ANU_YOLO

A collection of YOLO-based scripts and environment setups for face and object detection, designed for use on Raspberry Pi OS and other Linux systems.

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Table of Contents

• Project Overview
• Prerequisites
• Installation
• Model Weights
• Usage
• Group Task
• Credits

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Project Overview

This repository provides scripts for running YOLOv8-based face and object detection on images and videos. The main scripts, object.py and yolo_face.py, detects objects and faces using a YOLOv8n model and its retrained model and outputs annotated images or videos with bounding boxes and confidence scores, respectively. This introduces the newcomer to various ML concepts.

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Prerequisites

• Raspberry Pi OS or any modern Linux distribution
• Python 3.10 or higher (recommended: use Micromamba/Miniconda)
• Git

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Installation

0. Setup the Pi and hook it up to the monitors and make sure the display is working.

1. System Preparation

Open a terminal and execute the following commands in order:

echo "1) Update & upgrade system…"
sudo apt-get update -qq
sudo apt-get upgrade -y -qq

echo "2) Repair broken packages (if any)…"
sudo apt-get install -f -y -qq
sudo dpkg --configure -a

echo "3) Install core prerequisites: curl, unzip, compilers, media libs…"
sudo apt-get install -y -qq \
    curl unzip wget bzip2 build-essential cmake \
    libjpeg-dev libpng-dev libtiff-dev \
    libavcodec-dev libavformat-dev libswscale-dev ffmpeg \
    git ca-certificates

echo "4) Clean up unused packages & caches…"
sudo apt-get autoremove -y -qq
sudo apt-get autoclean -y -qq

echo "5) Ensure no broken dpkg state remains…"
sudo dpkg --configure -a
sudo apt-get install -f -y -qq

echo "6) Switch keyboard layout to US…"
sudo sed -i 's/^XKBLAYOUT=.*/XKBLAYOUT="us"/' /etc/default/keyboard
sudo DEBIAN_FRONTEND=noninteractive dpkg-reconfigure keyboard-configuration
sudo udevadm trigger --subsystem-match=input --action=change
sudo reboot

After rebooting, continue with the steps below.

2. Clone the Repository and Set Up the Environment

git clone https://www.github.com/<your_username>/ANU_YOLO
cd ANU_YOLO
chmod +x install_env.sh
./install_env.sh # Installs Miniconda and everything that you do not need to worry about

This script will install most required dependencies automatically.

3. (Optional) Create a Conda Environment

conda create -n yolo python=3.10
conda activate yolo
pip install opencv-python ultralytics face-recognition

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Model Weights

Download the YOLOv8 face detector weights from the official repository:

• lindevs/yolov8-face

Place the downloaded weights file (e.g., yolov8n-face-lindevs.pt) in the project root directory.

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Usage

Face Detection on Images

python yolo_face.py --source bus.jpg --output bus_face.jpg

• Input: bus.jpg (or any image file)
• Output: bus_face.jpg (annotated image with bounding boxes and confidence values)

Face Detection on Videos

python yolo_face.py --source path/to/video.mp4 --output path/to/output.mp4

• Input: Any video file
• Output: Annotated video with detected faces

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Group Task

Count the number of apples in the video: apples.mp4. Submit your script as apples.py (one per team). Please list your team name and members here:

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Credits for video: https://www.istockphoto.com/video/gala-apple-falling-gm472985251-20196243?utm_source=pixabay&utm_medium=affiliate&utm_campaign=sponsored_video&utm_content=srp_topbanner_media&utm_term=apple+fall

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Credits

• YOLOv8 by Ultralytics: https://docs.ultralytics.com/models/yolov8/
• Face model weights: https://github.com/lindevs/yolov8-face
• Josh Starmer: Videos --> I went to LSE to study Machine Learning in 2019 only to end up watching his videos for the exams.
• ChatGPT for a faux annotated image used in the presentation.