save

- annotation: 自适应字号系数 0.017→0.020（下限 8→9），标注文字更大更清晰
- measure: _brow_center 只取 FaceMesh 9 号点（眉间上点），不再与 151 取中点

Co-authored-by: Cursor <cursoragent@cursor.com>

.gitignore

@@ -49,3 +49,7 @@ 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

0716add-hair-api.json

@@ -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"
+    }
+  }
+}

add_hair.json

@@ -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": {

app.py

@@ -138,7 +138,47 @@ app = FastAPI(
 app.mount("/static", StaticFiles(directory="static"), name="static")
 
 # 不校验鉴权的路径前缀（供网关探测 / 文档 / 静态）
-_AUTH_EXEMPT = ("/health", "/docs", "/openapi.json", "/redoc", "/static", "/api/v1/debug")
+_AUTH_EXEMPT = ("/health", "/docs", "/openapi.json", "/redoc", "/static",
+               "/api/v1/debug", "/api/v1/redraw",
+               "/api/swapHair", "/hairColor")
+
+
+# ---------------------------------------------------------------------------
+# change_hair 代理路由（解决 CORS 问题）
+# ---------------------------------------------------------------------------
+_CHANGE_HAIR_BASE = "http://127.0.0.1:8801"
+
+
+@app.post("/api/swapHair/v1", tags=["change_hair"])
+async def proxy_swap_hair(request: Request):
+    """代理转发到 change_hair /api/swapHair/v1（换发型）"""
+    try:
+        import httpx
+        body = await request.body()
+        async with httpx.AsyncClient(timeout=300.0) as client:
+            resp = await client.post(f"{_CHANGE_HAIR_BASE}/api/swapHair/v1",
+                                    content=body,
+                                    headers={"Content-Type": "application/json"})
+            return JSONResponse(content=resp.json(), status_code=resp.status_code)
+    except Exception as e:
+        logger.exception("代理 swapHair 失败")
+        return err(1007, f"换发型服务异常：{e}")
+
+
+@app.post("/hairColor/v2", tags=["change_hair"])
+async def proxy_hair_color(request: Request):
+    """代理转发到 change_hair /hairColor/v2（换发色）"""
+    try:
+        import httpx
+        body = await request.body()
+        async with httpx.AsyncClient(timeout=300.0) as client:
+            resp = await client.post(f"{_CHANGE_HAIR_BASE}/hairColor/v2",
+                                    content=body,
+                                    headers={"Content-Type": "application/json"})
+            return JSONResponse(content=resp.json(), status_code=resp.status_code)
+    except Exception as e:
+        logger.exception("代理 hairColor 失败")
+        return err(1007, f"换发色服务异常：{e}")
 
 
 @app.middleware("http")
@@ -361,20 +401,36 @@ def _run_face_measure_data(image, variant="v1"):
         logger.warning("头发/耳朵分割失败，回退方案A：%s", seg_e)
 
     result = measure_face(landmarks, hair_mask, w, h, head_pose=head_pose)
+    discarded = result.hairline_discarded
     data = result.to_response()
+    vd = result.vertical
     if variant == "v6":
-        vd = result.vertical
-        base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
-        # 接口6 是三庭：去掉顶庭相关字段（top_court_cm / ratios.top_court / landmarks.hair_top）
-        data["four_courts"]["ratios"] = {
-            "upper_court": round(vd["upper_court_px"] / base_px, 3),
-            "middle_court": round(vd["middle_court_px"] / base_px, 3),
-            "lower_court": round(vd["lower_court_px"] / base_px, 3),
-        }
-        data["four_courts"].pop("top_court_cm", None)
-        data["face_total_height_cm"] = round(
-            result.upper_cm + result.middle_cm + result.lower_cm, 2)
-        data["landmarks"].pop("hair_top", None)
+        if discarded:
+            # 发际线弃用：接口6 的上庭也依赖发际线，一并置 null；只保留中/下庭。
+            base_px = vd["middle_court_px"] + vd["lower_court_px"]
+            data["four_courts"]["upper_court_cm"] = None
+            data["four_courts"]["ratios"] = {
+                "upper_court": None,
+                "middle_court": round(vd["middle_court_px"] / base_px, 3),
+                "lower_court": round(vd["lower_court_px"] / base_px, 3),
+            }
+            data["four_courts"].pop("top_court_cm", None)
+            data["face_total_height_cm"] = round(
+                result.middle_cm + result.lower_cm, 2)
+            data["landmarks"]["hairline"] = None
+        else:
+            base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
+            # 接口6 是三庭：去掉顶庭相关字段（top_court_cm / ratios.top_court / landmarks.hair_top）
+            data["four_courts"]["ratios"] = {
+                "upper_court": round(vd["upper_court_px"] / base_px, 3),
+                "middle_court": round(vd["middle_court_px"] / base_px, 3),
+                "lower_court": round(vd["lower_court_px"] / base_px, 3),
+            }
+            data["four_courts"].pop("top_court_cm", None)
+            data["face_total_height_cm"] = round(
+                result.upper_cm + result.middle_cm + result.lower_cm, 2)
+            # 注：landmarks.hair_top 保留返回（供前端/下游定位头顶），但顶庭数值、
+            # 占比、标注图仍按三庭处理，显示效果不变。
 
     # 七眼段宽度（cm）。eye1=左耳外段 eye2=左脸颊 eye3=左眼 eye4=两眼间距 eye5=右眼 eye6=右脸颊 eye7=右耳外段。
     # eye2~eye6（5段）只用内部分点，接口1/6 共用；eye1/eye7 需耳朵分割端线，仅接口1 有。
@@ -389,11 +445,14 @@ def _run_face_measure_data(image, variant="v1"):
             data["seven_eyes"][f"eye{i + 2}"] = (
                 None if (a is None or b is None) else round((b - a) / pc, 2))
         if variant != "v6":
-            # 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线
+            # 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线。
+            # 竖向范围：发际线弃用时用眉心做上界（hair_top 不可靠），否则用头顶。
             from face_analysis.annotation import _ear_edges_from_mask
+            top_y = (vd["brow_center"][1] if discarded
+                     else vd["hair_top"][1])
             head_l, head_r = _ear_edges_from_mask(
                 ear_mask, hair_mask,
-                result.vertical["hair_top"][1], result.vertical["chin_tip"][1],
+                top_y, vd["chin_tip"][1],
                 lcx, rcx, (lcx + rcx) / 2)
             data["seven_eyes"]["eye1"] = (
                 None if (head_l is None) else round((lcx - head_l) / pc, 2))
@@ -468,7 +527,7 @@ async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
 **标注图片 UI 规范**（真实版本生效）：
 - 字体/线条/箭头颜色：`#FFFFFF 100%`，透明底
 - 字号/线宽/虚线/箭头按图片短边自适应缩放
-- 四庭数值（名+数值两行，不带 cm）在图片**左侧**呈现，七眼段宽**上下穿插**展示，底部标「单位cm」
+- 四庭（名 + 数值带cm + 百分比 三行）在图片**左侧**呈现，七眼段宽**上下穿插**展示（数值带cm，下方另起一行标占头宽百分比）
 - 横线/竖线渐变消失并略超出端点；段宽/庭高用虚线 + 实心三角双箭头标示
 - 竖线含人头最左/最右端线（取自头发分割轮廓），共 8 线 7 段
 """,
@@ -573,7 +632,7 @@ async def face_measure(
 **标注图片 UI 规范**（真实版本生效）：
 - 字体/线条/箭头颜色：`#FFFFFF 100%`，透明底
 - 字号/线宽/虚线/箭头按图片短边自适应缩放
-- 三庭数值（名+数值两行，不带 cm）在图片**左侧**呈现，七眼段宽**上下穿插**展示，底部标「单位cm」
+- 三庭（名 + 数值带cm + 百分比 三行）在图片**左侧**呈现，七眼段宽**上下穿插**展示（数值带cm，下方另起一行标占头宽百分比）
 - 段宽/庭高用虚线 + 实心三角双箭头标示
 """,
     responses={
@@ -655,21 +714,27 @@ async def face_measure_v2(
 - 发际线类型 `hairline_type`（英文 key）
 - 顺序 `order`（本期固定 `1..N`，不排序）
 
+> **female 走「换发型」模式**：1..5 的生发图 `grown_image_base64` 由换发型（change_hair）
+> + Flux-2 整帧重绘（= 接口12 final 管线，整帧美颜+整帧重绘）生成，其余参数用固化默认值。
+> **6/7（bigflower/clasicalflower）与 male 一样走原生发（ComfyUI add_hair）管线**。
+> **male 全部走原生发（ComfyUI add_hair）管线**。入参与返回结构不变。
+> female 1..5 依赖 change_hair 与 ComfyUI(:8188) 均在跑。
+
 {_image_fields_desc}
 
 图片同时支持 `multipart/form-data` 文件上传（字段名 `image_file`）。
 
 ---
 
-- **gender**（必填）：`male` / `female`。决定返回的贴图集合（female 5 张 / male 4 张）。
+- **gender**（必填）：`male` / `female`。决定返回的贴图集合（female 7 张 / male 6 张）。
   非法或缺失返回 `1004`。
 - **hair_style**（必填）：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量。
-  `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`。
+  `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**：本期保留但不生效。
 
-`hairline_type` 取值：`ellipse` / `flower` / `heart` / `straight` / `wave`（female），
-`ellipse` / `m` / `straight` / `inverse_arc`（male）。
+`hairline_type` 取值：`ellipse` / `flower` / `heart` / `straight` / `wave` / `bigflower` / `clasicalflower`（female），
+`ellipse` / `m` / `straight` / `inverse_arc` / `heart` / `Softpetal`（male）。
 """,
     responses={
         200: {
@@ -708,17 +773,17 @@ async def hair_grow(
     image_url: Optional[str] = Form(default=None, description="图片 URL"),
     image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
     gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
-    hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-5 male:1-4"),
+    hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-7 male:1-6"),
     beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
     use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩（测试对比用）。false 时用干净原图生成（空遮罩，不烧模板线）"),
-    prompt: str = Form(default="补充遮罩区域的头发，加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
 ):
     # 1. gender 必填校验（非法/缺失 → 1004）
     if gender not in ("male", "female"):
         return err(1004, "gender 必填且只能为 male / female")
 
     # 2. hair_style 必填校验（解析逗号分隔，越界 → 1007）
-    max_styles = {"female": 5, "male": 4}[gender]
+    max_styles = {"female": 7, "male": 6}[gender]
     hair_styles = _parse_hair_styles(hair_style, max_styles)
     if hair_styles is None:
         return err(1007, f"hair_style 必填且为 1..{max_styles} 的整数（逗号分隔），收到 {hair_style!r}")
@@ -734,10 +799,18 @@ async def hair_grow(
 
     try:
         from fastapi.concurrency import run_in_threadpool
-        from hairline.service import generate_grow_results
 
-        # 预览 + 生发(ComfyUI) 都是阻塞且较慢，放线程池避免卡住事件循环
-        items = await run_in_threadpool(generate_grow_results, image, gender, use_mask, prompt, hair_styles)
+        # 预览 + 生发/换发型 都是阻塞且较慢，放线程池避免卡住事件循环。
+        # female：换发型 + Flux-2 整帧重绘（= 接口12 final 管线）；male：仍走原生发管线。
+        if gender == "female":
+            from hairline.service import generate_grow_results_swap
+            items = await run_in_threadpool(
+                generate_grow_results_swap, image, hair_styles, _V2_FINAL_DEFAULTS,
+                prompt=prompt)
+        else:
+            from hairline.service import generate_grow_results
+            items = await run_in_threadpool(
+                generate_grow_results, image, gender, use_mask, prompt, hair_styles)
         if items is None:
             return err(1001, "无法识别人像")
 
@@ -757,119 +830,160 @@ async def hair_grow(
 
 
 # ---------------------------------------------------------------------------
-# 接口 7：C 端生发 v2（add_hair2.json 工作流）
+# 调试接口：接口2 女性生发 分步计时
 # ---------------------------------------------------------------------------
-
-_WORKFLOW2_PATH = os.path.join(os.path.dirname(__file__), "add_hair2.json")
-
-
 @app.post(
-    "/api/v1/hair/grow-v2",
-    summary="接口7 C端生发 v2（add_hair2 工作流）",
-    tags=["生发"],
-    description=f"""
-输入用户正面照 + **性别** + **发型序号**，使用 add_hair2.json 工作流生成指定发际线类型的预览图与生发图。
-功能与接口2 完全一致，仅 ComfyUI 工作流不同。
-
-{_image_fields_desc}
-
-图片同时支持 `multipart/form-data` 文件上传（字段名 `image_file`）。
-
----
-
-- **gender**（必填）：`male` / `female`。决定返回的贴图集合（female 5 张 / male 4 张）。
-  非法或缺失返回 `1004`。
-- **hair_style**（必填）：`int`，发型序号。`female`：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；
-  `male`：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界返回 `1007`。
-- **beauty_enabled**：本期保留但不生效。
-
-`hairline_type` 取值：`ellipse` / `flower` / `heart` / `straight` / `wave`（female），
-`ellipse` / `m` / `straight` / `inverse_arc`（male）。
-""",
-    responses={
-        200: {
-            "description": "成功",
-            "content": {
-                "application/json": {
-                    "example": {
-                        "code": 0,
-                        "message": "success",
-                        "request_id": "mock-request-id",
-                        "data": {
-                            "results": [
-                                {"image_base64": "iVBORw0KGgo...", "hairline_type": "ellipse", "order": 1},
-                            ]
-                        },
-                    }
-                }
-            },
-        },
-        400: {
-            "description": "参数错误 / 图片识别失败",
-            "content": {
-                "application/json": {
-                    "examples": {
-                        "图片参数错误": {"value": {"code": 1007, "message": "图片参数错误：必须且只能传 image_file / image_url / image_base64 其中一个", "request_id": "x", "data": None}},
-                        "非正面照": {"value": {"code": 1003, "message": "角度问题，请上传正面照", "request_id": "x", "data": None}},
-                    }
-                }
-            },
-        },
-    },
+    "/api/v1/debug/grow-timing",
+    summary="调试-接口2女性生发分步计时",
+    tags=["调试"],
+    include_in_schema=False,
 )
-async def hair_grow_v2(
-    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 data:image/...;base64, 前缀）"),
-    gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
-    hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-5 male:1-4"),
-    beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
-    use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩（测试对比用）。false 时用干净原图生成（空遮罩，不烧模板线）"),
-    prompt: str = Form(default="补充遮罩区域的头发，加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+async def debug_grow_timing(
+    image_file: Optional[UploadFile] = File(default=None),
+    image_url: Optional[str] = Form(default=None),
+    image_base64: Optional[str] = Form(default=None),
+    hair_style: str = Form(default="2", description="发型序号(花瓣=2)，逗号分隔多选"),
+    webui_steps: Optional[int] = Form(default=None, description="swapHair webui img2img 采样步数，None=服务端默认(15)，可填10/15/20/25对比"),
+    redraw_max_side: Optional[int] = Form(default=None, description="ComfyUI重绘分辨率(长边像素)。None=默认896；0=原图不缩；其他如640/768/1024"),
+    redraw_prompt: Optional[str] = Form(default=None, description="ComfyUI重绘提示词，None=默认'填充遮罩区域的头发'"),
 ):
-    # 1. gender 必填校验（非法/缺失 → 1004）
-    if gender not in ("male", "female"):
-        return err(1004, "gender 必填且只能为 male / female")
+    """单图跑接口2女性生发，返回每个步骤的耗时 + 结果图，用于定位性能瓶颈。
 
-    # 2. hair_style 必填校验（解析逗号分隔，越界 → 1007）
-    max_styles = {"female": 5, "male": 4}[gender]
-    hair_styles = _parse_hair_styles(hair_style, max_styles)
-    if hair_styles is None:
-        return err(1007, f"hair_style 必填且为 1..{max_styles} 的整数（逗号分隔），收到 {hair_style!r}")
+    步骤拆分：
+      1. extract_context：人脸关键点检测 + 头发分割 + 发际线几何
+      2. [每个发型] generate_hairline_redraw：
+         2a. compute_mask：发际线遮罩计算
+         2b. _call_swap：调 change_hair 换发型（内含 webui SD1.5 推理，远程或本机）
+         2c. _composite：接缝融合（多频段/羽化）
+      3. [每个发型] _call_local_redraw：调本机 ComfyUI 用 Flux.2 重绘
+    """
+    import time as _time
+    from fastapi.concurrency import run_in_threadpool
 
-    # 3. 三选一取图
     raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
     if e is not None:
         return e
-
     image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR)
     if image is None:
         return err(1008, "图片格式不支持（仅 JPG / PNG）")
 
     try:
-        from fastapi.concurrency import run_in_threadpool
-        from hairline.service import generate_grow_results
+        max_styles = 7
+        hair_styles = _parse_hair_styles(hair_style, max_styles)
+        if hair_styles is None:
+            return err(1007, f"hair_style 必须为 1..{max_styles}")
 
-        # 预览 + 生发(ComfyUI) 都是阻塞且较慢，放线程池避免卡住事件循环
-        items = await run_in_threadpool(generate_grow_results, image, gender, use_mask, prompt, hair_styles, _WORKFLOW2_PATH)
-        if items is None:
+        t_total0 = _time.perf_counter()
+        timings = {"total_ms": 0, "extract_context_ms": 0, "per_hairstyle": []}
+
+        # 步骤1: extract_context
+        t0 = _time.perf_counter()
+        from hairline.service import extract_context as _ec, _call_local_redraw, _REDRAW_MAX_SIDE  # noqa
+        from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError  # noqa
+        from face_analysis.head_mask import SEGFORMER_HAIR  # noqa
+        ctx = await run_in_threadpool(_ec, image)
+        timings["extract_context_ms"] = int((_time.perf_counter() - t0) * 1000)
+        if ctx is None:
             return err(1001, "无法识别人像")
 
-        results = []
-        for p in items:
-            results.append({
-                "image_base64": _jpg_b64(p["image_bgr"]),               # 预览图 JPG
-                "grown_image_base64": (_png_to_jpg_b64(p["grown_png"])  # 生发图 JPG
-                                       if p["grown_png"] else None),
-                "hairline_type": p["hairline_type"],
-                "order": p["order"],
-            })
-        return ok({"results": results})
+        hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
+        h, w = image.shape[:2]
+        eff_side = _REDRAW_MAX_SIDE if redraw_max_side is None else redraw_max_side
+        redraw_img, hair_mask_redraw = image, hair_mask_reuse
+        downscale_info = None
+        if eff_side > 0 and max(h, w) > eff_side:
+            from hairline.service import _downscale_max_side
+            redraw_img, _rs = _downscale_max_side(image, eff_side)
+            _nh, _nw = redraw_img.shape[:2]
+            if hair_mask_redraw is not None:
+                hair_mask_redraw = cv2.resize(hair_mask_reuse.astype(np.uint8), (_nw, _nh),
+                                              interpolation=cv2.INTER_NEAREST).astype(bool)
+            downscale_info = {"from": f"{w}x{h}", "to": f"{_nw}x{_nh}", "max_side": eff_side}
+
+        textures_map = None
+        from hairline.service import get_texture_map, _FEMALE_KEY_TO_CHANG, load_texture_rgba, build_overlay_layer, load_ext_mesh
+        textures = get_texture_map()["female"]
+        items = [(s, textures[s - 1]) for s in hair_styles]
+
+        for order, (key, white_path) in items:
+            hs_t0 = _time.perf_counter()
+            entry = {"hairline_type": key, "order": order}
+            chang_id = _FEMALE_KEY_TO_CHANG.get(key)
+            entry["chang_id"] = chang_id
+            entry["ok"] = False
+            entry["error"] = None
+            entry["grown_b64"] = None
+            if chang_id is None:
+                entry["error"] = f"无对应 chang_id"
+                timings["per_hairstyle"].append(entry)
+                continue
+            try:
+                # 2a/2b/2c: generate_hairline_redraw (内部含 mask+swap+blend)
+                t0 = _time.perf_counter()
+                data = await run_in_threadpool(
+                    generate_hairline_redraw, redraw_img, chang_id,
+                    hair_mask=hair_mask_redraw, webui_steps=webui_steps, **_V2_FINAL_DEFAULTS)
+                t_redraw_pipeline = _time.perf_counter() - t0
+                _tm = data.get("timings_ms") or {}
+                entry["mask_ms"] = _tm.get("mask", 0)
+                entry["swap_ms"] = _tm.get("swap", 0)
+                entry["blend_ms"] = _tm.get("blend", 0)
+                entry["redraw_pipeline_ms"] = int(t_redraw_pipeline * 1000)
+
+                steps = data.get("steps") or {}
+                final_b64 = steps.get("final_base64") or ""
+                mask_b64 = steps.get("redraw_band_mask_base64") or ""
+                if not final_b64 or not mask_b64:
+                    entry["error"] = f"final/遮罩缺失(final={len(final_b64)} mask={len(mask_b64)})"
+                    timings["per_hairstyle"].append(entry)
+                    continue
+                if final_b64.startswith("data:"):
+                    final_b64 = final_b64.split(",", 1)[1]
+                if mask_b64.startswith("data:"):
+                    mask_b64 = mask_b64.split(",", 1)[1]
+
+                # 3: ComfyUI 重绘
+                t0 = _time.perf_counter()
+                # max_side: 0 或 None 都让 _call_local_redraw 用默认逻辑(外层已控制分辨率)
+                _ms = redraw_max_side if redraw_max_side is not None and redraw_max_side > 0 else None
+                grown_png = await run_in_threadpool(
+                    _call_local_redraw,
+                    base64.b64decode(final_b64), base64.b64decode(mask_b64),
+                    max_side=_ms, prompt=redraw_prompt)
+                entry["comfyui_redraw_ms"] = int((_time.perf_counter() - t0) * 1000)
+                if grown_png:
+                    entry["grown_b64"] = "data:image/jpeg;base64," + _png_to_jpg_b64(grown_png)
+                    entry["ok"] = True
+                else:
+                    entry["error"] = "ComfyUI 重绘返回空"
+            except NoFaceError:
+                entry["error"] = "未检出人脸"
+            except Exception as ex:  # noqa: BLE001
+                entry["error"] = str(ex)[:150]
+            entry["hairstyle_total_ms"] = int((_time.perf_counter() - hs_t0) * 1000)
+            timings["per_hairstyle"].append(entry)
+
+        timings["total_ms"] = int((_time.perf_counter() - t_total0) * 1000)
+        timings["downscale"] = downscale_info
+        timings["image_size"] = f"{w}x{h}"
+        return ok(timings)
     except Exception as ex:  # noqa: BLE001
-        logger.exception("接口7 处理异常")
+        logger.exception("debug/grow-timing 异常")
         return err(1007, f"处理失败：{ex}")
 
 
+# ---------------------------------------------------------------------------
+# 接口 7：C 端生发 v2 —— 已弃用（add_hair2.json 用 Klein-9b 大模型，会把常驻的
+# Klein-4b/Flux 挤出显存，导致接口2/3/5 耗时抖动；且业务已不再调用）。
+# 保留路由返回明确错误，避免老客户端拿到裸 404。
+# ---------------------------------------------------------------------------
+
+@app.post("/api/v1/hair/grow-v2", include_in_schema=False, deprecated=True)
+async def hair_grow_v2():
+    """接口7 已弃用：请改用 /api/v1/hair/grow（接口2）。"""
+    return err(1007, "接口7（/api/v1/hair/grow-v2）已弃用，请使用 /api/v1/hair/grow")
+
+
 # ---------------------------------------------------------------------------
 # 接口 3：B 端生发
 # ---------------------------------------------------------------------------
@@ -917,7 +1031,7 @@ async def hair_grow_b(
     marked_image_url: Optional[str] = Form(default=None, description="划线图片 URL"),
     marked_image_base64: Optional[str] = Form(default=None, description="划线图片 base64"),
     use_mask: bool = Form(default=True, description="是否画发际线（测试对比用）。false 时跳过划线检测、直接送划线图"),
-    prompt: str = Form(default="补充遮罩区域的头发，加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
 ):
     # 划线图三选一取图（只需这一张）
     marked_raw, e = await resolve_image_bytes(marked_image_file, marked_image_url, marked_image_base64)
@@ -1043,11 +1157,14 @@ async def face_features(
 ---
 
 **入参**（同接口2：先选性别，再多选发型）：
-- 必填 `gender`（`male`/`female`），决定发型集合（female 5 / male 4）。
+- 必填 `gender`（`male`/`female`），决定发型集合（female 7 / male 6）。
 - 必填 `hair_style`（发型序号，逗号分隔如 `1,2,3`），决定返回哪些发际线类型。缺失/越界/非法返回 `1007`。
-  `female`：1=ellipse,2=flower,3=heart,4=straight,5=wave；`male`：1=ellipse,2=inverse_arc,3=m,4=straight。
+  `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。
 - 可选 `use_mask` / `prompt`：同接口2 的生发控制参数。
   注：生发黑模板固定取 `hairline_texture_black/`（middle 档），即三档叠图分别用各自贴图、但生发目标固定 middle。
+- 可选 `generate_grow_image`（默认 `true`）：是否生成生发效果图（ComfyUI 生发，全流程最耗时）。
+  `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，大幅降低耗时。
 
 **返回说明**：
 
@@ -1123,15 +1240,16 @@ async def hairline_generate(
     image_url: Optional[str] = Form(default=None, description="图片 URL"),
     image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
     gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
-    hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-5 male:1-4"),
+    hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-7 male:1-6"),
     use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，测试对比用）"),
-    prompt: str = Form(default="补充遮罩区域的头发，加一点美颜", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
+    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
+    generate_grow_image: bool = Form(default=True, description="是否生成生发效果图（ComfyUI 生发，最耗时）。默认 true 出图；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"),
 ):
     if gender not in ("male", "female"):
         return err(1004, "gender 必填且只能为 male / female")
 
     # hair_style 必填（同接口2）：解析逗号分隔，缺失/越界/非法 → 1007
-    max_styles = {"female": 5, "male": 4}[gender]
+    max_styles = {"female": 7, "male": 6}[gender]
     hair_styles = _parse_hair_styles(hair_style, max_styles)
     if hair_styles is None:
         return err(1007, f"hair_style 必填且为 1..{max_styles} 的整数（逗号分隔），收到 {hair_style!r}")
@@ -1149,7 +1267,8 @@ async def hairline_generate(
         from hairline.service import generate_hairline_pngs
 
         res = await run_in_threadpool(
-            generate_hairline_pngs, image, gender, hair_styles, use_mask, prompt)
+            generate_hairline_pngs, image, gender, hair_styles, use_mask, prompt,
+            generate_grow_image=generate_grow_image)
         if res is None:
             return err(1001, "无法识别人像")
 
@@ -1414,13 +1533,11 @@ async def hairline_grow(
     color_match_strength: float = Form(default=1.0, description="颜色迁移强度（0~1，1=全迁移，<1 只迁移部分防过度改色），默认 1.0"),
     mb_feather_px: int = Form(default=1, description="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），默认 1"),
     transition_band_px: int = Form(default=-1, description="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），默认 -1"),
-    redraw: bool = Form(default=False, description="发际线带重绘开关：开启后额外跑一条分支——外推↔发际线带重绘，final输入，swapHair/Flux-2两路对比，结果单独展示（不替换final），默认 False"),
     inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充：0=保留原图(治染绿) | 1=填充噪声(默认/原始) | 2=纯色 | 3=潜变量噪声。默认 1"),
     mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素（原始11，越大颜色越易从边缘渗透），默认 11"),
     mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放（1.0=原始核尺寸，<1收缩防越界），默认 1.0"),
-    comfyui_prompt: Optional[str] = Form(default=None, description="redraw Flux-2路提示词，None用默认「补充遮罩区域的头发，加一点美颜」"),
 ):
-    """接口11：发际线生发 + 分步可视化。遮罩固定 pushed，融合默认 multiband（可选 seamless/two_stage/feather）。"""
+    """接口11：发际线生发 + 分步可视化（**不含重绘**，重绘见接口12）。遮罩固定 pushed，融合默认 multiband（可选 seamless/two_stage/feather）。"""
     raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
     if e is not None:
         return e
@@ -1435,10 +1552,10 @@ async def hairline_grow(
         from uuid import uuid4 as _uuid4
         rid = _uuid4().hex[:8]
         logger.info("[%s] 接口11 收到请求: hairline_push_cm=%s hairline_edge=%s mb_levels=%s "
-                    "blend=%s color_match=%s cm_strength=%s mb_feather_px=%s transition_band_px=%s redraw=%s "
+                    "blend=%s color_match=%s cm_strength=%s mb_feather_px=%s transition_band_px=%s "
                     "inpainting_fill=%s mask_blur=%s mask_dilate_scale=%s",
                     rid, hairline_push_cm, hairline_edge, mb_levels, blend_method,
-                    color_match, color_match_strength, mb_feather_px, transition_band_px, redraw,
+                    color_match, color_match_strength, mb_feather_px, transition_band_px,
                     inpainting_fill, mask_blur, mask_dilate_scale)
         try:
             data = await run_in_threadpool(
@@ -1450,8 +1567,8 @@ async def hairline_grow(
                 hairline_edge=hairline_edge, blend_method=blend_method,
                 color_match=color_match, color_match_strength=color_match_strength,
                 mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
-                redraw=redraw, inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-                mask_dilate_scale=mask_dilate_scale, comfyui_prompt=comfyui_prompt, rid=rid)
+                inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+                mask_dilate_scale=mask_dilate_scale, rid=rid)
         except NoFaceError:
             return err(1001, "无法识别人像")
         except SwapError as se:
@@ -1464,20 +1581,33 @@ async def hairline_grow(
 
 
 # ---------------------------------------------------------------------------
-# 接口 12：发际线生发（接口11 固定参数精简版）
+# 接口 12：发际线带重绘（调用接口11 的 ④final 作输入 + ⑤-①重绘带作遮罩，Flux-2 重绘）
 # ---------------------------------------------------------------------------
 
 @app.post(
     "/api/v1/hairline/grow_v2",
-    summary="接口12 发际线生发（固定 pushed 遮罩 + 多频段融合，仅返回最终图）",
+    summary="接口12 发际线带重绘（接口11 final + 发际线重绘带 → Flux-2 保色重绘）",
     tags=["生发"],
     description=f"""
-接口11 的固定参数精简版，适合生产直调。与接口11 共用同一管线，遮罩固定 pushed（发际线外推），
-融合固定 multiband（多频段金字塔）。本接口固定 `erode_cm=0.6`、`mb_levels=5` 不暴露，
-**只返回 `final_base64`**（最终合成图），不附带分步可视化。
+接口12 是接口11 的**下游重绘阶段**。内部先跑接口11 核心管线拿到 **④ 接缝融合最终图（final）**，
+再取 **⑤-① 发际线重绘带**（发际线沿外推方向 `band_lo_mult×push` ~ `band_hi_mult×push`
+之间、经 ①-a baseline 截断只留上部的带状区域，默认 0.5×~1.5×push）
+作为遮罩，用 **Flux-2（ComfyUI）** 做 reference-latent 保色重绘（不易染绿），重绘结果再与 final 融合。
 
-其余参数（hairline_id、seg_model、gen_backend、is_hr、denoising_strength、edge_erode_px、
-hairline_push_cm、hairline_edge 等）保留为可选 Form，调用方可按需覆盖。
+与接口11 的关系：接口11 只负责生成 final（不再含重绘）；接口12 负责在 final 上做发际线带重绘。
+接口11 的可调参数（seg_model/gen_backend/hairline_push_cm/blend_method/color_match 等）
+在本接口同样暴露，用于内部生成 final 与重绘带；另有 `comfyui_prompt` 控制 Flux-2 提示词。
+
+⚠️ 依赖 ComfyUI（默认 :8188）在跑，否则重绘失败会在 `data.redraw.c_error` 报告。
+
+**同时产出两版结果供对比**：
+- `redraw_full`：ComfyUI 整帧输出（全脸美颜 + 全脸重绘），与手动跑 ComfyUI 一致。
+- `redraw_band`：加发只在发际线带、美颜保留全脸（band 内用 ComfyUI 重绘，band 外 = final 结构
+  + 按 `beauty_alpha` 融入全脸美颜）。
+
+返回 `data.steps`：`input`（原图）/ `final`（接口11 的 ④，重绘输入基底）/
+`redraw_band_overlay`（⑤-① 重绘带可视化）/ `redraw_full`（A 整帧）/ `redraw_band`（B 局部加发+全脸美颜）/
+`redraw_c`（兼容旧字段，=redraw_full）。
 
 {_image_fields_desc}
 """,
@@ -1491,13 +1621,27 @@ async def hairline_grow_v2(
     hairgrow_strength: float = Form(default=0.75, description="区域生发强度（仅 hairgrow 后端），默认 0.75"),
     is_hr: bool = Form(default=False, description="高清模式（换发型输出 1152×1536，否则 576×768）"),
     seg_model: str = Form(default="segformer", description="头发分割模型：bisenet | segformer（默认 segformer）"),
+    erode_cm: float = Form(default=0.6, description="baseline 参考内缩距离（厘米），默认 0.6"),
     swap_mode: str = Form(default="ext_mask", description="换发型取图模式：ext_mask | as_is（默认 ext_mask）"),
     edge_erode_px: int = Form(default=3, description="贴图前遮罩内缩像素（防边缘露皮/光晕），默认 3"),
     denoising_strength: float = Form(default=0.6, description="换发型 webui 重绘强度（越大生发越激进），默认 0.6"),
+    mb_levels: int = Form(default=5, description="多频段金字塔层数（2~6），默认 5"),
     hairline_push_cm: float = Form(default=1.0, description="发际线外推距离（厘米），默认 1.0"),
     hairline_edge: str = Form(default="column", description="发际线提取方式：column | contour，默认 column"),
+    blend_method: str = Form(default="multiband", description="接缝融合方法：multiband | seamless | two_stage | feather | alpha_gradient"),
+    color_match: bool = Form(default=True, description="融合前 Reinhard 颜色迁移消除整体色差，默认 True"),
+    color_match_strength: float = Form(default=1.0, description="颜色迁移强度（0~1），默认 1.0"),
+    mb_feather_px: int = Form(default=1, description="多频段最细层掩码轻羽化像素，默认 1"),
+    transition_band_px: int = Form(default=-1, description="keep-region 过渡带边距（-1=自动），默认 -1"),
+    inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充：0=保留原图 | 1=噪声 | 2=纯色 | 3=潜变量。默认 1"),
+    mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素，默认 11"),
+    mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放，默认 1.0"),
+    comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，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」"),
+    beauty_alpha: float = Form(default=0.6, description="redraw_band 版 band 外的全脸美颜融入强度（0=band外无美颜纯用final，1≈整帧版），默认 0.6"),
+    band_lo_mult: float = Form(default=0.5, description="重绘带外推倍率下限（相对 hairline_push_cm，内轮廓=0×、原外推线=1.0×），默认 0.5"),
+    band_hi_mult: float = Form(default=1.5, description="重绘带外推倍率上限（相对 hairline_push_cm），默认 1.5"),
 ):
-    """接口12：发际线生发（固定 pushed 遮罩 + multiband 融合，仅返回最终图）。"""
+    """接口12：发际线带重绘。同时产出 redraw_full(整帧美颜) 与 redraw_band(局部加发+全脸美颜) 两版对比。"""
     raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
     if e is not None:
         return e
@@ -1508,43 +1652,190 @@ async def hairline_grow_v2(
 
     try:
         from fastapi.concurrency import run_in_threadpool
-        from face_analysis.hairline_grow import generate_hairline_grow, NoFaceError, SwapError
+        from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError, SwapError
+        from uuid import uuid4 as _uuid4
+        rid = _uuid4().hex[:8]
+        logger.info("[%s] 接口12 收到请求: hairline_id=%s hairline_push_cm=%s blend=%s comfyui_prompt=%r",
+                    rid, hairline_id, hairline_push_cm, blend_method, comfyui_prompt)
         try:
-            # 固定：mask_type=pushed、blend_method=multiband、erode_cm=0.6、mb_levels=5
             data = await run_in_threadpool(
-                generate_hairline_grow, image, hairline_id,
-                is_hr=is_hr, seg_model=seg_model, erode_cm=0.6, swap_mode=swap_mode,
+                generate_hairline_redraw, image, hairline_id,
+                is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm, swap_mode=swap_mode,
                 edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
                 gen_backend=gen_backend, hairgrow_strength=hairgrow_strength,
-                mb_levels=5, hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge)
+                mb_levels=mb_levels, hairline_push_cm=hairline_push_cm,
+                hairline_edge=hairline_edge, blend_method=blend_method,
+                color_match=color_match, color_match_strength=color_match_strength,
+                mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
+                inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+                mask_dilate_scale=mask_dilate_scale, comfyui_prompt=comfyui_prompt,
+                beauty_alpha=beauty_alpha, band_lo_mult=band_lo_mult,
+                band_hi_mult=band_hi_mult, rid=rid)
         except NoFaceError:
             return err(1001, "无法识别人像")
         except SwapError as se:
             return err(1007, f"换发型失败：{se}")
-        # 精简返回：只取最终合成图，丢掉接口11 的全部分步可视化
-        return ok({
-            "hairline_id": data["hairline_id"],
-            "image_size": data["image_size"],
-            "final_base64": data["steps"]["final_base64"],
-        })
+        logger.info("[%s] 接口12 成功返回", rid)
+        return ok(data)
     except Exception as ex:  # noqa: BLE001
         logger.exception("接口12 处理异常")
         return err(1007, f"处理失败：{ex}")
 
 
+# ---------------------------------------------------------------------------
+# 接口 12 final：精简版发际线带重绘（仅需图片 + 发型 ID，其余参数全用默认值）
+# ---------------------------------------------------------------------------
+
+# 接口12 final 固化的默认参数（= test_interface12.html 当前默认值，color_match 关闭）
+_V2_FINAL_DEFAULTS = dict(
+    gen_backend="swaphair", hairgrow_strength=0.75, is_hr=False, seg_model="segformer",
+    erode_cm=0.6, swap_mode="ext_mask", edge_erode_px=3, denoising_strength=0.6,
+    mb_levels=5, hairline_push_cm=0.8, hairline_edge="column", blend_method="two_stage",
+    color_match=False, color_match_strength=0.4, mb_feather_px=1, transition_band_px=-1,
+    inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0, comfyui_prompt=None,
+    beauty_alpha=0.6, band_lo_mult=0.5, band_hi_mult=1.5,
+)
+
+
+async def _run_v2_final(image_file, image_url, image_base64, hairline_id, tag):
+    """接口12 final / final v2 共用：仅需图片 + hairline_id，其余用固化默认值。
+    两者后端计算完全一致（同一次 ComfyUI 输出同时含 A 整帧与 B 局部+美颜），
+    差异仅在配套测试页展示哪一版。"""
+    raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
+    if e is not None:
+        return e
+
+    image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR)
+    if image is None:
+        return err(1008, "图片格式不支持（仅 JPG / PNG）")
+
+    try:
+        from fastapi.concurrency import run_in_threadpool
+        from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError, SwapError
+        from uuid import uuid4 as _uuid4
+        rid = _uuid4().hex[:8]
+        logger.info("[%s] %s 收到请求: hairline_id=%s（其余用默认值）", rid, tag, hairline_id)
+        try:
+            data = await run_in_threadpool(
+                generate_hairline_redraw, image, hairline_id,
+                rid=rid, **_V2_FINAL_DEFAULTS)
+        except NoFaceError:
+            return err(1001, "无法识别人像")
+        except SwapError as se:
+            return err(1007, f"换发型失败：{se}")
+        logger.info("[%s] %s 成功返回", rid, tag)
+        return ok(data)
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("%s 处理异常", tag)
+        return err(1007, f"处理失败：{ex}")
+
+
+@app.post(
+    "/api/v1/hairline/grow_v2_final",
+    summary="接口12 final 精简重绘（整帧重绘；仅图片 + 发型 ID）",
+    tags=["生发"],
+    description=f"""
+接口12 的**精简/生产版**：只需上传图片 + 选择 `hairline_id`，其余所有参数固化为当前调优默认值
+（hairline_push_cm=0.8 / blend_method=two_stage / **color_match=关闭** / color_match_strength=0.4 /
+beauty_alpha=0.6 / band_lo_mult=0.5 / band_hi_mult=1.5 等）。
+
+**最终重绘取整帧重绘**（`data.steps.redraw_full`，全脸美颜 + 全脸重绘）。
+返回结构与接口12 一致（`data.steps` 同时含 `redraw_full`（A 整帧）/ `redraw_band`（B 局部+美颜））。
+
+⚠️ 依赖 ComfyUI（默认 :8188）在跑。
+
+{_image_fields_desc}
+""",
+)
+async def hairline_grow_v2_final(
+    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 data:image/...;base64, 前缀）"),
+    hairline_id: str = Form(..., description="发际线类型 ID（= change_hair hair_id，如 chang_bolang）"),
+):
+    """接口12 final：整帧重绘版，仅需图片 + hairline_id。"""
+    return await _run_v2_final(image_file, image_url, image_base64, hairline_id, "接口12final")
+
+
+@app.post(
+    "/api/v1/hairline/grow_v2_final_v2",
+    summary="接口12 final v2 精简重绘（B 局部加发+全脸美颜；仅图片 + 发型 ID）",
+    tags=["生发"],
+    description=f"""
+接口12 final 的**局部加发版**：参数与 `grow_v2_final` 完全相同（color_match 关闭等），
+唯一区别是**最终重绘取 B 局部加发+全脸美颜**（`data.steps.redraw_band`：
+加发只在发际线带内、band 外保留 final 结构并按 beauty_alpha 融入全脸美颜）。
+
+返回结构与接口12 一致（`data.steps` 同时含 `redraw_full`（A 整帧）/ `redraw_band`（B 局部+美颜））。
+
+⚠️ 依赖 ComfyUI（默认 :8188）在跑。
+
+{_image_fields_desc}
+""",
+)
+async def hairline_grow_v2_final_v2(
+    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 data:image/...;base64, 前缀）"),
+    hairline_id: str = Form(..., description="发际线类型 ID（= change_hair hair_id，如 chang_bolang）"),
+):
+    """接口12 final v2：B 局部加发+全脸美颜版，仅需图片 + hairline_id。"""
+    return await _run_v2_final(image_file, image_url, image_base64, hairline_id, "接口12finalv2")
+
+
+# ---------------------------------------------------------------------------
+# 重绘端点（替代 local_test /api/generate）
+# ---------------------------------------------------------------------------
+
+@app.post(
+    "/api/v1/redraw",
+    summary="ComfyUI 重绘",
+    tags=["重绘"],
+    description="""
+传入人物图片 + 遮罩图片，直接调 ComfyUI（0716add-hair 工作流）执行局部重绘。
+替代原 local_test :8899 的 /api/generate 接口。
+
+**遮罩图片格式**：支持红色遮罩(R=255)、白色遮罩(R=G=B=255)、Alpha遮罩(A=255)，服务取所有通道最大值。
+**遮罩区域**表示需要重绘的部分，非遮罩区域保持原图不变。
+""",
+)
+async def api_redraw(
+    image_file: UploadFile = File(..., description="人物图片（JPG/PNG）"),
+    mask_file: UploadFile = File(..., description="遮罩图片（PNG，支持红/白/alpha 格式）"),
+    prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
+                       description="ComfyUI 提示词"),
+):
+    image_bytes = await image_file.read()
+    mask_bytes = await mask_file.read()
+    from fastapi.concurrency import run_in_threadpool
+    from hairline.redraw import run_redraw
+    try:
+        png_bytes = await run_in_threadpool(
+            run_redraw, image_bytes, mask_bytes, prompt)
+    except Exception as e:  # noqa: BLE001
+        logger.warning("重绘失败: %s", e)
+        return err(500, f"重绘失败: {e}")
+    b64 = base64.b64encode(png_bytes).decode()
+    return ok({"image_base64": f"data:image/png;base64,{b64}"})
+
+
 # ---------------------------------------------------------------------------
 # 调试：下载后端日志（接口11 遮罩计算全过程）
 # ---------------------------------------------------------------------------
 
 @app.get("/api/v1/debug/hairline_log", include_in_schema=False)
 async def download_hairline_log(rid: Optional[str] = None, tail: int = 500):
-    """返回 /home/xsl/hair/log/hairline_grow.log 的内容。
+    """返回 <仓库根>/log/hairline_grow.log 的内容。
 
     rid 非空时只返回该 request id 相关的行；tail 限制返回最后 N 行（默认 500）。
     供调试页"下载日志"按钮调用。
     """
     from fastapi.responses import PlainTextResponse
-    log_path = "/home/xsl/hair/log/hairline_grow.log"
+    log_path = os.getenv(
+        "HAIR_LOG_DIR",
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "log"),
+    )
+    log_path = os.path.join(log_path, "hairline_grow.log")
     try:
         with open(log_path, encoding="utf-8") as fh:
             lines = fh.readlines()

comfyui.service

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

docs/iface11_hair_test_run/response_meta.json

@@ -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": ""
+    }
+  }
+}

docs/iface3_implementation.md

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

docs/实现说明.md

@@ -68,7 +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）。

docs/接口11_hair_test运行记录.md

@@ -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
+```

docs/接口文档.md

@@ -20,7 +20,6 @@
 | 3 B 端生发 | POST | `/api/v1/hair/grow-b` |
 | 4 用户特征 | POST | `/api/v1/face/features` |
 | 5 发际线 PNG 生成 | POST | `/api/v1/hairline/generate` |
-| 7 C 端生发 v2 | POST | `/api/v1/hair/grow-v2` |
 
 ---
 
@@ -87,6 +86,7 @@
 | 1006 | 文件超出大小限制 | 单文件超过 1 MB |
 | 1007 | 图片参数错误 | file / url / base64 未传，或同时传了多个（三者严格互斥） |
 | 1008 | 图片格式不支持 | 非 JPG / PNG |
+| 1009 | 未授权 | 缺少或错误的 `X-Internal-Token`（`/api/*` 路径鉴权） |
 
 ---
 
@@ -109,6 +109,8 @@
 | four_courts | object | 四庭数据，见下表 |
 | seven_eyes | object | 七眼数据，见下表 |
 | landmarks | object | 关键分界点坐标（头顶 / 发际线 / 眉心 / 鼻翼下缘 / 下巴尖），原图像素坐标 |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 `four_courts`（四庭，自上而下）：
 
@@ -211,6 +213,8 @@
 | four_courts | object | 三庭数据（上/中/下庭，各含 cm 与 ratio；**无顶庭**） |
 | seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + **eye2~eye6** 共 5 段宽度） |
 | landmarks | object | 四个关键点像素坐标（发际线/眉心/鼻翼下缘/下巴尖） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 > 接口6 是**三庭五眼**：`four_courts`/`landmarks` 不含顶庭与头顶点（无 `top_court_cm`/`hair_top`）；`seven_eyes` 只含 **eye2~eye6**（左脸颊/左眼/两眼间距/右眼/右脸颊，5 段），**无 eye1/eye7**（耳外段需头发轮廓端线，仅接口1 有）。
 
@@ -261,7 +265,7 @@
 | 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 |
@@ -274,7 +278,7 @@
 |------|------|------|
 | image_url | string | 发际线曲线**透明 PNG** URL（仅白色发际线曲线，透明底，**不含人物**，需前端叠加原图显示） |
 | grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图，完整人像照片） |
-| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
+| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`（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**。
@@ -401,10 +405,11 @@
 
 | 参数 | 类型 | 必填 | 说明 |
 |------|------|------|------|
-| gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 5 / male 4）。缺失/非法返回 `1004` |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），决定返回哪些发际线类型。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。缺失/越界/非法返回 `1007` |
+| gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 7 / male 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。
 
@@ -422,11 +427,11 @@
 
 | 字段 | 类型 | 说明 |
 |------|------|------|
-| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
+| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`（male） |
 | image_middle_url | string | middle 档发际线曲线**透明 PNG** URL（仅曲线，透明底，**不含人物**，需叠加原图显示） |
 | image_high_url | string | high 档发际线曲线**透明 PNG** URL（同上，high 档曲线） |
 | image_low_url | string | low 档发际线曲线**透明 PNG** URL（同上，low 档曲线） |
-| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，完整人像照片，生发失败时为 `null`） |
+| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，完整人像照片，生发失败或 `generate_grow_image=false` 时为 `null`） |
 | order | int | 发型序号（= 传入的 hair_style 值） |
 
 > worker 侧返回 `image_middle_base64` / `image_high_base64` / `image_low_base64` / `grown_image_base64`，网关落盘后改写为上表对应的 `*_url`。
@@ -443,6 +448,8 @@
 | 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。
 
@@ -508,60 +515,6 @@
 
 ---
 
-## 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） |
-| 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）
-
-与接口 2 完全相同。`results`：发际线方案数组，**数量 = 所选发型数**。每个元素：
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| image_url | string | 发际线曲线**透明 PNG** URL（仅曲线，透明底，**不含人物**，需叠加原图显示） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图，完整人像照片） |
-| hairline_type | string | 发际线类型 key |
-| order | int | 排序序号 |
-
-> ⚠️ 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",
-        "order": 1
-      }
-    ]
-  }
-}
-```
-
----
-
 ## 汇总：输入输出一览
 
 | 接口 | 输入 | 主要输出 |
@@ -572,7 +525,6 @@
 | 3 B 端生发 | 划线图片 | 最合适发际线图片 + 生发后图片 |
 | 4 用户特征 | 用户照片 | 6 个用户特征字段（脸形/眉形/年龄/动静/性别/基因风格） |
 | 5 发际线 PNG | 用户照片 + gender + hair_style（多选） | 每个选中发型 middle/high/low 三档发际线叠图 + 生发图 + 最合适发际线面部中间点坐标 |
-| 7 C 端生发 v2 | 用户照片 + gender + hair_style | 同接口2，使用 add_hair2.json 工作流 |
 
 ---
 

face_analysis/annotation.py

@@ -6,9 +6,9 @@
 - 纵向竖线 8 条：人头最左 + 左脸颊/左眼外/内角/右眼内/外角/右脸颊 + 人头最右，
   把头宽切 7 段（七眼），段宽数值上下交替（上 3 / 下 4），带虚线双箭头。
   人头最左/最右取自耳朵分割外缘，看不到耳朵则省略该侧（最少 6 点 5 段）。
-- 四庭：图片左侧，「名」上「数值」下两行换行（不带 cm），带竖向虚线双箭头。
+- 四庭：图片左侧，「名」「数值(带 cm)」「百分比」三行换行，带竖向虚线双箭头。
 - 五条横线右侧标名：头顶/发际线/眉心/鼻翼下缘/下巴尖。
-- 单位 cm 统一标在底部「单位cm」。
+- 每段数值直接带 cm 后缀，下方另起一行标百分比（不再单独标底部「单位cm」）。
 中文字体用打包的思源黑体绝对路径加载，缺字体直接抛错（不静默降级成方块）。
 """
 import os
@@ -189,9 +189,8 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
       取自耳朵分割掩膜的外缘（方案 B，BiSeNet 类 7/8）；耳朵不可见（被头发/侧脸
       遮挡 → 掩膜空）或无掩膜时省略该侧端线，只画对应脸颊线。
     - 横向 5 条分界线：头顶/发际线/眉心/鼻翼下缘/下巴尖，右侧标名。
-    - 四庭（顶/上/中/下庭）在左侧：名 + 数值两行换行（无 cm），竖向虚线双箭头。
-    - 七眼段宽数值上下交替（上 3 / 下 4，无 cm），横向虚线双箭头。
-    - 底部统一标「单位cm」。
+    - 四庭（顶/上/中/下庭）在左侧：名 + 数值(带 cm) + 百分比三行换行，竖向虚线双箭头。
+    - 七眼段宽上下交替（上 3 / 下 4）：数值(带 cm) 上、百分比（占头宽比）下，横向虚线双箭头。
 
     variant="v6"（接口6）：去掉头顶横线与顶庭（只画发际线/眉心/鼻翼下缘/下巴尖 4 条
     横线 + 上/中/下庭），竖线纵向范围改为发际线→下巴尖，且不画人头最左/最右端线
@@ -203,7 +202,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     # --- 自适应尺寸：字号/线宽/虚线/箭头按短边缩放 ---
     s = min(w, h)
-    font_size = max(11, round(s * 0.026))     # 字体更小
+    font_size = max(9, round(s * 0.020))      # 字号上调一档
     line_w = max(1, round(s * 0.0022))
     dash_len = max(4, round(s * 0.008))
     gap_len = max(2, round(dash_len * 0.7))   # 虚线更稠密（间隙<划线）
@@ -213,7 +212,11 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
 
     buf = np.zeros((h, w, 4), dtype=np.uint8)
 
-    if variant == "v6":
+    # 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"]
@@ -240,7 +243,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     face_cx = (fx0 + fx1) / 2
     over = max(6, round(s * 0.030))           # 线超出包围盒的长度（参考图风格）
     face_half = (fx1 - fx0) / 2 + over        # 横线超出最外侧竖线一点
-    # v6 竖线纵向范围 = 发际线→下巴尖（不超出）；v1 = 头顶→下巴尖并两端超出一点
+    # 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1（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
 
@@ -256,70 +259,97 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     draw = ImageDraw.Draw(canvas)
     font = _load_font(font_size)
 
-    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字在线上方 ---
-    name_x = fx1 + pad
-    name_gap = max(2, round(pad * 1.6))       # 文字底部到线的间距（再上移）
+    # --- 2b. 每条横线在「中间线段」（两内眼角之间）中点画原点突出 ---
+    # 注意：原点不在整条线的中点 face_cx，而在被竖线切出的中间段（左内眼角↔右内眼角）
+    # 的正中，即脸的竖直中轴附近、两内眼角连线中点。
+    li_x = pts["left_inner"][0]
+    ri_x = pts["right_inner"][0]
+    dot_cx = (li_x + ri_x) / 2
+    dot_r = max(2, round(s * 0.0045))   # 原点半径，与 arrow_size 同档自适应
+    for cy in ys:
+        x0, y0 = dot_cx - dot_r, cy - dot_r
+        x1, y1 = dot_cx + dot_r, cy + dot_r
+        draw.ellipse((x0, y0, x1, y1), fill=LINE_COLOR)
+
+    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字纵向居中对齐到线 ---
+    name_x = fx1 + over + pad                  # 移到横线右端外侧一点（往右）
     for i, name in enumerate(order):
         text = _LINE_NAMES[name]
-        tw, th = _text_size(draw, text, font)
+        tw, _ = _text_size(draw, text, font)
         x = min(name_x, w - 2 - tw)           # 右侧越界时回收
-        draw.text((x, max(2, ys[i] - th - name_gap)), text, fill=LINE_COLOR, font=font)
+        # anchor="lm"：x 为左、y 为竖直中点 → 文字中线正好压在横线上（与线对齐）
+        draw.text((x, ys[i]), text, fill=LINE_COLOR, font=font, anchor="lm")
 
     # --- 3b. 左侧四庭：名 + 数值两行（无 cm）+ 竖向虚线双箭头 ---
-    if variant == "v6":
+    # 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）
+    court_total = sum(court_cm) or 1.0          # 各庭占比分母 = 四庭（v6 三庭）之和
     for i in range(n_court):
-        y_a, y_b = ys[i], ys[i + 1]
+        y_a, y_b = ys[court_start + i], ys[court_start + i + 1]
         # 竖向虚线双箭头，覆盖该庭高度（略收一点避免压到横线）
         inset = min(arrow_size, (y_b - y_a) * 0.12)
         draw_dashed_line_with_arrows(
             draw, arrow_x, y_a + inset, arrow_x, y_b - inset,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        # 名 + 数值两行，右对齐到箭头左侧
+        # 名 + 数值(带 cm) + 百分比三行，右对齐到箭头左侧
         name = court_name[i]
-        val = f"{court_cm[i]:.2f}"
+        val = f"{court_cm[i]:.2f}cm"
+        pct = f"{court_cm[i] / court_total * 100:.1f}%"
         nw, _ = _text_size(draw, name, font)
         vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         label_right = arrow_x - pad
         y_mid = (y_a + y_b) / 2
-        y_top = y_mid - line_h
+        y_top = y_mid - 1.5 * line_h
         draw.text((max(2, label_right - nw), y_top), name, fill=LINE_COLOR, font=font)
         draw.text((max(2, label_right - vw), y_top + line_h), val, fill=LINE_COLOR, font=font)
+        draw.text((max(2, label_right - pw), y_top + 2 * line_h), pct, fill=LINE_COLOR, font=font)
 
-    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值（无 cm） ---
-    # 文字与箭头间留更大间距，避免文字压住箭头
+    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值(带 cm) + 百分比 ---
+    # 每段两行：数值(带 cm) 上、百分比 下；百分比分母 = 整个头宽（七段之和）
     txt_off = arrow_size + pad * 2
-    y_arrow_top = max(txt_off + font_size + 2, fy0 - pad - arrow_size)
-    y_arrow_bot = min(h - txt_off - font_size - 2, fy1 + pad + arrow_size)
+    txt_block = 2 * line_h                     # 两行文字总高（数值 + 百分比）
+    y_arrow_top = max(txt_off + txt_block + 2, fy0 - pad - arrow_size)
+    y_arrow_bot = min(h - txt_off - txt_block - 2, fy1 + pad + arrow_size)
+    head_w = (xs[-1] - xs[0]) or 1.0           # 头宽（像素）= 百分比分母
     for i in range(len(xs) - 1):
         x_a, x_b = xs[i], xs[i + 1]
         if x_b - x_a < 1:
             continue
         seg_cm = (x_b - x_a) / pc
+        seg_pct = (x_b - x_a) / head_w * 100
         cx_seg = (x_a + x_b) / 2
-        text = f"{seg_cm:.2f}"
-        tw, th = _text_size(draw, text, font)
+        val = f"{seg_cm:.2f}cm"
+        pct = f"{seg_pct:.1f}%"
+        vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         inset = min(arrow_size, (x_b - x_a) * 0.12)
         on_top = (i % 2 == 1)                 # 奇数段在上 → 上 3 / 下 4
         y_arrow = y_arrow_top if on_top else y_arrow_bot
         draw_dashed_line_with_arrows(
             draw, x_a + inset, y_arrow, x_b - inset, y_arrow,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        ty = (y_arrow - th - txt_off) if on_top else (y_arrow + txt_off)
-        draw.text((cx_seg - tw / 2, ty), text, fill=LINE_COLOR, font=font)
-
-    # --- 5. 底部统一单位 ---
-    unit = "单位cm"
-    uw, uh = _text_size(draw, unit, font)
-    draw.text(((w - uw) / 2, h - uh - max(2, pad)), unit, fill=LINE_COLOR, font=font)
+        # 数值行在上、百分比行在下；on_top 时整块置于箭头上方，否则下方
+        text_top = (y_arrow - txt_off - txt_block) if on_top else (y_arrow + txt_off)
+        draw.text((cx_seg - vw / 2, text_top), val, fill=LINE_COLOR, font=font)
+        draw.text((cx_seg - pw / 2, text_top + line_h), pct, fill=LINE_COLOR, font=font)
 
     return canvas
 

face_analysis/face_mesh_landmarks.py

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

face_analysis/hairline_grow.py

@@ -47,8 +47,11 @@ from face_analysis.head_mask import (
     _draw_baseline,
 )
 
-# 调试日志：写 /home/xsl/hair/log/hairline_grow.log，每个步骤详细记录
-_LOG_DIR = "/home/xsl/hair/log"
+# 调试日志：写 <仓库根>/log/hairline_grow.log，每个步骤详细记录（可用 HAIR_LOG_DIR 覆盖）
+_LOG_DIR = os.getenv(
+    "HAIR_LOG_DIR",
+    os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "log"),
+)
 os.makedirs(_LOG_DIR, exist_ok=True)
 logger = logging.getLogger("hairline_grow")
 _log_fh = logging.FileHandler(os.path.join(_LOG_DIR, "hairline_grow.log"), encoding="utf-8")
@@ -99,11 +102,26 @@ def _png_b64(bgr_or_gray):
 
 
 def _gray_b64(gray_float):
-    """0~1 的浮点图 → 灰度 PNG data URI。"""
+    """0~1 的浮字图 → 灰度 PNG data URI。"""
     g = np.clip(gray_float * 255.0, 0, 255).astype(np.uint8)
     return _png_b64(g)
 
 
+def _red_mask_b64(mask_bool, h, w):
+    """布尔遮罩 → 纯红 alpha PNG data URI。
+    遮罩区域 RGBA=(255,0,0,255)，其余区域 RGBA=(0,0,0,0)。
+    供 ComfyUI 重绘接口（/api/v1/redraw）按 alpha 通道识别重绘区。
+
+    注意：cv2.imencode 写 PNG 用的是 **BGRA** 顺序（B,G,R,A），所以要得到
+    浏览器显示的红色 R=255，需赋值 (B=0,G=0,R=255,A=255)。
+    """
+    m = (mask_bool.astype(np.uint8)) * 255 if mask_bool is not None else np.zeros((h, w), np.uint8)
+    rgba = np.zeros((h, w, 4), np.uint8)
+    rgba[m > 0] = (0, 0, 255, 255)            # BGRA: B=0,G=0,R=255 → PNG 读出为红色 + 不透明
+    ok, buf = cv2.imencode(".png", rgba)
+    return "data:image/png;base64," + base64.b64encode(buf.tobytes()).decode() if ok else ""
+
+
 # ---------------------------------------------------------------------------
 # 步骤1：接口9 头发遮罩（复用 head_mask 构件）
 # ---------------------------------------------------------------------------
@@ -299,31 +317,48 @@ def _pushed_mask(hair_mask, upper, baseline_pts, push_px, rid="",
 
 
 
-def _redraw_band_mask(inner_pts, outer_pts, h, w, rid=""):
-    """重绘带遮罩：把发际线（①-f 内轮廓 inner_pts）和外推发际线（①-g outer_pts）
-    两条折线的端点连接成闭合多边形，填充得到带状区域，作为重绘 mask。
+def _redraw_band_mask(inner_pts, outer_pts, h, w, rid="", upper=None,
+                      lo_mult=0.5, hi_mult=1.5):
+    """重绘带遮罩：由发际线（①-f 内轮廓 inner_pts）沿径向外推方向，取
+    `lo_mult × push` 与 `hi_mult × push` 两条外推线之间的带状区域作为重绘 mask。
 
-    inner_pts / outer_pts 是一一对应的有序点列（outer = inner 径向外推 push_px），
-    故闭合环 = inner_pts（正向）+ outer_pts（反向）首尾相接。带只覆盖「现有头发下沿
-    到外推线」这一段（在头发一侧），正好是发际线交界处需要重绘融合的窄带。
+    inner_pts / outer_pts 是一一对应的有序点列（outer = inner 径向外推 1.0×push_px），
+    故每点的 1.0× 位移向量 = outer - inner；下界线 = inner + lo_mult×位移，
+    上界线 = inner + hi_mult×位移。闭合环 = 下界线（正向）+ 上界线（反向）首尾相接。
+
+    lo_mult / hi_mult：外推倍率（相对 hairline_push_cm）。默认 0.5 / 1.5，即带位于
+    0.5×push ~ 1.5×push 之间（以内轮廓为 0×、原外推线为 1.0×）。
+
+    upper：①-a baseline 以上区域布尔掩码。传入时把重绘带与它求交集，只保留 baseline
+    以上的部分（两侧鬓角落到 baseline 以下的段会被截掉）。
 
     返回 band_bool。
     """
     lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
     if len(inner_pts) < 2 or len(outer_pts) < 2:
         return np.zeros((h, w), dtype=bool)
-    # 闭合多边形：内轮廓正向 + 外推线反向，端点自然相连
-    ring = np.vstack([inner_pts.astype(np.int32), outer_pts[::-1].astype(np.int32)])
+    inner_f = np.asarray(inner_pts, dtype=np.float32)
+    outer_f = np.asarray(outer_pts, dtype=np.float32)
+    disp = outer_f - inner_f                       # 每点 1.0×push 的径向位移向量
+    lo_line = inner_f + float(lo_mult) * disp       # 下界外推线（lo_mult×push）
+    hi_line = inner_f + float(hi_mult) * disp       # 上界外推线（hi_mult×push）
+    # 闭合多边形：下界线正向 + 上界线反向，端点自然相连
+    ring = np.vstack([lo_line.astype(np.int32), hi_line[::-1].astype(np.int32)])
     band_u8 = np.zeros((h, w), dtype=np.uint8)
     cv2.fillPoly(band_u8, [ring], 255)
     band = band_u8 > 0
-    lg(f"_redraw_band_mask: 内轮廓点={len(inner_pts)} 外推点={len(outer_pts)} "
-      f"band像素={int(band.sum())}")
+    raw_px = int(band.sum())
+    # ①-a baseline 截断：只保留 baseline 以上的重绘带
+    if upper is not None:
+        band = band & upper
+    lg(f"_redraw_band_mask: 内轮廓点={len(inner_pts)} lo_mult={lo_mult} hi_mult={hi_mult} "
+      f"band像素(截断前)={raw_px} band像素(截断后)={int(band.sum())}")
     return band
 
 
 def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-                 hairline_push_cm=0.0, hairline_edge="column", rid=""):
+                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True,
+                 hair_mask=None):
     """算出布尔遮罩 + 可视化。
 
     seg_model: bisenet | segformer。
@@ -331,6 +366,10 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     hairline_push_cm: 仅 pushed 模式——发际线往头发方向外推的厘米数（进入现有头发）。
     hairline_edge: 仅 pushed 模式——发际线提取方式 column(逐列最低点) | contour(形态学轮廓)。
     rid: 调用方的 request id，用于日志关联。
+    render_viz: 是否生成各阶段叠图 overlay JPG（接口11 调试页用）。接口2/12 路径传 False
+        可跳过 6+ 张 base64 编码，省 ~80ms；数据字段（_inner_pts/_outer_pts/_upper_mask/
+        mask_pixels 等）始终返回，不受影响。
+    hair_mask: 预计算的头发布尔遮罩（来自 SegFormer parse）。传入时跳过重复分割，省 ~0.9s。
     返回 (mask_bool, viz_dict)。
     """
     lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
@@ -344,13 +383,16 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     upper = _upper_region_mask(baseline_pts, w, h)
     lg(f"baseline 第一点={baseline_pts[0]} 末点={baseline_pts[-1]} upper像素={int(upper.sum())}")
 
-    if seg_model == "bisenet":
-        hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
-    elif seg_model == "segformer":
-        hair_mask = _segformer_hair_mask(image_bgr)
+    if hair_mask is None:
+        if seg_model == "bisenet":
+            hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
+        elif seg_model == "segformer":
+            hair_mask = _segformer_hair_mask(image_bgr)
+        else:
+            raise ValueError(f"未知 seg_model: {seg_model}")
+        lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
     else:
-        raise ValueError(f"未知 seg_model: {seg_model}")
-    lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
+        lg(f"头发分割跳过(复用外部传入) hair_pixels={int(hair_mask.sum())}")
 
     top_fill = _fill_to_baseline(hair_mask, upper)      # 含额头，延伸到图底
     closed = _largest_cc(top_fill & upper)              # 闭合区域：头发+额头，底=基线
@@ -391,51 +433,55 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     # 遮罩计算过程可视化：
     #   eroded/closed 走 top_fill→closed/eroded 流程；
     #   pushed 走 baseline→头发分割→发际线→外推 流程，与 top_fill/closed 无关，故置空。
+    # render_viz=False（接口2/12 路径）时跳过 overlay JPG 编码，只保留数据字段。
     viz = {
         "erode_px": r,
         "hair_pixels": int(hair_mask.sum()),
         "closed_pixels": int(closed.sum()),
         "mask_pixels": int(mask_bool.sum()),
         # 1. 发际线分割线（baseline）：151 中心点标红，其余点标绿，黄线含左右延长线
-        "baseline_overlay_base64": _jpg_b64(_draw_baseline(image_bgr, baseline_pts, w)),
+        "baseline_overlay_base64": _jpg_b64(_draw_baseline(image_bgr, baseline_pts, w)) if render_viz else "",
         # 2. 分割线以上区域（upper 半区）：青色叠加
-        "upper_overlay_base64": _jpg_b64(_overlay(image_bgr, upper, (0, 255, 255))),
+        "upper_overlay_base64": _jpg_b64(_overlay(image_bgr, upper, (0, 255, 255))) if render_viz else "",
         # 3. 头发分割原始结果（hair_mask）：绿色叠加在原图上
-        "hair_seg_overlay_base64": _jpg_b64(_overlay(image_bgr, hair_mask, (0, 255, 0))),
+        "hair_seg_overlay_base64": _jpg_b64(_overlay(image_bgr, hair_mask, (0, 255, 0))) if render_viz else "",
         # 4. top_fill / closed —— 仅 eroded/closed 流程用；pushed 流程无关，留空
-        "top_fill_overlay_base64": "" if mask_type == "pushed"
+        "top_fill_overlay_base64": "" if (mask_type == "pushed" or not render_viz)
             else _jpg_b64(_overlay(image_bgr, top_fill, (255, 0, 0))),
-        "closed_overlay_base64": "" if mask_type == "pushed"
+        "closed_overlay_base64": "" if (mask_type == "pushed" or not render_viz)
             else _jpg_b64(_overlay(image_bgr, closed, (255, 0, 255))),
         # 5. pushed 模式专有（发际线提取/外推）—— 非 pushed 留空
         "hairline_overlay_base64": "",
         "pushed_overlay_base64": "",
         # —— 最终遮罩 ——
-        "mask_overlay_base64": _jpg_b64(_overlay(image_bgr, mask_bool, (0, 0, 255))),
-        "mask_base64": _png_b64((mask_bool.astype(np.uint8)) * 255),
+        "mask_overlay_base64": _jpg_b64(_overlay(image_bgr, mask_bool, (0, 0, 255))) if render_viz else "",
+        "mask_base64": _png_b64((mask_bool.astype(np.uint8)) * 255) if render_viz else "",
     }
     # pushed 模式：补充内轮廓提取 + 外推线可视化
     if pushed_info is not None:
         inner_pts, outer_pts, push_px = pushed_info
-        # ①-f 提取内轮廓：绿=头发内轮廓线（额头弧+两侧到下颌），黄=baseline 折线
-        hl_img = _draw_baseline(image_bgr, baseline_pts, w)  # 画 baseline（黄线+关键点）
-        hl_img = _draw_polyline(hl_img, inner_pts, (0, 255, 0), 3)
-        viz["hairline_overlay_base64"] = _jpg_b64(hl_img)
-        # ①-g 外推：圆心红点(151) + 内轮廓（绿）+ 外推线（青）+ 遮罩（红半透明）
-        ps_img = _draw_polyline(image_bgr.copy(), inner_pts, (0, 255, 0), 2)
-        ps_img = _draw_polyline(ps_img, outer_pts, (0, 255, 255), 3)
-        # 画圆心（151 点）红点，标示径向外推的中心（_idx151 上方已按值查到）
-        if center is not None:
-            cx151, cy151 = center
-            cv2.circle(ps_img, (cx151, cy151), 6, (0, 0, 255), -1, cv2.LINE_AA)
-            cv2.putText(ps_img, "151", (cx151 + 8, cy151 - 8),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
-        ps_img = _overlay(ps_img, mask_bool, (0, 0, 255), 0.3)
-        viz["pushed_overlay_base64"] = _jpg_b64(ps_img)
+        if render_viz:
+            # ①-f 提取内轮廓：绿=头发内轮廓线（额头弧+两侧到下颌），黄=baseline 折线
+            hl_img = _draw_baseline(image_bgr, baseline_pts, w)  # 画 baseline（黄线+关键点）
+            hl_img = _draw_polyline(hl_img, inner_pts, (0, 255, 0), 3)
+            viz["hairline_overlay_base64"] = _jpg_b64(hl_img)
+            # ①-g 外推：圆心红点(151) + 内轮廓（绿）+ 外推线（青）+ 遮罩（红半透明）
+            ps_img = _draw_polyline(image_bgr.copy(), inner_pts, (0, 255, 0), 2)
+            ps_img = _draw_polyline(ps_img, outer_pts, (0, 255, 255), 3)
+            # 画圆心（151 点）红点，标示径向外推的中心（_idx151 上方已按值查到）
+            if center is not None:
+                cx151, cy151 = center
+                cv2.circle(ps_img, (cx151, cy151), 6, (0, 0, 255), -1, cv2.LINE_AA)
+                cv2.putText(ps_img, "151", (cx151 + 8, cy151 - 8),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
+            ps_img = _overlay(ps_img, mask_bool, (0, 0, 255), 0.3)
+            viz["pushed_overlay_base64"] = _jpg_b64(ps_img)
         viz["push_px"] = push_px
         # 重绘带用原始数据：内轮廓点 + 外推点（供 _redraw_band_mask 连端点成带）
         viz["_inner_pts"] = inner_pts
         viz["_outer_pts"] = outer_pts
+        # baseline 以上区域，供重绘带按 ①-a baseline 截断（只留上面）
+        viz["_upper_mask"] = upper
     # 记录 viz 各字段是否非空（长度），便于排查前端取不到图的问题
     viz_summary = {k: (len(v) if isinstance(v, str) and v else 0)
                    for k, v in viz.items() if k.endswith("_base64")}
@@ -557,7 +603,7 @@ _REPAINT_WORKFLOW = os.path.join(os.path.dirname(os.path.dirname(__file__)), "ha
 
 
 def _call_comfyui(image_bgr, mask_bool, prompt=None):
-    """调本机 ComfyUI 的 Flux-2 inpaint 工作流（hair_repaint.json），返回与输入同分辨率的 BGR。
+    """调远端 ComfyUI 的 Flux-2 inpaint 工作流（hair_repaint.json），返回与输入同分辨率的 BGR。
 
     与 swapHair 的区别：ComfyUI 把「原图 VAE 编码作 reference latent + ColorMatch」双重保色，
     天生不易染色；提示词自由可调（中文）。mask 经 RGBA alpha 通道传入（透明=重绘区）。
@@ -565,10 +611,10 @@ def _call_comfyui(image_bgr, mask_bool, prompt=None):
     """
     import io
     from hairline.mask import compose_comfy_rgba
-    from hairline.comfyui import run as comfyui_run, ping
+    from hairline.comfyui import COMFYUI_URL, run as comfyui_run, ping
 
     if not ping():
-        raise SwapError("ComfyUI 不可达（http://127.0.0.1:8188），redraw Flux-2 路跳过")
+        raise SwapError(f"ComfyUI 不可达（{COMFYUI_URL}），redraw Flux-2 路跳过")
     mask_u8 = (mask_bool.astype(np.uint8)) * 255
     rgba_img = compose_comfy_rgba(image_bgr, mask_u8)   # alpha=255-mask：透明=重绘区
     buf = io.BytesIO()
@@ -804,47 +850,28 @@ def _composite(orig, swap_result, mask_bool, blend_method, feather_px, edge_erod
 # 主入口
 # ---------------------------------------------------------------------------
 
-def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
-                           erode_cm=0.6, swap_mode="ext_mask",
-                           edge_erode_px=3,
-                           denoising_strength=0.6, gen_backend="swaphair",
-                           hairgrow_strength=0.75, mb_levels=5,
-                           hairline_push_cm=1.0, hairline_edge="column",
-                           blend_method="multiband", color_match=True,
-                           color_match_strength=1.0, mb_feather_px=1,
-                           transition_band_px=-1, redraw=False,
-                           inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
-                           comfyui_prompt=None, rid=None):
-    """接口11 完整管线。返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
+               edge_erode_px, denoising_strength, gen_backend, hairgrow_strength,
+               mb_levels, hairline_push_cm, hairline_edge, blend_method, color_match,
+               color_match_strength, mb_feather_px, transition_band_px,
+               inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True,
+               hair_mask=None):
+    """接口11 共享核心：遮罩(pushed)→生成→硬贴回→接缝融合，产出 ④ final。
 
-    遮罩算法固定为 pushed（发际线外推）。
-    融合算法 blend_method 默认 multiband（多频段金字塔），可选 seamless(泊松)/
-    two_stage(泊松→多频段两段式)/feather(羽化)/alpha_gradient(距离变换)。
-    color_match 默认开启 Reinhard 颜色迁移消除整体色差（对 multiband/feather 有效）。
-    redraw=True 时额外跑一条「发际线带重绘」分支：
-      重绘区域 = 外推发际线↔内推发际线之间的带（以原内轮廓为中心，向头发/脸各推 push_cm）。
-      输入图 + 融合基底都用 final（④接缝融合最终图）。两路后端对比：
-      ② swapHair 路（final+band 重绘→final 融合）
-      ③ Flux-2 路（final+band 调 ComfyUI 保色重绘→final 融合）
-      结果在 steps.redraw_a/redraw_c 单独展示，不替换 final。
-    comfyui_prompt：Flux-2 路提示词，None 用默认「补充遮罩区域的头发，加一点美颜」。
-    inpainting_fill/mask_blur/mask_dilate_scale：透传 change_hair 服务端重绘参数（默认值
-      =服务端原始硬编码值，未传行为不变）。inpainting_fill=0 保留原图可治"染绿"。
-    rid: 调用方的 request id，用于日志关联。为 None 时自动生成。
+    不做任何重绘。返回中间产物 dict（供接口11 构造响应、接口12 取 final+重绘带用）：
+      final / swap_result / hard_paste / alpha / mask_bool / mask_viz /
+      px_per_cm / t_mask / t_swap / t_blend / h / w
+    未检出人脸抛 NoFaceError。
     """
     mask_type = "pushed"        # 固定：只支持 pushed 遮罩算法
-    # blend_method 由参数传入（默认 multiband，接口11 可覆盖）
-    if rid is None:
-        rid = uuid4().hex[:8]
-    logger.info("[%s] ===== generate_hairline_grow 开始 =====", rid)
-    logger.info("[%s] 参数(固定 mask=pushed): erode_cm=%s hairline_push_cm=%s hairline_edge=%r "
-                "mb_levels=%s seg=%s gen_backend=%s swap_mode=%s blend=%s color_match=%s "
-                "cm_strength=%s mb_feather_px=%s transition_band_px=%s redraw=%s "
-                "inpainting_fill=%s mask_blur=%s mask_dilate_scale=%s comfyui_prompt=%r",
+    logger.info("[%s] _grow_core 参数(固定 mask=pushed): erode_cm=%s hairline_push_cm=%s "
+                "hairline_edge=%r mb_levels=%s seg=%s gen_backend=%s swap_mode=%s blend=%s "
+                "color_match=%s cm_strength=%s mb_feather_px=%s transition_band_px=%s "
+                "inpainting_fill=%s mask_blur=%s mask_dilate_scale=%s",
                 rid, erode_cm, hairline_push_cm, hairline_edge, mb_levels,
                 seg_model, gen_backend, swap_mode, blend_method, color_match,
-                color_match_strength, mb_feather_px, transition_band_px, redraw,
-                inpainting_fill, mask_blur, mask_dilate_scale, comfyui_prompt)
+                color_match_strength, mb_feather_px, transition_band_px,
+                inpainting_fill, mask_blur, mask_dilate_scale)
     h, w = image_bgr.shape[:2]
     landmarks = detector.detect(image_bgr)
     if landmarks is None:
@@ -857,7 +884,8 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
     t0 = time.time()
     mask_bool, mask_viz = compute_mask(
         image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid)
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid,
+        render_viz=render_viz, hair_mask=hair_mask)
     t_mask = time.time() - t0
     logger.info("[%s] 步骤1 遮罩完成 耗时=%dms mask_pixels=%d", rid, int(t_mask*1000), int(mask_bool.sum()))
 
@@ -876,7 +904,7 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
     hard_paste = image_bgr.copy()
     hard_paste[mask_bool] = swap_result[mask_bool]
 
-    # 步骤4：接缝融合（默认 multiband）
+    # 步骤4：接缝融合（默认 multiband）→ ④ final
     t0 = time.time()
     final, alpha = _composite(
         image_bgr, swap_result, mask_bool, blend_method, 0, edge_erode_px,
@@ -885,69 +913,54 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         mb_feather_px=mb_feather_px, transition_band_px=transition_band_px)
     t_blend = time.time() - t0
 
-    # 步骤5（可选）：发际线带重绘分支 —— 开关 redraw=True 时执行，结果单独展示。
-    # 重绘区域 = 外推发际线↔内推发际线之间的带（以原内轮廓为中心，向头发/脸各推 push_cm）。
-    # 输入图 + 融合基底都用 final（④接缝融合最终图）。
-    redraw_viz = {
-        "redraw_band_overlay_base64": "",
-        "redraw_a_base64": "",
-        "redraw_c_base64": "",
+    return {
+        "final": final, "swap_result": swap_result, "hard_paste": hard_paste,
+        "alpha": alpha, "mask_bool": mask_bool, "mask_viz": mask_viz,
+        "px_per_cm": px_per_cm, "t_mask": t_mask, "t_swap": t_swap, "t_blend": t_blend,
+        "h": h, "w": w,
     }
-    redraw_info = {"enabled": False}
-    if redraw:
-        t0 = time.time()
-        logger.info("[%s] 步骤5 发际线带重绘 开始", rid)
-        # ① 算重绘带：发际线(内轮廓)↔外推发际线 两条折线端点相连组成的带
-        inner_pts = mask_viz.get("_inner_pts")
-        outer_pts = mask_viz.get("_outer_pts")
-        push_px = int(round(max(0.0, hairline_push_cm) * px_per_cm))
-        try:
-            band_mask = _redraw_band_mask(inner_pts, outer_pts, h, w, rid=rid)
-            if band_mask.sum() < 30:
-                raise RuntimeError("重绘带像素过少，可能内轮廓/外推线缺失")
-            logger.info("[%s] 步骤5 重绘带 push_px=%d band_pixels=%d",
-                        rid, push_px, int(band_mask.sum()))
-            redraw_viz["redraw_band_overlay_base64"] = _jpg_b64(
-                _overlay(final, band_mask, (255, 0, 255)))
-            redraw_info = {"enabled": True, "band_pixels": int(band_mask.sum()),
-                           "push_px": push_px}
-        except Exception as ex:  # noqa: BLE001
-            logger.exception("[%s] 步骤5 重绘带计算失败，整个重绘跳过", rid)
-            redraw_info = {"enabled": False, "error": f"band: {ex}"}
 
-        # ② swapHair 路：final + band 作 ext_mask 重绘 → final 作基底融合
-        if redraw_info.get("enabled"):
-            try:
-                redraw_a_raw = _call_swap(final, hairline_id, is_hr, band_mask, denoising_strength,
-                                          inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-                                          mask_dilate_scale=mask_dilate_scale)
-                final_a, _ = _composite(
-                    final, redraw_a_raw, band_mask, blend_method, 0, edge_erode_px,
-                    color_match=color_match, mb_levels=mb_levels,
-                    color_match_strength=color_match_strength,
-                    mb_feather_px=mb_feather_px, transition_band_px=transition_band_px)
-                redraw_viz["redraw_a_base64"] = _jpg_b64(final_a)
-                logger.info("[%s] 步骤5 swapHair路完成", rid)
-            except Exception as ex:  # noqa: BLE001
-                logger.warning("[%s] 步骤5 swapHair路失败，跳过: %s", rid, ex)
-                redraw_info["swap_error"] = str(ex)
 
-            # ③ Flux-2 路：final + band 调 ComfyUI（reference latent 保色，不染绿）→ final 融合
-            try:
-                prompt = comfyui_prompt if comfyui_prompt else "补充遮罩区域的头发，加一点美颜"
-                redraw_c_raw = _call_comfyui(final, band_mask, prompt=prompt)
-                final_c, _ = _composite(
-                    final, redraw_c_raw, band_mask, blend_method, 0, edge_erode_px,
-                    color_match=color_match, mb_levels=mb_levels,
-                    color_match_strength=color_match_strength,
-                    mb_feather_px=mb_feather_px, transition_band_px=transition_band_px)
-                redraw_viz["redraw_c_base64"] = _jpg_b64(final_c)
-                logger.info("[%s] 步骤5 Flux-2路完成", rid)
-            except Exception as ex:  # noqa: BLE001
-                logger.warning("[%s] 步骤5 Flux-2路失败，跳过: %s", rid, ex)
-                redraw_info["c_error"] = str(ex)
+# ---------------------------------------------------------------------------
+# 主入口
+# ---------------------------------------------------------------------------
 
-            logger.info("[%s] 步骤5 发际线带重绘完成 耗时=%dms", rid, int((time.time()-t0)*1000))
+def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
+                           erode_cm=0.6, swap_mode="ext_mask",
+                           edge_erode_px=3,
+                           denoising_strength=0.6, gen_backend="swaphair",
+                           hairgrow_strength=0.75, mb_levels=5,
+                           hairline_push_cm=1.0, hairline_edge="column",
+                           blend_method="multiband", 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,
+                           rid=None):
+    """接口11 完整管线（**不含重绘**，重绘见接口12 generate_hairline_redraw）。
+    返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+
+    遮罩算法固定为 pushed（发际线外推）。
+    融合算法 blend_method 默认 multiband（多频段金字塔），可选 seamless(泊松)/
+    two_stage(泊松→多频段两段式)/feather(羽化)/alpha_gradient(距离变换)。
+    color_match 默认开启 Reinhard 颜色迁移消除整体色差（对 multiband/feather 有效）。
+    inpainting_fill/mask_blur/mask_dilate_scale：透传 change_hair 服务端换发型重绘参数。
+    rid: 调用方的 request id，用于日志关联。为 None 时自动生成。
+    """
+    if rid is None:
+        rid = uuid4().hex[:8]
+    logger.info("[%s] ===== generate_hairline_grow 开始 =====", rid)
+    core = _grow_core(
+        image_bgr, hairline_id, is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm,
+        swap_mode=swap_mode, edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+        gen_backend=gen_backend, hairgrow_strength=hairgrow_strength, mb_levels=mb_levels,
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, blend_method=blend_method,
+        color_match=color_match, color_match_strength=color_match_strength,
+        mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
+        inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+        mask_dilate_scale=mask_dilate_scale, rid=rid)
+    mask_viz = core["mask_viz"]
+    alpha = core["alpha"]
+    w, h = core["w"], core["h"]
 
     data = {
         "hairline_id": hairline_id,
@@ -955,7 +968,7 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         "hairgrow_strength": round(float(hairgrow_strength), 3),
         "is_hr": is_hr,
         "seg_model": seg_model,
-        "mask_type": mask_type,
+        "mask_type": "pushed",
         "erode_cm": round(float(erode_cm), 2),
         "swap_mode": swap_mode,
         "blend_method": blend_method,
@@ -971,16 +984,16 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         "inpainting_fill": int(inpainting_fill),
         "mask_blur": int(mask_blur),
         "mask_dilate_scale": round(float(mask_dilate_scale), 3),
-        "px_per_cm": round(float(px_per_cm), 4),
+        "px_per_cm": round(float(core["px_per_cm"]), 4),
         "erode_px": mask_viz["erode_px"],
         "hair_pixels": mask_viz["hair_pixels"],
         "closed_pixels": mask_viz["closed_pixels"],
         "mask_pixels": mask_viz["mask_pixels"],
         "image_size": {"width": w, "height": h},
         "timings_ms": {
-            "mask": int(t_mask * 1000),
-            "swap": int(t_swap * 1000),
-            "blend": int(t_blend * 1000),
+            "mask": int(core["t_mask"] * 1000),
+            "swap": int(core["t_swap"] * 1000),
+            "blend": int(core["t_blend"] * 1000),
         },
         "steps": {
             "input_base64": _jpg_b64(image_bgr),
@@ -996,21 +1009,135 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
             # 最终遮罩
             "mask_overlay_base64": mask_viz["mask_overlay_base64"],
             "mask_base64": mask_viz["mask_base64"],
-            "swap_raw_base64": _jpg_b64(swap_result),
-            "hard_paste_base64": _jpg_b64(hard_paste),
+            "swap_raw_base64": _jpg_b64(core["swap_result"]),
+            "hard_paste_base64": _jpg_b64(core["hard_paste"]),
             "alpha_base64": _gray_b64(alpha) if alpha is not None else mask_viz["mask_base64"],
-            "final_base64": _jpg_b64(final),
-            # 步骤5（可选）：发际线带重绘分支 —— redraw=False 时为空串
-            "redraw_band_overlay_base64": redraw_viz["redraw_band_overlay_base64"],
-            "redraw_a_base64": redraw_viz["redraw_a_base64"],
-            "redraw_c_base64": redraw_viz["redraw_c_base64"],
+            "final_base64": _jpg_b64(core["final"]),
         },
-        "redraw": redraw_info,
-        "_rid": rid,  # 调用方的 request id，用于日志关联。为 None 时自动生成。
+        "_rid": rid,
     }
-    # 记录 steps 各图字段是否非空，供排查前端取图问题
     steps_summary = {k: (len(v) if isinstance(v, str) and v else 0)
                      for k, v in data["steps"].items() if k.endswith("_base64")}
     logger.info("[%s] 返回 steps 字段长度: %s", rid, steps_summary)
     logger.info("[%s] ===== generate_hairline_grow 完成 =====", rid)
     return data
+
+
+def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
+                             erode_cm=0.6, swap_mode="ext_mask",
+                             edge_erode_px=3,
+                             denoising_strength=0.6, gen_backend="swaphair",
+                             hairgrow_strength=0.75, mb_levels=5,
+                             hairline_push_cm=1.0, hairline_edge="column",
+                             blend_method="multiband", 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,
+                             comfyui_prompt=None, beauty_alpha=0.6,
+                             band_lo_mult=0.5, band_hi_mult=1.5, rid=None,
+                             hair_mask=None):
+    """接口12 发际线带重绘。内部先跑接口11 核心拿到 ④ final，再取 ⑤-① 发际线重绘带
+    （外推↔内推之间、经 baseline 截断只留上部）作遮罩。
+
+    **本接口不再做 Flux-2 重绘**：只产出 `final`（接缝融合基底）+ 纯红遮罩
+    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给后端
+    ComfyUI 重绘接口（/api/v1/redraw）完成。旧的 `redraw_full` / `redraw_band`
+    字段保留为空，仅作结构兼容。
+
+    返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+
+    comfyui_prompt：保留入参，但本接口不再使用（重绘提示词由外部服务自行决定）。
+    beauty_alpha：保留入参，但本接口不再使用（美颜由外部服务控制）。
+    band_lo_mult / band_hi_mult：重绘带外推倍率（相对 hairline_push_cm），带位于
+      lo×push ~ hi×push 之间（内轮廓=0×、原外推线=1.0×），默认 0.5 / 1.5。
+    其余参数含义与接口11 相同（用于内部生成 final 与重绘带）。
+    """
+    if rid is None:
+        rid = uuid4().hex[:8]
+    logger.info("[%s] ===== generate_hairline_redraw 开始 =====", rid)
+    core = _grow_core(
+        image_bgr, hairline_id, is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm,
+        swap_mode=swap_mode, edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+        gen_backend=gen_backend, hairgrow_strength=hairgrow_strength, mb_levels=mb_levels,
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, blend_method=blend_method,
+        color_match=color_match, color_match_strength=color_match_strength,
+        mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
+        inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+        mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False,
+        hair_mask=hair_mask)
+    final = core["final"]
+    mask_viz = core["mask_viz"]
+    w, h = core["w"], core["h"]
+    px_per_cm = core["px_per_cm"]
+
+    # ① 算重绘带（⑤-①）：发际线(内轮廓)↔外推发际线成带，经 baseline 截断只留上部
+    t0 = time.time()
+    inner_pts = mask_viz.get("_inner_pts")
+    outer_pts = mask_viz.get("_outer_pts")
+    upper_mask = mask_viz.get("_upper_mask")
+    push_px = int(round(max(0.0, hairline_push_cm) * px_per_cm))
+    redraw_band_overlay_b64 = ""
+    redraw_band_mask_b64 = ""   # 纯红 alpha PNG（遮罩区=(255,0,0,255)，其余全透明）
+    redraw_info = {"enabled": False}
+    band_mask = None
+    try:
+        band_mask = _redraw_band_mask(inner_pts, outer_pts, h, w, rid=rid, upper=upper_mask,
+                                      lo_mult=band_lo_mult, hi_mult=band_hi_mult)
+        if band_mask.sum() < 30:
+            raise RuntimeError("重绘带像素过少，可能内轮廓/外推线缺失")
+        logger.info("[%s] 重绘带 push_px=%d lo_mult=%s hi_mult=%s band_pixels=%d",
+                    rid, push_px, band_lo_mult, band_hi_mult, int(band_mask.sum()))
+        redraw_band_overlay_b64 = _jpg_b64(_overlay(final, band_mask, (255, 0, 255)))
+        # 纯红遮罩 PNG（供外部重绘服务按 alpha 识别重绘区）
+        redraw_band_mask_b64 = _red_mask_b64(band_mask, h, w)
+        redraw_info = {"enabled": True, "band_pixels": int(band_mask.sum()), "push_px": push_px,
+                       "band_lo_mult": float(band_lo_mult), "band_hi_mult": float(band_hi_mult)}
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("[%s] 重绘带计算失败，整个重绘跳过", rid)
+        redraw_info = {"enabled": False, "error": f"band: {ex}"}
+
+    # ② Flux-2 重绘已下线：本接口现在只产出 final（接缝融合基底）+ 纯红重绘带遮罩，
+    #    重绘交给后端 ComfyUI 重绘接口（/api/v1/redraw）完成。
+    #    下面保留 redraw_full_b64 / redraw_band_b64 为空，保持返回结构兼容（旧字段）。
+    redraw_full_b64 = ""
+    redraw_band_b64 = ""
+    t_redraw = time.time() - t0
+
+    data = {
+        "hairline_id": hairline_id,
+        "blend_method": blend_method,
+        "hairline_push_cm": round(float(hairline_push_cm), 2),
+        "comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
+        "beauty_alpha": beauty_alpha,
+        "px_per_cm": round(float(px_per_cm), 4),
+        "mask_pixels": mask_viz["mask_pixels"],
+        "image_size": {"width": w, "height": h},
+        "timings_ms": {
+            "mask": int(core["t_mask"] * 1000),
+            "swap": int(core["t_swap"] * 1000),
+            "blend": int(core["t_blend"] * 1000),
+            "redraw": int(t_redraw * 1000),
+        },
+        "steps": {
+            "input_base64": _jpg_b64(image_bgr),
+            # 接口11 的 ④ final —— 作为本接口的重绘输入基底
+            "final_base64": _jpg_b64(final),
+            # ⑤-① 发际线重绘带（紫，已按 baseline 截断只留上部）
+            "redraw_band_overlay_base64": redraw_band_overlay_b64,
+            # ⑤-② 发际线重绘带遮罩（纯红 alpha PNG，遮罩区=(255,0,0,255)）
+            "redraw_band_mask_base64": redraw_band_mask_b64,
+            # A：ComfyUI 整帧重绘+美颜（已下线，保留空字段兼容旧前端）
+            "redraw_full_base64": redraw_full_b64,
+            # B：加发只在发际线带、美颜保留全脸（已下线，保留空字段兼容旧前端）
+            "redraw_band_base64": redraw_band_b64,
+            # 兼容旧字段：指向 A（整帧版）
+            "redraw_c_base64": redraw_full_b64,
+        },
+        "redraw": redraw_info,
+        "_rid": rid,
+    }
+    steps_summary = {k: (len(v) if isinstance(v, str) and v else 0)
+                     for k, v in data["steps"].items() if k.endswith("_base64")}
+    logger.info("[%s] 返回 steps 字段长度: %s", rid, steps_summary)
+    logger.info("[%s] ===== generate_hairline_redraw 完成 =====", rid)
+    return data

face_analysis/head_mask.py

@@ -24,8 +24,9 @@ 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 = [123, 116, 143, 71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301, 372, 345, 352,]
+# 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

face_analysis/measure.py

@@ -12,9 +12,9 @@ from face_analysis.calibration import (
     estimate_scale_factor, normalized_to_pixel, pixel_distance, _lm_list,
 )
 from face_analysis.face_mesh_landmarks import (
-    GLABELLA_9, GLABELLA_151, NOSE_BOTTOM, CHIN_TIP,
+    GLABELLA_9, NOSE_BOTTOM, CHIN_TIP,
     LEFT_EYE_OUTER, LEFT_EYE_INNER, RIGHT_EYE_INNER, RIGHT_EYE_OUTER,
-    LEFT_CHEEK, RIGHT_CHEEK,
+    LEFT_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
 )
 from face_analysis.hair_segmenter import locate_hairline_by_segmentation
 
@@ -24,10 +24,8 @@ _TOP_RATIO = 0.22 / 0.28      # 顶庭 ÷ 中庭（≈ 0.786）
 
 
 def _brow_center(lm, w, h):
-    """眉心 = 索引 9 / 151 中点。"""
-    g9 = normalized_to_pixel(lm[GLABELLA_9], w, h)
-    g151 = normalized_to_pixel(lm[GLABELLA_151], w, h)
-    return (g9[0] + g151[0]) / 2, (g9[1] + g151[1]) / 2
+    """眉心 = 索引 9（眉间上点）。"""
+    return normalized_to_pixel(lm[GLABELLA_9], w, h)
 
 
 def estimate_vertical_landmarks(landmarks, image_width, image_height):
@@ -144,22 +142,47 @@ 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__":

face_analysis/pose.py

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

gateway.log

@@ -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]

hair-worker.service

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

hair_repaint.json

@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "补充遮罩区域的头发，加一点美颜"
+      "text": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
     },
     "class_type": "JjkText",
     "_meta": {

hairline/comfyui.py

@@ -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)
@@ -103,6 +107,14 @@ 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)
@@ -124,8 +136,11 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         except Exception:  # noqa: BLE001
             pass
 
-        # 3. 提交
-        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        # 3. 提交（front=True 时插队到队列最前）
+        payload = {"prompt": wf, "client_id": client_id}
+        if front:
+            payload["front"] = True
+        r = cli.post("/prompt", json=payload)
         r.raise_for_status()
         prompt_id = r.json()["prompt_id"]
 
@@ -144,7 +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}")
 

hairline/hairline_2d.py

@@ -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.
 #

hairline/redraw.py

@@ -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)

hairline/service.py

@@ -14,21 +14,87 @@ 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")
 
+# 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,
@@ -36,9 +102,8 @@ _TEXTURE_DIRS = {
     "low": os.path.join(_REPO, "hairline_texture_low"),
 }
 
-# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
-#    "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
-_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
+# torch 2.7.1+cu128 已支持 RTX 5090 (sm_120)，SegFormer 走 GPU（~0.05s/张）
+_SEG_DEVICE = os.getenv("SEG_DEVICE", "cuda")
 
 _landmarker = None
 _parser = None
@@ -69,10 +134,13 @@ def _gender_key(stem: str):
 
 
 def get_texture_map(level: str = "middle") -> dict:
-    """扫描指定档位贴图目录建 {gender: [(key, path)]}，按 key 排序、按档位缓存。
+    """扫描指定档位贴图目录建 {gender: [(key, path)]}，按显式顺序表排序、按档位缓存。
 
     level：middle(默认) / high / low，分别对应 hairline_texture[/_high|/_low]。
     文件名规范化去空格（如 `man_ inverse_arc.png` → key `inverse_arc`）。
+
+    排序依据 _HAIRSTYLE_ORDER：表内 key 按表序、表外未知 key 兜底排到末尾（再按字母序），
+    保证新增/重命名文件不会打乱现有 hair_style 序号。
     """
     if level not in _TEXTURE_DIRS:
         raise ValueError(f"hairline_level 必须是 middle/high/low，收到 {level!r}")
@@ -86,7 +154,9 @@ def get_texture_map(level: str = "middle") -> dict:
         if gender:
             mapping[gender].append((key, path))
     for g in mapping:
-        mapping[g].sort(key=lambda kp: kp[0])
+        order = _HAIRSTYLE_ORDER.get(g, [])
+        idx = {k: i for i, k in enumerate(order)}
+        mapping[g].sort(key=lambda kp: (idx.get(kp[0], len(idx)), kp[0]))
     _texture_maps[level] = mapping
     return mapping
 
@@ -100,13 +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)
@@ -143,7 +219,7 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
                          workflow_path: str | None = None):
     """指定发际线类型：发际线透明叠图(白线 RGBA) + 生发图(ComfyUI)。
 
-    hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..5，male: 1..4。
+    hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..7，male: 1..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）。
@@ -171,9 +247,14 @@ 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)
 
@@ -191,9 +272,14 @@ 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)
 
@@ -202,6 +288,115 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
     return results
 
 
+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。
+
+    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），
@@ -218,22 +413,51 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
             h, w = image_bgr.shape[:2]
             marked, mask = image_bgr, np.zeros((h, w), np.uint8)
         buf = io.BytesIO()
-        compose_comfy_rgba(marked, mask).save(buf, format="PNG")
+        compose_comfy_rgba(marked, mask).save(buf, format="PNG", compress_level=1)
         return comfyui.run(buf.getvalue(), prompt=prompt)
     except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
         logger.warning("接口5 生发图失败：%s", e)
         return None
 
 
+def _grow_native_one(image_bgr: np.ndarray, ctx: dict, white_path: str,
+                     prompt: str | None = None, unet_name: str | None = None):
+    """对单个发际线做原生生发（ComfyUI add_hair），与男性 generate_grow_results 一致。
+
+    接口2 女性新发型（bigflower/clasicalflower 等）无对应 change_hair LoRA，改走此路径：
+    黑模板 → build_inpaint_mask → 限边（_prep_comfy_input）→ comfyui.run(front=True)。
+    失败返回 None，不抛异常。结果按限边前原图尺寸放大回原尺寸（仅展示对齐）。
+    """
+    try:
+        h, w = image_bgr.shape[:2]
+        black = load_texture_rgba(_black_texture_path(white_path))
+        marked, mask = build_inpaint_mask(
+            image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+        m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
+        buf = io.BytesIO()
+        compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
+        # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
+        grown_png = comfyui.run(buf.getvalue(), prompt=prompt, front=True, unet_name=unet_name)
+        if gsc < 1.0 and grown_png:
+            grown_png = _upscale_png_to(grown_png, w, h)
+        return grown_png
+    except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
+        logger.warning("接口2 女原生生发图失败：%s", e)
+        return None
+
+
 def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
                            hair_styles: list[int], use_mask: bool = True,
-                           prompt: str | None = None):
+                           prompt: str | None = None,
+                           generate_grow_image: bool = True):
     """接口5：对选中发型返回 middle/high/low 三档发际线透明叠图 + 生发图(同接口2)。
 
     入参同接口2：先选 gender，再多选 hair_styles（必填，1-indexed 按贴图排序）。
     每个选中发型返回三档叠图（middle/high/low，RGBA 透明层只含发际线曲线）与一张生发图；
     三档贴图同名，生发黑模板固定取自 hairline_texture_black/（middle），故生发目标固定 middle 档。
     use_mask/prompt：同接口2 的生发参数。
+    generate_grow_image（默认 True）：是否生成生发图（ComfyUI，最耗时）。False 时跳过生发，
+        各发型 grown_png 恒为 None，可大幅降低耗时（仅留三档发际线叠图与中心点）。
     Returns: {"images":[{hairline_type,order,overlays:{middle,high,low}((H,W,4) RGBA 透明层),grown_png}],
               "best_centers":{"middle":(x,y),"high":(x,y),"low":(x,y)}}；无人脸 None。
               best_centers 取首个选中发型三档各自的发际线中点。
@@ -255,8 +479,9 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     tex_by_level = {lv: get_texture_map(lv)[gender] for lv in _TEXTURE_DIRS}
 
     # use_mask=False：干净原图+空遮罩与贴图无关，只跑一次 ComfyUI，选中项复用
+    # generate_grow_image=False：完全跳过生发（最耗时），grown_png 恒为 None
     shared_grown = None
-    if not use_mask:
+    if generate_grow_image and not use_mask:
         shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
 
     def _center_of(overlay):
@@ -275,9 +500,13 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
         for lv in _TEXTURE_DIRS:
             white = load_texture_rgba(tex_by_level[lv][s - 1][1])
             overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-        # 生发：固定 middle 黑模板
-        grown_png = shared_grown if not use_mask else \
-            _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
+        # 生发：固定 middle 黑模板（generate_grow_image=False 时跳过，恒 None）
+        if not generate_grow_image:
+            grown_png = None
+        elif not use_mask:
+            grown_png = shared_grown
+        else:
+            grown_png = _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
         images.append({"hairline_type": key, "order": s,
                        "overlays": overlays, "grown_png": grown_png})
         # best_centers：首个选中发型三档（middle/high/low）发际线中点
@@ -286,18 +515,73 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     return {"images": images, "best_centers": best_centers}
 
 
+# 接口3 送 ComfyUI 前限边，降低峰值显存，避免与接口2 切换时把 Flux 挤出。
+# 统一 prompt 后 Flux 不再被 CLIP 挤出，接口3 可用较高分辨率。可用 GROW_B_MAX_SIDE 覆盖。
+_GROW_B_MAX_SIDE = int(os.getenv("GROW_B_MAX_SIDE", "1024"))
+
+def _downscale_max_side(img_bgr: np.ndarray, max_side: int) -> tuple[np.ndarray, float]:
+    """长边超过 max_side 时等比例缩小；返回 (图, scale)，scale=新/旧。"""
+    h, w = img_bgr.shape[:2]
+    m = max(h, w)
+    if max_side <= 0 or m <= max_side:
+        return img_bgr, 1.0
+    scale = max_side / float(m)
+    nw = max(1, int(round(w * scale)))
+    nh = max(1, int(round(h * scale)))
+    out = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_AREA)
+    return out, scale
+
+
+def _upscale_png_to(png_bytes: bytes, out_w: int, out_h: int) -> bytes:
+    """把 Comfy 输出 PNG 双线性拉回原图尺寸（仅展示对齐，不增加推理细节）。"""
+    arr = np.frombuffer(png_bytes, np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
+    if img is None:
+        return png_bytes
+    if img.shape[1] == out_w and img.shape[0] == out_h:
+        return png_bytes
+    resized = cv2.resize(img, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
+    ok, buf = cv2.imencode(".png", resized)
+    return buf.tobytes() if ok else png_bytes
+
+
+def _prep_comfy_input(img_bgr: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
+    """单段 ComfyUI 生发（接口2男 / 接口3）送图前限边到 GROW_B_MAX_SIDE。
+    返回 (缩后图, 缩后遮罩, scale)；scale<1 时调用方需把结果放大回原尺寸。"""
+    h, w = img_bgr.shape[:2]
+    if _GROW_B_MAX_SIDE <= 0 or max(h, w) <= _GROW_B_MAX_SIDE:
+        return img_bgr, mask, 1.0
+    out, scale = _downscale_max_side(img_bgr, _GROW_B_MAX_SIDE)
+    nh, nw = out.shape[:2]
+    msk = cv2.resize(mask, (nw, nh), interpolation=cv2.INTER_NEAREST)
+    logger.info("接口2男/接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+                w, h, nw, nh, _GROW_B_MAX_SIDE)
+    return out, msk, scale
+
+
 def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str = None):
     """接口3：检测医生手绘发际线 → 遮罩 → 送 ComfyUI 生发（仅需划线图一张）。
 
     检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
     工作流提示词会清除黑线再生发）。
 
+    进 Comfy 前若长边 > GROW_B_MAX_SIDE（默认 896）会先等比例缩小，降低峰值显存；
+    输出再拉回原图尺寸。
+
     use_mask（默认 True）：是否启用自动检测的遮罩，用于测试对比。
       - True：检测手绘线 → 建遮罩 → alpha=255−mask（透明区=重绘区，节点44 画黄色参考区）。
       - False：跳过检测，直接送划线图，alpha 全 255（空遮罩，节点26 mask 为空），
         模型仅凭医生黑线参考生发。无需改工作流，唯一变量是遮罩。
     Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
     """
+    orig_h, orig_w = marked_bgr.shape[:2]
+    marked_bgr, _scale = _downscale_max_side(marked_bgr, _GROW_B_MAX_SIDE)
+    if _scale < 1.0:
+        logger.info(
+            "接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+            orig_w, orig_h, marked_bgr.shape[1], marked_bgr.shape[0], _GROW_B_MAX_SIDE,
+        )
+
     h, w = marked_bgr.shape[:2]
     if use_mask:
         rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
@@ -315,8 +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"}
 
 

local_test/README.md

@@ -0,0 +1,174 @@
+# 发型补全服务 API 文档
+
+## 服务概述
+
+本服务提供基于 ComfyUI 的发型补全（局部重绘）能力。通过传入人物图片和遮罩图片，调用 ComfyUI 工作流（`0716add-hair.json`）生成补全后的图片。
+
+## 技术栈
+
+- **框架**: Flask
+- **依赖**: requests, Pillow, numpy
+- **后端**: ComfyUI (http://127.0.0.1:8188)
+
+## 服务地址
+
+- **HTTP**: `http://127.0.0.1:8899`
+- **前端页面**: `http://127.0.0.1:8899/`
+- **API接口**: `http://127.0.0.1:8899/api/generate`
+
+## 启动方式
+
+### 使用脚本（推荐）
+
+```bash
+# 启动服务
+cd /home/ubuntu/hair/local_test
+./start.sh
+
+# 停止服务
+./stop.sh
+```
+
+### 直接运行
+
+```bash
+cd /home/ubuntu/hair/local_test
+/home/ubuntu/ComfyUI/venv/bin/python app.py
+```
+
+## API 接口
+
+### POST /api/generate
+
+调用 ComfyUI 工作流，传入图片和遮罩，返回生成结果。
+
+#### 请求参数
+
+| 参数名 | 类型 | 必填 | 说明 |
+|--------|------|------|------|
+| image | File | 是 | 人物图片（支持 jpg, png 等常见格式） |
+| mask | File | 是 | 遮罩图片（支持 jpg, png，遮罩区域可用红色/白色/alpha 通道标识） |
+| prompt | String | 否 | 提示词，默认值："填充遮罩区域的头发，皮肤加一点磨皮" |
+
+#### 遮罩图片格式说明
+
+服务支持多种遮罩格式，自动提取遮罩区域：
+
+| 格式类型 | 示例 | 遮罩区域标识 |
+|----------|------|--------------|
+| 红色遮罩 | 红色画笔绘制 | R=255 的像素 |
+| 白色遮罩 | 白色画笔绘制 | R=G=B=255 的像素 |
+| Alpha 遮罩 | 透明背景 | A=255 的像素 |
+
+服务会取所有通道的最大值作为遮罩强度，因此以上格式均可混用。
+
+**注意**: 遮罩区域表示需要重绘的部分，非遮罩区域保持原图不变。
+
+#### 请求示例（curl）
+
+```bash
+curl -X POST http://127.0.0.1:8899/api/generate \
+  -F "image=@/path/to/person.jpg" \
+  -F "mask=@/path/to/mask.png" \
+  -F "prompt=填充遮罩区域的头发，皮肤加一点磨皮" \
+  --output result.png
+```
+
+#### 请求示例（Python）
+
+```python
+import requests
+
+url = "http://127.0.0.1:8899/api/generate"
+files = {
+    "image": open("person.jpg", "rb"),
+    "mask": open("mask.png", "rb"),
+}
+data = {
+    "prompt": "填充遮罩区域的头发，皮肤加一点磨皮"
+}
+
+resp = requests.post(url, files=files, data=data, timeout=600)
+if resp.status_code == 200:
+    with open("result.png", "wb") as f:
+        f.write(resp.content)
+else:
+    print(f"Error: {resp.json()}")
+```
+
+#### 响应
+
+**成功 (HTTP 200)**:
+
+返回 PNG 图片二进制数据，Content-Type: `image/png`。
+
+**失败 (HTTP 4xx/5xx)**:
+
+返回 JSON 格式错误信息：
+
+```json
+{
+    "error": "错误描述"
+}
+```
+
+#### 错误码
+
+| 状态码 | 说明 |
+|--------|------|
+| 500 | 内部错误（文件处理失败、ComfyUI 返回错误等） |
+| 503 | 无法连接到 ComfyUI（服务未启动或端口错误） |
+| 500 | 超时（工作流执行超过 5 分钟） |
+
+## 工作流说明
+
+服务使用的工作流 `0716add-hair.json` 包含以下处理步骤：
+
+1. **加载模型**: Flux 2 Klein 9B (FP8) + Qwen 3.8B CLIP
+2. **图片上传**: 将原图与遮罩合成为 RGBA 格式上传至 ComfyUI
+3. **遮罩处理**: 填充孔洞 → 转换为图像 → 缩放 → 转换回遮罩
+4. **图像缩放**: 按比例缩放至合适尺寸（最大边长 1024，8 的倍数）
+5. **VAE 编码**: 将图像编码为 latent
+6. **采样生成**: 使用 Flux 模型 + ReferenceLatent 进行局部重绘
+7. **VAE 解码**: 将 latent 解码为图像
+8. **颜色匹配**: 使用 ColorMatch 保持颜色一致
+9. **保存结果**: 返回生成的图片
+
+## 前置依赖
+
+启动服务前需确保：
+
+1. **ComfyUI 已启动**: `http://127.0.0.1:8188` 可访问
+2. **模型文件存在**:
+   - `models/unet/flux2.0/flux-2-klein-9b-fp8.safetensors`
+   - `models/vae/flux2-vae.safetensors`
+   - `models/clip/qwen_3_8b_fp8mixed.safetensors`
+3. **虚拟环境已激活**: 使用 `/home/ubuntu/ComfyUI/venv/bin/python`
+
+## 文件结构
+
+```
+/home/ubuntu/hair/local_test/
+├── app.py          # Flask 后端服务
+├── index.html      # 前端测试页面
+├── test_api.py     # API 测试脚本
+├── README.md       # 本文档
+├── output/         # 测试结果输出目录
+├── 用来重绘.jpg     # 测试人物图片
+└── 用来重绘.png     # 测试遮罩图片
+```
+
+## 使用流程
+
+1. 启动 ComfyUI（`python main.py --listen`）
+2. 启动本服务（`python app.py`）
+3. 调用 API 或访问前端页面上传图片和遮罩
+4. 等待生成完成（通常 30-60 秒）
+5. 获取返回的 PNG 图片
+
+## 注意事项
+
+- 请求超时时间为 5 分钟，生成复杂图片可能需要较长时间
+- 遮罩图片尺寸需与人物图片一致，服务会自动缩放对齐
+- 建议使用红色或白色绘制遮罩，确保遮罩强度足够
+- 服务会自动对遮罩边缘进行高斯模糊（radius=4），避免硬边

local_test/app.py

@@ -0,0 +1,339 @@
+#!/usr/bin/env python3
+"""Hair inpainting service - calls ComfyUI workflow with image + mask."""
+import io
+import json
+import time
+import random
+import logging
+import traceback
+import requests
+from flask import Flask, request, jsonify, send_file
+import numpy as np
+from PIL import Image, ImageFilter
+
+# 让 PIL 支持 iPhone 的 HEIC/HEIF 照片（浏览器 accept="image/*" 会允许选中它们）。
+try:
+    from pillow_heif import register_heif_opener
+    register_heif_opener()
+    _HEIF_OK = True
+except Exception:
+    _HEIF_OK = False
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+log = logging.getLogger("hair")
+
+app = Flask(__name__)
+COMFYUI_URL = "http://127.0.0.1:8188"
+
+# 允许浏览器跨域直连本服务（如 hair 项目的测试页）。
+# 不引入 flask-cors 依赖，直接在响应头 + OPTIONS 预检里处理。
+_CORS_HEADERS = {
+    "Access-Control-Allow-Origin": "*",
+    "Access-Control-Allow-Methods": "POST, OPTIONS",
+    "Access-Control-Allow-Headers": "Content-Type",
+    "Access-Control-Expose-Headers": "X-Generate-Time",
+}
+
+
+@app.after_request
+def _add_cors(resp):
+    for k, v in _CORS_HEADERS.items():
+        resp.headers[k] = v
+    return resp
+
+
+@app.route("/api/generate", methods=["OPTIONS"])
+def _generate_preflight():
+    """CORS 预检：浏览器 POST 前会先发 OPTIONS。"""
+    return ("", 204)
+
+
+def build_workflow(image_filename, prompt_text, seed=None):
+    """Build ComfyUI API workflow from the 0716add-hair.json structure."""
+    if seed is None:
+        seed = random.randint(0, 2**53)
+
+    return {
+        # Loaders
+        "16": {"class_type": "UNETLoader", "inputs": {
+            "unet_name": "flux2.0/flux-2-klein-9b-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_8b_fp8mixed.safetensors",
+            "type": "flux2",
+            "device": "default"}},
+
+        # Input image (with mask in alpha channel)
+        "26": {"class_type": "LoadImage", "inputs": {
+            "image": image_filename}},
+
+        # Prompt
+        "60": {"class_type": "JjkText", "inputs": {
+            "text": prompt_text}},
+        "22": {"class_type": "CLIPTextEncode", "inputs": {
+            "clip": ["61", 0],
+            "text": ["60", 0]}},
+
+        # Image size
+        "31": {"class_type": "easy imageSize", "inputs": {
+            "image": ["26", 0]}},
+
+        # Mask processing: fill holes -> convert to image -> scale -> back to mask
+        "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"}},
+
+        # Scale image+mask by aspect ratio
+        "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"}},
+
+        # Preview (pass_through=true, just passes the image through)
+        "44": {"class_type": "ImageAndMaskPreview", "inputs": {
+            "image": ["32", 0],
+            "mask": ["32", 1],
+            "mask_opacity": 1,
+            "mask_color": "FFFF00",
+            "pass_through": True}},
+
+        # Get size of scaled image
+        "14": {"class_type": "GetImageSize+", "inputs": {
+            "image": ["44", 0]}},
+
+        # VAE encode the image
+        "13": {"class_type": "VAEEncode", "inputs": {
+            "pixels": ["44", 0],
+            "vae": ["3", 0]}},
+
+        # Flux model setup
+        "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]}},
+
+        # Empty latent for sampling
+        "7": {"class_type": "EmptySD3LatentImage", "inputs": {
+            "width": ["14", 0],
+            "height": ["14", 1],
+            "batch_size": 1}},
+
+        # Scheduler & guider
+        "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]}},
+
+        # Noise & sampler
+        "6": {"class_type": "RandomNoise", "inputs": {
+            "noise_seed": seed}},
+        "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]}},
+
+        # VAE decode
+        "10": {"class_type": "VAEDecode", "inputs": {
+            "samples": ["9", 0],
+            "vae": ["3", 0]}},
+
+        # Color match with original image
+        "62": {"class_type": "ColorMatch", "inputs": {
+            "image_ref": ["26", 0],
+            "image_target": ["10", 0],
+            "method": "mkl",
+            "strength": 1,
+            "multithread": True}},
+
+        # Save result
+        "17": {"class_type": "SaveImage", "inputs": {
+            "images": ["62", 0],
+            "filename_prefix": "hair_inpaint"}},
+    }
+
+
+@app.route("/")
+def index():
+    return send_file("index.html")
+
+
+@app.route("/api/generate", methods=["POST"])
+def generate():
+    t_start = time.time()
+    try:
+        if "image" not in request.files or "mask" not in request.files:
+            msg = f"缺少上传文件: files={list(request.files.keys())}"
+            log.warning(msg)
+            return jsonify({"error": msg}), 400
+        image_file = request.files["image"]
+        mask_file = request.files["mask"]
+        prompt_text = request.form.get("prompt", "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+        log.info(
+            "收到请求: image=%s mask=%s prompt=%r",
+            image_file.filename, mask_file.filename, prompt_text,
+        )
+
+        # Load original image as RGB
+        try:
+            image = Image.open(image_file).convert("RGB")
+        except Exception as e:
+            log.error("无法解码人物图片 %s: %s", image_file.filename, e)
+            hint = "" if _HEIF_OK else "（当前不支持 HEIC）"
+            return jsonify({
+                "error": f"无法识别人物图片格式{hint}，请改用 JPG/PNG: {e}"
+            }), 400
+
+        # Load mask and extract mask data from ALL channels (R, G, B, A)
+        # This handles different mask formats:
+        #   - Red mask (R=255 where drawn): user-provided PNG
+        #   - White mask (R=G=B=255 where drawn): frontend canvas
+        #   - Alpha mask (A=255 where drawn): transparent brush
+        try:
+            mask_img = Image.open(mask_file).convert("RGBA")
+        except Exception as e:
+            log.error("无法解码遮罩图片 %s: %s", mask_file.filename, e)
+            return jsonify({
+                "error": f"无法识别遮罩图片格式，请改用 JPG/PNG: {e}"
+            }), 400
+        mask_arr = np.array(mask_img)
+        # Use max of all channels: 255 where any color/alpha is drawn, 0 where empty
+        mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+        # Ensure mask matches image size
+        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)
+
+        # Apply slight blur for soft edges (similar to ComfyUI's brush)
+        mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+        # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+        # So alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+        # We want: drawn area (mask_data=255) -> inpaint -> alpha=0
+        #          undrawn area (mask_data=0) -> keep -> alpha=255
+        comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+        # Combine into RGBA (split RGB into separate channels first)
+        r, g, b = image.split()
+        rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+        # Upload to ComfyUI
+        img_bytes = io.BytesIO()
+        rgba.save(img_bytes, format="PNG")
+        img_bytes.seek(0)
+
+        upload_resp = requests.post(
+            f"{COMFYUI_URL}/upload/image",
+            files={"image": ("hair_input.png", img_bytes, "image/png")},
+            timeout=30,
+        )
+        upload_data = upload_resp.json()
+        if "name" not in upload_data:
+            log.error("ComfyUI 上传图片失败: %s", upload_data)
+            return jsonify({"error": f"Upload failed: {upload_data}"}), 500
+        image_filename = upload_data["name"]
+
+        # Build and queue workflow
+        workflow = build_workflow(image_filename, prompt_text)
+        prompt_resp = requests.post(
+            f"{COMFYUI_URL}/prompt",
+            json={"prompt": workflow},
+            timeout=30,
+        )
+        prompt_data = prompt_resp.json()
+        if "error" in prompt_data:
+            log.error(
+                "ComfyUI /prompt 校验失败: error=%s node_errors=%s",
+                prompt_data.get("error"), prompt_data.get("node_errors"),
+            )
+            return jsonify({"error": json.dumps(prompt_data["error"], ensure_ascii=False)}), 500
+        prompt_id = prompt_data["prompt_id"]
+
+        # Poll for completion (5 min timeout, 0.1s interval)
+        for _ in range(3000):
+            time.sleep(0.1)
+            history_resp = requests.get(
+                f"{COMFYUI_URL}/history/{prompt_id}", timeout=10
+            )
+            history_data = history_resp.json()
+            if prompt_id in history_data:
+                status = history_data[prompt_id].get("status", {})
+                if status.get("status_str") == "error":
+                    log.error(
+                        "ComfyUI 工作流执行失败: %s",
+                        json.dumps(status, ensure_ascii=False),
+                    )
+                    return jsonify({
+                        "error": "Workflow execution failed",
+                        "detail": status.get("messages", status),
+                    }), 500
+                outputs = history_data[prompt_id].get("outputs", {})
+                if "17" in outputs:  # SaveImage node
+                    image_info = outputs["17"]["images"][0]
+                    filename = image_info["filename"]
+                    subfolder = image_info.get("subfolder", "")
+                    img_type = image_info.get("type", "output")
+                    view_resp = requests.get(
+                        f"{COMFYUI_URL}/view",
+                        params={"filename": filename, "subfolder": subfolder, "type": img_type},
+                        timeout=30,
+                    )
+                    elapsed = time.time() - t_start
+                    log.info("重绘完成，服务端耗时 %.2fs", elapsed)
+                    resp = send_file(
+                        io.BytesIO(view_resp.content), mimetype="image/png"
+                    )
+                    resp.headers["X-Generate-Time"] = f"{elapsed:.2f}"
+                    return resp
+
+        return jsonify({"error": "Timeout: workflow did not complete in 5 minutes"}), 500
+
+    except requests.ConnectionError:
+        log.error("无法连接 ComfyUI @ %s", COMFYUI_URL)
+        return jsonify({"error": "Cannot connect to ComfyUI at " + COMFYUI_URL + ". Is it running?"}), 503
+    except Exception as e:
+        log.error("生成失败，未捕获异常:\n%s", traceback.format_exc())
+        return jsonify({"error": f"{type(e).__name__}: {e}"}), 500
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8899, debug=False)

local_test/bench.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+"""Benchmark ComfyUI hair-inpaint workflow across model / dtype / steps."""
+import io, time, sys
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+
+
+def prep_and_upload():
+    image = Image.open("用来重绘.jpg").convert("RGB")
+    mask_img = Image.open("用来重绘.png").convert("RGBA")
+    mask_data = np.max(np.array(mask_img), axis=2)
+    m = Image.fromarray(mask_data, mode="L")
+    if m.size != image.size:
+        m = m.resize(image.size, Image.LANCZOS)
+    m = m.filter(ImageFilter.GaussianBlur(radius=4))
+    alpha = Image.eval(m, lambda x: 255 - x)
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, alpha))
+    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+    up = requests.post(f"{COMFY}/upload/image",
+                       files={"image": ("hair_input.png", buf, "image/png")}).json()
+    return up["name"]
+
+
+def run_once(fname, model, dtype, steps):
+    wf = A.build_workflow(fname, "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+    wf["16"]["inputs"]["unet_name"] = model
+    wf["16"]["inputs"]["weight_dtype"] = dtype
+    wf["1"]["inputs"]["steps"] = steps
+    r = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()
+    if "prompt_id" not in r:
+        raise RuntimeError(f"submit failed: {str(r)[:300]}")
+    pid = r["prompt_id"]
+    deadline = time.time() + 180
+    while time.time() < deadline:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid not in h:
+            continue
+        st = h[pid].get("status", {})
+        if st.get("status_str") == "error":
+            for m in st.get("messages", []):
+                if m[0] == "execution_error":
+                    raise RuntimeError(str(m[1])[:300])
+            raise RuntimeError("execution error")
+        if "17" in h[pid].get("outputs", {}):
+            ts = {mm[0]: mm[1].get("timestamp") for mm in st["messages"]}
+            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
+    raise TimeoutError("run exceeded 180s")
+
+
+CONFIGS = [
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn",      6, "9B fp8 (当前)"),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 6, "9B fp8-fast"),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 4, "9B fp8-fast s4"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn",      6, "4B fp8"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn_fast", 6, "4B fp8-fast"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn_fast", 4, "4B fp8-fast s4"),
+]
+
+fname = prep_and_upload()
+print("input uploaded:", fname)
+print(f"{'配置':<22}{'warmup':>10}{'run1':>10}{'run2':>10}{'best':>10}")
+for model, dtype, steps, label in CONFIGS:
+    times = []
+    for i in range(3):  # 1 warmup + 2 measured
+        try:
+            t = run_once(fname, model, dtype, steps)
+        except Exception as e:
+            t = float('nan'); print("ERR", label, e)
+        times.append(t)
+    best = min(times[1:])
+    print(f"{label:<22}{times[0]:>9.2f}s{times[1]:>9.2f}s{times[2]:>9.2f}s{best:>9.2f}s")

local_test/bench2.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Steps sweep + resolution test on the working 9B fp8-fast config."""
+import io, time
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
+DTYPE = "fp8_e4m3fn_fast"
+
+
+def upload(scale=1.0):
+    image = Image.open("用来重绘.jpg").convert("RGB")
+    mask_img = Image.open("用来重绘.png").convert("RGBA")
+    if scale != 1.0:
+        w, h = image.size
+        w, h = int(w * scale) // 8 * 8, int(h * scale) // 8 * 8
+        image = image.resize((w, h), Image.LANCZOS)
+    mask_data = np.max(np.array(mask_img), axis=2)
+    m = Image.fromarray(mask_data, mode="L")
+    if m.size != image.size:
+        m = m.resize(image.size, Image.LANCZOS)
+    m = m.filter(ImageFilter.GaussianBlur(radius=4))
+    alpha = Image.eval(m, lambda x: 255 - x)
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, alpha))
+    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+    up = requests.post(f"{COMFY}/upload/image",
+                       files={"image": ("hair_input.png", buf, "image/png")}).json()
+    return up["name"], image.size
+
+
+def run(fname, steps):
+    wf = A.build_workflow(fname, "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
+    wf["16"]["inputs"]["unet_name"] = MODEL
+    wf["16"]["inputs"]["weight_dtype"] = DTYPE
+    wf["1"]["inputs"]["steps"] = steps
+    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
+    dl = time.time() + 120
+    while time.time() < dl:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid in h and "17" in h[pid].get("outputs", {}):
+            ts = {m[0]: m[1].get("timestamp") for m in h[pid]["status"]["messages"]}
+            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
+    return float("nan")
+
+
+print("=== 步数扫描 (9B fp8-fast, 原分辨率 1024x775) ===", flush=True)
+fname, sz = upload(1.0)
+run(fname, 6)  # warmup
+res = {}
+for s in [2, 3, 4, 6, 8]:
+    t = min(run(fname, s), run(fname, s))
+    res[s] = t
+    print(f"  steps={s}: {t:.2f}s", flush=True)
+# derive per-step cost & fixed overhead via two points
+per = (res[8] - res[2]) / 6
+fixed = res[2] - per * 2
+print(f"  -> 每步 ~{per:.3f}s, 固定开销(VAE/编码/colormatch/加载) ~{fixed:.2f}s", flush=True)
+
+print("\n=== 分辨率影响 (steps=4) ===", flush=True)
+for scale in [1.0, 0.75, 0.6]:
+    fn, s2 = upload(scale)
+    run(fn, 4)  # warmup
+    t = min(run(fn, 4), run(fn, 4))
+    print(f"  {s2[0]}x{s2[1]} ({s2[0]*s2[1]/1e6:.2f}MP): {t:.2f}s", flush=True)

local_test/bench_quality.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Generate result images at different step counts for quality comparison."""
+import io, time
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
+DTYPE = "fp8_e4m3fn_fast"
+OUT = "output"
+
+image = Image.open("用来重绘.jpg").convert("RGB")
+mask_img = Image.open("用来重绘.png").convert("RGBA")
+mask_data = np.max(np.array(mask_img), axis=2)
+m = Image.fromarray(mask_data, mode="L")
+if m.size != image.size:
+    m = m.resize(image.size, Image.LANCZOS)
+m = m.filter(ImageFilter.GaussianBlur(radius=4))
+alpha = Image.eval(m, lambda x: 255 - x)
+r, g, b = image.split()
+rgba = Image.merge("RGBA", (r, g, b, alpha))
+buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+fname = requests.post(f"{COMFY}/upload/image",
+                      files={"image": ("hair_input.png", buf, "image/png")}).json()["name"]
+
+# fixed seed for fair comparison
+SEED = 123456789
+imgs = []
+labels = []
+for steps in [2, 3, 4, 6]:
+    wf = A.build_workflow(fname, "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", seed=SEED)
+    wf["16"]["inputs"]["unet_name"] = MODEL
+    wf["16"]["inputs"]["weight_dtype"] = DTYPE
+    wf["1"]["inputs"]["steps"] = steps
+    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
+    t0 = time.time()
+    while True:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid in h and "17" in h[pid].get("outputs", {}):
+            ts = {mm[0]: mm[1].get("timestamp") for mm in h[pid]["status"]["messages"]}
+            dur = (ts["execution_success"] - ts["execution_start"]) / 1000.0
+            info = h[pid]["outputs"]["17"]["images"][0]
+            data = requests.get(f"{COMFY}/view", params={
+                "filename": info["filename"], "subfolder": info.get("subfolder", ""),
+                "type": info.get("type", "output")}).content
+            im = Image.open(io.BytesIO(data)).convert("RGB")
+            imgs.append(im)
+            labels.append(f"steps={steps}  {dur:.2f}s")
+            print(f"steps={steps}: {dur:.2f}s", flush=True)
+            break
+
+# build side-by-side contact sheet
+from PIL import ImageDraw
+h0 = imgs[0].height
+w0 = imgs[0].width
+pad = 10
+bar = 28
+sheet = Image.new("RGB", (w0 * len(imgs) + pad * (len(imgs) + 1),
+                          h0 + bar + pad * 2), (26, 26, 46))
+d = ImageDraw.Draw(sheet)
+for i, (im, lb) in enumerate(zip(imgs, labels)):
+    x = pad + i * (w0 + pad)
+    sheet.paste(im, (x, bar + pad))
+    d.text((x + 4, 6), lb, fill=(233, 69, 96))
+sheet.save(f"{OUT}/compare_steps.png")
+print("saved:", f"{OUT}/compare_steps.png", flush=True)

local_test/index.html

@@ -0,0 +1,192 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>发型补全工具</title>
+<style>
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { font-family: -apple-system, sans-serif; background: #1a1a2e; color: #eee; min-height: 100vh; padding: 20px; }
+h1 { text-align: center; margin-bottom: 20px; color: #e94560; font-size: 28px; }
+.container { max-width: 1400px; margin: 0 auto; display: grid; grid-template-columns: 1fr 1fr; gap: 24px; }
+.panel { background: #16213e; border-radius: 12px; padding: 20px; }
+.panel h2 { margin-bottom: 16px; font-size: 18px; color: #e94560; }
+.controls { display: flex; flex-wrap: wrap; gap: 12px; margin-bottom: 16px; align-items: center; }
+.controls label { font-size: 14px; color: #aaa; }
+input[type="file"] { color: #ddd; }
+input[type="text"] { flex: 1; min-width: 200px; padding: 8px 12px; border-radius: 6px; border: 1px solid #444; background: #0f3460; color: #eee; font-size: 14px; }
+button { padding: 10px 24px; border: none; border-radius: 6px; cursor: pointer; font-size: 14px; font-weight: 600; transition: all 0.2s; }
+.btn-upload { background: #0f3460; color: #eee; border: 1px solid #e94560; }
+.btn-upload:hover { background: #1a4a7a; }
+.btn-generate { background: #e94560; color: #fff; font-size: 16px; padding: 12px 36px; }
+.btn-generate:hover { background: #c73650; }
+.btn-generate:disabled { background: #555; cursor: not-allowed; }
+.image-wrapper { display: flex; gap: 12px; flex-wrap: wrap; border: 2px dashed #444; border-radius: 8px; padding: 12px; min-height: 300px; background: #0f3460; }
+.image-item { flex: 1; min-width: 200px; }
+.image-item img { max-width: 100%; border-radius: 6px; }
+.image-item h4 { font-size: 12px; color: #aaa; margin-bottom: 6px; }
+.placeholder { color: #666; font-size: 16px; text-align: center; padding: 60px 20px; width: 100%; }
+.result-area { display: flex; gap: 16px; flex-wrap: wrap; }
+.result-area img { max-width: 100%; border-radius: 8px; }
+.result-item { flex: 1; min-width: 250px; }
+.result-item h3 { font-size: 14px; color: #aaa; margin-bottom: 8px; text-align: center; }
+.loading { text-align: center; padding: 40px; color: #e94560; font-size: 18px; }
+.loading .spinner { display: inline-block; width: 40px; height: 40px; border: 4px solid #333; border-top-color: #e94560; border-radius: 50%; animation: spin 1s linear infinite; margin-bottom: 12px; }
+@keyframes spin { to { transform: rotate(360deg); } }
+.error { color: #ff6b6b; padding: 16px; background: #2a1a1a; border-radius: 8px; margin-top: 12px; }
+</style>
+</head>
+<body>
+<h1>💇 发型补全工具</h1>
+<div class="container">
+    <!-- Left: Input -->
+    <div class="panel">
+        <h2>1. 上传图片 & 遮罩</h2>
+        <div class="controls">
+            <label>人物图片:</label>
+            <input type="file" id="imageInput" accept="image/*" class="btn-upload">
+        </div>
+        <div class="controls">
+            <label>遮罩图片:</label>
+            <input type="file" id="maskInput" accept="image/*" class="btn-upload">
+        </div>
+        <div class="image-wrapper" id="imageWrapper">
+            <div class="placeholder" id="placeholder">请上传人物图片和遮罩图片</div>
+        </div>
+        <div class="controls" style="margin-top:16px">
+            <label>提示词:</label>
+            <input type="text" id="promptInput" value="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜">
+        </div>
+        <div style="text-align:center; margin-top:16px">
+            <button class="btn-generate" id="generateBtn" disabled>🚀 生成</button>
+        </div>
+    </div>
+
+    <!-- Right: Result -->
+    <div class="panel">
+        <h2>2. 对比结果</h2>
+        <div id="resultArea">
+            <div class="placeholder">生成结果将显示在这里</div>
+        </div>
+    </div>
+</div>
+
+<script>
+const imageInput = document.getElementById('imageInput');
+const maskInput = document.getElementById('maskInput');
+const imageWrapper = document.getElementById('imageWrapper');
+const placeholder = document.getElementById('placeholder');
+const promptInput = document.getElementById('promptInput');
+const generateBtn = document.getElementById('generateBtn');
+const resultArea = document.getElementById('resultArea');
+
+let originalImage = null;
+let maskImage = null;
+
+imageInput.addEventListener('change', (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+    const reader = new FileReader();
+    reader.onload = (ev) => {
+        const img = new Image();
+        img.onload = () => {
+            originalImage = { img: img, file: file };
+            updatePreview();
+            checkReady();
+        };
+        img.src = ev.target.result;
+    };
+    reader.readAsDataURL(file);
+});
+
+maskInput.addEventListener('change', (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+    const reader = new FileReader();
+    reader.onload = (ev) => {
+        const img = new Image();
+        img.onload = () => {
+            maskImage = { img: img, file: file };
+            updatePreview();
+            checkReady();
+        };
+        img.src = ev.target.result;
+    };
+    reader.readAsDataURL(file);
+});
+
+function updatePreview() {
+    placeholder.style.display = 'none';
+    let html = '';
+    if (originalImage) {
+        html += `<div class="image-item"><h4>人物图片</h4><img src="${originalImage.img.src}" alt="原图"></div>`;
+    }
+    if (maskImage) {
+        html += `<div class="image-item"><h4>遮罩图片</h4><img src="${maskImage.img.src}" alt="遮罩"></div>`;
+    }
+    imageWrapper.innerHTML = html;
+}
+
+function checkReady() {
+    generateBtn.disabled = !(originalImage && maskImage);
+}
+
+generateBtn.addEventListener('click', async () => {
+    if (!originalImage || !maskImage) return;
+
+    generateBtn.disabled = true;
+    generateBtn.textContent = '⏳ 生成中...';
+
+    const startTime = performance.now();
+    resultArea.innerHTML = '<div class="loading"><div class="spinner"></div><br>正在调用 ComfyUI 生成，请耐心等待...<div id="liveTimer" style="margin-top:8px;font-size:15px;color:#aaa">已用时 0.0s</div></div>';
+    const liveTimer = document.getElementById('liveTimer');
+    const timerId = setInterval(() => {
+        if (liveTimer) liveTimer.textContent = '已用时 ' + ((performance.now() - startTime) / 1000).toFixed(1) + 's';
+    }, 100);
+
+    try {
+        const formData = new FormData();
+        formData.append('image', originalImage.file, 'original.' + originalImage.file.name.split('.').pop());
+        formData.append('mask', maskImage.file, 'mask.' + maskImage.file.name.split('.').pop());
+        formData.append('prompt', promptInput.value);
+
+        const resp = await fetch('/api/generate', { method: 'POST', body: formData });
+        if (!resp.ok) {
+            const err = await resp.json();
+            throw new Error(err.error || 'Generation failed');
+        }
+
+        const resultBlob = await resp.blob();
+        const resultUrl = URL.createObjectURL(resultBlob);
+        const elapsed = ((performance.now() - startTime) / 1000).toFixed(1);
+        // 服务端纯推理耗时（若返回该响应头）
+        const serverTime = resp.headers.get('X-Generate-Time');
+        const serverInfo = serverTime ? `，服务端推理 ${parseFloat(serverTime).toFixed(1)}s` : '';
+
+        resultArea.innerHTML = `
+            <div style="text-align:center;margin-bottom:12px;color:#4ade80;font-size:16px;font-weight:600">
+                ⏱️ 本次重绘耗时 ${elapsed}s${serverInfo}
+            </div>
+            <div class="result-area">
+                <div class="result-item">
+                    <h3>原图</h3>
+                    <img src="${originalImage.img.src}" alt="原图">
+                </div>
+                <div class="result-item">
+                    <h3>生成结果</h3>
+                    <img src="${resultUrl}" alt="生成结果">
+                </div>
+            </div>
+        `;
+    } catch (err) {
+        const elapsed = ((performance.now() - startTime) / 1000).toFixed(1);
+        resultArea.innerHTML = `<div class="error">❌ ${err.message}<br><span style="color:#aaa;font-size:13px">（耗时 ${elapsed}s）</span></div>`;
+    } finally {
+        clearInterval(timerId);
+        generateBtn.disabled = false;
+        generateBtn.textContent = '🚀 生成';
+    }
+});
+</script>
+</body>
+</html>

local_test/start.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+# 启动发型补全服务
+
+cd "$(dirname "$0")"
+
+PID_FILE="hair_service.pid"
+LOG_FILE="hair_service.log"
+
+# 检查是否已在运行
+if [ -f "$PID_FILE" ]; then
+    PID=$(cat "$PID_FILE")
+    if kill -0 "$PID" 2>/dev/null; then
+        echo "服务已在运行 (PID: $PID)"
+        exit 0
+    else
+        echo "清理无效的 PID 文件..."
+        rm "$PID_FILE"
+    fi
+fi
+
+# 检查 ComfyUI 是否运行
+if ! curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:8188/ >/dev/null 2>&1; then
+    echo "警告: ComfyUI 未运行 (http://127.0.0.1:8188)"
+    echo "请先启动 ComfyUI: python /home/ubuntu/ComfyUI/main.py --listen"
+fi
+
+# 启动服务
+echo "启动发型补全服务..."
+/home/ubuntu/ComfyUI/venv/bin/python app.py >> "$LOG_FILE" 2>&1 &
+PID=$!
+echo "$PID" > "$PID_FILE"
+
+# 等待启动
+sleep 2
+if curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:8899/ | grep -q "200"; then
+    echo "服务启动成功!"
+    echo "本机: http://127.0.0.1:8899"
+    # 打印局域网 IP，方便其他机器访问（app.py 已绑定 0.0.0.0）
+    LAN_IPS=$(hostname -I 2>/dev/null | tr ' ' '\n' | grep -v '^$' || true)
+    if [ -n "$LAN_IPS" ]; then
+        echo "外网/局域网访问:"
+        for ip in $LAN_IPS; do
+            echo "  http://${ip}:8899"
+        done
+    fi
+    echo "PID: $PID"
+    echo "日志: $LOG_FILE"
+else
+    echo "服务启动失败，请检查日志: $LOG_FILE"
+    rm "$PID_FILE"
+    exit 1
+fi