Running AI payloads locally can be costly and maintaining models can be challenging. Leveraging Microsoft Azure cognitive services offers an affordable and efficient approach to accessing high-quality machine learning algorithms. In this series of lab notes, we will explore how to integrate the Raspberry Pi camera module with Azure intelligence.  

The Raspberry Pi Camera Module:

The Raspberry Pi camera modules are widely available, and the official Raspberry Pi camera module offers excellent support and easy setup. For this guide, we will utilize the camera module depicted in the figure below, which lacks auto-focus but is otherwise complete.  

This camera module enables Raspberry Pi's vision capabilities

Accessing Azure Cognitive Services:

Accessing Azure cognitive services involves working with a principal object that acts as an intermediary between the local client and the Microsoft server. The flowchart below illustrates the structure of the principal object.  

The principal object acts as an intermediate broker between the Microsoft server and the local client

To begin, sign up for a new account on portal.azure.com, where you can create a new resource of the type "Cognitive Services." Navigate to the "Keys and Endpoints" section within the resource management to find two access keys. These keys serve as cryptographic keys that grant access to your cognitive service utilization allowance.  

Using the Python API:

The Raspberry Pi camera module comes with a Python interface. Recently, an optimized and more standardized version of the camera library, called picamera2, has been released. You can find documentation about this library at https://www.raspberrypi.com/news/a-preview-release-of-the-picamera2-library/.  

For the following steps, we assume a Raspberry Pi 4 running the latest Raspberry Pi OS. The computer vision payload using Azure cognitive services can be implemented using the code snippet below:  

[## START - Python code snippet]  

The principal object acts as an intermediate broker between the Microsoft server and the local client

[## END - Python code snippet]  

In principle, we use a local file on the Raspberry Pi's memory to act as a dump for the information provided by the camera. In the next step, we use the above-mentioned broker object to act as a bridge transmitting the information to the backend. At this point, a quick experiment can be done. As we store the Raspberry Pi camera image to the memory card, we can extract it after running the program. The two features show how the recognition worked out  

The principal object acts as an intermediate broker between the Microsoft server and the local client

The principal object acts as an intermediate broker between the Microsoft server and the local client

Conclusion

Integrating Raspberry Pi computer vision with Azure cognitive services offers an affordable and straightforward approach to accessing high-quality machine learning algorithms. This article provides a glimpse into the possibilities, but due to space constraints, it only covers a small range of potential applications. We encourage you to explore the documentation to discover more about the Azure cognitive services interface for Raspberry Pi. With a similar code structure to the one presented here, you can perform various advanced image analysis tasks.