blocks|key|404375|text|您可以使用numpy，将图像转换为数组，找到所有非空列和行，然后从以下内容创建图像：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|404376|import+Image
import+numpy+as+np

image=Image.open('L_2d.png')
image.load()

image_data+=+np.asarray(image)
image_data_bw+=+image_data.max(axis=2)
non_empty_columns+=+np.where(image_data_bw.max(axis=0)>0)[0]
non_empty_rows+=+np.where(image_data_bw.max(axis=1)>0)[0]
cropBox+=+(min(non_empty_rows),+max(non_empty_rows),+min(non_empty_columns),+max(non_empty_columns))

image_data_new+=+image_data[cropBox[0]:cropBox[1]%2B1,+cropBox[2]:cropBox[3]%2B1+,+:]

new_image+=+Image.fromarray(image_data_new)
new_image.save('L_2d_cropped.png')|code-block|syntax|javascript|404377|结果看起来像是|404378|📷|atomic|offset|length|404379|​|404380|如果有什么不清楚的地方，尽管问。|404381|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/6feca4a735f77dc71b62e50dc4c641ef.png|imageAlt^0|0|0|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|13|8|@]|9|@$L|14|M|15|1|16]]|A|$]]|$1|N|3|O|5|6|7|17|8|@]|9|@]|A|$]]|$1|P|3|Q|5|6|7|18|8|@]|9|@]|A|$]]|$1|R|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|S|$T|$5|U|V|W|A|$X|Y|Z|-4]]]]

You can use numpy, convert the image to array, find all non-empty columns and rows and then create an image from these:

<pre><code>import Image
import numpy as np

image=Image.open('L_2d.png')
image.load()

image_data = np.asarray(image)
image_data_bw = image_data.max(axis=2)
non_empty_columns = np.where(image_data_bw.max(axis=0)&gt;0)[0]
non_empty_rows = np.where(image_data_bw.max(axis=1)&gt;0)[0]
cropBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))

image_data_new = image_data[cropBox[0]:cropBox[1]+1, cropBox[2]:cropBox[3]+1 , :]

new_image = Image.fromarray(image_data_new)
new_image.save('L_2d_cropped.png')
</code></pre>

The result looks like
<a href="https://i.stack.imgur.com/bwkWt.png" rel="noreferrer"><img src="https://i.stack.imgur.com/bwkWt.png" alt="cropped image"></a>

If anything is unclear, just ask.

blocks|key|186216|text|对我来说，它是这样工作的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|186217|import+Image

image=Image.open('L_2d.png')

imageBox+=+image.getbbox()
cropped=image.crop(imageBox)
cropped.save('L_2d_cropped.png')|code-block|syntax|javascript|186218|按mask=imageComponents[3]搜索边界时，仅按蓝色通道搜索。|offset|length|style|CODE|186219|entityMap^0|0|0|1|N|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

For me it works as:

<pre><code>import Image

image=Image.open('L_2d.png')

imageBox = image.getbbox()
cropped=image.crop(imageBox)
cropped.save('L_2d_cropped.png')
</code></pre>

When you search for boundaries by <code>mask=imageComponents[3]</code>, you search only by blue channel.

blocks|key|186303|text|我最近看到了这篇文章，并注意到PIL库发生了变化。我用openCV重新实现了这个功能：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|186304|import+cv2

def+crop_im(im,+padding=0.1):
++++"""
++++Takes+cv2+image,+im,+and+padding+%25+as+a+float,+padding,
++++and+returns+cropped+image.
++++"""
++++bw+=+cv2.cvtColor(im,+cv2.COLOR_BGR2GRAY)
++++rows,+cols+=+bw.shape
++++non_empty_columns+=+np.where(bw.min(axis=0)<255)[0]
++++non_empty_rows+=+np.where(bw.min(axis=1)<255)[0]
++++cropBox+=+(int(min(non_empty_rows)+*+(1+-+padding)),
++++++++++++++++int(min(max(non_empty_rows)+*+(1+%2B+padding),+rows)),
++++++++++++++++int(min(non_empty_columns)+*+(1+-+padding)),
++++++++++++++++int(min(max(non_empty_columns)+*+(1+%2B+padding),+cols)))
++++cropped+=+im[cropBox[0]:cropBox[1]%2B1,+cropBox[2]:cropBox[3]%2B1+,+:]

++++return+cropped

im+=+cv2.imread('testimage.png')
cropped+=+crop_im(im)
cv2.imshow('',+cropped)
cv2.waitKey(0)|code-block|syntax|javascript|186305|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Came across this post recently and noticed the PIL library has changed. I re-implemented this with openCV:
<pre><code>import cv2

def crop_im(im, padding=0.1):
 &quot;&quot;&quot;
 Takes cv2 image, im, and padding % as a float, padding,
 and returns cropped image.
 &quot;&quot;&quot;
 bw = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
 rows, cols = bw.shape
 non_empty_columns = np.where(bw.min(axis=0)&lt;255)[0]
 non_empty_rows = np.where(bw.min(axis=1)&lt;255)[0]
 cropBox = (int(min(non_empty_rows) * (1 - padding)),
 int(min(max(non_empty_rows) * (1 + padding), rows)),
 int(min(non_empty_columns) * (1 - padding)),
 int(min(max(non_empty_columns) * (1 + padding), cols)))
 cropped = im[cropBox[0]:cropBox[1]+1, cropBox[2]:cropBox[3]+1 , :]

 return cropped

im = cv2.imread('testimage.png')
cropped = crop_im(im)
cv2.imshow('', cropped)
cv2.waitKey(0)
</code></pre>

blocks|key|404517|text|我知道这篇文章很老了，但是，由于某些原因，建议的答案对我来说都不起作用。所以我从现有的答案中修改了我自己的版本：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|404518|import+Image
import+numpy+as+np
import+glob
import+shutil
import+os

grey_tolerance+=+0.7+#+(0,1)+=+crop+(more,less)

f+=+'test_image.png'
file,ext+=+os.path.splitext(f)

def+get_cropped_line(non_empty_elms,tolerance,S):
++++if+(sum(non_empty_elms)+==+0):
++++++++cropBox+=+()
++++else:
++++++++non_empty_min+=+non_empty_elms.argmax()
++++++++non_empty_max+=+S+-+non_empty_elms[::-1].argmax()%2B1
++++++++cropBox+=+(non_empty_min,non_empty_max)
++++return+cropBox

def+get_cropped_area(image_bw,tol):
++++max_val+=+image_bw.max()
++++tolerance+=+max_val*tol
++++non_empty_elms+=+(image_bw<=tolerance).astype(int)
++++S+=+non_empty_elms.shape
++++#+Traverse+rows
++++cropBox+=+[get_cropped_line(non_empty_elms[k,:],tolerance,S[1])+for+k+in+range(0,S[0])]
++++cropBox+=+filter(None,+cropBox)
++++xmin+=+[k[0]+for+k+in+cropBox]
++++xmax+=+[k[1]+for+k+in+cropBox]
++++#+Traverse+cols
++++cropBox+=+[get_cropped_line(non_empty_elms[:,k],tolerance,S[0])+for+k+in+range(0,S[1])]
++++cropBox+=+filter(None,+cropBox)
++++ymin+=+[k[0]+for+k+in+cropBox]
++++ymax+=+[k[1]+for+k+in+cropBox]
++++xmin+=+min(xmin)
++++xmax+=+max(xmax)
++++ymin+=+min(ymin)
++++ymax+=+max(ymax)
++++ymax+=+ymax-1+#+Not+sure+why+this+is+necessary,+but+it+seems+to+be.
++++cropBox+=+(ymin,+ymax-ymin,+xmin,+xmax-xmin)
++++return+cropBox

def+auto_crop(f,ext):
++++image=Image.open(f)
++++image.load()
++++image_data+=+np.asarray(image)
++++image_data_bw+=+image_data[:,:,0]%2Bimage_data[:,:,1]%2Bimage_data[:,:,2]
++++cropBox+=+get_cropped_area(image_data_bw,grey_tolerance)
++++image_data_new+=+image_data[cropBox[0]:cropBox[1]%2B1,+cropBox[2]:cropBox[3]%2B1+,+:]
++++new_image+=+Image.fromarray(image_data_new)
++++f_new+=+f.replace(ext,'')%2B'_cropped'%2Bext
++++new_image.save(f_new)|code-block|syntax|javascript|404519|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I know that this post is old but, for some reason, none of the suggested answers worked for me. So I hacked my own version from existing answers:

<pre><code>import Image
import numpy as np
import glob
import shutil
import os

grey_tolerance = 0.7 # (0,1) = crop (more,less)

f = 'test_image.png'
file,ext = os.path.splitext(f)

def get_cropped_line(non_empty_elms,tolerance,S):
 if (sum(non_empty_elms) == 0):
 cropBox = ()
 else:
 non_empty_min = non_empty_elms.argmax()
 non_empty_max = S - non_empty_elms[::-1].argmax()+1
 cropBox = (non_empty_min,non_empty_max)
 return cropBox

def get_cropped_area(image_bw,tol):
 max_val = image_bw.max()
 tolerance = max_val*tol
 non_empty_elms = (image_bw&lt;=tolerance).astype(int)
 S = non_empty_elms.shape
 # Traverse rows
 cropBox = [get_cropped_line(non_empty_elms[k,:],tolerance,S[1]) for k in range(0,S[0])]
 cropBox = filter(None, cropBox)
 xmin = [k[0] for k in cropBox]
 xmax = [k[1] for k in cropBox]
 # Traverse cols
 cropBox = [get_cropped_line(non_empty_elms[:,k],tolerance,S[0]) for k in range(0,S[1])]
 cropBox = filter(None, cropBox)
 ymin = [k[0] for k in cropBox]
 ymax = [k[1] for k in cropBox]
 xmin = min(xmin)
 xmax = max(xmax)
 ymin = min(ymin)
 ymax = max(ymax)
 ymax = ymax-1 # Not sure why this is necessary, but it seems to be.
 cropBox = (ymin, ymax-ymin, xmin, xmax-xmin)
 return cropBox

def auto_crop(f,ext):
 image=Image.open(f)
 image.load()
 image_data = np.asarray(image)
 image_data_bw = image_data[:,:,0]+image_data[:,:,1]+image_data[:,:,2]
 cropBox = get_cropped_area(image_data_bw,grey_tolerance)
 image_data_new = image_data[cropBox[0]:cropBox[1]+1, cropBox[2]:cropBox[3]+1 , :]
 new_image = Image.fromarray(image_data_new)
 f_new = f.replace(ext,'')+'_cropped'+ext
 new_image.save(f_new)
</code></pre>

blocks|key|404625|text|这是另一个使用pyvips的版本。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|404626|import+sys
import+pyvips

image+=+pyvips.Image.new_from_file(sys.argv[1])
left,+top,+width,+height+=+image.find_trim(threshold=2,+background=[255,+255,+255])
image+=+image.crop(left,+top,+width,+height)
image.write_to_file(sys.argv[2])|code-block|syntax|javascript|404627|pyvips修剪器对摄影图像很有用。它进行中值滤波，减去背景，找到超过阈值的像素，并删除该集合之外的第一行和最后一列。中位数和阈值意味着它不会被JPEG压缩之类的东西抛弃，在JPEG压缩中，噪声或不可见的压缩伪像可能会混淆其他修剪器。|404628|如果不提供background参数，它将使用(0，0)处的像素。JPEG默认为10，这对于threshold来说恰到好处。|style|CODE|404629|在这里，它在8k+x+8k+pixel+NASA+earth+image上运行|404630|$+time+./trim.py+/data/john/pics/city_lights_asia_night_8k.jpg+x.jpg
real++++0m1.868s
user++++0m13.204s
sys+++++0m0.280s
peak+memory:+100mb|404631|之前：|404632|​|404633|📷|atomic|404634|404635|之后：|404636|404637|404638|404639|有一个blog+post+with+some+more+discussion+here。|404640|entityMap|0|LINK|mutability|MUTABLE|url|https://pypi.python.org/pypi/pyvips|1|https://eoimages.gsfc.nasa.gov/images/imagerecords/79000/79790/city_lights_asia_night_8k.jpg|2|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/a906b2ef3b01c8c409e4e6b07be37f38.jpeg|imageAlt|3|https://ask.qcloudimg.com/http-save/yehe-900000/bbfe6330dabd6324f23a3c412fa5be1c.jpeg|4|http://libvips.blogspot.co.uk/2014/02/trim-auto-crop-with-ruby-vips.html^0|7|6|0|0|0|0|5|A|19|9|0|6|U|1|0|0|0|0|0|1|2|0|0|0|0|0|1|3|0|0|3|14|4|0^^$0|@$1|2|3|4|5|6|7|1R|8|@]|9|@$A|1S|B|1T|1|1U]]|C|$]]|$1|D|3|E|5|F|7|1V|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1W|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1X|8|@$A|1Y|B|1Z|M|N]|$A|20|B|21|M|N]]|9|@]|C|$]]|$1|O|3|P|5|6|7|22|8|@]|9|@$A|23|B|24|1|25]]|C|$]]|$1|Q|3|R|5|F|7|26|8|@]|9|@]|C|$G|H]]|$1|S|3|T|5|6|7|27|8|@]|9|@]|C|$]]|$1|U|3|V|5|6|7|28|8|@]|9|@]|C|$]]|$1|W|3|X|5|Y|7|29|8|@]|9|@$A|2A|B|2B|1|2C]]|C|$]]|$1|Z|3|V|5|6|7|2D|8|@]|9|@]|C|$]]|$1|10|3|11|5|6|7|2E|8|@]|9|@]|C|$]]|$1|12|3|V|5|6|7|2F|8|@]|9|@]|C|$]]|$1|13|3|X|5|Y|7|2G|8|@]|9|@$A|2H|B|2I|1|2J]]|C|$]]|$1|14|3|V|5|6|7|2K|8|@]|9|@]|C|$]]|$1|15|3|16|5|6|7|2L|8|@]|9|@$A|2M|B|2N|1|2O]]|C|$]]|$1|17|3|-4|5|6|7|2P|8|@]|9|@]|C|$]]]|18|$19|$5|1A|1B|1C|C|$1D|1E]]|1F|$5|1A|1B|1C|C|$1D|1G]]|1H|$5|1I|1B|1J|C|$1K|1L|1M|-4]]|1N|$5|1I|1B|1J|C|$1K|1O|1M|-4]]|1P|$5|1A|1B|1C|C|$1D|1Q]]]]

Here's another version using <a href="https://pypi.python.org/pypi/pyvips" rel="nofollow noreferrer">pyvips</a>.
<pre class="lang-py prettyprint-override"><code>import sys
import pyvips

image = pyvips.Image.new_from_file(sys.argv[1])
left, top, width, height = image.find_trim(threshold=2, background=[255, 255, 255])
image = image.crop(left, top, width, height)
image.write_to_file(sys.argv[2])
</code></pre>
The pyvips trimmer is useful for photographic images. It does a median filter, subtracts the background, finds pixels over the threshold, and removes up to the first and last row and column outside this set. The median and threshold mean it is not thrown off by things like JPEG compression, where noise or invisible compression artefacts can confuse other trimmers.
If you don't supply the <code>background</code> argument, it uses the pixel at (0, 0). <code>threshold</code> defaults to 10, which is about right for JPEG.
Here it is running on an <a href="https://eoimages.gsfc.nasa.gov/images/imagerecords/79000/79790/city_lights_asia_night_8k.jpg" rel="nofollow noreferrer">8k x 8k pixel NASA earth image</a>:
<pre><code>$ time ./trim.py /data/john/pics/city_lights_asia_night_8k.jpg x.jpg
real 0m1.868s
user 0m13.204s
sys 0m0.280s
peak memory: 100mb
</code></pre>
Before:
<a href="https://i.stack.imgur.com/4bMKz.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/4bMKz.jpg" alt="Earth at night before crop" /></a>
After:
<a href="https://i.stack.imgur.com/oalTI.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/oalTI.jpg" alt="Earth after crop" /></a>
There's a <a href="http://libvips.blogspot.co.uk/2014/02/trim-auto-crop-with-ruby-vips.html" rel="nofollow noreferrer">blog post with some more discussion here</a>.

blocks|key|186489|text|我测试了在这篇文章中回答的大部分答案，然而，我最终得到了我自己的答案。我用的是蟒蛇python3。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|186490|from+PIL+import+Image,+ImageChops

def+trim(im):
++++bg+=+Image.new(im.mode,+im.size,+im.getpixel((0,0)))
++++diff+=+ImageChops.difference(im,+bg)
++++diff+=+ImageChops.add(diff,+diff,+2.0,+-100)
++++#Bounding+box+given+as+a+4-tuple+defining+the+left,+upper,+right,+and+lower+pixel+coordinates.
++++#If+the+image+is+completely+empty,+this+method+returns+None.
++++bbox+=+diff.getbbox()
++++if+bbox:
++++++++return+im.crop(bbox)

if+__name__+==+"__main__":
++++bg+=+Image.open("test.jpg")+#+The+image+to+be+cropped
++++new_im+=+trim(bg)
++++new_im.show()|code-block|syntax|javascript|186491|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I tested most of the answers replied in this post, however, I was ended up my own answer. I used anaconda python3. 

<pre><code>from PIL import Image, ImageChops

def trim(im):
 bg = Image.new(im.mode, im.size, im.getpixel((0,0)))
 diff = ImageChops.difference(im, bg)
 diff = ImageChops.add(diff, diff, 2.0, -100)
 #Bounding box given as a 4-tuple defining the left, upper, right, and lower pixel coordinates.
 #If the image is completely empty, this method returns None.
 bbox = diff.getbbox()
 if bbox:
 return im.crop(bbox)

if __name__ == "__main__":
 bg = Image.open("test.jpg") # The image to be cropped
 new_im = trim(bg)
 new_im.show()
</code></pre>

blocks|key|404739|text|这是对snew的回复的改进，后者适用于透明背景。使用mathematical+morphology，我们可以让它在白色背景下工作(而不是透明的)，使用以下代码：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|404740|from+PIL+import+Image
from+skimage.io+import+imread
from+skimage.morphology+import+convex_hull_image
im+=+imread('L_2d.jpg')
plt.imshow(im)
plt.title('input+image')
plt.show()
#+create+a+binary+image
im1+=+1+-+rgb2gray(im)
threshold+=+0.5
im1[im1+<=+threshold]+=+0
im1[im1+>+threshold]+=+1
chull+=+convex_hull_image(im1)
plt.imshow(chull)
plt.title('convex+hull+in+the+binary+image')
plt.show()
imageBox+=+Image.fromarray((chull*255).astype(np.uint8)).getbbox()
cropped+=+Image.fromarray(im).crop(imageBox)
cropped.save('L_2d_cropped.jpg')
plt.imshow(cropped)
plt.show()|code-block|syntax|javascript|404741|​|404742|📷|atomic|404743|404744|404745|404746|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/2bf5f9f2d2e04c524c8d30ec91658bac.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/b83bf2a6e3a9576c49ad188ef3d7ba7f.png|2|https://ask.qcloudimg.com/http-save/yehe-900000/c1b77e68b6207eb8084f633ccd3c15c3.png^0|Q|N|0|0|0|0|1|0|0|0|1|1|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|15|8|@$9|16|A|17|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|19|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1A|8|@]|D|@$9|1B|A|1C|1|1D]]|E|$]]|$1|P|3|N|5|O|7|1E|8|@]|D|@$9|1F|A|1G|1|1H]]|E|$]]|$1|Q|3|N|5|O|7|1I|8|@]|D|@$9|1J|A|1K|1|1L]]|E|$]]|$1|R|3|L|5|6|7|1M|8|@]|D|@]|E|$]]|$1|S|3|-4|5|6|7|1N|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z|10|-4]]|11|$5|V|W|X|E|$Y|12|10|-4]]|13|$5|V|W|X|E|$Y|14|10|-4]]]]

This is an improvement over snew's reply, which works for transparent background. With <code>mathematical morphology</code> we can make it work on white background (instead of transparent), with the following code:

<pre><code>from PIL import Image
from skimage.io import imread
from skimage.morphology import convex_hull_image
im = imread('L_2d.jpg')
plt.imshow(im)
plt.title('input image')
plt.show()
# create a binary image
im1 = 1 - rgb2gray(im)
threshold = 0.5
im1[im1 &lt;= threshold] = 0
im1[im1 &gt; threshold] = 1
chull = convex_hull_image(im1)
plt.imshow(chull)
plt.title('convex hull in the binary image')
plt.show()
imageBox = Image.fromarray((chull*255).astype(np.uint8)).getbbox()
cropped = Image.fromarray(im).crop(imageBox)
cropped.save('L_2d_cropped.jpg')
plt.imshow(cropped)
plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/zxtm5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/zxtm5.png" alt="enter image description here"></a>
<a href="https://i.stack.imgur.com/2EWS5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/2EWS5.png" alt="enter image description here"></a>
<a href="https://i.stack.imgur.com/aXFxP.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/aXFxP.png" alt="enter image description here"></a>

blocks|key|404769|text|pilkit已经包含用于自动裁剪TrimBorderColor的处理器。像这样的SOmething应该可以工作：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|404770|from+pilkit.lib+import+Image
from+pilkit.processors+import+TrimBorderColor

img+=+Image.open('/path/to/my/image.png')
processor+=+TrimBorderColor()
new_img+=+processor.process(img)|code-block|syntax|javascript|404771|https://github.com/matthewwithanm/pilkit/blob/b24990167aacbaab3db6d8ec9a02f9ad42856898/pilkit/processors/crop.py#L33|404772|entityMap|0|LINK|mutability|MUTABLE|url^0|0|6|G|F|0|0|0|38|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@$9|U|A|V|B|C]|$9|W|A|X|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Z|8|@]|D|@$9|10|A|11|1|12]]|E|$]]|$1|M|3|-4|5|6|7|13|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|L]]]]

<code>pilkit</code> already contains processor for automatic cropping <code>TrimBorderColor</code>. SOmething like this should work:

<pre class="lang-py prettyprint-override"><code>from pilkit.lib import Image
from pilkit.processors import TrimBorderColor

img = Image.open('/path/to/my/image.png')
processor = TrimBorderColor()
new_img = processor.process(img)
</code></pre>

<a href="https://github.com/matthewwithanm/pilkit/blob/b24990167aacbaab3db6d8ec9a02f9ad42856898/pilkit/processors/crop.py#L33" rel="nofollow noreferrer">https://github.com/matthewwithanm/pilkit/blob/b24990167aacbaab3db6d8ec9a02f9ad42856898/pilkit/processors/crop.py#L33</a>

Can anyone help me figure out what's happening in my image auto-cropping script? I have a png image with a large transparent area/space. I would like to be able to automatically crop that space out and leave the essentials. Original image has a squared canvas, optimally it would be rectangular, encapsulating just the molecule.

here's the original image:
<img src="https://i.stack.imgur.com/AsypD.png" alt="Original Image">

Doing some googling i came across PIL/python code that was reported to work, however in my hands, running the code below over-crops the image.

<pre><code>import Image
import sys

image=Image.open('L_2d.png')
image.load()

imageSize = image.size
imageBox = image.getbbox()

imageComponents = image.split()

rgbImage = Image.new("RGB", imageSize, (0,0,0))
rgbImage.paste(image, mask=imageComponents[3])
croppedBox = rgbImage.getbbox()
print imageBox
print croppedBox
if imageBox != croppedBox:
 cropped=image.crop(croppedBox)
 print 'L_2d.png:', "Size:", imageSize, "New Size:",croppedBox
 cropped.save('L_2d_cropped.png')
</code></pre>

the output is this:<img src="https://i.stack.imgur.com/6Y5r6.png" alt="script&#39;s output">

Can anyone more familiar with image-processing/PLI can help me figure out the issue?

Automatically cropping an image with python/PIL

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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有人能帮我弄清楚我的图像自动裁剪脚本中发生了什么吗？我有一个很大的透明区域/空间的png图像。我希望能够自动裁剪出那个空间，并留下必要的东西。原始图像有一个正方形的画布，最好是长方形的，只封装分子。原图如下：​在做一些谷歌搜索时，我遇到了PIL/python代码，据报道它可以工作，但在我手中，运行下面的代码会过度裁剪图...

问使用python/PIL自动裁剪图像
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