ChessPrism/cline_docs/activeContext.md

Active Context

Current Status

• Screen capture module successfully implemented
• Window detection and capture working correctly
• SwiftUI interface with capture controls functioning
• Error handling system properly managing states
• Resource cleanup implemented
• Metal resource management optimized
• Automatic board detection and capture implemented
• Visual capture status indicator added
• Continuous board monitoring system implemented

Recent Changes

1. Implemented Continuous Board Monitoring:

   • Added separate monitoring and capture tasks:
     • Monitor constantly checks for chess boards (0.5s interval)
     • Capture processes frames when active (0.1s interval)
   • Auto-capture behavior:
     • Starts monitoring when app launches
     • Automatically starts capture when board appears
     • Stops capture (but keeps monitoring) when board disappears
     • Resumes capture when new board is detected
   • Fixed image conversion pipeline:
     • Proper NSImage → CGImage → CIImage conversion
     • Efficient resource management
     • Clean error handling

2. Enhanced Status Indication:

   • Visual status indicator shows capture state:
     • Green: Actively capturing board
     • Yellow: Waiting for board
     • Gray: Not capturing
   • Clear error messages for different states
   • Automatic status updates based on board detection

3. Resource Management Optimization:

   • Implemented shared CIContext pattern:
     • Prevents command queue exhaustion
     • Reduces Metal resource usage
     • Enables long-running captures
   • Proper cleanup on task completion
   • Efficient resource utilization

4. Improved Window Detection:

   • Using SCShareableContent for window access
   • Precise window identification:
     • Exact bundle ID matching (com.chess.iphone)
     • Window visibility verification (isOnScreen)
     • Size validation (width > 100 && height > 100)

5. Enhanced Capture System:

   • Continuous capture implementation:
     • Single persistent capture stream
     • Smooth frame processing (no flickering)
     • Efficient resource usage
   • Proper frame dimensions from window
   • Clean task cancellation handling
   • Main thread safety for UI updates

6. Error Handling:

   • Improved error resilience:
     • Continues monitoring even if capture stops
     • Only stops on critical errors
     • Shows error state without interrupting monitoring
   • Clear error states
   • Proper async/await usage
   • Task cancellation management
   • Thread-safe state updates

Current Focus

1. Board Detection:
   • Implement chess board recognition
   • Handle different board themes
   • Process captured frames efficiently

Next Steps

1. Implement board detection:
   • Pattern recognition for chess pieces
   • Board coordinate mapping
   • Position validation
2. Add position analysis
3. Create move detection system
4. Implement visual overlay
5. Integrate Stockfish engine

Known Issues

• Need to handle different chess.com themes
• Need to implement piece recognition
• Position analysis pending implementation