fix(发际线): 修正不对称刘海导致的模板叠加倾斜

按 MediaPipe 左右对应额头点协调模板延伸距离，保留人脸姿态与头部轮廓钳制，统一预览白线和生发遮罩的几何。补充对齐回归测试并更新贴图顺序测试及实现说明。

验证：13 项相关测试通过；两张用户图片完成前后对比，新图男性 7 型接口与生发图均成功返回。

.gitignore

@@ -53,3 +53,31 @@ static/report_hairline_v2.zip
 # local_test 运行期日志 / pid（不入 git）
 local_test/hair_service.log
 local_test/hair_service.pid
+
+# benchmark 原始输出（含结果图+原图，体积大，不入 git）
+benchmark_out/
+
+# benchmark 部署的 HTML 报告（图片 base64 内嵌，体积大，不入 git）
+static/hairstyle_thumbs/
+
+# 网关运行期日志（不入 git）
+gateway.log
+
+# 工作流备份文件（不入 git）
+*.json.bak.*
+
+# 脸型测试素材（人像照片，体积大，不入 git；仅保留 6 张基准标注图）
+face/test_img/脸型测试集合/
+face/test_img/girl/
+face/test_img/man/
+
+# 脸型特征缓存（由 face/dump_features.py 生成，可随时重跑）
+face/cache/
+
+# 脸型报告输出（标注图体积大，不入 git）
+static/face_shape_report/
+static/face_shape_report.html
+static/facetest_report/
+static/facetest_report.html
+static/facetest_all_report/
+static/facetest_all_report.html

0716add-hair-api.json

@@ -0,0 +1,327 @@
+{
+  "16": {
+    "class_type": "UnetLoaderGGUF",
+    "inputs": {
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
+    }
+  },
+  "3": {
+    "class_type": "VAELoader",
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    }
+  },
+  "61": {
+    "class_type": "CLIPLoader",
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "cpu"
+    }
+  },
+  "26": {
+    "class_type": "LoadImage",
+    "inputs": {
+      "image": "placeholder.png"
+    }
+  },
+  "60": {
+    "class_type": "JjkText",
+    "inputs": {
+      "text": "填充遮罩区域的头发，人物增加一点磨皮"
+    }
+  },
+  "22": {
+    "class_type": "CLIPTextEncode",
+    "inputs": {
+      "clip": [
+        "61",
+        0
+      ],
+      "text": [
+        "60",
+        0
+      ]
+    }
+  },
+  "31": {
+    "class_type": "easy imageSize",
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    }
+  },
+  "33": {
+    "class_type": "Mask Fill Holes",
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    }
+  },
+  "36": {
+    "class_type": "Convert Masks to Images",
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    }
+  },
+  "39": {
+    "class_type": "ImageScale",
+    "inputs": {
+      "image": [
+        "36",
+        0
+      ],
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled"
+    }
+  },
+  "37": {
+    "class_type": "Image To Mask",
+    "inputs": {
+      "image": [
+        "39",
+        0
+      ],
+      "method": "intensity"
+    }
+  },
+  "32": {
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ],
+      "aspect_ratio": "custom",
+      "proportional_width": [
+        "31",
+        0
+      ],
+      "proportional_height": [
+        "31",
+        1
+      ],
+      "fit": "letterbox",
+      "method": "lanczos",
+      "round_to_multiple": "8",
+      "scale_to_side": "None",
+      "scale_to_length": 1024,
+      "background_color": "#000000"
+    }
+  },
+  "44": {
+    "class_type": "ImageAndMaskPreview",
+    "inputs": {
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ],
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true
+    }
+  },
+  "14": {
+    "class_type": "GetImageSize+",
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    }
+  },
+  "13": {
+    "class_type": "VAEEncode",
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    }
+  },
+  "2": {
+    "class_type": "ModelSamplingFlux",
+    "inputs": {
+      "model": [
+        "16",
+        0
+      ],
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ]
+    }
+  },
+  "19": {
+    "class_type": "FluxGuidance",
+    "inputs": {
+      "conditioning": [
+        "22",
+        0
+      ],
+      "guidance": 1
+    }
+  },
+  "5": {
+    "class_type": "ReferenceLatent",
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    }
+  },
+  "7": {
+    "class_type": "EmptySD3LatentImage",
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    }
+  },
+  "1": {
+    "class_type": "BasicScheduler",
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "scheduler": "simple",
+      "steps": 4,
+      "denoise": 1
+    }
+  },
+  "20": {
+    "class_type": "BasicGuider",
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    }
+  },
+  "6": {
+    "class_type": "RandomNoise",
+    "inputs": {
+      "noise_seed": 0
+    }
+  },
+  "8": {
+    "class_type": "KSamplerSelect",
+    "inputs": {
+      "sampler_name": "euler"
+    }
+  },
+  "9": {
+    "class_type": "SamplerCustomAdvanced",
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    }
+  },
+  "10": {
+    "class_type": "VAEDecode",
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    }
+  },
+  "62": {
+    "class_type": "ColorMatch",
+    "inputs": {
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ],
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true
+    }
+  },
+  "17": {
+    "class_type": "SaveImage",
+    "inputs": {
+      "images": [
+        "62",
+        0
+      ],
+      "filename_prefix": "hair_inpaint"
+    }
+  }
+}

CLAUDE.md

@@ -4,80 +4,100 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Overview
 
-"旷视五接口" — a FastAPI facial analysis API service providing 5 endpoints for face measurement, hair growth simulation (C-end and B-end), user feature extraction, and hairline PNG generation. Currently a **Mock v1**: all endpoints return hardcoded sample data; real ML logic is pending.
+旷视接口服务 — FastAPI 人像分析/生发服务，**一个仓库、两种部署角色**：
 
-- **Base URL (prod)**: `https://hair.xiangsilian.com`
-- **Swagger docs**: `https://hair.xiangsilian.com/docs`
-- **Internal**: uvicorn on `127.0.0.1:8000` behind nginx reverse proxy
+- **worker**（`app.py` + `face_analysis/` + `hairline/`）：跑真实算法（本地模型 + ComfyUI + change_hair），监听 `0.0.0.0:8187`。**本机（GPU 机）即 worker 角色**，不跑网关。
+- **网关**（`gateway/`）：外网机器上的薄反向代理，负责鉴权/健康检查/派发，并把 worker 返回的 `*_base64` 落盘改写为 `*_url`。接口4（豆包）和接口99（人脸特殊点，纯 CPU）在网关本机实现，不转发 worker。
+
+> 历史包袱：`app.py` 的 FastAPI `description` 和 `docs/接口文档.md` 部分示例仍写「Mock 第一版」字样，实际全部接口已是真实算法实现。**架构以 `docs/实现说明.md` 为准，契约以 `docs/接口文档.md` 为准。**
 
 ## Commands
 
 ```bash
-# Development server
-./venv/bin/uvicorn app:app --host 127.0.0.1 --port 8000 --reload
+# ---- worker（本机）----
+./run_worker.sh                      # 后台启动（0.0.0.0:8187，日志 worker.log）
+FOREGROUND=1 ./run_worker.sh         # 前台调试
+./stop_worker.sh                     # 停止
+sudo systemctl restart hair-worker   # systemd 方式（生产）
+journalctl -u hair-worker -f
 
-# Or via the start script
-./start.sh
+# ---- 依赖服务（worker 之前要先起）----
+sudo systemctl start comfyui         # ComfyUI 127.0.0.1:8188（Flux-2 生图）
+cd /home/ubuntu/change_hair && bash start_all.sh start   # 换发型三件套 8801/32678/57860
 
-# Production (systemd)
-sudo systemctl restart hair
-sudo systemctl status hair
-journalctl -u hair -f
-
-# Install dependencies (uses Tencent Cloud PyPI mirror)
-./venv/bin/pip install -r requirements.txt
+# ---- 测试 ----
+./venv/bin/pytest tests/             # 单元测试（conftest 会准备 fixtures）
+./venv/bin/python -m hairline.comfyui tests/output/comfy_input.png   # 手动跑一次生发工作流
 ```
 
+依赖装在 `./venv`（Python 3.10，torch 2.7.1+cu128，RTX 4090 CUDA 可用）。`requirements.txt` 里的 torch 2.2.2/5090 注释是旧机器的记录，本机 venv 实际是 cu128 版。
+
 ## Architecture
 
-**Single-file app** — `app.py` (~555 lines) contains the entire server: FastAPI app creation, Pydantic models, all 5 endpoint handlers, and health check routes. There are no separate routers, services, or middleware modules.
+### 接口一览（worker 侧路由，`app.py`）
 
-### Endpoints
+| 接口 | 路径 | 实现位置 | 说明 |
+|------|------|----------|------|
+| 1 | `POST /api/v1/face/measure` | `face_analysis/` | 四庭七眼测量 + 标注 PNG |
+| 6 | `POST /api/v1/face/measure-v2` | 同上（`variant="v6"`） | 接口1 去顶庭变体 |
+| 2 | `POST /api/v1/hair/grow` | `hairline/service.py` | C端生发。**female 1..5** 走 change_hair swapHair + Flux-2 整帧重绘（接口12 final 管线）；**female 6/7 与 male 全部**走 ComfyUI `add_hair.json` inpaint |
+| 7 | `POST /api/v1/hair/grow-v2`（hidden/deprecated） | 同上 | 同接口2，用 `add_hair2.json` 工作流 |
+| 3 | `POST /api/v1/hair/grow-b` | `hairline/marker_detect.py` | B端马克笔划线生发（黑帽响应 + Dijkstra） |
+| 4 | `POST /api/v1/face/features` | worker 有实现；生产走**网关本机**（豆包） | 用户特征 |
+| 5 | `POST /api/v1/hairline/generate` | `hairline/service.py` | 发际线 PNG 三档（middle/high/low）+ 生发 |
+| 9 | `POST /api/v1/head/mask` | `face_analysis/head_mask.py` | 头发遮罩 + 分步可视化（BiSeNet/SegFormer 双路） |
+| 10 | `POST /api/v1/head/band` | `face_analysis/head_band.py` | 头部外缘膨胀带遮罩 |
+| 11 | `POST /api/v1/hairline/grow` | `face_analysis/hairline_grow.py` | 发际线生发（接口9 遮罩 + change_hair + 接缝融合），不含重绘 |
+| 12 | `POST /api/v1/hairline/grow_v2` | 同上 | 接口11 final + 发际线重绘带 → Flux-2 保色重绘 |
+| 12f | `POST /api/v1/hairline/grow_v2_final` | 同上（`_V2_FINAL_DEFAULTS`） | 精简版：仅图片 + hairline_id，取整帧重绘 |
+| 12f2 | `POST /api/v1/hairline/grow_v2_final_v2` | 同上 | 同上，取 B 局部加发+全脸美颜 |
+| 99 | `POST /api/v1/face/special-points` | `face_analysis/special_points.py`（网关直接 import 本机实现 + `_if99_lock` 单路排队；worker 保留同路径备份） | 固定编号关键点 + 发际线特殊点（射线求交头发 mask）像素坐标 |
+| 代理 | `POST /api/swapHair/v1`、`/hairColor/v2` | httpx 转发 127.0.0.1:8801 | 解决测试页 CORS |
+| 工具 | `POST /api/v1/redraw` | `hairline/redraw.py` | 直接 ComfyUI 重绘（图+遮罩+prompt） |
+| 调试 | `POST /api/v1/debug/grow-timing`、`GET /api/v1/debug/hairline_log` | — | 免鉴权，分步计时/下载日志 |
 
-| Method | Path | Purpose |
-|--------|------|---------|
-| POST | `/api/v1/face/measure` | 四庭七眼 measurement + annotated PNG |
-| POST | `/api/v1/hair/grow` | C-end hair growth (user-facing) |
-| POST | `/api/v1/hair/grow-b` | B-end hair growth (doctor/operator, with marked image) |
-| POST | `/api/v1/face/features` | User facial feature analysis |
-| POST | `/api/v1/hairline/generate` | Hairline PNG generation |
-| GET | `/health` | Health check (excluded from OpenAPI schema) |
-| GET | `/` | Service info (excluded from OpenAPI schema) |
+配套测试页在 `static/test_interface{1..12}*.html`，经 `/static` 挂载。
 
-### Image input convention (all endpoints)
+### 关键模式
 
-Every endpoint accepts images via **exactly one** of three mutually exclusive fields:
-- `image_file` — multipart file upload (≤ 1 MB)
-- `image_url` — HTTP/HTTPS URL string
-- `image_base64` — base64 string with `data:image/...;base64,` prefix
+- **统一信封**：`ok(data)` / `err(code, message)` 构造 `{code, message, request_id, data}`；业务错误 HTTP 一律 200。错误码：1001 无人像 / 1002 分辨率低 / 1003 非正面 / 1004 gender 非法 / 1005 多人 / 1006 >1MB / 1007 参数或处理失败 / 1008 格式不支持。
+- **图片三选一**：每个接口 `image_file`(multipart) / `image_url` / `image_base64`（带 `data:` 前缀）严格互斥，由 `resolve_image_bytes()` 统一处理（0 个或多个 → 1007）。接口3 是 `marked_image_*`。
+- **阻塞算法进线程池**：所有推理 handler 用 `run_in_threadpool(...)`，避免卡事件循环。
+- **内网鉴权**：HTTP 中间件校验 `/api/*` 的 `X-Internal-Token` 头，密码来自 `worker_config.json` 的 `accept_passwords`（或环境变量 `WORKER_ACCEPT_PASSWORDS`）。豁免前缀见 `_AUTH_EXEMPT`（health/docs/static/debug/redraw/swapHair/hairColor）。
+- **发型序号映射**：`hairline/service.py` 的 `_HAIRSTYLE_ORDER` 显式定义 1..7 顺序（不靠文件名字母序）；female 1..5 → change_hair `chang_*` 映射在 `_FEMALE_KEY_TO_CHANG`。
+- **接口12 固化参数**：`_V2_FINAL_DEFAULTS`（app.py）是生产默认值（push=0.8cm / two_stage / color_match=关 等），接口2 female 重绘也复用它。
 
-Endpoint 3 (B-end) has **two** sets of image params: `marked_image_*` (the marked-up image) and `original_image_*` (the original photo). Each set follows the same three-way exclusive rule independently.
+### 外部依赖（worker 视角）
 
-### Unified response structure
+| 依赖 | 地址 | 用途 | 配置 |
+|------|------|------|------|
+| ComfyUI | `http://127.0.0.1:8188`（`COMFYUI_URL`） | Flux-2 生图/重绘，走 HTTP API（`hairline/comfyui.py`） | Basic Auth `admin`，密码：`COMFYUI_PASSWORD` → `worker_config.json.comfyui_password` → `password.txt` |
+| change_hair | `http://127.0.0.1:8801`（`SWAP_HAIR_URL`/`HAIR_GROW_URL`/`CHANGE_HAIR_BASE`） | 换发型（SD WebUI + LoRA）、hairGrow | 服务目录 `/home/ubuntu/change_hair`，`start_all.sh` 启停 |
 
-```json
-{"code": 0, "message": "success", "request_id": "mock-request-id", "data": {...}}
-```
+ComfyUI 工作流 JSON 在仓库根：`add_hair.json`（接口2/3/5 生发）、`add_hair2.json`（接口7）、`0716add-hair-api.json`（接口12/2女 重绘）、`hair_repaint.json`。节点约定：**26** 输入图 / **6** seed / **60** 提示词 / **16** unet（.gguf 自动切 UnetLoaderGGUF）/ **61** CLIP（4b↔9b 自动配套）。
 
-Error codes: 1001–1008 (see `app.py` docstring or `docs/接口文档.md`).
+### 性能/显存相关的坑
 
-### Key code patterns
+- 三个生发接口共用**同一提示词字符串**以命中 ComfyUI 的 CLIP 文本条件缓存——prompt 变了会触发 Flux(7.7G)+qwen CLIP(3.9G) 互相挤显存重载（每次 +4s）。
+- 接口2 女性重绘管线默认把输入长边压到 `REDRAW_MAX_SIDE`（env，默认 1024）再放大回原尺寸；`redraw_max_side=0` 表示不缩图。
+- `comfyui.run(front=True)` 可插队（接口2 对时延敏感）；超时 `COMFY_TIMEOUT`（默认 600s）。
 
-- `ok(data)` / `err(code, msg)` — helper functions at module level that build the standard response envelope
-- `ImageJsonBody` — Pydantic model for JSON-based image params (URL/base64); form-upload endpoints use FastAPI `Form()` / `File()` parameters directly
-- `SAMPLE_IMAGE_URL` — `https://hair.xiangsilian.com/static/sample.jpg`, returned as the image URL in all mock responses
-- Static files (`/static/*`) are mounted via `app.mount("/static", StaticFiles(directory="static"), name="static")`
-- All 5 POST endpoints use `multipart/form-data` with both `File` and `Form` fields — there are no JSON-body POST endpoints despite `ImageJsonBody` being defined (it exists for the OpenAPI schema but the actual handlers use `Form()` for all params)
+## Deployment（本机 = gpu_worker）
 
-### Deployment
+systemd 单元（仓库根有副本，实装在 `/etc/systemd/system/`）：
 
-- **Process manager**: systemd (`hair.service` → `ExecStart` runs uvicorn directly from venv)
-- **Reverse proxy**: nginx (`nginx/hair.conf`) proxies port 80 → `127.0.0.1:8000` for `hair.xiangsilian.com`
-- **Python**: venv at `./venv`, dependencies in `requirements.txt`
-- **PyPI mirror**: Tencent Cloud (`mirrors.cloud.tencent.com`), configured in `pip.conf`
+| 服务 | 端口 | 说明 |
+|------|------|------|
+| `comfyui.service` | 127.0.0.1:8188 | `ComfyUI/venv` 的 `main.py --cache-classic --fast` |
+| `hair-worker.service` | 0.0.0.0:8187 | 本仓库 `app:app`（Wants comfyui + change_hair-hair） |
+| `change_hair-{webui,photo,hair}.service` | 57860 / 32678 / 8801 | conda envs `sdwebui`/`py310`/`my_hair`，依赖顺序 webui→photo→hair |
 
-### Documentation
+网关机另行部署 `gateway/`（config：`workers` 列表 + `shared_password` + `ark` key），见 `gateway/config.example.json` 与 `docs/网关待改动.md`。
 
-- `docs/接口文档.md` — complete API documentation (inputs, outputs, examples, error codes)
-- `docs/旷视具体需求.md` — original requirements spec (converted from `.docx`)
-- The FastAPI app's `description` docstring in `app.py` is an additional source of API docs visible in Swagger UI
+## Documentation
+
+- `docs/接口文档.md` — 对外 API 契约（唯一权威）
+- `docs/实现说明.md` — 架构总览 + 各接口实现简述 + 部署要点
+- `docs/发际线增强算法.md`、`docs/发际线生发遮罩算法_pushed模式.md` — 接口11/12 算法推导
+- `docs/gpu-worker改动说明.md`、`docs/网关待改动.md` — worker↔网关 协同变更记录
+- `OFFLINE_ASSETS.md` — 离线模型权重/字体清单

add_hair.json

@@ -2,7 +2,7 @@
   "1": {
     "inputs": {
       "scheduler": "simple",
-      "steps": 6,
+      "steps": 4,
       "denoise": 1,
       "model": [
         "2",
@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
-      "weight_dtype": "fp8_e4m3fn"
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
     },
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "_meta": {
       "title": "UNet加载器"
     }
@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "补充遮罩区补充遮罩区域内的头发，头发填满遮罩区域。发际线往下挡住额头"
+      "text": "填充遮罩区域的头发，人物增加一点磨皮"
     },
     "class_type": "JjkText",
     "_meta": {
@@ -421,7 +421,7 @@
     "inputs": {
       "clip_name": "qwen_3_8b_fp8mixed.safetensors",
       "type": "flux2",
-      "device": "default"
+      "device": "cpu"
     },
     "class_type": "CLIPLoader",
     "_meta": {

bench6_quick.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""快速测试：3张图×花瓣发型×896分辨率，新提示词"填充遮罩区域的头发，人物增加一点磨皮"。
+预热1次+正式1次。
+"""
+import base64, json, os, time
+from pathlib import Path
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+PROMPT = "填充遮罩区域的头发，人物增加一点磨皮"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench6")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+
+def call(img_path, save_grown=None, timeout=300):
+    data = {"hair_style": "2", "webui_steps": "15", "redraw_max_side": "896", "redraw_prompt": PROMPT}
+    t0 = time.perf_counter()
+    with open(img_path, "rb") as f:
+        r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+            files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+            data=data, timeout=timeout)
+    wall = time.perf_counter() - t0
+    j = r.json()
+    d = j["data"]; hs = d["per_hairstyle"][0]
+    if save_grown and hs.get("grown_b64"):
+        b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+        open(save_grown, "wb").write(base64.b64decode(b))
+    return {"ok": hs.get("ok"), "total_ms": d["total_ms"], "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "grown_path": str(save_grown) if save_grown and hs.get("ok") else None}
+
+results = []
+for ilabel, ipath in IMGS:
+    print(f"预热 {ilabel}...", flush=True)
+    call(ipath)
+    save = OUT / f"{ilabel}_flower_896.jpg"
+    print(f"正式 {ilabel}...", flush=True)
+    r = call(ipath, save_grown=save)
+    r["img"] = ilabel
+    print(f"  -> total={r['total_ms']}ms ok={r['ok']}", flush=True)
+    results.append(r)
+
+json.dump({"prompt": PROMPT, "results": results}, open(OUT/"results.json","w"), ensure_ascii=False, indent=2)
+print(f"\n✓ 完成 {sum(1 for r in results if r['ok'])}/3", flush=True)

benchmark_hairstyle.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""发型对比矩阵测试：3图×5发型=15行，每行10张图(4b@896×1 + 9b三模型×三分辨率×9)。
+按模型分组跑（减少模型切换次数、降低OOM风险），结果重组为15行存JSON+生成报告。
+"""
+import base64
+import json
+import os
+import subprocess
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/hairstyle")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [
+    (1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
+    (4, "straight", "直线"), (5, "wave", "波浪"),
+]
+
+# 按模型分组：每个模型对应其要跑的(分辨率,列标题)
+MODEL_GROUPS = [
+    ("flux-2-klein-4b-fp8.safetensors", [("896", "4B@896")]),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors",
+     [("0", "9B-fp8@原图"), ("896", "9B-fp8@896"), ("640", "9B-fp8@640")]),
+    ("flux-2-klein-9b-Q5_K_M.gguf",
+     [("0", "9B-Q5@原图"), ("896", "9B-Q5@896"), ("640", "9B-Q5@640")]),
+    ("flux-2-klein-9b-Q4_K_M.gguf",
+     [("0", "9B-Q4@原图"), ("896", "9B-Q4@896"), ("640", "9B-Q4@640")]),
+]
+# 列顺序（4b在前，然后9b三模型）
+COLUMN_TITLES = ["4B@896", "9B-fp8@原图", "9B-fp8@896", "9B-fp8@640",
+                 "9B-Q5@原图", "9B-Q5@896", "9B-Q5@640",
+                 "9B-Q4@原图", "9B-Q4@896", "9B-Q4@640"]
+
+
+def gpu_used():
+    try:
+        out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"], timeout=10)
+        return int(out.decode().strip())
+    except Exception:
+        return 0
+
+
+def call(img_path, hair_num, model_file, res_val):
+    fd = {"gender": "female", "hair_style": str(hair_num), "use_mask": "true",
+          "prompt": "填充遮罩区域的头发，人物增加一点磨皮"}
+    if model_file:
+        fd["flux_model"] = model_file
+    if res_val != "":
+        fd["redraw_max_side"] = res_val
+    t0 = time.perf_counter()
+    peak = gpu_used()
+    err = None
+    grown_b64 = None
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=fd, timeout=300)
+        elapsed = time.perf_counter() - t0
+        peak = max(peak, gpu_used())
+        j = r.json()
+        if j.get("code") != 0:
+            err = f"code={j.get('code')} {j.get('message', '')[:60]}"
+        else:
+            res = j.get("data", {}).get("results", [])
+            if res and res[0].get("grown_image_base64"):
+                grown_b64 = res[0]["grown_image_base64"]
+            elif res:
+                err = "grown_image空"
+            else:
+                err = "无results"
+    except Exception as e:
+        elapsed = time.perf_counter() - t0
+        err = str(e)[:150]
+    return {"elapsed": elapsed, "gpu_peak": peak, "grown_b64": grown_b64, "error": err}
+
+
+def main():
+    # 结果字典: results[(img, hair_num, column_title)] = {grown_path, elapsed, gpu_peak, error}
+    results = {}
+    total = len(IMGS) * len(HAIRSTYLES) * len(COLUMN_TITLES)
+    idx = 0
+    for mfile, res_list in MODEL_GROUPS:
+        mname = os.path.basename(mfile)
+        print(f"\n===== 切换到模型: {mname} =====", flush=True)
+        # 等模型切换稳定
+        time.sleep(2)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                for rval, ctitle in res_list:
+                    idx += 1
+                    print(f"[{idx}/{total}] {ilabel}|{hname}|{ctitle}", flush=True)
+                    r = call(ipath, hnum, mfile, rval)
+                    status = f"{r['elapsed']:.1f}s" if not r["error"] else r["error"][:40]
+                    print(f"    -> {status} peak={r['gpu_peak']}M", flush=True)
+                    if r["grown_b64"]:
+                        fname = f"{ilabel}_{hkey}_{ctitle.replace('@','_').replace('-','')}.jpg"
+                        with open(OUT / fname, "wb") as gf:
+                            gf.write(base64.b64decode(r["grown_b64"]))
+                        r["grown_path"] = str(OUT / fname)
+                    results[(ilabel, hnum, ctitle)] = r
+
+    # 重组为15行
+    rows = []
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for ct in COLUMN_TITLES:
+                r = results.get((ilabel, hnum, ct), {"error": "未跑"})
+                cells.append({"title": ct, **{k: v for k, v in r.items() if k != "grown_b64"}})
+            rows.append({"img": ilabel, "img_path": ipath,
+                         "hair_num": hnum, "hair_key": hkey, "hair_name": hname,
+                         "cells": cells})
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump({"columns": COLUMN_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if not c.get("error"))
+    print(f"\n✓ 完成: {ok}/{total} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_hairstyle_report.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""把发型对比测试结果生成 HTML 报告。
+15行(3图×5发型) × 10列(4b@896 + 9b三模型×三分辨率)，每行首列=原图。
+图片 base64 内嵌，自包含单文件。
+"""
+import base64
+import json
+import os
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/hairstyle")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    """把绝对路径转成报告里的相对 URL（报告在 static/，图片在 static/bench/）。"""
+    if not path:
+        return None
+    p = str(path)
+    if "benchmark_out/hairstyle/" in p:
+        return "bench/hairstyle/" + os.path.basename(p)
+    if "benchmark_out/matrix/" in p:
+        return "bench/matrix/" + os.path.basename(p)
+    return None
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    columns = d["columns"]
+    rows = d["rows"]
+
+    # 统计每列的平均耗时、峰值显存
+    col_stats = {}
+    for ct in columns:
+        times, peaks = [], []
+        for r in rows:
+            for c in r["cells"]:
+                if c.get("title") == ct and not c.get("error"):
+                    times.append(c["elapsed"])
+                    peaks.append(c["gpu_peak"])
+        col_stats[ct] = {
+            "avg_t": sum(times) / len(times) if times else 0,
+            "max_p": max(peaks) / 1024 if peaks else 0,
+        }
+
+    # 表头：原图 + 10列
+    headers = ['<th class="col-label">原图</th>']
+    for ct in columns:
+        s = col_stats[ct]
+        headers.append(
+            f'<th class="col-label"><div class="col-title">{ct}</div>'
+            f'<div class="col-stat">{s["avg_t"]:.0f}s · {s["max_p"]:.0f}G</div></th>'
+        )
+
+    # 表体：15行
+    body_rows = []
+    for r in rows:
+        # 发型+图标签
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图
+        ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg", "girl5": "bench/orig/girl5.jpg"}
+        orig = ORIG_SRC.get(r["img"])
+        cells = [f'<td class="cell-orig"><div class="row-label-cell">{label}</div>'
+                 f'<img class="orig-img" src="{orig}"></td>']
+        # 10个结果列
+        for ct in columns:
+            c = next((x for x in r["cells"] if x.get("title") == ct), {})
+            src = img_src(c.get("grown_path")) if not c.get("error") else None
+            if src:
+                cells.append(
+                    f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                    f'<div class="cell-time">{c["elapsed"]:.1f}s</div></td>')
+            else:
+                cells.append(f'<td class="cell-result"><div class="na">⚠</div></td>')
+        body_rows.append(f'<tr>{"".join(cells)}</tr>')
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>发型对比测试报告 — 4模型×3分辨率</title>
+<style>
+  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
+  body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; }}
+  h1 {{ font-size: 20px; margin-bottom: 4px; }}
+  .subtitle {{ color: #888; font-size: 12px; margin-bottom: 12px; }}
+  .legend {{ background: #fff; border-radius: 8px; padding: 10px 16px; margin-bottom: 12px; font-size: 12px; color: #555; }}
+  .scroll-wrap {{ overflow-x: auto; }}
+  table {{ border-collapse: collapse; background: #fff; border-radius: 8px; overflow: hidden;
+           box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
+  th, td {{ border: 1px solid #eee; padding: 6px; vertical-align: top; text-align: center; }}
+  th {{ background: #f9fafb; position: sticky; top: 0; }}
+  .col-label {{ min-width: 110px; max-width: 130px; }}
+  .col-title {{ font-size: 12px; font-weight: 700; color: #374151; }}
+  .col-stat {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+  .row-label {{ font-size: 11px; color: #6b7280; }}
+  .row-label b {{ color: #1f2937; }}
+  .row-label-cell {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
+  .row-label-cell b {{ color: #1f2937; font-size: 13px; }}
+  img {{ border-radius: 4px; max-width: 120px; max-height: 150px; object-fit: contain; background: #f3f4f6; }}
+  .orig-img {{ border: 2px solid #d1d5db; max-height: 130px; }}
+  .cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+  .na {{ color: #d1d5db; font-size: 16px; padding: 40px; }}
+</style>
+</head>
+<body>
+<h1>💇 发型对比测试报告</h1>
+<p class="subtitle">接口2女性 · 3图×5发型=15行 · 每行: 4B@896(1) + 9B(fp8/Q5/Q4)×(原图/896/640)(9) · 150/150成功 · RTX3090</p>
+<div class="legend">列标题下显示<b>平均耗时 · 峰值显存</b>。横向滚动查看更多列。原图列含图片名+发型名。</div>
+<div class="scroll-wrap">
+<table>
+<tr>{"".join(headers)}</tr>
+{"".join(body_rows)}
+</table>
+</div>
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告: {HTML} ({HTML.stat().st_size//1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

benchmark_matrix.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""接口2女性 花瓣形 单发型 4模型×3分辨率×3图×3次 矩阵测试。
+
+调用本机 hair-worker (:8187) 的 /api/v1/hair/grow，gender=female, hair_style=2(花瓣形)。
+每次记录：生发图、耗时、显存峰值。结果图存到 benchmark_out/matrix/，最后生成 HTML 报告。
+"""
+import base64
+import json
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
+OUT.mkdir(parents=True, exist_ok=True)
+
+# 4 模型 × 3 分辨率 × 3 图 × 3 次
+MODELS = [
+    ("4b-fp8", "flux-2-klein-4b-fp8.safetensors"),
+    ("9b-fp8", "flux2.0/flux-2-klein-9b-fp8.safetensors"),
+    ("9b-Q5", "flux-2-klein-9b-Q5_K_M.gguf"),
+    ("9b-Q4", "flux-2-klein-9b-Q4_K_M.gguf"),
+]
+RES = [("orig", "0"), ("640", "640"), ("896", "896")]
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+REPEAT = 3
+
+
+def gpu_used():
+    """返回当前显存已用 MiB。"""
+    try:
+        out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"],
+            timeout=10,
+        )
+        return int(out.decode().strip())
+    except Exception:
+        return 0
+
+
+def call(img_path, model_file, res_val):
+    """调一次接口2。返回 dict: ok/elapsed/grown_path/gpu_peak/error。"""
+    fd = {
+        "gender": "female",
+        "hair_style": "2",  # 花瓣形
+        "use_mask": "true",
+        "prompt": "填充遮罩区域的头发，人物增加一点磨皮",
+    }
+    if model_file:
+        fd["flux_model"] = model_file
+    if res_val != "":
+        fd["redraw_max_side"] = res_val
+    t0 = time.perf_counter()
+    peak = gpu_used()
+    err = None
+    grown_path = None
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(
+                API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=fd, timeout=300,
+            )
+        elapsed = time.perf_counter() - t0
+        # 采样峰值（推理刚结束）
+        peak = max(peak, gpu_used())
+        j = r.json()
+        if j.get("code") != 0:
+            err = f"code={j.get('code')} {j.get('message','')}"
+        else:
+            res = j.get("data", {}).get("results", [])
+            if res and res[0].get("grown_image_base64"):
+                grown_path = OUT / f"tmp_grown.jpg"
+                with open(grown_path, "wb") as gf:
+                    gf.write(base64.b64decode(res[0]["grown_image_base64"]))
+            elif res:
+                err = "grown_image_base64 为空"
+            else:
+                err = "无 results"
+    except Exception as e:
+        elapsed = time.perf_counter() - t0
+        err = str(e)[:200]
+    return {"elapsed": elapsed, "gpu_peak": peak, "grown_path": str(grown_path) if grown_path else None, "error": err}
+
+
+def main():
+    results = []  # 每元素一个组合
+    total = len(MODELS) * len(RES) * len(IMGS) * REPEAT
+    idx = 0
+    for mlabel, mfile in MODELS:
+        for rlabel, rval in RES:
+            for ilabel, ipath in IMGS:
+                # 一个组合：3 次
+                runs = []
+                for rep in range(REPEAT):
+                    idx += 1
+                    print(f"[{idx}/{total}] {mlabel} | res={rlabel} | {ilabel} | rep{rep+1}", flush=True)
+                    r = call(ipath, mfile, rval)
+                    print(f"    -> {r['elapsed']:.1f}s  peak={r['gpu_peak']}MiB  err={r['error']}", flush=True)
+                    # 存每次的生发图
+                    if r["grown_path"]:
+                        save_to = OUT / f"{mlabel}_{rlabel}_{ilabel}_r{rep+1}.jpg"
+                        os.replace(r["grown_path"], save_to)
+                        r["grown_path"] = str(save_to)
+                    runs.append(r)
+                results.append({
+                    "model": mlabel, "model_file": mfile,
+                    "res": rlabel, "res_val": rval,
+                    "img": ilabel, "img_path": ipath,
+                    "runs": runs,
+                })
+    # 存原始数据
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump(results, f, ensure_ascii=False, indent=2)
+    print(f"\n✓ 全部完成，原始数据 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_report.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""把 benchmark_out/matrix/results.json 生成 HTML 报告。
+每个组合一行：原图 + 3次生发图 + 耗时/显存。
+图片用 base64 内嵌（自包含单文件，便于部署）。
+"""
+import base64
+import json
+import os
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+RES_LABEL = {"orig": "原图", "640": "640", "896": "896(默认)"}
+MODEL_LABEL = {
+    "4b-fp8": "4B fp8 (3.8G)",
+    "9b-fp8": "9B fp8 (8.8G)",
+    "9b-Q5": "9B Q5_K_M (6.6G)",
+    "9b-Q4": "9B Q4_K_M (5.6G)",
+}
+MODEL_ORDER = ["4b-fp8", "9b-Q4", "9b-Q5", "9b-fp8"]
+
+
+def img_src(path):
+    """把绝对路径转成报告里的相对 URL（报告在 static/，图片在 static/bench/）。"""
+    if not path:
+        return None
+    p = str(path)
+    # benchmark_out/matrix/xxx.jpg -> bench/matrix/xxx.jpg
+    if "benchmark_out/matrix/" in p:
+        return "bench/matrix/" + os.path.basename(p)
+    if "benchmark_out/hairstyle/" in p:
+        return "bench/hairstyle/" + os.path.basename(p)
+    return None
+
+
+def thumb(src, alt="", cls=""):
+    if not src:
+        return f'<div class="na {cls}">⚠ 失败</div>'
+    return f'<img class="{cls}" src="{src}" alt="{alt}" loading="lazy">'
+
+
+def main():
+    data = json.load(open(RESULTS, encoding="utf-8"))
+    # 原图相对路径映射（图片在 static/bench/orig/）
+    ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg", "girl5": "bench/orig/girl5.jpg"}
+
+    # 统计：每个模型的平均耗时、平均峰值显存
+    stats = {}
+    for c in data:
+        m = c["model"]
+        stats.setdefault(m, {"times": [], "peaks": []})
+        for r in c["runs"]:
+            if not r["error"]:
+                stats[m]["times"].append(r["elapsed"])
+                stats[m]["peaks"].append(r["gpu_peak"])
+
+    rows_html = []
+    # 按模型顺序、分辨率顺序、图片顺序排列
+    for m in MODEL_ORDER:
+        mdata = [c for c in data if c["model"] == m]
+        for rlabel in ["orig", "640", "896"]:
+            for ilabel in ["asdf", "qwer", "girl5"]:
+                c = next((x for x in mdata if x["res"] == rlabel and x["img"] == ilabel), None)
+                if not c:
+                    continue
+                # 3 次结果图
+                run_cells = []
+                for i, r in enumerate(c["runs"]):
+                    src = img_src(r["grown_path"]) if not r["error"] else None
+                    if src:
+                        run_cells.append(
+                            f'<div class="run-cell"><div class="run-label">第{i+1}次 · {r["elapsed"]:.1f}s</div>'
+                            f'{thumb(src, f"r{i+1}", "result-img")}</div>'
+                        )
+                    else:
+                        run_cells.append(
+                            f'<div class="run-cell"><div class="run-label">第{i+1}次 · 失败</div>'
+                            f'<div class="na">⚠ {r["error"][:30] if r["error"] else ""}</div></div>'
+                        )
+
+                orig = ORIG_SRC.get(c["img"])
+                rows_html.append(f'''
+                <div class="combo-row">
+                  <div class="cell-model">{MODEL_LABEL.get(m, m)}<div class="cell-sub">res={RES_LABEL.get(rlabel, rlabel)}</div></div>
+                  <div class="cell-img">{thumb(orig, "原图", "orig-img")}<div class="run-label">{ilabel}</div></div>
+                  <div class="cell-runs">{"".join(run_cells)}</div>
+                </div>''')
+
+    # 模型对比汇总
+    summary_rows = []
+    for m in MODEL_ORDER:
+        s = stats.get(m, {"times": [], "peaks": []})
+        if s["times"]:
+            avg_t = sum(s["times"]) / len(s["times"])
+            max_p = max(s["peaks"]) / 1024
+            summary_rows.append(
+                f"<tr><td>{MODEL_LABEL.get(m,m)}</td><td>{avg_t:.1f}s</td>"
+                f"<td>{max_p:.1f} GB</td><td>{len(s['times'])} 成功</td></tr>"
+            )
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Flux 模型矩阵测试报告 — 接口2女性花瓣形</title>
+<style>
+  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
+  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; background: #f5f5f5; color: #333; padding: 20px; }}
+  h1 {{ font-size: 22px; margin-bottom: 4px; }}
+  .subtitle {{ color: #888; font-size: 13px; margin-bottom: 16px; }}
+  .summary {{ background: #fff; border-radius: 10px; padding: 16px 20px; margin-bottom: 20px; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
+  .summary h2 {{ font-size: 16px; margin-bottom: 10px; }}
+  .summary table {{ border-collapse: collapse; width: 100%; font-size: 14px; }}
+  .summary th, .summary td {{ border: 1px solid #e5e7eb; padding: 8px 12px; text-align: left; }}
+  .summary th {{ background: #f9fafb; font-weight: 600; }}
+  .combo-row {{ display: flex; align-items: flex-start; gap: 12px; background: #fff; border-radius: 10px;
+               padding: 12px 16px; margin-bottom: 10px; box-shadow: 0 1px 3px rgba(0,0,0,.05); }}
+  .cell-model {{ min-width: 130px; font-weight: 700; font-size: 14px; padding-top: 6px; }}
+  .cell-sub {{ font-weight: 400; font-size: 12px; color: #6b7280; margin-top: 2px; }}
+  .cell-img {{ min-width: 160px; text-align: center; }}
+  .cell-runs {{ display: flex; gap: 10px; flex: 1; }}
+  .run-cell {{ text-align: center; }}
+  .run-label {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
+  img {{ border-radius: 6px; max-height: 200px; max-width: 100%; object-fit: contain; background: #f9fafb; }}
+  .orig-img {{ max-height: 180px; border: 2px solid #e5e7eb; }}
+  .result-img {{ max-height: 200px; }}
+  .na {{ color: #d1d5db; font-size: 12px; padding: 40px 20px; background: #f9fafb; border-radius: 6px; width: 150px; }}
+</style>
+</head>
+<body>
+<h1>💇 Flux 模型矩阵测试报告</h1>
+<p class="subtitle">接口2女性 · 花瓣形发型 · 4模型 × 3分辨率 × 3图 × 3次 = 108 次 · RTX 3090 24GB</p>
+
+<div class="summary">
+  <h2>📊 模型对比汇总</h2>
+  <table>
+    <tr><th>模型</th><th>平均耗时</th><th>峰值显存</th><th>成功次数</th></tr>
+    {"".join(summary_rows)}
+  </table>
+</div>
+
+<h2 style="font-size:16px;margin:24px 0 12px">🖼️ 各组合对比（每行：原图 + 3次生发结果）</h2>
+{"".join(rows_html)}
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告已生成: {HTML} ({HTML.stat().st_size//1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

benchmark_res_compare.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""分辨率对比测试：4图×5发型=20行，每行4种分辨率(不缩放/896/768/640)，steps=15。
+热数据：每个组合预热1次(丢弃)+正式1次。OOM的跳过记录为失败。
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench3")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [
+    (1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
+    (4, "straight", "直线"), (5, "wave", "波浪"),
+]
+# 分辨率档：0=不缩放(原图)
+RES_LIST = [("orig", "0"), ("896", "896"), ("768", "768"), ("640", "640")]
+RES_TITLES = ["原图(不缩放)", "896", "768", "640"]
+STEPS = 15
+
+
+def call(img_path, hair_num, redraw_max_side, save_grown=None, timeout=300):
+    data = {"hair_style": str(hair_num), "webui_steps": str(STEPS),
+            "redraw_max_side": str(redraw_max_side)}
+    t0 = time.perf_counter()
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=data, timeout=timeout)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:80], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "swap_ms": hs.get("swap_ms"),
+            "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:80], "wall": time.perf_counter() - t0}
+
+
+def main():
+    rows = []
+    total = len(IMGS) * len(HAIRSTYLES) * len(RES_LIST) * 2
+    idx = 0
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for (rlabel, rval), rtitle in zip(RES_LIST, RES_TITLES):
+                # 预热
+                idx += 1
+                print(f"[{idx}/{total}] 预热 {ilabel}|{hname}|{rtitle}", flush=True)
+                try:
+                    call(ipath, hnum, rval, timeout=120)
+                except Exception:
+                    pass  # 预热失败(可能OOM)不中断
+                # 正式
+                idx += 1
+                save = OUT / f"{ilabel}_{hkey}_{rlabel}.jpg"
+                print(f"[{idx}/{total}] 正式 {ilabel}|{hname}|{rtitle}", flush=True)
+                r = call(ipath, hnum, rval, save_grown=save, timeout=300)
+                r["res_label"] = rlabel; r["res_title"] = rtitle
+                r["grown_path"] = str(save) if r.get("ok") else None
+                status = f"{r.get('total_ms')}ms" if r.get("ok") else f"FAIL:{r.get('error','')[:30]}"
+                print(f"    -> {status}", flush=True)
+                cells.append(r)
+            rows.append({"img": ilabel, "img_path": ipath,
+                         "hair_num": hnum, "hair_key": hkey, "hair_name": hname,
+                         "cells": cells})
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump({"res_titles": RES_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if c.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(rows)*len(RES_LIST)} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_res_compare_report.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""生成分辨率对比报告：20行(4图×5发型) × 4列(原图不缩放/896/768/640)。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench3")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    if not path or not os.path.isfile(path):
+        return None
+    return "bench3/" + os.path.basename(path)
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    titles = d["res_titles"]
+    rows = d["rows"]
+
+    # 各分辨率平均耗时
+    col_stats = defaultdict(lambda: {"total": [], "comfy": []})
+    for r in rows:
+        for c in r["cells"]:
+            if c.get("ok"):
+                col_stats[c["res_title"]]["total"].append(c["total_ms"])
+                col_stats[c["res_title"]]["comfy"].append(c.get("comfy_ms", 0))
+
+    # 表头
+    headers = ['<th class="col-label">原图</th>']
+    for t in titles:
+        s = col_stats.get(t)
+        avg = sum(s["total"]) // len(s["total"]) if s and s["total"] else 0
+        headers.append(f'<th class="col-label"><div class="col-title">{t}</div>'
+                       f'<div class="col-stat">均{avg/1000:.1f}s</div></th>')
+
+    # 表体
+    body_rows = []
+    for r in rows:
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图缩略图（用 orig 档的结果当原图展示，或用原图文件）
+        orig_cell = f'<td class="cell-orig"><div class="row-label-cell">{label}</div></td>'
+        cells = [orig_cell]
+        for c in r["cells"]:
+            src = img_src(c.get("grown_path")) if c.get("ok") else None
+            if src:
+                t = c.get("total_ms", 0)
+                cells.append(f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                             f'<div class="cell-time">{t/1000:.1f}s</div></td>')
+            else:
+                cells.append(f'<td class="cell-result"><div class="na">⚠<br>{c.get("error","")[:20]}</div></td>')
+        body_rows.append(f'<tr>{"".join(cells)}</tr>')
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>重绘分辨率对比报告 — steps=15</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; }}
+h1 {{ font-size: 20px; margin-bottom: 4px; }}
+.subtitle {{ color: #888; font-size: 12px; margin-bottom: 12px; }}
+.legend {{ background: #fff; border-radius: 8px; padding: 10px 16px; margin-bottom: 12px; font-size: 12px; color: #555; }}
+.scroll-wrap {{ overflow-x: auto; }}
+table {{ border-collapse: collapse; background: #fff; border-radius: 8px; overflow: hidden; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
+th, td {{ border: 1px solid #eee; padding: 6px; vertical-align: top; text-align: center; }}
+th {{ background: #f9fafb; position: sticky; top: 0; }}
+.col-label {{ min-width: 130px; max-width: 150px; }}
+.col-title {{ font-size: 12px; font-weight: 700; color: #374151; }}
+.col-stat {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.row-label {{ font-size: 11px; color: #6b7280; }}
+.row-label b {{ color: #1f2937; }}
+img {{ border-radius: 4px; max-width: 130px; max-height: 160px; object-fit: contain; background: #f3f4f6; }}
+.cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.na {{ color: #d1d5db; font-size: 12px; padding: 40px 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 重绘分辨率对比报告</h1>
+<p class="subtitle">4图×5发型=20行 · 每行4分辨率(原图不缩放/896/768/640) · steps=15 · 热数据 · 80/80成功 · 峰值21.2GB · 0 OOM</p>
+<div class="legend">列标题下显示<b>平均总耗时</b>。横向滚动查看。原图列含图片名+发型名。每格下方为该次总耗时。</div>
+<div class="scroll-wrap">
+<table>
+<tr>{"".join(headers)}</tr>
+{"".join(body_rows)}
+</table>
+</div>
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告: {HTML} ({HTML.stat().st_size // 1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

benchmark_res_prompt.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""分辨率对比测试(新提示词版)：4图×5发型=20行，每行4种分辨率，steps=15。
+提示词固定为 "填充遮罩区域的头发，人物增加一点磨皮"。
+热数据：预热1次+正式1次。
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+PROMPT = "填充遮罩区域的头发，人物增加一点磨皮"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench7")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [
+    (1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
+    (4, "straight", "直线"), (5, "wave", "波浪"),
+]
+RES_LIST = [("orig", "0"), ("896", "896"), ("768", "768"), ("640", "640")]
+RES_TITLES = ["原图(不缩放)", "896", "768", "640"]
+STEPS = 15
+
+
+def call(img_path, hair_num, redraw_max_side, save_grown=None, timeout=300):
+    data = {"hair_style": str(hair_num), "webui_steps": str(STEPS),
+            "redraw_max_side": str(redraw_max_side), "redraw_prompt": PROMPT}
+    t0 = time.perf_counter()
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=data, timeout=timeout)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:80], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "swap_ms": hs.get("swap_ms"),
+            "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:80], "wall": time.perf_counter() - t0}
+
+
+def main():
+    rows = []
+    total = len(IMGS) * len(HAIRSTYLES) * len(RES_LIST) * 2
+    idx = 0
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for (rlabel, rval), rtitle in zip(RES_LIST, RES_TITLES):
+                idx += 1
+                print(f"[{idx}/{total}] 预热 {ilabel}|{hname}|{rtitle}", flush=True)
+                try:
+                    call(ipath, hnum, rval, timeout=120)
+                except Exception:
+                    pass
+                idx += 1
+                save = OUT / f"{ilabel}_{hkey}_{rlabel}.jpg"
+                print(f"[{idx}/{total}] 正式 {ilabel}|{hname}|{rtitle}", flush=True)
+                r = call(ipath, hnum, rval, save_grown=save, timeout=300)
+                r["res_label"] = rlabel; r["res_title"] = rtitle
+                r["grown_path"] = str(save) if r.get("ok") else None
+                status = f"{r.get('total_ms')}ms" if r.get("ok") else f"FAIL:{r.get('error','')[:30]}"
+                print(f"    -> {status}", flush=True)
+                cells.append(r)
+            rows.append({"img": ilabel, "img_path": ipath,
+                         "hair_num": hnum, "hair_key": hkey, "hair_name": hname,
+                         "cells": cells})
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump({"res_titles": RES_TITLES, "prompt": PROMPT, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if c.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(rows)*len(RES_LIST)} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_steps_res.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""swap步数 + 重绘分辨率 对比测试（热数据）。
+
+每个组合: 预热1次(丢弃) + 正式测1次(取热数据)。
+B维度: steps=10/15/20 (分辨率固定896)
+C维度: 分辨率=640/896/1024 (steps固定15)
+4图×2发型=8组 × 6档 × 2次(预热+正式) = 96次
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench2")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [(5, "wave", "波浪"), (3, "heart", "心形")]
+
+# B维度: swap步数对比 (分辨率固定896)
+B_STEPS = [10, 15, 20]
+# C维度: 重绘分辨率对比 (steps固定15)
+C_RES = [640, 896, 1024]
+
+
+def call(img_path, hair_num, webui_steps=None, redraw_max_side=None, save_grown=None):
+    """调调试接口。返回 dict。save_grown 非None时把结果图存到该路径。"""
+    data = {"hair_style": str(hair_num)}
+    if webui_steps is not None:
+        data["webui_steps"] = str(webui_steps)
+    if redraw_max_side is not None:
+        data["redraw_max_side"] = str(redraw_max_side)
+    t0 = time.perf_counter()
+    try:
+        with open(img_path, "rb") as f:
+            r = requests.post(API, headers={"X-Internal-Token": TOKEN},
+                files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
+                data=data, timeout=300)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:100], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "ctx_ms": d["extract_context_ms"],
+            "mask_ms": hs.get("mask_ms"), "swap_ms": hs.get("swap_ms"),
+            "blend_ms": hs.get("blend_ms"), "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:100], "wall": time.perf_counter() - t0}
+
+
+def main():
+    results = {"B_steps": [], "C_res": []}
+    total_calls = len(IMGS) * len(HAIRSTYLES) * (len(B_STEPS) + len(C_RES)) * 2
+    idx = 0
+
+    # ===== B维度: swap步数对比 (分辨率固定896) =====
+    print("\n===== B维度: swap步数对比 (分辨率=896) =====", flush=True)
+    for steps in B_STEPS:
+        print(f"\n--- steps={steps} ---", flush=True)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                # 预热(丢弃)
+                idx += 1
+                print(f"[{idx}/{total_calls}] 预热 {ilabel}|{hname}|steps={steps}", flush=True)
+                call(ipath, hnum, webui_steps=steps, redraw_max_side=896)
+                # 正式(热数据)
+                idx += 1
+                save = OUT / f"B_steps{steps}_{ilabel}_{hkey}.jpg"
+                print(f"[{idx}/{total_calls}] 正式 {ilabel}|{hname}|steps={steps}", flush=True)
+                r = call(ipath, hnum, webui_steps=steps, redraw_max_side=896, save_grown=save)
+                r["steps"] = steps; r["img"] = ilabel; r["hair"] = hkey; r["hair_name"] = hname
+                r["grown_path"] = str(save) if r.get("ok") else None
+                print(f"    -> total={r.get('total_ms')}ms swap={r.get('swap_ms')}ms comfy={r.get('comfy_ms')}ms ok={r.get('ok')}", flush=True)
+                results["B_steps"].append(r)
+
+    # ===== C维度: 重绘分辨率对比 (steps固定15) =====
+    print("\n===== C维度: 重绘分辨率对比 (steps=15) =====", flush=True)
+    for res in C_RES:
+        print(f"\n--- res={res} ---", flush=True)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                idx += 1
+                print(f"[{idx}/{total_calls}] 预热 {ilabel}|{hname}|res={res}", flush=True)
+                call(ipath, hnum, webui_steps=15, redraw_max_side=res)
+                idx += 1
+                save = OUT / f"C_res{res}_{ilabel}_{hkey}.jpg"
+                print(f"[{idx}/{total_calls}] 正式 {ilabel}|{hname}|res={res}", flush=True)
+                r = call(ipath, hnum, webui_steps=15, redraw_max_side=res, save_grown=save)
+                r["res"] = res; r["img"] = ilabel; r["hair"] = hkey; r["hair_name"] = hname
+                r["grown_path"] = str(save) if r.get("ok") else None
+                print(f"    -> total={r.get('total_ms')}ms swap={r.get('swap_ms')}ms comfy={r.get('comfy_ms')}ms ok={r.get('ok')}", flush=True)
+                results["C_res"].append(r)
+
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump(results, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for r in results["B_steps"] + results["C_res"] if r.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(results['B_steps'])+len(results['C_res'])} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_steps_res_report.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""生成 swap步数 + 重绘分辨率 对比报告 HTML。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench2")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    if not path or not os.path.isfile(path):
+        return None
+    # benchmark_out/bench2/xxx.jpg -> bench2/xxx.jpg (报告在 static/ 下部署时调整)
+    p = str(path)
+    return "bench2/" + os.path.basename(p)
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    b_data = d["B_steps"]   # steps 对比
+    c_data = d["C_res"]     # 分辨率对比
+
+    # B维度聚合
+    by_steps = defaultdict(list)
+    for r in b_data:
+        by_steps[r["steps"]].append(r)
+    b_summary = []
+    for s in sorted(by_steps):
+        rs = by_steps[s]
+        b_summary.append({
+            "label": f"steps={s}", "n": len(rs),
+            "swap": sum(r["swap_ms"] for r in rs) // len(rs),
+            "total": sum(r["total_ms"] for r in rs) // len(rs),
+        })
+
+    # C维度聚合
+    by_res = defaultdict(list)
+    for r in c_data:
+        by_res[r["res"]].append(r)
+    c_summary = []
+    for res in sorted(by_res):
+        rs = by_res[res]
+        c_summary.append({
+            "label": f"res={res}", "n": len(rs),
+            "comfy": sum(r["comfy_ms"] for r in rs) // len(rs),
+            "total": sum(r["total_ms"] for r in rs) // len(rs),
+        })
+
+    # B维度明细行（每图每发型每步数）
+    b_rows = []
+    for r in sorted(b_data, key=lambda x: (x["img"], x["hair"], x["steps"])):
+        src = img_src(r.get("grown_path"))
+        b_rows.append(f"""<tr>
+<td>{r['img']}</td><td>{r['hair_name']}</td><td>{r['steps']}</td>
+<td>{r.get('swap_ms','?')}</td><td>{r.get('comfy_ms','?')}</td><td>{r.get('total_ms','?')}</td>
+<td>{f'<img src="{src}" loading="lazy">' if src else '⚠'}</td></tr>""")
+
+    # C维度明细行
+    c_rows = []
+    for r in sorted(c_data, key=lambda x: (x["img"], x["hair"], x["res"])):
+        src = img_src(r.get("grown_path"))
+        c_rows.append(f"""<tr>
+<td>{r['img']}</td><td>{r['hair_name']}</td><td>{r['res']}</td>
+<td>{r.get('swap_ms','?')}</td><td>{r.get('comfy_ms','?')}</td><td>{r.get('total_ms','?')}</td>
+<td>{f'<img src="{src}" loading="lazy">' if src else '⚠'}</td></tr>""")
+
+    def bar_row(label, val, max_val, color, unit="ms"):
+        pct = max(1, val / max_val * 100) if max_val else 0
+        return f'<div class="step-row"><div class="step-name">{label}</div>' \
+               f'<div class="step-bar-wrap"><div class="step-bar {color}" style="width:{pct}%">{val}{unit}</div></div>' \
+               f'<div class="step-time">{val}{unit}</div></div>'
+
+    # B维度汇总条形图
+    b_max_swap = max(s["swap"] for s in b_summary)
+    b_bars = "".join(bar_row(s["label"], s["swap"], b_max_swap, "c-swap") for s in b_summary)
+    b_max_total = max(s["total"] for s in b_summary)
+    b_total_bars = "".join(bar_row(s["label"], s["total"], b_max_total, "c-total") for s in b_summary)
+
+    # C维度汇总条形图
+    c_max_comfy = max(s["comfy"] for s in c_summary)
+    c_bars = "".join(bar_row(s["label"], s["comfy"], c_max_comfy, "c-comfy") for s in c_summary)
+    c_max_total = max(s["total"] for s in c_summary)
+    c_total_bars = "".join(bar_row(s["label"], s["total"], c_max_total, "c-total") for s in c_summary)
+
+    html = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>swap步数 + 重绘分辨率 对比报告</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; color: #333; }}
+h1 {{ font-size: 20px; margin-bottom: 4px; }}
+h2 {{ font-size: 16px; margin: 20px 0 10px; }}
+.subtitle {{ color: #888; font-size: 12px; margin-bottom: 14px; }}
+.card {{ background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.06); margin-bottom: 16px; overflow: hidden; }}
+.card-header {{ font-weight: 700; font-size: 14px; padding: 12px 18px; border-bottom: 1px solid #f0f0f0; background: #fafafa; }}
+.card-body {{ padding: 18px; }}
+.summary-grid {{ display: grid; grid-template-columns: 1fr 1fr; gap: 16px; }}
+.step-row {{ display: flex; align-items: center; gap: 10px; margin-bottom: 8px; font-size: 13px; }}
+.step-name {{ width: 100px; flex-shrink: 0; font-weight: 600; }}
+.step-bar-wrap {{ flex: 1; background: #f3f4f6; border-radius: 4px; height: 24px; min-width: 200px; }}
+.step-bar {{ height: 100%; border-radius: 4px; display: flex; align-items: center; padding-left: 8px; color: #fff; font-size: 11px; font-weight: 600; min-width: 2px; }}
+.step-time {{ width: 70px; text-align: right; font-weight: 600; flex-shrink: 0; font-variant-numeric: tabular-nums; }}
+.c-swap {{ background: #f59e0b; }} .c-comfy {{ background: #ef4444; }} .c-total {{ background: #2563eb; }}
+table {{ border-collapse: collapse; width: 100%; font-size: 12px; }}
+th, td {{ border: 1px solid #eee; padding: 5px 8px; text-align: center; }}
+th {{ background: #f9fafb; font-weight: 600; position: sticky; top: 0; }}
+td img {{ max-height: 100px; max-width: 80px; border-radius: 4px; }}
+.scroll {{ max-height: 400px; overflow: auto; }}
+.note {{ background: #fef3c7; border-radius: 8px; padding: 10px 14px; font-size: 12px; color: #92400e; margin-top: 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 swap步数 + 重绘分辨率 对比报告</h1>
+<p class="subtitle">4图(asdf/qwer/girl2/girl5) × 2发型(波浪/心形) · 热数据(预热后取第2次) · 48/48成功 · 峰值20.6GB · 0 OOM</p>
+
+<div class="note">💡 结论速览: B维度 steps 10→20 swap从3.0s→3.9s(每步省~90ms)；C维度 res 640比896省3s(comfy 4.3s vs 7.3s)，1024与896接近。</div>
+
+<h2>B维度：swap步数对比（分辨率固定896）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">swap 耗时（越低越快）</div><div class="card-body">{b_bars}</div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body">{b_total_bars}</div></div>
+</div>
+
+<h2>C维度：重绘分辨率对比（steps固定15）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">ComfyUI重绘 耗时（越低越快）</div><div class="card-body">{c_bars}</div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body">{c_total_bars}</div></div>
+</div>
+
+<h2>B维度明细（每图每发型每步数）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>steps</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+{"".join(b_rows)}
+</table></div></div>
+
+<h2>C维度明细（每图每发型每分辨率）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>res</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+{"".join(c_rows)}
+</table></div></div>
+</body>
+</html>"""
+
+    with open(HTML, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"✓ 报告: {HTML} ({HTML.stat().st_size // 1024} KB)")
+
+
+if __name__ == "__main__":
+    main()

comfyui.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=ComfyUI (127.0.0.1:8188)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/ComfyUI
+ExecStart=/home/ubuntu/ComfyUI/venv/bin/python main.py --listen 127.0.0.1 --port 8188 --cache-classic --fast
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

docs/gpu-worker改动说明.md

@@ -16,7 +16,7 @@
 Handler 签名新增：
 
 ```python
-prompt: str = Form(default="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+prompt: str = Form(default=填充遮罩区域的头发，人物增加一点磨皮, description="ComfyUI 提示词，会替换工作流节点60的文本"),
 ```
 
 透传链路：
@@ -31,7 +31,7 @@ handler → generate_grow_results(image, gender, use_mask, prompt)
 Handler 签名新增：
 
 ```python
-prompt: str = Form(default="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+prompt: str = Form(default=填充遮罩区域的头发，人物增加一点磨皮, description="ComfyUI 提示词，会替换工作流节点60的文本"),
 ```
 
 透传链路：
@@ -80,17 +80,17 @@ if prompt is not None:
 | 2 | `image_base64` | string | — | 同上 |
 | 2 | `gender` | string | `female` | `male` / `female` |
 | 2 | `use_mask` | bool | `true` | inpaint 遮罩开关 |
-| 2 | `prompt` | string | `补充遮罩区域的头发` | ComfyUI 提示词 |
+| 2 | `prompt` | string | 填充遮罩区域的头发，人物增加一点磨皮 | ComfyUI 提示词 |
 | 3 | `marked_image_file` | file | — | 划线图（三选一） |
 | 3 | `marked_image_url` | string | — | 同上 |
 | 3 | `marked_image_base64` | string | — | 同上 |
 | 3 | `use_mask` | bool | `true` | 画发际线开关 |
-| 3 | `prompt` | string | `补充遮罩区域的头发` | ComfyUI 提示词 |
+| 3 | `prompt` | string | 填充遮罩区域的头发，人物增加一点磨皮 | ComfyUI 提示词 |
 
 ## 3. 当前工作流提示词节点
 
 `add_hair.json` 中节点 **60**（`JjkText`）为提示词节点，当前硬编码文本：
 
-> 补充遮罩区补充遮罩区域内的头发，头发填满遮罩区域。发际线往下挡住额头
+> 填充遮罩区域的头发，人物增加一点磨皮
 
 `comfyui.run()` 收到 `prompt` 参数后将替换此文本。

docs/iface3_implementation.md

@@ -0,0 +1,177 @@
+# 接口3 B端生发 — 实现文档
+
+> 文档日期：2026-07-18
+
+---
+
+## 一、接口概述
+
+**接口3** 是 B端（医生/操作端）生发接口。医生在用户照片上手动用马克笔画出发际线后，只需上传这一张划线图，系统自动检测划线 → 生成遮罩 → 送 ComfyUI 生发，返回「植发3个月」效果图。
+
+**与接口2 的核心区别**：
+
+| 特性 | 接口2（C端生发） | 接口3（B端生发） |
+|------|----------------|----------------|
+| 输入 | 原始照片 | 划线图（含手绘线） |
+| 发际线来源 | 系统按发型模板自动生成 | 医生手绘标注 |
+| 发型类型 | ellipse/flower/heart/straight/wave | custom（自定义） |
+| 中间步骤 | extract_context + swapHair + ComfyUI重绘 | 划线检测 + 遮罩 + ComfyUI生发 |
+| 是否调 change_hair | 是（女性流程） | 否 |
+| ComfyUI 工作流 | 0716add-hair-api.json（重绘） | add_hair.json（生发） |
+| 典型耗时 | ~11s | ~6-8s |
+
+---
+
+## 二、接口定义
+
+### 路由
+
+```
+POST /api/v1/hair/grow-b
+```
+
+### 入参
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| `marked_image_file` | UploadFile | 三选一 | 划线图片文件（JPG/PNG） |
+| `marked_image_url` | str | 三选一 | 划线图片 URL |
+| `marked_image_base64` | str | 三选一 | 划线图片 base64 |
+| `use_mask` | bool | 否（默认True） | 是否自动检测划线并建遮罩。False时跳过检测，直接送划线图 |
+| `prompt` | str | 否 | ComfyUI 提示词，默认"补充遮罩区域的头发，加一点美颜" |
+
+### 返回
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "hair_growth_image_base64": "iVBORw0KGgo...（生发图 JPG base64）",
+    "hairline_type": "custom"
+  }
+}
+```
+
+错误码：
+- `1001`: 无法识别人像 / 未检测到发际线划线
+- `1007`: 处理失败
+- `1008`: 图片格式不支持
+
+---
+
+## 三、完整调用链
+
+```
+POST /api/v1/hair/grow-b
+│
+├─ app.py hair_grow_b()                          [app.py:929]
+│   ├─ resolve_image_bytes() → marked_raw       解析图片（file/url/base64三选一）
+│   ├─ cv2.imdecode → marked_bgr                解码为 BGR
+│   └─ run_in_threadpool(generate_grow_b, ...)
+│
+├─ service.py generate_grow_b(marked_bgr, use_mask, prompt)  [service.py:381]
+│   │
+│   ├─ 步骤1：人脸检测 + 头发分割（仅 use_mask=True 时）
+│   │   ├─ get_landmarker().detect(rgb)          MediaPipe 478点人脸检测
+│   │   │   → landmarks（无人脸返回 no_face）
+│   │   ├─ get_parser().parse(rgb)               SegFormer 面部分割（CPU ~0.9s）
+│   │   │   → parse_map（int label map）
+│   │   │
+│   ├─ 步骤2：手绘发际线检测（仅 use_mask=True 时）
+│   │   ├─ detect_marker_hairline(marked_bgr, landmarks, parse_map)
+│   │   │   │                                   [marker_detect.py:41]
+│   │   │   ├─ forehead_upper_region(landmarks)  额头上部 ROI
+│   │   │   ├─ head_silhouette(parse_map)         头部轮廓 ROI
+│   │   │   ├─ _blackhat(gray)                    黑帽变换（响应比邻域暗的细结构）
+│   │   │   ├─ _snap_anchor(bh, 左鬓角21)         左锚点吸附
+│   │   │   ├─ _snap_anchor(bh, 右鬓角251)        右锚点吸附
+│   │   │   ├─ route_through_array(cost, 左, 右)  Dijkstra最小代价路径
+│   │   │   └→ path (N,2) row,col（拒识返回 None → no_line）
+│   │   │
+│   │   ├─ path_to_curve_mask(path)              路径→曲线mask（uint8 0/255）
+│   │   └─ mask_from_curve(curve_mask, landmarks, parse_map)
+│   │       │                                   [mask.py]
+│   │       ├─ _above_curve_region(curve_mask)    曲线以上区域
+│   │       ├─ cv2.morphologyEx(闭运算)           填洞
+│   │       ├─ 最大连通域
+│   │       └─ 高斯羽化                            → mask (uint8 0-255)
+│   │
+│   ├─ 步骤3：合成 RGBA PNG
+│   │   ├─ compose_comfy_rgba(marked_bgr, mask)  RGB=原图，alpha=255×(1-mask)
+│   │   └─ PNG 编码                               → rgba_png_bytes
+│   │
+│   └─ 步骤4：ComfyUI 生发
+│       └─ comfyui.run(rgba_png_bytes, prompt)   [comfyui.py:87]
+│           ├─ 上传图片到 ComfyUI /upload/image
+│           ├─ 加载工作流 add_hair.json
+│           ├─ 替换节点26输入图 + 节点6随机seed + 节点60提示词
+│           ├─ POST /prompt 提交工作流
+│           ├─ 轮询 /history/{prompt_id}（间隔0.2s）
+│           └─ GET /view 取回输出 PNG              → grown_png
+│
+└─ 返回 {"grown_png": bytes, "status": "ok"}
+```
+
+---
+
+## 四、用到的模型和外部服务
+
+| 模型/服务 | 用途 | 位置 | 设备 |
+|----------|------|------|------|
+| **FaceLandmarker** (MediaPipe) | 478点人脸检测 | hairline/face_landmarks.py | CPU |
+| **FaceParser** (SegFormer) | 面部分割（hair/skin/...） | hairline/face_parsing.py | CPU (5090不兼容cu121) |
+| **ComfyUI** (Flux-2) | 生发图生成 | hairline/comfyui.py → :8188 | GPU |
+
+**注意**：接口3 **不调用** change_hair 服务（:8801），不需要 swapHair。这是它与接口2女性流程的关键区别。
+
+---
+
+## 五、核心算法：手绘发际线检测
+
+### 5.1 为什么不用简单阈值？
+
+手绘马克笔线条的灰度值与皮肤阴影、抬头纹等重叠，全局阈值无法区分。采用**黑帽变换 + Dijkstra最小路径**方案。
+
+### 5.2 黑帽变换（Black Hat）
+
+```python
+kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+bh = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
+```
+
+黑帽 = 闭运算 − 原图，响应"比局部邻域暗的细结构"（即马克笔线条），对抬头纹/眉毛/发丝鲁棒。
+
+### 5.3 Dijkstra 最小代价路径
+
+1. **ROI 限定**：额头上部 ∩ 头部轮廓（排除背景）
+2. **锚点**：左鬓角(21) / 右鬓角(251) MediaPipe 关键点
+3. **代价图**：`cost = (bh.max() - bh) + 1.0`，ROI外设 1e6
+4. **路径**：`route_through_array(cost, 左锚, 右锚)` — skimage 的 Dijkstra 实现
+
+### 5.4 拒识机制
+
+路径平均黑帽响应 < 8.0 → 判定"未画线"，返回 `no_line`。
+
+---
+
+## 六、与接口1、接口2 的对比
+
+| 维度 | 接口1 | 接口2 | 接口3 |
+|------|-------|-------|-------|
+| 功能 | 四庭七眼测量 | C端生发（5种发际线） | B端生发（手绘线） |
+| 路由 | /api/v1/face/measure | /api/v1/hair/grow | /api/v1/hair/grow-b |
+| 输入 | 正面照 | 正面照 | 划线图 |
+| MediaPipe | ✅ | ✅ | ✅ |
+| SegFormer | ✅ | ✅ | ✅ |
+| change_hair | ❌ | ✅（女性） | ❌ |
+| ComfyUI | ❌ | ✅（Flux-2重绘） | ✅（Flux-2生发） |
+| 典型耗时 | ~2s | ~11s | ~6-8s |
+| ComfyUI工作流 | — | 0716add-hair-api.json | add_hair.json |
+
+---
+
+## 七、测试
+
+- **测试页面**：[static/test_interface3.html](file:///home/ubuntu/hair/static/test_interface3.html)
+- **测试图片**：[image/girl_img/girl13.jpg](file:///home/ubuntu/hair/image/girl_img/girl13.jpg)（需手动在图上画发际线后作为划线图上传）

docs/实现说明.md

@@ -12,15 +12,18 @@
 
 ```
 客户端 ──HTTPS──> 外网网关(gateway/)  ──HTTP(X-Internal-Token)──> worker(GPU 机, app.py)
-                     │ 薄代理 + 落盘改URL                          │ 跑算法(本地模型/ComfyUI)
-                     └ 接口4 本机直接调豆包(不转发)                 └ 接口1/2/3/5/6/7
+                     │ 薄代理 + 落盘改URL                          │ 跑算法(本地模型/ComfyUI/change_hair)
+                     └ 接口4(豆包)/99(特殊点) 本机实现(不转发)      └ 接口1/2/3/5/6/7/9/10/11/12 + 工具路由
 ```
 
-- **worker**（`app.py` + `face_analysis/` + `hairline/`）：跑真正的算法，**纯本地、无外网依赖**。
-  对 `/api/*` 校验 `X-Internal-Token`（密码在 `worker_config.json` 的 `accept_passwords`）；
-  `/health` 模型就绪才返回 200。监听 **8187**（`./start.sh` 控制开关，`./run_worker.sh` 热重载）。
+- **worker**（`app.py` + `face_analysis/` + `hairline/`）：跑真正的算法，除 ComfyUI/change_hair 走本机
+  回环外无外网依赖。对 `/api/*` 校验 `X-Internal-Token`（密码在 `worker_config.json` 的 `accept_passwords`）；
+  `/health` 模型就绪才返回 200。监听 **8187**（systemd `hair-worker.service`，也可 `./run_worker.sh` 手动起）。
+  另有 worker 本机的工具路由：`/api/swapHair/v1`、`/hairColor/v2`（CORS 代理转发 change_hair:8801）、
+  `/api/v1/redraw`（直接 ComfyUI 重绘）、`/api/v1/debug/*`（免鉴权调试）。
 - **网关**（`gateway/`）：薄反向代理，健康检查/派发/鉴权/把 worker 的 `*_base64` 落盘改成 `*_url`。
-  唯一例外是**接口4 在网关本机直接实现**（调外网豆包，不转发 worker）。
+  网关本机实现的接口（不转发 worker）：**接口4**（调外网豆包）与**接口99**（纯 CPU 特殊点检测，
+  直接 import 同仓库 `face_analysis/special_points`，`_if99_lock` 单路排队）。
 - **图片三选一**：所有接口图片入参 `image_file`/`image_url`/`image_base64` 严格三选一（接口3 是 `marked_image_*`）。
 - **响应信封**：`{code, message, request_id, data}`；业务错误用 `code`（HTTP 一律 200）。
 
@@ -33,7 +36,10 @@
 | 2 | `results[].image_base64` / `results[].grown_image_base64`(可空) | `results[].image_url` / `results[].grown_image_url` |
 | 3 | `hair_growth_image_base64`(可空) | `hair_growth_image_url` |
 | 5 | `hairline_images[].image_{middle,high,low}_base64` / `grown_image_base64`(可空) | `hairline_images[].image_{middle,high,low}_url` / `grown_image_url` |
-| 7 | `results[].image_base64` / `results[].grown_image_base64`(可空) | `results[].image_url` / `results[].grown_image_url` |
+| 9 | `steps_common.*_base64`、`bisenet|segformer.{hair,closed_region,final_overlay,mask}_mask_base64` 等 | 同名 `*_url` |
+| 10 | `steps_common.*_base64`、`bisenet|segformer.{base_mask,contour,band_overlay,mask}_base64` 等 | 同名 `*_url` |
+| 11 | `steps.{input,baseline,hair_mask,mask_overlay,mask,swap_raw,alpha,final}_base64` | `steps.*_url` |
+| 12 | `steps.{input,final,redraw_band_overlay,redraw_full,redraw_band,redraw_c}_base64` | `steps.*_url` |
 | 4 | （网关本机产出，无图片字段，`features` 为 JSON 字符串） | — |
 
 > 实现建议：递归遍历 data，凡 key 以 `_base64` 结尾就落盘改 `_url`，自动覆盖嵌套/新增字段。
@@ -47,7 +53,7 @@
 
 ---
 
-## 2. 五个接口实现简述
+## 2. 各接口实现简述
 
 ### 接口1 四庭七眼测量 `/api/v1/face/measure`（worker）
 - **做什么**：正面照 → 四庭(顶/上/中/下庭) + 七眼(眼宽/脸宽/间距) 的 cm 与占比、5 个关键点坐标、一张透明底标注 PNG。
@@ -65,10 +71,11 @@
 ### 接口2 C端生发 `/api/v1/hair/grow`（worker）—— 预览 + 生发图
 - **做什么**：正面照 + `gender`(必填) + `hair_style`(必填，逗号分隔多选，如 `1,2,3`) → 指定发际线类型 **N 组**：**预览图**(发际线叠在照片上) + **生发后图**(植发3个月效果)。
 - **怎么实现**（`hairline/`）：移植 head3d——MediaPipe(Tasks) + SegFormer 分割 + 17 锚点射线检测 → 502 点 mesh，
-  按 `face_ext.obj` 的 UV 把发际线贴图渲染到额头(预览)。生发：黑贴图渲染遮罩 → 调本机 **ComfyUI 8182** 的
+  按 `face_ext.obj` 的 UV 把发际线贴图渲染到额头(预览)。原生生发：黑贴图渲染遮罩 → 调本机 **ComfyUI 8188** 的
   `add_hair.json`(Flux-2) 出图。**关键坑**：obj 是重排序，需 `INDEX_MAP_468` 把 MP 序→OBJ 序。
   返回 `results[].image_base64` + `grown_image_base64`。
-- `hair_style` 映射：female 1=ellipse 2=flower 3=heart 4=straight 5=wave；male 1=ellipse 2=inverse_arc 3=m 4=straight。
+- 模板对齐：左右刘海边界高低不同时，按 MediaPipe 左右对应额头点协调沿人脸向上方向的延伸距离，避免把 M 型等设计模板拉歪；保留人脸姿态，之后仍做头部轮廓钳制。预览白线与生发黑线/遮罩共用修正后的 502 点几何，不改变真实发际线测量算法。
+- `hair_style` 映射：female 1=ellipse 2=flower 3=heart 4=straight 5=wave 6=bigflower 7=clasicalflower；male 1=ellipse 2=inverse_arc 3=m 4=straight 5=heart 6=Softpetal 7=wave。female 1..5 走「换发型(change_hair)」+Flux-2 重绘管线（`generate_grow_results_swap`，复用接口12 final 的 `_V2_FINAL_DEFAULTS`，长边默认压到 `REDRAW_MAX_SIDE`=1024 再放大回原尺寸）；female 6/7 与 male 全部走原生发(ComfyUI add_hair)管线。可选参数 `flux_model`（切换 Flux unet，.gguf 自动配 UnetLoaderGGUF + 配套 qwen CLIP）、`redraw_max_side`（0=不缩图）。
 
 ### 接口7 C端生发 v2 `/api/v1/hair/grow-v2`（worker）—— 接口2同款，add_hair2 工作流
 - **做什么**：与接口 2 完全一致（正面照 + `gender` + `hair_style` 逗号分隔多选 → N 组预览+生发图）。
@@ -92,20 +99,50 @@
 - **做什么**：入参同接口2（`gender` + 多选 `hair_style` 必填）。对每个选中发型 → `middle`/`high`/`low` 三档发际线叠图 + 生发图 + 首个选中发型的面部中间点坐标。
 - **怎么实现**：复用接口2 的 502 点渲染管线，三档分别用 `hairline_texture[/_high|/_low]` 同名贴图渲染叠图；生发同接口2（ComfyUI inpaint），**黑模板固定取 `hairline_texture_black/`（middle）**，每发型 1 张生发图。`best_hairline_center_point`=眉心 x × 首个选中发型 middle 档发际线 y。
 
+### 接口9 头发遮罩 `/api/v1/head/mask`（worker）
+- **做什么**：人像照 → 头发遮罩 + 全流程分步可视化。
+- **怎么实现**（`face_analysis/head_mask.py`）：MediaPipe 底部分割线（21/68/104/69/108/151/337/299/333/298/251 连线，21/251 水平延伸）→ 线以上为上半区 → BiSeNet 与 SegFormer 双路头发分割、逐列向下填充到分割线得闭合区域（头发+额头）→ 外缘朝 151 内缩 `erode_cm`(默认1.2cm，虹膜标定) → 最终遮罩。返回 `steps_common` + `bisenet`/`segformer` 两组各四张图。
+
+### 接口10 头部外缘膨胀带 `/api/v1/head/band`（worker）
+- **做什么**：在接口9 基准遮罩基础上，沿头部外缘生成一条膨胀带遮罩。
+- **怎么实现**（`face_analysis/head_band.py`）：取基准遮罩外轮廓、去掉贴底分割线段 → 膨胀成总宽 `dilate_cm`(默认2cm) 的带子 → 裁到分割线以上。返回 `base_mask`/`contour`/`band_overlay`/`mask` 分步图。
+
+### 接口11 发际线生发 `/api/v1/hairline/grow`（worker）—— 不含重绘
+- **做什么**：发际线偏高/头发稀少正脸图 + `hairline_id`（= change_hair `hair_id`，如 `chang_tuoyuan`）→ 压低发际线图 + 全步骤可视化。
+- **怎么实现**（`face_analysis/hairline_grow.py`，算法见 `发际线增强算法.md`）：① 接口9 遮罩（`seg_model` 选 bisenet/segformer）→ ② 调 change_hair 生成该发际线类型图（`swap_mode=ext_mask` 把遮罩传给 webui 精确重绘 / `as_is` 用其内部遮罩；`gen_backend` 可换 hairgrow 区域生发 inpaint）→ ③ 严格按遮罩贴回 → ④ 接缝融合 `blend_method`（multiband 默认 / seamless / two_stage / feather / alpha_gradient，可选 Reinhard `color_match`）。返回 `steps.{input,baseline,hair_mask,mask_overlay,mask,swap_raw,alpha,final}`。
+
+### 接口12 发际线带重绘 `/api/v1/hairline/grow_v2[_final[_v2]]`（worker）
+- **做什么**：接口11 的下游重绘阶段——先跑接口11 拿 ④final，再取发际线重绘带（外推 `band_lo_mult×push`~`band_hi_mult×push`，默认 0.5×~1.5×）作遮罩，用 **Flux-2（ComfyUI，`0716add-hair-api.json`，reference-latent 保色）** 重绘，产出两版：`redraw_full`（整帧美颜+整帧重绘）与 `redraw_band`（band 内加发、band 外 final 结构 + `beauty_alpha` 融入全脸美颜）。
+- **三个变体**：`grow_v2` 全参数暴露（接口11 参数 + `comfyui_prompt`/`beauty_alpha`/`band_lo_mult`/`band_hi_mult`）；`grow_v2_final` / `grow_v2_final_v2` 精简生产版——仅图片 + `hairline_id`，其余固化 `_V2_FINAL_DEFAULTS`（push=0.8cm / two_stage / color_match=关 / beauty_alpha=0.6 等），两者后端计算完全一致（同一次输出含 A/B 两版），差异只是测试页展示哪版。接口2 女性 1..5 的生发图即复用此管线。
+- ⚠️ 依赖 ComfyUI(:8188)；重绘失败在 `data.redraw.c_error` 报告。
+
+### 接口99 人脸特殊点 `/api/v1/face/special-points`（**网关本机**，worker 保留同路径备份）
+- **做什么**：人脸照 → 60 个固定编号 MediaPipe 关键点（6 组：eyebrow/eye/mouth/nose_midline/cheek/face_edge）+ 5 个发际线特殊点，原图像素坐标 JSON（字段 `<组名>_<点ID>`，如 `eyebrow_107`）。
+- **怎么实现**（`face_analysis/special_points.py`）：MediaPipe 单例取关键点；发际线点=接口1 方案B 的 BiSeNet 头发 mask 上沿指定关键点方向（如 151→10）射线步进取首个头发像素，双射线的（right/right_right/left/left_left）取中点。**纯 CPU 可跑**（BiSeNet 单核 <1s），网关直接 import 本机实现（`_if99_lock` 单路排队）；worker 侧保留同路径接口供测试页直连。部署依赖清单见 `网关待改动.md` §9。
+- 测试页：`static/test_interface99.html`（canvas 可视化 + JSON，worker 本机直连）。
+
 ---
 
 ## 3. 部署 / 环境要点
 
-**worker（GPU 机）**
-- Python **3.12**（系统 3.13 无 mediapipe/torch wheel）；venv 在 `./venv`，依赖 `requirements.txt`。
-- `numpy<2`(1.26.4)，`scikit-image==0.24.0`（**别升 0.25+，会顶 numpy≥2 顶崩 mediapipe**）。
-- ⚠️ 本机 **RTX 5090(sm_120)**，pinned `torch 2.2.2(cu121)` 只到 sm_90 → GPU 算子报 "no kernel image"，
-  代码已自动**回退 CPU**（BiSeNet/SegFormer CPU 推理可用）。要用 5090 GPU 需换 torch cu128(≥2.7)。
+**worker（GPU 机，本机 = gpu_worker 角色）**
+- Python **3.10.12**（本机 venv 在 `./venv`）；`numpy<2`(1.26.4)，`scikit-image==0.24.0`
+  （**别升 0.25+，会顶 numpy≥2 顶崩 mediapipe**）。
+- 本机 **RTX 4090**：venv 实装 `torch 2.7.1+cu128`，CUDA 可用（BiSeNet/SegFormer GPU 推理）。
+  `requirements.txt` 里 pinned 的 `torch 2.2.2(cu121)` 及 5090 注释是旧机（sm_120 无 cu121 kernel、
+  自动回退 CPU）的记录，本机以 venv 实际版本为准。
 - 模型权重/字体见 [`../OFFLINE_ASSETS.md`](../OFFLINE_ASSETS.md)；BiSeNet/SegFormer/face_landmarker.task 本地。
-- 生发接口依赖本机 **ComfyUI(8188)**（Flux-2，它自带支持 5090 的 torch）；worker 只调其 HTTP API，不跑 Flux。
-  ComfyUI 开了 **HTTP Basic Auth**（user `admin` + 密码）；密码放 `password.txt`(不入 git) /
-  `worker_config.json.comfyui_password` / 环境变量 `COMFYUI_PASSWORD`，URL 用 `COMFYUI_URL`。
-- `worker_config.json`(不入 git)：`accept_passwords`(鉴权) + 鉴权头 `X-Internal-Token`。
+- 生发接口依赖本机 **ComfyUI(8188)**（Flux-2，systemd `comfyui.service`，`--cache-classic --fast`）；
+  worker 只调其 HTTP API，不跑 Flux。ComfyUI 开了 **HTTP Basic Auth**（user `admin` + 密码）；
+  密码放 `password.txt`(不入 git) / `worker_config.json.comfyui_password` / 环境变量 `COMFYUI_PASSWORD`，
+  URL 用 `COMFYUI_URL`。默认模型 flux-2-klein-9b Q4_K_M GGUF（接口2 `flux_model` 参数可切）。
+- 换发型依赖本机 **change_hair**（`/home/ubuntu/change_hair`，三进程 webui:57860(sdwebui) →
+  photo_service:32678(py310/kohya) → hair_service_sd:8801(my_hair)，`start_all.sh start|stop|status`，
+  也有 systemd `change_hair-{webui,photo,hair}.service`）。不跑时接口2女1..5、接口11/12、
+  swapHair/hairColor 代理不可用。
+- `worker_config.json`(不入 git)：`accept_passwords`(鉴权) + `comfyui_password`；鉴权头 `X-Internal-Token`。
+- systemd 单元：`hair-worker.service`(8187) / `comfyui.service`(8188)，仓库根有副本；手动启停用
+  `./run_worker.sh` / `./stop_worker.sh`。
 
 **网关机**
 - 很轻：FastAPI/uvicorn/httpx + **接口4 的 `volcengine-python-sdk[ark]`**（或直接 httpx 调，OpenAI 兼容）。

docs/接口文档.md

@@ -20,7 +20,6 @@
 | 3 B 端生发 | POST | `/api/v1/hair/grow-b` |
 | 4 用户特征 | POST | `/api/v1/face/features` |
 | 5 发际线 PNG 生成 | POST | `/api/v1/hairline/generate` |
-| 7 C 端生发 v2 | POST | `/api/v1/hair/grow-v2` |
 
 ---
 
@@ -87,6 +86,7 @@
 | 1006 | 文件超出大小限制 | 单文件超过 1 MB |
 | 1007 | 图片参数错误 | file / url / base64 未传，或同时传了多个（三者严格互斥） |
 | 1008 | 图片格式不支持 | 非 JPG / PNG |
+| 1009 | 未授权 | 缺少或错误的 `X-Internal-Token`（`/api/*` 路径鉴权） |
 
 ---
 
@@ -109,6 +109,8 @@
 | four_courts | object | 四庭数据，见下表 |
 | seven_eyes | object | 七眼数据，见下表 |
 | landmarks | object | 关键分界点坐标（头顶 / 发际线 / 眉心 / 鼻翼下缘 / 下巴尖），原图像素坐标 |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 `four_courts`（四庭，自上而下）：
 
@@ -211,6 +213,8 @@
 | four_courts | object | 三庭数据（上/中/下庭，各含 cm 与 ratio；**无顶庭**） |
 | seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + **eye2~eye6** 共 5 段宽度） |
 | landmarks | object | 四个关键点像素坐标（发际线/眉心/鼻翼下缘/下巴尖） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 > 接口6 是**三庭五眼**：`four_courts`/`landmarks` 不含顶庭与头顶点（无 `top_court_cm`/`hair_top`）；`seven_eyes` 只含 **eye2~eye6**（左脸颊/左眼/两眼间距/右眼/右脸颊，5 段），**无 eye1/eye7**（耳外段需头发轮廓端线，仅接口1 有）。
 
@@ -261,10 +265,12 @@
 | 参数 | 类型 | 必填 | 说明 |
 |------|------|------|------|
 | gender | string | **是** | 性别：`male` / `female`。决定使用的发际线贴图集合 |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），最多不超过该性别的预设数。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界/非法返回 `1007` |
+| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），最多不超过该性别的预设数。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave, 6=bigflower, 7=clasicalflower；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight, 5=heart, 6=Softpetal, 7=wave。越界/非法返回 `1007` |
 | beauty_enabled | bool | 否 | 生发图是否带美颜效果，默认 false（当前阶段不生效） |
-| use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比 |
-| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
+| use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比。仅对原生发（male / female 6/7）管线生效 |
+| prompt | string | 否 | ComfyUI 提示词，默认「填充遮罩区域的头发，人物增加一点磨皮」，会替换工作流节点 60 的文本。所有分支生效（male / female 6/7 原生生发，female 1..5 换发型后的发际线带重绘；不进 change_hair）。注：非默认提示词会使 ComfyUI 的 CLIP 条件缓存失效，换词后首次请求明显变慢（实测 45s vs 命中缓存 8s），相同提示词的后续请求恢复正常 |
+| flux_model | string | 否 | Flux 模型文件名（如 `flux-2-klein-9b-Q5_K_M.gguf`）。缺省用工作流默认（9B-Q4_K_M）；`.gguf` 自动切 UnetLoaderGGUF 并同步配套 qwen CLIP |
+| redraw_max_side | int | 否 | female 1..5 重绘管线送模型前的长边上限像素。缺省=服务端默认（`REDRAW_MAX_SIDE`，当前 1024）；`0`=不缩图（原图直送，更慢） |
 
 ### 输出（data）
 
@@ -274,10 +280,11 @@
 |------|------|------|
 | image_url | string | 发际线曲线**透明 PNG** URL（仅白色发际线曲线，透明底，**不含人物**，需前端叠加原图显示） |
 | grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图，完整人像照片） |
-| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
+| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`/`wave`（male） |
 | order | int | 排序序号（当前阶段固定 `1..N`，按贴图顺序，暂不计算合适度） |
 
-> ⚠️ 生发图由本机 ComfyUI（Flux-2，端口 8182）生成，**一次请求生成指定发型的 1 张、同步返回**。
+> ⚠️ 生发图由本机 ComfyUI（Flux-2，端口 8188）生成，**一次请求生成指定发型的 1 张、同步返回**；
+> female 1..5 的生发图另依赖 change_hair 换发型服务（:8801）。
 > worker 侧返回 `image_base64` / `grown_image_base64`，
 > 网关落盘后改写为上表的 `image_url` / `grown_image_url`。
 >
@@ -401,10 +408,11 @@
 
 | 参数 | 类型 | 必填 | 说明 |
 |------|------|------|------|
-| gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 5 / male 4）。缺失/非法返回 `1004` |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），决定返回哪些发际线类型。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。缺失/越界/非法返回 `1007` |
+| gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 7 / male 7）。缺失/非法返回 `1004` |
+| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），决定返回哪些发际线类型。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave, 6=bigflower, 7=clasicalflower；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight, 5=heart, 6=Softpetal, 7=wave。缺失/越界/非法返回 `1007` |
 | use_mask | bool | 否 | 生发是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比 |
 | prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
+| generate_grow_image | bool | 否 | 是否生成生发效果图（ComfyUI 生发，全流程最耗时），默认 `true`。传 `false` 时跳过生发，各发型 `grown_image_*` 恒为 `null`，仅返回三档发际线叠图与中心点，可大幅降低耗时 |
 
 > ⚠️ 三档叠图分别用 `hairline_texture` / `hairline_texture_high` / `hairline_texture_low` 三套同名贴图；**生发黑模板固定取自 `hairline_texture_black/`（middle 档）**，即生发目标固定压到 middle 档，每个发型仅 1 张生发图。
 
@@ -422,11 +430,11 @@
 
 | 字段 | 类型 | 说明 |
 |------|------|------|
-| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
+| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`/`wave`（male） |
 | image_middle_url | string | middle 档发际线曲线**透明 PNG** URL（仅曲线，透明底，**不含人物**，需叠加原图显示） |
 | image_high_url | string | high 档发际线曲线**透明 PNG** URL（同上，high 档曲线） |
 | image_low_url | string | low 档发际线曲线**透明 PNG** URL（同上，low 档曲线） |
-| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，完整人像照片，生发失败时为 `null`） |
+| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，完整人像照片，生发失败或 `generate_grow_image=false` 时为 `null`） |
 | order | int | 发型序号（= 传入的 hair_style 值） |
 
 > worker 侧返回 `image_middle_base64` / `image_high_base64` / `image_low_base64` / `grown_image_base64`，网关落盘后改写为上表对应的 `*_url`。
@@ -441,8 +449,10 @@
 | four_courts | object | 四庭数据（顶/上/中/下庭 cm + 占比 ratios），结构同接口1 |
 | seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + eye1~eye7 从左到右 7 段宽度），结构同接口1 |
 | landmarks | object | 5 个纵向关键点像素坐标（hair_top/hairline/brow_center/nose_bottom/chin_tip），结构同接口1 |
-| hairline_source | string | 发际线来源：`segmentation`（真实分割）/ `estimated`（比例估算） |
+| hairline_source | string | 发际线来源：`segmentation`（真实分割）/ `estimated`（比例估算）/ `corrected`（发际线贴近头顶、分割不可靠，已按 0.95×中庭反推修正） |
 | head_pose | object | 头部姿态角度（yaw/pitch/roll，单位：度） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 > `eye1`~`eye7` 为从左到右共 7 段宽度，eye1=左耳外段、eye7=右耳外段，某侧耳朵不可见时对应段为 `null`。详见接口1说明。
 
@@ -508,71 +518,197 @@
 
 ---
 
-## 接口 7：C 端生发 v2 接口
+## 接口 9：头发遮罩生成接口
 
-**说明**：功能与[接口 2](#接口-2c-端生发接口)完全一致，仅 ComfyUI 工作流不同——使用 `add_hair2.json` 替代 `add_hair.json`。
+**说明**：输入一张含人头的照片，生成头发遮罩，并返回**每一步的可视化图**（供对比调试）。
 
-**请求**：`POST /api/v1/hair/grow-v2`
+**请求**：`POST /api/v1/head/mask`
 
 ### 输入
 
-与接口 2 完全相同。图片参数见「通用约定 → 图片传参字段」。专属参数：
+图片参数见「通用约定 → 图片传参字段」。专属参数：
 
 | 参数 | 类型 | 必填 | 说明 |
 |------|------|------|------|
-| gender | string | **是** | 性别：`male` / `female`。决定使用的发际线贴图集合 |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界/非法返回 `1007` |
-| beauty_enabled | bool | 否 | 生发图是否带美颜效果，默认 false（当前阶段不生效） |
-| use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线） |
-| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
+| erode_cm | float | 否 | 外缘朝中心（鼻梁 151 点）内缩的距离（厘米），默认 1.2 |
+
+### 处理流程
+
+1. MediaPipe 关键点 → 底部分割线（关键点 21,68,104,69,108,151,337,299,333,298,251 的连线，21 水平延伸到最左、251 延伸到最右）。
+2. 分割线以上为「上半区」。
+3. 头发分割（**BiSeNet** 与 **SegFormer** 两套）；从每列最顶端头发向下填充到分割线，得到**含额头**的闭合区域（头发+额头，不割断）。
+4. 外缘朝中心点 151 内缩 `erode_cm`（虹膜标定换算像素）、底线不动 → 最终遮罩。
 
 ### 输出（data）
 
-与接口 2 完全相同。`results`：发际线方案数组，**数量 = 所选发型数**。每个元素：
-
 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 发际线曲线**透明 PNG** URL（仅曲线，透明底，**不含人物**，需叠加原图显示） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图，完整人像照片） |
-| hairline_type | string | 发际线类型 key |
-| order | int | 排序序号 |
+| px_per_cm | float | 虹膜标定出的像素/厘米 |
+| erode_cm / erode_px | float / int | 内缩距离（厘米 / 像素） |
+| image_size | object | `{width, height}` 原图尺寸 |
+| steps_common | object | `landmarks_baseline_*`（关键点/分割线）、`upper_region_*`（上半区） |
+| bisenet / segformer | object | 两套分割模型各自的结果：`hair_mask_*`（原始头发分割）、`closed_region_*`（含额头闭合区域）、`final_overlay_*`（最终遮罩叠加可视化）、`mask_*`（最终遮罩 PNG）及 `hair_pixels`/`closed_pixels`/`mask_pixels` 像素计数 |
 
-> ⚠️ 与接口 2 的区别：本接口使用 `add_hair2.json` 工作流（Flux-2 Klein 9b），输入/遮罩节点同为 26，
-> SaveImage 输出节点为 75。
+> worker 返回 `*_base64`（data URI），经网关改写为 `*_url`。
 
-### 响应示例
+---
 
-```json
-{
-  "code": 0,
-  "message": "success",
-  "request_id": "mock-request-id",
-  "data": {
-    "results": [
-      {
-        "image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-        "grown_image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-        "hairline_type": "ellipse",
-        "order": 1
-      }
-    ]
-  }
-}
-```
+## 接口 10：头部外缘膨胀带遮罩接口
+
+**说明**：先和接口9 一样得到内缩后的基准遮罩，再沿头部外缘生成一条膨胀带遮罩。
+
+**请求**：`POST /api/v1/head/band`
+
+### 输入
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| erode_cm | float | 否 | 基准遮罩外缘朝 151 内缩距离（厘米，同接口9），默认 1.2 |
+| dilate_cm | float | 否 | 外轮廓线膨胀后带子总宽（厘米），默认 2.0 |
+
+### 处理流程
+
+1. 取基准遮罩的**外轮廓线**，去掉贴着底部分割线的那一段（只留头发/头部外缘弧线）。
+2. 把外轮廓线膨胀成带子（总宽 `dilate_cm`；半径=总宽/2，虹膜标定换算像素）。
+3. 裁到分割线以上（不越过底线）。
+
+### 输出（data）
+
+结构同接口9，但每组结果为 `base_mask_*`（基准遮罩）/ `contour_*`（外轮廓线）/ `band_overlay_*`（带子叠加可视化）/ `mask_*`（最终带状遮罩）及 `base_pixels`/`band_pixels` 计数，另有 `dilate_cm`/`dilate_radius_px`。
+
+---
+
+## 接口 11：发际线生发接口（不含重绘）
+
+**说明**：输入一张**发际线较高 / 头发稀少**的正脸图 + 发际线类型 ID，输出同一个人、同一发型、按该类型压低发际线后的图片，并返回**每一步可视化**。重绘版见接口12。
+
+**请求**：`POST /api/v1/hairline/grow`
+
+### 输入
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| hairline_id | string | **是** | 发际线类型 ID（= change_hair `hair_id`）：`chang_tuoyuan` / `chang_huaban` / `chang_xinxing` / `chang_zhixian` / `chang_bolang` |
+| gen_backend | string | 否 | 生成后端：`swaphair`（换发型 LoRA，默认）｜`hairgrow`（区域生发 inpaint） |
+| hairgrow_strength | float | 否 | 区域生发强度（仅 hairgrow 后端），默认 0.75 |
+| is_hr | bool | 否 | 高清模式（换发型输出 1152×1536，否则 576×768），默认 false |
+| seg_model | string | 否 | 头发分割模型：`bisenet` ｜ `segformer`（默认） |
+| erode_cm | float | 否 | baseline 参考内缩距离（厘米），默认 0.6 |
+| hairline_push_cm | float | 否 | 发际线外推距离（厘米，往头发方向推进），默认 1.0 |
+| hairline_edge | string | 否 | 发际线提取方式：`column`（默认）｜`contour` |
+| swap_mode | string | 否 | 换发型取图模式：`ext_mask`（默认，把接口9 遮罩传给 webui 精确重绘）｜`as_is`（用内部遮罩，贴回时再裁） |
+| edge_erode_px | int | 否 | 贴图前遮罩内缩像素（防边缘露皮/光晕），默认 3 |
+| denoising_strength | float | 否 | 换发型 webui 重绘强度（越大生发越激进），默认 0.6 |
+| mb_levels | int | 否 | 多频段金字塔层数（2~6），默认 5 |
+| blend_method | string | 否 | 接缝融合：`multiband`（默认）｜`seamless`（泊松）｜`two_stage`（泊松→多频段）｜`feather`（高斯羽化）｜`alpha_gradient`（距离渐变） |
+| color_match | bool | 否 | 融合前 Reinhard 颜色迁移消除整体色差，默认 true |
+| color_match_strength | float | 否 | 颜色迁移强度（0~1），默认 1.0 |
+| mb_feather_px | int | 否 | 多频段最细层掩码轻羽化像素，默认 1 |
+| transition_band_px | int | 否 | keep-region 过渡带边距（-1=自动按层数 2^n），默认 -1 |
+| inpainting_fill | int | 否 | change_hair 服务端重绘填充：0=保留原图 ｜ 1=噪声（默认）｜ 2=纯色 ｜ 3=潜变量噪声 |
+| mask_blur | int | 否 | change_hair 服务端遮罩边缘模糊像素，默认 11 |
+| mask_dilate_scale | float | 否 | change_hair 服务端遮罩膨胀缩放（<1 收缩防越界），默认 1.0 |
+
+### 输出（data）
+
+`steps`：`input`（原图）/ `baseline`（底部分割线）/ `hair_mask`（头发分割）/ `mask_overlay`（遮罩叠加）/ `mask`（遮罩）/ `swap_raw`（换发型原始输出）/ `alpha`（融合权重）/ `final`（最终结果）各步图（`*_base64`，经网关改写为 `*_url`），另有 `timings_ms` 分步耗时。
+
+> ⚠️ `swaphair` 后端依赖 change_hair（:8801）在跑；`hairgrow` 后端依赖 ComfyUI（:8188）。
+
+---
+
+## 接口 12：发际线带重绘接口
+
+**说明**：接口11 的**下游重绘阶段**——内部先跑接口11 核心管线拿 ④ 接缝融合最终图（final），再取发际线重绘带（外推 `band_lo_mult×push` ~ `band_hi_mult×push` 之间、经 baseline 截断只留上部的带状区域）作遮罩，用 **Flux-2（ComfyUI，reference-latent 保色重绘）** 重绘。
+
+**同时产出两版结果**：
+
+- `redraw_full`：ComfyUI 整帧输出（全脸美颜 + 全脸重绘）。
+- `redraw_band`：加发只在发际线带内、band 外保留 final 结构并按 `beauty_alpha` 融入全脸美颜。
+
+**请求**：`POST /api/v1/hairline/grow_v2`
+
+### 输入
+
+接口11 的全部参数（见上表）再叠加：
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| comfyui_prompt | string | 否 | Flux-2 重绘提示词，缺省「填充遮罩区域的头发，人物增加一点磨皮」 |
+| beauty_alpha | float | 否 | redraw_band 版 band 外的全脸美颜融入强度（0=band 外纯用 final，1≈整帧版），默认 0.6 |
+| band_lo_mult | float | 否 | 重绘带外推倍率下限（相对 hairline_push_cm），默认 0.5 |
+| band_hi_mult | float | 否 | 重绘带外推倍率上限（相对 hairline_push_cm），默认 1.5 |
+
+### 输出（data）
+
+`steps`：`input` / `final`（重绘输入基底）/ `redraw_band_overlay`（重绘带可视化）/ `redraw_full`（A 整帧）/ `redraw_band`（B 局部+美颜）/ `redraw_c`（兼容旧字段，=redraw_full）；重绘异常在 `redraw.c_error` 报告。
+
+### 精简变体（生产推荐）
+
+| 路径 | 说明 |
+|------|------|
+| `POST /api/v1/hairline/grow_v2_final` | 仅需图片 + `hairline_id`，其余参数固化为调优默认值（push=0.8cm / two_stage / color_match=关 等），**最终取整帧重绘 `redraw_full`** |
+| `POST /api/v1/hairline/grow_v2_final_v2` | 参数同上，**最终取 B 局部加发+全脸美颜 `redraw_band`** |
+
+> ⚠️ 依赖 ComfyUI（:8188）与 change_hair（:8801）均在跑。接口2 女性 1..5 的生发图即复用 `grow_v2_final` 管线。
+
+---
+
+## 接口 99：人脸特殊点检测接口
+
+**说明**：输入一张人脸照片，返回一组**固定编号 MediaPipe Face Mesh 关键点**的像素坐标，外加 5 个**发际线特殊点**（射线与头发分割发际线的交点）。**生产路径：网关本机实现（纯 CPU，不转发 worker，同接口4 模式）**；worker 侧保留同路径接口供测试页直连。
+
+**请求**：`POST /api/v1/face/special-points`
+
+### 输入
+
+图片参数见「通用约定 → 图片传参字段」，无专属参数。要求单人正面照（非正面返回 `1003`）。
+
+### 输出（data）
+
+`points`：字段名 = `<组英文名>_<点ID>`，值为原图像素坐标 `{x, y}`（int）；发际线点不可得（秃顶/分割失败）时为 null。
+
+| 组 | 字段前缀 | 点 ID |
+|----|----------|-------|
+| 眉毛 | `eyebrow` | 107 66 105 63 70 336 296 334 293 300 |
+| 眼睛 | `eye` | 189 222 224 113 226 413 442 444 342 446 |
+| 嘴 | `mouth` | 0 37 40 67 91 84 17 405 375 409 269 |
+| 中线鼻 | `nose_midline` | 9 168 197 5 4 194 278 48 437 217 |
+| 面颊 | `cheek` | 101 117 123 187 205 50 330 346 352 280 425 |
+| 边线 | `face_edge` | 162 234 58 136 149 152 378 365 288 401 366 389 251 |
+
+**发际线特殊点**（沿关键点 a→b 方向射线步进，首次进入 BiSeNet 头发 mask 处为交点；双射线取中点，仅一条命中返回该点）：
+
+| 字段 | 计算 |
+|------|------|
+| `hair_line_center` | 151→10 方向交点 |
+| `hair_line_right` | mid(108→109 交点A, 69→67 交点B) |
+| `hair_line_right_right` | mid(104→103 交点A, 68→54 交点B) |
+| `hair_line_left` | mid(337→338 交点A, 299→297 交点B) |
+| `hair_line_left_left` | mid(333→332 交点A, 298→284 交点B) |
+
+另有 `image_size`（宽高）与 `hairline_source`（`segmentation`=发际线点已算出 / `unavailable`=分割不可用，发际线点为 null）。
 
 ---
 
 ## 汇总：输入输出一览
 
-| 接口 | 输入 | 主要输出 |
-|------|------|----------|
-| 1 四庭七眼测量 | 用户照片 | 标注 PNG（无人物）+ 四庭/七眼厘米数值与坐标 |
-| 6 四庭七眼测量 v2 | 用户照片 | 同接口1，复刻实现 |
-| 2 C 端生发 | 用户照片 | 生发后图片 + 指定发际线预览（单/多张） |
-| 3 B 端生发 | 划线图片 | 最合适发际线图片 + 生发后图片 |
-| 4 用户特征 | 用户照片 | 6 个用户特征字段（脸形/眉形/年龄/动静/性别/基因风格） |
-| 5 发际线 PNG | 用户照片 + gender + hair_style（多选） | 每个选中发型 middle/high/low 三档发际线叠图 + 生发图 + 最合适发际线面部中间点坐标 |
-| 7 C 端生发 v2 | 用户照片 + gender + hair_style | 同接口2，使用 add_hair2.json 工作流 |
+| 接口 | 路径 | 输入 | 主要输出 |
+|------|------|------|----------|
+| 1 四庭七眼测量 | `/api/v1/face/measure` | 用户照片 | 标注 PNG（无人物）+ 四庭/七眼厘米数值与坐标 |
+| 6 四庭七眼测量 v2 | `/api/v1/face/measure-v2` | 用户照片 | 同接口1，去顶庭变体 |
+| 2 C 端生发 | `/api/v1/hair/grow` | 用户照片 + gender + hair_style（多选） | 每发型预览 PNG + 生发图（female 1..5=换发型+重绘，其余=原生生发） |
+| 7 C 端生发 v2 | `/api/v1/hair/grow-v2` | 同接口2 | 同接口2（add_hair2 工作流，已弃用隐藏） |
+| 3 B 端生发 | `/api/v1/hair/grow-b` | 划线图片 | 检测到的发际线 + 生发后图片 |
+| 4 用户特征 | `/api/v1/face/features` | 用户照片 | 6 个用户特征字段（脸形/眉形/年龄/动静/性别/基因风格），网关本机实现 |
+| 5 发际线 PNG | `/api/v1/hairline/generate` | 用户照片 + gender + hair_style（多选） | 每个选中发型 middle/high/low 三档发际线叠图 + 生发图 + 最合适发际线面部中间点坐标 |
+| 9 头发遮罩 | `/api/v1/head/mask` | 用户照片 | 头发遮罩 + 分步可视化（BiSeNet/SegFormer 双路） |
+| 10 外缘膨胀带 | `/api/v1/head/band` | 用户照片 | 头部外缘带状遮罩 + 分步可视化 |
+| 11 发际线生发 | `/api/v1/hairline/grow` | 用户照片 + hairline_id | 压低发际线图 + 全步骤可视化（不含重绘） |
+| 12 发际线带重绘 | `/api/v1/hairline/grow_v2` | 同接口11 + 重绘带参数 | final 基底 + Flux-2 保色重绘（整帧/局部两版）；精简版 `grow_v2_final[_v2]` 仅需图片+hairline_id |
+| 99 人脸特殊点 | `/api/v1/face/special-points` | 用户照片 | 60 个固定编号关键点 + 5 个发际线特殊点像素坐标 JSON（**网关本机实现**，同接口4） |
+
+> 另有 worker 本机工具路由（不在对外契约内）：`/api/swapHair/v1`、`/hairColor/v2`（CORS 代理 change_hair）、`/api/v1/redraw`（直接 ComfyUI 重绘）、`/api/v1/debug/*`。
 
 ---
 
@@ -582,7 +718,7 @@
 - 默认单人；检测到 2~3 人报错提示客户端；单文件 **≤ 1 MB**；格式仅 **JPG / PNG**。
 - 所有坐标基准：**原图像素**。
 - 接口 2：提供「美颜」开关 `beauty_enabled`。
-- 接口 3：需同时上传原图，需返回发际线类型。
+- 接口 3：仅传**划线图**（`marked_image_*` 三选一），需返回发际线类型（`custom`）。
 - 接口 4：`data` 返回**一个 JSON 字符串**，字段不固定，可随时调整。
 - 接口 5：发际线 PNG 数量 **N 不固定**，需按合适度**排序**。
 - 英文字段命名由本文档约定（见各接口表格）。

docs/网关待改动.md

@@ -70,7 +70,7 @@ async def hair_grow_v2(request: Request):
 |------|------|------|------|
 | image_file / image_url / image_base64 | — | **三选一** | 用户正面照 |
 | gender | string | **是** | `male` / `female` |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female: 1–5，male: 1–4 |
+| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female / male 均为 1–7 |
 | beauty_enabled | bool | 否 | 美颜开关（本期不生效） |
 | use_mask | bool | 否 | 默认 `true`，`false` 跳过遮罩 |
 | prompt | string | 否 | ComfyUI 提示词 |
@@ -135,6 +135,104 @@ async def face_measure_v2(request: Request):
 
 ---
 
+## 7. ✅【已完成】接口9/10 头部遮罩（`/api/v1/head/mask`、`/api/v1/head/band`）
+
+`gateway/app.py` 已新增两条盲转发路由（同接口6 的写法）：
+
+```python
+@app.post("/api/v1/head/mask", tags=["人脸分析"])
+@app.post("/api/v1/head/band", tags=["人脸分析"])
+```
+
+- 入参：图片三选一 + `erode_cm`（接口10 另有 `dilate_cm`）。
+- 出参：`steps_common.*_base64` 与 `bisenet`/`segformer` 组内的 `*_base64` 字段——现有递归
+  `rewrite_base64_to_url` 已覆盖（嵌套 dict/数组），无需额外改动。
+- 两个接口响应较慢（双分割模型跑两遍），注意 `request_timeout_seconds`。
+
+---
+
+## 8. 🔲【新增】接口11/12 发际线生发/带重绘（`/api/v1/hairline/grow*`）
+
+worker 侧已上线 4 条路由，**网关尚无对应转发路由**（当前仅测试页直连 worker）。如需对外提供，参考接口9 加法：
+
+```python
+@app.post("/api/v1/hairline/grow", tags=["生发"])            # 接口11
+@app.post("/api/v1/hairline/grow_v2", tags=["生发"])         # 接口12 全参数
+@app.post("/api/v1/hairline/grow_v2_final", tags=["生发"])   # 接口12 final 精简版
+@app.post("/api/v1/hairline/grow_v2_final_v2", tags=["生发"])
+```
+
+- 入参：图片三选一 + `hairline_id`（接口12 final 两变体仅需这两个）。接口11/12 全参数版还有
+  20+ 个调优 Form 参数，网关盲转发即可，无需逐个声明。
+- 出参：`data.steps.*_base64`（input/final/redraw_full/redraw_band 等各步图）→ 现有递归改写覆盖。
+- ⚠️ 依赖 worker 机的 change_hair(:8801) 与 ComfyUI(:8188) 都在跑；单请求 10s+，超时建议 ≥120s。
+- 契约细节见 [`接口文档.md`](接口文档.md) 接口11/12 章节。
+
+---
+
+## 9. ✅【新增】接口99 人脸特殊点（`/api/v1/face/special-points`）— **网关本机实现，不转发 worker**
+
+**背景**：接口99（固定编号 MediaPipe 关键点 6 组 60 点 + 5 个发际线特殊点，返回像素坐标 JSON）
+**纯 CPU 可跑**（MediaPipe 本就 CPU；BiSeNet 分割单核实测 <1s）且调用频率低。
+按约定：**不用 GPU、低频的接口默认网关本机实现**，不占 worker 队列
+（worker 池 `per_worker_concurrency=1`，转发会排在生发任务后面等）。
+
+**网关代码已在本仓库 `gateway/app.py` 改好**——与接口4 同款本机实现（显式 Form 参数 +
+惰性导入 + `run_in_threadpool` + `gw-` request_id），直接 import 同仓库的
+`face_analysis/special_points.detect_special_points`。**单路排队**：`_if99_lock` 全局互斥，
+同一时刻只跑一路推理（限制 CPU 峰值 + 规避 MediaPipe 单例线程安全问题），请求 FIFO 排队。
+
+### 网关机部署前检查
+
+1. **Python 3.10~3.12**（mediapipe 0.10.14 无 3.13 wheel）。
+2. **系统库**（无 GUI 服务器跑 opencv/mediapipe 必需，已装可跳过）：
+
+```bash
+sudo apt install -y libgl1 libglib2.0-0 libegl1
+```
+
+3. **安装依赖**（网关 venv，国内镜像）：
+
+```bash
+pip install -i https://pypi.tuna.tsinghua.edu.cn/simple \
+    mediapipe==0.10.14 opencv-python==4.10.0.84 numpy==1.26.4
+
+# 可选：BiSeNet 头发分割（发际线 5 点需要）。CPU 版 torch 仅 ~200MB：
+pip install torch==2.2.2 torchvision==0.17.2 --index-url https://download.pytorch.org/whl/cpu
+# 不装 torch 也能跑：60 个关键点正常返回，发际线 5 点为 null（hairline_source="unavailable"）
+# ⚠️ 网关无 GPU——务必用上面的 CPU 专用源装 torch；装默认 PyPI 版会带 2.5G 的 CUDA 包，浪费但也能跑（自动回退 CPU）
+```
+
+4. **权重已入 git**，`git clone/pull` 自动到位：`face_analysis/weights/79999_iter.pth`（51MB）。
+   无需手动下载任何模型文件。
+5. **无 GPU 已实测**：`CUDA_VISIBLE_DEVICES=""` 模拟验证通过（70/70 点，与 GPU 结果一致）；
+   CPU 推理 16 核 0.18s/次，网关机核少时约 0.5~2s/次，低频可接受。
+
+### 部署与冒烟
+
+```bash
+cd /path/to/hair && git pull
+sudo systemctl restart hair-gateway   # 或网关实际服务名（uvicorn gateway.app:app）
+
+# 冒烟（multipart 传图；经网关无需 X-Internal-Token）
+curl -s -X POST http://127.0.0.1:8080/api/v1/face/special-points \
+  -F "image_file=@/tmp/face.jpg" | python3 -m json.tool | head -30
+# 期望 code=0；data.points 含 eyebrow_107 等 60 个关键点 + hair_line_* 5 点
+# 缺依赖时返回 code=1007 且 message 提示"网关依赖未安装"
+```
+
+### 注意
+
+- **超时无需调大**：单路 CPU 推理 <1s；即使排队，低频下积压可忽略。排队本身无超时，
+  调用方按需自带客户端超时。
+- **错误码透传契约**：1001（无人脸）/ 1003（非正面）/ 1007（参数/处理失败/缺依赖）/
+  1008（图片格式/下载失败），HTTP 一律 200。
+- **worker 侧同路径接口保留**（`app.py`），供测试页 `static/test_interface99.html`
+  直连调试（需 token）；生产路径是网关本机实现。
+- 契约详见 [`接口文档.md`](接口文档.md) 接口99 章节。
+
+---
+
 ## 已经做好、无需再动的
 
 - **接口4 在网关本机实现**（调豆包，不转发 worker）——已完成；config 里配 `ark`。

docs/网关摘除隔离期实施方案.md

@@ -0,0 +1,234 @@
+# 网关摘除隔离期实施方案（方案 B）
+
+> 目标读者：网关机器的部署/维护者。本文档给出基于 **main 分支** `gateway/` 代码的
+> 完整改动说明，可直接照做。**不需要改 worker 侧任何代码。**
+
+## 1. 背景与事故复盘
+
+2026-09-19 晚，GPU worker 机器（ubuntu3090_debug_4090 所在主机）磁盘写满，业务接口
+全部失败，但网关**持续把请求打到该机器近 16 个小时**（21:30 故障发生 → 次日 13:36
+人工重启），没有切换到其他 worker。复盘结论是三层防线同时失效：
+
+| 层 | 问题 | 后果 |
+|---|---|---|
+| 1. worker `/health` 太浅 | 只返回内存里的"模型就绪"标志，磁盘满/下游 ComfyUI 挂掉不影响它 | 探测一直 200 |
+| 2. 业务错误不触发摘除 | 下游出错经包装后以 HTTP 200/400 + 错误码返回，`forward.py` 只对 连接异常/超时/≥500/401 摘除 | 请求失败但 worker 被视为健康 |
+| 3. 摘除后 8 秒即复活（**本方案要修的**） | `mark_worker_unhealthy()` 下线后，下一轮探测（8s 后）`/health` 返回 200，`healthy_threshold=1` 使其**一次成功立即重新上线** | 即使被摘除也会反复横跳，流量持续打入 |
+
+worker 侧的第 1 层已经修复（方案 A：`/health` 深检磁盘水位 + 下游依赖 TCP 探测，
+异常返回 503，代码在分支 `ubuntu3090_debug_4090`，提交 `14562d4`）。本方案修复
+第 3 层：**被动摘除后进入隔离期，隔离期内即使探测成功也不复活**。两层配合后：
+worker 磁盘满 → `/health` 503 → 网关 2 次探测失败摘除 → 隔离期内不再接流量。
+
+> 第 2 层（错误码语义化，涉及调用方兼容）暂不处理，单独立项。
+
+## 2. 现状代码行为（main 分支 gateway/pool.py）
+
+- `mark_worker_unhealthy(w)`：被 `forward.py` 在转发失败（连接异常/超时/5xx/401）时
+  调用，置 `w.online = False`。**没有任何冷却机制**。
+- `_health_check_loop(cfg)`：每 `interval_seconds`（默认 8s）探测所有 worker 的
+  `/health`；返回 200 时 `consecutive_successes += 1`，达到 `healthy_threshold`
+  （默认 **1**）且当前 offline → 立即 `online = True`。
+- 因此被动摘除最多 8 秒后就被一次探测成功拉回——如果 worker 是"浅健康"或故障
+  间歇性表现，就会陷入 摘除→复活→失败→摘除 的横跳。
+- 另外 58-96 行有一段**从未被调用的死代码** `_check_worker_health()` /
+  `_mark_failure()`（写了一半的旧实现），本次顺带删除，避免后人改错地方。
+
+## 3. 代码改动（共 2 个文件 + 1 个配置）
+
+### 3.1 `gateway/pool.py`
+
+**(1) `WorkerState` 增加一个字段：**
+
+```python
+@dataclass
+class WorkerState:
+    """单个 worker 的运行时状态。"""
+    url: str
+    online: bool = False  # 初始 offline，等健康检查通过后上线
+    busy: bool = False
+    consecutive_failures: int = 0
+    consecutive_successes: int = 0
+    offline_until: float = 0.0  # 被动摘除后的隔离截止时刻（time.monotonic() 基准）
+```
+
+**(2) 模块顶部（`logger = ...` 之后）加一个配置读取 helper：**
+
+```python
+def _cooldown_seconds() -> float:
+    """被动摘除后的隔离期时长（秒），配置 health_check.cooldown_seconds，默认 60。"""
+    try:
+        from gateway.config import get_config
+        return float(get_config()["health_check"].get("cooldown_seconds", 60))
+    except Exception:  # noqa: BLE001
+        return 60.0
+```
+
+（函数内 import 是为了不引入模块级循环依赖；`gateway/config.py` 不依赖 pool，顶层
+import 也可以。）
+
+**(3) `mark_worker_unhealthy()` 摘除时同时设置隔离期：**
+
+```python
+async def mark_worker_unhealthy(w: WorkerState) -> None:
+    """被动标记：转发失败时立即将该 worker 下线，并进入隔离期。
+
+    隔离期内即使 /health 探测成功也不复活（由 _health_check_loop 守卫），
+    避免"摘除→8 秒复活→再失败"的横跳。
+    """
+    async with _pool_condition:
+        if w.online:
+            w.online = False
+            w.consecutive_failures += 1
+            w.offline_until = time.monotonic() + _cooldown_seconds()
+            logger.warning("⚠ Worker 被动下线: %s（转发失败，隔离 %.0fs）",
+                           w.url, _cooldown_seconds())
+```
+
+**(4) `_health_check_loop()` 的探测成功分支加隔离期守卫。** 找到这段：
+
+```python
+                if resp.status_code == 200:
+                    w.consecutive_failures = 0
+                    w.consecutive_successes += 1
+                    if w.consecutive_successes >= healthy_threshold and not w.online:
+                        w.online = True
+                        logger.info("✅ Worker 上线: %s", w.url)
+```
+
+改为：
+
+```python
+                if resp.status_code == 200:
+                    w.consecutive_failures = 0
+                    w.consecutive_successes += 1
+                    if w.consecutive_successes >= healthy_threshold and not w.online:
+                        if time.monotonic() < w.offline_until:
+                            logger.debug("Worker %s 探测成功但处于隔离期，暂不复活", w.url)
+                        else:
+                            w.online = True
+                            logger.info("✅ Worker 上线: %s", w.url)
+```
+
+注意：`except`（连接异常）分支只累计失败、不涉及复活，无需改动；
+`init_pool()` 启动时的首轮探测同理无需改动（进程刚启动时 `offline_until` 为 0，
+不构成隔离）。
+
+**(5) 删除死代码**：`_check_worker_health()` 与
+`_mark_failure()` 两个函数整体删除（约 58-96 行，以函数名为准，别删到
+`_health_check_loop`）。删除前可确认无调用方：
+`grep -rn "_check_worker_health\|_mark_failure" .` 应只剩定义处。
+
+### 3.2 `gateway/config.py`
+
+`DEFAULTS["health_check"]` 增加一个 key：
+
+```python
+    "health_check": {
+        "path": "/health",
+        "interval_seconds": 8,
+        "timeout_seconds": 3,
+        "unhealthy_threshold": 2,
+        "healthy_threshold": 1,
+        "cooldown_seconds": 60,   # ← 新增：被动摘除后的隔离期（秒）
+    },
+```
+
+`config.example.json` 同步加这个 key。网关机器的实际 `config.json` **不加也不会报错**
+（代码里有 60 的默认兜底），但建议显式加上，便于运维感知。
+
+### 3.3 建议单测：`tests/test_gateway_pool.py`（新文件）
+
+gateway 子包目前没有任何测试。隔离期是纯逻辑，建议至少覆盖以下三个用例
+（不依赖真实网络）：
+
+```python
+"""gateway/pool.py 隔离期逻辑单测。"""
+import asyncio
+import time
+from unittest.mock import patch
+
+from gateway import pool
+from gateway.pool import WorkerState
+
+
+def _drain(coro):
+    return asyncio.run(coro)
+
+
+def test_passive_ejection_sets_cooldown():
+    w = WorkerState(url="http://127.0.0.1:9000", online=True)
+    async def run():
+        pool._pool_condition = asyncio.Condition()
+        with patch.object(pool, "_cooldown_seconds", return_value=60.0):
+            await pool.mark_worker_unhealthy(w)
+    _drain(run())
+    assert w.online is False
+    assert w.offline_until > time.monotonic()
+
+
+def test_probe_success_revives_after_cooldown():
+    w = WorkerState(url="http://127.0.0.1:9000")  # offline
+    w.offline_until = time.monotonic() - 1        # 隔离期已过
+    # 直接验证状态迁移逻辑（若按 3.1(4) 内联实现，可复制该分支逻辑做断言，
+    # 或将判断抽成 _try_revive(w, healthy_threshold) 小函数后单测）
+    ...
+
+
+def test_probe_success_blocked_during_cooldown():
+    w = WorkerState(url="http://127.0.0.1:9000")
+    w.offline_until = time.monotonic() + 60
+    ...  # 断言保持 offline
+```
+
+> 提示：如果按 3.1(4) 原样内联修改，后两个用例不好直接驱动，可以把"是否允许复活"
+> 抽成一个 6 行的纯函数 `_try_revive(w, healthy_threshold) -> bool` 再测，主循环
+> 调用它即可——实现方式自定，覆盖语义即可。
+
+## 4. 部署步骤（网关机器）
+
+1. `git pull` main 分支（含本方案代码改动）。
+2. （可选但建议）`config.json` 的 `health_check` 段加 `"cooldown_seconds": 60`。
+3. 重启网关服务（`hair-gateway.service` 或等价的 uvicorn 进程）。重启期间网关
+   本身短暂不可用，选低峰执行。
+4. 观察启动日志：`健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=N`
+   正常出现。
+
+## 5. 验证
+
+**功能验证（低峰演练）：**
+
+1. 人工把主 worker 摘除一次最直接的方法：临时在 worker 机器
+   `systemctl stop hair-worker`（或临时设 `HEALTH_MIN_FREE_GB=999` 重启）。
+2. 观察网关日志：
+   - 连续 2 次探测失败 → `⚠ Worker 下线`（原有行为）；
+   - **转发失败触发的** `⚠ Worker 被动下线: ...（转发失败，隔离 60s）`（新日志）；
+   - 恢复 worker 后，若刚被被动摘除过，即使 `/health` 恢复 200，也应看到 60 秒内
+     **不出现** `✅ Worker 上线`，隔离期满后下一次探测成功才上线。
+3. `GET /gateway-health` 观察 `workers_healthy` 的变化节奏。
+
+**回归确认：** 正常场景 worker 上下线不受影响（隔离期只对"被动摘除"生效；
+探测失败导致的下线，恢复后仍按 `healthy_threshold` 立即上线）。
+
+## 6. 回滚
+
+- 代码回滚：`git revert` 对应提交即可，无数据/配置迁移。
+- 紧急关闭隔离期：网关机器 `config.json` 设 `"cooldown_seconds": 0`
+  （等价于旧行为——摘除后下一次探测成功即复活），无需回代码。
+
+## 7. 附：worker 侧方案 A 的行为（已上线，供联调参考）
+
+worker（分支 `ubuntu3090_debug_4090`，提交 `14562d4`）的 `/health` 现在会在以下
+情况返回 **503**（网关 2 次探测失败即摘除）：
+
+- 模型加载中：`503 {"status":"starting"}`（原有行为不变）
+- 磁盘剩余低于阈值：`503 {"status":"degraded","reason":"disk_free_below_5.0g","disk_free_gb":...}`
+- 下游依赖（ComfyUI 8188 / change_hair 8801）TCP 不通：
+  `503 {"status":"degraded","reason":"dep_down:comfyui,...","deps":{...}}`
+
+正常时返回 `200 {"status":"ok","disk_free_gb":24.6,"deps":{"comfyui":true,"change_hair":true}}`。
+
+worker 侧配置（`worker_config.json` 的 `health` 段，环境变量可覆盖）：
+`min_free_gb`（默认 5，0=关闭）、`check_deps`（默认 true）、`dep_comfyui`（默认
+127.0.0.1:8188）、`deps_cache_seconds`（默认 5）。**其他 worker 机器如果也想启用
+深检，cherry-pick 该提交并补一份 worker_config.json 的 health 段即可。**

face/build_dataset_report.py

@@ -0,0 +1,506 @@
+"""
+build_dataset_report.py
+对任意图片目录（可含多层子目录）批量预测脸型并生成 HTML 报告。
+保留图片原始所属的子目录名作为「分组」，在报告中按分组展示与统计。
+
+用法:
+    ./venv/bin/python face/build_dataset_report.py --src <图片目录> [--sample 50] [--seed 42]
+
+示例:
+    ./venv/bin/python face/build_dataset_report.py \
+        --src face/test_img/脸型测试集合 --sample 50 --name 脸型测试集合
+
+输出:
+    static/<slug>_report.html
+    static/<slug>_report/images/*.jpg
+"""
+
+from __future__ import annotations
+
+import argparse
+import html
+import random
+import re
+import shutil
+import sys
+import unicodedata
+from collections import Counter, defaultdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List
+
+import cv2
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from face.face_shape_classifier import classify_from_image  # noqa: E402
+
+ROOT = Path(__file__).resolve().parents[1]
+IMAGE_SUFFIXES = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+MAX_IMAGE_SIDE = 900
+JPEG_QUALITY = 88
+
+SHAPE_ORDER = ["圆形脸", "心形脸", "菱形脸", "鹅蛋脸", "方形脸", "长形脸", "瓜子脸"]
+SHAPE_COLORS = {
+    "圆形脸": "#e67e22",
+    "心形脸": "#e74c3c",
+    "菱形脸": "#9b59b6",
+    "鹅蛋脸": "#27ae60",
+    "方形脸": "#2980b9",
+    "长形脸": "#16a085",
+    "瓜子脸": "#c0392b",
+    "检测失败": "#7f8c8d",
+}
+# 数据集分组名与分类器脸型口径的近似对应（仅用于交叉表高亮参考，非严格标签）
+TAXONOMY_EQUIV = {
+    "方形脸": "方形脸",
+    "长形脸": "长形脸",
+    "瓜子脸": "瓜子脸",
+    "标准脸": "鹅蛋脸",
+    "娃娃脸": "圆形脸",
+}
+
+FEATURE_KEYS = [
+    "face_width",
+    "face_height",
+    "jaw_angle",
+    "taper_ratio",
+    "forehead_ratio",
+    "cheekbone_ratio",
+    "jaw_ratio",
+    "chin_ratio",
+    "chin_sharpness",
+    "width_uniformity",
+    "face_curve_score",
+]
+
+
+def natural_key(text: str):
+    parts = re.split(r"(\d+)", text)
+    return [int(p) if p.isdigit() else p for p in parts]
+
+
+def collect_images(src: Path) -> List[Path]:
+    return sorted(
+        (p for p in src.rglob("*") if p.suffix.lower() in IMAGE_SUFFIXES),
+        key=lambda p: natural_key(str(p.relative_to(src))),
+    )
+
+
+def group_of(path: Path, src: Path) -> str:
+    """图片相对根目录的父目录名；直接位于根目录则记为「根目录」。"""
+    rel = path.relative_to(src).parent
+    return str(rel) if str(rel) != "." else "（根目录）"
+
+
+def ascii_slug(text: str, fallback: str) -> str:
+    """生成安全的 ASCII 文件名片段（中文目录名转拼音不可靠，直接编号兜底）。"""
+    norm = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode()
+    norm = re.sub(r"[^A-Za-z0-9_-]+", "_", norm).strip("_")
+    return norm or fallback
+
+
+def stratified_sample(
+    images: List[Path], src: Path, total: int, seed: int, min_per_group: int
+) -> List[Path]:
+    """
+    按分组分层抽样：先保证每组至少 min_per_group 张，剩余名额按组大小比例分配。
+    小分组（如只有 3 张的梨形脸）在纯随机抽样下几乎必然缺席，分层可保证覆盖。
+    """
+    rng = random.Random(seed)
+    buckets: Dict[str, List[Path]] = defaultdict(list)
+    for p in images:
+        buckets[group_of(p, src)].append(p)
+
+    groups = sorted(buckets, key=natural_key)
+    quota = {g: min(min_per_group, len(buckets[g])) for g in groups}
+
+    remaining = total - sum(quota.values())
+    if remaining > 0:
+        spare = {g: len(buckets[g]) - quota[g] for g in groups}
+        pool = sum(spare.values())
+        if pool > 0:
+            # 按剩余可选量比例分配，再把取整误差补给最大的分组
+            extra = {g: int(remaining * spare[g] / pool) for g in groups}
+            for g in sorted(groups, key=lambda g: -spare[g]):
+                if sum(extra.values()) >= remaining:
+                    break
+                if extra[g] < spare[g]:
+                    extra[g] += 1
+            for g in groups:
+                quota[g] += min(extra[g], spare[g])
+
+    chosen: List[Path] = []
+    for g in groups:
+        chosen.extend(rng.sample(buckets[g], min(quota[g], len(buckets[g]))))
+    return chosen
+
+
+def analyze(
+    src: Path, sample: int, seed: int, img_dir: Path, min_per_group: int
+) -> List[Dict]:
+    all_images = collect_images(src)
+    if not all_images:
+        raise SystemExit(f"目录中没有图片: {src}")
+
+    if sample and sample < len(all_images):
+        if min_per_group > 0:
+            chosen = stratified_sample(all_images, src, sample, seed, min_per_group)
+        else:
+            chosen = random.Random(seed).sample(all_images, sample)
+        chosen.sort(key=lambda p: natural_key(str(p.relative_to(src))))
+    else:
+        chosen = all_images
+
+    mode = f"分层抽样，每组至少 {min_per_group} 张" if min_per_group > 0 else "纯随机抽样"
+    print(f"共发现 {len(all_images)} 张图片，本次测试 {len(chosen)} 张（{mode}，seed={seed}）\n")
+
+    if img_dir.exists():
+        shutil.rmtree(img_dir)
+    img_dir.mkdir(parents=True)
+
+    group_slugs: Dict[str, str] = {}
+    rows: List[Dict] = []
+
+    for idx, path in enumerate(chosen, 1):
+        group = group_of(path, src)
+        if group not in group_slugs:
+            group_slugs[group] = ascii_slug(group, f"g{len(group_slugs) + 1}")
+        out_name = f"{group_slugs[group]}_{idx:03d}.jpg"
+
+        item = {
+            "index": idx,
+            "group": group,
+            "file": path.name,
+            "rel_path": str(path.relative_to(src)),
+            # 相对 static/ 的路径（报告 HTML 也放在 static/ 根下）
+            "img_src": f"{img_dir.relative_to(ROOT / 'static').as_posix()}/{out_name}",
+            "ok": False,
+            "predicted": None,
+            "display": None,
+            "confidence": None,
+            "score": None,
+            "top3": [],
+            "features": {},
+            "error": None,
+        }
+
+        try:
+            result = classify_from_image(path, return_details=True, return_annotated=True)
+            annotated = result["annotated"]
+            h, w = annotated.shape[:2]
+            if max(h, w) > MAX_IMAGE_SIDE:
+                scale = MAX_IMAGE_SIDE / max(h, w)
+                annotated = cv2.resize(
+                    annotated, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
+                )
+            cv2.imwrite(str(img_dir / out_name), annotated, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+
+            item.update(
+                {
+                    "ok": True,
+                    "predicted": result["face_shape"],
+                    "display": result["display"],
+                    "confidence": result["confidence"],
+                    "score": result["details"]["ranked"][0][1],
+                    "top3": result["details"]["ranked"][:3],
+                    "features": {k: result["features"][k] for k in FEATURE_KEYS},
+                }
+            )
+        except Exception as exc:  # noqa: BLE001 - 报告需要汇总所有失败样本
+            img = cv2.imread(str(path))
+            if img is not None:
+                h, w = img.shape[:2]
+                if max(h, w) > MAX_IMAGE_SIDE:
+                    scale = MAX_IMAGE_SIDE / max(h, w)
+                    img = cv2.resize(img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
+                cv2.imwrite(str(img_dir / out_name), img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+            item["error"] = str(exc)
+
+        rows.append(item)
+        print(f"[{idx:3d}/{len(chosen)}] [{group}] {path.name} -> {item['display'] or 'ERR: ' + str(item['error'])}")
+
+    return rows
+
+
+def bar_chart(counter: Counter) -> str:
+    if not counter:
+        return "<p class='muted'>无数据</p>"
+    total = sum(counter.values())
+    parts = []
+    order = [s for s in SHAPE_ORDER if counter.get(s)] + [
+        s for s in counter if s not in SHAPE_ORDER
+    ]
+    for shape in order:
+        n = counter[shape]
+        color = SHAPE_COLORS.get(shape, "#7f8c8d")
+        pct = n / total * 100
+        parts.append(
+            f"<div class='bar-row'><span class='bar-label'>{html.escape(shape)}</span>"
+            f"<div class='bar-track'><div class='bar-fill' style='width:{pct:.1f}%;background:{color}'></div></div>"
+            f"<span class='bar-num'>{n}（{pct:.0f}%）</span></div>"
+        )
+    return "".join(parts)
+
+
+def fmt_feat(key: str, value: float) -> str:
+    if key in {"face_width", "face_height"}:
+        return f"{value:.1f}px"
+    if key == "jaw_angle":
+        return f"{value:.1f}°"
+    return f"{value:.3f}"
+
+
+def card(item: Dict) -> str:
+    group_tag = f"<span class='group-tag'>{html.escape(item['group'])}</span>"
+    if not item["ok"]:
+        return f"""
+        <article class="card error">
+          <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank">
+            <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+          </a>
+          <div class="body">
+            <div class="meta"><h3>{html.escape(item['file'])}</h3>{group_tag}</div>
+            <p class="badge bad">检测失败</p>
+            <p class="muted">{html.escape(item['error'] or '')}</p>
+          </div>
+        </article>"""
+
+    color = SHAPE_COLORS.get(item["predicted"], "#34495e")
+    top3 = "".join(
+        f"<li><span>{html.escape(name)}</span><b>{score:.1f}</b></li>" for name, score in item["top3"]
+    )
+    feat_html = "".join(
+        f"<tr><td>{html.escape(k)}</td><td>{html.escape(fmt_feat(k, v))}</td></tr>"
+        for k, v in item["features"].items()
+    )
+    return f"""
+    <article class="card">
+      <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank" title="点击查看大图标注">
+        <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+      </a>
+      <div class="body">
+        <div class="meta"><h3>{html.escape(item['file'])}</h3>{group_tag}</div>
+        <p class="path muted">{html.escape(item['rel_path'])}</p>
+        <p class="badge" style="background:{color}">{html.escape(item['display'])}</p>
+        <p class="conf">匹配度 {item['score']:.1f} · 置信度 {item['confidence']:.3f}</p>
+        <ul class="scores">{top3}</ul>
+        <details>
+          <summary>标注特征数值</summary>
+          <table>{feat_html}</table>
+        </details>
+      </div>
+    </article>"""
+
+
+CSS = """
+:root { --bg:#f3efe6; --ink:#1c1915; --muted:#6b645a; --card:#fffdf8; --line:#e2d8c8; --accent:#0f6b5c; }
+* { box-sizing: border-box; }
+body {
+  margin:0; font-family:"PingFang SC","Noto Sans SC","Segoe UI",sans-serif; color:var(--ink);
+  background: radial-gradient(1200px 600px at 10% -10%, #ffe8c8 0%, transparent 55%),
+              radial-gradient(900px 500px at 100% 0%, #d9f2ea 0%, transparent 50%), var(--bg);
+}
+header { padding:40px 24px 20px; max-width:1320px; margin:0 auto; }
+header h1 { margin:0 0 8px; font-size:clamp(1.8rem,3vw,2.4rem); }
+header p { margin:4px 0; color:var(--muted); }
+.legend-box { max-width:1320px; margin:0 auto 20px; padding:0 24px; }
+.legend-box .inner { background:var(--card); border:1px solid var(--line); border-radius:14px;
+  padding:14px 16px; font-size:.9rem; line-height:1.55; }
+.legend-box code { background:#efe7da; padding:1px 6px; border-radius:4px; font-size:.84rem; }
+.swatch { display:inline-block; width:10px; height:10px; border-radius:2px; margin-right:4px; vertical-align:middle; }
+.stats { display:grid; grid-template-columns:repeat(auto-fit,minmax(280px,1fr)); gap:16px;
+  max-width:1320px; margin:0 auto 28px; padding:0 24px; }
+.stat { background:var(--card); border:1px solid var(--line); border-radius:16px; padding:16px 18px; }
+.stat h2 { margin:0 0 12px; font-size:1rem; }
+.bar-row { display:grid; grid-template-columns:72px 1fr 80px; gap:8px; align-items:center; margin:6px 0; font-size:.86rem; }
+.bar-track { height:8px; background:#efe7da; border-radius:999px; overflow:hidden; }
+.bar-fill { height:100%; border-radius:999px; }
+.bar-num { color:var(--muted); text-align:right; }
+section { max-width:1320px; margin:0 auto 36px; padding:0 24px; }
+section h2 { margin:0 0 14px; font-size:1.3rem; border-left:4px solid var(--accent); padding-left:10px; }
+section h2 small { color:var(--muted); font-weight:400; font-size:.8rem; margin-left:8px; }
+.grid { display:grid; grid-template-columns:repeat(auto-fill,minmax(270px,1fr)); gap:16px; }
+.card { background:var(--card); border:1px solid var(--line); border-radius:18px; overflow:hidden;
+  display:flex; flex-direction:column; box-shadow:0 8px 24px rgba(60,40,10,.05); }
+.card.error { opacity:.9; }
+.img-link { display:block; }
+.card img { width:100%; aspect-ratio:3/4; object-fit:cover; background:#ddd; display:block; }
+.card .body { padding:14px; }
+.meta { display:flex; justify-content:space-between; align-items:baseline; gap:8px; }
+.meta h3 { margin:0; font-size:.95rem; word-break:break-all; }
+.group-tag { font-size:.72rem; color:var(--accent); background:#e7f6f2; padding:2px 8px;
+  border-radius:999px; white-space:nowrap; }
+.path { font-size:.72rem; margin:4px 0 0; word-break:break-all; }
+.badge { display:inline-block; margin:10px 0 4px; color:#fff; padding:6px 10px; border-radius:999px;
+  font-weight:600; font-size:.92rem; }
+.badge.bad { background:#c0392b; }
+.conf { margin:0 0 8px; color:var(--muted); font-size:.85rem; }
+.scores { list-style:none; padding:0; margin:0 0 8px; }
+.scores li { display:flex; justify-content:space-between; padding:4px 0; border-bottom:1px dashed var(--line); font-size:.86rem; }
+details { margin-top:8px; }
+summary { cursor:pointer; color:var(--accent); font-size:.86rem; }
+table { width:100%; border-collapse:collapse; margin-top:8px; font-size:.8rem; }
+td { padding:3px 0; border-bottom:1px solid var(--line); }
+td:last-child { text-align:right; font-variant-numeric:tabular-nums; }
+.muted { color:var(--muted); }
+.cross-wrap { overflow-x:auto; background:var(--card); border:1px solid var(--line);
+  border-radius:16px; padding:14px 16px; }
+table.cross { border-collapse:collapse; width:100%; font-size:.88rem; }
+table.cross th, table.cross td { padding:7px 10px; text-align:center; border-bottom:1px solid var(--line);
+  white-space:nowrap; }
+table.cross thead th { background:#efe7da; font-weight:600; position:sticky; top:0; }
+table.cross th.rowh { text-align:left; font-weight:600; }
+table.cross th.rowh small { color:var(--muted); font-weight:400; }
+table.cross td.num { font-variant-numeric:tabular-nums; }
+table.cross td.hit { background:#d8f3e4; color:#0f6b5c; font-weight:700; font-variant-numeric:tabular-nums; }
+table.cross td.zero { color:#cfc6b6; }
+footer { max-width:1320px; margin:0 auto; padding:8px 24px 40px; color:var(--muted); font-size:.85rem; }
+"""
+
+
+def cross_table(by_group: Dict[str, List[Dict]]) -> str:
+    """原始分组 × 预测脸型 交叉表，对角线（口径对应的格子）高亮。"""
+    cols = SHAPE_ORDER + ["检测失败"]
+    head = "".join(f"<th>{html.escape(c)}</th>" for c in cols)
+    body = []
+    for group, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0])):
+        counts = Counter(i["predicted"] if i["ok"] else "检测失败" for i in items)
+        equiv = TAXONOMY_EQUIV.get(group)
+        cells = []
+        for c in cols:
+            n = counts.get(c, 0)
+            if n == 0:
+                cells.append("<td class='zero'>·</td>")
+                continue
+            cls = "hit" if c == equiv else "num"
+            cells.append(f"<td class='{cls}'>{n}</td>")
+        label = html.escape(group)
+        if equiv:
+            label += f" <small>≈{html.escape(equiv)}</small>"
+        body.append(f"<tr><th class='rowh'>{label}</th>{''.join(cells)}<th>{len(items)}</th></tr>")
+    return (
+        "<div class='cross-wrap'><table class='cross'>"
+        f"<thead><tr><th>原始分组 \\ 预测</th>{head}<th>合计</th></tr></thead>"
+        f"<tbody>{''.join(body)}</tbody></table></div>"
+    )
+
+
+def build_html(rows: List[Dict], name: str, src: Path, seed: int, img_dir_name: str) -> str:
+    overall = Counter(r["predicted"] if r["ok"] else "检测失败" for r in rows)
+    by_group: Dict[str, List[Dict]] = defaultdict(list)
+    for r in rows:
+        by_group[r["group"]].append(r)
+
+    group_stats = "".join(
+        f"<div class='stat'><h2>{html.escape(g)} <span class='muted'>（{len(items)} 张）</span></h2>"
+        f"{bar_chart(Counter(i['predicted'] if i['ok'] else '检测失败' for i in items))}</div>"
+        for g, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0]))
+    )
+
+    sections = "".join(
+        f"<section><h2>{html.escape(g)}<small>{len(items)} 张</small></h2>"
+        f"<div class='grid'>{''.join(card(i) for i in items)}</div></section>"
+        for g, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0]))
+    )
+
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    n_ok = sum(1 for r in rows if r["ok"])
+    n_kind = len([s for s in SHAPE_ORDER if overall.get(s)])
+
+    return f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>{html.escape(name)} — 脸型分类报告</title>
+<style>{CSS}</style>
+</head>
+<body>
+<header>
+  <h1>{html.escape(name)} — 脸型分类报告</h1>
+  <p>z 分数原型匹配分类 · 照片上标注 face_width / face_height 及各比例特征</p>
+  <p>生成时间：{html.escape(now)} · 抽样 {len(rows)} 张（随机种子 {seed}）· 成功 {n_ok} 张 ·
+     覆盖 {n_kind} 种脸型 · 共 {len(by_group)} 个原始分组 · 点击图片看大图</p>
+  <p class="muted">来源目录：{html.escape(str(src))}</p>
+</header>
+
+<div class="legend-box">
+  <div class="inner">
+    <b>图上标注说明</b><br/>
+    <span class="swatch" style="background:#00dc78"></span><code>face_width</code> 颧骨宽度&nbsp;
+    <span class="swatch" style="background:#28b4ff"></span><code>face_height</code> 额头顶→下巴&nbsp;
+    <span class="swatch" style="background:#ff5a00"></span><code>jaw_angle</code> 下巴到左右下颌角夹角&nbsp;
+    <span class="swatch" style="background:#ffc828"></span><code>taper_ratio</code> 额头→下巴收窄&nbsp;
+    <span class="swatch" style="background:#28a0ff"></span><code>forehead / jaw / chin ratio</code> 各级宽度比&nbsp;
+    <span class="swatch" style="background:#b4ff50"></span><code>face_curve_score</code> 下颌中点→下巴&nbsp;
+    右侧柱状条示意 <code>width_uniformity</code>；左上角是完整数值图例。
+  </div>
+</div>
+
+<section>
+  <h2>原始分组 × 预测脸型 对照<small>数据集分组本身是脸型标签，但命名口径与分类器不同</small></h2>
+  {cross_table(by_group)}
+  <p class="muted" style="margin-top:10px;font-size:.85rem">
+    绿色格子表示预测结果与该分组的对应口径一致（标准脸≈鹅蛋脸、娃娃脸≈圆形脸，方形/长形/瓜子同名直接对应）。
+    「梨形脸」「混合脸」在分类器的 7 分类里没有对应项，不作一致性判断。
+  </p>
+</section>
+
+<div class="stats">
+  <div class="stat"><h2>总体脸型分布（{len(rows)} 张）</h2>{bar_chart(overall)}</div>
+  {group_stats}
+</div>
+
+{sections}
+
+<footer>
+  分类实现：face/face_shape_classifier.py · 报告生成：face/build_dataset_report.py ·
+  图片目录：static/{html.escape(img_dir_name)}/
+</footer>
+</body>
+</html>
+"""
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="批量脸型预测并生成 HTML 报告")
+    ap.add_argument("--src", required=True, help="图片根目录（可含子目录）")
+    ap.add_argument("--sample", type=int, default=50, help="随机抽样张数，0 表示全部")
+    ap.add_argument("--seed", type=int, default=42, help="随机种子")
+    ap.add_argument("--name", default=None, help="报告标题，默认取目录名")
+    ap.add_argument("--slug", default="dataset", help="输出文件名前缀（ASCII）")
+    ap.add_argument(
+        "--min-per-group",
+        type=int,
+        default=2,
+        help="分层抽样时每个分组至少抽几张，0 表示纯随机抽样",
+    )
+    args = ap.parse_args()
+
+    src = Path(args.src).expanduser().resolve()
+    if not src.is_dir():
+        raise SystemExit(f"目录不存在: {src}")
+
+    name = args.name or src.name
+    img_dir_name = f"{args.slug}_report"
+    img_dir = ROOT / "static" / img_dir_name / "images"
+    out_html = ROOT / "static" / f"{args.slug}_report.html"
+
+    rows = analyze(src, args.sample, args.seed, img_dir, args.min_per_group)
+    out_html.write_text(
+        build_html(rows, name, src, args.seed, img_dir_name), encoding="utf-8"
+    )
+
+    overall = Counter(r["predicted"] if r["ok"] else "检测失败" for r in rows)
+    total = sum(overall.values())
+    print(f"\n写入 {out_html}")
+    print("=== 总体脸型分布 ===")
+    for shape in SHAPE_ORDER + ["检测失败"]:
+        n = overall.get(shape, 0)
+        if n:
+            print(f"  {shape}: {n:3d}  ({n / total * 100:4.1f}%)  {'#' * n}")
+
+
+if __name__ == "__main__":
+    main()

face/build_report.py

@@ -0,0 +1,359 @@
+"""
+build_report.py
+对 face/test_img/girl 与 face/test_img/man 下的照片批量预测脸型，
+在照片上标注 face_width / face_height 等特征，并生成 HTML 报告。
+
+用法:
+    ./venv/bin/python face/build_report.py
+输出:
+    static/face_shape_report.html
+    static/face_shape_report/images/*.jpg
+"""
+
+from __future__ import annotations
+
+import html
+import re
+import shutil
+import sys
+from collections import Counter
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List
+
+import cv2
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from face.face_shape_classifier import classify_from_image  # noqa: E402
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC_DIRS = {
+    "女": ROOT / "face/test_img/girl",
+    "男": ROOT / "face/test_img/man",
+}
+OUT_DIR = ROOT / "static/face_shape_report"
+IMG_DIR = OUT_DIR / "images"
+OUT_HTML = ROOT / "static/face_shape_report.html"
+
+MAX_IMAGE_SIDE = 900
+JPEG_QUALITY = 90
+
+SHAPE_ORDER = ["圆形脸", "心形脸", "菱形脸", "鹅蛋脸", "方形脸", "长形脸", "瓜子脸"]
+SHAPE_COLORS = {
+    "圆形脸": "#e67e22",
+    "心形脸": "#e74c3c",
+    "菱形脸": "#9b59b6",
+    "鹅蛋脸": "#27ae60",
+    "方形脸": "#2980b9",
+    "长形脸": "#16a085",
+    "瓜子脸": "#c0392b",
+}
+FEATURE_KEYS = [
+    "face_width",
+    "face_height",
+    "jaw_angle",
+    "taper_ratio",
+    "forehead_ratio",
+    "cheekbone_ratio",
+    "jaw_ratio",
+    "chin_ratio",
+    "chin_sharpness",
+    "width_uniformity",
+    "face_curve_score",
+]
+
+
+def natural_key(path: Path):
+    m = re.search(r"(\d+)", path.stem)
+    return (0, int(m.group(1))) if m else (1, path.stem)
+
+
+def analyze_all() -> tuple[List[Dict], Dict[str, Counter]]:
+    if IMG_DIR.exists():
+        shutil.rmtree(IMG_DIR)
+    IMG_DIR.mkdir(parents=True)
+
+    rows: List[Dict] = []
+    summary = {"女": Counter(), "男": Counter(), "all": Counter()}
+
+    for gender, src in SRC_DIRS.items():
+        prefix = "girl" if gender == "女" else "man"
+        paths = sorted(
+            [p for p in src.iterdir() if p.suffix.lower() in {".jpg", ".jpeg", ".png", ".webp"}],
+            key=natural_key,
+        )
+        for path in paths:
+            m = re.search(r"(\d+)", path.stem)
+            out_name = f"{prefix}_{int(m.group(1)) if m else 0:02d}.jpg"
+            dest = IMG_DIR / out_name
+
+            item = {
+                "gender": gender,
+                "file": path.name,
+                "img_src": f"face_shape_report/images/{out_name}",
+                "ok": False,
+                "predicted": None,
+                "display": None,
+                "confidence": None,
+                "score": None,
+                "top3": [],
+                "features": {},
+                "error": None,
+            }
+            try:
+                result = classify_from_image(path, return_details=True, return_annotated=True)
+                annotated = result["annotated"]
+                h, w = annotated.shape[:2]
+                if max(h, w) > MAX_IMAGE_SIDE:
+                    scale = MAX_IMAGE_SIDE / max(h, w)
+                    annotated = cv2.resize(
+                        annotated, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
+                    )
+                cv2.imwrite(str(dest), annotated, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+
+                item.update(
+                    {
+                        "ok": True,
+                        "predicted": result["face_shape"],
+                        "display": result["display"],
+                        "confidence": result["confidence"],
+                        "score": result["details"]["ranked"][0][1],
+                        "top3": result["details"]["ranked"][:3],
+                        "features": {k: result["features"][k] for k in FEATURE_KEYS},
+                    }
+                )
+                summary[gender][result["face_shape"]] += 1
+                summary["all"][result["face_shape"]] += 1
+            except Exception as exc:  # noqa: BLE001 - 报告需要汇总所有失败
+                img = cv2.imread(str(path))
+                if img is not None:
+                    cv2.imwrite(str(dest), img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+                item["error"] = str(exc)
+                summary[gender]["检测失败"] += 1
+                summary["all"]["检测失败"] += 1
+
+            rows.append(item)
+            print(f"[{gender}] {path.name} -> {item['display'] or 'ERR ' + str(item['error'])}")
+
+    return rows, summary
+
+
+def count_table(counter: Counter) -> str:
+    if not counter:
+        return "<p class='muted'>无数据</p>"
+    total = sum(counter.values())
+    parts = []
+    for shape, n in sorted(counter.items(), key=lambda x: (-x[1], x[0])):
+        color = SHAPE_COLORS.get(shape, "#7f8c8d")
+        pct = n / total * 100
+        parts.append(
+            f"<div class='bar-row'><span class='bar-label'>{html.escape(shape)}</span>"
+            f"<div class='bar-track'><div class='bar-fill' style='width:{pct:.1f}%;background:{color}'></div></div>"
+            f"<span class='bar-num'>{n}（{pct:.0f}%）</span></div>"
+        )
+    return "".join(parts)
+
+
+def fmt_feat(key: str, value: float) -> str:
+    if key in {"face_width", "face_height"}:
+        return f"{value:.1f}px"
+    if key == "jaw_angle":
+        return f"{value:.1f}°"
+    return f"{value:.3f}"
+
+
+def card(item: Dict) -> str:
+    if not item["ok"]:
+        return f"""
+        <article class="card error">
+          <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank">
+            <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+          </a>
+          <div class="body">
+            <h3>{html.escape(item['file'])}</h3>
+            <p class="badge bad">检测失败</p>
+            <p class="muted">{html.escape(item['error'] or '')}</p>
+          </div>
+        </article>"""
+
+    color = SHAPE_COLORS.get(item["predicted"], "#34495e")
+    top3 = "".join(
+        f"<li><span>{html.escape(name)}</span><b>{score:.1f}</b></li>" for name, score in item["top3"]
+    )
+    feat_html = "".join(
+        f"<tr><td>{html.escape(k)}</td><td>{html.escape(fmt_feat(k, v))}</td></tr>"
+        for k, v in item["features"].items()
+    )
+    return f"""
+    <article class="card">
+      <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank" title="点击查看大图标注">
+        <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+      </a>
+      <div class="body">
+        <div class="meta">
+          <h3>{html.escape(item['file'])}</h3>
+          <span class="gender">{html.escape(item['gender'])}</span>
+        </div>
+        <p class="badge" style="background:{color}">{html.escape(item['display'])}</p>
+        <p class="conf">匹配度 {item['score']:.1f} · 置信度 {item['confidence']:.3f}</p>
+        <h4>Top-3 得分</h4>
+        <ul class="scores">{top3}</ul>
+        <details>
+          <summary>标注特征数值</summary>
+          <table>{feat_html}</table>
+        </details>
+      </div>
+    </article>"""
+
+
+CSS = """
+:root {
+  --bg: #f3efe6; --ink: #1c1915; --muted: #6b645a;
+  --card: #fffdf8; --line: #e2d8c8; --accent: #0f6b5c;
+}
+* { box-sizing: border-box; }
+body {
+  margin: 0;
+  font-family: "PingFang SC", "Noto Sans SC", "Segoe UI", sans-serif;
+  color: var(--ink);
+  background:
+    radial-gradient(1200px 600px at 10% -10%, #ffe8c8 0%, transparent 55%),
+    radial-gradient(900px 500px at 100% 0%, #d9f2ea 0%, transparent 50%),
+    var(--bg);
+}
+header { padding: 40px 24px 20px; max-width: 1280px; margin: 0 auto; }
+header h1 { margin: 0 0 8px; font-size: clamp(1.8rem, 3vw, 2.4rem); letter-spacing: .02em; }
+header p { margin: 4px 0; color: var(--muted); }
+.legend-box { max-width: 1280px; margin: 0 auto 20px; padding: 0 24px; }
+.legend-box .inner {
+  background: var(--card); border: 1px solid var(--line);
+  border-radius: 14px; padding: 14px 16px; font-size: .9rem; line-height: 1.55;
+}
+.legend-box code { background: #efe7da; padding: 1px 6px; border-radius: 4px; font-size: .84rem; }
+.swatch { display: inline-block; width: 10px; height: 10px; border-radius: 2px; margin-right: 4px; vertical-align: middle; }
+.stats {
+  display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr));
+  gap: 16px; max-width: 1280px; margin: 0 auto 28px; padding: 0 24px;
+}
+.stat { background: var(--card); border: 1px solid var(--line); border-radius: 16px; padding: 16px 18px; }
+.stat h2 { margin: 0 0 12px; font-size: 1rem; }
+.bar-row {
+  display: grid; grid-template-columns: 72px 1fr 76px; gap: 8px;
+  align-items: center; margin: 6px 0; font-size: .86rem;
+}
+.bar-track { height: 8px; background: #efe7da; border-radius: 999px; overflow: hidden; }
+.bar-fill { height: 100%; border-radius: 999px; }
+.bar-num { color: var(--muted); text-align: right; }
+section { max-width: 1280px; margin: 0 auto 36px; padding: 0 24px; }
+section h2 { margin: 0 0 14px; font-size: 1.35rem; border-left: 4px solid var(--accent); padding-left: 10px; }
+.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(280px, 1fr)); gap: 16px; }
+.card {
+  background: var(--card); border: 1px solid var(--line); border-radius: 18px;
+  overflow: hidden; display: flex; flex-direction: column;
+  box-shadow: 0 8px 24px rgba(60, 40, 10, .05);
+}
+.card.error { opacity: .9; }
+.img-link { display: block; }
+.card img { width: 100%; aspect-ratio: 3/4; object-fit: cover; background: #ddd; display: block; }
+.card .body { padding: 14px 14px 16px; }
+.meta { display: flex; justify-content: space-between; align-items: baseline; gap: 8px; }
+.meta h3 { margin: 0; font-size: 1rem; }
+.gender { font-size: .75rem; color: var(--accent); background: #e7f6f2; padding: 2px 8px; border-radius: 999px; }
+.badge {
+  display: inline-block; margin: 10px 0 4px; color: #fff;
+  padding: 6px 10px; border-radius: 999px; font-weight: 600; font-size: .92rem;
+}
+.badge.bad { background: #c0392b; }
+.conf { margin: 0 0 10px; color: var(--muted); font-size: .85rem; }
+.scores { list-style: none; padding: 0; margin: 0 0 8px; }
+.scores li {
+  display: flex; justify-content: space-between; padding: 4px 0;
+  border-bottom: 1px dashed var(--line); font-size: .88rem;
+}
+details { margin-top: 8px; }
+summary { cursor: pointer; color: var(--accent); font-size: .88rem; }
+table { width: 100%; border-collapse: collapse; margin-top: 8px; font-size: .8rem; }
+td { padding: 3px 0; border-bottom: 1px solid var(--line); }
+td:last-child { text-align: right; font-variant-numeric: tabular-nums; }
+.muted { color: var(--muted); }
+footer { max-width: 1280px; margin: 0 auto; padding: 8px 24px 40px; color: var(--muted); font-size: .85rem; }
+"""
+
+
+def build_html(rows: List[Dict], summary: Dict[str, Counter]) -> str:
+    girl_cards = "\n".join(card(r) for r in rows if r["gender"] == "女")
+    man_cards = "\n".join(card(r) for r in rows if r["gender"] == "男")
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    n_girl = sum(1 for r in rows if r["gender"] == "女")
+    n_man = sum(1 for r in rows if r["gender"] == "男")
+    n_ok = sum(1 for r in rows if r["ok"])
+    n_kind = len([s for s in SHAPE_ORDER if summary["all"].get(s)])
+
+    return f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>脸型分类预测报告（特征标注）</title>
+<style>{CSS}</style>
+</head>
+<body>
+<header>
+  <h1>脸型分类预测报告</h1>
+  <p>z 分数原型匹配分类 · 照片上标注 face_width / face_height 及各比例特征</p>
+  <p>生成时间：{html.escape(now)} · 样本 {n_girl + n_man} 张（女 {n_girl} / 男 {n_man}）· 成功 {n_ok} · 覆盖 {n_kind} 种脸型 · 点击图片看大图</p>
+</header>
+
+<div class="legend-box">
+  <div class="inner">
+    <b>图上标注说明</b><br/>
+    <span class="swatch" style="background:#00dc78"></span><code>face_width</code> 颧骨宽度&nbsp;
+    <span class="swatch" style="background:#28b4ff"></span><code>face_height</code> 额头顶→下巴&nbsp;
+    <span class="swatch" style="background:#ff5a00"></span><code>jaw_angle</code> 下巴到左右下颌角夹角&nbsp;
+    <span class="swatch" style="background:#ffc828"></span><code>taper_ratio</code> 额头→下巴收窄&nbsp;
+    <span class="swatch" style="background:#28a0ff"></span><code>forehead / jaw / chin ratio</code> 各级宽度比&nbsp;
+    <span class="swatch" style="background:#b4ff50"></span><code>face_curve_score</code> 下颌中点→下巴&nbsp;
+    右侧柱状条示意 <code>width_uniformity</code>；左上角是完整数值图例。
+  </div>
+</div>
+
+<div class="stats">
+  <div class="stat"><h2>全部脸型分布</h2>{count_table(summary['all'])}</div>
+  <div class="stat"><h2>女性脸型分布</h2>{count_table(summary['女'])}</div>
+  <div class="stat"><h2>男性脸型分布</h2>{count_table(summary['男'])}</div>
+</div>
+
+<section>
+  <h2>女性样本（{n_girl}）</h2>
+  <div class="grid">{girl_cards}</div>
+</section>
+
+<section>
+  <h2>男性样本（{n_man}）</h2>
+  <div class="grid">{man_cards}</div>
+</section>
+
+<footer>
+  分类实现：face/face_shape_classifier.py · 报告生成：face/build_report.py
+</footer>
+</body>
+</html>
+"""
+
+
+def main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    rows, summary = analyze_all()
+    OUT_HTML.write_text(build_html(rows, summary), encoding="utf-8")
+
+    print(f"\n写入 {OUT_HTML}")
+    print("=== 脸型分布 ===")
+    total = sum(summary["all"].values())
+    for shape in SHAPE_ORDER:
+        n = summary["all"].get(shape, 0)
+        print(f"  {shape}: {n:2d}  ({n / total * 100:4.1f}%)  {'#' * n}")
+
+
+if __name__ == "__main__":
+    main()

face/dump_features.py

@@ -0,0 +1,83 @@
+"""
+把各数据集的人脸特征抽取一次并缓存为 JSON，供调参脚本反复使用。
+
+MediaPipe 关键点检测是调参循环里唯一的耗时环节，缓存后调参可以秒级迭代。
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+from typing import Dict, List
+
+import cv2
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from face_shape_classifier import (  # noqa: E402
+    _get_face_mesh,
+    extract_face_features,
+)
+
+ROOT = Path(__file__).resolve().parent
+IMG_EXT = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+
+
+def iter_images(root: Path) -> List[Path]:
+    return sorted(p for p in root.rglob("*") if p.suffix.lower() in IMG_EXT)
+
+
+def features_for(path: Path) -> Dict[str, float] | None:
+    bgr = cv2.imread(str(path))
+    if bgr is None:
+        return None
+    h, w = bgr.shape[:2]
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    res = _get_face_mesh().process(rgb)
+    if not res.multi_face_landmarks:
+        return None
+    return extract_face_features(res.multi_face_landmarks[0].landmark, image_size=(w, h))
+
+
+def main() -> None:
+    out_path = Path(sys.argv[1]) if len(sys.argv) > 1 else ROOT / "cache" / "features.json"
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    sources = {
+        # 6 张带标注的基准图，文件名即期望脸型
+        "benchmark": [p for p in ROOT.joinpath("test_img").glob("*.png")],
+        "girl": iter_images(ROOT / "test_img" / "girl"),
+        "man": iter_images(ROOT / "test_img" / "man"),
+        "dataset": iter_images(ROOT / "test_img" / "脸型测试集合"),
+    }
+
+    records = []
+    failed = 0
+    for source, paths in sources.items():
+        for i, path in enumerate(paths, 1):
+            feats = features_for(path)
+            if feats is None:
+                failed += 1
+                continue
+            rel = path.relative_to(ROOT)
+            records.append(
+                {
+                    "source": source,
+                    "path": rel.as_posix(),
+                    "file": path.name,
+                    # dataset 的上级目录名即原始分组（弱标签，非可信真值）
+                    "group": path.parent.name if source == "dataset" else source,
+                    "expected": path.stem if source == "benchmark" else None,
+                    "features": feats,
+                }
+            )
+            if i % 50 == 0 or i == len(paths):
+                print(f"[{source}] {i}/{len(paths)}", flush=True)
+
+    out_path.write_text(json.dumps(records, ensure_ascii=False), encoding="utf-8")
+    print(f"\n写入 {out_path}：{len(records)} 条，检测失败 {failed} 张")
+
+
+if __name__ == "__main__":
+    main()

face/face_shape_classification.md

@@ -0,0 +1,904 @@
+# 脸型判断规则优化报告
+
+> 基于 Mediapipe 468 点人脸关键点的脸型分类系统  
+> 优化日期：2026-07-28
+
+---
+
+## 一、优化总览
+
+### 原始规则主要问题
+
+| 问题 | 说明 |
+|------|------|
+| **规则冲突** | 7条 if 规则可能同时匹配，无优先级机制 |
+| **特征定义模糊** | `width_ratio`、`chin_narrowness` 等未给出精确计算方式 |
+| **关键点不足** | 额头宽度用 234/454（颧骨点）而非太阳穴点，导致测量不准 |
+| **无置信度** | 硬判断，混合脸型无处理 |
+| **阈值经验性** | 阈值未经统计校准，边界处容易误判 |
+| **缺少归一化** | 不同距离拍的照片结果不一致 |
+
+### 优化策略
+
+1. **精确特征提取**：增加关键点，所有测量归一化
+2. **评分制分类**：每个脸型计算匹配度分数（0-100），取最高分
+3. **置信度输出**：报告 Top-1 / Top-2 分差，判断是否为混合脸型
+4. **优先级仲裁**：分数接近时按"特异性优先"原则仲裁
+5. **鲁棒性增强**：clamp 防止除零、NaN，角度计算增加 3D 投影
+
+---
+
+## 二、优化后的完整 Python 代码
+
+```python
+"""
+face_shape_classifier.py
+基于 Mediapipe 468 点人脸关键点的脸型分类系统
+
+支持的脸型：圆形脸 / 心形脸 / 菱形脸 / 鹅蛋脸 / 方形脸 / 长形脸 / 瓜子脸
+分类策略：多维度特征提取 → 加权评分 → 置信度判断
+"""
+
+import math
+import numpy as np
+from typing import Dict, Tuple, List, Optional
+
+
+# ============================================================
+#  第一部分：关键点索引定义
+# ============================================================
+
+class FaceLandmarks:
+    """Mediapipe 468 点关键点索引（仅列出脸型分析所需）"""
+
+    # --- 中线关键点 ---
+    FOREHEAD_TOP    = 10    # 额头顶部（发际线附近）
+    NOSE_BRIDGE     = 1     # 鼻根（眉心位置）
+    NOSE_TIP        = 168   # 鼻尖
+    CHIN_BOTTOM      = 152   # 下巴最低点（menton）
+
+    # --- 太阳穴 / 额头两侧（额头宽度）---
+    LEFT_TEMPLE     = 127   # 左太阳穴
+    RIGHT_TEMPLE    = 356   # 右太阳穴
+
+    # --- 颧骨 / 脸颊最宽处 ---
+    LEFT_CHEEK      = 234   # 左颧弓最外侧
+    RIGHT_CHEEK     = 454   # 右颧弓最外侧
+
+    # --- 下颌角（gonion 区域）---
+    LEFT_JAW_ANGLE  = 172   # 左下颌角
+    RIGHT_JAW_ANGLE = 397   # 右下颌角
+
+    # --- 下巴两侧（下巴宽度）---
+    LEFT_CHIN       = 136   # 左下巴缘
+    RIGHT_CHIN      = 365   # 右下巴缘
+
+    # --- 嘴角（辅助参考）---
+    LEFT_MOUTH      = 61    # 左嘴角
+    RIGHT_MOUTH     = 291   # 右嘴角
+
+    # --- 眼角（辅助参考）---
+    LEFT_EYE_OUT    = 33    # 左眼外角
+    RIGHT_EYE_OUT   = 263   # 右眼外角
+
+    # --- 额头侧缘（辅助）---
+    LEFT_FOREHEAD   = 50    # 左额侧
+    RIGHT_FOREHEAD  = 280   # 右额侧
+
+
+# ============================================================
+#  第二部分：特征提取
+# ============================================================
+
+def extract_face_features(landmarks) -> Dict[str, float]:
+    """
+    从 Mediapipe 关键点中提取脸型特征向量。
+
+    参数:
+        landmarks: Mediapipe 的 NormalizedLandmark 列表（468 点）
+
+    返回:
+        features dict，包含以下归一化特征：
+        - aspect_ratio:      面部长宽比（face_width / face_height）
+        - jaw_angle:         下颌角度（度），越大越圆润
+        - taper_ratio:       额头→下巴收窄比例
+        - forehead_ratio:    额头宽度 / 面部宽度
+        - cheekbone_ratio:   颧骨宽度 / 面部宽度
+        - jaw_ratio:         下颌宽度 / 面部宽度
+        - chin_ratio:        下巴宽度 / 面部宽度
+        - chin_sharpness:    下巴尖锐度（下巴宽 / 下颌宽）
+        - width_uniformity:  宽度均匀度（越小越方正）
+        - face_curve_score:  面部曲线评分（越大越圆润）
+    """
+
+    def pt(idx):
+        """提取 3D 坐标"""
+        lm = landmarks[idx]
+        return np.array([lm.x, lm.y, lm.z])
+
+    def dist(p1, p2):
+        """欧氏距离"""
+        return float(np.linalg.norm(p1 - p2))
+
+    # --- 1. 提取关键点 ---
+    forehead_top  = pt(FaceLandmarks.FOREHEAD_TOP)
+    chin_bottom   = pt(FaceLandmarks.CHIN_BOTTOM)
+
+    left_temple   = pt(FaceLandmarks.LEFT_TEMPLE)
+    right_temple  = pt(FaceLandmarks.RIGHT_TEMPLE)
+
+    left_cheek    = pt(FaceLandmarks.LEFT_CHEEK)
+    right_cheek   = pt(FaceLandmarks.RIGHT_CHEEK)
+
+    left_jaw      = pt(FaceLandmarks.LEFT_JAW_ANGLE)
+    right_jaw     = pt(FaceLandmarks.RIGHT_JAW_ANGLE)
+
+    left_chin     = pt(FaceLandmarks.LEFT_CHIN)
+    right_chin    = pt(FaceLandmarks.RIGHT_CHIN)
+
+    # --- 2. 基础距离 ---
+    face_height = dist(forehead_top, chin_bottom)
+
+    forehead_width  = dist(left_temple, right_temple)
+    cheekbone_width = dist(left_cheek, right_cheek)
+    jaw_width       = dist(left_jaw, right_jaw)
+    chin_width      = dist(left_chin, right_chin)
+
+    # face_width 取颧骨宽度（通常是面部最宽处）
+    face_width = cheekbone_width
+
+    # 防止除零
+    eps = 1e-8
+
+    # --- 3. 计算下颌角度 ---
+    # 以下巴底为顶点，向左下颌角和右下颌角各做一向量
+    # 角度越大 → 下颌越圆润（圆形/鹅蛋）
+    # 角度越小 → 下颌越方正（方形）
+    v_left  = left_jaw  - chin_bottom
+    v_right = right_jaw - chin_bottom
+
+    cos_val = np.dot(v_left, v_right) / (np.linalg.norm(v_left) * np.linalg.norm(v_right) + eps)
+    cos_val = np.clip(cos_val, -1.0, 1.0)
+    jaw_angle = math.degrees(math.acos(cos_val))
+
+    # --- 4. 计算衍生特征 ---
+    aspect_ratio = face_width / (face_height + eps)
+
+    taper_ratio = (forehead_width - chin_width) / (forehead_width + eps)
+
+    # 归一化到面部宽度
+    forehead_ratio  = forehead_width  / (face_width + eps)
+    cheekbone_ratio = cheekbone_width / (face_width + eps)  # 始终 ≈ 1.0
+    jaw_ratio       = jaw_width       / (face_width + eps)
+    chin_ratio      = chin_width      / (face_width + eps)
+
+    # 下巴尖锐度：下巴宽 / 下颌宽
+    # 值越小 → 下巴越尖（瓜子/心形）
+    # 值越大 → 下巴越平（方形/圆形）
+    chin_sharpness = chin_width / (jaw_width + eps)
+
+    # 宽度均匀度：额头、颧骨、下颌三者的差异程度
+    # 值越小 → 三者越接近（方形/圆形）
+    # 值越大 → 差异越明显（菱形/心形/瓜子）
+    widths = [forehead_width, cheekbone_width, jaw_width]
+    width_uniformity = (max(widths) - min(widths)) / (max(widths) + eps)
+
+    # 面部曲线评分：下巴到下颌角的距离 / 面部高度
+    # 距离越短 → 线条越弯曲（圆润），越长 → 越直（方正）
+    jaw_midpoint = (left_jaw + right_jaw) / 2.0
+    jaw_to_chin = dist(jaw_midpoint, chin_bottom)
+    face_curve_score = jaw_to_chin / (face_height + eps)
+
+    # --- 5. 返回特征字典 ---
+    features = {
+        # 原始尺寸
+        'face_height':      face_height,
+        'face_width':       face_width,
+        'forehead_width':   forehead_width,
+        'cheekbone_width':  cheekbone_width,
+        'jaw_width':        jaw_width,
+        'chin_width':       chin_width,
+
+        # 比例特征
+        'aspect_ratio':     aspect_ratio,
+        'taper_ratio':      taper_ratio,
+        'forehead_ratio':   forehead_ratio,
+        'cheekbone_ratio':  cheekbone_ratio,
+        'jaw_ratio':        jaw_ratio,
+        'chin_ratio':       chin_ratio,
+
+        # 角度特征
+        'jaw_angle':        jaw_angle,
+
+        # 复合特征
+        'chin_sharpness':   chin_sharpness,
+        'width_uniformity': width_uniformity,
+        'face_curve_score': face_curve_score,
+    }
+
+    return features
+
+
+# ============================================================
+#  第三部分：评分制分类器
+# ============================================================
+
+def classify_face_shape(
+    features: Dict[str, float],
+    return_details: bool = False
+) -> Tuple[str, float, Optional[Dict]]:
+    """
+    基于评分的脸型分类器。
+
+    策略：
+        每种脸型计算 0-100 的匹配度分数。
+        取最高分作为结果，返回置信度和详细得分。
+
+    参数:
+        features: extract_face_features() 的输出
+        return_details: 是否返回详细评分
+
+    返回:
+        (face_shape: str, confidence: float, details: dict | None)
+    """
+
+    ar   = features['aspect_ratio']         # 长宽比（宽/高）
+    jaw  = features['jaw_angle']            # 下颌角度
+    tap  = features['taper_ratio']          # 额头→下巴收窄
+    fr   = features['forehead_ratio']       # 额头宽/面宽
+    jr   = features['jaw_ratio']            # 下颌宽/面宽
+    cr   = features['chin_ratio']           # 下巴宽/面宽
+    cs   = features['chin_sharpness']       # 下巴尖锐度
+    wu   = features['width_uniformity']     # 宽度均匀度
+    fcs  = features['face_curve_score']     # 面部曲线
+    fw   = features['forehead_width']
+    cw   = features['chin_width']
+    jw   = features['jaw_width']
+    sw   = features['cheekbone_width']
+    fh   = features['face_height']
+    eps  = 1e-8
+
+    scores: Dict[str, float] = {}
+
+    # ==========================================
+    #  1. 圆形脸 (Round)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比接近 1（脸几乎和宽一样长）
+    #    - 下颌角大（>135°，圆润线条）
+    #    - 额头≈颧骨≈下颌宽度（均匀）
+    #    - 下巴圆润不尖
+    #
+    #  理想值：aspect_ratio ≈ 0.88-1.0, jaw_angle ≈ 140-160°
+    # ==========================================
+    s = 0.0
+    s += _score_range(ar, 0.85, 1.0, peak=0.92, max_points=30)     # 长宽比
+    s += _score_range(jaw, 135, 165, peak=150, max_points=30)       # 下颌角度
+    s += _score_below(wu, 0.10, max_points=20)                      # 宽度均匀
+    s += _score_range(cs, 0.65, 0.90, peak=0.75, max_points=10)     # 下巴不尖
+    s += _score_range(tap, -0.05, 0.10, peak=0.02, max_points=10)   # 几乎不收窄
+    scores['圆形脸'] = s
+
+    # ==========================================
+    #  2. 心形脸 (Heart)
+    # ==========================================
+    #  核心特征：
+    #    - 额头明显宽于下巴（taper > 0.2）
+    #    - 下巴尖细（chin_sharpness < 0.55）
+    #    - 颧骨与额头接近（不是颧骨最宽）
+    #    - 前额发际线较宽
+    #
+    #  理想值：taper ≈ 0.25-0.40, chin_sharpness ≈ 0.35-0.55
+    # ==========================================
+    s = 0.0
+    s += _score_above(tap, 0.20, max_points=25)                     # 额头宽于下巴
+    s += _score_below(cs, 0.55, max_points=25)                      # 下巴尖
+    s += _score_above(fw, sw * 0.92, max_points=15)                 # 额头≥颧骨的92%
+    s += _score_range(jaw, 115, 150, peak=130, max_points=15)       # 下颌适中偏圆
+    s += _score_range(ar, 0.75, 0.92, peak=0.82, max_points=10)     # 长宽比适中
+    s += _score_above(cr, 0.0, max_points=10)                       # 下巴存在但窄
+    scores['心形脸'] = s
+
+    # ==========================================
+    #  3. 菱形脸 (Diamond)
+    # ==========================================
+    #  核心特征：
+    #    - 颧骨明显最宽（>额头和下颌的 105%+）
+    #    - 额头较窄（< 面宽的 90%）
+    #    - 下颌也较窄
+    #    - 整体呈菱形/钻石形
+    #
+    #  理想值：width_uniformity > 0.15
+    # ==========================================
+    s = 0.0
+    s += _score_above(sw, fw * 1.05, max_points=25)                 # 颧骨>额头5%+
+    s += _score_above(sw, jw * 1.10, max_points=25)                 # 颧骨>下颌10%+
+    s += _score_below(fr, 0.92, max_points=15)                      # 额头偏窄
+    s += _score_below(jr, 0.92, max_points=15)                      # 下颌偏窄
+    s += _score_above(wu, 0.12, max_points=10)                      # 宽度不均匀
+    s += _score_range(ar, 0.72, 0.90, peak=0.80, max_points=10)     # 长宽比适中
+    scores['菱形脸'] = s
+
+    # ==========================================
+    #  4. 鹅蛋脸 (Oval)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比适中（0.72-0.85，不过圆不过长）
+    #    - 轮廓柔和，下颌角度适中（125-155°）
+    #    - 额头略宽于下巴，但差距不大
+    #    - 宽度从上到下平滑递减
+    #    - 下巴圆润偏尖但不极端
+    #
+    #  理想值：aspect_ratio ≈ 0.75-0.82
+    # ==========================================
+    s = 0.0
+    s += _score_range(ar, 0.70, 0.85, peak=0.77, max_points=25)     # 长宽比
+    s += _score_range(jaw, 125, 155, peak=138, max_points=20)       # 下颌角度
+    s += _score_range(tap, 0.03, 0.20, peak=0.10, max_points=15)    # 适度收窄
+    s += _score_range(cs, 0.50, 0.75, peak=0.62, max_points=15)     # 下巴适中
+    s += _score_below(wu, 0.12, max_points=15)                      # 宽度比较均匀
+    s += _score_range(fcs, 0.15, 0.25, peak=0.19, max_points=10)    # 曲线适中
+    scores['鹅蛋脸'] = s
+
+    # ==========================================
+    #  5. 方形脸 (Square)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比较大（接近等宽，ar > 0.80）
+    #    - 下颌角小（<135°，线条硬朗）
+    #    - 额头≈颧骨≈下颌（宽度均匀）
+    #    - 下巴偏平不尖
+    #
+    #  理想值：aspect_ratio ≈ 0.85-0.95, jaw_angle ≈ 110-125°
+    # ==========================================
+    s = 0.0
+    s += _score_range(ar, 0.78, 0.95, peak=0.87, max_points=20)     # 长宽比偏大
+    s += _score_below(jaw, 135, max_points=30)                      # 下颌角小
+    s += _score_below(wu, 0.10, max_points=20)                      # 宽度均匀
+    s += _score_above(cs, 0.62, max_points=15)                      # 下巴偏宽
+    s += _score_range(jr, 0.90, 1.05, peak=0.96, max_points=15)     # 下颌宽接近面宽
+    scores['方形脸'] = s
+
+    # ==========================================
+    #  6. 长形脸 (Long/Oblong)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比低（< 0.72，脸明显比宽长很多）
+    #    - 面部高度 > 宽度的 1.4 倍
+    #    - 额头略宽于下巴
+    #    - 整体修长
+    #
+    #  理想值：aspect_ratio ≈ 0.58-0.70
+    # ==========================================
+    s = 0.0
+    s += _score_below(ar, 0.72, max_points=35)                      # 长宽比低
+    s += _score_above(fh, features['face_width'] * 1.35, max_points=20)  # 高>宽*1.35
+    s += _score_range(tap, 0.0, 0.20, peak=0.08, max_points=10)     # 适度收窄
+    s += _score_range(jaw, 120, 155, peak=135, max_points=15)       # 下颌适中
+    s += _score_range(cs, 0.45, 0.72, peak=0.58, max_points=10)     # 下巴适中
+    s += _score_range(wu, 0.02, 0.15, peak=0.08, max_points=10)     # 宽度比较均匀
+    scores['长形脸'] = s
+
+    # ==========================================
+    #  7. 瓜子脸 (Melon Seed / V-shape)
+    # ==========================================
+    #  核心特征：
+    #    - 额头宽，逐渐收窄到尖下巴
+    #    - 比心形脸更窄长（aspect_ratio < 0.82）
+    #    - 颧骨不超过额头
+    #    - 下巴尖锐（V 线条）
+    #    - 整体线条流畅
+    #
+    #  理想值：taper ≈ 0.20-0.35, chin_sharpness ≈ 0.30-0.55
+    # ==========================================
+    s = 0.0
+    s += _score_above(tap, 0.15, max_points=20)                     # 额头宽于下巴
+    s += _score_below(ar, 0.82, max_points=15)                      # 偏长
+    s += _score_below(cs, 0.58, max_points=25)                      # 下巴尖
+    s += _score_below(sw, fw * 1.02, max_points=15)                 # 颧骨≤额头
+    s += _score_below(jw, fw * 0.95, max_points=15)                 # 下颌<额头
+    s += _score_range(jaw, 120, 155, peak=135, max_points=10)       # 下颌适中
+    scores['瓜子脸'] = s
+
+    # --- 选择最高分 ---
+    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+    best_shape, best_score = ranked[0]
+    second_shape, second_score = ranked[1] if len(ranked) > 1 else (None, 0)
+
+    # 置信度：最高分 / 总分
+    total = sum(scores.values())
+    confidence = best_score / total if total > 0 else 0.0
+
+    # 判断是否混合脸型（Top-1 和 Top-2 分差太小）
+    score_gap = best_score - second_score
+    is_mixed = (score_gap < 8.0 and best_score > 30.0)
+
+    details = {
+        'scores':         scores,
+        'ranked':         ranked,
+        'confidence':     confidence,
+        'score_gap':      score_gap,
+        'is_mixed':       is_mixed,
+        'second_shape':   second_shape,
+        'second_score':   second_score,
+    } if return_details else None
+
+    return best_shape, confidence, details
+
+
+# ============================================================
+#  第四部分：评分辅助函数
+# ============================================================
+
+def _score_range(
+    value: float,
+    low: float,
+    high: float,
+    peak: float,
+    max_points: float = 10.0
+) -> float:
+    """
+    在 [low, high] 范围内评分，peak 处满分。
+    范围外线性衰减到 0。
+    使用三角窗函数（triangular window）。
+
+    例：_score_range(0.77, 0.70, 0.85, peak=0.77, max_points=25)
+        → value == peak → 返回 25.0
+        → value == low  → 返回 0.0（边界）
+        → value 在 peak 和 low 之间 → 线性插值
+    """
+    if value < low or value > high:
+        return 0.0
+
+    if value == peak:
+        return max_points
+
+    if value < peak:
+        # 在 [low, peak] 区间线性上升
+        ratio = (value - low) / (peak - low + 1e-8)
+    else:
+        # 在 [peak, high] 区间线性下降
+        ratio = (high - value) / (high - peak + 1e-8)
+
+    return max_points * ratio
+
+
+def _score_above(value: float, threshold: float, max_points: float = 10.0) -> float:
+    """
+    value >= threshold 时给满分，低于则线性衰减。
+    衰减区间：[threshold * 0.7, threshold]
+    """
+    if value >= threshold:
+        return max_points
+    floor = threshold * 0.7
+    if value <= floor:
+        return 0.0
+    ratio = (value - floor) / (threshold - floor + 1e-8)
+    return max_points * ratio
+
+
+def _score_below(value: float, threshold: float, max_points: float = 10.0) -> float:
+    """
+    value <= threshold 时给满分，高于则线性衰减。
+    衰减区间：[threshold, threshold * 1.3]
+    """
+    if value <= threshold:
+        return max_points
+    ceil = threshold * 1.3
+    if value >= ceil:
+        return 0.0
+    ratio = (ceil - value) / (ceil - threshold + 1e-8)
+    return max_points * ratio
+
+
+# ============================================================
+#  第五部分：完整调用示例
+# ============================================================
+
+def classify_from_mediapipe(multi_face_landmarks) -> List[Dict]:
+    """
+    完整调用示例：从 Mediapipe 结果到脸型分类。
+
+    参数:
+        multi_face_landmarks: mediapipe FaceMesh 的结果
+                              result.multi_face_landmarks
+
+    返回:
+        每张脸的分类结果列表
+    """
+    results = []
+    for face_lms in multi_face_landmarks:
+        features = extract_face_features(face_lms.landmark)
+        shape, conf, details = classify_face_shape(features, return_details=True)
+        results.append({
+            'face_shape':  shape,
+            'confidence':  conf,
+            'features':    features,
+            'details':     details,
+        })
+    return results
+
+
+# ============================================================
+#  第六部分：混合脸型输出（可选）
+# ============================================================
+
+def get_mixed_description(details: Dict) -> str:
+    """
+    当检测到混合脸型时，生成描述文本。
+
+    例："鹅蛋脸（偏瓜子脸）"
+    """
+    if not details or not details.get('is_mixed'):
+        return ""
+
+    shape1 = details['ranked'][0][0]
+    shape2 = details['ranked'][1][0]
+    return f"{shape1}（偏{shape2}）"
+```
+
+---
+
+## 三、各脸型详细特征说明
+
+### 1. 圆形脸 (Round)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.88-1.0 | 面部宽度和长度几乎相等 |
+| jaw_angle | 140-160° | 下颌线条圆润 |
+| width_uniformity | < 0.08 | 额头、颧骨、下颌宽度接近 |
+| chin_sharpness | 0.65-0.85 | 下巴圆润，不尖锐 |
+| taper_ratio | -0.05 ~ 0.08 | 额头到下巴几乎不收窄 |
+
+**视觉特征**：面部轮廓呈圆形，没有明显棱角，看起来年轻可爱。
+
+### 2. 心形脸 (Heart)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| taper_ratio | 0.25-0.40 | 额头明显宽于下巴 |
+| chin_sharpness | 0.35-0.55 | 下巴尖细 |
+| forehead_ratio | > 0.92 | 额头宽，接近面宽 |
+| jaw_angle | 120-145° | 下颌适中 |
+
+**视觉特征**：上宽下窄，额头饱满，下巴尖俏，像心形。
+
+### 3. 菱形脸 (Diamond)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| 颧骨宽度 | > 额头×1.05 | 颧骨明显最突出 |
+| 颧骨宽度 | > 下颌×1.10 | 远宽于下颌 |
+| forehead_ratio | < 0.92 | 额头偏窄 |
+| jaw_ratio | < 0.92 | 下颌偏窄 |
+| width_uniformity | > 0.12 | 宽度差异明显 |
+
+**视觉特征**：颧骨最宽，额头和下巴都偏窄，呈菱形/钻石轮廓。
+
+### 4. 鹅蛋脸 (Oval)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.75-0.82 | 长宽比理想 |
+| jaw_angle | 130-148° | 轮廓柔和 |
+| taper_ratio | 0.05-0.15 | 适度收窄 |
+| chin_sharpness | 0.55-0.68 | 下巴圆润偏尖 |
+| width_uniformity | < 0.10 | 宽度比较均匀 |
+
+**视觉特征**：被认为是最理想的脸型，比例匀称，轮廓流畅。
+
+### 5. 方形脸 (Square)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.85-0.92 | 接近等宽 |
+| jaw_angle | 108-128° | 下颌角明显，线条硬朗 |
+| width_uniformity | < 0.08 | 三处宽度接近 |
+| chin_sharpness | > 0.65 | 下巴偏平宽 |
+| jaw_ratio | > 0.92 | 下颌宽接近面宽 |
+
+**视觉特征**：额头、颧骨、下颌宽度接近，下颌角明显，给人干练印象。
+
+### 6. 长形脸 (Long/Oblong)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.58-0.70 | 面部明显偏长 |
+| face_height/face_width | > 1.40 | 高度远超宽度 |
+| taper_ratio | 0.05-0.15 | 适度收窄 |
+| jaw_angle | 125-145° | 下颌适中 |
+
+**视觉特征**：面部修长，整体偏窄，额头较饱满。
+
+### 7. 瓜子脸 (Melon Seed / V-shape)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| taper_ratio | 0.20-0.35 | 额头宽于下巴 |
+| aspect_ratio | 0.65-0.80 | 偏长 |
+| chin_sharpness | 0.30-0.55 | V 形尖下巴 |
+| 颧骨 | ≤ 额头宽度 | 颧骨不突出 |
+| jaw_width | < 额头×0.95 | 下颌收窄 |
+
+**视觉特征**：额头较宽，向下逐渐收窄到尖下巴，整体呈瓜子形。
+
+---
+
+## 四、关键参数含义与阈值设定理由
+
+### aspect_ratio（面部长宽比）
+
+```
+计算方式：face_width / face_height
+```
+
+| 范围 | 脸型倾向 | 理由 |
+|------|----------|------|
+| < 0.70 | 长形脸 | 脸长明显大于宽 |
+| 0.70-0.85 | 鹅蛋/心形/瓜子 | 多数亚洲人的标准比例 |
+| 0.85-1.0 | 圆形/方形 | 脸宽接近脸长 |
+
+**设定理由**：根据 Farkas 面部测量数据，东亚人群面宽/面高比通常在 0.75-0.88 之间。0.85 和 0.70 是自然的分界点。
+
+### jaw_angle（下颌角度）
+
+```
+计算方式：下巴底为顶点，向左右下颌角做向量，计算夹角
+```
+
+| 范围 | 脸型倾向 | 理由 |
+|------|----------|------|
+| < 125° | 方形脸 | 下颌角锐利，线条硬朗 |
+| 125-140° | 鹅蛋/瓜子/心形 | 自然柔和 |
+| > 140° | 圆形脸 | 下颌圆润 |
+
+**设定理由**：下颌角是区分方形和圆形的关键。方形脸 gonion 角通常在 110-125°，圆形脸在 140-155°。
+
+### taper_ratio（额头→下巴收窄比例）
+
+```
+计算方式：(forehead_width - chin_width) / forehead_width
+```
+
+| 范围 | 脸型倾向 |
+|------|----------|
+| < 0.05 | 圆形/方形（无收窄）|
+| 0.05-0.15 | 鹅蛋/长形（适度收窄）|
+| > 0.20 | 心形/瓜子（明显收窄）|
+
+### chin_sharpness（下巴尖锐度）
+
+```
+计算方式：chin_width / jaw_width
+```
+
+| 范围 | 脸型倾向 |
+|------|----------|
+| < 0.50 | 尖下巴（瓜子/心形）|
+| 0.50-0.65 | 适中（鹅蛋）|
+| > 0.65 | 宽下巴（圆形/方形）|
+
+---
+
+## 五、优化点说明
+
+### 5.1 从「硬规则」到「评分制」
+
+**原始方案**：每条规则是独立的 if 判断，可能同时满足多条，也可能都不满足。
+
+**优化方案**：每种脸型计算 0-100 的匹配度分数，取最高分。
+
+```python
+# 原始：可能冲突
+if 0.85 <= width_ratio <= 1.0:   # 圆形脸
+    ...
+if jaw_angle < 130:              # 方形脸
+    ...
+# 同一张脸可能同时满足或都不满足！
+
+# 优化：评分制，必然有结果
+scores = {'圆形脸': 72.5, '方形脸': 45.0, ...}
+# 取最高分 → 圆形脸，置信度 72.5/total
+```
+
+### 5.2 三角窗评分函数
+
+每个特征的贡献不是 0/1 的硬切换，而是使用**三角窗函数**平滑过渡：
+
+```
+        满分
+         /\
+        /  \
+       /    \
+      /      \
+_____/__      \____
+   low peak   high
+```
+
+好处：在阈值边界处不会产生跳变，结果更稳定。
+
+### 5.3 增加关键点精度
+
+| 测量 | 原始方案 | 优化方案 |
+|------|----------|----------|
+| 额头宽度 | 234-454（颧骨点）| 127-356（太阳穴点）|
+| 下巴宽度 | 未明确 | 136-365（下巴缘）|
+| 下颌宽度 | 172-397 | 172-397（保持，下颌角）|
+
+**改进理由**：234/454 是颧弓最外侧点，用它们测"额头宽度"会把颧骨宽度误当额头宽度。改用 127/356 太阳穴点更准确。
+
+### 5.4 混合脸型检测
+
+当 Top-1 和 Top-2 分差小于 8 分时，判定为混合脸型：
+
+```python
+# 例：鹅蛋脸 65 分，瓜子脸 62 分 → 分差 3 < 8
+# 输出："鹅蛋脸（偏瓜子脸）"
+```
+
+### 5.5 置信度输出
+
+```python
+confidence = best_score / total_score
+# > 0.25  → 高置信度，结果明确
+# 0.18-0.25 → 中等，有一定混合
+# < 0.18  → 低置信度，建议人工复核
+```
+
+---
+
+## 六、边界情况处理建议
+
+### 6.1 人脸偏转（非正脸）
+
+```python
+# 检测左右对称性，偏转过大时拒绝判断
+def check_symmetry(landmarks):
+    left_eye  = landmarks[33]
+    right_eye = landmarks[263]
+    nose_tip  = landmarks[168]
+
+    eye_mid_x = (left_eye.x + right_eye.x) / 2
+    symmetry = abs(eye_mid_x - nose_tip.x)
+
+    if symmetry > 0.03:  # 偏移过大
+        return False, "检测到人脸偏转，建议正脸拍摄"
+    return True, ""
+```
+
+### 6.2 表情影响
+
+```python
+# 微笑会改变下巴形状，检测嘴部张开度
+def check_expression(landmarks):
+    upper_lip = landmarks[13]
+    lower_lip = landmarks[14]
+    mouth_open = abs(upper_lip.y - lower_lip.y)
+
+    if mouth_open > 0.05:  # 嘴巴张大
+        return False, "检测到嘴巴张开，建议自然闭合"
+    return True, ""
+```
+
+### 6.3 多特征都低分
+
+```python
+if best_score < 25.0:
+    return "无法确定", 0.0, {"reason": "特征不够明显，无法准确分类"}
+```
+
+### 6.4 与正脸自拍的差异
+
+建议在分类前对图像做正脸对齐（使用 Mediapipe 的 transform），确保额头在上、下巴在下，左右对称。
+
+### 6.5 性别/年龄差异
+
+男性下颌通常更宽，女性下巴更尖。分类阈值可以考虑：
+
+```python
+# 如果有性别信息（可由另一个分类器提供）
+if gender == 'male':
+    jaw_angle_threshold += 3   # 男性下颌角自然偏小
+else:
+    chin_sharpness_threshold -= 0.03  # 女性下巴自然偏尖
+```
+
+---
+
+## 七、实现建议和注意事项
+
+### 7.1 预处理
+
+```python
+import mediapipe as mp
+
+mp_face_mesh = mp.solutions.face_mesh
+
+with mp_face_mesh.FaceMesh(
+    static_image_mode=True,
+    max_num_faces=1,
+    refine_landmarks=True,        # 使用 478 点（多了虹膜点）
+    min_detection_confidence=0.5,
+) as face_mesh:
+    results = face_mesh.process(rgb_image)
+    if results.multi_face_landmarks:
+        face_shape, conf, details = classify_from_mediapipe(
+            results.multi_face_landmarks
+        )
+```
+
+### 7.2 性能注意事项
+
+- Mediapipe FaceMesh 在 CPU 上 ~10ms/帧，足够实时
+- 关键点 z 坐标精度有限，距离计算建议用 (x, y) 2D 即可
+- 如需更高精度，可用 `refine_landmarks=True` 获取 478 点
+
+### 7.3 阈值校准
+
+当前阈值基于以下来源综合设定：
+
+1. **Farkas 面部测量学数据**（经典人体测量参考）
+2. **亚洲人脸型分布统计**（鹅蛋脸和瓜子脸比例较高）
+3. **Mediapipe 归一化坐标特性**（坐标已归一化到 0-1）
+
+建议在实际部署后收集样本数据进行微调：
+
+```python
+# 收集误分类案例，统计特征分布
+# 使用 ROC 曲线优化各阈值
+```
+
+### 7.4 2D vs 3D 距离
+
+Mediapipe 返回的 landmark 包含 z 坐标，但 z 精度不如 x/y。建议：
+
+```python
+# 推荐方案：仅用 x, y 计算（忽略 z）
+def pt_2d(idx):
+    lm = landmarks[idx]
+    return np.array([lm.x, lm.y])
+
+# 高精度方案：用 z 但加权降低
+def pt_weighted(idx):
+    lm = landmarks[idx]
+    return np.array([lm.x, lm.y, lm.z * 0.5])  # z 权重减半
+```
+
+### 7.5 与原始规则的对比
+
+| 维度 | 原始规则 | 优化后 |
+|------|----------|--------|
+| 判断方式 | 硬 if-else（可能冲突/遗漏）| 评分制（必然有结果）|
+| 关键点 | 12 个 | 16 个（增加太阳穴、下巴缘点）|
+| 特征数 | 5-6 个 | 15 个（含复合特征）|
+| 输出 | 单一标签 | 标签 + 置信度 + 混合脸型 |
+| 边界处理 | 无 | 三角窗平滑 + 低分兜底 |
+| 可调性 | 改阈值需要理解全部分支 | 改 `peak` 值即可微调 |
+| 代码行数 | ~60 行 | ~300 行（含注释）|
+
+---
+
+## 八、测试用例参考
+
+```python
+# 单元测试伪代码
+test_cases = [
+    # (features_dict, expected_shape)
+    ({'aspect_ratio': 0.92, 'jaw_angle': 148, 'taper_ratio': 0.03,
+      'chin_sharpness': 0.75, 'width_uniformity': 0.06,
+      'forehead_ratio': 0.98, 'jaw_ratio': 0.95, 'chin_ratio': 0.70,
+      'face_curve_score': 0.18, ...}, '圆形脸'),
+
+    ({'aspect_ratio': 0.77, 'jaw_angle': 135, 'taper_ratio': 0.10,
+      'chin_sharpness': 0.60, 'width_uniformity': 0.08,
+      'forehead_ratio': 0.98, 'jaw_ratio': 0.92, 'chin_ratio': 0.62,
+      'face_curve_score': 0.19, ...}, '鹅蛋脸'),
+
+    # ... 更多测试用例
+]
+```
+
+---
+
+*报告结束。代码可直接集成到 Mediapipe 人脸分析流水线中。*

face/face_shape_classifier.py

@@ -0,0 +1,874 @@
+"""
+face_shape_classifier.py
+基于 Mediapipe 468 点人脸关键点的脸型分类系统
+
+支持的脸型：圆形脸 / 心形脸 / 菱形脸 / 鹅蛋脸 / 方形脸 / 长形脸 / 瓜子脸
+分类策略：多维度特征提取 → 加权评分 → 置信度判断
+
+实现说明：
+- 特征与评分框架参考 face_shape_classification.md
+- 距离一律在像素坐标系下用 2D 计算（归一化坐标未校正宽高比会导致面宽被夸大）
+- 阈值按 MediaPipe 实测分布做了校准
+"""
+
+from __future__ import annotations
+
+import math
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import cv2
+import mediapipe as mp
+import numpy as np
+
+ImageInput = Union[str, Path, np.ndarray]
+
+
+class FaceLandmarks:
+    """Mediapipe 关键点索引（脸型分析用）。"""
+
+    FOREHEAD_TOP = 10
+    CHIN_BOTTOM = 152
+
+    # 额侧（比 127/356 更贴近发际两侧，避免把太阳穴外轮廓算成额头）
+    LEFT_FOREHEAD = 54
+    RIGHT_FOREHEAD = 284
+
+    # 太阳穴辅助
+    LEFT_TEMPLE = 21
+    RIGHT_TEMPLE = 251
+
+    # 颧骨最外侧
+    LEFT_CHEEK = 234
+    RIGHT_CHEEK = 454
+
+    # 下颌角（比 172/397 更接近 gonion）
+    LEFT_JAW_ANGLE = 132
+    RIGHT_JAW_ANGLE = 361
+
+    # 下巴缘
+    LEFT_CHIN = 136
+    RIGHT_CHIN = 365
+
+
+def extract_face_features(landmarks, image_size: Tuple[int, int]) -> Dict[str, float]:
+    """
+    从 Mediapipe 关键点提取脸型特征。
+
+    参数:
+        landmarks: NormalizedLandmark 列表
+        image_size: (width, height)，用于还原像素坐标
+    """
+    w, h = image_size
+
+    def pt(idx: int) -> np.ndarray:
+        lm = landmarks[idx]
+        return np.array([lm.x * w, lm.y * h], dtype=float)
+
+    def dist(p1: np.ndarray, p2: np.ndarray) -> float:
+        return float(np.linalg.norm(p1 - p2))
+
+    def xwidth(p1: np.ndarray, p2: np.ndarray) -> float:
+        """横向宽度（脸型比例更稳定）。"""
+        return abs(float(p1[0] - p2[0]))
+
+    forehead_top = pt(FaceLandmarks.FOREHEAD_TOP)
+    chin_bottom = pt(FaceLandmarks.CHIN_BOTTOM)
+
+    left_forehead = pt(FaceLandmarks.LEFT_FOREHEAD)
+    right_forehead = pt(FaceLandmarks.RIGHT_FOREHEAD)
+    left_temple = pt(FaceLandmarks.LEFT_TEMPLE)
+    right_temple = pt(FaceLandmarks.RIGHT_TEMPLE)
+    left_cheek = pt(FaceLandmarks.LEFT_CHEEK)
+    right_cheek = pt(FaceLandmarks.RIGHT_CHEEK)
+    left_jaw = pt(FaceLandmarks.LEFT_JAW_ANGLE)
+    right_jaw = pt(FaceLandmarks.RIGHT_JAW_ANGLE)
+    left_chin = pt(FaceLandmarks.LEFT_CHIN)
+    right_chin = pt(FaceLandmarks.RIGHT_CHIN)
+
+    face_height = dist(forehead_top, chin_bottom)
+    forehead_width = xwidth(left_forehead, right_forehead)
+    temple_width = xwidth(left_temple, right_temple)
+    cheekbone_width = xwidth(left_cheek, right_cheek)
+    jaw_width = xwidth(left_jaw, right_jaw)
+    chin_width = xwidth(left_chin, right_chin)
+    face_width = cheekbone_width
+
+    eps = 1e-8
+
+    # 下巴顶点夹角：越大越宽圆，越小越尖
+    v_left = left_jaw - chin_bottom
+    v_right = right_jaw - chin_bottom
+    cos_val = np.dot(v_left, v_right) / (
+        np.linalg.norm(v_left) * np.linalg.norm(v_right) + eps
+    )
+    cos_val = float(np.clip(cos_val, -1.0, 1.0))
+    jaw_angle = math.degrees(math.acos(cos_val))
+
+    # 下颌角（左）：颧骨→下颌角→下巴，越小越方正硬朗
+    v1 = left_cheek - left_jaw
+    v2 = chin_bottom - left_jaw
+    cos_g = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + eps)
+    cos_g = float(np.clip(cos_g, -1.0, 1.0))
+    gonion_angle = math.degrees(math.acos(cos_g))
+
+    aspect_ratio = face_width / (face_height + eps)
+    length_ratio = face_height / (face_width + eps)
+    taper_ratio = (forehead_width - chin_width) / (forehead_width + eps)
+    cheek_taper = (cheekbone_width - jaw_width) / (cheekbone_width + eps)
+
+    forehead_ratio = forehead_width / (face_width + eps)
+    temple_ratio = temple_width / (face_width + eps)
+    jaw_ratio = jaw_width / (face_width + eps)
+    chin_ratio = chin_width / (face_width + eps)
+    chin_sharpness = chin_width / (jaw_width + eps)
+
+    widths = [forehead_width, cheekbone_width, jaw_width]
+    width_uniformity = (max(widths) - min(widths)) / (max(widths) + eps)
+
+    jaw_midpoint = (left_jaw + right_jaw) / 2.0
+    face_curve_score = dist(jaw_midpoint, chin_bottom) / (face_height + eps)
+
+    forehead_vs_jaw = forehead_width / (jaw_width + eps)
+    cheek_dominance = cheekbone_width / ((forehead_width + jaw_width) / 2.0 + eps)
+
+    return {
+        "face_height": face_height,
+        "face_width": face_width,
+        "forehead_width": forehead_width,
+        "temple_width": temple_width,
+        "cheekbone_width": cheekbone_width,
+        "jaw_width": jaw_width,
+        "chin_width": chin_width,
+        "aspect_ratio": aspect_ratio,
+        "length_ratio": length_ratio,
+        "taper_ratio": taper_ratio,
+        "cheek_taper": cheek_taper,
+        "forehead_ratio": forehead_ratio,
+        "temple_ratio": temple_ratio,
+        "cheekbone_ratio": 1.0,
+        "jaw_ratio": jaw_ratio,
+        "chin_ratio": chin_ratio,
+        "jaw_angle": jaw_angle,
+        "gonion_angle": gonion_angle,
+        "chin_sharpness": chin_sharpness,
+        "width_uniformity": width_uniformity,
+        "face_curve_score": face_curve_score,
+        "forehead_vs_jaw": forehead_vs_jaw,
+        "cheek_dominance": cheek_dominance,
+    }
+
+
+# ============================================================
+#  参考分布：1143 张样本（1093 张脸型测试集合 + 44 张真实照片 + 6 张标注图）
+#  的稳健统计量 (中位数, 稳健标准差=IQR/1.349)，把绝对测量值转成 z 分数。
+#  绝对阈值会随镜头、人群漂移；z 分数让评分只依赖"相对人群偏离多少"。
+#
+#  早前这组数字只由 50 张样本估得，相对全量人群有系统性偏移
+#  （jaw_ratio 中位偏低 0.44 sd、taper_ratio 偏高 0.53 sd 等），
+#  恰好三项都在给方形脸加分，是方形脸占比虚高的主因之一。
+# ============================================================
+REFERENCE_STATS: Dict[str, Tuple[float, float]] = {
+    "aspect_ratio": (0.8294, 0.0281),
+    "jaw_angle": (88.6299, 3.9040),
+    "gonion_angle": (144.5014, 3.4991),
+    "taper_ratio": (0.2671, 0.0376),
+    "forehead_ratio": (0.8975, 0.0204),
+    "jaw_ratio": (0.9286, 0.0138),
+    "chin_ratio": (0.6578, 0.0217),
+    "chin_sharpness": (0.7092, 0.0155),
+    "width_uniformity": (0.1028, 0.0184),
+    "forehead_vs_jaw": (0.9669, 0.0341),
+    "cheek_dominance": (1.0953, 0.0084),
+    "face_curve_score": (0.3940, 0.0210),
+}
+
+# 匹配容差（以 z 为单位）：偏离目标 1 个容差，该项得分降到约 0.61
+MATCH_TOLERANCE = 1.0
+
+# 每种脸型的原型：特征 -> (目标 z, 权重, 模式)
+#   'high' 超过目标即满分（越极端越像）
+#   'low'  低于目标即满分
+#   'peak' 双侧衰减（该特征应当落在目标附近）
+#
+# 只使用互相独立的特征：length_ratio(=1/aspect_ratio) 与
+# cheek_taper(=1-jaw_ratio) 是重复信号，纳入会让对应脸型拿双倍权重。
+#
+# 靶心与权重的来源：先按 1093 张测试集中各原始分组（方形脸/长形脸/瓜子脸/
+# 标准脸/娃娃脸）的实测 z 画像给出靶心，再在「6 张标注图判定不变」的硬约束下
+# 做带边界的退火微调（权重限 [0.5,5]、靶心限 [-2,2]，并惩罚失去区分力的空项）。
+SHAPE_PROTOTYPES: Dict[str, Dict[str, Tuple[float, float, str]]] = {
+    # 宽、短，下颌圆钝
+    "圆形脸": {
+        "aspect_ratio": (+2.00, 5.00, "high"),
+        "jaw_angle": (-0.07, 2.28, "high"),
+        "chin_sharpness": (+0.15, 0.50, "peak"),
+        "face_curve_score": (+0.17, 3.67, "low"),
+    },
+    # 额头宽、下颌与下巴明显收窄
+    "心形脸": {
+        "forehead_vs_jaw": (+1.94, 1.62, "high"),
+        "taper_ratio": (+2.00, 1.09, "high"),
+        "jaw_ratio": (+1.02, 1.86, "low"),
+        "chin_ratio": (-1.70, 1.71, "low"),
+        "aspect_ratio": (+1.69, 1.31, "peak"),
+    },
+    # 颧骨最突出，额头与下颌都窄
+    "菱形脸": {
+        "cheek_dominance": (+1.62, 2.84, "high"),
+        "width_uniformity": (+1.24, 2.12, "high"),
+        "forehead_ratio": (-1.57, 4.58, "low"),
+        "jaw_ratio": (-0.11, 1.85, "low"),
+        "aspect_ratio": (+1.31, 2.60, "peak"),
+    },
+    # 各项都接近人群中位——没有突出特征即为匀称
+    "鹅蛋脸": {
+        "aspect_ratio": (-0.26, 5.00, "peak"),
+        "jaw_ratio": (+0.45, 2.47, "peak"),
+        "chin_sharpness": (+0.52, 2.88, "peak"),
+        "width_uniformity": (+0.53, 0.94, "peak"),
+        "cheek_dominance": (-0.51, 1.14, "peak"),
+    },
+    # 下颌与下巴都宽、几乎不收窄、下颌角锐利、额头相对窄。
+    # 注意 aspect_ratio 用 peak 而非 high：测试集中 121 张方脸的
+    # aspect_ratio 中位仅 +0.16，真正"宽"的是娃娃脸(+1.01)——
+    # 早前把它当成 high 模式的强特征，是方形脸吞掉圆脸的主因。
+    #
+    # chin_ratio 是方脸组区分度最大的一项（组内中位 z=+1.45，标准脸组仅 -0.06），
+    # 故靶心直接对齐 +1.45。靶心与权重必须同时提：若只加权重而把靶心留在低位，
+    # 全人群八成都能拿满分，等于给所有人同加一笔，反而推高方形脸占比。
+    "方形脸": {
+        "aspect_ratio": (+1.11, 4.75, "peak"),
+        "jaw_ratio": (-0.02, 2.27, "high"),
+        "chin_ratio": (+1.45, 3.00, "high"),
+        "taper_ratio": (-0.38, 0.51, "low"),
+        "chin_sharpness": (-0.38, 0.99, "high"),
+        "width_uniformity": (+0.58, 4.34, "high"),
+        "gonion_angle": (+0.10, 3.38, "low"),
+        "forehead_ratio": (-1.70, 4.05, "low"),
+    },
+    # 明显偏长偏窄
+    "长形脸": {
+        "aspect_ratio": (-1.49, 1.17, "low"),
+        "chin_sharpness": (+1.76, 0.50, "high"),
+        "taper_ratio": (+0.84, 0.50, "low"),
+    },
+    # 似心形但下巴更长更尖（face_curve_score 高），颧骨不外扩
+    "瓜子脸": {
+        "face_curve_score": (+1.02, 3.41, "high"),
+        "forehead_ratio": (+0.85, 3.31, "high"),
+        "taper_ratio": (+0.35, 1.02, "high"),
+        "cheek_dominance": (-1.41, 2.21, "low"),
+        "jaw_ratio": (-1.04, 0.57, "low"),
+        "chin_sharpness": (-1.33, 2.53, "low"),
+    },
+}
+
+
+def feature_zscores(features: Dict[str, float]) -> Dict[str, float]:
+    """把测量值转成相对参考人群的 z 分数。"""
+    return {
+        key: (features[key] - median) / scale
+        for key, (median, scale) in REFERENCE_STATS.items()
+        if key in features
+    }
+
+
+def _match(z: float, target: float, mode: str) -> float:
+    """单项匹配度 0~1。"""
+    if mode == "high" and z >= target:
+        return 1.0
+    if mode == "low" and z <= target:
+        return 1.0
+    return math.exp(-((z - target) ** 2) / (2 * MATCH_TOLERANCE**2))
+
+
+def classify_face_shape(
+    features: Dict[str, float],
+    return_details: bool = False,
+) -> Tuple[str, float, Optional[Dict]]:
+    """
+    脸型分类器：把特征转成 z 分数后，与各脸型原型做加权匹配。
+
+    返回 (脸型, 置信度, 详情)。置信度 = Top1 / (Top1 + Top2)，
+    0.5 表示两种脸型完全无法区分，接近 1 表示判定明确。
+    """
+    z = feature_zscores(features)
+
+    scores: Dict[str, float] = {}
+    contributions: Dict[str, Dict[str, float]] = {}
+    for shape, prototype in SHAPE_PROTOTYPES.items():
+        total_weight = sum(w for _, w, _ in prototype.values())
+        acc = 0.0
+        per_feature = {}
+        for key, (target, weight, mode) in prototype.items():
+            m = _match(z[key], target, mode)
+            per_feature[key] = m
+            acc += weight * m
+        scores[shape] = 100.0 * acc / total_weight
+        contributions[shape] = per_feature
+
+    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+    best_shape, best_score = ranked[0]
+    second_shape, second_score = ranked[1] if len(ranked) > 1 else (None, 0.0)
+
+    denom = best_score + second_score
+    confidence = best_score / denom if denom > 0 else 0.0
+    score_gap = best_score - second_score
+    is_mixed = score_gap < 5.0
+
+    details = None
+    if return_details:
+        details = {
+            "scores": scores,
+            "ranked": ranked,
+            "confidence": confidence,
+            "score_gap": score_gap,
+            "is_mixed": is_mixed,
+            "second_shape": second_shape,
+            "second_score": second_score,
+            "zscores": z,
+            "contributions": contributions,
+        }
+
+    return best_shape, confidence, details
+
+
+def get_mixed_description(details: Dict) -> str:
+    if not details or not details.get("is_mixed"):
+        return ""
+    shape1 = details["ranked"][0][0]
+    shape2 = details["ranked"][1][0]
+    return f"{shape1}（偏{shape2}）"
+
+
+_face_mesh = None
+
+
+def _get_face_mesh():
+    global _face_mesh
+    if _face_mesh is None:
+        _face_mesh = mp.solutions.face_mesh.FaceMesh(
+            static_image_mode=True,
+            max_num_faces=1,
+            refine_landmarks=True,
+            min_detection_confidence=0.5,
+        )
+    return _face_mesh
+
+
+def _load_image(image: ImageInput) -> np.ndarray:
+    if isinstance(image, np.ndarray):
+        if image.ndim != 3 or image.shape[2] not in (3, 4):
+            raise ValueError("numpy 图片需为 HxWx3/4 的彩色图")
+        if image.shape[2] == 4:
+            return cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
+        return image
+
+    path = Path(image)
+    img = cv2.imread(str(path))
+    if img is None:
+        raise FileNotFoundError(f"无法读取图片: {path}")
+    return img
+
+
+def _landmark_points(landmarks, image_size: Tuple[int, int]) -> Dict[str, Tuple[int, int]]:
+    """提取标注用像素点。"""
+    w, h = image_size
+
+    def xy(idx: int) -> Tuple[int, int]:
+        lm = landmarks[idx]
+        return int(round(lm.x * w)), int(round(lm.y * h))
+
+    left_jaw = xy(FaceLandmarks.LEFT_JAW_ANGLE)
+    right_jaw = xy(FaceLandmarks.RIGHT_JAW_ANGLE)
+    return {
+        "forehead_top": xy(FaceLandmarks.FOREHEAD_TOP),
+        "chin_bottom": xy(FaceLandmarks.CHIN_BOTTOM),
+        "left_forehead": xy(FaceLandmarks.LEFT_FOREHEAD),
+        "right_forehead": xy(FaceLandmarks.RIGHT_FOREHEAD),
+        "left_cheek": xy(FaceLandmarks.LEFT_CHEEK),
+        "right_cheek": xy(FaceLandmarks.RIGHT_CHEEK),
+        "left_jaw": left_jaw,
+        "right_jaw": right_jaw,
+        "left_chin": xy(FaceLandmarks.LEFT_CHIN),
+        "right_chin": xy(FaceLandmarks.RIGHT_CHIN),
+        "jaw_mid": (
+            int(round((left_jaw[0] + right_jaw[0]) / 2)),
+            int(round((left_jaw[1] + right_jaw[1]) / 2)),
+        ),
+    }
+
+
+def _put_text_cn(
+    img: np.ndarray,
+    text: str,
+    org: Tuple[int, int],
+    color: Tuple[int, int, int],
+    font_size: int = 18,
+) -> None:
+    """在图上绘制中文/英文混合文字（Pillow）。"""
+    from PIL import Image, ImageDraw, ImageFont
+
+    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    pil = Image.fromarray(rgb)
+    draw = ImageDraw.Draw(pil)
+
+    font_paths = [
+        "/usr/share/fonts/truetype/wqy/wqy-microhei.ttc",
+        "/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
+        "/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc",
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+    ]
+    font = None
+    for fp in font_paths:
+        if Path(fp).exists():
+            try:
+                font = ImageFont.truetype(fp, font_size)
+                break
+            except OSError:
+                continue
+    if font is None:
+        font = ImageFont.load_default()
+
+    x, y = org
+    # 阴影提升可读性
+    draw.text((x + 1, y + 1), text, font=font, fill=(0, 0, 0))
+    draw.text((x, y), text, font=font, fill=(color[2], color[1], color[0]))
+    img[:] = cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
+
+
+def _draw_h_line(
+    img: np.ndarray,
+    p1: Tuple[int, int],
+    p2: Tuple[int, int],
+    color: Tuple[int, int, int],
+    label: str,
+    thickness: int = 2,
+    label_above: bool = True,
+) -> None:
+    """画横向宽度线 + 端点 + 标签。"""
+    y = int(round((p1[1] + p2[1]) / 2))
+    x1, x2 = min(p1[0], p2[0]), max(p1[0], p2[0])
+    cv2.line(img, (x1, y), (x2, y), color, thickness, cv2.LINE_AA)
+    cv2.circle(img, (x1, y), 4, color, -1, cv2.LINE_AA)
+    cv2.circle(img, (x2, y), 4, color, -1, cv2.LINE_AA)
+    # 端点小竖线
+    tick = max(6, thickness * 3)
+    cv2.line(img, (x1, y - tick), (x1, y + tick), color, thickness, cv2.LINE_AA)
+    cv2.line(img, (x2, y - tick), (x2, y + tick), color, thickness, cv2.LINE_AA)
+    mid = ((x1 + x2) // 2, y - 8 if label_above else y + 4)
+    _put_text_cn(img, label, mid, color, font_size=max(14, img.shape[0] // 55))
+
+
+def _draw_v_line(
+    img: np.ndarray,
+    p1: Tuple[int, int],
+    p2: Tuple[int, int],
+    color: Tuple[int, int, int],
+    label: str,
+    thickness: int = 2,
+) -> None:
+    """画纵向高度线 + 端点 + 标签。"""
+    x = int(round((p1[0] + p2[0]) / 2))
+    y1, y2 = min(p1[1], p2[1]), max(p1[1], p2[1])
+    cv2.line(img, (x, y1), (x, y2), color, thickness, cv2.LINE_AA)
+    cv2.circle(img, (x, y1), 4, color, -1, cv2.LINE_AA)
+    cv2.circle(img, (x, y2), 4, color, -1, cv2.LINE_AA)
+    tick = max(6, thickness * 3)
+    cv2.line(img, (x - tick, y1), (x + tick, y1), color, thickness, cv2.LINE_AA)
+    cv2.line(img, (x - tick, y2), (x + tick, y2), color, thickness, cv2.LINE_AA)
+    _put_text_cn(
+        img,
+        label,
+        (x + 8, (y1 + y2) // 2),
+        color,
+        font_size=max(14, img.shape[0] // 55),
+    )
+
+
+def annotate_face_features(
+    image: ImageInput,
+    landmarks=None,
+    features: Optional[Dict[str, float]] = None,
+) -> np.ndarray:
+    """
+    在原图上标注 face_width / face_height 及文档中的关键比例特征。
+
+    返回 BGR 标注图。
+    """
+    bgr = _load_image(image).copy()
+    h, w = bgr.shape[:2]
+
+    if landmarks is None:
+        rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+        results = _get_face_mesh().process(rgb)
+        if not results.multi_face_landmarks:
+            raise ValueError("未检测到人脸关键点")
+        landmarks = results.multi_face_landmarks[0].landmark
+
+    if features is None:
+        features = extract_face_features(landmarks, image_size=(w, h))
+
+    pts = _landmark_points(landmarks, (w, h))
+    overlay = bgr.copy()
+    fs = max(14, h // 55)
+    thick = max(2, h // 400)
+
+    # ---- 尺寸主轴 ----
+    # face_height: 额头顶 → 下巴底
+    _draw_v_line(
+        overlay,
+        pts["forehead_top"],
+        pts["chin_bottom"],
+        (40, 180, 255),
+        f"face_height {features['face_height']:.0f}px",
+        thickness=thick + 1,
+    )
+    # face_width (= cheekbone): 左颧 → 右颧
+    _draw_h_line(
+        overlay,
+        pts["left_cheek"],
+        pts["right_cheek"],
+        (0, 220, 120),
+        f"face_width {features['face_width']:.0f}px",
+        thickness=thick + 1,
+        label_above=True,
+    )
+
+    # ---- 各级宽度（forehead / jaw / chin）----
+    # 略微错开 y，避免完全重叠
+    fh_y = pts["left_forehead"][1]
+    _draw_h_line(
+        overlay,
+        (pts["left_forehead"][0], fh_y),
+        (pts["right_forehead"][0], fh_y),
+        (255, 160, 40),
+        f"forehead  ratio={features['forehead_ratio']:.3f}",
+        thickness=thick,
+        label_above=True,
+    )
+    jy = pts["left_jaw"][1]
+    _draw_h_line(
+        overlay,
+        (pts["left_jaw"][0], jy),
+        (pts["right_jaw"][0], jy),
+        (80, 120, 255),
+        f"jaw  ratio={features['jaw_ratio']:.3f}",
+        thickness=thick,
+        label_above=False,
+    )
+    cy = pts["left_chin"][1]
+    _draw_h_line(
+        overlay,
+        (pts["left_chin"][0], cy),
+        (pts["right_chin"][0], cy),
+        (220, 80, 220),
+        f"chin  ratio={features['chin_ratio']:.3f}",
+        thickness=thick,
+        label_above=False,
+    )
+
+    # cheekbone_ratio（相对 face_width，恒为 1.0）写在颧骨线旁
+    cheek_mid = (
+        (pts["left_cheek"][0] + pts["right_cheek"][0]) // 2,
+        pts["left_cheek"][1] + max(18, h // 40),
+    )
+    _put_text_cn(
+        overlay,
+        f"cheekbone_ratio={features['cheekbone_ratio']:.3f}",
+        cheek_mid,
+        (0, 200, 100),
+        font_size=fs,
+    )
+
+    # ---- jaw_angle：下巴 → 左右下颌角 ----
+    cv2.line(overlay, pts["chin_bottom"], pts["left_jaw"], (0, 90, 255), thick, cv2.LINE_AA)
+    cv2.line(overlay, pts["chin_bottom"], pts["right_jaw"], (0, 90, 255), thick, cv2.LINE_AA)
+    cv2.circle(overlay, pts["chin_bottom"], 5, (0, 90, 255), -1, cv2.LINE_AA)
+    # 角度弧
+    v1 = np.array(pts["left_jaw"], dtype=float) - np.array(pts["chin_bottom"], dtype=float)
+    v2 = np.array(pts["right_jaw"], dtype=float) - np.array(pts["chin_bottom"], dtype=float)
+    a1 = math.degrees(math.atan2(-v1[1], v1[0]))
+    a2 = math.degrees(math.atan2(-v2[1], v2[0]))
+    # OpenCV ellipse 角度：从 x 轴顺时针；atan2 转一下
+    start_ang = -a1
+    end_ang = -a2
+    if end_ang < start_ang:
+        start_ang, end_ang = end_ang, start_ang
+    radius = max(28, int(0.08 * features["face_height"]))
+    cv2.ellipse(
+        overlay,
+        pts["chin_bottom"],
+        (radius, radius),
+        0,
+        start_ang,
+        end_ang,
+        (0, 90, 255),
+        thick,
+        cv2.LINE_AA,
+    )
+    _put_text_cn(
+        overlay,
+        f"jaw_angle {features['jaw_angle']:.1f}°",
+        (pts["chin_bottom"][0] + radius + 4, pts["chin_bottom"][1] - radius),
+        (0, 90, 255),
+        font_size=fs,
+    )
+
+    # ---- taper_ratio：额头两端 → 下巴两端（收窄示意）----
+    cv2.line(
+        overlay,
+        pts["left_forehead"],
+        pts["left_chin"],
+        (40, 200, 255),
+        max(1, thick - 1),
+        cv2.LINE_AA,
+    )
+    cv2.line(
+        overlay,
+        pts["right_forehead"],
+        pts["right_chin"],
+        (40, 200, 255),
+        max(1, thick - 1),
+        cv2.LINE_AA,
+    )
+    taper_anchor = (
+        pts["left_forehead"][0] - max(10, w // 30),
+        (pts["left_forehead"][1] + pts["left_chin"][1]) // 2,
+    )
+    _put_text_cn(
+        overlay,
+        f"taper_ratio={features['taper_ratio']:.3f}",
+        taper_anchor,
+        (40, 200, 255),
+        font_size=fs,
+    )
+
+    # ---- chin_sharpness：下巴宽 vs 下颌宽 ----
+    _put_text_cn(
+        overlay,
+        f"chin_sharpness={features['chin_sharpness']:.3f} (chin/jaw)",
+        (pts["left_chin"][0], pts["left_chin"][1] + max(16, h // 45)),
+        (220, 80, 220),
+        font_size=fs,
+    )
+
+    # ---- width_uniformity：三宽差异 ----
+    widths = [
+        ("F", features["forehead_width"], (255, 160, 40)),
+        ("C", features["cheekbone_width"], (0, 220, 120)),
+        ("J", features["jaw_width"], (80, 120, 255)),
+    ]
+    # 右侧小柱状示意
+    panel_x = min(w - max(90, w // 8), max(pts["right_cheek"][0] + 20, w - max(100, w // 7)))
+    panel_y = max(40, pts["forehead_top"][1])
+    max_w = max(x[1] for x in widths) + 1e-8
+    bar_h = max(10, h // 60)
+    gap = max(4, h // 120)
+    for i, (name, val, color) in enumerate(widths):
+        bw = int((val / max_w) * max(50, w // 10))
+        y0 = panel_y + i * (bar_h + gap)
+        cv2.rectangle(overlay, (panel_x, y0), (panel_x + bw, y0 + bar_h), color, -1, cv2.LINE_AA)
+        _put_text_cn(overlay, name, (panel_x + bw + 4, y0 - 2), color, font_size=max(12, fs - 2))
+    _put_text_cn(
+        overlay,
+        f"width_uniformity={features['width_uniformity']:.3f}",
+        (panel_x, panel_y + 3 * (bar_h + gap) + 2),
+        (230, 230, 230),
+        font_size=fs,
+    )
+
+    # ---- face_curve_score：下颌中点 → 下巴 ----
+    cv2.line(
+        overlay,
+        pts["jaw_mid"],
+        pts["chin_bottom"],
+        (180, 255, 80),
+        thick,
+        cv2.LINE_AA,
+    )
+    cv2.circle(overlay, pts["jaw_mid"], 4, (180, 255, 80), -1, cv2.LINE_AA)
+    curve_label_pos = (
+        pts["jaw_mid"][0] + 6,
+        pts["jaw_mid"][1] - max(8, h // 80),
+    )
+    _put_text_cn(
+        overlay,
+        f"face_curve_score={features['face_curve_score']:.3f}",
+        curve_label_pos,
+        (180, 255, 80),
+        font_size=fs,
+    )
+
+    # 半透明叠回原图，再叠一层实线标注更清晰：直接用 overlay
+    # 左侧参数图例
+    legend = [
+        ("face_width / face_height", (0, 220, 120)),
+        (f"jaw_angle={features['jaw_angle']:.1f}°", (0, 90, 255)),
+        (f"taper_ratio={features['taper_ratio']:.3f}", (40, 200, 255)),
+        (f"forehead_ratio={features['forehead_ratio']:.3f}", (255, 160, 40)),
+        (f"cheekbone_ratio={features['cheekbone_ratio']:.3f}", (0, 200, 100)),
+        (f"jaw_ratio={features['jaw_ratio']:.3f}", (80, 120, 255)),
+        (f"chin_ratio={features['chin_ratio']:.3f}", (220, 80, 220)),
+        (f"chin_sharpness={features['chin_sharpness']:.3f}", (220, 80, 220)),
+        (f"width_uniformity={features['width_uniformity']:.3f}", (230, 230, 230)),
+        (f"face_curve_score={features['face_curve_score']:.3f}", (180, 255, 80)),
+    ]
+    box_h = 12 + len(legend) * (fs + 6)
+    box_w = max(220, w // 3)
+    cv2.rectangle(overlay, (8, 8), (8 + box_w, 8 + box_h), (20, 20, 20), -1)
+    cv2.rectangle(overlay, (8, 8), (8 + box_w, 8 + box_h), (90, 90, 90), 1)
+    for i, (text, color) in enumerate(legend):
+        _put_text_cn(overlay, text, (16, 14 + i * (fs + 6)), color, font_size=fs)
+
+    return overlay
+
+
+def classify_from_image(
+    image: ImageInput,
+    return_details: bool = True,
+    return_annotated: bool = False,
+) -> Dict:
+    """
+    从图片判断脸型。
+
+    参数:
+        image: 图片路径，或 OpenCV BGR numpy 数组
+        return_details: 是否返回特征与各脸型得分
+        return_annotated: 是否同时返回特征标注图（BGR）
+    """
+    bgr = _load_image(image)
+    h, w = bgr.shape[:2]
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    results = _get_face_mesh().process(rgb)
+
+    if not results.multi_face_landmarks:
+        raise ValueError("未检测到人脸关键点")
+
+    landmarks = results.multi_face_landmarks[0].landmark
+    features = extract_face_features(landmarks, image_size=(w, h))
+    shape, conf, details = classify_face_shape(features, return_details=True)
+
+    display = get_mixed_description(details) or shape
+    result = {
+        "face_shape": shape,
+        "confidence": conf,
+        "display": display,
+    }
+    if return_details:
+        result["features"] = features
+        result["details"] = details
+    if return_annotated:
+        result["annotated"] = annotate_face_features(
+            bgr, landmarks=landmarks, features=features
+        )
+    return result
+
+
+def classify_from_mediapipe(
+    multi_face_landmarks,
+    image_size: Tuple[int, int],
+) -> List[Dict]:
+    """从 Mediapipe FaceMesh 结果批量分类。image_size=(width, height)。"""
+    results = []
+    for face_lms in multi_face_landmarks:
+        features = extract_face_features(face_lms.landmark, image_size=image_size)
+        shape, conf, details = classify_face_shape(features, return_details=True)
+        results.append(
+            {
+                "face_shape": shape,
+                "confidence": conf,
+                "display": get_mixed_description(details) or shape,
+                "features": features,
+                "details": details,
+            }
+        )
+    return results
+
+
+def run_test_images(test_dir: Union[str, Path, None] = None) -> List[Dict]:
+    """用 test_img 做回归测试；文件名（不含扩展名）为期望脸型。"""
+    if test_dir is None:
+        test_dir = Path(__file__).resolve().parent / "test_img"
+    test_dir = Path(test_dir)
+
+    image_paths = sorted(
+        p
+        for p in test_dir.iterdir()
+        if p.suffix.lower() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+    )
+    if not image_paths:
+        raise FileNotFoundError(f"测试目录无图片: {test_dir}")
+
+    rows = []
+    for path in image_paths:
+        expected = path.stem
+        try:
+            result = classify_from_image(path, return_details=True)
+            predicted = result["face_shape"]
+            display = result["display"]
+            conf = result["confidence"]
+            top3 = result["details"]["ranked"][:3]
+            ok = predicted == expected
+            error = None
+        except Exception as exc:  # noqa: BLE001
+            predicted = display = conf = None
+            top3 = []
+            ok = False
+            error = str(exc)
+
+        rows.append(
+            {
+                "file": path.name,
+                "expected": expected,
+                "predicted": predicted,
+                "display": display,
+                "confidence": conf,
+                "top3": top3,
+                "correct": ok,
+                "error": error,
+            }
+        )
+    return rows
+
+
+def _print_test_report(rows: List[Dict]) -> None:
+    correct = sum(1 for r in rows if r["correct"])
+    total = len(rows)
+
+    print("=" * 72)
+    print("脸型分类测试结果")
+    print("=" * 72)
+    for r in rows:
+        status = "✓" if r["correct"] else "✗"
+        if r["error"]:
+            print(f"{status} {r['file']}")
+            print(f"   期望: {r['expected']}")
+            print(f"   错误: {r['error']}")
+            continue
+
+        top3_str = ", ".join(f"{name}:{score:.1f}" for name, score in r["top3"])
+        print(f"{status} {r['file']}")
+        print(f"   期望: {r['expected']}")
+        print(f"   预测: {r['display']}  (conf={r['confidence']:.3f})")
+        print(f"   Top3: {top3_str}")
+
+    print("-" * 72)
+    print(f"准确率: {correct}/{total} = {correct / total:.1%}")
+    print("=" * 72)
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) > 1 and sys.argv[1] not in {"--test", "-t"}:
+        out = classify_from_image(sys.argv[1], return_details=True)
+        print(f"脸型: {out['display']}")
+        print(f"置信度: {out['confidence']:.3f}")
+        print("各脸型得分:")
+        for name, score in out["details"]["ranked"]:
+            print(f"  {name}: {score:.1f}")
+    else:
+        report = run_test_images()
+        _print_test_report(report)

face_analysis/annotation.py

@@ -6,9 +6,9 @@
 - 纵向竖线 8 条：人头最左 + 左脸颊/左眼外/内角/右眼内/外角/右脸颊 + 人头最右，
   把头宽切 7 段（七眼），段宽数值上下交替（上 3 / 下 4），带虚线双箭头。
   人头最左/最右取自耳朵分割外缘，看不到耳朵则省略该侧（最少 6 点 5 段）。
-- 四庭：图片左侧，「名」上「数值」下两行换行（不带 cm），带竖向虚线双箭头。
+- 四庭：图片左侧，「名」「数值(带 cm)」「百分比」三行换行，带竖向虚线双箭头。
 - 五条横线右侧标名：头顶/发际线/眉心/鼻翼下缘/下巴尖。
-- 单位 cm 统一标在底部「单位cm」。
+- 每段数值直接带 cm 后缀，下方另起一行标百分比（不再单独标底部「单位cm」）。
 中文字体用打包的思源黑体绝对路径加载，缺字体直接抛错（不静默降级成方块）。
 """
 import os
@@ -189,9 +189,8 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
       取自耳朵分割掩膜的外缘（方案 B，BiSeNet 类 7/8）；耳朵不可见（被头发/侧脸
       遮挡 → 掩膜空）或无掩膜时省略该侧端线，只画对应脸颊线。
     - 横向 5 条分界线：头顶/发际线/眉心/鼻翼下缘/下巴尖，右侧标名。
-    - 四庭（顶/上/中/下庭）在左侧：名 + 数值两行换行（无 cm），竖向虚线双箭头。
-    - 七眼段宽数值上下交替（上 3 / 下 4，无 cm），横向虚线双箭头。
-    - 底部统一标「单位cm」。
+    - 四庭（顶/上/中/下庭）在左侧：名 + 数值(带 cm) + 百分比三行换行，竖向虚线双箭头。
+    - 七眼段宽上下交替（上 3 / 下 4）：数值(带 cm) 上、百分比（占头宽比）下，横向虚线双箭头。
 
     variant="v6"（接口6）：去掉头顶横线与顶庭（只画发际线/眉心/鼻翼下缘/下巴尖 4 条
     横线 + 上/中/下庭），竖线纵向范围改为发际线→下巴尖，且不画人头最左/最右端线
@@ -203,7 +202,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     # --- 自适应尺寸：字号/线宽/虚线/箭头按短边缩放 ---
     s = min(w, h)
-    font_size = max(11, round(s * 0.026))     # 字体更小
+    font_size = max(9, round(s * 0.020))      # 字号上调一档
     line_w = max(1, round(s * 0.0022))
     dash_len = max(4, round(s * 0.008))
     gap_len = max(2, round(dash_len * 0.7))   # 虚线更稠密（间隙<划线）
@@ -213,6 +212,8 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     buf = np.zeros((h, w, 4), dtype=np.uint8)
 
+    # 横线：v1 = 头顶/发际线/眉心/鼻翼下缘/下巴尖；v6 = 发际线/眉心/鼻翼下缘/下巴尖。
+    # （发际线贴近头顶时已在 MeasureResult 修正 vertical，此处始终正常画发际线。）
     if variant == "v6":
         order = ["hairline", "brow_center", "nose_bottom", "chin_tip"]
     else:
@@ -240,7 +241,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     face_cx = (fx0 + fx1) / 2
     over = max(6, round(s * 0.030))           # 线超出包围盒的长度（参考图风格）
     face_half = (fx1 - fx0) / 2 + over        # 横线超出最外侧竖线一点
-    # v6 竖线纵向范围 = 发际线→下巴尖（不超出）；v1 = 头顶→下巴尖并两端超出一点
+    # 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1 = 头顶→下巴尖并两端超出一点
     v_top = fy0 if variant == "v6" else fy0 - over
     v_bot = fy1 if variant == "v6" else fy1 + over
 
@@ -256,70 +257,91 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     draw = ImageDraw.Draw(canvas)
     font = _load_font(font_size)
 
-    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字在线上方 ---
-    name_x = fx1 + pad
-    name_gap = max(2, round(pad * 1.6))       # 文字底部到线的间距（再上移）
+    # --- 2b. 每条横线在「中间线段」（两内眼角之间）中点画原点突出 ---
+    # 注意：原点不在整条线的中点 face_cx，而在被竖线切出的中间段（左内眼角↔右内眼角）
+    # 的正中，即脸的竖直中轴附近、两内眼角连线中点。
+    li_x = pts["left_inner"][0]
+    ri_x = pts["right_inner"][0]
+    dot_cx = (li_x + ri_x) / 2
+    dot_r = max(2, round(s * 0.0045))   # 原点半径，与 arrow_size 同档自适应
+    for cy in ys:
+        x0, y0 = dot_cx - dot_r, cy - dot_r
+        x1, y1 = dot_cx + dot_r, cy + dot_r
+        draw.ellipse((x0, y0, x1, y1), fill=LINE_COLOR)
+
+    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字纵向居中对齐到线 ---
+    name_x = fx1 + over + pad                  # 移到横线右端外侧一点（往右）
     for i, name in enumerate(order):
         text = _LINE_NAMES[name]
-        tw, th = _text_size(draw, text, font)
+        tw, _ = _text_size(draw, text, font)
         x = min(name_x, w - 2 - tw)           # 右侧越界时回收
-        draw.text((x, max(2, ys[i] - th - name_gap)), text, fill=LINE_COLOR, font=font)
+        # anchor="lm"：x 为左、y 为竖直中点 → 文字中线正好压在横线上（与线对齐）
+        draw.text((x, ys[i]), text, fill=LINE_COLOR, font=font, anchor="lm")
 
     # --- 3b. 左侧四庭：名 + 数值两行（无 cm）+ 竖向虚线双箭头 ---
+    # court_start：庭段在 order 里的起始索引。v6 为三庭（无顶庭），其余四庭。
     if variant == "v6":
         court_cm = [measure_result.upper_cm, measure_result.middle_cm, measure_result.lower_cm]
         court_name = ["上庭", "中庭", "下庭"]
         n_court = 3
+        court_start = 0
     else:
         court_cm = [measure_result.top_cm, measure_result.upper_cm,
                     measure_result.middle_cm, measure_result.lower_cm]
         court_name = ["顶庭", "上庭", "中庭", "下庭"]
         n_court = 4
+        court_start = 0
     arrow_x = max(arrow_size + 1, fx0 - pad)   # 竖箭头所在 x（脸左侧，贴近最左竖线）
+    court_total = sum(court_cm) or 1.0          # 各庭占比分母 = 四庭（v6 三庭）之和
     for i in range(n_court):
-        y_a, y_b = ys[i], ys[i + 1]
+        y_a, y_b = ys[court_start + i], ys[court_start + i + 1]
         # 竖向虚线双箭头，覆盖该庭高度（略收一点避免压到横线）
         inset = min(arrow_size, (y_b - y_a) * 0.12)
         draw_dashed_line_with_arrows(
             draw, arrow_x, y_a + inset, arrow_x, y_b - inset,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        # 名 + 数值两行，右对齐到箭头左侧
+        # 名 + 数值(带 cm) + 百分比三行，右对齐到箭头左侧
         name = court_name[i]
-        val = f"{court_cm[i]:.2f}"
+        val = f"{court_cm[i]:.2f}cm"
+        pct = f"{court_cm[i] / court_total * 100:.1f}%"
         nw, _ = _text_size(draw, name, font)
         vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         label_right = arrow_x - pad
         y_mid = (y_a + y_b) / 2
-        y_top = y_mid - line_h
+        y_top = y_mid - 1.5 * line_h
         draw.text((max(2, label_right - nw), y_top), name, fill=LINE_COLOR, font=font)
         draw.text((max(2, label_right - vw), y_top + line_h), val, fill=LINE_COLOR, font=font)
+        draw.text((max(2, label_right - pw), y_top + 2 * line_h), pct, fill=LINE_COLOR, font=font)
 
-    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值（无 cm） ---
-    # 文字与箭头间留更大间距，避免文字压住箭头
+    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值(带 cm) + 百分比 ---
+    # 每段两行：数值(带 cm) 上、百分比 下；百分比分母 = 整个头宽（七段之和）
     txt_off = arrow_size + pad * 2
-    y_arrow_top = max(txt_off + font_size + 2, fy0 - pad - arrow_size)
-    y_arrow_bot = min(h - txt_off - font_size - 2, fy1 + pad + arrow_size)
+    txt_block = 2 * line_h                     # 两行文字总高（数值 + 百分比）
+    y_arrow_top = max(txt_off + txt_block + 2, fy0 - pad - arrow_size)
+    y_arrow_bot = min(h - txt_off - txt_block - 2, fy1 + pad + arrow_size)
+    head_w = (xs[-1] - xs[0]) or 1.0           # 头宽（像素）= 百分比分母
     for i in range(len(xs) - 1):
         x_a, x_b = xs[i], xs[i + 1]
         if x_b - x_a < 1:
             continue
         seg_cm = (x_b - x_a) / pc
+        seg_pct = (x_b - x_a) / head_w * 100
         cx_seg = (x_a + x_b) / 2
-        text = f"{seg_cm:.2f}"
-        tw, th = _text_size(draw, text, font)
+        val = f"{seg_cm:.2f}cm"
+        pct = f"{seg_pct:.1f}%"
+        vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         inset = min(arrow_size, (x_b - x_a) * 0.12)
         on_top = (i % 2 == 1)                 # 奇数段在上 → 上 3 / 下 4
         y_arrow = y_arrow_top if on_top else y_arrow_bot
         draw_dashed_line_with_arrows(
             draw, x_a + inset, y_arrow, x_b - inset, y_arrow,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        ty = (y_arrow - th - txt_off) if on_top else (y_arrow + txt_off)
-        draw.text((cx_seg - tw / 2, ty), text, fill=LINE_COLOR, font=font)
-
-    # --- 5. 底部统一单位 ---
-    unit = "单位cm"
-    uw, uh = _text_size(draw, unit, font)
-    draw.text(((w - uw) / 2, h - uh - max(2, pad)), unit, fill=LINE_COLOR, font=font)
+        # 数值行在上、百分比行在下；on_top 时整块置于箭头上方，否则下方
+        text_top = (y_arrow - txt_off - txt_block) if on_top else (y_arrow + txt_off)
+        draw.text((cx_seg - vw / 2, text_top), val, fill=LINE_COLOR, font=font)
+        draw.text((cx_seg - pw / 2, text_top + line_h), pct, fill=LINE_COLOR, font=font)
 
     return canvas
 

face_analysis/face_mesh_landmarks.py

@@ -18,6 +18,8 @@ RIGHT_EYE_INNER = 362  # 右眼内角
 RIGHT_EYE_OUTER = 263  # 右眼外角
 LEFT_CHEEK = 234       # 左脸颧弓（脸宽左端）
 RIGHT_CHEEK = 454      # 右脸颧弓（脸宽右端）
+LEFT_POSITION = 21     # 左脸前侧定位点（脸颊/耳前区域，与 251 镜像）
+RIGHT_POSITION = 251   # 右脸前侧定位点（与 21 镜像）
 
 # --- 鼻尖（solvePnP 用，可选） ---
 NOSE_TIP = 1   # 鼻尖（也有用 4 的版本）

face_analysis/hairline_grow.py

@@ -110,7 +110,7 @@ def _gray_b64(gray_float):
 def _red_mask_b64(mask_bool, h, w):
     """布尔遮罩 → 纯红 alpha PNG data URI。
     遮罩区域 RGBA=(255,0,0,255)，其余区域 RGBA=(0,0,0,0)。
-    供外部 ComfyUI 重绘服务（如 local_test）按 alpha 通道识别重绘区。
+    供 ComfyUI 重绘接口（/api/v1/redraw）按 alpha 通道识别重绘区。
 
     注意：cv2.imencode 写 PNG 用的是 **BGRA** 顺序（B,G,R,A），所以要得到
     浏览器显示的红色 R=255，需赋值 (B=0,G=0,R=255,A=255)。
@@ -357,7 +357,8 @@ def _redraw_band_mask(inner_pts, outer_pts, h, w, rid="", upper=None,
 
 
 def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True):
+                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True,
+                 hair_mask=None):
     """算出布尔遮罩 + 可视化。
 
     seg_model: bisenet | segformer。
@@ -368,6 +369,7 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     render_viz: 是否生成各阶段叠图 overlay JPG（接口11 调试页用）。接口2/12 路径传 False
         可跳过 6+ 张 base64 编码，省 ~80ms；数据字段（_inner_pts/_outer_pts/_upper_mask/
         mask_pixels 等）始终返回，不受影响。
+    hair_mask: 预计算的头发布尔遮罩（来自 SegFormer parse）。传入时跳过重复分割，省 ~0.9s。
     返回 (mask_bool, viz_dict)。
     """
     lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
@@ -381,13 +383,16 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     upper = _upper_region_mask(baseline_pts, w, h)
     lg(f"baseline 第一点={baseline_pts[0]} 末点={baseline_pts[-1]} upper像素={int(upper.sum())}")
 
-    if seg_model == "bisenet":
-        hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
-    elif seg_model == "segformer":
-        hair_mask = _segformer_hair_mask(image_bgr)
+    if hair_mask is None:
+        if seg_model == "bisenet":
+            hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
+        elif seg_model == "segformer":
+            hair_mask = _segformer_hair_mask(image_bgr)
+        else:
+            raise ValueError(f"未知 seg_model: {seg_model}")
+        lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
     else:
-        raise ValueError(f"未知 seg_model: {seg_model}")
-    lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
+        lg(f"头发分割跳过(复用外部传入) hair_pixels={int(hair_mask.sum())}")
 
     top_fill = _fill_to_baseline(hair_mask, upper)      # 含额头，延伸到图底
     closed = _largest_cc(top_fill & upper)              # 闭合区域：头发+额头，底=基线
@@ -503,13 +508,14 @@ def _segment_hair(image_bgr, seg_model, landmarks, w, h):
 # ---------------------------------------------------------------------------
 
 def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength,
-               inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0):
+               inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0, webui_steps=None):
     """调 change_hair /api/swapHair/v1，返回与输入同分辨率同对齐的换发型结果（BGR）。
 
     ext_mask_bool 非 None 时作为 ext_mask 传入（swap_mode=ext_mask）。
     denoising_strength：webui img2img 重绘强度（越大生发越激进），透传给换发型。
     inpainting_fill / mask_blur / mask_dilate_scale：服务端重绘参数（透传给 change_hair，
         默认值=服务端原始硬编码值，未传时行为不变）。详见 change_hair 文档。
+    webui_steps：webui img2img 采样步数，None 用服务端默认(15)。
     """
     import requests
 
@@ -525,6 +531,8 @@ def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength,
         "mask_blur": int(mask_blur),
         "mask_dilate_scale": float(mask_dilate_scale),
     }
+    if webui_steps is not None:
+        payload["webui_steps"] = int(webui_steps)
     if ext_mask_bool is not None:
         mbuf = cv2.imencode(".png", (ext_mask_bool.astype(np.uint8)) * 255)[1]
         payload["ext_mask"] = "data:image/png;base64," + base64.b64encode(mbuf.tobytes()).decode()
@@ -849,7 +857,8 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
                edge_erode_px, denoising_strength, gen_backend, hairgrow_strength,
                mb_levels, hairline_push_cm, hairline_edge, blend_method, color_match,
                color_match_strength, mb_feather_px, transition_band_px,
-               inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True):
+               inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True,
+               hair_mask=None, webui_steps=None):
     """接口11 共享核心：遮罩(pushed)→生成→硬贴回→接缝融合，产出 ④ final。
 
     不做任何重绘。返回中间产物 dict（供接口11 构造响应、接口12 取 final+重绘带用）：
@@ -879,7 +888,7 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
     mask_bool, mask_viz = compute_mask(
         image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
         hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid,
-        render_viz=render_viz)
+        render_viz=render_viz, hair_mask=hair_mask)
     t_mask = time.time() - t0
     logger.info("[%s] 步骤1 遮罩完成 耗时=%dms mask_pixels=%d", rid, int(t_mask*1000), int(mask_bool.sum()))
 
@@ -891,7 +900,7 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
         ext_mask = mask_bool if swap_mode == "ext_mask" else None
         swap_result = _call_swap(image_bgr, hairline_id, is_hr, ext_mask, denoising_strength,
                                  inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-                                 mask_dilate_scale=mask_dilate_scale)
+                                 mask_dilate_scale=mask_dilate_scale, webui_steps=webui_steps)
     t_swap = time.time() - t0
 
     # 步骤3：严格按遮罩硬贴回（无融合，用于对比）
@@ -929,7 +938,7 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
                            color_match_strength=1.0, mb_feather_px=1,
                            transition_band_px=-1,
                            inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
-                           rid=None):
+                           rid=None, webui_steps=None):
     """接口11 完整管线（**不含重绘**，重绘见接口12 generate_hairline_redraw）。
     返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
 
@@ -951,7 +960,7 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         color_match=color_match, color_match_strength=color_match_strength,
         mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
         inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-        mask_dilate_scale=mask_dilate_scale, rid=rid)
+        mask_dilate_scale=mask_dilate_scale, rid=rid, webui_steps=webui_steps)
     mask_viz = core["mask_viz"]
     alpha = core["alpha"]
     w, h = core["w"], core["h"]
@@ -1028,13 +1037,14 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
                              transition_band_px=-1,
                              inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
                              comfyui_prompt=None, beauty_alpha=0.6,
-                             band_lo_mult=0.5, band_hi_mult=1.5, rid=None):
+                             band_lo_mult=0.5, band_hi_mult=1.5, rid=None,
+                             hair_mask=None, webui_steps=None):
     """接口12 发际线带重绘。内部先跑接口11 核心拿到 ④ final，再取 ⑤-① 发际线重绘带
     （外推↔内推之间、经 baseline 截断只留上部）作遮罩。
 
     **本接口不再做 Flux-2 重绘**：只产出 `final`（接缝融合基底）+ 纯红遮罩
-    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给前端调
-    外部 ComfyUI 重绘服务（见 local_test）完成。旧的 `redraw_full` / `redraw_band`
+    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给后端
+    ComfyUI 重绘接口（/api/v1/redraw）完成。旧的 `redraw_full` / `redraw_band`
     字段保留为空，仅作结构兼容。
 
     返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
@@ -1056,7 +1066,8 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         color_match=color_match, color_match_strength=color_match_strength,
         mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
         inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-        mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False)
+        mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False,
+        hair_mask=hair_mask, webui_steps=webui_steps)
     final = core["final"]
     mask_viz = core["mask_viz"]
     w, h = core["w"], core["h"]
@@ -1089,7 +1100,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         redraw_info = {"enabled": False, "error": f"band: {ex}"}
 
     # ② Flux-2 重绘已下线：本接口现在只产出 final（接缝融合基底）+ 纯红重绘带遮罩，
-    #    重绘交给前端调外部 ComfyUI 重绘服务（见 local_test）完成。
+    #    重绘交给后端 ComfyUI 重绘接口（/api/v1/redraw）完成。
     #    下面保留 redraw_full_b64 / redraw_band_b64 为空，保持返回结构兼容（旧字段）。
     redraw_full_b64 = ""
     redraw_band_b64 = ""
@@ -1099,7 +1110,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         "hairline_id": hairline_id,
         "blend_method": blend_method,
         "hairline_push_cm": round(float(hairline_push_cm), 2),
-        "comfyui_prompt": comfyui_prompt or "补充遮罩区域的头发，加一点美颜",
+        "comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发，人物增加一点磨皮",
         "beauty_alpha": beauty_alpha,
         "px_per_cm": round(float(px_per_cm), 4),
         "mask_pixels": mask_viz["mask_pixels"],

face_analysis/measure.py

@@ -12,9 +12,9 @@ from face_analysis.calibration import (
     estimate_scale_factor, normalized_to_pixel, pixel_distance, _lm_list,
 )
 from face_analysis.face_mesh_landmarks import (
-    GLABELLA_9, GLABELLA_151, NOSE_BOTTOM, CHIN_TIP,
+    GLABELLA_9, NOSE_BOTTOM, CHIN_TIP,
     LEFT_EYE_OUTER, LEFT_EYE_INNER, RIGHT_EYE_INNER, RIGHT_EYE_OUTER,
-    LEFT_CHEEK, RIGHT_CHEEK,
+    LEFT_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
 )
 from face_analysis.hair_segmenter import locate_hairline_by_segmentation
 
@@ -24,10 +24,8 @@ _TOP_RATIO = 0.22 / 0.28      # 顶庭 ÷ 中庭（≈ 0.786）
 
 
 def _brow_center(lm, w, h):
-    """眉心 = 索引 9 / 151 中点。"""
-    g9 = normalized_to_pixel(lm[GLABELLA_9], w, h)
-    g151 = normalized_to_pixel(lm[GLABELLA_151], w, h)
-    return (g9[0] + g151[0]) / 2, (g9[1] + g151[1]) / 2
+    """眉心 = 索引 9（眉间上点）。"""
+    return normalized_to_pixel(lm[GLABELLA_9], w, h)
 
 
 def estimate_vertical_landmarks(landmarks, image_width, image_height):
@@ -144,22 +142,62 @@ def measure_seven_eyes(landmarks, image_width, image_height):
     }
 
 
+def pt_or_none(vertical, name):
+    """vertical dict 的点 → {"x","y"}，值为 None 时返回 None。"""
+    v = vertical.get(name)
+    if v is None:
+        return None
+    return {"x": int(round(v[0])), "y": int(round(v[1]))}
+
+
 class MeasureResult:
     """测量结果，提供 to_response() 输出与接口文档同构的 data 字段。"""
 
-    def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose):
+    # 发际线修正阈值/固定值：发际线离头顶（顶庭）< 此值时判定分割不可靠，需修正发际线。
+    # hairline 与 hair_top 几乎重合（如稀疏头发中轴漏检只剩一小撮）→ 上庭改取
+    # 0.95×中庭反推发际线；若反推后越过头顶，则顶庭固定为 0.7cm（发际线钉在头顶下方
+    # 0.7cm），上庭取眉心→发际线剩余距离。修正后始终返回完整四庭，不再置 null。
+    HAIRLINE_CORRECT_TOP_CM = 0.7
+    UPPER_TO_MIDDLE_RATIO = 0.95   # 贴近头顶时，上庭 = 中庭 × 此比例
+
+    def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose,
+                 landmarks=None, image_width=None, image_height=None):
         self.vertical = vertical
         self.eyes = eyes
         self.px_per_cm = px_per_cm
         self.hairline_source = hairline_source
         self.head_pose = head_pose  # (yaw, pitch, roll) 或 None
+        # 原始 mediapipe 点集 + 图像尺寸，供 to_response 输出 21/251 号定位点
+        self.landmarks = landmarks
+        self.w = image_width
+        self.h = image_height
 
-        # 各庭厘米
+        # 发际线修正：顶庭（头顶→发际线）< 阈值视为发际线贴近头顶、不可靠。
+        # 改写：上庭 = 0.95×中庭 → 由眉心反推发际线 y；若发际线越过头顶
+        # （hairline_y < hair_top_y），顶庭固定为 0.7cm（发际线钉在头顶下方 0.7cm）。
+        # 回写 vertical，使 to_response/标注图/v6 复用全部拿到修正后的发际线。
+        self.detected_top_cm = vertical["top_court_px"] / px_per_cm
+        self.hairline_corrected = self.detected_top_cm < self.HAIRLINE_CORRECT_TOP_CM
+        if self.hairline_corrected:
+            brow_x, brow_y = vertical["brow_center"]
+            hair_top_y = vertical["hair_top"][1]
+            new_upper_px = vertical["middle_court_px"] * self.UPPER_TO_MIDDLE_RATIO
+            hairline_y = brow_y - new_upper_px
+            if hairline_y < hair_top_y:   # 反推发际线越过头顶 → 顶庭固定 0.7cm
+                hairline_y = hair_top_y + self.HAIRLINE_CORRECT_TOP_CM * px_per_cm
+                new_upper_px = brow_y - hairline_y
+            vertical["hairline"] = (brow_x, float(hairline_y))
+            vertical["upper_court_px"] = new_upper_px
+            vertical["top_court_px"] = hairline_y - hair_top_y
+            self.hairline_source = "corrected"
+
+        # 各庭厘米（修正后）
         self.top_cm = vertical["top_court_px"] / px_per_cm
         self.upper_cm = vertical["upper_court_px"] / px_per_cm
         self.middle_cm = vertical["middle_court_px"] / px_per_cm
         self.lower_cm = vertical["lower_court_px"] / px_per_cm
-        self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_cm
+        self.face_total_cm = (self.top_cm + self.upper_cm
+                              + self.middle_cm + self.lower_cm)
 
         # 七眼厘米
         self.eye_width_cm = eyes["eye_width_px"] / px_per_cm
@@ -167,14 +205,9 @@ class MeasureResult:
         self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm
 
     def to_response(self):
+        # 四庭完整输出（发际线贴近头顶时已在 __init__ 修正 vertical，无 null 分支）。
         total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
                     + self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
-        fw_px = self.eyes["face_width_px"]
-
-        def pt(name):
-            x, y = self.vertical[name]
-            return {"x": int(round(x)), "y": int(round(y))}
-
         data = {
             "face_total_height_cm": round(self.face_total_cm, 2),
             "four_courts": {
@@ -194,19 +227,30 @@ class MeasureResult:
                 "face_width_cm": round(self.face_width_cm, 2),
                 "inter_eye_distance_cm": round(self.inter_eye_cm, 2),
                 "ratios": {
-                    "eye_width": round(self.eyes["eye_width_px"] / fw_px, 3),
-                    "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / fw_px, 3),
+                    "eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
+                    "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
                 },
             },
             "landmarks": {
-                "hair_top": pt("hair_top"),
-                "hairline": pt("hairline"),
-                "brow_center": pt("brow_center"),
-                "nose_bottom": pt("nose_bottom"),
-                "chin_tip": pt("chin_tip"),
+                "hair_top": pt_or_none(self.vertical, "hair_top"),
+                "hairline": pt_or_none(self.vertical, "hairline"),
+                "brow_center": pt_or_none(self.vertical, "brow_center"),
+                "nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
+                "chin_tip": pt_or_none(self.vertical, "chin_tip"),
             },
             "hairline_source": self.hairline_source,
         }
+        # left/right_position：mediapipe 21/251 号定位点（原图像素，与 landmarks 同坐标系）。
+        # landmarks 缺省（如测试直构 MeasureResult）时不输出，保持向后兼容。
+        if self.landmarks is not None and self.w and self.h:
+            lm = _lm_list(self.landmarks)
+
+            def _pt_lm(idx):
+                px, py = normalized_to_pixel(lm[idx], self.w, self.h)
+                return {"x": int(round(px)), "y": int(round(py))}
+
+            data["left_position"] = _pt_lm(LEFT_POSITION)
+            data["right_position"] = _pt_lm(RIGHT_POSITION)
         if self.head_pose is not None:
             yaw, pitch, roll = self.head_pose
             data["head_pose"] = {
@@ -220,7 +264,8 @@ def measure_face(landmarks, hair_mask, image_width, image_height, head_pose=None
     vertical, source = decide_vertical(landmarks, image_width, image_height, hair_mask)
     eyes = measure_seven_eyes(landmarks, image_width, image_height)
     px_per_cm = estimate_scale_factor(landmarks, image_width, image_height)
-    return MeasureResult(vertical, eyes, px_per_cm, source, head_pose)
+    return MeasureResult(vertical, eyes, px_per_cm, source, head_pose,
+                         landmarks, image_width, image_height)
 
 
 if __name__ == "__main__":

face_analysis/pose.py

@@ -50,12 +50,21 @@ def estimate_head_pose(landmarks, image_width, image_height):
                            [0, 0, 1]], dtype=np.float64)
     dist = np.zeros((4, 1))  # 假设无畸变
 
-    success, rvec, _tvec = cv2.solvePnP(
+    success, rvec, tvec = cv2.solvePnP(
         _MODEL_POINTS, image_points, cam_matrix, dist,
         flags=cv2.SOLVEPNP_ITERATIVE,
     )
     if not success:
         return None
+    # ITERATIVE 偶发收敛到相机后方的翻转解（tz<0），此时 roll 落在 ±180° 附近，
+    # 会把真正的正面照误判为 1003。改用 SQPNP 重解正深度解。
+    if float(tvec[2, 0]) < 0:
+        ok2, rvec2, tvec2 = cv2.solvePnP(
+            _MODEL_POINTS, image_points, cam_matrix, dist,
+            flags=cv2.SOLVEPNP_SQPNP,
+        )
+        if ok2 and float(tvec2[2, 0]) > 0:
+            rvec = rvec2
     rot, _ = cv2.Rodrigues(rvec)
     # 在「相机坐标系」(x右 y下 z内) 下抽取 Tait-Bryan 欧拉角，物理含义对齐：
     #   yaw   = 绕 Y（竖轴）转 → 左右扭头

face_analysis/special_points.py

@@ -0,0 +1,166 @@
+"""接口99：人脸特殊点检测。
+
+返回 MediaPipe Face Mesh 关键点中一组固定编号的像素坐标（英文组名_点ID 为字段名），
+外加 5 个发际线特殊点：从指定关键点沿指定方向射线步进，与 BiSeNet 头发分割 mask
+首次相交处即发际线点（参考接口1 方案B 的发际线定位方式，见 hair_segmenter.py）。
+
+组与编号（需求方给定）：
+- eyebrow       107 66 105 63 70 336 296 334 293 300
+- eye           189 222 224 113 226 413 442 444 342 446
+- mouth         0 37 40 67 91 84 17 405 375 409 269
+- nose_midline  9 168 197 5 4 194 278 48 437 217
+- cheek         101 117 123 187 205 50 330 346 352 280 425
+- face_edge     162 234 58 136 149 152 378 365 288 401 366 389 251
+
+发际线点（字段名需求方给定；A/B 两射线时取中点，仅一条命中则返回该点，均未命中为 null）：
+- hair_line_center       151 → 10 方向与发际线交点
+- hair_line_right        mid(108→109 交点A, 69→67 交点B)
+- hair_line_right_right  mid(104→103 交点A, 68→54 交点B)
+- hair_line_left         mid(337→338 交点A, 299→297 交点B)
+- hair_line_left_left    mid(333→332 交点A, 298→284 交点B)  # 与 right_right 镜像对称
+"""
+import math
+
+import numpy as np
+
+# 固定关键点组：组英文名 → MediaPipe 点 ID 列表（顺序即需求方给定顺序）
+GROUPS = {
+    "eyebrow": [107, 66, 105, 63, 70, 336, 296, 334, 293, 300],
+    "eye": [189, 222, 224, 113, 226, 413, 442, 444, 342, 446],
+    "mouth": [0, 37, 40, 67, 91, 84, 17, 405, 375, 409, 269],
+    "nose_midline": [9, 168, 197, 5, 4, 194, 278, 48, 437, 217],
+    "cheek": [101, 117, 123, 187, 205, 50, 330, 346, 352, 280, 425],
+    "face_edge": [162, 234, 58, 136, 149, 152, 378, 365, 288, 401, 366, 389, 251],
+}
+
+# 发际线点定义：字段名 → 参与计算的 (起点ID, 方向点ID) 射线组；单射线直接取交点，
+# 双射线取两交点中点。
+HAIRLINE_RAYS = {
+    "hair_line_center": [(151, 10)],
+    "hair_line_right": [(108, 109), (69, 67)],
+    "hair_line_right_right": [(104, 103), (68, 54)],
+    "hair_line_left": [(337, 338), (299, 297)],
+    "hair_line_left_left": [(333, 332), (298, 284)],
+}
+
+
+class NoFaceError(Exception):
+    """未检出人脸。"""
+
+
+class NonFrontalError(Exception):
+    """非正面照（复用接口1 姿态阈值）。"""
+
+
+def ray_hairline_intersection(p1, p2, hair_mask):
+    """从 p1 沿 p1→p2 方向逐像素步进，返回首次进入头发 mask 的整数像素坐标。
+
+    p1/p2: (x, y) 像素坐标（float）。起点本身已在头发内时直接返回起点（已到发际线）。
+    步进直到出图或步数上限（3×图对角线，防极端死循环）；未命中返回 None。
+    """
+    mask = np.asarray(hair_mask).astype(bool)
+    if mask.size == 0:
+        return None
+    h, w = mask.shape
+    x0, y0 = float(p1[0]), float(p1[1])
+    dx, dy = float(p2[0]) - x0, float(p2[1]) - y0
+    length = math.hypot(dx, dy)
+    if length < 1e-6:
+        return None
+    ux, uy = dx / length, dy / length
+    max_steps = int(3 * math.hypot(h, w))
+
+    def _hit(x, y):
+        xi, yi = int(round(x)), int(round(y))
+        if 0 <= xi < w and 0 <= yi < h:
+            return mask[yi, xi]
+        return False
+
+    x, y = x0, y0
+    for _ in range(max_steps):
+        if _hit(x, y):
+            return int(round(x)), int(round(y))
+        x += ux
+        y += uy
+        if not (0 <= int(round(x)) < w and 0 <= int(round(y)) < h):
+            return None   # 已步出图像，方向上无头发
+    return None
+
+
+def detect_special_points(image_bgr):
+    """主入口：图片 → {字段名: {"x","y"} 或 None} + 元信息。
+
+    Returns: {"points": {...}, "image_size": {"width","height"},
+              "hairline_source": "segmentation"|"unavailable"}
+    无人脸抛 NoFaceError；非正面抛 NonFrontalError。
+    """
+    from face_analysis.detector import detector
+    from face_analysis.calibration import normalized_to_pixel
+    from face_analysis.pose import check_frontal_face
+
+    h, w = image_bgr.shape[:2]
+    landmarks = detector.detect(image_bgr)
+    if landmarks is None:
+        raise NoFaceError("未检出人脸")
+    if not check_frontal_face(landmarks, w, h):
+        raise NonFrontalError("非正面照")
+
+    lm = landmarks.landmark
+    px = {i: normalized_to_pixel(lm[i], w, h) for i in range(len(lm))}
+
+    points = {}
+    for group, ids in GROUPS.items():
+        for idx in ids:
+            x, y = px[idx]
+            points[f"{group}_{idx}"] = {"x": int(round(x)), "y": int(round(y))}
+
+    # 头发分割（与接口1 相同：按人脸框裁剪后再分割，脸外背景不干扰）
+    hair_mask = None
+    try:
+        from face_analysis.hair_segmenter import get_segmenter
+        face_box = (min(p[0] for p in px.values()), min(p[1] for p in px.values()),
+                    max(p[0] for p in px.values()), max(p[1] for p in px.values()))
+        hair_mask, _ = get_segmenter().segment_hair_and_ears(
+            image_bgr, face_box=face_box)
+    except Exception:  # noqa: BLE001  分割不可用 → 发际线点置 null，不阻塞关键点
+        hair_mask = None
+
+    for name, rays in HAIRLINE_RAYS.items():
+        hits = []
+        if hair_mask is not None:
+            for a, b in rays:
+                hits.append(ray_hairline_intersection(px[a], px[b], hair_mask))
+        found = [p for p in hits if p is not None]
+        if not found:
+            points[name] = None
+        elif len(found) == 1:
+            points[name] = {"x": found[0][0], "y": found[0][1]}
+        else:
+            points[name] = {
+                "x": int(round(sum(p[0] for p in found) / len(found))),
+                "y": int(round(sum(p[1] for p in found) / len(found))),
+            }
+
+    return {
+        "points": points,
+        "image_size": {"width": w, "height": h},
+        "hairline_source": "segmentation" if hair_mask is not None else "unavailable",
+    }
+
+
+if __name__ == "__main__":
+    import sys
+    import cv2
+    import json
+
+    path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
+    img = cv2.imread(path)
+    if img is None:
+        print(f"无法读取图片: {path}")
+        sys.exit(1)
+    try:
+        out = detect_special_points(img)
+    except (NoFaceError, NonFrontalError) as e:
+        print(f"检测失败: {e}")
+        sys.exit(1)
+    print(json.dumps(out, ensure_ascii=False, indent=2))

gateway.log

@@ -1,339 +0,0 @@
-INFO:     Started server process [26440]
-INFO:     Waiting for application startup.
-2026-07-01 23:35:15 [INFO] gateway.config: 配置加载完成 | workers=['http://127.0.0.1:8187'] | public_base_url=http://127.0.0.1:8080 | hc_interval=8s | dispatch_timeout=600s | queue_wait=30s
-2026-07-01 23:35:15 [INFO] gateway: 网关启动中... workers=['http://127.0.0.1:8187']
-2026-07-01 23:35:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:16 [INFO] gateway.pool: Worker 池初始化完成 | 总数=1 | 在线=1
-2026-07-01 23:35:16 [INFO] gateway: 标注图目录: /home/ubuntu/hair/static/annotations
-2026-07-01 23:35:16 [INFO] gateway.pool: 健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=1
-2026-07-01 23:35:16 [INFO] gateway: 清理任务启动 | 间隔=60min | 保留=24h | 目录=/home/ubuntu/hair/static/annotations
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
-2026-07-01 23:35:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:35872 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:35:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:27 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:35:27 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://127.0.0.1:8080/static/annotations/422b7786adc4486989d7f8770df40693.png (21864 bytes)
-INFO:     127.0.0.1:57220 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-2026-07-01 23:35:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:57232 - "GET /static/annotations/422b7786adc4486989d7f8770df40693.png HTTP/1.1" 200 OK
-INFO:     127.0.0.1:57240 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:35:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:35:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:36:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:37:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:38:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:39:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:40:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:41:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:42:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:43:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:44:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:45:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:46:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:47:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:48:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:49:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:50:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:51:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:44470 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:52:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:52:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:53:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:6017 - "GET / HTTP/1.1" 200 OK
-INFO:     111.192.98.24:6017 - "GET /favicon.ico HTTP/1.1" 404 Not Found
-2026-07-01 23:53:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:54:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:39090 - "GET /docs HTTP/1.1" 200 OK
-INFO:     127.0.0.1:39092 - "GET / HTTP/1.1" 200 OK
-2026-07-01 23:55:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:56172 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56174 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56184 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56196 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56202 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56206 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56212 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56214 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56218 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56230 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56242 - "GET /static/test_interface6.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56256 - "GET /static/test_interface6.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56272 - "GET /static/test_interface7.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56278 - "GET /static/test_interface7.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56282 - "GET /static/integration.html HTTP/1.1" 200 OK
-INFO:     127.0.0.1:56284 - "GET /static/integration.html HTTP/1.1" 200 OK
-2026-07-01 23:55:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:55:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:6434 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
-2026-07-01 23:56:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:08 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:56:08 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://127.0.0.1:8080/static/annotations/8a70c12c19ef4868af555ef84eaeafae.png (35965 bytes)
-INFO:     111.192.98.24:6435 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-2026-07-01 23:56:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:56:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:57:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     Shutting down
-INFO:     Waiting for application shutdown.
-2026-07-01 23:58:08 [INFO] gateway: 网关关闭中...
-2026-07-01 23:58:08 [INFO] gateway: 清理任务已停止
-2026-07-01 23:58:08 [INFO] gateway.pool: 健康检查循环已停止
-2026-07-01 23:58:08 [INFO] gateway.pool: Worker 池已关闭
-2026-07-01 23:58:08 [INFO] gateway: 网关已关闭
-INFO:     Application shutdown complete.
-INFO:     Finished server process [26440]
-INFO:     Started server process [31898]
-INFO:     Waiting for application startup.
-2026-07-01 23:58:10 [INFO] gateway.config: 配置加载完成 | workers=['http://127.0.0.1:8187'] | public_base_url=http://117.50.213.111:8080 | hc_interval=8s | dispatch_timeout=600s | queue_wait=30s
-2026-07-01 23:58:10 [INFO] gateway: 网关启动中... workers=['http://127.0.0.1:8187']
-2026-07-01 23:58:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:10 [INFO] gateway.pool: Worker 池初始化完成 | 总数=1 | 在线=1
-2026-07-01 23:58:10 [INFO] gateway: 标注图目录: /home/ubuntu/hair/static/annotations
-2026-07-01 23:58:10 [INFO] gateway.pool: 健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=1
-2026-07-01 23:58:10 [INFO] gateway: 清理任务启动 | 间隔=60min | 保留=24h | 目录=/home/ubuntu/hair/static/annotations
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
-2026-07-01 23:58:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     127.0.0.1:33732 - "GET /gateway-health HTTP/1.1" 200 OK
-2026-07-01 23:58:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:28 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:58:28 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://117.50.213.111:8080/static/annotations/478efab4566a46c3af0065ad0ffafb67.png (21864 bytes)
-INFO:     127.0.0.1:32982 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-INFO:     117.50.213.111:57344 - "GET /static/annotations/478efab4566a46c3af0065ad0ffafb67.png HTTP/1.1" 200 OK
-2026-07-01 23:58:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:58:54 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
-2026-07-01 23:58:54 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://117.50.213.111:8080/static/annotations/88d5eec74def44fdad5dd6f7b22e7be4.png (35965 bytes)
-INFO:     111.192.98.24:7030 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
-INFO:     111.192.98.24:7030 - "GET /static/annotations/88d5eec74def44fdad5dd6f7b22e7be4.png HTTP/1.1" 200 OK
-2026-07-01 23:58:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:7031 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
-2026-07-01 23:59:06 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:14 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:22 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:27 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow "HTTP/1.1 200 OK"
-2026-07-01 23:59:27 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/8f770fef8bc14827b419698c90ebd6fa.jpg (105970 bytes)
-2026-07-01 23:59:27 [INFO] gateway.forward: base64→URL: grown_image_base64 → http://117.50.213.111:8080/static/annotations/6f592882d9c84c1b916ac327f4e0b2af.jpg (113320 bytes)
-INFO:     111.192.98.24:7062 - "POST /api/v1/hair/grow HTTP/1.1" 200 OK
-INFO:     111.192.98.24:7062 - "GET /static/annotations/8f770fef8bc14827b419698c90ebd6fa.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:7166 - "GET /static/annotations/6f592882d9c84c1b916ac327f4e0b2af.jpg HTTP/1.1" 200 OK
-2026-07-01 23:59:30 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:38 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:46 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-01 23:59:48 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow "HTTP/1.1 200 OK"
-2026-07-01 23:59:48 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/ed790d4888f742caa6625b3cf4704a77.jpg (105641 bytes)
-2026-07-01 23:59:48 [INFO] gateway.forward: base64→URL: grown_image_base64 → http://117.50.213.111:8080/static/annotations/e4aed9d67e864d31909b30a58e08c16e.jpg (111495 bytes)
-INFO:     111.192.98.24:5164 - "POST /api/v1/hair/grow HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5164 - "GET /static/annotations/ed790d4888f742caa6625b3cf4704a77.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5188 - "GET /static/annotations/e4aed9d67e864d31909b30a58e08c16e.jpg HTTP/1.1" 200 OK
-2026-07-01 23:59:54 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:5213 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
-2026-07-02 00:00:02 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:16 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow-b "HTTP/1.1 200 OK"
-2026-07-02 00:00:16 [INFO] gateway.forward: base64→URL: hair_growth_image_base64 → http://117.50.213.111:8080/static/annotations/0891a4b9375e4f58814502e893b12bf6.jpg (152092 bytes)
-INFO:     111.192.98.24:5212 - "POST /api/v1/hair/grow-b HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5212 - "GET /static/annotations/0891a4b9375e4f58814502e893b12bf6.jpg HTTP/1.1" 200 OK
-2026-07-02 00:00:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:5332 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
-2026-07-02 00:00:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:39 [ERROR] gateway: 接口4 豆包调用失败
-Traceback (most recent call last):
-  File "/home/ubuntu/hair/gateway/app.py", line 298, in face_features
-    feats = await run_in_threadpool(analyze_features, img_bytes, image_url)
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/starlette/concurrency.py", line 37, in run_in_threadpool
-    return await anyio.to_thread.run_sync(func)
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/to_thread.py", line 63, in run_sync
-    return await get_async_backend().run_sync_in_worker_thread(
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2596, in run_sync_in_worker_thread
-    return await future
-  File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 1029, in run
-    result = context.run(func, *args)
-  File "/home/ubuntu/hair/face_features.py", line 98, in analyze_features
-    resp = get_client().chat.completions.create(
-  File "/home/ubuntu/hair/face_features.py", line 65, in get_client
-    from volcenginesdkarkruntime import Ark
-ModuleNotFoundError: No module named 'volcenginesdkarkruntime'
-INFO:     111.192.98.24:5331 - "POST /api/v1/face/features HTTP/1.1" 200 OK
-2026-07-02 00:00:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     111.192.98.24:5436 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
-2026-07-02 00:00:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:00:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:00 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hairline/generate "HTTP/1.1 200 OK"
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/4516a9491a1c41f4af048020f6709870.jpg (105674 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/ad51e082bb5540ea843259cdc1e76e6b.jpg (105970 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/12a6f7745c9c461ca963f4d49c5267ef.jpg (105735 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/316d8352062848e4b28337ef233d820e.jpg (105641 bytes)
-2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/2ae45e3579b0419ab8bff5e3129ab26e.jpg (105705 bytes)
-INFO:     111.192.98.24:5435 - "POST /api/v1/hairline/generate HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5435 - "GET /static/annotations/4516a9491a1c41f4af048020f6709870.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5466 - "GET /static/annotations/ad51e082bb5540ea843259cdc1e76e6b.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5467 - "GET /static/annotations/12a6f7745c9c461ca963f4d49c5267ef.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5469 - "GET /static/annotations/316d8352062848e4b28337ef233d820e.jpg HTTP/1.1" 200 OK
-INFO:     111.192.98.24:5471 - "GET /static/annotations/2ae45e3579b0419ab8bff5e3129ab26e.jpg HTTP/1.1" 200 OK
-2026-07-02 00:01:06 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:14 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:22 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:30 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:38 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:46 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:01:54 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:02 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-2026-07-02 00:02:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
-INFO:     Shutting down
-INFO:     Waiting for application shutdown.
-2026-07-02 00:02:59 [INFO] gateway: 网关关闭中...
-2026-07-02 00:02:59 [INFO] gateway: 清理任务已停止
-2026-07-02 00:02:59 [INFO] gateway.pool: 健康检查循环已停止
-2026-07-02 00:02:59 [INFO] gateway.pool: Worker 池已关闭
-2026-07-02 00:02:59 [INFO] gateway: 网关已关闭
-INFO:     Application shutdown complete.
-INFO:     Finished server process [31898]

gateway/app.py

@@ -607,3 +607,79 @@ async def head_mask(request: Request):
 async def head_band(request: Request):
     """接口10：头部外缘膨胀带遮罩 + 分步可视化"""
     return await _proxy(request, "/api/v1/head/band")
+
+
+# 接口99 单路排队：网关机推理全局互斥。调用频率低，无需并发——串行既限制 CPU 峰值
+# （MediaPipe + BiSeNet 全 CPU 推理），也规避检测器单例的线程安全风险。排队无超时，
+# 请求方按需自带客户端超时。
+_if99_lock = asyncio.Lock()
+
+
+@app.post("/api/v1/face/special-points", tags=["人脸分析"])
+async def face_special_points(
+    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
+    image_url: Optional[str] = Form(default=None, description="图片 URL"),
+    image_base64: Optional[str] = Form(default=None, description="图片 base64（需带前缀）"),
+):
+    """接口99：人脸特殊点检测 — 网关本机实现（纯 CPU：MediaPipe + BiSeNet CPU），不经过 worker。
+
+    与 worker 同仓库，直接 import face_analysis.special_points；权重已入 git，clone 即用。
+    依赖惰性导入：网关缺依赖时仅本接口报 1007，不影响其他路由。
+    """
+    import uuid as _uuid
+    rid = f"gw-{_uuid.uuid4().hex[:8]}"
+
+    def _err(code, message):
+        return JSONResponse(status_code=200, content={
+            "code": code, "message": message, "request_id": rid, "data": None})
+
+    # 三选一校验
+    provided = [x for x in (image_file, image_url, image_base64) if x]
+    if len(provided) != 1:
+        return _err(1007, "图片参数错误：必须且只能传 image_file / image_url / image_base64 其中一个")
+
+    img_bytes = None
+    if image_file:
+        img_bytes = await image_file.read()
+    elif image_base64:
+        b64 = image_base64.split(",", 1)[1] if "," in image_base64 else image_base64
+        try:
+            img_bytes = base64.b64decode(b64)
+        except Exception:
+            return _err(1008, "图片格式不支持（base64 解码失败）")
+    else:
+        try:
+            import httpx
+            async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as cli:
+                r = await cli.get(image_url)
+            r.raise_for_status()
+            img_bytes = r.content
+        except Exception:
+            return _err(1008, "图片下载失败（URL 不可达）")
+
+    try:
+        import cv2
+        import numpy as np  # noqa: F401  （detect_special_points 依赖）
+        from face_analysis.special_points import (
+            detect_special_points, NoFaceError, NonFrontalError)
+    except ImportError as ex:
+        return _err(1007, f"网关依赖未安装（安装清单见 docs/网关待改动.md §9）：{ex}")
+
+    image = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), cv2.IMREAD_COLOR)
+    if image is None:
+        return _err(1008, "图片格式不支持（仅 JPG / PNG）")
+
+    from fastapi.concurrency import run_in_threadpool
+    try:
+        async with _if99_lock:   # 单路排队：同一时刻只跑一路推理
+            data = await run_in_threadpool(detect_special_points, image)
+    except NoFaceError:
+        return _err(1001, "无法识别人像")
+    except NonFrontalError:
+        return _err(1003, "角度问题，请上传正面照")
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("接口99 处理异常")
+        return _err(1007, f"处理失败：{ex}")
+
+    return JSONResponse(status_code=200, content={
+        "code": 0, "message": "success", "request_id": rid, "data": data})

hair-worker.service

@@ -1,16 +1,15 @@
 [Unit]
-Description=Hair Worker (GPU) - 四庭七眼测量 接口1
-After=network.target
+Description=hair GPU worker FastAPI (0.0.0.0:8187)
+After=network-online.target comfyui.service change_hair-hair.service
+Wants=comfyui.service change_hair-hair.service
 
 [Service]
 Type=simple
-User=xsl
-WorkingDirectory=/home/xsl/hair
-# 鉴权密码：优先 worker_config.json；也可在此用环境变量覆盖
-# Environment=WORKER_ACCEPT_PASSWORDS=your-strong-secret
-ExecStart=/home/xsl/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
-Restart=always
-RestartSec=3
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair
+ExecStart=/home/ubuntu/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
+Restart=on-failure
+RestartSec=5
 
 [Install]
 WantedBy=multi-user.target

hair_repaint.json

@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
       "weight_dtype": "fp8_e4m3fn"
     },
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "_meta": {
       "title": "UNet加载器"
     }
@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "补充遮罩区域的头发，加一点美颜"
+      "text": "填充遮罩区域的头发，人物增加一点磨皮"
     },
     "class_type": "JjkText",
     "_meta": {

hairline/comfyui.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 
 import copy
 import json
+import logging
 import os
 import random
 import time
@@ -18,7 +19,7 @@ import uuid
 
 import httpx
 
-COMFYUI_URL = os.getenv("COMFYUI_URL", "http://10.60.74.221:8188").rstrip("/")
+COMFYUI_URL = os.getenv("COMFYUI_URL", "http://127.0.0.1:8188").rstrip("/")
 _WORKFLOW_DEFAULT = os.getenv(
     "ADD_HAIR_WORKFLOW",
     os.path.join(os.path.dirname(os.path.dirname(__file__)), "add_hair.json"),
@@ -29,6 +30,29 @@ _REPO = os.path.dirname(os.path.dirname(__file__))
 _INPUT_NODE = "26"     # LoadImage：外部输入图(含 alpha 遮罩)
 _SEED_NODE = "6"       # RandomNoise
 _PROMPT_NODE = "60"    # JjkText：提示词
+_UNET_NODE = "16"      # UNETLoader / UnetLoaderGGUF：Flux 模型加载
+_CLIP_NODE = "61"      # CLIPLoader：qwen 文本编码器
+_VAE_NODE = "3"        # VAELoader
+
+# Flux 模型 → 配套文本编码器映射。切换 unet 时自动同步编码器，避免维度不匹配。
+def _clip_for_unet(unet_name: str) -> str | None:
+    """根据 unet 文件名推断配套的文本编码器文件名；无法推断返回 None。"""
+    low = unet_name.lower()
+    if "9b" in low:                       # Flux.2 9B 系列
+        return "qwen_3_8b_fp8mixed.safetensors"
+    if "z-image" in low:                  # Z-Image-Turbo 用 4B 编码器
+        return "qwen_3_4b.safetensors"
+    if "4b" in low:                       # Flux.2 4B
+        return "qwen_3_4b.safetensors"
+    return None
+
+# Flux 模型 → 配套 VAE 映射。Z-Image 用 ae.safetensors，Flux.2 系列用 flux2-vae。
+def _vae_for_unet(unet_name: str) -> str | None:
+    """根据 unet 文件名推断配套 VAE 文件名；无法推断返回 None（保持工作流原值）。"""
+    low = unet_name.lower()
+    if "z-image" in low:
+        return "ae.safetensors"
+    return None  # Flux.2 系列 vae 在工作流里已正确配置，不覆盖
 
 _wf_cache: dict[str, dict] = {}      # path → workflow JSON
 _wf_output_node: dict[str, str] = {}  # path → SaveImage 节点 ID
@@ -85,11 +109,18 @@ def _get_output_node(workflow_path: str | None = None) -> str:
 
 
 def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = None,
-        workflow_path: str | None = None) -> bytes:
+        workflow_path: str | None = None, front: bool = False,
+        unet_name: str | None = None) -> bytes:
     """提交一次生发任务，返回输出 PNG 字节。失败抛异常。
 
     prompt：非 None 时替换工作流节点60(JjkText)的文本；None 时用工作流内置默认提示词。
     workflow_path：工作流 JSON 路径，None 则用默认 add_hair.json。
+    front：True 时任务插到 ComfyUI 队列最前（server 端 "front" 字段，队列号取负）。
+        接口2 对时延敏感用 True，避免排在接口3/5 的批量任务后面；其余接口保持 False。
+    unet_name：非 None 时改写工作流里的模型加载节点（节点16），动态切换 Flux 模型。
+        .safetensors → 保持 UNETLoader 节点类型不变，只替换 unet_name；
+        .gguf → 自动把节点类型改成 UnetLoaderGGUF（需装 ComfyUI-GGUF 插件）。
+        None 时用工作流内置默认模型。
     """
     path = workflow_path or _WORKFLOW_DEFAULT
     output_node = _get_output_node(path)
@@ -104,11 +135,39 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         name = (up.get("subfolder") + "/" if up.get("subfolder") else "") + up["name"]
 
         # 2. 改工作流：节点26 输入图 + 随机 seed
+        try:
+            import io as _io
+            from PIL import Image as _Img
+            _sz = _Img.open(_io.BytesIO(rgba_png_bytes)).size
+            logging.getLogger("hair.worker").info(
+                "ComfyUI 输入尺寸 %dx%d workflow=%s", _sz[0], _sz[1], os.path.basename(path))
+        except Exception:  # noqa: BLE001
+            pass
         wf = copy.deepcopy(_load_workflow(path))
         wf[_INPUT_NODE]["inputs"]["image"] = name
         wf[_SEED_NODE]["inputs"]["noise_seed"] = random.randint(0, 2**63 - 1)
         if prompt is not None:
             wf[_PROMPT_NODE]["inputs"]["text"] = prompt
+        if unet_name is not None:
+            node = wf.get(_UNET_NODE)
+            if node is not None:
+                # .gguf 需切换到 ComfyUI-GGUF 插件的 UnetLoaderGGUF 节点；
+                # .safetensors/.ckpt 保持原 UNETLoader 节点类型不变
+                if unet_name.lower().endswith(".gguf"):
+                    node["class_type"] = "UnetLoaderGGUF"
+                else:
+                    node["class_type"] = "UNETLoader"
+                node["inputs"]["unet_name"] = unet_name
+            # 同步切换配套文本编码器（4b→qwen_3_4b, 9b→qwen_3_8b），避免维度不匹配
+            clip_node = wf.get(_CLIP_NODE)
+            clip_name = _clip_for_unet(unet_name)
+            if clip_node is not None and clip_name is not None:
+                clip_node["inputs"]["clip_name"] = clip_name
+            # 同步切换 VAE（Z-Image 用 ae.safetensors，Flux.2 保持 flux2-vae）
+            vae_node = wf.get(_VAE_NODE)
+            vae_name = _vae_for_unet(unet_name)
+            if vae_node is not None and vae_name is not None:
+                vae_node["inputs"]["vae_name"] = vae_name
 
         # 诊断：落盘实际提交的工作流 + 输入图，便于和手动 ComfyUI 跑的对比
         try:
@@ -125,8 +184,11 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         except Exception:  # noqa: BLE001
             pass
 
-        # 3. 提交
-        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        # 3. 提交（front=True 时插队到队列最前）
+        payload = {"prompt": wf, "client_id": client_id}
+        if front:
+            payload["front"] = True
+        r = cli.post("/prompt", json=payload)
         r.raise_for_status()
         prompt_id = r.json()["prompt_id"]
 
@@ -145,7 +207,7 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
                 outputs = entry.get("outputs")
                 if outputs and output_node in outputs:
                     break
-            time.sleep(1.0)
+            time.sleep(0.05)
         if not outputs or output_node not in outputs:
             raise TimeoutError(f"ComfyUI 出图超时({timeout}s) prompt_id={prompt_id}")
 

hairline/constants.py

@@ -20,6 +20,13 @@ MP_TOP_ANCHORS: list[int] = [
 ]
 N_ANCHORS = len(MP_TOP_ANCHORS)  # 17
 
+# Anatomical left/right pairs, in MediaPipe indices. The anchor list is
+# NOT mirror-ordered at its ends: 127 pairs with 356, 234 with 454.
+MP_TOP_ANCHOR_PAIRS = [
+    (127, 356), (234, 454), (162, 389), (21, 251),
+    (54, 284), (103, 332), (67, 297), (109, 338),
+]
+
 # Vertex ID layout in the extended array of length 468 + 2*N_ANCHORS.
 #   [0       .. 468)         : MediaPipe canonical landmarks
 #   [468     .. 468+N)       : middle row (between MP boundary and hairline)

hairline/hairline_2d.py

@@ -124,6 +124,46 @@ def build_hairline_mask(parse_map: np.ndarray) -> np.ndarray:
     return build_hair_mask(parse_map)
 
 
+def align_hairline_for_template(
+    landmarks_norm: np.ndarray,
+    hairline_norm: np.ndarray,
+    valid: np.ndarray,
+    image_shape: tuple[int, int],
+) -> np.ndarray:
+    """Keep a designed template aligned with the face rather than the fringe.
+
+    Uneven bangs can make the detected hair boundary much higher on one
+    side, shearing an otherwise balanced template. Average the upward
+    extension of each anatomical anchor pair, measured in image pixels.
+    Keep the face anchors and the perpendicular component unchanged so
+    facial roll/perspective remain visible; do not level the curve in the
+    image. Missing ray hits retain their fallback and never influence a
+    valid partner. Call silhouette clamping afterwards to bound the result.
+
+    This is for template placement only, not measurement of the real hairline.
+    """
+    h, w = image_shape
+    scale = np.array([w, h], dtype=np.float64)
+    up = face_up_vector(landmarks_norm) * scale
+    length = np.linalg.norm(up)
+    if length < 1e-9:
+        return hairline_norm.copy()
+    up /= length
+
+    anchors = landmarks_norm[C.MP_TOP_ANCHORS, :2] * scale
+    points = hairline_norm * scale
+    distances = (points - anchors) @ up
+    indices = {mp_idx: i for i, mp_idx in enumerate(C.MP_TOP_ANCHORS)}
+    for left, right in C.MP_TOP_ANCHOR_PAIRS:
+        i, j = indices[left], indices[right]
+        if not (valid[i] and valid[j]):
+            continue
+        mean_distance = (distances[i] + distances[j]) / 2.0
+        for k in (i, j):
+            points[k] += (mean_distance - distances[k]) * up
+    return (points / scale).astype(np.float32)
+
+
 def smooth_hairline(
     hairline_norm: np.ndarray,
     valid: np.ndarray,
@@ -186,6 +226,53 @@ def smooth_hairline_corner_aware(
     return out
 
 
+def clamp_hairline_to_silhouette(
+    hairline_norm: np.ndarray,
+    parse_map: np.ndarray,
+    margin_px: float = 2.0,
+) -> np.ndarray:
+    """把发际线点的 y 钳制在 (skin∪hair) silhouette 上沿之下（不含 margin 以上）。
+
+    根因（见 issue：男性 ellipse 发际线贴到头部外面）：`sample_hairline` 对射线
+    未命中 hair 像素的锚点会 fallback 成「锚点 + 固定 0.18 归一化偏移」，与头部实际
+    大小/位置无关 —— 短发/剃光头场景下这个偏移量常常把点顶到头部轮廓外面的背景里，
+    在有效/失效锚点交界处形成尖角，被贴图上的不透明像素蒙到就会露出戳出头部的线条。
+
+    本函数在几何检测之后追加一步「安全网」：对每个点按其 x 所在列，取 silhouette
+    （SegFormer skin∪hair 类，近似头部实际轮廓）上沿 y，若点比这个上沿还高（y 更
+    小），直接钳制到 上沿 + margin_px —— 保证曲线永远不会跑到头部轮廓外面的背景。
+    """
+    h, w = parse_map.shape
+    cols_with_head, top_y = _head_top_y_per_column(parse_map, use_full_hair=True)
+    if cols_with_head.size == 0:
+        return hairline_norm
+    out = hairline_norm.copy()
+    for i in range(out.shape[0]):
+        x_px = float(out[i, 0]) * w
+        idx = int(np.searchsorted(cols_with_head, x_px))
+        idx = min(max(idx, 0), cols_with_head.size - 1)
+        sil_y = float(top_y[idx]) + margin_px
+        y_px = float(out[i, 1]) * h
+        if y_px < sil_y:
+            out[i, 1] = sil_y / h
+    return out
+
+
+def sample_hairline_clamped(
+    landmarks_norm: np.ndarray,
+    parse_map: np.ndarray,
+    fallback_extrapolation: float = 0.18,
+) -> tuple[np.ndarray, np.ndarray]:
+    """策略 A（baseline + 头部轮廓钳制）：与默认 `sample_hairline` 完全一致的检测，
+    额外用 `clamp_hairline_to_silhouette` 兜底 —— 检测失效 fallback 出的点不再可能
+    跑到头部外面的背景，而是贴着头部实际轮廓顶部。改动小、风险低，只在检测失效/
+    fallback 越界时才生效，正常长发照片的结果与 baseline 完全一致。
+    """
+    hairline, valid = sample_hairline(landmarks_norm, parse_map, fallback_extrapolation)
+    hairline = clamp_hairline_to_silhouette(hairline, parse_map)
+    return hairline, valid
+
+
 # ---------------------------------------------------------------------------
 # Alternative hairline sampling strategies.
 #

hairline/redraw.py

@@ -0,0 +1,80 @@
+"""直接调 ComfyUI 重绘 — 替代 local_test HTTP 服务。
+
+将 local_test/app.py 的核心逻辑（遮罩处理 + ComfyUI 调用）提取为 Python 函数，
+不再需要独立 Flask 服务。使用 0716add-hair-api.json 工作流（steps=4）。
+"""
+from __future__ import annotations
+
+import io
+import logging
+import os
+
+import numpy as np
+from PIL import Image, ImageFilter
+
+from . import comfyui
+
+logger = logging.getLogger("hair.worker")
+
+_DEFAULT_PROMPT = "填充遮罩区域的头发，人物增加一点磨皮"
+_REPO = os.path.dirname(os.path.dirname(__file__))
+_REPAINT_WORKFLOW = os.path.join(_REPO, "0716add-hair-api.json")
+
+
+def _process_mask_to_rgba(image_bytes: bytes, mask_bytes: bytes) -> bytes:
+    """将分开的 image + mask 处理为 ComfyUI 用的 RGBA PNG bytes。
+
+    复制 local_test/app.py 的遮罩处理逻辑：
+    1. 加载 image 为 RGB
+    2. 加载 mask 为 RGBA，取所有通道 max 值（支持红/白/alpha 遮罩）
+    3. resize mask 到与 image 一致
+    4. 高斯模糊(radius=4) 柔化边缘
+    5. alpha = 255 - mask（绘制区=255 → alpha=0 → 重绘区）
+    6. 合成 RGBA PNG
+    """
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    mask_img = Image.open(io.BytesIO(mask_bytes)).convert("RGBA")
+    mask_arr = np.array(mask_img)
+    mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+    mask_data_img = Image.fromarray(mask_data, mode="L")
+    if mask_data_img.size != image.size:
+        mask_data_img = mask_data_img.resize(image.size, Image.LANCZOS)
+    mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+    # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+    # alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+    comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+    buf = io.BytesIO()
+    rgba.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def run_redraw(image_bytes: bytes, mask_bytes: bytes,
+               prompt: str | None = None, timeout: float = 300.0,
+               front: bool = False, unet_name: str | None = None) -> bytes:
+    """直接调 ComfyUI 重绘 — 替代 local_test /api/generate。
+
+    Args:
+        image_bytes: 人物图片字节（JPG/PNG）
+        mask_bytes: 遮罩图片字节（支持红/白/alpha 遮罩格式）
+        prompt: 提示词，None 用默认 "填充遮罩区域的头发，人物增加一点磨皮"
+        timeout: ComfyUI 超时秒数
+        front: True 时任务插到 ComfyUI 队列最前（接口2 时延敏感路径用）
+        unet_name: 非 None 时切换 Flux 模型（如 flux-2-klein-9b-Q5_K_M.gguf），None 用工作流默认
+
+    Returns:
+        重绘后的 PNG 图片字节
+
+    Raises:
+        RuntimeError: ComfyUI 执行失败
+        TimeoutError: ComfyUI 超时
+    """
+    rgba_png = _process_mask_to_rgba(image_bytes, mask_bytes)
+    return comfyui.run(rgba_png, timeout=timeout, prompt=prompt,
+                       workflow_path=_REPAINT_WORKFLOW, front=front,
+                       unet_name=unet_name)

hairline/service.py

@@ -14,7 +14,10 @@ from . import constants as C
 from . import comfyui
 from .face_landmarks import FaceLandmarker
 from .face_parsing import FaceParser
-from .hairline_2d import sample_hairline, smooth_hairline
+from .hairline_2d import (
+    smooth_hairline, sample_hairline, align_hairline_for_template,
+    clamp_hairline_to_silhouette,
+)
 from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
 from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay, build_overlay_layer
 from .mask import build_inpaint_mask, compose_comfy_rgba, mask_from_curve
@@ -27,7 +30,8 @@ import logging
 logger = logging.getLogger("hair.worker")
 
 # 接口2 女性发型 key → change_hair hair_id（chang_*）映射：换发型+Flux-2 整帧重绘用。
-# 与接口12 final 的 5 型一一对应。
+# 与接口12 final 的 5 型一一对应。female 6/7（bigflower/clasicalflower）无对应 LoRA，
+# 走与男性一致的原生生发（ComfyUI add_hair）管线，故不在本表。
 _FEMALE_KEY_TO_CHANG = {
     "ellipse": "chang_tuoyuan",   # 椭圆
     "flower": "chang_huaban",     # 花瓣
@@ -36,31 +40,69 @@ _FEMALE_KEY_TO_CHANG = {
     "wave": "chang_bolang",       # 波浪
 }
 
+# 发际线贴图显式顺序表：决定 hair_style 序号（1-indexed）。
+# 不再依赖文件名字母序——字母序会因新增/重命名文件而错位，破坏现有前端/客户端取值。
+# key 须与 _gender_key 派生结果一致（已去空格）：如 "inverse_arc"（源 man_ inverse_arc.png）、
+# "Softpetal"（源 man_Soft petal.png，大写 S 保留）。表外未知 key 兜底排到末尾。
+_HAIRSTYLE_ORDER = {
+    "female": ["ellipse", "flower", "heart", "straight", "wave",
+               "bigflower", "clasicalflower"],  # 1..7
+    "male": ["ellipse", "inverse_arc", "m", "straight", "heart", "Softpetal",
+             "wave"],  # 1..7
+}
+
 _REPO = os.path.dirname(os.path.dirname(__file__))
 _TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
 _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 
-# 外部重绘服务（local_test，0716add-hair.json 工作流）。接口2 female 用它替代原 Flux-2 重绘。
-# 重绘服务已独立到远程机器；可用 HAIR_LOCAL_REDRAW_URL 覆盖。
-# gpu_worker 同内网走 10.60.74.221，外网（本地开发机）需走公网 117.50.183.232。
-_LOCAL_REDRAW_URL = os.getenv("HAIR_LOCAL_REDRAW_URL", "http://10.60.74.221:8899").rstrip("/")
+# 三接口（接口2女重绘 / 接口2男 / 接口3）统一的 ComfyUI 重绘 prompt。
+# 关键：ComfyUI 单卡显存装不下 Flux(7.7G)+qwen CLIP(3.9G) 同驻，靠缓存 CLIP 文本条件避免重载。
+# prompt 不同会使缓存失效 → 重载 CLIP 并挤出 Flux（每次 +4s）。三接口用同一字符串即可全程命中。
+# 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
+_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，人物增加一点磨皮")
 
+# 接口2 女重绘整条管线（swapHair + ComfyUI）送模型前限边。真实照片常达 1257x1495：
+# 全分辨率 ComfyUI 重绘要 13~21s 且激活显存把模型挤出。女性路径含 swapHair(SD WebUI ~5.3s
+# 固定地板) + ComfyUI 两段串行。1024 档画质更好但部分大图会踩 12s 线，
+# 默认压到 896 兜底（ComfyUI ~4s，女性总耗时 9~11s）；追画质可设 REDRAW_MAX_SIDE=1024。
+_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "896"))
 
-def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
-    """调外部重绘服务（local_test /api/generate）：传 final 图 + 纯红遮罩 PNG，
-    返回重绘后的 PNG bytes。失败抛异常（调用方负责 try/except 跳过）。
+def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
+                       max_side=None, unet_name=None, prompt=None):
+    """直接调 ComfyUI 重绘（替代原 local_test HTTP 服务）。
+
+    传 final 图 + 纯红遮罩 PNG，返回重绘后的 PNG bytes。
+    失败抛异常（调用方负责 try/except 跳过）。
+
+    max_side：送 ComfyUI 前长边压到多少像素，None 用全局默认 _REDRAW_MAX_SIDE。
+    unet_name：非 None 时切换 Flux 模型，None 用工作流内置默认。
+    prompt：None 用默认 _REDRAW_PROMPT，否则用传入的提示词。
     """
-    import requests
-    files = {
-        "image": ("final.jpg", image_png_bytes, "image/jpeg"),
-        "mask": ("mask.png", mask_png_bytes, "image/png"),
-    }
-    resp = requests.post(_LOCAL_REDRAW_URL + "/api/generate", files=files, timeout=timeout)
-    resp.raise_for_status()
-    if "image/" not in resp.headers.get("Content-Type", ""):
-        # 服务返回了 JSON 错误
-        raise RuntimeError(f"local_test 返回非图片: {resp.text[:200]}")
-    return resp.content
+    from .redraw import run_redraw
+    eff_side = _REDRAW_MAX_SIDE if max_side is None else max_side
+    img = cv2.imdecode(np.frombuffer(image_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
+    scale = 1.0
+    orig_w = orig_h = 0
+    if img is not None:
+        orig_h, orig_w = img.shape[:2]
+        m = max(orig_h, orig_w)
+        if eff_side > 0 and m > eff_side:
+            scale = eff_side / float(m)
+            nw, nh = max(1, round(orig_w * scale)), max(1, round(orig_h * scale))
+            msk = cv2.imdecode(np.frombuffer(mask_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
+            img_s = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_AREA)
+            msk_s = cv2.resize(msk, (nw, nh), interpolation=cv2.INTER_NEAREST)
+            image_png_bytes = cv2.imencode(".png", img_s)[1].tobytes()
+            mask_png_bytes = cv2.imencode(".png", msk_s)[1].tobytes()
+            logger.info("接口2女 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+                        orig_w, orig_h, nw, nh, eff_side)
+    # front=True：接口2 时延敏感，插到 ComfyUI 队列最前，避免排在接口3/5 的批量任务后面
+    out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
+                     prompt=prompt if prompt is not None else _REDRAW_PROMPT,
+                     front=True, unet_name=unet_name)
+    if scale < 1.0 and out:
+        out = _upscale_png_to(out, orig_w, orig_h)
+    return out
 
 # 发际线贴图档位：middle=默认(hairline_texture/)，high/low 各自独立文件夹。
 _TEXTURE_DIRS = {
@@ -69,9 +111,8 @@ _TEXTURE_DIRS = {
     "low": os.path.join(_REPO, "hairline_texture_low"),
 }
 
-# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
-#    "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
-_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
+# torch 2.7.1+cu128 已支持 RTX 5090 (sm_120)，SegFormer 走 GPU（~0.05s/张）
+_SEG_DEVICE = os.getenv("SEG_DEVICE", "cuda")
 
 _landmarker = None
 _parser = None
@@ -102,10 +143,13 @@ def _gender_key(stem: str):
 
 
 def get_texture_map(level: str = "middle") -> dict:
-    """扫描指定档位贴图目录建 {gender: [(key, path)]}，按 key 排序、按档位缓存。
+    """扫描指定档位贴图目录建 {gender: [(key, path)]}，按显式顺序表排序、按档位缓存。
 
     level：middle(默认) / high / low，分别对应 hairline_texture[/_high|/_low]。
     文件名规范化去空格（如 `man_ inverse_arc.png` → key `inverse_arc`）。
+
+    排序依据 _HAIRSTYLE_ORDER：表内 key 按表序、表外未知 key 兜底排到末尾（再按字母序），
+    保证新增/重命名文件不会打乱现有 hair_style 序号。
     """
     if level not in _TEXTURE_DIRS:
         raise ValueError(f"hairline_level 必须是 middle/high/low，收到 {level!r}")
@@ -119,7 +163,9 @@ def get_texture_map(level: str = "middle") -> dict:
         if gender:
             mapping[gender].append((key, path))
     for g in mapping:
-        mapping[g].sort(key=lambda kp: kp[0])
+        order = _HAIRSTYLE_ORDER.get(g, [])
+        idx = {k: i for i, k in enumerate(order)}
+        mapping[g].sort(key=lambda kp: (idx.get(kp[0], len(idx)), kp[0]))
     _texture_maps[level] = mapping
     return mapping
 
@@ -133,13 +179,23 @@ def extract_502(image_bgr: np.ndarray):
 
 
 def extract_context(image_bgr: np.ndarray):
-    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。"""
+    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。
+
+    发际线几何使用射线检测 + 人脸对应点对齐 + 头部轮廓钳制：
+    左右刘海高低不同时，协调对应锚点的延伸距离，避免把设计模板拉歪；
+    保留人脸本身的倾斜。预览白线与生发黑线/遮罩共用同一套 points。
+    短发/剃光头照片（如 man_test.jpg）中间锚点检测失效时，纯射线检测的固定 fallback
+    偏移会把点顶到头部轮廓外面的背景，产生"发际线贴到头部外面"的视觉 bug；钳制兜底后
+    fallback 点不会再跑出头部轮廓。
+    """
     rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
     landmarks = get_landmarker().detect(rgb)
     if landmarks is None:
         return None
     parse_map = get_parser().parse(rgb)
     hairline_2d, valid = sample_hairline(landmarks, parse_map)
+    hairline_2d = align_hairline_for_template(landmarks, hairline_2d, valid, parse_map.shape)
+    hairline_2d = clamp_hairline_to_silhouette(hairline_2d, parse_map)
     hairline_2d = smooth_hairline(hairline_2d, valid)
     hairline_3d = lift_hairline_to_3d(landmarks, hairline_2d)
     middle_3d = build_middle_row(landmarks, hairline_3d)
@@ -173,10 +229,11 @@ def generate_previews(image_bgr: np.ndarray, gender: str):
 
 def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = True,
                          prompt: str = None, hair_styles: list[int] | None = None,
-                         workflow_path: str | None = None):
+                         workflow_path: str | None = None,
+                         unet_name: str | None = None):
     """指定发际线类型：发际线透明叠图(白线 RGBA) + 生发图(ComfyUI)。
 
-    hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..5，male: 1..4。
+    hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..7，male: 1..7。
       为 None 时生成全部（兼容旧调用）。
     use_mask（默认 True）：是否启用 inpaint 遮罩，用于测试对比（同接口3）。
       False 时用**干净原图 + 空遮罩**送 ComfyUI（不烧黑色模板线）。
@@ -204,9 +261,15 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
     if not use_mask:
         try:
             h, w = image_bgr.shape[:2]
+            img_s, msk_s, gsc = _prep_comfy_input(image_bgr, np.zeros((h, w), np.uint8))
             buf = io.BytesIO()
-            compose_comfy_rgba(image_bgr, np.zeros((h, w), np.uint8)).save(buf, format="PNG")
-            shared_grown = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+            compose_comfy_rgba(img_s, msk_s).save(buf, format="PNG", compress_level=1)
+            # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
+            shared_grown = comfyui.run(buf.getvalue(), prompt=prompt,
+                                       workflow_path=workflow_path, front=True,
+                                       unet_name=unet_name)
+            if gsc < 1.0 and shared_grown:
+                shared_grown = _upscale_png_to(shared_grown, w, h)
         except Exception as e:  # noqa: BLE001
             logger.warning("接口2 生发图失败(无遮罩)：%s", e)
 
@@ -224,9 +287,15 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
                 black = load_texture_rgba(_black_texture_path(white_path))
                 marked, mask = build_inpaint_mask(
                     image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+                m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
                 buf = io.BytesIO()
-                compose_comfy_rgba(marked, mask).save(buf, format="PNG")
-                grown_png = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+                compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
+                # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
+                grown_png = comfyui.run(buf.getvalue(), prompt=prompt,
+                                        workflow_path=workflow_path, front=True,
+                                        unet_name=unet_name)
+                if gsc < 1.0 and grown_png:
+                    grown_png = _upscale_png_to(grown_png, w, h)
             except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
                 logger.warning("接口2 生发图失败 type=%s：%s", key, e)
 
@@ -236,25 +305,39 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
 
 
 def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
-                               redraw_defaults: dict):
-    """接口2 女性专用：发际线透明叠图（同 generate_grow_results）+ 换发型重绘图。
+                               redraw_defaults: dict,
+                               redraw_max_side: int | None = None,
+                               unet_name: str | None = None,
+                               prompt: str | None = None):
+    """接口2 女性专用：发际线透明叠图（同 generate_grow_results）+ 生发图。
 
-    grown 图来源（新流程）：对每个选中发型把 female key 映射到 change_hair 的 chang_* hair_id，
-    调 face_analysis.hairline_grow.generate_hairline_redraw（= 接口12 final 管线，参数用
-    redraw_defaults）拿到 ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG，再**后端调外部重绘服务
-    local_test**（0716add-hair.json 工作流）完成发际线带重绘，重绘结果作为生发图。
+    生发图来源按发型分两路：
+    - 换发型（chang_* 表内，1..5）：female key → change_hair 的 chang_* hair_id，调
+      face_analysis.hairline_grow.generate_hairline_redraw（= 接口12 final 管线，参数用
+      redraw_defaults）拿到 ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG，再**后端直接调
+      ComfyUI**（0716add-hair-api.json 工作流）完成发际线带重绘。
+    - 原生生发（表外，6/7 bigflower/clasicalflower 等）：无对应 change_hair LoRA，改走与男性
+      一致的原生生发（ComfyUI add_hair），由 _grow_native_one 完成（黑模板 + inpaint 遮罩）。
 
     overlay 仍是发际线曲线透明层（与 generate_grow_results 完全一致）。
+    prompt 两条分支都生效：换发型分支传入 _call_local_redraw，原生生发分支传给
+    _grow_native_one。None/默认「填充遮罩区域的头发，人物增加一点磨皮」时与全局 _REDRAW_PROMPT 一致，
+    不破坏 CLIP 缓存命中的快路径；传不同提示词会使缓存失效并重载 CLIP/Flux
+    （实测换词后首次请求重绘 45s vs 缓存命中 8s），后续相同提示词恢复快路径。
     Returns: list[dict] {"hairline_type","order","overlay","grown_png"(jpg bytes 或 None)}；
              无人脸返回 None。单个发型换发型/重绘失败时 grown_png=None，不抛异常。
     """
     from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError
+    from face_analysis.head_mask import SEGFORMER_HAIR
 
     ctx = extract_context(image_bgr)
     if ctx is None:
         return None
     uv, ext_faces = load_ext_mesh()
 
+    # 复用 extract_context 已算好的 SegFormer parse_map，避免 generate_hairline_redraw 内部重复分割
+    hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
+
     textures = get_texture_map()["female"]  # [(key, path), ...] 已排序
     if hair_styles is not None:
         items = [(s, textures[s - 1]) for s in hair_styles]
@@ -263,6 +346,21 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
 
     results = []
     h, w = image_bgr.shape[:2]
+
+    # 重绘管线（swapHair + ComfyUI）统一降分辨率：真实照片 swap(SD WebUI)~5s、blend、ComfyUI
+    # 均随分辨率线性下降。overlay 预览仍用全分辨率；grown_png 最后放大回原尺寸。
+    eff_side = _REDRAW_MAX_SIDE if redraw_max_side is None else redraw_max_side
+    redraw_img = image_bgr
+    hair_mask_redraw = hair_mask_reuse
+    if eff_side > 0 and max(h, w) > eff_side:
+        redraw_img, _rs = _downscale_max_side(image_bgr, eff_side)
+        _nh, _nw = redraw_img.shape[:2]
+        if hair_mask_redraw is not None:
+            hair_mask_redraw = cv2.resize(hair_mask_reuse.astype(np.uint8), (_nw, _nh),
+                                          interpolation=cv2.INTER_NEAREST).astype(bool)
+        logger.info("接口2女 管线降分辨率: %dx%d → %dx%d (max_side=%d)",
+                    w, h, _nw, _nh, eff_side)
+
     for order, (key, white_path) in items:
         white = load_texture_rgba(white_path)
         overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
@@ -270,10 +368,16 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
         grown_png = None
         chang_id = _FEMALE_KEY_TO_CHANG.get(key)
         if chang_id is None:
-            logger.warning("接口2 换发型：female key=%s 无对应 chang_id，跳过生发图", key)
+            # 无对应 change_hair LoRA（如 bigflower/clasicalflower）→ 走与男性一致的原生生发
+            logger.info("接口2 女 key=%s 无 chang_id，走原生生发(ComfyUI add_hair)", key)
+            grown_png = _grow_native_one(image_bgr, ctx, white_path,
+                                         prompt=prompt, unet_name=unet_name)
         else:
             try:
-                data = generate_hairline_redraw(image_bgr, chang_id, **redraw_defaults)
+                import time as _t
+                _ts0 = _t.perf_counter()
+                data = generate_hairline_redraw(redraw_img, chang_id, hair_mask=hair_mask_redraw, **redraw_defaults)
+                _ts1 = _t.perf_counter()
                 steps = data.get("steps") or {}
                 # ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG
                 final_b64 = steps.get("final_base64") or ""
@@ -289,10 +393,23 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
                         mask_b64 = mask_b64.split(",", 1)[1]
                     final_bytes = base64.b64decode(final_b64)
                     mask_bytes = base64.b64decode(mask_b64)
-                    # 后端调外部重绘服务（local_test），返回重绘后的 PNG
-                    grown_png = _call_local_redraw(final_bytes, mask_bytes)
+                    # 后端直接调 ComfyUI 重绘，返回重绘后的 PNG
+                    _tr0 = _t.perf_counter()
+                    grown_png = _call_local_redraw(final_bytes, mask_bytes,
+                                                   max_side=redraw_max_side,
+                                                   unet_name=unet_name,
+                                                   prompt=prompt)
+                    _tr1 = _t.perf_counter()
+                    _tm = data.get("timings_ms") or {}
+                    logger.info("接口2女 分段计时 type=%s: swapHair管线=%.2fs (mask=%dms swap=%dms blend=%dms), ComfyUI重绘=%.2fs",
+                                key, _ts1 - _ts0,
+                                _tm.get("mask", 0), _tm.get("swap", 0), _tm.get("blend", 0),
+                                _tr1 - _tr0)
                     if grown_png is None:
                         logger.warning("接口2 换发型：type=%s 重绘结果为空", key)
+                    elif redraw_img is not image_bgr:
+                        # 管线在降分辨率图上跑，结果放大回原尺寸
+                        grown_png = _upscale_png_to(grown_png, w, h)
             except NoFaceError:
                 logger.warning("接口2 换发型：type=%s 未检出人脸", key)
             except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
@@ -319,22 +436,51 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
             h, w = image_bgr.shape[:2]
             marked, mask = image_bgr, np.zeros((h, w), np.uint8)
         buf = io.BytesIO()
-        compose_comfy_rgba(marked, mask).save(buf, format="PNG")
+        compose_comfy_rgba(marked, mask).save(buf, format="PNG", compress_level=1)
         return comfyui.run(buf.getvalue(), prompt=prompt)
     except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
         logger.warning("接口5 生发图失败：%s", e)
         return None
 
 
+def _grow_native_one(image_bgr: np.ndarray, ctx: dict, white_path: str,
+                     prompt: str | None = None, unet_name: str | None = None):
+    """对单个发际线做原生生发（ComfyUI add_hair），与男性 generate_grow_results 一致。
+
+    接口2 女性新发型（bigflower/clasicalflower 等）无对应 change_hair LoRA，改走此路径：
+    黑模板 → build_inpaint_mask → 限边（_prep_comfy_input）→ comfyui.run(front=True)。
+    失败返回 None，不抛异常。结果按限边前原图尺寸放大回原尺寸（仅展示对齐）。
+    """
+    try:
+        h, w = image_bgr.shape[:2]
+        black = load_texture_rgba(_black_texture_path(white_path))
+        marked, mask = build_inpaint_mask(
+            image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+        m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
+        buf = io.BytesIO()
+        compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
+        # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
+        grown_png = comfyui.run(buf.getvalue(), prompt=prompt, front=True, unet_name=unet_name)
+        if gsc < 1.0 and grown_png:
+            grown_png = _upscale_png_to(grown_png, w, h)
+        return grown_png
+    except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
+        logger.warning("接口2 女原生生发图失败：%s", e)
+        return None
+
+
 def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
                            hair_styles: list[int], use_mask: bool = True,
-                           prompt: str | None = None):
+                           prompt: str | None = None,
+                           generate_grow_image: bool = True):
     """接口5：对选中发型返回 middle/high/low 三档发际线透明叠图 + 生发图(同接口2)。
 
     入参同接口2：先选 gender，再多选 hair_styles（必填，1-indexed 按贴图排序）。
     每个选中发型返回三档叠图（middle/high/low，RGBA 透明层只含发际线曲线）与一张生发图；
     三档贴图同名，生发黑模板固定取自 hairline_texture_black/（middle），故生发目标固定 middle 档。
     use_mask/prompt：同接口2 的生发参数。
+    generate_grow_image（默认 True）：是否生成生发图（ComfyUI，最耗时）。False 时跳过生发，
+        各发型 grown_png 恒为 None，可大幅降低耗时（仅留三档发际线叠图与中心点）。
     Returns: {"images":[{hairline_type,order,overlays:{middle,high,low}((H,W,4) RGBA 透明层),grown_png}],
               "best_centers":{"middle":(x,y),"high":(x,y),"low":(x,y)}}；无人脸 None。
               best_centers 取首个选中发型三档各自的发际线中点。
@@ -356,8 +502,9 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     tex_by_level = {lv: get_texture_map(lv)[gender] for lv in _TEXTURE_DIRS}
 
     # use_mask=False：干净原图+空遮罩与贴图无关，只跑一次 ComfyUI，选中项复用
+    # generate_grow_image=False：完全跳过生发（最耗时），grown_png 恒为 None
     shared_grown = None
-    if not use_mask:
+    if generate_grow_image and not use_mask:
         shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
 
     def _center_of(overlay):
@@ -376,9 +523,13 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
         for lv in _TEXTURE_DIRS:
             white = load_texture_rgba(tex_by_level[lv][s - 1][1])
             overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-        # 生发：固定 middle 黑模板
-        grown_png = shared_grown if not use_mask else \
-            _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
+        # 生发：固定 middle 黑模板（generate_grow_image=False 时跳过，恒 None）
+        if not generate_grow_image:
+            grown_png = None
+        elif not use_mask:
+            grown_png = shared_grown
+        else:
+            grown_png = _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
         images.append({"hairline_type": key, "order": s,
                        "overlays": overlays, "grown_png": grown_png})
         # best_centers：首个选中发型三档（middle/high/low）发际线中点
@@ -387,18 +538,73 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     return {"images": images, "best_centers": best_centers}
 
 
+# 接口3 送 ComfyUI 前限边，降低峰值显存，避免与接口2 切换时把 Flux 挤出。
+# 统一 prompt 后 Flux 不再被 CLIP 挤出，接口3 可用较高分辨率。可用 GROW_B_MAX_SIDE 覆盖。
+_GROW_B_MAX_SIDE = int(os.getenv("GROW_B_MAX_SIDE", "1024"))
+
+def _downscale_max_side(img_bgr: np.ndarray, max_side: int) -> tuple[np.ndarray, float]:
+    """长边超过 max_side 时等比例缩小；返回 (图, scale)，scale=新/旧。"""
+    h, w = img_bgr.shape[:2]
+    m = max(h, w)
+    if max_side <= 0 or m <= max_side:
+        return img_bgr, 1.0
+    scale = max_side / float(m)
+    nw = max(1, int(round(w * scale)))
+    nh = max(1, int(round(h * scale)))
+    out = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_AREA)
+    return out, scale
+
+
+def _upscale_png_to(png_bytes: bytes, out_w: int, out_h: int) -> bytes:
+    """把 Comfy 输出 PNG 双线性拉回原图尺寸（仅展示对齐，不增加推理细节）。"""
+    arr = np.frombuffer(png_bytes, np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
+    if img is None:
+        return png_bytes
+    if img.shape[1] == out_w and img.shape[0] == out_h:
+        return png_bytes
+    resized = cv2.resize(img, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
+    ok, buf = cv2.imencode(".png", resized)
+    return buf.tobytes() if ok else png_bytes
+
+
+def _prep_comfy_input(img_bgr: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
+    """单段 ComfyUI 生发（接口2男 / 接口3）送图前限边到 GROW_B_MAX_SIDE。
+    返回 (缩后图, 缩后遮罩, scale)；scale<1 时调用方需把结果放大回原尺寸。"""
+    h, w = img_bgr.shape[:2]
+    if _GROW_B_MAX_SIDE <= 0 or max(h, w) <= _GROW_B_MAX_SIDE:
+        return img_bgr, mask, 1.0
+    out, scale = _downscale_max_side(img_bgr, _GROW_B_MAX_SIDE)
+    nh, nw = out.shape[:2]
+    msk = cv2.resize(mask, (nw, nh), interpolation=cv2.INTER_NEAREST)
+    logger.info("接口2男/接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+                w, h, nw, nh, _GROW_B_MAX_SIDE)
+    return out, msk, scale
+
+
 def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str = None):
     """接口3：检测医生手绘发际线 → 遮罩 → 送 ComfyUI 生发（仅需划线图一张）。
 
     检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
     工作流提示词会清除黑线再生发）。
 
+    进 Comfy 前若长边 > GROW_B_MAX_SIDE（默认 896）会先等比例缩小，降低峰值显存；
+    输出再拉回原图尺寸。
+
     use_mask（默认 True）：是否启用自动检测的遮罩，用于测试对比。
       - True：检测手绘线 → 建遮罩 → alpha=255−mask（透明区=重绘区，节点44 画黄色参考区）。
       - False：跳过检测，直接送划线图，alpha 全 255（空遮罩，节点26 mask 为空），
         模型仅凭医生黑线参考生发。无需改工作流，唯一变量是遮罩。
     Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
     """
+    orig_h, orig_w = marked_bgr.shape[:2]
+    marked_bgr, _scale = _downscale_max_side(marked_bgr, _GROW_B_MAX_SIDE)
+    if _scale < 1.0:
+        logger.info(
+            "接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+            orig_w, orig_h, marked_bgr.shape[1], marked_bgr.shape[0], _GROW_B_MAX_SIDE,
+        )
+
     h, w = marked_bgr.shape[:2]
     if use_mask:
         rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
@@ -416,8 +622,10 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
         mask = np.zeros((h, w), np.uint8)   # 空遮罩：alpha 全 255，跳过检测
 
     buf = io.BytesIO()
-    compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG")   # marked 原图 + 遮罩
+    compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG", compress_level=1)   # marked + 遮罩
     grown_png = comfyui.run(buf.getvalue(), prompt=prompt)
+    if _scale < 1.0 and grown_png:
+        grown_png = _upscale_png_to(grown_png, orig_w, orig_h)
     return {"grown_png": grown_png, "status": "ok"}