waves - z noise crack

¿Algún tutorial o recurso de ruido simple?

Question

Quiero crear un generador de ruido 3D similar a un terreno y, después de investigar un poco, llegué a la conclusión de que Simplex Noise es, con mucho, el mejor tipo de ruido para hacer esto.

Sin embargo, el nombre me parece bastante engañoso, ya que tengo muchos problemas para encontrar recursos sobre el tema y los recursos que encuentro a menudo no están bien escritos.

Básicamente, lo que busco es un buen recurso / tutorial que explique paso a paso cómo funciona el ruido simplex y cómo implementarlo en un programa.

No estoy buscando recursos que expliquen cómo usar una biblioteca o algo así.

Answer 1

Siguiendo una recomendación tutorial, intentaré explicar cómo usar una fuente java existente que crea una sola octava de ruido simplex.

Código de ruido simple

Esta parte del código fue creada por Stefan Gustavson y se colocó en el dominio público. Se puede encontrar here . Se cita aquí por conveniencia.

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.Random;
import javax.imageio.ImageIO;


/*
 * A speed-improved simplex noise algorithm for 2D, 3D and 4D in Java.
 *
 * Based on example code by Stefan Gustavson ([email protected]).
 * Optimisations by Peter Eastman ([email protected]).
 * Better rank ordering method by Stefan Gustavson in 2012.
 *
 * This could be speeded up even further, but it''s useful as it is.
 *
 * Version 2012-03-09
 *
 * This code was placed in the public domain by its original author,
 * Stefan Gustavson. You may use it as you see fit, but
 * attribution is appreciated.
 *
 */

public class SimplexNoise_octave {  // Simplex noise in 2D, 3D and 4D

  public static int RANDOMSEED=0;
  private static int NUMBEROFSWAPS=400;  

  private static Grad grad3[] = {new Grad(1,1,0),new Grad(-1,1,0),new Grad(1,-1,0),new Grad(-1,-1,0),
                                 new Grad(1,0,1),new Grad(-1,0,1),new Grad(1,0,-1),new Grad(-1,0,-1),
                                 new Grad(0,1,1),new Grad(0,-1,1),new Grad(0,1,-1),new Grad(0,-1,-1)};

  private static Grad grad4[]= {new Grad(0,1,1,1),new Grad(0,1,1,-1),new Grad(0,1,-1,1),new Grad(0,1,-1,-1),
                   new Grad(0,-1,1,1),new Grad(0,-1,1,-1),new Grad(0,-1,-1,1),new Grad(0,-1,-1,-1),
                   new Grad(1,0,1,1),new Grad(1,0,1,-1),new Grad(1,0,-1,1),new Grad(1,0,-1,-1),
                   new Grad(-1,0,1,1),new Grad(-1,0,1,-1),new Grad(-1,0,-1,1),new Grad(-1,0,-1,-1),
                   new Grad(1,1,0,1),new Grad(1,1,0,-1),new Grad(1,-1,0,1),new Grad(1,-1,0,-1),
                   new Grad(-1,1,0,1),new Grad(-1,1,0,-1),new Grad(-1,-1,0,1),new Grad(-1,-1,0,-1),
                   new Grad(1,1,1,0),new Grad(1,1,-1,0),new Grad(1,-1,1,0),new Grad(1,-1,-1,0),
                   new Grad(-1,1,1,0),new Grad(-1,1,-1,0),new Grad(-1,-1,1,0),new Grad(-1,-1,-1,0)};

  private static short p_supply[] = {151,160,137,91,90,15, //this contains all the numbers between 0 and 255, these are put in a random order depending upon the seed
  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 short p[]=new short[p_supply.length];

  // To remove the need for index wrapping, double the permutation table length
  private short perm[] = new short[512];
  private short permMod12[] = new short[512];
  public SimplexNoise_octave(int seed) {
    p=p_supply.clone();

    if (seed==RANDOMSEED){
        Random rand=new Random();
        seed=rand.nextInt();
    }

    //the random for the swaps
    Random rand=new Random(seed);

    //the seed determines the swaps that occur between the default order and the order we''re actually going to use
    for(int i=0;i<NUMBEROFSWAPS;i++){
        int swapFrom=rand.nextInt(p.length);
        int swapTo=rand.nextInt(p.length);

        short temp=p[swapFrom];
        p[swapFrom]=p[swapTo];
        p[swapTo]=temp;
    }


    for(int i=0; i<512; i++)
    {
      perm[i]=p[i & 255];
      permMod12[i] = (short)(perm[i] % 12);
    }
  }

  // Skewing and unskewing factors for 2, 3, and 4 dimensions
  private static final double F2 = 0.5*(Math.sqrt(3.0)-1.0);
  private static final double G2 = (3.0-Math.sqrt(3.0))/6.0;
  private static final double F3 = 1.0/3.0;
  private static final double G3 = 1.0/6.0;
  private static final double F4 = (Math.sqrt(5.0)-1.0)/4.0;
  private static final double G4 = (5.0-Math.sqrt(5.0))/20.0;

  // This method is a *lot* faster than using (int)Math.floor(x)
  private static int fastfloor(double x) {
    int xi = (int)x;
    return x<xi ? xi-1 : xi;
  }

  private static double dot(Grad g, double x, double y) {
    return g.x*x + g.y*y; }

  private static double dot(Grad g, double x, double y, double z) {
    return g.x*x + g.y*y + g.z*z; }

  private static double dot(Grad g, double x, double y, double z, double w) {
    return g.x*x + g.y*y + g.z*z + g.w*w; }


  // 2D simplex noise
  public double noise(double xin, double yin) {
    double n0, n1, n2; // Noise contributions from the three corners
    // Skew the input space to determine which simplex cell we''re in
    double s = (xin+yin)*F2; // Hairy factor for 2D
    int i = fastfloor(xin+s);
    int j = fastfloor(yin+s);
    double t = (i+j)*G2;
    double X0 = i-t; // Unskew the cell origin back to (x,y) space
    double Y0 = j-t;
    double x0 = xin-X0; // The x,y distances from the cell origin
    double y0 = yin-Y0;
    // For the 2D case, the simplex shape is an equilateral triangle.
    // Determine which simplex we are in.
    int i1, j1; // Offsets for second (middle) corner of simplex in (i,j) coords
    if(x0>y0) {i1=1; j1=0;} // lower triangle, XY order: (0,0)->(1,0)->(1,1)
    else {i1=0; j1=1;}      // upper triangle, YX order: (0,0)->(0,1)->(1,1)
    // A step of (1,0) in (i,j) means a step of (1-c,-c) in (x,y), and
    // a step of (0,1) in (i,j) means a step of (-c,1-c) in (x,y), where
    // c = (3-sqrt(3))/6
    double x1 = x0 - i1 + G2; // Offsets for middle corner in (x,y) unskewed coords
    double y1 = y0 - j1 + G2;
    double x2 = x0 - 1.0 + 2.0 * G2; // Offsets for last corner in (x,y) unskewed coords
    double y2 = y0 - 1.0 + 2.0 * G2;
    // Work out the hashed gradient indices of the three simplex corners
    int ii = i & 255;
    int jj = j & 255;
    int gi0 = permMod12[ii+perm[jj]];
    int gi1 = permMod12[ii+i1+perm[jj+j1]];
    int gi2 = permMod12[ii+1+perm[jj+1]];
    // Calculate the contribution from the three corners
    double t0 = 0.5 - x0*x0-y0*y0;
    if(t0<0) n0 = 0.0;
    else {
      t0 *= t0;
      n0 = t0 * t0 * dot(grad3[gi0], x0, y0);  // (x,y) of grad3 used for 2D gradient
    }
    double t1 = 0.5 - x1*x1-y1*y1;
    if(t1<0) n1 = 0.0;
    else {
      t1 *= t1;
      n1 = t1 * t1 * dot(grad3[gi1], x1, y1);
    }
    double t2 = 0.5 - x2*x2-y2*y2;
    if(t2<0) n2 = 0.0;
    else {
      t2 *= t2;
      n2 = t2 * t2 * dot(grad3[gi2], x2, y2);
    }
    // Add contributions from each corner to get the final noise value.
    // The result is scaled to return values in the interval [-1,1].
    return 70.0 * (n0 + n1 + n2);
  }


  // 3D simplex noise
  public double noise(double xin, double yin, double zin) {
    double n0, n1, n2, n3; // Noise contributions from the four corners
    // Skew the input space to determine which simplex cell we''re in
    double s = (xin+yin+zin)*F3; // Very nice and simple skew factor for 3D
    int i = fastfloor(xin+s);
    int j = fastfloor(yin+s);
    int k = fastfloor(zin+s);
    double t = (i+j+k)*G3;
    double X0 = i-t; // Unskew the cell origin back to (x,y,z) space
    double Y0 = j-t;
    double Z0 = k-t;
    double x0 = xin-X0; // The x,y,z distances from the cell origin
    double y0 = yin-Y0;
    double z0 = zin-Z0;
    // For the 3D case, the simplex shape is a slightly irregular tetrahedron.
    // Determine which simplex we are in.
    int i1, j1, k1; // Offsets for second corner of simplex in (i,j,k) coords
    int i2, j2, k2; // Offsets for third corner of simplex in (i,j,k) coords
    if(x0>=y0) {
      if(y0>=z0)
        { i1=1; j1=0; k1=0; i2=1; j2=1; k2=0; } // X Y Z order
        else if(x0>=z0) { i1=1; j1=0; k1=0; i2=1; j2=0; k2=1; } // X Z Y order
        else { i1=0; j1=0; k1=1; i2=1; j2=0; k2=1; } // Z X Y order
      }
    else { // x0<y0
      if(y0<z0) { i1=0; j1=0; k1=1; i2=0; j2=1; k2=1; } // Z Y X order
      else if(x0<z0) { i1=0; j1=1; k1=0; i2=0; j2=1; k2=1; } // Y Z X order
      else { i1=0; j1=1; k1=0; i2=1; j2=1; k2=0; } // Y X Z order
    }
    // A step of (1,0,0) in (i,j,k) means a step of (1-c,-c,-c) in (x,y,z),
    // a step of (0,1,0) in (i,j,k) means a step of (-c,1-c,-c) in (x,y,z), and
    // a step of (0,0,1) in (i,j,k) means a step of (-c,-c,1-c) in (x,y,z), where
    // c = 1/6.
    double x1 = x0 - i1 + G3; // Offsets for second corner in (x,y,z) coords
    double y1 = y0 - j1 + G3;
    double z1 = z0 - k1 + G3;
    double x2 = x0 - i2 + 2.0*G3; // Offsets for third corner in (x,y,z) coords
    double y2 = y0 - j2 + 2.0*G3;
    double z2 = z0 - k2 + 2.0*G3;
    double x3 = x0 - 1.0 + 3.0*G3; // Offsets for last corner in (x,y,z) coords
    double y3 = y0 - 1.0 + 3.0*G3;
    double z3 = z0 - 1.0 + 3.0*G3;
    // Work out the hashed gradient indices of the four simplex corners
    int ii = i & 255;
    int jj = j & 255;
    int kk = k & 255;
    int gi0 = permMod12[ii+perm[jj+perm[kk]]];
    int gi1 = permMod12[ii+i1+perm[jj+j1+perm[kk+k1]]];
    int gi2 = permMod12[ii+i2+perm[jj+j2+perm[kk+k2]]];
    int gi3 = permMod12[ii+1+perm[jj+1+perm[kk+1]]];
    // Calculate the contribution from the four corners
    double t0 = 0.6 - x0*x0 - y0*y0 - z0*z0;
    if(t0<0) n0 = 0.0;
    else {
      t0 *= t0;
      n0 = t0 * t0 * dot(grad3[gi0], x0, y0, z0);
    }
    double t1 = 0.6 - x1*x1 - y1*y1 - z1*z1;
    if(t1<0) n1 = 0.0;
    else {
      t1 *= t1;
      n1 = t1 * t1 * dot(grad3[gi1], x1, y1, z1);
    }
    double t2 = 0.6 - x2*x2 - y2*y2 - z2*z2;
    if(t2<0) n2 = 0.0;
    else {
      t2 *= t2;
      n2 = t2 * t2 * dot(grad3[gi2], x2, y2, z2);
    }
    double t3 = 0.6 - x3*x3 - y3*y3 - z3*z3;
    if(t3<0) n3 = 0.0;
    else {
      t3 *= t3;
      n3 = t3 * t3 * dot(grad3[gi3], x3, y3, z3);
    }
    // Add contributions from each corner to get the final noise value.
    // The result is scaled to stay just inside [-1,1]
    return 32.0*(n0 + n1 + n2 + n3);
  }


  // 4D simplex noise, better simplex rank ordering method 2012-03-09
  public double noise(double x, double y, double z, double w) {

    double n0, n1, n2, n3, n4; // Noise contributions from the five corners
    // Skew the (x,y,z,w) space to determine which cell of 24 simplices we''re in
    double s = (x + y + z + w) * F4; // Factor for 4D skewing
    int i = fastfloor(x + s);
    int j = fastfloor(y + s);
    int k = fastfloor(z + s);
    int l = fastfloor(w + s);
    double t = (i + j + k + l) * G4; // Factor for 4D unskewing
    double X0 = i - t; // Unskew the cell origin back to (x,y,z,w) space
    double Y0 = j - t;
    double Z0 = k - t;
    double W0 = l - t;
    double x0 = x - X0;  // The x,y,z,w distances from the cell origin
    double y0 = y - Y0;
    double z0 = z - Z0;
    double w0 = w - W0;
    // For the 4D case, the simplex is a 4D shape I won''t even try to describe.
    // To find out which of the 24 possible simplices we''re in, we need to
    // determine the magnitude ordering of x0, y0, z0 and w0.
    // Six pair-wise comparisons are performed between each possible pair
    // of the four coordinates, and the results are used to rank the numbers.
    int rankx = 0;
    int ranky = 0;
    int rankz = 0;
    int rankw = 0;
    if(x0 > y0) rankx++; else ranky++;
    if(x0 > z0) rankx++; else rankz++;
    if(x0 > w0) rankx++; else rankw++;
    if(y0 > z0) ranky++; else rankz++;
    if(y0 > w0) ranky++; else rankw++;
    if(z0 > w0) rankz++; else rankw++;
    int i1, j1, k1, l1; // The integer offsets for the second simplex corner
    int i2, j2, k2, l2; // The integer offsets for the third simplex corner
    int i3, j3, k3, l3; // The integer offsets for the fourth simplex corner
    // simplex[c] is a 4-vector with the numbers 0, 1, 2 and 3 in some order.
    // Many values of c will never occur, since e.g. x>y>z>w makes x<z, y<w and x<w
    // impossible. Only the 24 indices which have non-zero entries make any sense.
    // We use a thresholding to set the coordinates in turn from the largest magnitude.
    // Rank 3 denotes the largest coordinate.
    i1 = rankx >= 3 ? 1 : 0;
    j1 = ranky >= 3 ? 1 : 0;
    k1 = rankz >= 3 ? 1 : 0;
    l1 = rankw >= 3 ? 1 : 0;
    // Rank 2 denotes the second largest coordinate.
    i2 = rankx >= 2 ? 1 : 0;
    j2 = ranky >= 2 ? 1 : 0;
    k2 = rankz >= 2 ? 1 : 0;
    l2 = rankw >= 2 ? 1 : 0;
    // Rank 1 denotes the second smallest coordinate.
    i3 = rankx >= 1 ? 1 : 0;
    j3 = ranky >= 1 ? 1 : 0;
    k3 = rankz >= 1 ? 1 : 0;
    l3 = rankw >= 1 ? 1 : 0;
    // The fifth corner has all coordinate offsets = 1, so no need to compute that.
    double x1 = x0 - i1 + G4; // Offsets for second corner in (x,y,z,w) coords
    double y1 = y0 - j1 + G4;
    double z1 = z0 - k1 + G4;
    double w1 = w0 - l1 + G4;
    double x2 = x0 - i2 + 2.0*G4; // Offsets for third corner in (x,y,z,w) coords
    double y2 = y0 - j2 + 2.0*G4;
    double z2 = z0 - k2 + 2.0*G4;
    double w2 = w0 - l2 + 2.0*G4;
    double x3 = x0 - i3 + 3.0*G4; // Offsets for fourth corner in (x,y,z,w) coords
    double y3 = y0 - j3 + 3.0*G4;
    double z3 = z0 - k3 + 3.0*G4;
    double w3 = w0 - l3 + 3.0*G4;
    double x4 = x0 - 1.0 + 4.0*G4; // Offsets for last corner in (x,y,z,w) coords
    double y4 = y0 - 1.0 + 4.0*G4;
    double z4 = z0 - 1.0 + 4.0*G4;
    double w4 = w0 - 1.0 + 4.0*G4;
    // Work out the hashed gradient indices of the five simplex corners
    int ii = i & 255;
    int jj = j & 255;
    int kk = k & 255;
    int ll = l & 255;
    int gi0 = perm[ii+perm[jj+perm[kk+perm[ll]]]] % 32;
    int gi1 = perm[ii+i1+perm[jj+j1+perm[kk+k1+perm[ll+l1]]]] % 32;
    int gi2 = perm[ii+i2+perm[jj+j2+perm[kk+k2+perm[ll+l2]]]] % 32;
    int gi3 = perm[ii+i3+perm[jj+j3+perm[kk+k3+perm[ll+l3]]]] % 32;
    int gi4 = perm[ii+1+perm[jj+1+perm[kk+1+perm[ll+1]]]] % 32;
    // Calculate the contribution from the five corners
    double t0 = 0.6 - x0*x0 - y0*y0 - z0*z0 - w0*w0;
    if(t0<0) n0 = 0.0;
    else {
      t0 *= t0;
      n0 = t0 * t0 * dot(grad4[gi0], x0, y0, z0, w0);
    }
   double t1 = 0.6 - x1*x1 - y1*y1 - z1*z1 - w1*w1;
    if(t1<0) n1 = 0.0;
    else {
      t1 *= t1;
      n1 = t1 * t1 * dot(grad4[gi1], x1, y1, z1, w1);
    }
   double t2 = 0.6 - x2*x2 - y2*y2 - z2*z2 - w2*w2;
    if(t2<0) n2 = 0.0;
    else {
      t2 *= t2;
      n2 = t2 * t2 * dot(grad4[gi2], x2, y2, z2, w2);
    }
   double t3 = 0.6 - x3*x3 - y3*y3 - z3*z3 - w3*w3;
    if(t3<0) n3 = 0.0;
    else {
      t3 *= t3;
      n3 = t3 * t3 * dot(grad4[gi3], x3, y3, z3, w3);
    }
   double t4 = 0.6 - x4*x4 - y4*y4 - z4*z4 - w4*w4;
    if(t4<0) n4 = 0.0;
    else {
      t4 *= t4;
      n4 = t4 * t4 * dot(grad4[gi4], x4, y4, z4, w4);
    }
    // Sum up and scale the result to cover the range [-1,1]
    return 27.0 * (n0 + n1 + n2 + n3 + n4);
  }

  // Inner class to speed upp gradient computations
  // (array access is a lot slower than member access)
  private static class Grad
  {
    double x, y, z, w;

    Grad(double x, double y, double z)
    {
      this.x = x;
      this.y = y;
      this.z = z;
    }

    Grad(double x, double y, double z, double w)
    {
      this.x = x;
      this.y = y;
      this.z = z;
      this.w = w;
    }
  }

}

Francamente, considero que toda esta clase es una caja negra con un constructor public SimplexNoise_octave(int seed) y 3 métodos public double noise(double xin, double yin) , public double noise(double xin, double yin, double zin) y public double noise(double x, double y, double z, double w) .

Puede utilizar estos métodos exactamente como lo haría con los equivalentes de ruido perlin.

SimplexNoise_octave(int seed)

Cree 1 SimplexNoise_octave para cada octava que desee, cada una debe tener su propia semilla

public double noise(double xin, double yin)

Llame para obtener el valor de ruido particular para esa octava en esas coordenadas. Nota; las coordenadas deben ser preescaladas (más adelante). Las otras funciones de noise son las mismas pero para dimensiones más altas.

Creando Octavas

Al igual que en el ruido perlin, en general combinarás varias octavas de ruido para crear ruido fractal (que te da características de terreno). Tenga en cuenta que las alturas de terreno en 3D se crean mediante ruido 2D.

Varias octavas se combinan usando las siguientes razones

frequency = 2^i  
amplitude = persistence^i 

Para cada octava (i) se dividen las coordenadas de entrada por frecuencia y se multiplica el resultado por amplitud; Esto le da un aspecto de terreno. La persistencia se utiliza para afectar la apariencia del terreno, la alta persistencia (hacia 1) proporciona un terreno montañoso rocoso. La baja persistencia (hacia 0) da un terreno plano que varía lentamente. Ver la página de la etiqueta para más detalles.

A continuación se muestra un ejemplo de cómo se podría usar esto:

import java.util.Random;

public class SimplexNoise {

    SimplexNoise_octave[] octaves;
    double[] frequencys;
    double[] amplitudes;

    int largestFeature;
    double persistence;
    int seed;

    public SimplexNoise(int largestFeature,double persistence, int seed){
        this.largestFeature=largestFeature;
        this.persistence=persistence;
        this.seed=seed;

        //recieves a number (eg 128) and calculates what power of 2 it is (eg 2^7)
        int numberOfOctaves=(int)Math.ceil(Math.log10(largestFeature)/Math.log10(2));

        octaves=new SimplexNoise_octave[numberOfOctaves];
        frequencys=new double[numberOfOctaves];
        amplitudes=new double[numberOfOctaves];

        Random rnd=new Random(seed);

        for(int i=0;i<numberOfOctaves;i++){
            octaves[i]=new SimplexNoise_octave(rnd.nextInt());

            frequencys[i] = Math.pow(2,i);
            amplitudes[i] = Math.pow(persistence,octaves.length-i);




        }

    }


    public double getNoise(int x, int y){

        double result=0;

        for(int i=0;i<octaves.length;i++){
          //double frequency = Math.pow(2,i);
          //double amplitude = Math.pow(persistence,octaves.length-i);

          result=result+octaves[i].noise(x/frequencys[i], y/frequencys[i])* amplitudes[i];
        }


        return result;

    }

    public double getNoise(int x,int y, int z){

        double result=0;

        for(int i=0;i<octaves.length;i++){
          double frequency = Math.pow(2,i);
          double amplitude = Math.pow(persistence,octaves.length-i);

          result=result+octaves[i].noise(x/frequency, y/frequency,z/frequency)* amplitude;
        }


        return result;

    }
} 

Esto crea octavas que dan características de tamaño entre 1 y la característica más largestFeature , encontré que esto es útil pero no hay nada especial porque 1 es el tamaño más pequeño y puedes modificarlo. Produce entre -1 y 1, escala según sea necesario.

Uso

Un ejemplo de método principal que usaría esta clase es el siguiente

public static void main(String args[]){
    SimplexNoise simplexNoise=new SimplexNoise(100,0.1,5000);



    double xStart=0;
    double XEnd=500;
    double yStart=0;
    double yEnd=500;

    int xResolution=200;
    int yResolution=200;

    double[][] result=new double[xResolution][yResolution];

    for(int i=0;i<xResolution;i++){
        for(int j=0;j<yResolution;j++){
            int x=(int)(xStart+i*((XEnd-xStart)/xResolution));
            int y=(int)(yStart+j*((yEnd-yStart)/yResolution));
            result[i][j]=0.5*(1+simplexNoise.getNoise(x,y));
        }
    }

    ImageWriter.greyWriteImage(result);



}

Este método hace uso de mi propia clase ImageWriter solo para representar la salida en un archivo

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;

public class ImageWriter {
    //just convinence methods for debug

    public static void greyWriteImage(double[][] data){
        //this takes and array of doubles between 0 and 1 and generates a grey scale image from them

        BufferedImage image = new BufferedImage(data.length,data[0].length, BufferedImage.TYPE_INT_RGB);

        for (int y = 0; y < data[0].length; y++)
        {
          for (int x = 0; x < data.length; x++)
          {
            if (data[x][y]>1){
                data[x][y]=1;
            }
            if (data[x][y]<0){
                data[x][y]=0;
            }
              Color col=new Color((float)data[x][y],(float)data[x][y],(float)data[x][y]); 
            image.setRGB(x, y, col.getRGB());
          }
        }

        try {
            // retrieve image
            File outputfile = new File("saved.png");
            outputfile.createNewFile();

            ImageIO.write(image, "png", outputfile);
        } catch (IOException e) {
            //o no! Blank catches are bad
            throw new RuntimeException("I didn''t handle this very well");
        }
    }



}

Answer 2

webstaff.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf esta es una explicación bastante buena