8a4bad99d3
- 新增 app/baked/algorithm|pipeline，非部署参数不再走 env；Settings 保留 DB/HTTP/RTSP/海康/百度/MinIO/Demo - 移除 init_db_schema 与 reload 配置；main 仅 check_database；start*.sh 在 uvicorn 前执行 alembic upgrade head - 依赖 psycopg[binary] 供 Alembic 同步 URL；alembic/env 注释与预发清单更新 - 撕段门控消费管线、各视频/语音/归档调用改为 baked - 百度环境变量仅 BAIDU_APP_ID、BAIDU_API_KEY、BAIDU_SECRET_KEY 与 BAIDU_* 超时/ASR；人脸脚本与 baidu_speech 文案同步 - 全量单测与 .env.example 更新；.gitignore 忽略 refs/（本地权重/视频不入库） Made-with: Cursor
290 lines
9.5 KiB
Python
290 lines
9.5 KiB
Python
#!/usr/bin/env python3
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"""百度智能云人脸 1：N 搜索（独立脚本，不接入本仓库 FastAPI）。
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对应官方文档：人脸 1：N 搜索 — https://cloud.baidu.com/doc/FACE/s/Gk37c1uzc
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接口：POST https://aip.baidubce.com/rest/2.0/face/v3/search
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前置条件（本脚本不负责「建库 / 注册人脸」）：
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- 在控制台创建应用并开通「人脸识别」相关接口权限；
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- 已使用人脸库管理 API 或控制台建立用户组，并向库中注册用户与人脸照片；
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- 否则搜索会失败（例如未找到匹配用户、人脸库为空等）。人脸库管理说明见产品文档「人脸库管理」章节。
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配置从环境变量读取；启动时会从**仓库根目录**下的 `.env` 与**当前工作目录**下的 `.env` 加载（需已安装 `python-dotenv`，随 pydantic-settings 提供）。
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主要环境变量：
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BAIDU_APP_ID、BAIDU_API_KEY、BAIDU_SECRET_KEY（与 API 服务共用）
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BAIDU_FACE_GROUP_ID_LIST（与命令行 --groups 二选一；格式以百度人脸库文档为准，非法值由接口返回错误码）
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用法示例（输入为**文件夹**，遍历其下所有支持的图片并打印识别日志）：
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uv run python scripts/baidu_face_1n_search.py /path/to/photos
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支持格式：PNG、JPG、JPEG、BMP（单张 base64 建议 <2M，分辨率 <1920x1080，以官方文档为准）。
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"""
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from __future__ import annotations
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import argparse
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import base64
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import json
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import os
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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from aip import AipFace
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from dotenv import load_dotenv
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_PROJECT_ROOT = Path(__file__).resolve().parent.parent
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_IMAGE_SUFFIXES = {".png", ".jpg", ".jpeg", ".bmp"}
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def _validate_group_id_list(s: str) -> None:
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"""仅校验列表非空与数量上限；group_id 字符集等由百度接口校验。"""
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parts = [p.strip() for p in s.split(",") if p.strip()]
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if not parts:
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print("错误：group_id_list 解析后为空。", file=sys.stderr)
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sys.exit(2)
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if len(parts) > 10:
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print("错误：group_id 最多 10 个（逗号分隔）。", file=sys.stderr)
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sys.exit(2)
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def _load_dotenv_files() -> None:
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load_dotenv(_PROJECT_ROOT / ".env")
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load_dotenv()
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def _env(name: str) -> str:
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return (os.environ.get(name) or "").strip()
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def _env_int(name: str, default: int) -> int:
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v = _env(name)
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if v.isdigit() or (v.startswith("-") and v[1:].isdigit()):
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return int(v)
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return default
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def _face_client() -> AipFace:
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app_id = _env("BAIDU_APP_ID")
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api_key = _env("BAIDU_API_KEY")
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secret = _env("BAIDU_SECRET_KEY")
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if not app_id or not api_key or not secret:
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print(
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"错误：未配置百度凭据。\n"
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"请在 `.env` 或环境中设置：BAIDU_APP_ID、BAIDU_API_KEY、BAIDU_SECRET_KEY。\n",
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file=sys.stderr,
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)
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sys.exit(2)
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client = AipFace(app_id, api_key, secret)
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conn_ms = _env("BAIDU_CONNECTION_TIMEOUT_MS")
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sock_ms = _env("BAIDU_SOCKET_TIMEOUT_MS")
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if conn_ms.isdigit():
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client.setConnectionTimeoutInMillis(int(conn_ms))
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if sock_ms.isdigit():
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client.setSocketTimeoutInMillis(int(sock_ms))
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return client
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def _read_image_base64(path: Path) -> str:
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if not path.is_file():
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raise FileNotFoundError(str(path))
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raw = path.read_bytes()
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if not raw:
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raise ValueError("empty file")
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return base64.b64encode(raw).decode("ascii")
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def _ts() -> str:
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return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds")
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def _list_image_files(folder: Path, *, recursive: bool) -> list[Path]:
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if not folder.is_dir():
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print(f"错误：不是有效文件夹：{folder}", file=sys.stderr)
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sys.exit(2)
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if recursive:
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out: list[Path] = []
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for p in folder.rglob("*"):
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if p.is_file() and p.suffix.lower() in _IMAGE_SUFFIXES:
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out.append(p)
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else:
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out = [
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p
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for p in folder.iterdir()
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if p.is_file() and p.suffix.lower() in _IMAGE_SUFFIXES
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]
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return sorted(out, key=lambda p: p.name.lower())
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def _search_options_from_env_and_args(args: argparse.Namespace) -> dict[str, Any]:
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qc = _env("BAIDU_FACE_QUALITY_CONTROL") or "NONE"
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lc = _env("BAIDU_FACE_LIVENESS_CONTROL") or "NONE"
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if args.quality_control is not None:
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qc = args.quality_control
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if args.liveness_control is not None:
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lc = args.liveness_control
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max_n = _env_int("BAIDU_FACE_MAX_USER_NUM", 1) if args.max_user_num is None else args.max_user_num
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match_th = _env_int("BAIDU_FACE_MATCH_THRESHOLD", 80) if args.match_threshold is None else args.match_threshold
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return {
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"max_user_num": max(1, min(50, max_n)),
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"match_threshold": max(0, min(100, match_th)),
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"quality_control": qc,
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"liveness_control": lc,
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}
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def _resolve_group_id_list(args: argparse.Namespace) -> str:
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g = (args.group_id_list or "").strip() or _env("BAIDU_FACE_GROUP_ID_LIST")
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if not g:
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print(
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"错误：未指定人脸组。\n"
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"请设置环境变量 BAIDU_FACE_GROUP_ID_LIST，或传入命令行：--groups a,b",
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file=sys.stderr,
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)
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sys.exit(2)
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return g
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def _parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description="百度人脸 1：N 搜索：在指定人脸库组中，对文件夹内每张照片做相似度检索并打印识别日志。"
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)
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p.add_argument(
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"folder",
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type=Path,
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help="含照片的文件夹路径（仅处理 PNG/JPG/JPEG/BMP）",
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)
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p.add_argument(
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"--groups",
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dest="group_id_list",
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default=None,
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help="人脸组 id，逗号分隔，最多 10 个；未传时使用环境变量 BAIDU_FACE_GROUP_ID_LIST",
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)
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p.add_argument(
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"--max-user-num",
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type=int,
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default=None,
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help="覆盖环境变量 BAIDU_FACE_MAX_USER_NUM；返回前 N 个最相似用户（1–50）",
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)
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p.add_argument(
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"--match-threshold",
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type=int,
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default=None,
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help="覆盖环境变量 BAIDU_FACE_MATCH_THRESHOLD；0–100，默认 80",
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)
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p.add_argument(
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"--quality-control",
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choices=("NONE", "LOW", "NORMAL", "HIGH"),
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default=None,
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help="覆盖环境变量 BAIDU_FACE_QUALITY_CONTROL；默认 NONE",
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)
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p.add_argument(
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"--liveness-control",
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choices=("NONE", "LOW", "NORMAL", "HIGH"),
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default=None,
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help="覆盖环境变量 BAIDU_FACE_LIVENESS_CONTROL；默认 NONE",
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)
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p.add_argument(
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"--recursive",
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action="store_true",
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help="递归包含子目录中的图片",
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)
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p.add_argument(
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"--json",
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action="store_true",
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help="每张照片输出一行 JSON（file + API 原样响应），便于脚本解析",
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)
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return p.parse_args()
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def main() -> None:
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_load_dotenv_files()
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args = _parse_args()
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folder = args.folder.resolve()
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group_id_list = _resolve_group_id_list(args)
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_validate_group_id_list(group_id_list)
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options = _search_options_from_env_and_args(args)
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_group_log = f"[{_ts()}] 使用 group_id_list={group_id_list!r}"
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if args.json:
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print(_group_log, file=sys.stderr)
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else:
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print(_group_log)
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files = _list_image_files(folder, recursive=args.recursive)
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if not files:
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print(
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f"[{_ts()}] 文件夹内未找到支持格式的图片：{folder}（{', '.join(sorted(_IMAGE_SUFFIXES))}；可加 --recursive）",
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file=sys.stderr,
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)
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sys.exit(2)
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client = _face_client()
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n = len(files)
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any_error = False
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for i, path in enumerate(files, start=1):
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rel = path.name
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try:
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b64 = _read_image_base64(path)
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except (OSError, ValueError) as e:
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any_error = True
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print(
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f"[{_ts()}] [{i}/{n}] 文件 {rel!r} 读取失败：{e}",
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file=sys.stderr,
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)
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continue
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resp = client.search(b64, "BASE64", group_id_list, options)
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if args.json:
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line = {
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"file": str(path),
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"relpath": rel,
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"index": i,
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"total": n,
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"response": resp,
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}
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print(json.dumps(line, ensure_ascii=False))
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if resp.get("error_code", -1) != 0:
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any_error = True
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continue
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err = resp.get("error_code")
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if err != 0:
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any_error = True
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msg = resp.get("error_msg", "")
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print(
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f"[{_ts()}] [{i}/{n}] {rel!r} 识别失败 error_code={err} error_msg={msg!r}"
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)
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continue
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result = resp.get("result") or {}
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users = result.get("user_list") or []
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if not users:
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print(
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f"[{_ts()}] [{i}/{n}] {rel!r} 无匹配用户 user_list 为空（可检查人脸库或调低匹配阈值）"
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)
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continue
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for r, u in enumerate(users, start=1):
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gid = u.get("group_id", "")
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uid = u.get("user_id", "")
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info = u.get("user_info", "")
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score = u.get("score", "")
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tag = f" [{r}]" if len(users) > 1 else ""
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print(
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f"[{_ts()}] [{i}/{n}] {rel!r} 识别成功{tag} group_id={gid!r} "
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f"user_id={uid!r} user_info={info!r} score={score}"
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)
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if any_error:
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sys.exit(1)
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if __name__ == "__main__":
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main()
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