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desactivation de tous les scripts et ia, supression du modele yolo

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streaper2
2026-04-28 16:52:20 +02:00
parent e32833e366
commit fba3b41f2f
14 changed files with 382 additions and 285 deletions
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25
lib/services/target_detection_service.dart
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@@ -392,16 +392,21 @@ class TargetDetectionService {
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);
// Use YOLO-detected score (valeur) if available, otherwise calculate it
int score = impact.suggestedScore;
if (score <= 0) {
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,

117
lib/services/yolo_impact_detection_service.dart
View File

@@ -7,25 +7,41 @@ import 'target_detection_service.dart';
class YOLOImpactDetectionService {
Interpreter? _interpreter;
int _inputSize = 640;
List<int> _outputShape = [1, 17, 8400];
int _numClasses = 13;
static const String modelPath = 'assets/models/yolov11n_impact.tflite';
static const String modelPath = 'assets/models/yolov8n_32.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',
);
_interpreter = await Interpreter.fromAsset(modelPath);
// Get model metadata
final inputTensors = _interpreter!.getInputTensors();
if (inputTensors.isNotEmpty) {
// [1, 640, 640, 3] or [1, 3, 640, 640]
final shape = inputTensors[0].shape;
if (shape.length == 4) {
_inputSize = shape[1] == 3 ? shape[2] : shape[1];
}
}
final outputTensors = _interpreter!.getOutputTensors();
if (outputTensors.isNotEmpty) {
_outputShape = outputTensors[0].shape;
// Output is usually [1, 4 + num_classes, num_boxes]
if (_outputShape.length == 3) {
_numClasses = _outputShape[1] - 4;
}
}
print('YOLO Interpreter loaded successfully');
print('Model Input Size: $_inputSize');
print('Model Output Shape: $_outputShape (Classes: $_numClasses)');
} catch (e) {
print('Error loading YOLO model: $e');
}
@@ -37,31 +53,35 @@ class YOLOImpactDetectionService {
try {
final bytes = File(imagePath).readAsBytesSync();
final originalImage = img.decodeImage(bytes);
if (originalImage == null) return [];
final decodedImage = img.decodeImage(bytes);
if (decodedImage == null) return [];
final originalImage = img.bakeOrientation(decodedImage);
// YOLOv8/v11 usually takes 640x640
const int inputSize = 640;
final resizedImage = img.copyResize(
originalImage,
width: inputSize,
height: inputSize,
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]);
var input = _imageToByteListFloat32(resizedImage, _inputSize);
// Allocate output buffer dynamically
var output = List<double>.filled(
_outputShape.fold(1, (a, b) => a * b),
0
).reshape(_outputShape);
_interpreter!.run(input, output);
return _processOutput(
final results = _processOutput(
output[0],
originalImage.width,
originalImage.height,
);
print('YOLO Detection result count: ${results.length}');
return results;
} catch (e) {
print('Error during YOLO inference: $e');
return [];
@@ -76,33 +96,55 @@ class YOLOImpactDetectionService {
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) {
// output is typically [4 + numClasses, numBoxes]
final int rows = output.length;
final int numBoxes = output[0].length;
for (int i = 0; i < numBoxes; i++) {
double maxConfidence = 0;
int bestClass = 4;
// Find best class among all classes (indices 4 to rows-1)
for (int c = 4; c < rows; c++) {
if (output[c][i] > maxConfidence) {
maxConfidence = output[c][i];
bestClass = c;
}
}
if (maxConfidence > threshold) {
final int classIndex = bestClass - 4;
candidates.add(
_Detection(
x: output[0][i],
y: output[1][i],
// Use dynamic mapping: 0,1,2,3 are typically x,y,w,h
// We'll keep the current mapping for now as it matches user's previous model
y: output[0][i],
x: output[1][i],
w: output[2][i],
h: output[3][i],
confidence: confidence,
confidence: maxConfidence,
classIndex: classIndex,
),
);
}
}
// 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,
),
(det) {
// Score = ClassIndex (0 to 10) for impact models
int score = det.classIndex;
return DetectedImpactResult(
x: det.x,
y: det.y,
radius: 5.0,
suggestedScore: score,
);
},
)
.toList();
}
@@ -110,7 +152,6 @@ class YOLOImpactDetectionService {
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 = [];
@@ -164,11 +205,13 @@ class YOLOImpactDetectionService {
class _Detection {
final double x, y, w, h, confidence;
final int classIndex;
_Detection({
required this.x,
required this.y,
required this.w,
required this.h,
required this.confidence,
required this.classIndex,
});
}