Deploy Computer Vision Pipelines with OpenCV on Google Cloud C4A Axion VM

Signed-off-by: odidev <odidev@puresoftware.com>

Author: odidev

content/learning-paths/servers-and-cloud-computing/opencv-on-axion/_index.md

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+---
+title: Build Computer Vision Pipelines with OpenCV on Google Cloud C4A Axion VM
+description: Deploy and run OpenCV-based computer vision pipelines on Google Cloud Axion C4A Arm-based VMs, covering image processing, video pipelines, browser-based visualization, and integration with machine learning models.
+    
+minutes_to_complete: 45
+
+who_is_this_for: This is an introductory topic for DevOps engineers, software developers, and AI practitioners who want to build and run computer vision pipelines on SUSE Linux Enterprise Server (SLES) Arm64 using OpenCV, process images and videos, visualize outputs in real time, and integrate ML models.
+
+learning_objectives:
+    - Install and configure OpenCV on Google Cloud C4A Axion Arm64 instances
+    - Build image processing pipelines using OpenCV
+    - Develop video processing pipelines with real-time frame updates
+    - Visualize OpenCV outputs in the browser using an HTTP server
+    - Integrate OpenCV pipelines with machine learning models 
+
+prerequisites:
+  - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
+  - Basic familiarity with Python and Linux command line
+  - Understanding of basic image/video processing concepts
+
+author: Pareena Verma
+
+##### Tags
+skilllevels: Introductory
+subjects: ML
+cloud_service_providers:
+  - Google Cloud
+
+armips:
+  - Neoverse
+
+tools_software_languages:
+  - OpenCV
+  - Python
+  - NumPy
+  - Flask
+
+operatingsystems:
+  - Linux
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+
+further_reading:
+  - resource:
+      title: OpenCV official documentation
+      link: https://docs.opencv.org/
+      type: documentation
+
+  - resource:
+      title: OpenCV GitHub repository
+      link: https://github.com/opencv/opencv
+      type: documentation
+
+  - resource:
+      title: NumPy documentation
+      link: https://numpy.org/doc/
+      type: documentation
+
+weight: 1
+layout: "learningpathall"
+learning_path_main_page: yes
+---

content/learning-paths/servers-and-cloud-computing/opencv-on-axion/_next-steps.md

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+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/servers-and-cloud-computing/opencv-on-axion/background.md

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+---
+title: Get started with OpenCV on Google Axion C4A
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Explore Axion C4A Arm instances in Google Cloud
+
+Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for modern cloud workloads such as CI/CD pipelines, microservices, media processing, and general-purpose applications.
+
+The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability and performance benefits of the Arm architecture in Google Cloud.
+
+To learn more, see the Google blog [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).
+
+## Explore OpenCV on Google Axion C4A (Arm Neoverse V2)
+
+OpenCV (Open Source Computer Vision Library) is a widely used open-source library for building real-time computer vision and image processing applications. It provides optimized implementations for image transformations, video processing, object detection, and integration with machine learning models.
+
+Key capabilities of OpenCV include:
+
+* **Image Processing** for transformations, filtering, and feature extraction  
+* **Video Processing** for frame-by-frame analysis and real-time pipelines  
+* **Drawing & Visualization** for overlaying text, shapes, and annotations  
+* **ML Integration** for combining computer vision with machine learning models  
+
+Running OpenCV on Google Axion C4A Arm-based infrastructure enables efficient execution of image and video workloads by leveraging multi-core CPU parallelism. This allows:
+
+* Faster frame processing for video pipelines  
+* Efficient memory utilization for image transformations  
+* Improved performance-per-watt compared to x86 systems  
+* Cost-effective scaling for computer vision workloads  
+
+Common use cases include real-time video analytics, image transformation pipelines, automated inspection systems, and ML-powered vision applications.
+
+To learn more, visit the [OpenCV documentation](https://docs.opencv.org/) and explore the [OpenCV GitHub repository](https://github.com/opencv/opencv).
+
+## What you've accomplished and what's next
+
+In this section, you:
+
+* Explored Google Axion C4A Arm-based VMs and their benefits for computer vision workloads  
+* Understood OpenCV capabilities for image and video processing  
+* Learned how Arm architecture improves performance and efficiency for OpenCV pipelines  
+
+Next, you'll create a firewall rule to enable browser-based visualization of OpenCV pipelines running on your Arm-based virtual machine.d in this Learning Path.

content/learning-paths/servers-and-cloud-computing/opencv-on-axion/firewall-setup.md

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+---
+
+title: Create a firewall rule for OpenCV Browser Visualization
+
+weight: 3
+ 
+### FIXED, DO NOT MODIFY
+
+layout: learningpathall
+
+---
+ 
+Create a firewall rule in Google Cloud Console to expose the required port for OpenCV browser-based visualization.
+ 
+{{% notice Note %}}
+
+For help with GCP setup, see the Learning Path [Getting started with Google Cloud Platform](/learning-paths/servers-and-cloud-computing/csp/google/).
+
+{{% /notice %}}
+ 
+## Configure the firewall rule
+ 
+Navigate to the [Google Cloud Console](https://console.cloud.google.com/), go to **VPC Network > Firewall**, and select **Create firewall rule**.
+ 
+![Google Cloud Console VPC Network Firewall page showing the Create firewall rule button in the top menu bar#center](images/firewall-rule.png "Create a firewall rule in Google Cloud Console")
+ 
+Next, create the firewall rule that exposes the required port for OpenCV visualization.
+ 
+Set the **Name** of the new rule to "allow-opencv-port". Select your network that you intend to bind to your VM.
+ 
+Set **Direction of traffic** to "Ingress".  
+
+Set **Allow on match** to "Allow".  
+
+Set **Targets** to "Specified target tags".  
+
+Enter "allow-opencv" in the **Target tags** field.  
+
+Set **Source IPv4 ranges** to "0.0.0.0/0".
+ 
+![Google Cloud Console Create firewall rule form configured for OpenCV with Ingress and Allow settings#center](images/network-rule.png "Configuring the OpenCV firewall rule")
+ 
+## Configure port
+ 
+Under **Protocols and ports**, select **Specified protocols and ports**.
+ 
+Select the **TCP** checkbox and enter:
+ 
+```text
+8000
+```
+
+Then select **Create**.
+
+![Google Cloud Console Protocols and ports section showing TCP checkbox selected with port 8000 configured for OpenCV Browser Visualization#center](images/network-port.png "Setting Ray ports in the firewall rule")
+
+## What you've accomplished and what's next
+In this section, you:
+
+* Created a firewall rule for OpenCV visualization
+* Enabled external browser access to your VM
+* Exposed port 8000 for real-time pipeline outputs
+
+Next, you'll:
+
+* Run image and video pipelines
+* Integrate ML models with OpenCV
+* Optimize performance on Arm-based systems