Raspberry Pi Touch Display 2 is a portrait orientation touchscreen LCD display designed for interactive projects like tablets, entertainment systems, and information dashboards. Here, our documentation lead Nate Contino shows you how to connect a Touch Display 2 to your Raspberry Pi, use an on-screen keyboard, and change your screen orientation.

Touch Display 2 connects to a Raspberry Pi using a DSI connector and a GPIO connector. Raspberry Pi OS provides touchscreen drivers with support for five-finger multi-touch and an on-screen keyboard, providing full functionality without the need to connect a keyboard or mouse.

Specifications

• 1280×720px resolution, 24-bit RGB display
• 155×88 mm active area
• 7-inch diagonal display
• Powered directly by the host Raspberry Pi, requiring no separate power supply
• Supports up to five points of simultaneous multi-touch

Touch Display 2 is compatible with all models of Raspberry Pi from Raspberry Pi 1B+ onwards, except Raspberry Pi Zero and Zero 2 W, which lack a DSI connector.

The Touch Display 2 box contains the following parts (in left-to-right, top-to-bottom order in the Figure 1 image):

• Touch Display 2
• 8 × M2.5 screws
• 15-way to 15-way FFC
• 22-way to 15-way FFC for Raspberry Pi 5
• GPIO connector cable

Install

To connect a Touch Display 2 to a Raspberry Pi, use a Flat Flexible Cable (FFC) and a GPIO connector. The FFC you’ll use depends upon your Raspberry Pi model: for Raspberry Pi 5, use the included 22‑way to 15-way FFC; for any other Raspberry Pi model, use the included 15-way to 15-way FFC.

Once you have determined the correct FFC for your Raspberry Pi model, complete the following steps to connect your Touch Display 2 to your Raspberry Pi:

1. Disconnect your Raspberry Pi from power.

2. Lift the retaining clips on either side of the FFC connector on the Touch Display 2.

3. Insert one 15-way end of your FFC into the Touch Display 2 FFC connector, with the metal contacts facing upwards, away from the Touch Display 2.

4. While holding the FFC firmly in place, simultaneously push both retaining clips down on the FFC connector of the Touch Display 2.

5. Lift the retaining clips on either side of the DSI connector of your Raspberry Pi. This port should be marked with some variation of the term ‘DISPLAY’ or ‘DISP’. If your Raspberry Pi has multiple DSI connectors, prefer the port labelled ‘1’.

6. Insert the other end of your FFC into the Raspberry Pi DSI connector, with the metal contacts facing towards the Ethernet and USB-A ports.

7. While holding the FFC firmly in place, simultaneously push both retaining clips down on the DSI connector of your Raspberry Pi (see Figure 2 below).

8. Plug the GPIO connector cable into the port marked J1 on the Touch Display 2.

9. Connect the other (three-pin) end of the GPIO connector cable to pins 2, 4, and 6 of your Raspberry Pi’s GPIO. Connect the red cable (5 V power) to pin 2, and the black cable (ground) to pin 6. Viewed from above, with the Ethernet and USB-A ports facing down, these pins are located at the top right of the board, with pin 2 in the top right-most position (see Figure 3 below).

10. Optionally, use the included M2.5 screws to mount your Raspberry Pi to the back of the Touch Display 2:

11. Align the four corner stand-offs of your Raspberry Pi with the four mount points that surround the FFC connector and J1 port on the back of the Touch Display 2, taking special care not to pinch the FFC.

12. Insert the screws into the four corner stand‑offs and tighten until your Raspberry Pi is secure.

13. Reconnect your Raspberry Pi to power. It may take up to one minute to initialise the Touch Display 2 connection and begin displaying to the screen.

Use an on-screen keyboard

Raspberry Pi OS Bookworm and later include the Squeekboard on-screen keyboard by default. When a touch display is attached, the on-screen keyboard should automatically show when it is possible to enter text and automatically hide when it is not possible to enter text.

For applications which do not support text entry detection, use the keyboard icon at the right-hand end of the taskbar to manually show and hide the keyboard.

You can also permanently show or hide the on‑screen keyboard in the Display tab of Raspberry Pi Configuration or the Display section of raspi-config.

In Raspberry Pi OS releases prior to Bookworm, use matchbox-keyboard instead. If you use the Wayfire desktop compositor, use wvkbd instead.

Change screen orientation

If you want to physically rotate the display, or mount it in a specific position, select Screen Configuration from the Preferences menu. Right-click on the touch display rectangle (likely DSI-1) in the layout editor, select Orientation, then pick the best option to fit your needs.

Rotate the screen without a desktop

To set the screen orientation on a device that lacks a desktop environment, edit the
/boot/firmware/cmdline.txt configuration file to pass an orientation to the system. Add the following entry to the end of cmdline.txt:

video=DSI-1:720x1280@60,rotate=<rotation-value>

Replace the <rotation-value> placeholder with one of the following values, which correspond to the degree of rotation relative to the default on your display:

• 0
• 90
• 180
• 270

For example, a rotation value of 90 rotates the display 90 degrees clockwise. 180 rotates the display 180 degrees, or upside-down.

Note: It’s not possible to rotate the DSI display separately from the HDMI one with cmdline.txt. When you use DSI and HDMI simultaneously, they share the same rotation value.

This tutorial is an excerpt from the Raspberry Pi Documentation. Our extensive documentation covers all of our hardware, software, accessories, and microcontrollers as well as Raspberry Pi OS. If you still have questions, try posting on our forums, which are full of Raspberry Pi enthusiasts and some of the finest nerds on the planet.

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Raspberry Pi OS comes with Python pre-installed, and you need to use its virtual environments to install packages. The latest issue of The MagPi, out today, features this handy tutorial, penned by our documentation lead Nate Contino, to get you started.

Raspberry Pi OS comes with Python 3 pre-installed. Interfering with the system Python installation can cause problems for your operating system. When you install third-party Python libraries, always use the correct package-management tools.

On Linux, you can install python dependencies in two ways:

• use apt to install pre-configured system packages
• use pip to install libraries using Python’s dependency manager in a virtual environment

Install Python packages using apt

Packages installed via apt are packaged specifically for Raspberry Pi OS. These packages usually come pre-compiled, so they install faster. Because apt manages dependencies for all packages, installing with this method includes all of the sub-dependencies needed to run the package. And apt ensures that you don’t break other packages if you uninstall.

For instance, to install the Python 3 library that supports the Raspberry Pi Build HAT, run the following command:

$ sudo apt install python3-build-hat

To find Python packages distributed with apt, use apt search. In most cases, Python packages use the prefix python- or python3-: for instance, you can find the numpy package under the name python3-numpy.

Install Python libraries using pip

In older versions of Raspberry Pi OS, you could install libraries directly into the system version of Python using pip. Since Raspberry Pi OS Bookworm, users cannot install libraries directly into the system version of Python.

Instead, install libraries into a virtual environment (venv). To install a library at the system level for all users, install it with apt.

Attempting to install a Python package system-wide outputs an error similar to the following:

$ pip install buildhat
error: externally-managed-environment

× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
    python3-xyz, where xyz is the package you are trying to
    install.
    
    If you wish to install a non-Debian-packaged Python package,
    create a virtual environment using python3 -m venv path/to/venv.
    Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
    sure you have python3-full installed.
    
    For more information visit http://rptl.io/venv

note: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.
hint: See PEP 668 for the detailed specification.

Python users have long dealt with conflicts between OS package managers like apt and Python-specific package management tools like pip. These conflicts include both Python-level API incompatibilities and conflicts over file ownership.

Starting in Raspberry Pi OS Bookworm, packages installed via pip must be installed into a Python virtual environment (venv). A virtual environment is a container where you can safely install third-party modules so they won’t interfere with your system Python.

Use pip with virtual environments

To use a virtual environment, create a container to store the environment. There are several ways you can do this depending on how you want to work with Python:

per-project environments

Many users create separate virtual environments for each Python project. Locate the virtual environment in the root folder of each project, typically with a shared name like env. Run the following command from the root folder of each project to create a virtual environment configuration folder:

$ python -m venv env

Before you work on a project, run the following command from the root of the project to start using the virtual environment:

$ source env/bin/activate

You should then see a prompt similar to the following:

(env) $

When you finish working on a project, run the following command from any directory to leave the virtual environment:

$ deactivate

per-user environments

Instead of creating a virtual environment for each of your Python projects, you can create a single virtual environment for your user account. Activate that virtual environment before running any of your Python code. This approach can be more convenient for workflows that share many libraries across projects.

When creating a virtual environment for multiple projects across an entire user account, consider locating the virtual environment configuration files in your home directory. Store your configuration in a folder whose name begins with a period to hide the folder by default, preventing it from cluttering your home folder.

Use the following command to create a virtual environment in a hidden folder in the current user’s home directory:

$ python -m venv ~/.env

Run the following command from any directory to start using the virtual environment:

$ source ~/.env/bin/activate

You should then see a prompt similar to the following:

(.env) $

To leave the virtual environment, run the following command from any directory:

$ deactivate

Create a virtual environment

Run the following command to create a virtual environment configuration folder, replacing <env-name> with the name you would like to use for the virtual environment (e.g. env):

$ python -m venv <env-name>

Enter a virtual environment

Then, execute the bin/activate script in the virtual environment configuration folder to enter the virtual environment:

$ source <env-name>/bin/activate

You should then see a prompt similar to the following:

(<env-name>) $

The (<env-name>) command prompt prefix indicates that the current terminal session is in a virtual environment named <env-name>.

To check that you’re in a virtual environment, use pip list to view the list of installed packages:

(<env-name>) $ pip list
Package    Version
---------- -------
pip        23.0.1
setuptools 66.1.1

The list should be much shorter than the list of packages installed in your system Python. You can now safely install packages with pip. Any packages you install with pip while in a virtual environment only install to that virtual environment. In a virtual environment, the python or python3 commands automatically use the virtual environment’s version of Python and installed packages instead of the system Python.

Top Tip
Pass the –system-site-packages flag before the folder name to preload all of the currently installed packages in your system Python installation into the virtual environment.

Exit a virtual environment

To leave a virtual environment, run the following command:

(<env-name>) $ deactivate

Use the Thonny editor

We recommend Thonny for editing Python code on the Raspberry Pi.

By default, Thonny uses the system Python. However, you can switch to using a Python virtual environment by clicking on the interpreter menu in the bottom right of the Thonny window. Select a configured environment or configure a new virtual environment with Configure interpreter.

The MagPi #148 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!

The post Using Python with virtual environments | The MagPi #148 appeared first on Raspberry Pi.

As our product line expands, it can get confusing trying to keep track of all the different Raspberry Pi boards out there. Here is a high-level breakdown of Raspberry Pi models, including our flagship series, Zero series, Compute Module series, and Pico microcontrollers.

Raspberry Pi makes computers in several different series:

• The flagship series, often referred to by the shorthand ‘Raspberry Pi’, offers high-performance hardware, a full Linux operating system, and a variety of common ports in a form factor roughly the size of a credit card.

• The Zero series offers a full Linux operating system and essential ports at an affordable price point in a minimal form factor with low power consumption.

• The Compute Module series, often referred to by the shorthand ‘CM’, offers high-performance hardware and a full Linux operating system in a minimal form factor suitable for industrial and embedded applications. Compute Module models feature hardware equivalent to the corresponding flagship models but with fewer ports and no on-board GPIO pins. Instead, users should connect Compute Modules to a separate baseboard that provides the ports and pins required for a given application.

Additionally, Raspberry Pi makes the Pico series of tiny, versatile microcontroller boards. Pico models do not run Linux or allow for removable storage, but instead allow programming by flashing a binary onto on-board flash storage.

Flagship series

Model B indicates the presence of an Ethernet port. Model A indicates a lower-cost model in a smaller form factor with no Ethernet port, reduced RAM, and fewer USB ports to limit board height.

Model	SoC	Memory	GPIO	Connectivity
Raspberry Pi Model B	BCM2835	256MB, 512MB	26-pin GPIO header	HDMI, 2 × USB 2.0, CSI camera port, DSI display port, 3.5mm audio jack, RCA composite video, Ethernet (100Mb/s), SD card slot, micro USB power
Raspberry Pi Model A	BCM2835	256MB	26-pin GPIO header	HDMI, USB 2.0, CSI camera port, DSI display port, 3.5mm audio jack, RCA composite video, SD card slot, micro USB power
Raspberry Pi Model B+	BCM2835	512MB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, Ethernet (100Mb/s), microSD card slot, micro USB power
Raspberry Pi Model A+	BCM2835	256MB, 512MB	40-pin GPIO header	HDMI, USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, microSD card slot, micro USB power
Raspberry Pi 2 Model B	BCM2836 (in version 1.2, switched to BCM2837)	1 GB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, Ethernet (100Mb/s), microSD card slot, micro USB power
Raspberry Pi 3 Model B	BCM2837	1 GB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, Ethernet (100Mb/s), 2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.1, Bluetooth Low Energy (BLE), microSD card slot, micro USB power
Raspberry Pi 3 Model B+	BCM2837b0	1GB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, PoE-capable Ethernet (300Mb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi (100Mb/s), Bluetooth 4.2, Bluetooth Low Energy (BLE), microSD card slot, micro USB power
Raspberry Pi 3 Model A+	BCM2837b0	512 MB	40-pin GPIO header	HDMI, USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, 2.4/5GHz dual-band 802.11ac Wi-Fi (100Mb/s), Bluetooth 4.2, Bluetooth Low Energy (BLE), microSD card slot, micro USB power
Raspberry Pi 4 Model B	BCM2711	1GB, 2GB, 4GB, 8GB	40-pin GPIO header	2 × micro HDMI, 2 × USB 2.0, 2 × USB 3.0, CSI camera port, DSI display port, 3.5 mm AV jack, PoE-capable Gigabit Ethernet (1Gb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi (120Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE), microSD card slot, USB-C power (5V, 3A (15W))
Raspberry Pi 400	BCM2711	4GB	40-pin GPIO header	2 × micro HDMI, 2 × USB 2.0, 2 × USB 3.0, Gigabit Ethernet (1Gb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi (120Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE), microSD card slot, USB-C power (5V, 3A (15W))
Raspberry Pi 5	BCM2712 (2GB version uses BCM2712D0)	2GB, 4GB, 8GB	40-pin GPIO header	2 × micro HDMI, 2 × USB 2.0, 2 × USB 3.0, 2 ×  CSI camera/DSI display ports, single-lane PCIe FFC connector, UART connector, RTC battery connector, four-pin JST-SH PWM fan connector, PoE+-capable Gigabit Ethernet (1Gb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi 5 (300Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE), microSD card slot, USB-C power (5V, 5A (25W) or 5V, 3A (15W) with a 600mA peripheral limit)

For more information about the ports on the Raspberry Pi flagship series, see the Schematics and mechanical drawings.

Zero series

Models with the H suffix have header pins pre-soldered to the GPIO header. Models that lack the H suffix do not come with header pins attached to the GPIO header; the user must solder pins manually or attach a third-party pin kit.

All Zero models have the following connectivity:

• a microSD card slot
• a CSI camera port (version 1.3 of the original Zero introduced this port)
• a mini HDMI port
• 2 × micro USB ports (one for input power, one for external devices)

Model	SoC	Memory	GPIO	Wireless Connectivity
Raspberry Pi Zero	BCM2835	512MB	40-pin GPIO header (unpopulated)	none
Raspberry Pi Zero W	BCM2835	512MB	40-pin GPIO header (unpopulated)	2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.0, Bluetooth Low Energy (BLE)
Raspberry Pi Zero WH	BCM2835	512MB	40-pin GPIO header	2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.0, Bluetooth Low Energy (BLE)
Raspberry Pi Zero 2 W	RP3A0	512MB	40-pin GPIO header (unpopulated)	2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.2, Bluetooth Low Energy (BLE)

Compute Module series

Model	SoC	Memory	Storage	Form factor	Wireless Connectivity
Raspberry Pi Compute Module 1	BCM2835	512MB	4GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 3	BCM2837	1GB	0GB (Lite), 4GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 3+	BCM2837b0	1GB	0GB (Lite), 8GB, 16GB, 32GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 4S	BCM2711	1GB, 2GB, 4GB, 8GB	0GB (Lite), 8GB, 16GB, 32GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 4	BCM2711	1GB, 2GB, 4GB, 8GB	0GB (Lite), 8GB, 16GB, 32GB	dual 100-pin high density connectors	optional: 2.4/5GHz dual-band 802.11ac Wi-Fi 5 (300Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE)

For more information about Raspberry Pi Compute Modules, see the Compute Module documentation.

Pico microcontrollers

Models with the H suffix have header pins pre-soldered to the GPIO header. Models that lack the H suffix do not come with header pins attached to the GPIO header; the user must solder pins manually or attach a third-party pin kit.

Model	SoC	Memory	Storage	GPIO	Wireless Connectivity
Raspberry Pi Pico	RP2040	264KB	2MB	two 20-pin GPIO headers (unpopulated)	none
Raspberry Pi Pico H	RP2040	264KB	2MB	two 20-pin GPIO headers	none
Raspberry Pi Pico W	RP2040	264KB	2MB	two 20-pin GPIO headers (unpopulated)	2.4GHz single-band 802.11n Wi-Fi (10Mb/s), Bluetooth 5.2, Bluetooth Low Energy (BLE)
Raspberry Pi Pico WH	RP2040	264KB	2MB	two 20-pin GPIO headers	2.4GHz single-band 802.11n Wi-Fi (10Mb/s), Bluetooth 5.2, Bluetooth Low Energy (BLE)
Raspberry Pi Pico 2	RP2350	520KB	4MB	two 20-pin GPIO headers (unpopulated)	none

For more information about Raspberry Pi Pico models, see the Pico documentation.

If you’re interested in schematics, mechanical drawings, and information on thermal control, visit our documentation page.

The post Raspberry Pi product series explained appeared first on Raspberry Pi.

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.