About Project » History » Revision 13
Revision 12 (Pratama Kwee BRANDON, 11/06/2025 12:26 AM) → Revision 13/30 (Pratama Kwee BRANDON, 11/06/2025 12:26 AM)
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
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h2. VI. System Architecture
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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
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h2. X. Limitation & Future Enhancements
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!https://media.tenor.com/Q14Y3rSxX5wAAAAM/plan-roadmap.gif!