8a4bad99d3
- 新增 app/baked/algorithm|pipeline，非部署参数不再走 env；Settings 保留 DB/HTTP/RTSP/海康/百度/MinIO/Demo - 移除 init_db_schema 与 reload 配置；main 仅 check_database；start*.sh 在 uvicorn 前执行 alembic upgrade head - 依赖 psycopg[binary] 供 Alembic 同步 URL；alembic/env 注释与预发清单更新 - 撕段门控消费管线、各视频/语音/归档调用改为 baked - 百度环境变量仅 BAIDU_APP_ID、BAIDU_API_KEY、BAIDU_SECRET_KEY 与 BAIDU_* 超时/ASR；人脸脚本与 baidu_speech 文案同步 - 全量单测与 .env.example 更新；.gitignore 忽略 refs/（本地权重/视频不入库） Made-with: Cursor
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
"""时间窗聚合：按 ``consumable_vision_window_sec`` 桶内众数投票，产出 ``WindowInferenceReady``。
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从 ``CameraSessionManager._camera_worker`` 的时间窗计票逻辑独立出来，便于单测。
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消耗 TSV / 终端 Markdown 在 ``VisionClassificationHandler`` 中按「自动确认 / 待确认」分支写入，
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避免待确认事件在日志中先记成具体耗材名。
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"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass
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from app.baked import algorithm as ba
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from app.services.consumable_vision_algorithm import (
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ClsTop3,
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PredictionResult,
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cls_top3_to_prediction_result,
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window_bucket_to_best_snap,
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)
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from app.services.video.session_registry import (
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CameraStreamInferState,
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SurgerySessionState,
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)
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@dataclass(frozen=True)
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class WindowInferenceReady:
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"""单个已完成时间窗：原始 top3 快照 + 分类结果 + 墙钟区间（与 monotonic 窗对齐）。"""
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best: ClsTop3
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prediction: PredictionResult
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wall_lo: float
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wall_hi: float
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class WindowInferenceAggregator:
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"""负责把单路相机的推理快照按时间窗分桶，并产出「桶内最佳」结果。
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本类无状态：状态保存在 ``SurgerySessionState.camera_infer`` 中，
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便于与原逻辑保持一致；调用方在持有 ``state.lock`` 时调用下面的方法。
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"""
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def __init__(self) -> None:
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pass
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def ingest_snapshot_and_collect_ready(
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self,
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*,
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surgery_id: str,
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camera_id: str,
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snap: ClsTop3,
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state: SurgerySessionState,
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) -> list[WindowInferenceReady]:
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"""摄入一条推理快照，返回本次因桶满而产出的窗口列表。
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调用方必须已持有 ``state.lock``。
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"""
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_ = surgery_id
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_ = camera_id
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wsec = ba.CONSUMABLE_VISION_WINDOW_SEC
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ready: list[WindowInferenceReady] = []
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cis = state.camera_infer.setdefault(camera_id, CameraStreamInferState())
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if cis.stream_t0 is None:
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cis.stream_t0 = time.monotonic()
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cis.stream_wall_start = time.time()
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t_rel = time.monotonic() - cis.stream_t0
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cis.votes.append((t_rel, snap.t1_name, snap))
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current_b = int(t_rel // wsec)
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while cis.next_bucket < current_b:
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b = cis.next_bucket
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cis.next_bucket += 1
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lo, hi = b * wsec, (b + 1) * wsec
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bucket_pts = [(p, sn) for (t, p, sn) in cis.votes if lo <= t < hi]
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cis.votes = [
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(t, p, sn) for (t, p, sn) in cis.votes if not (lo <= t < hi)
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]
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if not bucket_pts:
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continue
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best = window_bucket_to_best_snap(bucket_pts)
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if best is None or cis.stream_wall_start is None:
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continue
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wall_lo = cis.stream_wall_start + lo
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wall_hi = cis.stream_wall_start + hi
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pred = cls_top3_to_prediction_result(best)
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ready.append(
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WindowInferenceReady(
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best=best,
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prediction=pred,
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wall_lo=wall_lo,
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wall_hi=wall_hi,
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)
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)
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return ready
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