2c5bf950c5
## 新架构：Oracle 集中计算 + NAS 代理展示 ### Oracle 端 (fam-edge) - 新增 frame_service: ffmpeg 视频抽帧 + VLM 人物定位裁剪头像（磁盘缓存） - 新增 /api/oracle/frame: 按 video_id+ts 抽帧返回 jpeg（带 token） - 新增 /api/oracle/avatar: 按 label 生成人物头像（VLM 定位人物 + 兜底整帧居中） - 新增 person_identifier: 人物身份识别模块 - Gemini 适配器支持 flash/flash-lite 双模型切换，429 自动降级 - frame_service VLM 全模型 429 时进入 10 分钟熔断，避免每次请求白打配额 - 兜底头像不落缓存，配额恢复后自动重试 VLM 精确定位 ### 人物合并硬规则校验（框架级修复） - person_service: LLM 合并结果落库前加硬冲突检测 - 性别冲突 → 绝不合并 - 年龄档跨未成年/成年 → 绝不合并（防止把爷爷/宝宝并进同一人） - oracle_db: upsert_person 入口剥离括号后缀（人物A（别名：人物B） → 人物A），消灭垃圾人物行 - 修复 set_canonical 丢弃 source 参数的 bug（旧代码硬编码 'manual' 导致错误合并被永久固化） - get_events_for_label: 只提取该身份组的特征文本，头像定位更精准 ### NAS 端 (fam-core) - 新增 img_proxy: /api/proxy/frame 和 /api/proxy/avatar 代理 Oracle 图片 - app.py 注册 img_bp 蓝图 - oracle_sync / db_layer / member_manager 同步人物表 ### UI 端 (fam-ui) - 事件时间轴: 每条事件卡片加时间点缩略帧 - 人物管理: 每人卡片加头像（150x150 圆角） - parse_persons: 剥离括号备注，与 Oracle 归一化一致 - 新增 EventItem 组件、Timeline 页改造 - Chat / ServiceStatus 页相应调整 ### 数据库 - scripts/ddl.sql: 同步表结构更新 - Oracle people 表: features_json / display_uid / source 字段完善
314 lines
15 KiB
Python
314 lines
15 KiB
Python
import json
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from fam_edge.oracle_db import OracleDB
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def _db(tmp_path):
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return OracleDB(str(tmp_path / "oracle.db"))
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def _set_heartbeat_age(db, age_sec):
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"""把心跳时间戳直接改写成"距现在 age_sec 秒前"，用于测试新鲜度阈值边界。"""
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from datetime import datetime, timedelta, timezone
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ts = (datetime.now(timezone(timedelta(hours=8))) - timedelta(seconds=age_sec))
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db.set_cursor('motion_heartbeat_at', ts.strftime('%Y-%m-%d %H:%M:%S'))
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def test_upsert_person_same_gender_merges_into_one_row(tmp_path):
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db = _db(tmp_path)
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db.upsert_person("人物A", features={"gender": "男", "hair": "短发黑色"})
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db.upsert_person("人物A", features={"gender": "男", "clothing": "蓝色T恤"})
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rows = db._conn.execute("SELECT * FROM people").fetchall()
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assert len(rows) == 1
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assert rows[0]["appearances"] == 2
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feats = json.loads(rows[0]["features_json"])
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assert feats["hair"] == "短发黑色"
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assert feats["clothing"] == "蓝色T恤"
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def test_upsert_person_gender_conflict_splits_into_new_label(tmp_path):
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"""核心诉求: 大模型给的 人物A/B/C 这类 uid 只在单次视频分析内稳定，不同视频
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独立编号，同一字符串完全可能撞到不同真人（实测 "人物A" 混了男女两个人）。
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性别冲突时不能直接合并覆盖，要拆成新 label，避免两个人的特征越merge越乱。"""
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db = _db(tmp_path)
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db.upsert_person("人物A", features={"gender": "男", "clothing": "蓝色Polo衫"})
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db.upsert_person("人物A", features={"gender": "女", "clothing": "白色上衣"})
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rows = {r["label"]: r for r in db._conn.execute("SELECT * FROM people").fetchall()}
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assert set(rows.keys()) == {"人物A", "人物A#2"}
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assert rows["人物A"]["appearances"] == 1
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assert json.loads(rows["人物A"]["features_json"])["gender"] == "男"
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assert rows["人物A#2"]["appearances"] == 1
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assert json.loads(rows["人物A#2"]["features_json"])["gender"] == "女"
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# 派生行的 display_uid 仍然记录原始大模型 uid，方便追溯来源
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assert rows["人物A#2"]["display_uid"] == "人物A"
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def test_upsert_person_gender_conflict_allocates_next_free_suffix(tmp_path):
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db = _db(tmp_path)
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db.upsert_person("人物A", features={"gender": "男"})
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db.upsert_person("人物A", features={"gender": "女"}) # -> 人物A#2
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db.upsert_person("人物A", features={"gender": "unknown"}) # unknown 不冲突，合并回 人物A
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db.upsert_person("人物A", features={"gender": "男", "hair": "光头"}) # 冲突，人物A 有 gender 男了不冲突；应仍合并
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labels = {r["label"] for r in db._conn.execute("SELECT label FROM people").fetchall()}
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assert labels == {"人物A", "人物A#2"}
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# 再来一次性别冲突（对着 人物A#2，性别女）应该分配 人物A#3，而不是复用 人物A#2
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db.upsert_person("人物A", features={"gender": "男"})
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db.upsert_person("人物A", features={"gender": "女"})
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# 这次新的女性冲突会先撞到 人物A（此时是男），分裂出下一个空闲后缀
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labels = {r["label"] for r in db._conn.execute("SELECT label FROM people").fetchall()}
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assert "人物A" in labels
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assert len(labels) >= 2
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def test_upsert_person_unknown_gender_never_triggers_split(tmp_path):
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db = _db(tmp_path)
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db.upsert_person("人物A", features={"gender": "男"})
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db.upsert_person("人物A", features={"gender": "unknown"})
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db.upsert_person("人物A", features={"gender": "未知"})
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rows = db._conn.execute("SELECT * FROM people").fetchall()
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assert len(rows) == 1
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assert rows[0]["appearances"] == 3
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def test_upsert_person_no_features_never_triggers_split(tmp_path):
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db = _db(tmp_path)
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db.upsert_person("人物A", features={"gender": "男"})
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db.upsert_person("人物A") # 无 features（source 更新等场景）
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rows = db._conn.execute("SELECT * FROM people").fetchall()
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assert len(rows) == 1
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assert rows[0]["appearances"] == 2
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# ----------------------------------------------------------------------
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# 运动侦测事件（NAS 推送）
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# ----------------------------------------------------------------------
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def test_record_motion_events_upserts_by_event_id(tmp_path):
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db = _db(tmp_path)
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n = db.record_motion_events([
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{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 1000, "duration": 5},
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{"event_id": 2, "camera_id": 2, "event_type": 10, "start_time": 2000, "duration": 3},
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])
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assert n == 2
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rows = db._conn.execute("SELECT * FROM ss_motion_events ORDER BY event_id").fetchall()
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assert len(rows) == 2
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# 重复推送同一个 event_id（幂等）应该更新而不是新增一行
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db.record_motion_events(
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[{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 1000, "duration": 99}])
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rows = db._conn.execute("SELECT * FROM ss_motion_events").fetchall()
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assert len(rows) == 2
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updated = db._conn.execute(
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"SELECT duration FROM ss_motion_events WHERE event_id=1").fetchone()
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assert updated['duration'] == 99
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def test_record_motion_events_skips_missing_event_id(tmp_path):
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db = _db(tmp_path)
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n = db.record_motion_events([{"camera_id": 2, "start_time": 1000}])
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assert n == 0
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def test_heartbeat_age_none_when_never_recorded(tmp_path):
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db = _db(tmp_path)
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assert db.get_motion_heartbeat_age_sec() is None
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def test_heartbeat_age_near_zero_right_after_recording(tmp_path):
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db = _db(tmp_path)
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db.record_motion_heartbeat()
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age = db.get_motion_heartbeat_age_sec()
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assert age is not None and age < 5
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def test_has_motion_in_range_local_fails_open_without_heartbeat(tmp_path):
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"""核心诉求: 从未收到过心跳（冷启动，NAS 推送链路还没接上）必须 fail-open，
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不能因为本地表是空的就悄悄跳过分析。"""
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db = _db(tmp_path)
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assert db.has_motion_in_range_local(1000, 2000) is None
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def test_has_motion_in_range_local_fails_open_when_heartbeat_stale(tmp_path):
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"""核心诉求: 表里有大量历史运动事件（曾经推送链路是健康的），但心跳已经
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过期太久（NAS 服务挂了/网络断了/DSM Webhook 规则被误关）——这时候不能信任
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"查询结果是 0 条 = 确认无运动"，必须当作链路已死，fail-open。"""
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db = _db(tmp_path)
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db.record_motion_events(
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[{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 500, "duration": 10}])
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_set_heartbeat_age(db, 1000) # 超过默认阈值 900s
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assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is None
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def test_has_motion_in_range_local_trusts_result_when_heartbeat_fresh(tmp_path):
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db = _db(tmp_path)
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db.record_motion_heartbeat()
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assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is False
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db.record_motion_events(
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[{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 2500, "duration": 5}])
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assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is True
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def test_has_motion_in_range_local_respects_heartbeat_boundary(tmp_path):
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db = _db(tmp_path)
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_set_heartbeat_age(db, 899)
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assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is not None
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_set_heartbeat_age(db, 901)
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assert db.has_motion_in_range_local(2000, 3000, max_heartbeat_age_sec=900) is None
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def test_has_motion_in_range_local_overlap_semantics(tmp_path):
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"""事件区间 [start_time, start_time+duration] 只要和查询窗口有重叠就算命中，
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不要求事件完全落在窗口内部（也不要求窗口完全覆盖事件）。"""
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db = _db(tmp_path)
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db.record_motion_heartbeat()
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# 事件在窗口开始之前就开始，但持续到窗口内 -> 应该命中
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db.record_motion_events(
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[{"event_id": 1, "camera_id": 2, "event_type": 10, "start_time": 1990, "duration": 20}])
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assert db.has_motion_in_range_local(2000, 3000) is True
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def test_has_motion_in_range_local_ignores_non_motion_event_type(tmp_path):
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db = _db(tmp_path)
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db.record_motion_heartbeat()
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db.record_motion_events(
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[{"event_id": 1, "camera_id": 2, "event_type": 99, "start_time": 2500, "duration": 5}])
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assert db.has_motion_in_range_local(2000, 3000) is False
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def test_has_motion_in_range_local_filters_by_camera_id(tmp_path):
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db = _db(tmp_path)
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db.record_motion_heartbeat()
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db.record_motion_events(
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[{"event_id": 1, "camera_id": 99, "event_type": 10, "start_time": 2500, "duration": 5}])
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assert db.has_motion_in_range_local(2000, 3000, camera_id=2) is False
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assert db.has_motion_in_range_local(2000, 3000, camera_id=99) is True
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# ----------------------------------------------------------------------
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# 人物对应关系表（video_id, raw_uid) -> canonical_name
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# ----------------------------------------------------------------------
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def _seed_video_with_events(db, filename="motion_1_1000.mp4"):
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vid = db.ensure_video(filename, f"/tmp/{filename}", event_start_time="2026-08-22 10:00:00")
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events = [
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{"timestamp": "10:00:01", "description": "在客厅走动", "people": ["人物A"],
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"person_appearances": [{"uid": "人物A", "features": {"gender": "男"}, "action": "走动"}]},
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{"timestamp": "10:00:05", "description": "坐下", "people": ["人物A", "人物B"],
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"person_appearances": [
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{"uid": "人物A", "features": {"gender": "男"}, "action": "坐下"},
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{"uid": "人物B", "features": {"gender": "女"}, "action": "站立"}]},
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]
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db.mark_video_processed(vid, "摘要", events, ["人物A", "人物B"], "gemini")
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return vid
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def test_set_identity_mapping_inserts_new_row(tmp_path):
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db = _db(tmp_path)
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assert db.set_identity_mapping(1, "人物A", "爷爷", source="auto_id") is True
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assert db.get_identity_map_for_video(1) == {"人物A": "爷爷"}
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def test_set_identity_mapping_updates_existing_non_manual_row(tmp_path):
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db = _db(tmp_path)
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db.set_identity_mapping(1, "人物A", "爷爷", source="auto_id")
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assert db.set_identity_mapping(1, "人物A", "爸爸", source="auto_id") is True
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assert db.get_identity_map_for_video(1) == {"人物A": "爸爸"}
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def test_set_identity_mapping_manual_protected_from_auto_overwrite(tmp_path):
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"""核心诉求: 人工纠正过的映射不能被后续自动识别悄悄改回去。"""
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db = _db(tmp_path)
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db.set_identity_mapping(1, "人物A", "爸爸", source="manual")
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changed = db.set_identity_mapping(1, "人物A", "爷爷", source="auto_id")
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assert changed is False
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assert db.get_identity_map_for_video(1) == {"人物A": "爸爸"}
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def test_set_identity_mapping_manual_can_override_manual(tmp_path):
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db = _db(tmp_path)
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db.set_identity_mapping(1, "人物A", "爸爸", source="manual")
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changed = db.set_identity_mapping(1, "人物A", "爷爷", source="manual")
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assert changed is True
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assert db.get_identity_map_for_video(1) == {"人物A": "爷爷"}
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def test_set_identity_mapping_no_change_returns_false(tmp_path):
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db = _db(tmp_path)
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db.set_identity_mapping(1, "人物A", "爷爷", source="auto_id")
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changed = db.set_identity_mapping(1, "人物A", "爷爷", source="auto_id")
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assert changed is False
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def test_get_identity_map_for_video_scoped_per_video(tmp_path):
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"""核心诉求: 同一个 raw_uid 字符串在不同视频里可能是不同真人，映射必须按
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video_id 隔离，不能串。"""
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db = _db(tmp_path)
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db.set_identity_mapping(1, "人物A", "爷爷", source="auto_id")
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db.set_identity_mapping(2, "人物A", "爸爸", source="auto_id")
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assert db.get_identity_map_for_video(1) == {"人物A": "爷爷"}
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assert db.get_identity_map_for_video(2) == {"人物A": "爸爸"}
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def test_rewrite_event_person_names_updates_events_and_video(tmp_path):
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db = _db(tmp_path)
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vid = _seed_video_with_events(db)
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db.rewrite_event_person_names(vid, {"人物A": "爷爷", "人物B": "媳妇"})
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rows = db._conn.execute(
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"SELECT person_list_json, person_appearances_json FROM events "
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"WHERE video_id=? ORDER BY id", (vid,)).fetchall()
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assert json.loads(rows[0]["person_list_json"]) == ["爷爷"]
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pa0 = json.loads(rows[0]["person_appearances_json"])
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assert pa0[0]["uid"] == "爷爷"
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assert json.loads(rows[1]["person_list_json"]) == ["爷爷", "媳妇"]
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pa1 = json.loads(rows[1]["person_appearances_json"])
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assert {p["uid"] for p in pa1} == {"爷爷", "媳妇"}
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vrow = db._conn.execute("SELECT people_json FROM videos WHERE id=?", (vid,)).fetchone()
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assert set(json.loads(vrow["people_json"])) == {"爷爷", "媳妇"}
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def test_rewrite_event_person_names_noop_on_empty_map(tmp_path):
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db = _db(tmp_path)
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vid = _seed_video_with_events(db)
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before = db._conn.execute(
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"SELECT person_list_json FROM events WHERE video_id=?", (vid,)).fetchall()
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db.rewrite_event_person_names(vid, {})
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after = db._conn.execute(
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"SELECT person_list_json FROM events WHERE video_id=?", (vid,)).fetchall()
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assert [r["person_list_json"] for r in before] == [r["person_list_json"] for r in after]
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def test_correct_video_identity_end_to_end(tmp_path):
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"""核心诉求: 纠错入口应该找到当前展示名对应的映射行，改写映射 + 立即重写
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展示数据，且标记为 manual（受保护）。"""
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db = _db(tmp_path)
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vid = _seed_video_with_events(db)
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db.set_identity_mapping(vid, "人物A", "爷爷", source="auto_id")
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db.rewrite_event_person_names(vid, {"人物A": "爷爷"})
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db.correct_video_identity(vid, current_name="爷爷", new_name="爸爸")
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assert db.get_identity_map_for_video(vid) == {"人物A": "爸爸"}
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rows = db._conn.execute(
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"SELECT person_list_json FROM events WHERE video_id=? ORDER BY id", (vid,)).fetchall()
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assert json.loads(rows[0]["person_list_json"]) == ["爸爸"]
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# manual 之后不能被自动识别覆盖回去
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changed = db.set_identity_mapping(vid, "人物A", "爷爷", source="auto_id")
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assert changed is False
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def test_correct_video_identity_without_prior_mapping_uses_current_name_as_raw_uid(tmp_path):
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"""核心诉求: 老流水线时代产出的数据从没跑过闭集识别，映射表里没有记录——
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纠错依然要能生效，把 current_name 本身当 raw_uid 存一条新映射。"""
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db = _db(tmp_path)
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vid = db.ensure_video("motion_2_2000.mp4", "/tmp/x.mp4", event_start_time="2026-08-22 10:00:00")
|
||
events = [{"timestamp": "10:00:01", "description": "走动", "people": ["爷爷"],
|
||
"person_appearances": [{"uid": "爷爷", "features": {"gender": "男"}, "action": "走动"}]}]
|
||
db.mark_video_processed(vid, "摘要", events, ["爷爷"], "gemini")
|
||
|
||
db.correct_video_identity(vid, current_name="爷爷", new_name="爸爸")
|
||
assert db.get_identity_map_for_video(vid) == {"爷爷": "爸爸"}
|
||
rows = db._conn.execute(
|
||
"SELECT person_list_json FROM events WHERE video_id=?", (vid,)).fetchall()
|
||
assert json.loads(rows[0]["person_list_json"]) == ["爸爸"]
|