save

save code
save
2026-08-04 22:37:14 +08:00 · 2026-08-04 22:37:04 +08:00 · 2026-08-04 22:37:04 +08:00 · 2026-07-30 23:03:33 +08:00 · 2026-07-27 23:17:22 +08:00 · 2026-07-27 23:10:54 +08:00
142 changed files with 10376 additions and 726 deletions
@@ -17,6 +17,10 @@ password.txt
 worker.pid
 worker.log

+# 请求日志（运行时生成，不入 git）
+gateway/request_log.jsonl
+gateway/request_log.jsonl.1
+
 # SegFormer 模型权重（~323MB，体积过大，不入 git，见 OFFLINE_ASSETS.md）
 hairline/models/face-parsing/model.safetensors

@@ -27,3 +31,25 @@ tests/output/

 # 本地临时遮罩测试页（不入 git）
 test_local.py
+
+# 运行期日志 / uvicorn 日志（不入 git）
+log/
+uvicorn.log
+uvicorn*.log
+
+# ZCode 工具目录（不入 git）
+.zcode/
+
+# 临时响应文件（不入 git）
+_grow*_resp.json
+
+# 测试素材图（体积大，不入 git）
+image/test/
+
+# 批量报告输出（生成图+原图，体积大，不入 git）
+static/report_hairline_v2/
+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
@@ -0,0 +1,327 @@
+{
+  "16": {
+    "class_type": "UNETLoader",
+    "inputs": {
+      "unet_name": "flux-2-klein-4b-fp8.safetensors",
+      "weight_dtype": "fp8_e4m3fn_fast"
+    }
+  },
+  "3": {
+    "class_type": "VAELoader",
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    }
+  },
+  "61": {
+    "class_type": "CLIPLoader",
+    "inputs": {
+      "clip_name": "qwen_3_4b.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，皮肤加一点磨皮，再加一点美颜"
+    }
+  },
+  "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"
+    }
+  }
+}
@@ -2,7 +2,7 @@
  "1": {
    "inputs": {
      "scheduler": "simple",
-      "steps": 6,
+      "steps": 4,
      "denoise": 1,
      "model": [
        "2",
@@ -170,8 +170,8 @@
  },
  "16": {
    "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
-      "weight_dtype": "fp8_e4m3fn"
+      "unet_name": "flux-2-klein-4b-fp8.safetensors",
+      "weight_dtype": "fp8_e4m3fn_fast"
    },
    "class_type": "UNETLoader",
    "_meta": {
@@ -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，皮肤加一点磨皮，再加一点美颜"
    },
    "class_type": "JjkText",
    "_meta": {
@@ -419,9 +419,9 @@
  },
  "61": {
    "inputs": {
-      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "clip_name": "qwen_3_4b.safetensors",
      "type": "flux2",
-      "device": "default"
+      "device": "cpu"
    },
    "class_type": "CLIPLoader",
    "_meta": {
@@ -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
@@ -0,0 +1,65 @@
+{
+  "code": 0,
+  "message": "success",
+  "request_id": "mock-request-id",
+  "data": {
+    "hairline_id": "chang_zhixian",
+    "gen_backend": "swaphair",
+    "hairgrow_strength": 0.75,
+    "is_hr": false,
+    "seg_model": "segformer",
+    "mask_type": "pushed",
+    "erode_cm": 0.6,
+    "swap_mode": "ext_mask",
+    "blend_method": "multiband",
+    "edge_erode_px": 3,
+    "mb_levels": 5,
+    "hairline_push_cm": 1.0,
+    "hairline_edge": "column",
+    "denoising_strength": 0.6,
+    "color_match": true,
+    "color_match_strength": 1.0,
+    "mb_feather_px": 1,
+    "transition_band_px": -1,
+    "inpainting_fill": 1,
+    "mask_blur": 11,
+    "mask_dilate_scale": 1.0,
+    "px_per_cm": 47.5311,
+    "erode_px": 29,
+    "hair_pixels": 186798,
+    "closed_pixels": 191712,
+    "mask_pixels": 140299,
+    "image_size": {
+      "width": 1257,
+      "height": 1495
+    },
+    "timings_ms": {
+      "mask": 1462,
+      "swap": 5596,
+      "blend": 220
+    },
+    "redraw": {
+      "enabled": false
+    },
+    "_rid": "bc7205a4",
+    "steps": {
+      "input_base64": "<omitted 427407 chars>",
+      "baseline_overlay_base64": "<omitted 435895 chars>",
+      "upper_overlay_base64": "<omitted 384479 chars>",
+      "hair_seg_overlay_base64": "<omitted 428039 chars>",
+      "top_fill_overlay_base64": "",
+      "closed_overlay_base64": "",
+      "hairline_overlay_base64": "<omitted 445467 chars>",
+      "pushed_overlay_base64": "<omitted 452803 chars>",
+      "mask_overlay_base64": "<omitted 420375 chars>",
+      "mask_base64": "<omitted 8026 chars>",
+      "swap_raw_base64": "<omitted 340791 chars>",
+      "hard_paste_base64": "<omitted 420679 chars>",
+      "alpha_base64": "<omitted 7762 chars>",
+      "final_base64": "<omitted 415811 chars>",
+      "redraw_band_overlay_base64": "",
+      "redraw_a_base64": "",
+      "redraw_c_base64": ""
+    }
+  }
+}
@@ -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）
@@ -0,0 +1,7 @@
+第一步 使用接口9 头发遮罩生成 的算法获取 mask
+第二部 改造 /home/xsl/change_hair 换发型的工作流， 换发型的参考文档在这里 /home/xsl/change_hair/docs/换发型集成文档.md
+1、原始换发型工作的遮罩用第一步算出来的遮罩
+2、然后换发型得到遮罩区域发际线的图片。
+3、严格按照遮罩区域把图片贴回到原图上面。
+4、贴图的时候融合贴图边缘和原图的接缝，可以采用羽化算法或者渐变alpha混合的算法，目的就是边缘要和原图过渡自然。 这里通过传入各种参数可以控制选哪种算法和控制过渡细节。
+最后一步返回生成特定样式的图片。
@@ -0,0 +1,115 @@
+# 发际线生发遮罩算法（pushed 模式）
+
+> 对应接口11 `/api/v1/hairline/grow`、接口12 `/api/v1/hairline/grow_v2`。
+> 遮罩算法固定为 pushed；融合算法默认 multiband（多频段金字塔），接口11 可切换 seamless/two_stage/feather。
+> 代码：`face_analysis/hairline_grow.py`（`_extract_hairline` / `_pushed_mask` / `compute_mask` / `_composite`）。
+
+## 概述
+
+pushed 是发际线生发的**唯一**遮罩算法。融合算法默认 multiband（多频段金字塔），接口11 暴露 `blend_method` 可切换为 seamless(泊松)/two_stage(泊松→多频段两段式)/feather(羽化)，便于对比调优。它从头发分割结果中提取「头发/皮肤交界线」（发际线），以眉心为圆心逐点径向外推一段距离，与 baseline 组成闭合区域作为最终遮罩。这样遮罩顶部会覆盖现有头发下沿一小段，贴回生发结果时顶部与真头发重叠、过渡自然。
+
+> 接口12 `/api/v1/hairline/grow_v2` 只需传 `image` + `hairline_id`，遮罩和融合全部固定，无需任何算法选择参数。
+
+## 算法流程（5 步）
+
+```
+①-a baseline 分割线      ← 眉骨/glabella 关键点折线（含 151 中心点）
+①-b 上半区 upper         ← baseline 以上的区域（裁剪范围）
+①-c 头发分割 hair_mask    ← segformer/bisenet 的原始头发像素
+①-f 头发内轮廓线          ← 取头发轮廓中朝脸一侧的那段（额头弧+两侧到下颌），有序折线
+①-g 径向外推 + 成带      ← 以 151 点为圆心把内轮廓逐点向外推 push_px，内轮廓↔外推线之间的带 = 最终遮罩
+```
+
+> ①-d（填充到基线 top_fill）、①-e（闭合区域 closed）是旧 eroded/closed 模式的中间产物，pushed 模式不走这条流程，前端不展示。
+
+### ①-a baseline 分割线
+
+MediaPipe 人脸关键点 `[21,68,104,69,108,151,337,299,333,298,251]` 连成折线（左端 21 → 中心 151 → 右端 251），再向左右边缘水平延长。151 点（glabella/眉心）是后续径向外推的圆心。代码 `_baseline_points` / `_draw_baseline`。
+
+### ①-b 上半区 upper
+
+baseline 折线以上的多边形区域（`_upper_region_mask`）。作为后续裁剪范围，保证遮罩不越界到下半脸。
+
+### ①-c 头发分割 hair_mask
+
+segformer（默认）或 bisenet 得到的头发二值掩码。
+
+### ①-f 头发内轮廓线（核心改动）
+
+代码 `_extract_hairline`。目标是提取「头发区域朝脸一侧的内轮廓线」：额头弧 + 左右两侧鬓角/脸颊边界，一直向下到下颌，是一条**有序折线**（不再是逐列一个 y 的数组，因为两侧近乎竖直、一个 x 对多个 y）。
+
+1. **取头发轮廓**：`hair_mask` 最大连通域，`cv2.findContours(RETR_EXTERNAL, CHAIN_APPROX_NONE)` 取稠密、保序的外轮廓点。
+2. **内侧判定**：轮廓同时含「朝背景的外侧剪影」和「朝脸的内轮廓」。对每个轮廓点，朝脸中心 151 方向采样 `sample_px`（≈0.4cm）像素，落点若是**非头发**像素 → 该点朝向脸（内轮廓）；否则是外侧剪影，丢弃。
+3. **取最长连续内侧段**：内轮廓点在闭合轮廓上本是一段连续弧，先做 1D 环形闭运算填掉判定抖动的小缝，再取最长连续 True 段并保序。
+4. **下颌截断**：丢掉 y > `chin_y`（下巴关键点 152 的 y）的点，把两侧末端截到下颌一带 → 得到「环脸」内轮廓弧。
+
+**关键点**：不再用 baseline 做水平截断、也不再逐列取下沿；截断改为「朝脸内侧」判定 + 下颌 y 截断，因此能同时拿到额头弧和两侧竖直边界。
+
+### ①-g 径向外推 + 闭合区域（最终遮罩）
+
+代码 `_pushed_mask`：
+
+1. **逐点径向外推**：圆心 = 151 点 (cx, cy)。对内轮廓上每个点 (x, y)，沿「从圆心指向它」的单位向量 `(ux, uy)` **向外**（远离脸中心 = 推进现有头发）外推 `push_px`，得到外推线（黄线）点 `(x + ux·push_px, y + uy·push_px)`。`push_px = hairline_push_cm × px_per_cm`（默认 1cm）。
+2. **逐列归并**：只取外推线中落在 baseline 以上的点，逐列取最靠上的 y 作为遮罩顶界 `pushed_y[x]`；空列线性插值填补。两侧鬓角落到 baseline 以下的段落自然被排除。
+3. **与 baseline 组闭合区域**：逐列从 `pushed_y[x]` 填充到 `baseline_y[x]`（仅 `pushed_y < baseline_y` 的列），`& upper` 去越界、`_largest_cc` 保留最大连通域。
+
+最终遮罩 = **外推发际线（①-g 黄线）与 baseline 分割线（①-a）组成的闭合区域**：顶界=外推发际线（覆盖现有头发约 push_cm），底界=baseline。与旧逻辑一致，区别只是 `pushed_y` 现在来自修正后的整条内轮廓，额头弧已延伸到两侧鬓角，额头遮罩宽度不再被截短。
+
+## 关键参数
+
+遮罩算法（pushed）固定。融合算法接口11 通过 `blend_method` 可切换（默认 multiband），其余融合参数均可调：
+
+| 参数 | 默认 | 说明 |
+|---|---|---|
+| `hairline_push_cm` | 1.0 | 内轮廓径向外推距离（厘米），= push_px / px_per_cm。`px_per_cm` 由虹膜直径标定 |
+| `hairline_edge` | `column` | 兼容保留的入参；内轮廓提取（轮廓+内侧判定）不再按它分支，取值不影响结果 |
+| `mb_levels` | 5 | 多频段金字塔层数（2~6，越大低频色差抹得越宽）|
+| `blend_method` | `multiband` | 接缝融合：multiband(多频段金字塔) / seamless(泊松) / two_stage(泊松→多频段,大色差) / feather(羽化) / alpha_gradient。接口12 固定 multiband |
+| `color_match` | `true` | 融合前 Reinhard 颜色迁移消除整体色差（multiband/feather/alpha_gradient 生效；seamless/two_stage 自带调色故跳过）|
+| `color_match_strength` | 1.0 | 颜色迁移强度（0~1，<1 只迁移部分，防 Reinhard 过度改色）|
+| `mb_feather_px` | 1 | 多频段最细层掩码轻羽化像素（0=不羽化），消除发丝边缘锯齿 |
+| `transition_band_px` | -1 | keep-region 过渡带边距（-1=自动按层数 `2**n`；>=0 用绝对像素与层数解耦）|
+| `edge_erode_px` | 3 | 贴图前遮罩内缩像素（防边缘露皮/光晕）|
+| `erode_cm` | 0.6（接口12 固定）| baseline 参考内缩距离，对 pushed 影响很小 |
+| `redraw` | `false` | 发际线带重绘开关：开启后用 final（④融合图）在「外推线↔发际线」带重绘，swapHair/Flux-2 两路对比，结果单独展示（不替换 final）|
+| `inpainting_fill` | 1 | change_hair 重绘填充：0=保留原图(治染绿) / 1=填充噪声(默认) / 2=纯色 / 3=潜变量噪声 |
+| `mask_blur` | 11 | change_hair 遮罩边缘模糊像素（越大颜色越易从边缘渗透）|
+| `mask_dilate_scale` | 1.0 | change_hair 遮罩膨胀核缩放（1.0=原始，<1 收缩防越界）|
+| `comfyui_prompt` | `null` | redraw Flux-2 路提示词，null 用默认「补充遮罩区域的头发，加一点美颜」|
+
+> 接口12 `/api/v1/hairline/grow_v2` 只需传 `image` + `hairline_id`，遮罩和融合全部用默认值（multiband + color_match=true），不暴露算法选择参数。
+
+### 融合方法选择建议
+
+- **multiband**（默认）：常规首选。低频抹色差、高频保发丝。需配合 `color_match=true` 消除整体色差。
+- **two_stage**：生成图与原图色差大时用。先泊松克隆统一色调，再多频段贴细节，兼顾调色与保发丝。比纯 seamless 更不易溢色。
+- **seamless**：纯泊松梯度域调和，色调统一干净，但可能整体改色/边缘溢色。
+- **feather / alpha_gradient**：单层 alpha 过渡，最轻量，但过渡带内色差不会被抹平，仅适合色差极小的场景。
+
+## 发际线带重绘（redraw，接口11 可选）
+
+`redraw=true` 时，在主流程（④接缝融合 final）之后额外跑一条重绘分支，结果单独展示（`steps.redraw_a` / `redraw_c`），**不替换** final。
+
+**重绘区域** = ①-g 外推发际线（`outer_pts`）与 ①-f 发际线（`inner_pts`）两条折线端点相连组成的带状闭合区域（宽度 ≈ `hairline_push_cm`，只覆盖发际线交界处）。
+
+**两路后端对比**（输入图 + 融合基底都用 final）：
+- **swapHair 路**（`redraw_a`）：final + 带遮罩调 change_hair → final 走 multiband 融合
+- **Flux-2 路**（`redraw_c`）：final + 带遮罩调 ComfyUI（`hair_repaint.json` 工作流）→ final 走 multiband 融合。Flux-2 经 reference latent + ColorMatch 双重保色，**不易染绿**
+
+> `inpainting_fill` / `mask_blur` / `mask_dilate_scale` 透传 change_hair 服务端（仅影响 swapHair 路）。`comfyui_prompt` 仅影响 Flux-2 路。
+> 两路独立容错：任一路失败只跳过该路，不影响另一路和主 final。
+> ⚠️ Flux-2 路需 ComfyUI（8188）在跑；swapHair 路需 change_hair（8801）在跑。
+
+## 与旧模式（eroded/closed，已移除）的区别
+
+| | eroded/closed（已移除） | pushed（当前唯一） |
+|---|---|---|
+| 遮罩来源 | top_fill（头发向下填充含额头）外缘内缩 | 头发内轮廓线 径向外推成带 |
+| 截断方式 | 用 baseline 组上半区 upper | 内侧判定 + 下颌 y 截断（不再用 baseline） |
+| 遮罩形状 | 整个额头闭合区域 | 沿内轮廓的环脸带（额头弧+两侧，压住现有头发 push_cm） |
+
+## 调试
+
+- 调试页：`http://<host>:8187/static/test_interface11_debug.html`（带前后端日志面板、下载日志按钮）
+- 后端日志：`/home/xsl/hair/log/hairline_grow.log`（按 `[rid]` 关联一次请求），下载接口 `/api/v1/debug/hairline_log?rid=<id>&tail=500`
+- 可视化步骤：①-a baseline / ①-b upper / ①-c 头发分割 / ①-f 交界线 / ①-g 外推+遮罩 / 最终遮罩 / 生成 / 贴回 / 融合
@@ -32,7 +32,7 @@
 | 6 | `annotated_image_base64` | `annotated_image_url` |
 | 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_base64` | `hairline_images[].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` |
 | 4 | （网关本机产出，无图片字段，`features` 为 JSON 字符串） | — |

@@ -56,9 +56,10 @@
  失败回退比例推算(方案A，`hairline_source` 透出)。标注图 numpy 向量化渐变线 + 思源黑体。返回 `annotated_image_base64`。
 - 门槛可配：`MIN_SHORT_SIDE`/`MIN_LONG_SIDE`(默认600/800)、姿态阈值 `FRONTAL_*_THR`(默认30°)。

-### 接口6 四庭七眼测量 v2 `/api/v1/face/measure-v2`（worker）—— 复刻接口1
- **做什么**：与接口 1 完全一致（正面照 → 四庭七眼 cm 与占比 + 5 个关键点 + 标注 PNG）。
- **怎么实现**：与接口 1 共用 `_face_measure_impl()`，零额外逻辑。对外路径 `/api/v1/face/measure-v2`。
+### 接口6 四庭七眼测量 v2 `/api/v1/face/measure-v2`（worker）—— 接口1 的去顶庭变体
+- **做什么**：基于接口 1，**去顶庭**：不画头顶横线、不返回顶庭数据（`four_courts` 仅上/中/下庭，`landmarks` 无 `hair_top`，`face_total_height_cm` 为三庭之和）。
+- **与接口1 的标注差异**（`create_annotated_image(variant="v6")`）：①竖线范围改为发际线→下巴尖；②不画人头最左/最右端线（仅七眼 6 点 5 段，接口1 为 8 线 7 段）；③左侧只标上/中/下庭。箭头/虚线/字体等与接口1 一致。
+- **怎么实现**：与接口 1 共用 `_face_measure_impl(variant="v6")`；v6 时标注走变体分支、数据由 app.py 边界删顶庭字段并重算三庭比例。
 - **网关改动**：新增路由 `POST /api/v1/face/measure-v2`，转发到 worker 同路径；base64→URL 改写无需改动。

 ### 接口2 C端生发 `/api/v1/hair/grow`（worker）—— 预览 + 生发图
@@ -67,7 +68,7 @@
  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
  `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。
+- `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。female 1..5 走「换发型(change_hair)」+Flux-2 重绘管线；female 6/7 与 male 全部走原生发(ComfyUI add_hair)管线。

 ### 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）。
@@ -88,8 +89,8 @@
  无人脸→1001。**唯一调外网的接口**：网关需可达 `ark.cn-beijing.volces.com`，API Key 走网关配置(不入 git)。

 ### 接口5 发际线PNG生成 `/api/v1/hairline/generate`（worker）
- **做什么**：照片 + `gender`(必填) → N 张发际线叠加图(同接口2预览) + 最佳(order1)发际线曲线的面部中间点坐标。
- **怎么实现**：复用接口2 的 502 点渲染管线，输出 N 张叠图 + `best_hairline_center_point`(眉心 x × 该处发际线 y)。无生发。
+- **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。

 ---

@@ -0,0 +1,182 @@
+# 接口11 运行记录 — `image/hair_test.jpg`
+
+> 实测时间：2026-07-15  
+> 调用：`POST http://127.0.0.1:8187/api/v1/hairline/grow`  
+> 鉴权：`X-Internal-Token: dev-shared-secret-2026`  
+> 输入图：`image/hair_test.jpg`（1257×1495）  
+> `hairline_id`：`chang_zhixian`（直线）；其余全部走接口默认值  
+> 业务结果：`code=0`，`rid=bc7205a4`  
+> 产物目录：`docs/iface11_hair_test_run/`
+
+---
+
+## 1. 本次调用用到的全部默认参数
+
+未在 Form 里显式传的参数均取 `app.py` / `generate_hairline_grow` 默认值；下表即本次实际生效值。
+
+| 参数 | 本次值 | 说明 |
+|------|--------|------|
+| `hairline_id` | `chang_zhixian` | **必填**。发际线类型 = change_hair 的 `hair_id`（直线） |
+| `gen_backend` | `swaphair` | 生成后端：换发型 LoRA |
+| `hairgrow_strength` | `0.75` | 仅 `hairgrow` 后端用；本次未走该路径 |
+| `is_hr` | `false` | 高清关闭（576×768 档，非 1152×1536） |
+| `seg_model` | `segformer` | 头发分割模型 |
+| `erode_cm` | `0.6` | baseline 参考内缩（cm）；pushed 下影响很小 |
+| `hairline_push_cm` | `1.0` | 发际线内轮廓径向外推距离（cm） |
+| `hairline_edge` | `column` | 兼容入参；当前内轮廓提取不再按它分支 |
+| `swap_mode` | `ext_mask` | 把 pushed 遮罩作为 `ext_mask` 传给 swapHair |
+| `edge_erode_px` | `3` | 贴图前遮罩内缩像素 |
+| `denoising_strength` | `0.6` | 换发型 webui 重绘强度 |
+| `mb_levels` | `5` | 多频段金字塔层数 |
+| `blend_method` | `multiband` | 接缝融合：多频段金字塔 |
+| `color_match` | `true` | 融合前 Reinhard 颜色迁移 |
+| `color_match_strength` | `1.0` | 颜色迁移强度（全迁移） |
+| `mb_feather_px` | `1` | 多频段最细层掩码轻羽化 |
+| `transition_band_px` | `-1` | keep-region 过渡带：自动按层数 `2**n` |
+| `redraw` | `false` | 发际线带重绘关闭 |
+| `inpainting_fill` | `1` | change_hair 填充噪声 |
+| `mask_blur` | `11` | change_hair 遮罩边缘模糊像素 |
+| `mask_dilate_scale` | `1.0` | change_hair 遮罩膨胀缩放 |
+| `comfyui_prompt` | `null` | 仅 `redraw`+Flux-2 路用；本次未用 |
+| `mask_type` | `pushed`（固定） | 代码写死，不可选 |
+
+图片入参：仅传了 `image_file`（三选一中的文件上传）。
+
+---
+
+## 2. 返回元数据（无 base64）
+
+| 字段 | 值 |
+|------|-----|
+| `px_per_cm` | 47.5311（虹膜直径标定） |
+| `erode_px` | 29（≈ 0.6cm × px_per_cm） |
+| `hair_pixels` | 186798 |
+| `closed_pixels` | 191712 |
+| `mask_pixels` | 140299 |
+| `image_size` | 1257 × 1495 |
+| `timings_ms.mask` | 1462 |
+| `timings_ms.swap` | 5596 |
+| `timings_ms.blend` | 220 |
+| `redraw.enabled` | false |
+| 总耗时（curl） | ≈ 7.4 s |
+
+完整精简 JSON：`docs/iface11_hair_test_run/response_meta.json`  
+完整原始响应（含 base64）：`docs/iface11_hair_test_run/response.json`
+
+---
+
+## 3. 管线分步说明与产物
+
+管线：① pushed 遮罩 → ② swapHair 生成 → ③ 硬贴回 → ④ multiband 融合。  
+各步图保存在 `docs/iface11_hair_test_run/steps/`。
+
+### ①-a 发际线分割线（baseline）
+
+- **做什么**：MediaPipe 关键点连成眉骨折线（中心为 151 眉心），并向左右边缘水平延长。
+- **图**：[`steps/baseline_overlay.jpg`](iface11_hair_test_run/steps/baseline_overlay.jpg)
+- **含义**：黄线 = baseline；151 中心点为后续径向外推圆心。
+
+### ①-b 分割线上半区（upper）
+
+- **做什么**：baseline 折线以上的多边形区域，作为后续裁剪范围。
+- **图**：[`steps/upper_overlay.jpg`](iface11_hair_test_run/steps/upper_overlay.jpg)
+- **含义**：青 = 上半区。
+
+### ①-c 头发分割（hair_seg）
+
+- **做什么**：SegFormer 得到头发二值掩码。
+- **图**：[`steps/hair_seg_overlay.jpg`](iface11_hair_test_run/steps/hair_seg_overlay.jpg)
+- **含义**：绿 = 原始头发像素（本次 `hair_pixels=186798`）。
+
+### ①-d / ①-e（旧 eroded/closed 中间步）
+
+- pushed 模式**不走**这两步；返回字段为空字符串。
+- `top_fill_overlay` / `closed_overlay`：本次无图。
+
+### ①-f 头发内轮廓线（hairline）
+
+- **做什么**：取头发朝脸一侧的内轮廓（额头弧 + 两侧到下颌），有序折线。
+- **图**：[`steps/hairline_overlay.jpg`](iface11_hair_test_run/steps/hairline_overlay.jpg)
+- **含义**：绿 = 内轮廓；黄 = baseline。
+
+### ①-g 外推发际线（pushed）
+
+- **做什么**：以眉心 151 为圆心，内轮廓逐点向外推 `hairline_push_cm=1.0`（≈ 47.5 px），与 baseline 组闭合区域。
+- **图**：[`steps/pushed_overlay.jpg`](iface11_hair_test_run/steps/pushed_overlay.jpg)
+- **含义**：青 = 外推线；红 = 外推遮罩区域。
+
+### ① 最终遮罩
+
+- **叠加图**：[`steps/mask_overlay.jpg`](iface11_hair_test_run/steps/mask_overlay.jpg) — 红 = 遮罩区（贴回/生成区）
+- **纯遮罩**：[`steps/mask.png`](iface11_hair_test_run/steps/mask.png) — 白 = 生成/贴回区  
+- 本次 `mask_pixels=140299`；贴图前再内缩 `edge_erode_px=3`。
+
+### ② 生成全帧（swap_raw）
+
+- **做什么**：`gen_backend=swaphair` + `swap_mode=ext_mask`，把遮罩交给 change_hair（`:8801`），LoRA=`chang_zhixian`，`denoising_strength=0.6`。
+- **图**：[`steps/swap_raw.jpg`](iface11_hair_test_run/steps/swap_raw.jpg)
+- **含义**：生成结果已与原图同分辨率对齐；耗时约 5.6 s。
+
+### ③ 严格按遮罩贴回（hard_paste）
+
+- **做什么**：遮罩内用生成图，遮罩外保持原图，无融合。
+- **图**：[`steps/hard_paste.jpg`](iface11_hair_test_run/steps/hard_paste.jpg)
+- **含义**：用于对比接缝融合前后差异。
+
+### ④ 融合权重 alpha + 最终结果
+
+- **做法**：`blend_method=multiband`，`mb_levels=5`，`color_match=true`（强度 1.0），`mb_feather_px=1`。
+- **alpha**：[`steps/alpha.png`](iface11_hair_test_run/steps/alpha.png) — 白 = 更多采用生成图
+- **最终输出**：[`steps/final.jpg`](iface11_hair_test_run/steps/final.jpg)（副本：[`final.jpg`](iface11_hair_test_run/final.jpg)）
+- **输入对照**：[`steps/input.jpg`](iface11_hair_test_run/steps/input.jpg)
+
+### ⑤ 发际线带重绘（本次关闭）
+
+`redraw=false`，故 `redraw_band_overlay` / `redraw_a` / `redraw_c` 均为空。
+
+---
+
+## 4. 最终输出
+
+**主结果文件**：[`docs/iface11_hair_test_run/final.jpg`](iface11_hair_test_run/final.jpg)
+
+含义：同一人、同一发型观感下，按直线发际线类型（`chang_zhixian`）压低发际线后的合成图；遮罩外像素保持原图不动。
+
+---
+
+## 5. 复现命令
+
+```bash
+curl -sS -X POST "http://127.0.0.1:8187/api/v1/hairline/grow" \
+  -H "X-Internal-Token: dev-shared-secret-2026" \
+  -F "image_file=@image/hair_test.jpg" \
+  -F "hairline_id=chang_zhixian" \
+  -o docs/iface11_hair_test_run/response.json
+```
+
+（其余参数全部省略即可走默认值。）
+
+---
+
+## 6. 产物清单
+
+```
+docs/接口11_hair_test运行记录.md          ← 本文档
+docs/iface11_hair_test_run/
+  final.jpg                               ← 最终结果
+  response.json                           ← 完整 API 响应（含 base64）
+  response_meta.json                      ← 去掉大图的元数据
+  steps/
+    input.jpg
+    baseline_overlay.jpg
+    upper_overlay.jpg
+    hair_seg_overlay.jpg
+    hairline_overlay.jpg
+    pushed_overlay.jpg
+    mask_overlay.jpg
+    mask.png
+    swap_raw.jpg
+    hard_paste.jpg
+    alpha.png
+    final.jpg
+```
@@ -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）：

@@ -128,18 +130,26 @@
 | face_width_cm | number | 脸宽（cm） |
 | inter_eye_distance_cm | number | 两眼间距（cm） |
 | ratios | object | 七眼各段占脸宽的比例 |
+| eye1 | number \| null | 从左到右第 1 段宽度（cm）：人头最左 → 左脸颊（左耳外侧段）。该侧耳朵不可见时为 null |
+| eye2 | number | 从左到右第 2 段宽度（cm）：左脸颊 → 左眼外角 |
+| eye3 | number | 从左到右第 3 段宽度（cm）：左眼外角 → 左眼内角（左眼宽度） |
+| eye4 | number | 从左到右第 4 段宽度（cm）：左眼内角 → 右眼内角（两眼间距） |
+| eye5 | number | 从左到右第 5 段宽度（cm）：右眼内角 → 右眼外角（右眼宽度） |
+| eye6 | number | 从左到右第 6 段宽度（cm）：右眼外角 → 右脸颊 |
+| eye7 | number \| null | 从左到右第 7 段宽度（cm）：右脸颊 → 人头最右（右耳外侧段）。该侧耳朵不可见时为 null |
+
+> `eye1`~`eye7` 为从左到右共 7 段宽度，与标注图竖线一一对应。最左/最右端线取自耳朵分割外缘；某侧耳朵被头发或侧脸遮挡（不可见）时该侧端线省略，对应 `eye1` 或 `eye7` 为 `null`（键始终保留），实际有效段为 5 或 6 段。`eye3`/`eye5` 为左右眼宽、`eye4` 为两眼间距，与 `eye_width_cm` / `inter_eye_distance_cm` 语义一致。

 ### 标注图片（UI）规范

 | 项目 | 要求 |
 |------|------|
-| 字体及线颜色 | `#FFFFFF` 100% |
-| 数值排布 | 四庭数值统一在图片**左侧**呈现；七眼间距**上下穿插**展示 |
-| 字体 | PingFangSC-Regular，字号 10pt |
-| 线宽 | 横线、竖线、虚线均为 1pt |
-| 线样式 | 横线、竖线渐变消失；虚线两侧呈现箭头 |
-
-> **【待确认】** 标注图片需提供设计稿后才能最终确定样式。
+| 颜色 | 字体及所有线/箭头 `#FFFFFF` 100%，透明底 |
+| 尺寸 | 字号/线宽/虚线/箭头按图片**短边自适应缩放**（非固定 pt） |
+| 横线 | 5 条分界线（头顶/发际线/眉心/鼻翼下缘/下巴尖），两端渐变消失并略超出最外侧竖线；线名在线**右上方** |
+| 竖线 | 人头最左 + 七眼 6 点 + 人头最右（最外两条取自头发分割轮廓），两端渐变消失并略超出头顶/下巴 |
+| 数值排布 | 四庭数值（名 + 数值两行，**不带 cm**）统一在图片**左侧**呈现；七眼段宽**上下穿插**展示；底部统一标「单位cm」 |
+| 线样式 | 段宽/庭高用**虚线 + 实心三角双箭头**标示（箭头尖端落在虚线两端） |

 ### 响应示例（当前 Mock 返回值）

@@ -162,7 +172,9 @@
      "eye_width_cm": 3.44,
      "face_width_cm": 24.08,
      "inter_eye_distance_cm": 3.44,
-      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 }
+      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 },
+      "eye1": 3.44, "eye2": 3.44, "eye3": 3.44, "eye4": 3.44,
+      "eye5": 3.44, "eye6": 3.44, "eye7": 3.44
    },
    "landmarks": {
      "hair_top": { "x": 540, "y": 120 },
@@ -179,7 +191,12 @@

 ## 接口 6：四庭七眼测量 v2 接口

-**说明**：功能与[接口 1](#接口-1四庭七眼测量标注接口)完全一致，复刻实现。输入用户正面照，返回四庭七眼测量数据和标注 PNG。
+**说明**：基于[接口 1](#接口-1四庭七眼测量标注接口)的变体。与接口 1 的差异：
+
+- **去顶庭**：不画头顶横线、不返回顶庭数据。`four_courts` 仅含上/中/下庭，`landmarks` 无 `hair_top`，`face_total_height_cm` 为三庭之和（不含顶庭）。
+- **竖线范围**：纵向竖线从**发际线**画到**下巴尖**（接口 1 为头顶→下巴尖）。
+- **不画人头最左/最右端线**：仅画七眼 6 点（左脸颊/左眼外角/左眼内角/右眼内角/右眼外角/右脸颊）共 5 段标尺，不取头发轮廓的头部端线（接口 1 会多出最左/最右 2 条头部端线、共 7 段）。
+- 其余（实心三角箭头、虚线样式、字体、单位cm、七眼数据）与接口 1 一致。

 **请求**：`POST /api/v1/face/measure-v2`

@@ -189,15 +206,17 @@

 ### 输出（data）

-与接口 1 完全相同。详见[接口 1 输出](#接口-1四庭七眼测量标注接口)。
-
 | 字段 | 类型 | 说明 |
 |------|------|------|
 | annotated_image_url | string | 标注图层 PNG URL（透明底，仅标注线/文字，不含人物） |
-| face_total_height_cm | number | 面部总高度（cm） |
-| four_courts | object | 四庭数据（顶庭/上庭/中庭/下庭，各含 cm 与 ratio） |
-| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距，各含 cm 与 ratio） |
-| landmarks | object | 五个关键点像素坐标 |
+| face_total_height_cm | number | 面部总高度（cm）= 上庭 + 中庭 + 下庭（**不含顶庭**） |
+| 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）。

 ### 响应示例

@@ -208,18 +227,20 @@
  "request_id": "mock-request-id",
  "data": {
    "annotated_image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-    "face_total_height_cm": 13.76,
+    "face_total_height_cm": 10.32,
    "four_courts": {
-      "top_court_cm": 3.44, "upper_court_cm": 3.44, "middle_court_cm": 3.44, "lower_court_cm": 3.44,
-      "ratios": { "top_court": 0.25, "upper_court": 0.25, "middle_court": 0.25, "lower_court": 0.25 }
+      "upper_court_cm": 3.44, "middle_court_cm": 3.44, "lower_court_cm": 3.44,
+      "ratios": { "upper_court": 0.333, "middle_court": 0.333, "lower_court": 0.333 }
    },
    "seven_eyes": {
      "eye_width_cm": 3.44, "face_width_cm": 24.08, "inter_eye_distance_cm": 3.44,
-      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 }
+      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 },
+      "eye2": 3.0, "eye3": 3.44, "eye4": 3.44, "eye5": 3.44, "eye6": 3.0
    },
    "landmarks": {
-      "hair_top": { "x": 540, "y": 120 }, "hairline": { "x": 540, "y": 430 },
-      "brow_center": { "x": 540, "y": 740 }, "nose_bottom": { "x": 540, "y": 1050 },
+      "hairline": { "x": 540, "y": 430 },
+      "brow_center": { "x": 540, "y": 740 },
+      "nose_bottom": { "x": 540, "y": 1050 },
      "chin_tip": { "x": 540, "y": 1360 }
    }
  }
@@ -230,10 +251,10 @@

 ## 接口 2：C 端生发接口

-**说明**：输入用户正面照 + 性别 + 发型序号（可多选），按指定发际线类型渲染预览图 + 生发图。
+**说明**：输入用户正面照 + 性别 + 发型序号（可多选），按指定发际线类型渲染发际线曲线透明 PNG + 生发图。

-> **每个方案返回两张图**：`image_url`=「原照片 + 发际线曲线叠加的**预览图**」；`grown_image_url`=
-> 经 ComfyUI/Flux 的「植发 3 个月**生发后图片**」。两者均已实现，实现简述见 [`实现说明.md`](实现说明.md)。
+> **每个方案返回两张图**：`image_url`=「发际线曲线**透明 PNG**（仅白色曲线，透明底，需叠加原图显示）」；`grown_image_url`=
+> 经 ComfyUI/Flux 的「植发 3 个月**生发后图片**」（完整人像照片）。两者均已实现，实现简述见 [`实现说明.md`](实现说明.md)。

 **请求**：`POST /api/v1/hair/grow`

@@ -244,9 +265,10 @@
 | 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。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 |

 ### 输出（data）

@@ -254,14 +276,16 @@

 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 方案**预览图** URL（发际线曲线叠加图） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
-| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
+| 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`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`（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**。
 > 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`。
+>
+> 💡 `image_url` 为透明底 PNG，前端需用绝对定位叠加到原图上显示（参考[测试页](https://hair.xiangsilian.com/static/test_interface2.html)的 `.img-stack` 叠加结构）。

 ### 响应示例

@@ -299,6 +323,7 @@
 |------|------|------|------|
 | marked_image_* | file / string | 是 | 已用马克笔标注发际线的图片，三选一 |
 | use_mask | bool | 否 | 是否画发际线，默认 `true`。`false` 时跳过划线检测、直接送划线图，模型仅凭手绘黑线生发，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 |

 ### 输出（data）

@@ -370,7 +395,7 @@

 ## 接口 5：发际线 PNG 生成接口

-**说明**：输入用户照片，返回 N 张用户发际线的 PNG 图片，并返回「最合适发际线」的面部中间点坐标。
+**说明**：入参同接口2（先选性别、再多选发型）。对每个选中发型返回 `middle` / `high` / `low` **三档**发际线叠图与**生发图**，并返回「最合适发际线」的面部中间点坐标。

 **请求**：`POST /api/v1/hairline/generate`

@@ -380,21 +405,53 @@

 | 参数 | 类型 | 必填 | 说明 |
 |------|------|------|------|
-| gender | string | **是** | 性别：`male` / `female`。决定返回的发际线集合（female 5 / male 4） |
+| gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 7 / male 6）。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。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。

 ### 输出（data）

 | 字段 | 类型 | 说明 |
 |------|------|------|
-| hairline_images | object[] | N 张发际线叠加图（发际线曲线叠在用户照片上，同接口2预览），**数量 = 该性别发际线数**，本期按贴图顺序，元素见下表 |
-| best_hairline_center_point | object | 最佳（order=1）发际线曲线的「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| hairline_images | object[] | **选中发型**列表，**数量 = 所选发型数**，元素见下表 |
+| best_hairline_center_point | object \| null | **首个选中发型**的 **middle 档**发际线曲线「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| high_hairline_center_point | object \| null | 同上，**high 档**发际线中点（发际线偏高） |
+| low_hairline_center_point | object \| null | 同上，**low 档**发际线中点（发际线偏低） |
+| face_measure | object \| null | **复用接口1**的四庭七眼测量**数值**（不含标注图）。独立流程，测量失败（无人脸/非正面/分割失败）时为 `null`，不影响发际线主结果。字段结构见下表 |

 `hairline_images` 元素：

 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 发际线叠加图 URL（worker 返回 `image_base64`，网关落盘后改写为 url） |
-| order | int | 排序序号（本期固定 `1..N`，暂不计算合适度） |
+| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`（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`。
+>
+> 💡 三档 `image_*_url` 为透明底 PNG，前端需用绝对定位叠加到原图上显示（参考[测试页](https://hair.xiangsilian.com/static/test_interface5.html)的 `.img-stack` 叠加结构）。`grown_image_url` 是完整人像照片，直接显示即可。
+
+`face_measure` 元素（与[接口1](#接口-1四庭七眼测量标注接口)的 `data` 同构，**不含** `annotated_image_*` 标注图字段）：
+
+| 字段 | 类型 | 说明 |
+|------|------|------|
+| face_total_height_cm | number | 全脸总高度（cm）= 四庭之和 |
+| 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） |
+| 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。

 ### 响应示例（当前 Mock 返回值）

@@ -405,67 +462,57 @@
  "request_id": "mock-request-id",
  "data": {
    "hairline_images": [
-      { "image_url": "https://hair.xiangsilian.com/static/sample.jpg", "order": 1 },
-      { "image_url": "https://hair.xiangsilian.com/static/sample.jpg", "order": 2 }
-    ],
-    "best_hairline_center_point": { "x": 540, "y": 430 }
-  }
-}
-```
-
---
-
-## 接口 7：C 端生发 v2 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`。
-
-**请求**：`POST /api/v1/hair/grow-v2`
-
-### 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：
-
-| 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） |
-
-### 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 | 方案**预览图** URL（发际线曲线叠加图） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
-| hairline_type | string | 发际线类型 key |
-| order | int | 排序序号 |
-
-> ⚠️ 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。
-
-### 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",
+        "image_middle_url": "https://hair.xiangsilian.com/static/annotations/mid1.png",
+        "image_high_url": "https://hair.xiangsilian.com/static/annotations/high1.png",
+        "image_low_url": "https://hair.xiangsilian.com/static/annotations/low1.png",
+        "grown_image_url": "https://hair.xiangsilian.com/static/annotations/grown1.png",
        "order": 1
+      },
+      {
+        "hairline_type": "heart",
+        "image_middle_url": "https://hair.xiangsilian.com/static/annotations/mid3.png",
+        "image_high_url": "https://hair.xiangsilian.com/static/annotations/high3.png",
+        "image_low_url": "https://hair.xiangsilian.com/static/annotations/low3.png",
+        "grown_image_base64": null,
+        "order": 3
      }
-    ]
+    ],
+    "best_hairline_center_point": { "x": 540, "y": 430 },
+    "high_hairline_center_point": { "x": 540, "y": 380 },
+    "low_hairline_center_point": { "x": 540, "y": 480 },
+    "face_measure": {
+      "face_total_height_cm": 26.76,
+      "four_courts": {
+        "top_court_cm": 5.77, "upper_court_cm": 5.93,
+        "middle_court_cm": 7.62, "lower_court_cm": 7.44,
+        "ratios": { "top_court": 0.216, "upper_court": 0.222,
+                    "middle_court": 0.285, "lower_court": 0.278 }
+      },
+      "seven_eyes": {
+        "eye_width_cm": 2.76, "face_width_cm": 15.08,
+        "inter_eye_distance_cm": 3.9,
+        "ratios": { "eye_width": 0.183, "inter_eye_distance": 0.259 },
+        "eye1": null, "eye2": 3.0, "eye3": 2.76, "eye4": 3.9,
+        "eye5": 2.76, "eye6": 3.0, "eye7": null
+      },
+      "landmarks": {
+        "hair_top": { "x": 504, "y": 103 },
+        "hairline": { "x": 504, "y": 228 },
+        "brow_center": { "x": 504, "y": 357 },
+        "nose_bottom": { "x": 505, "y": 522 },
+        "chin_tip": { "x": 506, "y": 683 }
+      },
+      "hairline_source": "segmentation",
+      "head_pose": { "yaw": -1.39, "pitch": 2.49, "roll": -0.06 }
+    }
  }
 }
 ```

+> 说明：生发失败的元素中，网关不改写 `null` 值，故字段名保持为 `grown_image_base64: null`（有值时才改写为 `grown_image_url`），与接口2生发失败项一致。
+
 ---

 ## 汇总：输入输出一览
@@ -477,8 +524,7 @@
 | 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 | N 张发际线 PNG + 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 |
+| 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 |

 ---

@@ -113,25 +113,25 @@ async def hair_grow_v2(request: Request):

 ---

-## 6. 🔲【新增】接口6 四庭七眼测量 v2（`/api/v1/face/measure-v2`）
+## 6. ✅【已完成】接口6 四庭七眼测量 v2（`/api/v1/face/measure-v2`）

-**背景**：worker 侧已新增接口 6，功能与接口 1 完全一致（复刻），共用同一实现。
+**背景**：worker 侧接口 6 是接口 1 的**去顶庭变体**（不再完全一致）：不画头顶横线/不返回顶庭数据、竖线范围发际线→下巴尖、不画人头最左/最右端线。worker 侧与接口 1 共用 `_face_measure_impl(variant="v6")`。

-**网关需新增一个路由**：
+**网关已新增路由**（`gateway/app.py` 已实现，网关机器 pull 后生效）：

 ```python
 # gateway/app.py

@app.post("/api/v1/face/measure-v2", tags=["人脸分析"])
 async def face_measure_v2(request: Request):
-    """接口6：四庭七眼测量 v2（复刻接口1）"""
+    """接口6：四庭七眼测量 v2（去顶庭 + 去头部端线）"""
    return await _proxy(request, "/api/v1/face/measure-v2")
 ```

 **无需额外改动**：
 - 入参：与接口 1 完全相同（image_file/url/base64 三选一）
 - 出参：`annotated_image_base64` → 经现有 `rewrite_base64_to_url` 自动改写为 `annotated_image_url`
- worker 侧与接口 1 共用 `_face_measure_impl()`，逻辑零差异
+- worker 侧 v6 差异（去顶庭字段、变体标注）由 `_face_measure_impl` 内部处理，网关透明转发

 ---

@@ -5,9 +5,10 @@
 - 四庭水平分界线：numpy 向量化渐变消失（中间亮、两侧渐隐）。
 - 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。
- 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。
+  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」「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
@@ -116,42 +117,84 @@ _LINE_NAMES = {
 }


-def _head_edges_from_mask(hair_mask, y0, y1, left_cheek_x, right_cheek_x, min_gap):
-    """从头发分割掩膜取人头最左/最右 x（仅在脸纵向范围 [y0,y1] 内统计）。
+def _grow_outward(start_col, fg_band, direction, limit):
+    """从 start_col 沿 direction(+1 右 / -1 左) 在前景带 fg_band 内逐列外扩。

-    返回 (head_left_x, head_right_x)；某侧无掩膜/向内噪声，或离脸颊线过近
-    （间距 < min_gap）则该侧为 None —— 即与脸颊线太近时只保留脸颊线。
+    用于回收被误标成「头发」的外耳轮廓：耳朵被头发遮挡时，外耳轮廓那一圈常被
+    分割并入头发类，故耳朵掩膜外缘会偏内。这里把外缘沿紧邻的前景（耳∪发）向外
+    延伸，最多 limit 列；一旦下一列无前景（背景间隙）立即停止，绝不窜到分离的
+    那缕头发上。返回外扩后的列号。
    """
-    if hair_mask is None:
+    w = fg_band.shape[1]
+    c = int(start_col)
+    for _ in range(int(limit)):
+        nc = c + direction
+        if not (0 <= nc < w) or not fg_band[:, nc].any():
+            break
+        c = nc
+    return c
+
+
+def _ear_edges_from_mask(ear_mask, hair_mask, y0, y1, left_cheek_x, right_cheek_x,
+                         face_center_x):
+    """从耳朵分割掩膜取人头最左/最右 x（仅在脸纵向范围 [y0,y1] 内统计）。
+
+    左线 = 脸中线左侧耳朵像素的最左列；右线 = 右侧耳朵像素的最右列；再沿紧邻的
+    前景（耳∪发）按脸宽自适应外扩，回收被误标成头发的外耳轮廓（见 _grow_outward）。
+    某侧耳朵不可见（被头发/侧脸遮挡 → 掩膜为空），或外缘未越过对应脸颊线（非真实
+    头宽边缘）时该侧返回 None —— 即「看不到耳朵就不画这条线」。
+    """
+    if ear_mask is None:
        return None, None
-    m = np.asarray(hair_mask)
+    m = np.asarray(ear_mask)
    if m.ndim == 3:
        m = m[..., 0]
+    m = m > 0
    h = m.shape[0]
    y0 = max(0, int(y0)); y1 = min(h - 1, int(y1))
    if y1 <= y0:
        return None, None
-    band = m[y0:y1 + 1] > 0
-    cols = np.where(band.any(axis=0))[0]
+    ear_band = m[y0:y1 + 1]
+    cols = np.where(ear_band.any(axis=0))[0]
    if cols.size == 0:
        return None, None
-    hl, hr = float(cols.min()), float(cols.max())
-    # 仅当确实在脸颊外侧、且与脸颊线间距足够大时才采用
-    return (hl if (left_cheek_x - hl) >= min_gap else None,
-            hr if (hr - right_cheek_x) >= min_gap else None)
+    # 前景带 = 耳∪发（外耳轮廓常被误标为发），外扩上限按脸宽自适应
+    fg_band = ear_band
+    if hair_mask is not None:
+        hm = np.asarray(hair_mask)
+        if hm.ndim == 3:
+            hm = hm[..., 0]
+        fg_band = ear_band | (hm[y0:y1 + 1] > 0)
+    grow = max(2, round(max(1.0, right_cheek_x - left_cheek_x) * 0.045))
+
+    left_cols = cols[cols < face_center_x]
+    right_cols = cols[cols > face_center_x]
+    # 左/右耳外缘（外扩后），且必须在对应脸颊线外侧（否则视为残缺/噪声，只保留脸颊线）
+    head_l = head_r = None
+    if right_cols.size:
+        edge = _grow_outward(right_cols.max(), fg_band, +1, grow)
+        head_r = float(edge) if edge >= right_cheek_x else None
+    if left_cols.size:
+        edge = _grow_outward(left_cols.min(), fg_band, -1, grow)
+        head_l = float(edge) if edge <= left_cheek_x else None
+    return head_l, head_r


-def create_annotated_image(image_bgr, measure_result, hair_mask=None):
+def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=None,
+                           variant="v1"):
    """生成标注图层 PNG（透明底 RGBA，尺寸同原图）。返回 PIL.Image。

    布局（对齐 img1.png 版本5）：
    - 纵向竖线：人头最左 + 七眼 6 点 + 人头最右，切 7 段（七眼）。人头最左/最右
-      取自头发分割掩膜的轮廓（方案 B）；无掩膜（方案 A 兜底）时省略头部端线，
-      只画 6 点 5 段。
+      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
+    （仅七眼 6 点 5 段，不取头部端）。
    """
    h, w = image_bgr.shape[:2]
    v = measure_result.vertical
@@ -159,31 +202,40 @@ def create_annotated_image(image_bgr, measure_result, hair_mask=None):

    # --- 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 * 0.013))
-    gap_len = max(4, round(dash_len * 1.2))   # 虚线更稀疏（间隙>划线）
-    arrow_size = max(2, round(s * 0.007))     # 箭头更小
+    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）
+    arrow_size = max(2, round(s * 0.0045))    # 箭头更小
    pad = max(4, round(s * 0.012))            # 文字与线的间距
    line_h = font_size + max(2, round(font_size * 0.18))

    buf = np.zeros((h, w, 4), dtype=np.uint8)

-    order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
+    # 发际线弃用（hairline_discarded）：保留头顶横线，去掉发际线横线，
+    # 也不标顶/上庭（缺发际线作边界，算不出）。横线 = 头顶/眉心/鼻翼下缘/下巴尖。
+    if getattr(measure_result, "hairline_discarded", False):
+        order = ["hair_top", "brow_center", "nose_bottom", "chin_tip"]
+    elif variant == "v6":
+        order = ["hairline", "brow_center", "nose_bottom", "chin_tip"]
+    else:
+        order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
    ys = [v[name][1] for name in order]

    pts = measure_result.eyes["points"]
    seven_keys = ["left_cheek", "left_outer", "left_inner",
                  "right_inner", "right_outer", "right_cheek"]
    base_xs = [pts[k][0] for k in seven_keys]
-    # 人头最左/最右：取自头发分割掩膜（方案B），脸纵向范围内统计；无掩膜则省略。
-    # 与脸颊线间距 < 脸宽×8% 视为太近，只保留脸颊线（不画头部端线）。
-    face_w = pts["right_cheek"][0] - pts["left_cheek"][0]
-    min_gap = max(1.0, face_w * 0.08)
-    head_l, head_r = _head_edges_from_mask(
-        hair_mask, ys[0], ys[-1], pts["left_cheek"][0], pts["right_cheek"][0], min_gap)
-    head_xs = [x for x in (head_l, head_r) if x is not None]
-    xs = sorted(base_xs + head_xs)           # 自左向右（6 或 7/8 点）
+    if variant == "v6":
+        xs = sorted(base_xs)                  # 接口6：仅七眼 6 点，不画人头最左/最右端线
+    else:
+        # 人头最左/最右：取自耳朵分割掩膜外缘（方案B，类7/8），脸纵向范围内统计。
+        # 看不到耳朵（被头发/侧脸遮挡 → 掩膜空）或外缘未越过脸颊线则省略该侧端线。
+        lcx, rcx = pts["left_cheek"][0], pts["right_cheek"][0]
+        head_l, head_r = _ear_edges_from_mask(
+            ear_mask, hair_mask, ys[0], ys[-1], lcx, rcx, (lcx + rcx) / 2)
+        head_xs = [x for x in (head_l, head_r) if x is not None]
+        xs = sorted(base_xs + head_xs)        # 自左向右（6 或 7/8 点）

    # 人脸/人头包围盒
    fx0, fx1 = xs[0], xs[-1]
@@ -191,77 +243,113 @@ def create_annotated_image(image_bgr, measure_result, hair_mask=None):
    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
+    # 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1（含发际线弃用）= 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

-    # --- 1. 横向 5 条分界线（渐变，覆盖头宽并超出一点） ---
+    # --- 1. 横向分界线（渐变，覆盖头宽并超出一点） ---
    for cy in ys:
        draw_gradient_horizontal_line(buf, face_cx, cy, half_length=face_half, width=line_w)

-    # --- 2. 纵向竖线（渐变，超出头顶/下巴一点） ---
+    # --- 2. 纵向竖线（渐变，覆盖 v_top→v_bot） ---
    for vx in xs:
-        draw_gradient_vertical_line(buf, vx, fy0 - over, fy1 + over, width=line_w)
+        draw_gradient_vertical_line(buf, vx, v_top, v_bot, width=line_w)

    canvas = Image.fromarray(buf, mode="RGBA")
    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 * 0.5))       # 文字底部到线的间距
+    # --- 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_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"]
+    # court_start：庭段在 order 里的起始索引。发际线弃用时 order 首位是头顶（无下界发际线，
+    # 顶/上庭不标），中庭从眉心开始 → 跳过 order[0]。
+    if getattr(measure_result, "hairline_discarded", False):
+        court_cm = [measure_result.middle_cm, measure_result.lower_cm]
+        court_name = ["中庭", "下庭"]
+        n_court = 2
+        court_start = 1
+    elif 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）
-    for i in range(4):
-        y_a, y_b = ys[i], ys[i + 1]
+    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[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） ---
-    # 文字与箭头间留出「箭头高度 + pad」，避免文字压住箭头
-    txt_off = arrow_size + pad
-    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)
+    # --- 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
+    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

@@ -285,15 +373,16 @@ if __name__ == "__main__":
        print("未检出人脸")
        sys.exit(1)
    mask = None
+    ears = None
    try:
        from face_analysis.hair_segmenter import get_segmenter
-        mask = get_segmenter().segment_hair(img)
+        mask, ears = get_segmenter().segment_hair_and_ears(img)
    except Exception as e:  # noqa: BLE001
        print(f"[warn] 分割不可用，回退方案 A：{e}")

    result = measure_face(lms, mask, w, h)
    t0 = time.time()
-    canvas = create_annotated_image(img, result, hair_mask=mask)
+    canvas = create_annotated_image(img, result, ear_mask=ears, hair_mask=mask)
    dt = time.time() - t0
    os.makedirs(os.path.dirname(out), exist_ok=True)
    canvas.save(out)
@@ -79,7 +79,7 @@ class Resnet18(nn.Module):
    def init_weight(self):
        # 优先本地骨干权重（内网离线），缺失才回退 torch model_zoo（会查缓存）。
        if os.path.isfile(_LOCAL_RESNET18):
-            state_dict = torch.load(_LOCAL_RESNET18, map_location="cpu")
+            state_dict = torch.load(_LOCAL_RESNET18, map_location="cpu", weights_only=False)
        else:
            state_dict = modelzoo.load_url(resnet18_url)
        self_state_dict = self.state_dict()
@@ -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）
@@ -1,7 +1,8 @@
-"""方案 B：BiSeNet 头发分割 + 发际线/头顶定位。
+"""方案 B：BiSeNet 头发/耳朵分割 + 发际线/头顶定位。

-加载 face-parsing BiSeNet（19 类，hair=17），对整图做像素级语义分割得到头发
-mask，再沿面部中轴线扫描得到真实发际线与头顶。GPU 可用时走 CUDA，否则 CPU。
+加载 face-parsing BiSeNet（CelebAMask-HQ 19 类，hair=17、l_ear=7、r_ear=8），对整图
+做像素级语义分割：得到头发 mask 用于沿面部中轴线扫描真实发际线与头顶，并得到耳朵
+mask 供标注图取人头最左/最右竖线（耳朵外缘）。GPU 可用时走 CUDA，否则 CPU。
 单例加载权重，避免每请求重载。详见技术方案 §1.4 / §4.0。
 """
 import os
@@ -15,6 +16,7 @@ import numpy as np

 _WEIGHTS = os.path.join(os.path.dirname(__file__), "weights", "79999_iter.pth")
 HAIR_CLASS = 17          # CelebAMask-HQ 19 类中 hair 的索引
+EAR_CLASSES = (7, 8)     # 7=l_ear / 8=r_ear（类名以人为参照，图像左右另行判定，不依赖类名）
 N_CLASSES = 19
 _INPUT_SIZE = 512        # BiSeNet 推理输入边长

@@ -50,7 +52,7 @@ class HairSegmenter:
        self._torch = torch
        self.device = _select_device(torch)
        self.net = BiSeNet(n_classes=N_CLASSES)
-        state = torch.load(weights_path, map_location="cpu")
+        state = torch.load(weights_path, map_location="cpu", weights_only=False)
        self.net.load_state_dict(state)
        self.net.to(self.device)
        self.net.eval()
@@ -59,8 +61,8 @@ class HairSegmenter:
            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        ])

-    def segment_hair(self, image_bgr):
-        """返回 hair_mask（H×W bool，True=头发），尺寸同输入原图。"""
+    def _parse(self, image_bgr):
+        """整图语义分割，返回原图尺寸的类别图（H×W int，值为 0–18 类别号）。"""
        torch = self._torch
        h, w = image_bgr.shape[:2]
        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
@@ -70,10 +72,49 @@ class HairSegmenter:
        with torch.no_grad():
            out = self.net(inp)[0]                        # 主输出 (1, C, 512, 512)
        parsing = out.squeeze(0).argmax(0).cpu().numpy()  # (512, 512) 类别图
-        hair_small = (parsing == HAIR_CLASS).astype(np.uint8)
        # 还原到原图尺寸（最近邻保持类别边界）
-        hair_mask = cv2.resize(hair_small, (w, h), interpolation=cv2.INTER_NEAREST)
-        return hair_mask.astype(bool)
+        return cv2.resize(parsing.astype(np.int32), (w, h),
+                          interpolation=cv2.INTER_NEAREST)
+
+    def segment_hair(self, image_bgr):
+        """返回 hair_mask（H×W bool，True=头发），尺寸同输入原图。"""
+        return self._parse(image_bgr) == HAIR_CLASS
+
+    def _parse_face_cropped(self, image_bgr, face_box):
+        """按人脸框裁剪后再分割，结果映射回原图尺寸（裁剪外填背景 0）。
+
+        BiSeNet 在 CelebAMask-HQ「紧裁对齐人脸」上训练，整张大场景图（全身/街拍，
+        脸只占一小块、背景复杂）会严重欠分割、丢耳朵。先按人脸放大裁剪，让脸接近
+        训练分布，耳朵/头发分割明显更稳。裁剪含足够上/侧边距以纳入发顶与双耳。
+        """
+        h, w = image_bgr.shape[:2]
+        x0, y0, x1, y1 = face_box
+        fw, fh = max(1.0, x1 - x0), max(1.0, y1 - y0)
+        cx0 = int(max(0, x0 - fw * 0.8)); cx1 = int(min(w, x1 + fw * 0.8))
+        cy0 = int(max(0, y0 - fh * 1.0)); cy1 = int(min(h, y1 + fh * 0.5))
+        if cx1 - cx0 < 2 or cy1 - cy0 < 2:
+            return self._parse(image_bgr)
+        full = np.zeros((h, w), dtype=np.int32)
+        full[cy0:cy1, cx0:cx1] = self._parse(image_bgr[cy0:cy1, cx0:cx1])
+        return full
+
+    def segment_hair_and_ears(self, image_bgr, face_box=None):
+        """单次推理返回 (hair_mask, ear_mask)，均为 H×W bool，尺寸同原图。
+
+        ear_mask = 左耳(7) ∪ 右耳(8)；耳朵被头发/侧脸遮挡时对应区域天然为空，
+        正好用于「看不到耳朵就不画线」的判定。两类合并、左右按图像位置另判，
+        不依赖以人为参照的类名（详见 EAR_CLASSES 注释）。
+
+        face_box=(x0,y0,x1,y1)（人脸关键点包围盒像素坐标）给定时先按人脸裁剪再
+        分割（见 _parse_face_cropped），整张大场景图也能稳定分出耳朵；不给则整图分割。
+        """
+        if face_box is None:
+            parsing = self._parse(image_bgr)
+        else:
+            parsing = self._parse_face_cropped(image_bgr, face_box)
+        hair_mask = parsing == HAIR_CLASS
+        ear_mask = np.isin(parsing, EAR_CLASSES)
+        return hair_mask, ear_mask


 _segmenter = None
@@ -0,0 +1,123 @@
+"""接口10：头部外缘膨胀带遮罩。
+
+先和接口9 一样得到**内缩后的基准遮罩**（含额头的闭合区域外缘朝151内缩 erode_cm、底线不动，默认1.2cm），
+在这个基础上：
+1. 取基准遮罩的**外轮廓线**（1px），去掉贴着底部分界线的那一段（只留头发/头部外缘弧线）。
+2. 把这条外轮廓线膨胀成带子（半径 = dilate_cm/2，即带子**总宽 ≈ dilate_cm**，默认 2cm）。
+3. 裁到分界线以上（不越过底线）。
+输出这条带子作为 mask。BiSeNet / SegFormer 两套并排对比，分步可视化。
+
+两个可调参数：erode_cm（同接口9 的内缩，默认1.2）+ dilate_cm（带子总宽，默认2）。
+复用 `head_mask` 的构件，避免重复实现。
+"""
+import cv2
+import numpy as np
+
+from face_analysis.detector import detector
+from face_analysis.calibration import estimate_scale_factor
+from face_analysis.head_mask import (
+    BASELINE_IDX, ERODE_CM, NoFaceError,
+    _baseline_points, _upper_region_mask, _fill_to_baseline, _largest_cc,
+    _bisenet_hair_mask, _segformer_hair_mask,
+    _erode, _overlay, _draw_baseline, _b64png, _mask_png,
+)
+
+DILATE_CM = 2.0          # 膨胀后带子总宽（厘米，默认；半径 = 总宽/2；可由入参覆盖）
+
+
+def _dilate(mask_bool, r):
+    """圆盘核膨胀半径 r（像素）。r<=0 原样返回。"""
+    if r <= 0:
+        return mask_bool.copy()
+    k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * r + 1, 2 * r + 1))
+    return cv2.dilate(mask_bool.astype(np.uint8), k).astype(bool)
+
+
+def _baseline_line_mask(baseline_pts, w, h, thickness):
+    """底部分割线（含左右水平延长线）画成一条带厚度的线，用于从外轮廓里剔除底边。"""
+    m = np.zeros((h, w), np.uint8)
+    y_l = baseline_pts[0][1]
+    y_r = baseline_pts[-1][1]
+    chain = [(0, y_l)] + baseline_pts + [(w - 1, y_r)]
+    for a, b in zip(chain[:-1], chain[1:]):
+        cv2.line(m, a, b, 1, thickness)
+    return m.astype(bool)
+
+
+def _outer_contour_no_bottom(region, baseline_band):
+    """区域外轮廓（1px）去掉贴着底部分界线的那一段。"""
+    contour = region & ~_erode(region, 1)
+    return contour & ~baseline_band
+
+
+def _model_result(image_bgr, hair_mask, upper, baseline_pts, baseline_band, r_erode, r_dilate, w):
+    """单个分割模型的分步结果（内缩后基准遮罩 / 外轮廓线 / 膨胀带 / 纯遮罩）。"""
+    top_fill = _fill_to_baseline(hair_mask, upper)                        # 含额头，延伸到图底
+    base = _largest_cc(_erode(top_fill, r_erode) & upper)                # 接口9 内缩后的基准遮罩
+    contour = _outer_contour_no_bottom(base, baseline_band)              # 外轮廓，去底线
+    band = _largest_cc(_dilate(contour, r_dilate) & upper)              # 膨胀成带、裁到底线以上
+    return {
+        "base_pixels": int(base.sum()),
+        "band_pixels": int(band.sum()),
+        "base_mask_base64": _b64png(
+            _draw_baseline(_overlay(image_bgr, base, (255, 150, 0)), baseline_pts, w)),
+        "contour_base64": _b64png(
+            _draw_baseline(_overlay(image_bgr, _dilate(contour, 2), (0, 255, 0)), baseline_pts, w)),
+        "band_overlay_base64": _b64png(
+            _draw_baseline(_overlay(image_bgr, band, (0, 0, 255)), baseline_pts, w)),
+        "mask_base64": _mask_png(band),
+    }
+
+
+def generate_head_band(image_bgr, erode_cm=ERODE_CM, dilate_cm=DILATE_CM):
+    """接口10 完整管线。返回可直接进 ok() 的 data dict。
+
+    erode_cm：基准遮罩外缘朝151 内缩距离（厘米，同接口9），页面可调，默认 1.2cm。
+    dilate_cm：外轮廓线膨胀后带子总宽（厘米），页面可调，默认 2cm。
+    未检出人脸抛 NoFaceError。单个分割模型异常不影响另一个（记为 {"error": ...}）。
+    """
+    h, w = image_bgr.shape[:2]
+    landmarks = detector.detect(image_bgr)
+    if landmarks is None:
+        raise NoFaceError()
+
+    erode_cm = max(0.0, float(erode_cm))
+    dilate_cm = max(0.0, float(dilate_cm))
+    px_per_cm = estimate_scale_factor(landmarks, w, h)
+    r_erode = int(round(erode_cm * px_per_cm))            # 内缩半径
+    r_dilate = int(round((dilate_cm / 2.0) * px_per_cm))  # 膨胀半径 = 总宽/2
+    baseline_pts = _baseline_points(landmarks, w, h)
+    upper = _upper_region_mask(baseline_pts, w, h)
+    # 剔除底边用的分界线带：几像素宽即可，独立于膨胀/内缩半径
+    baseline_band = _dilate(_baseline_line_mask(baseline_pts, w, h, 5), 2)
+
+    baseline_viz = _draw_baseline(image_bgr, baseline_pts, w)
+    data = {
+        "px_per_cm": round(float(px_per_cm), 4),
+        "erode_cm": round(erode_cm, 2),
+        "erode_px": r_erode,
+        "dilate_cm": round(dilate_cm, 2),
+        "dilate_radius_px": r_dilate,
+        "image_size": {"width": w, "height": h},
+        "baseline_landmarks": [
+            {"index": idx, "x": p[0], "y": p[1]}
+            for idx, p in zip(BASELINE_IDX, baseline_pts)
+        ],
+        "steps_common": {
+            "landmarks_baseline_base64": _b64png(baseline_viz),
+            "upper_region_base64": _b64png(_overlay(baseline_viz, upper, (0, 200, 0))),
+        },
+    }
+
+    seg_fns = {
+        "bisenet": lambda: _bisenet_hair_mask(image_bgr, landmarks, w, h),
+        "segformer": lambda: _segformer_hair_mask(image_bgr),
+    }
+    for name, fn in seg_fns.items():
+        try:
+            hair_mask = fn()
+            data[name] = _model_result(image_bgr, hair_mask, upper, baseline_pts,
+                                       baseline_band, r_erode, r_dilate, w)
+        except Exception as ex:  # noqa: BLE001  单模型失败不影响整体
+            data[name] = {"error": f"{type(ex).__name__}: {ex}"}
+    return data
@@ -0,0 +1,226 @@
+"""接口9：头发遮罩生成。
+
+流程（详见需求讨论）：
+1. MediaPipe 关键点检测。
+2. 底部分割线 = 关键点 [162,71,68,104,69,108,151,337,299,333,298,301,389] 的连线（左端162→中心151→右端389），
+   再把左端点 162 水平延伸到图片最左边、右端点 389 水平延伸到图片最右边。
+3. 上半区 = 分割线以上区域（多边形填充：左边缘→弧线→右边缘→上边缘闭合）。
+4. 头发分割：BiSeNet 与 SegFormer 各出一张 hair_mask（两套供对比）。
+5. 闭合区域（含额头）：每列从最顶端头发像素向下填充到分割线，把头发与画线之间的额头皮肤
+   也包进来（不再从发际线割断），底边即分割线。
+6. 外缘内缩 erode_cm（默认 1.2cm，可调）、底线不动：对「填充到图底的实心块」做半径 r 的腐蚀，
+   再与上半区相交。腐蚀只把外轮廓（顶/两侧）朝内（朝 151）收 r；平底边是相交后才产生的，
+   所以底线纹丝不动。cm→像素用虹膜标定（calibration.estimate_scale_factor）。
+
+对外返回每一步叠加在原图上的可视化图（base64 PNG，data URI），供测试页逐步展示。
+"""
+import base64
+
+import cv2
+import numpy as np
+
+from face_analysis.detector import detector
+from face_analysis.calibration import estimate_scale_factor, normalized_to_pixel
+
+# 底部分割线关键点（图像上从左到右，眉骨弧线 → 中心 151 → 右侧对称）
+# 左端 104 → 中心 151 → 右端 333；首末点向图片左右边缘水平延长
+# BASELINE_IDX = [104, 69, 108, 151, 337, 299, 333]
+# BASELINE_IDX = [34, 139, 71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301, 368, 264]
+BASELINE_IDX = [71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301]
+CENTER_IDX = 151          # 内缩方向的目标点（额头中心）
+ERODE_CM = 1.2            # 外缘内缩距离（厘米，默认；可由入参覆盖）
+SEGFORMER_HAIR = 13       # jonathandinu/face-parsing 中 hair 类索引
+
+
+class NoFaceError(Exception):
+    """未检测到人脸。"""
+
+
+# ---------------------------------------------------------------------------
+# 几何：分割线与上半区
+# ---------------------------------------------------------------------------
+
+def _px(landmarks, idx, w, h):
+    p = landmarks.landmark[idx]
+    return (int(round(p.x * w)), int(round(p.y * h)))
+
+
+def _baseline_points(landmarks, w, h):
+    """额头弧线各关键点的像素坐标（按 BASELINE_IDX 顺序，左→右）。"""
+    return [_px(landmarks, i, w, h) for i in BASELINE_IDX]
+
+
+def _upper_region_mask(baseline_pts, w, h):
+    """分割线以上区域（bool，H×W）。
+
+    多边形顶点：左上角 →（0, y左端）→ 弧线各点 →（w-1, y右端）→ 右上角，闭合后填充。
+    左端/右端为两段水平延长线（向图片左右边缘延伸）。
+    """
+    x0, y0 = baseline_pts[0]
+    x1, y1 = baseline_pts[-1]
+    poly = [(0, 0), (0, y0)] + baseline_pts + [(w - 1, y1), (w - 1, 0)]
+    mask = np.zeros((h, w), np.uint8)
+    cv2.fillPoly(mask, [np.array(poly, np.int32)], 1)
+    return mask.astype(bool)
+
+
+# ---------------------------------------------------------------------------
+# 头发分割（两套）
+# ---------------------------------------------------------------------------
+
+def _bisenet_hair_mask(image_bgr, landmarks, w, h):
+    """BiSeNet（接口1 同款）：先按人脸框裁剪再分割，稳住小脸大图。"""
+    from face_analysis.hair_segmenter import get_segmenter
+    pxs = [normalized_to_pixel(p, w, h) for p in landmarks.landmark]
+    face_box = (min(p[0] for p in pxs), min(p[1] for p in pxs),
+                max(p[0] for p in pxs), max(p[1] for p in pxs))
+    hair_mask, _ear = get_segmenter().segment_hair_and_ears(image_bgr, face_box=face_box)
+    return np.asarray(hair_mask, dtype=bool)
+
+
+def _segformer_hair_mask(image_bgr):
+    """复用接口2/3 的 SegFormer 单例（hairline.service.get_parser），
+    共用权重与设备策略（SEG_DEVICE，默认 cpu；本机 5090 上 CUDA 内核不可用故走 CPU）。"""
+    from hairline.service import get_parser
+    rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+    labels = get_parser().parse(rgb)
+    return labels == SEGFORMER_HAIR
+
+
+# ---------------------------------------------------------------------------
+# 形态学 & 可视化
+# ---------------------------------------------------------------------------
+
+def _erode(mask_bool, r):
+    """圆盘核腐蚀半径 r（像素）。r<=0 原样返回。"""
+    if r <= 0:
+        return mask_bool.copy()
+    k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * r + 1, 2 * r + 1))
+    return cv2.erode(mask_bool.astype(np.uint8), k).astype(bool)
+
+
+def _overlay(image, mask_bool, color, alpha=0.45):
+    """把纯色以 alpha 叠加到 mask 区域上（非 mask 区域保持原样）。"""
+    out = image.copy()
+    if mask_bool.any():
+        out[mask_bool] = (out[mask_bool] * (1 - alpha)
+                          + np.array(color, np.float32) * alpha).astype(np.uint8)
+    return out
+
+
+def _draw_baseline(image, baseline_pts, w):
+    """画分割线（含左右水平延长线）+ 关键点，中心点 151 标红。"""
+    out = image.copy()
+    y0 = baseline_pts[0][1]
+    y1 = baseline_pts[-1][1]
+    chain = [(0, y0)] + baseline_pts + [(w - 1, y1)]
+    for a, b in zip(chain[:-1], chain[1:]):
+        cv2.line(out, a, b, (0, 255, 255), 2, cv2.LINE_AA)
+    for idx, p in zip(BASELINE_IDX, baseline_pts):
+        col = (0, 0, 255) if idx == CENTER_IDX else (0, 200, 0)
+        cv2.circle(out, p, 4, col, -1, cv2.LINE_AA)
+        cv2.putText(out, str(idx), (p[0] + 4, p[1] - 6),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, col, 1, cv2.LINE_AA)
+    return out
+
+
+def _b64png(bgr):
+    """BGR 图 → data URI（PNG base64）。gateway 会把 *_base64 字段落盘改成 *_url。"""
+    ok, buf = cv2.imencode(".png", bgr)
+    return "data:image/png;base64," + base64.b64encode(buf.tobytes()).decode()
+
+
+def _mask_png(mask_bool):
+    """纯遮罩：白（255）为遮罩、黑为背景。"""
+    m = (mask_bool.astype(np.uint8)) * 255
+    return _b64png(cv2.cvtColor(m, cv2.COLOR_GRAY2BGR))
+
+
+# ---------------------------------------------------------------------------
+# 主入口
+# ---------------------------------------------------------------------------
+
+def _largest_cc(mask_bool):
+    """保留最大连通域，去掉背景里孤立的杂散头发列。空掩膜原样返回。"""
+    m = mask_bool.astype(np.uint8)
+    if m.sum() == 0:
+        return mask_bool
+    n, labels, stats, _ = cv2.connectedComponentsWithStats(m, connectivity=8)
+    if n <= 2:  # 只有背景 + 一个前景
+        return mask_bool
+    largest = 1 + int(np.argmax(stats[1:, cv2.CC_STAT_AREA]))
+    return labels == largest
+
+
+def _fill_to_baseline(hair_mask, upper):
+    """含额头的实心区域：每列从最顶端头发像素向下填充（延伸到图底，未按基线裁剪）。
+
+    这样头发与画线之间的额头皮肤被包进闭合区域（不再被割断）；未裁剪到基线是为了
+    后续腐蚀时底线不动（腐蚀在延伸到图底的实心块上做，再与上半区相交切平底边）。
+    """
+    has_hair = (hair_mask & upper).astype(np.uint8)
+    return np.maximum.accumulate(has_hair, axis=0).astype(bool)
+
+
+def _model_result(image_bgr, hair_mask, upper, baseline_pts, r, w):
+    """单个分割模型的分步结果（闭合区域 / 最终遮罩 / 可视化）。"""
+    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
+    final = _largest_cc(_erode(top_fill, r) & upper)   # 外缘朝151内缩 r、底线不动
+    return {
+        "hair_pixels": int(hair_mask.sum()),
+        "closed_pixels": int(closed.sum()),
+        "mask_pixels": int(final.sum()),
+        "hair_mask_base64": _b64png(_overlay(image_bgr, hair_mask, (255, 150, 0))),
+        "closed_region_base64": _b64png(
+            _draw_baseline(_overlay(image_bgr, closed, (255, 150, 0)), baseline_pts, w)),
+        "final_overlay_base64": _b64png(
+            _draw_baseline(_overlay(image_bgr, final, (0, 0, 255)), baseline_pts, w)),
+        "mask_base64": _mask_png(final),
+    }
+
+
+def generate_head_mask(image_bgr, erode_cm=ERODE_CM):
+    """接口9 完整管线。返回可直接进 ok() 的 data dict。
+
+    erode_cm：外缘朝 151 内缩的距离（厘米），页面可调，默认 1cm。
+    未检出人脸抛 NoFaceError。单个分割模型异常不影响另一个（记为 {"error": ...}）。
+    """
+    h, w = image_bgr.shape[:2]
+    landmarks = detector.detect(image_bgr)
+    if landmarks is None:
+        raise NoFaceError()
+
+    erode_cm = max(0.0, float(erode_cm))
+    px_per_cm = estimate_scale_factor(landmarks, w, h)
+    r = int(round(erode_cm * px_per_cm))
+    baseline_pts = _baseline_points(landmarks, w, h)
+    upper = _upper_region_mask(baseline_pts, w, h)
+
+    baseline_viz = _draw_baseline(image_bgr, baseline_pts, w)
+    data = {
+        "px_per_cm": round(float(px_per_cm), 4),
+        "erode_cm": round(erode_cm, 2),
+        "erode_px": r,
+        "image_size": {"width": w, "height": h},
+        "baseline_landmarks": [
+            {"index": idx, "x": p[0], "y": p[1]}
+            for idx, p in zip(BASELINE_IDX, baseline_pts)
+        ],
+        "steps_common": {
+            "landmarks_baseline_base64": _b64png(baseline_viz),
+            "upper_region_base64": _b64png(_overlay(baseline_viz, upper, (0, 200, 0))),
+        },
+    }
+
+    seg_fns = {
+        "bisenet": lambda: _bisenet_hair_mask(image_bgr, landmarks, w, h),
+        "segformer": lambda: _segformer_hair_mask(image_bgr),
+    }
+    for name, fn in seg_fns.items():
+        try:
+            hair_mask = fn()
+            data[name] = _model_result(image_bgr, hair_mask, upper, baseline_pts, r, w)
+        except Exception as ex:  # noqa: BLE001  单模型失败不影响整体
+            data[name] = {"error": f"{type(ex).__name__}: {ex}"}
+    return data
@@ -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 +142,47 @@ 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):
+    # 发际线弃用阈值：发际线离头顶（顶庭）< 此值时判定分割不可靠，弃用发际线。
+    # hairline 与 hair_top 几乎重合（如稀疏头发中轴漏检只剩一小撮），说明发际线
+    # 定位无意义 → 顶/上庭置 null、标注图不画头顶/发际线。
+    HAIRLINE_DISCARD_TOP_CM = 0.7
+
+    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
        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
+        # 发际线弃用判定：顶庭（头顶→发际线）过小视为发际线贴近头顶、不可靠。
+        # 弃用时 hairline_source 改为 "discarded"，face_total 只算中庭+下庭。
+        self.hairline_discarded = self.top_cm < self.HAIRLINE_DISCARD_TOP_CM
+        if self.hairline_discarded:
+            self.hairline_source = "discarded"
+            self.face_total_cm = self.middle_cm + self.lower_cm
+        else:
+            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
@@ -167,46 +190,88 @@ class MeasureResult:
        self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm

    def to_response(self):
-        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": {
-                "top_court_cm": round(self.top_cm, 2),
-                "upper_court_cm": round(self.upper_cm, 2),
-                "middle_court_cm": round(self.middle_cm, 2),
-                "lower_court_cm": round(self.lower_cm, 2),
-                "ratios": {
-                    "top_court": round(self.vertical["top_court_px"] / total_px, 3),
-                    "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
-                    "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
-                    "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
+        # 发际线弃用：顶/上庭相关字段置 null（保留键），ratio 分母只算中下庭；
+        # landmarks.hair_top/hairline 置 null。否则按四庭正常输出。
+        if self.hairline_discarded:
+            base_px = (self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
+            data = {
+                "face_total_height_cm": round(self.face_total_cm, 2),
+                "four_courts": {
+                    "top_court_cm": None,
+                    "upper_court_cm": None,
+                    "middle_court_cm": round(self.middle_cm, 2),
+                    "lower_court_cm": round(self.lower_cm, 2),
+                    "ratios": {
+                        "top_court": None,
+                        "upper_court": None,
+                        "middle_court": round(self.vertical["middle_court_px"] / base_px, 3),
+                        "lower_court": round(self.vertical["lower_court_px"] / base_px, 3),
+                    },
                },
-            },
-            "seven_eyes": {
-                "eye_width_cm": round(self.eye_width_cm, 2),
-                "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),
+                "seven_eyes": {
+                    "eye_width_cm": round(self.eye_width_cm, 2),
+                    "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"] / 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"),
-            },
-            "hairline_source": self.hairline_source,
-        }
+                "landmarks": {
+                    "hair_top": None,
+                    "hairline": None,
+                    "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,
+            }
+        else:
+            total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
+                        + self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
+            data = {
+                "face_total_height_cm": round(self.face_total_cm, 2),
+                "four_courts": {
+                    "top_court_cm": round(self.top_cm, 2),
+                    "upper_court_cm": round(self.upper_cm, 2),
+                    "middle_court_cm": round(self.middle_cm, 2),
+                    "lower_court_cm": round(self.lower_cm, 2),
+                    "ratios": {
+                        "top_court": round(self.vertical["top_court_px"] / total_px, 3),
+                        "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
+                        "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
+                        "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
+                    },
+                },
+                "seven_eyes": {
+                    "eye_width_cm": round(self.eye_width_cm, 2),
+                    "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"] / 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_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 +285,8 @@ 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__":
@@ -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
@@ -37,7 +37,8 @@ _PROMPT = (
    "基因风格（戏剧型/睿智型/自然型/古典型/优雅型/浪漫型/前卫型/少女型/少年型）"
 )

-_client = None
+_client = None                      # 缓存的 Ark client（api_key 变更时自动重建）
+_client_key: str | None = None      # _client 构建时使用的 api_key，用于检测配置变更


 def _load_api_key() -> str | None:
@@ -60,13 +61,21 @@ def _load_api_key() -> str | None:


 def get_client():
-    global _client
-    if _client is None:
+    """返回 Ark client。
+
+    client 全局缓存，但每次都会重新读取 api_key —— 一旦配置（环境变量 /
+    worker_config.json / gateway/config.json 的 ark_api_key）发生变化，
+    自动重建 client。这样换 key 后无需重启进程。
+    """
+    global _client, _client_key
+    key = _load_api_key()
+    if not key:
+        raise RuntimeError("缺少火山方舟 API Key（设 ARK_API_KEY 或 worker_config.json.ark_api_key）")
+    # client 未建、或 key 变了 → 重建
+    if _client is None or key != _client_key:
        from volcenginesdkarkruntime import Ark
-        key = _load_api_key()
-        if not key:
-            raise RuntimeError("缺少火山方舟 API Key（设 ARK_API_KEY 或 worker_config.json.ark_api_key）")
        _client = Ark(base_url=ARK_BASE_URL, api_key=key)
+        _client_key = key
    return _client


@@ -104,9 +113,22 @@ def analyze_features(image_bytes: bytes = None, image_url: str = None):
                {"type": "text", "text": _PROMPT},
            ],
        }],
+        max_tokens=1024,        # 限制输出长度，模型秒回
+        temperature=0,          # 固定输出，无随机采样，提速+结果稳定
+        stream=False,           # 关闭流式，单次返回结果更快
+        extra_body={
+            "thinking": {
+                "type": "disabled",  # 彻底关闭深度思考模式，提速50%+
+            },
+        },
    )
    text = resp.choices[0].message.content
-    raw = _parse_json(text)             # doubao 原始中文字段
+    logger.info("doubao raw response (first 500 chars): %s", text[:500])
+    try:
+        raw = _parse_json(text)         # doubao 原始中文字段
+    except (json.JSONDecodeError, ValueError) as e:
+        logger.error("doubao 返回非 JSON，原文: %s", text[:1000])
+        raise RuntimeError(f"豆包模型返回格式异常，无法解析为 JSON：{text[:200]}") from e
    if not has_face(raw):
        return None
    # 只保留 6 个英文字段（doubao 缺某字段则跳过）
@@ -0,0 +1,339 @@
+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]
@@ -15,10 +15,15 @@ from pathlib import Path
 from typing import Optional

 from fastapi import FastAPI, File, Form, Request, UploadFile
-from fastapi.responses import JSONResponse
+from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles

 from gateway.config import load_config
+from gateway.logging_middleware import (
+    get_stats,
+    init_logging as _init_req_logging,
+    request_logging_middleware,
+)

 # ---------------------------------------------------------------------------
 # 日志
@@ -43,6 +48,9 @@ async def lifespan(app: FastAPI):
    cfg = load_config()
    logger.info("网关启动中... workers=%s", cfg["workers"])

+    # 初始化请求日志
+    _init_req_logging(cfg)
+
    # 初始化健康池（阶段二实现）
    try:
        from gateway.pool import init_pool, shutdown_pool as _pool_shutdown
@@ -105,6 +113,9 @@ static_root.mkdir(parents=True, exist_ok=True)
 (static_root / "annotations").mkdir(parents=True, exist_ok=True)
 app.mount("/static", StaticFiles(directory=str(static_root)), name="static")

+# 请求日志中间件（在所有路由之前，静态文件之后）
+app.middleware("http")(request_logging_middleware)
+

 # ---------------------------------------------------------------------------
 # 健康检查（网关自身）
@@ -189,6 +200,7 @@ async def index():
        "service": "旷视五接口 — 网关",
        "version": "0.1.0",
        "docs": "/docs",
+        "stats": "/admin/stats",
        "integration_guide": "/static/integration.html",
        "test_pages": {
            "if1_measure": "/static/test_interface1.html",
@@ -196,11 +208,268 @@ async def index():
            "if3_hair_grow_b": "/static/test_interface3.html",
            "if4_features": "/static/test_interface4.html",
            "if5_hairline": "/static/test_interface5.html",
+            "if6_measure_v2": "/static/test_interface6.html",
            "if7_hair_grow_v2": "/static/test_interface7.html",
+            "if9_head_mask": "/static/test_interface9.html",
+            "if10_head_band": "/static/test_interface10.html",
        },
    }


+# ---------------------------------------------------------------------------
+# 请求统计仪表盘 HTML
+# ---------------------------------------------------------------------------
+
+_STATS_PAGE_HTML = """<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>网关请求统计</title>
+<style>
+  * { box-sizing: border-box; margin: 0; padding: 0; }
+  body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; background: #f5f5f5; color: #333; }
+  .container { max-width: 1300px; margin: 0 auto; padding: 24px; }
+  h1 { font-size: 22px; margin-bottom: 4px; }
+  .subtitle { color: #888; font-size: 13px; margin-bottom: 20px; }
+  .nav { margin-bottom: 20px; }
+  .nav a { color: #2563eb; text-decoration: none; font-size: 13px; }
+  .nav a:hover { text-decoration: underline; }
+
+  /* 汇总卡片 */
+  .stats-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(170px, 1fr)); gap: 14px; margin-bottom: 24px; }
+  .stat-card { background: #fff; border-radius: 12px; padding: 18px 20px; box-shadow: 0 1px 4px rgba(0,0,0,.06); }
+  .stat-card .value { font-size: 28px; font-weight: 700; color: #111827; }
+  .stat-card .label { font-size: 11px; color: #9ca3af; text-transform: uppercase; letter-spacing: .5px; margin-top: 4px; }
+  .stat-card.ok .value { color: #059669; }
+  .stat-card.warn .value { color: #d97706; }
+
+  /* 表格 */
+  .section { margin-bottom: 24px; }
+  .section h2 { font-size: 16px; margin-bottom: 10px; color: #374151; }
+  .table-wrap { background: #fff; border-radius: 12px; overflow: hidden; box-shadow: 0 1px 4px rgba(0,0,0,.06); }
+  table { width: 100%; border-collapse: collapse; }
+  th, td { padding: 9px 14px; text-align: left; border-bottom: 1px solid #f1f5f9; font-size: 13px; }
+  th { background: #f8fafc; font-weight: 700; color: #475569; font-size: 11px; text-transform: uppercase; letter-spacing: .3px; white-space: nowrap; }
+  tr:hover td { background: #fafbfc; }
+  td.mono { font-family: "SF Mono", "Fira Code", monospace; font-size: 12px; }
+  .badge { display: inline-block; padding: 1px 8px; border-radius: 10px; font-size: 11px; font-weight: 700; }
+  .badge-ok { background: #d1fae5; color: #065f46; }
+  .badge-err { background: #fee2e2; color: #991b1b; }
+  .badge-other { background: #f3f4f6; color: #6b7280; }
+  .duration-fast { color: #059669; }
+  .duration-mid { color: #d97706; }
+  .duration-slow { color: #dc2626; }
+
+  .footer { text-align: right; font-size: 12px; color: #9ca3af; margin-top: 20px; }
+  .auto-refresh { display: flex; align-items: center; gap: 8px; }
+  .auto-refresh input { accent-color: #2563eb; }
+  .empty { text-align: center; padding: 40px; color: #9ca3af; font-size: 14px; }
+
+  @media (max-width: 768px) {
+    .stats-grid { grid-template-columns: repeat(2, 1fr); }
+    th, td { padding: 6px 8px; font-size: 12px; }
+  }
+</style>
+</head>
+<body>
+<div class="container">
+  <h1>📊 网关请求统计</h1>
+  <p class="subtitle">实时请求监控 &nbsp;|&nbsp; 每 3 秒自动刷新</p>
+  <div class="nav">
+    <a href="/">← 返回首页</a> &nbsp;|&nbsp;
+    <a href="/docs">API 文档</a> &nbsp;|&nbsp;
+    <a href="/static/integration.html">接入指南</a>
+  </div>
+
+  <!-- 汇总卡片 -->
+  <div class="stats-grid">
+    <div class="stat-card"><div class="value" id="totalCount">—</div><div class="label">请求总数</div></div>
+    <div class="stat-card ok"><div class="value" id="successRate">—</div><div class="label">成功率（code=0）</div></div>
+    <div class="stat-card"><div class="value" id="avgTime">—</div><div class="label">平均响应时间</div></div>
+    <div class="stat-card"><div class="value" id="minTime">—</div><div class="label">最短响应</div></div>
+    <div class="stat-card warn"><div class="value" id="maxTime">—</div><div class="label">最长响应</div></div>
+  </div>
+
+  <!-- 按接口 -->
+  <div class="section">
+    <h2>📋 按接口统计</h2>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>路径</th><th>请求数</th><th>平均耗时</th><th>最大耗时</th><th>成功率</th></tr></thead>
+        <tbody id="endpointTable"><tr><td class="empty" colspan="5">暂无数据</td></tr></tbody>
+      </table>
+    </div>
+  </div>
+
+  <!-- 按 Worker -->
+  <div class="section">
+    <h2>🖥️ 按 GPU Worker 统计</h2>
+    <div class="table-wrap">
+      <table>
+        <thead><tr><th>Worker</th><th>请求数</th><th>平均耗时</th><th>成功率</th></tr></thead>
+        <tbody id="workerTable"><tr><td class="empty" colspan="4">暂无数据</td></tr></tbody>
+      </table>
+    </div>
+  </div>
+
+  <!-- 最近请求 -->
+  <div class="section">
+    <h2>🕐 最近请求（最新 100 条）</h2>
+    <div class="table-wrap" style="max-height:600px;overflow:auto;">
+      <table>
+        <thead><tr><th>时间</th><th>方法</th><th>路径</th><th>Worker</th><th>HTTP</th><th>业务码</th><th>耗时</th><th>客户端 IP</th></tr></thead>
+        <tbody id="recentTable"><tr><td class="empty" colspan="8">暂无数据</td></tr></tbody>
+      </table>
+    </div>
+  </div>
+
+  <div class="footer">
+    <label class="auto-refresh">
+      <input type="checkbox" id="autoRefresh" checked onchange="toggleAutoRefresh()"> 自动刷新（3s）
+    </label>
+    <span style="margin-left:16px" id="lastUpdated">加载中…</span>
+  </div>
+</div>
+
+<script>
+let _timer = null;
+
+function formatDuration(ms) {
+  if (ms < 1000) return ms.toFixed(1) + 'ms';
+  if (ms < 60000) return (ms / 1000).toFixed(2) + 's';
+  return (ms / 60000).toFixed(1) + 'min';
+}
+
+function durationClass(ms) {
+  if (ms < 500) return 'duration-fast';
+  if (ms < 2000) return 'duration-mid';
+  return 'duration-slow';
+}
+
+function badgeClass(code) {
+  if (code === 0) return 'badge-ok';
+  if (code !== null && code !== undefined && code !== 0) return 'badge-err';
+  return 'badge-other';
+}
+
+function badgeText(code) {
+  if (code === 0) return 'OK';
+  if (code !== null && code !== undefined) return 'ERR ' + code;
+  return '—';
+}
+
+async function refresh() {
+  try {
+    const r = await fetch('/admin/stats/json');
+    const data = await r.json();
+    const s = data.summary;
+
+    document.getElementById('totalCount').textContent = s.total.toLocaleString();
+    document.getElementById('successRate').textContent = s.success_rate + '%';
+    document.getElementById('avgTime').textContent = formatDuration(s.avg_duration_ms);
+    document.getElementById('minTime').textContent = formatDuration(s.min_duration_ms);
+    document.getElementById('maxTime').textContent = formatDuration(s.max_duration_ms);
+
+    // 按接口
+    let ehtml = '';
+    if (data.endpoints.length === 0) {
+      ehtml = '<tr><td class="empty" colspan="5">暂无数据</td></tr>';
+    } else {
+      data.endpoints.forEach(function(e) {
+        ehtml += '<tr>' +
+          '<td class="mono">' + e.path + '</td>' +
+          '<td>' + e.count + '</td>' +
+          '<td class="' + durationClass(e.avg_duration_ms) + '">' + formatDuration(e.avg_duration_ms) + '</td>' +
+          '<td>' + formatDuration(e.max_duration_ms) + '</td>' +
+          '<td><span class="badge ' + badgeClass(0) + '" style="opacity:' + (e.success_rate / 100) + '">' + e.success_rate + '%</span></td>' +
+          '</tr>';
+      });
+    }
+    document.getElementById('endpointTable').innerHTML = ehtml;
+
+    // 按 Worker
+    let whtml = '';
+    if (!data.workers || data.workers.length === 0) {
+      whtml = '<tr><td class="empty" colspan="4">暂无数据</td></tr>';
+    } else {
+      data.workers.forEach(function(w) {
+        whtml += '<tr>' +
+          '<td class="mono">' + w.worker + '</td>' +
+          '<td>' + w.count + '</td>' +
+          '<td class="' + durationClass(w.avg_duration_ms) + '">' + formatDuration(w.avg_duration_ms) + '</td>' +
+          '<td><span class="badge ' + badgeClass(0) + '" style="opacity:' + (w.success_rate / 100) + '">' + w.success_rate + '%</span></td>' +
+          '</tr>';
+      });
+    }
+    document.getElementById('workerTable').innerHTML = whtml;
+
+    // 最近请求
+    let rhtml = '';
+    if (data.recent.length === 0) {
+      rhtml = '<tr><td class="empty" colspan="8">暂无数据</td></tr>';
+    } else {
+      data.recent.forEach(function(entry) {
+        var ts = entry.timestamp.replace('T', ' ').substring(0, 23);
+        var workerDisplay = entry.worker || '—';
+        // 短 worker 显示：只取主机部分
+        if (workerDisplay.length > 30) {
+          workerDisplay = workerDisplay.replace(/^https?:\/\//, '').substring(0, 28) + '…';
+        }
+        rhtml += '<tr>' +
+          '<td class="mono">' + ts + '</td>' +
+          '<td>' + entry.method + '</td>' +
+          '<td class="mono">' + entry.path + '</td>' +
+          '<td class="mono" style="font-size:11px">' + workerDisplay + '</td>' +
+          '<td>' + entry.status_code + '</td>' +
+          '<td><span class="badge ' + badgeClass(entry.response_code) + '">' + badgeText(entry.response_code) + '</span></td>' +
+          '<td class="' + durationClass(entry.duration_ms) + '">' + formatDuration(entry.duration_ms) + '</td>' +
+          '<td class="mono">' + entry.client_ip + '</td>' +
+          '</tr>';
+      });
+    }
+    document.getElementById('recentTable').innerHTML = rhtml;
+
+    document.getElementById('lastUpdated').textContent = '最后更新: ' + new Date().toLocaleTimeString();
+  } catch(err) {
+    document.getElementById('lastUpdated').textContent = '加载失败: ' + err.message;
+  }
+}
+
+function toggleAutoRefresh() {
+  var checked = document.getElementById('autoRefresh').checked;
+  if (checked) {
+    _timer = setInterval(refresh, 3000);
+  } else {
+    clearInterval(_timer);
+    _timer = null;
+  }
+}
+
+refresh();
+_timer = setInterval(refresh, 3000);
+</script>
+</body>
+</html>"""
+
+
+# ---------------------------------------------------------------------------
+# 请求统计页面
+# ---------------------------------------------------------------------------
+
+
+@app.get("/admin/stats", include_in_schema=False)
+async def admin_stats():
+    """请求统计仪表盘（HTML 页面）。"""
+    return HTMLResponse(content=_STATS_PAGE_HTML)
+
+
+@app.get("/admin/stats/json", include_in_schema=False)
+async def admin_stats_json():
+    """请求统计数据（JSON，供页面轮询）。"""
+    return get_stats()
+
+
 # ---------------------------------------------------------------------------
 # 代理路由
 # ---------------------------------------------------------------------------
@@ -217,7 +486,7 @@ def _proxy(request: Request, path: str):

 # 声明各接口的 form 参数用于 OpenAPI schema（实际转发直接读 Request）
 _MEASURE_FORMS = {
-    "image_file": {"type": "file", "description": "上传图片文件（JPG/PNG，≤ 1 MB）"},
+    "image_file": {"type": "file", "description": "上传图片文件（JPG/PNG）"},
    "image_url": {"type": "string", "description": "图片 URL"},
    "image_base64": {"type": "string", "description": "图片 base64（需带前缀）"},
 }
@@ -240,6 +509,12 @@ async def face_measure(request: Request):
    return await _proxy(request, "/api/v1/face/measure")


+@app.post("/api/v1/face/measure-v2", tags=["人脸分析"])
+async def face_measure_v2(request: Request):
+    """接口6：四庭七眼测量标注 v2（去顶庭 + 去头部端线）"""
+    return await _proxy(request, "/api/v1/face/measure-v2")
+
+
@app.post("/api/v1/hair/grow", tags=["生发"])
 async def hair_grow(request: Request):
    """接口2：C端生发"""
@@ -254,7 +529,7 @@ async def hair_grow_b(request: Request):

@app.post("/api/v1/face/features", tags=["人脸分析"])
 async def face_features(
-    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG，≤ 1 MB）"),
+    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）"),
 ):
@@ -271,14 +546,7 @@ async def face_features(

    img_bytes = None
    if image_file:
-        raw = await image_file.read()
-        # TODO: 临时取消限制，后续恢复
-        # if len(raw) > 1_000_000:
-        #     return JSONResponse(status_code=200, content={
-        #         "code": 1006, "message": "文件超出 1 MB 限制",
-        #         "request_id": f"gw-{_uuid.uuid4().hex[:8]}", "data": None,
-        #     })
-        img_bytes = raw
+        img_bytes = await image_file.read()
    elif image_base64:
        b64 = image_base64
        if "," in b64:
@@ -327,3 +595,15 @@ async def hairline_generate(request: Request):
 async def hair_grow_v2(request: Request):
    """接口7：C端生发 v2（add_hair2 工作流）"""
    return await _proxy(request, "/api/v1/hair/grow-v2")
+
+
+@app.post("/api/v1/head/mask", tags=["人脸分析"])
+async def head_mask(request: Request):
+    """接口9：头发遮罩生成 + 分步可视化"""
+    return await _proxy(request, "/api/v1/head/mask")
+
+
+@app.post("/api/v1/head/band", tags=["人脸分析"])
+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")
@@ -33,6 +33,13 @@ DEFAULTS = {
        "interval_minutes": 60,
        "max_age_hours": 24,
    },
+    "request_log": {
+        "enabled": True,
+        "log_file": "gateway/request_log.jsonl",
+        "buffer_size": 2000,
+        "max_file_lines": 10000,
+        "max_file_age_days": 7,
+    },
 }

 _config_cache = None
@@ -162,6 +162,8 @@ async def proxy_request(request: Request, path: str) -> JSONResponse:
        worker = None
        try:
            worker = await acquire_worker(cfg)
+            # 记录当前使用的 worker，供日志中间件读取
+            request.state.worker_url = worker.url
        except NoWorkerAvailable:
            logger.warning("无可用 worker，返回 1007")
            return JSONResponse(
@@ -0,0 +1,406 @@
+"""请求日志中间件：为每个请求记录时间、路径、耗时等，并提供统计查询。
+
+- 内存环形缓冲区（最近 N 条）
+- JSON Lines 文件持久化（自动轮转）
+- ASGI 中间件透明捕获请求/响应
+"""
+
+import datetime
+import json
+import logging
+import time
+from collections import deque
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger("gateway.logging_middleware")
+
+
+# ---------------------------------------------------------------------------
+# 数据结构
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RequestLogEntry:
+    """单条请求日志。"""
+    timestamp: str          # ISO-8601
+    method: str
+    path: str
+    status_code: int
+    duration_ms: float
+    client_ip: str
+    worker: str = ""        # 处理请求的 worker URL（空串表示网关本地处理）
+    response_code: Optional[int] = None   # 响应 JSON 中的 code 字段
+    request_id: Optional[str] = None      # 响应 JSON 中的 request_id
+
+
+# ---------------------------------------------------------------------------
+# 环形缓冲区
+# ---------------------------------------------------------------------------
+
+
+class RingBuffer:
+    """固定大小的环形缓冲区，线程安全。"""
+
+    def __init__(self, maxlen: int = 2000):
+        self._deque: deque = deque(maxlen=maxlen)
+
+    def append(self, entry: RequestLogEntry) -> None:
+        self._deque.append(entry)
+
+    def snapshot(self) -> List[RequestLogEntry]:
+        """返回当前缓冲区副本（最新在前）。"""
+        return list(reversed(self._deque))
+
+    def __len__(self) -> int:
+        return len(self._deque)
+
+
+# ---------------------------------------------------------------------------
+# JSON Lines 文件写入（含轮转）
+# ---------------------------------------------------------------------------
+
+
+class LogFileWriter:
+    """追加写入 JSON Lines 日志文件，自动按行数 / 天数轮转。
+
+    轮转策略：保留 1 个备份 (.jsonl.1)，不保留更多历史。
+    """
+
+    def __init__(self, filepath: str, max_lines: int = 10000, max_age_days: int = 7):
+        self.filepath = Path(filepath)
+        self.max_lines = max_lines
+        self.max_age_seconds = max_age_days * 86400
+
+    def write(self, entry: RequestLogEntry) -> None:
+        try:
+            self._maybe_rotate()
+            self.filepath.parent.mkdir(parents=True, exist_ok=True)
+            line = json.dumps(asdict(entry), ensure_ascii=False) + "\n"
+            with open(self.filepath, "a", encoding="utf-8") as f:
+                f.write(line)
+        except Exception:
+            logger.warning("写入请求日志失败", exc_info=True)
+
+    def _maybe_rotate(self) -> None:
+        if not self.filepath.exists():
+            return
+
+        # 按天数轮转
+        mtime = self.filepath.stat().st_mtime
+        if time.time() - mtime > self.max_age_seconds:
+            self._rotate()
+            return
+
+        # 按行数轮转
+        try:
+            with open(self.filepath, "r", encoding="utf-8") as f:
+                count = sum(1 for _ in f)
+            if count >= self.max_lines:
+                self._rotate()
+        except Exception:
+            pass  # 读不到就算了，下次再说
+
+    def _rotate(self) -> None:
+        backup = self.filepath.with_suffix(".jsonl.1")
+        if backup.exists():
+            backup.unlink()
+        try:
+            self.filepath.rename(backup)
+            logger.info("请求日志已轮转: %s → %s", self.filepath.name, backup.name)
+        except Exception:
+            logger.warning("日志轮转失败", exc_info=True)
+
+
+# ---------------------------------------------------------------------------
+# 模块级全局状态（由 init_logging 初始化）
+# ---------------------------------------------------------------------------
+
+_buffer: Optional[RingBuffer] = None
+_writer: Optional[LogFileWriter] = None
+
+
+def init_logging(cfg: dict) -> None:
+    """初始化日志缓冲区与文件写入器。"""
+    global _buffer, _writer
+
+    log_cfg = cfg.get("request_log", {})
+    if not log_cfg.get("enabled", True):
+        logger.info("请求日志已禁用")
+        return
+
+    log_file = log_cfg.get("log_file", "gateway/request_log.jsonl")
+    log_path = Path(log_file)
+    if not log_path.is_absolute():
+        log_path = Path(__file__).resolve().parent.parent / log_file
+
+    _buffer = RingBuffer(maxlen=log_cfg.get("buffer_size", 2000))
+    _writer = LogFileWriter(
+        filepath=str(log_path),
+        max_lines=log_cfg.get("max_file_lines", 10000),
+        max_age_days=log_cfg.get("max_file_age_days", 7),
+    )
+
+    # 从历史日志文件加载最近 N 条到缓冲区
+    buffer_size = log_cfg.get("buffer_size", 2000)
+    loaded = _load_from_logfile(str(log_path), buffer_size)
+    if loaded > 0:
+        logger.info("从日志文件恢复 %d 条历史记录", loaded)
+
+    logger.info("请求日志已启用 | 缓冲=%d | 文件=%s",
+                 buffer_size, log_path)
+
+
+def _load_from_logfile(filepath: str, max_entries: int) -> int:
+    """从 JSON Lines 日志文件读取最近 max_entries 条到缓冲区。"""
+    try:
+        p = Path(filepath)
+        if not p.exists():
+            return 0
+
+        # 从文件末尾反向读取（高效处理大文件）
+        with open(p, "rb") as f:
+            # 估算：每条约 200 bytes，读最后 max_entries * 250 bytes 足够
+            chunk_size = max_entries * 250
+            f.seek(0, 2)  # 文件末尾
+            file_size = f.tell()
+            read_size = min(chunk_size, file_size)
+            f.seek(max(0, file_size - read_size))
+            raw = f.read().decode("utf-8", errors="replace")
+
+        # 跳过可能不完整的第一行
+        lines = raw.split("\n")
+        if file_size > read_size:
+            # 第一行可能不完整，跳过
+            lines = lines[1:]
+        # 去掉末尾空行
+        lines = [l for l in lines if l.strip()]
+
+        # 只取最后 max_entries 条
+        lines = lines[-max_entries:]
+
+        count = 0
+        for line in lines:
+            try:
+                data = json.loads(line)
+                entry = RequestLogEntry(
+                    timestamp=data.get("timestamp", ""),
+                    method=data.get("method", ""),
+                    path=data.get("path", ""),
+                    status_code=data.get("status_code", 0),
+                    duration_ms=data.get("duration_ms", 0.0),
+                    client_ip=data.get("client_ip", ""),
+                    worker=data.get("worker", ""),
+                    response_code=data.get("response_code"),
+                    request_id=data.get("request_id"),
+                )
+                _buffer.append(entry)
+                count += 1
+            except (json.JSONDecodeError, KeyError):
+                continue
+
+        return count
+    except Exception:
+        logger.warning("从日志文件恢复历史记录失败", exc_info=True)
+        return 0
+
+
+def is_initialized() -> bool:
+    return _buffer is not None
+
+
+# ---------------------------------------------------------------------------
+# ASGI 中间件
+# ---------------------------------------------------------------------------
+
+def _should_log(path: str) -> bool:
+    """只记录 API 请求（/api/ 路径），跳过静态文件、健康检查等。"""
+    return path.startswith("/api/")
+
+
+async def request_logging_middleware(request, call_next):
+    """记录每个请求的耗时、状态码等信息。"""
+
+    # 未初始化或不需要记录的路径 → 直接放行
+    if _buffer is None or not _should_log(request.url.path):
+        return await call_next(request)
+
+    start = time.perf_counter()
+
+    # 获取客户端 IP（优先级：X-Forwarded-For > X-Real-IP > client.host）
+    client_ip = request.client.host if request.client else "unknown"
+    forwarded = request.headers.get("x-forwarded-for")
+    if forwarded:
+        client_ip = forwarded.split(",")[0].strip()
+    else:
+        real_ip = request.headers.get("x-real-ip")
+        if real_ip:
+            client_ip = real_ip.strip()
+
+    response = await call_next(request)
+    duration_ms = round((time.perf_counter() - start) * 1000, 2)
+
+    # 读取 worker URL（由 forward.py 在转发时写入 request.state）
+    worker_url = getattr(request.state, "worker_url", None) or ""
+
+    # 提取响应 body 并解析业务字段（仅 JSON 响应）
+    response_code = None
+    request_id = None
+    content_type = response.headers.get("content-type", "")
+
+    if "application/json" in content_type or "application/json" in (response.media_type or ""):
+        # 读取 body（兼容 body_iterator 和 body 两种属性）
+        body = getattr(response, "body", None)
+        if body is None:
+            body = b""
+            async for chunk in response.body_iterator:
+                body += chunk
+        try:
+            data = json.loads(body)
+            response_code = data.get("code")
+            request_id = data.get("request_id")
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            pass
+
+        # 如果读取了 body_iterator，需要重建响应
+        if not hasattr(response, "body") or response.body is None:
+            from starlette.responses import Response as StarletteResponse
+            response = StarletteResponse(
+                content=body,
+                status_code=response.status_code,
+                headers=dict(response.headers),
+                media_type=response.media_type,
+            )
+
+    # 记录
+    now = datetime.datetime.utcnow()
+    entry = RequestLogEntry(
+        timestamp=now.strftime("%Y-%m-%dT%H:%M:%S.") +
+                   f"{now.microsecond // 1000:03d}Z",
+        method=request.method,
+        path=request.url.path,
+        status_code=response.status_code,
+        duration_ms=duration_ms,
+        client_ip=client_ip,
+        worker=worker_url,
+        response_code=response_code,
+        request_id=request_id,
+    )
+
+    _buffer.append(entry)
+    if _writer is not None:
+        _writer.write(entry)
+
+    return response
+
+
+# ---------------------------------------------------------------------------
+# 统计查询
+# ---------------------------------------------------------------------------
+
+
+def get_stats() -> Dict[str, Any]:
+    """基于缓冲区数据计算统计摘要，返回给统计页面使用。"""
+    if _buffer is None:
+        return {
+            "summary": {"total": 0, "success_rate": 0, "avg_duration_ms": 0,
+                         "min_duration_ms": 0, "max_duration_ms": 0},
+            "endpoints": [],
+            "workers": [],
+            "recent": [],
+            "last_updated": datetime.datetime.utcnow().isoformat() + "Z",
+        }
+
+    snapshot = _buffer.snapshot()
+    total = len(snapshot)
+
+    if total == 0:
+        return {
+            "summary": {"total": 0, "success_rate": 0, "avg_duration_ms": 0,
+                         "min_duration_ms": 0, "max_duration_ms": 0},
+            "endpoints": [],
+            "workers": [],
+            "recent": [],
+            "last_updated": datetime.datetime.utcnow().isoformat() + "Z",
+        }
+
+    # 汇总指标
+    durations = [e.duration_ms for e in snapshot]
+    ok_count = sum(1 for e in snapshot if e.response_code == 0)
+
+    # 按路径聚合
+    by_path: Dict[str, dict] = {}
+    for e in snapshot:
+        path = e.path
+        if path not in by_path:
+            by_path[path] = {"count": 0, "total_duration": 0.0, "ok": 0}
+        by_path[path]["count"] += 1
+        by_path[path]["total_duration"] += e.duration_ms
+        if e.response_code == 0:
+            by_path[path]["ok"] += 1
+
+    endpoints = sorted(
+        ({
+            "path": path,
+            "count": v["count"],
+            "avg_duration_ms": round(v["total_duration"] / v["count"], 1),
+            "max_duration_ms": round(
+                max(e.duration_ms for e in snapshot if e.path == path), 1),
+            "success_rate": round(v["ok"] / v["count"] * 100, 1),
+        } for path, v in by_path.items()),
+        key=lambda x: -x["count"],
+    )
+
+    # 最近 100 条（最新在前）
+    recent_100 = snapshot[:100]
+    recent = [
+        {
+            "timestamp": e.timestamp,
+            "method": e.method,
+            "path": e.path,
+            "status_code": e.status_code,
+            "duration_ms": e.duration_ms,
+            "client_ip": e.client_ip,
+            "worker": e.worker,
+            "response_code": e.response_code,
+            "request_id": e.request_id,
+        }
+        for e in recent_100
+    ]
+
+    # 按 worker 聚合
+    by_worker: Dict[str, dict] = {}
+    for e in snapshot:
+        w = e.worker or "(网关本地)"
+        if w not in by_worker:
+            by_worker[w] = {"count": 0, "total_duration": 0.0, "ok": 0}
+        by_worker[w]["count"] += 1
+        by_worker[w]["total_duration"] += e.duration_ms
+        if e.response_code == 0:
+            by_worker[w]["ok"] += 1
+
+    workers = sorted(
+        ({
+            "worker": w,
+            "count": v["count"],
+            "avg_duration_ms": round(v["total_duration"] / v["count"], 1),
+            "success_rate": round(v["ok"] / v["count"] * 100, 1) if v["count"] else 0,
+        } for w, v in by_worker.items()),
+        key=lambda x: -x["count"],
+    )
+
+    return {
+        "summary": {
+            "total": total,
+            "success_rate": round(ok_count / total * 100, 1),
+            "avg_duration_ms": round(sum(durations) / len(durations), 1),
+            "min_duration_ms": round(min(durations), 1),
+            "max_duration_ms": round(max(durations), 1),
+        },
+        "endpoints": endpoints,
+        "workers": workers,
+        "recent": recent,
+        "last_updated": datetime.datetime.utcnow().isoformat() + "Z",
+    }
@@ -223,15 +223,24 @@ def create_app(port: int, password: str) -> FastAPI:
        if x_internal_token != password:
            return JSONResponse(status_code=401, content={"detail": "unauthorized"})
        delay = float(request.query_params.get("delay", "1"))
+        _png = f"data:image/png;base64,{TINY_PNG_BASE64}"
        data = {
            "hairline_images": [
                {
-                    "image_base64": f"data:image/png;base64,{TINY_PNG_BASE64}",
+                    "hairline_type": "ellipse",
+                    "image_middle_base64": _png,
+                    "image_high_base64": _png,
+                    "image_low_base64": _png,
+                    "grown_image_base64": _png,
                    "order": 1,
                },
                {
-                    "image_base64": f"data:image/png;base64,{TINY_PNG_BASE64}",
-                    "order": 2,
+                    "hairline_type": "heart",
+                    "image_middle_base64": _png,
+                    "image_high_base64": _png,
+                    "image_low_base64": _png,
+                    "grown_image_base64": None,
+                    "order": 3,
                },
            ],
            "best_hairline_center_point": {"x": 540, "y": 430},
@@ -1,19 +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
-# 分辨率门槛（可选，默认 600/800）
-# Environment=MIN_SHORT_SIDE=600
-# Environment=MIN_LONG_SIDE=800
-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
@@ -0,0 +1,450 @@
+{
+  "1": {
+    "inputs": {
+      "scheduler": "simple",
+      "steps": 6,
+      "denoise": 1,
+      "model": [
+        "2",
+        0
+      ]
+    },
+    "class_type": "BasicScheduler",
+    "_meta": {
+      "title": "基本调度器"
+    }
+  },
+  "2": {
+    "inputs": {
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "model": [
+        "16",
+        0
+      ]
+    },
+    "class_type": "ModelSamplingFlux",
+    "_meta": {
+      "title": "采样算法（Flux）"
+    }
+  },
+  "3": {
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    },
+    "class_type": "VAELoader",
+    "_meta": {
+      "title": "加载VAE"
+    }
+  },
+  "5": {
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "ReferenceLatent",
+    "_meta": {
+      "title": "参考Latent"
+    }
+  },
+  "6": {
+    "inputs": {
+      "noise_seed": 808990860769642
+    },
+    "class_type": "RandomNoise",
+    "_meta": {
+      "title": "随机噪波"
+    }
+  },
+  "7": {
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    },
+    "class_type": "EmptySD3LatentImage",
+    "_meta": {
+      "title": "空Latent图像（SD3）"
+    }
+  },
+  "8": {
+    "inputs": {
+      "sampler_name": "euler"
+    },
+    "class_type": "KSamplerSelect",
+    "_meta": {
+      "title": "K采样器选择"
+    }
+  },
+  "9": {
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    },
+    "class_type": "SamplerCustomAdvanced",
+    "_meta": {
+      "title": "自定义采样器（高级）"
+    }
+  },
+  "10": {
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE解码"
+    }
+  },
+  "13": {
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE编码"
+    }
+  },
+  "14": {
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "GetImageSize+",
+    "_meta": {
+      "title": "🔧 Get Image Size"
+    }
+  },
+  "16": {
+    "inputs": {
+      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
+      "weight_dtype": "fp8_e4m3fn"
+    },
+    "class_type": "UNETLoader",
+    "_meta": {
+      "title": "UNet加载器"
+    }
+  },
+  "17": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "images": [
+        "62",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "保存图像"
+    }
+  },
+  "19": {
+    "inputs": {
+      "guidance": 1,
+      "conditioning": [
+        "22",
+        0
+      ]
+    },
+    "class_type": "FluxGuidance",
+    "_meta": {
+      "title": "Flux引导"
+    }
+  },
+  "20": {
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "BasicGuider",
+    "_meta": {
+      "title": "基本引导器"
+    }
+  },
+  "22": {
+    "inputs": {
+      "text": [
+        "60",
+        0
+      ],
+      "clip": [
+        "61",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP文本编码"
+    }
+  },
+  "26": {
+    "inputs": {
+      "image": "clipspace/clipspace-painted-masked-1784045785080.png [input]"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "加载图像"
+    }
+  },
+  "31": {
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "easy imageSize",
+    "_meta": {
+      "title": "图像尺寸"
+    }
+  },
+  "32": {
+    "inputs": {
+      "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",
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ]
+    },
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "_meta": {
+      "title": "LayerUtility: ImageScaleByAspectRatio V2"
+    }
+  },
+  "33": {
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    },
+    "class_type": "Mask Fill Holes",
+    "_meta": {
+      "title": "遮罩填充漏洞"
+    }
+  },
+  "36": {
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    },
+    "class_type": "Convert Masks to Images",
+    "_meta": {
+      "title": "遮罩到图像"
+    }
+  },
+  "37": {
+    "inputs": {
+      "method": "intensity",
+      "image": [
+        "39",
+        0
+      ]
+    },
+    "class_type": "Image To Mask",
+    "_meta": {
+      "title": "图像到遮罩"
+    }
+  },
+  "39": {
+    "inputs": {
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled",
+      "image": [
+        "36",
+        0
+      ]
+    },
+    "class_type": "ImageScale",
+    "_meta": {
+      "title": "缩放图像"
+    }
+  },
+  "44": {
+    "inputs": {
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true,
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ]
+    },
+    "class_type": "ImageAndMaskPreview",
+    "_meta": {
+      "title": "图像与遮罩预览"
+    }
+  },
+  "45": {
+    "inputs": {
+      "images": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "预览图像"
+    }
+  },
+  "53": {
+    "inputs": {
+      "rgthree_comparer": {
+        "images": [
+          {
+            "name": "A",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_kzrpg_00019_.png&type=temp&subfolder=&rand=0.8964945384546902"
+          },
+          {
+            "name": "B",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_kzrpg_00020_.png&type=temp&subfolder=&rand=0.6762414189274947"
+          }
+        ]
+      },
+      "image_a": [
+        "62",
+        0
+      ],
+      "image_b": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "Image Comparer (rgthree)",
+    "_meta": {
+      "title": "图像对比"
+    }
+  },
+  "60": {
+    "inputs": {
+      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
+    },
+    "class_type": "JjkText",
+    "_meta": {
+      "title": "Text"
+    }
+  },
+  "61": {
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "default"
+    },
+    "class_type": "CLIPLoader",
+    "_meta": {
+      "title": "加载CLIP"
+    }
+  },
+  "62": {
+    "inputs": {
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true,
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ]
+    },
+    "class_type": "ColorMatch",
+    "_meta": {
+      "title": "Color Match"
+    }
+  }
+}
@@ -1,8 +1,9 @@
 """ComfyUI 客户端：用 add_hair.json / add_hair2.json 工作流跑生发图（Flux-2 inpaint）。

-worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到本机 ComfyUI(默认 8188)，
+worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到远端 ComfyUI
+（默认 http://10.60.74.221:8188，可用环境变量 COMFYUI_URL 覆盖），
 替换工作流节点 26 的输入图、随机 seed，提交 /prompt，轮询 /history，取回 /view 输出。
-ComfyUI 开启了 HTTP Basic Auth（user `admin` + 密码），所有请求都带凭据。
+ComfyUI 若开启了 HTTP Basic Auth（user `admin` + 密码），所有请求都带凭据。

 支持多工作流：run() 可通过 workflow_path 指定不同工作流 JSON，自动检测 SaveImage 输出节点。
 """
@@ -10,6 +11,7 @@ from __future__ import annotations

 import copy
 import json
+import logging
 import os
 import random
 import time
@@ -84,11 +86,13 @@ 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) -> 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。
    """
    path = workflow_path or _WORKFLOW_DEFAULT
    output_node = _get_output_node(path)
@@ -103,14 +107,40 @@ 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

-        # 3. 提交
-        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        # 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:
+            import os as _os
+            _diag = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))),
+                                  "log", "comfyui_last_submit")
+            _os.makedirs(_diag, exist_ok=True)
+            with open(_os.path.join(_diag, "workflow.json"), "w", encoding="utf-8") as _f:
+                json.dump(wf, _f, ensure_ascii=False, indent=2)
+            with open(_os.path.join(_diag, "input.png"), "wb") as _f:
+                _f.write(rgba_png_bytes)
+            with open(_os.path.join(_diag, "prompt.txt"), "w", encoding="utf-8") as _f:
+                _f.write(prompt if prompt is not None else "(None=用工作流内置默认)")
+        except Exception:  # noqa: BLE001
+            pass
+
+        # 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"]

@@ -129,7 +159,7 @@ 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}")

@@ -186,6 +186,53 @@ 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.
 #
@@ -0,0 +1,78 @@
+"""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，皮肤加一点磨皮，再加一点美颜"
+_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) -> 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，皮肤加一点磨皮，再加一点美颜"
+        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）
+
+    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)
@@ -14,28 +14,100 @@ 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_clamped,
+)
 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
 from .marker_detect import detect_marker_hairline, path_to_curve_mask

+import base64
 import io
 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。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
+    "heart": "chang_xinxing",     # 心形
+    "straight": "chang_zhixian",  # 直线
+    "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"],  # 1..6
+}
+
 _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")

-# ⚠️ 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")
+# 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，皮肤加一点磨皮，再加一点美颜")
+
+# 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):
+    """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）。
+    """
+    from .redraw import run_redraw
+    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 _REDRAW_MAX_SIDE > 0 and m > _REDRAW_MAX_SIDE:
+            scale = _REDRAW_MAX_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, _REDRAW_MAX_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=_REDRAW_PROMPT, front=True)
+    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 = {
+    "middle": _TEXTURE_DIR,
+    "high": os.path.join(_REPO, "hairline_texture_high"),
+    "low": os.path.join(_REPO, "hairline_texture_low"),
+}
+
+# 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
-_texture_map = None
+_texture_maps: dict = {}  # {level: {gender: [(key, path)]}}，按档位缓存


 def get_landmarker() -> FaceLandmarker:
@@ -61,24 +133,32 @@ def _gender_key(stem: str):
    return None, None


-def get_texture_map() -> dict:
-    """扫描 hairline_texture/ 建 {gender: [(key, path)]}，按 key 排序、缓存。
+def get_texture_map(level: str = "middle") -> dict:
+    """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。
    """
-    global _texture_map
-    if _texture_map is not None:
-        return _texture_map
+    if level not in _TEXTURE_DIRS:
+        raise ValueError(f"hairline_level 必须是 middle/high/low，收到 {level!r}")
+    cached = _texture_maps.get(level)
+    if cached is not None:
+        return cached
    mapping: dict[str, list] = {"female": [], "male": []}
-    for path in sorted(glob.glob(os.path.join(_TEXTURE_DIR, "*.png"))):
+    for path in sorted(glob.glob(os.path.join(_TEXTURE_DIRS[level], "*.png"))):
        stem = os.path.splitext(os.path.basename(path))[0]
        gender, key = _gender_key(stem)
        if gender:
            mapping[gender].append((key, path))
    for g in mapping:
-        mapping[g].sort(key=lambda kp: kp[0])
-    _texture_map = mapping
-    return _texture_map
+        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


 def extract_502(image_bgr: np.ndarray):
@@ -90,13 +170,19 @@ 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。
+
+    发际线几何检测固定用 `sample_hairline_clamped`（射线检测 + 头部轮廓钳制）：
+    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，正常长发照片结果与旧行为一致。
+    """
    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, valid = sample_hairline_clamped(landmarks, 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)
@@ -131,15 +217,16 @@ 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):
-    """指定发际线类型：预览图(白线) + 生发图(ComfyUI)。
+    """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..6。
      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）。
    prompt（默认 None）：ComfyUI 提示词，非 None 时替换工作流节点60文本。
    workflow_path（默认 None）：ComfyUI 工作流 JSON 路径，None 用默认 add_hair.json。
-    Returns: list[dict] {"hairline_type","order","image_bgr"(预览), "grown_png"(bytes 或 None)}。
+    Returns: list[dict] {"hairline_type","order","overlay"((H,W,4) RGBA 透明层),
+                        "grown_png"(bytes 或 None)}。
    无人脸返回 None。某张 ComfyUI 失败时该项 grown_png=None，不抛异常。
    """
    if gender not in ("male", "female"):
@@ -160,16 +247,22 @@ 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)
+            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)

    results = []
+    h, w = image_bgr.shape[:2]
    for order, (key, white_path) in items:
        white = load_texture_rgba(white_path)
-        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
+        overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)

        if not use_mask:
            grown_png = shared_grown
@@ -179,24 +272,200 @@ 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)
+                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)

        results.append({"hairline_type": key, "order": order,
-                        "image_bgr": preview, "grown_png": grown_png})
+                        "overlay": overlay, "grown_png": grown_png})
    return results


-def generate_hairline_pngs(image_bgr: np.ndarray, gender: str):
-    """接口5：该性别全部发际线叠图(同接口2预览) + 最佳(order1)发际线曲线的面部中间点。
+def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
+                               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。

-    Returns: {"images":[{hairline_type,order,image_bgr}], "best_center":(x,y)}；无人脸 None。
+    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 仅用于原生生发分支（换发型分支的提示词由 redraw 流程内部固定）。
+    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]
+    else:
+        items = list(enumerate(textures, start=1))
+
+    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。
+    redraw_img = image_bgr
+    hair_mask_redraw = hair_mask_reuse
+    if _REDRAW_MAX_SIDE > 0 and max(h, w) > _REDRAW_MAX_SIDE:
+        redraw_img, _rs = _downscale_max_side(image_bgr, _REDRAW_MAX_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, _REDRAW_MAX_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)
+
+        grown_png = None
+        chang_id = _FEMALE_KEY_TO_CHANG.get(key)
+        if chang_id is None:
+            # 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:
+                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 ""
+                mask_b64 = steps.get("redraw_band_mask_base64") or ""
+                if not final_b64 or not mask_b64:
+                    logger.warning("接口2 换发型：type=%s final/遮罩缺失（final=%d mask=%d）",
+                                   key, len(final_b64), len(mask_b64))
+                else:
+                    # 去掉 data URI 前缀
+                    if final_b64.startswith("data:"):
+                        final_b64 = final_b64.split(",", 1)[1]
+                    if mask_b64.startswith("data:"):
+                        mask_b64 = mask_b64.split(",", 1)[1]
+                    final_bytes = base64.b64decode(final_b64)
+                    mask_bytes = base64.b64decode(mask_b64)
+                    # 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)
+                    _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
+                logger.warning("接口2 换发型图失败 type=%s：%s", key, e)
+
+        results.append({"hairline_type": key, "order": order,
+                        "overlay": overlay, "grown_png": grown_png})
+    return results
+
+
+def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
+                       use_mask: bool, prompt: str | None):
+    """对单个发际线做生发(ComfyUI)。黑模板固定取 hairline_texture_black/（middle），
+    与 hairline_level 无关（high/low 贴图与 middle 同名，basename 映射即落回 middle 黑模板）。
+    use_mask=False 时用干净原图+空遮罩（与贴图无关，white_path 可为 None）。
+    失败返回 None，不抛异常。
+    """
+    try:
+        if use_mask:
+            black = load_texture_rgba(_black_texture_path(white_path))
+            marked, mask = build_inpaint_mask(
+                image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+        else:
+            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", 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,
+                           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。
    """
    if gender not in ("male", "female"):
        raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
+    if not hair_styles:
+        raise ValueError("hair_styles 必填且不能为空")
    ctx = extract_context(image_bgr)
    if ctx is None:
        return None
@@ -206,20 +475,88 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str):
    # 面部中轴 x = 眉心(9/151 中点)
    face_cx = float((lm[9, 0] + lm[151, 0]) / 2 * w)

-    textures = get_texture_map()[gender]
-    images, best_center = [], None
-    for order, (key, path) in enumerate(textures, start=1):
-        white = load_texture_rgba(path)
-        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
-        images.append({"hairline_type": key, "order": order, "image_bgr": preview})
-        if order == 1:  # 最佳发际线曲线的中点（面部中轴处的发际线 y）
-            overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-            ys, xs = np.where(overlay[:, :, 3] > 40)
-            if xs.size:
-                near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
-                col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
-                best_center = (int(round(face_cx)), int(round(float(col_ys.mean()))))
-    return {"images": images, "best_center": best_center}
+    # 三档贴图表（同性别、同 key 顺序，因三个文件夹同名）
+    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 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):
+        """从某档发际线透明叠图取面部中轴处的发际线中点 (x,y)，无像素返回 None。"""
+        ys, xs = np.where(overlay[:, :, 3] > 40)
+        if not xs.size:
+            return None
+        near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
+        col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
+        return (int(round(face_cx)), int(round(float(col_ys.mean()))))
+
+    images, best_centers = [], None
+    for s in hair_styles:  # s = 1-indexed 发型序号
+        key, mid_path = tex_by_level["middle"][s - 1]
+        overlays = {}
+        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（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
+        if best_centers is None:
+            best_centers = {lv: _center_of(overlays[lv]) for lv in _TEXTURE_DIRS}
+    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):
@@ -228,12 +565,23 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
    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)
@@ -251,8 +599,10 @@ 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"}


--- a/Show More
+++ b/Show More