https://github.com/ksm26/object-detection-microservice
This repository provides a fully containerized microservice for object detection using YOLOv8 and FastAPI. With Docker and Docker Compose, developers can easily set up, run, and integrate advanced object recognition capabilities into their applications.
https://github.com/ksm26/object-detection-microservice
computervision containerization devops docker fastapi imageprocessing machinelearning microservices modelinference objectdetection yolov8 yolov8s
Last synced: 28 days ago
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This repository provides a fully containerized microservice for object detection using YOLOv8 and FastAPI. With Docker and Docker Compose, developers can easily set up, run, and integrate advanced object recognition capabilities into their applications.
- Host: GitHub
- URL: https://github.com/ksm26/object-detection-microservice
- Owner: ksm26
- Created: 2024-12-09T11:17:48.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-09T11:41:59.000Z (5 months ago)
- Last Synced: 2024-12-09T12:30:02.685Z (5 months ago)
- Topics: computervision, containerization, devops, docker, fastapi, imageprocessing, machinelearning, microservices, modelinference, objectdetection, yolov8, yolov8s
- Language: Python
- Homepage: https://github.com/ksm26/object-detection-microservice/tree/main
- Size: 20.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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.
---
## 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.
---
## Installation
Follow these steps to set up Docker and Docker Compose on your system.
### Step 1: Update System Packages
```bash
sudo apt update && sudo apt upgrade -y
```
### Step 2: Remove Older Docker Versions
```bash
sudo apt remove docker docker-engine docker.io containerd runc
```
### Step 3: Install Dependencies
```bash
sudo apt install -y apt-transport-https ca-certificates curl software-properties-common
```
### Step 4: Add Docker’s GPG Key
```bash
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
```bash
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
```
### Step 6: Install Docker
```bash
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io
docker --version
```
### Step 7: Install Docker Compose
```bash
sudo curl -L "https://github.com/docker/compose/releases/latest/download/docker-compose-$(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
```bash
sudo usermod -aG docker $USER
newgrp docker
```
## File Structure
```plaintext
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
```bash
git clone https://github.com/ksm26/object-detection-microservice
cd object_detection_microservice
```
### Step 2: Pre-download YOLOv8s Weights
```bash
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
```bash
docker-compose up --build
```
### Step 2: Test the Microservice
```bash
curl -X POST "http://localhost:8000/detect/" -F "file=@images/img1.jpg"
```
## Output
### JSON File: Contains detection details
JSON: ai_backend/output/_detections.json
```plaintext
[{"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_.jpg
## Restarting the Services
```bash
docker-compose down
docker-compose up
```