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>

tests/test_api.py

@@ -103,6 +103,39 @@ def test_grow_female_returns_5(client, monkeypatch):
     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
+
+
 def test_success_structure(client):
     r = _post(client, "frontal.jpg")
     body = r.json()

tests/test_marker.py

@@ -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