WildEye

WildEye is a Python-based application that utilizes machine learning models to identify and classify animals in images. The project leverages pre-trained models and custom scripts to detect various animal species, providing a user-friendly interface for seamless interaction.

Features

• Image Upload: Allows users to upload images for animal detection.
• Animal Classification: Utilizes machine learning models to identify and classify animals within the uploaded images.
• User Interface: Provides a simple and intuitive interface for users to interact with the application.

Technologies Used

• Python: Programming language used for application development.
• Flask: Web framework for building the web application.
• TensorFlow: Machine learning framework for model deployment.
• OpenCV: Library for image processing tasks.
• HTML/CSS: For designing the web interface.

Installation

To run the project locally, follow the steps below:

Clone the Repository:

git clone https://github.com/JimmYCHUU/WildEye.git

Navigate to the Project Directory:

cd animal-detector

Set Up the Virtual Environment:

It is recommended to use a virtual environment to manage dependencies:

python3 -m venv env
Activate the Virtual Environment:

On Windows:

.\env\Scripts\activate

On macOS/Linux:

source env/bin/activate

Install Dependencies:

Install the required Python packages:

pip install -r requirements.txt

Install System Dependencies:

Some system libraries are required for the application to function correctly:

sudo apt-get update && sudo apt-get install ffmpeg libsm6 libxext6

Run the Application:

Start the Flask development server:

python app.py

The application will be accessible at http://127.0.0.1:5000/.

Usage

• Open the application in your web browser.
• Upload an image using the provided upload interface.
• The application will process the image and display the detected animal species.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Submit a pull request.