We are enormously proud to reveal The Official Raspberry Pi Camera Module Guide (2nd edition), which is out now. David Plowman, a Raspberry Pi engineer specialising in camera software, algorithms, and image-processing hardware, authored this official guide.

This detailed book walks you through all the different types of Camera Module hardware, including Raspberry Pi Camera Module 3, High Quality Camera, Global Shutter Camera, and older models; discover how to attach them to Raspberry Pi and integrate vision technology with your projects. This edition also covers new code libraries, including the latest PiCamera2 Python library and rpicam command-line applications, as well as integration with the new Raspberry Pi AI Kit.

Save time with our starter guide

Our starter guide has clear diagrams explaining how to connect various Camera Modules to the new Raspberry Pi boards. It also explains how to fit custom lenses to HQ and GS Camera Modules using C-CS adaptors. Everything is outlined in step-by-step tutorials with diagrams and photographs, making it quick and easy to get your camera up and running.

Test your camera properly

You’ll discover how to connect your camera to a Raspberry Pi and test it using the new rpicam command-line applications — these replace the older libcam applications. The guide also covers the new PiCamera2 Python library, for integrating Camera Module technology with your software.

Get more from your images

Discover detailed information about how Camera Module works, and how to get the most from your images. You’ll learn how to use RAW formats and tuning files, HDR modes, and preview windows; custom resolutions, encoders, and file formats; target exposure and autofocus; shutter speed, and gain, enabling you to get the very best out of your imaging hardware.

Build smarter projects with AI Kit integration

A new chapter covers the integration of the AI Kit with Raspberry Pi Camera Modules to create smart imaging applications. This adds neural processing to your projects, enabling fast inference of objects captured by the camera.

Boost your skills with pre-built projects

The Official Raspberry Pi Camera Module Guide is packed with projects. Take selfies and stop-motion videos, experiment with high-speed and time-lapse photography, set up a security camera and smart door, build a bird box and wildlife camera trap, take your camera underwater, and much more! All of the code is tested and updated for the latest Raspberry Pi OS, and is available on GitHub for inspection.

Click here to pick up your copy of The Official Raspberry Pi Camera Module Guide (2nd edition).

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This #MagPiMonday, we’re hoping to inspire you to add artificial intelligence to your Raspberry Pi designs with this feature by Phil King, from the latest issue of The MagPi.

With their powerful AI accelerator modules, Raspberry Pi’s Camera Module and AI Kit open up exciting possibilities in computer vision and machine learning. The versatility of the Raspberry Pi platform, combined with AI capabilities, opens up a world of new possibilities for innovative smart projects. From creative experiments to practical applications like smart pill dispensers, makers are harnessing the kit’s potential to push the boundaries of AI. In this feature, we explore some standout projects, and hope they inspire you to embark on your own.

Peeper Pam boss detector

By VEEB Projects

AI computer vision can identify objects within a live camera view. In this project, VEEB’s Martin Spendiff and Vanessa Bradley have used it to detect humans in the frame, so you can tell if your boss is approaching behind you as you sit at your desk!

The project comprises two parts. A Raspberry Pi 5 equipped with a Camera Module and AI Kit handles the image recognition and also acts as a web server. This uses web sockets to send messages wirelessly to the ‘detector’ part — a Raspberry Pi Pico W and a voltmeter whose needle moves to indicate the level of AI certainty for the ID.

Having got their hands on an AI Kit — “a nice intro into computer vision” — it took the pair just three days to create Peeper Pam. “The most challenging bit was that we’d not used sockets — more efficient than the Pico constantly asking Raspberry Pi ‘do you see anything?’,” says Martin. “Raspberry Pi does all the heavy lifting, while Pico just listens for an ‘I’ve seen something’ signal.”

While he notes that you could get Raspberry Pi 5 to serve both functions, the two-part setup means you can place the camera in a different position to monitor a spot you can’t see. Also, by adapting the code from the project’s GitHub repo, there are lots of other uses if you get the AI to deter other objects. “Pigeons in the window box is one that we want to do,” Martin says.

Monster AI Pi PC

By Jeff Geerling

Never one to do things by halves, Jeff Geerling went overboard with Raspberry Pi AI Kit and built a Monster AI Pi PC with a total of eight neural processors. In fact, with 55 TOPS (trillions of operations per second), it’s faster than the latest AMD, Qualcomm, and Apple Silicon processors!

The NPU chips — including the AI Kit’s Hailo-8L — are connected to a large 12× PCIe slot card with a PEX 8619 switch capable of handling 16 PCI Express Gen 2 lanes. The card is then mounted on a Raspberry Pi 5 via a Pineboards uPCIty Lite HAT, which has an additional 12V PSU to supply the extra wattage needed for all those processors.

With a bit of jiggery-pokery with the firmware and drivers on Raspberry Pi, Jeff managed to get it working.

Car detection & tracking system

By Naveen

As a proof of concept, Japanese maker Naveen aimed to implement an automated system for identifying and monitoring cars at toll plazas to get an accurate tally of the vehicles entering and exiting.

With the extra processing power provided by a Raspberry AI Kit, the project uses Edge Impulse computer vision to detect and count cars in the view from a Camera Module Wide. “We opted for a wide lens because it can capture a larger area,” he says, “allowing the camera to monitor multiple lanes simultaneously.” He also needed to train and test a YOLOv5 machine learning model. All the details can be found on the project page via the link above, which could prove useful for learning how to train custom ML models for your own AI project.

Safety helmet detection system

By Shakhizat Nurgaliyev

Wearing a safety helmet on a building site is essential and could save your life. This computer vision project uses Raspberry Pi AI Kit with the advanced YOLOv8 machine learning model to quickly and accurately identify objects within the camera view, running at an impressive inference speed of 30fps.

The project page has a guide showing how to make use of Raspberry Pi AI Kit to achieve efficient AI inferencing for safety helmet detection. This includes details of the software installation and model training process, for which the maker has provided a link to a dataset of 5000 images with bounding box annotations for three classes: helmet, person, and head.

Accelerating MediaPipe models

By Mario Bergeron

Google’s MediaPipe is an open-source framework developed for building machine learning pipelines, especially useful for working with videos and images.

Having used MediaPipe on other platforms, Mario Bergeron decided to experiment with it on a Raspberry Pi AI Kit. On the project page (linked above) he details the process, including using his Python demo application with options to detect hands/palms, faces, or poses.

Mario’s test results show how much better the AI Kit’s Hailo-8L AI accelerator module performs compared to running reference TensorFlow Lite models on Raspberry Pi 5 alone: up to 5.8 times faster. With three models running for hand and landmarks detection, the frame rate is 26–28fps with one hand detected, and 22–25fps for two.

The MagPi #147 out NOW!

You can grab the new issue right now from Tesco, Sainsbury’s, Asda, WHSmith, and other newsagents, including the Raspberry Pi Store in Cambridge. It’s also available at our online store, which ships around the world. You can also get it via our app on Android or iOS.

You can also subscribe to the print version of The MagPi. Not only do we deliver it globally, but people who sign up to the six- or twelve-month print subscription get a FREE Raspberry Pi Pico W!

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