Files
LuaCsForBarotraumaEP/Barotrauma/BarotraumaShared/SharedSource/ProcGen/PerlinNoise.cs
2020-03-04 13:04:10 +01:00

184 lines
10 KiB
C#

using Microsoft.Xna.Framework;
using System;
using System.Collections.Generic;
using System.Text;
namespace Barotrauma
{
//By Adrian Biagioli (Flafla2)
//under a Creative Commons Attribution 4.0 International License.
public static class PerlinNoise
{
public static double OctavePerlin(double x, double y, double z, double frequency, int octaves, double persistence)
{
double total = 0;
double amplitude = 3;
for (int i = 0; i < octaves; i++)
{
total += CalculatePerlin(x * frequency, y * frequency, z * frequency) * amplitude;
amplitude *= persistence;
frequency *= 2;
}
return total;
}
// Hash lookup table as defined by Ken Perlin. This is a randomly
// arranged array of all numbers from 0-255 inclusive.
private static readonly int[] permutation =
{
151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,167, 43,172,9,
129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127, 4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180
};
private static readonly int[] p; // Doubled permutation to avoid overflow
private static readonly float[] cachedNoise;
private const int CacheResolution = 256;
static PerlinNoise()
{
p = new int[512];
for (int x = 0; x < 512; x++)
{
p[x] = permutation[x % 256];
}
float minValue = float.MaxValue;
float maxValue = float.MinValue;
cachedNoise = new float[CacheResolution * CacheResolution];
for (int x = 0; x < CacheResolution; x++)
{
for (int y = 0; y < CacheResolution; y++)
{
cachedNoise[x + CacheResolution * y] = (float)OctavePerlin(x / (double)CacheResolution, y / (double)CacheResolution, 0.5, 10, 4, 0.5f);
}
}
for (int i = 0; i < CacheResolution * CacheResolution; i++)
{
minValue = Math.Min(cachedNoise[i], minValue);
maxValue = Math.Max(cachedNoise[i], maxValue);
}
//normalize to 0-1 range
for (int i = 0; i < CacheResolution * CacheResolution; i++)
{
cachedNoise[i] = (cachedNoise[i] - minValue) / (maxValue - minValue);
}
}
/// <summary>
/// Sample a pre-generated perlin noise map. Faster than calculating the noise on the fly.
/// </summary>
/// <param name="x">Normalized x position. The noise map starts repeating after x > 1</param>
/// <param name="y">Normalized y position. The noise map starts repeating after y > 1</param>
/// <returns>A noise value between 0.0f and 1.0f</returns>
public static float GetPerlin(float x, float y)
{
x = Math.Abs(x) % 1.0f;
y = Math.Abs(y) % 1.0f;
float xIndex = x < 0.5f ? (x * 2.0f * CacheResolution) : CacheResolution - ((x - 0.5f) * 2.0f * CacheResolution);
xIndex = Math.Min(xIndex, CacheResolution - 1);
float yIndex = y < 0.5f ? (y * 2.0f * CacheResolution) : CacheResolution - ((y - 0.5f) * 2.0f * CacheResolution);
yIndex = Math.Min(yIndex, CacheResolution - 1);
int minX = (int)xIndex, maxX = (int)Math.Ceiling(xIndex);
int minY = (int)yIndex, maxY = (int)Math.Ceiling(yIndex);
return MathHelper.Lerp(
MathHelper.Lerp(cachedNoise[minX + minY * CacheResolution], cachedNoise[maxX + minY * CacheResolution], xIndex % 1.0f),
MathHelper.Lerp(cachedNoise[minX + maxY * CacheResolution], cachedNoise[maxX + maxY * CacheResolution], xIndex % 1.0f),
yIndex % 1.0f);
}
public static double CalculatePerlin(double x, double y, double z)
{
int xi = (int)x & 255; // Calculate the "unit cube" that the point asked will be located in
int yi = (int)y & 255; // The left bound is ( |_x_|,|_y_|,|_z_| ) and the right bound is that
int zi = (int)z & 255; // plus 1. Next we calculate the location (from 0.0 to 1.0) in that cube.
double xf = x - (int)x; // We also fade the location to smooth the result.
double yf = y - (int)y;
double zf = z - (int)z;
double u = Fade(xf);
double v = Fade(yf);
double w = Fade(zf);
int a = p[xi] + yi; // This here is Perlin's hash function. We take our x value (remember,
int aa = p[a] + zi; // between 0 and 255) and get a random value (from our p[] array above) between
int ab = p[a + 1] + zi; // 0 and 255. We then add y to it and plug that into p[], and add z to that.
int b = p[xi + 1] + yi; // Then, we get another random value by adding 1 to that and putting it into p[]
int ba = p[b] + zi; // and add z to it. We do the whole thing over again starting with x+1. Later
int bb = p[b + 1] + zi; // we plug aa, ab, ba, and bb back into p[] along with their +1's to get another set.
// in the end we have 8 values between 0 and 255 - one for each vertex on the unit cube.
// These are all interpolated together using u, v, and w below.
double x1, x2, y1, y2;
x1 = Lerp(Grad(p[aa], xf, yf, zf), // This is where the "magic" happens. We calculate a new set of p[] values and use that to get
Grad(p[ba], xf - 1, yf, zf), // our final gradient values. Then, we interpolate between those gradients with the u value to get
u); // 4 x-values. Next, we interpolate between the 4 x-values with v to get 2 y-values. Finally,
x2 = Lerp(Grad(p[ab], xf, yf - 1, zf), // we interpolate between the y-values to get a z-value.
Grad(p[bb], xf - 1, yf - 1, zf),
u); // When calculating the p[] values, remember that above, p[a+1] expands to p[xi]+yi+1 -- so you are
y1 = Lerp(x1, x2, v); // essentially adding 1 to yi. Likewise, p[ab+1] expands to p[p[xi]+yi+1]+zi+1] -- so you are adding
// to zi. The other 3 parameters are your possible return values (see grad()), which are actually
x1 = Lerp(Grad(p[aa + 1], xf, yf, zf - 1), // the vectors from the edges of the unit cube to the point in the unit cube itself.
Grad(p[ba + 1], xf - 1, yf, zf - 1),
u);
x2 = Lerp(Grad(p[ab + 1], xf, yf - 1, zf - 1),
Grad(p[bb + 1], xf - 1, yf - 1, zf - 1),
u);
y2 = Lerp(x1, x2, v);
return (Lerp(y1, y2, w) + 1) / 2; // For convenience we bound it to 0 - 1 (theoretical min/max before is -1 - 1)
}
public static double Grad(int hash, double x, double y, double z)
{
int h = hash & 15; // Take the hashed value and take the first 4 bits of it (15 == 0b1111)
double u = h < 8 /* 0b1000 */ ? x : y; // If the most signifigant bit (MSB) of the hash is 0 then set u = x. Otherwise y.
double v; // In Ken Perlin's original implementation this was another conditional operator (?:). I
// expanded it for readability.
if (h < 4 /* 0b0100 */) // If the first and second signifigant bits are 0 set v = y
v = y;
else if (h == 12 /* 0b1100 */ || h == 14 /* 0b1110*/)// If the first and second signifigant bits are 1 set v = x
v = x;
else // If the first and second signifigant bits are not equal (0/1, 1/0) set v = z
v = z;
return ((h & 1) == 0 ? u : -u) + ((h & 2) == 0 ? v : -v); // Use the last 2 bits to decide if u and v are positive or negative. Then return their addition.
}
public static double Fade(double t)
{
// Fade function as defined by Ken Perlin. This eases coordinate values
// so that they will "ease" towards integral values. This ends up smoothing
// the final output.
return t * t * t * (t * (t * 6 - 15) + 10); // 6t^5 - 15t^4 + 10t^3
}
public static double Lerp(double a, double b, double x)
{
return a + x * (b - a);
}
}
}