a0f2040816
Removed unused key TP_FILMNEGATIVE_REF_SPOTS. Style fix in language string. generateTranslationDiffs Film Negative values in History use newlines to reduce required width. Removed benchmark code.
404 lines
13 KiB
C++
404 lines
13 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 <http://www.gnu.org/licenses/>.
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*/
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#include <cmath>
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#include <iostream>
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#ifdef _OPENMP
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#include <omp.h>
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#endif
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#include "rawimagesource.h"
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#include "mytime.h"
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#include "opthelper.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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//#define BENCHMARK
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#include "StopWatch.h"
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namespace rtengine
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{
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extern const Settings* settings;
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}
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namespace
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{
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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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const rtengine::procparams::FilmNegativeParams& params,
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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() != rtengine::ST_BAYER && ri->getSensorType() != rtengine::ST_FUJI_XTRANS) {
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return false;
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}
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if (rtengine::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 = spotPos.x - spotSize; c < spotPos.x + spotSize; ++c) {
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for (int r = spotPos.y - spotSize; r < spotPos.y + spotSize; ++r) {
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const int ch = ri->getSensorType() == rtengine::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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}
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bool rtengine::RawImageSource::getFilmNegativeExponents(Coord2D spotA, Coord2D spotB, int tran, const 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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// 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, currentParams, 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, currentParams, 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", clearVals[0], clearVals[1], clearVals[2]);
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printf("Dense film values: R=%g G=%g B=%g\n", denseVals[0], denseVals[1], 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] = CLAMP(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", newExps[0], newExps[1], newExps[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)
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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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MyTime t1, t2, t3,t4, t5;
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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 = 0; row < H; row += 5) {
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const int c0 = ri->FC(row, 0);
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const int c1 = ri->FC(row, 5);
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int col = 0;
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for (; col < W - 5; col += 10) {
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cvs[c0].push_back(rawData[row][col]);
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cvs[c1].push_back(rawData[row][col + 5]);
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}
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if (col < W) {
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cvs[c0].push_back(rawData[row][col]);
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}
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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 = 0; row < H; row += 5) {
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const std::array<unsigned int, 6> cs = {
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ri->XTRANSFC(row, 0),
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ri->XTRANSFC(row, 5),
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ri->XTRANSFC(row, 10),
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ri->XTRANSFC(row, 15),
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ri->XTRANSFC(row, 20),
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ri->XTRANSFC(row, 25)
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};
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int col = 0;
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for (; col < W - 25; col += 30) {
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for (int c = 0; c < 6; ++c) {
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cvs[cs[c]].push_back(rawData[row][col + c * 5]);
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}
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}
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for (int c = 0; col < W; col += 5, ++c) {
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cvs[cs[c]].push_back(rawData[row][col]);
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}
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}
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}
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constexpr float MAX_OUT_VALUE = 65000.f;
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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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std::array<float, 3> medians; // Channel median values
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std::array<float, 3> mults = {
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1.f,
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1.f,
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1.f
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}; // Channel normalization multipliers
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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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findMinMaxPercentile(cvs[c].data(), cvs[c].size(), 0.5f, medians[c], 0.5f, medians[c], true);
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medians[c] = pow_F(rtengine::max(medians[c], 1.f), exps[c]);
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// Determine the channel multiplier so that N times the median becomes 65k. This clips away
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// the values in the dark border surrounding the negative (due to the film holder, for example),
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// the reciprocal of which have blown up to stellar values.
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mults[c] = MAX_OUT_VALUE / (medians[c] * 24.f);
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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: %zu, %zu, %zu\n", cvs[0].size(), cvs[1].size(), cvs[2].size());
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printf("Medians: %g %g %g\n", medians[0], medians[1], medians[2] );
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printf("Computed multipliers: %g %g %g\n", mults[0], mults[1], mults[2] );
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printf("Median calc time us: %d\n", t3.etime(t2));
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}
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constexpr float CLIP_VAL = 65535.f;
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t3.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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#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 std::array<float, 6> expsc = {
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exps[ri->XTRANSFC(row, 0)],
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exps[ri->XTRANSFC(row, 1)],
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exps[ri->XTRANSFC(row, 2)],
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exps[ri->XTRANSFC(row, 3)],
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exps[ri->XTRANSFC(row, 4)],
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exps[ri->XTRANSFC(row, 5)]
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};
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const std::array<float, 6> multsc = {
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mults[ri->XTRANSFC(row, 0)],
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mults[ri->XTRANSFC(row, 1)],
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mults[ri->XTRANSFC(row, 2)],
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mults[ri->XTRANSFC(row, 3)],
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mults[ri->XTRANSFC(row, 4)],
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mults[ri->XTRANSFC(row, 5)]
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};
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#ifdef __SSE2__
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const vfloat expsv0 = _mm_setr_ps(expsc[0], expsc[1], expsc[2], expsc[3]);
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const vfloat expsv1 = _mm_setr_ps(expsc[4], expsc[5], expsc[0], expsc[1]);
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const vfloat expsv2 = _mm_setr_ps(expsc[2], expsc[3], expsc[4], expsc[5]);
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const vfloat multsv0 = _mm_setr_ps(multsc[0], multsc[1], multsc[2], multsc[3]);
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const vfloat multsv1 = _mm_setr_ps(multsc[4], multsc[5], multsc[0], multsc[1]);
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const vfloat multsv2 = _mm_setr_ps(multsc[2], multsc[3], multsc[4], multsc[5]);
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for (; col < W - 11; col += 12) {
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STVFU(rawData[row][col], vminf(multsv0 * pow_F(vmaxf(LVFU(rawData[row][col]), onev), expsv0), clipv));
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STVFU(rawData[row][col + 4], vminf(multsv1 * pow_F(vmaxf(LVFU(rawData[row][col + 4]), onev), expsv1), clipv));
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STVFU(rawData[row][col + 8], vminf(multsv2 * pow_F(vmaxf(LVFU(rawData[row][col + 8]), onev), expsv2), clipv));
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}
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#endif // __SSE2__
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for (; col < W - 5; col += 6) {
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for (int c = 0; c < 6; ++c) {
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rawData[row][col + c] = rtengine::min(multsc[c] * pow_F(rtengine::max(rawData[row][col + c], 1.f), expsc[c]), CLIP_VAL);
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}
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}
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for (int c = 0; col < W; col++, c++) {
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rawData[row][col + c] = rtengine::min(multsc[c] * pow_F(rtengine::max(rawData[row][col + c], 1.f), expsc[c]), CLIP_VAL);
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}
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}
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}
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t4.set();
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if (settings->verbose) {
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printf("Pow loop time us: %d\n", t4.etime(t3));
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}
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t4.set();
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PixelsMap bitmapBads(W, H);
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int totBP = 0; // Hold count of bad pixels to correct
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if (ri->getSensorType() == ST_BAYER) {
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#ifdef _OPENMP
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#pragma omp parallel for reduction(+:totBP) schedule(dynamic,16)
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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 (rawData[i][j] >= MAX_OUT_VALUE) {
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bitmapBads.set(j, i);
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++totBP;
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}
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}
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}
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if (totBP > 0) {
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interpolateBadPixelsBayer(bitmapBads, rawData);
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}
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}
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else if (ri->getSensorType() == ST_FUJI_XTRANS) {
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#ifdef _OPENMP
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#pragma omp parallel for reduction(+:totBP) schedule(dynamic,16)
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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 (rawData[i][j] >= MAX_OUT_VALUE) {
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bitmapBads.set(j, i);
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totBP++;
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}
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}
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}
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if (totBP > 0) {
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interpolateBadPixelsXtrans(bitmapBads);
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}
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}
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t5.set();
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if (settings->verbose) {
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printf("Bad pixels count: %d\n", totBP);
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printf("Bad pixels interpolation time us: %d\n", t5.etime(t4));
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}
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}
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