/*
*  This file is part of RawTherapee.
*
*  Copyright (c) 2004-2010 Gabor Horvath <hgabor@rawtherapee.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 <https://www.gnu.org/licenses/>.

    *   adaptation to RawTherapee
    *   2015 Jacques Desmis <jdesmis@gmail.com>
    *   2015 Ingo Weyrich <heckflosse67@gmx.de>

    * D. J. Jobson, Z. Rahman, and G. A. Woodell. A multi-scale
    * Retinex for bridging the gap between color images and the
    * human observation of scenes. IEEE Transactions on Image Processing,
    * 1997, 6(7): 965-976

    * Fan Guo Zixing Cai Bin Xie Jin Tang
    * School of Information Science and Engineering, Central South University Changsha, China

    * Weixing Wang and Lian Xu
    * College of Physics and Information Engineering, Fuzhou University, Fuzhou, China

    * inspired from 2003 Fabien Pelisson <Fabien.Pelisson@inrialpes.fr>
    * some ideas taken (use of mask) Russell Cottrell - The Retinex .8bf Plugin

*/

#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>

#include "color.h"
#include "curves.h"
#include "gauss.h"
#include "improcfun.h"
#include "jaggedarray.h"
#include "median.h"
#include "opthelper.h"
#include "procparams.h"
#include "rawimagesource.h"
#include "rtengine.h"
#include "shmap.h"
#include "StopWatch.h"

namespace
{
void retinex_scales( float* scales, int nscales, int mode, int s, float high)
{
    if ( nscales == 1 ) {
        scales[0] =  (float)s / 2.f;
    } else if (nscales == 2) {
        scales[1] = (float) s / 2.f;
        scales[0] = (float) s;
    } else {
        float size_step = (float) s / (float) nscales;

        if (mode == 0) {
            for (int i = 0; i < nscales; ++i ) {
                scales[nscales - i - 1] = 2.0f + (float)i * size_step;
            }
        } else if (mode == 1) {
            size_step = (float)log(s - 2.0f) / (float) nscales;

            for (int i = 0; i < nscales; ++i ) {
                scales[nscales - i - 1] = 2.0f + (float)pow (10.f, (i * size_step) / log (10.f));
            }
        } else if (mode == 2) {
            size_step = (float) log(s - 2.0f) / (float) nscales;

            for ( int i = 0; i < nscales; ++i ) {
                scales[i] = s - (float)pow (10.f, (i * size_step) / log (10.f));
            }
        } else if (mode == 3) {
            size_step = (float) log(s - 2.0f) / (float) nscales;

            for ( int i = 0; i < nscales; ++i ) {
                scales[i] = high * s - (float)pow (10.f, (i * size_step) / log (10.f));
            }
        }
    }
}

void mean_stddv2( float **dst, float &mean, float &stddv, int W_L, int H_L, float &maxtr, float &mintr)
{
    // summation using double precision to avoid too large summation error for large pictures
    double vsquared = 0.f;
    double sum = 0.f;
    maxtr = -999999.f;
    mintr = 999999.f;
#ifdef _OPENMP
    #pragma omp parallel
#endif
    {
        float lmax = -999999.f, lmin = 999999.f;
#ifdef _OPENMP
        #pragma omp for reduction(+:sum,vsquared) nowait // this leads to differences, but parallel summation is more accurate
#endif

        for (int i = 0; i < H_L; i++ )
            for (int j = 0; j < W_L; j++) {
                sum += dst[i][j];
                vsquared += (dst[i][j] * dst[i][j]);

                lmax = dst[i][j] > lmax ? dst[i][j] : lmax;
                lmin = dst[i][j] < lmin ? dst[i][j] : lmin;
            }

#ifdef _OPENMP
        #pragma omp critical
#endif
        {
            maxtr = maxtr > lmax ? maxtr : lmax;
            mintr = mintr < lmin ? mintr : lmin;
        }

    }
    mean = sum / (double) (W_L * H_L);
    vsquared /= (double) W_L * H_L;
    stddv = ( vsquared - (mean * mean) );
    stddv = (float)sqrt(stddv);
}

}


namespace rtengine
{

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)
{
BENCHFUN
    if (!deh.enabled) {
        return;
    }

    constexpr float eps = 2.f;
    const bool useHsl = deh.retinexcolorspace == "HSLLOG";
    const bool useHslLin = deh.retinexcolorspace == "HSLLIN";
    const float offse = deh.offs; //def = 0  not use
    const int iter = deh.iter;
    const int gradient = deh.scal;
    int scal = deh.skal;
    const int nei = 2.8f * deh.neigh; //def = 220
    const float vart = deh.vart / 100.f;//variance
    const float gradvart = deh.grad;
    const float gradstr = deh.grads;
    const float strength = deh.str / 100.f; // Blend with original L channel data
    float limD = deh.limd;
    limD = pow(limD, 1.7f);//about 2500 enough
    limD *= useHslLin ? 10.f : 1.f;
    const float ilimD = 1.f / limD;
    const float hig = deh.highl / 100.f;

    const int H_L = height;
    const int W_L = width;

    constexpr float elogt = 2.71828f;
    bool lhutili = false;

    FlatCurve* shcurve = new FlatCurve(deh.lhcurve); //curve L=f(H)

    if (!shcurve || shcurve->isIdentity()) {
        if (shcurve) {
            delete shcurve;
            shcurve = nullptr;
        }
    } else {
        lhutili = true;
    }


    bool higplus = false ;
    int moderetinex = 2; // default to 2 ( deh.retinexMethod == "high" )

    if(deh.retinexMethod == "highliplus") {
        higplus = true;
        moderetinex = 3;
    } else if (deh.retinexMethod == "uni") {
        moderetinex = 0;
    } else if (deh.retinexMethod == "low") {
        moderetinex = 1;
    } else { /*if (deh.retinexMethod == "highli") */
        moderetinex = 3;
    }

    constexpr float aahi = 49.f / 99.f; ////reduce sensibility 50%
    constexpr float bbhi = 1.f - aahi;
    float high = bbhi + aahi * (float) deh.highl;

    for (int it = 1; it < iter + 1; it++) { //iter nb max of iterations
        float grad = 1.f;
        float sc = scal;

        if (gradient == 0) {
            grad = 1.f;
            sc = 3.f;
        } else if (gradient == 1) {
            grad = 0.25f * it + 0.75f;
            sc = -0.5f * it + 4.5f;
        } else if (gradient == 2) {
            grad = 0.5f * it + 0.5f;
            sc = -0.75f * it + 5.75f;
        } else if (gradient == 3) {
            grad = 0.666f * it + 0.333f;
            sc = -0.75f * it + 5.75f;
        } else if (gradient == 4) {
            grad = 0.8f * it + 0.2f;
            sc = -0.75f * it + 5.75f;
        } else if (gradient == 5) {
            if (moderetinex != 3) {
                grad = 2.5f * it - 1.5f;
            } else {
                float aa = (11.f * high - 1.f) / 4.f;
                float bb = 1.f - aa;
                grad = aa * it + bb;
            }

            sc = -0.75f * it + 5.75f;
        } else if (gradient == 6) {
            if (moderetinex != 3) {
                grad = 5.f * it - 4.f;
            } else {
                float aa = (21.f * high - 1.f) / 4.f;
                float bb = 1.f - aa;
                grad = aa * it + bb;
            }

            sc = -0.75f * it + 5.75f;
        } else if (gradient == -1) {
            grad = -0.125f * it + 1.125f;
            sc = 3.f;
        }

        if (iter == 1) {
            sc = scal;
        } else {
            //adjust sc in function of choice of scale by user if iterations
            if (scal < 3) {
                sc -= 1;
                if (sc < 1.f) {//avoid 0
                    sc = 1.f;
                }
            } else if (scal > 4) {
                sc += 1;
            }
        }

        float varx = vart;
        float limdx = limD;
        float ilimdx = ilimD;

        if (gradvart != 0) {
            if (gradvart == 1) {
                varx = vart * (-0.125f * it + 1.125f);
                limdx = limD * (-0.125f * it + 1.125f);
                ilimdx = 1.f / limdx;
            } else if (gradvart == 2) {
                varx = vart * (-0.2f * it + 1.2f);
                limdx = limD * (-0.2f * it + 1.2f);
                ilimdx = 1.f / limdx;
            } else if (gradvart == -1) {
                varx = vart * (0.125f * it + 0.875f);
                limdx = limD * (0.125f * it + 0.875f);
                ilimdx = 1.f / limdx;
            } else if (gradvart == -2) {
                varx = vart * (0.4f * it + 0.6f);
                limdx = limD * (0.4f * it + 0.6f);
                ilimdx = 1.f / limdx;
            }
        }

        scal = round(sc);
        float ks = 1.f;

        if (gradstr != 0) {
            if (gradstr == 1) {
                if (it <= 3) {
                    ks = -0.3f * it + 1.6f;
                } else {
                    ks = 0.5f;
                }
            } else if (gradstr == 2) {
                if (it <= 3) {
                    ks = -0.6f * it + 2.2f;
                } else {
                    ks = 0.3f;
                }
            } else if (gradstr == -1) {
                if (it <= 3) {
                    ks = 0.2f * it + 0.6f;
                } else {
                    ks = 1.2f;
                }
            } else if (gradstr == -2) {
                if (it <= 3) {
                    ks = 0.4f * it + 0.2f;
                } else {
                    ks = 1.5f;
                }
            }
        }

        const float strengthx = ks * strength;

        constexpr auto maxRetinexScales = 8;
        float RetinexScales[maxRetinexScales];

        retinex_scales(RetinexScales, scal, moderetinex, nei / grad, high);

        const int shHighlights = deh.highlights;
        const int shShadows = deh.shadows;

        int mapmet = 0;

        if(deh.mapMethod == "map") {
            mapmet = 2;
        } else if(deh.mapMethod == "mapT") {
            mapmet = 3;
        } else if(deh.mapMethod == "gaus") {
            mapmet = 4;
        }

        const double shradius = mapmet == 4 ? (double) deh.radius : 40.;

        int viewmet = 0;

        if(deh.viewMethod == "mask") {
            viewmet = 1;
        } else if(deh.viewMethod == "tran") {
            viewmet = 2;
        } else if(deh.viewMethod == "tran2") {
            viewmet = 3;
        } else if(deh.viewMethod == "unsharp") {
            viewmet = 4;
        }

        std::unique_ptr<JaggedArray<float>> srcBuffer(new JaggedArray<float>(W_L, H_L));
        float** src = *(srcBuffer.get());

#ifdef _OPENMP
        #pragma omp parallel for
#endif

        for (int i = 0; i < H_L; i++)
            for (int j = 0; j < W_L; j++) {
                src[i][j] = luminance[i][j] + eps;
                luminance[i][j] = 0.f;
            }

        JaggedArray<float> out(W_L, H_L);
        JaggedArray<float>& tran = out; // tran and out can safely use the same buffer

        const float logBetaGain = xlogf(16384.f);
        float pond = logBetaGain / (float) scal;

        if(!useHslLin) {
            pond /= log(elogt);
        }

        std::unique_ptr<SHMap> shmap;
        if (((mapmet == 2 || mapmet == 3 || mapmet == 4) && it == 1)) {
            shmap.reset(new SHMap(W_L, H_L));
        }

        std::unique_ptr<float[]> buffer;
        if (mapmet > 0) {
            buffer.reset(new float[W_L * H_L]);
        }

        for (int scale = scal - 1; scale >= 0; --scale) {
            if (scale == scal - 1) {
                gaussianBlur(src, out, W_L, H_L, RetinexScales[scale], true);
            } else { // reuse result of last iteration
                // out was modified in last iteration => restore it
                if((((mapmet == 2 && scale > 1) || mapmet == 3 || mapmet == 4) || (mapmet > 0 && mapcontlutili)) && it == 1) {
#ifdef _OPENMP
                    #pragma omp parallel for
#endif

                    for (int i = 0; i < H_L; i++) {
                        for (int j = 0; j < W_L; j++) {
                            out[i][j] = buffer[i * W_L + j];
                        }
                    }
                }

                gaussianBlur(out, out, W_L, H_L, sqrtf(SQR(RetinexScales[scale]) - SQR(RetinexScales[scale + 1])), true);
            }

            if ((((mapmet == 2 && scale > 2) || mapmet == 3 || mapmet == 4) || (mapmet > 0 && mapcontlutili)) && it == 1 && scale > 0) {
                // out will be modified => store it for use in next iteration.
#ifdef _OPENMP
                #pragma omp parallel for
#endif

                for (int i = 0; i < H_L; i++) {
                    for (int j = 0; j < W_L; j++) {
                        buffer[i * W_L + j] = out[i][j];
                    }
                }
            }
            int h_th = 0;
            int s_th = 0;
            if (((mapmet == 2 && scale > 2) || mapmet == 3 || mapmet == 4) && it == 1) {
                shmap->updateL(out, shradius, true, 1);
                h_th = shmap->max_f - deh.htonalwidth * (shmap->max_f - shmap->avg) / 100;
                s_th = deh.stonalwidth * (shmap->avg - shmap->min_f) / 100;
            }

            if (mapmet > 0 && mapcontlutili && it == 1) {
#ifdef _OPENMP
                #pragma omp parallel for
#endif

                for (int i = 0; i < H_L; i++) {
                    for (int j = 0; j < W_L; j++) {
                        out[i][j] = mapcurve[2.f * out[i][j]];
                    }
                }

            }

            if (((mapmet == 2 && scale > 2) || mapmet == 3 || mapmet == 4) && it == 1) {
                const float hWeight = (100.f - shHighlights) / 100.f;
                const float sWeight = (100.f - shShadows) / 100.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++) {
                        const float mapval = 1.f + shmap->map[i][j];
                        float factor;

                        if (mapval > h_th) {
                            factor = (h_th + hWeight * (mapval - h_th)) / mapval;
                        } else if (mapval < s_th) {
                            factor = (s_th - sWeight * (s_th - mapval)) / mapval;
                        } else {
                            factor = 1.f;
                        }

                        out[i][j] *= factor;
                    }
                }
            }

#ifdef _OPENMP
            #pragma omp parallel for
#endif

            for (int i = 0; i < H_L; i++) {
                int j = 0;

#ifdef __SSE2__
                const vfloat pondv = F2V(pond);
                const vfloat limMinv = F2V(ilimdx);
                const vfloat limMaxv = F2V(limdx);
                if( useHslLin) {
                    for (; j < W_L - 3; j += 4) {
                        STVFU(luminance[i][j], LVFU(luminance[i][j]) + pondv * vclampf(LVFU(src[i][j]) / LVFU(out[i][j]), limMinv, limMaxv));
                    }
                } else {
                    for (; j < W_L - 3; j += 4) {
                        STVFU(luminance[i][j], LVFU(luminance[i][j]) + pondv * xlogf(vclampf(LVFU(src[i][j]) / LVFU(out[i][j]), limMinv, limMaxv)));
                    }
                }

#endif

                if(useHslLin) {
                    for (; j < W_L; j++) {
                        luminance[i][j] +=  pond * LIM(src[i][j] / out[i][j], ilimdx, limdx);
                    }
                } else {
                    for (; j < W_L; j++) {
                        luminance[i][j] +=  pond * xlogf(LIM(src[i][j] / out[i][j], ilimdx, limdx)); //  /logt ?
                    }
                }
            }
        }

        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) - 0.5f;
                        } else {
                            valparam = shcurve->getVal(Color::huelab_to_huehsv2(HH)) - 0.5f;
                        }

                        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;
        }
    }
}

}
