/**
 * @file bilateral.cpp
 * @brief Bilateral filtering
 *
 * 
 * This file is a part of LuminanceHDR package, based on pfstmo.
 * ---------------------------------------------------------------------- 
 * Copyright (C) 2003,2004 Grzegorz Krawczyk
 * 
 *  This program 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 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program 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 this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 * ---------------------------------------------------------------------- 
 * 
 * @author Rafal Mantiuk, <mantiuk@mpi-sb.mpg.de>
 * @author Grzegorz Krawczyk, <krawczyk@mpi-sb.mpg.de>
 *
 * $Id: bilateral.cpp,v 1.3 2008/09/09 00:56:49 rafm Exp $
 */

#include "arch/math.h"

#include "TonemappingOperators/pfstmo.h"
#include "Libpfs/array2d.h"
#include "Libpfs/progress.h"

#ifdef BRANCH_PREDICTION
#define likely(x)       __builtin_expect((x),1)
#define unlikely(x)     __builtin_expect((x),0)
#else
#define likely(x)       (x)
#define unlikely(x)     (x)
#endif

inline int max( int a, int b )
{ return (a>b) ? a : b; }

inline int min( int a, int b )
{ return (a<b) ? a : b; }


// support functions

void gaussianKernel( pfs::Array2Df *kern, float sigma )
{
    float sigma2Sqr = 2.0f * sigma * sigma;
    int col_2 = kern->getCols() / 2;
    int row_2 = kern->getRows() / 2;
    for( int y = 0; y < kern->getRows(); y++ ) {
        for( int x = 0; x < kern->getCols(); x++ ) {
            float rx = (float)(x - col_2);
            float ry = (float)(y - row_2);
            float d2 = rx*rx + ry*ry;
            (*kern)(x, y) = exp( -d2 / sigma2Sqr );
        }
    }
}

class GaussLookup {
    float *gauss;
    float maxVal;
    float scaleFactor;
    int N;

public:
    GaussLookup( float sigma, int N ) : N(N)
    {
        float sigma2Sqr = 2.0f * sigma * sigma;
        maxVal = sqrt(-logf(0.01f) * sigma2Sqr);
        gauss = new float[N];
        for( int i = 0; i < N; i++) {
            float x = (float)i / (float)(N-1) * maxVal;
            gauss[i] = exp(-x * x / (sigma2Sqr));
        }
        scaleFactor = (float)(N-1) / maxVal;
    }

    ~GaussLookup()
    {
        delete[] gauss;
    }

    float getValue( float x )
    {
        x = fabs( x );
        if( unlikely( x > maxVal ) ) return 0;
        return gauss[ (int)(x*scaleFactor) ];
    }
};

void bilateralFilter(const pfs::Array2Df *I, pfs::Array2Df *J,
                     float sigma_s, float sigma_r,
                     pfs::Progress& ph)
{
    const pfs::Array2Df *X1 = I;     // intensity data     // DAVIDE : CHECK THIS!

    // x +- sigma_s*2 should contain 95% of the Gaussian distrib
    int sKernelSize = (int)( sigma_s*4 + 0.5 ) + 1;
    int sKernelSize_2 = sKernelSize / 2;

    pfs::Array2Df sKernel(sKernelSize, sKernelSize);
    gaussianKernel( &sKernel, sigma_s );
    GaussLookup gauss( sigma_r, 256 );

    for( int y = 0; y < I->getRows(); y++ )
    {
        ph.setValue( y * 100 / I->getRows() );

        for( int x = 0; x < I->getCols(); x++ )
        {
            float val = 0;
            float k = 0;
            float I_s = (*X1)(x,y);	//!! previously 'I' not 'X1'

            if( unlikely( !finite( I_s ) ) )
                I_s = 0.0f;

            for( int py = max( 0, y - sKernelSize_2), pymax = min( I->getRows(), y + sKernelSize_2);
                py < pymax; py++ )
            {
                for( int px = max( 0, x - sKernelSize_2), pxmax = min( I->getCols(), x + sKernelSize_2);
                    px < pxmax; px++ )
                {
                    float I_p = (*X1)(px, py);	//!! previously 'I' not 'X1'
                    if( unlikely( !finite( I_p ) ) )
                        I_p = 0.0f;

                    float mult = sKernel(px-x + sKernelSize_2, py-y + sKernelSize_2) *
                        gauss.getValue( I_p - I_s );

                    float Ixy = (*I)(px, py);
                    if( unlikely( !finite( Ixy ) ) )
                        Ixy = 0.0f;          

                    val += Ixy * mult;	//!! but here we want 'I'
                    k += mult;
                }
            }
            //avoid division by 0 when k is close to 0
            //         (*J)(x,y) = fabs(k) > 0.00000001 ? val/k : 0.;
            (*J)(x,y) = val/k;
        }
    }
}
