Python Traffic Signs Recognition System

This project is a Traffic Sign Classification System built using Python, TensorFlow/Keras, and Tkinter. It allows users to upload an image of a traffic sign, and the system will classify the sign using a pre-trained deep learning model.

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Features

• User-friendly GUI: Built with Tkinter for an intuitive user experience.
• Deep learning-powered classification: Uses a trained traffic_classifier.h5 model for accurate predictions.
• Predefined traffic sign classes: Supports the classification of 43 different traffic signs based on a standardized dataset.
• Model Improvement Capability: Allows training on new images to enhance model accuracy and robustness.

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Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Required Python libraries (install via requirements.txt)

Installation

1. Clone the Repository
   Clone the project to your local machine:

   git clone https://github.com/Victor-M16/Python-Traffic-Signs-Recognition-System.git
   cd Python-Traffic-Signs-Recognition-System

2. Install Dependencies
   Install the required Python packages:

   pip install -r requirements.txt

3. Run the Application
   Launch the GUI application:

   python gui.py

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Usage

Traffic Sign Classification

1. Start the Application
   Run the application and wait for the GUI to appear.

2. Upload an Image

   • Click the "Upload an image" button.
   • Use the file explorer to select an image of a traffic sign.

3. Classify the Image

   • Click the "Classify Image" button to analyze the uploaded image.
   • The predicted traffic sign will be displayed at the bottom of the GUI.

Model Improvement with New Images

This project supports incremental training to improve the model using new images. Follow these steps:

1. Prepare Your Data

   • Organize new traffic sign images into folders named according to their class IDs (e.g., 0, 1, 2, etc.).
   • Place these folders in a directory named train.

2. Update the Model

   • Run the training script provided in the repository to include new images in the training process:

     python train.py

   • The script will:
     • Load existing data and the new images.
     • Preprocess the data by resizing images to 30x30 pixels and normalizing them.
     • Retrain the model with both old and new data.

3. Replace the Old Model

   • The updated model will be saved as my_model.h5.
   • Replace the existing traffic_classifier.h5 in the gui.py code with my_model.h5 to use the improved version.

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Classes

The system can recognize the following traffic signs:

1. Speed limit (20km/h)
2. Speed limit (30km/h)
3. Speed limit (50km/h)
   ...
   (Complete list available in the source code.)

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File Structure

• gui.py: Main application script for the GUI.
• train.py: Script for training the model with new or additional images.
• traffic_classifier.h5: Pre-trained model for classifying traffic signs.
• requirements.txt: Python dependencies for the project.

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License

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

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Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

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Acknowledgments

• The pre-trained model is based on a standardized traffic signs dataset.
• Tkinter was used to design the GUI for easy interaction.

Enjoy using the Traffic Sign Classification System! 🚦