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Interfacing the Logitech C270 Webcam with the NVIDIA Jetson Nano

Updated: Jul 4, 2023

In this post, I'm going to guide you through the process of interfacing the Logitech C270 Webcam with the NVIDIA Jetson Nano. This tutorial is ideal for beginners, so I'll be explaining each step in detail, covering each line of code and the reason behind it.

Why Choose the Jetson Nano?

The NVIDIA Jetson Nano is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. Coupled with a webcam, the Jetson Nano can serve as a powerful tool for a multitude of computer vision applications.


Before we start, please ensure you have the following:

  • NVIDIA Jetson Nano Developer Kit

  • Logitech C270 Webcam

  • Monitor, mouse, and keyboard

  • Jetson Nano Developer Kit SD Card Image (JetPack)

We are assuming that your Jetson Nano has been set up with the JetPack Image, and you have a terminal ready to enter the following commands. We're not explicitly installing Python3 because the Jetson Nano already has it installed out-of-the-box with the JetPack Image.

Step 1: Installing the Necessary Software and Libraries

Open your terminal on Jetson Nano and update the system using the following commands:

sudo apt-get update
sudo apt-get upgrade

Next, we will install the OpenCV library, which is essential for our project. OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library, which includes several hundreds of computer vision algorithms. You can use it to process images and videos to detect faces, identify objects, classify human actions in videos, etc.

Open up a terminal and enter the following line to install OpenCV

sudo apt-get install python3-opencv

Step 2: Testing the Webcam

Now it's time to plug in your webcam into one of the USB ports of the Jetson Nano. To check whether the webcam is working correctly, we will use a simple Python script.

Open up a new Python file in your terminal:


This opens a new file called `` in a text editor. Paste the following Python code in it:

import cv2
cap = cv2.VideoCapture(0)
while True:
    ret, frame =
    cv2.imshow('Webcam Live View', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):

Here's a line-by-line breakdown of what this script does:

- `import cv2`: This line imports the OpenCV library, which we installed earlier.

- `cap = cv2.VideoCapture(0)`: This line creates a VideoCapture object. The number 0 specifies that we want to use the first camera attached to the computer, which in this case is our webcam.

- `while True:`: This is an infinite loop where we will continuously capture frames from the webcam.

- `ret, frame =`: The `read()` function reads one frame from the video source (in this case, the webcam). It returns two values: a boolean (`ret`) indicating the success of the read operation, and the image (`frame`) itself.

- `cv2.imshow('Webcam Live View', frame)`: This function creates a window with the name 'Webcam Live View' and shows the image in `frame` in this window.

- `if cv2.waitKey(1) & 0xFF == ord('q')`: This line has a couple of operations. `cv2.waitKey(1)` waits for 1 millisecond

for a key event, and returns the ASCII value of the key pressed. The `& 0xFF` operation is a bitwise AND that ensures we only keep the last 8 bits of the number returned by `cv2.waitKey(1)`. The `== ord('q')` checks if the key pressed was 'q'. If 'q' was pressed, the expression will be True and the loop will break, stopping the video feed.

- `cap.release()`: This line releases the webcam.

- `cv2.destroyAllWindows()`: This finally destroys all the windows created during the process.

Save the file and close the text editor. Run the script with this command in the terminal:


If your webcam is correctly set up, a window named 'Webcam Live View' will appear, displaying the live video from your webcam.

Congratulations! You've successfully interfaced your Logitech C270 Webcam with the NVIDIA Jetson Nano. In one of the future blog posts, we will dive deeper into using the webcam feed for object detection. Stay tuned!

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