G12-2025-Final Project

h1. 💡 About Project

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h2. I. Project Overview

Our project focuses on developing a *high-quality image composition system* that seamlessly merges two or more projected images into a single, visually consistent display.  

Using **Python** and **OpenCV**, we employ advanced image processing techniques such as *gamma correction*, *alpha blending*, and *intensity modification* to eliminate brightness and color mismatches between overlapping regions.  

The project is organized into sub-teams responsible for software development, UML design, testing, and wiki management. Each member plays a key role in ensuring collaborative progress and well-structured documentation.  

By integrating **Doxygen** for code documentation and **Redmine** for project tracking, we aim to produce a well-documented, scalable, and reproducible system for real-time image correction and blending.

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h2. II. Motivation & Problem Statement

When using multiple projectors to display a single image, visible seams or brightness inconsistencies often occur in overlapping regions. These inconsistencies degrade image quality and make the final projection appear uneven.  

Manual calibration methods are time-consuming and prone to human error.  

Our motivation is to develop a software-based approach that automates the alignment and blending process, ensuring seamless image projection.  

By leveraging the **OpenCV** library, the system can detect overlapping areas, apply brightness corrections, and blend images smoothly — eliminating the need for costly hardware-based calibration systems.

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h2. III. Objectives

* To develop an automated image blending system capable of merging two or more projections into a single seamless image.  

* To apply *gamma correction* and *intensity modification* techniques to balance color and brightness across overlapping regions.  

* To implement *alpha blending* for smooth transitions between images.  

* To design and visualize the system architecture using **UML diagrams**.  

* To document the entire project using **Doxygen** and manage tasks via **Redmine**.

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h2. IV. Key Features

*1. Automated Image Blending*

Uses OpenCV and user-defined parameters to automatically blend two projected images, ensuring accurate overlap and alignment.

*2. Gamma Correction and Intensity Adjustment*

Employs advanced color and brightness correction algorithms to maintain consistent luminance across blended areas, effectively removing visible seams and mismatches.

*3. Video Blending*

Leverages GPU acceleration through PyTorch to calculate per-pixel brightness for video streams, enabling real-time blending and correction.

*4. User-Friendly Graphical Interface*

Provides an intuitive GUI that allows users to select interpolation modes, specify overlap pixels, and control blending parameters easily.

*5. Modular System Architecture*

Designed using UML-based class structures that divide the project into smaller, manageable components, improving scalability and ease of feature expansion.

*6. Comprehensive Documentation and Project Management*

Integrates Doxygen for automated code documentation and Redmine for task tracking, ensuring transparent collaboration and efficient workflow management.

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h2. V. Algorithm and Theoretical Framework

h2. VI. System Architecture

_(Add later)_

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h2. VII. Methodology and Development Process

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a. Requirement analysis

b. Testing and Validation

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h2. VIII. Technology Stack

* *Python (OpenCV, NumPy)*  

* *Doxygen*  

* *Redmine*  

* *Astah*  

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h2. IX. Application & Impact

_(Add later)_

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h2. X. Limitation & Future Enhancements

_(Add later)_

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