Любые простые шумовые пособия или ресурсы?

Ответ 1

В рекомендации руководства я попытаюсь объяснить, как использовать существующий источник java, который создает одну октаву симплексного шума.

Симплексный шумовой код

Эта часть кода была создана Стефаном Густавсоном и помещена в общественное достояние. Здесь можно найти здесь. Это цитируется здесь для удобства

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 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;
    }
  }

}

Откровенно говоря, я считаю, что весь этот класс является черным ящиком с открытым конструктором public SimplexNoise_octave(int seed) и тремя общедоступными методами public double noise(double xin, double yin), public double noise(double xin, double yin, double zin) и public double noise(double x, double y, double z, double w).

Вы можете использовать эти методы точно так же, как и эквиваленты шума perlin.

SimplexNoise_octave(int seed)

Создайте 1 SimplexNoise_octave для каждой нужной октавы, каждая из которых должна иметь собственное семя

public double noise(double xin, double yin)

Позвоните, чтобы получить конкретное значение шума для этой октавы в этих координатах. Заметка; координаты должны быть предварительно масштабированы (более поздние). Другие функции noise одинаковы, но для более высоких значений.

Создание октав

Как и в перлинговом шуме, вы в целом объедините несколько октав шума для создания фрактального шума (который дает вам характерные для местности характеристики). Обратите внимание, что высоты 3D-ландшафта создаются 2D-шумом.

Несколько октав объединяются с использованием следующих соотношений

frequency = 2^i  
amplitude = persistence^i 

Для каждой октавы (i) вы разделяете входные координаты по частоте и умножаете результат на амплитуду; это дает местность, как внешний вид. Стойкость используется для воздействия на внешний вид местности, высокая стойкость (к 1) дает скалистую горную местность. низкое сопротивление (в направлении 0) дает медленно меняющуюся плоскую поверхность. Подробнее см. на странице теги.

Ниже приведен пример того, как это можно использовать:

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;

    }
} 

Это создает октавы, которые предоставляют функции размером от 1 до largestFeature, я нашел, что это полезно, но нет ничего особенного в том, что 1 является наименьшим размером, и вы можете это изменить. Он выводит от -1 до 1, масштабируя по мере необходимости.

Использование

Примером основного метода, который будет использовать этот класс, является

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);



}

Этот метод использует мой собственный класс ImageWriter только для визуализации вывода в файл

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");
        }
    }



}

Ответ 2

http://webstaff.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf это довольно хорошее объяснение.