xslandClaude Opus 4.8
bf76923591
按需求方意见——B端只需上传一张已画好发际线的图，用不着原图： - 入参去掉 original_image_*，只保留 marked_image_*(三选一) - 输出去掉 best_hairline_image_url，只返回 hair_growth_image_url + hairline_type - ComfyUI 输入图改用 marked 划线图原样(add_hair.json 本就是"画了线的照片"， 提示词清除黑线)；检测路径只用于建遮罩，不再重画干净线/不需对齐原图 - service.generate_grow_b 签名改 (marked_bgr) 单参 - 同步文档：接口文档/接口3技术方案/网关映射表(去掉接口3 best_hairline 行) - 测试更新：grow-b 只传 marked，断言无 best_hairline 字段，44全绿 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
237 lines
9.5 KiB
Python
237 lines
9.5 KiB
Python
"""接口2 服务层：模型单例 + 性别贴图映射 + 「照片→N 张发际线预览图」管线。
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把 head3d 的 extract_hairline 步骤包成单例复用（避免每请求重建模型），再按性别
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对每张贴图调 render.render_hairline_overlay 生成预览图。
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"""
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from __future__ import annotations
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import glob
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import os
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import cv2
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import numpy as np
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from . import constants as C
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from . import comfyui
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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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from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay, build_overlay_layer
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from .mask import build_inpaint_mask, compose_comfy_rgba, mask_from_curve
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from .marker_detect import detect_marker_hairline, path_to_curve_mask
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import io
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import logging
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logger = logging.getLogger("hair.worker")
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_REPO = os.path.dirname(os.path.dirname(__file__))
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_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
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_BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
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# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
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# "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
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_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
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_landmarker = None
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_parser = None
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_texture_map = None
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def get_landmarker() -> FaceLandmarker:
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global _landmarker
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if _landmarker is None:
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_landmarker = FaceLandmarker(static_image_mode=True)
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return _landmarker
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def get_parser() -> FaceParser:
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global _parser
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if _parser is None:
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_parser = FaceParser(device=_SEG_DEVICE)
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return _parser
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def _gender_key(stem: str):
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"""文件名 stem → (gender, key)；非 girl_/man_ 前缀返回 (None, None)。"""
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if stem.startswith("girl_"):
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return "female", stem[5:].replace(" ", "").strip()
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if stem.startswith("man_"):
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return "male", stem[4:].replace(" ", "").strip()
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return None, None
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def get_texture_map() -> dict:
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"""扫描 hairline_texture/ 建 {gender: [(key, path)]}，按 key 排序、缓存。
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文件名规范化去空格（如 `man_ inverse_arc.png` → key `inverse_arc`）。
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"""
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global _texture_map
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if _texture_map is not None:
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return _texture_map
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mapping: dict[str, list] = {"female": [], "male": []}
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for path in sorted(glob.glob(os.path.join(_TEXTURE_DIR, "*.png"))):
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stem = os.path.splitext(os.path.basename(path))[0]
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gender, key = _gender_key(stem)
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if gender:
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mapping[gender].append((key, path))
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for g in mapping:
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mapping[g].sort(key=lambda kp: kp[0])
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_texture_map = mapping
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return _texture_map
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def extract_502(image_bgr: np.ndarray):
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"""照片(BGR) → (points502 MP序, valid17)。无人脸返回 (None, None)。"""
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ctx = extract_context(image_bgr)
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if ctx is None:
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return None, None
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return ctx["points"], ctx["valid"]
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def extract_context(image_bgr: np.ndarray):
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"""照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。"""
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rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
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landmarks = get_landmarker().detect(rgb)
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if landmarks is None:
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return None
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parse_map = get_parser().parse(rgb)
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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 = assemble_full(landmarks, middle_3d, hairline_3d)
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return {"landmarks": landmarks, "parse_map": parse_map, "points": points, "valid": valid}
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def _black_texture_path(white_path: str) -> str:
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"""白贴图路径 → 同名黑贴图路径（hairline_texture_black/）。"""
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return os.path.join(_BLACK_TEXTURE_DIR, os.path.basename(white_path))
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def generate_previews(image_bgr: np.ndarray, gender: str):
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"""生成该性别全部发际线预览图（仅预览，不生发）。
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Returns: list[dict] {"hairline_type", "image_bgr", "order"}；无人脸返回 None。
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"""
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if gender not in ("male", "female"):
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raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
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ctx = extract_context(image_bgr)
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if ctx is None:
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return None
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uv, ext_faces = load_ext_mesh()
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results = []
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for order, (key, path) in enumerate(get_texture_map()[gender], start=1):
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preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv,
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load_texture_rgba(path))
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results.append({"hairline_type": key, "image_bgr": preview, "order": order})
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return results
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def generate_grow_results(image_bgr: np.ndarray, gender: str):
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"""该性别全部发际线：预览图(白线) + 生发图(ComfyUI)。同步、串行。
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Returns: list[dict] {"hairline_type","order","image_bgr"(预览), "grown_png"(bytes 或 None)}。
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无人脸返回 None。某张 ComfyUI 失败时该项 grown_png=None，不影响其余。
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"""
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if gender not in ("male", "female"):
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raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
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ctx = extract_context(image_bgr)
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if ctx is None:
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return None
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uv, ext_faces = load_ext_mesh()
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results = []
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for order, (key, white_path) in enumerate(get_texture_map()[gender], start=1):
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white = load_texture_rgba(white_path)
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preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
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grown_png = None
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try:
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black = load_texture_rgba(_black_texture_path(white_path))
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marked, mask = build_inpaint_mask(
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image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
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buf = io.BytesIO()
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compose_comfy_rgba(marked, mask).save(buf, format="PNG")
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grown_png = comfyui.run(buf.getvalue())
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except Exception as e: # noqa: BLE001 单张失败不拖垮整请求
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logger.warning("接口2 生发图失败 type=%s：%s", key, e)
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results.append({"hairline_type": key, "order": order,
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"image_bgr": preview, "grown_png": grown_png})
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return results
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def generate_hairline_pngs(image_bgr: np.ndarray, gender: str):
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"""接口5：该性别全部发际线叠图(同接口2预览) + 最佳(order1)发际线曲线的面部中间点。
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Returns: {"images":[{hairline_type,order,image_bgr}], "best_center":(x,y)}；无人脸 None。
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"""
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if gender not in ("male", "female"):
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raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
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ctx = extract_context(image_bgr)
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if ctx is None:
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return None
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h, w = image_bgr.shape[:2]
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uv, ext_faces = load_ext_mesh()
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lm = ctx["landmarks"]
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# 面部中轴 x = 眉心(9/151 中点)
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face_cx = float((lm[9, 0] + lm[151, 0]) / 2 * w)
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textures = get_texture_map()[gender]
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images, best_center = [], None
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for order, (key, path) in enumerate(textures, start=1):
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white = load_texture_rgba(path)
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preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
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images.append({"hairline_type": key, "order": order, "image_bgr": preview})
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if order == 1: # 最佳发际线曲线的中点（面部中轴处的发际线 y）
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overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
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ys, xs = np.where(overlay[:, :, 3] > 40)
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if xs.size:
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near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
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col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
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best_center = (int(round(face_cx)), int(round(float(col_ys.mean()))))
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return {"images": images, "best_center": best_center}
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def generate_grow_b(marked_bgr: np.ndarray):
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"""接口3：检测医生手绘发际线 → 遮罩 → 送 ComfyUI 生发（仅需划线图一张）。
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检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
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工作流提示词会清除黑线再生发）。
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Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
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"""
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rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
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landmarks = get_landmarker().detect(rgb)
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if landmarks is None:
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return {"grown_png": None, "status": "no_face"}
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parse_map = get_parser().parse(rgb)
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path = detect_marker_hairline(marked_bgr, landmarks, parse_map)
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if path is None:
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return {"grown_png": None, "status": "no_line"}
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h, w = marked_bgr.shape[:2]
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line_w = max(2, int(w * 0.006))
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curve_mask = path_to_curve_mask(path, h, w, thickness=max(3, line_w))
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mask = mask_from_curve(curve_mask, landmarks, parse_map)
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buf = io.BytesIO()
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compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG") # marked 原图 + 遮罩
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grown_png = comfyui.run(buf.getvalue())
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return {"grown_png": grown_png, "status": "ok"}
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if __name__ == "__main__":
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import sys
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g = sys.argv[2] if len(sys.argv) > 2 else "female"
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img = cv2.imread(sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg")
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os.makedirs("tests/output", exist_ok=True)
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print("texture map:", {k: [kp[0] for kp in v] for k, v in get_texture_map().items()})
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res = generate_previews(img, g)
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if res is None:
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print("无人脸")
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sys.exit(1)
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for r in res:
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out = f"tests/output/preview_{g}_{r['hairline_type']}.png"
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cv2.imwrite(out, r["image_bgr"])
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print(f" order={r['order']} type={r['hairline_type']} -> {out}")
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