🎯 About Project - G12-2025¶

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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.

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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II. 💡 Motivation & Problem Statement¶

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 {background}

Current Limitations¶

• ⏱️ Manual calibration is time-consuming
• 🎯 Human error in manual adjustments
• 💰 Hardware-based solutions are expensive
• 🔧 Limited flexibility for different setups

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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III. 🎯 Objectives¶

#	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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IV. 🌟 Key Features¶

Core Capabilities¶

🎨 Image Processing Features:

• Multi-image composition and merging
• Automatic overlap detection
• Real-time brightness adjustment
• Color consistency maintenance
• Seamless boundary blending

Technical Features¶

• ⚡ Performance: Real-time processing capability
• 🔧 Flexibility: Supports 2+ projector configurations
• 📊 Accuracy: Pixel-level precision in blending
• 🎯 Reliability: Consistent results across different inputs

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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V. 🏗️ System Architecture¶

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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VI. 🔬 Methodology and Development Process¶

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+

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 {background}

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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VII. 🛠️ Technology Stack¶

Programming & Libraries¶

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Core Technologies:

|_. Technology |_. Purpose |_. Version | | | Primary language | 3.9+ | | | Image processing | 4.x | | | Numerical computations | Latest | {background}

Development Tools¶

• 🎨 Astah - UML diagram creation and management
• 📚 Doxygen - Automated documentation generation
• 📊 Redmine - Project management and issue tracking
• 🔧 Git - Version control and collaboration

Development Environment¶

• IDE: PyCharm / VS Code
• OS: Cross-platform (Windows, macOS, Linux)
• Testing: pytest framework
• CI/CD: GitHub Actions (planned)

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VIII. 🌍 Application & Impact¶

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 {background}

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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IX. 🚀 Limitations & Future Enhancements¶

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 {background}

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 {background}

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
View Team | Technical Design | Progress Log {background}