如何从JPEG裁剪多个矩形或正方形？ [英] How to crop multiple rectangles or squares from JPEG?

问题描述

我有一个jpeg，我想在其中裁剪包含图形的部分（底部的那部分）。

I have a jpeg from where I want to crop a portion containing graph (the one in the bottom portion).

到目前为止，我使用此代码来实现相同：

As of now I used this code to achieve the same:

from PIL import Image

img = Image.open(r'D:\aakash\graph2.jpg')
area = (20, 320, 1040, 590)
img2 = img.crop(area)
# img.show()
img2.show()

但是我通过多次猜测x1，y1，x2，y2来实现这一点

But I achieved this by guessing the x1, y1, x2, y2 multiple times to arrive at this (guess work).

裁剪前的图像：

Image before cropping:

裁剪后的图像：

Image after cropping:

我完全是新手基于某些逻辑的图像裁剪。如果位置相同，如何成功裁剪所有图形以创建单独的图像？

I'm totally novice in image cropping based on some logic. How can I successfully crop all graphs to create separate images given the positions are same?

更新：我相信，这不可能重复之所以要解决这个问题，是因为尽管逻辑上是相同的，但是集群逻辑的工作方式却有所不同。在这个问题中，只有2条垂直白线可以划分，但是这里有2条水平线和2条垂直线，我几乎不知道如何使用KMeans解决此类图像聚类。

Update: I believe, it is not a possible duplicate of that problem because, even though logically that's same, but the way the clustering logic will work is different. In that question, there are only 2 vertical white lines to divide, but here, there's two horizontal and two vertical lines, and I hardly have a clue on how to use KMeans to solve this kind of image clustering.

在 sklearn's KMeans 方面的专家的帮助下解决此类问题将受到高度赞赏。

Help from someone who's an expert with sklearn's KMeans to solve this kind of problem shall be highly appreciated.

推荐答案

这是另一种方法，但是使用PIL / Pillow和 skimage 而不是OpenCV：

Here's another way to do it, but using PIL/Pillow and skimage rather than OpenCV:

#!/usr/local/bin/python3

import numpy as np
from PIL import Image, ImageFilter
from skimage.measure import label, regionprops

# Load image and make Numpy version and greyscale PIL version
pim = Image.open('article.jpg')
n   = np.array(pim)
pgr = pim.convert('L')

# Threshold to make black and white
thr = pgr.point(lambda p: p < 230 and 255)
# Following line is just for debug
thr.save('result-1.png')

# Median filter to remove noise
fil = thr.filter(ImageFilter.MedianFilter(11))
# Following line is just for debug
fil.save('result-2.png')

# Make Numpy version for skimage to use
nim = np.array(fil)

# Image is now white blobs on black background, so label() it
label_image=label(nim)

# Iterate through blobs, saving each to disk
i=0
for region in regionprops(label_image):
   if region.area >= 100:
      # Extract rectangle containing blob and save
      name="blob-" + str(i) + ".png"
      minr, minc, maxr, maxc = region.bbox
      Image.fromarray(n[minr:maxr,minc:maxc,:]).save(name)
      i = i + 1

给出以下输出图像：

中间的调试映像为 result-1.png ：

和结果2。 png ：

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