# How to perform edge detection using Sobel X and Sobel Y in cv2 – easiest explanation – 2023

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 do it…

## Step 1 – Let’s import the required packages.

```import cv2
import matplotlib.pyplot as plt```

## Step 2 – Let’s read the image.

```imgpath = "test.tiff"
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 – Let’s find the edges using Sobel X and Sobel Y in cv2.

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

```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.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()```