Object Detection Microservice

This repository contains a containerized microservice for object detection using YOLOv8. The project leverages Docker, FastAPI, and YOLOv8 to provide an efficient and user-friendly solution for object detection.

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Prerequisites

Before starting, ensure you have the following installed:

• Docker: For containerization of services.
• Docker Compose: To manage multi-container applications.
• Ubuntu: This guide assumes an Ubuntu-based system.

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Installation

Follow these steps to set up Docker and Docker Compose on your system.

Step 1: Update System Packages

sudo apt update && sudo apt upgrade -y

Step 2: Remove Older Docker Versions

sudo apt remove docker docker-engine docker.io containerd runc

Step 3: Install Dependencies

sudo apt install -y apt-transport-https ca-certificates curl software-properties-common

Step 4: Add Docker’s GPG Key

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

Step 5: Add Docker Repository

echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

Step 6: Install Docker

sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io
docker --version

Step 7: Install Docker Compose

sudo curl -L "https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
docker-compose --version

Step 8: Add User to Docker Group

sudo usermod -aG docker $USER
newgrp docker

File Structure

object_detection_microservice/
├── ai_backend/
│   ├── Dockerfile
│   ├── main.py
│   ├── models/
│   │   └── yolov8s.pt
│   ├── output/
│   └── requirements.txt
├── ui_backend/
│   ├── Dockerfile
│   ├── main.py
│   └── requirements.txt
├── docker-compose.yml
└── images/
    └── img1.jpg

Project Setup

Step 1: Clone the Repository

git clone https://github.com/ksm26/object-detection-microservice
cd object_detection_microservice

Step 2: Pre-download YOLOv8s Weights

mkdir -p ai_backend/models
wget https://github.com/ultralytics/yolov3/releases/download/v9.6.0/yolov8s.pt -O ai_backend/models/yolov8s.pt

Running the Microservice

Step 1: Start the Services

docker-compose up --build

Step 2: Test the Microservice

curl -X POST "http://localhost:8000/detect/" -F "file=@images/img1.jpg"

Output

JSON File: Contains detection details

JSON: ai_backend/output/<image_name>_detections.json

[{"bbox": [[171.80197143554688, 177.1391143798828, 276.281494140625, 426.3130187988281]], "confidence": 0.787072479724884, "class": 15, "label": "cat"}, {"bbox": [[255.08807373046875, 65.53730773925781, 426.2208557128906, 423.4767761230469]], "confidence": 0.7535444498062134, "class": 16, "label": "dog"}, {"bbox": [[253.9145965576172, 65.65737915039062, 424.5438232421875, 423.5783386230469]], "confidence": 0.3015751242637634, "class": 15, "label": "cat"}]

Processed Image: The image with bounding boxes drawn

Image: ai_backend/output/predict_<image_name>.jpg

Restarting the Services

docker-compose down
docker-compose up