About Project » History » Revision 10
Revision 9 (Pratama Kwee BRANDON, 11/06/2025 12:18 AM) → Revision 10/30 (Pratama Kwee BRANDON, 11/06/2025 12:25 AM)
!https://media.tenor.com/cb9L14uH-YAAAAAM/cool-fun.gif! h1. 💡 About Project --- 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. --- 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. --- 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**. --- 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. --- h2. V. Algorithm and Theoretical Framework h2. VI. System Architecture _(Add later)_ --- h2. VII. VI. Methodology and Development Process _(Add later)_ a. Requirement analysis b. Testing and Validation --- h2. VIII. VII. Technology Stack * *Python (OpenCV, NumPy)* * *Doxygen* * *Redmine* * *Astah* --- h2. IX. VIII. Application & Impact _(Add later)_ --- h2. X. IX. Limitation & Future Enhancements _(Add later)_ --- !https://media.tenor.com/Q14Y3rSxX5wAAAAM/plan-roadmap.gif!