03 — Linux, Python & AI-Ready Raspberry Pi OS

Setup

Linux

Python

Learn essential Linux terminal commands and Python basics on Raspberry Pi OS, then prepare the Raspberry Pi 5 for computer-vision and AI workloads.

Overview

With Raspberry Pi OS installed and the desktop configured, this tutorial covers the Linux terminal commands and Python basics you will use throughout the labs, then walks through installing the packages needed to run computer-vision and AI workloads on the Raspberry Pi 5.

1. Open a Terminal

Open a terminal in any of the following ways:

Click the terminal icon on the Panel
Press Ctrl+Alt+T
Connect remotely with ssh username@hostname.local

2. Linux Essentials

Moving Around the Filesystem

Command	Purpose
`pwd`	Show the current directory
`ls`	List files
`ls -la`	List files, including hidden files
`cd folder`	Enter a directory
`cd ..`	Move up one directory
`cd ~`	Go to your home directory
`mkdir folder`	Create a directory
`cp source destination`	Copy a file
`mv source destination`	Move or rename a file
`rm file`	Delete a file
`clear`	Clear the terminal
`history`	Show recent commands

Warning

Linux commands such as rm normally do not move files to a recycle bin. Double-check paths before deleting files.

Permissions and sudo

sudo runs a command with administrator privileges — required for system changes such as installing software or editing configuration files:

sudo raspi-config

Make a script executable:

chmod +x script.sh

Installing Software with apt

sudo apt update
sudo apt install package-name -y

The first command refreshes the package list; the second installs the requested package. Remove a package with:

sudo apt remove package-name

Editing Files with nano

nano filename.txt

Shortcut	Action
`Ctrl+O`	Save (write out)
`Ctrl+X`	Exit
`Ctrl+K`	Cut a line
`Ctrl+U`	Paste a line

3. Python Essentials

Check the Installed Version

Raspberry Pi OS ships with Python 3 pre-installed:

python3 --version
pip3 --version

Thonny: the Bundled IDE

Thonny (Main Menu → Programming → Thonny) is a beginner-friendly Python IDE that comes preinstalled on Raspberry Pi OS. It includes a code editor, a variables viewer, and a step-through debugger — useful for early labs before moving to VS Code.

Virtual Environments

Create an isolated environment for course projects:

python3 -m venv ~/cv_env
source ~/cv_env/bin/activate

Note

Recent Raspberry Pi OS releases mark the system Python as externally managed, so a plain pip install outside a virtual environment will refuse to run. Always activate a venv first for course work.

Installing Packages with pip

With the virtual environment activated:

pip install numpy opencv-python

Running a Script

python3 my_script.py

4. Prepare Raspberry Pi OS for AI & Computer Vision

Install OpenCV

sudo apt install -y python3-opencv

Or, inside a virtual environment:

pip install opencv-python

Install picamera2

picamera2 is the official library for the Raspberry Pi Camera Module and usually comes preinstalled. If it is missing:

sudo apt install -y python3-picamera2

Install a Lightweight ML Runtime

Full TensorFlow or PyTorch will run on a Pi 5 but are heavy for real-time inference. For course labs, prefer a lightweight runtime with a quantized model:

pip install tflite-runtime
pip install onnxruntime

tflite-runtime runs TensorFlow Lite (.tflite) models; onnxruntime runs models exported in the cross-framework ONNX format.

Optional AI Accelerators

For labs that need faster on-device inference, the Pi 5 supports:

Coral USB Accelerator (Edge TPU) — fast INT8 TensorFlow Lite inference over USB
Raspberry Pi AI Kit (Hailo-8 / Hailo-8L) — an M.2 accelerator connected via the Pi 5's PCIe interface for high-throughput on-device inference

Quick Test

Verify the camera and OpenCV are working together:

import cv2

cap = cv2.VideoCapture(0)
ret, frame = cap.read()
print("Frame captured:", ret, frame.shape if ret else None)
cap.release()

Run it:

python3 camera_test.py

A successful run prints Frame captured: True followed by the frame's height, width, and channel count.

Good Habits

Keep the system updated regularly: sudo apt update && sudo apt full-upgrade -y
Use a separate virtual environment per project
Back up your SD card image before making major changes, using Raspberry Pi Imager's read/backup option

Next Steps

You are ready to start the lab exercises. Continue exploring the Visual Studio Code and NumPy tutorials for the rest of the development workflow used in this course.