About Project¶

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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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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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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IV. Key Features¶

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V. System Architecture¶

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VI. Methodology and Development Process¶

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VII. Technology Stack¶

• Python (OpenCV, NumPY)
• Doxygen
• Redmine
• Astah

VIII. Application & Impact¶

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IX. Limitation & Future Enhancements¶

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