利用PerlinNoise实现图像雾化效果的python代码

下面是利用Perlin Noise实现图像雾化效果的Python代码，需要使用到numpy和Pillow库：

import numpy as np
from PIL import Image

# 生成Perlin Noise噪声矩阵
def generate_perlin_noise(shape, res=16):
    lin = np.linspace(0, 1, res+1, endpoint=False).reshape(-1, 1)
    X, Y = np.meshgrid(lin, lin)
    grid = np.stack((X, Y), axis=-1)
    grid_shape = (res, res, 1, 2)
    grid = np.pad(grid, ((0, 1), (0, 1), (0, 0), (0, 0)), mode='wrap')
    grid = np.reshape(grid, grid_shape)

    noise_shape = (shape[0]//res, shape[1]//res, 1, 2)
    noise = np.random.normal(size=noise_shape)
    corners = np.array([[[0,0], [0,1], [1,0], [1,1]]]).repeat(noise_shape[0], axis=0).repeat(noise_shape[1], axis=1)
    dot_products = np.sum(np.concatenate((corners, noise), axis=-1) * grid, axis=-1)
    weights = fade(np.modf(X*res)[0]), fade(np.modf(Y*res)[0])
    weights = np.repeat(np.expand_dims(weights, axis=-1), 2, axis=-1)
    noise = np.sum(weights * dot_products, axis=-2)
    return noise

# 平滑处理
def smoothstep(x):
    return x * x * (3 - 2 * x)

# 加权平均
def lerp(a, b, t):
    return a + t * (b - a)

# 淡化函数
def fade(t):
    return smoothstep(t)

# 将图像转为灰度图像
def gray_scale(image):
    return image.convert('L')

# 二值化处理
def binary(image, threshold):
    return image.point(lambda x: 255 if x>threshold else 0)

# 图像雾化
def fog(image, noise_scale=10, noise_res=16, smooth_scale=30, threshold=150):
    # 将图像转为灰度图像
    gray_image = gray_scale(image)

    # 生成Perlin Noise噪声矩阵
    noise = generate_perlin_noise(gray_image.size, res=noise_res)

    # 平滑处理
    smooth_noise = np.zeros_like(noise)
    for y in range(smooth_noise.shape[0]):
        for x in range(smooth_noise.shape[1]):
            smooth_noise[y, x] = np.mean(noise[max(0,y-smooth_scale):min(smooth_noise.shape[0],y+smooth_scale), max(0,x-smooth_scale):min(smooth_noise.shape[1],x+smooth_scale)])

    # 加权平均
    new_image = Image.new('L', gray_image.size)
    for y in range(new_image.size[1]):
        for x in range(new_image.size[0]):
            t = smooth_noise[y//noise_scale, x//noise_scale]
            new_image.putpixel((x, y), int(lerp(gray_image.getpixel((x, y)), 255, t)))

    # 二值化处理
    new_image = binary(new_image, threshold)

    # 叠加原图像
    result_image = Image.new('RGBA', image.size, (255, 255, 255, 0))
    result_image.paste(image, (0, 0), new_image)
    return result_image

# 测试代码
if __name__ == '__main__':
    image = Image.open('test.jpg')
    result_image = fog(image)
    result_image.show()

需要说明的是，上述代码中的Perlin Noise算法实现参考自[这里](https://github.com/FlorianWilk/perlin-noise/blob/master/perlin_noise.py)。