Thin-Plate Spline Illumination Estimation (TPS) 🌈

Welcome to the TPS repository! This project focuses on the Thin-Plate Spline Illumination Estimation Automatic White Balancing Method. It aims to enhance color constancy in digital images, making them appear more natural and vibrant.

Table of Contents

• Introduction
• Key Features
• Topics Covered
• Installation
• Usage
• Contributing
• License
• Acknowledgments

Introduction

In the realm of digital image processing, achieving accurate color representation is crucial. The TPS method leverages thin-plate splines for illumination estimation and automatic white balancing. This approach ensures that images maintain their true colors, regardless of varying lighting conditions.

Whether you're working on camera calibration, color measurement, or enhancing your vision systems, this repository provides the tools you need.

Key Features

• Automatic White Balancing: Adjusts image colors based on lighting conditions.
• Illumination Estimation: Estimates light sources in the scene for better color representation.
• Easy Integration: Simple to integrate into existing image processing workflows.
• Robust Performance: Maintains color accuracy across diverse lighting environments.

Topics Covered

This repository touches on several important topics in the field of image processing:

• Camera Calibration: Ensures that images are captured accurately.
• Color Constancy: Maintains consistent color under different lighting.
• Color Measurement: Quantifies colors in images.
• Color Vision: Studies how humans perceive color.
• Digital Image Processing: Involves techniques to manipulate images.
• Illumination: Focuses on the light in images.
• Image Registration: Aligns multiple images for comparison.
• Interpolation: Estimates values between known data points.
• Light Sources: Analyzes different types of lighting.
• Neural Networks: Uses AI for advanced image processing tasks.
• Vision: Explores how systems interpret visual information.
• Visual Optics: Studies how light interacts with lenses and sensors.

Installation

To get started with the TPS method, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Jomelson/tps.git
   cd tps

2. Install Dependencies: Make sure you have Python installed. Then, install the required packages:

   pip install -r requirements.txt

3. Download and Execute the Latest Release: Visit the Releases section to download the latest version. Follow the instructions in the release notes to execute the program.

Usage

Using the TPS method is straightforward. Here’s a simple example:

1. Load Your Image: Use the provided functions to load an image into the program.

2. Apply the TPS Method: Call the TPS function with your image as an argument:

   from tps import apply_tps
   balanced_image = apply_tps(input_image)

3. Save the Output: Save the processed image to your desired location:

   balanced_image.save("output_image.jpg")

This simple workflow allows you to enhance the color accuracy of your images quickly.

Contributing

We welcome contributions from the community! Here’s how you can help:

1. Fork the Repository: Create your own copy of the repository.
2. Create a Branch: Use a descriptive name for your branch.
3. Make Changes: Implement your features or fixes.
4. Submit a Pull Request: Describe your changes and why they are beneficial.

Please ensure that your code adheres to the existing style and includes tests where applicable.

License

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

Acknowledgments

We appreciate the contributions of the open-source community. Special thanks to those who have provided feedback and suggestions for improvement.

For more detailed information, including updates and improvements, please check the Releases section.