blocks|key|2690787|text|很抱歉，我不能给你一个Python解决方案，但我可以向你展示技术和结果，只需在命令行中使用ImageMagick：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2690788|从这个开始：|2690789|​|2690790|📷|atomic|2690791|2690792|首先，使用法线立方或双线性插值将图像缩小到16x16像素，然后使用“最近邻”插值将图像缩放回其原始大小：|2690793|convert+paddington.png+-resize+16x16+-scale+400x400+result.png|code-block|syntax|javascript|2690794|2690795|2690796|2690797|关键字|2690798|像素化，帕丁顿，ImageMagick，命令行，命令行，图像，图像处理，最近邻插值。|2690799|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/bc1a7dd062741789065a6877a17708a6.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/c078a614d6266420f5371dd0f6963b7f.png^0|19|B|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|0|3|0|0^^$0|@$1|2|3|4|5|6|7|1C|8|@$9|1D|A|1E|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1F|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|1G|8|@]|D|@]|E|$]]|$1|J|3|K|5|L|7|1H|8|@]|D|@$9|1I|A|1J|1|1K]]|E|$]]|$1|M|3|I|5|6|7|1L|8|@]|D|@]|E|$]]|$1|N|3|O|5|6|7|1M|8|@]|D|@]|E|$]]|$1|P|3|Q|5|R|7|1N|8|@]|D|@]|E|$S|T]]|$1|U|3|I|5|6|7|1O|8|@]|D|@]|E|$]]|$1|V|3|K|5|L|7|1P|8|@]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|W|3|I|5|6|7|1T|8|@]|D|@]|E|$]]|$1|X|3|Y|5|6|7|1U|8|@$9|1V|A|1W|B|C]]|D|@]|E|$]]|$1|Z|3|10|5|6|7|1X|8|@]|D|@]|E|$]]|$1|11|3|-4|5|6|7|1Y|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|18|19|-4]]|1A|$5|14|15|16|E|$17|1B|19|-4]]]]

Sorry, I can't give you a Python solution, but I can show you the technique and the result, just using ImageMagick at the command-line:

Starting with this:

<a href="https://i.stack.imgur.com/KmyrX.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/KmyrX.png" alt="enter image description here"></a>

First, resize the image down to 16x16 pixels using normal cubic, or bilinear interpolation, then scale the image back up to its original size using "nearest neighbour" interpolation:

<pre><code>convert paddington.png -resize 16x16 -scale 400x400 result.png
</code></pre>

<a href="https://i.stack.imgur.com/lRwKa.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/lRwKa.png" alt="enter image description here"></a>

Keywords:

Pixelate, pixellate, pixelated, pixellated, Paddington, ImageMagick, command-line, commandline, image, image processing, nearest neighbour interpolation.

blocks|key|25866|text|九个月过去了，我现在可以拼凑出一些Python了--按照你最初关于如何用Python和PIL/Pillow对图像进行像素化的要求。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|25867|#!/usr/local/bin/python3
from+PIL+import+Image

#+Open+Paddington
img+=+Image.open("paddington.png")

#+Resize+smoothly+down+to+16x16+pixels
imgSmall+=+img.resize((16,16),resample=Image.BILINEAR)

#+Scale+back+up+using+NEAREST+to+original+size
result+=+imgSmall.resize(img.size,Image.NEAREST)

#+Save
result.save('result.png')|code-block|syntax|javascript|25868|原始图像|offset|length|style|BOLD|25869|​|25870|📷|atomic|25871|25872|结果|25873|25874|25875|25876|25877|帕丁顿太可爱了--他只是需要被像素化一下！|25878|如果您将其缩小到32x32像素(而不是16x16)，然后重新调整大小，则会得到：|25879|25880|25881|25882|关键字|25883|像素化，Paddington，Python，Pillow，PIL，图像，图像处理，最近邻插值。|25884|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/0fc0b4ce66e238aeec7ec5fc6a754e17.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/1c19c8c711c3af584586e2b95d2ee145.png|2|https://ask.qcloudimg.com/http-save/yehe-900000/a6d9f7450e89c0e7e4562d1628c42ae1.png^0|0|0|0|4|0|0|0|1|0|0|0|0|2|0|0|0|1|1|0|0|0|0|0|0|0|1|2|0|0|0|3|0|0^^$0|@$1|2|3|4|5|6|7|1M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1O|8|@$I|1P|J|1Q|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|1R|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|1S|8|@]|9|@$I|1T|J|1U|1|1V]]|A|$]]|$1|R|3|N|5|6|7|1W|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1X|8|@$I|1Y|J|1Z|K|L]]|9|@]|A|$]]|$1|U|3|N|5|6|7|20|8|@]|9|@]|A|$]]|$1|V|3|P|5|Q|7|21|8|@]|9|@$I|22|J|23|1|24]]|A|$]]|$1|W|3|N|5|6|7|25|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|26|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|27|8|@]|9|@]|A|$]]|$1|10|3|11|5|6|7|28|8|@]|9|@]|A|$]]|$1|12|3|N|5|6|7|29|8|@]|9|@]|A|$]]|$1|13|3|P|5|Q|7|2A|8|@]|9|@$I|2B|J|2C|1|2D]]|A|$]]|$1|14|3|N|5|6|7|2E|8|@]|9|@]|A|$]]|$1|15|3|16|5|6|7|2F|8|@$I|2G|J|2H|K|L]]|9|@]|A|$]]|$1|17|3|18|5|6|7|2I|8|@]|9|@]|A|$]]|$1|19|3|-4|5|6|7|2J|8|@]|9|@]|A|$]]]|1A|$1B|$5|1C|1D|1E|A|$1F|1G|1H|-4]]|1I|$5|1C|1D|1E|A|$1F|1J|1H|-4]]|1K|$5|1C|1D|1E|A|$1F|1L|1H|-4]]]]

Nine months pass and I can now cobble together some Python - per your original request on how to pixellate an image with Python and PIL/Pillow.

<pre><code>#!/usr/local/bin/python3
from PIL import Image

# Open Paddington
img = Image.open("paddington.png")

# Resize smoothly down to 16x16 pixels
imgSmall = img.resize((16,16),resample=Image.BILINEAR)

# Scale back up using NEAREST to original size
result = imgSmall.resize(img.size,Image.NEAREST)

# Save
result.save('result.png')
</code></pre>

Original Image

<a href="https://i.stack.imgur.com/Meuq2.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Meuq2.png" alt="enter image description here"></a>

Result

<a href="https://i.stack.imgur.com/WEn8R.png" rel="noreferrer"><img src="https://i.stack.imgur.com/WEn8R.png" alt="enter image description here"></a>

<hr>

Paddington is so cute - he just needed to be pixellated!

If you take him down to 32x32 pixels (instead of 16x16) and then resize back up, you get:

<a href="https://i.stack.imgur.com/8IKen.png" rel="noreferrer"><img src="https://i.stack.imgur.com/8IKen.png" alt="enter image description here"></a>

Keywords:

Pixelate, pixellate, pixelated, pixellated, Paddington, Python, Pillow, PIL, image, image processing, nearest neighbour interpolation.

blocks|key|24572|text|以下是我的解决方案|type|unstyled|depth|inlineStyleRanges|entityRanges|data|24573|import+numpy+as+np
import+matplotlib.pyplot+as+plt

def+pixelate_rgb(img,+window):
++++n,+m,+_+=+img.shape
++++n,+m+=+n+-+n+%25+window,+m+-+m+%25+window
++++img1+=+np.zeros((n,+m,+3))
++++for+x+in+range(0,+n,+window):
++++++++for+y+in+range(0,+m,+window):
++++++++++++img1[x:x%2Bwindow,y:y%2Bwindow]+=+img[x:x%2Bwindow,y:y%2Bwindow].mean(axis=(0,1))
++++return+img1

img+=+plt.imread('test.png')

fig,+ax+=+plt.subplots(1,+4,+figsize=(20,10))

ax[0].imshow(pixelate_rgb(img,+5))
ax[1].imshow(pixelate_rgb(img,+10))
ax[2].imshow(pixelate_rgb(img,+20))
ax[3].imshow(pixelate_rgb(img,+30))

#+remove+frames
[a.set_axis_off()+for+a+in+ax.flatten()]
plt.subplots_adjust(wspace=0.03,+hspace=0)|code-block|syntax|javascript|24574|​|24575|📷|atomic|offset|length|24576|24577|另一个想法是处理灰度图像：|24578|def+pixelate_bin(img,+window,+threshold):
++++n,+m+=+img.shape
++++n,+m+=+n+-+n+%25+window,+m+-+m+%25+window
++++img1+=+np.zeros((n,m))
++++for+x+in+range(0,+n,+window):
++++++++for+y+in+range(0,+m,+window):
++++++++++++if+img[x:x%2Bwindow,y:y%2Bwindow].mean()+>+threshold:
++++++++++++++++img1[x:x%2Bwindow,y:y%2Bwindow]+=+1
++++return+img1

#+convert+image+to+grayscale
img+=+np.dot(plt.imread('test.png'),+[0.299+,+0.587,+0.114])

fig,+ax+=+plt.subplots(1,+3,+figsize=(15,10))

plt.tight_layout()
ax[0].imshow(pixelate_bin(img,+5,+.2),+cmap='gray')
ax[1].imshow(pixelate_bin(img,+5,+.3),+cmap='gray')
ax[2].imshow(pixelate_bin(img,+5,+.45),+cmap='gray')

#+remove+frames
[a.set_axis_off()+for+a+in+ax.flatten()]
plt.subplots_adjust(wspace=0.03,+hspace=0)|24579|24580|24581|24582|请记住：+png的值介于0和1之间，而jpg的值介于0和255之间|style|BOLD|CODE|24583|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/62114240759a4bb48e75fd15914cf77a.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/723f71f4185042194d5dd27bf4f3a5bc.png^0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|0|4|5|3|J|3|0^^$0|@$1|2|3|4|5|6|7|1B|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1C|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1D|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|1E|8|@]|9|@$L|1F|M|1G|1|1H]]|A|$]]|$1|N|3|H|5|6|7|1I|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1J|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|1K|8|@]|9|@]|A|$E|F]]|$1|S|3|H|5|6|7|1L|8|@]|9|@]|A|$]]|$1|T|3|J|5|K|7|1M|8|@]|9|@$L|1N|M|1O|1|1P]]|A|$]]|$1|U|3|H|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1R|8|@$L|1S|M|1T|X|Y]|$L|1U|M|1V|X|Z]|$L|1W|M|1X|X|Z]]|9|@]|A|$]]|$1|10|3|-4|5|6|7|1Y|8|@]|9|@]|A|$]]]|11|$12|$5|13|14|15|A|$16|17|18|-4]]|19|$5|13|14|15|A|$16|1A|18|-4]]]]

Here is my solution
<pre><code>import numpy as np
import matplotlib.pyplot as plt

def pixelate_rgb(img, window):
 n, m, _ = img.shape
 n, m = n - n % window, m - m % window
 img1 = np.zeros((n, m, 3))
 for x in range(0, n, window):
 for y in range(0, m, window):
 img1[x:x+window,y:y+window] = img[x:x+window,y:y+window].mean(axis=(0,1))
 return img1

img = plt.imread('test.png')

fig, ax = plt.subplots(1, 4, figsize=(20,10))

ax[0].imshow(pixelate_rgb(img, 5))
ax[1].imshow(pixelate_rgb(img, 10))
ax[2].imshow(pixelate_rgb(img, 20))
ax[3].imshow(pixelate_rgb(img, 30))

# remove frames
[a.set_axis_off() for a in ax.flatten()]
plt.subplots_adjust(wspace=0.03, hspace=0)
</code></pre>
<a href="https://i.stack.imgur.com/aPrl7.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/aPrl7.png" alt="enter image description here" /></a>
One more idea is to process graycale image:
<pre><code>def pixelate_bin(img, window, threshold):
 n, m = img.shape
 n, m = n - n % window, m - m % window
 img1 = np.zeros((n,m))
 for x in range(0, n, window):
 for y in range(0, m, window):
 if img[x:x+window,y:y+window].mean() &gt; threshold:
 img1[x:x+window,y:y+window] = 1
 return img1

# convert image to grayscale
img = np.dot(plt.imread('test.png'), [0.299 , 0.587, 0.114])

fig, ax = plt.subplots(1, 3, figsize=(15,10))

plt.tight_layout()
ax[0].imshow(pixelate_bin(img, 5, .2), cmap='gray')
ax[1].imshow(pixelate_bin(img, 5, .3), cmap='gray')
ax[2].imshow(pixelate_bin(img, 5, .45), cmap='gray')

# remove frames
[a.set_axis_off() for a in ax.flatten()]
plt.subplots_adjust(wspace=0.03, hspace=0)
</code></pre>
<a href="https://i.stack.imgur.com/b4TVk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/b4TVk.png" alt="enter image description here" /></a>
Keep in mind: <code>png</code> has values between 0 and 1, whereas <code>jpg</code> between 0 and 255

I need to find an way to reduce a square image to 256 big pixels with python, preferably with the matplotlib and pillow libraries. 

Got any ideas ?

How to pixelate a square image to 256 big pixels with python?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我需要找到一种使用python将正方形图像缩小到256大像素的方法，最好是使用matplotlib和pillow库。有什么想法吗？

问如何使用python将正方形图像像素化到256个大像素？
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