https://github.com/theaniketraj/plastecure
An Innovative Platform that simplifies the process of plastic waste dispensing.
https://github.com/theaniketraj/plastecure
group-project tech-fest
Last synced: 6 months ago
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An Innovative Platform that simplifies the process of plastic waste dispensing.
- Host: GitHub
- URL: https://github.com/theaniketraj/plastecure
- Owner: theaniketraj
- Created: 2025-05-06T20:32:12.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-09-06T06:37:01.000Z (11 months ago)
- Last Synced: 2025-09-06T08:30:50.189Z (11 months ago)
- Topics: group-project, tech-fest
- Language: HTML
- Homepage: https://plastecure.netlify.app
- Size: 2.93 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Plastecure
> **Healing the Earth, one bottle at a time.**
Plastecure is a smart recycling bin system designed to automate and incentivize the process of recycling plastic bottles. It combines hardware and software to create an interactive and rewarding experience for users.
## Overview
The Plastecure system consists of a physical smart bin and a web application. Users interact with the bin to deposit plastic bottles. The bin automatically detects the bottle, and the user's account is credited with points. The web application provides a dashboard for users to track their contributions and rewards.
The core of the system is a machine learning model that visually identifies plastic bottles, ensuring that only correct items are recycled.
## Features
* **Automated Bottle Detection**: An ESP32-CAM captures an image of the deposited item, and a Python server with a PyTorch model verifies if it's a bottle.
* **User Authentication & Rewards**: Users can log in with a unique ID on the machine's keypad. The system tracks the number of bottles deposited and allocates points.
* **Real-time Bin Status**: An ultrasonic sensor monitors the fill level of the bin, and an LCD screen displays the status.
* **Web Dashboard**: A user-friendly web interface for users to view their recycling statistics and other information.
* **Hardware Integration**: The system uses an Arduino to control the physical components like servos, sensors, and the user interface (keypad and LCD).
## Tech Stack
### Hardware
- **Arduino Uno**: Main controller for the smart bin's hardware components.
- **ESP32-CAM**: For capturing images for bottle detection.
- **ESP32**: For I2C communication with the Arduino and potentially Wi-Fi connectivity to the backend.
- **Sensors**:
- PIR Motion Sensor (on ESP32-CAM)
- Ultrasonic Sensor (for bin level)
- IR Sensor (for bottle counting)
- **Actuators**:
- Servo Motors
- **User Interface**:
- I2C LCD Display
- 4x4 Matrix Keypad
### Software & Cloud
- **Backend**:
- **Flask**: Python web server to handle image processing.
- **PyTorch**: For the bottle detection machine learning model.
- **Frontend**:
- HTML5
- CSS3
- JavaScript
- **Database & Backend-as-a-Service**:
- **Firebase**: For user authentication, real-time database, and analytics.
- **Programming Languages**:
- **C++ (Arduino/ESP)**
- **Python**
- **JavaScript**
## Setup and Installation
### 1. API Server
The API server is responsible for the machine learning-based bottle detection.
1. **Navigate to the API directory:**
```bash
cd API
```
2. **Install Python dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the server:**
```bash
python server_debug.py
```
The server will start on `http://localhost:5000`. For the ESP32-CAM to access it, you'll need to expose this local server to the internet using a tool like [ngrok](https://ngrok.com/).
### 2. Hardware (Arduino & ESPs)
1. **Open the `.ino` files** in the `Machine Codes` directory with the Arduino IDE.
2. **Install necessary libraries** for the Arduino Uno, ESP32, and ESP32-CAM (e.g., `LiquidCrystal_I2C`, `Keypad`, `Servo`, `WiFi`, `HTTPClient`, `ArduinoJson`).
3. **Configure the code:**
* In `esp32cam.ino`, update the Wi-Fi credentials (`ssid` and `password`) and the server URL (`serverBase`) to your ngrok forwarding address.
* In `new_arduino.ino`, ensure the I2C address for the LCD and ESP32 are correct.
4. **Upload the code** to the respective boards.
### 3. Web Application
1. **Open `index.html`** in your browser to view the landing page.
2. **Firebase Configuration**: The Firebase configuration is present in `index.html`. Make sure your Firebase project has Authentication and Realtime Database enabled. The security rules for the database should be configured to allow authorized reads and writes.
## Project Structure
```
.
├── API/ # Backend Flask server for ML model
│ ├── server_debug.py
│ └── requirements.txt
├── Machine Codes/ # Code for the microcontrollers
│ ├── Arduino Uno/
│ └── esp32 cam/
├── assets/ # Images and other static assets
├── scripts/ # JavaScript for the web pages
├── style/ # CSS stylesheets
└── *.html # HTML files for the web application
```
## Contributing
Contributions are welcome! Please feel free to submit a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.