Build computer vision pipelines with OpenCV on Google Axion
Introduction
Understand OpenCV on Google Axion C4A
Create a firewall rule for OpenCV browser visualization
Create a Google Axion C4A Arm virtual machine
Build OpenCV pipelines on Google Axion
Use machine learning models with OpenCV on Google Axion
Next Steps

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These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which C4A machine type should I use?
Use the c4a-standard-4 machine type with four vCPUs and 16 GB of memory.
Which port do I open for the browser view, and how do I check that it works?
Open TCP port 8000 with a VPC firewall rule. After you start the project’s HTTP server, visit http://EXTERNAL_IP:8000. A page that loads confirms the rule and service are configured.
What Python version do I need during setup?
Install Python 3.11. Additionally, install development tools with zypper to support building OpenCV’s pip package.
Where are the project files and virtual environment created?
Use the ~/opencv-project directory and a virtual environment named cv-env. Run subsequent scripts from that directory with the environment activated.
How do I save the trained model and load it in the OpenCV pipeline?
Use joblib.dump in the training script to save the classifier as iris_model.joblib and the label names as iris_labels.joblib. In the OpenCV pipeline, use joblib.load to load both files so you can generate predictions without retraining the model.

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