309 lines
13 KiB
Python
309 lines
13 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 AliasChoices, Field, field_validator, model_validator
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from pydantic_settings import BaseSettings, SettingsConfigDict
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def _fish_api_env_file() -> Path:
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"""fish_api/.env — 与启动 cwd 无关，避免从仓库根跑 uvicorn 时读不到 .env。"""
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return Path(__file__).resolve().parents[1] / ".env"
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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 models_dir() -> Path:
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"""仓库内统一权重目录（YOLO / DGCNN / PointNet / X3D / SAM 等），与 FishMeasure 代码目录解耦。"""
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return fish_repo_root() / "models"
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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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def _default_sqlite_path() -> Path:
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return fish_repo_root() / "fish_api" / ".data" / "app.db"
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def _default_action_output_root() -> Path:
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return fish_repo_root() / "fish_api" / ".data" / "action_output"
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def _default_measure_debug_log_dir() -> Path:
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"""DGCNN 体重推算过程等可调试文本（与终端一致的 calculation log）。"""
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return fish_repo_root() / "fish_api" / ".data" / "logs" / "measure"
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(
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env_file=_fish_api_env_file(),
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env_file_encoding="utf-8",
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extra="ignore",
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)
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#: 对外可访问的 API 基址（无末尾 `/`），用于 biomass 等 JSON 里 `video_left` / `video_right` 的绝对 URL。环境变量：**PUBLIC_BASE_URL**
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public_base_url: str = Field(
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default="http://127.0.0.1:8000",
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validation_alias=AliasChoices("PUBLIC_BASE_URL", "public_base_url"),
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)
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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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sqlite_path: Path = Field(default_factory=_default_sqlite_path)
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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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#: FishMeasure 推理输出（与 SQLite、媒体缓存同属 fish_api/.data；启动脚本默认保留，设置 CLEAR_MEASURE_OUTPUT=1 可清空）
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measure_output_root: Path = fish_repo_root() / "fish_api" / ".data" / "measure_output"
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#: 体重推算过程等调试文本写入目录（默认 fish_api/.data/logs/measure）。**MEASURE_DEBUG_LOG_DIR**
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measure_debug_log_dir: Path = Field(
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default_factory=_default_measure_debug_log_dir,
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validation_alias=AliasChoices("MEASURE_DEBUG_LOG_DIR", "measure_debug_log_dir"),
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)
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#: 为 False 时不写入上述目录（仍打 logger）。**MEASURE_DEBUG_LOG_WRITE**
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measure_debug_log_write: bool = Field(
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default=True,
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validation_alias=AliasChoices("MEASURE_DEBUG_LOG_WRITE", "measure_debug_log_write"),
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)
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#: FishAction 侧预留目录（与 measure 对称；启动脚本默认保留，设置 CLEAR_ACTION_OUTPUT=1 可清空）
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action_output_root: Path = Field(default_factory=_default_action_output_root)
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python_fish_measure: str = ""
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python_fish_action: str = ""
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#: SAM/CUDA 设备（cuda 或 cpu）
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sam_device: str = "cuda"
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#: 为 True 时在视频右上角显示大型 weight/length 标签（10倍字体）
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predict_show_large_labels_at_top_right: bool = False
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#: FishMeasure 中 YOLO 置信度见 ``measure_yolo_conf`` / ``MEASURE_YOLO_CONF``；其余脚本内参数仍可在 FishMeasure 目录修改。
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#: FishAction 核心参数见 ``action_checkpoint`` 等。
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#: FishAction X3D 模型路径（不设则用 models/action_x3d/checkpoint_best.pt）
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action_checkpoint: Optional[str] = None
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#: 为 True 时复用已有 cloud/*.ply（传 --reuse-existing-clouds）
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#: 为 False 时强制重新生成点云（传 --no-reuse-existing-clouds）
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measure_reuse_existing_clouds: bool = True
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#: YOLO 检测置信度，传给 ``predict_weigth_from_svo2.py --conf``（与 FishMeasure ``run_predict_from_svo2_fish9.sh`` 等使用的 0.8 对齐）。**MEASURE_YOLO_CONF**
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measure_yolo_conf: float = Field(
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default=0.8,
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ge=0.0,
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le=1.0,
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validation_alias=AliasChoices("MEASURE_YOLO_CONF", "measure_yolo_conf"),
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)
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#: 传给 FishMeasure ``--filter-pointcloud``（默认开启，与 fish9 脚本对齐）。
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measure_filter_pointcloud: bool = Field(
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default=True,
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validation_alias=AliasChoices(
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"MEASURE_FILTER_POINTCLOUD", "measure_filter_pointcloud"
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),
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)
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#: 传给 FishMeasure ``--use-density-filter``（默认开启，与 fish9 脚本对齐）。
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measure_use_density_filter: bool = Field(
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default=True,
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validation_alias=AliasChoices(
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"MEASURE_USE_DENSITY_FILTER", "measure_use_density_filter"
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),
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)
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#: 传给 FishMeasure ``--use-pointcloud-classifier``（默认开启，与 fish9 脚本对齐）。
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measure_use_pointcloud_classifier: bool = Field(
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default=True,
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validation_alias=AliasChoices(
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"MEASURE_USE_POINTCLOUD_CLASSIFIER", "measure_use_pointcloud_classifier"
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),
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)
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#: PointNet2 点云分类器阈值，传给 ``--pointcloud-classifier-threshold``。
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measure_pointcloud_classifier_threshold: float = Field(
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default=0.7,
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ge=0.0,
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le=1.0,
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validation_alias=AliasChoices(
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"MEASURE_POINTCLOUD_CLASSIFIER_THRESHOLD",
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"measure_pointcloud_classifier_threshold",
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),
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)
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#: 点云分类器模型路径，传给 ``--pointcloud-classifier``。
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measure_pointcloud_classifier: Optional[Path] = Field(
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default=None,
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validation_alias=AliasChoices(
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"MEASURE_POINTCLOUD_CLASSIFIER", "measure_pointcloud_classifier"
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),
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)
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#: 传给 FishMeasure ``--use-flatness-filter``（默认开启，与 fish9 脚本对齐）。
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measure_use_flatness_filter: bool = Field(
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default=True,
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validation_alias=AliasChoices(
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"MEASURE_USE_FLATNESS_FILTER", "measure_use_flatness_filter"
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),
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)
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#: 平整度阈值，传给 ``--flatness-threshold``。
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measure_flatness_threshold: float = Field(
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default=55.0,
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validation_alias=AliasChoices(
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"MEASURE_FLATNESS_THRESHOLD", "measure_flatness_threshold"
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),
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)
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# ── 体重聚合规则（传给 predict_weigth_from_svo2.py → test_dgcnn_weight_estimator.py） ──
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#: DGCNN top-K 帧数，传给 ``--weight-top-k``。**MEASURE_WEIGHT_TOP_K**
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measure_weight_top_k: int = Field(
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default=5,
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ge=1,
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validation_alias=AliasChoices("MEASURE_WEIGHT_TOP_K", "measure_weight_top_k"),
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)
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#: 按长度选 top-K，传给 ``--weight-top-by-length``。**MEASURE_WEIGHT_TOP_BY_LENGTH**
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measure_weight_top_by_length: bool = Field(
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default=True,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_TOP_BY_LENGTH", "measure_weight_top_by_length"
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),
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)
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#: top-K 按长度选时，若 K 个平均长度 > 此值则切为按重量选，传给 ``--weight-length-switch-mm``。**MEASURE_WEIGHT_LENGTH_SWITCH_MM**
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measure_weight_length_switch_mm: float = Field(
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default=319.0,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_LENGTH_SWITCH_MM", "measure_weight_length_switch_mm"
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),
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)
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#: 几何过滤：length > 此值的帧排除，传给 ``--weight-max-length-mm``（0 关闭）。**MEASURE_WEIGHT_MAX_LENGTH_MM**
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measure_weight_max_length_mm: float = Field(
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default=400.0,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_MAX_LENGTH_MM", "measure_weight_max_length_mm"
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),
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)
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#: 几何过滤：PCA 长/宽 < 此值的帧排除，传给 ``--weight-min-length-width-ratio``（0 关闭）。**MEASURE_WEIGHT_MIN_LENGTH_WIDTH_RATIO**
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measure_weight_min_length_width_ratio: float = Field(
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default=1.5,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_MIN_LENGTH_WIDTH_RATIO", "measure_weight_min_length_width_ratio"
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),
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)
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#: 全池均值模式，传给 ``--weight-average-all-after-filter``。**MEASURE_WEIGHT_AVERAGE_ALL_AFTER_FILTER**
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measure_weight_average_all_after_filter: bool = Field(
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default=False,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_AVERAGE_ALL_AFTER_FILTER", "measure_weight_average_all_after_filter"
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),
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)
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#: 全池均值 > 此值时改用 max（规则 A），传给 ``--weight-average-all-fallback-max-if-mean-over-g``（0 关闭）。**MEASURE_WEIGHT_AVG_ALL_FALLBACK_MAX_G**
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measure_weight_avg_all_fallback_max_g: float = Field(
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default=400.0,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_AVG_ALL_FALLBACK_MAX_G", "measure_weight_avg_all_fallback_max_g"
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),
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)
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#: 全池 candidates 均值 > 此值时改用 max（规则 B, 440g 保护），传给 ``--weight-mean-pool-fallback-max-if-over-g``（0 关闭）。**MEASURE_WEIGHT_MEAN_POOL_FALLBACK_MAX_G**
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measure_weight_mean_pool_fallback_max_g: float = Field(
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default=440.0,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_MEAN_POOL_FALLBACK_MAX_G", "measure_weight_mean_pool_fallback_max_g"
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),
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)
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#: 异常值剔除开关，传给 ``--weight-remove-outliers``。**MEASURE_WEIGHT_REMOVE_OUTLIERS**
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measure_weight_remove_outliers: bool = Field(
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default=False,
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_REMOVE_OUTLIERS", "measure_weight_remove_outliers"
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),
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)
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#: 异常值剔除方法（iqr / zscore），传给 ``--weight-outlier-method``。**MEASURE_WEIGHT_OUTLIER_METHOD**
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measure_weight_outlier_method: str = Field(
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default="iqr",
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validation_alias=AliasChoices(
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"MEASURE_WEIGHT_OUTLIER_METHOD", "measure_weight_outlier_method"
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),
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)
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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 SQLite 最新结果）
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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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#: 状态管理：true=持久化到 SQLite（重启后记住），false=内存模式（重启后清空）
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action_watch_use_state_file: bool = True
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#: 优先作为「水上视频」源文件；未设置时在 ACTION_WATCH_DIR 取最新 .mp4（FishAction 输入）。**BIOMASS_WATER_VIDEO_SOURCE**
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biomass_water_video_source: Optional[Path] = None
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#: 发布到 MEDIA_ROOT 的 H.264 文件名。**BIOMASS_WATER_VIDEO_MEDIA_NAME**
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biomass_water_video_media_name: str = "biomass_water_surface.mp4"
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#: 优先作为「声呐视频」源文件；未设置时在 BIOMASS_SONAR_VIDEO_DIR 取最新 .mp4。**BIOMASS_SONAR_VIDEO_SOURCE**
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biomass_sonar_video_source: Optional[Path] = None
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#: 声呐 MP4 目录（与 ACTION_WATCH_DIR 独立，避免与水面视频混用）。**BIOMASS_SONAR_VIDEO_DIR**
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biomass_sonar_video_dir: Optional[Path] = None
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#: 是否在 SONAR_VIDEO_DIR 中递归查找 .mp4。**BIOMASS_SONAR_VIDEO_RECURSIVE**
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biomass_sonar_video_recursive: bool = False
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#: 发布到 MEDIA_ROOT 的 H.264 文件名。**BIOMASS_SONAR_VIDEO_MEDIA_NAME**
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biomass_sonar_video_media_name: str = "biomass_sonar.mp4"
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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 SQLite 最新结果）
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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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#: 状态管理：true=持久化到 SQLite（重启后记住），false=内存模式（重启后清空）
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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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"biomass_water_video_source",
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"biomass_sonar_video_source",
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"biomass_sonar_video_dir",
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"measure_watch_dir",
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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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md = models_dir()
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if not self.action_checkpoint:
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object.__setattr__(
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self, "action_checkpoint", str(md / "action_x3d" / "checkpoint_best.pt")
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)
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if self.measure_pointcloud_classifier is None:
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object.__setattr__(
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self,
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"measure_pointcloud_classifier",
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self.fish_measure_root
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/ "pointcloud_classifier"
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/ "Pointnet_Pointnet2_pytorch"
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/ "log"
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/ "classification"
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/ "fish_pointnet2_finetune"
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/ "checkpoints"
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/ "best_model.pth",
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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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