Question

我在为项目生成Perlin噪声时遇到问题。当我想了解如何正确使用库时，我尝试逐步遵循此页面：https://medium.com/@yvanscher/playing-with-perlin-noise-generating-realistic-archipelagos-b59f004d8401 在第一部分中，有代码：

import noise
import numpy as np
from scipy.misc import toimage

shape = (1024,1024)
scale = 100.0
octaves = 6
persistence = 0.5
lacunarity = 2.0

world = np.zeros(shape)
for i in range(shape[0]):
    for j in range(shape[1]):
        world[i][j] = noise.pnoise2(i/scale, 
                                    j/scale, 
                                    octaves=octaves, 
                                    persistence=persistence, 
                                    lacunarity=lacunarity, 
                                    repeatx=1024, 
                                    repeaty=1024, 
                                    base=0)

toimage(world).show()

我将其复制粘贴到最后，并进行很小的更改（toimage已过时），所以我有：

import noise
import numpy as np
from PIL import Image

shape = (1024,1024)
scale = 100
octaves = 6
persistence = 0.5
lacunarity = 2.0
seed = np.random.randint(0,100)

world = np.zeros(shape)
for i in range(shape[0]):
    for j in range(shape[1]):
        world[i][j] = noise.pnoise2(i/scale,
                                    j/scale,
                                    octaves=octaves,
                                    persistence=persistence,
                                    lacunarity=lacunarity,
                                    repeatx=1024,
                                    repeaty=1024,
                                    base=seed)

Image.fromarray(world, mode='L').show()

我尝试了很多衍射模式，但是这种噪声甚至不接近相干噪声。我的结果是类似this（mode ='L'）。有人可以解释一下，我在做什么错？

Answer 1

这是工作代码。我随意清洗了一下。有关详细信息，请参见评论。最后建议：测试代码时，请使用matplotlib进行可视化。其imshow()函数比PIL更健壮。

import noise
import numpy as np
from PIL import Image

shape = (1024,1024)
scale = .5
octaves = 6
persistence = 0.5
lacunarity = 2.0
seed = np.random.randint(0,100)

world = np.zeros(shape)

# make coordinate grid on [0,1]^2
x_idx = np.linspace(0, 1, shape[0])
y_idx = np.linspace(0, 1, shape[1])
world_x, world_y = np.meshgrid(x_idx, y_idx)

# apply perlin noise, instead of np.vectorize, consider using itertools.starmap()
world = np.vectorize(noise.pnoise2)(world_x/scale,
                        world_y/scale,
                        octaves=octaves,
                        persistence=persistence,
                        lacunarity=lacunarity,
                        repeatx=1024,
                        repeaty=1024,
                        base=seed)

# here was the error: one needs to normalize the image first. Could be done without copying the array, though
img = np.floor((world + .5) * 255).astype(np.uint8) # <- Normalize world first
Image.fromarray(img, mode='L').show()

Answer 2

如果有人跟着我，使用噪音库，您应该使用

进行归一化

img = np.floor((world + 1) * 127).astype(np.uint8)

这样，就不会有任何异常颜色的斑点。

Python噪声库中的Perlin噪声

2 个答案: