e4c6127619
- Add top1_name/top1_conf to TSV and show top1–3 in pending markdown - Add 相对开录 column and pass since_recording_start from surgery start - Track surgery_started_wall and format_elapsed_mmss_since in session registry - Remove ffmpeg stream_loop from synthetic/demo fake RTSP (play once) - Fix fake_rtsp_from_file poll loop indentation; update README - Extend consumption TSV tests; add face test PNGs under tests/faces Made-with: Cursor
563 lines
22 KiB
Python
563 lines
22 KiB
Python
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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from app.database import AsyncSessionLocal
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from app.domain.consumption import SurgeryConsumptionStored
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from app.repositories.surgery_results import SurgeryResultRepository
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from app.services.consumable_vision_algorithm import (
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ConsumableVisionAlgorithmService,
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PredictionResult,
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)
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from app.services.video.archive_persister import ArchivePersister
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from app.services.video.backend_resolver import BackendResolver
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from app.services.video.classification_handler import VisionClassificationHandler
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from app.services.video.hikvision_runtime import HikvisionInitRefCount, HikvisionRuntime
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from app.services.video.inference_aggregator import WindowInferenceAggregator
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from app.services.video.session_registry import (
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CameraStreamInferState,
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PendingConsumableConfirmation,
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RunningSurgery,
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SurgerySessionRegistry,
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SurgerySessionState,
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format_elapsed_mmss_since,
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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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from app.services.consumption_tsv_log import (
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append_consumption_log_summary,
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init_consumption_log_file,
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print_consumption_summary_markdown,
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)
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from app.services.voice_file_log import init_voice_log_file
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from app.surgery_errors import SurgeryPipelineError
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__all__ = [
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"CameraSessionManager",
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"CameraStreamInferState",
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"PendingConsumableConfirmation",
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"RunningSurgery",
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"SurgerySessionState",
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]
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class CameraSessionManager:
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"""Per-surgery camera orchestration.
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本类负责：
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1. 开始/停止手术：创建 `SurgerySessionState`、拉起相机 worker、停录时收尾。
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2. 把「语音确认所需的内存态」委托给 ``SurgerySessionRegistry``（实现 `PendingConfirmationStore`）。
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3. 把「结果写库 + 失败重试 + durable fallback」委托给 ``ArchivePersister``。
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对外接口保持不变，上游（``SurgeryPipeline`` / ``VoiceConfirmationService``）无需感知拆分。
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"""
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def __init__(
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self,
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*,
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settings: Settings,
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vision_algorithm: ConsumableVisionAlgorithmService,
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hikvision_runtime: HikvisionRuntime | None,
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result_repository: SurgeryResultRepository | None = None,
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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()
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self._archive = archive_persister or ArchivePersister(
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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()
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self._classifier_handler = VisionClassificationHandler(
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registry=self._registry,
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)
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# ------------------------------------------------------------------
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# 生命周期
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# ------------------------------------------------------------------
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async def start_archive_retry_loop(self) -> None:
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await self._archive.recover_from_durable_fallback()
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await self._archive.start_retry_loop()
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async def shutdown(self) -> None:
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await self._archive.shutdown()
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ids = self._registry.active_ids()
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for sid in ids:
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try:
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await self.stop_surgery(sid, require_active=False)
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except Exception as exc:
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logger.warning("shutdown stop_surgery {}: {}", sid, exc)
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# ------------------------------------------------------------------
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# Surgery start / stop
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# ------------------------------------------------------------------
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async def start_surgery(
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self,
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surgery_id: str,
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camera_ids: list[str],
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candidate_consumables: list[str],
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) -> None:
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if self._registry.has_active(surgery_id):
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raise SurgeryPipelineError(
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"RECORDING_CANNOT_START",
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"该手术已在录制中，请勿重复开始。",
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)
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stale = await self._archive.take_archived_details(surgery_id)
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if stale is not None:
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logger.warning(
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"surgery_id={} 仍有未落库归档，尝试写入数据库后再开始新会话",
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surgery_id,
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)
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if self._archive.repository is None:
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logger.error(
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"surgery_id={} 有内存归档但未配置数据库仓库，无法持久化；"
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"开始新会话将丢弃该归档（仅开发/无库模式）",
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surgery_id,
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)
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else:
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ok = await self._archive.persist_or_archive(surgery_id, stale)
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if not ok:
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raise SurgeryPipelineError(
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"RECORDING_CANNOT_START",
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"该手术号存在尚未写入数据库的历史结果，请修复数据库或等待自动重试成功后再开始。",
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)
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resolved = self._vision.effective_candidate_consumables(candidate_consumables)
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if not resolved:
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raise SurgeryPipelineError(
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"RECORDING_CANNOT_START",
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"耗材候选为空：请在请求中传入 candidate_consumables，或提供有效的 consumable_classifier_labels.yaml / 分类模型。",
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)
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if not any(str(x).strip() for x in candidate_consumables):
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logger.info(
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"surgery {}: candidate_consumables 未提供，使用默认全量 {} 项",
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surgery_id,
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len(resolved),
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)
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name_to_code = self._vision.build_name_mapping(resolved)
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state = SurgerySessionState(
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candidate_consumables=list(resolved),
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name_to_code=name_to_code,
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surgery_started_wall=time.time(),
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)
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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 = 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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asyncio.create_task(
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self._camera_worker(
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surgery_id=surgery_id,
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camera_id=cam_id,
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stream_ready=ready,
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stop_event=stop_event,
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state=state,
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),
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name=f"camera:{surgery_id}:{cam_id}",
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)
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)
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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)
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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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timeout=open_timeout,
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)
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state.ready.set()
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except TimeoutError as exc:
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logger.error(
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"Surgery {} cameras not all ready within {}s",
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surgery_id,
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open_timeout,
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)
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await self.stop_surgery(surgery_id, require_active=True)
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raise SurgeryPipelineError(
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"RECORDING_CANNOT_START",
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"开录未能确认：部分摄像头在超时内未成功拉到首帧。",
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) from exc
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except Exception:
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await self.stop_surgery(surgery_id, require_active=True)
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raise
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async def stop_surgery(self, surgery_id: str, *, require_active: bool = True) -> None:
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run = await self._registry.unregister(surgery_id)
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if run is None:
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if require_active:
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raise SurgeryPipelineError(
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"RECORDING_NOT_STOPPED",
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"停录未能完成：当前没有该手术的活跃录制会话。",
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)
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return
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run.stop_event.set()
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results = await asyncio.gather(*run.tasks, return_exceptions=True)
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for res in results:
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if isinstance(res, BaseException):
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logger.warning("surgery task finished with error: {}", res)
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details = list(run.state.details)
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detail_rows = [
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SurgeryConsumptionDetail(
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item_id=r.item_id,
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item_name=r.item_name,
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qty=r.qty,
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doctor_id=r.doctor_id,
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timestamp=r.timestamp,
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)
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for r in details
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]
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summ = build_consumption_summary(detail_rows)
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totals = {s.item_id: (s.item_name, s.total_quantity) for s in summ}
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append_consumption_log_summary(surgery_id, totals)
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print_consumption_summary_markdown(totals)
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await self._archive.persist_or_archive(surgery_id, details)
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# ------------------------------------------------------------------
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# PendingConfirmationStore 协议委托
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# ------------------------------------------------------------------
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def live_consumption_if_active(
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self, surgery_id: str
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) -> list[SurgeryConsumptionStored] | None:
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return self._registry.live_consumption_if_active(surgery_id)
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def archived_consumption_fallback(
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self, surgery_id: str
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) -> list[SurgeryConsumptionStored] | None:
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return self._archive.archived_details(surgery_id)
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def record_voice_trace(
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self,
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surgery_id: str,
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*,
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asr_text: str | None,
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error: str | None,
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) -> None:
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self._registry.record_voice_trace(surgery_id, asr_text=asr_text, error=error)
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def get_pending_confirmation_by_id(
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self,
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surgery_id: str,
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confirmation_id: str,
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) -> PendingConsumableConfirmation | None:
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return self._registry.get_pending_confirmation_by_id(surgery_id, confirmation_id)
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def get_surgery_candidate_consumables(self, surgery_id: str) -> list[str]:
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return self._registry.get_surgery_candidate_consumables(surgery_id)
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async def record_voice_parse_failure(
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self, surgery_id: str, confirmation_id: str
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) -> tuple[int, int]:
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return await self._registry.record_voice_parse_failure(surgery_id, confirmation_id)
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def next_pending_confirmation(
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self, surgery_id: str
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) -> PendingConsumableConfirmation | None:
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return self._registry.next_pending_confirmation(surgery_id)
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async def resolve_pending_confirmation(
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self,
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surgery_id: str,
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confirmation_id: str,
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*,
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chosen_label: str | None,
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rejected: bool,
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) -> None:
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await self._registry.resolve_pending_confirmation(
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surgery_id,
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confirmation_id,
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chosen_label=chosen_label,
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rejected=rejected,
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)
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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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surgery_id: str,
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camera_id: str,
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stream_ready: asyncio.Event,
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stop_event: asyncio.Event,
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state: SurgerySessionState,
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) -> None:
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kind = self._resolver.backend_for_camera(camera_id)
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hik_user_id: int | None = None
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hik_init_retained = False
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try:
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url, hik_user_id, hik_init_retained = await self._resolve_rtsp_url(
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camera_id=camera_id,
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kind=kind,
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)
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assert url is not None
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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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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.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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runner = None
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if runner is not None:
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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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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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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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|
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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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|
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if snap is None:
|
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return
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|
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if bp.VIDEO_LOG_INFERENCE_RESULTS:
|
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logger.info(
|
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"Vision result surgery={} camera={} 相对开录={} top1={}({:.3f}) top2={}({:.3f}) top3={}({:.3f})",
|
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surgery_id,
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camera_id,
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format_elapsed_mmss_since(
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state.surgery_started_wall,
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at_epoch=time.time(),
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),
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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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|
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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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|
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worker = CameraStreamWorker(
|
||
surgery_id=surgery_id,
|
||
camera_id=camera_id,
|
||
url=url,
|
||
)
|
||
await worker.run(
|
||
stream_ready=stream_ready,
|
||
stop_event=stop_event,
|
||
frame_handler=_frame_handler,
|
||
)
|
||
finally:
|
||
if hik_user_id is not None and self._hik is not None:
|
||
await asyncio.to_thread(self._hik.logout, hik_user_id)
|
||
if hik_init_retained and self._hik is not None:
|
||
HikvisionInitRefCount.release(self._hik)
|
||
|
||
async def _handle_classification_result(
|
||
self,
|
||
*,
|
||
state: SurgerySessionState,
|
||
cls_res: PredictionResult,
|
||
) -> None:
|
||
"""Deprecated test-shim：沿用旧签名，转发给 ``VisionClassificationHandler``。
|
||
|
||
保留此方法是因为单元测试直接调用了它。新代码应使用 ``self._classifier_handler.handle``。
|
||
"""
|
||
await self._classifier_handler.handle(state=state, cls_res=cls_res)
|
||
|
||
async def _resolve_rtsp_url(
|
||
self,
|
||
*,
|
||
camera_id: str,
|
||
kind: VideoBackendKind,
|
||
) -> tuple[str, int | None, bool]:
|
||
"""Returns (url, hikvision_user_id, whether NET_DVR_Init refcount was retained)."""
|
||
if kind != VideoBackendKind.HIKVISION_SDK:
|
||
return self._resolver.rtsp_url_for_camera(camera_id), None, False
|
||
|
||
if self._hik is None:
|
||
if self._s.hikvision_sdk_fallback_to_rtsp:
|
||
logger.warning(
|
||
"Hikvision SDK not loaded; fallback to RTSP for camera {}",
|
||
camera_id,
|
||
)
|
||
return self._resolver.rtsp_url_for_camera(camera_id), None, False
|
||
raise RuntimeError("Hikvision SDK requested but libhcnetsdk.so not loaded")
|
||
|
||
if not (
|
||
self._s.hikvision_device_ip.strip()
|
||
and self._s.hikvision_user.strip()
|
||
and self._s.hikvision_password.strip()
|
||
):
|
||
if self._s.hikvision_sdk_fallback_to_rtsp:
|
||
logger.warning(
|
||
"Hikvision credentials incomplete; fallback to RTSP for camera {}",
|
||
camera_id,
|
||
)
|
||
return self._resolver.rtsp_url_for_camera(camera_id), None, False
|
||
raise RuntimeError("Hikvision SDK requires HIKVISION_DEVICE_IP, user, password")
|
||
|
||
HikvisionInitRefCount.retain(self._hik)
|
||
try:
|
||
login = await asyncio.to_thread(
|
||
lambda: self._hik.login_v30(
|
||
ip=self._s.hikvision_device_ip.strip(),
|
||
port=int(self._s.hikvision_device_port),
|
||
username=self._s.hikvision_user.strip(),
|
||
password=self._s.hikvision_password.strip(),
|
||
)
|
||
)
|
||
except Exception as exc:
|
||
HikvisionInitRefCount.release(self._hik)
|
||
if self._s.hikvision_sdk_fallback_to_rtsp:
|
||
logger.warning("Hikvision login failed ({}); fallback to RTSP", exc)
|
||
return self._resolver.rtsp_url_for_camera(camera_id), None, False
|
||
raise
|
||
|
||
url = self._resolver.rtsp_url_after_hikvision_login(camera_id)
|
||
return url, login.user_id, True
|