在Python中使用OpenCV（CV2）对图像进行边缘检测

转载：https://blog.csdn.net/cumtb2002/article/details/107798767

 

Modules used:

使用的模块：

For this, we will use the opencv-python module which provides us various functions to work on images.

为此，我们将使用opencv-python模块，该模块为我们提供了处理图像的各种功能。

Download opencv-python

下载opencv-python

1.  
   General Way:
2.  
   pip install opencv-python
3.  
    
4.  
   Pycharm Users:
5.  
   Go to the project Interpreter and install this module from there.

opencv-python Module:

opencv-python模块：

opencv-python is a python library that will solve the Computer Vision Problems and provides us various functions to edit the Images.

opencv-python是一个python库，它将解决计算机视觉问题并为我们提供编辑图像的各种功能。

Note: The edge Detection is possible only in grayscale Image.

注意：只能在灰度图像中进行边缘检测。

What we will do in this script?

我们将在此脚本中做什么？

To detect the edges of the images we will use opencv-python various Functions and Provide thresholds.

为了检测图像的边缘，我们将使用opencv-python的各种功能并提供阈值。

In this article we will detect the edge of the Image with the help of various functions and the accuracy of edge increases as we go down,

在本文中，我们将借助各种功能来检测图像的边缘，并且当我们下降时边缘的精度会提高，

• Sobel Function: This Function will create the Horizontal and vertical edges and after that, we will use the Bitwise or operator to combine them

  Sobel函数 ：此函数将创建水平边缘和垂直边缘，然后，我们将使用按位或运算符将它们组合

• Laplacian Function: This Function is the simplest Function in which we just have to put the Grayscale Variable into it, and we will get the edge detected image.

  拉普拉斯函数 ：此函数是最简单的函数，只需要将灰度变量放入其中，就可以得到边缘检测到的图像。

• Canny Function: This is the most powerful function for edge detection and most accurate.

  Canny功能 ：这是边缘检测功能最强大且最准确的功能。

Let's see the code:

让我们看一下代码：

1)使用Sobel函数 (1) Using Sobel Function)

1.  
   # importing the module
2.  
   import cv2
3.  
    
4.  
   # read the image and store the data in a variable
5.  
   image=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")
6.  
    
7.  
   # make it grayscale
8.  
   Gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
9.  
    
10.  
    # Make it with the help of sobel
11.  
    # make the sobel_horizontal
12.  
    # For horizontal x axis=1 and yaxis=0
13.  
    # for vertical x axis=0 and y axis=1
14.  
    Horizontal=cv2.Sobel(Gray,0,1,0,cv2.CV_64F)
15.  
     
16.  
    # the thresholds are like
17.  
    # (variable,0,<x axis>,<y axis>,cv2.CV_64F)
18.  
    Vertical=cv2.Sobel(Gray,0,0,1,cv2.CV_64F)
19.  
     
20.  
    # DO the Bitwise operation
21.  
    Bitwise_Or=cv2.bitwise_or(Horizontal,Vertical)
22.  
     
23.  
    # Show the Edged Image
24.  
    cv2.imshow("Sobel Image",Bitwise_Or)
25.  
    cv2.imshow("Original Image",Gray)
26.  
    cv2.waitKey(0)
27.  
    cv2.destroyAllWindows()

Output:

输出：

2)拉普拉斯函数 (2) Laplacian Function)

1.  
   # importing the module
2.  
   import cv2
3.  
    
4.  
   # read the image and store the data in a variable
5.  
   image=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")
6.  
    
7.  
   # make it grayscale
8.  
   Gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
9.  
    
10.  
    # Make Laplacian Function
11.  
    Lappy=cv2.Laplacian(Gray,cv2.CV_64F)
12.  
     
13.  
    cv2.imshow("Laplacian",Lappy)
14.  
    cv2.imshow("Original",Gray)
15.  
    cv2.waitKey(0)
16.  
    cv2.destroyAllWindows()

Output:

输出：

3)使用Canny函数 (3) Using Canny Function)

1.  
   # importing the module
2.  
   import cv2
3.  
    
4.  
   # read the image and store the data in a variable
5.  
   image=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")
6.  
    
7.  
   # make it grayscale
8.  
   Gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
9.  
    
10.  
    # Make canny Function
11.  
    canny=cv2.Canny(Gray,40,140)
12.  
     
13.  
    # the threshold is varies bw 0 and 255
14.  
    cv2.imshow("Canny",canny)
15.  
    cv2.imshow("Original",Gray)
16.  
    cv2.waitKey(0)
17.  
    cv2.destroyAllWindows()

Output:

输出：

翻译自: https://www.includehelp.com/python/edge-detection-of-image-using-opencv-cv2.aspx