58 lines
1.3 KiB
Python
58 lines
1.3 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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# Generate a height map using Perlin noise
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def generate_perlin_noise(size, scale, seed=None):
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if seed:
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np.random.seed(seed)
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def fade(t):
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return 6 * t**5 - 15 * t**4 + 10 * t**3
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def lerp(a, b, t):
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return a + t * (b - a)
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def gradient(h, x, y):
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vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
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g = vectors[h % 4]
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return g[:, :, 0] * x + g[:, :, 1] * y
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lin = np.linspace(0, scale, size, endpoint=False)
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x, y = np.meshgrid(lin, lin)
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p = np.arange(256, dtype=int)
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np.random.shuffle(p)
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p = np.stack([p, p]).flatten()
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xi = x.astype(int) & 255
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yi = y.astype(int) & 255
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xf = x - xi
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yf = y - yi
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u = fade(xf)
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v = fade(yf)
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n00 = gradient(p[p[xi] + yi], xf, yf)
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n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
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n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
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n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
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x1 = lerp(n00, n10, u)
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x2 = lerp(n01, n11, u)
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return lerp(x1, x2, v)
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# Parameters for the height map
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size = 512
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scale = 100
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# Generate the height map
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height_map = generate_perlin_noise(size, scale, seed=42)
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# Plot the height map
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plt.imshow(height_map, cmap='gray')
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plt.axis('off')
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plt.savefig('heightmap.png', bbox_inches='tight', pad_inches=0)
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plt.show()
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