307 lines
11 KiB
Dart
307 lines
11 KiB
Dart
import 'dart:math' as math;
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import 'package:flutter/foundation.dart';
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import '../detection/detection_result.dart';
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import '../detection/motion_aggregator.dart';
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import '../detection/nms.dart';
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import '../reminder/reminder.dart';
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@immutable
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class CameraUiState {
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final bool modelReady;
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final List<DetectionResult> results;
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final int rotation;
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final int imageWidthPx;
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final int imageHeightPx;
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final double debugHighestScore;
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final int debugDetectCalls;
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final int debugDetectErrors;
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final String? debugLastError;
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final int framesReceived;
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final int debugLastMs;
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final String debugYuv;
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/// 近 [CameraViewModel.latencyWindow] 帧推理耗时滚动平均（毫秒；无样本为 null）。
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/// 给 C 端用户看的设备识别延迟。
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final double? latencyAvgMs;
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const CameraUiState({
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this.modelReady = false,
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this.results = const [],
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this.rotation = 90,
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this.imageWidthPx = 0,
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this.imageHeightPx = 0,
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this.debugHighestScore = 0,
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this.debugDetectCalls = 0,
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this.debugDetectErrors = 0,
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this.debugLastError,
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this.framesReceived = 0,
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this.debugLastMs = 0,
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this.debugYuv = '',
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this.latencyAvgMs,
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});
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}
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/// 检测结果置信度分级与轨迹确认。
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///
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/// - [highConf]（0.35）：高于此分直接确认显示；真实目标多为 0.1~0.2，
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/// 高于 0.35 视为强证据。
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/// - 低于 0.35 的框：需要多帧稳定（[confirmFrames] 帧）或 活动证据
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/// （运动区域/背景新出现区域重叠）才确认显示。
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/// - 同位置去重（2026-09-03）：多模型对同一目标交替检出时各轨迹都会在
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/// 忘记窗内持续显示 → 轨迹层跨标签同目标补挂 + 显示层
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/// [dedupeVisibleOverlaps] 同类别强重叠只保留高置信度框。
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class CameraViewModel extends ChangeNotifier {
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static const int maxTracks = 30;
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static const double motionBoost = 0.15;
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static const double highConf = 0.35;
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static const int confirmFrames = 3;
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static const double associateRadius = 0.12;
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static const int displayAgeMs = 500;
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static const int forgetMs = 2000;
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/// 延迟滚动平均窗口（帧数）：单帧抖动大，取近期均值给用户展示
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static const int latencyWindow = 30;
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final List<int> _procWindow = [];
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/// 推理后台 isolate 是否就绪（由相机页创建 worker 后设置）
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bool modelReady = false;
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final Reminder reminder;
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CameraUiState _state;
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CameraUiState get state => _state;
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final Map<int, _Track> _tracks = {};
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int _nextTrackId = 0;
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CameraViewModel({required this.reminder}) : _state = const CameraUiState();
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void setModelReady(bool ready) {
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if (modelReady == ready) return;
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modelReady = ready;
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// 换 worker / 停识别：旧窗口样本作废，从零起算
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_procWindow.clear();
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_state = CameraUiState(modelReady: ready);
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notifyListeners();
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}
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/// 帧分析回调（分析流调用）
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void onFramesAnalyzed(
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List<DetectionResult> results,
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int rotation,
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int imageWidthPx,
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int imageHeightPx,
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List<MotionRegion> motionRegions,
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List<MotionRegion> noveltyRegions, {
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int detectCalls = 0,
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int detectErrors = 0,
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String? lastError,
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int framesReceived = 0,
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int lastProcessMs = 0,
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String yuvDiag = '',
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}) {
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final now = DateTime.now().millisecondsSinceEpoch;
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_associate(results, motionRegions, noveltyRegions, now);
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final visible = <DetectionResult>[];
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for (final t in _tracks.values) {
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if (now - t.firstSeenMs < displayAgeMs) continue;
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if (now - t.lastSeenMs > forgetMs) continue;
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if (!_shouldDisplay(t, motionRegions, noveltyRegions)) continue;
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var r = t.result;
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// 低分确认目标 + 活动证据 → 分数提升，便于视觉区分
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if (t.confirmed &&
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r.score < highConf &&
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_hasActivity(r, motionRegions, noveltyRegions)) {
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r = r.copyWith(score: (r.score + motionBoost).clamp(0.0, 1.0));
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}
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visible.add(r.copyWith(confirmed: t.confirmed));
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}
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// 提醒：仅新确认的目标物种轨迹（label 非 suspect 即目标——单物种模型
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// class 0、综合模型各物种索引 0..N-1；确认瞬间触发一次，10s 冷却在 Reminder 内）
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for (final t in _tracks.values) {
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final isSuspect = t.result.label == 'suspect';
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if (isSuspect || !t.confirmed || t.reminded) continue;
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final age = now - t.firstSeenMs;
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if (age >= displayAgeMs && age <= displayAgeMs + 1600 &&
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now - t.lastSeenMs <= 300) {
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t.reminded = true;
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reminder.onDetected(t.label);
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}
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}
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var highest = 0.0;
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for (final r in results) {
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if (r.score > highest) highest = r.score;
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}
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if (lastProcessMs > 0) {
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_procWindow.add(lastProcessMs);
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if (_procWindow.length > latencyWindow) {
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_procWindow.removeAt(0);
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}
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}
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final latencyAvg = _procWindow.isEmpty
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? null
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: _procWindow.reduce((a, b) => a + b) / _procWindow.length;
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_state = CameraUiState(
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modelReady: modelReady,
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results: dedupeVisibleOverlaps(visible),
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rotation: rotation,
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imageWidthPx: imageWidthPx,
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imageHeightPx: imageHeightPx,
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debugHighestScore: highest,
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debugDetectCalls: detectCalls,
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debugDetectErrors: detectErrors,
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debugLastError: lastError,
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framesReceived: framesReceived,
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debugLastMs: lastProcessMs,
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debugYuv: yuvDiag.isNotEmpty ? yuvDiag : _state.debugYuv,
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latencyAvgMs: latencyAvg,
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);
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notifyListeners();
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}
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/// 检测框 → 轨迹关联：按中心距离就近匹配（同标签优先，跨标签收紧距离），
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/// 未匹配则新建候选轨迹。
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void _associate(
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List<DetectionResult> results,
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List<MotionRegion> motionRegions,
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List<MotionRegion> noveltyRegions,
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int now) {
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final matched = <int>{};
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for (final r in results) {
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if (!_plausible(r)) continue;
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_Track? best;
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var bestD = associateRadius;
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for (final t in _tracks.values) {
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if (matched.contains(t.id)) continue;
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final d = _centerDist(t.result, r);
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// 同标签宽松匹配；跨标签（目标↔疑似 抖动）收紧到 60%
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final limit = t.label == r.label ? bestD : associateRadius * 0.6;
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if (d < limit) {
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bestD = d;
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best = t;
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}
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}
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// 跨标签同目标补挂（2026-09-03 用户实测修订：重叠位置只保留高置信度框）：
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// 中心距超过跨标签收紧半径、但几何强重叠指向同一位置（不同模型对同一
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// 目标的框偏移/紧致度不同）的检测并入既有轨迹，防止同目标两条轨迹并存
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// → 同位置双名常驻/重复提醒。疑似↔目标（不同类别预警）不并入。
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if (best == null) {
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_Track? adopt;
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var adoptD = double.infinity;
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for (final t in _tracks.values) {
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if (matched.contains(t.id)) continue;
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if (t.isSuspect != r.isSuspect || !sameTarget(t.result, r)) continue;
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final d = _centerDist(t.result, r);
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if (d < adoptD) {
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adoptD = d;
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adopt = t;
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}
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}
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best = adopt;
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}
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if (best != null) {
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matched.add(best.id);
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best.update(r, now);
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best.seenCount++;
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if (best.seenCount >= confirmFrames || r.score >= highConf ||
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_hasActivity(r, motionRegions, noveltyRegions)) {
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best.confirmed = true;
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}
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} else {
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final t = _Track(_nextTrackId++, now, r);
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t.seenCount = 1;
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t.confirmed = r.score >= highConf ||
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_hasActivity(r, motionRegions, noveltyRegions);
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_tracks[t.id] = t;
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}
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}
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_tracks.removeWhere(
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(id, t) => !matched.contains(id) && now - t.lastSeenMs > forgetMs);
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}
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/// 显示判定（按类别策略）：
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/// - 疑似（生境预警）：设计意图是常驻静态预警，始终显示（渲染侧弱化）
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/// - 目标物种：确认轨迹直接显示；未确认的只有在高分或活动证据时才显示
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bool _shouldDisplay(_Track t, List<MotionRegion> motionRegions,
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List<MotionRegion> noveltyRegions) {
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if (t.label == 'suspect') return true;
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if (t.confirmed) return true;
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return t.result.score >= highConf ||
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_hasActivity(t.result, motionRegions, noveltyRegions);
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}
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/// 活动证据：与运动区域或背景新出现区域重叠
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bool _hasActivity(DetectionResult r, List<MotionRegion> motionRegions,
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List<MotionRegion> noveltyRegions) =>
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motionRegions.any((m) => MotionAggregator.centerInRegion(r, m)) ||
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noveltyRegions.any((m) => MotionAggregator.centerInRegion(r, m));
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/// 物理合理性过滤：宽高比与相对尺寸（目标物种 20-100px@720 量级）
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bool _plausible(DetectionResult r) {
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final h = r.height;
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final w = r.width;
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if (w <= 0 || h <= 0) return false;
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final aspect = w / h;
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if (aspect < 0.3 || aspect > 3.0) return false;
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if (r.label == 'suspect') return h >= 0.01 && h <= 0.5;
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return h >= 0.01 && h <= 0.3;
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}
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double _centerDist(DetectionResult a, DetectionResult b) =>
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math.sqrt(math.pow(a.centerX - b.centerX, 2) +
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math.pow(a.centerY - b.centerY, 2));
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@override
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void dispose() {
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reminder.release();
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super.dispose();
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}
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}
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class _Track {
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final int id;
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final String label;
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int firstSeenMs;
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int lastSeenMs;
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int seenCount = 0;
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bool confirmed = false;
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bool reminded = false;
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DetectionResult result;
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_Track(this.id, this.firstSeenMs, this.result)
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: lastSeenMs = firstSeenMs,
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label = result.label;
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/// 当前框是否疑似类别（随关联的最新检测更新——轨迹框在跨标签补挂后
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/// 会换成其他标签的框；轨迹 label 仅创建时记账）
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bool get isSuspect => result.isSuspect;
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void update(DetectionResult r, int now) {
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lastSeenMs = now;
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result = r;
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}
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}
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/// 同屏抑制（2026-09-03 用户实测修订：同一位置/重叠位置出现同/异模型检出的
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/// 物种只保留高置信度者）。根因：多个模型对同一目标**交替**检出时，每帧合并
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/// 层只压掉当帧低分框，但各自轨迹都落在 2s 忘记窗内持续显示 → 同位置双名
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/// 常驻。显示层兜底：可见框内同类别（都目标/都疑似）、且 [sameTarget] 强
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/// 重叠指向同一位置的框每帧只保留最高分者。异类别（目标×疑似生境预警）是
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/// 两种语义不同的框，同位置也各自保留。
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List<DetectionResult> dedupeVisibleOverlaps(List<DetectionResult> visible) {
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if (visible.length <= 1) return visible;
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final sorted = [...visible]..sort((a, b) => b.score.compareTo(a.score));
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final kept = <DetectionResult>[];
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for (final r in sorted) {
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if (!kept.any((k) => k.isSuspect == r.isSuspect && sameTarget(k, r))) {
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kept.add(r);
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}
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}
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return kept;
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}
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