TheMaddax
4b8935afd1
- Add automatic capture start/stop based on board detection - Implement cursor-free snapshot system: * Add SCStreamConfiguration cursor control * Add temporary capture session management * Ensure clean snapshots without cursor artifacts - Add visual feedback: * Status indicator (green/yellow/gray) * Snapshot preview below board * Clear capture state indication - Update documentation: * Add snapshot system patterns * Document cursor control implementation * Update technical constraints
199 lines
4.9 KiB
Markdown
199 lines
4.9 KiB
Markdown
# Product Context
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## Project Overview
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ChessPrism is an advanced chess analysis tool that enhances the online chess experience by providing real-time analysis and move suggestions. It works by capturing and analyzing the chess board from popular chess websites and platforms.
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## Core Problems Solved
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### Chess Analysis Accessibility
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- Makes professional-level chess analysis accessible during online play
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- Provides real-time insights without manual position input
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- Integrates seamlessly with existing chess platforms
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### Visual Recognition
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- Accurately detects chess board from screen content
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- Recognizes board coordinates and boundaries
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- Handles various board themes and orientations
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- Maintains accuracy during game play
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- Provides clean board snapshots for analysis
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### Real-time Processing
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- Captures and processes screen content in real-time
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- Provides immediate feedback and analysis
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- Maintains performance during long sessions
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- Supports manual snapshot capture for detailed analysis
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## User Experience Goals
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### Seamless Integration
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1. Non-intrusive Operation
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- Works with Chess.com desktop app
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- Minimal setup requirements
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- Automatic board detection and tracking
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- Clean snapshot capture without cursor interference
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2. Intuitive Interface
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- Clear visualization of analysis
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- Easy-to-understand suggestions
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- Minimal user intervention required
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- Visual feedback for capture states
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- Manual snapshot control
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### Reliable Detection
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1. Board Recognition
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- Two-phase detection strategy:
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* Pattern recognition for known interfaces
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* Coordinate-based fallback for reliability
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- Proper coordinate system handling
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- Consistent board capture across sessions
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- High-quality snapshots for analysis
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2. Position Analysis
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- Accurate piece recognition (planned)
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- Current position evaluation (planned)
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- Move suggestion visualization (planned)
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## Target Users
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### Chess Players
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- Amateur to intermediate players
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- Chess.com desktop app users
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- Players seeking to improve
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### Use Cases
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1. Learning
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- Understanding position evaluation
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- Learning from mistakes
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- Exploring alternative moves
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- Analyzing specific positions via snapshots
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2. Analysis
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- Real-time position assessment
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- Move validation
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- Strategic planning
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- Detailed position study
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## Product Requirements
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### Essential Features
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1. Board Detection (Current Focus)
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- Accurate boundary recognition
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- Full board capture
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- Support for Chess.com desktop app
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- Reliable coordinate transformations
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- Clean snapshot capability
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2. Position Analysis (Planned)
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- Real-time evaluation
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- Move suggestions
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- Tactical opportunities
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3. User Interface
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- Analysis overlay
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- Control panel
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- Settings management
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- Snapshot controls
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- Visual status indicators
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### Quality Standards
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1. Accuracy
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- Reliable board detection
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- Complete board capture
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- Precise coordinate handling
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- Clean snapshots without artifacts
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2. Performance
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- Real-time processing
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- Minimal resource usage
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- Stable operation
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- Efficient snapshot handling
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3. Usability
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- Intuitive controls
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- Clear feedback
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- Minimal setup
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- Simple snapshot workflow
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## Success Metrics
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### Technical Metrics
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- Board detection accuracy rate
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- Full board capture success rate
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- Processing speed per frame
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- Error recovery rate
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- Snapshot quality assessment
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### User Metrics
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- Setup success rate
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- Analysis accuracy
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- User engagement time
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- Feature utilization
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- Snapshot usage patterns
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## Current Challenges
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### Board Detection
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1. Coordinate Systems
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- Vision framework (bottom-left origin)
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- NSImage/CGImage (bottom-left origin)
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- SwiftUI (top-left origin)
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- Proper transformations between systems
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2. Detection Accuracy
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- Full board capture
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- Consistent positioning
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- Reliable boundaries
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- Clean snapshots
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### Next Steps
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1. Refine board detection
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- Improve coordinate handling
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- Ensure full board capture
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- Validate transformations
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- Optimize snapshot quality
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2. Move to position analysis
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- Piece recognition
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- Position evaluation
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- Move suggestions
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## Future Enhancements
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### Planned Features
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1. Advanced Analysis
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- Deep position evaluation
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- Opening recognition
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- Endgame tablebases
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- Position comparison from snapshots
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2. Learning Tools
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- Mistake analysis
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- Improvement suggestions
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- Progress tracking
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- Position database from snapshots
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3. Customization
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- Analysis depth control
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- Visual preference settings
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- Platform-specific optimizations
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- Snapshot management options
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## Product Roadmap
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### Current Phase
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- Core board detection system
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- Coordinate system handling
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- Basic user interface
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- Manual snapshot system
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### Next Phase
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- Position analysis
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- Move suggestion system
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- Visual overlay implementation
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- Enhanced snapshot analysis
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### Future Phase
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- Advanced analysis features
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- Learning tools integration
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- Customization options
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- Snapshot database and comparison tools
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