https://github.com/alihassanml/yolo-final-year-project
This project is a YOLO (You Only Look Once) object detection system built using FastAPI. It provides real-time predictions and visualizations, leveraging the YOLO model for detecting specific activities or objects. This project is developed for a final-year academic project.
https://github.com/alihassanml/yolo-final-year-project
fastapi opencv yolo
Last synced: 7 months ago
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This project is a YOLO (You Only Look Once) object detection system built using FastAPI. It provides real-time predictions and visualizations, leveraging the YOLO model for detecting specific activities or objects. This project is developed for a final-year academic project.
- Host: GitHub
- URL: https://github.com/alihassanml/yolo-final-year-project
- Owner: alihassanml
- License: mit
- Created: 2025-01-20T16:08:20.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-21T12:26:30.000Z (8 months ago)
- Last Synced: 2025-02-21T13:28:37.038Z (8 months ago)
- Topics: fastapi, opencv, yolo
- Language: Python
- Homepage:
- Size: 33.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YOLO-Based Object Detection with FastAPI - Final Year Project
This project is a YOLO (You Only Look Once) object detection system built using FastAPI. It provides real-time predictions and visualizations, leveraging the YOLO model for detecting specific activities or objects. This project is developed for a final-year academic project.
## Features
- **Object Detection:** Predict objects in uploaded images using a custom YOLO model (`best5.pt`).
- **Custom Trained Classes:** Detects the following classes:
- Drinking
- Eating
- Violence
- Sleeping
- Smoking
- Walking
- Weapon
- **Real-Time Prediction API:** Upload images and get annotated predictions with bounding boxes and confidence scores.
- **Database Storage:** Stores prediction results (class name, accuracy, and timestamp) in a database.
- **Data Visualization:** View class-wise prediction counts in a bar chart.
## Technology Stack
- **Backend Framework:** FastAPI
- **Model:** YOLO (Ultralytics)
- **Database:** SQLAlchemy with SQLite
- **Frontend:** Jinja2 Templates, HTML, CSS, JavaScript (Bootstrap, Chart.js)
## How to Run the Project
### Prerequisites
1. Python 3.8+ installed.
2. Install dependencies using the `requirements.txt` file.
### Steps to Run
1. Clone the repository:
```bash
git clone https://github.com/alihassanml/Yolo-Final-Year-Project.git
cd Yolo-Final-Year-Project
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Linux/Mac
venv\Scripts\activate # On Windows
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Start the FastAPI server:
```bash
uvicorn app:app --reload
```
5. Open your browser and navigate to:
```
http://127.0.0.1:8000
```
6. Use the web interface to upload images and view predictions.
### API Endpoints
- `GET /`
Returns the homepage.
- `POST /predict`
Upload an image and receive predictions with annotated bounding boxes.
- `GET /predictions`
Retrieve a list of all predictions stored in the database.
- `GET /class-counts`
Get class-wise counts of predictions for visualization.
## Project Structure
```
Yolo-Final-Year-Project/
│
├── app.py # Main FastAPI application
├── model/
│ ├── best5.pt # YOLO pre-trained model weights
│ └── __init__.py # Package initialization
│
├── database.py # Database configuration
├── model.py # SQLAlchemy models
├── static/
│ ├── style.css # CSS for styling
│ ├── script.js # JavaScript functionality
│ └── uploads/ # Uploaded images folder
│
├── templates/
│ └── index.html # Jinja2 template for the frontend
│
├── requirements.txt # Python dependencies
├── README.md # Project documentation
└── .gitignore # Files to ignore in the repository
```
## Future Enhancements
- Add video-based detection for live streams.
- Integrate a user authentication system for secure access.
- Expand the model to detect additional classes.
- Include export functionality for predictions (e.g., CSV or Excel).
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
### Author
Developed by [Ali Hassan](https://github.com/alihassanml). For questions or feedback, feel free to reach out.
```
---
### `requirements.txt`
```plaintext
fastapi==0.95.2
uvicorn==0.23.0
sqlalchemy==2.0.21
jinja2==3.1.2
pydantic==2.0.3
opencv-python==4.8.0.74
numpy==1.24.3
ultralytics==8.0.30
Pillow==9.5.0
chart.js==3.9.1
```
---
### Steps to Upload to GitHub
1. Save the `README.md` and `requirements.txt` in the root directory of your project.
2. Commit and push the changes to your GitHub repository:
```bash
git add README.md requirements.txt
git commit -m "Added README and requirements.txt for FastAPI-based YOLO project"
git push origin main
```