feat(接口2): 移植head3d发际线管线 + 接口2实现方案文档

- 从head3d复制发际线检测管线到 hairline/ 包：MediaPipe Tasks + SegFormer分割
  + 17锚点射线检测 + 502点mesh(face_ext.obj)+UV
- 复制模型：face_landmarker.task(3.7MB)、SegFormer config/preprocessor
  (model.safetensors 340MB 单独下载中)
- 新增 docs/接口2-C端生发-技术实现方案.md：第一步=发际线曲线叠加预览图，
  新增gender必填参数，按性别贴图数量输出(female5/male4)，hairline_type英文key，
  服务端cv2逐三角形warp渲染器(head3d只有浏览器端Three.js渲染)
- 接口文档.md 接口2章节同步：gender参数、输出语义、错误码说明
- hairline_texture/ 9张发际线贴图入库

hairline/_mediapipe_subprocess.py

@@ -0,0 +1,67 @@
+"""Standalone MediaPipe FaceLandmarker runner used by web_service.py.
+
+Running MediaPipe Tasks in the same process as the Flask dev server can
+segfault under WSL (D3D12 EGL backend). Spawning a fresh subprocess per
+request keeps the web server alive and lets us inject WSL-friendly env
+vars before any mediapipe import.
+
+Usage:
+    python -m python._mediapipe_subprocess <image_path> <output_npy>
+"""
+from __future__ import annotations
+
+import os
+import sys
+
+os.environ.setdefault("LIBGL_ALWAYS_SOFTWARE", "1")
+os.environ.setdefault("MESA_LOADER_DRIVER_OVERRIDE", "llvmpipe")
+os.environ.setdefault("GALLIUM_DRIVER", "llvmpipe")
+os.environ.setdefault("MEDIAPIPE_DISABLE_GPU", "1")
+os.environ.setdefault("EGL_PLATFORM", "surfaceless")
+
+import numpy as np  # noqa: E402
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_DIR = os.path.dirname(THIS_DIR)
+if PROJECT_DIR not in sys.path:
+    sys.path.insert(0, PROJECT_DIR)
+
+from python.face_landmarks import FaceLandmarker  # noqa: E402
+
+
+def main() -> int:
+    if len(sys.argv) != 3:
+        print(
+            "usage: python -m python._mediapipe_subprocess <image_path> <output_npy>",
+            file=sys.stderr,
+        )
+        return 2
+
+    image_path, output_path = sys.argv[1], sys.argv[2]
+
+    import cv2
+
+    bgr = cv2.imread(image_path)
+    if bgr is None:
+        print(f"could not read image: {image_path}", file=sys.stderr)
+        return 3
+
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    rgb = np.ascontiguousarray(rgb, dtype=np.uint8)
+
+    landmarker = FaceLandmarker(static_image_mode=True)
+    try:
+        landmarks = landmarker.detect(rgb)
+    finally:
+        landmarker.close()
+
+    if landmarks is None:
+        print("no face detected", file=sys.stderr)
+        return 4
+
+    np.save(output_path, landmarks.astype(np.float32))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())