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Zoë Gidiere
2023-10-07 17:59:29 -06:00
parent 2f1b8690ff
commit 29e5b9f3ff
5 changed files with 42 additions and 35 deletions
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48
common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/samplers/noise/PseudoErosionSampler.java
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@@ -8,6 +8,7 @@
package com.dfsek.terra.addons.noise.samplers.noise;
import com.dfsek.terra.api.noise.NoiseSampler;
import com.dfsek.terra.api.util.MathUtil;
import static com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction.PRIME_X;
import static com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction.PRIME_Y;
@@ -214,6 +215,8 @@ public class PseudoErosionSampler implements NoiseSampler {
private final double frequency;
private final double inverseFrequency;
private final double cellularJitter;
private final NoiseSampler lookup;
@@ -221,6 +224,7 @@ public class PseudoErosionSampler implements NoiseSampler {
public PseudoErosionSampler(long salt, double frequency, NoiseSampler lookup, double jitterModifier) {
this.salt = salt;
this.frequency = frequency;
this.inverseFrequency = 1 / frequency;
this.lookup = lookup;
this.cellularJitter = 0.43701595 * jitterModifier;
}
@@ -236,21 +240,27 @@ public class PseudoErosionSampler implements NoiseSampler {
// Precompute cell positions and lookup values
double[] cellData = new double[PRECOMPUTE_SIZE * PRECOMPUTE_SIZE * 3];
for(int xi = -PRECOMPUTE_RADIUS; xi <= PRECOMPUTE_RADIUS; xi++) {
int gridXi = gridX + xi;
int gridXiPrimed = gridXi * PRIME_X;
int xiKey = ((xi + PRECOMPUTE_RADIUS) * PRECOMPUTE_SIZE) + PRECOMPUTE_RADIUS;
for(int yi = -PRECOMPUTE_RADIUS; yi <= PRECOMPUTE_RADIUS; yi++) {
int jitterIdx = jitterIdx2D(seed, gridX + xi, gridY + yi);
int gridYi = gridY + yi;
int jitterIdx = hash(seed, gridXiPrimed, gridYi * PRIME_Y) & (255 << 1);
double jitterX = RAND_VECS_2D[jitterIdx] * cellularJitter;
double jitterY = RAND_VECS_2D[jitterIdx | 1] * cellularJitter;
double cellX = gridX + xi + jitterX;
double cellY = gridY + yi + jitterY;
double cellX = gridXi + jitterX;
double cellY = gridYi + jitterY;
// Transform to actual coordinates for lookup
double actualCellX = cellX * 1 / frequency;
double actualCellY = cellY * 1 / frequency;
double actualCellX = cellX * inverseFrequency;
double actualCellY = cellY * inverseFrequency;
double lookup = this.lookup.noise(seed, actualCellX, actualCellY);
// Calculate linear index for flattened array
int linearIndex = ((xi + PRECOMPUTE_RADIUS) * PRECOMPUTE_SIZE + (yi + PRECOMPUTE_RADIUS)) * 3;
int linearIndex = (xiKey + yi) * 3;
cellData[linearIndex] = cellX;
cellData[linearIndex + 1] = cellY;
@@ -260,6 +270,8 @@ public class PseudoErosionSampler implements NoiseSampler {
// Iterate over nearby cells
for(int xi = -NEARBY_CELLS_RADIUS; xi <= NEARBY_CELLS_RADIUS; xi++) {
int xiKey = ((xi + PRECOMPUTE_RADIUS) * PRECOMPUTE_SIZE) + PRECOMPUTE_RADIUS;
for(int yi = -NEARBY_CELLS_RADIUS; yi <= NEARBY_CELLS_RADIUS; yi++) {
// Find cell position with the lowest lookup value within moore neighborhood of neighbor
@@ -267,8 +279,10 @@ public class PseudoErosionSampler implements NoiseSampler {
double connectedCellX = 0;
double connectedCellY = 0;
for(int xni = xi - MAX_CONNECTION_RADIUS; xni <= xi + MAX_CONNECTION_RADIUS; xni++) {
int xniKey = ((xni + PRECOMPUTE_RADIUS) * PRECOMPUTE_SIZE) + PRECOMPUTE_RADIUS;
for(int yni = yi - MAX_CONNECTION_RADIUS; yni <= yi + MAX_CONNECTION_RADIUS; yni++) {
int linearIndex = ((xni + PRECOMPUTE_RADIUS) * PRECOMPUTE_SIZE + (yni + PRECOMPUTE_RADIUS)) * 3;
int linearIndex = (xniKey + yni) * 3;
double lookup = cellData[linearIndex + 2];
if(lookup < lowestLookup) {
lowestLookup = lookup;
@@ -278,7 +292,7 @@ public class PseudoErosionSampler implements NoiseSampler {
}
}
int linearIndex = ((xi + PRECOMPUTE_RADIUS) * PRECOMPUTE_SIZE + (yi + PRECOMPUTE_RADIUS)) * 3;
int linearIndex = (xiKey + yi) * 3;
double cellX = cellData[linearIndex];
double cellY = cellData[linearIndex + 1];
@@ -307,34 +321,30 @@ public class PseudoErosionSampler implements NoiseSampler {
if (x1 == x2 && y1 == y2) {
// If positions are the same, just return the distance from the point
return Math.sqrt(Math.pow(x1dx, 2) + Math.pow(y1dx, 2));
return MathUtil.hypot(x1dx, y1dx);
}
double ldx = x1 - x2;
double ldy = y1 - y2;
double ldxSquared = Math.pow(ldx, 2);
double ldySquared = Math.pow(ldy, 2);
double lineLengthSquared = Math.pow(ldx, 2) + Math.pow(ldy, 2);
double x2dx = x - x2;
double y2dx = y - y2;
double dotProduct = Math.fma(ldy, y1dx, (ldx * x1dx));
double lt = dotProduct / (ldySquared + ldxSquared); // Position along the line
double lt = dotProduct / lineLengthSquared; // Position along the line
if (lt > 0) {
return Math.sqrt(Math.pow(x1dx, 2) + Math.pow(y1dx, 2)); // Distance between point 1 and position
return MathUtil.hypot(x1dx, y1dx); // Distance between point 1 and position
} else if (lt < -1) {
return Math.sqrt(Math.pow(x2dx, 2) + Math.pow(y2dx, 2)); // Distance between point 2 and position
return MathUtil.hypot(x2dx, y2dx); // Distance between point 2 and position
} else {
double distance = Math.fma(ldy, x1dx, (-(ldx * y1dx))) / Math.sqrt(ldxSquared + ldySquared);
double distance = Math.fma(ldy, x1dx, (-(ldx * y1dx))) / Math.sqrt(lineLengthSquared);
return Math.abs(distance); // Distance from the line
}
}
private static int jitterIdx2D(int seed, int x, int y) {
return hash(seed, x * PRIME_X, y * PRIME_Y) & (255 << 1);
}
public double getNoiseRaw(long sl, double x, double y, double z) {
// TODO

5
common/addons/library-image/src/main/java/com/dfsek/terra/addons/image/noisesampler/ChannelNoiseSampler.java
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@@ -4,8 +4,7 @@ import com.dfsek.terra.addons.image.colorsampler.ColorSampler;
import com.dfsek.terra.addons.image.util.ColorUtil;
import com.dfsek.terra.addons.image.util.ColorUtil.Channel;
import com.dfsek.terra.api.noise.NoiseSampler;
import static com.dfsek.terra.addons.image.util.MathUtil.lerp;
import com.dfsek.terra.api.util.MathUtil;
public class ChannelNoiseSampler implements NoiseSampler {
@@ -30,7 +29,7 @@ public class ChannelNoiseSampler implements NoiseSampler {
int sample = colorSampler.apply((int) x, (int) y);
int premultiplied = premultiply ? ColorUtil.premultiply(sample) : sample;
double channelValue = channel.from(premultiplied);
return normalize ? lerp(channelValue, 0, -1, 255, 1) : channelValue;
return normalize ? MathUtil.linearMap(channelValue, 0, -1, 255, 1) : channelValue;
}
@Override

7
common/addons/library-image/src/main/java/com/dfsek/terra/addons/image/operator/DistanceTransform.java
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@@ -4,8 +4,7 @@ import com.dfsek.terra.addons.image.image.Image;
import com.dfsek.terra.addons.image.util.ColorUtil;
import com.dfsek.terra.addons.image.util.ColorUtil.Channel;
import com.dfsek.terra.api.noise.NoiseSampler;
import static com.dfsek.terra.addons.image.util.MathUtil.lerp;
import com.dfsek.terra.api.util.MathUtil;
/**
@@ -157,11 +156,11 @@ public class DistanceTransform {
double d = distances[x][y];
distances[x][y] = switch(normalization) {
case None -> distances[x][y];
case Linear -> lerp(d, minDistance, -1, maxDistance, 1);
case Linear -> MathUtil.linearMap(d, minDistance, -1, maxDistance, 1);
case SmoothPreserveZero -> {
if(minDistance > 0 || maxDistance < 0) {
// Can't preserve zero if it is not contained in range so just lerp
yield lerp(distances[x][y], minDistance, -1, maxDistance, 1);
yield MathUtil.linearMap(distances[x][y], minDistance, -1, maxDistance, 1);
} else {
if(d > 0) {
yield Math.pow(d/maxDistance, 2);

9
common/addons/library-image/src/main/java/com/dfsek/terra/addons/image/util/MathUtil.java
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@@ -1,9 +0,0 @@
package com.dfsek.terra.addons.image.util;
public class MathUtil {
private MathUtil() {}
public static double lerp(double x, double x1, double y1, double x2, double y2) {
return (((y1-y2)*(x-x1))/(x1-x2))+y1;
}
}

8
common/api/src/main/java/com/dfsek/terra/api/util/MathUtil.java
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@@ -251,4 +251,12 @@ public final class MathUtil {
public static double interpQuintic(double t) {
return t * t * t * (t * (t * 6 - 15) + 10);
}
public static double linearMap(double x, double x1, double y1, double x2, double y2) {
return (((y1-y2)*(x-x1))/(x1-x2))+y1;
}
public static double hypot(double x, double y) {
return Math.sqrt(Math.pow(x, 2) + Math.pow(y, 2));
}
}