555 lines
17 KiB
C++
555 lines
17 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 <http://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 <heckflosse@i-weyrich.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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*/
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#include <string.h>
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#include "rtengine.h"
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#include "gauss.h"
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#include "rawimagesource.h"
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#include "improcfun.h"
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#include "opthelper.h"
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#include "StopWatch.h"
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#define MAX_RETINEX_SCALES 8
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#define clipretinex( val, minv, maxv ) (( val = (val < minv ? minv : val ) ) > maxv ? maxv : val )
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#define med3(a0,a1,a2,a3,a4,a5,a6,a7,a8,median) { \
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pp[0]=a0; pp[1]=a1; pp[2]=a2; pp[3]=a3; pp[4]=a4; pp[5]=a5; pp[6]=a6; pp[7]=a7; pp[8]=a8; \
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PIX_SORT(pp[1],pp[2]); PIX_SORT(pp[4],pp[5]); PIX_SORT(pp[7],pp[8]); \
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PIX_SORT(pp[0],pp[1]); PIX_SORT(pp[3],pp[4]); PIX_SORT(pp[6],pp[7]); \
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PIX_SORT(pp[1],pp[2]); PIX_SORT(pp[4],pp[5]); PIX_SORT(pp[7],pp[8]); \
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PIX_SORT(pp[0],pp[3]); PIX_SORT(pp[5],pp[8]); PIX_SORT(pp[4],pp[7]); \
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PIX_SORT(pp[3],pp[6]); PIX_SORT(pp[1],pp[4]); PIX_SORT(pp[2],pp[5]); \
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PIX_SORT(pp[4],pp[7]); PIX_SORT(pp[4],pp[2]); PIX_SORT(pp[6],pp[4]); \
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PIX_SORT(pp[4],pp[2]); median=pp[4];} //pp4 = median
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namespace rtengine
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{
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extern const Settings* settings;
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static float RetinexScales[MAX_RETINEX_SCALES];
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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 += dst[i][j];
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vsquared += (dst[i][j] * dst[i][j]);
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if ( dst[i][j] > lmax) {
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lmax = dst[i][j] ;
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}
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if ( dst[i][j] < lmin) {
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lmin = dst[i][j] ;
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}
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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 - (mean * mean) );
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stddv = (float)sqrt(stddv);
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}
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void mean_stddv( float **dst, float &mean, float &stddv, int W_L, int H_L, const float factor, 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 = 0.f;
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mintr = 0.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 = 0.f, lmin = 0.f;
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#ifdef _OPENMP
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#pragma omp for reduction(+:sum,vsquared) // this can lead 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 += dst[i][j];
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vsquared += (dst[i][j] * dst[i][j]);
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if ( dst[i][j] > lmax) {
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lmax = dst[i][j] ;
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}
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if ( dst[i][j] < lmin) {
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lmin = dst[i][j] ;
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}
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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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sum *= factor;
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maxtr *= factor;
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mintr *= factor;
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vsquared *= (factor * factor);
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mean = sum / (float) (W_L * H_L);
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vsquared /= (float) W_L * H_L;
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stddv = ( vsquared - (mean * mean) );
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stddv = (float)sqrt(stddv);
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}
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void RawImageSource::MSR(float** luminance, float** originalLuminance, float **exLuminance, int width, int height, RetinexParams deh, const RetinextransmissionCurve & dehatransmissionCurve, float &minCD, float &maxCD, float &mini, float &maxi, float &Tmean, float &Tsigma, float &Tmin, float &Tmax)
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{
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if (deh.enabled) {//enabled
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float mean, stddv, maxtr, mintr;
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// float mini, delta, maxi;
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float delta;
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float eps = 2.f;
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bool useHsl = deh.retinexcolorspace == "HSLLOG";
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bool useHslLin = deh.retinexcolorspace == "HSLLIN";
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float gain2 = (float) deh.gain / 100.f; //def =1 not use
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gain2 = useHslLin ? gain2 * 0.5f : gain2;
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float offse = (float) deh.offs; //def = 0 not use
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int scal = deh.scal; //def=3
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scal = 3;//disabled scal
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int nei = (int) 2.8f * deh.neigh; //def = 220
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float vart = (float)deh.vart / 100.f;//variance
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float strength = (float) deh.str / 100.f; // Blend with original L channel data
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float limD = (float) 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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float ilimD = 1.f / limD;
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int moderetinex = 2; // default to 2 ( deh.retinexMethod == "high" )
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float hig = ((float) deh.highl) / 100.f;
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bool higplus = false ;
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float elogt;
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float hl = deh.baselog;
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if(hl >= 2.71828f) {
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elogt = 2.71828f + SQR(SQR(hl - 2.71828f));
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} else {
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elogt = hl;
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}
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elogt = 2.71828f;//disabled baselog
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FlatCurve* shcurve = NULL;//curve L=f(H)
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bool lhutili = false;
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if (deh.enabled) {
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shcurve = new FlatCurve(deh.lhcurve);
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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 = NULL;
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}
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} else {
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lhutili = true;
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}
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}
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if(deh.retinexMethod == "highliplus") {
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higplus = true;
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}
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if (deh.retinexMethod == "uni") {
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moderetinex = 0;
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}
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if (deh.retinexMethod == "low") {
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moderetinex = 1;
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}
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if (deh.retinexMethod == "highli" || deh.retinexMethod == "highliplus") {
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moderetinex = 3;
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}
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float aahi = 49.f / 99.f; ////reduce sensibility 50%
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float bbhi = 1.f - aahi;
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float high;
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high = bbhi + aahi * (float) deh.highl;
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retinex_scales( RetinexScales, scal, moderetinex, nei, high );
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int H_L = height;
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int W_L = width;
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float *src[H_L] ALIGNED16;
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float *srcBuffer = new float[H_L * W_L];
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for (int i = 0; i < H_L; i++) {
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src[i] = &srcBuffer[i * W_L];
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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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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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float *out[H_L] ALIGNED16;
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float *outBuffer = new float[H_L * W_L];
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for (int i = 0; i < H_L; i++) {
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out[i] = &outBuffer[i * W_L];
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}
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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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float *buffer = new float[W_L * H_L];;
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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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for ( int scale = scal - 1; scale >= 0; scale-- ) {
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if(scale == scal - 1) {
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gaussianBlur<float> (src, out, W_L, H_L, RetinexScales[scale], buffer);
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} else { // reuse result of last iteration
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gaussianBlur<float> (out, out, W_L, H_L, sqrtf(SQR(RetinexScales[scale]) - SQR(RetinexScales[scale + 1])), buffer);
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}
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#ifdef __SSE2__
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vfloat pondv = F2V(pond);
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vfloat limMinv = F2V(ilimD);
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vfloat limMaxv = F2V(limD);
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#endif
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#ifdef _OPENMP
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#pragma omp 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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if(useHslLin) {
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for (; j < W_L - 3; j += 4) {
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_mm_storeu_ps(&luminance[i][j], LVFU(luminance[i][j]) + pondv * (LIMV(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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_mm_storeu_ps(&luminance[i][j], LVFU(luminance[i][j]) + pondv * xlogf(LIMV(LVFU(src[i][j]) / LVFU(out[i][j]), limMinv, limMaxv) ));
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}
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}
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#endif
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if(useHslLin) {
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for (; j < W_L; j++) {
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luminance[i][j] += pond * (LIM(src[i][j] / out[i][j], ilimD, limD));
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}
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} else {
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for (; j < W_L; j++) {
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luminance[i][j] += pond * xlogf(LIM(src[i][j] / out[i][j], ilimD, limD)); // /logt ?
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}
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}
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}
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}
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}
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delete [] buffer;
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delete [] outBuffer;
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delete [] srcBuffer;
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mean = 0.f;
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stddv = 0.f;
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// I call mean_stddv2 instead of mean_stddv ==> logBetaGain
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mean_stddv2( luminance, mean, stddv, W_L, H_L, maxtr, mintr);
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// printf("mean=%f std=%f delta=%f maxtr=%f mintr=%f\n", mean, stddv, delta, maxtr, mintr);
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// mean_stddv( luminance, mean, stddv, W_L, H_L, logBetaGain, maxtr, mintr);
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if (dehatransmissionCurve && mean != 0.f && stddv != 0.f) { //if curve
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float asig = 0.166666f / stddv;
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float bsig = 0.5f - asig * mean;
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float amax = 0.333333f / (maxtr - mean - stddv);
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float bmax = 1.f - amax * maxtr;
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float amin = 0.333333f / (mean - stddv - mintr);
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float bmin = -amin * mintr;
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asig *= 500.f;
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bsig *= 500.f;
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amax *= 500.f;
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bmax *= 500.f;
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amin *= 500.f;
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bmin *= 500.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 absciss;
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#ifdef _OPENMP
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#pragma omp 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++) { //for mintr to maxtr evalate absciss in function of original transmission
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if (LIKELY(fabsf(luminance[i][j] - mean) < stddv)) {
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absciss = asig * luminance[i][j] + bsig;
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} else if (luminance[i][j] >= mean) {
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absciss = amax * luminance[i][j] + bmax;
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} else { /*if(luminance[i][j] <= mean - stddv)*/
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absciss = amin * luminance[i][j] + bmin;
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}
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luminance[i][j] *= (-1.f + 4.f * dehatransmissionCurve[absciss]); //new transmission
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}
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}
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// median filter on transmission ==> reduce artifacts
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if (deh.medianmap) {
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int wid = W_L;
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int hei = H_L;
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float *tmL[hei] ALIGNED16;
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float *tmLBuffer = new float[wid * hei];
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int borderL = 1;
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for (int i = 0; i < hei; i++) {
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tmL[i] = &tmLBuffer[i * wid];
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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 = borderL; i < hei - borderL; i++) {
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float pp[9], temp;
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for (int j = borderL; j < wid - borderL; j++) {
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med3(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], tmL[i][j]); //3x3
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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 = borderL; i < hei - borderL; i++ ) {
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for (int j = borderL; j < wid - borderL; j++) {
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luminance[i][j] = tmL[i][j];
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}
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}
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delete [] tmLBuffer;
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}
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// I call mean_stddv2 instead of mean_stddv ==> logBetaGain
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// mean_stddv( luminance, mean, stddv, W_L, H_L, 1.f, maxtr, mintr);
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mean_stddv2( luminance, mean, stddv, W_L, H_L, maxtr, mintr);
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}
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float epsil = 0.1f;
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mini = mean - vart * stddv;
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if (mini < mintr) {
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mini = mintr + epsil;
|
|
}
|
|
|
|
maxi = mean + vart * stddv;
|
|
|
|
if (maxi > maxtr) {
|
|
maxi = maxtr - epsil;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
float cdfactor = gain2 * 32768.f / delta;
|
|
maxCD = -9999999.f;
|
|
minCD = 9999999.f;
|
|
|
|
|
|
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel
|
|
#endif
|
|
{
|
|
float cdmax = -999999.f, cdmin = 999999.f;
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp for
|
|
#endif
|
|
|
|
for ( int i = 0; i < H_L; i ++ )
|
|
for (int j = 0; j < W_L; j++) {
|
|
// float cd = cdfactor * ( luminance[i][j] * logBetaGain - mini ) + offse;
|
|
float cd = cdfactor * ( luminance[i][j] - mini ) + offse;
|
|
|
|
if(cd > cdmax) {
|
|
cdmax = cd;
|
|
}
|
|
|
|
if(cd < cdmin) {
|
|
cdmin = cd;
|
|
}
|
|
|
|
float str = strength;
|
|
|
|
if(lhutili) { // S=f(H)
|
|
{
|
|
float HH = exLuminance[i][j];
|
|
float valparam;
|
|
|
|
if(useHsl || useHslLin) {
|
|
valparam = float((shcurve->getVal(HH) - 0.5f));
|
|
} else {
|
|
valparam = float((shcurve->getVal(Color::huelab_to_huehsv2(HH)) - 0.5f));
|
|
}
|
|
|
|
str *= (1.f + 2.f * valparam);
|
|
}
|
|
}
|
|
|
|
// if(exLuminance[i][j] > 65535.f*hig && higplus) str *= hig;
|
|
luminance[i][j] = clipretinex( cd, 0.f, 32768.f ) * str + (1.f - str) * originalLuminance[i][j];
|
|
}
|
|
|
|
#ifdef _OPENMP
|
|
#pragma omp critical
|
|
#endif
|
|
{
|
|
maxCD = maxCD > cdmax ? maxCD : cdmax;
|
|
minCD = minCD < cdmin ? minCD : cdmin;
|
|
}
|
|
|
|
}
|
|
// printf("cdmin=%f cdmax=%f\n",minCD, maxCD);
|
|
Tmean = mean;
|
|
Tsigma = stddv;
|
|
Tmin = mintr;
|
|
Tmax = maxtr;
|
|
|
|
if (shcurve) {
|
|
delete shcurve;
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
}
|