# Active Context

## Current Status
- Screen capture module implemented
- Pattern recognition-based board detection implemented
- Coordinate-based detection retained as fallback
- SwiftUI interface for capture controls added
- Proper resource cleanup implemented
- Error handling system in place

## Recent Changes
- Refined pattern recognition approach for chess board detection:
  1. Modified rectangle detection parameters:
     - Using 0.3-0.5 aspect ratio for taller rectangles
     - Increased minimum size to 0.4
     - Single observation for precision
  2. Improved board extraction:
     - Using detected width as reference
     - Positioning square board at top of detected area
     - Using offsetBy for vertical positioning
  3. Simplified coordinate handling:
     - Direct rectangle detection with Vision framework
     - Proper coordinate system transformations
     - Maintained pattern matching for accuracy

## Current Challenges
- Board detection still not capturing full height
- Need to investigate if issue is with:
  1. Detection parameters
  2. Coordinate transformations
  3. View rendering constraints
  4. Window/display configuration

## Next Steps
1. Further refine board detection:
   - Test different aspect ratios
   - Validate coordinate transformations
   - Review view constraints
2. Add board position analysis
3. Implement move detection
4. Create visual overlay system
5. Integrate Stockfish engine

## Known Issues
- Board detection showing only bottom portion
- Need to validate pattern detection accuracy
- Need to handle multiple display configurations
- Requires testing with different screen resolutions
- Need to add proper error recovery
- Requires additional permission handling for production