如何在Python中检测边缘并裁剪图像 [英] How to detect edge and crop an image in Python

问题描述

我是Python图像处理的新手，我正在尝试解决一个常见问题.我有一个带有人签名的图像.我想找到边缘并将其裁剪以适合图像中的签名.

I'm new to Image Processing in Python and I'm trying to solve a common problem. I have an image having a signature of a person. I want to find the edges and crop it to fit the signature in the image.

我尝试了Canny Edge Detection，并使用一系列使用PIL，CV2的解决方案(文章和答案)裁剪图像，但是似乎没有任何效果.我正在寻找一个可行的解决方案.

I tried Canny Edge Detection and cropping the image using a list of existing solutions (articles & answers) using PIL, CV2, but none seem to work. I'm looking for a working solution.

我尝试过的一些解决方案:

Some solutions I tried:

1. 在边缘检测后从各个方向裁剪图像

   如何裁剪出最大的矩形图片

   还有更多...尽管看起来很简单，但是没有任何工作.使用任何现有解决方案时，我都遇到错误或无法预期的输出.

   and many more... None worked although seems very simple. I encountered either errors or not expected output using any of the existing solutions.

   推荐答案

   您需要的是阈值化.在OpenCV中，您可以使用 .

   What you need is thresholding. In OpenCV you can accomplish this using cv2.threshold().

   我开枪了.我的方法如下:

   I took a shot at it. My approach was the following:

   1. 转换为灰度
   2. 阈值图像仅获得签名而没有其他内容
   3. 查找那些在阈值图像中显示的像素
   4. 以原始灰度围绕该区域
   5. 从作物中创建一个新的阈值图像，显示效果不严格

   这是我的尝试，我认为效果很好.

   Here was my attempt, I think it worked pretty well.

   import cv2
   import numpy as np
   
   # load image
   img = cv2.imread('image.jpg') 
   rsz_img = cv2.resize(img, None, fx=0.25, fy=0.25) # resize since image is huge
   gray = cv2.cvtColor(rsz_img, cv2.COLOR_BGR2GRAY) # convert to grayscale
   
   # threshold to get just the signature
   retval, thresh_gray = cv2.threshold(gray, thresh=100, maxval=255, type=cv2.THRESH_BINARY)
   
   # find where the signature is and make a cropped region
   points = np.argwhere(thresh_gray==0) # find where the black pixels are
   points = np.fliplr(points) # store them in x,y coordinates instead of row,col indices
   x, y, w, h = cv2.boundingRect(points) # create a rectangle around those points
   x, y, w, h = x-10, y-10, w+20, h+20 # make the box a little bigger
   crop = gray[y:y+h, x:x+w] # create a cropped region of the gray image
   
   # get the thresholded crop
   retval, thresh_crop = cv2.threshold(crop, thresh=200, maxval=255, type=cv2.THRESH_BINARY)
   
   # display
   cv2.imshow("Cropped and thresholded image", thresh_crop) 
   cv2.waitKey(0)
   

   结果如下:

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