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).

The post The Official Raspberry Pi Camera Module Guide out now: build amazing vision-based projects appeared first on Raspberry Pi.

The Pine64 November update introduces the PineCam, a successor to the PineCube IP camera. With a redesigned structure and enhanced features, the PineCam is aimed at applications like monitoring, video streaming, and hardware experimentation. The device is built on the SG2000 System-on-Chip from the Oz64 single board computer covered in October. This SoC combines two […]

In this guest post, Ramona Rayner from our partner Sony shows you how to quickly explore different models and AI capabilities, and how you can easily build applications on top of the Raspberry Pi AI Camera.

The recently launched Raspberry Pi AI Camera is an extremely capable piece of hardware, enabling you to build powerful AI applications on your Raspberry Pi. By offloading the AI inference to the IMX500 accelerator chip, more computational resources are available to handle application logic right on the edge! We are very curious to see what you will be creating and we are keen to give you more tools to do so. This post will cover how to quickly explore different models and AI capabilities, and how to easily build applications on top of the Raspberry Pi AI Camera.

If you didn’t have the chance to go through the Getting Started guide, make sure to check that out first to verify that your AI Camera is set up correctly.

Explore pre-trained models

A great way to start exploring the possibilities of the Raspberry Pi AI Camera is to try out some of the pre-trained models that are available in the IMX500 Model Zoo. To simplify the exploration process, consider using a GUI Tool, designed to quickly upload different models and see the real-time inference results on the AI Camera.

In order to start the GUI Tool, make sure to have Node.js installed. (Verify Node.js is installed by running node --version in the terminal.) And build and run the tool by running the following commands in the root of the repository:

make build

./dist/run.sh

The GUI Tool will be accessible on http://127.0.0.1:3001. To see a model in action:

• Add a custom model by clicking the ADD button located at the top right corner of the interface.
• Provide the necessary details to add a custom network and upload the network.rpk file, and the (optional) labels.txt file.
• Select the model and navigate to Camera Preview to see the model in action!

Here are just a few of the models available in the IMX500 Model Zoo:

Network Name	Network Type	Post Processor	Color Format	Preserve Aspect Ratio	Network File	Labels File
mobilenet_v2	packaged	Classification	RGB	True	network.rpk	imagenet_labels.txt
efficientdet_lite0_pp	packaged	Object Detection (EfficientDet Lite0)	RGB	True	network.rpk	coco_labels.txt
deeplabv3plus	packaged	Segmentation	RGB	False	network.rpk	–
posenet	packaged	Pose Estimation	RGB	False	network.rpk	–

Exploring the different models gives you insight into the camera’s capabilities and enables you to identify the model that best suits your requirements. When you think you’ve found it, it’s time to build an application.

Building applications

Plenty of CPU is available to run applications on the Raspberry Pi while model inference is taking place on the IMX500. To demonstrate this we’ll run a Workout Monitoring sample application.

The goal is to count real-time exercise repetitions by detecting and tracking people performing common exercises like pull-ups, push-ups, ab workouts and squats. The app will count repetitions for each person in the frame, making sure multiple people can work out simultaneously and compete while getting automated rep counting.

To run the example, clone the sample apps repository and make sure to download the HigherHRNet model from the Raspberry Pi IMX500 Model Zoo.

Make sure you have OpenCV with Qt available:

sudo apt install python3-opencv

And from the root of the repository run:

python3 -m venv venv --system-site-packages
source venv/bin/activate
cd examples/workout-monitor/
pip install -e .

Switching between exercises is straightforward; simply provide the appropriate --exercise argument as one of pullup, pushup, abworkout or squat.

workout-monitor --model /path/to/imx500_network_higherhrnet_coco.rpk
 --exercise pullup

Note that this application is running:

• Model post-processing to interpret the model’s output tensor into bounding boxes and skeleton keypoints
• A tracker module (ByteTrack) to give the detected people a unique ID so that you can count individual people’s exercise reps
• A matcher module to increase the accuracy of the tracker results, by matching people over frames so as not to lose their IDs
• CV2 visualisation to visualise the results of the detections and see the results of the application

And all of this in real time, on the edge, while the IMX500 is taking care of the AI inference!

Now both you and the AI Camera are testing out each other’s limits. How many pull-ups can you do?

We hope by this point you’re curious to explore further; you can discover more sample applications on GitHub.

The post Bringing real-time edge AI applications to developers appeared first on Raspberry Pi.

The Banana Pi BPI-CanMV-K230D-Zero is a compact and low-power single-board computer built around the Kendryte K230D dual-core XuanTie C908 RISC-V chip with an integrated third-generation Knowledge Process Unit (KPU) for AI computation. It follows the form factor of the Raspberry Pi Zero or Raspberry Pi Zero 2W board and targets IoT and ML applications. The SBC comes with 128MB of LPDDR4 RAM and uses a microSD card slot for storage. Additional features of this board include dual MIPI-CSI camera inputs for 4K video, a 40-pin GPIO header for I2C, UART, SPI, PWM, and more. Wireless features include 2.4GHz WiFi, USB 2.0 with OTG, and microphone support. These features make this SBC suitable for applications such as AI tasks such as image, video, and audio processing. Banana Pi BPI-CanMV-K230D-Zero Specifications SoC – Kendryte K230D CPU CPU1 – 64-bit RISC-V processor @ 1.6GHz with RVV 1.0 support CPU2 – 64-bit RISC-V processor [...]

The post $29 Banana Pi BPI-CanMV-K230D-Zero features Kendryte K230D RISC-V SoC for AIoT applications appeared first on CNX Software - Embedded Systems News.

Orange Pi 4A is a new low-cost credit card-size single board computer (SBC) powered by an Allwinner T527 octa-core Cortex-A55 processor with a 2TOP NPU and offered with either 2GB or 4GB RAM. The board also comes with multiple storage options: a 128 or 256Mbit SPI NOR flash for the bootloader, an eMMC socket for up to 128GB modules, an M.2 socket for NVMe SSDs, and a microSD card slot. It’s also equipped with four USB 2.0 ports, a gigabit Ethernet port, three display interfaces (HDMI, MIPI DSI, eDP),  two camera interfaces, and a 40-pin “Raspberry Pi” header. The Orange Pi 4A is somewhat equivalent to an octa-core Raspberry Pi 3/4 with some extra features. Orange Pi 4A specifications: SoC – Allwinner T527 CPU Octa-core Arm Cortex-A55 @ up to 1.8GHz (four cores) and up to 1.42 GHz (four cores) XuanTie E906 RISC-V core @ 200MHz GPU – Arm Mali-G57 [...]

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We’re now ready to announce the winners of CNX Software’s Giveaway Week 2024. We offered some of the review samples we tested (and some we did not test) in the last year, and for the fourth year running, RAKwireless also gave away two IoT development kits shipped directly to winners. This year’s prizes also included a RISC-V motherboard, a 3D depth camera, a few Arm development boards, two touchscreen displays, and an Alder Lake-N mini PC/router. All those products can be seen in the photo, minus some accessories. You’ll find more than seven devices because we organized the third Giveaway Week on CNX Software Thailand simultaneously with four prizes. We had seven winners on CNX Software: Jupiter RISC-V mini-ITX motherboard – François-Denis, Canada Orbbec Femto mega 3D depth and 4K RGB camera  – Reifu, Japan RAKwireless Blues.ONE LoRaWAN, LTE-M, and NB-IoT devkit – OldCrow, Portugal Mixtile Core 3588E development kit [...]

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Forlinx FET-MX95xx-C is a system-on-module (SoM) based on NXP i.MX 95 SoC with up to six Cortex-A55 cores, an Arm Cortex-M7 real-time core clocked at 800 MHz, an Arm Cortex-M33 “safety” core clocked at 333 MHz, and equipped with 8GB LPDDR4x and 64GB eMMC flash. The company also provides the feature-rich OK-MX95xx-C development board based on the i.MX 95 module with a wide range of interfaces such as dual GbE, a 10GbE SFP+ cage, terminal blocks with RS485 and CAN Bus interface, three USB Type-A ports, two PCIe slots, and more. Forlinx FET-MX95xx-C system-on-module Specifications: SoC – NXP i.MX 9596 CPU 6x Arm Cortex-A55 application cores clocked at 1.8 GHz (industrial) with 32K I-cache and D-cache, 64KB L2 cache, and 512KB L3 cache Arm Cortex-M7 real-time core clocked at 800 MHz Arm Cortex-M33 safety core clocked at 333 MHz GPU – Arm Mali-G310 V2 GPU for 2D/3D acceleration with support [...]

The post Forlinx launches NXP i.MX 95 SoM and development board with 10GbE, CAN Bus, RS485, and more appeared first on CNX Software - Embedded Systems News.

The second prize of Giveaway Week 2024 is the Orbbec Femto Mega 3D depth and 4K RGB camera powered by an NVIDIA Jetson Nano module and featuring Microsoft ToF technology found in Hololens and Azure Kinect DevKit. The camera connects to Windows or Linux host computers through USB or Ethernet and is supported by the Orbbec SDK with the NVIDIA Jetson Nano running depth vision algorithms to convert raw data to precise depth images. I first reviewed the Orbbec Femto Mega using the Orbbec Viewer for a quick test connected to an Ubuntu laptop (as shown above) before switching to a more complex demo using the Orbbec SDK for body tracking in Windows 11. Although it was satisfying once it worked, I struggled quite a lot to run the body tracking demo in Windows 11, so there’s a learning curve, and after you have this working, you’d still need to [...]

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The Orbbec Gemini 335Lg is a 3D Depth and RGB camera in the Gemini 330 series, built with a GMSL2/FAKRA connector to support the connectivity needs of autonomous mobile robots (AMRs) and robotic arms in demanding environments. As an enhancement of the Gemini 335L, the 335Lg features a GMSL2 serializer and FAKRA-Z connector ensuring reliable performance in industrial applications requiring high mobility and precision. The Gemini 335Lg integrates with the Orbbec SDK, enabling flexible platform support across deserialization chips, carrier boards, and computing boxes, including NVIDIA’s Jetson AGX Orin and AGX Xavier. The device can operate in both USB and GMSL (MIPI) modes, which can be toggled via a switch next to the 8-pin sync port, with GMSL as the default. The GMSL2/FAKRA connection provides high-quality streaming with synchronized multi-device capability, enhancing adaptability for complex setups. Previously, we covered several 3D cameras from Orbbec, including the Orbbec Femto Mega 3D [...]

The post Orbbec Gemini 335Lg 3D depth and RGB camera features MX6800 ASIC, GMSL2/FAKRA connector for multi-device sync on NVIDIA Jetson Platforms appeared first on CNX Software - Embedded Systems News.

Seeed Studio has recently launched the OpenUC2 10x AI portable microscope built around the XIAO ESP32-S3 Sense module. Designed for educational, environmental research, health monitoring, and prototyping applications this microscope features an OV2640 camera with a 10x magnification with precise motorized focusing, high-resolution imaging, and real-time TinyML processing for image handling. The microscope is modular and open-source making it easy to customize and expand its features using 3D-printed parts, motorized stages, and additional sensors. It supports Wi-Fi connectivity with a durable body, uses USB-C for power and swappable objectives make it usable in various applications. Previously we have written about similar portable microscopes like the ioLight microscope and the KoPa W5 Wi-Fi Microscope, and Jean-Luc also tested a cheap USB microscope to read part number of components. Feel free to check those out if you are looking for a cheap microscope. OpenUC2 10x specifications: Wireless MCU – Espressif Systems ESP32-S3 CPU [...]

The post OpenUC2 10x is an ESP32-S3 portable microscope with AI-powered real-time image analysis appeared first on CNX Software - Embedded Systems News.

Waveshare has recently launched the ESP32-S3-ETH development board with an Ethernet RJ45 jack, a camera interface, and compatibility with Raspberry Pi Pico HAT expansion boards. This board includes a microSD card interface and supports OV2640 and OV5640 camera modules. Additionally, it offers an optional Power over Ethernet (PoE) module, making it ideal for applications such as smart home projects, AI-enhanced computer vision, and image acquisition. Previously, we have written about LILYGO T-ETH-Lite, an ESP32-S3 board with Ethernet and optional PoE support. We have also written about LuckFox Pico Pro and Pico Max, Rockchip RV1106-powered development boards with 10/100M Ethernet and camera support. The ESP32-S3-ETH board is like a combination of those two, where you get an ESP32-S3 microcontroller, Ethernet (with optional PoE), and a camera interface. ESP32-S3 ETH development board specifications: Wireless module ESP32-S3R8 MCU – ESP32-S3 dual-core LX7 microprocessor @ up to 240 MHz with Vector extension for machine learning Memory – 8MB PSRAM Storage [...]

The post Waveshare ESP32-S3 ETH board provides Ethernet and camera connectors, supports Raspberry Pi Pico HATs appeared first on CNX Software - Embedded Systems News.

Orbbec unveiled the Orbbec Perceptor Developer Kit at ROSCon 2024 in Odense, Denmark, offering a comprehensive, out-of-the-box solution for autonomous mobile robot development. Developed in collaboration with NVIDIA, the OPDK is designed to streamline application development for dynamic environments, such as warehouses and factories. The OPDK integrates four Gemini 335L Depth+RGB cameras with the NVIDIA […]

If you’ve got your hands on the Raspberry Pi AI Camera that we launched a few weeks ago, you might be looking for a bit of help to get up and running with it – it’s a bit different from our other camera products. We’ve raided our documentation to bring you this Getting started guide. If you work through the steps here you’ll have your camera performing object detection and pose estimation, even if all this is new to you. Then you can dive into the rest of our AI Camera documentation to take things further.

Here we describe how to run the pre-packaged MobileNet SSD (object detection) and PoseNet (pose estimation) neural network models on the Raspberry Pi AI Camera.

Prerequisites

We’re assuming that you’re using the AI Camera attached to either a Raspberry Pi 4 or a Raspberry Pi 5. With minor changes, you can follow these instructions on other Raspberry Pi models with a camera connector, including the Raspberry Pi Zero 2 W and Raspberry Pi 3 Model B+.

First, make sure that your Raspberry Pi runs the latest software. Run the following command to update:

sudo apt update && sudo apt full-upgrade

The AI Camera has an integrated RP2040 chip that handles neural network model upload to the camera, and we’ve released a new RP2040 firmware that greatly improves upload speed. AI Cameras shipping from now onwards already have this update, and if you have an earlier unit, you can update it yourself by following the firmware update instructions in this forum post. This should take no more than one or two minutes, but please note before you start that it’s vital nothing disrupts the process. If it does – for example, if the camera becomes disconnected, or if your Raspberry Pi loses power – the camera will become unusable and you’ll need to return it to your reseller for a replacement. Cameras with the earlier firmware are entirely functional, and their performance is identical in every respect except for model upload speed.

Install the IMX500 firmware

In addition to updating the RP2040 firmware if required, the AI camera must download runtime firmware onto the IMX500 sensor during startup. To install these firmware files onto your Raspberry Pi, run the following command:

sudo apt install imx500-all

This command:

• installs the /lib/firmware/imx500_loader.fpk and /lib/firmware/imx500_firmware.fpk firmware files required to operate the IMX500 sensor
• places a number of neural network model firmware files in /usr/share/imx500-models/
• installs the IMX500 post-processing software stages in rpicam-apps
• installs the Sony network model packaging tools

NOTE: The IMX500 kernel device driver loads all the firmware files when the camera starts, and this may take several minutes if the neural network model firmware has not been previously cached. The demos we’re using here display a progress bar on the console to indicate firmware loading progress.

Reboot

Now that you’ve installed the prerequisites, restart your Raspberry Pi:

sudo reboot

Run example applications

Once all the system packages are updated and firmware files installed, we can start running some example applications. As mentioned earlier, the Raspberry Pi AI Camera integrates fully with libcamera, rpicam-apps, and Picamera2. This blog post concentrates on rpicam-apps, but you’ll find more in our AI Camera documentation.

rpicam-apps

The rpicam-apps camera applications include IMX500 object detection and pose estimation stages that can be run in the post-processing pipeline. For more information about the post-processing pipeline, see the post-processing documentation.

The examples on this page use post-processing JSON files located in /usr/share/rpicam-assets/.

Object detection

The MobileNet SSD neural network performs basic object detection, providing bounding boxes and confidence values for each object found. imx500_mobilenet_ssd.json contains the configuration parameters for the IMX500 object detection post-processing stage using the MobileNet SSD neural network.

imx500_mobilenet_ssd.json declares a post-processing pipeline that contains two stages:

1. imx500_object_detection, which picks out bounding boxes and confidence values generated by the neural network in the output tensor
2. object_detect_draw_cv, which draws bounding boxes and labels on the image

The MobileNet SSD tensor requires no significant post-processing on your Raspberry Pi to generate the final output of bounding boxes. All object detection runs directly on the AI Camera.

The following command runs rpicam-hello with object detection post-processing:

rpicam-hello -t 0s --post-process-file /usr/share/rpi-camera-assets/imx500_mobilenet_ssd.json --viewfinder-width 1920 --viewfinder-height 1080 --framerate 30

After running the command, you should see a viewfinder that overlays bounding boxes on objects recognised by the neural network:

To record video with object detection overlays, use rpicam-vid instead:

rpicam-vid -t 10s -o output.264 --post-process-file /usr/share/rpi-camera-assets/imx500_mobilenet_ssd.json --width 1920 --height 1080 --framerate 30

You can configure the imx500_object_detection stage in many ways.

For example, max_detections defines the maximum number of objects that the pipeline will detect at any given time. threshold defines the minimum confidence value required for the pipeline to consider any input as an object.

The raw inference output data of this network can be quite noisy, so this stage also performs some temporal filtering and applies hysteresis. To disable this filtering, remove the temporal_filter config block.

Pose estimation

The PoseNet neural network performs pose estimation, labelling key points on the body associated with joints and limbs. imx500_posenet.json contains the configuration parameters for the IMX500 pose estimation post-processing stage using the PoseNet neural network.

imx500_posenet.json declares a post-processing pipeline that contains two stages:

1. imx500_posenet, which fetches the raw output tensor from the PoseNet neural network
2. plot_pose_cv, which draws line overlays on the image

The AI Camera performs basic detection, but the output tensor requires additional post-processing on your host Raspberry Pi to produce final output.

The following command runs rpicam-hello with pose estimation post-processing:

rpicam-hello -t 0s --post-process-file /usr/share/rpi-camera-assets/imx500_posenet.json --viewfinder-width 1920 --viewfinder-height 1080 --framerate 30

You can configure the imx500_posenet stage in many ways.

For example, max_detections defines the maximum number of bodies that the pipeline will detect at any given time. threshold defines the minimum confidence value required for the pipeline to consider input as a body.

Picamera2

For examples of image classification, object detection, object segmentation, and pose estimation using Picamera2, see the picamera2 GitHub repository.

Most of the examples use OpenCV for some additional processing. To install the dependencies required to run OpenCV, run the following command:

sudo apt install python3-opencv python3-munkres

Now download the picamera2 repository to your Raspberry Pi to run the examples. You’ll find example files in the root directory, with additional information in the README.md file.

Run the following script from the repository to run YOLOv8 object detection:

python imx500_object_detection_demo.py --model /usr/share/imx500-models/imx500_network_yolov8n_pp.rpk --ignore-dash-labels -r

To try pose estimation in Picamera2, run the following script from the repository:

python imx500_pose_estimation_higherhrnet_demo.py

To explore further, including how things work under the hood and how to convert existing models to run on the Raspberry Pi AI Camera, see our documentation.

The post How to get started with your Raspberry Pi AI Camera appeared first on Raspberry Pi.

The summer, and Louis Wood’s internship with our Maker in Residence, was creeping to a close without his final build making it off the ground. But as if by magic, on his very last day, Louis got his handmade drone flying.

3D-printed CAD design

The journey of building a custom drone began with designing in CAD software. My initial design was fully 3D-printed with an enclosed structure and cantilevered arms to support point forces. The honeycomb lid provided cooling, and the enclosure allowed for embedded XT-60 and MR-30 connections, creating a clean and integrated look. Inside, I ensured all electrical components were rigidly mounted to avoid unwanted movement that could destabilise the flight.

Testing quickly revealed that 3D-printed frames were brittle, often breaking during crashes. Moreover, the limitations of my printer’s build area meant that motor placement was cramped. To overcome these issues, I CNC-routed a new frame from 4 mm carbon fibre, increasing the wheelbase for better stability. Using Carveco software, I generated toolpaths and cut the frame on a WorkBee CNC in our Maker Lab. After two hours, I had a sturdy, assembled frame ready for electronics.

Not one, not two, but three Raspberry Pis

For the drone’s brain, I used a Raspberry Pi Pico 2 connected to an MPU6050 gyroscope for real-time orientation data and an IBUS protocol receiver for streamlined control inputs. Initially, I faced issues with signal processing due to the delay of handling five separate PWM signals. Switching to IBUS sped up the loop frequency by tenfold, which greatly improved flight response. The Pico handled PID (Proportional-Integral-Derivative) calculations for stability, and a 4-in-1 ESC managed the motor signals. The drone also carries a Raspberry Pi Zero with a Camera Module 2 and an analogue VTX for real-time FPV (first-person view) flying.

Programming was based on Tim Hanewich’s Scout flight controller code, implementing a ‘rate’ mode controller that uses PID values to maintain desired angular velocities. Fine-tuning the PID gains was essential; improper settings could lead to instability and dangerous oscillations. I followed a careful tuning process, starting with low values for each parameter and slowly increasing them.

To make the process safer, I constructed a testing rig to isolate each axis and simulate flight conditions. This allowed me to achieve a rough tune before moving on to actual flight tests, ultimately ensuring the drone’s safe and stable performance.

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This canny way to transfer analogue film to digital was greatly improved by using Raspberry Pi, as Rosie Hattersley discovered in issue 145 of The MagPi.

Gugusse is a French term meaning something ‘quite flimsy’, explains software engineer and photography fan Denis-Carl Robidoux. The word seemed apt to describe the 3D-printed project: a “flimsy and purely mechanical machine to transfer film.” 

Denis-Carl created Gugusse as a volunteer at the Montreal museum where his girlfriend works. He was “their usual pro bono volunteer guy for anything special with media, [and] they asked me if I could transfer some rolls of 16mm film to digital.” Dissatisfied with the resulting Gugusse Roller mechanism, he eventually decided to set about improving upon it with a little help from Raspberry Pi. Results from the Gugusse Roller’s digitisation process can be admired on YouTube.

New and improved

Denis-Carl brought decades of Linux coding (“since the era when you had to write your own device drivers to make your accessories to work with it”), and a career making drivers for jukeboxes and high-level automation scripts, to the digitisation conundrum. Raspberry Pi clearly offered potential: “Actually, there was no other way to get a picture of this quality at this price level for this DIY project.” However, the Raspberry Pi Camera Module v2 Denis-Carl originally used wasn’t ideal for the macro photography approach and alternative lenses involved in transferring film. The module design was geared up for a lens in close proximity to the camera sensor, and Bayer mosaics aligned for extremities of incoming light were at odds with his needs. “But then came Raspberry Pi HQ camera, which didn’t have the Bayer mosaic alignment issue and was a good 12Mp, enough to perform 4K scans.” 

Scene stealer

Denis-Carl always intended the newer Gugusse Roller design to be sprocketless, since this would allow it to scan any film format. This approach meant the device needed to be able to detect the film holes optically: “I managed this with an incoming light at 45 degrees and a light sensitive resistor placed at 45 degrees but in the opposite direction.” It was “a Eureka moment” when he finally made it work. Once the tension is set, the film scrolls smoothly past the HQ camera, which captures each frame as a DNG file once the system detects the controlling arms are correctly aligned and after an interval for any vibration to dissipate. 

The Gugusse Roller uses Raspberry Pi 4 to control the HQ Camera, three stepper motors, and three GPIO inputs. So far it has scanned thousands of rolls of film, including trailers of classics such as Jaws, and other, lesser-known treasures. The idea has also caught the imagination of more than a dozen followers who have gone on to build their own Gugusse Roller using Denis-Carl’s instructions — check out other makers’ builds on Facebook.

The post Gugusse Roller transfers analogue film to digital with Raspberry Pi appeared first on Raspberry Pi.

We previously noted that Raspberry Pi showcased a Raspberry Pi Zero 2W with a Raspberry Pi AI camera based on a Sony IMX500 intelligent vision sensor at Embedded World 2024, but it was not available at the time. The good news is that the Raspberry Pi AI camera is now available for $70 from your favorite distributor. This follows the launch of the more powerful Raspberry Pi AI Kit designed for the Raspberry Pi 5 with a 13 TOPS Hailo-8L NPU connected through PCIe. The AI camera based on a Sony IMX500 AI camera sensor assisted by a Raspberry Pi RP2040 to handle neural network and firmware management is less powerful, but can still perform many of the same tasks including object detection and body segmentation, and works on any Raspberry Pi board with a MIPI CSI connector, while the AI Kit only works on the latest Pi 5 board. [...]

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Raspberry Pi has launched a new low-cost AI Camera, bringing powerful AI capabilities to all Raspberry Pi models. Unlike the AI Kit, which is limited to the Raspberry Pi 5, the AI Camera is more versatile and compatible with a wider range of devices.   The core of the Raspberry Pi AI Camera is the […]

People have been using Raspberry Pi products to build artificial intelligence projects for almost as long as we’ve been making them. As we’ve released progressively more powerful devices, the range of applications that we can support natively has increased; but in any generation there will always be some workloads that require an external accelerator, like the Raspberry Pi AI Kit, which we launched in June.

The AI Kit is an awesomely powerful piece of hardware, capable of performing thirteen trillion operations per second. But it is only compatible with Raspberry Pi 5, and requires a separate camera module to capture visual data. We are very excited therefore to announce a new addition to our camera product line: the Raspberry Pi AI Camera.

The AI Camera is built around a Sony IMX500 image sensor with an integrated AI accelerator. It can run a wide variety of popular neural network models, with low power consumption and low latency, leaving the processor in your Raspberry Pi free to perform other tasks.

Key features of the Raspberry Pi AI Camera include:

• 12 MP Sony IMX500 Intelligent Vision Sensor
• Sensor modes: 4056×3040 at 10fps, 2028×1520 at 30fps
• 1.55 µm × 1.55 µm cell size
• 78-degree field of view with manually adjustable focus
• Integrated RP2040 for neural network and firmware management

The AI Camera can be connected to all Raspberry Pi models, including Raspberry Pi Zero, using our regular camera ribbon cables.

Using Sony’s suite of AI tools, existing neural network models using frameworks such as TensorFlow or PyTorch can be converted to run efficiently on the AI Camera. Alternatively, new models can be designed to take advantage of the AI accelerator’s specific capabilities.

Under the hood

To make use of the integrated AI accelerator, we must first upload a model. On older Raspberry Pi devices this process uses the I2C protocol, while on Raspberry Pi 5 we are able to use a much faster custom two-wire protocol. The camera end of the link is managed by an on-board RP2040 microcontroller; an attached 16MB flash device caches recently used models, allowing us to skip the upload step in many cases.

Once the sensor has started streaming, the IMX500 operates as a standard Bayer image sensor, much like the one on Raspberry Pi Camera Module 3. An integrated Image Signal Processor (ISP) performs basic image processing steps on the sensor frame (principally Bayer-to-RGB conversion and cropping/rescaling), and feeds the processed frame directly into the AI accelerator. Once the neural network model has processed the frame, its output is transferred to the host Raspberry Pi together with the Bayer frame over the CSI-2 camera bus.

Integration with Raspberry Pi libcamera

A key benefit of the AI Camera is its seamless integration with our Raspberry Pi camera software stack. Under the hood, libcamera processes the Bayer frame using our own ISP, just as it would for any sensor.

We also parse the neural network results to generate an output tensor, and synchronise it with the processed Bayer frame. Both of these are returned to the application during libcamera’s request completion step.

The Raspberry Pi camera frameworks — Picamera2 and rpicam-apps, and indeed any libcamera-based application — can retrieve the output tensor, correctly synchronised with the sensor frame. Here’s an example of an object detection neural network model (MobileNet SSD) running under rpicam-apps and performing inference on a 1080p video at 30fps.

This demo uses the postprocessing framework in rpicam-apps to generate object bounding boxes from the output tensor and draw them on the image. This stage takes no more than 300 lines of code to implement. An equivalent application built using Python and Picamera2 requires many fewer lines of code.

Another example below shows a pose estimation neural network model (PoseNet) performing inference on a 1080p video at 30fps.

Although these examples were recorded using a Raspberry Pi 4, they run with the same inferencing performance on a Raspberry Pi Zero!

Together with Sony, we have released a number of popular visual neural network models optimised for the AI Camera in our model zoo, along with visualisation example scripts using Picamera2.

Which product should I buy?

Should you buy a Raspberry Pi AI Kit, or a Raspberry Pi AI Camera? The AI Kit has higher theoretical performance than the AI Camera, and can support a broader range of models, but is only compatible with Raspberry Pi 5. The AI Camera is more compact, has a lower total cost if you don’t already own a camera, and is compatible with all models of Raspberry Pi.

Ultimately, both products provide great acceleration performance for common models, and both have been optimised to work smoothly with our camera software stack.

Getting started and going further

Check out our Getting Started Guide. There you’ll find instructions on installing the AI Camera hardware, setting up the software environment, and running the examples and neural networks in our model zoo.

Sony’s AITRIOS Developer site has more technical resources on the IMX500 sensor, in particular the IMX500 Converter and IMX500 Package documentation, which will be useful for users who want to run custom-trained networks on the AI Camera.

We’ve been inspired by the incredible AI projects you’ve built over the years with Raspberry Pi, and your hard work and inventiveness encourages us to invest in the tools that will help you go further. The arrival of first the AI Kit, and now the AI Camera, opens up a whole new world of opportunities for high-resolution, high-frame rate, high-quality visual AI: we don’t know what you’re going to build with them, but we’re sure it will be awesome.

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