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admin
2026-08-26 18:15:54 +08:00
parent 54d343b739
commit a4568d8a55
79 changed files with 11264 additions and 560 deletions
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flutter_app/lib/app.dart
+4
View File
@@ -8,6 +8,7 @@ import 'camera/camera_screen.dart';
import 'container.dart';
import 'home/home_screen.dart';
import 'legal/terms_screen.dart';
import 'models/model_manager.dart';
import 'payment/paywall_screen.dart';
import 'update/update_checker.dart';
import 'update/update_screen.dart';
@@ -93,6 +94,9 @@ class _StartupGateState extends State<StartupGate> {
}
final token = await session.readToken();
if (!mounted) return;
// 模型热更新：后台拉取模型目录并下载/更新各数据集模型（不阻塞启动，
// 相机页打开前若未就绪会兜底等待；下载失败回退内置资产模型）
ModelManager.instance.refresh();
Navigator.of(context)
.pushReplacementNamed(token == null ? '/login' : '/home');
}
flutter_app/lib/camera/camera_screen.dart
+34 -2
View File
@@ -6,6 +6,7 @@ import 'package:permission_handler/permission_handler.dart';
import 'package:wakelock_plus/wakelock_plus.dart';
import '../detection/detector_worker.dart';
import '../models/model_manager.dart';
import '../reminder/reminder.dart';
import 'app_camera_controller.dart';
import 'camera_view_model.dart';
@@ -119,8 +120,18 @@ class _CameraScreenState extends State<CameraScreen> {
setState(() => _permissionGranted = granted);
if (!granted) return;
// 模型热更新：优先使用已下载的数据集模型（启动时后台拉取；此处兜底等待，
// 下载慢/失败不阻塞相机启动——无下载模型时 worker 回退内置资产）
if (!ModelManager.instance.ready) {
try {
await ModelManager.instance
.refresh()
.timeout(const Duration(seconds: 15));
} catch (_) {}
}
// 模型加载/推理在后台 isolate，不阻塞 UI；worker 为 null 时仅预览并提示
final worker = await DetectorWorker.create();
final worker = await DetectorWorker.create(
models: ModelManager.instance.models);
final viewModel = CameraViewModel(reminder: Reminder());
viewModel.setModelReady(worker != null);
final analyzer = FrameAnalyzer(worker: worker, viewModel: viewModel);
@@ -276,6 +287,27 @@ class _CameraScreenState extends State<CameraScreen> {
return Stack(
fit: StackFit.expand,
children: [
// 模型热更新下载失败提示（已加载模型仍可用，仅提示补更新）
if (vm.state.modelReady && ModelManager.instance.error != null)
Positioned(
left: 16,
right: 16,
top: MediaQuery.of(context).padding.top + 56,
child: Container(
padding:
const EdgeInsets.symmetric(horizontal: 12, vertical: 6),
decoration: BoxDecoration(
color: Colors.black54,
borderRadius: BorderRadius.circular(8),
),
child: Text(
ModelManager.instance.error!,
textAlign: TextAlign.center,
style: const TextStyle(color: Colors.orange, fontSize: 12),
),
),
),
// 模型未加载时仅显示相机预览，不做检测标注（横幅置于顶栏下方，避免与底部诊断行重叠）
if (!vm.state.modelReady)
Positioned(
@@ -305,7 +337,7 @@ class _CameraScreenState extends State<CameraScreen> {
crossAxisAlignment: CrossAxisAlignment.center,
children: [
Text(
'阈值:${(_minScore * 100).toStringAsFixed(0)}% 模型:${vm.state.modelReady ? '已加载' : '未加载'} 帧:${vm.state.framesReceived} 流:${camera?.streamCallbacks ?? 0} 推理:${vm.state.debugDetectCalls}次 异常:${vm.state.debugDetectErrors}次 处理:${vm.state.debugLastMs}ms 最高分:${(vm.state.debugHighestScore * 100).toStringAsFixed(1)}% 图:${vm.state.imageWidthPx}x${vm.state.imageHeightPx} 传感:${camera?.sensorOrientation ?? '-'} 屏转:${camera?.displayDegrees ?? '-'} 旋:${camera?.rotationDegrees ?? 0} turn:${camera?.quarterTurns ?? '-'}',
'阈值:${(_minScore * 100).toStringAsFixed(0)}% 模型:${vm.state.modelReady ? ModelManager.instance.modelsLabel : '未加载'} 帧:${vm.state.framesReceived} 流:${camera?.streamCallbacks ?? 0} 推理:${vm.state.debugDetectCalls}次 异常:${vm.state.debugDetectErrors}次 处理:${vm.state.debugLastMs}ms 最高分:${(vm.state.debugHighestScore * 100).toStringAsFixed(1)}% 图:${vm.state.imageWidthPx}x${vm.state.imageHeightPx} 传感:${camera?.sensorOrientation ?? '-'} 屏转:${camera?.displayDegrees ?? '-'} 旋:${camera?.rotationDegrees ?? 0} turn:${camera?.quarterTurns ?? '-'}',
style: const TextStyle(color: Colors.white70, fontSize: 12),
),
if (_nativeStats.isNotEmpty)
flutter_app/lib/camera/detection_overlay.dart
+5 -2
View File
@@ -87,10 +87,13 @@ class _OverlayPainter extends CustomPainter {
_drawDashedRect(canvas, box, paint);
}
// 标签：框上方，含距离
// 标签：框上方，含距离；多模型时标注来源模型名（内置资产不标）
final dist = _distanceLabel(r);
final modelTag = r.modelName.isNotEmpty && r.modelName != '内置'
? '[${r.modelName}]'
: '';
final text =
'${_labels[r.label] ?? r.label} ${(r.score * 100).toInt()}%$dist';
'${_labels[r.label] ?? r.label}$modelTag ${(r.score * 100).toInt()}%$dist';
final textPainter = TextPainter(
text: TextSpan(
text: text,
flutter_app/lib/detection/detection_result.dart
+8
View File
@@ -9,6 +9,10 @@ class DetectionResult {
/// 轨迹已确认（多帧稳定/高分/活动确认），false = 候选，渲染为虚线
final bool confirmed;
/// 产出该框的模型（数据集 id 与名称；内置资产模型为 -1/空）
final int modelId;
final String modelName;
const DetectionResult({
required this.label,
required this.score,
@@ -17,6 +21,8 @@ class DetectionResult {
required this.right,
required this.bottom,
this.confirmed = true,
this.modelId = -1,
this.modelName = '',
});
double get width => right - left;
@@ -40,6 +46,8 @@ class DetectionResult {
right: right ?? this.right,
bottom: bottom ?? this.bottom,
confirmed: confirmed ?? this.confirmed,
modelId: modelId,
modelName: modelName,
);
}
flutter_app/lib/detection/detector_worker.dart
+131 -51
View File
@@ -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;
}
flutter_app/lib/detection/tflite_detector.dart
+30 -10
View File
@@ -11,7 +11,9 @@ import 'nms.dart';
/// cx/cy/w/h 已归一化，类别得分已过 sigmoid；按 out[dim][anchor] 索引。
/// 输入为 NCHW [1, 3, 704, 704]（litert 导出保留 torch 布局）。
class TfliteDetector {
static const int inputSize = 704;
// 输入尺寸取自模型本身（ultralytics litert 导出 NCHW [1,3,H,W]，各数据集
// 训练 imgsz 可不同），默认 704 兜底
static const int defaultInputSize = 704;
// 野鸡数据置信度普遍偏低（0.1~0.2 量级），保留低分池供运动检测提升；
// 可运行时调整（设置页滑块），默认 0.10
double minScore = 0.10;
@@ -24,39 +26,55 @@ class TfliteDetector {
final List<String> _labels;
final int _numClasses;
final int _numAnchors;
final int inputSize;
final Float32List _input =
Float32List(1 * inputSize * inputSize * 3);
/// 模型身份（多模型并行推理区分来源；内置资产模型为 -1/空）
final int modelId;
final String modelName;
late final Float32List _input;
/// 输出按模型形状 [1, 4+nc, anchors] 的嵌套 List 组织，
/// run() 要求输出对象形状与模型完全一致（扁平 List 会被拒）。
final List<List<List<double>>> _output;
TfliteDetector._(this._interpreter, this._labels, this._numClasses,
this._numAnchors, this._output);
this._numAnchors, this._output, this.inputSize, this.modelId,
this.modelName) {
_input = Float32List(1 * inputSize * inputSize * 3);
}
/// 模型缺失或加载失败返回 null（App 降级为仅预览）。
/// 在后台 isolate 内调用（模型字节由主 isolate 读取后传入）。
static Future<TfliteDetector?> fromBuffer(
Uint8List bytes, List<String> labels) async {
Uint8List bytes,
List<String> labels, {
int modelId = -1,
String modelName = '',
}) async {
try {
final interpreter = Interpreter.fromBuffer(
bytes,
options: InterpreterOptions()..threads = 4,
);
return TfliteDetector._fromModel(interpreter, labels);
return TfliteDetector._fromModel(
interpreter, labels, modelId, modelName);
} catch (_) {
return null;
}
}
/// 输出布局 [1, 4+nc, anchors] 取自模型本身，类别数不与 labels 文件长度耦合。
factory TfliteDetector._fromModel(
Interpreter interpreter, List<String> labels) {
factory TfliteDetector._fromModel(Interpreter interpreter,
List<String> labels, int modelId, String modelName) {
final shape = interpreter.getOutputTensor(0).shape;
final numClasses =
shape.length >= 3 && shape[1] > 4 ? shape[1] - 4 : labels.length;
final numAnchors = shape.length >= 3 && shape[2] > 0 ? shape[2] : 2100;
final inputShape = interpreter.getInputTensor(0).shape;
final inputSize = inputShape.length >= 4
? inputShape[3]
: defaultInputSize;
final output = List.generate(
1,
(_) => List.generate(
@@ -64,8 +82,8 @@ class TfliteDetector {
(_) => List<double>.filled(numAnchors, 0),
),
);
return TfliteDetector._(
interpreter, labels, numClasses, numAnchors, output);
return TfliteDetector._(interpreter, labels, numClasses, numAnchors,
output, inputSize, modelId, modelName);
}
/// 原始数据接口（后台 isolate 用，不依赖 CameraImage）。
@@ -463,6 +481,8 @@ class TfliteDetector {
top: (cy - h / 2).clamp(0.0, 1.0),
right: (cx + w / 2).clamp(0.0, 1.0),
bottom: (cy + h / 2).clamp(0.0, 1.0),
modelId: modelId,
modelName: modelName,
));
}
final kept = nms(boxes, iouThreshold);
flutter_app/lib/models/model_manager.dart
+285
View File
@@ -0,0 +1,285 @@
import 'dart:convert';
import 'dart:io';
import 'package:crypto/crypto.dart' show sha256;
import 'package:flutter/foundation.dart';
import 'package:http/http.dart' as http;
import 'package:path_provider/path_provider.dart';
import '../config/app_config.dart';
/// 模型目录条目（GET /api/v1/app/update 响应 data.models[]）。
/// 服务器发布模型后随版本检查一同下发，App 按目录逐数据集下载/更新。
class ModelCatalogItem {
final int datasetId;
final String datasetName;
final String version;
final List<String> labels;
final int sizeBytes;
final String sha256;
final String downloadUrl;
const ModelCatalogItem({
required this.datasetId,
required this.datasetName,
required this.version,
required this.labels,
required this.sizeBytes,
required this.sha256,
required this.downloadUrl,
});
factory ModelCatalogItem.fromJson(Map<String, dynamic> j) =>
ModelCatalogItem(
datasetId: (j['datasetId'] as num?)?.toInt() ?? 0,
datasetName: j['datasetName'] as String? ?? '',
version: j['version'] as String? ?? '',
labels: (j['labels'] as List? ?? const [])
.map((e) => e.toString())
.toList(),
sizeBytes: (j['sizeBytes'] as num?)?.toInt() ?? 0,
sha256: j['sha256'] as String? ?? '',
downloadUrl: j['downloadUrl'] as String? ?? '',
);
}
/// 已就绪模型（字节 + 标签，供推理 worker 加载；含内置资产回退模型）
class ModelBundle {
final int datasetId;
final String datasetName;
final String version;
final List<String> labels;
final Uint8List bytes;
const ModelBundle({
required this.datasetId,
required this.datasetName,
required this.version,
required this.labels,
required this.bytes,
});
}
/// 模型热更新管理：启动时拉取模型目录（随 /app/update 公开接口下发，无需登录态），
/// 按需下载/校验/持久化各数据集模型，供相机页多模型并行推理。
///
/// 存储：应用私有目录 `models/<datasetId>/`（model.tflite + labels.json + meta.json），
/// meta 记录 {version, sha256}，服务器发布新版本时按版本+摘要重下，不重复下载旧模型。
class ModelManager extends ChangeNotifier {
static final ModelManager instance = ModelManager._();
final String baseUrl;
final http.Client _client;
final Future<Directory> Function()? _rootDirOverride;
List<ModelBundle> _models = const [];
bool _ready = false;
bool _refreshing = false;
String? _error;
Future<void>? _inFlight;
ModelManager._({String? baseUrl, http.Client? client})
: this(baseUrl: baseUrl, client: client);
/// 可注入 baseUrl / client / 存储根目录（单测用）
@visibleForTesting
ModelManager({
String? baseUrl,
http.Client? client,
Future<Directory> Function()? rootDir,
}) : baseUrl = baseUrl ?? AppConfig.apiBaseUrl,
_client = client ?? http.Client(),
_rootDirOverride = rootDir;
/// 已就绪模型列表（空 = 无服务器模型，回退内置资产）
List<ModelBundle> get models => _models;
/// 是否成功拉取过目录（即使下载失败也为 true，用于区分"从未联网"与"目录为空"）
bool get ready => _ready;
/// 最近一次同步的错误信息（下载失败/校验失败等；目录为空不算错误）
String? get error => _error;
bool get refreshing => _refreshing;
/// 模型名摘要（诊断行展示）：内置 / 数据集名×n
String get modelsLabel {
if (_models.isEmpty) return '内置';
return _models.map((m) => m.datasetName).join(',');
}
/// 拉取目录并同步本地模型；并发调用共享同一进行中的刷新。
Future<void> refresh() {
if (_refreshing) return _inFlight ?? Future.value();
_refreshing = true;
_inFlight = _doRefresh().whenComplete(() {
_refreshing = false;
_inFlight = null;
notifyListeners();
});
return _inFlight!;
}
Future<void> _doRefresh() async {
try {
final res = await _client
.get(Uri.parse('$baseUrl/api/v1/app/update'))
.timeout(const Duration(seconds: 8));
// 服务器 Content-Type 无 charset，http 包默认按 latin1 解码会乱码 → 显式 utf8
final body =
jsonDecode(utf8.decode(res.bodyBytes)) as Map<String, dynamic>;
final data = body['data'] as Map<String, dynamic>? ?? const {};
final list = data['models'] as List? ?? const [];
final catalog = list
.map((e) => ModelCatalogItem.fromJson(e as Map<String, dynamic>))
.toList();
final failed = <String>[];
for (final item in catalog) {
if (!await _ensureLocal(item)) failed.add(item.datasetName);
}
await _prune(catalog);
_models = await _loadBundles(catalog);
_ready = true;
_error = failed.isEmpty
? null
: '模型下载失败:${failed.join(',')}（重试旧模型或稍后再试）';
} catch (e) {
if (!_ready) _error = '模型目录拉取失败:$e';
// 已就绪过则保留旧模型，不覆盖 error（下载级错误优先展示）
}
}
/// 保证目录条目在本地可用：meta 匹配且文件在 → 跳过；否则下载并校验 sha256。
Future<bool> _ensureLocal(ModelCatalogItem item) async {
final dir = await _modelDir(item.datasetId);
try {
final meta = await _readMeta(dir);
final file = File('${dir.path}/model.tflite');
if (meta != null &&
meta['version'] == item.version &&
meta['sha256'] == item.sha256 &&
await file.exists()) {
return true;
}
// 版本更新或文件缺失：下载校验（失败重试一次）
for (var attempt = 0; attempt < 2; attempt++) {
final ok = await _downloadAndVerify(item, dir, file);
if (ok) return true;
await file.delete().catchError((_) => file);
await File('${dir.path}/model.tflite.part')
.delete()
.catchError((_) => file);
}
debugPrint('[ModelManager] 下载失败: ${item.datasetName} ${item.version}');
return false;
} catch (e) {
debugPrint('[ModelManager] _ensureLocal ${item.datasetName}: $e');
return false;
}
}
Future<bool> _downloadAndVerify(
ModelCatalogItem item, Directory dir, File file) async {
final part = File('${file.path}.part');
final sink = part.openWrite();
try {
final res = await _client
.send(http.Request('GET', Uri.parse('$baseUrl${item.downloadUrl}')))
.timeout(const Duration(minutes: 3));
if (res.statusCode != 200) return false;
await res.stream.pipe(sink);
await sink.close();
final bytes = await part.readAsBytes();
final hex = sha256.convert(bytes).toString();
if (item.sha256.isNotEmpty && hex != item.sha256) {
debugPrint('[ModelManager] sha256 不匹配: ${item.datasetName} '
'want=${item.sha256} got=$hex');
return false;
}
await part.rename(file.path);
await dir.create(recursive: true);
await File('${dir.path}/labels.json')
.writeAsString(jsonEncode(item.labels));
await File('${dir.path}/meta.json').writeAsString(jsonEncode({
'version': item.version,
'sha256': item.sha256,
}));
debugPrint('[ModelManager] 已下载 ${item.datasetName} '
'${bytes.length}B -> ${file.path}');
return true;
} catch (e) {
await sink.close().catchError((_) {});
debugPrint('[ModelManager] 下载异常 ${item.datasetName}: $e');
return false;
}
}
/// 清理服务器目录中已下线的数据集模型（不再发布则删本地）
Future<void> _prune(List<ModelCatalogItem> catalog) async {
final root = await _rootDir();
if (!await root.exists()) return;
final keep = catalog.map((c) => '${c.datasetId}').toSet();
await for (final e in root.list()) {
if (e is Directory) {
// 目录 URI 末尾带 '/'，pathSegments 末位为空串 → 过滤后取目录名
final name = e.uri.pathSegments.where((s) => s.isNotEmpty).last;
if (!keep.contains(name)) {
await e.delete(recursive: true).catchError((_) => e);
}
}
}
}
Future<List<ModelBundle>> _loadBundles(
List<ModelCatalogItem> catalog) async {
final bundles = <ModelBundle>[];
for (final item in catalog) {
try {
final dir = await _modelDir(item.datasetId);
final file = File('${dir.path}/model.tflite');
if (!await file.exists()) continue;
final labels = await File('${dir.path}/labels.json').exists()
? (jsonDecode(
await File('${dir.path}/labels.json').readAsString())
as List)
.map((e) => e.toString())
.toList()
: item.labels;
bundles.add(ModelBundle(
datasetId: item.datasetId,
datasetName: item.datasetName,
version: item.version,
labels: labels,
bytes: await file.readAsBytes(),
));
} catch (e) {
debugPrint('[ModelManager] 读取 ${item.datasetName} 失败: $e');
}
}
return bundles;
}
Future<Map<String, dynamic>?> _readMeta(Directory dir) async {
final f = File('${dir.path}/meta.json');
if (!await f.exists()) return null;
try {
return jsonDecode(await f.readAsString()) as Map<String, dynamic>;
} catch (_) {
return null;
}
}
Future<Directory> _rootDir() async {
if (_rootDirOverride != null) return _rootDirOverride();
final support = await getApplicationSupportDirectory();
return Directory('${support.path}/models');
}
Future<Directory> _modelDir(int datasetId) async {
final root = await _rootDir();
final dir = Directory('${root.path}/$datasetId');
await dir.create(recursive: true);
return dir;
}
}
flutter_app/lib/update/update_checker.dart
+3 -1
View File
@@ -39,7 +39,9 @@ class UpdateChecker {
final res = await _client
.get(Uri.parse('$baseUrl/api/v1/app/update'))
.timeout(const Duration(seconds: 8));
final body = jsonDecode(res.body) as Map<String, dynamic>;
// 服务器 Content-Type 无 charset，http 包默认按 latin1 解码会乱码 → 显式 utf8
final body =
jsonDecode(utf8.decode(res.bodyBytes)) as Map<String, dynamic>;
final data = body['data'] as Map<String, dynamic>? ?? const {};
return AppUpdateInfo(
version: data['version'] as String? ?? '',