342 lines
11 KiB
C++
342 lines
11 KiB
C++
/* -*- C++ -*-
|
|
*
|
|
* This file is part of RawTherapee.
|
|
*
|
|
* Copyright (c) 2018 Alberto Griggio <alberto.griggio@gmail.com>
|
|
*
|
|
* RawTherapee is free software: you can redistribute it and/or modify
|
|
* it under the terms of the GNU General Public License as published by
|
|
* the Free Software Foundation, either version 3 of the License, or
|
|
* (at your option) any later version.
|
|
*
|
|
* RawTherapee is distributed in the hope that it will be useful,
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
* GNU General Public License for more details.
|
|
*
|
|
* You should have received a copy of the GNU General Public License
|
|
* along with RawTherapee. If not, see <http://www.gnu.org/licenses/>.
|
|
*/
|
|
|
|
#include "rawimagesource.h"
|
|
#include "rtthumbnail.h"
|
|
#include "curves.h"
|
|
#include "color.h"
|
|
#include "rt_math.h"
|
|
#include "iccstore.h"
|
|
#include "../rtgui/mydiagonalcurve.h"
|
|
#include "improcfun.h"
|
|
//#define BENCHMARK
|
|
#include "StopWatch.h"
|
|
#include <iostream>
|
|
|
|
|
|
namespace rtengine {
|
|
|
|
extern const Settings *settings;
|
|
|
|
namespace {
|
|
|
|
struct CdfInfo {
|
|
std::vector<int> cdf;
|
|
int min_val;
|
|
int max_val;
|
|
|
|
CdfInfo(): cdf(256), min_val(-1), max_val(-1) {}
|
|
};
|
|
|
|
|
|
CdfInfo getCdf(const IImage8 &img)
|
|
{
|
|
CdfInfo ret;
|
|
|
|
for (int y = 0; y < img.getHeight(); ++y) {
|
|
for (int x = 0; x < img.getWidth(); ++x) {
|
|
int lum = LIM(int(Color::rgbLuminance(float(img.r(y, x)), float(img.g(y, x)), float(img.b(y, x)))), 0, 255);
|
|
++ret.cdf[lum];
|
|
}
|
|
}
|
|
|
|
int sum = 0;
|
|
for (size_t i = 0; i < ret.cdf.size(); ++i) {
|
|
if (ret.cdf[i] > 0) {
|
|
if (ret.min_val < 0) {
|
|
ret.min_val = i;
|
|
}
|
|
ret.max_val = i;
|
|
}
|
|
sum += ret.cdf[i];
|
|
ret.cdf[i] = sum;
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
|
|
int findMatch(int val, const std::vector<int> &cdf, int j)
|
|
{
|
|
if (cdf[j] <= val) {
|
|
for (; j < int(cdf.size()); ++j) {
|
|
if (cdf[j] == val) {
|
|
return j;
|
|
} else if (cdf[j] > val) {
|
|
return (cdf[j] - val <= val - cdf[j-1] ? j : j-1);
|
|
}
|
|
}
|
|
return 255;
|
|
} else {
|
|
for (; j >= 0; --j) {
|
|
if (cdf[j] == val) {
|
|
return j;
|
|
} else if (cdf[j] < val) {
|
|
return (val - cdf[j] <= cdf[j+1] - val ? j : j+1);
|
|
}
|
|
}
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
|
|
void mappingToCurve(const std::vector<int> &mapping, std::vector<double> &curve)
|
|
{
|
|
curve.clear();
|
|
|
|
int idx = 15;
|
|
for (; idx < int(mapping.size()); ++idx) {
|
|
if (mapping[idx] >= idx) {
|
|
break;
|
|
}
|
|
}
|
|
if (idx == int(mapping.size())) {
|
|
for (idx = 1; idx < int(mapping.size()); ++idx) {
|
|
if (mapping[idx] >= idx) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
auto coord = [](int v) -> double { return double(v)/255.0; };
|
|
auto doit =
|
|
[&](int start, int stop, int step, bool addstart) -> void
|
|
{
|
|
int prev = start;
|
|
if (addstart && mapping[start] >= 0) {
|
|
curve.push_back(coord(start));
|
|
curve.push_back(coord(mapping[start]));
|
|
}
|
|
for (int i = start; i < stop; ++i) {
|
|
int v = mapping[i];
|
|
if (v < 0) {
|
|
continue;
|
|
}
|
|
bool change = i > 0 && v != mapping[i-1];
|
|
int diff = i - prev;
|
|
if ((change && std::abs(diff - step) <= 1) || diff > step * 2) {
|
|
curve.push_back(coord(i));
|
|
curve.push_back(coord(v));
|
|
prev = i;
|
|
}
|
|
}
|
|
};
|
|
|
|
curve.push_back(0.0);
|
|
curve.push_back(0.0);
|
|
|
|
int start = 0;
|
|
while (start < idx && (mapping[start] < 0 || start < idx / 2)) {
|
|
++start;
|
|
}
|
|
|
|
const int npoints = 8;
|
|
int step = std::max(int(mapping.size())/npoints, 1);
|
|
int end = mapping.size();
|
|
if (idx <= end / 3) {
|
|
doit(start, idx, idx / 2, true);
|
|
doit(idx, end, (end - idx) / 3, false);
|
|
} else {
|
|
doit(start, idx, idx > step ? step : idx / 2, true);
|
|
doit(idx, int(mapping.size()), step, idx - step > step / 2 && std::abs(curve[curve.size()-2] - coord(idx)) > 0.01);
|
|
}
|
|
|
|
if (curve.size() > 2 && (1 - curve[curve.size()-2] <= step / (256.0 * 3))) {
|
|
curve.pop_back();
|
|
curve.pop_back();
|
|
}
|
|
|
|
curve.push_back(1.0);
|
|
curve.push_back(1.0);
|
|
|
|
if (curve.size() < 4) {
|
|
curve = { DCT_Linear }; // not enough points, fall back to linear
|
|
} else {
|
|
curve.insert(curve.begin(), DCT_Spline);
|
|
}
|
|
}
|
|
|
|
} // namespace
|
|
|
|
|
|
void RawImageSource::getAutoMatchedToneCurve(const ColorManagementParams &cp, std::vector<double> &outCurve)
|
|
{
|
|
BENCHFUN
|
|
|
|
if (settings->verbose) {
|
|
std::cout << "performing histogram matching for " << getFileName() << " on the embedded thumbnail" << std::endl;
|
|
}
|
|
|
|
const auto same_profile =
|
|
[](const ColorManagementParams &a, const ColorManagementParams &b) -> bool
|
|
{
|
|
return (a.inputProfile == b.inputProfile
|
|
&& a.toneCurve == b.toneCurve
|
|
&& a.applyLookTable == b.applyLookTable
|
|
&& a.applyBaselineExposureOffset == b.applyBaselineExposureOffset
|
|
&& a.applyHueSatMap == b.applyHueSatMap
|
|
&& a.dcpIlluminant == b.dcpIlluminant);
|
|
};
|
|
|
|
if (!histMatchingCache.empty() && same_profile(histMatchingParams, cp)) {
|
|
if (settings->verbose) {
|
|
std::cout << "tone curve found in cache" << std::endl;
|
|
}
|
|
outCurve = histMatchingCache;
|
|
return;
|
|
}
|
|
|
|
outCurve = { DCT_Linear };
|
|
|
|
int fw, fh;
|
|
getFullSize(fw, fh, TR_NONE);
|
|
if (getRotateDegree() == 90 || getRotateDegree() == 270) {
|
|
std::swap(fw, fh);
|
|
}
|
|
int skip = 3;
|
|
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: full raw image size is " << fw << "x" << fh << std::endl;
|
|
}
|
|
|
|
ProcParams neutral;
|
|
neutral.icm = cp;
|
|
neutral.raw.bayersensor.method = RAWParams::BayerSensor::getMethodString(RAWParams::BayerSensor::Method::FAST);
|
|
neutral.raw.xtranssensor.method = RAWParams::XTransSensor::getMethodString(RAWParams::XTransSensor::Method::FAST);
|
|
neutral.icm.outputProfile = "sRGB";
|
|
|
|
std::unique_ptr<IImage8> source;
|
|
{
|
|
RawMetaDataLocation rml;
|
|
eSensorType sensor_type;
|
|
int w, h;
|
|
std::unique_ptr<Thumbnail> thumb(Thumbnail::loadQuickFromRaw(getFileName(), rml, sensor_type, w, h, 1, false, true, true));
|
|
if (!thumb) {
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: no thumbnail found, generating a neutral curve" << std::endl;
|
|
}
|
|
histMatchingCache = outCurve;
|
|
histMatchingParams = cp;
|
|
return;
|
|
} else if (w * 10 < fw) {
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: the embedded thumbnail is too small: " << w << "x" << h << std::endl;
|
|
}
|
|
histMatchingCache = outCurve;
|
|
histMatchingParams = cp;
|
|
return;
|
|
}
|
|
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
|
|
source.reset(thumb->quickProcessImage(neutral, fh / skip, TI_Nearest));
|
|
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: extracted embedded thumbnail" << std::endl;
|
|
}
|
|
}
|
|
|
|
std::unique_ptr<IImage8> target;
|
|
{
|
|
RawMetaDataLocation rml;
|
|
eSensorType sensor_type;
|
|
double scale;
|
|
int w = fw / skip, h = fh / skip;
|
|
std::unique_ptr<Thumbnail> thumb(Thumbnail::loadFromRaw(getFileName(), rml, sensor_type, w, h, 1, false, false, true));
|
|
if (!thumb) {
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: raw decoding failed, generating a neutral curve" << std::endl;
|
|
}
|
|
histMatchingCache = outCurve;
|
|
histMatchingParams = cp;
|
|
return;
|
|
}
|
|
target.reset(thumb->processImage(neutral, sensor_type, fh / skip, TI_Nearest, getMetaData(), scale, false, true));
|
|
|
|
int sw = source->getWidth(), sh = source->getHeight();
|
|
int tw = target->getWidth(), th = target->getHeight();
|
|
float thumb_ratio = float(std::max(sw, sh)) / float(std::min(sw, sh));
|
|
float target_ratio = float(std::max(tw, th)) / float(std::min(tw, th));
|
|
int cx = 0, cy = 0;
|
|
if (std::abs(thumb_ratio - target_ratio) > 0.01) {
|
|
if (thumb_ratio > target_ratio) {
|
|
// crop the height
|
|
int ch = th - (tw * float(sh) / float(sw));
|
|
cy += ch / 2;
|
|
th -= ch;
|
|
} else {
|
|
// crop the width
|
|
int cw = tw - (th * float(sw) / float(sh));
|
|
cx += cw / 2;
|
|
tw -= cw;
|
|
}
|
|
if (settings->verbose) {
|
|
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;
|
|
}
|
|
|
|
if (cx || cy) {
|
|
Image8 *tmp = new Image8(tw, th);
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for
|
|
#endif
|
|
for (int y = 0; y < th; ++y) {
|
|
for (int x = 0; x < tw; ++x) {
|
|
tmp->r(y, x) = target->r(y+cy, x+cx);
|
|
tmp->g(y, x) = target->g(y+cy, x+cx);
|
|
tmp->b(y, x) = target->b(y+cy, x+cx);
|
|
}
|
|
}
|
|
target.reset(tmp);
|
|
}
|
|
}
|
|
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: generated neutral rendering" << std::endl;
|
|
}
|
|
}
|
|
if (target->getWidth() != source->getWidth() || target->getHeight() != source->getHeight()) {
|
|
Image8 *tmp = new Image8(source->getWidth(), source->getHeight());
|
|
target->resizeImgTo(source->getWidth(), source->getHeight(), TI_Nearest, tmp);
|
|
target.reset(tmp);
|
|
}
|
|
CdfInfo scdf = getCdf(*source);
|
|
CdfInfo tcdf = getCdf(*target);
|
|
|
|
std::vector<int> mapping;
|
|
int j = 0;
|
|
for (int i = 0; i < int(tcdf.cdf.size()); ++i) {
|
|
j = findMatch(tcdf.cdf[i], scdf.cdf, j);
|
|
if (i >= tcdf.min_val && i <= tcdf.max_val && j >= scdf.min_val && j <= scdf.max_val) {
|
|
mapping.push_back(j);
|
|
} else {
|
|
mapping.push_back(-1);
|
|
}
|
|
}
|
|
|
|
mappingToCurve(mapping, outCurve);
|
|
|
|
if (settings->verbose) {
|
|
std::cout << "histogram matching: generated curve with " << outCurve.size()/2 << " control points" << std::endl;
|
|
}
|
|
|
|
histMatchingCache = outCurve;
|
|
histMatchingParams = cp;
|
|
}
|
|
|
|
} // namespace rtengine
|