chaulmark/ChessPrism
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ChessPrism/cline_docs/productContext.md
TheMaddax 4b8935afd1 feat: Implement auto-capture and cursor-free snapshots 
- 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
2025-01-07 09:44:37 -06:00

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Product Context

Project Overview

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.

Core Problems Solved

Chess Analysis Accessibility

  • Makes professional-level chess analysis accessible during online play
  • Provides real-time insights without manual position input
  • Integrates seamlessly with existing chess platforms

Visual Recognition

  • Accurately detects chess board from screen content
  • Recognizes board coordinates and boundaries
  • Handles various board themes and orientations
  • Maintains accuracy during game play
  • Provides clean board snapshots for analysis

Real-time Processing

  • Captures and processes screen content in real-time
  • Provides immediate feedback and analysis
  • Maintains performance during long sessions
  • Supports manual snapshot capture for detailed analysis

User Experience Goals

Seamless Integration

  1. Non-intrusive Operation

    • Works with Chess.com desktop app
    • Minimal setup requirements
    • Automatic board detection and tracking
    • Clean snapshot capture without cursor interference

  2. Intuitive Interface

    • Clear visualization of analysis
    • Easy-to-understand suggestions
    • Minimal user intervention required
    • Visual feedback for capture states
    • Manual snapshot control

Reliable Detection

  1. Board Recognition

    • Two-phase detection strategy:
      • Pattern recognition for known interfaces
      • Coordinate-based fallback for reliability
    • Proper coordinate system handling
    • Consistent board capture across sessions
    • High-quality snapshots for analysis

  2. Position Analysis

    • Accurate piece recognition (planned)
    • Current position evaluation (planned)
    • Move suggestion visualization (planned)

Target Users

Chess Players

  • Amateur to intermediate players
  • Chess.com desktop app users
  • Players seeking to improve

Use Cases

  1. Learning

    • Understanding position evaluation
    • Learning from mistakes
    • Exploring alternative moves
    • Analyzing specific positions via snapshots

  2. Analysis

    • Real-time position assessment
    • Move validation
    • Strategic planning
    • Detailed position study

Product Requirements

Essential Features

  1. Board Detection (Current Focus)

    • Accurate boundary recognition
    • Full board capture
    • Support for Chess.com desktop app
    • Reliable coordinate transformations
    • Clean snapshot capability

  2. Position Analysis (Planned)

    • Real-time evaluation
    • Move suggestions
    • Tactical opportunities

  3. User Interface

    • Analysis overlay
    • Control panel
    • Settings management
    • Snapshot controls
    • Visual status indicators

Quality Standards

  1. Accuracy

    • Reliable board detection
    • Complete board capture
    • Precise coordinate handling
    • Clean snapshots without artifacts

  2. Performance

    • Real-time processing
    • Minimal resource usage
    • Stable operation
    • Efficient snapshot handling

  3. Usability

    • Intuitive controls
    • Clear feedback
    • Minimal setup
    • Simple snapshot workflow

Success Metrics

Technical Metrics

  • Board detection accuracy rate
  • Full board capture success rate
  • Processing speed per frame
  • Error recovery rate
  • Snapshot quality assessment

User Metrics

  • Setup success rate
  • Analysis accuracy
  • User engagement time
  • Feature utilization
  • Snapshot usage patterns

Current Challenges

Board Detection

  1. Coordinate Systems

    • Vision framework (bottom-left origin)
    • NSImage/CGImage (bottom-left origin)
    • SwiftUI (top-left origin)
    • Proper transformations between systems

  2. Detection Accuracy

    • Full board capture
    • Consistent positioning
    • Reliable boundaries
    • Clean snapshots

Next Steps

  1. Refine board detection

    • Improve coordinate handling
    • Ensure full board capture
    • Validate transformations
    • Optimize snapshot quality

  2. Move to position analysis

    • Piece recognition
    • Position evaluation
    • Move suggestions

Future Enhancements

Planned Features

  1. Advanced Analysis

    • Deep position evaluation
    • Opening recognition
    • Endgame tablebases
    • Position comparison from snapshots

  2. Learning Tools

    • Mistake analysis
    • Improvement suggestions
    • Progress tracking
    • Position database from snapshots

  3. Customization

    • Analysis depth control
    • Visual preference settings
    • Platform-specific optimizations
    • Snapshot management options

Product Roadmap

Current Phase

  • Core board detection system
  • Coordinate system handling
  • Basic user interface
  • Manual snapshot system

Next Phase

  • Position analysis
  • Move suggestion system
  • Visual overlay implementation
  • Enhanced snapshot analysis

Future Phase

  • Advanced analysis features
  • Learning tools integration
  • Customization options
  • Snapshot database and comparison tools