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
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
"""Main CLI: image -> JSON of 502 3D points consumable by the SDK.
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Pipeline:
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1. MediaPipe FaceMesh -> 468 normalized landmarks
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2. Face parsing (HF SegFormer) -> per-pixel class map
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3. Hairline curve detection + per-anchor ray casting -> 17 hairline 2D points
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4. Lift 2D hairline points to 3D using anchor Z + curvature offset
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5. Interpolate 17 middle-row 3D points
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6. Concatenate into (502, 3) and emit JSON
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Output JSON schema:
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{
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"image": {"width": W, "height": H, "path": "..."},
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"n_total": 502,
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"n_mp": 468,
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"n_extension": 34,
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"layout": ["mp[0..468)", "middle[468..485)", "hairline[485..502)"],
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"points": [[x_norm, y_norm, z_relative], ...] # length 502
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"valid_hairline": [true/false, ...] # length 17
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}
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Usage:
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py -3 python/extract_hairline.py path/to/image.jpg
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py -3 python/extract_hairline.py path/to/image.jpg --out data/out.json
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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import time
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import numpy as np
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if __package__ is None or __package__ == "":
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from python import constants as C
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from python.face_landmarks import FaceLandmarker
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from python.face_parsing import FaceParser
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from python.hairline_2d import sample_hairline, smooth_hairline
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from python.lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
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else:
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from . import constants as C
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from .face_landmarks import FaceLandmarker
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from .face_parsing import FaceParser
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from .hairline_2d import sample_hairline, smooth_hairline
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from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
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def run(image_path: str, out_path: str | None = None, device: str | None = None) -> dict:
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import cv2
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bgr = cv2.imread(image_path)
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if bgr is None:
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raise FileNotFoundError(f"could not read image: {image_path}")
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
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H, W = rgb.shape[:2]
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t0 = time.perf_counter()
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lmk = FaceLandmarker(static_image_mode=True)
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landmarks = lmk.detect(rgb)
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lmk.close()
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if landmarks is None:
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raise RuntimeError("no face detected")
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t1 = time.perf_counter()
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print(f" [time] mediapipe: {(t1 - t0)*1000:.0f} ms")
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parser = FaceParser(device=device)
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parse_map = parser.parse(rgb)
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t2 = time.perf_counter()
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print(f" [time] parsing: {(t2 - t1)*1000:.0f} ms")
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hairline_2d, valid = sample_hairline(landmarks, parse_map)
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hairline_2d = smooth_hairline(hairline_2d, valid)
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hairline_3d = lift_hairline_to_3d(landmarks, hairline_2d)
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middle_3d = build_middle_row(landmarks, hairline_3d)
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points_full = assemble_full(landmarks, middle_3d, hairline_3d)
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t3 = time.perf_counter()
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print(f" [time] hairline: {(t3 - t2)*1000:.0f} ms")
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record = {
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"image": {"width": int(W), "height": int(H), "path": image_path},
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"n_total": C.N_TOTAL,
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"n_mp": C.N_MP,
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"n_extension": C.N_EXT,
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"layout": [
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f"mp[0..{C.N_MP})",
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f"middle[{C.MIDDLE_START}..{C.HAIRLINE_START})",
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f"hairline[{C.HAIRLINE_START}..{C.N_TOTAL})",
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],
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"points": points_full.tolist(),
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"valid_hairline": valid.tolist(),
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}
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if out_path is None:
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base = os.path.splitext(os.path.basename(image_path))[0]
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out_path = os.path.join("data", base + ".json")
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os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
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with open(out_path, "w", encoding="utf-8") as f:
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json.dump(record, f, ensure_ascii=False, indent=2)
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print(f" wrote {out_path}")
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return record
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def main() -> None:
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ap = argparse.ArgumentParser(description="Extract MediaPipe + hairline points from one image.")
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ap.add_argument("image", help="path to input image (jpg/png)")
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ap.add_argument("--out", default=None, help="output JSON path")
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ap.add_argument("--device", default=None, help="torch device (cpu/cuda); auto if omitted")
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args = ap.parse_args()
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run(args.image, args.out, args.device)
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if __name__ == "__main__":
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main()
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