* The result is a continuous function that interpolates a smooth path along a * series random points. The function is consitent, so given the same * parameters, it will always return the same value. The smoothing function is * based on the Improving Noise paper presented at Siggraph 2002. *
* Computing noise for one and two dimensions can make use of the 3D problem * space by just setting the un-needed dimensions to a fixed value. * * @author Justin Couch * @edited by Ondřej Hruška * @version $Revision: 1.4 $ * @source http://code.j3d.org/download.html */ public class PerlinNoiseGenerator { // Constants for setting up the Perlin-1 noise functions private static final int B = 0x1000; private static final int BM = 0xff; private static final int N = 0x1000; /** Default seed to use for the random number generation */ private static final int DEFAULT_SEED = 100; /** Default sample size to work with */ private static final int DEFAULT_SAMPLE_SIZE = 256; private final Random rand = new Random(DEFAULT_SEED); /** Permutation array for the improved noise function */ private final int[] p_imp; /** P array for perline 1 noise */ private int[] p; private double[][] g3; private double[][] g2; private double[] g1; /** * Create a new noise creator with the default seed value */ public PerlinNoiseGenerator() { this(DEFAULT_SEED); } /** * Create a new noise creator with the given seed value for the randomness * * @param seed The seed value to use */ public PerlinNoiseGenerator(long seed) { p_imp = new int[DEFAULT_SAMPLE_SIZE << 1]; int i, j, k; rand.setSeed(seed); // Calculate the table of psuedo-random coefficients. for (i = 0; i < DEFAULT_SAMPLE_SIZE; i++) p_imp[i] = i; // generate the psuedo-random permutation table. while (--i > 0) { k = p_imp[i]; j = (int) (rand.nextLong() & DEFAULT_SAMPLE_SIZE); p_imp[i] = p_imp[j]; p_imp[j] = k; } initPerlin1(); } /** * Computes noise function for three dimensions at the point (x,y,z). * * @param x x dimension parameter * @param y y dimension parameter * @param z z dimension parameter * @return the noise value at the point (x, y, z) */ public double improvedNoise(double x, double y, double z) { // Constraint the point to a unit cube final int uc_x = (int) Math.floor(x) & 255; final int uc_y = (int) Math.floor(y) & 255; final int uc_z = (int) Math.floor(z) & 255; // Relative location of the point in the unit cube final double xo = x - Math.floor(x); final double yo = y - Math.floor(y); final double zo = z - Math.floor(z); // Fade curves for x, y and z final double u = fade(xo); final double v = fade(yo); final double w = fade(zo); // Generate a hash for each coordinate to find out where in the cube // it lies. final int a = p_imp[uc_x] + uc_y; final int aa = p_imp[a] + uc_z; final int ab = p_imp[a + 1] + uc_z; final int b = p_imp[uc_x + 1] + uc_y; final int ba = p_imp[b] + uc_z; final int bb = p_imp[b + 1] + uc_z; // blend results from the 8 corners based on the noise function final double c1 = grad(p_imp[aa], xo, yo, zo); final double c2 = grad(p_imp[ba], xo - 1, yo, zo); final double c3 = grad(p_imp[ab], xo, yo - 1, zo); final double c4 = grad(p_imp[bb], xo - 1, yo - 1, zo); final double c5 = grad(p_imp[aa + 1], xo, yo, zo - 1); final double c6 = grad(p_imp[ba + 1], xo - 1, yo, zo - 1); final double c7 = grad(p_imp[ab + 1], xo, yo - 1, zo - 1); final double c8 = grad(p_imp[bb + 1], xo - 1, yo - 1, zo - 1); return lerp(w, lerp(v, lerp(u, c1, c2), lerp(u, c3, c4)), lerp(v, lerp(u, c5, c6), lerp(u, c7, c8))); } /** * 1-D noise generation function using the original perlin algorithm. * * @param x Seed for the noise function * @return The noisy output */ public double noise1(double x) { final double t = x + N; final int bx0 = ((int) t) & BM; final int bx1 = (bx0 + 1) & BM; final double rx0 = t - (int) t; final double rx1 = rx0 - 1; final double sx = sCurve(rx0); final double u = rx0 * g1[p[bx0]]; final double v = rx1 * g1[p[bx1]]; return lerp(sx, u, v); } /** * Create noise in a 2D space using the orignal perlin noise algorithm. * * @param x The X coordinate of the location to sample * @param y The Y coordinate of the location to sample * @return A noisy value at the given position */ public double noise2(double x, double y) { double t = x + N; final int bx0 = ((int) t) & BM; final int bx1 = (bx0 + 1) & BM; final double rx0 = t - (int) t; final double rx1 = rx0 - 1; t = y + N; final int by0 = ((int) t) & BM; final int by1 = (by0 + 1) & BM; final double ry0 = t - (int) t; final double ry1 = ry0 - 1; final int i = p[bx0]; final int j = p[bx1]; final int b00 = p[i + by0]; final int b10 = p[j + by0]; final int b01 = p[i + by1]; final int b11 = p[j + by1]; final double sx = sCurve(rx0); final double sy = sCurve(ry0); double[] q = g2[b00]; double u = rx0 * q[0] + ry0 * q[1]; q = g2[b10]; double v = rx1 * q[0] + ry0 * q[1]; final double a = lerp(sx, u, v); q = g2[b01]; u = rx0 * q[0] + ry1 * q[1]; q = g2[b11]; v = rx1 * q[0] + ry1 * q[1]; final double b = lerp(sx, u, v); return lerp(sy, a, b); } /** * Create noise in a 3D space using the orignal perlin noise algorithm. * * @param x The X coordinate of the location to sample * @param y The Y coordinate of the location to sample * @param z The Z coordinate of the location to sample * @return A noisy value at the given position */ public double noise3(double x, double y, double z) { double t = x + N; final int bx0 = ((int) t) & BM; final int bx1 = (bx0 + 1) & BM; final double rx0 = t - (int) t; final double rx1 = rx0 - 1; t = y + N; final int by0 = ((int) t) & BM; final int by1 = (by0 + 1) & BM; final double ry0 = t - (int) t; final double ry1 = ry0 - 1; t = z + N; final int bz0 = ((int) t) & BM; final int bz1 = (bz0 + 1) & BM; final double rz0 = t - (int) t; final double rz1 = rz0 - 1; final int i = p[bx0]; final int j = p[bx1]; final int b00 = p[i + by0]; final int b10 = p[j + by0]; final int b01 = p[i + by1]; final int b11 = p[j + by1]; t = sCurve(rx0); final double sy = sCurve(ry0); final double sz = sCurve(rz0); double[] q = g3[b00 + bz0]; double u = (rx0 * q[0] + ry0 * q[1] + rz0 * q[2]); q = g3[b10 + bz0]; double v = (rx1 * q[0] + ry0 * q[1] + rz0 * q[2]); double a = lerp(t, u, v); q = g3[b01 + bz0]; u = (rx0 * q[0] + ry1 * q[1] + rz0 * q[2]); q = g3[b11 + bz0]; v = (rx1 * q[0] + ry1 * q[1] + rz0 * q[2]); double b = lerp(t, u, v); final double c = lerp(sy, a, b); q = g3[b00 + bz1]; u = (rx0 * q[0] + ry0 * q[1] + rz1 * q[2]); q = g3[b10 + bz1]; v = (rx1 * q[0] + ry0 * q[1] + rz1 * q[2]); a = lerp(t, u, v); q = g3[b01 + bz1]; u = (rx0 * q[0] + ry1 * q[1] + rz1 * q[2]); q = g3[b11 + bz1]; v = (rx1 * q[0] + ry1 * q[1] + rz1 * q[2]); b = lerp(t, u, v); final double d = lerp(sy, a, b); return lerp(sz, c, d); } /** * Create a turbulent noise output based on the core noise function. This * uses the noise as a base function and is suitable for creating clouds, * marble and explosion effects. For example, a typical marble effect would * set the colour to be: * *
* sin(point + turbulence(point) * point.x);
*
*
* @param x
* @param y
* @param z
* @param loF
* @param hiF
* @return value
*/
public double imporvedTurbulence(double x, double y, double z, double loF, double hiF)
{
double p_x = x + 123.456f;
double p_y = y;
double p_z = z;
double t = 0;
double f;
for (f = loF; f < hiF; f *= 2) {
t += Math.abs(improvedNoise(p_x, p_y, p_z)) / f;
p_x *= 2;
p_y *= 2;
p_z *= 2;
}
return t - 0.3;
}
/**
* Create a turbulance function in 2D using the original perlin noise
* function.
*
* @param x The X coordinate of the location to sample
* @param y The Y coordinate of the location to sample
* @param freq The frequency of the turbluance to create
* @return The value at the given coordinates
*/
public double turbulence2(double x, double y, double freq)
{
double t = 0;
do {
t += noise2(freq * x, freq * y) / freq;
freq *= 0.5f;
} while (freq >= 1);
return t;
}
/**
* Create a turbulance function in 3D using the original perlin noise
* function.
*
* @param x The X coordinate of the location to sample
* @param y The Y coordinate of the location to sample
* @param z The Z coordinate of the location to sample
* @param freq The frequency of the turbluance to create
* @return The value at the given coordinates
*/
public double turbulence3(double x, double y, double z, double freq)
{
double t = 0;
do {
t += noise3(freq * x, freq * y, freq * z) / freq;
freq *= 0.5f;
} while (freq >= 1);
return t;
}
/**
* Create a 1D tileable noise function for the given width.
*
* @param x The X coordinate to generate the noise for
* @param w The width of the tiled block
* @return The value of the noise at the given coordinate
*/
public double tileableNoise1(double x, double w)
{
return (noise1(x) * (w - x) + noise1(x - w) * x) / w;
}
/**
* Create a 2D tileable noise function for the given width and height.
*
* @param x The X coordinate to generate the noise for
* @param y The Y coordinate to generate the noise for
* @param w The width of the tiled block
* @param h The height of the tiled block
* @return The value of the noise at the given coordinate
*/
public double tileableNoise2(double x, double y, double w, double h)
{
return (noise2(x, y) * (w - x) * (h - y) + noise2(x - w, y) * x * (h - y) + noise2(x, y - h) * (w - x) * y + noise2(x - w, y - h) * x * y) / (w * h);
}
/**
* Create a 3D tileable noise function for the given width, height and
* depth.
*
* @param x The X coordinate to generate the noise for
* @param y The Y coordinate to generate the noise for
* @param z The Z coordinate to generate the noise for
* @param w The width of the tiled block
* @param h The height of the tiled block
* @param d The depth of the tiled block
* @return The value of the noise at the given coordinate
*/
public double tileableNoise3(double x, double y, double z, double w, double h, double d)
{
return (noise3(x, y, z) * (w - x) * (h - y) * (d - z) + noise3(x - w, y, z) * x * (h - y) * (d - z) + noise3(x, y - h, z) * (w - x) * y * (d - z) + noise3(x - w, y - h, z) * x * y * (d - z) + noise3(x, y, z - d) * (w - x) * (h - y) * z + noise3(x - w, y, z - d) * x * (h - y) * z + noise3(x, y - h, z - d) * (w - x) * y * z + noise3(x - w, y - h, z - d) * x * y * z) / (w * h * d);
}
/**
* Create a turbulance function that can be tiled across a surface in 2D.
*
* @param x The X coordinate of the location to sample
* @param y The Y coordinate of the location to sample
* @param w The width to tile over
* @param h The height to tile over
* @param freq The frequency of the turbluance to create
* @return The value at the given coordinates
*/
public double tileableTurbulence2(double x, double y, double w, double h, double freq)
{
double t = 0;
do {
t += tileableNoise2(freq * x, freq * y, w * freq, h * freq) / freq;
freq *= 0.5f;
} while (freq >= 1);
return t;
}
/**
* Create a turbulance function that can be tiled across a surface in 3D.
*
* @param x The X coordinate of the location to sample
* @param y The Y coordinate of the location to sample
* @param z The Z coordinate of the location to sample
* @param w The width to tile over
* @param h The height to tile over
* @param d The depth to tile over
* @param freq The frequency of the turbluance to create
* @return The value at the given coordinates
*/
public double tileableTurbulence3(double x, double y, double z, double w, double h, double d, double freq)
{
double t = 0;
do {
t += tileableNoise3(freq * x, freq * y, freq * z, w * freq, h * freq, d * freq) / freq;
freq *= 0.5f;
} while (freq >= 1);
return t;
}
/**
* Simple lerp function using doubles.
*/
private double lerp(double t, double a, double b)
{
return a + t * (b - a);
}
/**
* Fade curve calculation which is 6t^5 - 15t^4 + 10t^3. This is the new
* algorithm, where the old one used to be 3t^2 - 2t^3.
*
* @param t The t parameter to calculate the fade for
* @return the drop-off amount.
*/
private double fade(double t)
{
return t * t * t * (t * (t * 6 - 15) + 10);
}
/**
* Calculate the gradient function based on the hash code.
*/
private double grad(int hash, double x, double y, double z)
{
// Convert low 4 bits of hash code into 12 gradient directions.
final int h = hash & 15;
final double u = (h < 8 || h == 12 || h == 13) ? x : y;
final double v = (h < 4 || h == 12 || h == 13) ? y : z;
return ((h & 1) == 0 ? u : -u) + ((h & 2) == 0 ? v : -v);
}
/**
* S-curve function for value distribution for Perlin-1 noise function.
*/
private double sCurve(double t)
{
return (t * t * (3 - 2 * t));
}
/**
* 2D-vector normalisation function.
*/
private void normalize2(double[] v)
{
final double s = 1 / Math.sqrt(v[0] * v[0] + v[1] * v[1]);
v[0] *= s;
v[1] *= s;
}
/**
* 3D-vector normalisation function.
*/
private void normalize3(double[] v)
{
final double s = 1 / Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
v[0] *= s;
v[1] *= s;
v[2] *= s;
}
/**
* Initialise the lookup arrays used by Perlin 1 function.
*/
private void initPerlin1()
{
p = new int[B + B + 2];
g3 = new double[B + B + 2][3];
g2 = new double[B + B + 2][2];
g1 = new double[B + B + 2];
int i, j, k;
for (i = 0; i < B; i++) {
p[i] = i;
g1[i] = (((rand.nextDouble() * Integer.MAX_VALUE) % (B + B)) - B) / B;
for (j = 0; j < 2; j++)
g2[i][j] = (((rand.nextDouble() * Integer.MAX_VALUE) % (B + B)) - B) / B;
normalize2(g2[i]);
for (j = 0; j < 3; j++)
g3[i][j] = (((rand.nextDouble() * Integer.MAX_VALUE) % (B + B)) - B) / B;
normalize3(g3[i]);
}
while (--i > 0) {
k = p[i];
j = (int) ((rand.nextDouble() * Integer.MAX_VALUE) % B);
p[i] = p[j];
p[j] = k;
}
for (i = 0; i < B + 2; i++) {
p[B + i] = p[i];
g1[B + i] = g1[i];
for (j = 0; j < 2; j++)
g2[B + i][j] = g2[i][j];
for (j = 0; j < 3; j++)
g3[B + i][j] = g3[i][j];
}
}
}