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?
答案 0 :(得分:2)
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()
答案 1 :(得分:1)
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。