preparation du modele yolo

lib/features/analysis/analysis_provider.dart

@@ -523,6 +523,20 @@ class AnalysisProvider extends ChangeNotifier {
     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; // The corrected image is always square (side x side)
+        notifyListeners();
+      }
+
+      // 2. Detect the target on the straight/corrected image
       final result = await _opencvTargetService.detectTarget(_imagePath!);
 
       if (result.success) {

lib/main.dart

@@ -10,6 +10,7 @@ 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 'services/yolo_impact_detection_service.dart';
 
 void main() async {
   WidgetsFlutterBinding.ensureInitialized();
@@ -33,9 +34,13 @@ void main() async {
         Provider<ImageProcessingService>(
           create: (_) => ImageProcessingService(),
         ),
+        Provider<YOLOImpactDetectionService>(
+          create: (_) => YOLOImpactDetectionService(),
+        ),
         Provider<TargetDetectionService>(
           create: (context) => TargetDetectionService(
             imageProcessingService: context.read<ImageProcessingService>(),
+            yoloService: context.read<YOLOImpactDetectionService>(),
           ),
         ),
         Provider<ScoreCalculatorService>(
@@ -44,9 +49,7 @@ void main() async {
         Provider<GroupingAnalyzerService>(
           create: (_) => GroupingAnalyzerService(),
         ),
-        Provider<SessionRepository>(
+        Provider<SessionRepository>(create: (_) => SessionRepository()),
-          create: (_) => SessionRepository(),
-        ),
       ],
       child: const BullyApp(),
     ),

lib/services/distortion_correction_service.dart

@@ -676,4 +676,399 @@ class DistortionCorrectionService {
     points[2] = br;
     points[3] = bl;
   }
+
+  /// Corrige la perspective en reformant le plus grand ovale (ellipse) en un cercle parfait,
+  /// sans recadrer agressivement l'image entière.
+  Future<String> correctPerspectiveUsingOvals(String imagePath) async {
+    try {
+      final src = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
+      if (src.isEmpty) throw Exception("Impossible de charger l'image");
+
+      final gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY);
+      final blurred = cv.gaussianBlur(gray, (5, 5), 0);
+
+      final thresh = cv.threshold(
+        blurred,
+        0,
+        255,
+        cv.THRESH_BINARY | cv.THRESH_OTSU,
+      );
+      final edges = cv.canny(blurred, thresh.$1 * 0.5, thresh.$1);
+
+      final contoursResult = cv.findContours(
+        edges,
+        cv.RETR_EXTERNAL,
+        cv.CHAIN_APPROX_SIMPLE,
+      );
+      final contours = contoursResult.$1;
+
+      if (contours.isEmpty) return imagePath;
+
+      cv.RotatedRect? bestEllipse;
+      double maxArea = 0;
+
+      for (final contour in contours) {
+        if (contour.length < 5) continue;
+        final area = cv.contourArea(contour);
+        if (area < 1000) continue;
+
+        final ellipse = cv.fitEllipse(contour);
+        if (area > maxArea) {
+          maxArea = area;
+          bestEllipse = ellipse;
+        }
+      }
+
+      if (bestEllipse == null) return imagePath;
+
+      // The goal here is to morph the bestEllipse into a perfect circle, while
+      // keeping the image the same size and the center of the ellipse in the same place.
+      // We'll use the average of the width and height (or max) to define the target circle
+      final targetRadius =
+          math.max(bestEllipse.size.width, bestEllipse.size.height) / 2.0;
+
+      // Extract the 4 bounding box points of the ellipse
+      final boxPoints = cv.boxPoints(bestEllipse);
+      final List<cv.Point> srcPoints = [];
+      for (int i = 0; i < boxPoints.length; i++) {
+        srcPoints.add(cv.Point(boxPoints[i].x.toInt(), boxPoints[i].y.toInt()));
+      }
+      _sortPoints(srcPoints);
+
+      // Calculate the size of the perfectly squared output image
+      final int side = (targetRadius * 2).toInt();
+
+      final List<cv.Point> dstPoints = [
+        cv.Point(0, 0), // Top-Left
+        cv.Point(side, 0), // Top-Right
+        cv.Point(side, side), // Bottom-Right
+        cv.Point(0, side), // Bottom-Left
+      ];
+
+      // Morph the target region into a perfect square, cropping the rest of the image
+      final M = cv.getPerspectiveTransform(
+        cv.VecPoint.fromList(srcPoints),
+        cv.VecPoint.fromList(dstPoints),
+      );
+
+      final corrected = cv.warpPerspective(src, M, (side, side));
+
+      final tempDir = await getTemporaryDirectory();
+      final timestamp = DateTime.now().millisecondsSinceEpoch;
+      final outputPath = '${tempDir.path}/corrected_oval_$timestamp.jpg';
+
+      cv.imwrite(outputPath, corrected);
+
+      return outputPath;
+    } catch (e) {
+      print('Erreur correction perspective ovales: $e');
+      return imagePath;
+    }
+  }
+
+  /// Corrige la distorsion et la profondeur (perspective) en créant un maillage
+  /// basé sur la concentricité des différents cercles de la cible pour trouver le meilleur plan.
+  Future<String> correctPerspectiveWithConcentricMesh(String imagePath) async {
+    try {
+      final src = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
+      if (src.isEmpty) throw Exception("Impossible de charger l'image");
+
+      final gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY);
+      final blurred = cv.gaussianBlur(gray, (5, 5), 0);
+      final thresh = cv.threshold(
+        blurred,
+        0,
+        255,
+        cv.THRESH_BINARY | cv.THRESH_OTSU,
+      );
+      final edges = cv.canny(blurred, thresh.$1 * 0.5, thresh.$1);
+
+      final contoursResult = cv.findContours(
+        edges,
+        cv.RETR_LIST,
+        cv.CHAIN_APPROX_SIMPLE,
+      );
+      final contours = contoursResult.$1;
+      if (contours.isEmpty) return imagePath;
+
+      List<cv.RotatedRect> ellipses = [];
+      for (final contour in contours) {
+        if (contour.length < 5) continue;
+        if (cv.contourArea(contour) < 500) continue;
+        ellipses.add(cv.fitEllipse(contour));
+      }
+
+      if (ellipses.isEmpty) return imagePath;
+
+      // Find the largest ellipse to serve as our central reference
+      ellipses.sort(
+        (a, b) => (b.size.width * b.size.height).compareTo(
+          a.size.width * a.size.height,
+        ),
+      );
+      final largestEllipse = ellipses.first;
+      final maxDist =
+          math.max(largestEllipse.size.width, largestEllipse.size.height) *
+          0.15;
+
+      // Group all ellipses that are roughly concentric with the largest one
+      List<cv.RotatedRect> concentricGroup = [];
+      for (final e in ellipses) {
+        final dx = e.center.x - largestEllipse.center.x;
+        final dy = e.center.y - largestEllipse.center.y;
+        if (math.sqrt(dx * dx + dy * dy) < maxDist) {
+          concentricGroup.add(e);
+        }
+      }
+
+      if (concentricGroup.length < 2) {
+        print(
+          "Pas assez de cercles concentriques pour le maillage, utilisation de la méthode simple.",
+        );
+        return await correctPerspectiveUsingOvals(imagePath);
+      }
+
+      final targetRadius =
+          math.max(largestEllipse.size.width, largestEllipse.size.height) / 2.0;
+      final int side = (targetRadius * 2.4).toInt(); // Add padding
+      final double cx = side / 2.0;
+      final double cy = side / 2.0;
+
+      List<cv.Point2f> srcPointsList = [];
+      List<cv.Point2f> dstPointsList = [];
+
+      for (final ellipse in concentricGroup) {
+        final box = cv.boxPoints(ellipse);
+        final m0 = cv.Point2f(
+          (box[0].x + box[1].x) / 2,
+          (box[0].y + box[1].y) / 2,
+        );
+        final m1 = cv.Point2f(
+          (box[1].x + box[2].x) / 2,
+          (box[1].y + box[2].y) / 2,
+        );
+        final m2 = cv.Point2f(
+          (box[2].x + box[3].x) / 2,
+          (box[2].y + box[3].y) / 2,
+        );
+        final m3 = cv.Point2f(
+          (box[3].x + box[0].x) / 2,
+          (box[3].y + box[0].y) / 2,
+        );
+
+        final d02 = math.sqrt(
+          math.pow(m0.x - m2.x, 2) + math.pow(m0.y - m2.y, 2),
+        );
+        final d13 = math.sqrt(
+          math.pow(m1.x - m3.x, 2) + math.pow(m1.y - m3.y, 2),
+        );
+
+        cv.Point2f maj1, maj2, min1, min2;
+        double r;
+
+        if (d02 > d13) {
+          maj1 = m0;
+          maj2 = m2;
+          min1 = m1;
+          min2 = m3;
+          r = d02 / 2.0;
+        } else {
+          maj1 = m1;
+          maj2 = m3;
+          min1 = m0;
+          min2 = m2;
+          r = d13 / 2.0;
+        }
+
+        // Sort maj1 and maj2 so maj1 is left/top
+        if ((maj1.x - maj2.x).abs() > (maj1.y - maj2.y).abs()) {
+          if (maj1.x > maj2.x) {
+            final t = maj1;
+            maj1 = maj2;
+            maj2 = t;
+          }
+        } else {
+          if (maj1.y > maj2.y) {
+            final t = maj1;
+            maj1 = maj2;
+            maj2 = t;
+          }
+        }
+
+        // Sort min1 and min2 so min1 is top/left
+        if ((min1.y - min2.y).abs() > (min1.x - min2.x).abs()) {
+          if (min1.y > min2.y) {
+            final t = min1;
+            min1 = min2;
+            min2 = t;
+          }
+        } else {
+          if (min1.x > min2.x) {
+            final t = min1;
+            min1 = min2;
+            min2 = t;
+          }
+        }
+
+        srcPointsList.addAll([maj1, maj2, min1, min2]);
+        dstPointsList.addAll([
+          cv.Point2f(cx - r, cy),
+          cv.Point2f(cx + r, cy),
+          cv.Point2f(cx, cy - r),
+          cv.Point2f(cx, cy + r),
+        ]);
+
+        // Add ellipse centers mapping perfectly to the origin to force concentric depth alignment
+        srcPointsList.add(cv.Point2f(ellipse.center.x, ellipse.center.y));
+        dstPointsList.add(cv.Point2f(cx, cy));
+      }
+
+      // We explicitly convert points to VecPoint to use findHomography standard binding
+      final srcVec = cv.VecPoint.fromList(
+        srcPointsList.map((p) => cv.Point(p.x.toInt(), p.y.toInt())).toList(),
+      );
+      final dstVec = cv.VecPoint.fromList(
+        dstPointsList.map((p) => cv.Point(p.x.toInt(), p.y.toInt())).toList(),
+      );
+
+      final M = cv.findHomography(
+        cv.Mat.fromVec(srcVec),
+        cv.Mat.fromVec(dstVec),
+        method: cv.RANSAC,
+      );
+
+      if (M.isEmpty) {
+        return await correctPerspectiveUsingOvals(imagePath);
+      }
+
+      final corrected = cv.warpPerspective(src, M, (side, side));
+
+      final tempDir = await getTemporaryDirectory();
+      final timestamp = DateTime.now().millisecondsSinceEpoch;
+      final outputPath = '${tempDir.path}/corrected_mesh_$timestamp.jpg';
+      cv.imwrite(outputPath, corrected);
+
+      return outputPath;
+    } catch (e) {
+      print('Erreur correction perspective maillage concentrique: $e');
+      return imagePath;
+    }
+  }
+
+  /// Corrige la perspective en détectant les 4 coins de la feuille (quadrilatère)
+  ///
+  /// Cette méthode cherche le plus grand polygone à 4 côtés (le bord du papier)
+  /// et le déforme pour en faire un carré parfait.
+  Future<String> correctPerspectiveUsingQuadrilateral(String imagePath) async {
+    try {
+      final src = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
+      if (src.isEmpty) throw Exception("Impossible de charger l'image");
+
+      final gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY);
+      // Flou plus important pour ignorer les détails internes (cercles, trous)
+      final blurred = cv.gaussianBlur(gray, (9, 9), 0);
+
+      // Canny edge detector
+      final thresh = cv.threshold(
+        blurred,
+        0,
+        255,
+        cv.THRESH_BINARY | cv.THRESH_OTSU,
+      );
+      final edges = cv.canny(blurred, thresh.$1 * 0.5, thresh.$1);
+
+      // Pour la détection de la feuille (les bords peuvent être discontinus à cause de l'éclairage)
+      final kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5));
+      final closedEdges = cv.morphologyEx(edges, cv.MORPH_CLOSE, kernel);
+
+      // Find contours
+      final contoursResult = cv.findContours(
+        closedEdges,
+        cv.RETR_EXTERNAL,
+        cv.CHAIN_APPROX_SIMPLE,
+      );
+      final contours = contoursResult.$1;
+
+      cv.VecPoint? bestQuad;
+      double maxArea = 0;
+
+      final minArea = src.rows * src.cols * 0.1; // Au moins 10% de l'image
+
+      for (final contour in contours) {
+        final area = cv.contourArea(contour);
+        if (area < minArea) continue;
+
+        final peri = cv.arcLength(contour, true);
+        // Approximation polygonale (tolérance = 2% à 5% du périmètre)
+        final approx = cv.approxPolyDP(contour, 0.04 * peri, true);
+
+        if (approx.length == 4) {
+          if (area > maxArea) {
+            maxArea = area;
+            bestQuad = approx;
+          }
+        }
+      }
+
+      // Fallback
+      if (bestQuad == null) {
+        print(
+          "Aucun papier quadrilatère détecté, on utilise les cercles à la place.",
+        );
+        return await correctPerspectiveUsingCircles(imagePath);
+      }
+
+      // Convert to List<cv.Point>
+      final List<cv.Point> srcPoints = [];
+      for (int i = 0; i < bestQuad.length; i++) {
+        srcPoints.add(bestQuad[i]);
+      }
+
+      _sortPoints(srcPoints);
+
+      // Calculate max width and height
+      double widthA = _distanceCV(srcPoints[2], srcPoints[3]);
+      double widthB = _distanceCV(srcPoints[1], srcPoints[0]);
+      int dstWidth = math.max(widthA, widthB).toInt();
+
+      double heightA = _distanceCV(srcPoints[1], srcPoints[2]);
+      double heightB = _distanceCV(srcPoints[0], srcPoints[3]);
+      int dstHeight = math.max(heightA, heightB).toInt();
+
+      // Since standard target paper forms a square, we force the resulting warp to be a perfect square.
+      int side = math.max(dstWidth, dstHeight);
+
+      final List<cv.Point> dstPoints = [
+        cv.Point(0, 0),
+        cv.Point(side, 0),
+        cv.Point(side, side),
+        cv.Point(0, side),
+      ];
+
+      final M = cv.getPerspectiveTransform(
+        cv.VecPoint.fromList(srcPoints),
+        cv.VecPoint.fromList(dstPoints),
+      );
+
+      final corrected = cv.warpPerspective(src, M, (side, side));
+
+      final tempDir = await getTemporaryDirectory();
+      final timestamp = DateTime.now().millisecondsSinceEpoch;
+      final outputPath = '${tempDir.path}/corrected_quad_$timestamp.jpg';
+
+      cv.imwrite(outputPath, corrected);
+
+      return outputPath;
+    } catch (e) {
+      print('Erreur correction perspective quadrilatère: $e');
+      // Fallback
+      return await correctPerspectiveUsingCircles(imagePath);
+    }
+  }
+
+  double _distanceCV(cv.Point p1, cv.Point p2) {
+    final dx = p2.x - p1.x;
+    final dy = p2.y - p1.y;
+    return math.sqrt(dx * dx + dy * dy);
+  }
 }

lib/services/opencv_impact_detection_service.dart

@@ -153,7 +153,7 @@ class OpenCVImpactDetectionService {
         );
 
         final contours = contoursResult.$1;
-        // hierarchy is item2
+        // hierarchy is $2
 
         for (int i = 0; i < contours.length; i++) {
           final contour = contours[i];

lib/services/target_detection_service.dart

@@ -2,9 +2,12 @@ import 'dart:math' as math;
 import '../data/models/target_type.dart';
 import 'image_processing_service.dart';
 import 'opencv_impact_detection_service.dart';
+import 'yolo_impact_detection_service.dart';
 
-export 'image_processing_service.dart' show ImpactDetectionSettings, ReferenceImpact, ImpactCharacteristics;
+export 'image_processing_service.dart'
-export 'opencv_impact_detection_service.dart' show OpenCVDetectionSettings, OpenCVDetectedImpact;
+    show ImpactDetectionSettings, ReferenceImpact, ImpactCharacteristics;
+export 'opencv_impact_detection_service.dart'
+    show OpenCVDetectionSettings, OpenCVDetectedImpact;
 
 class TargetDetectionResult {
   final double centerX; // Relative (0-1)
@@ -52,18 +55,19 @@ class DetectedImpactResult {
 class TargetDetectionService {
   final ImageProcessingService _imageProcessingService;
   final OpenCVImpactDetectionService _opencvService;
+  final YOLOImpactDetectionService _yoloService;
 
   TargetDetectionService({
     ImageProcessingService? imageProcessingService,
     OpenCVImpactDetectionService? opencvService,
-  })  : _imageProcessingService = imageProcessingService ?? ImageProcessingService(),
+    YOLOImpactDetectionService? yoloService,
-        _opencvService = opencvService ?? OpenCVImpactDetectionService();
+  }) : _imageProcessingService =
+           imageProcessingService ?? ImageProcessingService(),
+       _opencvService = opencvService ?? OpenCVImpactDetectionService(),
+       _yoloService = yoloService ?? YOLOImpactDetectionService();
 
   /// Detect target and impacts from an image file
-  TargetDetectionResult detectTarget(
+  TargetDetectionResult detectTarget(String imagePath, TargetType targetType) {
-    String imagePath,
-    TargetType targetType,
-  ) {
     try {
       // Detect main target
       final mainTarget = _imageProcessingService.detectMainTarget(imagePath);
@@ -84,7 +88,13 @@ class TargetDetectionService {
       // Convert impacts to relative coordinates and calculate scores
       final detectedImpacts = impacts.map((impact) {
         final score = targetType == TargetType.concentric
-            ? _calculateConcentricScore(impact.x, impact.y, centerX, centerY, radius)
+            ? _calculateConcentricScore(
+                impact.x,
+                impact.y,
+                centerX,
+                centerY,
+                radius,
+              )
             : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
 
         return DetectedImpactResult(
@@ -149,9 +159,9 @@ class TargetDetectionService {
 
     // Vertical zones
     if (dy < -0.25) return 5; // Head zone (top)
-    if (dy < 0.0) return 5;   // Center mass (upper body)
+    if (dy < 0.0) return 5; // Center mass (upper body)
-    if (dy < 0.15) return 4;  // Body
+    if (dy < 0.15) return 4; // Body
-    if (dy < 0.35) return 3;  // Lower body
+    if (dy < 0.35) return 3; // Lower body
 
     return 0; // Outside target
   }
@@ -177,7 +187,13 @@ class TargetDetectionService {
       return impacts.map((impact) {
         final score = targetType == TargetType.concentric
             ? _calculateConcentricScoreWithRings(
-                impact.x, impact.y, centerX, centerY, radius, ringCount)
+                impact.x,
+                impact.y,
+                centerX,
+                centerY,
+                radius,
+                ringCount,
+              )
             : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
 
         return DetectedImpactResult(
@@ -221,7 +237,10 @@ class TargetDetectionService {
     String imagePath,
     List<ReferenceImpact> references,
   ) {
-    return _imageProcessingService.analyzeReferenceImpacts(imagePath, references);
+    return _imageProcessingService.analyzeReferenceImpacts(
+      imagePath,
+      references,
+    );
   }
 
   /// Detect impacts based on reference characteristics (calibrated detection)
@@ -245,7 +264,13 @@ class TargetDetectionService {
       return impacts.map((impact) {
         final score = targetType == TargetType.concentric
             ? _calculateConcentricScoreWithRings(
-                impact.x, impact.y, centerX, centerY, radius, ringCount)
+                impact.x,
+                impact.y,
+                centerX,
+                centerY,
+                radius,
+                ringCount,
+              )
             : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
 
         return DetectedImpactResult(
@@ -283,7 +308,13 @@ class TargetDetectionService {
       return impacts.map((impact) {
         final score = targetType == TargetType.concentric
             ? _calculateConcentricScoreWithRings(
-                impact.x, impact.y, centerX, centerY, radius, ringCount)
+                impact.x,
+                impact.y,
+                centerX,
+                centerY,
+                radius,
+                ringCount,
+              )
             : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
 
         return DetectedImpactResult(
@@ -315,9 +346,7 @@ class TargetDetectionService {
   }) {
     try {
       // Convertir les références au format OpenCV
-      final refPoints = references
+      final refPoints = references.map((r) => (x: r.x, y: r.y)).toList();
-          .map((r) => (x: r.x, y: r.y))
-          .toList();
 
       final impacts = _opencvService.detectFromReferences(
         imagePath,
@@ -328,7 +357,13 @@ class TargetDetectionService {
       return impacts.map((impact) {
         final score = targetType == TargetType.concentric
             ? _calculateConcentricScoreWithRings(
-                impact.x, impact.y, centerX, centerY, radius, ringCount)
+                impact.x,
+                impact.y,
+                centerX,
+                centerY,
+                radius,
+                ringCount,
+              )
             : _calculateSilhouetteScore(impact.x, impact.y, centerX, centerY);
 
         return DetectedImpactResult(
@@ -343,4 +378,41 @@ class TargetDetectionService {
       return [];
     }
   }
+
+  /// Détecte les impacts en utilisant YOLOv8
+  Future<List<DetectedImpactResult>> detectImpactsWithYOLO(
+    String imagePath,
+    TargetType targetType,
+    double centerX,
+    double centerY,
+    double radius,
+    int ringCount,
+  ) async {
+    try {
+      final impacts = await _yoloService.detectImpacts(imagePath);
+
+      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) {
+      print('Erreur détection YOLOv8: $e');
+      return [];
+    }
+  }
 }

lib/services/yolo_impact_detection_service.dart

@@ -0,0 +1,174 @@
+import 'dart:io';
+import 'dart:math' as math;
+import 'dart:typed_data';
+import 'package:tflite_flutter/tflite_flutter.dart';
+import 'package:image/image.dart' as img;
+import 'target_detection_service.dart';
+
+class YOLOImpactDetectionService {
+  Interpreter? _interpreter;
+
+  static const String modelPath = 'assets/models/yolov11n_impact.tflite';
+  static const String labelsPath = 'assets/models/labels.txt';
+
+  Future<void> init() async {
+    if (_interpreter != null) return;
+
+    try {
+      // Try loading the specific YOLOv11 model first, fallback to v8 if not found
+      try {
+        _interpreter = await Interpreter.fromAsset(modelPath);
+      } catch (e) {
+        print('YOLOv11 model not found at $modelPath, trying YOLOv8 fallback');
+        _interpreter = await Interpreter.fromAsset(
+          'assets/models/yolov8n_impact.tflite',
+        );
+      }
+
+      print('YOLO Interpreter loaded successfully');
+    } catch (e) {
+      print('Error loading YOLO model: $e');
+    }
+  }
+
+  Future<List<DetectedImpactResult>> detectImpacts(String imagePath) async {
+    if (_interpreter == null) await init();
+    if (_interpreter == null) return [];
+
+    try {
+      final bytes = File(imagePath).readAsBytesSync();
+      final originalImage = img.decodeImage(bytes);
+      if (originalImage == null) return [];
+
+      // YOLOv8/v11 usually takes 640x640
+      const int inputSize = 640;
+      final resizedImage = img.copyResize(
+        originalImage,
+        width: inputSize,
+        height: inputSize,
+      );
+
+      // Prepare input tensor
+      var input = _imageToByteListFloat32(resizedImage, inputSize);
+
+      // Raw YOLO output shape usually [1, 4 + num_classes, 8400]
+      // For single class "impact", it's [1, 5, 8400]
+      var output = List<double>.filled(1 * 5 * 8400, 0).reshape([1, 5, 8400]);
+
+      _interpreter!.run(input, output);
+
+      return _processOutput(
+        output[0],
+        originalImage.width,
+        originalImage.height,
+      );
+    } catch (e) {
+      print('Error during YOLO inference: $e');
+      return [];
+    }
+  }
+
+  List<DetectedImpactResult> _processOutput(
+    List<List<double>> output,
+    int imgWidth,
+    int imgHeight,
+  ) {
+    final List<_Detection> candidates = [];
+    const double threshold = 0.25;
+
+    // output is [5, 8400] -> [x, y, w, h, conf]
+    for (int i = 0; i < 8400; i++) {
+      final double confidence = output[4][i];
+      if (confidence > threshold) {
+        candidates.add(
+          _Detection(
+            x: output[0][i],
+            y: output[1][i],
+            w: output[2][i],
+            h: output[3][i],
+            confidence: confidence,
+          ),
+        );
+      }
+    }
+
+    // Apply Non-Max Suppression (NMS)
+    final List<_Detection> suppressed = _nms(candidates);
+
+    return suppressed
+        .map(
+          (det) => DetectedImpactResult(
+            x: det.x / 640.0,
+            y: det.y / 640.0,
+            radius: 5.0,
+            suggestedScore: 0,
+          ),
+        )
+        .toList();
+  }
+
+  List<_Detection> _nms(List<_Detection> detections) {
+    if (detections.isEmpty) return [];
+
+    // Sort by confidence descending
+    detections.sort((a, b) => b.confidence.compareTo(a.confidence));
+
+    final List<_Detection> selected = [];
+    final List<bool> active = List.filled(detections.length, true);
+
+    for (int i = 0; i < detections.length; i++) {
+      if (!active[i]) continue;
+
+      selected.add(detections[i]);
+
+      for (int j = i + 1; j < detections.length; j++) {
+        if (!active[j]) continue;
+
+        if (_iou(detections[i], detections[j]) > 0.45) {
+          active[j] = false;
+        }
+      }
+    }
+
+    return selected;
+  }
+
+  double _iou(_Detection a, _Detection b) {
+    final double areaA = a.w * a.h;
+    final double areaB = b.w * b.h;
+
+    final double x1 = math.max(a.x - a.w / 2, b.x - b.w / 2);
+    final double y1 = math.max(a.y - a.h / 2, b.y - b.h / 2);
+    final double x2 = math.min(a.x + a.w / 2, b.x + b.w / 2);
+    final double y2 = math.min(a.y + a.h / 2, b.y + b.h / 2);
+
+    final double intersection = math.max(0.0, x2 - x1) * math.max(0.0, y2 - y1);
+    return intersection / (areaA + areaB - intersection);
+  }
+
+  Uint8List _imageToByteListFloat32(img.Image image, int inputSize) {
+    var convertedBytes = Float32List(1 * inputSize * inputSize * 3);
+    var buffer = Float32List.view(convertedBytes.buffer);
+    int pixelIndex = 0;
+    for (int i = 0; i < inputSize; i++) {
+      for (int j = 0; j < inputSize; j++) {
+        var pixel = image.getPixel(j, i);
+        buffer[pixelIndex++] = (pixel.r / 255.0);
+        buffer[pixelIndex++] = (pixel.g / 255.0);
+        buffer[pixelIndex++] = (pixel.b / 255.0);
+      }
+    }
+    return convertedBytes.buffer.asUint8List();
+  }
+}
+
+class _Detection {
+  final double x, y, w, h, confidence;
+  _Detection({
+    required this.x,
+    required this.y,
+    required this.w,
+    required this.h,
+    required this.confidence,
+  });
+}

linux/flutter/generated_plugins.cmake

@@ -7,6 +7,7 @@ list(APPEND FLUTTER_PLUGIN_LIST
 )
 
 list(APPEND FLUTTER_FFI_PLUGIN_LIST
+  tflite_flutter
 )
 
 set(PLUGIN_BUNDLED_LIBRARIES)

pubspec.lock

@@ -536,6 +536,14 @@ packages:
       url: "https://pub.dev"
     source: hosted
     version: "2.2.0"
+  quiver:
+    dependency: transitive
+    description:
+      name: quiver
+      sha256: ea0b925899e64ecdfbf9c7becb60d5b50e706ade44a85b2363be2a22d88117d2
+      url: "https://pub.dev"
+    source: hosted
+    version: "3.2.2"
   sky_engine:
     dependency: transitive
     description: flutter
@@ -653,6 +661,14 @@ packages:
       url: "https://pub.dev"
     source: hosted
     version: "0.7.9"
+  tflite_flutter:
+    dependency: "direct main"
+    description:
+      name: tflite_flutter
+      sha256: ffb8651fdb116ab0131d6dc47ff73883e0f634ad1ab12bb2852eef1bbeab4a6a
+      url: "https://pub.dev"
+    source: hosted
+    version: "0.10.4"
   typed_data:
     dependency: transitive
     description:

pubspec.yaml

@@ -64,6 +64,9 @@ dependencies:
   # Image processing for impact detection
   image: ^4.1.7
 
+  # Machine Learning for YOLOv8
+  tflite_flutter: ^0.10.4
+
 dev_dependencies:
   flutter_test:
     sdk: flutter

tests/find_homography_test.dart

@@ -0,0 +1,12 @@
+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

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

tests/test_homography.dart

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

windows/flutter/generated_plugins.cmake

@@ -7,6 +7,7 @@ list(APPEND FLUTTER_PLUGIN_LIST
 )
 
 list(APPEND FLUTTER_FFI_PLUGIN_LIST
+  tflite_flutter
 )
 
 set(PLUGIN_BUNDLED_LIBRARIES)