python - 如何使Perlin噪声发生器更平滑？

我正在尝试使用Perlin杂讯产生器制作 map 图块，但我注意到我的杂讯太尖锐，我的意思是，它的标高太多而且没有平坦的地方，而且它们看起来不像是 Alpine ，岛屿，湖泊或其他任何事物；他们似乎过于随意，而且有很多高峰。
在问题的结尾，需要进行一些更改才能修复它。
该问题的重要代码是:
1D:

def Noise(self, x):     # I wrote this noise function but it seems too random
    random.seed(x)
    number = random.random()
    if number < 0.5:
        final = 0 - number * 2
    elif number > 0.5:
        final = number * 2
    return final

 def Noise(self, x):     # I found this noise function on the internet
    x = (x<<13) ^ x
    return ( 1.0 - ( (x * (x * x * 15731 + 789221) + 1376312589) & 0x7fffffff) / 1073741824.0)

2D:

def Noise(self, x, y):     # I wrote this noise function but it seems too random
    n = x + y
    random.seed(n)
    number = random.random()
    if number < 0.5:
        final = 0 - number * 2
    elif number > 0.5:
        final = number * 2
    return final

def Noise(self, x, y):     # I found this noise function on the internet
    n = x + y * 57
    n = (n<<13) ^ n
    return ( 1.0 - ( (x * (x * x * 15731 + 789221) + 1376312589) & 0x7fffffff) / 1073741824.0)

我在代码中留下了1D和2D Perlin噪声，因为可能有人对此感兴趣:
(花了我很长时间才能找到一些代码，所以我认为有人会很高兴在这里找到一个示例)。
您不需要Matplotlib或NumPy发出噪音；我只是用它们来制作图形并更好地查看结果。

import random
import matplotlib.pyplot as plt              # To make graphs
from mpl_toolkits.mplot3d import Axes3D      # To make 3D graphs
import numpy as np                           # To make graphs

class D():     # Base of classes D1 and D2
    def Cubic_Interpolate(self, v0, v1, v2, v3, x):
        P = (v3 - v2) - (v0 - v1)
        Q = (v0 - v1) - P
        R = v2 - v0
        S = v1
        return P * x**3 + Q * x**2 + R * x + S

class D1(D):
    def __init__(self, lenght, octaves):
        self.result = self.Perlin(lenght, octaves)

    def Noise(self, x):     # I wrote this noise function but it seems too random
        random.seed(x)
        number = random.random()
        if number < 0.5:
            final = 0 - number * 2
        elif number > 0.5:
            final = number * 2
        return final

    def Noise(self, x):     # I found this noise function on the internet
        x = (x<<13) ^ x
        return ( 1.0 - ( (x * (x * x * 15731 + 789221) + 1376312589) & 0x7fffffff) / 1073741824.0)

    def Perlin(self, lenght, octaves):
        result = []
        for x in range(lenght):
            value = 0
            for y in range(octaves):
                frequency = 2 ** y
                amplitude = 0.25 ** y
                value += self.Interpolate_Noise(x * frequency) * amplitude
            result.append(value)
            print(f"{x} / {lenght} ({x/lenght*100:.2f}%): {round(x/lenght*10) * '#'} {(10-round(x/lenght*10)) * ' '}. Remaining {lenght-x}.")     # I don't use `os.system('cls')` because it slow down the code.
        return result

    def Smooth_Noise(self, x):
        return self.Noise(x) / 2 + self.Noise(x-1) / 4 + self.Noise(x+1) / 4

    def Interpolate_Noise(self, x):
        round_x = round(x)
        frac_x  = x - round_x
        v0 = self.Smooth_Noise(round_x - 1)
        v1 = self.Smooth_Noise(round_x)
        v2 = self.Smooth_Noise(round_x + 1)
        v3 = self.Smooth_Noise(round_x + 2)
        return self.Cubic_Interpolate(v0, v1, v2, v3, frac_x)

    def graph(self, *args):
        plt.plot(np.array(self.result), '-', label = "Line")
        for x in args:
            plt.axhline(y=x, color='r', linestyle='-')
        plt.xlabel('X')
        plt.ylabel('Y')
        plt.title("Simple Plot")
        plt.legend()
        plt.show()

class D2(D):
    def __init__(self, lenght, octaves = 1):

        self.lenght_axes = round(lenght ** 0.5)
        self.lenght = self.lenght_axes ** 2

        self.result = self.Perlin(self.lenght, octaves)

    def Noise(self, x, y):     # I wrote this noise function but it seems too random
        n = x + y
        random.seed(n)
        number = random.random()
        if number < 0.5:
            final = 0 - number * 2
        elif number > 0.5:
            final = number * 2
        return final

    def Noise(self, x, y):     # I found this noise function on the internet
        n = x + y * 57
        n = (n<<13) ^ n
        return ( 1.0 - ( (x * (x * x * 15731 + 789221) + 1376312589) & 0x7fffffff) / 1073741824.0)

    def Smooth_Noise(self, x, y):
        corners = (self.Noise(x - 1, y - 1) + self.Noise(x + 1, y - 1) + self.Noise(x - 1, y + 1) + self.Noise(x + 1, y + 1) ) / 16
        sides   = (self.Noise(x - 1, y) + self.Noise(x + 1, y) + self.Noise(x, y - 1)  + self.Noise(x, y + 1) ) /  8
        center  =  self.Noise(x, y) / 4
        return corners + sides + center

    def Interpolate_Noise(self, x, y):

        round_x = round(x)
        frac_x  = x - round_x

        round_y = round(y)
        frac_y  = y - round_y

        v11 = self.Smooth_Noise(round_x - 1, round_y - 1)
        v12 = self.Smooth_Noise(round_x    , round_y - 1)
        v13 = self.Smooth_Noise(round_x + 1, round_y - 1)
        v14 = self.Smooth_Noise(round_x + 2, round_y - 1)
        i1 = self.Cubic_Interpolate(v11, v12, v13, v14, frac_x)

        v21 = self.Smooth_Noise(round_x - 1, round_y)
        v22 = self.Smooth_Noise(round_x    , round_y)
        v23 = self.Smooth_Noise(round_x + 1, round_y)
        v24 = self.Smooth_Noise(round_x + 2, round_y)
        i2 = self.Cubic_Interpolate(v21, v22, v23, v24, frac_x)

        v31 = self.Smooth_Noise(round_x - 1, round_y + 1)
        v32 = self.Smooth_Noise(round_x    , round_y + 1)
        v33 = self.Smooth_Noise(round_x + 1, round_y + 1)
        v34 = self.Smooth_Noise(round_x + 2, round_y + 1)
        i3 = self.Cubic_Interpolate(v31, v32, v33, v34, frac_x)

        v41 = self.Smooth_Noise(round_x - 1, round_y + 2)
        v42 = self.Smooth_Noise(round_x    , round_y + 2)
        v43 = self.Smooth_Noise(round_x + 1, round_y + 2)
        v44 = self.Smooth_Noise(round_x + 2, round_y + 2)
        i4 = self.Cubic_Interpolate(v41, v42, v43, v44, frac_x)

        return self.Cubic_Interpolate(i1, i2, i3, i4, frac_y)

    def Perlin(self, lenght, octaves):
        result = []
        for x in range(lenght):
            value = 0
            for y in range(octaves):
                frequency = 2 ** y
                amplitude = 0.25 ** y
                value += self.Interpolate_Noise(x * frequency, x * frequency) * amplitude
            result.append(value)
            print(f"{x} / {lenght} ({x/lenght*100:.2f}%): {round(x/lenght*10) * '#'} {(10-round(x/lenght*10)) * ' '}. Remaining {lenght-x}.")     # I don't use `os.system('cls')` because it slow down the code.
        return result

    def graph(self, color = 'viridis'):
        # Other colors: https://matplotlib.org/examples/color/colormaps_reference.html
        fig = plt.figure()
        Z = np.array(self.result).reshape(self.lenght_axes, self.lenght_axes)

        ax = fig.add_subplot(1, 2, 1, projection='3d')
        X = np.arange(self.lenght_axes)
        Y = np.arange(self.lenght_axes)
        X, Y = np.meshgrid(X, Y)
        d3 = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=color, linewidth=0, antialiased=False)
        fig.colorbar(d3)

        ax = fig.add_subplot(1, 2, 2)
        d2 = ax.imshow(Z, cmap=color, interpolation='none')
        fig.colorbar(d2)

        plt.show()

问题在于输出似乎不适用于 map 。
使用以下命令查看此输出:

test = D2(1000, 3)
test.graph()

我正在寻找更平滑的东西。
也许很难在2D噪波中注意到我在说什么，但是在1D场景中要容易得多:

test = D1(1000, 3)
test.graph()

互联网的噪声功能具有较小的峰值和较不频繁的峰值，但仍然过多。我正在寻找更平滑的东西。
可能是这样的:

或这个:

附言:我是根据this pseudocode制作的。
编辑:
皮卡列克语:

即使值较低，它也有峰，没有曲线或平滑/平坦的线。
geza:解决方案
多亏了geza's suggestions，我找到了解决问题的方法:

def Perlin(self, lenght_axes, octaves, zoom = 0.01, amplitude_base = 0.5):
    result = []

    for y in range(lenght_axes):
        line = []
        for x in range(lenght_axes):
            value = 0
            for o in range(octaves):
                frequency = 2 ** o
                amplitude = amplitude_base ** o
                value += self.Interpolate_Noise(x * frequency * zoom, y * frequency * zoom) * amplitude
            line.append(value)
        result.append(line)
        print(f"{y} / {lenght_axes} ({y/lenght_axes*100:.2f}%): {round(y/lenght_axes*20) * '#'} {(20-round(y/lenght_axes*20)) * ' '}. Remaining {lenght_axes-y}.")
    return result

其他修改是:

Z = np.array(self.result)而不是图形函数中的此Z = np.array(self.result).reshape(self.lenght_axes, self.lenght_axes)。

在math.floor()和import math变量的round()函数中使用Interpolate_Noise(记住round_x)代替round_y。

将return(第二个)中的Noise行修改为return ( 1.0 - ( (n * (n * n * 15731 + 789221) + 1376312589) & 0x7fffffff) / 1073741824.0)。D2(10000, 10)
现在唯一奇怪的是山(黄色)总是在同一地方附近，但是我认为这是更改Noise函数中数字的问题。

最佳答案

我在您的代码中发现了这些错误:

您需要将Interpolate_Noise参数乘以“缩放”到 map 中(例如，将x与0.01相乘)。如果在一维的情况下执行此操作，您将看到生成的函数已经好得多了。

将 Octave 音阶数从3增加到更大的值(3个 Octave 音阶不会产生太多细节)

使用幅度0.5 ^ Octave ，而不是0.25 ^ Octave (但是您可以使用此参数，因此0.25本质上不是很糟糕，但是它没有提供太多的细节)

对于2D情况，您需要有2个外部循环(一个用于水平，一个用于垂直。当然，您仍然需要 Octave 循环)。因此，您需要在水平和垂直位置正确地“索引”噪声，而不仅仅是x和x。

完全删除平滑。 Perlin噪音不需要它。

2D噪声函数有一个错误:它在返回表达式

中使用x而不是n 三次插值时的

，您可以使用round而不是math.floor。

这是我的一个答案，它是一种简单的(C++)实现类似Perlin(不是适当的Perlin)噪声的方法:https://stackoverflow.com/a/45121786/8157187

关于python - 如何使Perlin噪声发生器更平滑？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/47837968/