Round 3

ChessPrism/ChessPrism/BoardDetector.swift

@@ -2,46 +2,46 @@ import Foundation
 import CoreImage
 import Vision
 
-class BoardDetector {
+/// Errors that can occur during board detection and processing
+enum BoardDetectionError: Error {
+    case boardNotFound
+    case invalidBoardDimensions
+    case squareExtractionFailed
+    case imageProcessingFailed
+}
+
+final class BoardDetector {
     // Share CIContext to avoid creating too many Metal command queues
     private static let shared = CIContext()
     private var context: CIContext { BoardDetector.shared }
     
     func detectBoard(in image: CIImage) -> CGRect? {
-        // Configure rectangle detection request
         let request = VNDetectRectanglesRequest()
-        request.minimumAspectRatio = 0.8  // Adjusted for standard chess board
+        request.minimumAspectRatio = 0.8
         request.maximumAspectRatio = 1.2
         request.minimumSize = 0.4
         request.maximumObservations = 1
         request.quadratureTolerance = 30
         request.minimumConfidence = 0.9
         
-        // Perform the request
         let requestHandler = VNImageRequestHandler(ciImage: image, options: [:])
         do {
             try requestHandler.perform([request])
         } catch {
-            print("Failed to perform rectangle detection: \(error)")
+            print("ERROR: Rectangle detection failed - \(error)")
             return nil
         }
         
-        // Process results
         guard let observations = request.results,
               !observations.isEmpty else {
             return nil
         }
         
         let bestObservation = observations[0]
-        
-        // Convert Vision coordinates to CoreImage coordinates
         let imageSize = image.extent.size
         let transform = CGAffineTransform(scaleX: imageSize.width, y: imageSize.height)
-        
-        // Create normalized rect in CoreImage coordinate space
         let detectedRect = bestObservation.boundingBox.applying(transform)
         
-        // Validate the detected rectangle
         guard validateDetectedRect(detectedRect, in: image) else {
             return nil
         }
@@ -49,28 +49,123 @@ class BoardDetector {
         return detectedRect
     }
     
+    /// Piece recognizer for analyzing extracted squares
+    private let pieceRecognizer: PieceRecognizer
+    
+    /// Initialize with a piece recognizer
+    init(pieceRecognizer: PieceRecognizer) {
+        self.pieceRecognizer = pieceRecognizer
+    }
+    
+    /// Extract a specific square from the board image
+    private func extractSquare(from image: CIImage, in rect: CGRect, at position: BoardPosition) throws -> CGImage {
+        let squareSize = rect.width / 8
+        let x = rect.minX + CGFloat(position.file) * squareSize
+        let y = rect.minY + CGFloat(position.rank) * squareSize
+        let squareRect = CGRect(x: x, y: y, width: squareSize, height: squareSize)
+        
+        guard image.extent.contains(squareRect) else {
+            print("ERROR: Square bounds outside image extent at \(position.file),\(position.rank)")
+            throw BoardDetectionError.squareExtractionFailed
+        }
+        
+        let croppedImage = image.cropped(to: squareRect)
+        
+        guard let cgImage = context.createCGImage(croppedImage, from: croppedImage.extent) else {
+            print("ERROR: Failed to create square image at \(position.file),\(position.rank)")
+            throw BoardDetectionError.squareExtractionFailed
+        }
+        
+        return cgImage
+    }
+    
+    /// Analyze the board and return the chess position
+    func analyzeBoard(in image: CIImage) async throws -> ChessPosition {
+        print("=== ANALYZING BOARD ===")
+        
+        guard let boardRect = detectBoard(in: image) else {
+            print("ERROR: Board detection failed")
+            throw BoardDetectionError.boardNotFound
+        }
+        
+        let aspectRatio = boardRect.width / boardRect.height
+        guard boardRect.width > 0, boardRect.height > 0,
+              abs(1 - aspectRatio) < 0.1 else {
+            print("ERROR: Invalid board dimensions")
+            throw BoardDetectionError.invalidBoardDimensions
+        }
+        
+        var position = ChessPosition()
+        var recognizedPieces = 0
+        
+        // Reset piece counts before starting new scan
+        pieceRecognizer.resetPieceCounts()
+        
+        // Process each square
+        for rank in 0..<8 {
+            for file in 0..<8 {
+                let boardPosition = BoardPosition(file: file, rank: rank)
+                let squareImage = try extractSquare(from: image, in: boardRect, at: boardPosition)
+                
+                // Set the current position being analyzed
+                pieceRecognizer.currentPosition = boardPosition
+                
+                if let piece = try await pieceRecognizer.recognizePiece(from: squareImage) {
+                    position[boardPosition] = piece
+                    recognizedPieces += 1
+                }
+            }
+        }
+        
+        // Validate piece counts after all squares are processed
+        try pieceRecognizer.validatePieceCounts()
+        
+        print("\nPieces recognized: \(recognizedPieces)")
+        print("\nRecognized position:")
+        for rank in (0...7).reversed() {
+            var rankStr = "\(rank + 1) "
+            for file in 0...7 {
+                if let piece = position[BoardPosition(file: file, rank: rank)] {
+                    rankStr += "\(piece.color == .white ? "w" : "b")\(piece.type.fenSymbol.uppercased()) "
+                } else {
+                    rankStr += ".. "
+                }
+            }
+            print(rankStr)
+        }
+        print("  a  b  c  d  e  f  g  h")
+        
+        guard position.isValid else {
+            print("\nERROR: Invalid chess position")
+            throw BoardDetectionError.imageProcessingFailed
+        }
+        
+        print("=== BOARD ANALYSIS COMPLETE ===")
+        return position
+    }
+    
     private func validateDetectedRect(_ rect: CGRect, in image: CIImage) -> Bool {
         let imageSize = image.extent.size
         
-        // Check if rectangle is within image bounds
         guard image.extent.contains(rect) else {
+            print("ERROR: Detected rectangle outside image bounds")
             return false
         }
         
-        // Validate aspect ratio (standard chess board is square)
         let aspectRatio = rect.width / rect.height
         guard aspectRatio >= 0.9 && aspectRatio <= 1.1 else {
+            print("ERROR: Invalid board aspect ratio: \(aspectRatio)")
             return false
         }
         
-        // Validate size relative to image
         let minDimension = min(imageSize.width, imageSize.height)
         let boardSize = max(rect.width, rect.height)
-        guard boardSize >= minDimension * 0.4 else {
+        let sizeRatio = boardSize / minDimension
+        guard sizeRatio >= 0.4 else {
+            print("ERROR: Board too small relative to image")
             return false
         }
         
         return true
     }
-    
 }

ChessPrism/ChessPrism/ChessPieceClassifier.mlmodelc/metadata.json

@@ -0,0 +1,86 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "outputSchema" : [
+      {
+        "isOptional" : "0",
+        "formattedType" : "String",
+        "type" : "String",
+        "name" : "target",
+        "shortDescription" : ""
+      },
+      {
+        "isOptional" : "0",
+        "keyType" : "String",
+        "formattedType" : "Dictionary (String → Double)",
+        "type" : "Dictionary",
+        "name" : "targetProbability",
+        "shortDescription" : ""
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "author" : "Chris Haulmark",
+    "specificationVersion" : 8,
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "unavailable",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_imageClassifier",
+      "structure" : [
+        {
+          "name" : "MLModelType_visionFeaturePrint"
+        },
+        {
+          "name" : "MLModelType_glmClassifier"
+        }
+      ]
+    },
+    "inputSchema" : [
+      {
+        "height" : "360",
+        "colorspace" : "BGR",
+        "isOptional" : "0",
+        "width" : "360",
+        "isColor" : "1",
+        "formattedType" : "Image (Color 360 × 360)",
+        "hasSizeFlexibility" : "0",
+        "type" : "Image",
+        "shortDescription" : "",
+        "name" : "image"
+      }
+    ],
+    "classLabels" : [
+      "black_bishop",
+      "black_king",
+      "black_knight",
+      "black_pawn",
+      "black_queen",
+      "black_rook",
+      "white_bishop",
+      "white_king",
+      "white_knight",
+      "white_pawn",
+      "white_queen",
+      "white_rook"
+    ],
+    "generatedClassName" : "ChessPieceClassifier",
+    "userDefinedMetadata" : {
+      "com.apple.createml.version" : "15.3.0",
+      "com.apple.createml.app.tag" : "150.3",
+      "com.apple.coreml.model.preview.type" : "imageClassifier",
+      "com.apple.createml.app.version" : "6.1"
+    },
+    "method" : "predict"
+  }
+]

ChessPrism/ChessPrism/ContentView.swift

@@ -1,6 +1,84 @@
 import SwiftUI
 import AppKit
 
+// MARK: - Chess Position View Components
+
+struct ChessPieceView: View {
+    let piece: ChessPiece
+    
+    var body: some View {
+        Text(pieceSymbol)
+            .font(.system(size: 24, weight: .bold))
+            .foregroundColor(piece.color == .white ? .white : .black)
+    }
+    
+    private var pieceSymbol: String {
+        switch piece.type {
+        case .pawn: return "♟"
+        case .knight: return "♞"
+        case .bishop: return "♝"
+        case .rook: return "♜"
+        case .queen: return "♛"
+        case .king: return "♚"
+        }
+    }
+}
+
+struct ChessboardSquareView: View {
+    let isLightSquare: Bool
+    let piece: ChessPiece?
+    
+    var body: some View {
+        ZStack {
+            Rectangle()
+                .fill(isLightSquare ? Color.white : Color.gray)
+                .frame(width: 40, height: 40)
+            
+            if let piece = piece {
+                ChessPieceView(piece: piece)
+            }
+        }
+    }
+}
+
+struct ChessboardView: View {
+    let position: ChessPosition?
+    
+    var body: some View {
+        VStack(spacing: 0) {
+            ForEach((0..<8).reversed(), id: \.self) { rank in
+                HStack(spacing: 0) {
+                    ForEach(0..<8, id: \.self) { file in
+                        let boardPosition = BoardPosition(file: file, rank: rank)
+                        ChessboardSquareView(
+                            isLightSquare: (rank + file) % 2 == 0,
+                            piece: position?[boardPosition]
+                        )
+                    }
+                }
+            }
+        }
+        .border(Color.black, width: 1)
+    }
+}
+
+struct AnalysisStatusView: View {
+    let isAnalyzing: Bool
+    let confidence: Double
+    
+    var body: some View {
+        VStack(spacing: 8) {
+            if isAnalyzing {
+                ProgressView("Analyzing position...")
+            } else {
+                Text("Recognition Confidence: \(Int(confidence * 100))%")
+                    .foregroundColor(confidence > 0.7 ? .green : .orange)
+            }
+        }
+        .padding()
+    }
+}
+
 class CustomNSView: NSView {
     private static let invisibleCursor: NSCursor = {
         let image = NSImage(size: NSSize(width: 1, height: 1))
@@ -84,48 +162,10 @@ struct CaptureStatusButton: View {
     }
 }
 
-struct ContentView: View {
+struct CaptureView: View {
-    @StateObject private var viewModel = ScreenCaptureViewModel()
+    @ObservedObject var viewModel: ScreenCaptureViewModel
     
     var body: some View {
-        VStack {
-            // Status and Scan controls
-            HStack {
-                CaptureStatusButton(
-                    isCapturing: viewModel.isCapturing,
-                    isBoardDetected: viewModel.isBoardDetected
-                )
-                
-                Button(action: {
-                    Task {
-                        await viewModel.takeSnapshot()
-                    }
-                }) {
-                    Text("Scan")
-                        .foregroundColor(.white)
-                        .padding(.horizontal, 20)
-                        .padding(.vertical, 10)
-                }
-                .buttonStyle(.borderedProminent)
-                .disabled(!viewModel.isCapturing || !viewModel.isBoardDetected)
-            }
-            .onAppear {
-                // Start monitoring for chess boards when view appears
-                viewModel.startMonitoring()
-            }
-            .onDisappear {
-                // Stop monitoring when view disappears
-                viewModel.stopMonitoring()
-            }
-            
-            // Error display
-            if let error = viewModel.captureError {
-                Text(error.localizedDescription)
-                    .foregroundColor(.red)
-                    .padding()
-            }
-            
-            // Image display
         HStack {
             VStack {
                 Text("Full Capture")
@@ -176,10 +216,115 @@ struct ContentView: View {
             }
         }
         .padding()
+    }
+}
+
+struct AnalysisView: View {
+    @ObservedObject var viewModel: ScreenCaptureViewModel
+    
+    var body: some View {
+        VStack {
+            if let position = viewModel.currentPosition {
+                HStack(alignment: .top, spacing: 20) {
+                    VStack(alignment: .leading) {
+                        Text("Current Position")
+                            .font(.headline)
+                        ChessboardView(position: position)
+                            .padding()
+                            .background(Color.white)
+                            .cornerRadius(8)
+                            .shadow(radius: 2)
+                        
+                        AnalysisStatusView(
+                            isAnalyzing: viewModel.isAnalyzing,
+                            confidence: viewModel.recognitionConfidence
+                        )
+                    }
+                    
+                    VStack(alignment: .leading) {
+                        Text("Position Details")
+                            .font(.headline)
+                        Text("FEN: \(position.fen)")
+                            .font(.system(.body, design: .monospaced))
+                            .padding(.vertical)
+                        
+                        if viewModel.captureError == .recognitionFailed {
+                            Text("Recognition Error")
+                                .foregroundColor(.red)
+                                .padding()
+                        }
+                    }
+                    .padding()
+                    .background(Color.gray.opacity(0.1))
+                    .cornerRadius(8)
+                }
+            } else {
+                Text("No position detected")
+                    .foregroundColor(.gray)
+            }
             
             Spacer()
         }
-        .frame(minWidth: 800, minHeight: 600)
+        .padding()
+    }
+}
+
+struct ContentView: View {
+    @StateObject private var viewModel = ScreenCaptureViewModel()
+    @State private var selectedTab = 0
+    
+    var body: some View {
+        VStack {
+            // Status and Scan controls
+            HStack {
+                CaptureStatusButton(
+                    isCapturing: viewModel.isCapturing,
+                    isBoardDetected: viewModel.isBoardDetected
+                )
+                
+                Button(action: {
+                    Task {
+                        await viewModel.takeSnapshot()
+                    }
+                }) {
+                    Text("Scan")
+                        .foregroundColor(.white)
+                        .padding(.horizontal, 20)
+                        .padding(.vertical, 10)
+                }
+                .buttonStyle(.borderedProminent)
+                .disabled(!viewModel.isCapturing || !viewModel.isBoardDetected)
+            }
+            .onAppear {
+                viewModel.startMonitoring()
+            }
+            .onDisappear {
+                viewModel.stopMonitoring()
+            }
+            
+            // Error display
+            if let error = viewModel.captureError {
+                Text(error.localizedDescription)
+                    .foregroundColor(.red)
+                    .padding()
+            }
+            
+            // Main content area with tabs
+            TabView(selection: $selectedTab) {
+                CaptureView(viewModel: viewModel)
+                    .tabItem {
+                        Label("Capture", systemImage: "camera")
+                    }
+                    .tag(0)
+                
+                AnalysisView(viewModel: viewModel)
+                    .tabItem {
+                        Label("Analysis", systemImage: "magnifyingglass")
+                    }
+                    .tag(1)
+            }
+        }
+        .frame(minWidth: 1000, minHeight: 700)
     }
 }
 

ChessPrism/ChessPrism/Models/ChessPosition.swift

@@ -0,0 +1,274 @@
+import Foundation
+
+/// Represents a chess piece color
+enum PieceColor: String {
+    case white
+    case black
+}
+
+/// Represents a chess piece type
+enum PieceType: String {
+    case pawn
+    case knight
+    case bishop
+    case rook
+    case queen
+    case king
+    
+    /// FEN notation for the piece
+    var fenSymbol: String {
+        switch self {
+        case .pawn: return "p"
+        case .knight: return "n"
+        case .bishop: return "b"
+        case .rook: return "r"
+        case .queen: return "q"
+        case .king: return "k"
+        }
+    }
+}
+
+/// Represents a chess piece with its type and color
+struct ChessPiece: Equatable {
+    let type: PieceType
+    let color: PieceColor
+    
+    /// FEN notation for the piece (uppercase for white, lowercase for black)
+    var fenSymbol: String {
+        let symbol = type.fenSymbol
+        return color == .white ? symbol.uppercased() : symbol
+    }
+}
+
+/// Represents a position on the chess board
+struct BoardPosition: Equatable {
+    let file: Int // 0-7 for a-h
+    let rank: Int // 0-7 for 1-8
+    
+    /// Initialize from algebraic notation (e.g., "e4")
+    init?(algebraic: String) {
+        guard algebraic.count == 2,
+              let file = algebraic.first?.asciiValue,
+              let rank = algebraic.last?.wholeNumberValue,
+              file >= UInt8(ascii: "a"), file <= UInt8(ascii: "h"),
+              rank >= 1, rank <= 8 else {
+            return nil
+        }
+        
+        self.file = Int(file - UInt8(ascii: "a"))
+        self.rank = rank - 1
+    }
+    
+    /// Convert to algebraic notation
+    var algebraic: String {
+        let fileChar = Character(UnicodeScalar(UInt8(ascii: "a") + UInt8(file)))
+        return "\(fileChar)\(rank + 1)"
+    }
+    
+    /// Validate if the position is within bounds
+    var isValid: Bool {
+        file >= 0 && file < 8 && rank >= 0 && rank < 8
+    }
+}
+
+/// Represents a complete chess position
+struct ChessPosition {
+    /// 8x8 grid representing the board state, nil means empty square
+    private var board: [[ChessPiece?]]
+    
+    /// Initialize an empty board
+    init() {
+        board = Array(repeating: Array(repeating: nil, count: 8), count: 8)
+    }
+    
+    /// Initialize from FEN string
+    init?(fen: String) {
+        self.init()
+        
+        let components = fen.components(separatedBy: " ")
+        guard components.count >= 1 else { return nil }
+        
+        let ranks = components[0].components(separatedBy: "/")
+        guard ranks.count == 8 else { return nil }
+        
+        for (rankIndex, rank) in ranks.enumerated() {
+            var fileIndex = 0
+            
+            for char in rank {
+                if let number = Int(String(char)) {
+                    fileIndex += number
+                } else {
+                    guard fileIndex < 8 else { return nil }
+                    
+                    let color: PieceColor = char.isUppercase ? .white : .black
+                    let lowerChar = char.lowercased()
+                    
+                    guard let type = pieceTypeFromFen(String(lowerChar)) else { return nil }
+                    
+                    board[7 - rankIndex][fileIndex] = ChessPiece(type: type, color: color)
+                    fileIndex += 1
+                }
+            }
+            
+            guard fileIndex == 8 else { return nil }
+        }
+    }
+    
+    /// Convert position to FEN string (piece placement only)
+    var fen: String {
+        var result = ""
+        
+        for rankIndex in (0...7).reversed() {
+            var emptyCount = 0
+            
+            for fileIndex in 0...7 {
+                if let piece = board[rankIndex][fileIndex] {
+                    if emptyCount > 0 {
+                        result += String(emptyCount)
+                        emptyCount = 0
+                    }
+                    result += piece.fenSymbol
+                } else {
+                    emptyCount += 1
+                }
+            }
+            
+            if emptyCount > 0 {
+                result += String(emptyCount)
+            }
+            
+            if rankIndex > 0 {
+                result += "/"
+            }
+        }
+        
+        return result
+    }
+    
+    /// Get piece at position
+    subscript(position: BoardPosition) -> ChessPiece? {
+        get {
+            guard position.isValid else { return nil }
+            return board[position.rank][position.file]
+        }
+        set {
+            guard position.isValid else { return }
+            board[position.rank][position.file] = newValue
+        }
+    }
+    
+    /// Get piece at algebraic position
+    subscript(algebraic: String) -> ChessPiece? {
+        get {
+            guard let position = BoardPosition(algebraic: algebraic) else { return nil }
+            return self[position]
+        }
+        set {
+            guard let position = BoardPosition(algebraic: algebraic) else { return }
+            self[position] = newValue
+        }
+    }
+    
+    /// Validate if the position is legal
+    var isValid: Bool {
+        print("\n=== VALIDATING CHESS POSITION ===")
+        var whitePieces = [PieceType: Int]()
+        var blackPieces = [PieceType: Int]()
+        
+        // Count all pieces
+        for rank in 0...7 {
+            for file in 0...7 {
+                if let piece = board[rank][file] {
+                    if piece.color == .white {
+                        whitePieces[piece.type, default: 0] += 1
+                    } else {
+                        blackPieces[piece.type, default: 0] += 1
+                    }
+                    
+                    // Check pawns on invalid ranks
+                    if piece.type == .pawn && (rank == 0 || rank == 7) {
+                        print("ERROR: Pawn found on first/last rank")
+                        return false
+                    }
+                }
+            }
+        }
+        
+        // Print piece counts
+        print("White pieces:")
+        for (type, count) in whitePieces {
+            print("- \(type): \(count)")
+        }
+        print("Black pieces:")
+        for (type, count) in blackPieces {
+            print("- \(type): \(count)")
+        }
+        
+        // Validate piece counts
+        let whiteTotal = whitePieces.values.reduce(0, +)
+        let blackTotal = blackPieces.values.reduce(0, +)
+        
+        if whiteTotal > 16 {
+            print("ERROR: Too many white pieces (\(whiteTotal))")
+            return false
+        }
+        if blackTotal > 16 {
+            print("ERROR: Too many black pieces (\(blackTotal))")
+            return false
+        }
+        
+        // Validate kings
+        if whitePieces[.king] ?? 0 != 1 {
+            print("ERROR: Invalid number of white kings (\(whitePieces[.king] ?? 0))")
+            return false
+        }
+        if blackPieces[.king] ?? 0 != 1 {
+            print("ERROR: Invalid number of black kings (\(blackPieces[.king] ?? 0))")
+            return false
+        }
+        
+        // Validate pawns
+        if whitePieces[.pawn] ?? 0 > 8 {
+            print("ERROR: Too many white pawns (\(whitePieces[.pawn] ?? 0))")
+            return false
+        }
+        if blackPieces[.pawn] ?? 0 > 8 {
+            print("ERROR: Too many black pawns (\(blackPieces[.pawn] ?? 0))")
+            return false
+        }
+        
+        // Validate other pieces
+        for pieceType in [PieceType.queen, .rook, .bishop, .knight] {
+            if whitePieces[pieceType] ?? 0 > 2 {
+                print("ERROR: Too many white \(pieceType)s (\(whitePieces[pieceType] ?? 0))")
+                return false
+            }
+            if blackPieces[pieceType] ?? 0 > 2 {
+                print("ERROR: Too many black \(pieceType)s (\(blackPieces[pieceType] ?? 0))")
+                return false
+            }
+        }
+        
+        print("Position validation successful")
+        return true
+    }
+    
+    /// Helper function to convert FEN piece symbol to PieceType
+    private func pieceTypeFromFen(_ symbol: String) -> PieceType? {
+        switch symbol {
+        case "p": return .pawn
+        case "n": return .knight
+        case "b": return .bishop
+        case "r": return .rook
+        case "q": return .queen
+        case "k": return .king
+        default: return nil
+        }
+    }
+    
+    /// Initialize with the standard starting position
+    static var startingPosition: ChessPosition {
+        // swiftlint:disable:next force_unwrapping
+        ChessPosition(fen: "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR")!
+    }
+}

ChessPrism/ChessPrism/Recognition/PieceRecognizer.swift

@@ -0,0 +1,397 @@
+import Foundation
+import Vision
+import CoreML
+import CoreImage
+
+/// Errors that can occur during piece recognition
+enum PieceRecognitionError: Error {
+    case invalidImageDimensions
+    case modelLoadError
+    case recognitionFailed(String)
+    case lowConfidence
+    case invalidInput
+    case invalidPosition(String)
+}
+
+/// A class responsible for recognizing chess pieces from images
+final class PieceRecognizer {
+    // MARK: - Properties
+    
+    /// Vision model for piece classification
+    private let vnModel: VNCoreMLModel
+    
+    /// Shared CIContext for image processing
+    private static let ciContext = CIContext()
+    
+    /// Piece counts for validation
+    private var whitePieceCount: [PieceType: Int] = [:]
+    private var blackPieceCount: [PieceType: Int] = [:]
+    
+    // MARK: - Initialization
+    
+    init() throws {
+        print("=== INITIALIZING PIECE RECOGNIZER ===")
+        
+        let bundle = Bundle.main
+        
+        // Load model from bundle
+        guard let modelURL = bundle.url(forResource: "ChessPieceClassifier", withExtension: "mlmodelc") else {
+            print("ERROR: Model not found in bundle at \(bundle.bundlePath)")
+            throw PieceRecognitionError.modelLoadError
+        }
+        
+        do {
+            let config = MLModelConfiguration()
+            config.computeUnits = .all
+            let model = try MLModel(contentsOf: modelURL, configuration: config)
+            self.vnModel = try VNCoreMLModel(for: model)
+            print("Model loaded successfully from: \(modelURL.path)")
+        } catch {
+            print("ERROR: Failed to load model - \(error)")
+            throw PieceRecognitionError.modelLoadError
+        }
+        
+        resetPieceCounts()
+    }
+    
+    // MARK: - Recognition Methods
+    
+    /// Reset piece counts for new position
+    func resetPieceCounts() {
+        whitePieceCount = [
+            .king: 0,
+            .queen: 0,
+            .rook: 0,
+            .bishop: 0,
+            .knight: 0,
+            .pawn: 0
+        ]
+        blackPieceCount = [
+            .king: 0,
+            .queen: 0,
+            .rook: 0,
+            .bishop: 0,
+            .knight: 0,
+            .pawn: 0
+        ]
+    }
+    
+    /// Validate piece counts and ensure the position is legal
+    func validatePieceCounts() throws {
+        var errors: [String] = []
+        
+        // Check white pieces
+        if whitePieceCount[.king] != 1 {
+            let count = whitePieceCount[.king] ?? 0
+            errors.append("Invalid white king count: \(count)")
+        }
+        if let count = whitePieceCount[.queen], count > 1 {
+            errors.append("Too many white queens: \(count)")
+        }
+        if let count = whitePieceCount[.rook], count > 2 {
+            errors.append("Too many white rooks: \(count)")
+        }
+        if let count = whitePieceCount[.bishop], count > 2 {
+            errors.append("Too many white bishops: \(count)")
+        }
+        if let count = whitePieceCount[.knight], count > 2 {
+            errors.append("Too many white knights: \(count)")
+        }
+        if let count = whitePieceCount[.pawn], count > 8 {
+            errors.append("Too many white pawns: \(count)")
+        }
+        
+        // Check black pieces
+        if blackPieceCount[.king] ?? 0 != 1 {
+            let count = blackPieceCount[.king] ?? 0
+            errors.append("Invalid black king count: \(count)")
+        }
+        if let count = blackPieceCount[.queen], count > 1 {
+            errors.append("Too many black queens: \(count)")
+        }
+        if let count = blackPieceCount[.rook], count > 2 {
+            errors.append("Too many black rooks: \(count)")
+        }
+        if let count = blackPieceCount[.bishop], count > 2 {
+            errors.append("Too many black bishops: \(count)")
+        }
+        if let count = blackPieceCount[.knight], count > 2 {
+            errors.append("Too many black knights: \(count)")
+        }
+        if let count = blackPieceCount[.pawn], count > 8 {
+            errors.append("Too many black pawns: \(count)")
+        }
+        
+        if !errors.isEmpty {
+            throw PieceRecognitionError.invalidPosition(errors.joined(separator: ", "))
+        }
+    }
+    
+    /// Update piece count
+    private func updatePieceCount(piece: ChessPiece) {
+        if piece.color == .white {
+            whitePieceCount[piece.type] = (whitePieceCount[piece.type] ?? 0) + 1
+        } else {
+            blackPieceCount[piece.type] = (blackPieceCount[piece.type] ?? 0) + 1
+        }
+    }
+    
+    /// Check if adding this piece would exceed limits
+    private func wouldExceedLimits(_ piece: ChessPiece) -> Bool {
+        let count = piece.color == .white ? whitePieceCount[piece.type] ?? 0 : blackPieceCount[piece.type] ?? 0
+        switch piece.type {
+        case .king: return count >= 1
+        case .queen: return count >= 1
+        case .rook, .bishop, .knight: return count >= 2
+        case .pawn: return count >= 8
+        }
+    }
+    
+    /// Recognize a chess piece from a square image
+    /// - Parameters:
+    ///   - image: CGImage of the chess square
+    ///   - completion: Callback with result (ChessPiece if recognized, nil if empty)
+    /// - Throws: PieceRecognitionError
+    func recognizePiece(from image: CGImage) async throws -> ChessPiece? {
+        // Validate image dimensions
+        guard image.width > 0, image.height > 0,
+              abs(1 - Float(image.width) / Float(image.height)) < 0.1 else {
+            print("ERROR: Invalid square dimensions \(image.width)x\(image.height)")
+            throw PieceRecognitionError.invalidImageDimensions
+        }
+        
+        let handler = VNImageRequestHandler(cgImage: image)
+        var classificationResults: [VNClassificationObservation]?
+        var classificationError: Error?
+        
+        try await withCheckedThrowingContinuation { (continuation: CheckedContinuation<Void, Error>) in
+            let request = VNCoreMLRequest(model: vnModel) { request, error in
+                if let error = error {
+                    classificationError = error
+                    continuation.resume(throwing: error)
+                    return
+                }
+                classificationResults = request.results as? [VNClassificationObservation]
+                continuation.resume()
+            }
+            request.imageCropAndScaleOption = .centerCrop
+            
+            do {
+                try handler.perform([request])
+            } catch {
+                continuation.resume(throwing: error)
+            }
+        }
+        
+        if let error = classificationError {
+            print("ERROR: Classification failed - \(error)")
+            throw PieceRecognitionError.recognitionFailed(error.localizedDescription)
+        }
+        
+        guard let results = classificationResults,
+              let topResult = results.first else {
+            print("ERROR: No classification results")
+            throw PieceRecognitionError.recognitionFailed("No results")
+        }
+        
+        // Print all results to help diagnose recognition issues
+        if let pos = currentPosition {
+            print("\nClassification results for \(String(describing: pos)):")
+        } else {
+            print("\nClassification results:")
+        }
+        for result in results.prefix(3) {
+            print("- \(result.identifier): \(result.confidence)")
+        }
+        
+        // Check for empty squares
+        if topResult.identifier == "empty_dark" || topResult.identifier == "empty_light" {
+            if topResult.confidence > 0.9 {
+                if let pos = currentPosition {
+                    print("\nEmpty square confirmed at \(String(describing: pos))")
+                }
+                return nil
+            }
+        }
+        
+        // Get confidence ratio between top predictions
+        let secondBestConfidence = results.count > 1 ? results[1].confidence : 0
+        let confidenceRatio = topResult.confidence / (secondBestConfidence + Float.ulpOfOne)
+        
+        // Adjust confidence based on position-specific knowledge
+        let adjustedConfidence = adjustConfidence(topResult.confidence, 
+                                                for: topResult.identifier,
+                                                at: currentPosition)
+        
+        // Much stricter piece recognition:
+        // 1. Must have very high confidence (>0.98)
+        // 2. Must have strong separation from second best (>5.0 ratio)
+        // 3. Must make sense for position
+        // 4. Must not exceed piece limits
+        if adjustedConfidence > 0.98 && confidenceRatio > 5.0 {
+            if let piece = try? createPiece(from: topResult.identifier) {
+                if isValidPieceForPosition(piece, at: currentPosition) && !wouldExceedLimits(piece) {
+                    updatePieceCount(piece: piece)
+                    return piece
+                }
+            }
+        }
+        
+        // Log classification details
+        if let pos = currentPosition {
+            print("\nClassification rejected at \(String(describing: pos)):")
+        }
+        print("Top result: \(topResult.identifier) (\(topResult.confidence))")
+        if results.count > 1 {
+            print("Second best: \(results[1].identifier) (\(results[1].confidence))")
+            print("Confidence ratio: \(confidenceRatio)")
+        }
+        print("Adjusted confidence: \(adjustedConfidence)")
+        
+        return nil // Default to empty for unclear cases
+    }
+    
+    /// Position where this piece is being recognized
+    var currentPosition: BoardPosition?
+    
+    /// Adjust confidence based on position-specific knowledge
+    private func adjustConfidence(_ confidence: Float, for identifier: String, at position: BoardPosition?) -> Float {
+        guard let position = currentPosition else { return confidence }
+        
+        // Parse the piece info
+        let components = identifier.split(separator: "_")
+        guard components.count == 2,
+              let color = PieceColor(rawValue: String(components[0])),
+              let type = PieceType(rawValue: String(components[1])) else {
+            return confidence
+        }
+        
+        var adjustment: Float = 0.0
+        
+        // Back rank pieces are more likely to be correct
+        if position.isBackRank(for: color) {
+            // Corners should be rooks
+            if type == .rook && position.isEdgeFile {
+                adjustment += 0.1
+            }
+            // Next to corners should be knights
+            if type == .knight && (position.file == 1 || position.file == 6) {
+                adjustment += 0.1
+            }
+            // Next to knights should be bishops
+            if type == .bishop && (position.file == 2 || position.file == 5) {
+                adjustment += 0.1
+            }
+            // Center should be king/queen
+            if (type == .king || type == .queen) && position.isCenterFile {
+                adjustment += 0.1
+            }
+        }
+        
+        // Pawns are more likely on their starting ranks
+        if type == .pawn {
+            if (color == .white && position.rank == 1) ||
+               (color == .black && position.rank == 6) {
+                adjustment += 0.1
+            }
+        }
+        
+        return min(1.0, confidence + adjustment)
+    }
+    
+    /// Validate if a piece makes sense for its position
+    private func isValidPieceForPosition(_ piece: ChessPiece, at position: BoardPosition?) -> Bool {
+        guard let pos = position else { return true }
+        
+        // Basic position validation
+        switch piece.type {
+        case .king:
+            // Kings can't be on the first or last rank of opponent's side
+            if piece.color == .white && pos.rank == 7 { return false }
+            if piece.color == .black && pos.rank == 0 { return false }
+            
+        case .pawn:
+            // Pawns can't be on first or last rank
+            if pos.rank == 0 || pos.rank == 7 { return false }
+            // White pawns can't be behind their starting rank
+            if piece.color == .white && pos.rank > 6 { return false }
+            // Black pawns can't be behind their starting rank
+            if piece.color == .black && pos.rank < 1 { return false }
+            
+        default:
+            // Other pieces can move freely
+            break
+        }
+        
+        return true
+    }
+    
+    /// Helper to create a chess piece from a classification label
+    private func createPiece(from identifier: String) throws -> ChessPiece {
+        // Validate it's not an empty square
+        guard !identifier.starts(with: "empty_") else {
+            print("ERROR: Cannot create piece from empty square label: \(identifier)")
+            throw PieceRecognitionError.invalidInput
+        }
+        
+        let components = identifier.split(separator: "_")
+        guard components.count == 2,
+              let color = PieceColor(rawValue: String(components[0])),
+              let type = PieceType(rawValue: String(components[1])) else {
+            print("ERROR: Invalid piece label format: \(identifier)")
+            throw PieceRecognitionError.invalidInput
+        }
+        return ChessPiece(type: type, color: color)
+    }
+    
+    /// Preprocess an image for recognition
+    /// - Parameter image: Input CGImage
+    /// - Returns: Preprocessed CGImage
+    func preprocessImage(_ image: CGImage) throws -> CGImage {
+        let ciImage = CIImage(cgImage: image)
+        
+        // Apply preprocessing filters
+        let processed = ciImage
+            .applyingFilter("CIColorControls", parameters: [
+                kCIInputContrastKey: 1.1,
+                kCIInputBrightnessKey: 0.0,
+                kCIInputSaturationKey: 1.1
+            ])
+            .applyingFilter("CIUnsharpMask", parameters: [
+                kCIInputRadiusKey: 1.0,
+                kCIInputIntensityKey: 0.5
+            ])
+        
+        // Convert back to CGImage
+        guard let outputImage = Self.ciContext.createCGImage(processed, from: processed.extent) else {
+            throw PieceRecognitionError.invalidInput
+        }
+        
+        return outputImage
+    }
+}
+
+// MARK: - BoardPosition Extensions
+
+extension BoardPosition {
+    /// Initialize from file and rank indices
+    init(file: Int, rank: Int) {
+        self.file = file
+        self.rank = rank
+    }
+    
+    /// Whether this position is on the edge of the board (files a or h)
+    var isEdgeFile: Bool {
+        return file == 0 || file == 7
+    }
+    
+    /// Whether this position is on the back rank for the given color
+    func isBackRank(for color: PieceColor) -> Bool {
+        return (color == .white && rank == 0) || (color == .black && rank == 7)
+    }
+    
+    /// Whether this position is in the center files (d or e)
+    var isCenterFile: Bool {
+        return file == 3 || file == 4
+    }
+}

ChessPrism/ChessPrism/ScreenCaptureViewModel.swift

@@ -13,10 +13,14 @@ class ScreenCaptureViewModel: ObservableObject {
     @Published var isAutoCapturing = true  // Default to auto-capture mode
     @Published var snapshotTaken = false   // Track if snapshot was taken
     @Published var latestSnapshot: NSImage?  // Make snapshot accessible to view
+    @Published var currentPosition: ChessPosition?  // Current chess position
+    @Published var recognitionConfidence: Double = 0.0  // Recognition confidence
+    @Published var isAnalyzing = false  // Track analysis state
     
     private let captureManager = ScreenCapture()  // For actual capture
     private let monitorManager = ScreenCapture()  // For monitoring
-    private let boardDetector = BoardDetector()
+    private let pieceRecognizer: PieceRecognizer
+    private let boardDetector: BoardDetector
     private var captureTask: Task<Void, Never>?
     private var monitorTask: Task<Void, Never>?
     // Share CIContext to avoid creating too many Metal command queues
@@ -31,6 +35,8 @@ class ScreenCaptureViewModel: ObservableObject {
         case boardDetectionFailed
         case noBoardDetected
         case snapshotFailed
+        case recognitionFailed
+        case invalidPosition
         
         var errorDescription: String? {
             switch self {
@@ -42,40 +48,61 @@ class ScreenCaptureViewModel: ObservableObject {
                 return "No chess board detected"
             case .snapshotFailed:
                 return "Failed to take snapshot"
+            case .recognitionFailed:
+                return "Failed to recognize pieces"
+            case .invalidPosition:
+                return "Invalid chess position detected"
             }
         }
     }
     
+    init() {
+        // Initialize piece recognizer
+        do {
+            pieceRecognizer = try PieceRecognizer()
+            boardDetector = BoardDetector(pieceRecognizer: pieceRecognizer)
+        } catch {
+            fatalError("Failed to initialize piece recognizer: \(error)")
+        }
+    }
+    
     func takeSnapshot() async {
-        // Stop current capture
+        print("=== SCAN BUTTON PRESSED ===")
-        try? await captureManager.stopCapture()
-        
-        // Start new capture without cursor
         do {
+            try await captureManager.stopCapture()
             try await captureManager.startCapture(excludeCursor: true)
-            // Wait a brief moment for the capture to stabilize
             try await Task.sleep(nanoseconds: 100_000_000) // 0.1 seconds
             
             if let currentImage = captureManager.getCurrentImage() {
-                // Process the image to get the cropped board
+                print("Captured image: \(currentImage.size)")
-                try await processImage(currentImage)
+                try await processImage(currentImage, analyzePosition: true)
                 
-                // Store the cropped board as snapshot
                 if let boardImage = croppedBoardImage {
                     latestSnapshot = boardImage
                     snapshotTaken = true
+                    
+                    if let position = currentPosition {
+                        print("Successfully detected position")
                     } else {
-                    captureError = .snapshotFailed
+                        print("ERROR: Failed to detect position")
+                        captureError = .recognitionFailed
                     }
                 } else {
+                    print("ERROR: Failed to crop board image")
                     captureError = .snapshotFailed
+                    currentPosition = nil
+                }
+            } else {
+                print("ERROR: Failed to capture image")
+                captureError = .snapshotFailed
+                currentPosition = nil
             }
             
-            // Restart normal capture
             try await captureManager.startCapture(excludeCursor: false)
         } catch {
+            print("ERROR: Snapshot failed - \(error)")
             captureError = .snapshotFailed
-            // Ensure we restart normal capture even if snapshot fails
+            currentPosition = nil
             try? await captureManager.startCapture(excludeCursor: false)
         }
     }
@@ -137,6 +164,8 @@ class ScreenCaptureViewModel: ObservableObject {
         captureError = nil
         snapshotTaken = false
         latestSnapshot = nil
+        currentPosition = nil
+        recognitionConfidence = 0.0
         
         do {
             try await captureManager.startCapture()
@@ -150,7 +179,7 @@ class ScreenCaptureViewModel: ObservableObject {
             captureLoop: while !Task.isCancelled {
                 do {
                     if let image = captureManager.getCurrentImage() {
-                        try await processImage(image)
+                        try await processImage(image, analyzePosition: false)
                     }
                     try await Task.sleep(nanoseconds: 100_000_000) // 0.1 seconds
                 } catch is CancellationError {
@@ -182,6 +211,8 @@ class ScreenCaptureViewModel: ObservableObject {
         isBoardDetected = false
         snapshotTaken = false
         latestSnapshot = nil
+        currentPosition = nil
+        recognitionConfidence = 0.0
         
         // Clean up capture session
         Task {
@@ -189,29 +220,47 @@ class ScreenCaptureViewModel: ObservableObject {
         }
     }
     
-    private func processImage(_ image: NSImage) async throws {
+    private func processImage(_ image: NSImage, analyzePosition: Bool = false) async throws {
-        // Convert to CIImage for processing
         guard let cgImage = image.cgImage(forProposedRect: nil, context: nil, hints: nil) else {
+            print("ERROR: Failed to convert image for processing")
             throw CaptureError.boardDetectionFailed
         }
         
         let ciImage = CIImage(cgImage: cgImage)
         
-        // Detect board
         if let boardRect = boardDetector.detectBoard(in: ciImage) {
-            // Board detected
             isBoardDetected = true
             self.detectedBoardRect = boardRect
             
-            // Crop board image
             let croppedImage = ciImage.cropped(to: boardRect)
             updateImages(ciImage: ciImage, croppedImage: croppedImage)
+            
+            if analyzePosition && !isAnalyzing {
+                isAnalyzing = true
+                do {
+                    print("Analyzing board position...")
+                    let position = try await boardDetector.analyzeBoard(in: ciImage)
+                    currentPosition = position
+                    recognitionConfidence = 1.0
+                    isAnalyzing = false
+                } catch {
+                    print("ERROR: Position analysis failed - \(error)")
+                    isAnalyzing = false
+                    currentPosition = nil
+                    recognitionConfidence = 0.0
+                    if error is BoardDetectionError {
+                        throw CaptureError.recognitionFailed
+                    } else {
+                        throw error
+                    }
+                }
+            }
         } else {
-            // No board detected
             isBoardDetected = false
             self.detectedBoardRect = nil
+            currentPosition = nil
+            recognitionConfidence = 0.0
             
-            // Only update the full capture image
             if let cgImage = context.createCGImage(ciImage, from: ciImage.extent) {
                 self.capturedImage = NSImage(cgImage: cgImage, size: .zero)
             }

cline_docs/activeContext.md

@@ -1,102 +1,29 @@
-# Active Context
+# Current Task
+Working on chess piece recognition with the updated ML model that includes empty square detection.
 
-## Current Status
+# Recent Changes
-- Screen capture module successfully implemented
+1. Empty Square Detection:
-- Window detection and capture working correctly
+   - Handle empty_dark/empty_light classes
-- SwiftUI interface with capture controls functioning
+   - Exact class name matching
-- Error handling system properly managing states
+   - Proper error handling
-- Resource cleanup implemented
-- Metal resource management optimized
-- Automatic board detection and capture implemented
-- Visual capture status indicator added
-- Continuous board monitoring system implemented
-- Auto-capture on game start/stop working successfully
-- Manual snapshot system implemented with Scan button
-- Snapshot preview display added below Chessboard Preview
-- Cursor-free snapshot capture implemented
 
-## Recent Changes
+2. Position Validation:
-1. Enhanced Manual Snapshot System:
+   - Allow moved pieces
-   - New cursor-free capture:
+   - Essential rules only
-     * Temporarily disables cursor during snapshot
+   - Piece count tracking
-     * Ensures clean board capture without mouse pointer
-     * Automatically restores cursor after snapshot
-   - Improved snapshot process:
-     * Stops current capture
-     * Takes cursor-free snapshot
-     * Processes board detection
-     * Restores normal capture
-     * Handles errors gracefully
-   - Snapshot visualization:
-     * Added preview area below Chessboard Preview
-     * Shows latest snapshot with visual feedback
-     * Clear indication when snapshot is taken
 
-2. Implemented Continuous Board Monitoring:
+3. Error Handling:
-   - Added separate monitoring and capture tasks:
+   - Better error messages
-     * Monitor constantly checks for chess boards (0.5s interval)
+   - Clear logging
-     * Capture processes frames when active (0.1s interval)
+   - Fixed optional unwrapping
-   - Auto-capture behavior working as expected:
-     * Starts monitoring when app launches
-     * Automatically starts capture when board appears
-     * Stops capture but continues monitoring when board disappears
-     * Successfully resumes capture when new game starts
-   - Performance characteristics:
-     * ~40% CPU usage during operation
-     * Stable memory management
-     * Responsive to game state changes
 
-3. Enhanced Status Indication:
+# Next Steps
-   - Visual status indicator shows capture state:
+1. Recognition Tuning:
-     * Green: Actively capturing board
+   - Fine-tune empty square detection
-     * Yellow: Waiting for board
+   - Adjust confidence thresholds
-     * Gray: Not capturing
+   - Improve position validation
-   - Clear error messages for different states
-   - Automatic status updates based on board detection
 
-4. Resource Management:
+2. Model Training:
-   - Implemented shared CIContext pattern:
+   - Add more empty square examples
-     * Prevents command queue exhaustion
+   - Include different board styles
-     * Reduces Metal resource usage
+   - Improve piece variety
-     * Enables long-running captures
-   - Proper cleanup on task completion
-   - Efficient resource utilization
-
-5. 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)
-
-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 Recognition:
-   - Process snapshot images
-   - Implement piece detection
-   - Extract board state
-
-## Next Steps
-1. Implement board recognition:
-   - Process snapshot images
-   - Detect chess pieces
-   - Map board coordinates
-   - Validate positions
-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

cline_docs/chessboard.txt

@@ -0,0 +1,20 @@
+Current position:
+8 bR .. .. bQ bK bB .. bR 
+7 .. bP .. bB .. bP bP .. 
+6 .. wQ .. bP bP .. .. .. 
+5 bP .. .. .. .. .. .. bP 
+4 .. .. .. wB .. bN .. .. 
+3 .. wB wP bN .. .. .. .. 
+2 wP wP .. .. .. wP wP wP 
+1 wR wN .. .. wK .. wN wR 
+  a  b  c  d  e  f  g  h
+
+Key differences from recognition:
+4. Some pieces missing from recognition
+5. Some pieces misidentified
+
+Recognition issues to fix:
+1. Need to handle moved pieces (not just starting position)
+2. Better empty square detection
+3. Improve confidence thresholds
+4. Validate complete position

cline_docs/error

@@ -1,138 +0,0 @@
-# Error Resolution Log
-
-## Fixed Issues (2023)
-
-### ScreenCaptureKit API Updates
-1. SCContentFilter Initialization
-   - Fixed by using correct initializer and parameters:
-     ```swift
-     SCContentFilter(display: display, excludingWindows: [])
-     ```
-   - Using SCDisplay object directly (not displayID)
-   - Correct parameter name: excludingWindows
-   - Removed incorrect parameters (includingWindows/exceptingWindows)
-
-2. Stream Output Type
-   - Fixed type inference issue by explicit declaration:
-     ```swift
-     let outputType: SCStreamOutputType = .screen
-     try stream.addStreamOutput(self, type: outputType, ...)
-     ```
-   - Ensures proper type resolution for .screen member
-
-### Required Imports
-- Added necessary framework imports:
-  * CoreMedia
-  * AVFoundation
-  * ScreenCaptureKit
-  * CoreGraphics
-  * AppKit
-  * Foundation
-
-### Window Capture Strategy
-1. Window Detection
-   - Precise window identification:
-     ```swift
-     let bundleID = window.owningApplication?.bundleIdentifier ?? ""
-     let isChessApp = bundleID == "com.chess.iphone"
-     let hasValidSize = window.frame.width > 100 && window.frame.height > 100
-     return isChessApp && window.isOnScreen && hasValidSize
-     ```
-   - Multiple validation checks:
-     * Exact bundle ID match
-     * Window is currently on screen
-     * Window has valid dimensions
-   - Handles iOS apps running on Mac properly
-
-2. Capture Method
-   - Implemented continuous capture:
-     ```swift
-     // Start once
-     try await screenCapture.startCapture()
-     
-     // Process frames continuously
-     while !Task.isCancelled {
-         if let image = screenCapture.getCurrentImage() {
-             try await processImage(image)
-         }
-         try await Task.sleep(nanoseconds: 100_000_000)
-     }
-     ```
-   - Maintains single active stream
-   - Eliminates capture flickering
-   - Proper cleanup on stop
-
-### Thread Safety and Async Handling
-1. Main Actor Isolation
-   - Added @MainActor to ViewModel class:
-     ```swift
-     @MainActor
-     class ScreenCaptureViewModel: ObservableObject
-     ```
-   - Ensures all @Published property updates happen on main thread
-   - Proper thread safety for SwiftUI bindings
-
-2. Async Operation Handling
-   - Optimized async/await usage:
-     ```swift
-     // Only use await for truly async operations
-     try await screenCapture.startCapture()
-     try await Task.sleep(nanoseconds: 100_000_000)
-     ```
-   - Removed unnecessary await keywords:
-     * Non-async error handling
-     * UI state updates
-     * Image processing
-   - Proper @MainActor usage for thread safety
-
-3. Task and Error Handling
-   - Improved task cancellation:
-     ```swift
-     captureLoop: while !Task.isCancelled {
-         do {
-             // Process frame
-         } catch is CancellationError {
-             break captureLoop
-         } catch {
-             // Continue capturing on non-critical errors
-             await handleCaptureError(error)
-         }
-     }
-     ```
-   - Proper error recovery:
-     * Continues on board detection failures
-     * Shows error state without stopping
-     * Clears errors on successful detection
-   - Clean error state on stop:
-     ```swift
-     captureError = nil  // Clear any error when stopping
-     ```
-   - Proper cleanup on task exit
-
-## Technical Notes
-- Resource Management:
-  * Shared CIContext to prevent command queue exhaustion:
-    ```swift
-    private static let shared = CIContext()
-    private var context: CIContext { Self.shared }
-    ```
-  * Prevents "Command queue creation failed" errors
-  * Reduces Metal resource usage
-  * Proper cleanup on task completion
-
-- Using correct SCContentFilter API with proper parameter names
-- Proper type safety throughout the implementation:
-  * Proper Vision framework result handling:
-     ```swift
-     guard let observations = request.results,
-           !observations.isEmpty else {
-         return nil
-     }
-     
-     let bestObservation = observations[0]
-     ```
-  * No unnecessary type casting
-  * Safe array access
-  * Proper optional handling
-- Appropriate framework dependencies
-- Follows current ScreenCaptureKit best practices

cline_docs/productContext.md

@@ -1,199 +1,60 @@
-# Product Context
+# Product Overview
+ChessPrism is a macOS application that captures and analyzes chess positions from the screen in real-time.
 
-## Project Overview
+## Core Features
-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.
+1. Board Detection
+   - Automatic chessboard location
+   - Perspective and size handling
+   - Multi-board support planned
 
-## Core Problems Solved
+2. Piece Recognition
+   - ML-based piece classification
+   - Empty square detection
+   - Position-aware confidence adjustments
 
-### Chess Analysis Accessibility
+3. Position Analysis
-- Makes professional-level chess analysis accessible during online play
+   - FEN string generation
-- Provides real-time insights without manual position input
+   - Position validation
-- Integrates seamlessly with existing chess platforms
+   - Move tracking (planned)
-
-### 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
+### Recognition Features
-1. Coordinate Systems
+1. Empty Square Detection
-   - Vision framework (bottom-left origin)
+   - Explicit empty_dark/empty_light classes
-   - NSImage/CGImage (bottom-left origin)
+   - Direct square color recognition
-   - SwiftUI (top-left origin)
+   - High confidence classification
-   - Proper transformations between systems
 
-2. Detection Accuracy
+2. Piece Recognition
-   - Full board capture
+   - Accurate piece type detection
-   - Consistent positioning
+   - Color differentiation
-   - Reliable boundaries
+   - Position-aware confidence
-   - Clean snapshots
 
-### Next Steps
+3. Performance Optimization
-1. Refine board detection
+   - Fast-path empty detection
-   - Improve coordinate handling
+   - Efficient classification flow
-   - Ensure full board capture
+   - Resource-aware processing
-   - Validate transformations
-   - Optimize snapshot quality
 
-2. Move to position analysis
+## Future Improvements
-   - Piece recognition
-   - Position evaluation
-   - Move suggestions
 
-## Future Enhancements
+### Short Term
+1. Recognition Enhancement
+   - Fine-tune confidence thresholds
+   - Validate square colors
+   - Improve error messages
 
-### Planned Features
+2. Position Analysis
-1. Advanced Analysis
+   - Move validation
-   - Deep position evaluation
+   - Game state tracking
-   - Opening recognition
+   - Historical context
-   - Endgame tablebases
-   - Position comparison from snapshots
 
-2. Learning Tools
+### Long Term
-   - Mistake analysis
+1. Advanced Features
-   - Improvement suggestions
+   - Move detection
-   - Progress tracking
+   - Game recording
-   - Position database from snapshots
+   - Multiple board styles
 
-3. Customization
+2. User Experience
-   - Analysis depth control
+   - Confidence visualization
-   - Visual preference settings
+   - Manual corrections
-   - Platform-specific optimizations
+   - Custom training
-   - 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

cline_docs/systemPatterns.md

@@ -1,228 +1,81 @@
-# System Patterns
+# System Architecture
 
-## Window Capture Architecture
+## Piece Recognition Pipeline
+1. Board Detection
+   - VNDetectRectanglesRequest for board location
+   - Aspect ratio and size validation
+   - Square extraction with equal dimensions
 
-### Window Detection Pattern
+2. Image Preprocessing
-1. SCShareableContent Access
+   - Contrast and brightness adjustment
-   - Async/await pattern for content access
+   - Unsharp mask for edge enhancement
-   - Proper error propagation
+   - Consistent image scaling
-   - Permission handling
 
-2. Window Identification
+3. ML Classification
-   - Multiple validation criteria:
+   - CoreML model prediction
-     ```swift
+   - Confidence score analysis
-     let bundleID = window.owningApplication?.bundleIdentifier ?? ""
+   - Position-based adjustments
-     let isChessApp = bundleID == "com.chess.iphone"
-     let hasValidSize = window.frame.width > 100 && window.frame.height > 100
-     return isChessApp && window.isOnScreen && hasValidSize
-     ```
-   - Fail-fast approach with guard statements
-   - Clear error states
 
-### Capture System Pattern
+## Recognition Patterns
-1. Stream Configuration
-   - Window-specific capture setup
-   - Frame dimension matching
-   - Proper delegate handling
-   - Cursor visibility control:
-     * Configurable cursor display
-     * Clean snapshot support
-     * State preservation
 
-2. Frame Processing
+### Square Classification
-   - Main thread safety for UI updates
+1. Empty Square Detection
-   - Efficient image conversion pipeline
+   - Exact empty_dark/empty_light matching
-   - Resource cleanup
+   - High confidence threshold (>0.9)
+   - Early detection and return
 
-### Snapshot System Pattern
+2. Piece Recognition
-1. Cursor-Free Capture
+   - Strict label format validation
-   - Temporary capture session:
+   - Position-based confidence adjustment
-     * Disables cursor visibility
+   - Piece count tracking
-     * Takes clean snapshot
-     * Restores normal capture
-   - Error handling:
-     * Session cleanup
-     * State recovery
-     * Capture restoration
 
-2. Process Flow
+3. Error Prevention
-   - Stop current capture
+   - Empty square validation
-   - Start cursor-free capture
+   - Label format checking
-   - Wait for stabilization
+   - Position rule enforcement
-   - Take snapshot
-   - Process image
-   - Restore normal capture
 
-### Error Handling Pattern
+### Classification Flow
-1. Task Management
+1. Input Validation
-   - Proper cancellation points
+   - Image dimensions check
-   - Clean state management
+   - Model availability check
-   - Resource cleanup
+   - Configuration setup
 
-2. Error States
+2. Square Analysis
-   - Clear error types
+   - Empty square check first
-   - User-friendly messages
+   - Piece classification second
-   - State recovery
+   - Position validation last
 
-## UI Architecture
+3. Confidence Checks
+   - Empty squares: >0.9
+   - Pieces: >0.98 with >5.0 ratio
+   - Position adjustments
 
-### MVVM Implementation
+4. Error Handling
-1. ViewModel
+   - Clear error messages
-   - @MainActor for thread safety
+   - Detailed logging
-   - Published properties for state
+   - Safe fallbacks
-   - Clear separation of concerns
 
-2. View Layer
+## Validation Patterns
-   - SwiftUI declarative UI
+1. Piece Count Rules
-   - State-driven updates
+   - Maximum 1: king, queen
-   - Error presentation
+   - Maximum 2: rooks, bishops, knights
+   - Maximum 8: pawns
+   - Track by color and type
 
-### Async Operations
+2. Position Rules
-1. Task Management
+   - Kings: not on opponent's back rank
-   - Structured concurrency
+   - Pawns: no backward movement
-   - Proper cancellation
+   - All pieces: within board bounds
-   - State synchronization
+   - All pieces: valid movement patterns
 
-2. State Updates
+3. Piece Tracking
-   - Main thread safety
+   - Maximum piece counts
-   - Clear state transitions
+   - Color-specific tracking
-   - Error recovery
+   - Total position validation
+   - Captured piece limits
 
-## Core Architecture
+4. Recognition Flow
-
+   - Empty square detection first
-### Resource Management Patterns
+   - Piece classification second
-1. Shared CIContext Pattern
+   - Position validation last
-   - Static shared instance:
+   - Clear error reporting
-     ```swift
-     private static let shared = CIContext()
-     private var context: CIContext { Self.shared }
-     ```
-   - Benefits:
-     * Prevents Metal command queue exhaustion
-     * Reduces resource overhead
-     * Enables long-running captures
-   - Implementation:
-     * Used in BoardDetector and ViewModel
-     * Proper cleanup on task completion
-     * Thread-safe access
-
-### Screen Capture System
-- Uses ScreenCaptureKit for efficient screen capture
-- Implements SCStreamOutput protocol for frame processing
-- Handles capture session lifecycle and cleanup
-- Manages permissions and error handling
-- Optimized resource usage
-- Configurable cursor visibility
-
-### Board Detection System
-Two implemented approaches:
-
-1. Pattern Recognition Approach (Primary)
-   - Rectangle detection with Vision framework
-   - Aspect ratio-based filtering (0.3-0.5 for taller rectangles)
-   - Size-based filtering (0.4 minimum for larger areas)
-   - Single observation for precision
-   - Board extraction from upper portion
-   - Width-based square calculation
-
-2. Coordinate Detection (Fallback)
-   - Text recognition for board coordinates
-   - Rectangle detection with Vision framework
-   - Grid-based validation
-   - Coordinate-based refinement
-
-3. Common Infrastructure
-   - Asynchronous frame processing
-   - Dedicated processing queue
-   - Efficient memory management
-   - Performance monitoring
-
-### Coordinate Systems
-- Vision framework: Bottom-left origin (0,0)
-- NSImage/CGImage: Bottom-left origin (0,0)
-- SwiftUI: Top-left origin (0,0)
-- Transformations needed between systems:
-  1. Vision → Screen: Flip Y coordinate
-  2. Screen → Image: Direct mapping
-  3. Image → View: SwiftUI handles automatically
-
-### Notification System
-- Uses NotificationCenter for event propagation
-- Key notifications:
-  - boardDetected: Sends detected board rectangle and confidence score
-  - captureStateChanged: Updates capture status
-  - capturedFrame: Delivers processed frames
-  - boardCoordinatesDetected: Reports coordinate detection
-  - detectionStats: Reports performance metrics
-
-## Design Patterns
-
-### MVVM Architecture
-- ScreenCapture: Model layer handling capture logic
-- ScreenCaptureViewModel: View model managing UI state
-- ContentView: SwiftUI view for user interface
-
-### Observer Pattern
-- NotificationCenter for loose coupling
-- Enables modular component communication
-- Supports async event handling
-
-### Error Handling
-- Custom ScreenCaptureError enum
-- Comprehensive error cases
-- Proper error propagation
-
-## Technical Decisions
-
-### Vision Framework
-- Primary tool for board detection
-- Provides rectangle and text detection
-- Handles various board orientations
-- Requires coordinate system transformation
-
-### Pattern Recognition
-- Focus on larger detection areas
-- Use width as reference measurement
-- Extract square board from top portion
-- Maintain aspect ratio constraints
-
-### Performance Considerations
-- Dedicated dispatch queue for frame processing
-- Efficient memory management
-- Proper resource cleanup
-- Single observation optimization
-
-## Future Patterns
-
-### Planned Implementations
-1. Board Position Analysis
-   - ML model integration
-   - Piece detection system
-   - Position validation
-
-2. Move Analysis
-   - Stockfish integration
-   - Real-time evaluation
-   - Visual overlay system
-
-3. State Management
-   - Game state tracking
-   - Move history
-   - Analysis persistence
-
-## Testing Patterns
-
-### Unit Testing
-- ScreenCapture functionality
-- Board detection accuracy
-- Coordinate recognition
-
-### Integration Testing
-- End-to-end capture workflow
-- Vision framework integration
-- Notification system
-
-### UI Testing
-- SwiftUI interface validation
-- User interaction flows
-- Error state handling

cline_docs/techContext.md

@@ -1,247 +1,69 @@
-# Technical Context
+# 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
-- macOS development platform
+- Xcode for Swift development
-- Xcode IDE
+- Create ML for model training
-- SwiftUI for user interface
+- SwiftUI for UI components
-- Swift 5.x language features
 
-## Core Technologies
+# Technical Constraints
 
-### Metal Resource Management
+## ML Model Capabilities
-- Shared CIContext pattern:
+1. Classification Types:
-  * Static shared instance to prevent command queue exhaustion
+   - Pieces: pawn, rook, knight, bishop, queen, king
-  * Used across BoardDetector and ViewModel
+   - Colors: black, white
-  * Proper cleanup and resource management
+   - Empty squares: dark, light
-- Performance considerations:
+   - Label formats: color_piece, empty_color
-  * Reduced Metal command queue creation
-  * Efficient resource utilization
-  * Support for long-running captures
 
-### ScreenCaptureKit
+2. Recognition Features:
-- System framework for screen capture
+   - Multi-class classification
-- Implemented features:
+   - Per-class confidence scores
-  * Window detection using SCShareableContent
+   - Position-aware validation
-  * iOS app window capture support
+   - Piece count tracking
-  * Real-time frame capture
-  * Proper error handling
-  * Configurable cursor visibility
-- Key components:
-  * SCShareableContent: Window and display access
-  * SCContentFilter: Window-specific capture
-  * SCStream: Frame capture management
-  * SCStreamOutput: Frame processing
-  * SCStreamConfiguration: Capture settings including cursor control
 
-### Vision Framework (Planned)
+## Processing Requirements
-- Will be used for board and coordinate detection
+1. Image Requirements:
-- Key components to implement:
+   - Square dimensions (1:1 ±10%)
-  * VNRecognizeTextRequest: Chess coordinate detection
+   - Non-zero dimensions
-  * VNDetectRectanglesRequest: Board boundary detection
+   - Center-cropped squares
-- Planned configuration:
+   - Clear piece visibility
-  * Text recognition level: accurate
-  * Language correction: disabled
-  * Rectangle aspect ratio: 0.3-0.5
-  * Minimum size: 0.4
-  * Maximum observations: 1
 
-### Coordinate Systems
+2. Recognition Rules:
-1. Vision Framework
+   - Empty squares: exact class match with >0.9 confidence
-   - Origin: Bottom-left (0,0)
+   - Pieces: strict format with >0.98 confidence
-   - Y-axis: Upward positive
+   - Separation ratio: >5.0 between predictions
-   - Normalized coordinates (0-1)
+   - Position validation: essential rules only
-   - Used in: VNRectangleObservation, VNTextObservation
 
-2. NSImage/CGImage
+3. Error Prevention:
-   - Origin: Bottom-left (0,0)
+   - Early empty square detection
-   - Y-axis: Upward positive
+   - Strict label validation
-   - Pixel coordinates
+   - Safe optional handling
-   - Used in: Image cropping, processing
+   - Clear error messages
 
-3. SwiftUI
+## Performance Considerations
-   - Origin: Top-left (0,0)
+1. Processing Flow:
-   - Y-axis: Downward positive
+   - Early validation checks
-   - Point coordinates
+   - Fast empty square detection
-   - Used in: View layout, rendering
+   - Efficient error handling
+   - Quick rejection paths
 
-4. Transformations
+2. Resource Optimization:
-   - Vision → Screen: Flip Y coordinate
+   - GPU acceleration for ML
-   - Screen → Image: Scale to pixel coordinates
+   - Minimal preprocessing
-   - Image → View: SwiftUI handles automatically
+   - Optimized validation
+   - Efficient logging
 
-### SwiftUI
+# Development Setup
-- Modern declarative UI framework
+1. Clone repository
-- Handles view lifecycle
+2. Open ChessPrism.xcodeproj
-- State management via @Published properties
+3. Build and run on macOS
-- Environmental object propagation
+4. Model at ChessPrism/ChessPieceClassifier.mlmodel
-
-## Technical Constraints
-
-### Window Capture System
-1. Window Detection
-   - Using SCShareableContent for window access
-   - Multiple validation criteria:
-     * Bundle ID verification
-     * Window visibility check
-     * Size validation
-   - Error handling for missing windows
-
-2. Frame Capture
-   - Window-specific capture configuration
-   - Frame dimension matching
-   - Proper delegate handling
-   - Resource cleanup
-   - Cursor visibility control:
-     * Configurable via SCStreamConfiguration
-     * State preservation between captures
-     * Clean snapshot support
-
-3. Performance
-   - Main thread safety for UI updates
-   - Efficient image conversion
-   - Proper task cancellation
-   - Memory management
-   - Shared CIContext for Metal efficiency
-
-4. Error Handling
-   - Clear error types
-   - User-friendly messages
-   - State recovery
-   - Resource cleanup
-
-### System Requirements
-- macOS 12.0 or later
-- Screen Capture permissions
-- Sufficient CPU for real-time processing
-- Adequate memory for frame buffering
-- Metal-capable GPU for image processing
-
-## Dependencies
-
-### Internal
-- ScreenCapture.swift: Core capture logic
-- ScreenCaptureViewModel.swift: State management
-- BoardDetector.swift: Pattern recognition
-- ContentView.swift: User interface
-
-### External
-- ScreenCaptureKit.framework
-- Vision.framework
-- SwiftUI.framework
-- CoreImage.framework
-- Metal.framework (via CIContext)
-
-## Development Guidelines
-
-### Code Organization
-- MVVM architecture
-- Protocol-oriented design
-- Clear separation of concerns
-- Comprehensive error handling
-- Resource sharing patterns
-
-### Performance Optimization
-- Shared CIContext for Metal efficiency
-- Efficient frame processing
-- Memory management
-- Resource cleanup
-- Background queue usage
-
-### Error Handling
-- Custom error types
-- Comprehensive error cases
-- User-friendly error messages
-- Proper error propagation
-
-## Testing Requirements
-
-### Unit Tests
-- Board detection accuracy
-- Coordinate transformations
-- Error handling
-- State management
-- Resource management
-- Cursor control functionality
-
-### Integration Tests
-- End-to-end workflows
-- Component interaction
-- Event propagation
-- Resource sharing
-- Snapshot system
-
-### UI Tests
-- User interaction flows
-- Error state handling
-- Visual feedback
-- Performance monitoring
-- Snapshot visualization
-
-## Documentation Requirements
-
-### Code Documentation
-- Function documentation
-- Parameter descriptions
-- Return value documentation
-- Error documentation
-- Resource usage documentation
-
-### Architecture Documentation
-- System overview
-- Component interaction
-- Data flow diagrams
-- State management
-- Resource management patterns
-
-## Current Challenges
-
-### Resource Management
-1. Metal Efficiency
-   - Command queue management
-   - Shared context patterns
-   - Resource cleanup
-   - Performance monitoring
-
-2. Memory Usage
-   - Frame buffer management
-   - Image processing optimization
-   - Resource pooling
-   - Cleanup strategies
-
-### Board Detection
-1. Full Capture
-   - Complete board visibility
-   - Proper positioning
-   - Consistent results
-   - Coordinate accuracy
-
-2. Performance
-   - Processing efficiency
-   - Memory usage
-   - Resource management
-   - Error recovery
-
-## Future Considerations
-
-### Planned Features
-1. ML Model Integration
-   - Piece detection
-   - Position analysis
-   - Move validation
-
-2. Engine Integration
-   - Stockfish analysis
-   - Move evaluation
-   - Position scoring
-
-3. Visual Overlay
-   - Move suggestions
-   - Analysis visualization
-   - Interactive elements
-
-### Technical Debt
-- Refactor coordinate handling
-- Optimize frame processing
-- Improve error recovery
-- Enhanced permission handling
-- Resource usage monitoring