因此，我试图生成Perlin噪声并将其保存到图像文件中。我已经正确保存了图像，但噪点看起来并不像Perlin噪点。
这是我的代码:
package com.egs.survivalsim.util;
import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
import com.egs.survivalsim.MainComponent;
public class Noise {
MainComponent main;
public double noise(int x,int y){
x = x + y * 57;
x = ((x << 13) ^ x);
double t = (x * (x * x * 15731 + 789221) + 1376312589) & 0x7fffffff;
return 1 - t * 0.000000000931322574615478515625;
}
public double sNoise(int x, int y){
double corners = (noise(x - 1, y - 1) + noise(x + 1, y - 1) + noise(x - 1, y + 1) + noise(x + 1, y + 1)) * 0.0625;
double sides = (noise(x - 1, y) + noise(x + 1, y) + noise(x, y - 1) + noise(x, y + 1)) * 0.125;
double center = noise(x, y) * 0.25;
return corners + sides + center;
}
public double lInterpoleLin(double a, double b, double x){
return a * (1 - x) + b * x;
}
public double lInterpoleCos(double a, double b, double x){
double ft = x * 3.1415927;
double f = (1 - Math.cos(ft)) * 0.001;
return a * (1 - f) + b * f;
}
public double iNoise(double x, double y){
int iX = (int) x;
int iY = (int) y;
double dX = x - iX;
double dY = y - iY;
double p1 = sNoise(iX, iY);
double p2 = sNoise(iX + 1, iY);
double p3 = sNoise(iX, iY + 1);
double p4 = sNoise(iX + 1, iY + 1);
double i1 = lInterpoleCos(p1 ,p2 ,dX);
double i2 = lInterpoleCos(p3, p4, dX);
return lInterpoleCos(i1, i2, dY);
}
public double pNoise(double x, double y, double persistence, int octave){
double result;
double amplitude = 1;
int frequence = 1;
result = 0;
for(int n = 0; n < octave; n++){
frequence <<= 1;
amplitude *= persistence;
result += iNoise(x * frequence, y * frequence) * amplitude;
}
return result;
}
public void startNoise(MainComponent main){
System.out.println("Generating noise map");
this.main = main;
System.out.println("Width: " + main.worldWidth);
System.out.println("Height: " + main.worldHeight);
BufferedImage image = new BufferedImage(main.worldWidth, main.worldHeight, BufferedImage.TYPE_INT_RGB);
for(int y = 0; y < main.worldHeight; y++){
for(int x = 0; x < main.worldWidth; x++){
double c = pNoise((double) x - main.worldWidth, (double) y - main.worldHeight, 0.2, 2);
c *= 128.0;
c += 127.0;
if(c > 255.0){
c = 255.0;
}
if(c < 0.0){
c = 0.0;
}
int r = (int) c;
int g = (int) c;
int b = (int) c;
if(c>128)
r>>=1;
if(c>128)
b>>=1;
int color = new Color(r, g, b).getRGB();
image.setRGB(x, y, color);
main.noiseArray[x][y] = (int) c;
}
}
File fileImage = new File("noise.png");
try {
ImageIO.write(image, "png", fileImage);
} catch (IOException e) {
e.printStackTrace();
}
System.out.println("Noise map generated");
}
}
最佳答案
不，确实看起来有点奇怪。首先，噪音的产生似乎存在问题。其次，我真的不知道为什么要创建彩色图像。最快的解决方案是查看Ken Perlin的最新出版物，并查看其reference implementation。
通过简单的复制和粘贴以及几行额外的Java代码，您将获得漂亮的图像。
请注意，我确实只是将其砍下来，这意味着我手动缩放了采样范围，以使其看起来不错，并且由于我不知道Perlin噪声值在哪个范围内，因此我只是重新缩放了数组以适合范围为[0，255]。
在下面的代码中，只有main很重要。其余的则从Perlin复制而来。
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
public class Noise {
static final int p[] = new int[512], 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
};
static {
for (int i = 0; i < 256; i++) p[256 + i] = p[i] = permutation[i];
}
static public double noise(double x, double y, double z) {
int X = (int) Math.floor(x) & 255, // FIND UNIT CUBE THAT
Y = (int) Math.floor(y) & 255, // CONTAINS POINT.
Z = (int) Math.floor(z) & 255;
x -= Math.floor(x); // FIND RELATIVE X,Y,Z
y -= Math.floor(y); // OF POINT IN CUBE.
z -= Math.floor(z);
double u = fade(x), // COMPUTE FADE CURVES
v = fade(y), // FOR EACH OF X,Y,Z.
w = fade(z);
int A = p[X] + Y, AA = p[A] + Z, AB = p[A + 1] + Z, // HASH COORDINATES OF
B = p[X + 1] + Y, BA = p[B] + Z, BB = p[B + 1] + Z; // THE 8 CUBE CORNERS,
return lerp(w, lerp(v, lerp(u, grad(p[AA], x, y, z), // AND ADD
grad(p[BA], x - 1, y, z)), // BLENDED
lerp(u, grad(p[AB], x, y - 1, z), // RESULTS
grad(p[BB], x - 1, y - 1, z))),// FROM 8
lerp(v, lerp(u, grad(p[AA + 1], x, y, z - 1), // CORNERS
grad(p[BA + 1], x - 1, y, z - 1)), // OF CUBE
lerp(u, grad(p[AB + 1], x, y - 1, z - 1),
grad(p[BB + 1], x - 1, y - 1, z - 1))));
}
static double fade(double t) {
return t * t * t * (t * (t * 6 - 15) + 10);
}
static double lerp(double t, double a, double b) {
return a + t * (b - a);
}
static double grad(int hash, double x, double y, double z) {
int h = hash & 15; // CONVERT LO 4 BITS OF HASH CODE
double u = h < 8 ? x : y, // INTO 12 GRADIENT DIRECTIONS.
v = h < 4 ? y : h == 12 || h == 14 ? x : z;
return ((h & 1) == 0 ? u : -u) + ((h & 2) == 0 ? v : -v);
}
public static void main(String[] args) {
final int WIDTH = 2 * 256, HEIGHT = 256;
double[] data = new double[WIDTH * HEIGHT];
int count = 0;
for (int y = 0; y < HEIGHT; y++) {
for (int x = 0; x < WIDTH; x++) {
data[count++] = PerlinNoise.noise(20.0 * x / WIDTH, 10.0 * y / HEIGHT, 0);
}
}
double minValue = data[0], maxValue = data[0];
for (int i = 0; i < data.length; i++) {
minValue = Math.min(data[i], minValue);
maxValue = Math.max(data[i], maxValue);
}
int[] pixelData = new int[WIDTH * HEIGHT];
for (int i = 0; i < data.length; i++) {
pixelData[i] = (int) (255 * (data[i] - minValue) / (maxValue - minValue));
}
BufferedImage img = new BufferedImage(WIDTH, HEIGHT, BufferedImage.TYPE_BYTE_GRAY);
img.getRaster().setPixels(0, 0, WIDTH, HEIGHT, pixelData);
File output = new File("image.jpg");
try {
ImageIO.write(img, "jpg", output);
} catch (IOException e) {
e.printStackTrace();
}
}
}