xslandClaude Opus 4.8
554b64a916
第一步：按性别把发际线类型贴图渲染到照片，输出 N 张发际线叠加预览图。 - hairline/render.py: 解析 face_ext.obj(502 UV + 64 ribbon扩展面) + OpenCV 逐三角 仿射 warp 渲染器；关键修复——face_ext.obj 是 OBJ序，用 INDEX_MAP_468 把 MP序 502点重排成 OBJ序后再投影，否则 ribbon 会错贴到中脸 - hairline/service.py: FaceLandmarker+SegFormer 单例 + 性别贴图映射(扫描去空格) + generate_previews 管线(female5/male4) - 集成点修复: face_landmarks DEFAULT_MODEL_PATH 改 hairline/models/; constants HF_FACE_PARSER_MODEL 改本地路径(离线) - app.py: /api/v1/hair/grow 接真实实现，gender 必填(非法→1004)，返回 results[].image_base64(不落盘)，校验/鉴权同接口1；lifespan 预热接口2单例； 补 logging.basicConfig - 依赖: transformers==4.45.2；SegFormer 权重走 hf-mirror 下载(见 OFFLINE_ASSETS) - 测试: tests/test_hairline.py(mesh/重排/贴图映射) + test_api 接口2用例，31 全绿 注：SegFormer 受 5090/torch 限制走 CPU(~2.5s/张)，换 cu128 可 SEG_DEVICE=cuda。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
105 lines
3.9 KiB
Python
105 lines
3.9 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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# 模型在 hairline/models/ 下（模块即在 hairline/ 根），故只取一层 dirname。
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DEFAULT_MODEL_PATH = os.path.join(
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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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