Files
hair/hairline/_mediapipe_subprocess.py
T
xsl db32fa12e5 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
2026-06-14 16:59:39 +08:00

68 lines
1.8 KiB
Python

"""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())