how to use edge detection using gradient magnitude in python

1import numpy as np
2import cv2
3
4# Load the image, convert it to grayscale, and show it
5image = cv2.imread("hand.png")
6image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
7cv2.imshow("Greyscale", image)
8
9# Compute the Laplacian of the image
10lap = cv2.Laplacian(image, cv2.CV_64F)
11lap = np.uint8(np.absolute(lap))
12cv2.imshow("Laplacian", lap)
13cv2.waitKey(0)
14
15# Compute gradients along the X and Y axis, respectively
16sobelX = cv2.Sobel(image, cv2.CV_64F, 1, 0)
17sobelY = cv2.Sobel(image, cv2.CV_64F, 0, 1)
18
19# The sobelX and sobelY images are now of the floating
20# point data type -- we need to take care when converting
21# back to an 8-bit unsigned integer that we do not miss
22# any images due to clipping values outside the range
23# of [0, 255]. First, we take the absolute value of the
24# graident magnitude images, THEN we convert them back
25# to 8-bit unsigned integers
26sobelX = np.uint8(np.absolute(sobelX))
27sobelY = np.uint8(np.absolute(sobelY))
28
29# We can combine our Sobel gradient images using our
30# bitwise OR
31sobelCombined = cv2.bitwise_or(sobelX, sobelY)
32
33# Show our Sobel images
34cv2.imshow("Sobel X", sobelX)
35cv2.imshow("Sobel Y", sobelY)
36cv2.imshow("Sobel Combined", sobelCombined)
37cv2.waitKey(0)
38