7baaa72965
Add MEASURE_WATCH_* settings and measure_watch background loop parallel to action watch. Gitignore SAM 2.4GB weight and fix corrupted DGCNN checkpoint. Clear stale outputs for fresh rerun. Made-with: Cursor
126 lines
3.9 KiB
Python
126 lines
3.9 KiB
Python
from __future__ import annotations
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from functools import lru_cache
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from pathlib import Path
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from typing import Optional
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from pydantic import Field, field_validator, model_validator
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from pydantic_settings import BaseSettings, SettingsConfigDict
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def fish_repo_root() -> Path:
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# fish_api/app/settings.py -> parent[2] = repo root (contains FishMeasure/, fish_api/)
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return Path(__file__).resolve().parents[2]
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def _default_stream_tmp() -> Path:
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return fish_repo_root() / "fish_api" / ".data" / "ingest"
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def _default_media_root() -> Path:
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return fish_repo_root() / "fish_api" / ".data" / "media"
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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public_base_url: str = "http://127.0.0.1:8000"
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ingest_api_key: str = ""
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stream_tmp_dir: Path = Field(default_factory=_default_stream_tmp)
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media_root: Path = Field(default_factory=_default_media_root)
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fish_measure_root: Path = fish_repo_root() / "FishMeasure"
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fish_action_root: Path = fish_repo_root() / "FishAction"
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measure_output_root: Path = fish_repo_root() / "FishMeasure" / "output_weight_estimator"
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python_fish_measure: str = ""
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python_fish_action: str = ""
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yolo_model: Optional[str] = None
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weight_checkpoint: Optional[str] = None
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sam_device: str = "cuda"
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predict_conf: float = 0.5
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predict_imgsz: int = 640
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predict_max_frames: int = 0
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predict_frame_stride: int = 1
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action_checkpoint: Optional[str] = None
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action_clips_per_video: int = 8
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action_batch_size: int = 4
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action_num_workers: int = 2
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#: 非空时由 fish_api 在后台持续扫描该目录中的新 MP4 并跑 FishAction（与 ingest 共用 app_state）
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action_watch_dir: Optional[Path] = None
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action_watch_poll_interval: float = Field(default=2.0, ge=0.1)
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action_watch_stable_polls: int = Field(default=3, ge=1)
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action_watch_recursive: bool = False
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#: 默认：<action_watch_dir>/.fishaction_watch_processed.json
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action_watch_state_file: Optional[Path] = None
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action_watch_use_state_file: bool = True
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#: 非空时后台持续扫描该目录中的新 .svo2 并跑 FishMeasure（与 ingest 共用 app_state）
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measure_watch_dir: Optional[Path] = None
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measure_watch_poll_interval: float = Field(default=2.0, ge=0.1)
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measure_watch_stable_polls: int = Field(default=3, ge=1)
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measure_watch_recursive: bool = False
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measure_watch_state_file: Optional[Path] = None
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measure_watch_use_state_file: bool = True
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default_fish_species: str = "大黄鱼"
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@field_validator(
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"action_watch_dir",
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"action_watch_state_file",
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"measure_watch_dir",
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"measure_watch_state_file",
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mode="before",
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)
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@classmethod
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def _empty_str_path_none(cls, v: object) -> object:
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if v is None:
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return None
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if isinstance(v, str) and not v.strip():
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return None
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return v
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@model_validator(mode="after")
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def _default_paths(self) -> "Settings":
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if not self.yolo_model:
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object.__setattr__(
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self,
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"yolo_model",
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str(
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self.fish_measure_root
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/ "runs/train/fish_detection_20251127_104658/weights/best.pt"
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),
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)
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if not self.weight_checkpoint:
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object.__setattr__(
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self,
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"weight_checkpoint",
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str(
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self.fish_measure_root
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/ "weight_estimator/runs/dgcnn_20260312_171043/best.pt"
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),
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)
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if not self.action_checkpoint:
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object.__setattr__(
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self,
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"action_checkpoint",
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str(self.fish_action_root / "checkpoints/ptv_x3d_m/checkpoint_best.pt"),
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)
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return self
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@lru_cache
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def get_settings() -> Settings:
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return Settings()
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