在Python中使用OpenCV（CV2）对图像进行边缘检测

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

General Way:
pip install opencv-python
Pycharm Users:
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)

# importing the module
import cv2
# read the image and store the data in a variable
image=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")
# make it grayscale
Gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Make it with the help of sobel
# make the sobel_horizontal
# For horizontal x axis=1 and yaxis=0
# for vertical x axis=0 and y axis=1
Horizontal=cv2.Sobel(Gray,0,1,0,cv2.CV_64F)
# the thresholds are like 
# (variable,0,<x axis>,<y axis>,cv2.CV_64F)
Vertical=cv2.Sobel(Gray,0,0,1,cv2.CV_64F)
# DO the Bitwise operation
Bitwise_Or=cv2.bitwise_or(Horizontal,Vertical)
# Show the Edged Image
cv2.imshow("Sobel Image",Bitwise_Or)
cv2.imshow("Original Image",Gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

输出：

Python | Edge Detection of Image using OpenCV (CV2) (1)

2)拉普拉斯函数 (2) Laplacian Function)

# importing the module
import cv2
# read the image and store the data in a variable
image=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")
# make it grayscale
Gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Make Laplacian Function
Lappy=cv2.Laplacian(Gray,cv2.CV_64F)
cv2.imshow("Laplacian",Lappy)
cv2.imshow("Original",Gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

输出：

Python | Edge Detection of Image using OpenCV (CV2) (2)

3)使用Canny函数 (3) Using Canny Function)

# importing the module
import cv2
# read the image and store the data in a variable
image=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")
# make it grayscale
Gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Make canny Function
canny=cv2.Canny(Gray,40,140)
# the threshold is varies bw 0 and 255
cv2.imshow("Canny",canny)
cv2.imshow("Original",Gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

输出：