726 lines
24 KiB
C++
726 lines
24 KiB
C++
/*
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* This file is part of RawTherapee.
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*
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* Copyright (c) 2004-2010 Gabor Horvath <hgabor@rawtherapee.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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* adaptation to RawTherapee
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* 2015 Jacques Desmis <jdesmis@gmail.com>
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* 2015 Ingo Weyrich <heckflosse67@gmx.de>
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* D. J. Jobson, Z. Rahman, and G. A. Woodell. A multi-scale
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* Retinex for bridging the gap between color images and the
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* human observation of scenes. IEEE Transactions on Image Processing,
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* 1997, 6(7): 965-976
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* Fan Guo Zixing Cai Bin Xie Jin Tang
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* School of Information Science and Engineering, Central South University Changsha, China
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* Weixing Wang and Lian Xu
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* College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
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* inspired from 2003 Fabien Pelisson <Fabien.Pelisson@inrialpes.fr>
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* some ideas taken (use of mask) Russell Cottrell - The Retinex .8bf Plugin
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*/
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#include <cmath>
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include "color.h"
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#include "curves.h"
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#include "gauss.h"
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#include "improcfun.h"
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#include "jaggedarray.h"
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#include "median.h"
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#include "opthelper.h"
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#include "procparams.h"
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#include "rawimagesource.h"
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#include "rtengine.h"
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#include "shmap.h"
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#include "StopWatch.h"
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namespace
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{
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void retinex_scales( float* scales, int nscales, int mode, int s, float high)
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{
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if ( nscales == 1 ) {
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scales[0] = (float)s / 2.f;
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} else if (nscales == 2) {
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scales[1] = (float) s / 2.f;
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scales[0] = (float) s;
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} else {
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float size_step = (float) s / (float) nscales;
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if (mode == 0) {
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for (int i = 0; i < nscales; ++i ) {
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scales[nscales - i - 1] = 2.0f + (float)i * size_step;
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}
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} else if (mode == 1) {
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size_step = (float)log(s - 2.0f) / (float) nscales;
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for (int i = 0; i < nscales; ++i ) {
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scales[nscales - i - 1] = 2.0f + (float)pow (10.f, (i * size_step) / log (10.f));
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}
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} else if (mode == 2) {
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size_step = (float) log(s - 2.0f) / (float) nscales;
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for ( int i = 0; i < nscales; ++i ) {
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scales[i] = s - (float)pow (10.f, (i * size_step) / log (10.f));
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}
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} else if (mode == 3) {
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size_step = (float) log(s - 2.0f) / (float) nscales;
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for ( int i = 0; i < nscales; ++i ) {
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scales[i] = high * s - (float)pow (10.f, (i * size_step) / log (10.f));
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}
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}
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}
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}
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void mean_stddv2( float **dst, float &mean, float &stddv, int W_L, int H_L, float &maxtr, float &mintr)
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{
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// summation using double precision to avoid too large summation error for large pictures
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double vsquared = 0.f;
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double sum = 0.f;
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maxtr = -999999.f;
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mintr = 999999.f;
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#ifdef _OPENMP
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#pragma omp parallel
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#endif
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{
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float lmax = -999999.f, lmin = 999999.f;
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#ifdef _OPENMP
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#pragma omp for reduction(+:sum,vsquared) nowait // this leads to differences, but parallel summation is more accurate
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#endif
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for (int i = 0; i < H_L; i++ )
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for (int j = 0; j < W_L; j++) {
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sum += static_cast<double>(dst[i][j]);
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vsquared += rtengine::SQR<double>(dst[i][j]);
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lmax = dst[i][j] > lmax ? dst[i][j] : lmax;
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lmin = dst[i][j] < lmin ? dst[i][j] : lmin;
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}
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#ifdef _OPENMP
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#pragma omp critical
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#endif
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{
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maxtr = maxtr > lmax ? maxtr : lmax;
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mintr = mintr < lmin ? mintr : lmin;
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}
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}
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mean = sum / (double) (W_L * H_L);
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vsquared /= (double) W_L * H_L;
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stddv = vsquared - rtengine::SQR<double>(mean);
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stddv = std::sqrt(stddv);
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}
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}
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namespace rtengine
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{
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void RawImageSource::MSR(float** luminance, float** originalLuminance, float **exLuminance, const LUTf& mapcurve, bool mapcontlutili, int width, int height, const procparams::RetinexParams &deh, const RetinextransmissionCurve & dehatransmissionCurve, const RetinexgaintransmissionCurve & dehagaintransmissionCurve, float &minCD, float &maxCD, float &mini, float &maxi, float &Tmean, float &Tsigma, float &Tmin, float &Tmax)
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{
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BENCHFUN
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if (!deh.enabled) {
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return;
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}
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constexpr float eps = 2.f;
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const bool useHsl = deh.retinexcolorspace == "HSLLOG";
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const bool useHslLin = deh.retinexcolorspace == "HSLLIN";
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const float offse = deh.offs; //def = 0 not use
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const int iter = deh.iter;
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const int gradient = deh.scal;
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int scal = deh.skal;
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const int nei = 2.8f * deh.neigh; //def = 220
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const float vart = deh.vart / 100.f;//variance
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const float gradvart = deh.grad;
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const float gradstr = deh.grads;
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const float strength = deh.str / 100.f; // Blend with original L channel data
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float limD = deh.limd;
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limD = pow(limD, 1.7f);//about 2500 enough
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limD *= useHslLin ? 10.f : 1.f;
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const float ilimD = 1.f / limD;
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const float hig = deh.highl / 100.f;
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const int H_L = height;
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const int W_L = width;
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constexpr float elogt = 2.71828f;
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bool lhutili = false;
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FlatCurve* shcurve = new FlatCurve(deh.lhcurve); //curve L=f(H)
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if (!shcurve || shcurve->isIdentity()) {
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if (shcurve) {
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delete shcurve;
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shcurve = nullptr;
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}
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} else {
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lhutili = true;
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}
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bool higplus = false ;
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int moderetinex = 2; // default to 2 ( deh.retinexMethod == "high" )
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if(deh.retinexMethod == "highliplus") {
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higplus = true;
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moderetinex = 3;
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} else if (deh.retinexMethod == "uni") {
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moderetinex = 0;
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} else if (deh.retinexMethod == "low") {
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moderetinex = 1;
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} else { /*if (deh.retinexMethod == "highli") */
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moderetinex = 3;
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}
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constexpr float aahi = 49.f / 99.f; ////reduce sensibility 50%
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constexpr float bbhi = 1.f - aahi;
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float high = bbhi + aahi * (float) deh.highl;
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for (int it = 1; it < iter + 1; it++) { //iter nb max of iterations
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float grad = 1.f;
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float sc = scal;
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if (gradient == 0) {
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grad = 1.f;
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sc = 3.f;
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} else if (gradient == 1) {
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grad = 0.25f * it + 0.75f;
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sc = -0.5f * it + 4.5f;
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} else if (gradient == 2) {
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grad = 0.5f * it + 0.5f;
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sc = -0.75f * it + 5.75f;
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} else if (gradient == 3) {
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grad = 0.666f * it + 0.333f;
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sc = -0.75f * it + 5.75f;
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} else if (gradient == 4) {
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grad = 0.8f * it + 0.2f;
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sc = -0.75f * it + 5.75f;
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} else if (gradient == 5) {
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if (moderetinex != 3) {
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grad = 2.5f * it - 1.5f;
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} else {
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float aa = (11.f * high - 1.f) / 4.f;
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float bb = 1.f - aa;
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grad = aa * it + bb;
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}
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sc = -0.75f * it + 5.75f;
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} else if (gradient == 6) {
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if (moderetinex != 3) {
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grad = 5.f * it - 4.f;
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} else {
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float aa = (21.f * high - 1.f) / 4.f;
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float bb = 1.f - aa;
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grad = aa * it + bb;
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}
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sc = -0.75f * it + 5.75f;
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} else if (gradient == -1) {
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grad = -0.125f * it + 1.125f;
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sc = 3.f;
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}
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if (iter == 1) {
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sc = scal;
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} else {
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//adjust sc in function of choice of scale by user if iterations
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if (scal < 3) {
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sc -= 1;
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if (sc < 1.f) {//avoid 0
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sc = 1.f;
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}
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} else if (scal > 4) {
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sc += 1;
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}
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}
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float varx = vart;
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float limdx = limD;
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float ilimdx = ilimD;
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if (gradvart != 0) {
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if (gradvart == 1) {
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varx = vart * (-0.125f * it + 1.125f);
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limdx = limD * (-0.125f * it + 1.125f);
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ilimdx = 1.f / limdx;
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} else if (gradvart == 2) {
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varx = vart * (-0.2f * it + 1.2f);
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limdx = limD * (-0.2f * it + 1.2f);
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ilimdx = 1.f / limdx;
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} else if (gradvart == -1) {
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varx = vart * (0.125f * it + 0.875f);
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limdx = limD * (0.125f * it + 0.875f);
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ilimdx = 1.f / limdx;
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} else if (gradvart == -2) {
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varx = vart * (0.4f * it + 0.6f);
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limdx = limD * (0.4f * it + 0.6f);
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ilimdx = 1.f / limdx;
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}
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}
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scal = round(sc);
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float ks = 1.f;
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if (gradstr != 0) {
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if (gradstr == 1) {
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if (it <= 3) {
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ks = -0.3f * it + 1.6f;
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} else {
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ks = 0.5f;
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}
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} else if (gradstr == 2) {
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if (it <= 3) {
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ks = -0.6f * it + 2.2f;
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} else {
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ks = 0.3f;
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}
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} else if (gradstr == -1) {
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if (it <= 3) {
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ks = 0.2f * it + 0.6f;
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} else {
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ks = 1.2f;
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}
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} else if (gradstr == -2) {
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if (it <= 3) {
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ks = 0.4f * it + 0.2f;
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} else {
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ks = 1.5f;
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}
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}
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}
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const float strengthx = ks * strength;
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constexpr auto maxRetinexScales = 8;
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float RetinexScales[maxRetinexScales];
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retinex_scales(RetinexScales, scal, moderetinex, nei / grad, high);
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const int shHighlights = deh.highlights;
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const int shShadows = deh.shadows;
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int mapmet = 0;
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if(deh.mapMethod == "map") {
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mapmet = 2;
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} else if(deh.mapMethod == "mapT") {
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mapmet = 3;
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} else if(deh.mapMethod == "gaus") {
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mapmet = 4;
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}
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const double shradius = mapmet == 4 ? (double) deh.radius : 40.;
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int viewmet = 0;
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if(deh.viewMethod == "mask") {
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viewmet = 1;
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} else if(deh.viewMethod == "tran") {
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viewmet = 2;
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} else if(deh.viewMethod == "tran2") {
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viewmet = 3;
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} else if(deh.viewMethod == "unsharp") {
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viewmet = 4;
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}
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std::unique_ptr<JaggedArray<float>> srcBuffer(new JaggedArray<float>(W_L, H_L));
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float** src = *(srcBuffer.get());
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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 i = 0; i < H_L; i++)
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for (int j = 0; j < W_L; j++) {
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src[i][j] = luminance[i][j] + eps;
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luminance[i][j] = 0.f;
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}
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JaggedArray<float> out(W_L, H_L);
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JaggedArray<float>& tran = out; // tran and out can safely use the same buffer
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const float logBetaGain = xlogf(16384.f);
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float pond = logBetaGain / (float) scal;
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if(!useHslLin) {
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pond /= log(elogt);
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}
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std::unique_ptr<SHMap> shmap;
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if (((mapmet == 2 || mapmet == 3 || mapmet == 4) && it == 1)) {
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shmap.reset(new SHMap(W_L, H_L));
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}
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std::unique_ptr<float[]> buffer;
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if (mapmet > 0) {
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buffer.reset(new float[W_L * H_L]);
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}
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for (int scale = scal - 1; scale >= 0; --scale) {
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if (scale == scal - 1) {
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gaussianBlur(src, out, W_L, H_L, RetinexScales[scale], true);
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} else { // reuse result of last iteration
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// out was modified in last iteration => restore it
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if((((mapmet == 2 && scale > 1) || mapmet == 3 || mapmet == 4) || (mapmet > 0 && mapcontlutili)) && it == 1) {
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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 i = 0; i < H_L; i++) {
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for (int j = 0; j < W_L; j++) {
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out[i][j] = buffer[i * W_L + j];
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}
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}
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}
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gaussianBlur(out, out, W_L, H_L, sqrtf(SQR(RetinexScales[scale]) - SQR(RetinexScales[scale + 1])), true);
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}
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if ((((mapmet == 2 && scale > 2) || mapmet == 3 || mapmet == 4) || (mapmet > 0 && mapcontlutili)) && it == 1 && scale > 0) {
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// out will be modified => store it for use in next iteration.
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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 i = 0; i < H_L; i++) {
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for (int j = 0; j < W_L; j++) {
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buffer[i * W_L + j] = out[i][j];
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}
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}
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}
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int h_th = 0;
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int s_th = 0;
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if (((mapmet == 2 && scale > 2) || mapmet == 3 || mapmet == 4) && it == 1) {
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shmap->updateL(out, shradius, true, 1);
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h_th = shmap->max_f - deh.htonalwidth * (shmap->max_f - shmap->avg) / 100;
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s_th = deh.stonalwidth * (shmap->avg - shmap->min_f) / 100;
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}
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if (mapmet > 0 && mapcontlutili && it == 1) {
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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 i = 0; i < H_L; i++) {
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for (int j = 0; j < W_L; j++) {
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out[i][j] = mapcurve[2.f * out[i][j]];
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}
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}
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}
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if (((mapmet == 2 && scale > 2) || mapmet == 3 || mapmet == 4) && it == 1) {
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const float hWeight = (100.f - shHighlights) / 100.f;
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const float sWeight = (100.f - shShadows) / 100.f;
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#ifdef _OPENMP
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#pragma omp parallel for schedule(dynamic,16)
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#endif
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for (int i = 0; i < H_L; i++) {
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for (int j = 0; j < W_L; j++) {
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const float mapval = 1.f + shmap->map[i][j];
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float factor;
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if (mapval > h_th) {
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factor = (h_th + hWeight * (mapval - h_th)) / mapval;
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} else if (mapval < s_th) {
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factor = (s_th - sWeight * (s_th - mapval)) / mapval;
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} else {
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factor = 1.f;
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}
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out[i][j] *= factor;
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}
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}
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}
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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 i = 0; i < H_L; i++) {
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int j = 0;
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#ifdef __SSE2__
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const vfloat pondv = F2V(pond);
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const vfloat limMinv = F2V(ilimdx);
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const vfloat limMaxv = F2V(limdx);
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if( useHslLin) {
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for (; j < W_L - 3; j += 4) {
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STVFU(luminance[i][j], LVFU(luminance[i][j]) + pondv * vclampf(LVFU(src[i][j]) / LVFU(out[i][j]), limMinv, limMaxv));
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}
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} else {
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for (; j < W_L - 3; j += 4) {
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STVFU(luminance[i][j], LVFU(luminance[i][j]) + pondv * xlogf(vclampf(LVFU(src[i][j]) / LVFU(out[i][j]), limMinv, limMaxv)));
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}
|
|
}
|
|
|
|
#endif
|
|
|
|
if(useHslLin) {
|
|
for (; j < W_L; j++) {
|
|
luminance[i][j] += pond * LIM(src[i][j] / out[i][j], ilimdx, limdx);
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|
}
|
|
} else {
|
|
for (; j < W_L; j++) {
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|
luminance[i][j] += pond * xlogf(LIM(src[i][j] / out[i][j], ilimdx, limdx)); // /logt ?
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|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
srcBuffer.reset();
|
|
|
|
float mean = 0.f;
|
|
float stddv = 0.f;
|
|
// I call mean_stddv2 instead of mean_stddv ==> logBetaGain
|
|
|
|
float maxtr, mintr;
|
|
mean_stddv2(luminance, mean, stddv, W_L, H_L, maxtr, mintr);
|
|
//printf("mean=%f std=%f delta=%f maxtr=%f mintr=%f\n", mean, stddv, delta, maxtr, mintr);
|
|
|
|
//mean_stddv( luminance, mean, stddv, W_L, H_L, logBetaGain, maxtr, mintr);
|
|
if (dehatransmissionCurve && mean != 0.f && stddv != 0.f) { //if curve
|
|
float asig = 0.166666f / stddv;
|
|
float bsig = 0.5f - asig * mean;
|
|
float amax = 0.333333f / (maxtr - mean - stddv);
|
|
float bmax = 1.f - amax * maxtr;
|
|
float amin = 0.333333f / (mean - stddv - mintr);
|
|
float bmin = -amin * mintr;
|
|
|
|
asig *= 500.f;
|
|
bsig *= 500.f;
|
|
amax *= 500.f;
|
|
bmax *= 500.f;
|
|
amin *= 500.f;
|
|
bmin *= 500.f;
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for schedule(dynamic,16)
|
|
#endif
|
|
|
|
for (int i = 0; i < H_L; i++ ) {
|
|
for (int j = 0; j < W_L; j++) { //for mintr to maxtr evalate absciss in function of original transmission
|
|
float absciss;
|
|
if (LIKELY(fabsf(luminance[i][j] - mean) < stddv)) {
|
|
absciss = asig * luminance[i][j] + bsig;
|
|
} else if (luminance[i][j] >= mean) {
|
|
absciss = amax * luminance[i][j] + bmax;
|
|
} else { /*if(luminance[i][j] <= mean - stddv)*/
|
|
absciss = amin * luminance[i][j] + bmin;
|
|
}
|
|
|
|
//TODO : move multiplication by 4.f and subtraction of 1.f inside the curve
|
|
luminance[i][j] *= (-1.f + 4.f * dehatransmissionCurve[absciss]); //new transmission
|
|
|
|
if(viewmet == 3 || viewmet == 2) {
|
|
tran[i][j] = luminance[i][j];
|
|
}
|
|
}
|
|
}
|
|
|
|
// median filter on transmission ==> reduce artifacts
|
|
if (deh.medianmap && it == 1) { //only one time
|
|
JaggedArray<float> tmL(W_L, H_L);
|
|
constexpr int borderL = 1;
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for
|
|
#endif
|
|
|
|
for (int i = borderL; i < H_L - borderL; i++) {
|
|
for (int j = borderL; j < W_L - borderL; j++) {
|
|
tmL[i][j] = median(luminance[i][j], luminance[i - 1][j], luminance[i + 1][j], luminance[i][j + 1], luminance[i][j - 1], luminance[i - 1][j - 1], luminance[i - 1][j + 1], luminance[i + 1][j - 1], luminance[i + 1][j + 1]); //3x3
|
|
}
|
|
}
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for
|
|
#endif
|
|
|
|
for (int i = borderL; i < H_L - borderL; i++ ) {
|
|
for (int j = borderL; j < W_L - borderL; j++) {
|
|
luminance[i][j] = tmL[i][j];
|
|
}
|
|
}
|
|
}
|
|
|
|
// I call mean_stddv2 instead of mean_stddv ==> logBetaGain
|
|
//mean_stddv( luminance, mean, stddv, W_L, H_L, 1.f, maxtr, mintr);
|
|
mean_stddv2(luminance, mean, stddv, W_L, H_L, maxtr, mintr);
|
|
}
|
|
|
|
constexpr float epsil = 0.1f;
|
|
|
|
mini = mean - varx * stddv;
|
|
|
|
if (mini < mintr) {
|
|
mini = mintr + epsil;
|
|
}
|
|
|
|
maxi = mean + varx * stddv;
|
|
|
|
if (maxi > maxtr) {
|
|
maxi = maxtr - epsil;
|
|
}
|
|
|
|
float delta = maxi - mini;
|
|
//printf("maxi=%f mini=%f mean=%f std=%f delta=%f maxtr=%f mintr=%f\n", maxi, mini, mean, stddv, delta, maxtr, mintr);
|
|
|
|
if ( !delta ) {
|
|
delta = 1.0f;
|
|
}
|
|
|
|
// coeff for auto "transmission" with 2 sigma #95% data
|
|
const float aza = 16300.f / (2.f * stddv);
|
|
const float azb = -aza * (mean - 2.f * stddv);
|
|
const float bza = 16300.f / (2.f * stddv);
|
|
const float bzb = 16300.f - bza * (mean);
|
|
|
|
//prepare work for curve gain
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for
|
|
#endif
|
|
|
|
for (int i = 0; i < H_L; i++) {
|
|
for (int j = 0; j < W_L; j++) {
|
|
luminance[i][j] = luminance[i][j] - mini;
|
|
}
|
|
}
|
|
|
|
mean = 0.f;
|
|
stddv = 0.f;
|
|
// I call mean_stddv2 instead of mean_stddv ==> logBetaGain
|
|
|
|
mean_stddv2(luminance, mean, stddv, W_L, H_L, maxtr, mintr);
|
|
float asig = 0.f, bsig = 0.f, amax = 0.f, bmax = 0.f, amin = 0.f, bmin = 0.f;
|
|
|
|
if (dehagaintransmissionCurve && mean != 0.f && stddv != 0.f) { //if curve
|
|
asig = 0.166666f / stddv;
|
|
bsig = 0.5f - asig * mean;
|
|
amax = 0.333333f / (maxtr - mean - stddv);
|
|
bmax = 1.f - amax * maxtr;
|
|
amin = 0.333333f / (mean - stddv - mintr);
|
|
bmin = -amin * mintr;
|
|
|
|
asig *= 500.f;
|
|
bsig *= 500.f;
|
|
amax *= 500.f;
|
|
bmax *= 500.f;
|
|
amin *= 500.f;
|
|
bmin *= 500.f;
|
|
}
|
|
|
|
const float cdfactor = 32768.f / delta;
|
|
maxCD = -9999999.f;
|
|
minCD = 9999999.f;
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for reduction(max:maxCD) reduction(min:minCD) schedule(dynamic, 16)
|
|
#endif
|
|
|
|
for ( int i = 0; i < H_L; i ++ ) {
|
|
for (int j = 0; j < W_L; j++) {
|
|
float gan;
|
|
|
|
if (dehagaintransmissionCurve && mean != 0.f && stddv != 0.f) {
|
|
float absciss;
|
|
|
|
if (LIKELY(fabsf(luminance[i][j] - mean) < stddv)) {
|
|
absciss = asig * luminance[i][j] + bsig;
|
|
} else if (luminance[i][j] >= mean) {
|
|
absciss = amax * luminance[i][j] + bmax;
|
|
} else { /*if(luminance[i][j] <= mean - stddv)*/
|
|
absciss = amin * luminance[i][j] + bmin;
|
|
}
|
|
|
|
|
|
// float cd = cdfactor * ( luminance[i][j] - mini ) + offse;
|
|
// TODO : move multiplication by 2.f inside the curve
|
|
gan = 2.f * dehagaintransmissionCurve[absciss]; //new gain function transmission
|
|
} else {
|
|
gan = 0.5f;
|
|
}
|
|
|
|
const float cd = gan * cdfactor * luminance[i][j] + offse;
|
|
|
|
maxCD = cd > maxCD ? cd : maxCD;
|
|
minCD = cd < minCD ? cd : minCD;
|
|
|
|
float str = strengthx;
|
|
|
|
if (lhutili && it == 1) { // S=f(H)
|
|
{
|
|
const float HH = exLuminance[i][j];
|
|
float valparam;
|
|
|
|
if(useHsl || useHslLin) {
|
|
valparam = shcurve->getVal(HH) - 0.5;
|
|
} else {
|
|
valparam = shcurve->getVal(Color::huelab_to_huehsv2(HH)) - 0.5;
|
|
}
|
|
|
|
str *= (1.f + 2.f * valparam);
|
|
}
|
|
}
|
|
|
|
if (higplus && exLuminance[i][j] > 65535.f * hig) {
|
|
str *= hig;
|
|
}
|
|
|
|
if (viewmet == 0) {
|
|
luminance[i][j] = intp(str, LIM(cd, 0.f, 32768.f), originalLuminance[i][j]);
|
|
} else if (viewmet == 1) {
|
|
luminance[i][j] = out[i][j];
|
|
} else if (viewmet == 4) {
|
|
luminance[i][j] = originalLuminance[i][j] + str * (originalLuminance[i][j] - out[i][j]);//unsharp
|
|
} else if (viewmet == 2) {
|
|
if(tran[i][j] <= mean) {
|
|
luminance[i][j] = azb + aza * tran[i][j]; //auto values
|
|
} else {
|
|
luminance[i][j] = bzb + bza * tran[i][j];
|
|
}
|
|
} else { /*if (viewmet == 3) */
|
|
luminance[i][j] = 1000.f + tran[i][j] * 700.f; //arbitrary values to help display log values which are between -20 to + 30 - usage values -4 + 5
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
Tmean = mean;
|
|
Tsigma = stddv;
|
|
Tmin = mintr;
|
|
Tmax = maxtr;
|
|
|
|
if (shcurve) {
|
|
delete shcurve;
|
|
shcurve = nullptr;
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|