xsl
db32fa12e5
- 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
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
"""MediaPipe FaceMesh wrappers returning 468 3D landmarks in [0,1] x/y space.
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`FaceLandmarker` uses the modern mediapipe.tasks.vision API and requires
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models/face_landmarker.task.
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`SolutionsFaceLandmarker` uses the older mediapipe.solutions.face_mesh API.
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It is useful as a CPU fallback in WSL environments where the Tasks API may
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segfault while initializing EGL/OpenGL.
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"""
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from __future__ import annotations
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import os
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import numpy as np
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DEFAULT_MODEL_PATH = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
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"models", "face_landmarker.task",
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)
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class FaceLandmarker:
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def __init__(self, static_image_mode: bool = True, model_path: str | None = None):
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import mediapipe as mp
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from mediapipe.tasks import python as mp_python
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from mediapipe.tasks.python import vision
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path = model_path or DEFAULT_MODEL_PATH
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if not os.path.isfile(path):
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raise FileNotFoundError(
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f"face_landmarker.task not found at {path}. "
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"Download it from "
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"https://storage.googleapis.com/mediapipe-models/face_landmarker/"
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"face_landmarker/float16/1/face_landmarker.task"
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)
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running_mode = vision.RunningMode.IMAGE if static_image_mode else vision.RunningMode.VIDEO
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options = vision.FaceLandmarkerOptions(
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base_options=mp_python.BaseOptions(model_asset_path=path),
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running_mode=running_mode,
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num_faces=1,
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output_face_blendshapes=False,
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output_facial_transformation_matrixes=False,
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)
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self._detector = vision.FaceLandmarker.create_from_options(options)
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self._mp = mp
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def detect(self, image_rgb: np.ndarray) -> np.ndarray | None:
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"""Returns (468, 3) float32 of normalized x, y and relative z, or None.
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The task model produces 478 landmarks (468 face + 10 iris); we return
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only the first 468 to match the canonical FaceMesh topology used by
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the SDK's OBJ file.
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"""
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mp_image = self._mp.Image(image_format=self._mp.ImageFormat.SRGB, data=image_rgb)
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result = self._detector.detect(mp_image)
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if not result.face_landmarks:
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return None
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lm = result.face_landmarks[0][:468]
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arr = np.array([[p.x, p.y, p.z] for p in lm], dtype=np.float32)
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return arr
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def close(self):
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try:
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self._detector.close()
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except Exception:
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pass
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class SolutionsFaceLandmarker:
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"""CPU-oriented fallback using mediapipe.solutions.face_mesh."""
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def __init__(self, static_image_mode: bool = True):
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import mediapipe as mp
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try:
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face_mesh_module = mp.solutions.face_mesh
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except AttributeError as exc:
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raise RuntimeError(
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"当前 mediapipe 包不包含 mediapipe.solutions.face_mesh。"
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"请使用 web_service.py 的默认 parsing backend，或安装包含 solutions API 的 mediapipe 版本。"
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) from exc
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self._face_mesh = face_mesh_module.FaceMesh(
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static_image_mode=static_image_mode,
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max_num_faces=1,
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refine_landmarks=False,
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min_detection_confidence=0.5,
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)
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def detect(self, image_rgb: np.ndarray) -> np.ndarray | None:
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"""Returns (468, 3) float32 of normalized x, y and relative z, or None."""
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image_rgb.flags.writeable = False
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result = self._face_mesh.process(image_rgb)
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image_rgb.flags.writeable = True
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if not result.multi_face_landmarks:
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return None
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lm = result.multi_face_landmarks[0].landmark[:468]
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return np.array([[p.x, p.y, p.z] for p in lm], dtype=np.float32)
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def close(self):
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try:
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self._face_mesh.close()
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except Exception:
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pass
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