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outCurve = histMatchingCache;
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return;
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}
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outCurve = { DCT_Linear };
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int fw, fh;
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getFullSize(fw, fh, TR_NONE);
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if (getRotateDegree() == 90 || getRotateDegree() == 270) {
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std::swap(fw, fh);
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}
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int skip = 3;
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if (settings->verbose) {
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std::cout << "histogram matching: full raw image size is " << fw << "x" << fh << std::endl;
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}
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ProcParams neutral;
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neutral.icm = cp;
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neutral.raw.bayersensor.method = RAWParams::BayerSensor::getMethodString(RAWParams::BayerSensor::Method::FAST);
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neutral.raw.xtranssensor.method = RAWParams::XTransSensor::getMethodString(RAWParams::XTransSensor::Method::FAST);
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neutral.icm.outputProfile = ColorManagementParams::NoICMString;
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std::unique_ptr<IImage8> source;
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{
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RawMetaDataLocation rml;
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eSensorType sensor_type;
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int w, h;
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std::unique_ptr<Thumbnail> thumb(Thumbnail::loadQuickFromRaw(getFileName(), rml, sensor_type, w, h, 1, false, true, true));
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if (!thumb) {
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if (settings->verbose) {
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std::cout << "histogram matching: no thumbnail found, generating a neutral curve" << std::endl;
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}
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histMatchingCache = outCurve;
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*histMatchingParams = cp;
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return;
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} else if (w * 33 < fw || w * h < 19200) {
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// Some cameras have extremely small thumbs, for example Canon PowerShot A3100 IS has 128x96 thumbs.
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// For them we skip histogram matching.
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// With 160x120 thumbs from RICOH GR DIGITAL 2 it works fine, so we use 19200 as limit.
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if (settings->verbose) {
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std::cout << "histogram matching: the embedded thumbnail is too small: " << w << "x" << h << std::endl;
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}
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histMatchingCache = outCurve;
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*histMatchingParams = cp;
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return;
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}
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skip = LIM(skip * fh / h, 6, 10); // adjust the skip factor -- the larger the thumbnail, the less we should skip to get a good match
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source.reset(thumb->quickProcessImage(neutral, fh / skip, TI_Nearest));
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if (settings->verbose) {
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std::cout << "histogram matching: extracted embedded thumbnail" << std::endl;
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}
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}
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std::unique_ptr<IImage8> target;
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{
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RawMetaDataLocation rml;
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eSensorType sensor_type;
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double scale;
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int w = fw / skip, h = fh / skip;
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std::unique_ptr<Thumbnail> thumb(Thumbnail::loadFromRaw(getFileName(), rml, sensor_type, w, h, 1, false, false, true));
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if (!thumb) {
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if (settings->verbose) {
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std::cout << "histogram matching: raw decoding failed, generating a neutral curve" << std::endl;
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}
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histMatchingCache = outCurve;
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*histMatchingParams = cp;
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return;
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}
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target.reset(thumb->processImage(neutral, sensor_type, fh / skip, TI_Nearest, getMetaData(), scale, false, true));
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int sw = source->getWidth(), sh = source->getHeight();
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int tw = target->getWidth(), th = target->getHeight();
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float thumb_ratio = float(std::max(sw, sh)) / float(std::min(sw, sh));
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float target_ratio = float(std::max(tw, th)) / float(std::min(tw, th));
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if (std::abs(thumb_ratio - target_ratio) > 0.01) {
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int cx = 0, cy = 0;
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if (thumb_ratio > target_ratio) {
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// crop the height
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int ch = th - (tw * float(sh) / float(sw));
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cy += ch / 2;
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th -= ch;
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} else {
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// crop the width
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int cw = tw - (th * float(sw) / float(sh));
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cx += cw / 2;
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tw -= cw;
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}
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if (settings->verbose) {
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std::cout << "histogram matching: cropping target to get an aspect ratio of " << round(thumb_ratio * 100)/100.0 << ":1, new size is " << tw << "x" << th << std::endl;
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}
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if (cx || cy) {
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Image8 *tmp = new Image8(tw, th);
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#ifdef _OPENMP
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#pragma omp parallel for
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#endif
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for (int y = 0; y < th; ++y) {
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for (int x = 0; x < tw; ++x) {
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tmp->r(y, x) = target->r(y+cy, x+cx);
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tmp->g(y, x) = target->g(y+cy, x+cx);
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tmp->b(y, x) = target->b(y+cy, x+cx);
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}
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}
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target.reset(tmp);
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}
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}
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if (settings->verbose) {
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std::cout << "histogram matching: generated neutral rendering" << std::endl;
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}
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}
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if (target->getWidth() != source->getWidth() || target->getHeight() != source->getHeight()) {
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Image8 *tmp = new Image8(source->getWidth(), source->getHeight());
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target->resizeImgTo(source->getWidth(), source->getHeight(), TI_Nearest, tmp);
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target.reset(tmp);
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}
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CdfInfo scdf = getCdf(*source);
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CdfInfo tcdf = getCdf(*target);
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std::vector<int> mapping;
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int j = 0;
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for (int i = 0; i < int(tcdf.cdf.size()); ++i) {
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j = findMatch(tcdf.cdf[i], scdf.cdf, j);
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if (i >= tcdf.min_val && i <= tcdf.max_val && j >= scdf.min_val && j <= scdf.max_val) {
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mapping.push_back(j);
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} else {
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mapping.push_back(-1);
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}
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}
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mappingToCurve(mapping, outCurve);
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if (settings->verbose) {
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std::cout << "histogram matching: generated curve with " << outCurve.size()/2 << " control points" << std::endl;
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}
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histMatchingCache = outCurve;
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*histMatchingParams = cp;
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}
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} // namespace rtengine
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