ChessPrism/cline_docs/techContext.md

Technologies Used

Core ML & Vision

• ChessPieceClassifier.mlmodel for piece recognition
• VNCoreMLModel for image classification
• Vision framework for board detection

Image Processing

• CoreImage for preprocessing
• CIColorControls and CIUnsharpMask filters
• CGImage for image manipulation

Development Environment

• Xcode for Swift development
• Create ML for model training
• SwiftUI for UI components

Technical Constraints

ML Model Capabilities

1. Classification Types:

   • Pieces: pawn, rook, knight, bishop, queen, king
   • Colors: black, white
   • Empty squares: dark, light
   • Label formats: color_piece, empty_color

2. Recognition Features:

   • Multi-class classification
   • Per-class confidence scores
   • Position-aware validation
   • Piece count tracking

Processing Requirements

1. Image Requirements:

   • Square dimensions (1:1 ±10%)
   • Non-zero dimensions
   • Center-cropped squares
   • Clear piece visibility

2. Recognition Rules:

   • Empty squares: exact class match with >0.9 confidence
   • Pieces: strict format with >0.98 confidence
   • Separation ratio: >5.0 between predictions
   • Position validation: essential rules only

3. Error Prevention:

   • Early empty square detection
   • Strict label validation
   • Safe optional handling
   • Clear error messages

Performance Considerations

1. Processing Flow:

   • Early validation checks
   • Fast empty square detection
   • Efficient error handling
   • Quick rejection paths

2. Resource Optimization:

   • GPU acceleration for ML
   • Minimal preprocessing
   • Optimized validation
   • Efficient logging

Development Setup

1. Clone repository
2. Open ChessPrism.xcodeproj
3. Build and run on macOS
4. Model at ChessPrism/ChessPieceClassifier.mlmodel