使用 python/PIL 自动裁剪图像 [英] Automatically cropping an image with python/PIL

问题描述

谁能帮我弄清楚我的图像自动裁剪脚本中发生了什么?我有一个带有大透明区域/空间的 png 图像.我希望能够自动裁剪该空间并留下必需品.原始图像有一个正方形的画布，最好是矩形的，只包含分子.

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.

这是原图:

通过谷歌搜索，我发现 PIL/python 代码被报告可以工作，但在我手中，运行下面的代码过度裁剪图像.

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.

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')

输出是这样的:

任何更熟悉图像处理/PLI 的人能帮我找出问题吗?

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

推荐答案

您可以使用 numpy，将图像转换为数组，找到所有非空的列和行，然后从这些列和行创建图像:

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

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]+1, cropBox[2]:cropBox[3]+1 , :]

new_image = Image.fromarray(image_data_new)
new_image.save('L_2d_cropped.png')

结果看起来像

如果有什么不清楚的，尽管问.

If anything is unclear, just ask.

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