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Moved median calculation block before exponentiation. This way findMinMaxPercentile() can be used again because it works on the original raw file values (which are in a much more reasonable range). Patch kindly provided by @heckflosse ;-)

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rom9
2019-06-11 20:08:46 +02:00
parent 223ae8abce
commit e1c9197ed5
1 changed files with 57 additions and 58 deletions
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115
rtengine/filmnegativeproc.cc
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@@ -134,6 +134,61 @@ void RawImageSource::filmNegativeProcess(const procparams::FilmNegativeParams &p
MyTime t1, t2, t3,t4, t5, t6;
t1.set();
// Channel vectors to calculate medians
std::vector<float> cvs[3] = {
std::vector<float>(),
std::vector<float>(),
std::vector<float>()
};
// Sample one every 5 pixels, and push the value in the appropriate channel vector.
// Chose an odd step, not multiple of the CFA size, to get a chance to visit each channel.
if(ri->getSensorType() == ST_BAYER) {
for (int row = 0; row < H; row+=5) {
for (int col = 0; col < W; col+=5) {
int c = FC(row, col); // three colors, 0=R, 1=G, 2=B
cvs[c].push_back(rawData[row][col]);
}
}
} else if(ri->getSensorType() == ST_FUJI_XTRANS) {
for (int row = 0; row < H; row+=5) {
for (int col = 0; col < W; col+=5) {
int c = ri->XTRANSFC(row, col); // three colors, 0=R, 1=G, 2=B
cvs[c].push_back(rawData[row][col]);
}
}
}
const float MAX_OUT_VALUE = 65000.f;
t2.set();
if (settings->verbose)
printf("Median vector fill loop time us: %d\n", t2.etime(t1));
float medians[3]; // Channel median values
float mults[3] = { 1.f }; // Channel normalization multipliers
for (int c=0; c<3; c++) {
// Find median values for each channel
if(cvs[c].size() > 0) {
findMinMaxPercentile(&cvs[c][0], cvs[c].size(), 0.5f, medians[c], 0.5f, medians[c], true);
medians[c] = pow_F(max(medians[c], 1.f), -exps[c]);
// Determine the channel multipler so that N times the median becomes 65k. This clips away
// the values in the dark border surrounding the negative (due to the film holder, for example),
// the reciprocal of which have blown up to stellar values.
mults[c] = MAX_OUT_VALUE / (medians[c] * 24);
}
}
t3.set();
if (settings->verbose) {
printf("Sample count : %lu, %lu, %lu\n", cvs[0].size(), cvs[1].size(), cvs[2].size());
printf("Medians : %g %g %g\n", medians[0], medians[1], medians[2] );
printf("Computed multipliers : %g %g %g\n", mults[0], mults[1], mults[2] );
printf("Median calc time us: %d\n", t3.etime(t2));
}
if(ri->getSensorType() == ST_BAYER) {
#ifdef _OPENMP
#pragma omp parallel for schedule(dynamic, 16)
@@ -191,65 +246,9 @@ void RawImageSource::filmNegativeProcess(const procparams::FilmNegativeParams &p
}
t2.set();
if (settings->verbose)
printf("Pow loop time us: %d\n", t2.etime(t1));
// Channel vectors to calculate medians
std::vector<float> cvs[3] = {
std::vector<float>(),
std::vector<float>(),
std::vector<float>()
};
// Sample one every 5 pixels, and push the value in the appropriate channel vector.
// Chose an odd step, not multiple of the CFA size, to get a chance to visit each channel.
if(ri->getSensorType() == ST_BAYER) {
for (int row = 0; row < H; row+=5) {
for (int col = 0; col < W; col+=5) {
int c = FC(row, col); // three colors, 0=R, 1=G, 2=B
cvs[c].push_back(rawData[row][col]);
}
}
} else if(ri->getSensorType() == ST_FUJI_XTRANS) {
for (int row = 0; row < H; row+=5) {
for (int col = 0; col < W; col+=5) {
int c = ri->XTRANSFC(row, col); // three colors, 0=R, 1=G, 2=B
cvs[c].push_back(rawData[row][col]);
}
}
}
const float MAX_OUT_VALUE = 65000.f;
t3.set();
if (settings->verbose)
printf("Median vector fill loop time us: %d\n", t3.etime(t2));
float medians[3]; // Channel median values
float mults[3] = { 1.f }; // Channel normalization multipliers
for (int c=0; c<3; c++) {
// Find median values for each channel
if(cvs[c].size() > 0) {
std::sort(cvs[c].begin(), cvs[c].end());
medians[c] = cvs[c].at(cvs[c].size() / 2);
// Determine the channel multipler so that N times the median becomes 65k. This clips away
// the values in the dark border surrounding the negative (due to the film holder, for example),
// the reciprocal of which have blown up to stellar values.
mults[c] = MAX_OUT_VALUE / (medians[c] * 24);
}
}
t4.set();
if (settings->verbose) {
printf("Sample count : %lu, %lu, %lu\n", cvs[0].size(), cvs[1].size(), cvs[2].size());
printf("Medians : %g %g %g\n", medians[0], medians[1], medians[2] );
printf("Computed multipliers : %g %g %g\n", mults[0], mults[1], mults[2] );
printf("Median calc time us: %d\n", t4.etime(t3));
}
if (settings->verbose)
printf("Pow loop time us: %d\n", t4.etime(t3));
if(ri->getSensorType() == ST_BAYER) {