Tutorials

Self-study guides for the software and programming tools used in Computer Vision.

These tutorials cover the software and programming tools used throughout the course. They are designed for self-study and can be revisited whenever you need a refresher.

 

00 — Software & Tools Checklist

Install and verify the full software toolchain — Python/Conda, an IDE, Git, and (optionally) Docker and cloud CLIs — before the first lab, plus a step-by-step setup walkthrough.

 

01 — Raspberry Pi 5 Setup

Install Raspberry Pi OS on a Raspberry Pi 5, complete first boot, and configure the desktop, panel, and menu for the lab.

 

02 — Git and GitHub

Learn the Git workflow used to download, track, save, and share course code.

 

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

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

 

04 — Python and Conda Environment Setup

Install Conda and create the course cv environment used for Python, OpenCV, PyTorch, and assignments.

 

05 — Visual Studio Code

Configure VS Code to use the course cv Conda environment, notebooks, and the Python debugger.

 

06 — NumPy for Computer Vision

Interactive NumPy refresher focused on arrays, images, vectorization, matrix operations, and data types.

 

07 — Getting Started with Pydantic

Learn Pydantic basics: define typed data models, validate inputs, and catch configuration errors early in your computer-vision projects.

 

08 — Camera Calibration: Video Tutorials

Nine recommended calibration videos turned into step-by-step tutorials with plain-English notes, OpenCV 5 code, quizzes and a lab for each, ending with a Raspberry Pi camera in 15 minutes.

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