# Technical Context

## Development Environment
- macOS Application
- Swift & SwiftUI
- Xcode 14+
- Target: macOS 12.3+

## Core Technologies
1. Vision Framework
   - VNDetectRectanglesRequest for board detection
   - VNCoreMLRequest for piece classification
   - VNImageRequestHandler for image processing

2. Core ML Model
   - Name: ChessPieceClassifier.mlmodel
   - Input: RGB/RGBA images
   - Output: Classification label
   - Categories (exact names):
     ```swift
     white_pawn, white_knight, white_bishop, white_rook, white_queen, white_king,
     black_pawn, black_knight, black_bishop, black_rook, black_queen, black_king
     ```

3. ScreenCaptureKit
   - Window capture at 30 FPS
   - Configurable cursor visibility
   - Chess.com window detection

## Image Processing
1. Preprocessing Pipeline
   - Contrast enhancement (1.3x)
   - Edge sharpening
   - Noise reduction
   - Color normalization

2. Square Extraction
   - Aspect ratio validation
   - Size normalization
   - Center crop

## Model Integration
1. Loading
   ```swift
   let config = MLModelConfiguration()
   config.computeUnits = .all
   let model = try MLModel(contentsOf: modelURL)
   let vnModel = try VNCoreMLModel(for: model)
   ```

2. Classification
   ```swift
   let request = VNCoreMLRequest(model: vnModel)
   request.imageCropAndScaleOption = .centerCrop
   ```

3. Result Handling
   - Confidence threshold: 0.75
   - Empty square fallback
   - Direct category mapping

## Dependencies
- Foundation
- Vision
- CoreML
- CoreImage
- ScreenCaptureKit
- SwiftUI

## Error Handling
- Invalid dimensions
- Model loading failures
- Recognition errors
- Low confidence results

## File Organization
```
ChessPrism/
├── Models/
│   ├── SquareClassification.swift  # Model output mapping
│   └── ChessPosition.swift         # Board state
├── Recognition/
│   ├── PieceRecognizer.swift      # ML integration
│   ├── FenGenerator.swift         # Position encoding
│   └── MoveDetector.swift         # Move analysis
└── Core/
    ├── BoardDetector.swift        # Square extraction
    └── ScreenCapture.swift        # Window capture