feat(接口5): 发际线PNG生成(真实实现，替换Mock)

复用接口2 预览管线：gender 必填 → 该性别全部发际线叠加图(同接口2预览) + 最佳(order=1)发际线曲线的面部中间点坐标。无生发(不调 ComfyUI)。 - hairline/service.py: generate_hairline_pngs——N张发际线叠图 + best_center(面部中轴眉心x × order1曲线在该处的y) - app.py: /hairline/generate 真实实现，新增 gender 必填(非法→1004)，返回 hairline_images[].image_base64 + best_hairline_center_point；无人脸→1001；重活线程池 - 接口文档/OpenAPI: 接口5 新增 gender 入参 + 输出改 base64(网关改url) - 测试: test_api 接口5(gender必填/N张/center点)，44全绿实测(5090): female 5张叠图 + center{x,y}，~2.5s(无Flux)。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
feat(接口3): B端生发-马克笔发际线检测+生发(替换Mock)
2026-06-15 00:20:56 +08:00 · 2026-06-15 00:08:05 +08:00 · 2026-06-14 23:42:30 +08:00 · 2026-06-14 23:32:21 +08:00
25 changed files with 1448 additions and 61 deletions
@@ -0,0 +1,450 @@
+{
+  "1": {
+    "inputs": {
+      "scheduler": "simple",
+      "steps": 6,
+      "denoise": 1,
+      "model": [
+        "2",
+        0
+      ]
+    },
+    "class_type": "BasicScheduler",
+    "_meta": {
+      "title": "基本调度器"
+    }
+  },
+  "2": {
+    "inputs": {
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "model": [
+        "16",
+        0
+      ]
+    },
+    "class_type": "ModelSamplingFlux",
+    "_meta": {
+      "title": "采样算法（Flux）"
+    }
+  },
+  "3": {
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    },
+    "class_type": "VAELoader",
+    "_meta": {
+      "title": "加载VAE"
+    }
+  },
+  "5": {
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "ReferenceLatent",
+    "_meta": {
+      "title": "参考Latent"
+    }
+  },
+  "6": {
+    "inputs": {
+      "noise_seed": 690815303279000
+    },
+    "class_type": "RandomNoise",
+    "_meta": {
+      "title": "随机噪波"
+    }
+  },
+  "7": {
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    },
+    "class_type": "EmptySD3LatentImage",
+    "_meta": {
+      "title": "空Latent图像（SD3）"
+    }
+  },
+  "8": {
+    "inputs": {
+      "sampler_name": "euler"
+    },
+    "class_type": "KSamplerSelect",
+    "_meta": {
+      "title": "K采样器选择"
+    }
+  },
+  "9": {
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    },
+    "class_type": "SamplerCustomAdvanced",
+    "_meta": {
+      "title": "自定义采样器（高级）"
+    }
+  },
+  "10": {
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE解码"
+    }
+  },
+  "13": {
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE编码"
+    }
+  },
+  "14": {
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "GetImageSize+",
+    "_meta": {
+      "title": "🔧 Get Image Size"
+    }
+  },
+  "16": {
+    "inputs": {
+      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
+      "weight_dtype": "fp8_e4m3fn"
+    },
+    "class_type": "UNETLoader",
+    "_meta": {
+      "title": "UNet加载器"
+    }
+  },
+  "17": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "images": [
+        "62",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "保存图像"
+    }
+  },
+  "19": {
+    "inputs": {
+      "guidance": 1,
+      "conditioning": [
+        "22",
+        0
+      ]
+    },
+    "class_type": "FluxGuidance",
+    "_meta": {
+      "title": "Flux引导"
+    }
+  },
+  "20": {
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "BasicGuider",
+    "_meta": {
+      "title": "基本引导器"
+    }
+  },
+  "22": {
+    "inputs": {
+      "text": [
+        "60",
+        0
+      ],
+      "clip": [
+        "61",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP文本编码"
+    }
+  },
+  "26": {
+    "inputs": {
+      "image": "clipspace/clipspace-painted-masked-1780841075435.png [input]"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "加载图像"
+    }
+  },
+  "31": {
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "easy imageSize",
+    "_meta": {
+      "title": "图像尺寸"
+    }
+  },
+  "32": {
+    "inputs": {
+      "aspect_ratio": "custom",
+      "proportional_width": [
+        "31",
+        0
+      ],
+      "proportional_height": [
+        "31",
+        1
+      ],
+      "fit": "letterbox",
+      "method": "lanczos",
+      "round_to_multiple": "8",
+      "scale_to_side": "None",
+      "scale_to_length": 1024,
+      "background_color": "#000000",
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ]
+    },
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "_meta": {
+      "title": "LayerUtility: ImageScaleByAspectRatio V2"
+    }
+  },
+  "33": {
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    },
+    "class_type": "Mask Fill Holes",
+    "_meta": {
+      "title": "遮罩填充漏洞"
+    }
+  },
+  "36": {
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    },
+    "class_type": "Convert Masks to Images",
+    "_meta": {
+      "title": "遮罩到图像"
+    }
+  },
+  "37": {
+    "inputs": {
+      "method": "intensity",
+      "image": [
+        "39",
+        0
+      ]
+    },
+    "class_type": "Image To Mask",
+    "_meta": {
+      "title": "图像到遮罩"
+    }
+  },
+  "39": {
+    "inputs": {
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled",
+      "image": [
+        "36",
+        0
+      ]
+    },
+    "class_type": "ImageScale",
+    "_meta": {
+      "title": "缩放图像"
+    }
+  },
+  "44": {
+    "inputs": {
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true,
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ]
+    },
+    "class_type": "ImageAndMaskPreview",
+    "_meta": {
+      "title": "图像与遮罩预览"
+    }
+  },
+  "45": {
+    "inputs": {
+      "images": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "预览图像"
+    }
+  },
+  "53": {
+    "inputs": {
+      "rgthree_comparer": {
+        "images": [
+          {
+            "name": "A",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_nljpo_00001_.png&type=temp&subfolder=&rand=0.7003773747423834"
+          },
+          {
+            "name": "B",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_nljpo_00002_.png&type=temp&subfolder=&rand=0.10574778041280719"
+          }
+        ]
+      },
+      "image_a": [
+        "62",
+        0
+      ],
+      "image_b": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "Image Comparer (rgthree)",
+    "_meta": {
+      "title": "图像对比"
+    }
+  },
+  "60": {
+    "inputs": {
+      "text": "严格保留原图人物五官、面部肤色、神态、服饰、背景、原图光影与整体色调，画面除划线区域外全部细节保持原样不变；先清除画面内所有黑色标注划线，仅在原划线划定范围内生成植发术后 3 个月长发效果，新生头发生长边界刚好止于原划线位置，新发沿原划线轮廓边缘和外部原生发丝交错融合、自然无缝衔接，无生硬分界线；发色和自身原生黑发保持统一，发丝细腻写实、带有真实毛发纹理与自然光泽，发量均匀，符合植发 3 个月刚长出的轻微稀疏生长状态，毛发走向遵从头部原生头发生长规律，头发光影明暗和原图环境统一，整体写实自然，无贴片假发质感。"
+    },
+    "class_type": "JjkText",
+    "_meta": {
+      "title": "Text"
+    }
+  },
+  "61": {
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "default"
+    },
+    "class_type": "CLIPLoader",
+    "_meta": {
+      "title": "加载CLIP"
+    }
+  },
+  "62": {
+    "inputs": {
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true,
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ]
+    },
+    "class_type": "ColorMatch",
+    "_meta": {
+      "title": "Color Match"
+    }
+  }
+}
@@ -504,17 +504,22 @@ async def hair_grow(
        return err(1002, "人像分辨率过低")

    try:
-        from hairline.service import generate_previews
+        from fastapi.concurrency import run_in_threadpool
+        from hairline.service import generate_grow_results

-        previews = generate_previews(image, gender)   # 无人脸 → None
-        if previews is None:
+        # 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)
+        if items is None:
            return err(1001, "无法识别人像")

        results = []
-        for p in previews:
-            ok_enc, png = cv2.imencode(".png", p["image_bgr"])
+        for p in items:
+            _ok, png = cv2.imencode(".png", p["image_bgr"])
+            grown_b64 = (base64.b64encode(p["grown_png"]).decode()
+                         if p["grown_png"] else None)
            results.append({
                "image_base64": base64.b64encode(png.tobytes()).decode(),
+                "grown_image_base64": grown_b64,
                "hairline_type": p["hairline_type"],
                "order": p["order"],
            })
@@ -554,9 +559,9 @@ async def hair_grow(
                        "message": "success",
                        "request_id": "mock-request-id",
                        "data": {
-                            "best_hairline_image_url": SAMPLE_IMAGE_URL,
-                            "hair_growth_image_url": SAMPLE_IMAGE_URL,
-                            "hairline_type": "花瓣形",
+                            "best_hairline_image_base64": "iVBORw0KGgo...（原图）",
+                            "hair_growth_image_base64": "iVBORw0KGgo...（生发图）",
+                            "hairline_type": "custom",
                        },
                    }
                }
@@ -580,12 +585,46 @@ async def hair_grow_b(
    original_image_url: Optional[str] = Form(default=None, description="原始用户照片 URL"),
    original_image_base64: Optional[str] = Form(default=None, description="原始用户照片 base64"),
 ):
-    data = {
-        "best_hairline_image_url": SAMPLE_IMAGE_URL,
-        "hair_growth_image_url": SAMPLE_IMAGE_URL,
-        "hairline_type": "花瓣形",
-    }
-    return ok(data)
+    # 1. 两组图各三选一取图
+    marked_raw, e = await resolve_image_bytes(marked_image_file, marked_image_url, marked_image_base64)
+    if e is not None:
+        return e
+    orig_raw, e = await resolve_image_bytes(original_image_file, original_image_url, original_image_base64)
+    if e is not None:
+        return e
+    if len(marked_raw) > MAX_FILE_BYTES or len(orig_raw) > MAX_FILE_BYTES:
+        return err(1006, "文件超出 1 MB 限制")
+
+    marked = cv2.imdecode(np.frombuffer(marked_raw, np.uint8), cv2.IMREAD_COLOR)
+    original = cv2.imdecode(np.frombuffer(orig_raw, np.uint8), cv2.IMREAD_COLOR)
+    if marked is None or original is None:
+        return err(1008, "图片格式不支持（仅 JPG / PNG）")
+
+    h, w = marked.shape[:2]
+    short_side, long_side = min(w, h), max(w, h)
+    if short_side < MIN_SHORT_SIDE or long_side < MIN_LONG_SIDE:
+        return err(1002, "人像分辨率过低")
+
+    try:
+        from fastapi.concurrency import run_in_threadpool
+        from hairline.service import generate_grow_b
+
+        res = await run_in_threadpool(generate_grow_b, marked, original)
+        if res["status"] == "no_face":
+            return err(1001, "无法识别人像")
+        if res["status"] == "no_line":
+            return err(1001, "未检测到发际线划线，请确认划线图额头有清晰的手绘发际线")
+
+        grown_b64 = base64.b64encode(res["grown_png"]).decode() if res["grown_png"] else None
+        data = {
+            "best_hairline_image_base64": base64.b64encode(orig_raw).decode(),  # 原图原样
+            "hair_growth_image_base64": grown_b64,
+            "hairline_type": "custom",
+        }
+        return ok(data)
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("接口3 处理异常")
+        return err(1007, f"处理失败：{ex}")


 # ---------------------------------------------------------------------------
@@ -682,10 +721,15 @@ async def face_features(

 ---

+**入参**：新增必填 `gender`（`male`/`female`），决定返回的发际线集合（female 5 / male 4）。
+
 **返回说明**：

- `hairline_images`：发际线 PNG 列表，数量 N 不固定，已按合适度**从高到低排序**（`order=1` 最合适）
- `best_hairline_center_point`：最合适发际线的面部中间点坐标，以**原图像素**为基准（左上角为原点，x 向右，y 向下）
+- `hairline_images`：发际线叠加图列表（发际线曲线叠加在用户照片上，同接口2预览），
+  数量 = 该性别的发际线类型数，本期按贴图顺序 `order=1..N`（暂不计算合适度）。
+  worker 返回 `image_base64`，网关落盘后改写为 `image_url`。
+- `best_hairline_center_point`：最佳（`order=1`）发际线曲线的**面部中间点**坐标，
+  以**原图像素**为基准（左上角为原点，x 向右，y 向下）。
 """,
    responses={
        200: {
@@ -698,8 +742,8 @@ async def face_features(
                        "request_id": "mock-request-id",
                        "data": {
                            "hairline_images": [
-                                {"image_url": SAMPLE_IMAGE_URL, "order": 1},
-                                {"image_url": SAMPLE_IMAGE_URL, "order": 2},
+                                {"image_base64": "iVBORw0KGgo...", "order": 1},
+                                {"image_base64": "iVBORw0KGgo...", "order": 2},
                            ],
                            "best_hairline_center_point": {"x": 540, "y": 430},
                        },
@@ -721,15 +765,50 @@ async def hairline_generate(
    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG，≤ 1 MB）"),
    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）"),
 ):
-    data = {
-        "hairline_images": [
-            {"image_url": SAMPLE_IMAGE_URL, "order": 1},
-            {"image_url": SAMPLE_IMAGE_URL, "order": 2},
-        ],
-        "best_hairline_center_point": {"x": 540, "y": 430},
-    }
-    return ok(data)
+    if gender not in ("male", "female"):
+        return err(1004, "gender 必填且只能为 male / female")
+
+    raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
+    if e is not None:
+        return e
+    if len(raw) > MAX_FILE_BYTES:
+        return err(1006, "文件超出 1 MB 限制")
+
+    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）")
+
+    h, w = image.shape[:2]
+    short_side, long_side = min(w, h), max(w, h)
+    if short_side < MIN_SHORT_SIDE or long_side < MIN_LONG_SIDE:
+        return err(1002, "人像分辨率过低")
+
+    try:
+        from fastapi.concurrency import run_in_threadpool
+        from hairline.service import generate_hairline_pngs
+
+        res = await run_in_threadpool(generate_hairline_pngs, image, gender)
+        if res is None:
+            return err(1001, "无法识别人像")
+
+        hairline_images = []
+        for it in res["images"]:
+            _ok, png = cv2.imencode(".png", it["image_bgr"])
+            hairline_images.append({
+                "image_base64": base64.b64encode(png.tobytes()).decode(),
+                "order": it["order"],
+            })
+        c = res["best_center"]
+        data = {
+            "hairline_images": hairline_images,
+            "best_hairline_center_point": ({"x": c[0], "y": c[1]} if c else None),
+        }
+        return ok(data)
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("接口5 处理异常")
+        return err(1007, f"处理失败：{ex}")


 # ---------------------------------------------------------------------------
@@ -242,5 +242,114 @@ transformers>=4.40         # SegFormer 人脸分割

 ---

-> **文档版本**: v1.0 ｜ **创建日期**: 2026-06-14 ｜ 算法来源: head3d（502 点 mesh + UV）｜ 运行位置: worker(GPU)
-> **本期产出**: 发际线曲线叠加预览图（非最终生发图）
+## 10. 第二步：生发图生成（ComfyUI + Flux inpaint）★ 新增
+
+> 在「发际线预览」基础上，**新增真实生发后图片**：把发际线划线 + 遮罩送入本机
+> ComfyUI（Flux-2 Klein 9b，端口 **8182**）跑 `add_hair.json` 工作流，得到「植发 3
+> 个月」效果图。**worker 不跑 Flux**，只做图像准备 + 调 ComfyUI HTTP API + 取回结果。
+
+### 10.1 需求与决策（已与需求方确认）
+
+| 项 | 决策 |
+|----|------|
+| 生成数量 | **一次请求生成该性别全部 N 种**（female 5 / male 4），与预览一一对应 |
+| 遮罩区域 | **头发区域 ∪ 新发际线以下** —— SegFormer 头部(头发)区域，下边界拓到新发际线曲线 |
+| 返回方式 | **同步阻塞**到 ComfyUI 出图再返回（N 张串行，单请求耗时可达数分钟，网关需调大超时） |
+| ComfyUI 接入 | 标准 HTTP API（`/upload/image` + `/prompt` + `/history` + `/view`），8182 无鉴权，自定义节点已装齐，`noise_seed` 每次随机 |
+
+### 10.2 `add_hair.json` 工作流解读
+
+Flux-2 Klein 9b 的参考式局部重绘（denoise=1 + ReferenceLatent）：
+- **节点 26 `LoadImage`** 是唯一外部输入，同时给出 **图像**（[0]）和 **遮罩**（[1]，从 PNG 的
+  alpha 通道取，ComfyUI 约定 `mask = 1 − alpha`，即 **alpha 透明处 = 要重绘的区域**）。
+- 提示词（节点 60）：保留原图一切，**先清除画面内所有黑色标注划线**，仅在划线范围内生成
+  「植发 3 个月」头发，发际线边界刚好止于划线处，与原生发自然衔接。
+- 节点 32/37/39/44 做遮罩填洞、缩放到 1024、ImageAndMaskPreview 组装 → VAEEncode 参考。
+- 节点 10 VAEDecode → 节点 62 ColorMatch（与原图调色一致）→ 节点 17 SaveImage = 生发图。
+
+> **关键**：worker 要做的就是**程序化复现「手绘 painted-masked」的输入**——给节点 26 一张
+> RGBA：**RGB = 画了黑色发际线划线的照片，alpha = 遮罩（重绘区透明）**。工作流其余不动。
+
+### 10.3 遮罩算法（参考 `/home/xsl/headmark`，用黑贴图简化）
+
+headmark（发际线蒙板工具）的 5 步法：① MediaPipe 取**额头上半区域** → ② **整个头部分割** →
+③ 两者**交集** = ROI → ④ 在 ROI 内**找发际线**（手绘划线）→ ⑤ 发际线 + 头型**围成闭合区域**填充 = 遮罩。
+
+> **我们的简化**：发际线不是手绘、需要检测的；而是用 `hairline_texture_black/` 的黑曲线
+> **程序化渲染**出来——位置已知，**省掉 headmark 第 4 步的检测**，直接拿渲染出的曲线当边界。
+
+```
+已有：502 点、SegFormer parse_map
+build_inpaint_mask(photo, parse_map, landmarks, hairline_texture_black/t):
+  [1] upper_region = MediaPipe 额头边界关键点
+        [21,68,104,69,108,151,337,299,333,298,251] 连线，向上+两侧补到图像边缘，填充
+        （= headmark step1：发际线以上的"上部区域"）
+  [2] head_mask   = SegFormer 头部轮廓（hair ∪ skin ∪ 其余面部类，排除 bg/neck/cloth）
+        （= headmark step2；headmark 用 head-segmentation 包/Selfie，本项目复用已加载的 SegFormer）
+  [3] roi = upper_region ∩ head_mask                          （= headmark step3）
+  [4] 划线图 marked + curve_mask = render(photo, 502点, 黑贴图 t)   # 烧黑线 + 得到曲线像素
+  [5] mask = roi 中"在发际线曲线以上(更小 y)"的部分 → 闭运算去洞 + 取最大连通域填充 + 轻羽化
+        （= headmark step5：发际线曲线 + ROI 上边界围成的闭合区域）
+  return marked(划线图), mask
+```
+
+for 每种发际线贴图 t（该性别全部）：
+- `marked, mask = build_inpaint_mask(...)`
+- `comfy_input = RGBA(rgb=marked, alpha=255*(1−mask))`  # **透明=重绘区**，对齐 ComfyUI `mask=1−alpha`
+- `grown = comfyui_run(comfy_input)`（§10.4）
+- `results[t] = { preview(白线预览,已有), grown(生发图,新增) }`
+
+- `hairline_texture_black/`：与 `hairline_texture/` 同 9 张曲线，但**黑色**，烧划线 + 当遮罩下边界。
+- **头部分割来源**：先复用已加载的 SegFormer（零新增依赖）；若头型轮廓不够干净，可改用
+  headmark 同款 `head-segmentation` 包（子进程，避免与 MediaPipe GPU 冲突）。
+- 遮罩边界精度 **M5 必须 dump 可视化核验**（划线图 / ROI / 最终 mask 三张叠图）。
+
+### 10.4 ComfyUI 客户端（`hairline/comfyui.py`，新增）
+
+```
+COMFYUI_URL = env COMFYUI_URL (默认 http://127.0.0.1:8182)
+WORKFLOW    = add_hair.json（启动时加载一次）
+run(comfy_input_png_bytes):
+  1. POST /upload/image (multipart) → {name, subfolder, type:"input"}
+  2. wf = deepcopy(WORKFLOW); wf["26"]["inputs"]["image"] = name
+     wf["6"]["inputs"]["noise_seed"] = 随机
+  3. POST /prompt {prompt: wf, client_id} → prompt_id
+  4. 轮询 GET /history/{prompt_id} 直到完成（带超时）
+  5. node "17".images[0] → GET /view?filename&subfolder&type=output → PNG bytes
+  返回 PNG bytes
+```
+
+### 10.5 接口契约变更（同步更新 `接口文档.md`）
+
+`/api/v1/hair/grow` 的 `results[]` 每项**新增生发图字段**（worker 返回 base64，网关落盘改 URL）：
+
+| 字段 | 说明 |
+|------|------|
+| `image_base64` | （已有）发际线**预览图**（白线叠加） |
+| `grown_image_base64` | （新增）**生发后图片**（ComfyUI 出图） |
+| `hairline_type` / `order` | 同前 |
+
+> ⚠️ **同步 + N 张 Flux** → 单请求很慢。网关/前端超时要放大；worker 自身并发=1。
+> 失败处理：某张 ComfyUI 失败时该项 `grown_image_base64` 置空并标记，不整请求失败（待定，实现期确认）。
+
+### 10.6 开发步骤（M5+）
+
+| 阶段 | 内容 | 验证 |
+|------|------|------|
+| **M5 遮罩** | `build_inpaint_mask` + 黑线渲染 + 合成 RGBA | 目视：划线图正确、遮罩=头发∪发际线以下，透明区对 |
+| **M6 ComfyUI 客户端** | `comfyui.py` 跑通一张（8182 起服务后） | 上传→prompt→取回 PNG，得到生发图 |
+| **M7 接 service/app** | generate 时每种附带 grown，handler 返回新字段 | curl：results 含 grown_image_base64（合法 PNG） |
+| **M8 文档/测试** | 更新接口文档；mock ComfyUI 的单测 + 真机冒烟 | pytest 绿；真机端到端出生发图 |
+
+### 10.7 新增风险
+
+1. **耗时**：同步 N 张 Flux，单请求数分钟级；需评估是否后续改异步/队列。
+2. **ComfyUI 依赖外部进程**：8182 未起/模型未加载/节点缺失 → 该接口失败；worker `/health` 不体现 ComfyUI 状态（可加可选探测）。
+3. **遮罩精度**：直接决定生发位置与自然度；M5 必须可视化核验，必要时拿手绘样本标定。
+4. **GPU 共享**：ComfyUI 与接口1/2 的 CPU 推理同机；显存/算力调度需观察（ComfyUI 自带 torch，支持 5090）。
+
+---
+
+> **文档版本**: v1.1 ｜ **创建日期**: 2026-06-14（v1.1：新增 §10 生发图生成 ComfyUI 管线）
+> 算法来源: head3d（502 点 mesh + UV）+ Flux-2 Klein 9b（ComfyUI add_hair.json）｜ 运行位置: worker(GPU) + 本机 ComfyUI(8182)
+> **产出**: ① 发际线曲线叠加预览图　② 生发后图片（植发 3 个月效果）
@@ -0,0 +1,93 @@
+# 接口 3：B 端生发 — 技术实现方案（马克笔发际线检测 + 生发）
+
+> 在 **高性能 worker（GPU 机）** 实现，与接口 1/2 同机。对外经网关代理。
+> B 端：医生在患者额头**用马克笔画出规划的发际线**，拍照上传。系统**检测这条手绘线**，
+> 据此生成生发图。检测算法移植自 `/home/xsl/headmark` 的调研结论（黑帽 + Dijkstra）。
+
+---
+
+## 0. 契约（对齐 `接口文档.md` 接口3，不变）
+
+`POST /api/v1/hair/grow-b`
+
+| 输入 | 说明 |
+|------|------|
+| `marked_image_*` | 已划线（医生标注发际线）的图，三选一，必填 |
+| `original_image_*` | 原始用户照片，三选一，必填 |
+
+| 输出 data | 决策 |
+|-----------|------|
+| `best_hairline_image_url` | **= 原图 original** 原样返回（worker 返回 `best_hairline_image_base64`） |
+| `hair_growth_image_url` | **生发后图片**（ComfyUI，worker 返回 `hair_growth_image_base64`） |
+| `hairline_type` | 固定 **`"custom"`**（手绘定制） |
+
+> 落盘改 URL 由网关做（架构同接口1/2）。
+
+---
+
+## 1. 马克笔发际线检测（核心，源自 headmark 调研）
+
+headmark `docs/detection_research.md` 结论：全局灰度阈值不可用（笔迹平均灰度反而高于阈值、
+与皮肤阴影分布重叠）；推荐 **黑帽响应图 + 端点锚定 Dijkstra 最小路径**，实测误差 ≤0.5px(GT锚点)。
+本项目用 **MediaPipe 锚点**（非 GT）实测平均 3.2px、中位 0px —— 对生成遮罩足够（线会膨胀成带）。
+
+```
+detect_marker_hairline(marked_bgr, landmarks, parse_map):
+  [1] ROI = forehead_upper_region(landmarks) ∩ head_silhouette(parse_map)   # 复用接口2 mask.py
+  [2] 黑帽响应 bh = MORPH_BLACKHAT(gray, ksize=max(15,int(w*0.025)|1))；ROI 外置 0
+  [3] 锚点 = MediaPipe 21(左鬓角)/251(右鬓角)，各自小窗口(≈w*3%)内吸附到 bh 最大处
+  [4] 代价 cost = bh.max()-bh+1；ROI 外设 1e6；
+      path = skimage.graph.route_through_array(cost, 左锚, 右锚, fully_connected, geometric)
+  [5] 拒识：path 平均 bh 响应 < 阈值(可调) → None（上层返回 1001 "未检测到发际线划线"）
+  return path  # (N,2) row,col
+```
+
+- 依赖：**`scikit-image==0.24.0`**。⚠️ 0.25+ 强依赖 numpy≥2，会顶掉 mediapipe 的 numpy<2 →
+  mediapipe/SegFormer 全崩。**必须锁 0.24.x**。
+- 复用接口2：`forehead_upper_region` / `head_silhouette`（`hairline/mask.py`）、SegFormer / MediaPipe 单例。
+
+## 2. 遮罩 + 原图重画干净线
+
+- **遮罩**：path → 画成 curve_mask → 复用接口2 `_above_curve_region` + `head` + `_clean_mask`
+  得到"发际线以上闭合区域"。
+- **ComfyUI 输入图**：用 **原图 original**（按需缩放到 marked 尺寸对齐坐标），**重画一条干净黑线**
+  （检测 path 膨胀成线宽），避免医生手绘的毛刺/杂线干扰生成。
+- 合成 RGBA：RGB=重画线的原图，alpha=255−mask（透明=重绘区）。复用 `compose_comfy_rgba`。
+
+## 3. 生发（复用接口2 ComfyUI 客户端）
+
+`hairline/comfyui.run(rgba_png)` → 跑 `add_hair.json`（Flux-2）→ 生发图 PNG。同步。
+
+## 4. worker handler（`/api/v1/hair/grow-b`）
+
+```
+1. marked + original 各三选一取图（复用 resolve_image_bytes）+ 校验(大小/解码/分辨率)
+2. 在 marked 上：landmarks(MediaPipe)+parse(SegFormer) → detect_marker_hairline
+   - 无人脸 → 1001；未检测到画线 → 1001 "未检测到发际线划线"
+3. 遮罩 + 原图重画线 → RGBA → comfyui.run → 生发图
+4. return ok({ best_hairline_image_base64: 原图, hair_growth_image_base64: 生发图,
+              hairline_type: "custom" })
+异常 → 1007；重活 run_in_threadpool。
+```
+
+## 5. 开发步骤
+
+| 阶段 | 内容 | 验证 |
+|------|------|------|
+| **M1 检测** | `hairline/marker_detect.py`（黑帽+锚点+Dijkstra+拒识） | headmark test_image：检测线贴合真值；无线图被拒识 |
+| **M2 遮罩+重画** | path→遮罩(复用) + 原图重画干净线 + RGBA 合成 | 目视：干净线在原图、遮罩贴合 |
+| **M3 接 app** | grow-b 真实实现 + 输出字段 + 1001 | curl：best=原图/grown 合法PNG/type=custom；无线→1001 |
+| **M4 测试** | 检测/mask 单测 + mock-ComfyUI 集成 + 真机冒烟 | pytest 绿；真机出生发图 |
+
+## 6. 风险
+
+1. **锚点偏差/路径端点偏移**：MediaPipe 21/251 吸附后仍可能在鬓角端有偏移（实测 max~42px，少数点）。
+   膨胀成带 + 遮罩闭合可吸收；必要时改进吸附窗口或端点截断。
+2. **没画线/画线极浅**：靠拒识阈值（路径平均黑帽响应）兜底，阈值需在更多真实图上标定。
+3. **marked 与 original 尺寸/对齐不一致**：按 marked 坐标系处理，original 缩放对齐；若两图非同源（不同姿态）会错位——约定二者为"同一张照片的划线版/原始版"。
+4. **抬头纹/眉毛/发丝干扰**：黑帽 + ROI + Dijkstra 平滑已大幅抑制（调研验证抬头纹零干扰），极端情况可在代价图抑制头发区域。
+
+---
+
+> **文档版本**: v1.0 ｜ **创建日期**: 2026-06-15 ｜ 检测来源: headmark（黑帽+Dijkstra）｜
+> 生发: 复用接口2 ComfyUI(add_hair.json) ｜ 运行位置: worker(GPU) + 本机 ComfyUI(8182)
@@ -198,10 +198,15 @@

 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 方案预览图 URL（当前 = 发际线叠加图） |
+| image_url | string | 方案**预览图** URL（发际线曲线叠加图） |
+| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
 | hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
 | order | int | 排序序号（当前阶段固定 `1..N`，按贴图顺序，暂不计算合适度） |

+> ⚠️ 生发图由本机 ComfyUI（Flux-2，端口 8182）生成，**一次请求生成全部 N 张、同步返回**，
+> 单请求耗时可达数分钟，调用方超时需放大。worker 侧返回 `image_base64` / `grown_image_base64`，
+> 网关落盘后改写为上表的 `image_url` / `grown_image_url`。
+
 ### 响应示例

 ```json
@@ -211,8 +216,8 @@
  "request_id": "mock-request-id",
  "data": {
    "results": [
-      { "image_url": "https://hair.xiangsilian.com/static/annotations/uuid1.png", "hairline_type": "ellipse", "order": 1 },
-      { "image_url": "https://hair.xiangsilian.com/static/annotations/uuid2.png", "hairline_type": "flower", "order": 2 }
+      { "image_url": "https://hair.xiangsilian.com/static/annotations/uuid1.png", "grown_image_url": "https://hair.xiangsilian.com/static/annotations/grown1.png", "hairline_type": "ellipse", "order": 1 },
+      { "image_url": "https://hair.xiangsilian.com/static/annotations/uuid2.png", "grown_image_url": "https://hair.xiangsilian.com/static/annotations/grown2.png", "hairline_type": "flower", "order": 2 }
    ]
  }
 }
@@ -310,21 +315,25 @@

 ### 输入

-图片参数见「通用约定 → 图片传参字段」。本接口无其他专属参数。
+图片参数见「通用约定 → 图片传参字段」。专属参数：
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| gender | string | **是** | 性别：`male` / `female`。决定返回的发际线集合（female 5 / male 4） |

 ### 输出（data）

 | 字段 | 类型 | 说明 |
 |------|------|------|
-| hairline_images | object[] | N 张用户发际线 PNG，**数量 N 不固定**，已按合适度排序，元素见下表 |
-| best_hairline_center_point | object | 最合适发际线的「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| hairline_images | object[] | N 张发际线叠加图（发际线曲线叠在用户照片上，同接口2预览），**数量 = 该性别发际线数**，本期按贴图顺序，元素见下表 |
+| best_hairline_center_point | object | 最佳（order=1）发际线曲线的「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |

 `hairline_images` 元素：

 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 发际线 PNG 图片 URL |
-| order | int | 排序序号（1 = 最合适，依次递增） |
+| image_url | string | 发际线叠加图 URL（worker 返回 `image_base64`，网关落盘后改写为 url） |
+| order | int | 排序序号（本期固定 `1..N`，暂不计算合适度） |

 ### 响应示例（当前 Mock 返回值）

@@ -0,0 +1,110 @@
+"""ComfyUI 客户端：用 add_hair.json 工作流跑生发图（Flux-2 inpaint）。
+
+worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到本机 ComfyUI(默认 8182)，
+替换工作流节点 26 的输入图、随机 seed，提交 /prompt，轮询 /history，取回 /view 输出。
+"""
+from __future__ import annotations
+
+import copy
+import json
+import os
+import random
+import time
+import uuid
+
+import httpx
+
+COMFYUI_URL = os.getenv("COMFYUI_URL", "http://127.0.0.1:8182").rstrip("/")
+WORKFLOW_PATH = os.getenv(
+    "ADD_HAIR_WORKFLOW",
+    os.path.join(os.path.dirname(os.path.dirname(__file__)), "add_hair.json"),
+)
+COMFY_TIMEOUT = float(os.getenv("COMFYUI_TIMEOUT", "600"))   # 单张出图最长等待(秒)
+
+_INPUT_NODE = "26"     # LoadImage：外部输入图(含 alpha 遮罩)
+_SEED_NODE = "6"       # RandomNoise
+_OUTPUT_NODE = "17"    # SaveImage
+
+_workflow = None
+
+
+def _load_workflow() -> dict:
+    global _workflow
+    if _workflow is None:
+        with open(WORKFLOW_PATH, encoding="utf-8") as f:
+            _workflow = json.load(f)
+    return _workflow
+
+
+def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT) -> bytes:
+    """提交一次生发任务，返回输出 PNG 字节。失败抛异常。"""
+    client_id = uuid.uuid4().hex
+    with httpx.Client(base_url=COMFYUI_URL, timeout=30.0) as cli:
+        # 1. 上传输入图（含 alpha 遮罩）到 ComfyUI input 目录
+        fname = f"hair_{client_id}.png"
+        r = cli.post("/upload/image", files={"image": (fname, rgba_png_bytes, "image/png")},
+                     data={"overwrite": "true", "type": "input"})
+        r.raise_for_status()
+        up = r.json()
+        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
+        wf = copy.deepcopy(_load_workflow())
+        wf[_INPUT_NODE]["inputs"]["image"] = name
+        wf[_SEED_NODE]["inputs"]["noise_seed"] = random.randint(0, 2**63 - 1)
+
+        # 3. 提交
+        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        r.raise_for_status()
+        prompt_id = r.json()["prompt_id"]
+
+        # 4. 轮询 /history
+        deadline = time.time() + timeout
+        outputs = None
+        while time.time() < deadline:
+            hr = cli.get(f"/history/{prompt_id}")
+            hr.raise_for_status()
+            hist = hr.json()
+            if prompt_id in hist:
+                entry = hist[prompt_id]
+                status = entry.get("status", {})
+                if status.get("status_str") == "error":
+                    raise RuntimeError(f"ComfyUI 执行报错: {status}")
+                outputs = entry.get("outputs")
+                if outputs and _OUTPUT_NODE in outputs:
+                    break
+            time.sleep(1.0)
+        if not outputs or _OUTPUT_NODE not in outputs:
+            raise TimeoutError(f"ComfyUI 出图超时({timeout}s) prompt_id={prompt_id}")
+
+        # 5. 取回输出图
+        imgs = outputs[_OUTPUT_NODE].get("images") or []
+        if not imgs:
+            raise RuntimeError("ComfyUI 输出无图像")
+        info = imgs[0]
+        vr = cli.get("/view", params={"filename": info["filename"],
+                                      "subfolder": info.get("subfolder", ""),
+                                      "type": info.get("type", "output")})
+        vr.raise_for_status()
+        return vr.content
+
+
+def ping() -> bool:
+    """探测 ComfyUI 是否在线（/system_stats）。"""
+    try:
+        with httpx.Client(base_url=COMFYUI_URL, timeout=3.0) as cli:
+            return cli.get("/system_stats").status_code == 200
+    except Exception:  # noqa: BLE001
+        return False
+
+
+if __name__ == "__main__":
+    import sys
+    inp = sys.argv[1] if len(sys.argv) > 1 else "tests/output/comfy_input.png"
+    print("ComfyUI:", COMFYUI_URL, "online:", ping())
+    with open(inp, "rb") as f:
+        png = run(f.read())
+    out = "tests/output/grown.png"
+    with open(out, "wb") as f:
+        f.write(png)
+    print(f"生发图已存 {out}（{len(png)} bytes）")
@@ -0,0 +1,102 @@
+"""接口3：马克笔手绘发际线检测（黑帽响应图 + 端点锚定 Dijkstra 最小路径）。
+
+源自 /home/xsl/headmark 调研结论：全局灰度阈值不可用（笔迹平均灰度反高于阈值、
+与皮肤阴影分布重叠）；黑帽变换响应"比局部邻域暗的细结构"，叠加 ROI + 两鬓角锚点间
+最小代价路径，对抬头纹/眉毛/发丝鲁棒。复用接口2 的 ROI（额头上部 ∩ 头部分割）。
+"""
+from __future__ import annotations
+
+import cv2
+import numpy as np
+from skimage.graph import route_through_array
+
+from .mask import forehead_upper_region, head_silhouette
+
+# 鬓角锚点（MediaPipe canonical 索引）：21 左、251 右
+ANCHOR_LEFT = 21
+ANCHOR_RIGHT = 251
+# 拒识阈值：路径平均黑帽响应低于此值 → 判"未检测到画线"（待真实图标定）
+MIN_MEAN_RESPONSE = 8.0
+
+
+def _blackhat(gray: np.ndarray, w: int) -> np.ndarray:
+    k = max(15, int(w * 0.025) | 1)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+    return cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel).astype(np.float32)
+
+
+def _snap_anchor(bh_roi: np.ndarray, x: int, y: int, w: int):
+    """在 (x,y) 周围窗口内吸附到黑帽响应最大处，返回 (row, col)。"""
+    win = max(8, int(w * 0.03))
+    h, ww = bh_roi.shape
+    x0, x1 = max(0, x - win), min(ww, x + win)
+    y0, y1 = max(0, y - win), min(h, y + win)
+    sub = bh_roi[y0:y1, x0:x1]
+    if sub.size == 0 or sub.max() <= 0:
+        return (int(np.clip(y, 0, h - 1)), int(np.clip(x, 0, ww - 1)))
+    dy, dx = np.unravel_index(int(np.argmax(sub)), sub.shape)
+    return (y0 + dy, x0 + dx)
+
+
+def detect_marker_hairline(marked_bgr: np.ndarray, landmarks_mp: np.ndarray,
+                           parse_map: np.ndarray, min_mean_response: float = MIN_MEAN_RESPONSE):
+    """检测手绘发际线，返回路径 (N,2) row,col；未检出/被拒识返回 None。"""
+    h, w = marked_bgr.shape[:2]
+    roi = cv2.bitwise_and(forehead_upper_region(landmarks_mp, w, h),
+                          head_silhouette(parse_map)) > 0
+    if roi.sum() == 0:
+        return None
+
+    gray = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2GRAY)
+    bh = _blackhat(gray, w)
+    bh_roi = bh * roi
+
+    al = _snap_anchor(bh_roi, int(landmarks_mp[ANCHOR_LEFT, 0] * w),
+                      int(landmarks_mp[ANCHOR_LEFT, 1] * h), w)
+    ar = _snap_anchor(bh_roi, int(landmarks_mp[ANCHOR_RIGHT, 0] * w),
+                      int(landmarks_mp[ANCHOR_RIGHT, 1] * h), w)
+
+    cost = (bh.max() - bh) + 1.0
+    cost[~roi] = 1e6                                  # 禁止路径走出 ROI
+    path, _ = route_through_array(cost, al, ar, fully_connected=True, geometric=True)
+    path = np.asarray(path)
+
+    # 拒识：路径平均黑帽响应过低 → 没画线（强行找出的伪路径）
+    if float(bh[path[:, 0], path[:, 1]].mean()) < min_mean_response:
+        return None
+    return path
+
+
+def path_to_curve_mask(path: np.ndarray, h: int, w: int, thickness: int = 3) -> np.ndarray:
+    """把路径画成曲线 mask（uint8 0/255），用作遮罩下边界 / 重画干净线。"""
+    m = np.zeros((h, w), np.uint8)
+    pts = path[:, ::-1].reshape(-1, 1, 2)            # (row,col)→(x,y)
+    cv2.polylines(m, [pts], False, 255, thickness, lineType=cv2.LINE_AA)
+    return m
+
+
+if __name__ == "__main__":
+    import sys
+    from .service import get_landmarker, get_parser
+
+    path_img = sys.argv[1] if len(sys.argv) > 1 else "/home/xsl/headmark/test_image/input1.png"
+    img = cv2.imread(path_img)
+    if img is None:
+        print(f"无法读取 {path_img}"); sys.exit(1)
+    h, w = img.shape[:2]
+    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    lm = get_landmarker().detect(rgb)
+    if lm is None:
+        print("未检出人脸"); sys.exit(1)
+    pm = get_parser().parse(rgb)
+    p = detect_marker_hairline(img, lm, pm)
+    if p is None:
+        print("未检测到发际线划线（拒识）"); sys.exit(0)
+    print(f"检测到画线：{len(p)} 点")
+    vis = img.copy()
+    cv2.polylines(vis, [p[:, ::-1].reshape(-1, 1, 2)], False, (0, 0, 255), 2)
+    import os
+    os.makedirs("tests/output", exist_ok=True)
+    name = os.path.splitext(os.path.basename(path_img))[0]
+    cv2.imwrite(f"tests/output/marker_{name}.png", vis)
+    print(f"saved tests/output/marker_{name}.png")
@@ -0,0 +1,151 @@
+"""接口2 第二步：inpaint 遮罩 + 黑色发际线划线合成（参考 headmark 5步法）。
+
+算法（用 hairline_texture_black 渲染黑线替代 headmark 的手绘检测）：
+  ① 额头上部区域：MediaPipe 额头边界关键点连线，向上+两侧补到图像边缘填充
+  ② 头部轮廓：SegFormer 头部类（hair∪skin∪…，排除 bg/neck/cloth）
+  ③ ROI = ① ∩ ②
+  ④ 渲染黑色发际线 → 烧进照片(marked) + 得到曲线像素
+  ⑤ mask = ROI 中"发际线曲线以上"，闭运算去洞 + 最大连通域 + 轻羽化
+合成 RGBA：RGB=marked，alpha=255×(1−mask)（透明=重绘区，对齐 ComfyUI mask=1−alpha）。
+"""
+from __future__ import annotations
+
+import cv2
+import numpy as np
+from PIL import Image
+
+from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay, build_overlay_layer
+
+# headmark 额头边界关键点（MediaPipe canonical 索引，左→右沿上额）
+FOREHEAD_LANDMARKS = [21, 68, 104, 69, 108, 151, 337, 299, 333, 298, 251]
+# SegFormer 头部类（含 skin..hat；排除 bg=0 / ear_r=15 / neck_l=16 / neck=17 / cloth=18）
+_HEAD_CLASSES = list(range(1, 15))
+
+
+def forehead_upper_region(landmarks_mp: np.ndarray, w: int, h: int) -> np.ndarray:
+    """headmark step1：额头边界关键点以上的"上部区域"填充 mask（uint8 0/255）。"""
+    pts = [(int(landmarks_mp[i, 0] * w), int(landmarks_mp[i, 1] * h)) for i in FOREHEAD_LANDMARKS]
+    left_ext = (0, pts[0][1])
+    right_ext = (w - 1, pts[-1][1])
+    polygon = np.array([left_ext] + pts + [right_ext, (w - 1, 0), (0, 0)], dtype=np.int32)
+    m = np.zeros((h, w), np.uint8)
+    cv2.fillPoly(m, [polygon], 255)
+    return m
+
+
+def head_silhouette(parse_map: np.ndarray) -> np.ndarray:
+    """headmark step2：SegFormer 头部轮廓 mask（uint8 0/255）。"""
+    return (np.isin(parse_map, _HEAD_CLASSES).astype(np.uint8) * 255)
+
+
+def _curve_bottom_per_column(curve_mask: np.ndarray):
+    """每列发际线曲线的**最低**像素 y（线下沿），返回 (xs, ys) 仅含有曲线的列。"""
+    ys_idx, xs_idx = np.where(curve_mask > 0)
+    if xs_idx.size == 0:
+        return None, None
+    w = curve_mask.shape[1]
+    bottom = np.full(w, -1, np.int32)
+    np.maximum.at(bottom, xs_idx, ys_idx)
+    cols = np.where(bottom >= 0)[0]
+    return cols, bottom[cols]
+
+
+def _above_curve_region(curve_mask: np.ndarray, h: int, w: int) -> np.ndarray:
+    """由发际线曲线得到"曲线以上"区域（uint8 0/255）。
+
+    曲线 x 跨度内逐列插值出下沿 y_line(x)，两侧按端点 y 水平延伸；
+    above = 所有 y ≤ y_line(x)。曲线缺失（极端）则返回全 1（交给 ROI 兜底）。
+    """
+    cols, ybot = _curve_bottom_per_column(curve_mask)
+    if cols is None:
+        return np.full((h, w), 255, np.uint8)
+    x0, x1 = int(cols.min()), int(cols.max())
+    # 全列插值 y_line：[x0,x1] 内线性插值，两侧水平延伸
+    yline = np.interp(np.arange(w), cols, ybot,
+                      left=float(ybot[0]), right=float(ybot[-1])).astype(np.int32)
+    yy = np.arange(h)[:, None]                  # (h,1)
+    above = (yy <= yline[None, :]).astype(np.uint8) * 255   # (h,w)
+    return above
+
+
+def _clean_mask(mask: np.ndarray, w: int) -> np.ndarray:
+    """闭运算去洞 + 取最大连通域填充 + 轻羽化。"""
+    k = max(3, (int(w * 0.015) | 1))
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+    closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    cnts, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    out = np.zeros_like(mask)
+    if cnts:
+        largest = max(cnts, key=cv2.contourArea)
+        cv2.drawContours(out, [largest], -1, 255, -1)
+    # 轻羽化（柔化边缘，利于扩散衔接）
+    out = cv2.GaussianBlur(out, (0, 0), sigmaX=max(1.0, w * 0.004))
+    return out
+
+
+def mask_from_curve(curve_mask: np.ndarray, landmarks_mp: np.ndarray,
+                    parse_map: np.ndarray) -> np.ndarray:
+    """由发际线曲线 + ROI(额头上部 ∩ 头部) 围成"曲线以上"闭合遮罩（uint8 0..255）。
+
+    接口2(模板渲染曲线) 与 接口3(检测路径曲线) 共用。
+    """
+    h, w = curve_mask.shape[:2]
+    roi = cv2.bitwise_and(forehead_upper_region(landmarks_mp, w, h),
+                          head_silhouette(parse_map))
+    above = _above_curve_region(curve_mask, h, w)
+    return _clean_mask(cv2.bitwise_and(roi, above), w)
+
+
+def build_inpaint_mask(photo_bgr: np.ndarray, landmarks_mp: np.ndarray,
+                       parse_map: np.ndarray, points502: np.ndarray,
+                       black_texture_rgba: np.ndarray):
+    """接口2：返回 (marked_bgr 划线图, mask uint8 0..255 重绘区)。"""
+    h, w = photo_bgr.shape[:2]
+    uv, ext_faces = load_ext_mesh()
+    marked = render_hairline_overlay(photo_bgr, points502, ext_faces, uv, black_texture_rgba)
+    overlay = build_overlay_layer(h, w, points502, ext_faces, uv, black_texture_rgba)
+    curve_mask = (overlay[:, :, 3] > 40).astype(np.uint8) * 255
+    mask = mask_from_curve(curve_mask, landmarks_mp, parse_map)
+    return marked, mask
+
+
+def compose_comfy_rgba(marked_bgr: np.ndarray, mask: np.ndarray) -> Image.Image:
+    """合成 ComfyUI LoadImage 用的 RGBA：RGB=划线图，alpha=255−mask（透明=重绘区）。"""
+    rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
+    alpha = (255 - mask).astype(np.uint8)
+    rgba = np.dstack([rgb, alpha])
+    return Image.fromarray(rgba, mode="RGBA")
+
+
+if __name__ == "__main__":
+    import sys, os
+    from .service import get_landmarker, get_parser
+
+    path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
+    tex_name = sys.argv[2] if len(sys.argv) > 2 else "girl_straight"
+    img = cv2.imread(path)
+    h, w = img.shape[:2]
+    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    from .hairline_2d import sample_hairline, smooth_hairline
+    from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
+    lm = get_landmarker().detect(rgb)
+    parse_map = get_parser().parse(rgb)
+    h2d, valid = sample_hairline(lm, parse_map); h2d = smooth_hairline(h2d, valid)
+    h3d = lift_hairline_to_3d(lm, h2d); mid = build_middle_row(lm, h3d)
+    pts = assemble_full(lm, mid, h3d)
+
+    black = load_texture_rgba(f"hairline_texture_black/{tex_name}.png")
+    marked, mask = build_inpaint_mask(img, lm, parse_map, pts, black)
+    os.makedirs("tests/output", exist_ok=True)
+    cv2.imwrite("tests/output/mask_marked.png", marked)
+    cv2.imwrite("tests/output/mask_binary.png", mask)
+    # 三联可视化：划线图 / ROI / mask 叠加
+    upper = forehead_upper_region(lm, w, h); head = head_silhouette(parse_map)
+    roi = cv2.bitwise_and(upper, head)
+    vis = marked.copy()
+    vis[roi > 0] = (vis[roi > 0] * 0.6 + np.array([0, 40, 0])).clip(0, 255).astype(np.uint8)
+    vis[mask > 128] = (vis[mask > 128] * 0.4 + np.array([0, 0, 150])).clip(0, 255).astype(np.uint8)
+    cv2.imwrite("tests/output/mask_vis.png", vis)
+    compose_comfy_rgba(marked, mask).save("tests/output/comfy_input.png")
+    print(f"saved mask_marked/mask_binary/mask_vis/comfy_input；mask 像素 {int((mask>128).sum())}")
@@ -78,19 +78,30 @@ def render_hairline_overlay(photo_bgr: np.ndarray,
    points502_norm: (502, 3) 归一化坐标（x,y ∈ [0,1]），**MP 顺序**（extract_hairline 输出）。
    """
    H, W = photo_bgr.shape[:2]
-    TH, TW = texture_rgba.shape[:2]
-    pts_obj = mp_order_to_obj_order(points502_norm)              # MP序 → OBJ序
-    img_xy = pts_obj[:, :2] * np.array([W, H], dtype=np.float32)  # (502,2)
+    overlay = build_overlay_layer(H, W, points502_norm, ext_faces, uv502, texture_rgba)
+    # alpha 合成（RGBA→BGR：贴图 RGB 顺序需反成 BGR）
+    a = overlay[:, :, 3:4] / 255.0
+    rgb = overlay[:, :, :3][..., ::-1]               # RGB→BGR
+    out = photo_bgr.astype(np.float32) * (1.0 - a) + rgb * a
+    return np.clip(out, 0, 255).astype(np.uint8)

-    overlay = np.zeros((H, W, 4), np.float32)        # 累积曲线层 RGBA
+
+def build_overlay_layer(H, W, points502_norm, ext_faces, uv502, texture_rgba) -> np.ndarray:
+    """渲染发际线曲线层，返回 (H, W, 4) float32 RGBA（未合成到照片）。
+
+    供渲染合成（render_hairline_overlay）与遮罩（mask.py 取 alpha=曲线像素）共用。
+    """
+    TH, TW = texture_rgba.shape[:2]
+    pts_obj = mp_order_to_obj_order(points502_norm)
+    img_xy = pts_obj[:, :2] * np.array([W, H], dtype=np.float32)
+    overlay = np.zeros((H, W, 4), np.float32)
    tex = texture_rgba.astype(np.float32)
    for (i, j, k) in ext_faces:
        dst = img_xy[[i, j, k]].astype(np.float32)
        # UV → 贴图像素；flipY：贴图 y = (1 - v_raw) * TH（与 head3d Three.js flipY=true 一致）
        src = np.array([[uv502[v][0] * TW, (1.0 - uv502[v][1]) * TH] for v in (i, j, k)],
                       dtype=np.float32)
-        # 退化三角形（投影到一条线）跳过，避免 getAffineTransform 奇异
-        if cv2.contourArea(dst.astype(np.int32)) < 1.0:
+        if cv2.contourArea(dst.astype(np.int32)) < 1.0:   # 退化三角形跳过
            continue
        M = cv2.getAffineTransform(src, dst)
        warped = cv2.warpAffine(tex, M, (W, H), flags=cv2.INTER_LINEAR,
@@ -99,9 +110,4 @@ def render_hairline_overlay(photo_bgr: np.ndarray,
        cv2.fillConvexPoly(tri_mask, dst.astype(np.int32), 255)
        sel = tri_mask > 0
        overlay[sel] = warped[sel]
-
-    # alpha 合成（RGBA→BGR：贴图 RGB 顺序需反成 BGR）
-    a = overlay[:, :, 3:4] / 255.0
-    rgb = overlay[:, :, :3][..., ::-1]               # RGB→BGR
-    out = photo_bgr.astype(np.float32) * (1.0 - a) + rgb * a
-    return np.clip(out, 0, 255).astype(np.uint8)
+    return overlay
@@ -11,13 +11,23 @@ import cv2
 import numpy as np

 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 .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
-from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay
+from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay, build_overlay_layer
+from .mask import build_inpaint_mask, compose_comfy_rgba, mask_from_curve
+from .marker_detect import detect_marker_hairline, path_to_curve_mask

-_TEXTURE_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "hairline_texture")
+import io
+import logging
+
+logger = logging.getLogger("hair.worker")
+
+_REPO = os.path.dirname(os.path.dirname(__file__))
+_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
+_BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")

 # ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
 #    "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
@@ -73,40 +83,152 @@ def get_texture_map() -> dict:

 def extract_502(image_bgr: np.ndarray):
    """照片(BGR) → (points502 MP序, valid17)。无人脸返回 (None, None)。"""
+    ctx = extract_context(image_bgr)
+    if ctx is None:
+        return None, None
+    return ctx["points"], ctx["valid"]
+
+
+def extract_context(image_bgr: np.ndarray):
+    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。"""
    rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
    landmarks = get_landmarker().detect(rgb)
    if landmarks is None:
-        return None, None
+        return None
    parse_map = get_parser().parse(rgb)
    hairline_2d, valid = sample_hairline(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)
    points = assemble_full(landmarks, middle_3d, hairline_3d)
-    return points, valid
+    return {"landmarks": landmarks, "parse_map": parse_map, "points": points, "valid": valid}
+
+
+def _black_texture_path(white_path: str) -> str:
+    """白贴图路径 → 同名黑贴图路径（hairline_texture_black/）。"""
+    return os.path.join(_BLACK_TEXTURE_DIR, os.path.basename(white_path))


 def generate_previews(image_bgr: np.ndarray, gender: str):
-    """生成该性别全部发际线预览图。
+    """生成该性别全部发际线预览图（仅预览，不生发）。

-    Returns: list[dict]，每项 {"hairline_type": key, "image_bgr": ndarray, "order": 1..N}。
-    无人脸返回 None。gender 必须是 male/female。
+    Returns: list[dict] {"hairline_type", "image_bgr", "order"}；无人脸返回 None。
    """
    if gender not in ("male", "female"):
        raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
-    points, _valid = extract_502(image_bgr)
-    if points is None:
+    ctx = extract_context(image_bgr)
+    if ctx is None:
        return None
-
    uv, ext_faces = load_ext_mesh()
    results = []
    for order, (key, path) in enumerate(get_texture_map()[gender], start=1):
-        tex = load_texture_rgba(path)
-        preview = render_hairline_overlay(image_bgr, points, ext_faces, uv, tex)
+        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv,
+                                          load_texture_rgba(path))
        results.append({"hairline_type": key, "image_bgr": preview, "order": order})
    return results


+def generate_grow_results(image_bgr: np.ndarray, gender: str):
+    """该性别全部发际线：预览图(白线) + 生发图(ComfyUI)。同步、串行。
+
+    Returns: list[dict] {"hairline_type","order","image_bgr"(预览), "grown_png"(bytes 或 None)}。
+    无人脸返回 None。某张 ComfyUI 失败时该项 grown_png=None，不影响其余。
+    """
+    if gender not in ("male", "female"):
+        raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
+    ctx = extract_context(image_bgr)
+    if ctx is None:
+        return None
+    uv, ext_faces = load_ext_mesh()
+    results = []
+    for order, (key, white_path) in enumerate(get_texture_map()[gender], start=1):
+        white = load_texture_rgba(white_path)
+        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
+
+        grown_png = None
+        try:
+            black = load_texture_rgba(_black_texture_path(white_path))
+            marked, mask = build_inpaint_mask(
+                image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+            buf = io.BytesIO()
+            compose_comfy_rgba(marked, mask).save(buf, format="PNG")
+            grown_png = comfyui.run(buf.getvalue())
+        except Exception as e:  # noqa: BLE001  单张失败不拖垮整请求
+            logger.warning("接口2 生发图失败 type=%s：%s", key, e)
+
+        results.append({"hairline_type": key, "order": order,
+                        "image_bgr": preview, "grown_png": grown_png})
+    return results
+
+
+def generate_hairline_pngs(image_bgr: np.ndarray, gender: str):
+    """接口5：该性别全部发际线叠图(同接口2预览) + 最佳(order1)发际线曲线的面部中间点。
+
+    Returns: {"images":[{hairline_type,order,image_bgr}], "best_center":(x,y)}；无人脸 None。
+    """
+    if gender not in ("male", "female"):
+        raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
+    ctx = extract_context(image_bgr)
+    if ctx is None:
+        return None
+    h, w = image_bgr.shape[:2]
+    uv, ext_faces = load_ext_mesh()
+    lm = ctx["landmarks"]
+    # 面部中轴 x = 眉心(9/151 中点)
+    face_cx = float((lm[9, 0] + lm[151, 0]) / 2 * w)
+
+    textures = get_texture_map()[gender]
+    images, best_center = [], None
+    for order, (key, path) in enumerate(textures, start=1):
+        white = load_texture_rgba(path)
+        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
+        images.append({"hairline_type": key, "order": order, "image_bgr": preview})
+        if order == 1:  # 最佳发际线曲线的中点（面部中轴处的发际线 y）
+            overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
+            ys, xs = np.where(overlay[:, :, 3] > 40)
+            if xs.size:
+                near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
+                col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
+                best_center = (int(round(face_cx)), int(round(float(col_ys.mean()))))
+    return {"images": images, "best_center": best_center}
+
+
+def generate_grow_b(marked_bgr: np.ndarray, original_bgr: np.ndarray):
+    """接口3：检测医生手绘发际线 → 遮罩 → 原图重画干净线 → ComfyUI 生发。
+
+    Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
+    """
+    rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
+    landmarks = get_landmarker().detect(rgb)
+    if landmarks is None:
+        return {"grown_png": None, "status": "no_face"}
+    parse_map = get_parser().parse(rgb)
+    path = detect_marker_hairline(marked_bgr, landmarks, parse_map)
+    if path is None:
+        return {"grown_png": None, "status": "no_line"}
+
+    h, w = marked_bgr.shape[:2]
+    # 原图对齐到 marked 坐标系（同一张照片的原始版/划线版，尺寸应一致）
+    orig = original_bgr
+    if orig.shape[:2] != (h, w):
+        orig = cv2.resize(orig, (w, h), interpolation=cv2.INTER_AREA)
+
+    # 在原图上重画干净黑线（膨胀成笔迹宽度），替代医生手绘的毛刺
+    line_w = max(2, int(w * 0.006))
+    marked_clean = orig.copy()
+    cv2.polylines(marked_clean, [path[:, ::-1].reshape(-1, 1, 2)], False,
+                  (0, 0, 0), line_w, lineType=cv2.LINE_AA)
+
+    # 遮罩：检测路径曲线 + ROI 闭合
+    curve_mask = path_to_curve_mask(path, h, w, thickness=max(3, line_w))
+    mask = mask_from_curve(curve_mask, landmarks, parse_map)
+
+    buf = io.BytesIO()
+    compose_comfy_rgba(marked_clean, mask).save(buf, format="PNG")
+    grown_png = comfyui.run(buf.getvalue())
+    return {"grown_png": grown_png, "status": "ok"}
+
+
 if __name__ == "__main__":
    import sys
    g = sys.argv[2] if len(sys.argv) > 2 else "female"
@@ -22,5 +22,9 @@ torchvision==0.17.2
 # MediaPipe Tasks(FaceLandmarker) 用已装的 mediapipe；新增 SegFormer 人脸分割：
 transformers==4.45.2     # SegFormer 人脸分割（jonathandinu/face-parsing，本地权重）

+# 接口3：B端生发（马克笔发际线检测）
+# ⚠️ 必须 0.24.x —— 0.25+ 强依赖 numpy>=2，会顶掉 mediapipe 需要的 numpy<2
+scikit-image==0.24.0     # route_through_array（黑帽响应图上的 Dijkstra 最小路径）
+
 # 测试
 pytest==8.3.3
@@ -81,7 +81,16 @@ def test_grow_missing_gender_1004(client):
    assert r.json()["code"] == 1004


-def test_grow_female_returns_5(client):
+_PNG_1x1 = base64.b64decode(
+    "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
+)
+
+
+def test_grow_female_returns_5(client, monkeypatch):
+    # mock ComfyUI：不依赖 8182、不跑 Flux，只验证管线接线 + grown 字段
+    import hairline.comfyui as comfy
+    monkeypatch.setattr(comfy, "run", lambda *a, **k: _PNG_1x1)
+
    files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
    r = client.post(GROW, headers=H, files=files, data={"gender": "female"})
    body = r.json()
@@ -90,9 +99,63 @@ def test_grow_female_returns_5(client):
    assert [x["hairline_type"] for x in results] == ["ellipse", "flower", "heart", "straight", "wave"]
    assert [x["order"] for x in results] == [1, 2, 3, 4, 5]
    assert base64.b64decode(results[0]["image_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
+    assert base64.b64decode(results[0]["grown_image_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
    assert "image_url" not in results[0]


+GROWB = "/api/v1/hair/grow-b"
+
+
+def test_growb_missing_original_1007(client):
+    files = {"marked_image_file": ("m.jpg", open(fixture("marked_hairline.jpg"), "rb"), "application/octet-stream")}
+    r = client.post(GROWB, headers=H, files=files)
+    assert r.json()["code"] == 1007
+
+
+def test_growb_no_line_1001(client):
+    f = lambda: open(fixture("frontal.jpg"), "rb")
+    files = {"marked_image_file": ("m.jpg", f(), "application/octet-stream"),
+             "original_image_file": ("o.jpg", f(), "application/octet-stream")}
+    r = client.post(GROWB, headers=H, files=files)
+    assert r.json()["code"] == 1001
+
+
+def test_growb_success(client, monkeypatch):
+    import hairline.comfyui as comfy
+    monkeypatch.setattr(comfy, "run", lambda *a, **k: _PNG_1x1)
+    fm = open(fixture("marked_hairline.jpg"), "rb")
+    fo = open(fixture("marked_hairline.jpg"), "rb")
+    files = {"marked_image_file": ("m.jpg", fm, "application/octet-stream"),
+             "original_image_file": ("o.jpg", fo, "application/octet-stream")}
+    body = client.post(GROWB, headers=H, files=files).json()
+    assert body["code"] == 0, body
+    d = body["data"]
+    assert d["hairline_type"] == "custom"
+    assert base64.b64decode(d["hair_growth_image_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
+    assert d["best_hairline_image_base64"]                 # 原图原样(非空)
+    assert "best_hairline_image_url" not in d
+
+
+HLGEN = "/api/v1/hairline/generate"
+
+
+def test_hairline_gen_missing_gender_1004(client):
+    files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
+    assert client.post(HLGEN, headers=H, files=files).json()["code"] == 1004
+
+
+def test_hairline_gen_female(client):
+    files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
+    body = client.post(HLGEN, headers=H, files=files, data={"gender": "female"}).json()
+    assert body["code"] == 0, body
+    d = body["data"]
+    assert [x["order"] for x in d["hairline_images"]] == [1, 2, 3, 4, 5]
+    assert base64.b64decode(d["hairline_images"][0]["image_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
+    c = d["best_hairline_center_point"]
+    assert 0 <= c["x"] <= 682 and 0 <= c["y"] <= 811        # 落在原图范围内
+    assert "image_url" not in d["hairline_images"][0]
+
+
 def test_success_structure(client):
    r = _post(client, "frontal.jpg")
    body = r.json()
@@ -0,0 +1,42 @@
+"""接口3 马克笔检测测试：辅助函数(纯numpy) + 真实样本检测/拒识。"""
+import cv2
+import numpy as np
+from conftest import fixture
+
+from hairline.marker_detect import (
+    detect_marker_hairline, path_to_curve_mask, _snap_anchor,
+)
+from hairline.service import get_landmarker, get_parser
+
+
+def test_path_to_curve_mask():
+    path = np.array([[10, 5], [10, 50], [10, 90]])     # 水平线 row=10
+    m = path_to_curve_mask(path, 100, 100, thickness=3)
+    assert m[10, 50] == 255
+    assert m[90, 50] == 0
+
+
+def test_snap_anchor_moves_to_peak():
+    bh = np.zeros((100, 100), np.float32)
+    bh[40, 30] = 99.0                                  # 峰值在 (40,30)
+    r, c = _snap_anchor(bh, x=33, y=42, w=1000)        # 起点附近 → 应吸到峰值
+    assert (r, c) == (40, 30)
+
+
+def _ctx(path_img):
+    img = cv2.imread(path_img)
+    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    lm = get_landmarker().detect(rgb)
+    pm = get_parser().parse(rgb)
+    return img, lm, pm
+
+
+def test_detect_real_marked():
+    img, lm, pm = _ctx(fixture("marked_hairline.jpg"))
+    path = detect_marker_hairline(img, lm, pm)
+    assert path is not None and len(path) > 50          # 检测到画线
+
+
+def test_reject_clean_photo():
+    img, lm, pm = _ctx(fixture("frontal.jpg"))
+    assert detect_marker_hairline(img, lm, pm) is None  # 无画线 → 拒识
@@ -0,0 +1,47 @@
+"""接口2 第二步 遮罩几何单元测试（纯 numpy/cv2，无需 SegFormer/torch）。"""
+import numpy as np
+
+from hairline.mask import (
+    forehead_upper_region, head_silhouette, _above_curve_region,
+    compose_comfy_rgba, FOREHEAD_LANDMARKS,
+)
+
+
+def test_forehead_upper_region():
+    w = h = 200
+    lm = np.zeros((468, 3), np.float32)
+    for i in FOREHEAD_LANDMARKS:          # 额头边界点都放在 y=0.4
+        lm[i, 0] = 0.5
+        lm[i, 1] = 0.4
+    m = forehead_upper_region(lm, w, h)
+    assert m[10, 100] == 255              # 上方在区域内
+    assert m[150, 100] == 0               # 下方(0.75h)在区域外
+
+
+def test_head_silhouette():
+    pm = np.zeros((50, 50), np.int32)
+    pm[:25] = 13                          # hair
+    pm[25:40] = 1                         # skin
+    pm[40:] = 0                           # bg
+    m = head_silhouette(pm)
+    assert m[10, 10] == 255 and m[30, 10] == 255   # hair/skin → 头部
+    assert m[45, 10] == 0                            # bg → 非头部
+
+
+def test_above_curve_region():
+    h = w = 100
+    curve = np.zeros((h, w), np.uint8)
+    curve[60, 20:80] = 255               # 水平发际线在 row=60
+    above = _above_curve_region(curve, h, w)
+    assert above[30, 50] == 255          # 线以上
+    assert above[80, 50] == 0            # 线以下
+
+
+def test_compose_comfy_rgba_alpha():
+    marked = np.zeros((10, 10, 3), np.uint8)
+    mask = np.zeros((10, 10), np.uint8)
+    mask[2:5, 2:5] = 255                 # 重绘区
+    rgba = np.array(compose_comfy_rgba(marked, mask))
+    assert rgba.shape == (10, 10, 4)
+    assert rgba[3, 3, 3] == 0            # 重绘区 alpha=0(透明)
+    assert rgba[8, 8, 3] == 255         # 非重绘区 alpha=255(不透明)
Author	SHA1	Message	Date
xslandClaude Opus 4.8	38161d1b50	feat(接口5): 发际线PNG生成(真实实现，替换Mock) 复用接口2 预览管线：gender 必填 → 该性别全部发际线叠加图(同接口2预览) + 最佳(order=1)发际线曲线的面部中间点坐标。无生发(不调 ComfyUI)。 - hairline/service.py: generate_hairline_pngs——N张发际线叠图 + best_center(面部中轴眉心x × order1曲线在该处的y) - app.py: /hairline/generate 真实实现，新增 gender 必填(非法→1004)，返回 hairline_images[].image_base64 + best_hairline_center_point；无人脸→1001；重活线程池 - 接口文档/OpenAPI: 接口5 新增 gender 入参 + 输出改 base64(网关改url) - 测试: test_api 接口5(gender必填/N张/center点)，44全绿实测(5090): female 5张叠图 + center{x,y}，~2.5s(无Flux)。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-15 00:20:56 +08:00
xslandClaude Opus 4.8	ce95a508c1	feat(接口3): B端生发-马克笔发际线检测+生发(替换Mock) 医生在额头用马克笔画规划发际线 → 检测该线 → 生发。检测算法源自 /home/xsl/headmark。 - hairline/marker_detect.py: 黑帽响应图(MORPH_BLACKHAT)+鬓角锚点(MediaPipe 21/251吸附) +skimage route_through_array 最小路径检测画线；路径平均响应阈值拒识无画线 (headmark 调研：全局灰度阈值不可用，黑帽+Dijkstra 实测误差≤0.5px) - hairline/mask.py: 抽出 mask_from_curve(曲线+ROI闭合)，接口2/3共用 - hairline/service.py: generate_grow_b——检测→遮罩→原图重画干净线→ComfyUI生发 - app.py: /hair/grow-b 真实实现，marked+original各三选一+校验；输出 best_hairline_image_base64(=原图)/hair_growth_image_base64/hairline_type="custom"；无人脸或未检测到画线→1001；重活进线程池 - requirements: scikit-image==0.24.0 (⚠️锁0.24，0.25+强依赖numpy>=2会顶掉mediapipe的numpy<2) - 文档: docs/接口3-B端生发-技术实现方案.md - 测试: test_marker.py(检测/拒识/辅助) + test_api grow-b(mock ComfyUI)，42全绿实测(5090): grow-b ~6.4s，生发图把额头发际线补到医生画线、清除划线、人物保持。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-15 00:08:05 +08:00
xslandClaude Opus 4.8	94ad95850e	feat(接口2): 新增生发后图片(ComfyUI/Flux inpaint) 在发际线预览基础上，每种发际线再出一张「植发3个月」生发图： - hairline/mask.py: headmark 5步法遮罩(额头上部区域∩SegFormer头部=ROI，取发际线曲线以上闭合区域)；用 hairline_texture_black 渲染黑线替代手绘检测； compose_comfy_rgba 合成 RGBA(alpha=255-mask, 透明=重绘区, 对齐 ComfyUI mask=1-alpha) - hairline/comfyui.py: ComfyUI 客户端(默认8182)，/upload/image+/prompt(改节点26+随机seed) +轮询/history+/view 取回生发图 - hairline/render.py: 抽出 build_overlay_layer 供遮罩取曲线像素 - hairline/service.py: extract_context 一次出 landmarks/parse_map/502点； generate_grow_results 每种=预览+生发图(同步串行N张，单张ComfyUI失败则grown置空不拖垮整请求) - app.py: /hair/grow 返回 results[].grown_image_base64；重活放线程池避免卡事件循环 - add_hair.json 工作流 + hairline_texture_black/ 黑贴图入库 - 测试: test_mask.py(遮罩几何) + test_api mock ComfyUI 验 grown 字段，35 全绿实测(5090): female 5张生发图同步约18s；预览/生发图人物五官服饰背景保持、黑线已清除。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-14 23:42:30 +08:00
xslandClaude Opus 4.8	4c69bb4623	docs(接口2): 新增生发图设计(§10 ComfyUI+Flux) + headmark遮罩算法 - 技术方案 §10：生发图生成管线（add_hair.json 工作流解读、遮罩算法、 ComfyUI 客户端、契约变更、M5~M8 步骤、风险） - 遮罩算法参考 /home/xsl/headmark 5步法，用 hairline_texture_black 渲染黑线替代手绘检测：额头上部区域 ∩ 头部分割 = ROI，取发际线曲线以上闭合区域 - 接口文档：results[] 新增 grown_image_url(生发后图片) + 同步/超时说明 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-14 23:32:21 +08:00