feat: 配置写死与 baked 模块，Alembic 建表，百度仅 BAIDU_*
- 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
This commit is contained in:
Kevin
@@ -2,9 +2,13 @@ from __future__ import annotations
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import asyncio
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from loguru import logger
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from app.baked import algorithm as ba
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from app.baked import pipeline as bp
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from app.config import Settings
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from sqlalchemy.ext.asyncio import async_sessionmaker
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@@ -27,6 +31,15 @@ from app.services.video.session_registry import (
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SurgerySessionRegistry,
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SurgerySessionState,
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)
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from app.services.tear_gated_segment_consumption.product_map import (
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load_tear_segment_name_to_id,
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resolve_tear_segment_labels_yaml_path,
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)
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from app.services.tear_gated_segment_consumption.report import write_tear_segment_txt
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from app.services.tear_gated_segment_consumption.runner import (
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TearGatedSegmentModelBundle,
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TearGatedSegmentRunner,
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)
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from app.services.video.stream_worker import CameraStreamWorker, redact_rtsp_url
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from app.services.video.types import VideoBackendKind
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from app.schemas import SurgeryConsumptionDetail, build_consumption_summary
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@@ -69,21 +82,21 @@ class CameraSessionManager:
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session_factory: async_sessionmaker | None = None,
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registry: SurgerySessionRegistry | None = None,
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archive_persister: ArchivePersister | None = None,
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tear_segment_models: TearGatedSegmentModelBundle | None = None,
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) -> None:
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self._s = settings
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self._vision = vision_algorithm
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self._hik = hikvision_runtime
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self._tear_models = tear_segment_models
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self._session_factory: async_sessionmaker = session_factory or AsyncSessionLocal
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self._resolver = BackendResolver(settings, hikvision_runtime=hikvision_runtime)
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self._registry = registry or SurgerySessionRegistry(settings=settings)
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self._registry = registry or SurgerySessionRegistry()
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self._archive = archive_persister or ArchivePersister(
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settings=settings,
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repository=result_repository,
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session_factory=self._session_factory,
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)
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self._aggregator = WindowInferenceAggregator(settings=settings)
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self._aggregator = WindowInferenceAggregator()
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self._classifier_handler = VisionClassificationHandler(
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settings=settings,
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registry=self._registry,
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)
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@@ -157,7 +170,7 @@ class CameraSessionManager:
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stop_event = asyncio.Event()
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readies = [asyncio.Event() for _ in camera_ids]
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tasks: list[asyncio.Task[None]] = []
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open_timeout = self._s.video_open_timeout_sec + 5.0
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open_timeout = bp.VIDEO_OPEN_TIMEOUT_SEC + 5.0
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for cam_id, ready in zip(camera_ids, readies, strict=True):
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tasks.append(
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@@ -175,9 +188,18 @@ class CameraSessionManager:
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run = RunningSurgery(stop_event=stop_event, state=state, tasks=tasks)
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init_consumption_log_file(surgery_id)
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init_voice_log_file(surgery_id, self._s)
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init_voice_log_file(surgery_id)
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await self._registry.register(surgery_id, run)
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if ba.TEAR_SEGMENT_ENABLED:
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primary = (ba.TEAR_SEGMENT_PRIMARY_CAMERA_ID or "").strip()
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if primary and primary not in camera_ids:
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logger.warning(
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"撕段算法已开启但主摄 id={!r} 不在本台开录 camera_ids={} 中，该路不会跑撕段流水线",
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primary,
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camera_ids,
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)
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try:
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await asyncio.wait_for(
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asyncio.gather(*(r.wait() for r in readies)),
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@@ -292,6 +314,45 @@ class CameraSessionManager:
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# ------------------------------------------------------------------
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# Camera worker（拉流 + 推理节流 + 时间窗分桶 + 分类结果处理）
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# ------------------------------------------------------------------
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async def _finalize_tear_segment_runner(
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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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state: SurgerySessionState,
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runner: TearGatedSegmentRunner,
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) -> None:
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recs = runner.finalize()
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for rec in recs:
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wall_ts = runner.wall_time_for_record(rec)
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detail_ts = datetime.fromtimestamp(wall_ts, tz=timezone.utc)
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await self._registry.append_confirmed_detail(
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state=state,
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item_id=rec.item_id,
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item_name=rec.item_name,
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doctor_id=bp.VIDEO_RESULT_DOCTOR_ID,
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source="tear_segment",
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cooldown_key=f"{surgery_id}:tear_seg:{rec.segment_index}",
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detail_timestamp=detail_ts,
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)
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if ba.TEAR_SEGMENT_LOG_TXT and recs:
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raw_tpl = (ba.TEAR_SEGMENT_LOG_TXT_PATH or "").strip()
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if raw_tpl and "{surgery_id}" in raw_tpl:
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p = Path(raw_tpl.format(surgery_id=surgery_id)).expanduser()
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elif raw_tpl:
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p = Path(raw_tpl).expanduser()
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else:
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p = Path(f"logs/tear_segment_{surgery_id}.txt")
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labels_src = str(resolve_tear_segment_labels_yaml_path())
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write_tear_segment_txt(
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path=p,
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surgery_id=surgery_id,
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camera_id=camera_id,
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labels_source=labels_src,
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records=recs,
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)
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logger.info("撕段报告已写: {}", p)
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async def _camera_worker(
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self,
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*,
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@@ -311,74 +372,119 @@ class CameraSessionManager:
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)
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assert url is not None
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last_infer = 0.0
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async def _frame_handler(frame: object) -> None:
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nonlocal last_infer
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now = time.monotonic()
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if now - last_infer < self._s.video_inference_interval_sec:
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await asyncio.sleep(0.01)
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return
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last_infer = now
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primary = (ba.TEAR_SEGMENT_PRIMARY_CAMERA_ID or "").strip()
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use_tear_req = (
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ba.TEAR_SEGMENT_ENABLED
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and self._tear_models is not None
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and primary
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and camera_id == primary
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)
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runner: TearGatedSegmentRunner | None = None
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if use_tear_req:
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name_to_id = load_tear_segment_name_to_id()
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try:
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snap = await asyncio.to_thread(
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self._vision.infer_frame_bgr,
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frame,
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state.name_to_code,
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)
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self._tear_models.ensure_loaded()
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runner = self._tear_models.create_runner(name_to_id)
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except Exception as exc:
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logger.debug(
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"Inference skip camera={} surgery={}: {}",
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logger.exception(
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"撕段模型未就绪，本路回退为原时间窗算法 camera={} surgery={}: {}",
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camera_id,
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surgery_id,
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exc,
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)
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return
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runner = None
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if snap is None:
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return
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if runner is not None:
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if self._s.video_log_inference_results:
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logger.info(
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"Vision result surgery={} camera={} top1={}({:.3f}) top2={}({:.3f}) top3={}({:.3f})",
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surgery_id,
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camera_id,
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snap.t1_name,
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snap.t1_conf,
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snap.t2_name,
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snap.t2_conf,
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snap.t3_name,
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snap.t3_conf,
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async def _frame_handler_tear(frame: object) -> None:
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await asyncio.to_thread(runner.process_frame_bgr, frame)
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w_tear = CameraStreamWorker(
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surgery_id=surgery_id,
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camera_id=camera_id,
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url=url,
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)
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try:
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await w_tear.run(
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stream_ready=stream_ready,
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stop_event=stop_event,
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frame_handler=_frame_handler_tear,
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)
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async with state.lock:
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ready_windows = self._aggregator.ingest_snapshot_and_collect_ready(
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finally:
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await self._finalize_tear_segment_runner(
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surgery_id=surgery_id,
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camera_id=camera_id,
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snap=snap,
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state=state,
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runner=runner,
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)
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else:
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last_infer = 0.0
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for win in ready_windows:
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await self._classifier_handler.handle(
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state=state,
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cls_res=win.prediction,
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ready=win,
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surgery_id=surgery_id,
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camera_id=camera_id,
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)
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async def _frame_handler(frame: object) -> None:
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nonlocal last_infer
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now = time.monotonic()
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if now - last_infer < bp.VIDEO_INFERENCE_INTERVAL_SEC:
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await asyncio.sleep(0.01)
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return
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last_infer = now
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try:
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snap = await asyncio.to_thread(
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self._vision.infer_frame_bgr,
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frame,
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state.name_to_code,
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)
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except Exception as exc:
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logger.debug(
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"Inference skip camera={} surgery={}: {}",
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camera_id,
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surgery_id,
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exc,
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)
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return
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worker = CameraStreamWorker(
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settings=self._s,
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surgery_id=surgery_id,
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camera_id=camera_id,
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url=url,
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)
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await worker.run(
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stream_ready=stream_ready,
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stop_event=stop_event,
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frame_handler=_frame_handler,
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)
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if snap is None:
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return
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if bp.VIDEO_LOG_INFERENCE_RESULTS:
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logger.info(
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"Vision result surgery={} camera={} top1={}({:.3f}) top2={}({:.3f}) top3={}({:.3f})",
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surgery_id,
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camera_id,
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snap.t1_name,
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snap.t1_conf,
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snap.t2_name,
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snap.t2_conf,
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snap.t3_name,
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snap.t3_conf,
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)
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async with state.lock:
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ready_windows = self._aggregator.ingest_snapshot_and_collect_ready(
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surgery_id=surgery_id,
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camera_id=camera_id,
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snap=snap,
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state=state,
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)
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for win in ready_windows:
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await self._classifier_handler.handle(
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state=state,
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cls_res=win.prediction,
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ready=win,
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surgery_id=surgery_id,
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camera_id=camera_id,
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)
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worker = CameraStreamWorker(
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surgery_id=surgery_id,
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camera_id=camera_id,
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url=url,
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)
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await worker.run(
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stream_ready=stream_ready,
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stop_event=stop_event,
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frame_handler=_frame_handler,
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)
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finally:
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if hik_user_id is not None and self._hik is not None:
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await asyncio.to_thread(self._hik.logout, hik_user_id)
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