1

flutter_app/lib/detection/detector_worker.dart

@@ -7,17 +7,27 @@ import 'package:flutter/foundation.dart' show debugPrint;
 import 'package:flutter/services.dart' show rootBundle;
 
 import '../camera/motion_detector.dart';
+import '../models/model_manager.dart';
 import 'background_model.dart';
 import 'detection_result.dart';
+import 'nms.dart';
 import 'tflite_detector.dart';
 import 'visual_prior.dart';
 
 /// 推理工作单元：模型加载与检测全部在后台 isolate 执行，
 /// 主 isolate 只投递帧数据、接收结果，UI 不被推理阻塞（iOS 真机卡顿根因）。
+///
+/// 多模型并行推理：传入 [models]（各数据集下载模型）后，每帧逐模型推理，
+/// 结果按类别分组跨模型 NMS 合并（同标签重复框取高分，不同标签互不压制）；
+/// 无下载模型时回退内置资产模型。
 class DetectorWorker {
   static const String modelAsset = 'assets/model.tflite';
   static const String labelsAsset = 'assets/labels.txt';
 
+  /// 内置资产回退模型的标识
+  static const int builtinModelId = -1;
+  static const String builtinModelName = '内置';
+
   final Isolate _isolate;
   final ReceivePort _responses;
 
@@ -54,16 +64,26 @@ class DetectorWorker {
     });
   }
 
-  /// 读取模型资产并启动后台推理 isolate；加载失败返回 null（App 降级为仅预览）。
-  static Future<DetectorWorker?> create() async {
+  /// 加载模型并启动后台推理 isolate；加载失败返回 null（App 降级为仅预览）。
+  /// [models] 为空时回退内置资产模型（模型缺失同样返回 null）。
+  static Future<DetectorWorker?> create({List<ModelBundle>? models}) async {
     try {
-      final data = await rootBundle.load(modelAsset);
-      final modelBytes =
-          data.buffer.asUint8List(data.offsetInBytes, data.lengthInBytes);
-      final labels = (await rootBundle.loadString(labelsAsset))
-          .split('\n')
-          .where((l) => l.trim().isNotEmpty)
-          .toList();
+      final payload = <List<Object?>>[];
+      if (models != null && models.isNotEmpty) {
+        for (final m in models) {
+          payload.add(
+              [m.bytes, m.labels, m.datasetId, m.datasetName]);
+        }
+      } else {
+        final data = await rootBundle.load(modelAsset);
+        final modelBytes =
+            data.buffer.asUint8List(data.offsetInBytes, data.lengthInBytes);
+        final labels = (await rootBundle.loadString(labelsAsset))
+            .split('\n')
+            .where((l) => l.trim().isNotEmpty)
+            .toList();
+        payload.add([modelBytes, labels, builtinModelId, builtinModelName]);
+      }
 
       final responses = ReceivePort();
       final isolate = await Isolate.spawn(_workerMain, responses.sendPort);
@@ -73,7 +93,7 @@ class DetectorWorker {
           .timeout(const Duration(seconds: 10),
               onTimeout: () => throw TimeoutException('worker port timeout'));
       worker._port = port;
-      port.send(['load', modelBytes, labels]);
+      port.send(['load', payload]);
       await worker._ready.future
           .timeout(const Duration(seconds: 20), onTimeout: () {
         throw TimeoutException('model load timeout');
@@ -147,6 +167,8 @@ class DetectorWorker {
             top: v[3] as double,
             right: v[4] as double,
             bottom: v[5] as double,
+            modelId: v.length > 6 ? (v[6] as num).toInt() : -1,
+            modelName: v.length > 7 ? v[7] as String : '',
           );
         }).toList();
         final motion = (list[5] as List)
@@ -202,7 +224,7 @@ Future<void> _workerMain(SendPort mainPort) async {
   mainPort.send(['port', control.sendPort]);
   mainPort.send(['log', 'worker-start']);
 
-  TfliteDetector? detector;
+  List<TfliteDetector> detectors = const [];
   MotionDetector? motion;
   BackgroundModel? background;
   var lastDualMs = 0; // 双字节序推理诊断节流
@@ -214,12 +236,35 @@ Future<void> _workerMain(SendPort mainPort) async {
         case 'load':
           mainPort.send(['log', 'load-received']);
           try {
-            detector = await TfliteDetector.fromBuffer(
-                list[1] as Uint8List, (list[2] as List).cast<String>());
-            if (detector == null) {
-              mainPort.send(['load-error', 'fromBuffer 返回 null']);
+            // 多模型：逐模型加载，单个失败不阻塞其余；全部失败才报错
+            final loaded = <TfliteDetector>[];
+            final failures = <String>[];
+            for (final entry in list[1] as List) {
+              final e = entry as List;
+              final name = e.length > 3 ? e[3] as String : '';
+              final d = await TfliteDetector.fromBuffer(
+                e[0] as Uint8List,
+                (e[1] as List).cast<String>(),
+                modelId: (e[2] as num).toInt(),
+                modelName: name,
+              );
+              if (d == null) {
+                failures.add(name.isEmpty ? 'unknown' : name);
+              } else {
+                loaded.add(d);
+              }
+            }
+            if (loaded.isEmpty) {
+              mainPort.send([
+                'load-error',
+                '模型加载失败:${failures.join(',')} '
+                    '(fromBuffer 返回 null)'
+              ]);
             } else {
-              mainPort.send(['log', 'fromBuffer-ok']);
+              detectors = loaded;
+              mainPort.send(['log',
+                  'loaded=${loaded.map((d) => d.modelName).join(',')} '
+                      'failed=${failures.isEmpty ? '-' : failures.join(',')}']);
               motion = MotionDetector();
               background = BackgroundModel();
               mainPort.send(['ready']);
@@ -229,10 +274,9 @@ Future<void> _workerMain(SendPort mainPort) async {
           }
           break;
         case 'frame':
-          final d = detector;
           final m = motion;
           final b = background;
-          if (d == null || m == null || b == null) break;
+          if (detectors.isEmpty || m == null || b == null) break;
           final frame = list[1] as List;
           final planes = (frame[0] as List).cast<Uint8List>();
           final strides = (frame[1] as List).cast<int>();
@@ -302,42 +346,49 @@ Future<void> _workerMain(SendPort mainPort) async {
               uv1 += ' v:min=$vMin max=$vMax mean=${(vSum / n2).toStringAsFixed(0)}';
             }
           }
-          // 首帧（或相机重启后）自适应判定 YUV 值域与色序，再跑正式推理
-          if (!isBgra && !d.yuvModeKnown) {
-            d.decideYuvChroma(
-              planes: planes,
-              strides: strides,
-              width: width,
-              height: height,
-            );
-          }
-          var results = d.detectRaw(
-            planes: planes,
-            strides: strides,
-            width: width,
-            height: height,
-            isBgra: isBgra,
-            rgbaOrder: rgbaOrder,
-          );
-          // 自愈：判定后 1.5s 内无检测且帧可用 → 用实时帧重跑完整判定
-          // （首帧模糊/暗帧导致启发式猜错时，画面稳定后 oracle 可分胜负）
-          if (!isBgra && d.yuvModeKnown && !d.yuvRetried &&
-              results.length <= 1 &&
-              DateTime.now().millisecondsSinceEpoch - d.yuvDecisionMs > 1500 &&
-              d.retryDecision(
+          // 多模型并行推理：每模型先首帧自适应判定 YUV 值域/色序，再逐模型推理；
+          // 汇总后按类别分组跨模型 NMS 合并（同标签重复框取高分，异标签互不压制）
+          var results = <DetectionResult>[];
+          for (final d in detectors) {
+            if (!isBgra && !d.yuvModeKnown) {
+              d.decideYuvChroma(
                 planes: planes,
                 strides: strides,
                 width: width,
                 height: height,
-              )) {
-            results = d.detectRaw(
+              );
+            }
+            var dets = d.detectRaw(
               planes: planes,
               strides: strides,
               width: width,
               height: height,
               isBgra: isBgra,
+              rgbaOrder: rgbaOrder,
             );
+            // 自愈：判定后 1.5s 内无检测且帧可用 → 用实时帧重跑完整判定
+            // （首帧模糊/暗帧导致启发式猜错时，画面稳定后 oracle 可分胜负）
+            if (!isBgra && d.yuvModeKnown && !d.yuvRetried &&
+                dets.length <= 1 &&
+                DateTime.now().millisecondsSinceEpoch - d.yuvDecisionMs >
+                    1500 &&
+                d.retryDecision(
+                  planes: planes,
+                  strides: strides,
+                  width: width,
+                  height: height,
+                )) {
+              dets = d.detectRaw(
+                planes: planes,
+                strides: strides,
+                width: width,
+                height: height,
+                isBgra: isBgra,
+              );
+            }
+            results.addAll(dets);
           }
+          results = mergeAcrossModels(results, TfliteDetector.iouThreshold);
           // 低分野鸡框过视觉先验（颜色/位置），减少户外误报
           results = VisualPrior.filter(
             results,
@@ -358,14 +409,14 @@ Future<void> _workerMain(SendPort mainPort) async {
           if (isBgra &&
               DateTime.now().millisecondsSinceEpoch - lastDualMs > 3000) {
             lastDualMs = DateTime.now().millisecondsSinceEpoch;
-            final a = d.diagnoseOrder(
+            final a = detectors.first.diagnoseOrder(
                 planes: planes,
                 strides: strides,
                 width: width,
                 height: height,
                 isBgra: true,
                 rgbaOrder: false);
-            final b = d.diagnoseOrder(
+            final b = detectors.first.diagnoseOrder(
                 planes: planes,
                 strides: strides,
                 width: width,
@@ -382,8 +433,16 @@ Future<void> _workerMain(SendPort mainPort) async {
             width,
             height,
             results
-                .map((r) =>
-                    [r.label, r.score, r.left, r.top, r.right, r.bottom])
+                .map((r) => [
+                      r.label,
+                      r.score,
+                      r.left,
+                      r.top,
+                      r.right,
+                      r.bottom,
+                      r.modelId,
+                      r.modelName,
+                    ])
                 .toList(),
             motionRegions
                 .map((mr) => [mr.left, mr.top, mr.right, mr.bottom])
@@ -395,17 +454,21 @@ Future<void> _workerMain(SendPort mainPort) async {
             'planes=${planes.length} yLen=${yPlane.length} stride=${strides[0]} '
                 'y:min=$yMin max=$yMax mean=${yMean.toStringAsFixed(1)} '
                 'diff=${yDiff < 0 ? '-' : yDiff.toStringAsFixed(3)} '
-                'uv1:[$uv1] | ${d.yuvDiag}$dualDiag',
+                'uv1:[$uv1] | ${detectors.first.yuvDiag}$dualDiag',
           ]);
           break;
         case 'reset':
           motion?.reset();
           background?.reset();
-          detector?.resetYuvMode();
+          for (final d in detectors) {
+            d.resetYuvMode();
+          }
           break;
         case 'set-min-score':
-          detector?.minScore = (list[1] as num).toDouble();
-          mainPort.send(['log', 'min-score=${detector?.minScore}']);
+          for (final d in detectors) {
+            d.minScore = (list[1] as num).toDouble();
+          }
+          mainPort.send(['log', 'min-score=${detectors.isEmpty ? '-' : detectors.first.minScore}']);
       }
     } catch (e, st) {
       mainPort.send([
@@ -415,3 +478,20 @@ Future<void> _workerMain(SendPort mainPort) async {
     }
   }
 }
+
+/// 多模型结果合并：按类别分组，组内 NMS（不同模型检出同一目标时取高分）。
+/// 各模型类别体系独立（如野鸡/疑似 vs 野兔/疑似），不同类别互不压制。
+List<DetectionResult> mergeAcrossModels(
+    List<DetectionResult> all, double iouThreshold) {
+  if (all.length <= 1) return all;
+  final byLabel = <String, List<DetectionResult>>{};
+  for (final r in all) {
+    byLabel.putIfAbsent(r.label, () => []).add(r);
+  }
+  final merged = <DetectionResult>[];
+  for (final group in byLabel.values) {
+    merged.addAll(nms(group, iouThreshold));
+  }
+  merged.sort((a, b) => b.score.compareTo(a.score));
+  return merged;
+}