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Remove repeated code in cellular sampler

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Astrash 2023-09-27 13:39:51 +10:00
parent 5eeb5af6c4
commit 3f9ead0d66
1 changed files with 65 additions and 200 deletions
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189
common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/samplers/noise/CellularSampler.java
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@ -240,10 +240,6 @@ public class CellularSampler extends NoiseFunction {
double centerX = x; double centerX = x;
double centerY = y; double centerY = y;
switch(distanceFunction) {
default:
case Euclidean:
case EuclideanSq:
for(int xi = xr - 1; xi <= xr + 1; xi++) { for(int xi = xr - 1; xi <= xr + 1; xi++) {
int yPrimed = yPrimedBase; int yPrimed = yPrimedBase;
@ -254,7 +250,11 @@ public class CellularSampler extends NoiseFunction {
double vecX = (xi - x) + RAND_VECS_2D[idx] * cellularJitter; double vecX = (xi - x) + RAND_VECS_2D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_2D[idx | 1] * cellularJitter; double vecY = (yi - y) + RAND_VECS_2D[idx | 1] * cellularJitter;
double newDistance = vecX * vecX + vecY * vecY; double newDistance = switch(distanceFunction) {
case Manhattan -> fastAbs(vecX) + fastAbs(vecY);
case Hybrid -> (fastAbs(vecX) + fastAbs(vecY)) + (vecX * vecX + vecY * vecY);
default -> vecX * vecX + vecY * vecY;
};
distance1 = fastMax(fastMin(distance1, newDistance), distance0); distance1 = fastMax(fastMin(distance1, newDistance), distance0);
if(newDistance < distance0) { if(newDistance < distance0) {
@ -272,68 +272,6 @@ public class CellularSampler extends NoiseFunction {
} }
xPrimed += PRIME_X; xPrimed += PRIME_X;
} }
break;
case Manhattan:
for(int xi = xr - 1; xi <= xr + 1; xi++) {
int yPrimed = yPrimedBase;
for(int yi = yr - 1; yi <= yr + 1; yi++) {
int hash = hash(seed, xPrimed, yPrimed);
int idx = hash & (255 << 1);
double vecX = (xi - x) + RAND_VECS_2D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_2D[idx | 1] * cellularJitter;
double newDistance = fastAbs(vecX) + fastAbs(vecY);
distance1 = fastMax(fastMin(distance1, newDistance), distance0);
if(newDistance < distance0) {
distance0 = newDistance;
closestHash = hash;
centerX = ((xi + RAND_VECS_2D[idx] * cellularJitter) / frequency);
centerY = ((yi + RAND_VECS_2D[idx | 1] * cellularJitter) / frequency);
} else if(newDistance < distance1) {
distance2 = distance1;
distance1 = newDistance;
} else if(newDistance < distance2) {
distance2 = newDistance;
}
yPrimed += PRIME_Y;
}
xPrimed += PRIME_X;
}
break;
case Hybrid:
for(int xi = xr - 1; xi <= xr + 1; xi++) {
int yPrimed = yPrimedBase;
for(int yi = yr - 1; yi <= yr + 1; yi++) {
int hash = hash(seed, xPrimed, yPrimed);
int idx = hash & (255 << 1);
double vecX = (xi - x) + RAND_VECS_2D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_2D[idx | 1] * cellularJitter;
double newDistance = (fastAbs(vecX) + fastAbs(vecY)) + (vecX * vecX + vecY * vecY);
distance1 = fastMax(fastMin(distance1, newDistance), distance0);
if(newDistance < distance0) {
distance0 = newDistance;
closestHash = hash;
centerX = ((xi + RAND_VECS_2D[idx] * cellularJitter) / frequency);
centerY = ((yi + RAND_VECS_2D[idx | 1] * cellularJitter) / frequency);
} else if(newDistance < distance1) {
distance2 = distance1;
distance1 = newDistance;
} else if(newDistance < distance2) {
distance2 = newDistance;
}
yPrimed += PRIME_Y;
}
xPrimed += PRIME_X;
}
break;
}
if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) { if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) {
distance0 = fastSqrt(distance0); distance0 = fastSqrt(distance0);
@ -383,99 +321,30 @@ public class CellularSampler extends NoiseFunction {
double centerY = y; double centerY = y;
double centerZ = z; double centerZ = z;
for(int xi = xr - 1; xi <= xr + 1; xi++) {
int yPrimed = yPrimedBase;
for(int yi = yr - 1; yi <= yr + 1; yi++) {
int zPrimed = zPrimedBase;
for(int zi = zr - 1; zi <= zr + 1; zi++) {
int hash = hash(seed, xPrimed, yPrimed, zPrimed);
int idx = hash & (255 << 2);
double vecX = (xi - x) + RAND_VECS_3D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_3D[idx | 1] * cellularJitter;
double vecZ = (zi - z) + RAND_VECS_3D[idx | 2] * cellularJitter;
double newDistance = 0;
switch(distanceFunction) { switch(distanceFunction) {
case Euclidean: case Euclidean, EuclideanSq -> newDistance = vecX * vecX + vecY * vecY + vecZ * vecZ;
case EuclideanSq: case Manhattan -> newDistance = fastAbs(vecX) + fastAbs(vecY) + fastAbs(vecZ);
for(int xi = xr - 1; xi <= xr + 1; xi++) { case Hybrid -> {
int yPrimed = yPrimedBase; newDistance = (fastAbs(vecX) + fastAbs(vecY) + fastAbs(vecZ)) + (vecX * vecX + vecY * vecY + vecZ * vecZ);
for(int yi = yr - 1; yi <= yr + 1; yi++) {
int zPrimed = zPrimedBase;
for(int zi = zr - 1; zi <= zr + 1; zi++) {
int hash = hash(seed, xPrimed, yPrimed, zPrimed);
int idx = hash & (255 << 2);
double vecX = (xi - x) + RAND_VECS_3D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_3D[idx | 1] * cellularJitter;
double vecZ = (zi - z) + RAND_VECS_3D[idx | 2] * cellularJitter;
double newDistance = vecX * vecX + vecY * vecY + vecZ * vecZ;
if(newDistance < distance0) {
distance0 = newDistance;
closestHash = hash;
centerX = ((xi + RAND_VECS_3D[idx] * cellularJitter) / frequency);
centerY = ((yi + RAND_VECS_3D[idx | 1] * cellularJitter) / frequency);
centerZ = ((zi + RAND_VECS_3D[idx | 2] * cellularJitter) / frequency);
} else if(newDistance < distance1) {
distance2 = distance1;
distance1 = newDistance;
} else if(newDistance < distance2) {
distance2 = newDistance;
}
zPrimed += PRIME_Z;
}
yPrimed += PRIME_Y;
}
xPrimed += PRIME_X;
}
break;
case Manhattan:
for(int xi = xr - 1; xi <= xr + 1; xi++) {
int yPrimed = yPrimedBase;
for(int yi = yr - 1; yi <= yr + 1; yi++) {
int zPrimed = zPrimedBase;
for(int zi = zr - 1; zi <= zr + 1; zi++) {
int hash = hash(seed, xPrimed, yPrimed, zPrimed);
int idx = hash & (255 << 2);
double vecX = (xi - x) + RAND_VECS_3D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_3D[idx | 1] * cellularJitter;
double vecZ = (zi - z) + RAND_VECS_3D[idx | 2] * cellularJitter;
double newDistance = fastAbs(vecX) + fastAbs(vecY) + fastAbs(vecZ);
if(newDistance < distance0) {
distance0 = newDistance;
closestHash = hash;
centerX = ((xi + RAND_VECS_3D[idx] * cellularJitter) / frequency);
centerY = ((yi + RAND_VECS_3D[idx | 1] * cellularJitter) / frequency);
centerZ = ((zi + RAND_VECS_3D[idx | 2] * cellularJitter) / frequency);
} else if(newDistance < distance1) {
distance2 = distance1;
distance1 = newDistance;
} else if(newDistance < distance2) {
distance2 = newDistance;
}
zPrimed += PRIME_Z;
}
yPrimed += PRIME_Y;
}
xPrimed += PRIME_X;
}
break;
case Hybrid:
for(int xi = xr - 1; xi <= xr + 1; xi++) {
int yPrimed = yPrimedBase;
for(int yi = yr - 1; yi <= yr + 1; yi++) {
int zPrimed = zPrimedBase;
for(int zi = zr - 1; zi <= zr + 1; zi++) {
int hash = hash(seed, xPrimed, yPrimed, zPrimed);
int idx = hash & (255 << 2);
double vecX = (xi - x) + RAND_VECS_3D[idx] * cellularJitter;
double vecY = (yi - y) + RAND_VECS_3D[idx | 1] * cellularJitter;
double vecZ = (zi - z) + RAND_VECS_3D[idx | 2] * cellularJitter;
double newDistance = (fastAbs(vecX) + fastAbs(vecY) + fastAbs(vecZ)) +
(vecX * vecX + vecY * vecY + vecZ * vecZ);
distance1 = fastMax(fastMin(distance1, newDistance), distance0); distance1 = fastMax(fastMin(distance1, newDistance), distance0);
}
}
if(newDistance < distance0) { if(newDistance < distance0) {
distance0 = newDistance; distance0 = newDistance;
closestHash = hash; closestHash = hash;
@ -494,10 +363,6 @@ public class CellularSampler extends NoiseFunction {
} }
xPrimed += PRIME_X; xPrimed += PRIME_X;
} }
break;
default:
break;
}
if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) { if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) {
distance0 = fastSqrt(distance0); distance0 = fastSqrt(distance0);