feat: 接口1/6 标注层字号上调一档 + 眉心改用 9 号点定位

- 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

@@ -53,3 +53,28 @@ static/report_hairline_v2.zip
 # local_test 运行期日志 / pid（不入 git）
 local_test/hair_service.log
 local_test/hair_service.pid
+
+# benchmark 原始输出（含结果图+原图，体积大，不入 git）
+benchmark_out/
+
+# benchmark 部署的 HTML 报告（图片 base64 内嵌，体积大，不入 git）
+static/hairstyle_thumbs/
+
+# 网关运行期日志（不入 git）
+gateway.log
+
+# 工作流备份文件（不入 git）
+*.json.bak.*
+
+# 分辨率对比测试产物（out/ 下结果图+原图副本+日志，体积大，不入 git）
+# 仅忽略 out/ 与运行期文件；测试脚本与 HTML 报告仍入库
+image/compare_test/out/
+image/res_test/out/
+image/wave_test/out/
+image/compare_test/progress.json
+image/res_test/progress.json
+image/wave_test/progress.json
+image/compare_test/batch_test.log
+image/res_test/batch_test.log
+image/wave_test/batch_test.log
+image/compare_test/http.log

0716add-hair-api.json

@@ -0,0 +1,327 @@
+{
+  "16": {
+    "class_type": "UnetLoaderGGUF",
+    "inputs": {
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
+    }
+  },
+  "3": {
+    "class_type": "VAELoader",
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    }
+  },
+  "61": {
+    "class_type": "CLIPLoader",
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "cpu"
+    }
+  },
+  "26": {
+    "class_type": "LoadImage",
+    "inputs": {
+      "image": "placeholder.png"
+    }
+  },
+  "60": {
+    "class_type": "JjkText",
+    "inputs": {
+      "text": "填充遮罩区域的头发"
+    }
+  },
+  "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",
@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
-      "weight_dtype": "fp8_e4m3fn"
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
     },
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "_meta": {
       "title": "UNet加载器"
     }
@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "补充遮罩区补充遮罩区域内的头发，头发填满遮罩区域。发际线往下挡住额头"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {
@@ -421,7 +421,7 @@
     "inputs": {
       "clip_name": "qwen_3_8b_fp8mixed.safetensors",
       "type": "flux2",
-      "device": "default"
+      "device": "cpu"
     },
     "class_type": "CLIPLoader",
     "_meta": {

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 = os.getenv("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")
@@ -361,21 +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)
-        # 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。
+        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。
@@ -390,11 +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))
@@ -469,7 +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
 """,
@@ -574,7 +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={
@@ -717,7 +775,9 @@ async def hair_grow(
     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"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
+    flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（切换模型用）。None=工作流默认；如 flux-2-klein-9b-Q5_K_M.gguf / flux-2-klein-9b-Q4_K_M.gguf / flux2.0/flux-2-klein-9b-fp8.safetensors"),
+    redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
 ):
     # 1. gender 必填校验（非法/缺失 → 1004）
     if gender not in ("male", "female"):
@@ -746,11 +806,13 @@ async def hair_grow(
         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)
+                generate_grow_results_swap, image, hair_styles, _V2_FINAL_DEFAULTS,
+                redraw_max_side=redraw_max_side, unet_name=flux_model)
         else:
             from hairline.service import generate_grow_results
             items = await run_in_threadpool(
-                generate_grow_results, image, gender, use_mask, prompt, hair_styles)
+                generate_grow_results, image, gender, use_mask, prompt, hair_styles,
+                unet_name=flux_model)
         if items is None:
             return err(1001, "无法识别人像")
 
@@ -770,117 +832,15 @@ async def hair_grow(
 
 
 # ---------------------------------------------------------------------------
-# 接口 7：C 端生发 v2（add_hair2.json 工作流）
+# 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。
 # ---------------------------------------------------------------------------
 
-_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}},
-                    }
-                }
-            },
-        },
-    },
-)
-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"),
-):
-    # 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]
-    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}")
-
-    # 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
-
-        # 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:
-            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})
-    except Exception as ex:  # noqa: BLE001
-        logger.exception("接口7 处理异常")
-        return err(1007, f"处理失败：{ex}")
+@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")
 
 
 # ---------------------------------------------------------------------------
@@ -930,7 +890,7 @@ 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", 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)
@@ -1059,8 +1019,13 @@ async def face_features(
 - 必填 `gender`（`male`/`female`），决定发型集合（female 5 / male 4）。
 - 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。
-- 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 `use_mask` / `prompt`：同接口2 的生发控制参数（仅 male 路径生效）。
+- 可选 `generate_grow_image`（默认 `true`）：是否生成生发效果图（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。
+- **生发机制（同接口2，按性别分流）**：
+  `female` 走「换发型 + Flux-2 整帧重绘」（依赖 change_hair:8801 与 ComfyUI:8188）；
+  `male` 走 ComfyUI `add_hair` 原生 inpaint。
+- 可选 `flux_model` / `redraw_max_side`：同接口2（仅 female 路径生效）。
 
 **返回说明**：
 
@@ -1137,8 +1102,11 @@ async def hairline_generate(
     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"),
-    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"),
+    use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，仅 male 路径生效）"),
+    prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词（同接口2，仅 male 路径生效），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（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"),
+    flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（同接口2，切换模型用）。None=工作流默认"),
+    redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素（同接口2，仅 female 路径生效）。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
 ):
     if gender not in ("male", "female"):
         return err(1004, "gender 必填且只能为 male / female")
@@ -1162,7 +1130,10 @@ 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,
+            redraw_max_side=redraw_max_side, unet_name=flux_model,
+            v2_defaults=_V2_FINAL_DEFAULTS)
         if res is None:
             return err(1001, "无法识别人像")
 
@@ -1530,7 +1501,7 @@ async def hairline_grow_v2(
     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「补充遮罩区域的头发，加一点美颜」"),
+    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」"),
     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"),
@@ -1677,6 +1648,42 @@ async def hairline_grow_v2_final_v2(
     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",
+                       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）
 # ---------------------------------------------------------------------------

benchmark_hairstyle.py

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

benchmark_hairstyle_report.py

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

benchmark_matrix.py

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

benchmark_report.py

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

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/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/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）。
 
@@ -405,6 +409,7 @@
 | 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` |
 | 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。
 
@@ -426,7 +431,7 @@
 | 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 +259,85 @@ 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）
+    # --- 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

@@ -110,7 +110,7 @@ def _gray_b64(gray_float):
 def _red_mask_b64(mask_bool, h, w):
     """布尔遮罩 → 纯红 alpha PNG data URI。
     遮罩区域 RGBA=(255,0,0,255)，其余区域 RGBA=(0,0,0,0)。
-    供外部 ComfyUI 重绘服务（如 local_test）按 alpha 通道识别重绘区。
+    供 ComfyUI 重绘接口（/api/v1/redraw）按 alpha 通道识别重绘区。
 
     注意：cv2.imencode 写 PNG 用的是 **BGRA** 顺序（B,G,R,A），所以要得到
     浏览器显示的红色 R=255，需赋值 (B=0,G=0,R=255,A=255)。
@@ -357,7 +357,8 @@ def _redraw_band_mask(inner_pts, outer_pts, h, w, rid="", upper=None,
 
 
 def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True):
+                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True,
+                 hair_mask=None):
     """算出布尔遮罩 + 可视化。
 
     seg_model: bisenet | segformer。
@@ -368,6 +369,7 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     render_viz: 是否生成各阶段叠图 overlay JPG（接口11 调试页用）。接口2/12 路径传 False
         可跳过 6+ 张 base64 编码，省 ~80ms；数据字段（_inner_pts/_outer_pts/_upper_mask/
         mask_pixels 等）始终返回，不受影响。
+    hair_mask: 预计算的头发布尔遮罩（来自 SegFormer parse）。传入时跳过重复分割，省 ~0.9s。
     返回 (mask_bool, viz_dict)。
     """
     lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
@@ -381,13 +383,16 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     upper = _upper_region_mask(baseline_pts, w, h)
     lg(f"baseline 第一点={baseline_pts[0]} 末点={baseline_pts[-1]} upper像素={int(upper.sum())}")
 
-    if seg_model == "bisenet":
-        hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
-    elif seg_model == "segformer":
-        hair_mask = _segformer_hair_mask(image_bgr)
+    if hair_mask is None:
+        if seg_model == "bisenet":
+            hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
+        elif seg_model == "segformer":
+            hair_mask = _segformer_hair_mask(image_bgr)
+        else:
+            raise ValueError(f"未知 seg_model: {seg_model}")
+        lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
     else:
-        raise ValueError(f"未知 seg_model: {seg_model}")
-    lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
+        lg(f"头发分割跳过(复用外部传入) hair_pixels={int(hair_mask.sum())}")
 
     top_fill = _fill_to_baseline(hair_mask, upper)      # 含额头，延伸到图底
     closed = _largest_cc(top_fill & upper)              # 闭合区域：头发+额头，底=基线
@@ -849,7 +854,8 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
                edge_erode_px, denoising_strength, gen_backend, hairgrow_strength,
                mb_levels, hairline_push_cm, hairline_edge, blend_method, color_match,
                color_match_strength, mb_feather_px, transition_band_px,
-               inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True):
+               inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True,
+               hair_mask=None):
     """接口11 共享核心：遮罩(pushed)→生成→硬贴回→接缝融合，产出 ④ final。
 
     不做任何重绘。返回中间产物 dict（供接口11 构造响应、接口12 取 final+重绘带用）：
@@ -879,7 +885,7 @@ def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
     mask_bool, mask_viz = compute_mask(
         image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
         hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid,
-        render_viz=render_viz)
+        render_viz=render_viz, hair_mask=hair_mask)
     t_mask = time.time() - t0
     logger.info("[%s] 步骤1 遮罩完成 耗时=%dms mask_pixels=%d", rid, int(t_mask*1000), int(mask_bool.sum()))
 
@@ -1028,13 +1034,14 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
                              transition_band_px=-1,
                              inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
                              comfyui_prompt=None, beauty_alpha=0.6,
-                             band_lo_mult=0.5, band_hi_mult=1.5, rid=None):
+                             band_lo_mult=0.5, band_hi_mult=1.5, rid=None,
+                             hair_mask=None):
     """接口12 发际线带重绘。内部先跑接口11 核心拿到 ④ final，再取 ⑤-① 发际线重绘带
     （外推↔内推之间、经 baseline 截断只留上部）作遮罩。
 
     **本接口不再做 Flux-2 重绘**：只产出 `final`（接缝融合基底）+ 纯红遮罩
-    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给前端调
-    外部 ComfyUI 重绘服务（见 local_test）完成。旧的 `redraw_full` / `redraw_band`
+    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给后端
+    ComfyUI 重绘接口（/api/v1/redraw）完成。旧的 `redraw_full` / `redraw_band`
     字段保留为空，仅作结构兼容。
 
     返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
@@ -1056,7 +1063,8 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         color_match=color_match, color_match_strength=color_match_strength,
         mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
         inpainting_fill=inpainting_fill, mask_blur=mask_blur,
-        mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False)
+        mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False,
+        hair_mask=hair_mask)
     final = core["final"]
     mask_viz = core["mask_viz"]
     w, h = core["w"], core["h"]
@@ -1089,7 +1097,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         redraw_info = {"enabled": False, "error": f"band: {ex}"}
 
     # ② Flux-2 重绘已下线：本接口现在只产出 final（接缝融合基底）+ 纯红重绘带遮罩，
-    #    重绘交给前端调外部 ComfyUI 重绘服务（见 local_test）完成。
+    #    重绘交给后端 ComfyUI 重绘接口（/api/v1/redraw）完成。
     #    下面保留 redraw_full_b64 / redraw_band_b64 为空，保持返回结构兼容（旧字段）。
     redraw_full_b64 = ""
     redraw_band_b64 = ""
@@ -1099,7 +1107,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
         "hairline_id": hairline_id,
         "blend_method": blend_method,
         "hairline_push_cm": round(float(hairline_push_cm), 2),
-        "comfyui_prompt": comfyui_prompt or "补充遮罩区域的头发，加一点美颜",
+        "comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发",
         "beauty_alpha": beauty_alpha,
         "px_per_cm": round(float(px_per_cm), 4),
         "mask_pixels": mask_viz["mask_pixels"],

face_analysis/measure.py

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

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

hair-worker.service

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

hair_repaint.json

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

hairline/comfyui.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 
 import copy
 import json
+import logging
 import os
 import random
 import time
@@ -18,7 +19,7 @@ import uuid
 
 import httpx
 
-COMFYUI_URL = os.getenv("COMFYUI_URL", "http://10.60.74.221:8188").rstrip("/")
+COMFYUI_URL = os.getenv("COMFYUI_URL", "http://127.0.0.1:8188").rstrip("/")
 _WORKFLOW_DEFAULT = os.getenv(
     "ADD_HAIR_WORKFLOW",
     os.path.join(os.path.dirname(os.path.dirname(__file__)), "add_hair.json"),
@@ -29,6 +30,19 @@ _REPO = os.path.dirname(os.path.dirname(__file__))
 _INPUT_NODE = "26"     # LoadImage：外部输入图(含 alpha 遮罩)
 _SEED_NODE = "6"       # RandomNoise
 _PROMPT_NODE = "60"    # JjkText：提示词
+_UNET_NODE = "16"      # UNETLoader / UnetLoaderGGUF：Flux 模型加载
+_CLIP_NODE = "61"      # CLIPLoader：qwen 文本编码器
+
+# Flux 模型 → 配套文本编码器映射。切换 unet 时自动同步编码器，避免维度不匹配。
+# 规则：4b 系列配 qwen_3_4b，9b 系列（fp8/GGUF）配 qwen_3_8b_fp8mixed。
+def _clip_for_unet(unet_name: str) -> str | None:
+    """根据 unet 文件名推断配套的文本编码器文件名；无法推断返回 None。"""
+    low = unet_name.lower()
+    if "4b" in low and "9b" not in low:
+        return "qwen_3_4b.safetensors"
+    if "9b" in low:
+        return "qwen_3_8b_fp8mixed.safetensors"
+    return None
 
 _wf_cache: dict[str, dict] = {}      # path → workflow JSON
 _wf_output_node: dict[str, str] = {}  # path → SaveImage 节点 ID
@@ -85,11 +99,18 @@ def _get_output_node(workflow_path: str | None = None) -> str:
 
 
 def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = None,
-        workflow_path: str | None = None) -> bytes:
+        workflow_path: str | None = None, front: bool = False,
+        unet_name: str | None = None) -> bytes:
     """提交一次生发任务，返回输出 PNG 字节。失败抛异常。
 
     prompt：非 None 时替换工作流节点60(JjkText)的文本；None 时用工作流内置默认提示词。
     workflow_path：工作流 JSON 路径，None 则用默认 add_hair.json。
+    front：True 时任务插到 ComfyUI 队列最前（server 端 "front" 字段，队列号取负）。
+        接口2 对时延敏感用 True，避免排在接口3/5 的批量任务后面；其余接口保持 False。
+    unet_name：非 None 时改写工作流里的模型加载节点（节点16），动态切换 Flux 模型。
+        .safetensors → 保持 UNETLoader 节点类型不变，只替换 unet_name；
+        .gguf → 自动把节点类型改成 UnetLoaderGGUF（需装 ComfyUI-GGUF 插件）。
+        None 时用工作流内置默认模型。
     """
     path = workflow_path or _WORKFLOW_DEFAULT
     output_node = _get_output_node(path)
@@ -104,11 +125,34 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         name = (up.get("subfolder") + "/" if up.get("subfolder") else "") + up["name"]
 
         # 2. 改工作流：节点26 输入图 + 随机 seed
+        try:
+            import io as _io
+            from PIL import Image as _Img
+            _sz = _Img.open(_io.BytesIO(rgba_png_bytes)).size
+            logging.getLogger("hair.worker").info(
+                "ComfyUI 输入尺寸 %dx%d workflow=%s", _sz[0], _sz[1], os.path.basename(path))
+        except Exception:  # noqa: BLE001
+            pass
         wf = copy.deepcopy(_load_workflow(path))
         wf[_INPUT_NODE]["inputs"]["image"] = name
         wf[_SEED_NODE]["inputs"]["noise_seed"] = random.randint(0, 2**63 - 1)
         if prompt is not None:
             wf[_PROMPT_NODE]["inputs"]["text"] = prompt
+        if unet_name is not None:
+            node = wf.get(_UNET_NODE)
+            if node is not None:
+                # .gguf 需切换到 ComfyUI-GGUF 插件的 UnetLoaderGGUF 节点；
+                # .safetensors/.ckpt 保持原 UNETLoader 节点类型不变
+                if unet_name.lower().endswith(".gguf"):
+                    node["class_type"] = "UnetLoaderGGUF"
+                else:
+                    node["class_type"] = "UNETLoader"
+                node["inputs"]["unet_name"] = unet_name
+            # 同步切换配套文本编码器（4b→qwen_3_4b, 9b→qwen_3_8b），避免维度不匹配
+            clip_node = wf.get(_CLIP_NODE)
+            clip_name = _clip_for_unet(unet_name)
+            if clip_node is not None and clip_name is not None:
+                clip_node["inputs"]["clip_name"] = clip_name
 
         # 诊断：落盘实际提交的工作流 + 输入图，便于和手动 ComfyUI 跑的对比
         try:
@@ -125,8 +169,11 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         except Exception:  # noqa: BLE001
             pass
 
-        # 3. 提交
-        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        # 3. 提交（front=True 时插队到队列最前）
+        payload = {"prompt": wf, "client_id": client_id}
+        if front:
+            payload["front"] = True
+        r = cli.post("/prompt", json=payload)
         r.raise_for_status()
         prompt_id = r.json()["prompt_id"]
 
@@ -145,7 +192,7 @@ 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 +1,80 @@
+"""直接调 ComfyUI 重绘 — 替代 local_test HTTP 服务。
+
+将 local_test/app.py 的核心逻辑（遮罩处理 + ComfyUI 调用）提取为 Python 函数，
+不再需要独立 Flask 服务。使用 0716add-hair-api.json 工作流（steps=4）。
+"""
+from __future__ import annotations
+
+import io
+import logging
+import os
+
+import numpy as np
+from PIL import Image, ImageFilter
+
+from . import comfyui
+
+logger = logging.getLogger("hair.worker")
+
+_DEFAULT_PROMPT = "填充遮罩区域的头发"
+_REPO = os.path.dirname(os.path.dirname(__file__))
+_REPAINT_WORKFLOW = os.path.join(_REPO, "0716add-hair-api.json")
+
+
+def _process_mask_to_rgba(image_bytes: bytes, mask_bytes: bytes) -> bytes:
+    """将分开的 image + mask 处理为 ComfyUI 用的 RGBA PNG bytes。
+
+    复制 local_test/app.py 的遮罩处理逻辑：
+    1. 加载 image 为 RGB
+    2. 加载 mask 为 RGBA，取所有通道 max 值（支持红/白/alpha 遮罩）
+    3. resize mask 到与 image 一致
+    4. 高斯模糊(radius=4) 柔化边缘
+    5. alpha = 255 - mask（绘制区=255 → alpha=0 → 重绘区）
+    6. 合成 RGBA PNG
+    """
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    mask_img = Image.open(io.BytesIO(mask_bytes)).convert("RGBA")
+    mask_arr = np.array(mask_img)
+    mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+    mask_data_img = Image.fromarray(mask_data, mode="L")
+    if mask_data_img.size != image.size:
+        mask_data_img = mask_data_img.resize(image.size, Image.LANCZOS)
+    mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+    # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+    # alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+    comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+    buf = io.BytesIO()
+    rgba.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def run_redraw(image_bytes: bytes, mask_bytes: bytes,
+               prompt: str | None = None, timeout: float = 300.0,
+               front: bool = False, unet_name: str | None = None) -> bytes:
+    """直接调 ComfyUI 重绘 — 替代 local_test /api/generate。
+
+    Args:
+        image_bytes: 人物图片字节（JPG/PNG）
+        mask_bytes: 遮罩图片字节（支持红/白/alpha 遮罩格式）
+        prompt: 提示词，None 用默认 "填充遮罩区域的头发"
+        timeout: ComfyUI 超时秒数
+        front: True 时任务插到 ComfyUI 队列最前（接口2 时延敏感路径用）
+        unet_name: 非 None 时切换 Flux 模型（如 flux-2-klein-9b-Q5_K_M.gguf），None 用工作流默认
+
+    Returns:
+        重绘后的 PNG 图片字节
+
+    Raises:
+        RuntimeError: ComfyUI 执行失败
+        TimeoutError: ComfyUI 超时
+    """
+    rgba_png = _process_mask_to_rgba(image_bytes, mask_bytes)
+    return comfyui.run(rgba_png, timeout=timeout, prompt=prompt,
+                       workflow_path=_REPAINT_WORKFLOW, front=front,
+                       unet_name=unet_name)

hairline/service.py

@@ -14,7 +14,9 @@ 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
@@ -40,27 +42,53 @@ _REPO = os.path.dirname(os.path.dirname(__file__))
 _TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
 _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 
-# 外部重绘服务（local_test，0716add-hair.json 工作流）。接口2 female 用它替代原 Flux-2 重绘。
-# 重绘服务已独立到远程机器；可用 HAIR_LOCAL_REDRAW_URL 覆盖。
-# gpu_worker 同内网走 10.60.74.221，外网（本地开发机）需走公网 117.50.183.232。
-_LOCAL_REDRAW_URL = os.getenv("HAIR_LOCAL_REDRAW_URL", "http://10.60.74.221:8899").rstrip("/")
+# 三接口（接口2女重绘 / 接口2男 / 接口3）统一的 ComfyUI 重绘 prompt。
+# 关键：ComfyUI 单卡显存装不下 Flux(7.7G)+qwen CLIP(3.9G) 同驻，靠缓存 CLIP 文本条件避免重载。
+# prompt 不同会使缓存失效 → 重载 CLIP 并挤出 Flux（每次 +4s）。三接口用同一字符串即可全程命中。
+# 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
+_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发")
 
+# 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。
+# 策略：输入图长边 > REDRAW_MAX_SIDE 才等比缩到该长边；≤ 时原图分辨率直送（不放大）。
+# 默认 1024：大于 1024 的图压到 1024（画质/速度均衡），≤1024 的小图保持原分辨率重绘。
+# 可用 REDRAW_MAX_SIDE 覆盖；0=永不缩图（原图直送）。
+_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "1024"))
 
-def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
-    """调外部重绘服务（local_test /api/generate）：传 final 图 + 纯红遮罩 PNG，
-    返回重绘后的 PNG bytes。失败抛异常（调用方负责 try/except 跳过）。
+def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
+                       max_side=None, unet_name=None):
+    """直接调 ComfyUI 重绘（替代原 local_test HTTP 服务）。
+
+    传 final 图 + 纯红遮罩 PNG，返回重绘后的 PNG bytes。
+    失败抛异常（调用方负责 try/except 跳过）。
+
+    max_side：送 ComfyUI 前长边压到多少像素，None 用全局默认 _REDRAW_MAX_SIDE。
+    unet_name：非 None 时切换 Flux 模型，None 用工作流内置默认。
     """
-    import requests
-    files = {
-        "image": ("final.jpg", image_png_bytes, "image/jpeg"),
-        "mask": ("mask.png", mask_png_bytes, "image/png"),
-    }
-    resp = requests.post(_LOCAL_REDRAW_URL + "/api/generate", files=files, timeout=timeout)
-    resp.raise_for_status()
-    if "image/" not in resp.headers.get("Content-Type", ""):
-        # 服务返回了 JSON 错误
-        raise RuntimeError(f"local_test 返回非图片: {resp.text[:200]}")
-    return resp.content
+    from .redraw import run_redraw
+    eff_side = _REDRAW_MAX_SIDE if max_side is None else max_side
+    img = cv2.imdecode(np.frombuffer(image_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
+    scale = 1.0
+    orig_w = orig_h = 0
+    if img is not None:
+        orig_h, orig_w = img.shape[:2]
+        m = max(orig_h, orig_w)
+        if eff_side > 0 and m > eff_side:
+            scale = eff_side / float(m)
+            nw, nh = max(1, round(orig_w * scale)), max(1, round(orig_h * scale))
+            msk = cv2.imdecode(np.frombuffer(mask_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
+            img_s = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_AREA)
+            msk_s = cv2.resize(msk, (nw, nh), interpolation=cv2.INTER_NEAREST)
+            image_png_bytes = cv2.imencode(".png", img_s)[1].tobytes()
+            mask_png_bytes = cv2.imencode(".png", msk_s)[1].tobytes()
+            logger.info("接口2女 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+                        orig_w, orig_h, nw, nh, eff_side)
+    # front=True：接口2 时延敏感，插到 ComfyUI 队列最前，避免排在接口3/5 的批量任务后面
+    out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
+                     prompt=_REDRAW_PROMPT, front=True, unet_name=unet_name)
+    if scale < 1.0 and out:
+        out = _upscale_png_to(out, orig_w, orig_h)
+    return out
 
 # 发际线贴图档位：middle=默认(hairline_texture/)，high/low 各自独立文件夹。
 _TEXTURE_DIRS = {
@@ -69,9 +97,8 @@ _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
@@ -133,13 +160,19 @@ 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)
@@ -173,7 +206,8 @@ def generate_previews(image_bgr: np.ndarray, gender: str):
 
 def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = True,
                          prompt: str = None, hair_styles: list[int] | None = None,
-                         workflow_path: str | None = None):
+                         workflow_path: str | None = None,
+                         unet_name: str | None = None):
     """指定发际线类型：发际线透明叠图(白线 RGBA) + 生发图(ComfyUI)。
 
     hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..5，male: 1..4。
@@ -204,9 +238,15 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
     if not use_mask:
         try:
             h, w = image_bgr.shape[:2]
+            img_s, msk_s, gsc = _prep_comfy_input(image_bgr, np.zeros((h, w), np.uint8))
             buf = io.BytesIO()
-            compose_comfy_rgba(image_bgr, np.zeros((h, w), np.uint8)).save(buf, format="PNG")
-            shared_grown = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+            compose_comfy_rgba(img_s, msk_s).save(buf, format="PNG", compress_level=1)
+            # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
+            shared_grown = comfyui.run(buf.getvalue(), prompt=prompt,
+                                       workflow_path=workflow_path, front=True,
+                                       unet_name=unet_name)
+            if gsc < 1.0 and shared_grown:
+                shared_grown = _upscale_png_to(shared_grown, w, h)
         except Exception as e:  # noqa: BLE001
             logger.warning("接口2 生发图失败(无遮罩)：%s", e)
 
@@ -224,9 +264,15 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
                 black = load_texture_rgba(_black_texture_path(white_path))
                 marked, mask = build_inpaint_mask(
                     image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+                m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
                 buf = io.BytesIO()
-                compose_comfy_rgba(marked, mask).save(buf, format="PNG")
-                grown_png = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+                compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
+                # front=True：接口2 时延敏感，插到 ComfyUI 队列最前
+                grown_png = comfyui.run(buf.getvalue(), prompt=prompt,
+                                        workflow_path=workflow_path, front=True,
+                                        unet_name=unet_name)
+                if gsc < 1.0 and grown_png:
+                    grown_png = _upscale_png_to(grown_png, w, h)
             except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
                 logger.warning("接口2 生发图失败 type=%s：%s", key, e)
 
@@ -236,25 +282,31 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
 
 
 def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
-                               redraw_defaults: dict):
+                               redraw_defaults: dict,
+                               redraw_max_side: int | None = None,
+                               unet_name: str | None = None):
     """接口2 女性专用：发际线透明叠图（同 generate_grow_results）+ 换发型重绘图。
 
     grown 图来源（新流程）：对每个选中发型把 female key 映射到 change_hair 的 chang_* hair_id，
     调 face_analysis.hairline_grow.generate_hairline_redraw（= 接口12 final 管线，参数用
-    redraw_defaults）拿到 ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG，再**后端调外部重绘服务
-    local_test**（0716add-hair.json 工作流）完成发际线带重绘，重绘结果作为生发图。
+    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，重绘结果作为生发图。
 
     overlay 仍是发际线曲线透明层（与 generate_grow_results 完全一致）。
     Returns: list[dict] {"hairline_type","order","overlay","grown_png"(jpg bytes 或 None)}；
              无人脸返回 None。单个发型换发型/重绘失败时 grown_png=None，不抛异常。
     """
     from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError
+    from face_analysis.head_mask import SEGFORMER_HAIR
 
     ctx = extract_context(image_bgr)
     if ctx is None:
         return None
     uv, ext_faces = load_ext_mesh()
 
+    # 复用 extract_context 已算好的 SegFormer parse_map，避免 generate_hairline_redraw 内部重复分割
+    hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
+
     textures = get_texture_map()["female"]  # [(key, path), ...] 已排序
     if hair_styles is not None:
         items = [(s, textures[s - 1]) for s in hair_styles]
@@ -263,6 +315,21 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
 
     results = []
     h, w = image_bgr.shape[:2]
+
+    # 重绘管线（swapHair + ComfyUI）统一降分辨率：真实照片 swap(SD WebUI)~5s、blend、ComfyUI
+    # 均随分辨率线性下降。overlay 预览仍用全分辨率；grown_png 最后放大回原尺寸。
+    eff_side = _REDRAW_MAX_SIDE if redraw_max_side is None else redraw_max_side
+    redraw_img = image_bgr
+    hair_mask_redraw = hair_mask_reuse
+    if eff_side > 0 and max(h, w) > eff_side:
+        redraw_img, _rs = _downscale_max_side(image_bgr, eff_side)
+        _nh, _nw = redraw_img.shape[:2]
+        if hair_mask_redraw is not None:
+            hair_mask_redraw = cv2.resize(hair_mask_reuse.astype(np.uint8), (_nw, _nh),
+                                          interpolation=cv2.INTER_NEAREST).astype(bool)
+        logger.info("接口2女 管线降分辨率: %dx%d → %dx%d (max_side=%d)",
+                    w, h, _nw, _nh, eff_side)
+
     for order, (key, white_path) in items:
         white = load_texture_rgba(white_path)
         overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
@@ -273,7 +340,10 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
             logger.warning("接口2 换发型：female key=%s 无对应 chang_id，跳过生发图", key)
         else:
             try:
-                data = generate_hairline_redraw(image_bgr, chang_id, **redraw_defaults)
+                import time as _t
+                _ts0 = _t.perf_counter()
+                data = generate_hairline_redraw(redraw_img, chang_id, hair_mask=hair_mask_redraw, **redraw_defaults)
+                _ts1 = _t.perf_counter()
                 steps = data.get("steps") or {}
                 # ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG
                 final_b64 = steps.get("final_base64") or ""
@@ -289,10 +359,22 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
                         mask_b64 = mask_b64.split(",", 1)[1]
                     final_bytes = base64.b64decode(final_b64)
                     mask_bytes = base64.b64decode(mask_b64)
-                    # 后端调外部重绘服务（local_test），返回重绘后的 PNG
-                    grown_png = _call_local_redraw(final_bytes, mask_bytes)
+                    # 后端直接调 ComfyUI 重绘，返回重绘后的 PNG
+                    _tr0 = _t.perf_counter()
+                    grown_png = _call_local_redraw(final_bytes, mask_bytes,
+                                                   max_side=redraw_max_side,
+                                                   unet_name=unet_name)
+                    _tr1 = _t.perf_counter()
+                    _tm = data.get("timings_ms") or {}
+                    logger.info("接口2女 分段计时 type=%s: swapHair管线=%.2fs (mask=%dms swap=%dms blend=%dms), ComfyUI重绘=%.2fs",
+                                key, _ts1 - _ts0,
+                                _tm.get("mask", 0), _tm.get("swap", 0), _tm.get("blend", 0),
+                                _tr1 - _tr0)
                     if grown_png is None:
                         logger.warning("接口2 换发型：type=%s 重绘结果为空", key)
+                    elif redraw_img is not image_bgr:
+                        # 管线在降分辨率图上跑，结果放大回原尺寸
+                        grown_png = _upscale_png_to(grown_png, w, h)
             except NoFaceError:
                 logger.warning("接口2 换发型：type=%s 未检出人脸", key)
             except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
@@ -319,7 +401,7 @@ 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)
@@ -328,13 +410,24 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | 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,
+                           redraw_max_side: int | None = None,
+                           unet_name: str | None = None,
+                           v2_defaults: dict | None = None):
     """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。
+
+    生发机制（同接口2，按性别分流）：
+      - female：generate_grow_results_swap（swapHair + Flux-2 整帧重绘）
+      - male：generate_grow_results（ComfyUI add_hair inpaint）
+    redraw_max_side / unet_name / v2_defaults：female 路径参数，同接口2。
+      male 路径仅用 unet_name；redraw_max_side/v2_defaults 对 male 无意义（忽略）。
+    use_mask/prompt：仅 male 路径生效（同接口2 male）。
+    generate_grow_image（默认 True）：是否生成生发图（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。
@@ -355,10 +448,27 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     # 三档贴图表（同性别、同 key 顺序，因三个文件夹同名）
     tex_by_level = {lv: get_texture_map(lv)[gender] for lv in _TEXTURE_DIRS}
 
-    # use_mask=False：干净原图+空遮罩与贴图无关，只跑一次 ComfyUI，选中项复用
-    shared_grown = None
-    if not use_mask:
-        shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
+    # 生发图（同接口2，按性别分流）：一次性算出所有选中发型的生发图，按 order 对应回叠图。
+    # female→generate_grow_results_swap（swapHair+Flux-2 整帧重绘）；
+    # male→generate_grow_results（ComfyUI add_hair inpaint）。
+    # generate_grow_image=False 时跳过，grown_by_order 为空 dict（各发型 grown_png 恒 None）。
+    grown_by_order: dict[int, bytes | None] = {}
+    if generate_grow_image:
+        try:
+            if gender == "female":
+                items = generate_grow_results_swap(
+                    image_bgr, hair_styles, v2_defaults or {},
+                    redraw_max_side=redraw_max_side, unet_name=unet_name)
+            else:
+                items = generate_grow_results(
+                    image_bgr, gender, use_mask, prompt, hair_styles,
+                    unet_name=unet_name)
+            if items is None:
+                return None  # 无人脸（同接口2 的 None 语义）
+            for it in items:
+                grown_by_order[it["order"]] = it.get("grown_png")
+        except Exception as e:  # noqa: BLE001  整批生发失败不拖垮叠图主结果
+            logger.warning("接口5 生发批量失败（gender=%s）：%s", gender, e)
 
     def _center_of(overlay):
         """从某档发际线透明叠图取面部中轴处的发际线中点 (x,y)，无像素返回 None。"""
@@ -371,14 +481,13 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
 
     images, best_centers = [], None
     for s in hair_styles:  # s = 1-indexed 发型序号
-        key, mid_path = tex_by_level["middle"][s - 1]
+        key, _mid_path = tex_by_level["middle"][s - 1]
         overlays = {}
         for lv in _TEXTURE_DIRS:
             white = load_texture_rgba(tex_by_level[lv][s - 1][1])
             overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-        # 生发：固定 middle 黑模板
-        grown_png = shared_grown if not use_mask else \
-            _grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
+        # 生发图：从按性别算好的结果里按 order 取（generate_grow_image=False 时缺省 None）
+        grown_png = grown_by_order.get(s)
         images.append({"hairline_type": key, "order": s,
                        "overlays": overlays, "grown_png": grown_png})
         # best_centers：首个选中发型三档（middle/high/low）发际线中点
@@ -387,18 +496,73 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     return {"images": images, "best_centers": best_centers}
 
 
+# 接口3 送 ComfyUI 前限边，降低峰值显存，避免与接口2 切换时把 Flux 挤出。
+# 统一 prompt 后 Flux 不再被 CLIP 挤出，接口3 可用较高分辨率。可用 GROW_B_MAX_SIDE 覆盖。
+_GROW_B_MAX_SIDE = int(os.getenv("GROW_B_MAX_SIDE", "1024"))
+
+def _downscale_max_side(img_bgr: np.ndarray, max_side: int) -> tuple[np.ndarray, float]:
+    """长边超过 max_side 时等比例缩小；返回 (图, scale)，scale=新/旧。"""
+    h, w = img_bgr.shape[:2]
+    m = max(h, w)
+    if max_side <= 0 or m <= max_side:
+        return img_bgr, 1.0
+    scale = max_side / float(m)
+    nw = max(1, int(round(w * scale)))
+    nh = max(1, int(round(h * scale)))
+    out = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_AREA)
+    return out, scale
+
+
+def _upscale_png_to(png_bytes: bytes, out_w: int, out_h: int) -> bytes:
+    """把 Comfy 输出 PNG 双线性拉回原图尺寸（仅展示对齐，不增加推理细节）。"""
+    arr = np.frombuffer(png_bytes, np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
+    if img is None:
+        return png_bytes
+    if img.shape[1] == out_w and img.shape[0] == out_h:
+        return png_bytes
+    resized = cv2.resize(img, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
+    ok, buf = cv2.imencode(".png", resized)
+    return buf.tobytes() if ok else png_bytes
+
+
+def _prep_comfy_input(img_bgr: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
+    """单段 ComfyUI 生发（接口2男 / 接口3）送图前限边到 GROW_B_MAX_SIDE。
+    返回 (缩后图, 缩后遮罩, scale)；scale<1 时调用方需把结果放大回原尺寸。"""
+    h, w = img_bgr.shape[:2]
+    if _GROW_B_MAX_SIDE <= 0 or max(h, w) <= _GROW_B_MAX_SIDE:
+        return img_bgr, mask, 1.0
+    out, scale = _downscale_max_side(img_bgr, _GROW_B_MAX_SIDE)
+    nh, nw = out.shape[:2]
+    msk = cv2.resize(mask, (nw, nh), interpolation=cv2.INTER_NEAREST)
+    logger.info("接口2男/接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+                w, h, nw, nh, _GROW_B_MAX_SIDE)
+    return out, msk, scale
+
+
 def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str = None):
     """接口3：检测医生手绘发际线 → 遮罩 → 送 ComfyUI 生发（仅需划线图一张）。
 
     检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
     工作流提示词会清除黑线再生发）。
 
+    进 Comfy 前若长边 > GROW_B_MAX_SIDE（默认 896）会先等比例缩小，降低峰值显存；
+    输出再拉回原图尺寸。
+
     use_mask（默认 True）：是否启用自动检测的遮罩，用于测试对比。
       - True：检测手绘线 → 建遮罩 → alpha=255−mask（透明区=重绘区，节点44 画黄色参考区）。
       - False：跳过检测，直接送划线图，alpha 全 255（空遮罩，节点26 mask 为空），
         模型仅凭医生黑线参考生发。无需改工作流，唯一变量是遮罩。
     Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
     """
+    orig_h, orig_w = marked_bgr.shape[:2]
+    marked_bgr, _scale = _downscale_max_side(marked_bgr, _GROW_B_MAX_SIDE)
+    if _scale < 1.0:
+        logger.info(
+            "接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
+            orig_w, orig_h, marked_bgr.shape[1], marked_bgr.shape[0], _GROW_B_MAX_SIDE,
+        )
+
     h, w = marked_bgr.shape[:2]
     if use_mask:
         rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
@@ -416,8 +580,10 @@ 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"}
 
 

image/compare_test/archive_20260725/batch_test.log

@@ -0,0 +1,75 @@
+=== 接口2 female 批量对比测试 ===
+矩阵: 4图 × 5发型 × 2档 = 40 次
+已跳过 0 个已完成项
+
+[1/40] ▶ girl2_ellipse_default896 (side=None)... ✅ 10.8s  出图 1082x1081 (204589B)
+[2/40] ▶ girl2_ellipse_origin0 (side=0)... ✅ 13.3s  出图 1088x1088 (232252B)
+[3/40] ▶ girl2_flower_default896 (side=None)... ✅ 11.0s  出图 1082x1081 (210875B)
+[4/40] ▶ girl2_flower_origin0 (side=0)... ✅ 12.5s  出图 1088x1088 (236073B)
+[5/40] ▶ girl2_heart_default896 (side=None)... ✅ 11.9s  出图 1082x1081 (198911B)
+[6/40] ▶ girl2_heart_origin0 (side=0)... ✅ 12.2s  出图 1088x1088 (249893B)
+[7/40] ▶ girl2_straight_default896 (side=None)... ✅ 12.1s  出图 1082x1081 (211803B)
+[8/40] ▶ girl2_straight_origin0 (side=0)... ✅ 12.3s  出图 1088x1088 (235377B)
+[9/40] ▶ girl2_wave_default896 (side=None)... ✅ 12.7s  出图 1082x1081 (207736B)
+[10/40] ▶ girl2_wave_origin0 (side=0)... ✅ 11.9s  出图 1088x1088 (238081B)
+[11/40] ▶ girl5_ellipse_default896 (side=None)... ✅ 9.6s  出图 768x752 (105867B)
+[12/40] ▶ girl5_ellipse_origin0 (side=0)... ✅ 7.3s  出图 768x752 (112963B)
+[13/40] ▶ girl5_flower_default896 (side=None)... ✅ 8.0s  出图 768x752 (114378B)
+[14/40] ▶ girl5_flower_origin0 (side=0)... ✅ 7.2s  出图 768x752 (108495B)
+[15/40] ▶ girl5_heart_default896 (side=None)... ✅ 8.1s  出图 768x752 (113874B)
+[16/40] ▶ girl5_heart_origin0 (side=0)... ✅ 7.6s  出图 768x752 (103160B)
+[17/40] ▶ girl5_straight_default896 (side=None)... ✅ 8.4s  出图 768x752 (104492B)
+[18/40] ▶ girl5_straight_origin0 (side=0)... ✅ 7.2s  出图 768x752 (100941B)
+[19/40] ▶ girl5_wave_default896 (side=None)... ✅ 8.4s  出图 768x752 (120122B)
+[20/40] ▶ girl5_wave_origin0 (side=0)... ✅ 7.7s  出图 768x752 (113282B)
+[21/40] ▶ qwer_ellipse_default896 (side=None)... ✅ 10.2s  出图 1288x1678 (261812B)
+[22/40] ▶ qwer_ellipse_origin0 (side=0)... ✅ 30.0s  出图 1280x1680 (331013B)
+[23/40] ▶ qwer_flower_default896 (side=None)... ✅ 10.2s  出图 1288x1678 (312834B)
+[24/40] ▶ qwer_flower_origin0 (side=0)... ✅ 25.0s  出图 1280x1680 (328605B)
+[25/40] ▶ qwer_heart_default896 (side=None)... ✅ 11.1s  出图 1288x1678 (331337B)
+[26/40] ▶ qwer_heart_origin0 (side=0)... ✅ 25.0s  出图 1280x1680 (355982B)
+[27/40] ▶ qwer_straight_default896 (side=None)... ✅ 10.7s  出图 1288x1678 (281665B)
+[28/40] ▶ qwer_straight_origin0 (side=0)... ✅ 23.4s  出图 1280x1680 (322183B)
+[29/40] ▶ qwer_wave_default896 (side=None)... ✅ 11.1s  出图 1288x1678 (289322B)
+[30/40] ▶ qwer_wave_origin0 (side=0)... ✅ 25.8s  出图 1280x1680 (351719B)
+[31/40] ▶ asdf_ellipse_default896 (side=None)... ✅ 11.2s  出图 1254x1666 (285362B)
+[32/40] ▶ asdf_ellipse_origin0 (side=0)... ✅ 25.7s  出图 1248x1664 (286768B)
+[33/40] ▶ asdf_flower_default896 (side=None)... ✅ 10.7s  出图 1254x1666 (266946B)
+[34/40] ▶ asdf_flower_origin0 (side=0)... ✅ 22.9s  出图 1248x1664 (353668B)
+[35/40] ▶ asdf_heart_default896 (side=None)... ✅ 10.4s  出图 1254x1666 (271975B)
+[36/40] ▶ asdf_heart_origin0 (side=0)... ✅ 22.9s  出图 1248x1664 (291983B)
+[37/40] ▶ asdf_straight_default896 (side=None)... ✅ 9.8s  出图 1254x1666 (258665B)
+[38/40] ▶ asdf_straight_origin0 (side=0)... ✅ 24.0s  出图 1248x1664 (279883B)
+[39/40] ▶ asdf_wave_default896 (side=None)... ✅ 11.9s  出图 1254x1666 (286515B)
+[40/40] ▶ asdf_wave_origin0 (side=0)... ✅ 24.3s  出图 1248x1664 (295694B)
+
+======================================================================
+汇总报告
+======================================================================
+
+--- 速度对比（秒，✓=成功 ✗=失败）---
+图       发型               默认896         原图0        差值
+girl2   ellipse          10.8✓       13.3✓      +2.5
+girl2   flower           11.0✓       12.5✓      +1.5
+girl2   heart            11.9✓       12.2✓      +0.3
+girl2   straight         12.1✓       12.3✓      +0.2
+girl2   wave             12.7✓       11.9✓      -0.8
+girl5   ellipse           9.6✓        7.3✓      -2.3
+girl5   flower            8.0✓        7.2✓      -0.8
+girl5   heart             8.1✓        7.6✓      -0.5
+girl5   straight          8.4✓        7.2✓      -1.2
+girl5   wave              8.4✓        7.7✓      -0.7
+qwer    ellipse          10.2✓       30.0✓     +19.8
+qwer    flower           10.2✓       25.0✓     +14.8
+qwer    heart            11.1✓       25.0✓     +13.9
+qwer    straight         10.7✓       23.4✓     +12.7
+qwer    wave             11.1✓       25.8✓     +14.7
+asdf    ellipse          11.2✓       25.7✓     +14.5
+asdf    flower           10.7✓       22.9✓     +12.2
+asdf    heart            10.4✓       22.9✓     +12.5
+asdf    straight          9.8✓       24.0✓     +14.2
+asdf    wave             11.9✓       24.3✓     +12.4
+
+结果图: /home/ubuntu/hair/image/compare_test/out/
+CSV报告: /home/ubuntu/hair/image/compare_test/out/report.csv
+JSON明细: /home/ubuntu/hair/image/compare_test/out/report.json

image/compare_test/archive_20260725/report.csv

@@ -0,0 +1,41 @@
+img,style,side,ok,elapsed_s,out_w,out_h,err
+girl2,ellipse,default896,True,10.8,1082,1081,
+girl2,ellipse,origin0,True,13.3,1088,1088,
+girl2,flower,default896,True,11.0,1082,1081,
+girl2,flower,origin0,True,12.5,1088,1088,
+girl2,heart,default896,True,11.9,1082,1081,
+girl2,heart,origin0,True,12.2,1088,1088,
+girl2,straight,default896,True,12.1,1082,1081,
+girl2,straight,origin0,True,12.3,1088,1088,
+girl2,wave,default896,True,12.7,1082,1081,
+girl2,wave,origin0,True,11.9,1088,1088,
+girl5,ellipse,default896,True,9.6,768,752,
+girl5,ellipse,origin0,True,7.3,768,752,
+girl5,flower,default896,True,8.0,768,752,
+girl5,flower,origin0,True,7.2,768,752,
+girl5,heart,default896,True,8.1,768,752,
+girl5,heart,origin0,True,7.6,768,752,
+girl5,straight,default896,True,8.4,768,752,
+girl5,straight,origin0,True,7.2,768,752,
+girl5,wave,default896,True,8.4,768,752,
+girl5,wave,origin0,True,7.7,768,752,
+qwer,ellipse,default896,True,10.2,1288,1678,
+qwer,ellipse,origin0,True,30.0,1280,1680,
+qwer,flower,default896,True,10.2,1288,1678,
+qwer,flower,origin0,True,25.0,1280,1680,
+qwer,heart,default896,True,11.1,1288,1678,
+qwer,heart,origin0,True,25.0,1280,1680,
+qwer,straight,default896,True,10.7,1288,1678,
+qwer,straight,origin0,True,23.4,1280,1680,
+qwer,wave,default896,True,11.1,1288,1678,
+qwer,wave,origin0,True,25.8,1280,1680,
+asdf,ellipse,default896,True,11.2,1254,1666,
+asdf,ellipse,origin0,True,25.7,1248,1664,
+asdf,flower,default896,True,10.7,1254,1666,
+asdf,flower,origin0,True,22.9,1248,1664,
+asdf,heart,default896,True,10.4,1254,1666,
+asdf,heart,origin0,True,22.9,1248,1664,
+asdf,straight,default896,True,9.8,1254,1666,
+asdf,straight,origin0,True,24.0,1248,1664,
+asdf,wave,default896,True,11.9,1254,1666,
+asdf,wave,origin0,True,24.3,1248,1664,

image/compare_test/archive_20260725/report.json

@@ -0,0 +1,562 @@
+[
+ {
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+  "img": "girl2",
+  "style": "ellipse",
+  "style_idx": 1,
+  "side": "default896",
+  "side_val": null,
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+  "elapsed": 10.8,
+  "err": "",
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+  "out_h": 1081,
+  "bytes": 204589
+ },
+ {
+  "key": "girl2_ellipse_origin0",
+  "img": "girl2",
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+  "side": "origin0",
+  "side_val": 0,
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+  "err": "",
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+  "out_h": 1088,
+  "bytes": 232252
+ },
+ {
+  "key": "girl2_flower_default896",
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+  "style_idx": 2,
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+  "side_val": null,
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+  "err": "",
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+  "out_h": 1081,
+  "bytes": 210875
+ },
+ {
+  "key": "girl2_flower_origin0",
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+ },
+ {
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+  "out_h": 1081,
+  "bytes": 198911
+ },
+ {
+  "key": "girl2_heart_origin0",
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+  "out_h": 1088,
+  "bytes": 249893
+ },
+ {
+  "key": "girl2_straight_default896",
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+  "out_h": 1081,
+  "bytes": 211803
+ },
+ {
+  "key": "girl2_straight_origin0",
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+  "err": "",
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+  "out_h": 1088,
+  "bytes": 235377
+ },
+ {
+  "key": "girl2_wave_default896",
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+  "bytes": 207736
+ },
+ {
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+  "out_h": 1088,
+  "bytes": 238081
+ },
+ {
+  "key": "girl5_ellipse_default896",
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+  "side": "default896",
+  "side_val": null,
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+  "elapsed": 9.6,
+  "err": "",
+  "out_w": 768,
+  "out_h": 752,
+  "bytes": 105867
+ },
+ {
+  "key": "girl5_ellipse_origin0",
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+  "side": "origin0",
+  "side_val": 0,
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+  "elapsed": 7.3,
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+  "bytes": 112963
+ },
+ {
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+  "side_val": null,
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+  "elapsed": 8.0,
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+  "out_h": 752,
+  "bytes": 114378
+ },
+ {
+  "key": "girl5_flower_origin0",
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+  "out_h": 752,
+  "bytes": 108495
+ },
+ {
+  "key": "girl5_heart_default896",
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+  "err": "",
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+  "out_h": 752,
+  "bytes": 113874
+ },
+ {
+  "key": "girl5_heart_origin0",
+  "img": "girl5",
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+  "err": "",
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+  "out_h": 752,
+  "bytes": 103160
+ },
+ {
+  "key": "girl5_straight_default896",
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+  "out_h": 752,
+  "bytes": 104492
+ },
+ {
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+  "bytes": 100941
+ },
+ {
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+  "bytes": 120122
+ },
+ {
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+ },
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+ },
+ {
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+ },
+ {
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+  "bytes": 331337
+ },
+ {
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+ },
+ {
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+  "bytes": 289322
+ },
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+  "bytes": 351719
+ },
+ {
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+  "bytes": 285362
+ },
+ {
+  "key": "asdf_ellipse_origin0",
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+  "bytes": 286768
+ },
+ {
+  "key": "asdf_flower_default896",
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+  "out_h": 1666,
+  "bytes": 266946
+ },
+ {
+  "key": "asdf_flower_origin0",
+  "img": "asdf",
+  "style": "flower",
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+  "side": "origin0",
+  "side_val": 0,
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+  "elapsed": 22.9,
+  "err": "",
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+  "out_h": 1664,
+  "bytes": 353668
+ },
+ {
+  "key": "asdf_heart_default896",
+  "img": "asdf",
+  "style": "heart",
+  "style_idx": 3,
+  "side": "default896",
+  "side_val": null,
+  "ok": true,
+  "elapsed": 10.4,
+  "err": "",
+  "out_w": 1254,
+  "out_h": 1666,
+  "bytes": 271975
+ },
+ {
+  "key": "asdf_heart_origin0",
+  "img": "asdf",
+  "style": "heart",
+  "style_idx": 3,
+  "side": "origin0",
+  "side_val": 0,
+  "ok": true,
+  "elapsed": 22.9,
+  "err": "",
+  "out_w": 1248,
+  "out_h": 1664,
+  "bytes": 291983
+ },
+ {
+  "key": "asdf_straight_default896",
+  "img": "asdf",
+  "style": "straight",
+  "style_idx": 4,
+  "side": "default896",
+  "side_val": null,
+  "ok": true,
+  "elapsed": 9.8,
+  "err": "",
+  "out_w": 1254,
+  "out_h": 1666,
+  "bytes": 258665
+ },
+ {
+  "key": "asdf_straight_origin0",
+  "img": "asdf",
+  "style": "straight",
+  "style_idx": 4,
+  "side": "origin0",
+  "side_val": 0,
+  "ok": true,
+  "elapsed": 24.0,
+  "err": "",
+  "out_w": 1248,
+  "out_h": 1664,
+  "bytes": 279883
+ },
+ {
+  "key": "asdf_wave_default896",
+  "img": "asdf",
+  "style": "wave",
+  "style_idx": 5,
+  "side": "default896",
+  "side_val": null,
+  "ok": true,
+  "elapsed": 11.9,
+  "err": "",
+  "out_w": 1254,
+  "out_h": 1666,
+  "bytes": 286515
+ },
+ {
+  "key": "asdf_wave_origin0",
+  "img": "asdf",
+  "style": "wave",
+  "style_idx": 5,
+  "side": "origin0",
+  "side_val": 0,
+  "ok": true,
+  "elapsed": 24.3,
+  "err": "",
+  "out_w": 1248,
+  "out_h": 1664,
+  "bytes": 295694
+ }
+]

image/compare_test/batch_test.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""接口2 female 批量对比测试：4图 × 5发型 × 3分辨率档位 = 60次请求。
+串行执行，记录耗时与成败，结果图按 图_发型_档位 命名保存。
+用法: python3 batch_test.py
+支持断点续跑（progress.json）；新增档位时只会补跑未完成项。
+"""
+import base64, csv, json, os, sys, time, traceback
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+TIMEOUT = 600
+OUT = "/home/ubuntu/hair/image/compare_test/out"
+PROGRESS = "/home/ubuntu/hair/image/compare_test/progress.json"
+
+IMG_DIR = "/home/ubuntu/hair/image"
+IMAGES = [
+    ("girl2",  f"{IMG_DIR}/girl_img/girl2.jpg"),
+    ("girl5",  f"{IMG_DIR}/girl_img/girl5.jpg"),
+    ("qwer",   f"{IMG_DIR}/qwer.jpg"),
+    ("asdf",   f"{IMG_DIR}/asdf.jpg"),
+]
+# female: 1=ellipse 2=flower 3=heart 4=straight 5=wave
+STYLES = [
+    (1, "ellipse"), (2, "flower"), (3, "heart"), (4, "straight"), (5, "wave"),
+]
+# 三档: 默认896 / 显式1024 / 原图直送0
+SIDES = [
+    ("default896", None),   # 不传 → 后端默认896
+    ("side1024",   1024),   # 长边压到 1024
+    ("origin0",    0),      # 原图直送
+]
+SIDE_LABEL = {
+    "default896": "默认896",
+    "side1024": "1024",
+    "origin0": "原图0",
+}
+
+def load_progress():
+    if os.path.exists(PROGRESS):
+        try:
+            return json.load(open(PROGRESS))
+        except Exception:
+            pass
+    return {"done": [], "results": []}
+
+def save_progress(prog):
+    json.dump(prog, open(PROGRESS, "w"), ensure_ascii=False, indent=1)
+
+def run_one(img_name, img_path, style_idx, style_key, side_name, side_val):
+    """跑单次请求，返回 dict 结果。"""
+    key = f"{img_name}_{style_key}_{side_name}"
+    with open(img_path, "rb") as f:
+        img_b64 = base64.b64encode(f.read()).decode()
+    data = {
+        "image_base64": "data:image/jpeg;base64," + img_b64,
+        "gender": "female",
+        "hair_style": str(style_idx),
+    }
+    if side_val is not None:
+        data["redraw_max_side"] = str(side_val)
+    t0 = time.time()
+    rec = {"key": key, "img": img_name, "style": style_key, "style_idx": style_idx,
+           "side": side_name, "side_val": side_val, "ok": False,
+           "elapsed": 0.0, "err": "", "out_w": 0, "out_h": 0}
+    try:
+        r = requests.post(API, data=data, headers={"X-Internal-Token": TOKEN}, timeout=TIMEOUT)
+        rec["elapsed"] = round(time.time() - t0, 1)
+        d = r.json()
+        if d.get("code") != 0:
+            rec["err"] = f"code={d.get('code')} {d.get('message','')}"[:200]
+            return rec
+        results = (d.get("data") or {}).get("results") or []
+        if not results:
+            rec["err"] = "空结果"
+            return rec
+        it = results[0]
+        grown = it.get("grown_image_base64")
+        if not grown:
+            rec["err"] = "无生发图(grown_png=None, 重绘失败)"
+            return rec
+        raw = base64.b64decode(grown)
+        from PIL import Image
+        import io as _io
+        im = Image.open(_io.BytesIO(raw))
+        rec["out_w"], rec["out_h"] = im.size
+        out_path = f"{OUT}/{key}.jpg"
+        with open(out_path, "wb") as fo:
+            fo.write(raw)
+        rec["ok"] = True
+        rec["bytes"] = len(raw)
+    except requests.exceptions.Timeout:
+        rec["elapsed"] = round(time.time() - t0, 1)
+        rec["err"] = f"超时(>{TIMEOUT}s)"
+    except Exception as e:
+        rec["elapsed"] = round(time.time() - t0, 1)
+        rec["err"] = f"{type(e).__name__}: {str(e)[:180]}"
+    return rec
+
+def main():
+    os.makedirs(OUT, exist_ok=True)
+    prog = load_progress()
+    done_keys = set(prog["done"])
+    total = len(IMAGES) * len(STYLES) * len(SIDES)
+    print(f"=== 接口2 female 批量对比测试 ===")
+    print(f"矩阵: {len(IMAGES)}图 × {len(STYLES)}发型 × {len(SIDES)}档 = {total} 次")
+    print(f"已跳过 {len(done_keys)} 个已完成项\n")
+
+    idx = 0
+    for img_name, img_path in IMAGES:
+        for style_idx, style_key in STYLES:
+            for side_name, side_val in SIDES:
+                idx += 1
+                key = f"{img_name}_{style_key}_{side_name}"
+                if key in done_keys:
+                    print(f"[{idx}/{total}] ⏭  跳过已完成 {key}")
+                    continue
+                print(f"[{idx}/{total}] ▶ {key} (side={side_val})...", end=" ", flush=True)
+                rec = run_one(img_name, img_path, style_idx, style_key, side_name, side_val)
+                prog["results"].append(rec)
+                prog["done"].append(key)
+                save_progress(prog)
+                if rec["ok"]:
+                    print(f"✅ {rec['elapsed']}s  出图 {rec['out_w']}x{rec['out_h']} ({rec['bytes']}B)")
+                else:
+                    print(f"❌ {rec['elapsed']}s  {rec['err']}")
+                time.sleep(2)  # 串行间隔，避免队列堆积
+
+    # 汇总
+    print("\n" + "="*70)
+    print("汇总报告")
+    print("="*70)
+    write_report(prog["results"])
+    print(f"\n结果图: {OUT}/")
+    print(f"CSV报告: {OUT}/report.csv")
+    print(f"JSON明细: {OUT}/report.json")
+
+def write_report(results):
+    # CSV
+    csv_path = f"{OUT}/report.csv"
+    with open(csv_path, "w", newline="") as f:
+        w = csv.writer(f)
+        w.writerow(["img","style","side","ok","elapsed_s","out_w","out_h","err"])
+        for r in results:
+            w.writerow([r["img"],r["style"],r["side"],r["ok"],r["elapsed"],
+                        r["out_w"],r["out_h"],r["err"]])
+    # JSON
+    json.dump(results, open(f"{OUT}/report.json","w"), ensure_ascii=False, indent=1)
+    # 控制台速度对比表
+    print("\n--- 速度对比（秒，✓=成功 ✗=失败）---")
+    headers = ["图", "发型"] + [SIDE_LABEL[s] for s, _ in SIDES]
+    print(f"{headers[0]:<8}{headers[1]:<10}" + "".join(f"{h:>12}" for h in headers[2:]))
+    for img_name, _ in IMAGES:
+        for _, style_key in STYLES:
+            cells = []
+            for side_name, _ in SIDES:
+                r = next((x for x in results if x["img"]==img_name and x["style"]==style_key and x["side"]==side_name), None)
+                cells.append(f"{r['elapsed']}{'✓' if r['ok'] else '✗'}" if r else "-")
+            print(f"{img_name:<8}{style_key:<10}" + "".join(f"{c:>12}" for c in cells))
+
+if __name__ == "__main__":
+    main()

image/compare_test/gen_report.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+"""把 report.json + 结果图生成为自包含 HTML 报告（支持 896 / 1024 / 原图0 三档）。"""
+import json, os, html
+
+OUT = "/home/ubuntu/hair/image/compare_test/out"
+REPORT_HTML = os.path.join(OUT, "report.html")
+results = json.load(open(os.path.join(OUT, "report.json")))
+
+IMAGES = ["girl2", "girl5", "qwer", "asdf"]
+STYLES = ["ellipse", "flower", "heart", "straight", "wave"]
+SIDES = [
+    ("default896", "默认 896", "#2563eb", "d"),
+    ("side1024",   "1024",     "#10b981", "m"),
+    ("origin0",    "原图直送 0", "#f59e0b", "o"),
+]
+IMG_LONGSIDE = {"girl2": 1082, "girl5": 767, "qwer": 1678, "asdf": 1666}
+STYLE_CN = {"ellipse": "椭圆", "flower": "花瓣", "heart": "心形", "straight": "直线", "wave": "波浪"}
+
+def get(img, style, side):
+    for r in results:
+        if r["img"] == img and r["style"] == style and r["side"] == side:
+            return r
+    return None
+
+# 总览统计（>60s 视为冷启动/异常，不进均值）
+total = len(results)
+ok = sum(1 for r in results if r["ok"])
+COLD_S = 60.0
+
+def avg(seq):
+    seq = [x for x in seq if x is not None]
+    return sum(seq) / len(seq) if seq else 0
+
+def times(side, img=None):
+    out = []
+    for r in results:
+        if not r["ok"] or r["side"] != side:
+            continue
+        if img is not None and r["img"] != img:
+            continue
+        if r["elapsed"] >= COLD_S:
+            continue
+        out.append(r["elapsed"])
+    return out
+
+cold_n = sum(1 for r in results if r["ok"] and r["elapsed"] >= COLD_S)
+side_avgs = {sid: avg(times(sid)) for sid, *_ in SIDES}
+all_times = [t for sid, *_ in SIDES for t in times(sid)]
+max_bar = max(all_times + [1])
+
+# 构造速度对比图数据：每图 × 三档
+chart_rows = []
+for img in IMAGES:
+    chart_rows.append((img, IMG_LONGSIDE[img], [avg(times(sid, img)) for sid, *_ in SIDES]))
+
+def bar_svg():
+    bar_h = 22
+    gap = 12
+    label_w = 78
+    chart_w = 760
+    n_bars = len(SIDES)
+    rows = len(chart_rows)
+    h = rows * (bar_h * n_bars + gap) + 40
+    parts = [f'<svg viewBox="0 0 {label_w + chart_w + 80} {h}" class="chart">']
+    y = 10
+    for img, longside, avgs in chart_rows:
+        base = avgs[0] if avgs and avgs[0] else 1
+        for i, ((sid, label, color, _), a) in enumerate(zip(SIDES, avgs)):
+            yi = y + i * bar_h
+            w = int(a / max_bar * chart_w) if a else 0
+            warn = "⚠" if (i > 0 and a > base * 1.8) else ""
+            parts.append(f'<rect x="{label_w}" y="{yi}" width="{w}" height="{bar_h-4}" rx="3" fill="{color}"/>')
+            parts.append(f'<text x="{label_w + w + 6}" y="{yi + bar_h - 10}" class="barlabel">{a:.1f}s {warn}</text>')
+            parts.append(f'<text x="{label_w-8}" y="{yi + bar_h - 10}" class="rowlabel" text-anchor="end">{html.escape(label)}</text>')
+        parts.append(f'<text x="0" y="{y + bar_h - 2}" class="imglabel">{img}<tspan class="imgside">长边{longside}</tspan></text>')
+        y += bar_h * n_bars + gap
+    parts.append("</svg>")
+    return "".join(parts)
+
+def img_cell(r, cls):
+    if not r:
+        return f'<td class="{cls} fail">缺失</td>'
+    fname = r["key"] + ".jpg"
+    status = "✅" if r["ok"] else "❌"
+    t = f'{r["elapsed"]}s'
+    dim = f'{r["out_w"]}×{r["out_h"]}'
+    err = f'<div class="err">{html.escape(r["err"])}</div>' if r["err"] else ""
+    img_tag = (f'<img loading="lazy" src="{fname}" onclick="openImg(this.src)" alt="{html.escape(r["key"])}">'
+               if r["ok"] else '<div class="noimg">无图</div>')
+    return (f'<td class="{cls}"><div class="thumb">{img_tag}</div>'
+            f'<div class="meta">{status} {t} · {dim}</div>{err}</td>')
+
+def compare_cards():
+    out = []
+    for img in IMAGES:
+        out.append(f'<div class="card"><div class="card-h">📷 {html.escape(img)} <span class="tag">原图长边 {IMG_LONGSIDE[img]}px</span></div><div class="card-b">')
+        heads = "".join(f'<th class="{cls}">{html.escape(label)}</th>' for _, label, _, cls in SIDES)
+        out.append(f'<table class="cmp"><thead><tr><th>发型</th>{heads}</tr></thead><tbody>')
+        for style in STYLES:
+            sc = STYLE_CN[style]
+            cells = "".join(img_cell(get(img, style, sid), cls) for sid, _, _, cls in SIDES)
+            out.append(f'<tr><td class="sname">{html.escape(style)}<span>{sc}</span></td>{cells}</tr>')
+        out.append("</tbody></table></div></div>")
+    return "".join(out)
+
+# 结论
+small = [img for img in IMAGES if IMG_LONGSIDE[img] <= 1100]
+large = [img for img in IMAGES if IMG_LONGSIDE[img] > 1100]
+def ratio_range(num_side, den_side="default896"):
+    ratios = []
+    for img in large:
+        den = avg(times(den_side, img))
+        num = avg(times(num_side, img))
+        if den:
+            ratios.append(num / den)
+    if not ratios:
+        return 0, 0
+    return min(ratios), max(ratios)
+
+r1024_lo, r1024_hi = ratio_range("side1024")
+r0_lo, r0_hi = ratio_range("origin0")
+cold_note = f"冷启动 {cold_n} 次（≥{COLD_S:.0f}s）已从均值剔除。" if cold_n else ""
+conclusion = (
+    f"原图长边 ≤ 1100（{'/'.join(small)}）时三档耗时接近；"
+    f"长边 > 1600（{'/'.join(large)}）时相对默认896："
+    f"<b>1024 约 {r1024_lo:.1f}~{r1024_hi:.1f}×</b>，"
+    f"<b>原图直送约 {r0_lo:.1f}~{r0_hi:.1f}×</b>。"
+    f"{cold_note}画质对比见下方三列并排，点击可放大。"
+)
+
+avg896 = side_avgs.get("default896", 0)
+avg1024 = side_avgs.get("side1024", 0)
+avg0 = side_avgs.get("origin0", 0)
+ratio1024 = avg1024 / avg896 if avg896 else 0
+ratio0 = avg0 / avg896 if avg896 else 0
+
+legend = "".join(
+    f'<span><i style="background:{color}"></i>{html.escape(label)}</span>'
+    for _, label, color, _ in SIDES
+)
+
+html_doc = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>接口2 分辨率对比测试报告</title>
+<style>
+  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
+  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; line-height: 1.5; padding: 20px; }}
+  .wrap {{ max-width: 1400px; margin: 0 auto; }}
+  h1 {{ font-size: 24px; margin-bottom: 4px; }}
+  .sub {{ color: #6b7280; font-size: 13px; margin-bottom: 20px; }}
+  .summary {{ display: grid; grid-template-columns: repeat(5, 1fr); gap: 12px; margin-bottom: 24px; }}
+  .stat {{ background: #fff; border-radius: 10px; padding: 16px; box-shadow: 0 1px 3px rgba(0,0,0,.06); }}
+  .stat .num {{ font-size: 26px; font-weight: 700; }}
+  .stat .lbl {{ font-size: 12px; color: #6b7280; margin-top: 2px; }}
+  .stat.d .num {{ color: #2563eb; }}
+  .stat.m .num {{ color: #10b981; }}
+  .stat.o .num {{ color: #f59e0b; }}
+  .card {{ background: #fff; border-radius: 12px; box-shadow: 0 1px 4px rgba(0,0,0,.07); margin-bottom: 20px; overflow: hidden; }}
+  .card-h {{ padding: 12px 18px; background: #fafafa; border-bottom: 1px solid #f0f0f0; font-weight: 700; font-size: 15px; display: flex; align-items: center; gap: 8px; }}
+  .card-h .tag {{ font-size: 11px; font-weight: 500; color: #6b7280; background: #f3f4f6; padding: 2px 8px; border-radius: 10px; }}
+  .card-b {{ padding: 16px; }}
+  .chart {{ width: 100%; height: auto; max-width: 920px; }}
+  .barlabel {{ font-size: 12px; fill: #374151; font-weight: 600; }}
+  .rowlabel {{ font-size: 11px; fill: #6b7280; }}
+  .imglabel {{ font-size: 14px; fill: #111; font-weight: 700; }}
+  .imgside {{ font-size: 10px; fill: #9ca3af; font-weight: 400; }}
+  table.cmp {{ width: 100%; border-collapse: collapse; table-layout: fixed; }}
+  table.cmp th {{ font-size: 12px; color: #6b7280; font-weight: 600; padding: 8px; text-align: center; border-bottom: 2px solid #f0f0f0; }}
+  table.cmp td {{ padding: 8px; border-bottom: 1px solid #f6f6f6; vertical-align: top; text-align: center; }}
+  table.cmp th.d, table.cmp td.d {{ background: #eff6ff; }}
+  table.cmp th.m, table.cmp td.m {{ background: #ecfdf5; }}
+  table.cmp th.o, table.cmp td.o {{ background: #fffbeb; }}
+  td.sname {{ font-weight: 600; text-align: left; width: 90px; }}
+  td.sname span {{ display: block; font-size: 11px; color: #9ca3af; font-weight: 400; }}
+  .thumb {{ background: #222; border-radius: 6px; overflow: hidden; margin-bottom: 4px; cursor: zoom-in; }}
+  .thumb img {{ width: 100%; height: 200px; object-fit: contain; display: block; }}
+  .noimg {{ color: #d1d5db; font-size: 12px; padding: 40px 0; }}
+  .meta {{ font-size: 11px; color: #6b7280; }}
+  .err {{ font-size: 10px; color: #dc2626; margin-top: 2px; }}
+  .note {{ background: #fef3c7; border-left: 3px solid #f59e0b; padding: 12px 16px; border-radius: 6px; font-size: 13px; margin-bottom: 20px; }}
+  .legend {{ display: flex; gap: 20px; font-size: 12px; color: #6b7280; margin-bottom: 12px; flex-wrap: wrap; }}
+  .legend span {{ display: inline-flex; align-items: center; gap: 5px; }}
+  .legend i {{ width: 12px; height: 12px; border-radius: 2px; display: inline-block; }}
+  .overlay {{ display: none; position: fixed; inset: 0; background: rgba(0,0,0,.9); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 30px; }}
+  .overlay.active {{ display: flex; }}
+  .overlay img {{ max-width: 95%; max-height: 95%; object-fit: contain; border-radius: 4px; }}
+  @media (max-width: 900px) {{
+    .summary {{ grid-template-columns: repeat(2, 1fr); }}
+    .thumb img {{ height: 160px; }}
+  }}
+</style>
+</head>
+<body>
+<div class="wrap">
+  <h1>接口2 生发 · 分辨率对比测试报告</h1>
+  <p class="sub">POST /api/v1/hair/grow · female · 4 张图 × 5 发型 × 3 档分辨率 = {total} 次 · 串行</p>
+
+  <div class="summary">
+    <div class="stat"><div class="num">{ok}/{total}</div><div class="lbl">成功 / 总数</div></div>
+    <div class="stat d"><div class="num">{avg896:.1f}s</div><div class="lbl">默认896 平均</div></div>
+    <div class="stat m"><div class="num">{avg1024:.1f}s</div><div class="lbl">1024 平均 · ×{ratio1024:.2f}</div></div>
+    <div class="stat o"><div class="num">{avg0:.1f}s</div><div class="lbl">原图直送 平均 · ×{ratio0:.2f}</div></div>
+    <div class="stat"><div class="num" style="color:{'#dc2626' if ratio0>1.3 else '#16a34a'}">×{ratio0:.2f}</div><div class="lbl">原图/默认 倍率</div></div>
+  </div>
+
+  <div class="note">
+    <b>结论：</b>{conclusion}
+  </div>
+
+  <div class="card">
+    <div class="card-h">平均耗时对比（按图分组，单位：秒）</div>
+    <div class="card-b">
+      <div class="legend">{legend}</div>
+      {bar_svg()}
+    </div>
+  </div>
+
+  <h2 style="font-size:18px;margin:8px 0 14px">画质对比（左→右：默认896 · 1024 · 原图直送）</h2>
+  {compare_cards()}
+
+</div>
+<div class="overlay" id="overlay" onclick="this.classList.remove('active')">
+  <img id="overlayImg" src="">
+</div>
+<script>
+function openImg(src) {{
+  document.getElementById('overlayImg').src = src;
+  document.getElementById('overlay').classList.add('active');
+}}
+</script>
+</body>
+</html>
+"""
+
+with open(REPORT_HTML, "w") as f:
+    f.write(html_doc)
+# 同步首页
+with open(os.path.join(OUT, "index.html"), "w") as f:
+    f.write(html_doc)
+print(f"已生成: {REPORT_HTML}")
+print(f"图片目录: {OUT}")

image/compare_test/report-server.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=接口2 分辨率对比测试报告 HTTP 服务 (8848)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair/image/compare_test/out
+ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8848 --bind 0.0.0.0
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

image/reports/index.html

@@ -0,0 +1,47 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>测试报告索引</title>
+<style>
+  * { box-sizing: border-box; margin: 0; padding: 0; }
+  body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; padding: 40px 20px; }
+  .wrap { max-width: 720px; margin: 0 auto; }
+  h1 { font-size: 24px; margin-bottom: 4px; }
+  .sub { color: #6b7280; font-size: 13px; margin-bottom: 28px; }
+  .card { display: block; background: #fff; border-radius: 12px; box-shadow: 0 1px 4px rgba(0,0,0,.07); padding: 20px 24px; margin-bottom: 14px; text-decoration: none; color: inherit; transition: .15s; border-left: 4px solid #2563eb; }
+  .card:hover { transform: translateX(4px); box-shadow: 0 4px 12px rgba(0,0,0,.1); }
+  .card .title { font-size: 16px; font-weight: 700; margin-bottom: 4px; }
+  .card .desc { font-size: 13px; color: #6b7280; }
+  .card .url { font-size: 11px; color: #9ca3af; margin-top: 6px; font-family: monospace; }
+  .card.wave { border-left-color: #16a34a; }
+  .card.v2 { border-left-color: #f59e0b; }
+  .card.v1 { border-left-color: #2563eb; }
+</style>
+</head>
+<body>
+<div class="wrap">
+  <h1>📊 测试报告索引</h1>
+  <p class="sub">接口2 / 接口5 分辨率对比测试报告合集</p>
+
+  <a class="card wave" href="wave/">
+    <div class="title">💇 wave发型 · 5档分辨率对比（最新）</div>
+    <div class="desc">21图 × 5档(原图/1024/896/768/640) = 105次 · wave发型 · female</div>
+    <div class="url">/wave/</div>
+  </a>
+
+  <a class="card v2" href="v2/">
+    <div class="title">💄 5图 · 5档分辨率对比 v2</div>
+    <div class="desc">5图 × 3发型(花瓣/心形/波浪) × 5档 = 75次 · female</div>
+    <div class="url">/v2/</div>
+  </a>
+
+  <a class="card v1" href="v1/">
+    <div class="title">💄 4图 · 2档分辨率对比 v1</div>
+    <div class="desc">4图 × 5发型 × 2档(默认896/原图) = 40次 · female</div>
+    <div class="url">/v1/</div>
+  </a>
+</div>
+</body>
+</html>

image/reports/report-server.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=测试报告统一HTTP服务 (8850, 路径区分: /wave /v2 /v1)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair/image/reports
+ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8850 --bind 0.0.0.0
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

image/reports/v1

@@ -0,0 +1 @@
+/home/ubuntu/hair/image/compare_test/out

image/reports/v2

@@ -0,0 +1 @@
+/home/ubuntu/hair/image/res_test/out

image/reports/wave

@@ -0,0 +1 @@
+/home/ubuntu/hair/image/wave_test/out

image/res_test/batch_test.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""接口2 female 分辨率对比测试 v2: 5图 × 3发型 × 5档 = 75次。
+串行执行，记录耗时与成败，结果图按 图_发型_档位 命名保存。
+支持断点续跑（progress.json）。
+"""
+import base64, csv, json, os, sys, time, io
+import requests
+from PIL import Image
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+TIMEOUT = 600
+OUT = "/home/ubuntu/hair/image/res_test/out"
+PROGRESS = "/home/ubuntu/hair/image/res_test/progress.json"
+
+IMG_DIR = "/home/ubuntu/hair/image"
+IMAGES = [
+    ("girl2", f"{IMG_DIR}/girl_img/girl2.jpg"),
+    ("girl5", f"{IMG_DIR}/girl_img/girl5.jpg"),
+    ("qwer",  f"{IMG_DIR}/qwer.jpg"),
+    ("asdf",  f"{IMG_DIR}/asdf.jpg"),
+    ("girl7", f"{IMG_DIR}/girl_img/girl7.jpg"),
+]
+# female: 2=flower(花瓣) 3=heart(心形) 5=wave(波浪)
+STYLES = [(2, "flower"), (3, "heart"), (5, "wave")]
+# 5档: 原图(0) / 1024 / 896 / 768 / 640
+SIDES = [
+    ("origin",  0),
+    ("s1024",   1024),
+    ("s896",    896),
+    ("s768",    768),
+    ("s640",    640),
+]
+
+def load_progress():
+    if os.path.exists(PROGRESS):
+        try:
+            return json.load(open(PROGRESS))
+        except Exception:
+            pass
+    return {"done": [], "results": []}
+
+def save_progress(prog):
+    json.dump(prog, open(PROGRESS, "w"), ensure_ascii=False, indent=1)
+
+def run_one(img_name, img_path, style_idx, style_key, side_name, side_val):
+    key = f"{img_name}_{style_key}_{side_name}"
+    with open(img_path, "rb") as f:
+        img_b64 = base64.b64encode(f.read()).decode()
+    data = {
+        "image_base64": "data:image/jpeg;base64," + img_b64,
+        "gender": "female",
+        "hair_style": str(style_idx),
+        "redraw_max_side": str(side_val),  # 含原图档=0
+    }
+    t0 = time.time()
+    rec = {"key": key, "img": img_name, "style": style_key, "style_idx": style_idx,
+           "side": side_name, "side_val": side_val, "ok": False,
+           "elapsed": 0.0, "err": "", "out_w": 0, "out_h": 0, "bytes": 0}
+    try:
+        r = requests.post(API, data=data, headers={"X-Internal-Token": TOKEN}, timeout=TIMEOUT)
+        rec["elapsed"] = round(time.time() - t0, 1)
+        d = r.json()
+        if d.get("code") != 0:
+            rec["err"] = f"code={d.get('code')} {d.get('message','')}"[:200]
+            return rec
+        results = (d.get("data") or {}).get("results") or []
+        if not results:
+            rec["err"] = "空结果"
+            return rec
+        it = results[0]
+        grown = it.get("grown_image_base64")
+        if not grown:
+            rec["err"] = "无生发图(重绘失败/OOM?)"
+            return rec
+        raw = base64.b64decode(grown)
+        im = Image.open(io.BytesIO(raw))
+        rec["out_w"], rec["out_h"] = im.size
+        out_path = f"{OUT}/{key}.jpg"
+        with open(out_path, "wb") as fo:
+            fo.write(raw)
+        rec["ok"] = True
+        rec["bytes"] = len(raw)
+    except requests.exceptions.Timeout:
+        rec["elapsed"] = round(time.time() - t0, 1)
+        rec["err"] = f"超时(>{TIMEOUT}s)"
+    except Exception as e:
+        rec["elapsed"] = round(time.time() - t0, 1)
+        rec["err"] = f"{type(e).__name__}: {str(e)[:180]}"
+    return rec
+
+def main():
+    prog = load_progress()
+    done_keys = set(prog["done"])
+    total = len(IMAGES) * len(STYLES) * len(SIDES)
+    print(f"=== 接口2 female 分辨率对比测试 v2 ===")
+    print(f"矩阵: {len(IMAGES)}图 × {len(STYLES)}发型 × {len(SIDES)}档 = {total} 次")
+    print(f"已跳过 {len(done_keys)} 个已完成项\n")
+
+    idx = 0
+    for img_name, img_path in IMAGES:
+        for style_idx, style_key in STYLES:
+            for side_name, side_val in SIDES:
+                idx += 1
+                key = f"{img_name}_{style_key}_{side_name}"
+                if key in done_keys:
+                    print(f"[{idx}/{total}] ⏭  跳过 {key}")
+                    continue
+                print(f"[{idx}/{total}] ▶ {key} (side={side_val})...", end=" ", flush=True)
+                rec = run_one(img_name, img_path, style_idx, style_key, side_name, side_val)
+                prog["results"].append(rec)
+                prog["done"].append(key)
+                save_progress(prog)
+                if rec["ok"]:
+                    print(f"✅ {rec['elapsed']}s  {rec['out_w']}x{rec['out_h']} ({rec['bytes']}B)")
+                else:
+                    print(f"❌ {rec['elapsed']}s  {rec['err']}")
+                time.sleep(2)
+
+    print("\n" + "=" * 70)
+    print("汇总")
+    print("=" * 70)
+    write_report(prog["results"])
+    print(f"\n结果图: {OUT}/")
+    print(f"CSV: {OUT}/report.csv   JSON: {OUT}/report.json")
+
+def write_report(results):
+    with open(f"{OUT}/report.csv", "w", newline="") as f:
+        w = csv.writer(f)
+        w.writerow(["img", "style", "side", "side_val", "ok", "elapsed_s", "out_w", "out_h", "bytes", "err"])
+        for r in results:
+            w.writerow([r["img"], r["style"], r["side"], r["side_val"], r["ok"],
+                        r["elapsed"], r["out_w"], r["out_h"], r["bytes"], r["err"]])
+    json.dump(results, open(f"{OUT}/report.json", "w"), ensure_ascii=False, indent=1)
+
+    # 控制台速度表：按 图×发型 分行，5档列
+    print("\n--- 速度对比（秒）---")
+    print(f"{'图·发型':<18}{'原图0':>8}{'1024':>8}{'896':>8}{'768':>8}{'640':>8}")
+    for img_name, _ in IMAGES:
+        for _, style_key in STYLES:
+            cells = []
+            for side_name, _ in SIDES:
+                r = next((x for x in results if x["img"] == img_name and x["style"] == style_key and x["side"] == side_name), None)
+                cells.append(f"{r['elapsed']}✓" if r and r["ok"] else (f"{r['elapsed']}✗" if r else "-"))
+            print(f"{img_name+'·'+style_key:<18}{cells[0]:>8}{cells[1]:>8}{cells[2]:>8}{cells[3]:>8}{cells[4]:>8}")
+
+if __name__ == "__main__":
+    main()

image/res_test/gen_report.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python3
+"""生成 v2 分辨率对比报告：每行=原图+5档，按图×发型组织15行。"""
+import json, os, html
+
+OUT = "/home/ubuntu/hair/image/res_test/out"
+REPORT = os.path.join(OUT, "report.html")
+results = json.load(open(os.path.join(OUT, "report.json")))
+
+IMAGES = ["girl2", "girl5", "qwer", "asdf", "girl7"]
+STYLES = [("flower", "花瓣"), ("heart", "心形"), ("wave", "波浪")]
+SIDES = [("origin", "原图直送", 0), ("s1024", "1024", 1024),
+         ("s896", "896", 896), ("s768", "768", 768), ("s640", "640", 640)]
+LONGSIDE = {"girl2": 1082, "girl5": 767, "qwer": 1678, "asdf": 1666, "girl7": 925}
+
+def get(img, style, side):
+    for r in results:
+        if r["img"] == img and r["style"] == style and r["side"] == side:
+            return r
+    return None
+
+# 统计
+total = len(results)
+ok = sum(1 for r in results if r["ok"])
+
+# 每档平均耗时（排除冷启动异常值：girl2_flower_origin=135s 明显是冷启动）
+def avg(side, exclude_first_cold=False):
+    ts = []
+    for r in results:
+        if r["side"] == side and r["ok"]:
+            if exclude_first_cold and r["key"] == "girl2_flower_origin":
+                continue  # 跳过冷启动
+            ts.append(r["elapsed"])
+    return sum(ts) / len(ts) if ts else 0
+
+avg_origin = avg("origin", exclude_first_cold=True)
+avg_1024 = avg("s1024")
+avg_896 = avg("s896")
+avg_768 = avg("s768")
+avg_640 = avg("s640")
+avgs = [("origin", "原图直送", avg_origin, 0),
+        ("s1024", "1024", avg_1024, 1024),
+        ("s896", "896", avg_896, 896),
+        ("s768", "768", avg_768, 768),
+        ("s640", "640", avg_640, 640)]
+
+# 速度色阶：以全部耗时的 min-max 映射颜色（绿→黄→红）
+all_t = sorted([r["elapsed"] for r in results if r["ok"] and r["key"] != "girl2_flower_origin"])
+tmin, tmax = all_t[0], all_t[-1]
+def speed_color(t):
+    if tmax == tmin:
+        return "#16a34a"
+    ratio = (t - tmin) / (tmax - tmin)  # 0=最快(绿) 1=最慢(红)
+    if ratio < 0.33:
+        return "#16a34a"  # 绿
+    elif ratio < 0.66:
+        return "#f59e0b"  # 橙
+    else:
+        return "#dc2626"  # 红
+
+# 表格行
+rows_html = []
+for img in IMAGES:
+    for style_key, style_cn in STYLES:
+        # 原图格
+        orig_cell = (f'<td class="cell orig">'
+                     f'<div class="thumb"><img loading="lazy" src="orig_{img}.jpg" '
+                     f'onclick="openImg(this.src)" alt="原图"></div>'
+                     f'<div class="meta">📷 原图</div></td>')
+        # 5档格
+        side_cells = []
+        for side_name, side_lbl, side_val in SIDES:
+            r = get(img, style_key, side_name)
+            if r and r["ok"]:
+                col = speed_color(r["elapsed"])
+                cell = (f'<td class="cell">'
+                        f'<div class="thumb"><img loading="lazy" src="{r["key"]}.jpg" '
+                        f'onclick="openImg(this.src)" alt="{html.escape(r["key"])}"></div>'
+                        f'<div class="meta"><b style="color:{col}">{r["elapsed"]}s</b> · '
+                        f'{r["out_w"]}×{r["out_h"]}</div></td>')
+            elif r:
+                cell = (f'<td class="cell fail"><div class="thumb noimg">❌</div>'
+                        f'<div class="meta err">{html.escape(r["err"][:30])}</div></td>')
+            else:
+                cell = '<td class="cell fail"><div class="thumb noimg">—</div></td>'
+            side_cells.append(cell)
+        row_label = (f'<td class="rowlabel">'
+                     f'<div class="rimg">{html.escape(img)}</div>'
+                     f'<div class="rs">{html.escape(style_cn)}</div>'
+                     f'<div class="rl">长边 {LONGSIDE[img]}</div></td>')
+        rows_html.append("<tr>" + row_label + orig_cell + "".join(side_cells) + "</tr>")
+
+# 顶部平均耗时卡
+def stat_card(lbl, val, col, sub=""):
+    return (f'<div class="stat" style="border-left:3px solid {col}">'
+            f'<div class="num" style="color:{col}">{val:.1f}s</div>'
+            f'<div class="lbl">{lbl}{sub}</div></div>')
+
+stat_cards = "".join([
+    stat_card("原图直送", avg_origin, "#dc2626", "<br><span class='dim'>排除冷启动</span>"),
+    stat_card("1024", avg_1024, "#f59e0b"),
+    stat_card("896 (默认)", avg_896, "#2563eb"),
+    stat_card("768", avg_768, "#16a34a"),
+    stat_card("640", avg_640, "#0d9488"),
+])
+
+html_doc = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>接口2 分辨率对比测试 v2</title>
+<style>
+  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
+  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; line-height: 1.5; padding: 16px; }}
+  .wrap {{ max-width: 100%; margin: 0 auto; }}
+  h1 {{ font-size: 22px; margin-bottom: 4px; }}
+  .sub {{ color: #6b7280; font-size: 12px; margin-bottom: 16px; }}
+  .stats {{ display: flex; gap: 10px; margin-bottom: 16px; flex-wrap: wrap; }}
+  .stat {{ background: #fff; border-radius: 8px; padding: 12px 14px; box-shadow: 0 1px 3px rgba(0,0,0,.06); flex: 1; min-width: 110px; }}
+  .stat .num {{ font-size: 22px; font-weight: 700; }}
+  .stat .lbl {{ font-size: 11px; color: #6b7280; margin-top: 2px; }}
+  .stat .dim {{ color: #9ca3af; font-size: 10px; }}
+  .summary-bar {{ background: #fff; border-radius: 8px; padding: 12px 16px; margin-bottom: 16px; box-shadow: 0 1px 3px rgba(0,0,0,.06); font-size: 13px; }}
+  .summary-bar b {{ color: #dc2626; }}
+  .table-wrap {{ overflow-x: auto; background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.07); }}
+  table {{ border-collapse: collapse; min-width: 100%; }}
+  th, td {{ vertical-align: top; }}
+  thead th {{ position: sticky; top: 0; background: #f9fafb; z-index: 2; padding: 10px 8px; font-size: 12px; color: #374151; border-bottom: 2px solid #e5e7eb; text-align: center; }}
+  thead th.orig-h {{ background: #fef3c7; }}
+  tbody td {{ border-bottom: 1px solid #f3f4f6; padding: 8px; }}
+  tbody tr:hover {{ background: #f9fafb; }}
+  td.rowlabel {{ text-align: left; padding: 8px 12px; position: sticky; left: 0; background: #fff; z-index: 1; min-width: 90px; box-shadow: 2px 0 4px rgba(0,0,0,.04); }}
+  tbody tr:hover td.rowlabel {{ background: #f9fafb; }}
+  .rimg {{ font-weight: 700; font-size: 14px; }}
+  .rs {{ font-size: 12px; color: #6b7280; }}
+  .rl {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+  .cell {{ width: 180px; min-width: 180px; text-align: center; }}
+  .cell.orig {{ width: 180px; background: #fffbeb; }}
+  .thumb {{ background: #1f2937; border-radius: 6px; overflow: hidden; margin-bottom: 4px; cursor: zoom-in; }}
+  .thumb img {{ width: 100%; height: 240px; object-fit: contain; display: block; }}
+  .thumb.noimg {{ color: #d1d5db; font-size: 16px; padding: 100px 0; text-align: center; }}
+  .meta {{ font-size: 11px; color: #6b7280; }}
+  .meta.err {{ color: #dc2626; }}
+  .legend {{ display: inline-flex; gap: 12px; font-size: 11px; color: #6b7280; margin-left: 12px; }}
+  .legend span {{ display: inline-flex; align-items: center; gap: 4px; }}
+  .legend i {{ width: 10px; height: 10px; border-radius: 2px; display: inline-block; }}
+  .overlay {{ display: none; position: fixed; inset: 0; background: rgba(0,0,0,.92); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 24px; }}
+  .overlay.active {{ display: flex; }}
+  .overlay img {{ max-width: 96%; max-height: 96%; object-fit: contain; border-radius: 4px; }}
+</style>
+</head>
+<body>
+<div class="wrap">
+  <h1>💄 接口2 生发 · 5档分辨率对比报告</h1>
+  <p class="sub">POST /api/v1/hair/grow · female · 5 图 × 3 发型(花瓣/心形/波浪) × 5 档分辨率 = 75 次 · 串行 · 4090 (24G)</p>
+
+  <div class="stats">
+    <div class="stat" style="border-left:3px solid #16a34a"><div class="num" style="color:#16a34a">{ok}/{total}</div><div class="lbl">成功 / 总数</div></div>
+    {stat_cards}
+  </div>
+
+  <div class="summary-bar">
+    📊 <b>结论：</b>耗时随送图分辨率单调下降。<b>大图(qwer/asdf 长边~1670) 原图直送需 ~27-30s，是 896 档(10s) 的近 3 倍</b>；
+    中小图(girl2/girl5/girl7 长边 767-1082)各档差异较小(6-15s)。
+    <b>4090 24G 全程无 OOM</b>，75/75 成功。<b>画质对比</b>见下表（横向滑动），点击任意图可放大。
+    <span class="legend">
+      <span><i style="background:#16a34a"></i>快(&lt;{tmin+ (tmax-tmin)*0.33:.0f}s)</span>
+      <span><i style="background:#f59e0b"></i>中等</span>
+      <span><i style="background:#dc2626"></i>慢(&gt;{tmin+ (tmax-tmin)*0.66:.0f}s)</span>
+    </span>
+  </div>
+
+  <div class="table-wrap">
+    <table>
+      <thead>
+        <tr>
+          <th>图 · 发型</th>
+          <th class="orig-h">📷 原图</th>
+          <th>原图直送 (0)<br><span class="dim">不缩放</span></th>
+          <th>1024</th>
+          <th>896<br><span class="dim">默认</span></th>
+          <th>768</th>
+          <th>640</th>
+        </tr>
+      </thead>
+      <tbody>
+        {"".join(rows_html)}
+      </tbody>
+    </table>
+  </div>
+</div>
+<div class="overlay" id="overlay" onclick="this.classList.remove('active')"><img id="overlayImg" src=""></div>
+<script>
+function openImg(src) {{
+  document.getElementById('overlayImg').src = src;
+  document.getElementById('overlay').classList.add('active');
+}}
+</script>
+</body>
+</html>
+"""
+with open(REPORT, "w") as f:
+    f.write(html_doc)
+print(f"已生成: {REPORT}")

image/res_test/report-server-v2.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=接口2 5档分辨率对比报告 v2 (8849)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair/image/res_test/out
+ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8849 --bind 0.0.0.0
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

image/wave_test/batch_test.py

@@ -0,0 +1,143 @@
+#!/usr/bin/env python3
+"""接口2 female wave发型 分辨率对比测试 v3: 21图 × 5档 = 105次。
+串行执行，记录耗时与成败，结果图按 图_档位 命名保存。支持断点续跑。
+"""
+import base64, csv, json, os, time, io
+import requests
+from PIL import Image
+
+API = "http://127.0.0.1:8187/api/v1/hair/grow"
+TOKEN = "dev-shared-secret-2026"
+TIMEOUT = 600
+OUT = "/home/ubuntu/hair/image/wave_test/out"
+PROGRESS = "/home/ubuntu/hair/image/wave_test/progress.json"
+
+IMG_DIR = "/home/ubuntu/hair/image"
+# 21张图：19张girl_img + asdf + qwer
+IMAGES = []
+for f in sorted(os.listdir(os.path.join(IMG_DIR, "girl_img"))):
+    if f.lower().endswith((".jpg", ".jpeg", ".png")):
+        IMAGES.append((os.path.splitext(f)[0], os.path.join(IMG_DIR, "girl_img", f)))
+IMAGES.append(("asdf", os.path.join(IMG_DIR, "asdf.jpg")))
+IMAGES.append(("qwer", os.path.join(IMG_DIR, "qwer.jpg")))
+
+# wave = female hair_style 5
+STYLE_IDX = 5
+# 5档: 原图(0) / 1024 / 896 / 768 / 640
+SIDES = [
+    ("origin", 0),
+    ("s1024",  1024),
+    ("s896",   896),
+    ("s768",   768),
+    ("s640",   640),
+]
+
+def load_progress():
+    if os.path.exists(PROGRESS):
+        try:
+            return json.load(open(PROGRESS))
+        except Exception:
+            pass
+    return {"done": [], "results": []}
+
+def save_progress(prog):
+    json.dump(prog, open(PROGRESS, "w"), ensure_ascii=False, indent=1)
+
+def run_one(img_name, img_path, side_name, side_val):
+    key = f"{img_name}_{side_name}"
+    with open(img_path, "rb") as f:
+        img_b64 = base64.b64encode(f.read()).decode()
+    data = {
+        "image_base64": "data:image/jpeg;base64," + img_b64,
+        "gender": "female",
+        "hair_style": str(STYLE_IDX),
+        "redraw_max_side": str(side_val),
+    }
+    t0 = time.time()
+    rec = {"key": key, "img": img_name, "side": side_name, "side_val": side_val,
+           "ok": False, "elapsed": 0.0, "err": "", "out_w": 0, "out_h": 0, "bytes": 0}
+    try:
+        r = requests.post(API, data=data, headers={"X-Internal-Token": TOKEN}, timeout=TIMEOUT)
+        rec["elapsed"] = round(time.time() - t0, 1)
+        d = r.json()
+        if d.get("code") != 0:
+            rec["err"] = f"code={d.get('code')} {d.get('message','')}"[:200]
+            return rec
+        results = (d.get("data") or {}).get("results") or []
+        if not results:
+            rec["err"] = "空结果"
+            return rec
+        it = results[0]
+        grown = it.get("grown_image_base64")
+        if not grown:
+            rec["err"] = "无生发图(重绘失败/OOM?)"
+            return rec
+        raw = base64.b64decode(grown)
+        im = Image.open(io.BytesIO(raw))
+        rec["out_w"], rec["out_h"] = im.size
+        out_path = f"{OUT}/{key}.jpg"
+        with open(out_path, "wb") as fo:
+            fo.write(raw)
+        rec["ok"] = True
+        rec["bytes"] = len(raw)
+    except requests.exceptions.Timeout:
+        rec["elapsed"] = round(time.time() - t0, 1)
+        rec["err"] = f"超时(>{TIMEOUT}s)"
+    except Exception as e:
+        rec["elapsed"] = round(time.time() - t0, 1)
+        rec["err"] = f"{type(e).__name__}: {str(e)[:180]}"
+    return rec
+
+def main():
+    prog = load_progress()
+    done_keys = set(prog["done"])
+    total = len(IMAGES) * len(SIDES)
+    print(f"=== 接口2 female wave 分辨率对比测试 v3 ===")
+    print(f"矩阵: {len(IMAGES)}图 × {len(SIDES)}档 = {total} 次 (发型固定 wave)")
+    print(f"已跳过 {len(done_keys)} 个已完成项\n")
+
+    idx = 0
+    for img_name, img_path in IMAGES:
+        for side_name, side_val in SIDES:
+            idx += 1
+            key = f"{img_name}_{side_name}"
+            if key in done_keys:
+                print(f"[{idx}/{total}] ⏭  跳过 {key}")
+                continue
+            print(f"[{idx}/{total}] ▶ {key} (side={side_val})...", end=" ", flush=True)
+            rec = run_one(img_name, img_path, side_name, side_val)
+            prog["results"].append(rec)
+            prog["done"].append(key)
+            save_progress(prog)
+            if rec["ok"]:
+                print(f"✅ {rec['elapsed']}s  {rec['out_w']}x{rec['out_h']} ({rec['bytes']}B)")
+            else:
+                print(f"❌ {rec['elapsed']}s  {rec['err']}")
+            time.sleep(2)
+
+    print("\n" + "=" * 70)
+    print("汇总")
+    print("=" * 70)
+    write_report(prog["results"])
+    print(f"\n结果图: {OUT}/")
+    print(f"CSV: {OUT}/report.csv   JSON: {OUT}/report.json")
+
+def write_report(results):
+    with open(f"{OUT}/report.csv", "w", newline="") as f:
+        w = csv.writer(f)
+        w.writerow(["img", "side", "side_val", "ok", "elapsed_s", "out_w", "out_h", "bytes", "err"])
+        for r in results:
+            w.writerow([r["img"], r["side"], r["side_val"], r["ok"],
+                        r["elapsed"], r["out_w"], r["out_h"], r["bytes"], r["err"]])
+    json.dump(results, open(f"{OUT}/report.json", "w"), ensure_ascii=False, indent=1)
+
+    # 各档平均耗时
+    print("\n--- 各档平均耗时（秒）---")
+    for side_name, side_val in SIDES:
+        ts = [r["elapsed"] for r in results if r["side"] == side_name and r["ok"]]
+        if ts:
+            avg = sum(ts) / len(ts)
+            print(f"  {side_name:8s} (={side_val:>4}): 平均 {avg:5.1f}s  [{min(ts):.1f}~{max(ts):.1f}]  成功 {len(ts)}")
+
+if __name__ == "__main__":
+    main()

image/wave_test/gen_report.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+"""生成 v3 wave发型 分辨率对比报告：每行=原图+5档，21行；含各档耗时统计。"""
+import json, os, html
+
+OUT = "/home/ubuntu/hair/image/wave_test/out"
+REPORT = os.path.join(OUT, "report.html")
+results = json.load(open(os.path.join(OUT, "report.json")))
+
+SIDES = [("origin", "原图直送", 0), ("s1024", "1024", 1024),
+         ("s896", "896", 896), ("s768", "768", 768), ("s640", "640", 640)]
+
+# 图片顺序（与测试脚本一致）：girl_img 排序 + asdf + qwer
+IMG_DIR = "/home/ubuntu/hair/image"
+IMAGES = []
+for f in sorted(os.listdir(os.path.join(IMG_DIR, "girl_img"))):
+    if f.lower().endswith((".jpg", ".jpeg", ".png")):
+        IMAGES.append(os.path.splitext(f)[0])
+IMAGES.append("asdf")
+IMAGES.append("qwer")
+
+def get(img, side):
+    for r in results:
+        if r["img"] == img and r["side"] == side:
+            return r
+    return None
+
+# 统计
+total = len(results)
+ok = sum(1 for r in results if r["ok"])
+
+# 各档统计
+def avg(side):
+    ts = [r["elapsed"] for r in results if r["side"] == side and r["ok"]]
+    return sum(ts)/len(ts) if ts else 0
+avgs = {s[0]: avg(s[0]) for s in SIDES}
+
+# 速度色阶
+all_t = sorted(r["elapsed"] for r in results if r["ok"])
+tmin, tmax = all_t[0], all_t[-1]
+def speed_color(t):
+    if tmax == tmin: return "#16a34a"
+    ratio = (t - tmin) / (tmax - tmin)
+    if ratio < 0.33: return "#16a34a"
+    elif ratio < 0.66: return "#f59e0b"
+    else: return "#dc2626"
+
+# 表格行
+rows_html = []
+for img in IMAGES:
+    orig_cell = (f'<td class="cell orig">'
+                 f'<div class="thumb"><img loading="lazy" src="orig_{img}.jpg" '
+                 f'onclick="openImg(this.src)" alt="原图"></div>'
+                 f'<div class="meta">📷 原图</div></td>')
+    side_cells = []
+    for side_name, side_lbl, side_val in SIDES:
+        r = get(img, side_name)
+        if r and r["ok"]:
+            col = speed_color(r["elapsed"])
+            cell = (f'<td class="cell">'
+                    f'<div class="thumb"><img loading="lazy" src="{r["key"]}.jpg" '
+                    f'onclick="openImg(this.src)" alt="{html.escape(r["key"])}"></div>'
+                    f'<div class="meta"><b style="color:{col}">{r["elapsed"]}s</b></div></td>')
+        elif r:
+            cell = (f'<td class="cell fail"><div class="thumb noimg">❌</div>'
+                    f'<div class="meta err">{html.escape(r["err"][:30])}</div></td>')
+        else:
+            cell = '<td class="cell fail"><div class="thumb noimg">—</div></td>'
+        side_cells.append(cell)
+    rows_html.append("<tr>" + orig_cell + "".join(side_cells) + "</tr>")
+
+# 各档统计卡
+def stat_card(lbl, val, col, rng=""):
+    return (f'<div class="stat" style="border-left:3px solid {col}">'
+            f'<div class="num" style="color:{col}">{val:.1f}s</div>'
+            f'<div class="lbl">{lbl}{rng}</div></div>')
+
+stat_cards = "".join([
+    stat_card("原图直送(0)", avgs["origin"], "#dc2626"),
+    stat_card("1024", avgs["s1024"], "#f59e0b"),
+    stat_card("896", avgs["s896"], "#2563eb"),
+    stat_card("768", avgs["s768"], "#16a34a"),
+    stat_card("640", avgs["s640"], "#0d9488"),
+])
+
+html_doc = f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>wave发型 5档分辨率对比</title>
+<style>
+  * {{ box-sizing: border-box; margin: 0; padding: 0; }}
+  body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Microsoft YaHei", sans-serif; background: #f3f4f6; color: #1f2937; line-height: 1.5; padding: 16px; }}
+  .wrap {{ max-width: 100%; margin: 0 auto; }}
+  h1 {{ font-size: 22px; margin-bottom: 4px; }}
+  .sub {{ color: #6b7280; font-size: 12px; margin-bottom: 16px; }}
+  .stats {{ display: flex; gap: 10px; margin-bottom: 16px; flex-wrap: wrap; }}
+  .stat {{ background: #fff; border-radius: 8px; padding: 12px 14px; box-shadow: 0 1px 3px rgba(0,0,0,.06); flex: 1; min-width: 110px; }}
+  .stat .num {{ font-size: 22px; font-weight: 700; }}
+  .stat .lbl {{ font-size: 11px; color: #6b7280; margin-top: 2px; }}
+  .summary-bar {{ background: #fff; border-radius: 8px; padding: 12px 16px; margin-bottom: 16px; box-shadow: 0 1px 3px rgba(0,0,0,.06); font-size: 13px; }}
+  .summary-bar b {{ color: #dc2626; }}
+  .legend {{ display: inline-flex; gap: 12px; font-size: 11px; color: #6b7280; margin-left: 12px; }}
+  .legend span {{ display: inline-flex; align-items: center; gap: 4px; }}
+  .legend i {{ width: 10px; height: 10px; border-radius: 2px; display: inline-block; }}
+  .table-wrap {{ overflow-x: auto; background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.07); }}
+  table {{ border-collapse: collapse; min-width: 100%; }}
+  th, td {{ vertical-align: top; }}
+  thead th {{ position: sticky; top: 0; background: #f9fafb; z-index: 2; padding: 10px 8px; font-size: 12px; color: #374151; border-bottom: 2px solid #e5e7eb; text-align: center; }}
+  thead th.orig-h {{ background: #fef3c7; }}
+  tbody td {{ border-bottom: 1px solid #f3f4f6; padding: 8px; }}
+  tbody tr:hover {{ background: #f9fafb; }}
+  .cell {{ width: 200px; min-width: 200px; text-align: center; }}
+  .cell.orig {{ background: #fffbeb; }}
+  .thumb {{ background: #1f2937; border-radius: 6px; overflow: hidden; margin-bottom: 4px; cursor: zoom-in; }}
+  .thumb img {{ width: 100%; height: 260px; object-fit: contain; display: block; }}
+  .thumb.noimg {{ color: #d1d5db; font-size: 16px; padding: 110px 0; text-align: center; }}
+  .meta {{ font-size: 11px; color: #6b7280; }}
+  .meta.err {{ color: #dc2626; }}
+  .overlay {{ display: none; position: fixed; inset: 0; background: rgba(0,0,0,.92); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 24px; }}
+  .overlay.active {{ display: flex; }}
+  .overlay img {{ max-width: 96%; max-height: 96%; object-fit: contain; border-radius: 4px; }}
+</style>
+</head>
+<body>
+<div class="wrap">
+  <h1>💇 wave发型 · 5档分辨率对比报告</h1>
+  <p class="sub">POST /api/v1/hair/grow · female · wave(波浪) · 21 图 × 5 档分辨率 = 105 次 · 串行 · 4090 (24G)</p>
+
+  <div class="stats">
+    <div class="stat" style="border-left:3px solid #16a34a"><div class="num" style="color:#16a34a">{ok}/{total}</div><div class="lbl">成功 / 总数</div></div>
+    {stat_cards}
+  </div>
+
+  <div class="summary-bar">
+    📊 <b>结论：</b>耗时随分辨率单调下降。<b>大图(asdf 1666/qwer 1678) 原图直送需 24~26s，是 1024 档(11s) 的 2.3 倍</b>；
+    中小图(≤1024) 各档差异较小(6~13s)，因小图本身不触发缩图。
+    <b>4090 24G 全程无 OOM</b>，105/105 成功。<b>画质对比</b>见下表（横向滑动），点击任意图可放大。
+    <span class="legend">
+      <span><i style="background:#16a34a"></i>快(&lt;{(tmin+(tmax-tmin)*0.33):.0f}s)</span>
+      <span><i style="background:#f59e0b"></i>中等</span>
+      <span><i style="background:#dc2626"></i>慢(&gt;{(tmin+(tmax-tmin)*0.66):.0f}s)</span>
+    </span>
+  </div>
+
+  <div class="table-wrap">
+    <table>
+      <thead>
+        <tr>
+          <th class="orig-h">📷 原图</th>
+          <th>原图直送 (0)<br><span class="dim">不缩放</span></th>
+          <th>1024</th>
+          <th>896<br><span class="dim">默认</span></th>
+          <th>768</th>
+          <th>640</th>
+        </tr>
+      </thead>
+      <tbody>
+        {"".join(rows_html)}
+      </tbody>
+    </table>
+  </div>
+</div>
+<div class="overlay" id="overlay" onclick="this.classList.remove('active')"><img id="overlayImg" src=""></div>
+<script>
+function openImg(src) {{
+  document.getElementById('overlayImg').src = src;
+  document.getElementById('overlay').classList.add('active');
+}}
+</script>
+</body>
+</html>
+"""
+with open(REPORT, "w") as f:
+    f.write(html_doc)
+print(f"已生成: {REPORT}")

image/wave_test/report-server-wave.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=wave发型5档分辨率对比报告 (8850)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair/image/wave_test/out
+ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python -m http.server 8850 --bind 0.0.0.0
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

local_test/app.py

@@ -206,7 +206,7 @@ def generate():
             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")
+        prompt_text = request.form.get("prompt", "填充遮罩区域的头发")
         log.info(
             "收到请求: image=%s mask=%s prompt=%r",
             image_file.filename, mask_file.filename, prompt_text,

local_test/bench.py

@@ -27,7 +27,7 @@ def prep_and_upload():
 
 
 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")
+    wf = A.build_workflow(fname, "填充遮罩区域的头发")
     wf["16"]["inputs"]["unet_name"] = model
     wf["16"]["inputs"]["weight_dtype"] = dtype
     wf["1"]["inputs"]["steps"] = steps

local_test/bench2.py

@@ -33,7 +33,7 @@ def upload(scale=1.0):
 
 
 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")
+    wf = A.build_workflow(fname, "填充遮罩区域的头发")
     wf["16"]["inputs"]["unet_name"] = MODEL
     wf["16"]["inputs"]["weight_dtype"] = DTYPE
     wf["1"]["inputs"]["steps"] = steps

local_test/bench_quality.py

@@ -30,7 +30,7 @@ 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", seed=SEED)
+    wf = A.build_workflow(fname, "填充遮罩区域的头发", seed=SEED)
     wf["16"]["inputs"]["unet_name"] = MODEL
     wf["16"]["inputs"]["weight_dtype"] = DTYPE
     wf["1"]["inputs"]["steps"] = steps

local_test/index.html

@@ -55,7 +55,7 @@ button { padding: 10px 24px; border: none; border-radius: 6px; cursor: pointer;
         </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">
+            <input type="text" id="promptInput" value="填充遮罩区域的头发">
         </div>
         <div style="text-align:center; margin-top:16px">
             <button class="btn-generate" id="generateBtn" disabled>🚀 生成</button>

local_test/test_api.py

@@ -25,7 +25,7 @@ resp = requests.post(
         "image": ("original.jpg", img_data, "image/jpeg"),
         "mask": ("mask.png", mask_data, "image/png"),
     },
-    data={"prompt": "填充遮罩区域的头发，皮肤加一点磨皮"},
+    data={"prompt": "填充遮罩区域的头发"},
     timeout=600,
 )