Java中的Perlin Noise问题

所以我试图生成perlin噪音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噪音。

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  255.0){ c = 255.0; } 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"); } } 

Answer 1

不，确实这看起来有点奇怪。 首先，噪音的产生似乎存在问题。 其次，我真的不知道你为什么要创建一个彩色图像。 最快的解决方案是查看Ken Perlin的最新出版物，并查看他的参考实现 。

通过简单的复制和粘贴以及几行额外的java代码，您可以获得漂亮的图像。

请注意，我真的只是将其砍掉，这意味着我手动缩放采样范围以使其看起来很好并且由于我不知道Perlin噪声的值在哪个范围内，我只需将数组重新调整为适合进入范围[0,255]。

在下面的代码中，只有main很重要。 其余的是从Perlin复制的。

 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(); } } }