Added an implementation of the Fast Guided Filter
derived directly from the pseudo-code of the paper: Fast Guided Filter by Kaiming He, Jian Sun available at https://arxiv.org/abs/1505.00996
This commit is contained in:
Alberto Griggio
@@ -127,6 +127,7 @@ set(RTENGINESOURCEFILES
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xtrans_demosaic.cc
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vng4_demosaic_RT.cc
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ipsoftlight.cc
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guidedfilter.cc
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)
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if(LENSFUN_HAS_LOAD_DIRECTORY)
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@@ -35,6 +35,8 @@ namespace rtengine
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template<class T, class A> void boxblur (T** src, A** dst, int radx, int rady, int W, int H)
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{
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//box blur image; box range = (radx,rady)
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radx = min(radx, W-1);
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rady = min(rady, H-1);
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AlignedBuffer<float>* buffer = new AlignedBuffer<float> (W * H);
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float* temp = buffer->data;
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194
rtengine/guidedfilter.cc
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194
rtengine/guidedfilter.cc
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@@ -0,0 +1,194 @@
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/* -*- C++ -*-
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*
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* This file is part of RawTherapee.
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*
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* Copyright (c) 2018 Alberto Griggio <alberto.griggio@gmail.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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/**
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* This is a Fast Guided Filter implementation, derived directly from the
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* pseudo-code of the paper:
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*
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* Fast Guided Filter
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* by Kaiming He, Jian Sun
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*
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* available at https://arxiv.org/abs/1505.00996
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*/
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#include "guidedfilter.h"
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#include "boxblur.h"
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#include "rescale.h"
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#include "imagefloat.h"
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namespace rtengine {
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#if 0
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# define DEBUG_DUMP(arr) \
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do { \
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Imagefloat im(arr.width(), arr.height()); \
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const char *out = "/tmp/" #arr ".tif"; \
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for (int y = 0; y < im.getHeight(); ++y) { \
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for (int x = 0; x < im.getWidth(); ++x) { \
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im.r(y, x) = im.g(y, x) = im.b(y, x) = arr[y][x] * 65535.f; \
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} \
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} \
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im.saveTIFF(out, 16); \
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} while (false)
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#else
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# define DEBUG_DUMP(arr)
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#endif
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void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2D<float> &dst, int r, float epsilon, bool multithread, int subsampling)
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{
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const int W = src.width();
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const int H = src.height();
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enum Op { MUL, DIVEPSILON, ADD, SUB, ADDMUL, SUBMUL };
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const auto apply =
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[=](Op op, array2D<float> &res, const array2D<float> &a, const array2D<float> &b, const array2D<float> &c=array2D<float>()) -> void
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{
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const int w = res.width();
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const int h = res.height();
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#ifdef _OPENMP
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#pragma omp parallel for if (multithread)
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#endif
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for (int y = 0; y < h; ++y) {
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for (int x = 0; x < w; ++x) {
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float r;
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float aa = a[y][x];
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float bb = b[y][x];
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switch (op) {
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case MUL:
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r = aa * bb;
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break;
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case DIVEPSILON:
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r = aa / (bb + epsilon);
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break;
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case ADD:
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r = aa + bb;
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break;
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case SUB:
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r = aa - bb;
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break;
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case ADDMUL:
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r = aa * bb + c[y][x];
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break;
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case SUBMUL:
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r = c[y][x] - (aa * bb);
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break;
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default:
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assert(false);
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r = 0;
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break;
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}
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res[y][x] = r;
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}
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}
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};
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// use the terminology of the paper (Algorithm 2)
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const array2D<float> &I = guide;
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const array2D<float> &p = src;
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array2D<float> &q = dst;
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const auto f_mean =
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[](array2D<float> &d, array2D<float> &s, int rad) -> void
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{
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boxblur<float, float>(s, d, rad, rad, s.width(), s.height());
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};
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const auto f_subsample =
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[=](array2D<float> &d, const array2D<float> &s) -> void
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{
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rescaleBilinear(s, d, multithread);
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};
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const auto f_upsample = f_subsample;
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const int w = W / subsampling;
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const int h = H / subsampling;
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array2D<float> I1(w, h);
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array2D<float> p1(w, h);
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f_subsample(I1, I);
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f_subsample(p1, p);
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DEBUG_DUMP(I);
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DEBUG_DUMP(p);
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DEBUG_DUMP(I1);
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DEBUG_DUMP(p1);
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float r1 = float(r) / subsampling;
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array2D<float> meanI(w, h);
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f_mean(meanI, I1, r1);
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DEBUG_DUMP(meanI);
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array2D<float> meanp(w, h);
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f_mean(meanp, p1, r1);
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DEBUG_DUMP(meanp);
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array2D<float> &corrIp = p1;
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apply(MUL, corrIp, I1, p1);
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f_mean(corrIp, corrIp, r1);
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DEBUG_DUMP(corrIp);
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array2D<float> &corrI = I1;
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apply(MUL, corrI, I1, I1);
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f_mean(corrI, corrI, r1);
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DEBUG_DUMP(corrI);
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array2D<float> &varI = corrI;
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apply(SUBMUL, varI, meanI, meanI, corrI);
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DEBUG_DUMP(varI);
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array2D<float> &covIp = corrIp;
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apply(SUBMUL, covIp, meanI, meanp, corrIp);
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DEBUG_DUMP(covIp);
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array2D<float> &a = varI;
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apply(DIVEPSILON, a, covIp, varI);
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DEBUG_DUMP(a);
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array2D<float> &b = covIp;
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apply(SUBMUL, b, a, meanI, meanp);
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DEBUG_DUMP(b);
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array2D<float> &meana = a;
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f_mean(meana, a, r1);
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DEBUG_DUMP(meana);
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array2D<float> &meanb = b;
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f_mean(meanb, b, r1);
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DEBUG_DUMP(meanb);
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array2D<float> meanA(W, H);
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f_upsample(meanA, meana);
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DEBUG_DUMP(meanA);
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array2D<float> &meanB = q;
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f_upsample(meanB, meanb);
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DEBUG_DUMP(meanB);
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apply(ADDMUL, q, meanA, I, meanB);
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DEBUG_DUMP(q);
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}
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} // namespace rtengine
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29
rtengine/guidedfilter.h
Normal file
29
rtengine/guidedfilter.h
Normal file
@@ -0,0 +1,29 @@
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/* -*- C++ -*-
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*
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* This file is part of RawTherapee.
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*
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* Copyright (c) 2018 Alberto Griggio <alberto.griggio@gmail.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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#pragma once
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#include "array2D.h"
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namespace rtengine {
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void guidedFilter(const array2D<float> &guide, const array2D<float> &src, array2D<float> &dst, int r, float epsilon, bool multithread, int subsampling=4);
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} // namespace rtengine
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