Found via codespell v2.0.dev0 ``` codespell -q 3 -I ../rawtherapy-whitelist.txt -S ./rtdata/languages -L bord,hist,fo,reall,bloc,alph,dof,thre,makro,chang,currentry,portugues,vektor,ue ```
571 lines
19 KiB
C++
571 lines
19 KiB
C++
/*
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* This file is part of RawTherapee.
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*
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* Copyright (c) 2019 Alberto Romei <aldrop8@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 <https://www.gnu.org/licenses/>.
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*/
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#include <cmath>
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#include <iostream>
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#include "rawimage.h"
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#include "rawimagesource.h"
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#include "coord.h"
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#include "mytime.h"
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#include "opthelper.h"
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#include "pixelsmap.h"
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#include "procparams.h"
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#include "rt_algo.h"
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#include "rtengine.h"
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#include "sleef.h"
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//#define BENCHMARK
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#include "StopWatch.h"
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namespace
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{
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using rtengine::ST_BAYER;
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using rtengine::ST_FUJI_XTRANS;
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using rtengine::settings;
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bool channelsAvg(
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const rtengine::RawImage* ri,
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int width,
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int height,
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const float* cblacksom,
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rtengine::Coord spotPos,
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int spotSize,
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std::array<float, 3>& avgs
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)
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{
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avgs = {}; // Channel averages
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if (ri->getSensorType() != ST_BAYER && ri->getSensorType() != ST_FUJI_XTRANS) {
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return false;
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}
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if (settings->verbose) {
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printf("Spot coord: x=%d y=%d\n", spotPos.x, spotPos.y);
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}
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const int half_spot_size = spotSize / 2;
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const int& x1 = spotPos.x - half_spot_size;
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const int& x2 = spotPos.x + half_spot_size;
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const int& y1 = spotPos.y - half_spot_size;
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const int& y2 = spotPos.y + half_spot_size;
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if (x1 < 0 || x2 > width || y1 < 0 || y2 > height) {
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return false; // Spot goes outside bounds, bail out.
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}
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std::array<int, 3> pxCount = {}; // Per-channel sample counts
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for (int c = x1; c < x2; ++c) {
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for (int r = y1; r < y2; ++r) {
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const int ch = ri->getSensorType() == ST_BAYER ? ri->FC(r, c) : ri->XTRANSFC(r, c);
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++pxCount[ch];
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// Sample the original unprocessed values from RawImage, subtracting black levels.
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// Scaling is irrelevant, as we are only interested in the ratio between two spots.
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avgs[ch] += ri->data[r][c] - cblacksom[ch];
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}
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}
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for (int ch = 0; ch < 3; ++ch) {
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avgs[ch] /= pxCount[ch];
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}
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return true;
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}
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void calcMedians(
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const rtengine::RawImage* ri,
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float** data,
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int x1, int y1, int x2, int y2,
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std::array<float, 3>& meds
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)
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{
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MyTime t1, t2, t3;
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t1.set();
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// Channel vectors to calculate medians
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std::array<std::vector<float>, 3> cvs;
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// Sample one every 5 pixels, and push the value in the appropriate channel vector.
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// Choose an odd step, not a multiple of the CFA size, to get a chance to visit each channel.
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if (ri->getSensorType() == ST_BAYER) {
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for (int row = y1; row < y2; row += 5) {
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const int c0 = ri->FC(row, x1 + 0);
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const int c1 = ri->FC(row, x1 + 5);
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int col = x1;
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for (; col < x2 - 5; col += 10) {
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cvs[c0].push_back(data[row][col]);
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cvs[c1].push_back(data[row][col + 5]);
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}
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if (col < x2) {
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cvs[c0].push_back(data[row][col]);
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}
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}
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} else if (ri->getSensorType() == ST_FUJI_XTRANS) {
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for (int row = y1; row < y2; row += 5) {
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const std::array<unsigned int, 6> cs = {
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ri->XTRANSFC(row, x1 + 0),
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ri->XTRANSFC(row, x1 + 5),
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ri->XTRANSFC(row, x1 + 10),
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ri->XTRANSFC(row, x1 + 15),
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ri->XTRANSFC(row, x1 + 20),
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ri->XTRANSFC(row, x1 + 25)
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};
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int col = x1;
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for (; col < x2 - 25; col += 30) {
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for (int c = 0; c < 6; ++c) {
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cvs[cs[c]].push_back(data[row][col + c * 5]);
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}
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}
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for (int c = 0; col < x2; col += 5, ++c) {
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cvs[cs[c]].push_back(data[row][col]);
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}
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}
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}
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t2.set();
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if (settings->verbose) {
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printf("Median vector fill loop time us: %d\n", t2.etime(t1));
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}
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t2.set();
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for (int c = 0; c < 3; ++c) {
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// Find median values for each channel
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if (!cvs[c].empty()) {
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rtengine::findMinMaxPercentile(cvs[c].data(), cvs[c].size(), 0.5f, meds[c], 0.5f, meds[c], true);
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}
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}
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t3.set();
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if (settings->verbose) {
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printf("Sample count: R=%zu, G=%zu, B=%zu\n", cvs[0].size(), cvs[1].size(), cvs[2].size());
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printf("Median calc time us: %d\n", t3.etime(t2));
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}
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}
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std::array<double, 3> calcWBMults(
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const rtengine::ColorTemp& wb,
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const rtengine::ImageMatrices& imatrices,
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const rtengine::RawImage *ri,
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const float ref_pre_mul[4])
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{
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std::array<double, 3> wb_mul;
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double r, g, b;
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wb.getMultipliers(r, g, b);
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wb_mul[0] = imatrices.cam_rgb[0][0] * r + imatrices.cam_rgb[0][1] * g + imatrices.cam_rgb[0][2] * b;
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wb_mul[1] = imatrices.cam_rgb[1][0] * r + imatrices.cam_rgb[1][1] * g + imatrices.cam_rgb[1][2] * b;
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wb_mul[2] = imatrices.cam_rgb[2][0] * r + imatrices.cam_rgb[2][1] * g + imatrices.cam_rgb[2][2] * b;
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for (int c = 0; c < 3; ++c) {
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wb_mul[c] = ri->get_pre_mul(c) / wb_mul[c] / ref_pre_mul[c];
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}
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// Normalize max channel gain to 1.0
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float mg = rtengine::max(wb_mul[0], wb_mul[1], wb_mul[2]);
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for (int c = 0; c < 3; ++c) {
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wb_mul[c] /= mg;
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}
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return wb_mul;
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}
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}
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bool rtengine::RawImageSource::getFilmNegativeExponents(Coord2D spotA, Coord2D spotB, int tran, const procparams::FilmNegativeParams ¤tParams, std::array<float, 3>& newExps)
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{
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newExps = {
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static_cast<float>(currentParams.redRatio * currentParams.greenExp),
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static_cast<float>(currentParams.greenExp),
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static_cast<float>(currentParams.blueRatio * currentParams.greenExp)
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};
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constexpr int spotSize = 32; // TODO: Make this configurable?
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Coord spot;
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std::array<float, 3> clearVals;
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std::array<float, 3> denseVals;
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// Get channel averages in the two spots, sampling from the original ri->data buffer.
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// NOTE: rawData values might be affected by CA correction, FlatField, etc, so:
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// rawData[y][x] == (ri->data[y][x] - cblacksom[c]) * scale_mul[c]
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// is not always true. To calculate exponents on the exact values, we should keep
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// a copy of the rawData buffer after preprocessing. Worth the memory waste?
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// Sample first spot
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transformPosition(spotA.x, spotA.y, tran, spot.x, spot.y);
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if (!channelsAvg(ri, W, H, cblacksom, spot, spotSize, clearVals)) {
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return false;
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}
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// Sample second spot
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transformPosition(spotB.x, spotB.y, tran, spot.x, spot.y);
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if (!channelsAvg(ri, W, H, cblacksom, spot, spotSize, denseVals)) {
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return false;
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}
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// Detect which one is the dense spot, based on green channel
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if (clearVals[1] < denseVals[1]) {
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std::swap(clearVals, denseVals);
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}
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if (settings->verbose) {
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printf("Clear film values: R=%g G=%g B=%g\n", static_cast<double>(clearVals[0]), static_cast<double>(clearVals[1]), static_cast<double>(clearVals[2]));
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printf("Dense film values: R=%g G=%g B=%g\n", static_cast<double>(denseVals[0]), static_cast<double>(denseVals[1]), static_cast<double>(denseVals[2]));
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}
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const float denseGreenRatio = clearVals[1] / denseVals[1];
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// Calculate logarithms in arbitrary base
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const auto logBase =
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[](float base, float num) -> float
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{
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return std::log(num) / std::log(base);
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};
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// Calculate exponents for each channel, based on the ratio between the bright and dark values,
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// compared to the ratio in the reference channel (green)
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for (int ch = 0; ch < 3; ++ch) {
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if (ch == 1) {
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newExps[ch] = 1.f; // Green is the reference channel
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} else {
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newExps[ch] = rtengine::LIM(logBase(clearVals[ch] / denseVals[ch], denseGreenRatio), 0.3f, 4.f);
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}
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}
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if (settings->verbose) {
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printf("New exponents: R=%g G=%g B=%g\n", static_cast<double>(newExps[0]), static_cast<double>(newExps[1]), static_cast<double>(newExps[2]));
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}
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return true;
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}
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bool rtengine::RawImageSource::getRawSpotValues(Coord2D spotCoord, int spotSize, int tran, const procparams::FilmNegativeParams ¶ms, std::array<float, 3>& rawValues)
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{
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Coord spot;
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transformPosition(spotCoord.x, spotCoord.y, tran, spot.x, spot.y);
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if (settings->verbose) {
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printf("Transformed coords: %d,%d\n", spot.x, spot.y);
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}
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if (spotSize < 4) {
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return false;
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}
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// Calculate averages of raw unscaled channels
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if (!channelsAvg(ri, W, H, cblacksom, spot, spotSize, rawValues)) {
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return false;
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}
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if (settings->verbose) {
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printf("Raw spot values: R=%g, G=%g, B=%g\n", rawValues[0], rawValues[1], rawValues[2]);
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}
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return true;
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}
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void rtengine::RawImageSource::filmNegativeProcess(const procparams::FilmNegativeParams ¶ms, std::array<float, 3>& filmBaseValues)
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{
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// BENCHFUNMICRO
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if (!params.enabled) {
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return;
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}
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// Exponents are expressed as positive in the parameters, so negate them in order
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// to get the reciprocals.
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const std::array<float, 3> exps = {
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static_cast<float>(-params.redRatio * params.greenExp),
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static_cast<float>(-params.greenExp),
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static_cast<float>(-params.blueRatio * params.greenExp)
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};
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constexpr float MAX_OUT_VALUE = 65000.f;
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// Get multipliers for a known, fixed WB setting, that will be the starting point
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// for balancing the converted image.
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const std::array<double, 3> wb_mul = calcWBMults(
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ColorTemp(3500., 1., 1., "Custom"), imatrices, ri, ref_pre_mul);
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if (rtengine::settings->verbose) {
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printf("Fixed WB mults: %g %g %g\n", wb_mul[0], wb_mul[1], wb_mul[2]);
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}
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// Compensation factor to restore the non-autoWB initialGain (see RawImageSource::load)
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const float autoGainComp = camInitialGain / initialGain;
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std::array<float, 3> mults; // Channel normalization multipliers
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// If film base values are set in params, use those
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if (filmBaseValues[0] <= 0.f) {
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// ...otherwise, the film negative tool might have just been enabled on this image,
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// without any previous setting. So, estimate film base values from channel medians
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std::array<float, 3> medians;
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// Special value for backwards compatibility with profiles saved by RT 5.7
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const bool oldChannelScaling = filmBaseValues[0] == -1.f;
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// If using the old channel scaling method, get medians from the whole current image,
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// reading values from the already-scaled rawData buffer.
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if (oldChannelScaling) {
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calcMedians(ri, rawData, 0, 0, W, H, medians);
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} else {
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// Cut 20% border from medians calculation. It will probably contain outlier values
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// from the film holder, which will bias the median result.
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const int bW = W * 20 / 100;
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const int bH = H * 20 / 100;
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calcMedians(ri, rawData, bW, bH, W - bW, H - bH, medians);
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}
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// Un-scale rawData medians
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for (int c = 0; c < 3; ++c) {
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medians[c] /= scale_mul[c];
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}
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if (settings->verbose) {
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printf("Channel medians: R=%g, G=%g, B=%g\n", medians[0], medians[1], medians[2]);
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}
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for (int c = 0; c < 3; ++c) {
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// Estimate film base values, so that in the output data, each channel
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// median will correspond to 1/24th of MAX.
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filmBaseValues[c] = pow_F(24.f / 512.f, 1.f / exps[c]) * medians[c];
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if (oldChannelScaling) {
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// If using the old channel scaling method, apply WB multipliers here to undo their
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// effect later, since fixed wb compensation was not used in previous version.
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// Also, undo the effect of the autoGainComp factor (see below).
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filmBaseValues[c] /= pow_F((wb_mul[c] / autoGainComp), 1.f / exps[c]);// / autoGainComp;
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}
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}
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}
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// Calculate multipliers based on previously obtained film base input values.
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// Apply current scaling coefficients to raw, unscaled base values.
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std::array<float, 3> fb = {
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filmBaseValues[0] * scale_mul[0],
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filmBaseValues[1] * scale_mul[1],
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filmBaseValues[2] * scale_mul[2]
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};
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if (settings->verbose) {
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printf("Input film base values: %g %g %g\n", fb[0], fb[1], fb[2]);
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}
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for (int c = 0; c < 3; ++c) {
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// Apply channel exponents, to obtain the corresponding base values in the output data
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fb[c] = pow_F(rtengine::max(fb[c], 1.f), exps[c]);
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// Determine the channel multiplier so that the film base value is 1/512th of max.
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mults[c] = (MAX_OUT_VALUE / 512.f) / fb[c];
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// Un-apply the fixed WB multipliers, to reverse their effect later in the WB tool.
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// This way, the output image will be adapted to this fixed WB setting
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mults[c] /= wb_mul[c];
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// Also compensate for the initialGain difference between the default scaling (forceAutoWB=true)
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// and the non-autoWB scaling (see camInitialGain).
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// This effectively restores camera scaling multipliers, and gives us stable multipliers
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// (not depending on image content).
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mults[c] *= autoGainComp;
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}
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if (settings->verbose) {
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printf("Output film base values: %g %g %g\n", static_cast<double>(fb[0]), static_cast<double>(fb[1]), static_cast<double>(fb[2]));
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printf("Computed multipliers: %g %g %g\n", static_cast<double>(mults[0]), static_cast<double>(mults[1]), static_cast<double>(mults[2]));
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}
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constexpr float CLIP_VAL = 65535.f;
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MyTime t1, t2, t3;
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t1.set();
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if (ri->getSensorType() == ST_BAYER) {
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#ifdef __SSE2__
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const vfloat onev = F2V(1.f);
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const vfloat clipv = F2V(CLIP_VAL);
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#endif
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#ifdef _OPENMP
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#pragma omp parallel for schedule(dynamic, 16)
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#endif
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for (int row = 0; row < H; ++row) {
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int col = 0;
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// Avoid trouble with zeroes, minimum pixel value is 1.
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const float exps0 = exps[FC(row, col)];
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const float exps1 = exps[FC(row, col + 1)];
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const float mult0 = mults[FC(row, col)];
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const float mult1 = mults[FC(row, col + 1)];
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#ifdef __SSE2__
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const vfloat expsv = _mm_setr_ps(exps0, exps1, exps0, exps1);
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const vfloat multsv = _mm_setr_ps(mult0, mult1, mult0, mult1);
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for (; col < W - 3; col += 4) {
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STVFU(rawData[row][col], vminf(multsv * pow_F(vmaxf(LVFU(rawData[row][col]), onev), expsv), clipv));
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}
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#endif // __SSE2__
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for (; col < W - 1; col += 2) {
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rawData[row][col] = rtengine::min(mult0 * pow_F(rtengine::max(rawData[row][col], 1.f), exps0), CLIP_VAL);
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rawData[row][col + 1] = rtengine::min(mult1 * pow_F(rtengine::max(rawData[row][col + 1], 1.f), exps1), CLIP_VAL);
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}
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if (col < W) {
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rawData[row][col] = rtengine::min(mult0 * pow_F(rtengine::max(rawData[row][col], 1.f), exps0), CLIP_VAL);
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}
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}
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} else if (ri->getSensorType() == ST_FUJI_XTRANS) {
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#ifdef __SSE2__
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const vfloat onev = F2V(1.f);
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const vfloat clipv = F2V(CLIP_VAL);
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|
#endif
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|
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|
#ifdef _OPENMP
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|
#pragma omp parallel for schedule(dynamic, 16)
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|
#endif
|
|
|
|
for (int row = 0; row < H; row ++) {
|
|
int col = 0;
|
|
// Avoid trouble with zeroes, minimum pixel value is 1.
|
|
const std::array<float, 6> expsc = {
|
|
exps[ri->XTRANSFC(row, 0)],
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|
exps[ri->XTRANSFC(row, 1)],
|
|
exps[ri->XTRANSFC(row, 2)],
|
|
exps[ri->XTRANSFC(row, 3)],
|
|
exps[ri->XTRANSFC(row, 4)],
|
|
exps[ri->XTRANSFC(row, 5)]
|
|
};
|
|
const std::array<float, 6> multsc = {
|
|
mults[ri->XTRANSFC(row, 0)],
|
|
mults[ri->XTRANSFC(row, 1)],
|
|
mults[ri->XTRANSFC(row, 2)],
|
|
mults[ri->XTRANSFC(row, 3)],
|
|
mults[ri->XTRANSFC(row, 4)],
|
|
mults[ri->XTRANSFC(row, 5)]
|
|
};
|
|
#ifdef __SSE2__
|
|
const vfloat expsv0 = _mm_setr_ps(expsc[0], expsc[1], expsc[2], expsc[3]);
|
|
const vfloat expsv1 = _mm_setr_ps(expsc[4], expsc[5], expsc[0], expsc[1]);
|
|
const vfloat expsv2 = _mm_setr_ps(expsc[2], expsc[3], expsc[4], expsc[5]);
|
|
const vfloat multsv0 = _mm_setr_ps(multsc[0], multsc[1], multsc[2], multsc[3]);
|
|
const vfloat multsv1 = _mm_setr_ps(multsc[4], multsc[5], multsc[0], multsc[1]);
|
|
const vfloat multsv2 = _mm_setr_ps(multsc[2], multsc[3], multsc[4], multsc[5]);
|
|
|
|
for (; col < W - 11; col += 12) {
|
|
STVFU(rawData[row][col], vminf(multsv0 * pow_F(vmaxf(LVFU(rawData[row][col]), onev), expsv0), clipv));
|
|
STVFU(rawData[row][col + 4], vminf(multsv1 * pow_F(vmaxf(LVFU(rawData[row][col + 4]), onev), expsv1), clipv));
|
|
STVFU(rawData[row][col + 8], vminf(multsv2 * pow_F(vmaxf(LVFU(rawData[row][col + 8]), onev), expsv2), clipv));
|
|
}
|
|
|
|
#endif // __SSE2__
|
|
|
|
for (; col < W - 5; col += 6) {
|
|
for (int c = 0; c < 6; ++c) {
|
|
rawData[row][col + c] = rtengine::min(multsc[c] * pow_F(rtengine::max(rawData[row][col + c], 1.f), expsc[c]), CLIP_VAL);
|
|
}
|
|
}
|
|
|
|
for (int c = 0; col < W; col++, c++) {
|
|
rawData[row][col + c] = rtengine::min(multsc[c] * pow_F(rtengine::max(rawData[row][col + c], 1.f), expsc[c]), CLIP_VAL);
|
|
}
|
|
}
|
|
}
|
|
|
|
t2.set();
|
|
|
|
if (settings->verbose) {
|
|
printf("Pow loop time us: %d\n", t2.etime(t1));
|
|
}
|
|
|
|
t2.set();
|
|
|
|
PixelsMap bitmapBads(W, H);
|
|
|
|
int totBP = 0; // Hold count of bad pixels to correct
|
|
|
|
if (ri->getSensorType() == ST_BAYER) {
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for reduction(+:totBP) schedule(dynamic,16)
|
|
#endif
|
|
|
|
for (int i = 0; i < H; ++i) {
|
|
for (int j = 0; j < W; ++j) {
|
|
if (rawData[i][j] >= MAX_OUT_VALUE) {
|
|
bitmapBads.set(j, i);
|
|
++totBP;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (totBP > 0) {
|
|
interpolateBadPixelsBayer(bitmapBads, rawData);
|
|
}
|
|
|
|
} else if (ri->getSensorType() == ST_FUJI_XTRANS) {
|
|
#ifdef _OPENMP
|
|
#pragma omp parallel for reduction(+:totBP) schedule(dynamic,16)
|
|
#endif
|
|
|
|
for (int i = 0; i < H; ++i) {
|
|
for (int j = 0; j < W; ++j) {
|
|
if (rawData[i][j] >= MAX_OUT_VALUE) {
|
|
bitmapBads.set(j, i);
|
|
totBP++;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (totBP > 0) {
|
|
interpolateBadPixelsXtrans(bitmapBads);
|
|
}
|
|
}
|
|
|
|
t3.set();
|
|
|
|
if (settings->verbose) {
|
|
printf("Bad pixels count: %d\n", totBP);
|
|
printf("Bad pixels interpolation time us: %d\n", t3.etime(t2));
|
|
}
|
|
}
|