如何使用 Pillow 模糊 Python 中的图像

Question

I am trying to take an image and blur it in Python using Pillow.

The only things that are imported are random and Image.

The only functions of Image I can use are open, size, load, new, close, show and save.

I know that I have to take the average RGB values of the pixels surrounding every pixel and then set that value to be the RGB value of the center pixel.

The problem is that I don't know how to get these values and look at specific edge cases where there are fewer adjacent pixels.

I am not allowed to import anything and can only use lists, loops, if statements, dictionaries, and tuples.

Answer 1

Take a look at this: https://en.wikipedia.org/wiki/Kernel_%28image_processing%29

Basically, you'll want to loop over the image and compute the new value for each pixel. If you can change the size of the image, then you can simply ignore the borders and therefore the edge cases. Otherwise, you'll need to apply one of the edge-handling techniques listed in the wiki page.

Good luck!!

Answer 2

You can use numpy.lib.stride_tricks.as_strided in order to create a window along the two spatial dimensions which can then be used to average neighboring points via mean . The following uses a (3, 3) window, ie only considering direct neighbors but different values are possible of course. To work on the edges, the original image first gets padded by repeating the edge values. Then the window averaging process can be repeated an arbitrary number times, depending on the desired blur factor. Here is some example code:

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

original_image = np.array(Image.open('example.jpg'))
image = np.pad(original_image, ((1,), (1,), (0,)), mode='edge')
window = np.lib.stride_tricks.as_strided(
    image,
    image.shape + (3, 3),
    image.strides + image.strides[:2]
)[:-2, :-2]

for __ in range(10):
    image[1:-1, 1:-1] = window.mean(axis=(-1, -2))

plt.title('Original')
plt.imshow(original_image)
plt.figure()
plt.title('Blurred')
plt.imshow(image[1:-1, 1:-1])

plt.show()

And here are the two image versions for comparison:

^{Photo by Cameron Venti on Unsplash}