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https://github.com/victor-m16/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.
https://github.com/victor-m16/python-traffic-signs-recognition-system
deep-learning keras python
Last synced: 4 days ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/victor-m16/python-traffic-signs-recognition-system
- Owner: Victor-M16
- License: mit
- Created: 2023-05-18T17:24:28.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-12-01T10:41:57.000Z (2 months ago)
- Last Synced: 2024-12-16T01:12:53.897Z (about 2 months ago)
- Topics: deep-learning, keras, python
- Language: Python
- Homepage:
- Size: 2.66 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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.
---
## 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.
---
## 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:
```bash
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:
```bash
pip install -r requirements.txt
```
3. **Run the Application**
Launch the GUI application:
```bash
python gui.py
```
---
## 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:
```bash
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.
---
## 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.)*
---
## 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.
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## Contributing
Contributions are welcome! Feel free to fork the repository and submit a pull request.
---
## 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! 🚦