Perlin 噪声发生器无法工作,看起来不流畅
我观看了一些教程并尝试用 python 创建 Perlin 噪声生成器。 它接收 x 和 y 方向上向量数量的元组以及数组之间像素距离的比例,然后计算每个像素与其周围 4 个数组中的每一个之间的点积,然后对它们进行双线性插值获取像素的值。
这是代码:
from PIL import Image
import numpy as np
scale = 16
size = np.array([8, 8])
vectors = []
for i in range(size[0]):
for j in range(size[1]):
rand = np.random.rand() * 2 * np.pi
vectors.append(np.array([np.cos(rand), np.sin(rand)]))
interpolated_map = np.zeros(size * scale)
def interpolate(x1, x2, w):
t = (w % scale) / scale
return (x2 - x1) * t + x1
def dot_product(a, b):
return a[0] * b[0] + a[1] * b[1]
for i in range(size[1] * scale):
for j in range(size[0] * scale):
dot_products = []
for m in range(4):
corner_vector_x = round(i / scale) + (m % 2)
corner_vector_y = round(j / scale) + int(m / 2)
x = i - corner_vector_x * scale
y = j - corner_vector_y * scale
if corner_vector_x >= size[0]:
corner_vector_x = 0
if corner_vector_y >= size[1]:
corner_vector_y = 0
corner_vector = vectors[corner_vector_x + corner_vector_y * (size[0])]
distance_vector = np.array([x, y])
dot_products.append(dot_product(corner_vector, distance_vector))
x1 = interpolate(dot_products[0], dot_products[1], i)
x2 = interpolate(dot_products[2], dot_products[3], i)
interpolated_map[i][j] = (interpolate(x1, x2, j) / 2 + 1) * 255
img = Image.fromarray(interpolated_map)
img.show()
我不知道出了什么问题,我尝试观看多个不同的教程,阅读一堆不同的文章,但结果总是相同的。
I watched some tutorials and tried to create a Perlin noise generator in python.
It takes in a tuple for the number of vectors in the x and y directions and a scale for the distance in pixels between the arrays, then calculates the dot product between each pixel and each of the 4 arrays surrounding it, It then interpolates them bilinearly to get the pixel's value.
here's the code:
from PIL import Image
import numpy as np
scale = 16
size = np.array([8, 8])
vectors = []
for i in range(size[0]):
for j in range(size[1]):
rand = np.random.rand() * 2 * np.pi
vectors.append(np.array([np.cos(rand), np.sin(rand)]))
interpolated_map = np.zeros(size * scale)
def interpolate(x1, x2, w):
t = (w % scale) / scale
return (x2 - x1) * t + x1
def dot_product(a, b):
return a[0] * b[0] + a[1] * b[1]
for i in range(size[1] * scale):
for j in range(size[0] * scale):
dot_products = []
for m in range(4):
corner_vector_x = round(i / scale) + (m % 2)
corner_vector_y = round(j / scale) + int(m / 2)
x = i - corner_vector_x * scale
y = j - corner_vector_y * scale
if corner_vector_x >= size[0]:
corner_vector_x = 0
if corner_vector_y >= size[1]:
corner_vector_y = 0
corner_vector = vectors[corner_vector_x + corner_vector_y * (size[0])]
distance_vector = np.array([x, y])
dot_products.append(dot_product(corner_vector, distance_vector))
x1 = interpolate(dot_products[0], dot_products[1], i)
x2 = interpolate(dot_products[2], dot_products[3], i)
interpolated_map[i][j] = (interpolate(x1, x2, j) / 2 + 1) * 255
img = Image.fromarray(interpolated_map)
img.show()
I don't know what's going wrong, I've tried watching multiple different tutorials, reading a bunch of different articles, but the result is always the same.
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