diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..4025eeb
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,22 @@
+FROM skin-py-cpu-base:v1.0.2
+
+WORKDIR /app
+
+ENV PYTHONUNBUFFERED=1
+ENV PIP_NO_CACHE_DIR=1
+
+COPY wanzheng.py .
+COPY api_server.py .
+COPY config ./config
+COPY best.pt .
+COPY shape_predictor_68_face_landmarks.dat .
+
+EXPOSE 8071
+
+CMD ["python", "api_server.py"]
+
+# 服务器执行 nvidia-smi 查看CUDA版本 比如 CUDA 12.1 就是/whl/cu121
+# RUN pip install \
+#     torch==2.4.1 \
+#     torchvision==0.19.1 \
+#     --index-url https://download.pytorch.org/whl/cu121
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diff --git a/__pycache__/test_regressions.cpython-39.pyc b/__pycache__/test_regressions.cpython-39.pyc
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diff --git a/__pycache__/wanzheng.cpython-39.pyc b/__pycache__/wanzheng.cpython-39.pyc
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diff --git a/api_server.py b/api_server.py
new file mode 100644
index 0000000..a9e450b
--- /dev/null
+++ b/api_server.py
@@ -0,0 +1,428 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+皮肤分析 API 接口服务
+"""
+
+import sys
+import os
+import threading
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Query
+from pydantic import BaseModel, Field
+import uvicorn
+import logging
+from datetime import datetime
+from typing import Optional
+import subprocess
+from pathlib import Path
+from config.settings import (
+    ANALYSIS_ROOT,
+    API_HOST,
+    API_CALLBACK,
+    MODEL_PATH,
+    PORE_ENABLED,
+    PORE_PREDICTOR_PATH,
+)
+
+# ==================== 日志配置 ====================
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s | %(levelname)s | %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# ==================== FastAPI 应用 ====================
+app = FastAPI(
+    title="皮肤分析任务接口",
+    description="接收文件夹名称，触发皮肤分析任务",
+    version="1.0.0"
+)
+
+# ==================== 数据存储 ====================
+task_store = {}
+active_tasks = set()
+task_store_lock = threading.RLock()
+
+# ==================== 数据模型 ====================
+class TaskRequest(BaseModel):
+    folder_name: str = Field(..., description="文件夹全路径")
+    task_id: str = Field(..., description="目标文件夹名称（任务 ID）")
+    task_type: Optional[str] = Field(
+        default=None,
+        description="任务类型，pore 表示仅执行大毛孔检测",
+    )
+
+class TaskResponse(BaseModel):
+    code: int = Field(..., description="状态码")
+    message: str = Field(..., description="消息")
+    task_id: str = Field(..., description="任务 ID")
+    status: str = Field(..., description="任务状态")
+
+class TaskStatus(BaseModel):
+    task_id: str = Field(..., description="任务 ID")
+    status: str = Field(..., description="状态")
+    created_at: str = Field(..., description="创建时间")
+    completed_at: Optional[str] = Field(default=None, description="完成时间")
+    error: Optional[str] = Field(default=None, description="错误信息")
+
+class CallbackRequest(BaseModel):
+    task_id: str = Field(..., description="任务 ID")
+    status: int = Field(..., ge=0, le=1, description="处理状态，1=成功，0=失败")
+
+
+def validate_task_id(task_id: str) -> str:
+    """校验会进入文件路径、日志和命令参数的任务 ID。"""
+    if not task_id or len(task_id) < 3 or len(task_id) > 128:
+        raise ValueError("任务 ID 格式错误，长度必须为 3 到 128 个字符")
+    if task_id in {".", ".."} or task_id.startswith("-"):
+        raise ValueError("任务 ID 格式错误")
+    if any(char in task_id for char in ("/", "\\")):
+        raise ValueError("任务 ID 不能包含路径分隔符")
+    if any(ord(char) < 32 or ord(char) == 127 for char in task_id):
+        raise ValueError("任务 ID 不能包含控制字符")
+    return task_id
+
+
+def normalize_task_type(task_type: Optional[str]) -> Optional[str]:
+    """规范化可选任务类型；空值保持现有完整分析流程。"""
+    normalized = (task_type or "").strip().lower()
+    if not normalized:
+        return None
+    if normalized != "pore":
+        raise ValueError(f"不支持的任务类型：{task_type}")
+    return normalized
+
+
+def resolve_input_dir(folder_name: str, task_id: str, now: Optional[datetime] = None) -> Path:
+    """解析输入目录，并确保目录始终位于分析数据根目录内。"""
+    root = Path(ANALYSIS_ROOT).resolve()
+    candidate = Path(folder_name)
+    if not candidate.is_absolute():
+        candidate = root / (now or datetime.now()).strftime("%Y%m%d") / task_id
+
+    candidate = candidate.resolve()
+    try:
+        candidate.relative_to(root)
+    except ValueError as exc:
+        raise ValueError(f"输入目录必须位于 {root} 内") from exc
+    if candidate == root:
+        raise ValueError("输入目录不能是分析数据根目录本身")
+    return candidate
+
+
+def update_task(task_id: str, **updates) -> None:
+    """线程安全地更新仍存在的任务。"""
+    with task_store_lock:
+        task = task_store.get(task_id)
+        if task is not None:
+            task.update(updates)
+
+
+def finish_task(task_id: str, status: str, error: Optional[str]) -> None:
+    """原子写入最终状态并解除运行标记，避免迟到回调覆盖结果。"""
+    with task_store_lock:
+        task = task_store.get(task_id)
+        if task is not None:
+            task.update(
+                status=status,
+                error=error,
+                completed_at=datetime.now().isoformat(),
+            )
+        active_tasks.discard(task_id)
+
+# ==================== 后台任务 ====================
+def run_analysis_task(
+    task_id: str,
+    request: TaskRequest,
+    input_dir: Path,
+    task_type: Optional[str] = None,
+):
+    """后台运行分析任务"""
+    
+    logger.info(f"🚀 开始执行任务：{task_id}")
+
+    try:
+        update_task(task_id, status="running")
+
+        # 再次解析路径，防止任务排队期间目录被替换为越界符号链接。
+        revalidated_input_dir = resolve_input_dir(str(input_dir), task_id)
+        if revalidated_input_dir != input_dir:
+            raise Exception("输入目录在任务执行前发生变化")
+        input_dir = revalidated_input_dir
+
+        # 检查目录是否存在
+        if not input_dir.is_dir():
+            raise Exception(f"输入目录不存在或不是目录: {input_dir}")
+
+        # ==================== 脚本路径 ====================
+
+        script_path = Path(__file__).parent / "wanzheng.py"
+
+        if not script_path.exists():
+            raise Exception(f"分析脚本不存在: {script_path}")
+
+        # ==================== 模型路径 ====================
+
+        pore_only = task_type == "pore"
+        model_path = Path(MODEL_PATH)
+
+        if not pore_only and not model_path.is_file():
+            raise Exception(f"模型文件不存在: {model_path}")
+
+        # ==================== 构建命令 ====================
+
+        cmd = [
+            sys.executable,
+
+            str(script_path),
+
+            str(input_dir),
+
+            "--workers", "4",
+
+            "--analysis-root", str(Path(ANALYSIS_ROOT).resolve()),
+
+            "--api-url", f"{API_HOST.rstrip('/')}/{API_CALLBACK.lstrip('/')}",
+
+            "--task-id", task_id,
+        ]
+
+        if pore_only:
+            cmd.extend([
+                "--task-type", "pore",
+                "--pore-predictor", PORE_PREDICTOR_PATH,
+            ])
+        else:
+            cmd.extend(["--model", str(model_path)])
+            if PORE_ENABLED:
+                cmd.extend(["--pore-predictor", PORE_PREDICTOR_PATH])
+            else:
+                cmd.append("--disable-pores")
+
+        logger.info(f"🔧 命令参数：{cmd}")
+        logger.info(f"📋 执行命令：{' '.join(cmd)}")
+        logger.info(f"📁 输入目录：{input_dir}")
+
+        # ==================== 执行脚本 ====================
+
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            encoding="utf-8",
+            errors="replace",
+            timeout=3600
+        )
+
+        # ==================== 输出日志 ====================
+
+        if result.stdout:
+            logger.info(f"📋 标准输出：\n{result.stdout}")
+
+        if result.stderr:
+            if result.returncode == 0:
+                logger.info(f"📋 进程日志：\n{result.stderr}")
+            else:
+                logger.error(f"📋 错误输出：\n{result.stderr}")
+
+        # ==================== 判断执行结果 ====================
+
+        if result.returncode == 0:
+            finish_task(task_id, status="completed", error=None)
+
+            logger.info(f"✅ 任务完成：{task_id}")
+
+        else:
+
+            finish_task(
+                task_id,
+                status="failed",
+                error=result.stderr or f"分析进程退出码：{result.returncode}"
+            )
+
+            logger.error(
+                f"❌ 任务失败：{task_id} "
+                f"(code={result.returncode})"
+            )
+
+    except subprocess.TimeoutExpired:
+        finish_task(
+            task_id,
+            status="failed",
+            error="任务超时（超过1小时）"
+        )
+
+        logger.error(f"⏰ 任务超时：{task_id}")
+
+    except Exception as e:
+        finish_task(
+            task_id,
+            status="failed",
+            error=str(e)
+        )
+
+        logger.exception(f"❌ 任务异常：{task_id}")
+
+# ==================== API 接口 ====================
+@app.get("/")
+async def root():
+    return {
+        "service": "皮肤分析任务接口",
+        "version": "1.0.0",
+        "status": "running"
+    }
+
+@app.get("/health")
+async def health_check():
+    with task_store_lock:
+        statuses = [task["status"] for task in task_store.values()]
+    return {
+        "status": "healthy",
+        "timestamp": datetime.now().isoformat(),
+        "tasks": {
+            "total": len(statuses),
+            "pending": statuses.count("pending"),
+            "running": statuses.count("running"),
+            "completed": statuses.count("completed"),
+            "failed": statuses.count("failed")
+        }
+    }
+
+@app.post("/api/v1/analysis/submit", response_model=TaskResponse)
+async def submit_task(request: TaskRequest, background_tasks: BackgroundTasks):
+    # 使用传入的 task_id，如果没有则从 folder_name 提取
+    task_id = request.task_id if request.task_id else Path(request.folder_name).name
+
+    try:
+        validate_task_id(task_id)
+        task_type = normalize_task_type(request.task_type)
+        input_dir = resolve_input_dir(request.folder_name, task_id)
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    with task_store_lock:
+        if task_id in active_tasks:
+            existing_task = task_store[task_id]
+            return TaskResponse(
+                code=200,
+                message="任务已存在，正在运行中",
+                task_id=task_id,
+                status=existing_task["status"]
+            )
+
+        task_store[task_id] = {
+            "task_id": task_id,
+            "status": "pending",
+            "created_at": datetime.now().isoformat(),
+            "completed_at": None,
+            "error": None,
+            "request": {
+                "folder_name": request.folder_name,
+                "task_id": request.task_id,
+                "task_type": task_type,
+            }
+        }
+        active_tasks.add(task_id)
+
+    background_tasks.add_task(
+        run_analysis_task,
+        task_id,
+        request,
+        input_dir,
+        task_type,
+    )
+    
+    logger.info(f"📝 任务已提交：{task_id}")
+    
+    return TaskResponse(
+        code=200,
+        message="任务提交成功",
+        task_id=task_id,
+        status="pending"
+    )
+
+@app.post("/api/v1/analysis/callback")
+async def analysis_callback(request: CallbackRequest):
+    try:
+        task_id = validate_task_id(request.task_id)
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    callback_status = "completed" if request.status == 1 else "failed"
+
+    with task_store_lock:
+        if task_id not in task_store:
+            task_store[task_id] = {
+                "task_id": task_id,
+                "status": callback_status,
+                "created_at": datetime.now().isoformat(),
+                "completed_at": datetime.now().isoformat(),
+                "error": None if request.status == 1 else "分析任务回调失败",
+                "request": None
+            }
+        else:
+            current_status = task_store[task_id]["status"]
+            is_finished = current_status in {"completed", "failed"} and task_id not in active_tasks
+            if not is_finished:
+                task_store[task_id]["status"] = callback_status
+                task_store[task_id]["completed_at"] = datetime.now().isoformat()
+                task_store[task_id]["error"] = (
+                    None if request.status == 1 else "分析任务回调失败"
+                )
+
+        status = task_store[task_id]["status"]
+
+    logger.info(f"📩 收到分析回调：{task_id} status={request.status}")
+    return {"code": 200, "message": "回调接收成功", "task_id": task_id, "status": status}
+
+@app.get("/api/v1/analysis/status/{task_id}", response_model=TaskStatus)
+async def get_task_status(task_id: str):
+    with task_store_lock:
+        task = task_store.get(task_id)
+        if task is not None:
+            task = dict(task)
+    if task is None:
+        raise HTTPException(status_code=404, detail=f"任务不存在：{task_id}")
+    return TaskStatus(
+        task_id=task["task_id"],
+        status=task["status"],
+        created_at=task["created_at"],
+        completed_at=task.get("completed_at"),
+        error=task.get("error")
+    )
+
+@app.get("/api/v1/analysis/list")
+async def list_tasks(
+    status: Optional[str] = Query(default=None),
+    limit: int = Query(default=20, ge=1, le=100)
+):
+    with task_store_lock:
+        tasks = [dict(task) for task in task_store.values()]
+    
+    if status:
+        tasks = [t for t in tasks if t["status"] == status]
+    
+    tasks = sorted(tasks, key=lambda x: x["created_at"], reverse=True)
+    total = len(tasks)
+    tasks = tasks[:limit]
+    
+    return {
+        "total": total,
+        "tasks": tasks
+    }
+
+# ==================== 启动服务 ====================
+if __name__ == "__main__":
+    print("="*60)
+    print("皮肤分析 API 服务")
+    print("="*60)
+    print("监听地址：http://0.0.0.0:8071")
+    print("提交接口：POST /api/v1/analysis/submit")
+    print("状态查询：GET /api/v1/analysis/status/{task_id}")
+    print("健康检查：GET /health")
+    print("="*60)
+    print()
+    
+    uvicorn.run(app, host="0.0.0.0", port=8071)
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diff --git a/config/settings.py b/config/settings.py
new file mode 100644
index 0000000..15dec5c
--- /dev/null
+++ b/config/settings.py
@@ -0,0 +1,35 @@
+import os
+from pathlib import Path
+
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+API_HOST = os.getenv(
+    "API_HOST",
+    "http://skin-test-api.ailuowan.com"
+)
+
+API_CALLBACK = os.getenv(
+    "API_CALLBACK",
+    "/api/v1/analysis/callback"
+)
+
+ANALYSIS_ROOT = os.getenv(
+    "ANALYSIS_ROOT",
+    "/data/analysis"
+)
+
+MODEL_PATH = os.getenv(
+    "MODEL_PATH",
+    "/app/best.pt"
+)
+
+PORE_PREDICTOR_PATH = os.getenv(
+    "PORE_PREDICTOR_PATH",
+    str(PROJECT_ROOT / "shape_predictor_68_face_landmarks.dat")
+)
+
+PORE_ENABLED = os.getenv(
+    "PORE_ENABLED",
+    "1"
+).strip().lower() not in {"0", "false", "no", "off"}
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..597ccda
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,48 @@
+services:
+  # 哪个项目-什么语言-做什么
+  skin-py-analysis:
+    build: /data/www/py-skin-analysis-worker
+    image: analysis-py:latest
+    working_dir: /app
+    expose:
+      - "8071"
+    volumes:
+      - /data/www/py-skin-analysis-worker:/app
+      - /data/analysis:/data/analysis
+    environment:
+      - PYTHONUNBUFFERED=1
+    command: python api_server.py
+    restart: unless-stopped
+    networks:
+      - default
+      - common-network
+networks:
+  default:
+    external: true
+    name: skin-network
+
+  common-network:
+    external: true
+
+# services:
+#   # 皮肤分析 API 服务
+#   skin-api:
+#     build: .
+#     image: skin-analysis:latest
+#     container_name: skin-analysis-api
+#     ports:
+#       - "8071:8071"
+#     volumes:
+#       # 挂载数据目录
+#       - D:/data/analysis:/data/analysis
+#     environment:
+#       - PYTHONUNBUFFERED=1
+#     command: python api_server.py
+#     restart: unless-stopped
+#     deploy:
+#       resources:
+#         reservations:
+#           devices:
+#             - driver: nvidia
+#               count: 1
+#               capabilities: [gpu]
\ No newline at end of file
diff --git a/facepp_skin_analysis.json b/facepp_skin_analysis.json
new file mode 100644
index 0000000..d5fe743
--- /dev/null
+++ b/facepp_skin_analysis.json
@@ -0,0 +1,10414 @@
+{
+  "request_id": "1786419532,7fbd2b44-fcf1-4a58-a3cf-2ae49974abf3",
+  "time_used": 3257,
+  "result": {
+    "skin_age": {
+      "value": 29
+    },
+    "eye_pouch": {
+      "value": 1,
+      "confidence": 0.97373587
+    },
+    "eye_pouch_severity": {
+      "value": 0,
+      "confidence": 0.92456836
+    },
+    "dark_circle": {
+      "value": 1,
+      "confidence": 1
+    },
+    "dark_circle_severity": {
+      "value": 1,
+      "confidence": 1
+    },
+    "forehead_wrinkle": {
+      "value": 0,
+      "confidence": 0.8329094
+    },
+    "crows_feet": {
+      "value": 0,
+      "confidence": 0.9784701
+    },
+    "eye_finelines": {
+      "value": 0,
+      "confidence": 0.75139356
+    },
+    "glabella_wrinkle": {
+      "value": 0,
+      "confidence": 0.96045905
+    },
+    "nasolabial_fold": {
+      "value": 0,
+      "confidence": 0.383613
+    },
+    "skin_type": {
+      "skin_type": 3,
+      "details": {
+        "0": {
+          "value": 0,
+          "confidence": 0.047169436
+        },
+        "1": {
+          "value": 0,
+          "confidence": 0.012784755
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+          "radius": 1
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+          "radius": 1
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+          "radius": 2
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+  },
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+    "left": 322,
+    "width": 1753,
+    "height": 1753
+  }
+}
\ No newline at end of file
diff --git a/shape_predictor_68_face_landmarks.dat b/shape_predictor_68_face_landmarks.dat
new file mode 100644
index 0000000..e0ec20d
Binary files /dev/null and b/shape_predictor_68_face_landmarks.dat differ
diff --git a/wanzheng.py b/wanzheng.py
new file mode 100644
index 0000000..7ca4d10
--- /dev/null
+++ b/wanzheng.py
@@ -0,0 +1,1275 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+皮肤分析工具 - 后端 API 版
+输出 JSON 数据到文件夹，完成后回调后端接口
+"""
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from pathlib import Path
+
+import os
+import argparse
+import threading
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+import logging
+from typing import Dict, List, Optional, Tuple
+import requests
+
+from config.settings import API_HOST, API_CALLBACK, PORE_PREDICTOR_PATH
+
+try:
+    import dlib
+    DLIB_IMPORT_ERROR = None
+except (ImportError, OSError) as exc:
+    dlib = None
+    DLIB_IMPORT_ERROR = exc
+
+# ==================== 日志配置 ====================
+def setup_logging(verbose: bool = False):
+    level = logging.DEBUG if verbose else logging.INFO
+    logging.basicConfig(
+        level=level,
+        format='%(asctime)s | %(levelname)s | %(message)s',
+        datefmt='%H:%M:%S'
+    )
+    return logging.getLogger(__name__)
+
+logger = setup_logging()
+
+# Ultralytics model instances are not safe to invoke from multiple threads.
+# Keep the OpenCV stages concurrent while serializing only model inference.
+MODEL_INFERENCE_LOCK = threading.Lock()
+PORE_MODEL_LOAD_LOCK = threading.Lock()
+
+PORE_COLOR = (207, 95, 123)
+PORE_MIN_RADIUS = 3
+PORE_MAX_RADIUS = 8
+PORE_CONTRAST_THRESHOLD = 22.0
+PORE_CIRCULARITY_THRESHOLD = 0.55
+PORE_MAX_COUNT = 2000
+PORE_FOREHEAD_RATIO = 0.38
+IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.webp'}
+PORE_IMAGE_NAMES = (
+    'cross_left.jpg',
+    'cross_mid.jpg',
+    'cross_right.jpg',
+)
+
+# ==================== 支持中文路径的读写函数 ====================
+def cv2_imread_chinese(file_path: str) -> Optional[np.ndarray]:
+    if not os.path.exists(file_path):
+        logger.warning(f"文件不存在：{file_path}")
+        return None
+    try:
+        img = cv2.imdecode(np.fromfile(file_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+        return img
+    except Exception as e:
+        logger.error(f"读取失败 {file_path}: {e}")
+        return None
+
+def cv2_imwrite_chinese(file_path: str, img: np.ndarray) -> bool:
+    try:
+        ext = os.path.splitext(file_path)[1]
+        if ext.lower() not in ['.jpg', '.jpeg', '.png', '.bmp']:
+            ext = '.jpg'
+        encoded_ok, encoded = cv2.imencode(ext, img)
+        if not encoded_ok:
+            logger.error(f"图像编码失败 {file_path}")
+            return False
+        encoded.tofile(file_path)
+        return True
+    except Exception as e:
+        logger.error(f"写入失败 {file_path}: {e}")
+        return False
+
+# ==================== YOLO 痤疮检测 ====================
+def detect_acne(img: np.ndarray, image_path: str, model: YOLO, output_path: str) -> Tuple[bool, Dict, str]:
+    try:
+        # 直接全图检测
+        with MODEL_INFERENCE_LOCK:
+            results = model(img, conf=0.2, verbose=False)
+
+        annotated_img = img.copy()
+        class_counts = {}
+        
+        for result in results:
+            if result.boxes is not None:
+                for box in result.boxes:
+                    cls_id = int(box.cls[0])
+                    class_name = model.names[cls_id]
+                    x1, y1, x2, y2 = map(int, box.xyxy[0])
+                    conf = float(box.conf[0])
+                    
+                    color = (0, 0, 255)
+                    cv2.rectangle(annotated_img, (x1, y1), (x2, y2), color, 2)
+                    label = f"{class_name} {conf:.2f}"
+                    (text_width, text_height), baseline = cv2.getTextSize(
+                        label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2
+                    )
+                    cv2.rectangle(annotated_img, (x1, y1 - text_height - baseline), 
+                                (x1 + text_width, y1), color, -1)
+                    cv2.putText(annotated_img, label, (x1, y1 - baseline), 
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+                    
+                    class_counts[class_name] = class_counts.get(class_name, 0) + 1
+
+        if cv2_imwrite_chinese(output_path, annotated_img):
+            logger.debug(f"YOLO 完成：{os.path.basename(output_path)}")
+            return True, class_counts, ""
+        else:
+            return False, {}, "写入失败"
+
+    except Exception as e:
+        logger.error(f"YOLO 失败：{e}")
+        return False, {}, str(e)
+
+def build_lut(anchors: np.ndarray) -> np.ndarray:
+    """从锚点构建自定义 256 级颜色映射 LUT"""
+    lut = np.zeros((256, 3), dtype=np.uint8)
+    for c in range(3):
+        lut[:, c] = np.interp(np.arange(256), anchors[:, 0], anchors[:, c + 1])
+    return lut.reshape(256, 1, 3)
+
+
+def detect_skin_region(img: np.ndarray):
+    """多色彩空间皮肤检测，返回 (skin_soft, skin_binary, a_float)"""
+    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+    _, a_ch, _ = cv2.split(lab)
+    a_float = a_ch.astype(np.float32)
+
+    mask_lab = cv2.inRange(lab, (40, 120, 105), (235, 185, 175))
+    ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+    mask_ycrcb = cv2.inRange(ycrcb, (40, 133, 77), (240, 175, 135))
+    skin_mask = cv2.bitwise_or(mask_lab, mask_ycrcb)
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
+    skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_CLOSE, kernel, iterations=3)
+    skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_OPEN, kernel, iterations=1)
+    skin_mask = cv2.dilate(skin_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7)), iterations=1)
+
+    contours, _ = cv2.findContours(skin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if contours:
+        largest = max(contours, key=cv2.contourArea)
+        skin_mask = np.zeros_like(skin_mask)
+        cv2.drawContours(skin_mask, [largest], -1, 255, -1)
+        hull = cv2.convexHull(largest)
+        cv2.drawContours(skin_mask, [hull], -1, 255, -1)
+
+    skin_soft = cv2.GaussianBlur(skin_mask.astype(np.float32), (21, 21), 0) / 255.0
+    return skin_soft, skin_mask, a_float
+
+
+def compute_heatmap_idx(a_float: np.ndarray, skin_mask: np.ndarray,
+                        baseline_percentile: float, sensitivity: float) -> np.ndarray:
+    """计算红度热力图索引（0-255）"""
+    skin_pixels = a_float[skin_mask > 128]
+    if len(skin_pixels) > 100:
+        baseline = np.percentile(skin_pixels, baseline_percentile)
+    else:
+        baseline = np.median(a_float)
+    redness = np.maximum(0, a_float - baseline) * sensitivity
+    vmax = np.percentile(redness, 98)
+    if vmax > 0:
+        idx = np.clip(redness, 0, vmax) / vmax * 255.0
+    else:
+        idx = np.zeros_like(a_float)
+    return np.clip(idx, 0, 255).astype(np.uint8)
+
+
+def add_dark_details(result: np.ndarray, img: np.ndarray,
+                     intensity: float = 0.50) -> np.ndarray:
+    """叠加原图暗部细节（眉毛/头发/眼睛），保持五官可见"""
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    dark = (255.0 - gray.astype(np.float32)) / 255.0
+    dark = np.clip(dark * intensity, 0, 1)
+    dark = cv2.GaussianBlur(dark, (5, 5), 0)
+    return (result.astype(np.float32) * (1 - np.dstack([dark] * 3))).astype(np.uint8)
+
+
+# ==================== 大毛孔检测 ====================
+def detect_pore_haar_face(gray: np.ndarray):
+    if not hasattr(cv2, "CascadeClassifier"):
+        return None
+    cascade_names = [
+        "haarcascade_frontalface_default.xml",
+        "haarcascade_frontalface_alt2.xml",
+        "haarcascade_profileface.xml",
+    ]
+    for name in cascade_names:
+        cascade = cv2.CascadeClassifier(cv2.data.haarcascades + name)
+        faces = cascade.detectMultiScale(
+            gray,
+            scaleFactor=1.05,
+            minNeighbors=3,
+            minSize=(120, 120),
+        )
+        if len(faces) > 0:
+            x, y, w, h = max(faces, key=lambda rect: rect[2] * rect[3])
+            margin = int(w * 0.18)
+            return dlib.rectangle(
+                max(0, x - margin),
+                max(0, y - margin),
+                min(gray.shape[1], x + w + margin),
+                min(gray.shape[0], y + h + margin),
+            )
+    return None
+
+
+def detect_pore_landmarks(img: np.ndarray, detector, predictor):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = detector(gray, 1)
+    if not faces:
+        # 侧脸在常规上采样下可能漏检，仅在失败时提高一次精度。
+        faces = detector(gray, 2)
+    if faces:
+        face = max(faces, key=lambda rect: rect.width() * rect.height())
+    else:
+        face = detect_pore_haar_face(gray)
+        if face is None:
+            return None
+    shape = predictor(gray, face)
+    return np.array(
+        [(shape.part(index).x, shape.part(index).y) for index in range(68)],
+        dtype=np.int32,
+    )
+
+
+def build_pore_face_mask(img: np.ndarray, landmarks: np.ndarray,
+                         forehead_ratio: float) -> np.ndarray:
+    jaw = landmarks[0:17]
+    brow = landmarks[17:27]
+    chin = landmarks[8]
+    brow_center_y = int(np.mean(brow[:, 1]))
+    face_height = max(1, chin[1] - brow_center_y)
+    top_y = max(0, brow_center_y - int(face_height * forehead_ratio))
+    top_points = np.array([
+        [landmarks[16][0], top_y],
+        [landmarks[27][0], top_y],
+        [landmarks[0][0], top_y],
+    ], dtype=np.int32)
+    face_poly = np.vstack([jaw, top_points])
+    mask = np.zeros(img.shape[:2], np.uint8)
+    cv2.fillPoly(mask, [face_poly], 255, cv2.LINE_AA)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (31, 31))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2)
+    mask = cv2.GaussianBlur(mask, (11, 11), 0)
+    _, mask = cv2.threshold(mask, 20, 255, cv2.THRESH_BINARY)
+    return mask
+
+
+def build_pore_exclusion_mask(img: np.ndarray,
+                              landmarks: np.ndarray) -> np.ndarray:
+    exclude = np.zeros(img.shape[:2], np.uint8)
+    regions = [
+        landmarks[17:22],
+        landmarks[22:27],
+        landmarks[36:42],
+        landmarks[42:48],
+        landmarks[48:60],
+        landmarks[[33, 48, 54, 8, 6, 10]],
+    ]
+    scales = [1.8, 1.8, 2.0, 2.0, 1.7, 1.4]
+    for points, scale in zip(regions, scales):
+        center = points.mean(axis=0)
+        expanded = center + (points - center) * scale
+        hull = cv2.convexHull(expanded.astype(np.int32))
+        cv2.fillConvexPoly(exclude, hull, 255, cv2.LINE_AA)
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    dark = (gray < 55).astype(np.uint8) * 255
+    dark_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (17, 17))
+    dark = cv2.dilate(dark, dark_kernel, iterations=1)
+    exclude = cv2.bitwise_or(exclude, dark)
+    exclude_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9))
+    return cv2.dilate(exclude, exclude_kernel, iterations=1)
+
+
+def pore_catmull_rom(points: np.ndarray, num: int = 12) -> np.ndarray:
+    points = np.array(points, dtype=np.float64)
+    count = len(points)
+    result = []
+    for index in range(count):
+        p0 = points[(index - 1) % count]
+        p1 = points[index]
+        p2 = points[(index + 1) % count]
+        p3 = points[(index + 2) % count]
+        for t in np.linspace(0, 1, num, endpoint=False):
+            t2 = t * t
+            t3 = t2 * t
+            x = 0.5 * (
+                2 * p1[0] + (-p0[0] + p2[0]) * t
+                + (2 * p0[0] - 5 * p1[0] + 4 * p2[0] - p3[0]) * t2
+                + (-p0[0] + 3 * p1[0] - 3 * p2[0] + p3[0]) * t3
+            )
+            y = 0.5 * (
+                2 * p1[1] + (-p0[1] + p2[1]) * t
+                + (2 * p0[1] - 5 * p1[1] + 4 * p2[1] - p3[1]) * t2
+                + (-p0[1] + 3 * p1[1] - 3 * p2[1] + p3[1]) * t3
+            )
+            result.append((x, y))
+    return np.array(result, dtype=np.int32)
+
+
+def build_pore_roi_mask(img: np.ndarray,
+                        landmarks: np.ndarray) -> np.ndarray:
+    height, width = img.shape[:2]
+    roi = np.zeros((height, width), np.uint8)
+    nose_bottom_y = max(landmarks[31][1], landmarks[35][1])
+    right_34 = landmarks[34].copy()
+    right_34[1] = min(right_34[1], nose_bottom_y)
+    right_32 = landmarks[32].copy()
+    right_32[1] = min(right_32[1], nose_bottom_y)
+    right_41 = landmarks[41].copy()
+    right_41[1] += 5
+    right_46 = landmarks[46].copy()
+    right_46[1] += 5
+
+    point_35 = landmarks[35]
+    point_14 = landmarks[14]
+    point_31 = landmarks[31]
+    point_2 = landmarks[2]
+    cheek_right = np.array([
+        int(point_35[0] * 0.6 + point_14[0] * 0.4),
+        int(point_35[1] * 0.6 + point_14[1] * 0.4) + 12,
+    ], dtype=np.int32)
+    cheek_left = np.array([
+        int(point_31[0] * 0.6 + point_2[0] * 0.4),
+        int(point_31[1] * 0.6 + point_2[1] * 0.4) + 12,
+    ], dtype=np.int32)
+    contour = np.array([
+        landmarks[2], right_41, landmarks[28], right_46, landmarks[14],
+        cheek_right, landmarks[35], right_34, landmarks[30], right_32,
+        landmarks[31], cheek_left,
+    ], dtype=np.int32)
+    cv2.fillPoly(roi, [pore_catmull_rom(contour)], 255, cv2.LINE_AA)
+
+    eye_bottom_y = int(np.max(landmarks[36:48, 1])) + 5
+    roi[:eye_bottom_y, :] = 0
+    roi = cv2.GaussianBlur(roi, (9, 9), 0)
+    _, roi = cv2.threshold(roi, 20, 255, cv2.THRESH_BINARY)
+    return roi
+
+
+def build_pore_skin_mask(img: np.ndarray, face_mask: np.ndarray,
+                         exclude_mask: np.ndarray,
+                         roi_mask: np.ndarray) -> np.ndarray:
+    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+    ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+    skin = cv2.bitwise_or(
+        cv2.inRange(lab, (35, 118, 105), (245, 188, 178)),
+        cv2.inRange(ycrcb, (35, 130, 75), (245, 180, 140)),
+    )
+    skin = cv2.bitwise_and(skin, face_mask)
+    skin = cv2.bitwise_and(skin, cv2.bitwise_not(exclude_mask))
+    skin = cv2.bitwise_and(skin, roi_mask)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (13, 13))
+    skin = cv2.morphologyEx(skin, cv2.MORPH_CLOSE, kernel, iterations=2)
+    return cv2.morphologyEx(skin, cv2.MORPH_OPEN, kernel, iterations=1)
+
+
+def detect_big_pores_dog(img: np.ndarray, skin_mask: np.ndarray,
+                         min_radius: int, max_radius: int,
+                         contrast_threshold: float,
+                         circularity_threshold: float,
+                         max_pores: int) -> List[Dict]:
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(12, 12))
+    gray_eq = clahe.apply(gray)
+    candidates = []
+    height, width = gray_eq.shape
+
+    for radius in range(min_radius, max_radius + 1):
+        first = cv2.GaussianBlur(gray_eq, (0, 0), sigmaX=radius * 0.6)
+        second = cv2.GaussianBlur(gray_eq, (0, 0), sigmaX=radius * 1.6)
+        dog = second.astype(np.float32) - first.astype(np.float32)
+        local_max = dog == cv2.dilate(
+            dog,
+            np.ones((radius * 2 + 1, radius * 2 + 1), np.uint8),
+        )
+        valid = local_max & (skin_mask > 0) & (dog >= contrast_threshold)
+
+        ys, xs = np.where(valid)
+        for y, x in zip(ys, xs):
+            patch_radius = min(
+                radius + 2,
+                min(x, y, width - 1 - x, height - 1 - y),
+            )
+            if patch_radius < 2:
+                continue
+            patch = gray_eq[
+                y - patch_radius:y + patch_radius + 1,
+                x - patch_radius:x + patch_radius + 1,
+            ]
+            if patch.shape[0] < 5 or patch.shape[1] < 5:
+                continue
+
+            _, binary = cv2.threshold(
+                patch,
+                0,
+                255,
+                cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU,
+            )
+            contours, _ = cv2.findContours(
+                binary,
+                cv2.RETR_EXTERNAL,
+                cv2.CHAIN_APPROX_SIMPLE,
+            )
+            if not contours:
+                continue
+            contour = max(contours, key=cv2.contourArea)
+            area = cv2.contourArea(contour)
+            if area < 3 or area > np.pi * (radius + 2) ** 2:
+                continue
+            perimeter = cv2.arcLength(contour, True)
+            if perimeter < 1:
+                continue
+            circularity = 4 * np.pi * area / (perimeter * perimeter)
+            if circularity < circularity_threshold:
+                continue
+
+            local_background = float(np.mean(gray_eq[
+                max(0, y - radius * 2):y + radius * 2 + 1,
+                max(0, x - radius * 2):x + radius * 2 + 1,
+            ]))
+            real_contrast = local_background - float(gray_eq[y, x])
+            if real_contrast < contrast_threshold * 0.6:
+                continue
+            candidates.append({
+                "x": int(x),
+                "y": int(y),
+                "radius": radius,
+                "score": float(dog[y, x]),
+                "contrast": real_contrast,
+                "circularity": float(circularity),
+                "area": float(area),
+            })
+
+    candidates.sort(key=lambda candidate: -candidate["contrast"])
+    used = np.zeros((height, width), bool)
+    selected = []
+    for candidate in candidates:
+        if len(selected) >= max_pores:
+            break
+        x, y = candidate["x"], candidate["y"]
+        spacing = candidate["radius"] + 2
+        y0, y1 = max(0, y - spacing), min(height, y + spacing + 1)
+        x0, x1 = max(0, x - spacing), min(width, x + spacing + 1)
+        if used[y0:y1, x0:x1].any():
+            continue
+        selected.append(candidate)
+        used[y0:y1, x0:x1] = True
+    return selected
+
+
+def draw_big_pores(img: np.ndarray, pores: List[Dict]) -> np.ndarray:
+    output = img.copy()
+    overlay = output.copy()
+    for pore in pores:
+        radius = 3 if pore["contrast"] > 22 else 2
+        cv2.circle(
+            overlay,
+            (pore["x"], pore["y"]),
+            radius,
+            PORE_COLOR,
+            1,
+            cv2.LINE_AA,
+        )
+    return cv2.addWeighted(overlay, 0.85, output, 0.15, 0)
+
+
+def load_pore_predictor(predictor_path: str):
+    """加载 dlib 模型，并兼容 Windows 下包含中文的绝对路径。"""
+    path = Path(predictor_path).resolve()
+    with PORE_MODEL_LOAD_LOCK:
+        try:
+            return dlib.shape_predictor(str(path))
+        except RuntimeError:
+            if str(path).isascii() or not path.name.isascii():
+                raise
+
+            original_directory = Path.cwd()
+            try:
+                os.chdir(str(path.parent))
+                return dlib.shape_predictor(path.name)
+            finally:
+                os.chdir(str(original_directory))
+
+
+class PoreAnalyzer:
+    """每个任务复用一次 dlib 模型，毛孔失败不影响原有分析阶段。"""
+
+    def __init__(self, predictor_path: str):
+        if dlib is None:
+            raise RuntimeError(f"dlib 不可用：{DLIB_IMPORT_ERROR}")
+        if not Path(predictor_path).is_file():
+            raise FileNotFoundError(f"缺少 dlib 关键点模型：{predictor_path}")
+        self.detector = dlib.get_frontal_face_detector()
+        self.predictor = load_pore_predictor(predictor_path)
+        self.landmark_lock = threading.Lock()
+
+    def analyze(self, img: np.ndarray, pores_dir: Path,
+                stem: str) -> Tuple[bool, Dict, str]:
+        try:
+            with self.landmark_lock:
+                landmarks = detect_pore_landmarks(
+                    img,
+                    self.detector,
+                    self.predictor,
+                )
+            if landmarks is None:
+                return False, {}, "未检测到人脸"
+
+            face_mask = build_pore_face_mask(
+                img,
+                landmarks,
+                PORE_FOREHEAD_RATIO,
+            )
+            exclude_mask = build_pore_exclusion_mask(img, landmarks)
+            roi_mask = build_pore_roi_mask(img, landmarks)
+            skin_mask = build_pore_skin_mask(
+                img,
+                face_mask,
+                exclude_mask,
+                roi_mask,
+            )
+            pores = detect_big_pores_dog(
+                img,
+                skin_mask,
+                PORE_MIN_RADIUS,
+                PORE_MAX_RADIUS,
+                PORE_CONTRAST_THRESHOLD,
+                PORE_CIRCULARITY_THRESHOLD,
+                PORE_MAX_COUNT,
+            )
+            roi_area = int(np.sum(skin_mask > 0))
+            average_size = (
+                float(np.mean([pore["radius"] * 2 for pore in pores]))
+                if pores else 0.0
+            )
+
+            annotated = draw_big_pores(img, pores)
+            outline_path = pores_dir / f"pore_outline_{stem}.jpg"
+            if not cv2_imwrite_chinese(str(outline_path), annotated):
+                return False, {}, "毛孔标注图写入失败"
+
+            stats_path = pores_dir / f"pore_stats_{stem}.txt"
+            with open(stats_path, "w", encoding="utf-8") as file:
+                file.write(f"毛孔数量: {len(pores)}\n")
+                file.write(f"平均毛孔大小: {average_size:.1f} px（直径）\n")
+                file.write(f"ROI: {roi_area} px\n")
+
+            stats = {
+                "pore_count": len(pores),
+                "pore_average_size": average_size,
+                "pore_roi_area": roi_area,
+            }
+            return True, stats, ""
+        except Exception as exc:
+            return False, {}, str(exc)
+
+
+# ==================== RBX 红区分析 ====================
+def analyze_red_zone(img: np.ndarray, output_path: str) -> Tuple[bool, Dict, str]:
+    try:
+        # 直接全图皮肤检测
+        lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+        _, a_ch, _ = cv2.split(lab)
+        a_float = a_ch.astype(np.float32)
+
+        mask_lab = cv2.inRange(lab, (40, 120, 105), (235, 185, 175))
+        ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+        mask_ycrcb = cv2.inRange(ycrcb, (40, 133, 77), (240, 175, 135))
+        skin_mask = cv2.bitwise_or(mask_lab, mask_ycrcb)
+
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
+        skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_CLOSE, kernel, iterations=3)
+        skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_OPEN, kernel, iterations=1)
+        skin_mask = cv2.dilate(skin_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7)), iterations=1)
+
+        contours, _ = cv2.findContours(skin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if contours:
+            largest = max(contours, key=cv2.contourArea)
+            skin_mask = np.zeros_like(skin_mask)
+            cv2.drawContours(skin_mask, [largest], -1, 255, -1)
+            hull = cv2.convexHull(largest)
+            cv2.drawContours(skin_mask, [hull], -1, 255, -1)
+
+        skin_soft = cv2.GaussianBlur(skin_mask.astype(np.float32), (31, 31), 0) / 255.0
+        skin_pixels = a_float[skin_mask > 128]
+        if len(skin_pixels) > 100:
+            baseline_a = np.percentile(skin_pixels, 15)
+        else:
+            baseline_a = np.median(a_float)
+
+        redness_map = np.maximum(0, a_float - baseline_a) * 1.8
+        vmax = np.percentile(redness_map, 98)
+        if vmax > 0:
+            redness_map = np.clip(redness_map, 0, vmax)
+            heatmap_idx = (redness_map / vmax) * 255.0
+        else:
+            heatmap_idx = np.zeros_like(a_float)
+        heatmap_idx = np.clip(heatmap_idx, 0, 255).astype(np.uint8)
+        heatmap_idx = cv2.bilateralFilter(heatmap_idx, 9, 75, 75)
+        heatmap_idx = cv2.GaussianBlur(heatmap_idx, (7, 7), 0)
+
+        # 皮肤健康度统计
+        skin_area = np.sum(skin_mask > 128)
+        if skin_area > 0:
+            within_skin = heatmap_idx[skin_mask > 128]
+            normal_pct = float(np.sum(within_skin < 55) / skin_area * 100)
+            mild_pct = float(np.sum((within_skin >= 55) & (within_skin < 110)) / skin_area * 100)
+            warning_pct = float(np.sum((within_skin >= 110) & (within_skin < 170)) / skin_area * 100)
+            severe_pct = float(np.sum(within_skin >= 170) / skin_area * 100)
+            health_score = float(normal_pct * 1.0 + mild_pct * 0.7 + warning_pct * 0.4 + severe_pct * 0.1)
+        else:
+            normal_pct = mild_pct = warning_pct = severe_pct = health_score = 0.0
+        health_stats = {
+            'health_score': health_score, 'normal_pct': normal_pct,
+            'mild_pct': mild_pct, 'warning_pct': warning_pct, 'severe_pct': severe_pct,
+        }
+
+        alpha_red = heatmap_idx.astype(np.float32) / 255.0
+        alpha_red = np.clip(alpha_red * 1.5, 0, 1)
+        alpha_red = cv2.GaussianBlur(alpha_red, (5, 5), 0)
+
+        anchors = np.array([
+            [0,   232, 228, 242],
+            [3,   205, 170, 220],
+            [6,   150, 90,  215],
+            [10,  70,  30,  210],
+            [15,  35,  15,  225],
+            [20,  18,  14,  242],
+            [255, 2,   2,   97 ],
+        ], dtype=np.float32)
+        heatmap_color = cv2.applyColorMap(heatmap_idx, build_lut(anchors))
+        heatmap_color = cv2.GaussianBlur(heatmap_color, (3, 3), 0)
+
+        hsv = cv2.cvtColor(heatmap_color, cv2.COLOR_BGR2HSV).astype(np.float32)
+        hsv[:, :, 1] = np.clip(hsv[:, :, 1] * 1.5, 0, 255)
+        heatmap_color = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
+        heatmap_float = heatmap_color.astype(np.float32) / 255.0
+
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32)
+        skin_texture = cv2.GaussianBlur(gray, (5, 5), 0)
+        skin_texture = np.clip(215 + skin_texture * 0.12, 215, 252)
+        skin_texture = np.dstack([skin_texture] * 3) / 255.0
+
+        alpha_3 = np.dstack([alpha_red] * 3)
+        result = skin_texture * (1 - alpha_3 * 0.8) + heatmap_float * (alpha_3 * 0.8)
+
+        skin_alpha = np.dstack([skin_soft] * 3)
+
+        highpass = gray - cv2.GaussianBlur(gray, (11, 11), 0)
+        highpass = np.clip(highpass * 0.08, -8, 8)
+        highpass = cv2.GaussianBlur(highpass, (3, 3), 0)
+        highpass_3 = np.dstack([highpass] * 3) / 255.0
+        result = np.clip(result + highpass_3 * skin_alpha * 0.5, 0, 1)
+
+        dark_mask = np.clip(1.0 - gray / 55.0, 0, 1)
+        dark_mask = cv2.GaussianBlur(dark_mask, (11, 11), 0)
+        dark_3 = np.dstack([dark_mask] * 3)
+        white = np.ones_like(heatmap_float)
+        result = result * (1 - dark_3) + white * dark_3
+
+        result = (result * 255).astype(np.uint8)
+
+        if cv2_imwrite_chinese(output_path, result):
+            logger.debug(f"红区完成：{os.path.basename(output_path)}")
+            return True, health_stats, ""
+        return False, {}, "写入失败"
+    except Exception as e:
+        logger.error(f"红区失败：{e}")
+        return False, {}, str(e)
+
+# ==================== 人脸红区检测 ====================
+def analyze_face_red(img: np.ndarray, output_path: str) -> Tuple[bool, str]:
+    """交叉偏振人脸皮肤分析：红度 + 色素"""
+    try:
+        # 直接全图皮肤检测
+        lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+        l_ch, a_ch, b_ch = cv2.split(lab)
+        a_float = a_ch.astype(np.float32)
+        l_float = l_ch.astype(np.float32)
+
+        mask_lab = cv2.inRange(lab, (40, 120, 105), (235, 185, 175))
+        ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+        mask_ycrcb = cv2.inRange(ycrcb, (40, 133, 77), (240, 175, 135))
+        skin = cv2.bitwise_or(mask_lab, mask_ycrcb)
+        k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
+        skin = cv2.morphologyEx(skin, cv2.MORPH_CLOSE, k, iterations=3)
+
+        contours, _ = cv2.findContours(skin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if contours:
+            skin[:] = 0
+            cv2.drawContours(skin, [max(contours, key=cv2.contourArea)], -1, 255, -1)
+
+        skin_soft = cv2.GaussianBlur(skin.astype(np.float32), (15, 15), 0) / 255.0
+
+        # 3. 红度分析
+        skin_pixels = a_float[skin > 128]
+        baseline = np.percentile(skin_pixels, 20) if len(skin_pixels) > 100 else np.median(a_float)
+        redness = np.maximum(0, a_float - baseline) * 2.0
+        vmax = np.percentile(redness[skin > 128], 97) if np.sum(skin > 128) > 100 else 0
+        red_map = np.clip(redness / vmax * 255.0, 0, 255).astype(np.uint8) if vmax > 0 else np.zeros_like(a_float, dtype=np.uint8)
+        red_map = cv2.bilateralFilter(red_map, 7, 50, 50)
+
+        # 4. 色素分析
+        l_blur = cv2.GaussianBlur(l_float, (31, 31), 0)
+        pigment = np.abs(l_float - l_blur)
+        p_max = np.percentile(pigment[skin > 128], 95) if np.sum(skin > 128) > 100 else 0
+        pig_map = np.clip(pigment / p_max * 255.0, 0, 255).astype(np.uint8) if p_max > 0 else np.zeros_like(l_float, dtype=np.uint8)
+
+        # 5. 可视化：原图 + 红度(红) + 色素(蓝)
+        img_f = img.astype(np.float32)
+        skin_3 = np.dstack([skin_soft] * 3)
+
+        red_alpha = np.clip(red_map.astype(np.float32) / 200.0, 0, 1)
+        red_alpha = cv2.GaussianBlur(red_alpha, (5, 5), 0)
+        red_ol = np.zeros_like(img)
+        red_ol[:, :, 2] = 180
+        red_3 = np.dstack([red_alpha] * 3)
+        result = img_f * (1 - red_3 * 0.5) + red_ol.astype(np.float32) * (red_3 * 0.5)
+
+        pig_alpha = np.clip(pig_map.astype(np.float32) / 200.0, 0, 1)
+        pig_alpha = cv2.GaussianBlur(pig_alpha, (5, 5), 0)
+        pig_ol = np.zeros_like(img)
+        pig_ol[:, :, 0] = 160
+        pig_3 = np.dstack([pig_alpha] * 3)
+        result = result * (1 - pig_3 * 0.4) + pig_ol.astype(np.float32) * (pig_3 * 0.4)
+
+        # 非皮肤压暗
+        dark = img_f * 0.3 + 50
+        result = result * skin_3 + dark * (1 - skin_3)
+        result = np.clip(result, 0, 255).astype(np.uint8)
+
+        if cv2_imwrite_chinese(output_path, result):
+            logger.debug(f"交叉偏振分析完成：{os.path.basename(output_path)}")
+            return True, ""
+        return False, "写入失败"
+    except Exception as e:
+        logger.error(f"交叉偏振分析失败：{e}")
+        return False, str(e)
+
+# ==================== 热力敏感图分析 ====================
+def analyze_heat_sensitivity(img: np.ndarray, output_path: str) -> Tuple[bool, str]:
+    try:
+        # 直接全图皮肤检测
+        skin_soft, skin_binary, a_float = detect_skin_region(img)
+        heatmap_idx = compute_heatmap_idx(a_float, skin_binary, 30, 1.8)
+        heatmap_idx = cv2.GaussianBlur(heatmap_idx, (7, 7), 0)
+        heatmap_idx = cv2.bilateralFilter(heatmap_idx, 7, 60, 60)
+
+        anchors = np.array([
+            [0,   192, 97,  16 ],
+            [1,   233, 241, 86 ],
+            [60,  8,   213, 204],
+            [100, 0,   250, 245],
+            [160, 0,   150, 255],
+            [245, 33,  38,  200],
+            [255, 4,   38,  152],
+        ], dtype=np.float32)
+        heatmap_color = cv2.applyColorMap(heatmap_idx, build_lut(anchors))
+        heatmap_color = cv2.convertScaleAbs(heatmap_color, alpha=1.05, beta=0)
+
+        result = (heatmap_color.astype(np.float32) * 0.92 +
+                  np.full_like(img, 255, dtype=np.float32) * 0.08)
+        result = np.clip(result, 0, 255).astype(np.uint8)
+
+        result = add_dark_details(result, img)
+
+        if cv2_imwrite_chinese(output_path, result):
+            logger.debug(f"热力敏感图完成：{os.path.basename(output_path)}")
+            return True, ""
+        return False, "写入失败"
+    except Exception as e:
+        logger.error(f"热力敏感图失败：{e}")
+        return False, str(e)
+
+# ==================== UV 荧光分析 ====================
+def detect_skin_uv(img: np.ndarray):
+    """UV 照片皮肤检测 — 亮度阈值 + 形态学"""
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # UV 照片: 皮肤有微弱的 UV 反射/荧光, 背景几乎全黑
+    _, skin = cv2.threshold(gray, 18, 255, cv2.THRESH_BINARY)
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
+    skin = cv2.morphologyEx(skin, cv2.MORPH_CLOSE, kernel, iterations=3)
+    skin = cv2.morphologyEx(skin, cv2.MORPH_OPEN, kernel, iterations=1)
+    skin = cv2.dilate(skin, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7)), iterations=1)
+
+    contours, _ = cv2.findContours(skin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if contours:
+        largest = max(contours, key=cv2.contourArea)
+        skin = np.zeros_like(skin)
+        cv2.drawContours(skin, [largest], -1, 255, -1)
+        hull = cv2.convexHull(largest)
+        cv2.drawContours(skin, [hull], -1, 255, -1)
+
+    skin_soft = cv2.GaussianBlur(skin.astype(np.float32), (21, 21), 0) / 255.0
+    return skin_soft, skin
+
+
+def analyze_pigment(img: np.ndarray, output_path: str) -> Tuple[bool, str]:
+    """UV 荧光分析 — 黑底 + 皮肤灰度 + 荧光绿点"""
+    try:
+        skin_soft, skin_binary = detect_skin_uv(img)
+
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32)
+        clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(16, 16))
+        skin_base = clahe.apply(gray.astype(np.uint8)).astype(np.float32)
+
+        skin_base = skin_base * skin_soft
+        skin_base = np.power(skin_base / 255.0, 1.2) * 255.0
+
+        skin_base_3 = np.dstack([skin_base / 255.0] * 3)
+        result_u8 = (np.clip(skin_base_3, 0, 1) * 255).astype(np.uint8)
+
+        if cv2_imwrite_chinese(output_path, result_u8):
+            logger.debug(f"UV 荧光完成：{os.path.basename(output_path)}")
+            return True, ""
+        else:
+            return False, "写入失败"
+
+    except Exception as e:
+        logger.error(f"UV 荧光失败：{e}")
+        return False, str(e)
+
+def analyze_pores_for_image(result: Dict, img: np.ndarray,
+                            output_base: Path,
+                            pore_analyzer: Optional[PoreAnalyzer]) -> None:
+    """执行单张图片的毛孔分析，并将结果写回统一结果对象。"""
+    if pore_analyzer is None:
+        result['pore_status'] = 'error'
+        result['pore_error'] = '毛孔检测未初始化'
+        return
+
+    try:
+        success, pore_stats, error = pore_analyzer.analyze(
+            img,
+            output_base / "pores",
+            result['stem'],
+        )
+        if success:
+            result.update(pore_stats)
+            result['pore_status'] = 'success'
+            logger.info(
+                f"{result['filename']} 毛孔完成："
+                f"{result['pore_count']} 个"
+            )
+        else:
+            result['pore_status'] = 'error'
+            result['pore_error'] = error
+            logger.warning(f"{result['filename']} 毛孔检测失败：{error}")
+    except Exception as exc:
+        result['pore_status'] = 'error'
+        result['pore_error'] = str(exc)
+        logger.exception(f"{result['filename']} 毛孔检测异常")
+
+
+# ==================== 单张图片处理 ====================
+def process_single_image(args: Tuple) -> Dict:
+    if len(args) == 4:
+        img_path, model, output_base, verbose = args
+        pore_analyzer = None
+        task_type = None
+    elif len(args) == 5:
+        img_path, model, output_base, verbose, pore_analyzer = args
+        task_type = None
+    else:
+        img_path, model, output_base, verbose, pore_analyzer, task_type = args
+
+    pore_only = task_type == 'pore'
+    
+    if img_path.suffix.lower() not in IMAGE_EXTENSIONS:
+        return {'status': 'skip', 'filename': img_path.name}
+    
+    logger.info(f"处理：{img_path.name}")
+    
+    img = cv2_imread_chinese(str(img_path))
+    if img is None:
+        return {'status': 'error', 'filename': img_path.name, 'error': '读取失败'}
+
+    img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
+
+    result = {
+        'status': 'success',
+        'filename': img_path.name,
+        'stem': img_path.stem,
+        'acne_counts': {},
+        'health_score': 0,
+        'normal_pct': 0,
+        'mild_pct': 0,
+        'warning_pct': 0,
+        'severe_pct': 0,
+        'errors': [],
+        'pore_status': 'disabled' if pore_analyzer is None else 'pending',
+        'pore_count': 0,
+        'pore_average_size': 0,
+        'pore_roi_area': 0,
+        'pore_error': None,
+    }
+    completed_stages = 0
+
+    if pore_only:
+        analyze_pores_for_image(result, img, output_base, pore_analyzer)
+        if result['pore_status'] == 'success':
+            result['status'] = 'success'
+        else:
+            result['status'] = 'error'
+            logger.error(
+                f"{img_path.name} 毛孔处理失败：{result['pore_error']}"
+            )
+        return result
+
+    # 1. YOLO 痤疮检测
+    try:
+        yolo_out = output_base / "yolo" / f"yolo_{img_path.stem}.jpg"
+        success, acne_counts, error = detect_acne(img, str(img_path), model, str(yolo_out))
+        if success:
+            result['acne_counts'] = acne_counts
+            completed_stages += 1
+        else:
+            result['errors'].append(f"YOLO: {error}")
+    except Exception as e:
+        result['errors'].append(f"YOLO: {e}")
+
+    # 3. RBX 红区分析
+    try:
+        red_out = output_base / "redzone" / f"red_{img_path.stem}.jpg"
+        success, health_stats, error = analyze_red_zone(img, str(red_out))
+        if success:
+            result.update(health_stats)
+            completed_stages += 1
+        else:
+            result['errors'].append(f"红区：{error}")
+    except Exception as e:
+        result['errors'].append(f"红区：{e}")
+
+    # 4. 人脸红区检测
+    try:
+        face_red_out = output_base / "face_red" / f"face_red_{img_path.stem}.jpg"
+        success, error = analyze_face_red(img, str(face_red_out))
+        if success:
+            completed_stages += 1
+        else:
+            result['errors'].append(f"人脸红区：{error}")
+    except Exception as e:
+        result['errors'].append(f"人脸红区：{e}")
+
+    # 6. 热力敏感图分析
+    try:
+        sensitivity_out = output_base / "sensitivity" / f"sensitivity_{img_path.stem}.jpg"
+        success, error = analyze_heat_sensitivity(img, str(sensitivity_out))
+        if success:
+            completed_stages += 1
+        else:
+            result['errors'].append(f"热力敏感图：{error}")
+    except Exception as e:
+        result['errors'].append(f"热力敏感图：{e}")
+
+    # 7. 色素铅图分析
+    try:
+        pig_out = output_base / "pigment" / f"pigment_{img_path.stem}.jpg"
+        success, error = analyze_pigment(img, str(pig_out))
+        if success:
+            completed_stages += 1
+        else:
+            result['errors'].append(f"色素：{error}")
+    except Exception as e:
+        result['errors'].append(f"色素：{e}")
+
+    # 8. 大毛孔检测是附加阶段，不改变原五项分析的成功状态。
+    if pore_analyzer is not None:
+        analyze_pores_for_image(result, img, output_base, pore_analyzer)
+
+    if result['errors']:
+        if completed_stages > 0:
+            result['status'] = 'partial'
+            logger.warning(f"{img_path.name} 部分失败：{result['errors']}")
+        else:
+            result['status'] = 'error'
+            logger.error(f"{img_path.name} 处理失败：{result['errors']}")
+    else:
+        logger.info(f"{img_path.name} 完成")
+    
+    return result
+
+# ==================== 保存文本报告 ====================
+def save_text_report(results: List[Dict], texts_dir: Path) -> bool:
+    try:
+        # 确保texts目录存在
+        if not texts_dir.exists():
+            texts_dir.mkdir(parents=True, exist_ok=True)
+        
+        text_path = texts_dir / "analysis_report.txt"
+
+        valid = [r for r in results if r.get('health_score', 0) > 0]
+        if valid:
+            avg_health = np.mean([r['health_score'] for r in valid])
+            avg_normal = np.mean([r['normal_pct'] for r in valid])
+            avg_mild = np.mean([r['mild_pct'] for r in valid])
+            avg_warning = np.mean([r['warning_pct'] for r in valid])
+            avg_severe = np.mean([r['severe_pct'] for r in valid])
+        else:
+            avg_health = avg_normal = avg_mild = avg_warning = avg_severe = 0.0
+
+        with open(text_path, 'w', encoding='utf-8') as f:
+            f.write(f"Health Score::{avg_health:.1f} / 100;;\n")
+            f.write(f"Normal area::{avg_normal:.1f}%;;\n")
+            f.write(f"Mild abnormality::{avg_mild:.1f}%;;\n")
+            f.write(f"Moderate abnormality::{avg_warning:.1f}%;;\n")
+            f.write(f"Severe abnormality::{avg_severe:.1f}%;;\n")
+
+        logger.info(f"文本报告已保存：{text_path}")
+        return True
+        
+    except Exception as e:
+        logger.error(f"文本报告生成失败：{e}")
+        return False
+
+# ==================== 回调后端接口 ====================
+def callback_api(task_id: str, status: int, api_url: str = API_HOST+API_CALLBACK):
+    """完成后回调后端接口"""
+    try:
+        payload = {
+            "task_id": task_id,
+            "status": status  # 1=成功，0=失败
+        }
+        response = requests.post(api_url, json=payload, timeout=10)
+        if response.status_code == 200:
+            logger.info(f"回调成功：{task_id}")
+            return True
+        else:
+            logger.warning(f"回调失败：{response.status_code, api_url}")
+            return False
+    except Exception as e:
+        logger.error(f"回调异常：{e}")
+        return False
+
+
+def resolve_input_dir(input_dir: Path, analysis_root: Optional[str] = None) -> Path:
+    """解析输入目录；API 调用时额外限制在共享分析目录内。"""
+    if analysis_root is None:
+        return input_dir
+
+    resolved = input_dir.resolve()
+    root = Path(analysis_root).resolve()
+    try:
+        resolved.relative_to(root)
+    except ValueError as exc:
+        raise ValueError(f"输入目录必须位于 {root} 内") from exc
+    if resolved == root:
+        raise ValueError("输入目录不能是分析数据根目录本身")
+    return resolved
+
+# ==================== 主函数 ====================
+def main() -> int:
+    global logger
+    
+    parser = argparse.ArgumentParser(description='皮肤分析工具 - 后端 API 版')
+    parser.add_argument('--task-id', type=str,
+                        help='任务 ID')
+    parser.add_argument('--task-type', choices=['pore'], default=None,
+                        help='任务类型，pore 表示仅执行大毛孔检测')
+    parser.add_argument('input_dir', type=str,
+                       help='输入目录完整路径（后端传入的 folder_name）')
+    parser.add_argument('--model', '-m', type=str, default='best.pt',
+                       help='YOLO 痤疮模型路径 (默认：cuochuang.pt)')
+    parser.add_argument('--workers', '-w', type=int, default=3,
+                        help='并发工作线程数 (默认：3)')
+    parser.add_argument('--pore-predictor', type=str,
+                        default=PORE_PREDICTOR_PATH,
+                        help='dlib 68 点人脸关键点模型路径')
+    parser.add_argument('--disable-pores', action='store_true',
+                        help='关闭附加的大毛孔检测')
+    parser.add_argument('--analysis-root', type=str,
+                        help='限制输入目录所在的分析数据根目录（API 调用时使用）')
+    parser.add_argument('--verbose', '-v', action='store_true',
+                       help='详细输出模式')
+    parser.add_argument('--api-url', type=str, default=API_HOST+API_CALLBACK,
+                        help='回调 API 地址')
+    args = parser.parse_args()
+    pore_only = args.task_type == 'pore'
+
+    # 初始化日志
+    logger = setup_logging(args.verbose)
+
+    # 输入输出路径
+    raw_input_dir = Path(args.input_dir)
+    try:
+        input_dir = resolve_input_dir(raw_input_dir, args.analysis_root)
+    except ValueError as e:
+        task_id = args.task_id or raw_input_dir.name
+        logger.error(f"输入目录校验失败：{e}")
+        callback_api(task_id, status=0, api_url=args.api_url)
+        return 1
+    task_id = args.task_id or input_dir.name
+    output_base = input_dir.parent / 'output'
+
+    logger.info("=" * 60)
+    logger.info("皮肤分析工具 - 后端 API 版")
+    logger.info("=" * 60)
+    logger.info(f"任务 ID: {task_id}")
+    logger.info(f"输入目录：{input_dir}")
+    logger.info(f"输出目录：{output_base}")
+    logger.info(f"模型路径：{args.model}")
+    logger.info(f"并发线程：{args.workers}")
+    logger.info(f"任务类型：{args.task_type or '完整分析'}")
+    logger.info(
+        f"毛孔检测：{'开启' if pore_only or not args.disable_pores else '关闭'}"
+    )
+    if pore_only or not args.disable_pores:
+        logger.info(f"毛孔关键点模型：{args.pore_predictor}")
+    logger.info(f"回调地址：{args.api_url}")
+
+    model_path = args.model
+
+    if not input_dir.is_dir():
+        logger.error(f"输入目录不存在或不是目录：{input_dir}")
+        callback_api(task_id, status=0, api_url=args.api_url)
+        return 1
+
+    if args.workers < 1:
+        logger.error(f"并发线程数必须大于 0：{args.workers}")
+        callback_api(task_id, status=0, api_url=args.api_url)
+        return 1
+
+    # 创建输出目录
+    texts_dir = output_base / 'texts'
+
+    try:
+        # 首先创建输出目录
+        output_base.mkdir(exist_ok=True, parents=True)
+        logger.info(f"输出目录创建成功：{output_base}")
+
+        # 毛孔专用任务不创建其他分析阶段的目录。
+        subdirs = (
+            ['pores']
+            if pore_only
+            else ['yolo', 'redzone', 'face_red', 'sensitivity', 'pigment', 'texts']
+        )
+        for subdir in subdirs:
+            subdir_path = output_base / subdir
+            subdir_path.mkdir(exist_ok=True)
+            logger.info(f"子目录创建成功：{subdir_path}")
+    except Exception as e:
+        logger.error(f"目录创建失败：{e}")
+        callback_api(task_id, status=0, api_url=args.api_url)
+        return 1
+
+    # 完整分析中毛孔是附加能力；毛孔专用任务则要求初始化成功。
+    pore_analyzer = None
+    if pore_only or not args.disable_pores:
+        try:
+            pores_dir = output_base / 'pores'
+            pores_dir.mkdir(exist_ok=True)
+            pore_analyzer = PoreAnalyzer(args.pore_predictor)
+            logger.info("大毛孔检测模型加载成功")
+        except Exception as e:
+            if pore_only:
+                logger.error(f"大毛孔检测模型加载失败：{e}")
+                callback_api(task_id, status=0, api_url=args.api_url)
+                return 1
+            logger.warning(f"大毛孔检测已跳过：{e}")
+
+    model = None
+    if not pore_only:
+        # 加载痤疮模型
+        logger.info("加载 YOLO 痤疮模型...")
+        try:
+            model = YOLO(model_path)
+            logger.info("痤疮模型加载成功")
+        except Exception as e:
+            logger.error(f"痤疮模型加载失败：{e}")
+            callback_api(task_id, status=0, api_url=args.api_url)
+            return 1
+    
+    # 获取图片列表
+    try:
+        directory_entries = list(input_dir.iterdir())
+        if pore_only:
+            files_by_name = {
+                path.name.lower(): path
+                for path in directory_entries
+                if path.is_file()
+            }
+            image_files = [
+                files_by_name[name]
+                for name in PORE_IMAGE_NAMES
+                if name in files_by_name
+            ]
+            missing_images = [
+                name for name in PORE_IMAGE_NAMES
+                if name not in files_by_name
+            ]
+            if missing_images:
+                logger.warning(
+                    f"毛孔任务缺少固定图片：{', '.join(missing_images)}"
+                )
+        else:
+            image_files = directory_entries
+        total_images = len(image_files)
+        logger.info(f"发现 {total_images} 个待处理文件")
+    except Exception as e:
+        logger.error(f"获取图片列表失败：{e}")
+        callback_api(task_id, status=0, api_url=args.api_url)
+        return 1
+    
+    # 并发处理
+    results = []
+    tasks = [(img_path, model, output_base, args.verbose, pore_analyzer, args.task_type)
+             for img_path in image_files]
+    
+    logger.info("开始并发处理...")
+    try:
+        with ThreadPoolExecutor(max_workers=args.workers) as executor:
+            futures = {executor.submit(process_single_image, task): task 
+                       for task in tasks}
+            
+            for future in tqdm(as_completed(futures), total=len(futures), 
+                              desc="处理进度", unit="图"):
+                result = future.result()
+                results.append(result)
+    except Exception as e:
+        logger.error(f"并发处理失败：{e}")
+        callback_api(task_id, status=0, api_url=args.api_url)
+        return 1
+    
+    if not pore_only:
+        # 保存文本报告
+        logger.info("\n保存文本报告中...")
+        logger.info(f"文本目录：{texts_dir}")
+        if not save_text_report(results, texts_dir):
+            callback_api(task_id, status=0, api_url=args.api_url)
+            return 1
+        logger.info("文本报告保存成功")
+    
+    # 统计总结
+    success = sum(1 for r in results if r['status'] == 'success')
+    partial = sum(1 for r in results if r['status'] == 'partial')
+    errors = sum(1 for r in results if r['status'] == 'error')
+    skipped = sum(1 for r in results if r['status'] == 'skip')
+    
+    logger.info("\n" + "=" * 60)
+    logger.info("处理结束")
+    logger.info(f"成功：{success} | 部分：{partial} | 失败：{errors} | 跳过：{skipped}")
+    logger.info(f"输出目录：{output_base}")
+    if not pore_only:
+        logger.info(f"文本报告：{texts_dir / 'analysis_report.txt'}")
+    logger.info("=" * 60)
+    
+    # 回调后端接口（状态 1=成功）
+    status = 1 if (success + partial) > 0 else 0
+    if status == 0:
+        logger.error("没有任何图片成功完成分析")
+    callback_api(task_id, status=status, api_url=args.api_url)
+    return 0 if status == 1 else 1
+
+if __name__ == "__main__":
+    raise SystemExit(main())