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.

Table of Contents

### 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)

- 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]]]]])*

src | input image |

ddepth | depth of the output image |

dx and dy | dx and dy specify whether Sobel-x or Sobel-y is to be used |

ksize | kernel 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 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()

### 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 projects, deep learning projects, computer vision projects, NLP projects, Flask projects at machinelearningprojects.net**.