G12-2025-Final Project

h1. 🎯 About Project - G12-2025

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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* - For brightness normalization

* 🔄 *Alpha Blending* - For smooth transitions

* ✨ *Intensity Modification* - For color consistency

The project is organized into specialized 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.

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*Key Integration Tools:*

* 📚 *Doxygen* - Automated code documentation

* 📊 *Redmine* - Project tracking and management

* 🎨 *Astah* - UML diagram creation

Our goal is 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

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h3. The Challenge

When using multiple projectors to display a single image, several issues arise:

⚠️ *Common Problems:*

* Visible seams in overlapping regions

* Brightness inconsistencies between projectors

* Color mismatches at boundaries

* Uneven final projection quality

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h3. Current Limitations

* ⏱️ Manual calibration is time-consuming

* 🎯 Human error in manual adjustments

* 💰 Hardware-based solutions are expensive

* 🔧 Limited flexibility for different setups

h3. Our Solution

✨ *Software-Based Automation*

We develop an intelligent system that:

1. *Detects* overlapping areas automatically

2. *Applies* brightness corrections in real-time

3. *Blends* images smoothly using alpha blending

4. *Eliminates* the need for costly hardware calibration

By leveraging the *OpenCV* library, our system automates the entire alignment and blending process, ensuring seamless image projection with minimal manual intervention.

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h2. III. 🎯 Objectives

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|_. # |_. Objective |_. Status |

| 1 | Develop automated image blending system for multiple projections | 🔄 In Progress |

| 2 | Apply gamma correction and intensity modification techniques | ✅ Algorithm Designed |

| 3 | Implement alpha blending for smooth transitions | ✅ Implemented |

| 4 | Design system architecture using UML diagrams | ✅ Complete |

| 5 | Document entire project using Doxygen | 🔄 Ongoing |

| 6 | Manage tasks and track progress via Redmine | ✅ Active |

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h2. IV. 🌟 Key Features

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h3. Core Capabilities

*🎨 Image Processing Features:*

* Multi-image composition and merging

* Automatic overlap detection

* Real-time brightness adjustment

* Color consistency maintenance

* Seamless boundary blending

h3. Technical Features

* *⚡ Performance:* Real-time processing capability

* *🔧 Flexibility:* Supports 2+ projector configurations

* *📊 Accuracy:* Pixel-level precision in blending

* *🎯 Reliability:* Consistent results across different inputs

h3. System Features

* *📚 Documentation:* Comprehensive Doxygen-generated docs

* *🧪 Testing:* Automated test suite for quality assurance

* *🔄 Version Control:* Git-based collaborative development

* *📊 Project Management:* Redmine integration for tracking

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h2. V. 🏗️ System Architecture

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h3. Architecture Overview

Our system follows a modular design pattern with clear separation of concerns:

*Layer 1: Input Processing*

* Image acquisition from multiple sources

* Pre-processing and validation

*Layer 2: Analysis & Correction*

* Overlap detection algorithms

* Gamma correction calculations

* Intensity adjustment computations

*Layer 3: Blending & Composition*

* Alpha blending engine

* Final image composition

* Real-time rendering

*Layer 4: Output & Display*

* Optimized image delivery

* Multi-projector synchronization

_For detailed UML diagrams, see [[UML Diagrams]]_

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h2. VI. 🔬 Methodology and Development Process

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h3. Development Approach

We follow an *Agile-inspired iterative development process*:

|_. Phase |_. Activities |_. Duration |

| Planning | Requirements analysis, team formation | Week 1-2 |

| Design | UML diagrams, architecture design | Week 3-4 |

| Development | Core algorithm implementation | Week 5-8 |

| Testing | Unit testing, integration testing | Week 7-9 |

| Documentation | Code docs, wiki, user guides | Ongoing |

| Refinement | Optimization and bug fixes | Week 10+ |

h3. Quality Assurance Process

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*Testing Strategy:*

1. *Unit Testing* - Individual component validation

2. *Integration Testing* - System-wide functionality

3. *Performance Testing* - Speed and efficiency metrics

4. *User Acceptance Testing* - Real-world scenario validation

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h3. Collaboration Tools

* *Version Control:* Git & GitHub for code management

* *Project Tracking:* Redmine for task and issue management

* *Communication:* Regular team meetings and Slack

* *Documentation:* Doxygen for code, Wiki for project info

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h2. VII. 🛠️ Technology Stack

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h3. Programming & Libraries

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*Core Technologies:*

|_. Technology |_. Purpose |_. Version |

| !https://img.shields.io/badge/Python-3.9+-blue! | Primary language | 3.9+ |

| !https://img.shields.io/badge/OpenCV-4.x-green! | Image processing | 4.x |

| !https://img.shields.io/badge/NumPy-Latest-orange! | Numerical computations | Latest |

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h3. Development Tools

* *🎨 Astah* - UML diagram creation and management

* *📚 Doxygen* - Automated documentation generation

* *📊 Redmine* - Project management and issue tracking

* *🔧 Git* - Version control and collaboration

h3. Development Environment

* *IDE:* PyCharm / VS Code

* *OS:* Cross-platform (Windows, macOS, Linux)

* *Testing:* pytest framework

* *CI/CD:* GitHub Actions (planned)

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h2. VIII. 🌍 Application & Impact

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h3. Real-World Applications

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*Industry Use Cases:*

🎭 *Entertainment & Events*

* Large-scale concert projections

* Theater and stage productions

* Immersive art installations

🏢 *Corporate & Education*

* Conference room presentations

* Educational institutions

* Training facilities

🎮 *Gaming & Simulation*

* Flight simulators

* Virtual reality environments

* Gaming arcades

🏛️ *Museums & Exhibitions*

* Interactive displays

* Historical recreations

* Planetariums

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h3. Project Impact

* *Cost Reduction:* Eliminates expensive hardware calibration systems

* *Time Savings:* Automated process vs. manual adjustment

* *Quality Improvement:* Consistent, reproducible results

* *Accessibility:* Software-based solution available to more users

* *Scalability:* Easily adapts to different projector configurations

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h2. IX. 🚀 Limitations & Future Enhancements

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h3. Current Limitations

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⚠️ *Known Constraints:*

* Processing time increases with image resolution

* Requires compatible projector specifications

* Limited to static image composition (no video yet)

* Calibration needed for each new setup

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h3. Planned Enhancements

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*Phase 2 Features:*

🎥 *Video Support*

* Real-time video blending

* Multi-stream synchronization

🤖 *AI Integration*

* Machine learning for automatic calibration

* Intelligent scene detection

⚡ *Performance Optimization*

* GPU acceleration

* Parallel processing implementation

🌐 *Extended Compatibility*

* Support for more projector models

* Cloud-based processing option

📱 *User Interface*

* GUI for non-technical users

* Mobile app for remote control

🔧 *Advanced Features*

* 3D projection mapping

* Curved surface support

* Dynamic brightness adjustment

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h3. Research Opportunities

* Integration with IoT devices for smart environments

* Edge computing implementation for distributed systems

* Advanced color science algorithms

* Virtual reality applications

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*📅 Last Updated:* October 30, 2025

*📝 Maintained by:* Documentation Team

[[Group Members|View Team]] | [[UML Diagrams|Technical Design]] | [[Class Activity|Progress Log]]

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