Garbaggio Web Application

Garbaggio is a web application designed to facilitate the collection and sharing of free items available around town. Users can upload photos of items, which are then automatically analyzed using a machine learning model to detect objects. Detected objects are stored in a database along with their location and timestamp information.

Features

Object Detection: Utilizes a pre-trained Mask R-CNN model from PyTorch to detect objects in uploaded photos.
Database Storage: Stores detected objects in an SQLite database (database.db) with attributes including object name, latitude, longitude, and timestamp.
Web Interface: Provides a user-friendly web interface where users can upload photos and view detected objects.
Responsive Design: Built with Flask (backend) and Vue.js (frontend) for a modern and responsive user experience.

Tech Stack

Backend: Flask (Python)
- Flask-SQLAlchemy: ORM for database management
- Flask-CORS: Handling Cross-Origin Resource Sharing
Frontend: Vue.js
- Axios: HTTP client for making requests to the Flask backend
Machine Learning: PyTorch (Torchvision)
- Used for object detection with a Mask R-CNN model

Installation

Prerequisites

Python 3.8+ installed on your system
Node.js and npm (Node Package Manager) installed for Vue.js frontend development

Setup

Clone the repository:
```
git clone <repository_url>
cd Garbaggio
```

Create and activate a virtual environment (recommended):

python -m venv venv
.\venv\Scripts\activate  # On Windows
source venv/bin/activate  # On macOS/Linux

Install Python dependencies:
```
pip install -r requirements.txt
```
Install Node.js dependencies:
```
cd frontend
npm install
```
Run the application:
- Start the Flask backend:
```
python backend/app.py
```
- Start the Vue.js frontend (in a separate terminal):
```
cd frontend
npm run serve
```
Access the application: Open a web browser and go to http://localhost:8080 (or the URL shown in the terminal after running npm run serve) to use the Garbaggio web application.

Usage

Upload Photo:
- Click on the upload button and select a photo of an item available for pickup.
- Enter the latitude and longitude coordinates of the item's location.
Object Detection:
- The uploaded photo is processed using a pre-trained Mask R-CNN model.
- Detected objects are displayed on the screen.
Database Storage:
- Detected objects, along with their location and timestamp, are stored in the SQLite database (backend/database.db).

Contributing

Contributions are welcome! If you'd like to enhance the Garbaggio web application, please fork the repository and create a pull request with your proposed changes. Ensure your code follows the project's coding style and includes relevant tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

The Mask R-CNN model used for object detection is based on the torchvision library from PyTorch.
Flask and Vue.js communities for their excellent documentation and resources.

noveoko/garbaggio