8b2eac9a3d
The pipette part is already working quite nice but need to be finished. The widgets part needs more work...
384 lines
10 KiB
C++
384 lines
10 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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*/
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#include "shmap.h"
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#include "gauss.h"
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#include "rtengine.h"
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#include "rt_math.h"
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#include "rawimagesource.h"
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#include "sleef.c"
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#undef THREAD_PRIORITY_NORMAL
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#ifdef __SSE2__
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#include "sleefsseavx.c"
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#endif // __SSE2__
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namespace rtengine {
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extern const Settings* settings;
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SHMap::SHMap (int w, int h, bool multiThread) : W(w), H(h), multiThread(multiThread) {
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map = new float*[H];
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for (int i=0; i<H; i++)
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map[i] = new float[W];
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}
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SHMap::~SHMap () {
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for (int i=0; i<H; i++)
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delete [] map[i];
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delete [] map;
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}
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void SHMap::update (Imagefloat* img, double radius, double lumi[3], bool hq, int skip) {
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// fill with luminance
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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; i++)
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for (int j=0; j<W; j++) {
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map[i][j] = lumi[0]*std::max(img->r(i,j),0.f) + lumi[1]*std::max(img->g(i,j),0.f) + lumi[2]*std::max(img->b(i,j),0.f);
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}
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if (!hq) {
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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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AlignedBufferMP<double>* pBuffer = new AlignedBufferMP<double> (max(W,H));
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gaussHorizontal<float> (map, map, *pBuffer, W, H, radius);
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gaussVertical<float> (map, map, *pBuffer, W, H, radius);
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delete pBuffer;
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}
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}
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else {
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//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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//experimental dirpyr shmap
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float thresh = 100*radius;//1000;
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LUTf rangefn(0x10000);
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float ** dirpyrlo[2];
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int intfactor = 1024;//16384;
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//set up range functions
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for (int i=0; i<0x10000; i++) {
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//rangefn[i] = (int)(((thresh)/((double)(i) + (thresh)))*intfactor);
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rangefn[i] = static_cast<int>(xexpf(-(min(10.0f,(static_cast<float>(i)*i) / (thresh*thresh))))*intfactor);
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//if (rangefn[i]<0 || rangefn[i]>intfactor)
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//printf("i=%d rangefn=%d arg=%f \n",i,rangefn[i], float(i*i) / (thresh*thresh));
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}
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dirpyrlo[0] = allocArray<float> (W, H);
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dirpyrlo[1] = allocArray<float> (W, H);
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int scale=1;
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int level=0;
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int indx=0;
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dirpyr_shmap(map, dirpyrlo[indx], W, H, rangefn, 0, scale );
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scale *= 2;
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level += 1;
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indx = 1-indx;
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while (skip*scale<16) {
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dirpyr_shmap(dirpyrlo[1-indx], dirpyrlo[indx], W, H, rangefn, level, scale );
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scale *= 2;
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level += 1;
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indx = 1-indx;
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}
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dirpyr_shmap(dirpyrlo[1-indx], map, W, H, rangefn, level, scale );
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freeArray<float>(dirpyrlo[0], H);
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freeArray<float>(dirpyrlo[1], H);
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//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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/*
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// anti-alias filtering the result
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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; i++)
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for (int j=0; j<W; j++)
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if (i>0 && j>0 && i<H-1 && j<W-1)
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map[i][j] = (buffer[i-1][j-1]+buffer[i-1][j]+buffer[i-1][j+1]+buffer[i][j-1]+buffer[i][j]+buffer[i][j+1]+buffer[i+1][j-1]+buffer[i+1][j]+buffer[i+1][j+1])/9;
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else
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map[i][j] = buffer[i][j];
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*/
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}
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// update average, minimum, maximum
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float _avg = 0.0f;
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min_f = 65535;
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max_f = 0;
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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 _min_f = 65535.0f;
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float _max_f = 0.0f;
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float _val;
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#ifdef _OPENMP
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#pragma omp for reduction(+:_avg) nowait
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#endif
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for (int i=32; i<H-32; i++)
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for (int j=32; j<W-32; j++) {
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_val = map[i][j];
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if (_val < _min_f)
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_min_f = _val;
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if (_val > _max_f)
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_max_f = _val;
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_avg += _val;
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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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if(_min_f < min_f )
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min_f = _min_f;
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if(_max_f > max_f )
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max_f = _max_f;
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}
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}
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_avg /= ((H-64)*(W-64));
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avg = _avg;
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}
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void SHMap::forceStat (float max_, float min_, float avg_) {
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max_f = max_;
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min_f = min_;
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avg = avg_;
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}
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#if defined( __SSE__ ) && defined( WIN32 )
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__attribute__((force_align_arg_pointer)) void SHMap::dirpyr_shmap(float ** data_fine, float ** data_coarse, int width, int height, LUTf & rangefn, int level, int scale)
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#else
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void SHMap::dirpyr_shmap(float ** data_fine, float ** data_coarse, int width, int height, LUTf & rangefn, int level, int scale)
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#endif
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{
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//scale is spacing of directional averaging weights
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//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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// calculate weights, compute directionally weighted average
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int scalewin, halfwin;
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if(level < 2) {
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halfwin = 1;
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scalewin = halfwin*scale;
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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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#if defined( __SSE2__ ) && defined( __x86_64__ )
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__m128 dirwtv, valv, normv;
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#endif // __SSE2__
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int j;
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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 < height; i++) {
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float dirwt;
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for(j = 0; j < scalewin; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j%scale; jnbr<=j+scalewin; jnbr+=scale) {
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dirwt = ( rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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#if defined( __SSE2__ ) && defined( __x86_64__ )
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for(; j < (width-scalewin)-3; j+=4)
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{
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valv= _mm_setzero_ps();
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normv= _mm_setzero_ps();
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<=j+scalewin; jnbr+=scale) {
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dirwtv = ( rangefn[_mm_cvttps_epi32(vabsf(LVFU(data_fine[inbr][jnbr])-LVFU(data_fine[i][j])))] );
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valv += dirwtv*LVFU(data_fine[inbr][jnbr]);
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normv += dirwtv;
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}
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}
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_mm_storeu_ps( &data_coarse[i][j], valv/normv);
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}
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for(; j < width-scalewin; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<=j+scalewin; jnbr+=scale) {
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dirwt = ( rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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#else
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for(; j < width-scalewin; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<=j+scalewin; jnbr+=scale) {
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dirwt = ( rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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#endif
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for(; j < width; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<width; jnbr+=scale) {
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dirwt = ( rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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}
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}
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}
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else {
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halfwin=2;
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scalewin = halfwin*scale;
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int domker[5][5] = {{1,1,1,1,1},{1,2,2,2,1},{1,2,2,2,1},{1,2,2,2,1},{1,1,1,1,1}};
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//generate domain kernel
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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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#if defined( __SSE2__ ) && defined( __x86_64__ )
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__m128 dirwtv, valv, normv;
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float domkerv[5][5][4] __attribute__ ((aligned (16))) = {{{1,1,1,1},{1,1,1,1},{1,1,1,1},{1,1,1,1},{1,1,1,1}},{{1,1,1,1},{2,2,2,2},{2,2,2,2},{2,2,2,2},{1,1,1,1}},{{1,1,1,1},{2,2,2,2},{2,2,2,2},{2,2,2,2},{1,1,1,1}},{{1,1,1,1},{2,2,2,2},{2,2,2,2},{2,2,2,2},{1,1,1,1}},{{1,1,1,1},{1,1,1,1},{1,1,1,1},{1,1,1,1},{1,1,1,1}}};
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#endif // __SSE2__
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int j;
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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 < height; i++) {
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float dirwt;
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for(j = 0; j < scalewin; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j%scale; jnbr<=j+scalewin; jnbr+=scale) {
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dirwt = ( domker[(inbr-i)/scale+halfwin][(jnbr-j)/scale+halfwin] * rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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#if defined( __SSE2__ ) && defined( __x86_64__ )
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for(; j < width-scalewin-3; j+=4)
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{
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valv = _mm_setzero_ps();
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normv = _mm_setzero_ps();
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<=j+scalewin; jnbr+=scale) {
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dirwtv = ( _mm_load_ps((float*)&domkerv[(inbr-i)/scale+halfwin][(jnbr-j)/scale+halfwin]) * rangefn[_mm_cvttps_epi32(vabsf(LVFU(data_fine[inbr][jnbr])-LVFU(data_fine[i][j])))] );
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valv += dirwtv*LVFU(data_fine[inbr][jnbr]);
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normv += dirwtv;
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}
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}
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_mm_storeu_ps( &data_coarse[i][j], valv/normv);
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}
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for(; j < width-scalewin; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<=j+scalewin; jnbr+=scale) {
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dirwt = ( domker[(inbr-i)/scale+halfwin][(jnbr-j)/scale+halfwin] * rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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#else
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for(; j < width-scalewin; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<=j+scalewin; jnbr+=scale) {
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dirwt = ( domker[(inbr-i)/scale+halfwin][(jnbr-j)/scale+halfwin] * rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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#endif
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for(; j < width; j++)
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{
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float val=0;
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float norm=0;
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for(int inbr=max(i-scalewin,i%scale); inbr<=min(i+scalewin, height-1); inbr+=scale) {
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for (int jnbr=j-scalewin; jnbr<width; jnbr+=scale) {
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dirwt = ( domker[(inbr-i)/scale+halfwin][(jnbr-j)/scale+halfwin] * rangefn[abs(data_fine[inbr][jnbr]-data_fine[i][j])] );
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val += dirwt*data_fine[inbr][jnbr];
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norm += dirwt;
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}
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}
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data_coarse[i][j] = val/norm; // low pass filter
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}
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}
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}
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}
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}
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}//end of SHMap
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