chore: suppression du code mort (détection auto, distorsion, ML Kit)

- Supprime 5 services inatteignables depuis l'UI (~3 000 lignes) :
  distortion_correction, image_processing, target_detection,
  opencv_impact_detection, target_rectify
- AnalysisProvider allégé (835 -> ~360 lignes) : retrait de la détection
  par références, de la détection auto d'impacts, du workflow distorsion
  et du doublon moveShot
- Retire les dépendances inutilisées google_mlkit_object_detection et
  google_mlkit_document_scanner du pubspec
- Le bouton ↻ du Plotting efface désormais tous les impacts en un clic
  (clearShots) sans relancer la détection auto ni toucher la calibration
- Nettoie les paramètres morts de TargetOverlay (referenceImpacts,
  onAddShot), le flag _isSelectingReferences, _buildActionButtons vide
  et le résidu _detectionTimer de capture_screen
- Supprime le dossier tests/ (brouillons d'expérimentation OpenCV)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

lib/features/analysis/analysis_provider.dart

@@ -1,8 +1,8 @@
 /// Gestionnaire d'état pour l'analyse des cibles (ChangeNotifier).
 ///
-/// Gère le workflow complet d'analyse : chargement d'image, détection de cible,
+/// Gère le workflow complet d'analyse : chargement d'image, gestion des
-/// gestion des impacts (manuels et automatiques), calcul des scores,
+/// impacts placés manuellement, calcul des scores, analyse de groupement
-/// analyse de groupement et sauvegarde des sessions.
+/// et sauvegarde des sessions.
 library;
 
 import 'dart:io';
@@ -13,37 +13,25 @@ import '../../data/models/target_analysis.dart';
 import '../../data/models/shot.dart';
 import '../../data/models/target_type.dart';
 import '../../data/repositories/session_repository.dart';
-import '../../services/target_detection_service.dart';
 import '../../services/score_calculator_service.dart';
 import '../../services/grouping_analyzer_service.dart';
-import '../../services/distortion_correction_service.dart';
-import '../../services/opencv_target_service.dart';
 import '../../services/ai_export_service.dart';
 
 enum AnalysisState { initial, loading, success, error }
 
 class AnalysisProvider extends ChangeNotifier {
-  final TargetDetectionService _detectionService;
   final ScoreCalculatorService _scoreCalculatorService;
   final GroupingAnalyzerService _groupingAnalyzerService;
   final SessionRepository _sessionRepository;
-  final DistortionCorrectionService _distortionService;
-  final OpenCVTargetService _opencvTargetService;
   final Uuid _uuid = const Uuid();
 
   AnalysisProvider({
-    required TargetDetectionService detectionService,
     required ScoreCalculatorService scoreCalculatorService,
     required GroupingAnalyzerService groupingAnalyzerService,
     required SessionRepository sessionRepository,
-    DistortionCorrectionService? distortionService,
+  })  : _scoreCalculatorService = scoreCalculatorService,
-    OpenCVTargetService? opencvTargetService,
-  }) : _detectionService = detectionService,
-        _scoreCalculatorService = scoreCalculatorService,
         _groupingAnalyzerService = groupingAnalyzerService,
-        _sessionRepository = sessionRepository,
+        _sessionRepository = sessionRepository;
-        _distortionService = distortionService ?? DistortionCorrectionService(),
-        _opencvTargetService = opencvTargetService ?? OpenCVTargetService();
 
   AnalysisState _state = AnalysisState.initial;
   String? _errorMessage;
@@ -53,7 +41,7 @@ class AnalysisProvider extends ChangeNotifier {
   // AJOUT PROTECTION DU PLOTTING : Stockage permanent de la rotation du Crop
   double _cropRotation = 0.0;
 
-  // Target detection results
+  // Target calibration
   double _targetCenterX = 0.5;
   double _targetCenterY = 0.5;
   double _targetRadius = 0.4;
@@ -71,15 +59,6 @@ class AnalysisProvider extends ChangeNotifier {
   // Grouping results
   GroupingResult? _groupingResult;
 
-  // Reference-based detection
-  List<Shot> _referenceImpacts = [];
-  ImpactCharacteristics? _learnedCharacteristics;
-
-  // Distortion correction
-  bool _distortionCorrectionEnabled = false;
-  DistortionParameters? _distortionParams;
-  String? _correctedImagePath;
-
   // Getters
   AnalysisState get state => _state;
   String? get errorMessage => _errorMessage;
@@ -100,21 +79,6 @@ class AnalysisProvider extends ChangeNotifier {
 
   int get totalScore => _scoreResult?.totalScore ?? 0;
   int get shotCount => _shots.length;
-  List<Shot> get referenceImpacts => List.unmodifiable(_referenceImpacts);
-  ImpactCharacteristics? get learnedCharacteristics => _learnedCharacteristics;
-  bool get hasLearnedCharacteristics => _learnedCharacteristics != null;
-
-  // Distortion correction getters
-  bool get distortionCorrectionEnabled => _distortionCorrectionEnabled;
-  DistortionParameters? get distortionParams => _distortionParams;
-  String? get correctedImagePath => _correctedImagePath;
-  bool get hasDistortion => _distortionParams?.needsCorrection ?? false;
-
-  /// Retourne le chemin de l'image à afficher (corrigée si activée, originale sinon)
-  String? get displayImagePath =>
-      _distortionCorrectionEnabled && _correctedImagePath != null
-          ? _correctedImagePath
-          : _imagePath;
 
   /// Modifie et mémorise la rotation de l'image pour le Plotting
   void setCropRotation(double rotation) {
@@ -122,14 +86,11 @@ class AnalysisProvider extends ChangeNotifier {
     notifyListeners();
   }
 
-  /// Analyze an image
+  /// Charge l'image et initialise les paramètres de cible par défaut.
-  ///
+  /// Le placement des impacts et la calibration se font ensuite manuellement.
-  /// [autoAnalyze] determines if we should run automatic detection immediately.
-  /// If false, only the image is loaded and default target parameters are set.
   Future<void> analyzeImage(
     String imagePath,
     TargetType targetType, {
-        bool autoAnalyze = true,
     Offset? manualCenter,
   }) async {
     _state = AnalysisState.loading;
@@ -147,55 +108,13 @@ class AnalysisProvider extends ChangeNotifier {
       _imageAspectRatio = frame.image.width / frame.image.height;
       frame.image.dispose();
 
-      if (!autoAnalyze) {
-        // Just setup default values without running detection
       _targetCenterX = manualCenter?.dx ?? 0.5;
       _targetCenterY = manualCenter?.dy ?? 0.5;
       _targetRadius = 0.4;
       _targetInnerRadius = 0.04;
 
-        // Initialize empty shots list
       _shots = [];
 
-        _state = AnalysisState.success;
-        notifyListeners();
-        return;
-      }
-
-      final result = await _detectionService.detectTargetAsync(
-        imagePath,
-        targetType,
-      );
-
-      if (!result.success) {
-        _state = AnalysisState.error;
-        _errorMessage = result.errorMessage;
-        notifyListeners();
-        return;
-      }
-
-      _targetCenterX = result.centerX;
-      _targetCenterY = result.centerY;
-      _targetRadius = result.radius;
-      _targetInnerRadius = result.radius * 0.1;
-
-      // Create shots from detected impacts
-      _shots = result.impacts.map((impact) {
-        return Shot(
-          id: _uuid.v4(),
-          x: impact.x,
-          y: impact.y,
-          score: impact.suggestedScore,
-          analysisId: '',
-        );
-      }).toList();
-
-      // Calculate scores
-      _recalculateScores();
-
-      // Calculate grouping
-      _recalculateGrouping();
-
       _state = AnalysisState.success;
       notifyListeners();
     } catch (e) {
@@ -216,22 +135,18 @@ class AnalysisProvider extends ChangeNotifier {
     notifyListeners();
   }
 
-  /// Remove a shot
+  /// Efface tous les impacts en un clic (bouton ↻ de l'écran Plotting).
-  void removeShot(String shotId) {
+  /// La calibration (centre, rayon, anneaux) n'est pas touchée.
-    _shots.removeWhere((shot) => shot.id == shotId);
+  void clearShots() {
+    _shots.clear();
     _recalculateScores();
     _recalculateGrouping();
     notifyListeners();
   }
 
-  /// Move a shot to a new position
+  /// Remove a shot
-  void moveShot(String shotId, double newX, double newY) {
+  void removeShot(String shotId) {
-    final index = _shots.indexWhere((shot) => shot.id == shotId);
+    _shots.removeWhere((shot) => shot.id == shotId);
-    if (index == -1) return;
-
-    final newScore = _calculateShotScore(newX, newY);
-    _shots[index] = _shots[index].copyWith(x: newX, y: newY, score: newScore);
-
     _recalculateScores();
     _recalculateGrouping();
     notifyListeners();
@@ -247,265 +162,6 @@ class AnalysisProvider extends ChangeNotifier {
     notifyListeners();
   }
 
-  /// Auto-detect impacts using image processing
-  Future<int> autoDetectImpacts({
-    int darkThreshold = 80,
-    int minImpactSize = 20,
-    int maxImpactSize = 500,
-    double minCircularity = 0.6,
-    double minFillRatio = 0.5,
-    bool clearExisting = false,
-  }) async {
-    if (_imagePath == null || _targetType == null) return 0;
-
-    final settings = ImpactDetectionSettings(
-      darkThreshold: darkThreshold,
-      minImpactSize: minImpactSize,
-      maxImpactSize: maxImpactSize,
-      minCircularity: minCircularity,
-      minFillRatio: minFillRatio,
-    );
-
-    final detectedImpacts = _detectionService.detectImpactsOnly(
-      _imagePath!,
-      _targetType!,
-      _targetCenterX,
-      _targetCenterY,
-      _targetRadius,
-      _ringCount,
-      settings,
-    );
-
-    if (clearExisting) {
-      _shots.clear();
-    }
-
-    // Add detected impacts as shots
-    for (final impact in detectedImpacts) {
-      final score = _calculateShotScore(impact.x, impact.y);
-      final shot = Shot(
-        id: _uuid.v4(),
-        x: impact.x,
-        y: impact.y,
-        score: score,
-        analysisId: '',
-      );
-      _shots.add(shot);
-    }
-
-    _recalculateScores();
-    _recalculateGrouping();
-    notifyListeners();
-
-    return detectedImpacts.length;
-  }
-
-  /// Auto-detect impacts using OpenCV (Hough Circles + Contours)
-  Future<int> autoDetectImpactsWithOpenCV({
-    double cannyThreshold1 = 50,
-    double cannyThreshold2 = 150,
-    double minDist = 20,
-    double param1 = 100,
-    double param2 = 30,
-    int minRadius = 5,
-    int maxRadius = 50,
-    int minSize = 5,
-    int maxSize = 1000,
-    int blurSize = 5,
-    bool useContourDetection = true,
-    double minCircularity = 0.6,
-    double minContourArea = 50,
-    double maxContourArea = 5000,
-    bool clearExisting = false,
-  }) async {
-    if (_imagePath == null || _targetType == null) return 0;
-
-    final settings = OpenCVDetectionSettings(
-      cannyThreshold1: cannyThreshold1,
-      cannyThreshold2: cannyThreshold2,
-      minDist: minDist,
-      param1: param1,
-      param2: param2,
-      minRadius: minRadius,
-      maxRadius: maxRadius,
-      blurSize: blurSize,
-      useContourDetection: useContourDetection,
-      minCircularity: minCircularity,
-      minContourArea: minContourArea,
-      maxContourArea: maxContourArea,
-    );
-
-    final detectedImpacts = _detectionService.detectImpactsWithOpenCV(
-      _imagePath!,
-      _targetType!,
-      _targetCenterX,
-      _targetCenterY,
-      _targetRadius,
-      _ringCount,
-      settings: settings,
-    );
-
-    if (clearExisting) {
-      _shots.clear();
-    }
-
-    // Add detected impacts as shots
-    for (final impact in detectedImpacts) {
-      final score = _calculateShotScore(impact.x, impact.y);
-      final shot = Shot(
-        id: _uuid.v4(),
-        x: impact.x,
-        y: impact.y,
-        score: score,
-        analysisId: '',
-      );
-      _shots.add(shot);
-    }
-
-    _recalculateScores();
-    _recalculateGrouping();
-    notifyListeners();
-
-    return detectedImpacts.length;
-  }
-
-  /// Detect impacts with OpenCV using reference points
-  Future<int> detectFromReferencesWithOpenCV({
-    double tolerance = 2.0,
-    bool clearExisting = false,
-  }) async {
-    if (_imagePath == null ||
-        _targetType == null ||
-        _referenceImpacts.length < 2) {
-      return 0;
-    }
-
-    // Convertir les références
-    final references = _referenceImpacts
-        .map((shot) => ReferenceImpact(x: shot.x, y: shot.y))
-        .toList();
-
-    final detectedImpacts = _detectionService
-        .detectImpactsWithOpenCVFromReferences(
-      _imagePath!,
-      _targetType!,
-      _targetCenterX,
-      _targetCenterY,
-      _targetRadius,
-      _ringCount,
-      references,
-      tolerance: tolerance,
-    );
-
-    if (clearExisting) {
-      _shots.clear();
-    }
-
-    // Add detected impacts as shots
-    for (final impact in detectedImpacts) {
-      final score = _calculateShotScore(impact.x, impact.y);
-      final shot = Shot(
-        id: _uuid.v4(),
-        x: impact.x,
-        y: impact.y,
-        score: score,
-        analysisId: '',
-      );
-      _shots.add(shot);
-    }
-
-    _recalculateScores();
-    _recalculateGrouping();
-    notifyListeners();
-
-    return detectedImpacts.length;
-  }
-
-  /// Add a reference impact for calibrated detection
-  void addReferenceImpact(double x, double y) {
-    final score = _calculateShotScore(x, y);
-    final shot = Shot(id: _uuid.v4(), x: x, y: y, score: score, analysisId: '');
-    _referenceImpacts.add(shot);
-    notifyListeners();
-  }
-
-  /// Remove a reference impact
-  void removeReferenceImpact(String shotId) {
-    _referenceImpacts.removeWhere((shot) => shot.id == shotId);
-    _learnedCharacteristics = null;
-    notifyListeners();
-  }
-
-  /// Clear all reference impacts
-  void clearReferenceImpacts() {
-    _referenceImpacts.clear();
-    _learnedCharacteristics = null;
-    notifyListeners();
-  }
-
-  /// Learn characteristics from reference impacts
-  bool learnFromReferences() {
-    if (_imagePath == null || _referenceImpacts.length < 2) return false;
-
-    final references = _referenceImpacts
-        .map((shot) => ReferenceImpact(x: shot.x, y: shot.y))
-        .toList();
-
-    _learnedCharacteristics = _detectionService.analyzeReferenceImpacts(
-      _imagePath!,
-      references,
-    );
-
-    notifyListeners();
-    return _learnedCharacteristics != null;
-  }
-
-  /// Auto-detect impacts using learned reference characteristics
-  Future<int> detectFromReferences({
-    double tolerance = 2.0,
-    bool clearExisting = false,
-  }) async {
-    if (_imagePath == null ||
-        _targetType == null ||
-        _learnedCharacteristics == null) {
-      return 0;
-    }
-
-    final detectedImpacts = _detectionService.detectImpactsFromReferences(
-      _imagePath!,
-      _targetType!,
-      _targetCenterX,
-      _targetCenterY,
-      _targetRadius,
-      _ringCount,
-      _learnedCharacteristics!,
-      tolerance: tolerance,
-    );
-
-    if (clearExisting) {
-      _shots.clear();
-    }
-
-    // Add detected impacts as shots
-    for (final impact in detectedImpacts) {
-      final score = _calculateShotScore(impact.x, impact.y);
-      final shot = Shot(
-        id: _uuid.v4(),
-        x: impact.x,
-        y: impact.y,
-        score: score,
-        analysisId: '',
-      );
-      _shots.add(shot);
-    }
-
-    _recalculateScores();
-    _recalculateGrouping();
-    notifyListeners();
-
-    return detectedImpacts.length;
-  }
-
   /// Adjust target position
   void adjustTargetPosition(
     double centerX,
@@ -539,118 +195,6 @@ class AnalysisProvider extends ChangeNotifier {
     notifyListeners();
   }
 
-  /// Auto-calibrate target using OpenCV
-  Future<bool> autoCalibrateTarget() async {
-    if (_imagePath == null) return false;
-
-    try {
-      // 1. Attempt to correct perspective/distortion first
-      final correctedPath = await _distortionService
-          .correctPerspectiveWithConcentricMesh(_imagePath!);
-
-      if (correctedPath != _imagePath) {
-        _imagePath = correctedPath;
-        _correctedImagePath = correctedPath;
-        _distortionCorrectionEnabled = true;
-        _imageAspectRatio = 1.0;
-        notifyListeners();
-      }
-
-      // 2. Detect the target on the straight/corrected image
-      final result = await _opencvTargetService.detectTarget(_imagePath!);
-
-      if (result.success) {
-        adjustTargetPosition(
-          result.centerX,
-          result.centerY,
-          result.radius * 0.1,
-          result.radius,
-        );
-        return true;
-      }
-      return false;
-    } catch (e) {
-      debugPrint('Auto-calibration error: $e');
-      return false;
-    }
-  }
-
-  /// Calcule les paramètres de distorsion basés sur la calibration actuelle
-  void calculateDistortion() {
-    _distortionParams = _distortionService.calculateDistortionFromCalibration(
-      targetCenterX: _targetCenterX,
-      targetCenterY: _targetCenterY,
-      targetRadius: _targetRadius,
-      imageAspectRatio: _imageAspectRatio,
-    );
-    notifyListeners();
-  }
-
-  /// Applique la correction de distorsion à l'image
-  /// Crée une nouvelle image corrigée et la sauvegarde
-  Future<void> applyDistortionCorrection() async {
-    if (_imagePath == null || _distortionParams == null) return;
-
-    try {
-      _correctedImagePath = await _distortionService.applyCorrection(
-        _imagePath!,
-        _distortionParams!,
-      );
-      _distortionCorrectionEnabled = true;
-      notifyListeners();
-    } catch (e) {
-      _errorMessage = 'Erreur lors de la correction: $e';
-      notifyListeners();
-    }
-  }
-
-  /// Active ou désactive l'affichage de l'image corrigée
-  void setDistortionCorrectionEnabled(bool enabled) {
-    if (enabled && _correctedImagePath == null && _distortionParams != null) {
-      // Si on active mais pas encore d'image corrigée, la créer
-      applyDistortionCorrection();
-    } else {
-      _distortionCorrectionEnabled = enabled;
-      notifyListeners();
-    }
-  }
-
-  /// Calcule ET applique la correction pour un feedback immédiat
-  Future<void> calculateAndApplyDistortion() async {
-    // 1. Calcul des paramètres (votre code actuel)
-    _distortionParams = _distortionService.calculateDistortionFromCalibration(
-      targetCenterX: _targetCenterX,
-      targetCenterY: _targetCenterY,
-      targetRadius: _targetRadius,
-      imageAspectRatio: _imageAspectRatio,
-    );
-
-    // 2. Vérification si une correction est réellement nécessaire
-    if (_distortionParams != null && _distortionParams!.needsCorrection) {
-      // 3. Application immédiate de la transformation (méthode asynchrone)
-      await applyDistortionCorrection();
-    } else {
-      notifyListeners();
-    }
-  }
-
-  Future<void> runFullDistortionWorkflow() async {
-    _state = AnalysisState.loading;
-    notifyListeners();
-
-    try {
-      calculateDistortion();
-      await applyDistortionCorrection();
-      _distortionCorrectionEnabled = true;
-      _state = AnalysisState.success;
-    } catch (e) {
-      _errorMessage = "Erreur de rendu : $e";
-      _state = AnalysisState.error;
-    } finally {
-      notifyListeners();
-    }
-  }
-
   int _calculateShotScore(double x, double y) {
     if (_targetType == TargetType.concentric) {
       return _scoreCalculatorService.calculateConcentricScore(
@@ -807,11 +351,6 @@ class AnalysisProvider extends ChangeNotifier {
     _shots = [];
     _scoreResult = null;
     _groupingResult = null;
-    _referenceImpacts = [];
-    _learnedCharacteristics = null;
-    _distortionCorrectionEnabled = false;
-    _distortionParams = null;
-    _correctedImagePath = null;
     notifyListeners();
   }
 

lib/features/analysis/analysis_screen.dart

@@ -14,7 +14,6 @@ import '../../core/theme/app_theme.dart';
 import '../../data/models/target_type.dart';
 import '../../data/models/shot.dart';
 import '../../data/repositories/session_repository.dart';
-import '../../services/target_detection_service.dart';
 import '../../services/score_calculator_service.dart';
 import '../../services/grouping_analyzer_service.dart';
 import '../../services/wallet_identity_service.dart';
@@ -61,7 +60,6 @@ class AnalysisScreen extends StatelessWidget {
     return ChangeNotifierProvider(
       create: (context) {
         final p = AnalysisProvider(
-          detectionService: context.read<TargetDetectionService>(),
           scoreCalculatorService: context.read<ScoreCalculatorService>(),
           groupingAnalyzerService: context.read<GroupingAnalyzerService>(),
           sessionRepository: context.read<SessionRepository>(),
@@ -73,7 +71,6 @@ class AnalysisScreen extends StatelessWidget {
         p.analyzeImage(
           imagePath,
           targetType,
-          autoAnalyze: false,
           manualCenter: manualCenterOffset,
         );
         return p;
@@ -111,7 +108,6 @@ class _AnalysisScreenContentState extends State<_AnalysisScreenContent> {
 
   // Forcé à TRUE pour démarrer sur l'ajustement des cercles
   bool _isCalibrating = true;
-  bool _isSelectingReferences = false;
   bool _isAtBottom = false;
 
   final ScrollController _scrollController = ScrollController();
@@ -192,7 +188,6 @@ class _AnalysisScreenContentState extends State<_AnalysisScreenContent> {
     if (validated == true) {
       setState(() {
         _isCalibrating = false;
-        _isSelectingReferences = false;
       });
     } else {
       _enterCalibration();
@@ -250,17 +245,17 @@ class _AnalysisScreenContentState extends State<_AnalysisScreenContent> {
           },
         ),
         actions: [
-          if (!_isCalibrating && !_isSelectingReferences)
+          // Remise à zéro des impacts : efface tous les impacts en un clic,
+          // sans modifier la calibration (centre, rayon, anneaux).
+          if (!_isCalibrating)
             IconButton(
               icon: const Icon(Icons.refresh),
-              onPressed: () => provider.analyzeImage(
+              tooltip: 'Effacer tous les impacts',
-                context.read<AnalysisProvider>().imagePath!,
+              onPressed: () => provider.clearShots(),
-                context.read<AnalysisProvider>().targetType!,
-              ),
             ),
           // Nuage d'export vers le backend IA : visible uniquement si l'analyse
           // a réussi ET que l'utilisateur a activé l'option dans les Paramètres.
-          if (!_isCalibrating && !_isSelectingReferences)
+          if (!_isCalibrating)
             FutureBuilder<bool>(
               future: WalletIdentityService().isUploadEnabled(),
               builder: (context, snapshot) {
@@ -470,8 +465,6 @@ class _AnalysisScreenContentState extends State<_AnalysisScreenContent> {
                             targetCenterX: provider.targetCenterX,
                             targetCenterY: provider.targetCenterY,
                           ),
-                        const SizedBox(height: 12),
-                        _buildActionButtons(context, provider),
                         const SizedBox(height: 50),
                       ],
                     ),
@@ -647,10 +640,6 @@ class _AnalysisScreenContentState extends State<_AnalysisScreenContent> {
     );
   }
 
-  Widget _buildActionButtons(BuildContext context, AnalysisProvider provider) {
-    return const Column(children: [Row(children: [])]);
-  }
-
   void _showShotDetails(
       BuildContext context,
       AnalysisProvider provider,

lib/features/analysis/widgets/target_overlay.dart

@@ -1,9 +1,9 @@
 /// Overlay visuel de la cible.
 ///
-/// Dessine les anneaux de la cible, les impacts détectés, le cercle de groupement
+/// Dessine les anneaux de la cible, les impacts et le cercle de groupement.
-/// et les impacts de référence. Gère uniquement la SÉLECTION d'impacts existants
+/// Gère uniquement la SÉLECTION d'impacts existants (tap sur un impact).
-/// (tap sur un impact). L'AJOUT d'un impact est délégué à l'écran parent pour
+/// L'AJOUT d'un impact est délégué à l'écran parent pour éviter tout conflit
-/// éviter tout conflit de gestes avec le zoom/pan de l'InteractiveViewer.
+/// de gestes avec le zoom/pan de l'InteractiveViewer.
 library;
 
 import 'package:flutter/material.dart';
@@ -20,11 +20,9 @@ class TargetOverlay extends StatelessWidget {
   final int ringCount;
   final List<double>? ringRadii;
   final void Function(Shot shot)? onShotTapped;
-  final void Function(double x, double y)? onAddShot;
   final double? groupingCenterX;
   final double? groupingCenterY;
   final double? groupingDiameter;
-  final List<Shot>? referenceImpacts;
   final double zoomScale;
   final bool showRings;
 
@@ -38,11 +36,9 @@ class TargetOverlay extends StatelessWidget {
     this.ringCount = 10,
     this.ringRadii,
     this.onShotTapped,
-    this.onAddShot,
     this.groupingCenterX,
     this.groupingCenterY,
     this.groupingDiameter,
-    this.referenceImpacts,
     this.zoomScale = 1.0,
     this.showRings = false,
   });
@@ -72,7 +68,6 @@ class TargetOverlay extends StatelessWidget {
           groupingCenterX: groupingCenterX,
           groupingCenterY: groupingCenterY,
           groupingDiameter: groupingDiameter,
-          referenceImpacts: referenceImpacts,
           zoomScale: zoomScale,
           showRings: showRings,
         ),
@@ -127,7 +122,6 @@ class _TargetOverlayPainter extends CustomPainter {
   final double? groupingCenterX;
   final double? groupingCenterY;
   final double? groupingDiameter;
-  final List<Shot>? referenceImpacts;
   final double zoomScale;
   final bool showRings;
 
@@ -142,7 +136,6 @@ class _TargetOverlayPainter extends CustomPainter {
     this.groupingCenterX,
     this.groupingCenterY,
     this.groupingDiameter,
-    this.referenceImpacts,
     this.zoomScale = 1.0,
     this.showRings = false,
   });
@@ -163,13 +156,6 @@ class _TargetOverlayPainter extends CustomPainter {
     for (final shot in shots) {
       _drawImpact(canvas, size, shot);
     }
-
-    // Draw reference impacts (with different color)
-    if (referenceImpacts != null) {
-      for (final ref in referenceImpacts!) {
-        _drawReferenceImpact(canvas, size, ref);
-      }
-    }
   }
 
   void _drawTargetCenter(Canvas canvas, Size size) {
@@ -319,48 +305,6 @@ class _TargetOverlayPainter extends CustomPainter {
     );
   }
 
-  void _drawReferenceImpact(Canvas canvas, Size size, Shot ref) {
-    final x = ref.x * size.width;
-    final y = ref.y * size.height;
-
-    // Tailles fixes divisées par le zoom pour rester constantes à l'écran
-    final outerRadius = 12 / zoomScale;
-    final innerRadius = 10 / zoomScale;
-    final strokeWidth = 3 / zoomScale;
-    final fontSize = 12 / zoomScale;
-
-    // Draw outer circle (white outline for visibility)
-    final outlinePaint = Paint()
-      ..color = Colors.white
-      ..style = PaintingStyle.stroke
-      ..strokeWidth = strokeWidth;
-    canvas.drawCircle(Offset(x, y), outerRadius, outlinePaint);
-
-    // Draw reference marker (purple)
-    final refPaint = Paint()
-      ..color = Colors.deepPurple
-      ..style = PaintingStyle.fill;
-    canvas.drawCircle(Offset(x, y), innerRadius, refPaint);
-
-    // Draw "R" to indicate reference
-    final textPainter = TextPainter(
-      text: TextSpan(
-        text: 'R',
-        style: TextStyle(
-          color: Colors.white,
-          fontSize: fontSize,
-          fontWeight: FontWeight.bold,
-        ),
-      ),
-      textDirection: TextDirection.ltr,
-    );
-    textPainter.layout();
-    textPainter.paint(
-      canvas,
-      Offset(x - textPainter.width / 2, y - textPainter.height / 2),
-    );
-  }
-
   @override
   bool shouldRepaint(covariant _TargetOverlayPainter oldDelegate) {
     return shots != oldDelegate.shots ||
@@ -372,7 +316,6 @@ class _TargetOverlayPainter extends CustomPainter {
         groupingCenterX != oldDelegate.groupingCenterX ||
         groupingCenterY != oldDelegate.groupingCenterY ||
         groupingDiameter != oldDelegate.groupingDiameter ||
-        referenceImpacts != oldDelegate.referenceImpacts ||
         zoomScale != oldDelegate.zoomScale ||
         showRings != oldDelegate.showRings;
   }

lib/features/capture/capture_screen.dart

@@ -128,7 +128,6 @@ class _CaptureScreenState extends State<CaptureScreen>
 
   // Détection OpenCV (cible circulaire) — on garde le résultat COMPLET
   TargetDetectionResult? _targetResult; // NOUVEAU : centre + rayon de la cible
-  Timer? _detectionTimer;
   bool _isAnalyzingFrame = false;
 
   // NOUVEAU : Données IMU en temps réel
@@ -148,7 +147,6 @@ class _CaptureScreenState extends State<CaptureScreen>
   void dispose() {
     _cameraController?.dispose();
     _scanAnimationController.dispose();
-    _detectionTimer?.cancel();
     _parallelismSubscription?.cancel(); // NOUVEAU
     _parallelismService.dispose();      // NOUVEAU
     super.dispose();
@@ -300,9 +298,6 @@ class _CaptureScreenState extends State<CaptureScreen>
   // Détection OpenCV périodique (inchangée)
   // ─────────────────────────────────────────────────────────────────────────
   void _startAlignmentDetection() {
-    _detectionTimer?.cancel();
-    _detectionTimer = null;
-
     DateTime? lastAnalysis;
 
     _cameraController!.startImageStream((CameraImage cameraImage) async {
@@ -362,8 +357,6 @@ class _CaptureScreenState extends State<CaptureScreen>
   }
 
   void _stopAlignmentDetection() {
-    _detectionTimer?.cancel();
-    _detectionTimer = null;
     try {
       if (_cameraController != null &&
           _cameraController!.value.isStreamingImages) {

lib/main.dart

@@ -7,10 +7,8 @@ import 'package:sqflite_common_ffi/sqflite_ffi.dart';
 import 'app.dart';
 import 'core/theme/theme_provider.dart';
 import 'data/repositories/session_repository.dart';
-import 'services/target_detection_service.dart';
 import 'services/score_calculator_service.dart';
 import 'services/grouping_analyzer_service.dart';
-import 'services/image_processing_service.dart';
 import 'features/session/session_provider.dart';
 
 void main() async {
@@ -32,14 +30,6 @@ void main() async {
   runApp(
     MultiProvider(
       providers: [
-        Provider<ImageProcessingService>(
-          create: (_) => ImageProcessingService(),
-        ),
-        Provider<TargetDetectionService>(
-          create: (context) => TargetDetectionService(
-            imageProcessingService: context.read<ImageProcessingService>(),
-          ),
-        ),
         Provider<ScoreCalculatorService>(
           create: (_) => ScoreCalculatorService(),
         ),

lib/services/opencv_impact_detection_service.dart

@@ -1,228 +0,0 @@
-/// Service de détection d'impacts utilisant OpenCV.
-library;
-
-import 'dart:math' as math;
-import 'package:opencv_dart/opencv_dart.dart' as cv;
-
-/// Paramètres de détection d'impacts OpenCV
-class OpenCVDetectionSettings {
-  /// Seuil Canny bas pour la détection de contours
-  final double cannyThreshold1;
-
-  /// Seuil Canny haut pour la détection de contours
-  final double cannyThreshold2;
-
-  /// Distance minimale entre les centres des cercles détectés
-  final double minDist;
-
-  /// Paramètre 1 de HoughCircles (seuil Canny interne)
-  final double param1;
-
-  /// Paramètre 2 de HoughCircles (seuil d'accumulation)
-  final double param2;
-
-  /// Rayon minimum des cercles en pixels
-  final int minRadius;
-
-  /// Rayon maximum des cercles en pixels
-  final int maxRadius;
-
-  /// Taille du flou gaussien (doit être impair)
-  final int blurSize;
-
-  /// Utiliser la détection de contours en plus de Hough
-  final bool useContourDetection;
-
-  /// Circularité minimale pour la détection par contours (0-1)
-  final double minCircularity;
-
-  /// Surface minimale des contours
-  final double minContourArea;
-
-  /// Surface maximale des contours
-  final double maxContourArea;
-
-  const OpenCVDetectionSettings({
-    this.cannyThreshold1 = 50,
-    this.cannyThreshold2 = 150,
-    this.minDist = 20,
-    this.param1 = 100,
-    this.param2 = 30,
-    this.minRadius = 5,
-    this.maxRadius = 50,
-    this.blurSize = 5,
-    this.useContourDetection = true,
-    this.minCircularity = 0.6,
-    this.minContourArea = 50,
-    this.maxContourArea = 5000,
-  });
-}
-
-/// Résultat de détection d'impact
-class OpenCVDetectedImpact {
-  /// Position X normalisée (0-1)
-  final double x;
-
-  /// Position Y normalisée (0-1)
-  final double y;
-
-  /// Rayon en pixels
-  final double radius;
-
-  /// Score de confiance (0-1)
-  final double confidence;
-
-  /// Méthode de détection utilisée
-  final String method;
-
-  const OpenCVDetectedImpact({
-    required this.x,
-    required this.y,
-    required this.radius,
-    this.confidence = 1.0,
-    this.method = 'unknown',
-  });
-}
-
-/// Service de détection d'impacts utilisant OpenCV
-class OpenCVImpactDetectionService {
-  /// Détecte les impacts dans une image en utilisant OpenCV
-  List<OpenCVDetectedImpact> detectImpacts(
-    String imagePath, {
-    OpenCVDetectionSettings settings = const OpenCVDetectionSettings(),
-  }) {
-    try {
-      final img = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
-      if (img.isEmpty) return [];
-
-      final gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY);
-
-      // Apply blur to reduce noise
-      final blurKSize = (settings.blurSize, settings.blurSize);
-      final blurred = cv.gaussianBlur(gray, blurKSize, 2, sigmaY: 2);
-
-      final List<OpenCVDetectedImpact> detectedImpacts = [];
-
-      final circles = cv.HoughCircles(
-        blurred,
-        cv.HOUGH_GRADIENT,
-        1,
-        settings.minDist,
-        param1: settings.param1,
-        param2: settings.param2,
-        minRadius: settings.minRadius,
-        maxRadius: settings.maxRadius,
-      );
-
-      if (circles.rows > 0 && circles.cols > 0) {
-        // Mat shape: (1, N, 3) usually for HoughCircles (CV_32FC3)
-        // We use at<Vec3f> directly.
-
-        for (int i = 0; i < circles.cols; i++) {
-          final vec = circles.at<cv.Vec3f>(0, i);
-          final x = vec.val1;
-          final y = vec.val2;
-          final r = vec.val3;
-
-          detectedImpacts.add(
-            OpenCVDetectedImpact(
-              x: x / img.cols,
-              y: y / img.rows,
-              radius: r,
-              confidence: 0.8,
-              method: 'hough',
-            ),
-          );
-        }
-      }
-
-      // 2. Contour Detection (if enabled)
-      if (settings.useContourDetection) {
-        // Canny edge detection
-        final edges = cv.canny(
-          blurred,
-          settings.cannyThreshold1,
-          settings.cannyThreshold2,
-        );
-
-        // Find contours
-        final contoursResult = cv.findContours(
-          edges,
-          cv.RETR_EXTERNAL,
-          cv.CHAIN_APPROX_SIMPLE,
-        );
-
-        final contours = contoursResult.$1;
-        // hierarchy is $2
-
-        for (int i = 0; i < contours.length; i++) {
-          final contour = contours[i];
-
-          // Filter by area
-          final area = cv.contourArea(contour);
-          if (area < settings.minContourArea ||
-              area > settings.maxContourArea) {
-            continue;
-          }
-
-          // Filter by circularity
-          final perimeter = cv.arcLength(contour, true);
-          if (perimeter == 0) continue;
-          final circularity = 4 * math.pi * area / (perimeter * perimeter);
-
-          if (circularity < settings.minCircularity) continue;
-
-          // Get bounding circle
-          final enclosingCircle = cv.minEnclosingCircle(contour);
-          final center = enclosingCircle.$1;
-          final radius = enclosingCircle.$2;
-
-          // Avoid duplicates (simple distance check against Hough results)
-          bool isDuplicate = false;
-          for (final existing in detectedImpacts) {
-            final dx = existing.x * img.cols - center.x;
-            final dy = existing.y * img.rows - center.y;
-            final dist = math.sqrt(dx * dx + dy * dy);
-            if (dist < radius) {
-              isDuplicate = true;
-              break;
-            }
-          }
-
-          if (!isDuplicate) {
-            detectedImpacts.add(
-              OpenCVDetectedImpact(
-                x: center.x / img.cols,
-                y: center.y / img.rows,
-                radius: radius,
-                confidence: circularity, // Use circularity as confidence
-                method: 'contour',
-              ),
-            );
-          }
-        }
-      }
-
-      return detectedImpacts;
-    } catch (e) {
-      // print('OpenCV Error: $e');
-      return [];
-    }
-  }
-
-  /// Détecte les impacts en utilisant une image de référence
-  List<OpenCVDetectedImpact> detectFromReferences(
-    String imagePath,
-    List<({double x, double y})> referencePoints, {
-    double tolerance = 2.0,
-  }) {
-    // Basic implementation: use average color/brightness of reference points
-    // This is a placeholder for a more complex template matching or feature matching
-
-    // For now, we can just run the standard detection but filter results
-    // based on properties of the reference points (e.g. size/radius if we had it).
-
-    // Returning standard detection for now to enable the feature.
-    return detectImpacts(imagePath);
-  }
-}

lib/services/target_detection_service.dart

@@ -1,461 +0,0 @@
-import 'dart:math' as math;
-import 'package:flutter/foundation.dart';
-import '../data/models/target_type.dart';
-import 'image_processing_service.dart';
-import 'opencv_impact_detection_service.dart';
-export 'image_processing_service.dart'
-    show ImpactDetectionSettings, ReferenceImpact, ImpactCharacteristics;
-export 'opencv_impact_detection_service.dart'
-    show OpenCVDetectionSettings, OpenCVDetectedImpact;
-
-// ============================================================================
-// STRUCT ET FONCTION GLOBALE POUR LE PARALLÉLISME (THREAD SECONDAIRE)
-// ============================================================================
-
-/// Conteneur de données pour envoyer les paramètres à l'Isolate d'arrière-plan.
-class DetectionPayload {
-  final String imagePath;
-  final TargetType targetType;
-
-  // On recrée les instances à l'intérieur du thread isolé car les objets ne se partagent pas entre threads.
-  DetectionPayload({
-    required this.imagePath,
-    required this.targetType,
-  });
-}
-
-/// FONCTION EXÉCUTÉE EN PARALLÈLE : Tourne sur un autre cœur du processeur.
-/// L'interface graphique reste à 120 FPS et totalement fluide.
-TargetDetectionResult runParallelTargetDetection(DetectionPayload payload) {
-  // 1. Initialisation locale des services dans le sous-thread
-  final imageProcessingService = ImageProcessingService();
-
-  try {
-    // 2. Détection de la cible principale (Calcul lourd)
-    final mainTarget = imageProcessingService.detectMainTarget(payload.imagePath);
-
-    double centerX = 0.5;
-    double centerY = 0.5;
-    double radius = 0.4;
-
-    if (mainTarget != null) {
-      centerX = mainTarget.centerX;
-      centerY = mainTarget.centerY;
-      radius = mainTarget.radius;
-    }
-
-    // 3. Détection des impacts (Calcul lourd)
-    final impacts = imageProcessingService.detectImpacts(payload.imagePath);
-
-    // 4. Calcul mathématique des scores relatifs
-    final detectedImpacts = impacts.map((impact) {
-      final score = payload.targetType == TargetType.concentric
-          ? _staticCalculateConcentricScore(
-        impact.x,
-        impact.y,
-        centerX,
-        centerY,
-        radius,
-      )
-          : _staticCalculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
-
-      return DetectedImpactResult(
-        x: impact.x,
-        y: impact.y,
-        radius: impact.radius,
-        suggestedScore: score,
-      );
-    }).toList();
-
-    return TargetDetectionResult(
-      centerX: centerX,
-      centerY: centerY,
-      radius: radius,
-      impacts: detectedImpacts,
-    );
-  } catch (e) {
-    return TargetDetectionResult.error('Erreur de detection parallèle: $e');
-  }
-}
-
-// Fonctions mathématiques pures nécessaires à l'Isolate (statiques)
-int _staticCalculateConcentricScore(double impactX, double impactY, double centerX, double centerY, double targetRadius) {
-  final dx = impactX - centerX;
-  final dy = impactY - centerY;
-  final distance = math.sqrt(dx * dx + dy * dy) / targetRadius;
-  if (distance <= 0.1) return 10;
-  if (distance <= 0.2) return 9;
-  if (distance <= 0.3) return 8;
-  if (distance <= 0.4) return 7;
-  if (distance <= 0.5) return 6;
-  if (distance <= 0.6) return 5;
-  if (distance <= 0.7) return 4;
-  if (distance <= 0.8) return 3;
-  if (distance <= 0.9) return 2;
-  if (distance <= 1.0) return 1;
-  return 0;
-}
-
-int _staticCalculateSilhouetteScore(double impactX, double impactY, double centerX, double centerY) {
-  final dx = (impactX - centerX).abs();
-  final dy = impactY - centerY;
-  if (dx > 0.15) return 0;
-  if (dy < -0.25) return 5;
-  if (dy < 0.0) return 5;
-  if (dy < 0.15) return 4;
-  if (dy < 0.35) return 3;
-  return 0;
-}
-
-// ============================================================================
-// FIN DU BLOC DE PARALLÉLISME
-// ============================================================================
-
-
-class TargetDetectionResult {
-  final double centerX; // Relative (0-1)
-  final double centerY; // Relative (0-1)
-  final double radius; // Relative (0-1)
-  final List<DetectedImpactResult> impacts;
-  final bool success;
-  final String? errorMessage;
-
-  TargetDetectionResult({
-    required this.centerX,
-    required this.centerY,
-    required this.radius,
-    required this.impacts,
-    this.success = true,
-    this.errorMessage,
-  });
-
-  factory TargetDetectionResult.error(String message) {
-    return TargetDetectionResult(
-      centerX: 0.5,
-      centerY: 0.5,
-      radius: 0.4,
-      impacts: [],
-      success: false,
-      errorMessage: message,
-    );
-  }
-}
-
-class DetectedImpactResult {
-  final double x; // Relative (0-1)
-  final double y; // Relative (0-1)
-  final double radius; // Absolute pixels
-  final int suggestedScore;
-
-  DetectedImpactResult({
-    required this.x,
-    required this.y,
-    required this.radius,
-    required this.suggestedScore,
-  });
-}
-
-class TargetDetectionService {
-  final ImageProcessingService _imageProcessingService;
-  final OpenCVImpactDetectionService _opencvService;
-
-  TargetDetectionService({
-    ImageProcessingService? imageProcessingService,
-    OpenCVImpactDetectionService? opencvService,
-  }) : _imageProcessingService =
-      imageProcessingService ?? ImageProcessingService(),
-        _opencvService = opencvService ?? OpenCVImpactDetectionService();
-
-  /// Detect target and impacts from an image file ASYNCHRONOUSLY in a separate Thread.
-  /// CORRECTION : Utilise désormais 'compute' pour basculer en arrière-plan immédiat.
-  Future<TargetDetectionResult> detectTargetAsync(String imagePath, TargetType targetType) async {
-    final payload = DetectionPayload(imagePath: imagePath, targetType: targetType);
-    // Déclenche l'exécution isolée en tâche de fond
-    return await compute(runParallelTargetDetection, payload);
-  }
-
-  /// Gardée pour rétrocompatibilité synchrone si nécessaire
-  TargetDetectionResult detectTarget(String imagePath, TargetType targetType) {
-    try {
-      final mainTarget = _imageProcessingService.detectMainTarget(imagePath);
-
-      double centerX = 0.5;
-      double centerY = 0.5;
-      double radius = 0.4;
-
-      if (mainTarget != null) {
-        centerX = mainTarget.centerX;
-        centerY = mainTarget.centerY;
-        radius = mainTarget.radius;
-      }
-
-      final impacts = _imageProcessingService.detectImpacts(imagePath);
-
-      final detectedImpacts = impacts.map((impact) {
-        final score = targetType == TargetType.concentric
-            ? _calculateConcentricScore(
-          impact.x,
-          impact.y,
-          centerX,
-          centerY,
-          radius,
-        )
-            : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
-
-        return DetectedImpactResult(
-          x: impact.x,
-          y: impact.y,
-          radius: impact.radius,
-          suggestedScore: score,
-        );
-      }).toList();
-
-      return TargetDetectionResult(
-        centerX: centerX,
-        centerY: centerY,
-        radius: radius,
-        impacts: detectedImpacts,
-      );
-    } catch (e) {
-      return TargetDetectionResult.error('Erreur de detection: $e');
-    }
-  }
-
-  int _calculateConcentricScore(
-      double impactX,
-      double impactY,
-      double centerX,
-      double centerY,
-      double targetRadius,
-      ) {
-    final dx = impactX - centerX;
-    final dy = impactY - centerY;
-    final distance = math.sqrt(dx * dx + dy * dy) / targetRadius;
-
-    if (distance <= 0.1) return 10;
-    if (distance <= 0.2) return 9;
-    if (distance <= 0.3) return 8;
-    if (distance <= 0.4) return 7;
-    if (distance <= 0.5) return 6;
-    if (distance <= 0.6) return 5;
-    if (distance <= 0.7) return 4;
-    if (distance <= 0.8) return 3;
-    if (distance <= 0.9) return 2;
-    if (distance <= 1.0) return 1;
-    return 0;
-  }
-
-  int _calculateSilhouetteScore(
-      double impactX,
-      double impactY,
-      double centerX,
-      double centerY,
-      ) {
-    final dx = (impactX - centerX).abs();
-    final dy = impactY - centerY;
-
-    if (dx > 0.15) return 0;
-    if (dy < -0.25) return 5;
-    if (dy < 0.0) return 5;
-    if (dy < 0.15) return 4;
-    if (dy < 0.35) return 3;
-
-    return 0;
-  }
-
-  List<DetectedImpactResult> detectImpactsOnly(
-      String imagePath,
-      TargetType targetType,
-      double centerX,
-      double centerY,
-      double radius,
-      int ringCount,
-      ImpactDetectionSettings settings,
-      ) {
-    try {
-      final impacts = _imageProcessingService.detectImpactsWithSettings(
-        imagePath,
-        settings,
-      );
-
-      return impacts.map((impact) {
-        final score = targetType == TargetType.concentric
-            ? _calculateConcentricScoreWithRings(
-          impact.x,
-          impact.y,
-          centerX,
-          centerY,
-          radius,
-          ringCount,
-        )
-            : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
-
-        return DetectedImpactResult(
-          x: impact.x,
-          y: impact.y,
-          radius: impact.radius,
-          suggestedScore: score,
-        );
-      }).toList();
-    } catch (e) {
-      return [];
-    }
-  }
-
-  int _calculateConcentricScoreWithRings(
-      double impactX,
-      double impactY,
-      double centerX,
-      double centerY,
-      double targetRadius,
-      int ringCount,
-      ) {
-    final dx = impactX - centerX;
-    final dy = impactY - centerY;
-    final distance = math.sqrt(dx * dx + dy * dy) / targetRadius;
-
-    for (int i = 0; i < ringCount; i++) {
-      final zoneRadius = (i + 1) / ringCount;
-      if (distance <= zoneRadius) {
-        return 10 - i;
-      }
-    }
-
-    return 0;
-  }
-
-  ImpactCharacteristics? analyzeReferenceImpacts(
-      String imagePath,
-      List<ReferenceImpact> references,
-      ) {
-    return _imageProcessingService.analyzeReferenceImpacts(
-      imagePath,
-      references,
-    );
-  }
-
-  List<DetectedImpactResult> detectImpactsFromReferences(
-      String imagePath,
-      TargetType targetType,
-      double centerX,
-      double centerY,
-      double radius,
-      int ringCount,
-      ImpactCharacteristics characteristics, {
-        double tolerance = 2.0,
-      }) {
-    try {
-      final impacts = _imageProcessingService.detectImpactsFromReferences(
-        imagePath,
-        characteristics,
-        tolerance: tolerance,
-      );
-
-      return impacts.map((impact) {
-        final score = targetType == TargetType.concentric
-            ? _calculateConcentricScoreWithRings(
-          impact.x,
-          impact.y,
-          centerX,
-          centerY,
-          radius,
-          ringCount,
-        )
-            : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
-
-        return DetectedImpactResult(
-          x: impact.x,
-          y: impact.y,
-          radius: impact.radius,
-          suggestedScore: score,
-        );
-      }).toList();
-    } catch (e) {
-      return [];
-    }
-  }
-
-  List<DetectedImpactResult> detectImpactsWithOpenCV(
-      String imagePath,
-      TargetType targetType,
-      double centerX,
-      double centerY,
-      double radius,
-      int ringCount, {
-        OpenCVDetectionSettings? settings,
-      }) {
-    try {
-      final impacts = _opencvService.detectImpacts(
-        imagePath,
-        settings: settings ?? const OpenCVDetectionSettings(),
-      );
-
-      return impacts.map((impact) {
-        final score = targetType == TargetType.concentric
-            ? _calculateConcentricScoreWithRings(
-          impact.x,
-          impact.y,
-          centerX,
-          centerY,
-          radius,
-          ringCount,
-        )
-            : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
-
-        return DetectedImpactResult(
-          x: impact.x,
-          y: impact.y,
-          radius: impact.radius,
-          suggestedScore: score,
-        );
-      }).toList();
-    } catch (e) {
-      debugPrint('Erreur détection OpenCV: $e');
-      return [];
-    }
-  }
-
-  List<DetectedImpactResult> detectImpactsWithOpenCVFromReferences(
-      String imagePath,
-      TargetType targetType,
-      double centerX,
-      double centerY,
-      double radius,
-      int ringCount,
-      List<ReferenceImpact> references, {
-        double tolerance = 2.0,
-      }) {
-    try {
-      final refPoints = references.map((r) => (x: r.x, y: r.y)).toList();
-
-      final impacts = _opencvService.detectFromReferences(
-        imagePath,
-        refPoints,
-        tolerance: tolerance,
-      );
-
-      return impacts.map((impact) {
-        final score = targetType == TargetType.concentric
-            ? _calculateConcentricScoreWithRings(
-          impact.x,
-          impact.y,
-          centerX,
-          centerY,
-          radius,
-          ringCount,
-        )
-            : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
-
-        return DetectedImpactResult(
-          x: impact.x,
-          y: impact.y,
-          radius: impact.radius,
-          suggestedScore: score,
-        );
-      }).toList();
-    } catch (e) {
-      debugPrint('Erreur détection OpenCV depuis références: $e');
-      return [];
-    }
-  }
-}

lib/services/target_rectify_service.dart

@@ -1,259 +0,0 @@
-import 'dart:async';
-import 'dart:math' as math;
-import 'package:flutter/foundation.dart';
-import 'package:opencv_dart/opencv_dart.dart' as cv;
-
-/// Résultat d'une tentative de redressement de cible.
-class RectifyResult {
-  /// Chemin du fichier image redressé (ou original si échec).
-  final String outputPath;
-
-  /// true si une cible a été détectée et redressée, false si on a renvoyé
-  /// l'image d'origine sans transformation.
-  final bool rectified;
-
-  /// Angle d'inclinaison estimé de la cible AVANT redressement, en degrés.
-  /// (0 = déjà de face). Utile pour informer l'utilisateur.
-  final double estimatedTiltDegrees;
-
-  /// Message de diagnostic (utile pour debug / affichage).
-  final String message;
-
-  const RectifyResult({
-    required this.outputPath,
-    required this.rectified,
-    required this.estimatedTiltDegrees,
-    required this.message,
-  });
-}
-
-/// Service qui redresse une cible RONDE (cercles concentriques) photographiée
-/// de biais, en la ramenant parfaitement de face.
-///
-/// Principe :
-///   1. Détecter le plus grand contour ~circulaire de l'image.
-///   2. Ajuster une ELLIPSE sur ce contour (fitEllipse).
-///   3. Un cercle vu en perspective devient une ellipse : on calcule la
-///      transformation de perspective qui remappe cette ellipse vers un
-///      CERCLE parfait, et on l'applique à toute l'image.
-///   4. La cible apparaît alors de face.
-///
-/// L'image résultat est carrée et centrée sur la cible.
-class TargetRectifyService {
-  /// Taille (en pixels) du côté de l'image carrée de sortie.
-  final int outputSize;
-
-  /// Marge autour de la cible dans l'image de sortie (1.0 = cible pile au bord,
-  /// 1.3 = 30 % de marge autour). Garde un peu de contexte.
-  final double marginFactor;
-
-  /// En dessous de cet écart d'axes (ratio petit/grand axe proche de 1),
-  /// la cible est considérée déjà de face → pas de warp inutile.
-  final double minTiltRatioToRectify;
-
-  TargetRectifyService({
-    this.outputSize = 1024,
-    this.marginFactor = 1.25,
-    this.minTiltRatioToRectify = 0.985,
-  });
-
-  /// Redresse l'image située à [inputPath]. Écrit le résultat dans
-  /// [outputPath] et renvoie un [RectifyResult].
-  ///
-  /// Ne bloque jamais : en cas d'échec de détection, renvoie l'image
-  /// d'origine (rectified = false) pour ne pas perdre la photo du tireur.
-  Future<RectifyResult> rectify({
-    required String inputPath,
-    required String outputPath,
-  }) async {
-    cv.Mat? src;
-    cv.Mat? gray;
-    cv.Mat? blurred;
-    cv.Mat? edges;
-    try {
-      src = cv.imread(inputPath, flags: cv.IMREAD_COLOR);
-      if (src.isEmpty) {
-        return RectifyResult(
-          outputPath: inputPath,
-          rectified: false,
-          estimatedTiltDegrees: 0,
-          message: 'Image illisible',
-        );
-      }
-
-      // ── 1. Prétraitement ────────────────────────────────────────────────
-      gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY);
-      blurred = cv.gaussianBlur(gray, (5, 5), 2, sigmaY: 2);
-      edges = cv.canny(blurred, 60, 160);
-
-      // Dilatation légère pour fermer les contours brisés
-      final cv.Mat kernel = cv.getStructuringElement(
-        cv.MORPH_ELLIPSE,
-        (3, 3),
-      );
-      final cv.Mat dilated = cv.dilate(edges, kernel);
-
-      // ── 2. Recherche du meilleur contour elliptique ──────────────────────
-      final (contours, _) = cv.findContours(
-        dilated,
-        cv.RETR_EXTERNAL,
-        cv.CHAIN_APPROX_SIMPLE,
-      );
-
-      final double imgArea = (src.width * src.height).toDouble();
-      cv.RotatedRect? bestEllipse;
-      double bestScore = 0;
-
-      for (int i = 0; i < contours.length; i++) {
-        final c = contours[i];
-        if (c.length < 5) continue; // fitEllipse exige >= 5 points
-
-        final double area = cv.contourArea(c);
-        // On ignore les contours minuscules et ceux qui couvrent presque tout
-        if (area < imgArea * 0.03 || area > imgArea * 0.97) continue;
-
-        final cv.RotatedRect e = cv.fitEllipse(c);
-        final ep = e.points; // 4 sommets
-        if (ep.length < 4) continue;
-        final double ecx = (ep[0].x + ep[1].x + ep[2].x + ep[3].x) / 4.0;
-        final double ecy = (ep[0].y + ep[1].y + ep[2].y + ep[3].y) / 4.0;
-        final double mAx = (ep[0].x + ep[1].x) / 2.0;
-        final double mAy = (ep[0].y + ep[1].y) / 2.0;
-        final double mBx = (ep[1].x + ep[2].x) / 2.0;
-        final double mBy = (ep[1].y + ep[2].y) / 2.0;
-        final double w =
-            2 * math.sqrt(math.pow(mAx - ecx, 2) + math.pow(mAy - ecy, 2));
-        final double h =
-            2 * math.sqrt(math.pow(mBx - ecx, 2) + math.pow(mBy - ecy, 2));
-        if (w <= 1 || h <= 1) continue;
-
-        // À quel point le contour ressemble-t-il vraiment à son ellipse ?
-        // On compare l'aire du contour à l'aire de l'ellipse ajustée.
-        final double ellipseArea = math.pi * (w / 2) * (h / 2);
-        if (ellipseArea <= 0) continue;
-        final double fitRatio = area / ellipseArea; // ~1 si bon ajustement
-        if (fitRatio < 0.7 || fitRatio > 1.3) continue;
-
-        // Score = taille de l'ellipse (on veut la cible la plus grande)
-        final double score = ellipseArea;
-        if (score > bestScore) {
-          bestScore = score;
-          bestEllipse = e;
-        }
-      }
-
-      if (bestEllipse == null) {
-        return RectifyResult(
-          outputPath: inputPath,
-          rectified: false,
-          estimatedTiltDegrees: 0,
-          message: 'Aucune cible circulaire détectée',
-        );
-      }
-
-      // ── 3. Extraire les 4 sommets de l'ellipse (robuste à la version d'API) ─
-      // RotatedRect.points renvoie les 4 coins de la boîte englobant l'ellipse.
-      // On en dérive nous-mêmes le centre, les demi-axes et l'orientation, ce
-      // qui évite de dépendre de la forme exacte de `.size` / `.center`
-      // (record vs objet) qui varie selon les versions d'opencv_dart.
-      final pts = bestEllipse.points; // List<Point2f> de 4 sommets
-
-      double px(int i) => pts[i].x;
-      double py(int i) => pts[i].y;
-
-      // Centre = moyenne des 4 sommets
-      final double cx = (px(0) + px(1) + px(2) + px(3)) / 4.0;
-      final double cy = (py(0) + py(1) + py(2) + py(3)) / 4.0;
-
-      // Milieux de deux côtés adjacents → extrémités des deux demi-axes
-      // Côté 0-1 et côté 1-2 (ordre des sommets d'un RotatedRect)
-      final double m01x = (px(0) + px(1)) / 2.0;
-      final double m01y = (py(0) + py(1)) / 2.0;
-      final double m12x = (px(1) + px(2)) / 2.0;
-      final double m12y = (py(1) + py(2)) / 2.0;
-
-      // Demi-axes = distance centre → milieu de chaque côté
-      final double axisA =
-          math.sqrt(math.pow(m01x - cx, 2) + math.pow(m01y - cy, 2));
-      final double axisB =
-          math.sqrt(math.pow(m12x - cx, 2) + math.pow(m12y - cy, 2));
-
-      final double majorAxis = math.max(axisA, axisB);
-      final double minorAxis = math.min(axisA, axisB);
-      if (majorAxis <= 1) {
-        return RectifyResult(
-          outputPath: inputPath,
-          rectified: false,
-          estimatedTiltDegrees: 0,
-          message: 'Ellipse dégénérée',
-        );
-      }
-
-      final double axisRatio = minorAxis / majorAxis; // 1 = cercle parfait
-      final double tiltDeg =
-          math.acos(axisRatio.clamp(0.0, 1.0)) * (180.0 / math.pi);
-
-      // Déjà quasiment de face → on ne touche pas (évite le flou inutile)
-      if (axisRatio >= minTiltRatioToRectify) {
-        cv.imwrite(outputPath, src);
-        return RectifyResult(
-          outputPath: outputPath,
-          rectified: false,
-          estimatedTiltDegrees: tiltDeg,
-          message: 'Cible déjà de face',
-        );
-      }
-
-      // ── 4. Construire la transformation de perspective ────────────────────
-      // Source : extrémités des deux axes de l'ellipse (4 points).
-      // Destination : extrémités des axes d'un cercle parfait centré.
-      // On obtient les extrémités en prolongeant centre→milieu-de-côté.
-      final srcPts = cv.VecPoint.fromList([
-        cv.Point((cx + (m01x - cx)).round(), (cy + (m01y - cy)).round()),
-        cv.Point((cx - (m01x - cx)).round(), (cy - (m01y - cy)).round()),
-        cv.Point((cx + (m12x - cx)).round(), (cy + (m12y - cy)).round()),
-        cv.Point((cx - (m12x - cx)).round(), (cy - (m12y - cy)).round()),
-      ]);
-
-      // Cible : cercle parfait centré, rayon R, dans une image carrée.
-      // L'axe "A" (m01) devient l'axe horizontal, l'axe "B" (m12) le vertical.
-      final double out = outputSize.toDouble();
-      final double center = out / 2;
-      final double radius = (out / 2) / marginFactor;
-      final dstPts = cv.VecPoint.fromList([
-        cv.Point((center + radius).round(), center.round()),
-        cv.Point((center - radius).round(), center.round()),
-        cv.Point(center.round(), (center + radius).round()),
-        cv.Point(center.round(), (center - radius).round()),
-      ]);
-
-      final cv.Mat transform = cv.getPerspectiveTransform(srcPts, dstPts);
-
-      final cv.Mat warped = cv.warpPerspective(
-        src,
-        transform,
-        (outputSize, outputSize),
-        flags: cv.INTER_LINEAR,
-        borderMode: cv.BORDER_CONSTANT,
-      );
-
-      cv.imwrite(outputPath, warped);
-
-      return RectifyResult(
-        outputPath: outputPath,
-        rectified: true,
-        estimatedTiltDegrees: tiltDeg,
-        message: 'Cible redressée (inclinaison ${tiltDeg.toStringAsFixed(1)}°)',
-      );
-    } catch (e) {
-      debugPrint('TargetRectify erreur: $e');
-      // En cas de pépin, on renvoie l'original pour ne jamais perdre la photo
-      return RectifyResult(
-        outputPath: inputPath,
-        rectified: false,
-        estimatedTiltDegrees: 0,
-        message: 'Erreur de traitement: $e',
-      );
-    }
-  }
-}

pubspec.lock

@@ -293,30 +293,6 @@ packages:
       url: "https://pub.dev"
     source: hosted
     version: "2.1.3"
-  google_mlkit_commons:
-    dependency: transitive
-    description:
-      name: google_mlkit_commons
-      sha256: "3e69fea4211727732cc385104e675ad1e40b29f12edd492ee52fa108423a6124"
-      url: "https://pub.dev"
-    source: hosted
-    version: "0.11.1"
-  google_mlkit_document_scanner:
-    dependency: "direct main"
-    description:
-      name: google_mlkit_document_scanner
-      sha256: "67428ddb853880c8185049a5834cd328e6420921a74786f6aadee0b76f8536bd"
-      url: "https://pub.dev"
-    source: hosted
-    version: "0.2.1"
-  google_mlkit_object_detection:
-    dependency: "direct main"
-    description:
-      name: google_mlkit_object_detection
-      sha256: "9dd35886972e18747e22098f8ebee78d30716a99a789bb2e3a65a24229e031e7"
-      url: "https://pub.dev"
-    source: hosted
-    version: "0.15.1"
   hooks:
     dependency: transitive
     description:

pubspec.yaml

@@ -38,11 +38,9 @@ dependencies:
   cupertino_icons: ^1.0.8
   sensors_plus: ^4.0.2
   opencv_dart: ^2.1.0
-  google_mlkit_object_detection: ^0.15.0
 
   # Image capture from camera/gallery
   image_picker: ^1.2.1
-  google_mlkit_document_scanner: ^0.2.0
 
   # Local database for history
   sqflite: ^2.3.2
@@ -73,9 +71,6 @@ dependencies:
   crypto: ^3.0.7
   camera: ^0.12.0+1
 
-  # Machine Learning for YOLOv8
-  # tflite_flutter: ^0.11.0
-
 dev_dependencies:
   flutter_test:
     sdk: flutter
@@ -100,7 +95,6 @@ flutter:
 
   # To add assets to your application, add an assets section, like this:
   # assets:
-  #   - assets/models/yolov8n_32.tflite
   #   - images/a_dot_burr.jpeg
   #   - images/a_dot_ham.jpeg
 

tests/find_homography_test.dart

@@ -1,12 +0,0 @@
-import 'package:opencv_dart/opencv_dart.dart' as cv;
-
-void main() {
-  var p1 = cv.VecPoint.fromList([cv.Point(0, 0), cv.Point(1, 1)]);
-  var p2 = cv.VecPoint2f.fromList([cv.Point2f(0, 0), cv.Point2f(1, 1)]);
-
-  // Is it p1.mat ?
-  // Or is it cv.findHomography(p1, p1) but actually needs specific types ?
-  cv.Mat mat1 = cv.Mat.fromVec(p1);
-  cv.Mat mat2 = cv.Mat.fromVec(p2);
-  cv.findHomography(mat1, mat2);
-}

tests/opencv_quad_test.dart

@@ -1,7 +0,0 @@
-import 'package:opencv_dart/opencv_dart.dart' as cv;
-
-void main() {
-  print(cv.approxPolyDP);
-  print(cv.arcLength);
-  print(cv.contourArea);
-}

tests/test_homography.dart

@@ -1,5 +0,0 @@
-import 'package:opencv_dart/opencv_dart.dart' as cv;
-
-void main() {
-  print(cv.findHomography);
-}