About Project » History » Revision 15
Revision 14 (Pratama Kwee BRANDON, 11/06/2025 12:56 AM) → Revision 15/30 (Sandeep GANESAN, 11/06/2025 09:34 AM)
h1. 💡 About Project --- h2. I. Project Overview Our project focuses on developing a high-quality *high-quality image composition system capable of system* that seamlessly merging merges two or more projected images into a single, visually uniform consistent display. The goal is to ensure that the combined image appears continuous Using **Python** and free from visible seams, color shifts, or brightness inconsistencies. Built using Python and OpenCV, the system applies a series of **OpenCV**, we employ advanced image-processing image processing techniques including gamma correction, alpha blending, such as *gamma correction*, *alpha blending*, and intensity adjustment *intensity modification* to harmonize eliminate brightness and color mismatches between overlapping areas. These methods allow us to dynamically compensate for lighting variations and surface irregularities, resulting in a more accurate and visually pleasing projection output. regions. The project team is divided organized into multiple sub-groups, each focusing on specific responsibilities such as sub-teams responsible for software development, UML design, testing, and wiki management. This structure encourages effective collaboration, clear communication, and consistent Each member plays a key role in ensuring collaborative progress across all development phases. To maintain transparency and ensure reproducibility, we integrate Doxygen well-structured documentation. By integrating **Doxygen** for detailed source-code code documentation and Redmine **Redmine** for structured task tracking and project coordination. Together, these tools support tracking, we aim to produce a development environment that prioritizes scalability, maintainability, well-documented, scalable, and long-term usability. Ultimately, the project aims to deliver a robust framework reproducible system for real-time image correction and blending, serving as a foundation for future extensions in projection mapping, interactive displays, and multi-screen visualization systems. 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 _(Add later)_ --- h2. VI. System Architecture _(Add later)_ --- h2. VII. Requirement Analysis This defines the functional requirement of the project and outlining what the system needs to accomplish *- Image Input and Processing* -The system must accept image files and video files as input. -The system must split a given image into two sub-images (left and right) with a specific overlap region -The system must allow users to choose a one out of the three blending mode (linear, quadratic, or gaussian). -The system must apply blending algorithms using OpenCV and PyTorch for GPU-accelerated computation for videos. -The system must save the blended images (left.png, right.png) locally after processing. *- Video Frame Blending* -The system must be able to process individual video frames sequentially for real-time blending. -The system must output a smooth blended video stream without visible seams. *- Graphical User Interface (GUI)* -The system must let the user select the overlap pixel value from the GUI. -The system must let the user choose the blending algorithm from the GUI. -The system must be able to run both image and video blending modes in the GUI. -The system must let the user view the original, left, and right images in real-time in the GUI. -The system must be able display the blended outputs in fullscreen mode from the GUI. *- Error Handling and Feedback* -The system must handle missing files and display warnings or error messages appropriately. -The GUI must handle invalid user input without crashing the program. --- h2. VIII. Technology Stack * *Python (OpenCV, NumPy)* * *Doxygen* * *Redmine* * *Astah* --- h2. IX. Application & Impact _(Add later)_ --- h2. X. Limitation & Future Enhancements _(Add later)_ --- !https://media.tenor.com/Q14Y3rSxX5wAAAAM/plan-roadmap.gif!