What is the method for extracting image edges in Python?

Commonly used image edge detection methods in Python include Sobel operator, Canny edge detection, and Laplacian operator. These methods can be implemented using the OpenCV library.

1. Sobel Operator: The Sobel operator is a gradient-based method for edge detection that calculates the gradients of an image in both horizontal and vertical directions, merging them to produce the final gradient image. This technique can effectively detect edges in an image.

import cv2
import numpy as np

img = cv2.imread('image.jpg', 0)
edges = cv2.Sobel(img, cv2.CV_64F, 1, 1, ksize=5)

cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

1. Canny edge detection: The Canny edge detection is a classic algorithm that combines multiple steps including Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold detection to detect edges in an image.

import cv2

img = cv2.imread('image.jpg', 0)
edges = cv2.Canny(img, 100, 200)

cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

1. The Laplacian operator is a method of edge detection based on second-order derivatives, used to detect edges in images by obtaining the second derivative of the image and detecting edges based on the derivative values.

import cv2

img = cv2.imread('image.jpg', 0)
edges = cv2.Laplacian(img, cv2.CV_64F)

cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

These methods can be chosen based on the actual situation to achieve the best edge detection results.