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Capture sharpening: cleanup

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This commit is contained in:
Ingo Weyrich
2019-11-11 15:52:31 +01:00
parent d32c570383
commit af4bf34b6b
1 changed files with 58 additions and 68 deletions
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126
rtengine/capturesharpening.cc
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@@ -99,33 +99,48 @@ void compute3x3kernel(float sigma, float kernel[3][3]) {
}
}
inline void gauss3x3div (float** RESTRICT src, float** RESTRICT dst, float** RESTRICT divBuffer, const int W, const int H, const float kernel[3][3])
inline void initTile(float** dst, const int tileSize)
{
// first rows
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < tileSize; ++j) {
dst[i][j] = 1.f;
}
}
// left and right border
for (int i = 3; i < tileSize - 3; ++i) {
dst[i][0] = dst[i][1] = dst[i][2] = 1.f;
dst[i][tileSize - 3] = dst[i][tileSize - 2] = dst[i][tileSize - 1] = 1.f;
}
// last rows
for (int i = tileSize - 3 ; i < tileSize; ++i) {
for (int j = 0; j < tileSize; ++j) {
dst[i][j] = 1.f;
}
}
}
inline void gauss3x3div (float** RESTRICT src, float** RESTRICT dst, float** RESTRICT divBuffer, const int tileSize, const float kernel[3][3])
{
const float c11 = kernel[0][0];
const float c10 = kernel[0][1];
const float c00 = kernel[1][1];
for (int i = 1; i < H - 1; i++) {
dst[i][0] = 1.f;
for (int j = 1; j < W - 1; j++) {
for (int i = 1; i < tileSize - 1; i++) {
for (int j = 1; j < tileSize - 1; j++) {
const float val = c11 * (src[i - 1][j - 1] + src[i - 1][j + 1] + src[i + 1][j - 1] + src[i + 1][j + 1]) +
c10 * (src[i - 1][j] + src[i][j - 1] + src[i][j + 1] + src[i + 1][j]) +
c00 * src[i][j];
dst[i][j] = divBuffer[i][j] / std::max(val, 0.00001f);
}
dst[i][W - 1] = 1.f;
}
// first and last row
for (int j = 0; j < W; ++j) {
dst[0][j] = 1.f;
}
for (int j = 0; j < W; ++j) {
dst[H - 1][j] = 1.f;
}
}
inline void gauss5x5div (float** RESTRICT src, float** RESTRICT dst, float** RESTRICT divBuffer, const int W, const int H, const float kernel[5][5])
inline void gauss5x5div (float** RESTRICT src, float** RESTRICT dst, float** RESTRICT divBuffer, const int tileSize, const float kernel[5][5])
{
const float c21 = kernel[0][1];
@@ -134,10 +149,9 @@ inline void gauss5x5div (float** RESTRICT src, float** RESTRICT dst, float** RES
const float c10 = kernel[1][2];
const float c00 = kernel[2][2];
for (int i = 2; i < H - 2; ++i) {
dst[i][0] = dst[i][1] = 1.f;
for (int i = 2; i < tileSize - 2; ++i) {
// I tried hand written SSE code but gcc vectorizes better
for (int j = 2; j < W - 2; ++j) {
for (int j = 2; j < tileSize - 2; ++j) {
const float val = c21 * (src[i - 2][j - 1] + src[i - 2][j + 1] + src[i - 1][j - 2] + src[i - 1][j + 2] + src[i + 1][j - 2] + src[i + 1][j + 2] + src[i + 2][j - 1] + src[i + 2][j + 1]) +
c20 * (src[i - 2][j] + src[i][j - 2] + src[i][j + 2] + src[i + 2][j]) +
c11 * (src[i - 1][j - 1] + src[i - 1][j + 1] + src[i + 1][j - 1] + src[i + 1][j + 1]) +
@@ -146,23 +160,10 @@ inline void gauss5x5div (float** RESTRICT src, float** RESTRICT dst, float** RES
dst[i][j] = divBuffer[i][j] / std::max(val, 0.00001f);
}
dst[i][W - 2] = dst[i][W - 1] = 1.f;
}
// first and last rows
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < W; ++j) {
dst[i][j] = 1.f;
}
}
for (int i = H - 2 ; i < H; ++i) {
for (int j = 0; j < W; ++j) {
dst[i][j] = 1.f;
}
}
}
inline void gauss7x7div(float** RESTRICT src, float** RESTRICT dst, float** RESTRICT divBuffer, const int W, const int H, const float kernel[7][7])
inline void gauss7x7div(float** RESTRICT src, float** RESTRICT dst, float** RESTRICT divBuffer, const int tileSize, const float kernel[7][7])
{
const float c31 = kernel[0][2];
@@ -174,10 +175,9 @@ inline void gauss7x7div(float** RESTRICT src, float** RESTRICT dst, float** REST
const float c10 = kernel[2][3];
const float c00 = kernel[3][3];
for (int i = 3; i < H - 3; ++i) {
dst[i][0] = dst[i][1] = dst[i][2] = 1.f;
for (int i = 3; i < tileSize - 3; ++i) {
// I tried hand written SSE code but gcc vectorizes better
for (int j = 3; j < W - 3; ++j) {
for (int j = 3; j < tileSize - 3; ++j) {
const float val = c31 * (src[i - 3][j - 1] + src[i - 3][j + 1] + src[i - 1][j - 3] + src[i - 1][j + 3] + src[i + 1][j - 3] + src[i + 1][j + 3] + src[i + 3][j - 1] + src[i + 3][j + 1]) +
c30 * (src[i - 3][j] + src[i][j - 3] + src[i][j + 3] + src[i + 3][j]) +
c22 * (src[i - 2][j - 2] + src[i - 2][j + 2] + src[i + 2][j - 2] + src[i + 2][j + 2]) +
@@ -189,30 +189,17 @@ inline void gauss7x7div(float** RESTRICT src, float** RESTRICT dst, float** REST
dst[i][j] = divBuffer[i][j] / std::max(val, 0.00001f);
}
dst[i][W - 3] = dst[i][W - 2] = dst[i][W - 1] = 1.f;
}
// first and last rows
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < W; ++j) {
dst[i][j] = 1.f;
}
}
for (int i = H - 3 ; i < H; ++i) {
for (int j = 0; j < W; ++j) {
dst[i][j] = 1.f;
}
}
}
inline void gauss3x3mult(float** RESTRICT src, float** RESTRICT dst, const int W, const int H, const float kernel[3][3])
inline void gauss3x3mult(float** RESTRICT src, float** RESTRICT dst, const int tileSize, const float kernel[3][3])
{
const float c11 = kernel[0][0];
const float c10 = kernel[0][1];
const float c00 = kernel[1][1];
for (int i = 1; i < H - 1; i++) {
for (int j = 1; j < W - 1; j++) {
for (int i = 1; i < tileSize - 1; i++) {
for (int j = 1; j < tileSize - 1; j++) {
const float val = c11 * (src[i - 1][j - 1] + src[i - 1][j + 1] + src[i + 1][j - 1] + src[i + 1][j + 1]) +
c10 * (src[i - 1][j] + src[i][j - 1] + src[i][j + 1] + src[i + 1][j]) +
c00 * src[i][j];
@@ -222,7 +209,7 @@ inline void gauss3x3mult(float** RESTRICT src, float** RESTRICT dst, const int W
}
inline void gauss5x5mult (float** RESTRICT src, float** RESTRICT dst, const int W, const int H, const float kernel[5][5])
inline void gauss5x5mult (float** RESTRICT src, float** RESTRICT dst, const int tileSize, const float kernel[5][5])
{
const float c21 = kernel[0][1];
@@ -231,9 +218,9 @@ inline void gauss5x5mult (float** RESTRICT src, float** RESTRICT dst, const int
const float c10 = kernel[1][2];
const float c00 = kernel[2][2];
for (int i = 2; i < H - 2; ++i) {
for (int i = 2; i < tileSize - 2; ++i) {
// I tried hand written SSE code but gcc vectorizes better
for (int j = 2; j < W - 2; ++j) {
for (int j = 2; j < tileSize - 2; ++j) {
const float val = c21 * (src[i - 2][j - 1] + src[i - 2][j + 1] + src[i - 1][j - 2] + src[i - 1][j + 2] + src[i + 1][j - 2] + src[i + 1][j + 2] + src[i + 2][j - 1] + src[i + 2][j + 1]) +
c20 * (src[i - 2][j] + src[i][j - 2] + src[i][j + 2] + src[i + 2][j]) +
c11 * (src[i - 1][j - 1] + src[i - 1][j + 1] + src[i + 1][j - 1] + src[i + 1][j + 1]) +
@@ -245,7 +232,7 @@ inline void gauss5x5mult (float** RESTRICT src, float** RESTRICT dst, const int
}
}
inline void gauss7x7mult(float** RESTRICT src, float** RESTRICT dst, const int W, const int H, const float kernel[7][7])
inline void gauss7x7mult(float** RESTRICT src, float** RESTRICT dst, const int tileSize, const float kernel[7][7])
{
const float c31 = kernel[0][2];
@@ -257,9 +244,9 @@ inline void gauss7x7mult(float** RESTRICT src, float** RESTRICT dst, const int W
const float c10 = kernel[2][3];
const float c00 = kernel[3][3];
for (int i = 3; i < H - 3; ++i) {
for (int i = 3; i < tileSize - 3; ++i) {
// I tried hand written SSE code but gcc vectorizes better
for (int j = 3; j < W - 3; ++j) {
for (int j = 3; j < tileSize - 3; ++j) {
const float val = c31 * (src[i - 3][j - 1] + src[i - 3][j + 1] + src[i - 1][j - 3] + src[i - 1][j + 3] + src[i + 1][j - 3] + src[i + 1][j + 3] + src[i + 3][j - 1] + src[i + 3][j + 1]) +
c30 * (src[i - 3][j] + src[i][j - 3] + src[i][j + 3] + src[i + 3][j]) +
c22 * (src[i - 2][j - 2] + src[i - 2][j + 2] + src[i + 2][j - 2] + src[i + 2][j + 2]) +
@@ -543,6 +530,7 @@ BENCHFUN
array2D<float> tmpIThr(fullTileSize, fullTileSize);
array2D<float> tmpThr(fullTileSize, fullTileSize);
array2D<float> lumThr(fullTileSize, fullTileSize);
initTile(tmpThr, fullTileSize);
#ifdef _OPENMP
#pragma omp for schedule(dynamic,2) collapse(2)
#endif
@@ -570,14 +558,14 @@ BENCHFUN
if (is3x3) {
for (int k = 0; k < iterations; ++k) {
// apply 3x3 gaussian blur and divide luminance by result of gaussian blur
gauss3x3div(tmpIThr, tmpThr, lumThr, fullTileSize, fullTileSize, kernel3);
gauss3x3mult(tmpThr, tmpIThr, fullTileSize, fullTileSize, kernel3);
gauss3x3div(tmpIThr, tmpThr, lumThr, fullTileSize, kernel3);
gauss3x3mult(tmpThr, tmpIThr, fullTileSize, kernel3);
}
} else if (is5x5) {
for (int k = 0; k < iterations; ++k) {
// apply 5x5 gaussian blur and divide luminance by result of gaussian blur
gauss5x5div(tmpIThr, tmpThr, lumThr, fullTileSize, fullTileSize, kernel5);
gauss5x5mult(tmpThr, tmpIThr, fullTileSize, fullTileSize, kernel5);
gauss5x5div(tmpIThr, tmpThr, lumThr, fullTileSize, kernel5);
gauss5x5mult(tmpThr, tmpIThr, fullTileSize, kernel5);
}
} else {
if (sigmaCornerOffset != 0.0) {
@@ -586,17 +574,17 @@ BENCHFUN
if (sigmaTile >= 0.4f) {
float lkernel7[7][7];
compute7x7kernel(static_cast<float>(sigma) + distanceFactor * distance, lkernel7);
for (int k = 0; k < iterations - 1; ++k) {
for (int k = 0; k < iterations; ++k) {
// apply 7x7 gaussian blur and divide luminance by result of gaussian blur
gauss7x7div(tmpIThr, tmpThr, lumThr, fullTileSize, fullTileSize, lkernel7);
gauss7x7mult(tmpThr, tmpIThr, fullTileSize, fullTileSize, lkernel7);
gauss7x7div(tmpIThr, tmpThr, lumThr, fullTileSize, lkernel7);
gauss7x7mult(tmpThr, tmpIThr, fullTileSize, lkernel7);
}
}
} else {
for (int k = 0; k < iterations; ++k) {
// apply 7x7 gaussian blur and divide luminance by result of gaussian blur
gauss7x7div(tmpIThr, tmpThr, lumThr, fullTileSize, fullTileSize, kernel7);
gauss7x7mult(tmpThr, tmpIThr, fullTileSize, fullTileSize, kernel7);
gauss7x7div(tmpIThr, tmpThr, lumThr, fullTileSize, kernel7);
gauss7x7mult(tmpThr, tmpIThr, fullTileSize, kernel7);
}
}
}
@@ -643,7 +631,7 @@ void RawImageSource::captureSharpening(const procparams::CaptureSharpeningParams
plistener->setProgress(0.0);
}
BENCHFUN
const float xyz_rgb[3][3] = { // XYZ from RGB
constexpr float xyz_rgb[3][3] = { // XYZ from RGB
{ 0.412453, 0.357580, 0.180423 },
{ 0.212671, 0.715160, 0.072169 },
{ 0.019334, 0.119193, 0.950227 }
@@ -659,6 +647,8 @@ BENCHFUN
array2D<float> clipMask(W, H);
constexpr float clipLimit = 0.95f;
constexpr float maxSigma = 1.15f;
if (getSensorType() == ST_BAYER) {
const float whites[2][2] = {
{(ri->get_white(FC(0,0)) - c_black[FC(0,0)]) * scale_mul[FC(0,0)] * clipLimit, (ri->get_white(FC(0,1)) - c_black[FC(0,1)]) * scale_mul[FC(0,1)] * clipLimit},
@@ -667,7 +657,7 @@ BENCHFUN
buildClipMaskBayer(rawData, W, H, clipMask, whites);
const unsigned int fc[2] = {FC(0,0), FC(1,0)};
if (sharpeningParams.autoRadius) {
radius = std::min(calcRadiusBayer(rawData, W, H, 1000.f, clipVal, fc), 1.15f);
radius = std::min(calcRadiusBayer(rawData, W, H, 1000.f, clipVal, fc), maxSigma);
}
} else if (getSensorType() == ST_FUJI_XTRANS) {
float whites[6][6];
@@ -695,14 +685,14 @@ BENCHFUN
}
}
if (sharpeningParams.autoRadius) {
radius = std::min(calcRadiusXtrans(rawData, W, H, 1000.f, clipVal, i, j), 1.15f);
radius = std::min(calcRadiusXtrans(rawData, W, H, 1000.f, clipVal, i, j), maxSigma);
}
} else if (ri->get_colors() == 1) {
buildClipMaskMono(rawData, W, H, clipMask, (ri->get_white(0) - c_black[0]) * scale_mul[0] * clipLimit);
if (sharpeningParams.autoRadius) {
const unsigned int fc[2] = {0, 0};
radius = std::min(calcRadiusBayer(rawData, W, H, 1000.f, clipVal, fc), 1.15f);
radius = std::min(calcRadiusBayer(rawData, W, H, 1000.f, clipVal, fc), maxSigma);
}
}