How to perform edge detection using Sobel X and Sobel Y in cv2 – easiest explanation – 2022

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So in today’s blog, we are going to see the magic of edge detection using Sobel X and Sobel Y in cv2. Sobel X and Sobel Y are first-order derivatives. I can assure you that, you will be amazed after watching the results. So without any further due, let’s dive into it.

Let’s do it…

Step 1 – Let’s import the required packages.

import cv2
import matplotlib.pyplot as plt

Step 2 – Lets read the image.

imgpath = "test.tiff"
img = cv2.imread(imgpath, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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  • Here we are reading our image and as always converting it back to the RGB format because we know that cv2 reads the image in BGR format by default.

Step 3 – Lets find the edges using Sobel X and Sobel Y in cv2.

Syntax cv2.Sobel(src, ddepth, dx, dy[, ksize[, scale[, delta[, borderType]]]]])

srcinput image
ddepthdepth of the output image
dx and dydx and dy specify whether Sobel-x or Sobel-y is to be used
ksizekernel size
edgesx = cv2.Sobel(img, -1, dx=1, dy=0, ksize=1)
edgesy = cv2.Sobel(img, -1, dx=0, dy=1, ksize=1)
Sobel X
Sobel X
Sobel Y
Sobel Y
  • Sobel X simply finds the first-order derivative in the X-direction. It means that it will detect only those edges which are changing in the X direction (see 1st image).
  • Similarly, Sobel Y finds the first-order derivative in the Y direction. It means that it will detect only those edges which are changing in the Y direction (see 2nd image).

Read my blog on second-order derivative: HOW TO DETECT EDGES USING LAPLACIAN 2ND ORDER DERIVATIVE IN PYTHON USING OPENCV

Step 4 – Let’s merge these results of Sobel X and Sobel Y in cv2.

edges = edgesx + edgesy
  • We can simply add edges in the X direction and edges in the Y direction to get the overall edges in our image (see final image).

Step 5 – Plot the results.

output = [img, edgesx, edgesy, edges]
titles = ['Original', 'x', 'y', 'Edges']

for i in range(4):
    plt.subplot(2, 2, i + 1)
    plt.imshow(output[i], cmap='gray')
    plt.title(titles[i])
    plt.xticks([])
    plt.yticks([])
plt.show()
Sobel X and Sobel Y
Final results

Let’s see the whole code…

import cv2
import matplotlib.pyplot as plt

imgpath = "test.tiff"
img = cv2.imread(imgpath, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

edgesx = cv2.Sobel(img, -1, dx=1, dy=0, ksize=1)
edgesy = cv2.Sobel(img, -1, dx=0, dy=1, ksize=1)

edges = edgesx + edgesy

output = [img, edgesx, edgesy, edges]
titles = ['Original', 'x', 'y', 'Edges']

for i in range(4):
    plt.subplot(2, 2, i + 1)
    plt.imshow(output[i], cmap='gray')
    plt.title(titles[i])
    plt.xticks([])
    plt.yticks([])
plt.show()

NOTE Read more about Sobel on OpenCV’s documentation page.

Do let me know if there’s any query regarding Sobel X and Sobel Y in cv2 by contacting me on email or LinkedIn.

So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time …

Read my previous post: ROTATING AND SCALING IMAGES – A FUN APPLICATION IN PYTHON USING OPENCV

Check out my other machine learning projectsdeep learning projectscomputer vision projectsNLP projectsFlask projects at machinelearningprojects.net.

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