我想创建椒盐噪声函数（PIL 和 Numpy）答案

【问题标题】：i want to create salt and pepper noise function (PIL and Numpy)我想创建椒盐噪声函数（PIL 和 Numpy）
【发布时间】：2018-11-03 13:44:43
【问题描述】：

我想创建椒盐噪声函数。输入是noise_density，即输出图像中作为噪声的像素数量，它应该返回的值是噪声图像数据源

def salt_pepper(noise_density):



noisesource = ColumnDataSource(data={'image': [noiseImage]})
return noisesource

【问题讨论】：

标签： python numpy python-imaging-library

【解决方案1】：

这个函数返回一个[密度]x[密度]像素的图像，使用numpy生成一个随机数组，使用PIL从数组中生成图像本身。

def salt_pepper(density):
    imarray = numpy.random.rand(density,density,3) * 255
    return Image.fromarray(imarray.astype('uint8')).convert('L')

现在，例如，您可以运行

salt_pepper(500)

生成一个 500x500px 的图像文件。

当然，一定要

import numpy
from PIL import Image

【讨论】：

考虑添加至少一些 cmets - 解释，代码的作用以及它如何解决问题中的问题，很少无法改善答案。

【解决方案2】：

我想出了一个矢量化解决方案，我确信可以改进/简化它。尽管界面与请求的界面不完全一样，但代码非常简单（而且速度很快?），我相信它可以很容易地适应。

import numpy as np
from PIL import Image

def salt_and_pepper(image, prob=0.05):
    # If the specified `prob` is negative or zero, we don't need to do anything.
    if prob <= 0:
        return image

    arr = np.asarray(image)
    original_dtype = arr.dtype

    # Derive the number of intensity levels from the array datatype.
    intensity_levels = 2 ** (arr[0, 0].nbytes * 8)

    min_intensity = 0
    max_intensity = intensity_levels - 1

    # Generate an array with the same shape as the image's:
    # Each entry will have:
    # 1 with probability: 1 - prob
    # 0 or np.nan (50% each) with probability: prob
    random_image_arr = np.random.choice(
        [min_intensity, 1, np.nan], p=[prob / 2, 1 - prob, prob / 2], size=arr.shape
    )

    # This results in an image array with the following properties:
    # - With probability 1 - prob: the pixel KEEPS ITS VALUE (it was multiplied by 1)
    # - With probability prob/2: the pixel has value zero (it was multiplied by 0)
    # - With probability prob/2: the pixel has value np.nan (it was multiplied by np.nan)
    # We need to to `arr.astype(np.float)` to make sure np.nan is a valid value.
    salt_and_peppered_arr = arr.astype(np.float) * random_image_arr

    # Since we want SALT instead of NaN, we replace it.
    # We cast the array back to its original dtype so we can pass it to PIL.
    salt_and_peppered_arr = np.nan_to_num(
        salt_and_peppered_arr, nan=max_intensity
    ).astype(original_dtype)

    return Image.fromarray(salt_and_peppered_arr)

您可以像这样加载黑白版本的Lena：

lena = Image.open("lena.ppm")
bwlena = Image.fromarray(np.asarray(lena).mean(axis=2).astype(np.uint8))

最后，你可以保存几个例子：

salt_and_pepper(bwlena, prob=0.1).save("sp01lena.png", "PNG")
salt_and_pepper(bwlena, prob=0.3).save("sp03lena.png", "PNG")

结果：

https://i.ibb.co/J2y9HXS/sp01lena.png

https://i.ibb.co/VTm5Vy2/sp03lena.png

【讨论】：