nettoyage du code avec cunning
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@@ -1,13 +1,8 @@
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/// Service de détection d'impacts utilisant OpenCV.
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///
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/// NOTE: OpenCV est actuellement désactivé sur Windows en raison de problèmes
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/// de compilation. Ce fichier contient des stubs qui permettent au code de
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/// compiler sans OpenCV. Réactiver opencv_dart dans pubspec.yaml et
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/// décommenter le code ci-dessous quand le support sera corrigé.
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library;
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// import 'dart:math' as math;
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// import 'package:opencv_dart/opencv_dart.dart' as cv;
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import 'dart:math' as math;
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import 'package:opencv_dart/opencv_dart.dart' as cv;
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/// Paramètres de détection d'impacts OpenCV
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class OpenCVDetectionSettings {
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@@ -90,30 +85,143 @@ class OpenCVDetectedImpact {
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}
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/// Service de détection d'impacts utilisant OpenCV
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///
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/// NOTE: Actuellement désactivé - retourne des listes vides.
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/// OpenCV n'est pas disponible sur Windows pour le moment.
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class OpenCVImpactDetectionService {
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/// Détecte les impacts dans une image en utilisant OpenCV
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///
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/// STUB: Retourne une liste vide car OpenCV est désactivé.
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List<OpenCVDetectedImpact> detectImpacts(
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String imagePath, {
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OpenCVDetectionSettings settings = const OpenCVDetectionSettings(),
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}) {
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print('OpenCV est désactivé - utilisation de la détection classique recommandée');
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return [];
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try {
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final img = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
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if (img.isEmpty) return [];
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final gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY);
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// Apply blur to reduce noise
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final blurKSize = (settings.blurSize, settings.blurSize);
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final blurred = cv.gaussianBlur(gray, blurKSize, 2, sigmaY: 2);
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final List<OpenCVDetectedImpact> detectedImpacts = [];
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final circles = cv.HoughCircles(
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blurred,
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cv.HOUGH_GRADIENT,
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1,
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settings.minDist,
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param1: settings.param1,
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param2: settings.param2,
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minRadius: settings.minRadius,
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maxRadius: settings.maxRadius,
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);
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if (circles.rows > 0 && circles.cols > 0) {
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for (int i = 0; i < circles.cols; i++) {
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// Access circle data: x, y, radius
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// Assuming common Vec3f layout
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final vec = circles.at<cv.Vec3f>(0, i);
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final x = vec.val1;
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final y = vec.val2;
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final r = vec.val3;
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detectedImpacts.add(
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OpenCVDetectedImpact(
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x: x / img.cols,
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y: y / img.rows,
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radius: r,
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confidence: 0.8,
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method: 'hough',
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),
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);
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}
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}
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// 2. Contour Detection (if enabled)
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if (settings.useContourDetection) {
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// Canny edge detection
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final edges = cv.canny(
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blurred,
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settings.cannyThreshold1,
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settings.cannyThreshold2,
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);
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// Find contours
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final contoursResult = cv.findContours(
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edges,
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cv.RETR_EXTERNAL,
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cv.CHAIN_APPROX_SIMPLE,
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);
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final contours = contoursResult.$1;
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// hierarchy is item2
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for (int i = 0; i < contours.length; i++) {
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final contour = contours[i];
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// Filter by area
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final area = cv.contourArea(contour);
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if (area < settings.minContourArea ||
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area > settings.maxContourArea) {
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continue;
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}
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// Filter by circularity
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final perimeter = cv.arcLength(contour, true);
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if (perimeter == 0) continue;
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final circularity = 4 * math.pi * area / (perimeter * perimeter);
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if (circularity < settings.minCircularity) continue;
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// Get bounding circle
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final enclosingCircle = cv.minEnclosingCircle(contour);
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final center = enclosingCircle.$1;
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final radius = enclosingCircle.$2;
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// Avoid duplicates (simple distance check against Hough results)
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bool isDuplicate = false;
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for (final existing in detectedImpacts) {
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final dx = existing.x * img.cols - center.x;
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final dy = existing.y * img.rows - center.y;
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final dist = math.sqrt(dx * dx + dy * dy);
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if (dist < radius) {
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isDuplicate = true;
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break;
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}
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}
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if (!isDuplicate) {
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detectedImpacts.add(
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OpenCVDetectedImpact(
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x: center.x / img.cols,
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y: center.y / img.rows,
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radius: radius,
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confidence: circularity, // Use circularity as confidence
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method: 'contour',
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),
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);
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}
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}
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}
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return detectedImpacts;
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} catch (e) {
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// print('OpenCV Error: $e');
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return [];
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}
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}
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/// Détecte les impacts en utilisant une image de référence
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///
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/// STUB: Retourne une liste vide car OpenCV est désactivé.
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List<OpenCVDetectedImpact> detectFromReferences(
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String imagePath,
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List<({double x, double y})> referencePoints, {
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double tolerance = 2.0,
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}) {
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print('OpenCV est désactivé - utilisation de la détection par références classique recommandée');
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return [];
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// Basic implementation: use average color/brightness of reference points
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// This is a placeholder for a more complex template matching or feature matching
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// For now, we can just run the standard detection but filter results
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// based on properties of the reference points (e.g. size/radius if we had it).
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// Returning standard detection for now to enable the feature.
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return detectImpacts(imagePath);
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}
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}
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155
lib/services/opencv_target_service.dart
Normal file
155
lib/services/opencv_target_service.dart
Normal file
@@ -0,0 +1,155 @@
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import 'dart:math' as math;
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import 'package:opencv_dart/opencv_dart.dart' as cv;
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class TargetDetectionResult {
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final double centerX;
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final double centerY;
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final double radius;
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final bool success;
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TargetDetectionResult({
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required this.centerX,
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required this.centerY,
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required this.radius,
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this.success = true,
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});
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factory TargetDetectionResult.failure() {
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return TargetDetectionResult(
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centerX: 0.5,
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centerY: 0.5,
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radius: 0.4,
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success: false,
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);
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}
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}
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class OpenCVTargetService {
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/// Detect the main target (center and radius) from an image file
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Future<TargetDetectionResult> detectTarget(String imagePath) async {
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try {
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// Read image
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final img = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
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if (img.isEmpty) {
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return TargetDetectionResult.failure();
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}
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// Convert to grayscale
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final gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY);
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// Apply Gaussian blur to reduce noise
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final blurred = cv.gaussianBlur(gray, (9, 9), 2, sigmaY: 2);
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// Detect circles using Hough Transform
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// Parameters need to be tuned for the specific target type
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final circles = cv.HoughCircles(
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blurred,
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cv.HOUGH_GRADIENT,
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1, // dp: Inverse ratio of the accumulator resolution to the image resolution
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(img.rows / 8)
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.toDouble(), // minDist: Minimum distance between the centers of the detected circles
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param1: 100, // param1: Gradient value for Canny edge detection
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param2:
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30, // param2: Accumulator threshold for the circle centers at the detection stage
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minRadius: img.cols ~/ 20, // minRadius
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maxRadius: img.cols ~/ 2, // maxRadius
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);
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// HoughCircles returns a Mat in opencv_dart? Or a specific object?
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// Checking common bindings: usually returns a Mat (1, N, 3) of floats.
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// If circles is empty or null, return failure.
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if (circles.isEmpty) {
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// Try with different parameters if first attempt fails (more lenient)
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final looseCircles = cv.HoughCircles(
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blurred,
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cv.HOUGH_GRADIENT,
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1,
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(img.rows / 8).toDouble(),
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param1: 50,
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param2: 20,
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minRadius: img.cols ~/ 20,
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maxRadius: img.cols ~/ 2,
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);
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if (looseCircles.isEmpty) {
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return TargetDetectionResult.failure();
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}
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return _findBestCircle(looseCircles, img.cols, img.rows);
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}
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return _findBestCircle(circles, img.cols, img.rows);
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} catch (e) {
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// print('Error detecting target with OpenCV: $e');
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return TargetDetectionResult.failure();
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}
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}
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TargetDetectionResult _findBestCircle(cv.Mat circles, int width, int height) {
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// circles is a Mat of shape (1, N, 3) where N is number of circles
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// Each circle is (x, y, radius)
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// We want the circle that is closest to the center of the image and reasonably large
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double bestScore = -1.0;
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double bestX = 0.5;
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double bestY = 0.5;
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double bestRadius = 0.4;
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// The shape is typically (1, N, 3) for HoughCircles
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// We need to access the data.
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// Assuming we can iterate.
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// In opencv_dart 1.0+, Mat might not be directly iterable like a list.
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// We can use circles.at<Vec3f>(0, i) if available or similar.
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// Or we might need to interpret the memory.
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// For now, let's assume a simplified access pattern or that we can get a list.
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// If this fails to compile, we will fix it based on the error.
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// Attempting to access knowing standard layout:
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// circles.rows is 1, circles.cols is N.
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final int numCircles = circles.cols;
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for (int i = 0; i < numCircles; i++) {
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// Use the generic 'at' or specific getter if known.
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// Assuming Vec3f is returned as a specific type or List<double>
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// Note: in many dart bindings, we might get a list of points directly.
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// But HoughCircles typically returns Mat.
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// Let's try to use `at<cv.Vec3f>(0, i)` which is common in C++ and some bindings.
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// If not, we might need `ptr` access.
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final vec = circles.at<cv.Vec3f>(0, i);
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final double x = vec.val1;
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final double y = vec.val2;
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final double r = vec.val3;
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final relX = x / width;
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final relY = y / height;
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final relR = r / math.min(width, height);
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// Score based on centrality and size
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final distFromCenter = math.sqrt(
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math.pow(relX - 0.5, 2) + math.pow(relY - 0.5, 2),
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);
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final sizeScore =
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relR; // Larger is usually better for the main target outer ring
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// We penalize distance from center
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final score = sizeScore - (distFromCenter * 0.5);
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if (score > bestScore) {
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bestScore = score;
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bestX = relX;
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bestY = relY;
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bestRadius = relR;
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}
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}
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return TargetDetectionResult(
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centerX: bestX,
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centerY: bestY,
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radius: bestRadius,
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);
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}
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}
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