feat: implement base architecture and core repositories for weapon tracking and target analysis functionality

2026-05-08 20:29:18 +02:00
parent 5dd58da51c
commit 774dbfcf40
37 changed files with 3687 additions and 2713 deletions
--- a/lib/services/ai_export_service.dart
+++ b/lib/services/ai_export_service.dart
@@ -1,4 +1,5 @@
 import 'dart:convert';
+import 'package:flutter/foundation.dart';
 import 'dart:io';
 import 'package:http/http.dart' as http;
 import 'package:device_info_plus/device_info_plus.dart';
@@ -37,7 +38,7 @@ class AiExportService {
        deviceData['os'] = 'Windows ${windowsInfo.majorVersion}.${windowsInfo.minorVersion}';
      }
    } catch (e) {
-      print('Erreur lors de la récupération des infos appareil: $e');
+      debugPrint('Erreur lors de la récupération des infos appareil: $e');
    }

    return deviceData;
@@ -131,15 +132,15 @@ class AiExportService {

      if (response.statusCode == 200) {
        final responseData = await response.stream.bytesToString();
-        print('Export réussi: $responseData');
+        debugPrint('Export réussi: $responseData');
        return true;
      } else {
        final errorData = await response.stream.bytesToString();
-        print('Erreur d\'export: ${response.statusCode} - $errorData');
+        debugPrint('Erreur d\'export: ${response.statusCode} - $errorData');
        return false;
      }
    } catch (e) {
-      print('Exception lors de l\'export: $e');
+      debugPrint('Exception lors de l\'export: $e');
      return false;
    }
  }
--- a/lib/services/distortion_correction_service.dart
+++ b/lib/services/distortion_correction_service.dart
@@ -8,6 +8,7 @@ library;
 import 'dart:io';
 import 'dart:math' as math;
 import 'package:image/image.dart' as img;
+import 'package:flutter/foundation.dart';
 import 'package:opencv_dart/opencv_dart.dart' as cv;
 import 'package:path_provider/path_provider.dart';

@@ -562,8 +563,9 @@ class DistortionCorrectionService {
      double maxArea = 0;

      for (final contour in contours) {
-        if (contour.length < 5)
+        if (contour.length < 5) {
          continue; // fitEllipse nécessite au moins 5 points
+        }

        final area = cv.contourArea(contour);
        if (area < 1000) continue; // Ignorer les trop petits bruits
@@ -636,7 +638,7 @@ class DistortionCorrectionService {
      return outputPath;
    } catch (e) {
      // En cas d'erreur, retourner l'image originale
-      print('Erreur correction perspective cercles: $e');
+      debugPrint('Erreur correction perspective cercles: $e');
      return imagePath;
    }
  }
@@ -761,7 +763,7 @@ class DistortionCorrectionService {

      return outputPath;
    } catch (e) {
-      print('Erreur correction perspective ovales: $e');
+      debugPrint('Erreur correction perspective ovales: $e');
      return imagePath;
    }
  }
@@ -822,7 +824,7 @@ class DistortionCorrectionService {
      }

      if (concentricGroup.length < 2) {
-        print(
+        debugPrint(
          "Pas assez de cercles concentriques pour le maillage, utilisation de la méthode simple.",
        );
        return await correctPerspectiveUsingOvals(imagePath);
@@ -950,7 +952,7 @@ class DistortionCorrectionService {

      return outputPath;
    } catch (e) {
-      print('Erreur correction perspective maillage concentrique: $e');
+      debugPrint('Erreur correction perspective maillage concentrique: $e');
      return imagePath;
    }
  }
@@ -1012,7 +1014,7 @@ class DistortionCorrectionService {

      // Fallback
      if (bestQuad == null) {
-        print(
+        debugPrint(
          "Aucun papier quadrilatère détecté, on utilise les cercles à la place.",
        );
        return await correctPerspectiveUsingCircles(imagePath);
@@ -1060,7 +1062,7 @@ class DistortionCorrectionService {

      return outputPath;
    } catch (e) {
-      print('Erreur correction perspective quadrilatère: $e');
+      debugPrint('Erreur correction perspective quadrilatère: $e');
      // Fallback
      return await correctPerspectiveUsingCircles(imagePath);
    }
--- a/lib/services/image_processing_service.dart
+++ b/lib/services/image_processing_service.dart
@@ -1,4 +1,5 @@
 import 'dart:io';
+import 'package:flutter/foundation.dart';
 import 'dart:math' as math;
 import 'package:image/image.dart' as img;

@@ -189,7 +190,7 @@ class ImageProcessingService {
        );
      }).toList();
    } catch (e) {
-      print('Error detecting impacts: $e');
+      debugPrint('Error detecting impacts: $e');
      return [];
    }
  }
@@ -236,14 +237,14 @@ class ImageProcessingService {
      final fillRatios = <double>[];
      final thresholds = <double>[];

-      print('Analyzing ${references.length} reference impacts...');
+      debugPrint('Analyzing ${references.length} reference impacts...');

      for (int refIndex = 0; refIndex < references.length; refIndex++) {
        final ref = references[refIndex];
        final centerX = (ref.x * width).round().clamp(0, width - 1);
        final centerY = (ref.y * height).round().clamp(0, height - 1);

-        print('Reference $refIndex at ($centerX, $centerY)');
+        debugPrint('Reference $refIndex at ($centerX, $centerY)');

        // AMÉLIORATION : Recherche du point le plus sombre dans une zone plus large
        int darkestX = centerX;
@@ -275,7 +276,7 @@ class ImageProcessingService {
          if (darkestLum < 50 && r > 5) break;
        }

-        print('  Darkest point at ($darkestX, $darkestY), lum=$darkestLum');
+        debugPrint('  Darkest point at ($darkestX, $darkestY), lum=$darkestLum');

        // Now find the blob at the darkest point using adaptive threshold
        final blobResult = _findBlobAtPoint(blurred, darkestX, darkestY, width, height);
@@ -286,15 +287,15 @@ class ImageProcessingService {
          circularities.add(blobResult.circularity);
          fillRatios.add(blobResult.fillRatio);
          thresholds.add(blobResult.threshold);
-          print('  Found blob: size=${blobResult.size}, circ=${blobResult.circularity.toStringAsFixed(2)}, '
+          debugPrint('  Found blob: size=${blobResult.size}, circ=${blobResult.circularity.toStringAsFixed(2)}, '
                'fill=${blobResult.fillRatio.toStringAsFixed(2)}, threshold=${blobResult.threshold.toStringAsFixed(0)}');
        } else {
-          print('  No valid blob found at this reference');
+          debugPrint('  No valid blob found at this reference');
        }
      }

      if (luminances.isEmpty) {
-        print('ERROR: No valid blobs found from any reference!');
+        debugPrint('ERROR: No valid blobs found from any reference!');
        return null;
      }

@@ -329,11 +330,11 @@ class ImageProcessingService {
        avgDarkThreshold: avgThreshold,
      );

-      print('Learned characteristics: $result');
+      debugPrint('Learned characteristics: $result');

      return result;
    } catch (e) {
-      print('Error analyzing reference impacts: $e');
+      debugPrint('Error analyzing reference impacts: $e');
      return null;
    }
  }
@@ -680,7 +681,7 @@ class ImageProcessingService {
      // Calculate minimum fill ratio - impacts pleins
      final minFillRatio = (characteristics.avgFillRatio - 0.2).clamp(0.35, 0.85);

-      print('Detection params: thresholds=$thresholds, size=$minSize-$maxSize, '
+      debugPrint('Detection params: thresholds=$thresholds, size=$minSize-$maxSize, '
            'circ>=$effectiveMinCircularity, fill>=$minFillRatio');

      // Détecter avec plusieurs seuils et combiner les résultats
@@ -722,7 +723,7 @@ class ImageProcessingService {
        return true;
      }).toList();

-      print('Found ${filteredBlobs.length} impacts after filtering (from ${mergedBlobs.length} merged)');
+      debugPrint('Found ${filteredBlobs.length} impacts after filtering (from ${mergedBlobs.length} merged)');

      // Convert to relative coordinates
      return filteredBlobs.map((blob) {
@@ -733,7 +734,7 @@ class ImageProcessingService {
        );
      }).toList();
    } catch (e) {
-      print('Error detecting impacts from references: $e');
+      debugPrint('Error detecting impacts from references: $e');
      return [];
    }
  }
@@ -776,50 +777,6 @@ class ImageProcessingService {
    return merged;
  }

-  /// Detect dark spots with adaptive luminance range
-  List<_Blob> _detectDarkSpotsAdaptive(
-    img.Image image,
-    int minLuminance,
-    int maxLuminance,
-    int minSize,
-    int maxSize, {
-    double minCircularity = 0.5,
-    double minFillRatio = 0.5,
-  }) {
-    final width = image.width;
-    final height = image.height;
-
-    // Create binary mask of pixels within luminance range
-    final mask = List.generate(height, (_) => List.filled(width, false));
-
-    for (int y = 0; y < height; y++) {
-      for (int x = 0; x < width; x++) {
-        final pixel = image.getPixel(x, y);
-        final luminance = img.getLuminance(pixel);
-        mask[y][x] = luminance >= minLuminance && luminance <= maxLuminance;
-      }
-    }
-
-    // Find connected components
-    final visited = List.generate(height, (_) => List.filled(width, false));
-    final blobs = <_Blob>[];
-
-    for (int y = 0; y < height; y++) {
-      for (int x = 0; x < width; x++) {
-        if (mask[y][x] && !visited[y][x]) {
-          final blob = _floodFill(mask, visited, x, y, width, height);
-          if (blob.size >= minSize &&
-              blob.size <= maxSize &&
-              blob.circularity >= minCircularity &&
-              blob.fillRatio >= minFillRatio) {
-            blobs.add(blob);
-          }
-        }
-      }
-    }
-
-    return _filterOverlappingBlobs(blobs);
-  }

  /// Detect dark spots in a grayscale image using blob detection
  List<_Blob> _detectDarkSpots(
--- a/lib/services/statistics_service.dart
+++ b/lib/services/statistics_service.dart
@@ -66,25 +66,12 @@ class PrecisionStats {

 /// Standard deviation statistics
 class StdDevStats {
-  /// Standard deviation of X positions
  final double stdDevX;
-
-  /// Standard deviation of Y positions
  final double stdDevY;
-
-  /// Combined standard deviation (radial)
  final double stdDevRadial;
-
-  /// Standard deviation of scores
  final double stdDevScore;
-
-  /// Mean X position
  final double meanX;
-
-  /// Mean Y position
  final double meanY;
-
-  /// Mean score
  final double meanScore;

  const StdDevStats({
@@ -98,18 +85,11 @@ class StdDevStats {
  });
 }

-/// Regional distribution (quadrants or sectors)
+/// Regional distribution
 class RegionalStats {
-  /// Shot distribution by quadrant (top-left, top-right, bottom-left, bottom-right)
  final Map<String, int> quadrantDistribution;
-
-  /// Shot distribution by sector (N, NE, E, SE, S, SW, W, NW, Center)
  final Map<String, int> sectorDistribution;
-
-  /// Dominant direction (where most shots land)
  final String dominantDirection;
-
-  /// Bias offset from center
  final double biasX;
  final double biasY;

@@ -157,16 +137,16 @@ class StatisticsService {
  SessionStatistics calculateStatistics(
    List<Session> sessions, {
    StatsPeriod period = StatsPeriod.all,
-    double targetCenterX = 0.5,
-    double targetCenterY = 0.5,
  }) {
    // Filter sessions by period
    final filteredSessions = _filterByPeriod(sessions, period);

-    // Collect all shots
+    // Collect all shots from all analyses in all sessions
    final allShots = <Shot>[];
    for (final session in filteredSessions) {
-      allShots.addAll(session.shots);
+      for (final analysis in session.analyses) {
+        allShots.addAll(analysis.shots);
+      }
    }

    if (allShots.isEmpty) {
@@ -183,14 +163,14 @@ class StatisticsService {
    // Calculate heat map
    final heatMap = _calculateHeatMap(allShots, gridSize: 5);

-    // Calculate precision
-    final precision = _calculatePrecision(allShots, targetCenterX, targetCenterY);
+    // Calculate precision (using relative center 0.5, 0.5)
+    final precision = _calculatePrecision(allShots, 0.5, 0.5);

    // Calculate standard deviation
    final stdDev = _calculateStdDev(allShots);

    // Calculate regional distribution
-    final regional = _calculateRegional(allShots, targetCenterX, targetCenterY);
+    final regional = _calculateRegional(allShots, 0.5, 0.5);

    return SessionStatistics(
      totalShots: totalShots,
@@ -224,20 +204,13 @@ class StatisticsService {

  /// Calculate heat map
  HeatMap _calculateHeatMap(List<Shot> shots, {int gridSize = 5}) {
-    // Initialize grid
-    final grid = List.generate(
-      gridSize,
-      (_) => List.generate(gridSize, (_) => <Shot>[]),
-    );
-
-    // Assign shots to grid cells
+    final grid = List.generate(gridSize, (_) => List.generate(gridSize, (_) => <Shot>[]));
    for (final shot in shots) {
      final col = (shot.x * gridSize).floor().clamp(0, gridSize - 1);
      final row = (shot.y * gridSize).floor().clamp(0, gridSize - 1);
      grid[row][col].add(shot);
    }

-    // Find max count for normalization
    int maxCount = 0;
    for (final row in grid) {
      for (final cell in row) {
@@ -245,16 +218,12 @@ class StatisticsService {
      }
    }

-    // Create heat zones
    final zones = <List<HeatZone>>[];
    for (int row = 0; row < gridSize; row++) {
      final rowZones = <HeatZone>[];
      for (int col = 0; col < gridSize; col++) {
        final cellShots = grid[row][col];
-        final avgScore = cellShots.isEmpty
-            ? 0.0
-            : cellShots.fold<int>(0, (sum, s) => sum + s.score) / cellShots.length;
-
+        final avgScore = cellShots.isEmpty ? 0.0 : cellShots.fold<int>(0, (sum, s) => sum + s.score) / cellShots.length;
        rowZones.add(HeatZone(
          row: row,
          col: col,
@@ -266,30 +235,15 @@ class StatisticsService {
      zones.add(rowZones);
    }

-    return HeatMap(
-      gridSize: gridSize,
-      zones: zones,
-      maxShotsInZone: maxCount,
-      totalShots: shots.length,
-    );
+    return HeatMap(gridSize: gridSize, zones: zones, maxShotsInZone: maxCount, totalShots: shots.length);
  }

  /// Calculate precision statistics
-  PrecisionStats _calculatePrecision(
-    List<Shot> shots,
-    double centerX,
-    double centerY,
-  ) {
+  PrecisionStats _calculatePrecision(List<Shot> shots, double centerX, double centerY) {
    if (shots.isEmpty) {
-      return const PrecisionStats(
-        avgDistanceFromCenter: 0,
-        groupingDiameter: 0,
-        precisionScore: 0,
-        consistencyScore: 0,
-      );
+      return const PrecisionStats(avgDistanceFromCenter: 0, groupingDiameter: 0, precisionScore: 0, consistencyScore: 0);
    }

-    // Calculate distances from center
    final distances = shots.map((shot) {
      final dx = shot.x - centerX;
      final dy = shot.y - centerY;
@@ -297,8 +251,6 @@ class StatisticsService {
    }).toList();

    final avgDistance = distances.reduce((a, b) => a + b) / distances.length;
-
-    // Calculate grouping (spread between shots)
    double maxSpread = 0;
    for (int i = 0; i < shots.length; i++) {
      for (int j = i + 1; j < shots.length; j++) {
@@ -309,45 +261,29 @@ class StatisticsService {
      }
    }

-    // Calculate standard deviation of distances (consistency)
-    final meanDist = avgDistance;
    double variance = 0;
    for (final d in distances) {
-      variance += math.pow(d - meanDist, 2);
+      variance += math.pow(d - avgDistance, 2);
    }
    final stdDevDist = math.sqrt(variance / distances.length);

-    // Precision score: based on average distance from center (0-100)
-    // 0 distance = 100 score, 0.5 distance = 0 score
    final precisionScore = math.max(0, (1 - avgDistance * 2) * 100);
-
-    // Consistency score: based on grouping tightness (0-100)
-    // Lower spread = higher consistency
    final consistencyScore = math.max(0, (1 - stdDevDist * 5) * 100);

    return PrecisionStats(
-      avgDistanceFromCenter: avgDistance.toDouble(),
-      groupingDiameter: maxSpread.toDouble(),
-      precisionScore: precisionScore.clamp(0.0, 100.0).toDouble(),
-      consistencyScore: consistencyScore.clamp(0.0, 100.0).toDouble(),
+      avgDistanceFromCenter: avgDistance,
+      groupingDiameter: maxSpread,
+      precisionScore: precisionScore.toDouble().clamp(0.0, 100.0),
+      consistencyScore: consistencyScore.toDouble().clamp(0.0, 100.0),
    );
  }

  /// Calculate standard deviation statistics
  StdDevStats _calculateStdDev(List<Shot> shots) {
    if (shots.isEmpty) {
-      return const StdDevStats(
-        stdDevX: 0,
-        stdDevY: 0,
-        stdDevRadial: 0,
-        stdDevScore: 0,
-        meanX: 0.5,
-        meanY: 0.5,
-        meanScore: 0,
-      );
+      return const StdDevStats(stdDevX: 0, stdDevY: 0, stdDevRadial: 0, stdDevScore: 0, meanX: 0.5, meanY: 0.5, meanScore: 0);
    }

-    // Calculate means
    double sumX = 0, sumY = 0, sumScore = 0;
    for (final shot in shots) {
      sumX += shot.x;
@@ -358,7 +294,6 @@ class StatisticsService {
    final meanY = sumY / shots.length;
    final meanScore = sumScore / shots.length;

-    // Calculate variances
    double varianceX = 0, varianceY = 0, varianceScore = 0;
    for (final shot in shots) {
      varianceX += math.pow(shot.x - meanX, 2);
@@ -369,18 +304,11 @@ class StatisticsService {
    varianceY /= shots.length;
    varianceScore /= shots.length;

-    final stdDevX = math.sqrt(varianceX);
-    final stdDevY = math.sqrt(varianceY);
-    final stdDevScore = math.sqrt(varianceScore);
-
-    // Radial standard deviation
-    final stdDevRadial = math.sqrt(varianceX + varianceY);
-
    return StdDevStats(
-      stdDevX: stdDevX,
-      stdDevY: stdDevY,
-      stdDevRadial: stdDevRadial,
-      stdDevScore: stdDevScore,
+      stdDevX: math.sqrt(varianceX),
+      stdDevY: math.sqrt(varianceY),
+      stdDevRadial: math.sqrt(varianceX + varianceY),
+      stdDevScore: math.sqrt(varianceScore),
      meanX: meanX,
      meanY: meanY,
      meanScore: meanScore,
@@ -388,42 +316,13 @@ class StatisticsService {
  }

  /// Calculate regional distribution
-  RegionalStats _calculateRegional(
-    List<Shot> shots,
-    double centerX,
-    double centerY,
-  ) {
+  RegionalStats _calculateRegional(List<Shot> shots, double centerX, double centerY) {
    if (shots.isEmpty) {
-      return const RegionalStats(
-        quadrantDistribution: {},
-        sectorDistribution: {},
-        dominantDirection: 'Centre',
-        biasX: 0,
-        biasY: 0,
-      );
+      return const RegionalStats(quadrantDistribution: {}, sectorDistribution: {}, dominantDirection: 'Centre', biasX: 0, biasY: 0);
    }

-    // Quadrant distribution
-    final quadrants = <String, int>{
-      'Haut-Gauche': 0,
-      'Haut-Droite': 0,
-      'Bas-Gauche': 0,
-      'Bas-Droite': 0,
-    };
-
-    // Sector distribution (8 sectors + center)
-    final sectors = <String, int>{
-      'N': 0,
-      'NE': 0,
-      'E': 0,
-      'SE': 0,
-      'S': 0,
-      'SO': 0,
-      'O': 0,
-      'NO': 0,
-      'Centre': 0,
-    };
-
+    final quadrants = <String, int>{'Haut-Gauche': 0, 'Haut-Droite': 0, 'Bas-Gauche': 0, 'Bas-Droite': 0};
+    final sectors = <String, int>{'N': 0, 'NE': 0, 'E': 0, 'SE': 0, 'S': 0, 'SO': 0, 'O': 0, 'NO': 0, 'Centre': 0};
    double sumDx = 0, sumDy = 0;

    for (final shot in shots) {
@@ -432,16 +331,12 @@ class StatisticsService {
      sumDx += dx;
      sumDy += dy;

-      // Quadrant
      if (dy < 0) {
-        quadrants[dx < 0 ? 'Haut-Gauche' : 'Haut-Droite'] =
-            quadrants[dx < 0 ? 'Haut-Gauche' : 'Haut-Droite']! + 1;
+        quadrants[dx < 0 ? 'Haut-Gauche' : 'Haut-Droite'] = quadrants[dx < 0 ? 'Haut-Gauche' : 'Haut-Droite']! + 1;
      } else {
-        quadrants[dx < 0 ? 'Bas-Gauche' : 'Bas-Droite'] =
-            quadrants[dx < 0 ? 'Bas-Gauche' : 'Bas-Droite']! + 1;
+        quadrants[dx < 0 ? 'Bas-Gauche' : 'Bas-Droite'] = quadrants[dx < 0 ? 'Bas-Gauche' : 'Bas-Droite']! + 1;
      }

-      // Sector
      final distance = math.sqrt(dx * dx + dy * dy);
      if (distance < 0.1) {
        sectors['Centre'] = sectors['Centre']! + 1;
@@ -452,11 +347,6 @@ class StatisticsService {
      }
    }

-    // Calculate bias
-    final biasX = sumDx / shots.length;
-    final biasY = sumDy / shots.length;
-
-    // Find dominant direction
    String dominant = 'Centre';
    int maxCount = 0;
    sectors.forEach((key, value) {
@@ -470,14 +360,12 @@ class StatisticsService {
      quadrantDistribution: quadrants,
      sectorDistribution: sectors,
      dominantDirection: dominant,
-      biasX: biasX,
-      biasY: biasY,
+      biasX: sumDx / shots.length,
+      biasY: sumDy / shots.length,
    );
  }

  String _angleToSector(double angle) {
-    // Angle is in degrees, -180 to 180
-    // 0 = East, 90 = South, -90 = North, 180/-180 = West
    if (angle >= -22.5 && angle < 22.5) return 'E';
    if (angle >= 22.5 && angle < 67.5) return 'SE';
    if (angle >= 67.5 && angle < 112.5) return 'S';
@@ -491,41 +379,12 @@ class StatisticsService {

  SessionStatistics _emptyStatistics(StatsPeriod period, List<Session> sessions) {
    return SessionStatistics(
-      totalShots: 0,
-      totalScore: 0,
-      avgScore: 0,
-      maxScore: 0,
-      minScore: 0,
-      heatMap: const HeatMap(
-        gridSize: 5,
-        zones: [],
-        maxShotsInZone: 0,
-        totalShots: 0,
-      ),
-      precision: const PrecisionStats(
-        avgDistanceFromCenter: 0,
-        groupingDiameter: 0,
-        precisionScore: 0,
-        consistencyScore: 0,
-      ),
-      stdDev: const StdDevStats(
-        stdDevX: 0,
-        stdDevY: 0,
-        stdDevRadial: 0,
-        stdDevScore: 0,
-        meanX: 0.5,
-        meanY: 0.5,
-        meanScore: 0,
-      ),
-      regional: const RegionalStats(
-        quadrantDistribution: {},
-        sectorDistribution: {},
-        dominantDirection: 'Centre',
-        biasX: 0,
-        biasY: 0,
-      ),
-      sessions: sessions,
-      period: period,
+      totalShots: 0, totalScore: 0, avgScore: 0, maxScore: 0, minScore: 0,
+      heatMap: const HeatMap(gridSize: 5, zones: [], maxShotsInZone: 0, totalShots: 0),
+      precision: const PrecisionStats(avgDistanceFromCenter: 0, groupingDiameter: 0, precisionScore: 0, consistencyScore: 0),
+      stdDev: const StdDevStats(stdDevX: 0, stdDevY: 0, stdDevRadial: 0, stdDevScore: 0, meanX: 0.5, meanY: 0.5, meanScore: 0),
+      regional: const RegionalStats(quadrantDistribution: {}, sectorDistribution: {}, dominantDirection: 'Centre', biasX: 0, biasY: 0),
+      sessions: sessions, period: period,
    );
  }
 }
--- a/lib/services/target_detection_service.dart
+++ b/lib/services/target_detection_service.dart
@@ -1,4 +1,5 @@
 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';
@@ -320,7 +321,7 @@ class TargetDetectionService {
        );
      }).toList();
    } catch (e) {
-      print('Erreur détection OpenCV: $e');
+      debugPrint('Erreur détection OpenCV: $e');
      return [];
    }
  }
@@ -369,7 +370,7 @@ class TargetDetectionService {
        );
      }).toList();
    } catch (e) {
-      print('Erreur détection OpenCV depuis références: $e');
+      debugPrint('Erreur détection OpenCV depuis références: $e');
      return [];
    }
  }
--- a/lib/services/wallet_identity_service.dart
+++ b/lib/services/wallet_identity_service.dart
@@ -4,6 +4,7 @@ import 'package:crypto/crypto.dart';
 import 'package:shared_preferences/shared_preferences.dart';
 import 'package:device_info_plus/device_info_plus.dart';
 import 'dart:io';
+import 'package:flutter/foundation.dart';

 class WalletIdentityService {
  static const String _prefsKey = 'wallet_identity_phrase';
@@ -86,7 +87,7 @@ class WalletIdentityService {
        deviceId = windowsInfo.deviceId;
      }
    } catch (e) {
-      print('Erreur lecture device ID: $e');
+      debugPrint('Erreur lecture device ID: $e');
    }
    
    // Add a salt to the device ID