xslandCursor
98b9108837
问题：接口2 与接口3/5 乱序调用时耗时抖动（最差 15~22s）。两个根因： 1. GPU 24G 常驻 21.4G，Flux-2(3.9G) 无法完全驻留显存，每次采样动态换页， 速度随空闲显存波动（2s~8s）； 2. ComfyUI 单队列 FIFO，接口2 排在接口3/5 批量任务后面。 改动： - hairline/comfyui.py: run() 新增 front 参数，/prompt 带 "front": true 插队到队列最前； redraw.py 透传；service.py 接口2 三处调用（女重绘 + 男有/无遮罩）传 front=True， 接口3/5 仍走普通队列。 - add_hair.json / 0716add-hair-api.json: 节点61 CLIPLoader device default→cpu。 qwen CLIP(4G) 不再占显存（文本条件缓存常年命中），ComfyUI 显存 8.8G→4.5G， Flux-2 完全驻留，采样稳定 ~3-5s。代价：换 prompt 后首次请求 CPU 编码 ~11s（一次性）。 - 提示词全局统一为「填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜」： app.py 4处默认值、service.py _REDRAW_PROMPT、redraw.py _DEFAULT_PROMPT、 4个工作流节点60内置文案、测试页(test_interface2/3/7/12/12_final)、local_test。 任何两个不同 prompt 交替提交都会打爆 CLIP 编码缓存（--cache-classic 只存最近一次）， 之前测试页旧文案与服务端不一致导致交替测试每次 +11s。 - app.py: 接口7 /api/v1/hair/grow-v2 下线（业务弃用；add_hair2.json 的 Klein-9b 会把常驻 Klein-4b 挤出显存）。保留 stub 返回 1007 明确报错，避免裸 404。 实测（1024 档）：接口2女 8.5~10s、接口2男 ~5s、接口3 ~7-10s，交替混跑无尖刺。 Co-authored-by: Cursor <cursoragent@cursor.com> (cherry-picked from ubuntu3090 e7b62f2；已适配 main 分支代码结构：main 无 _REDRAW_PROMPT/_REDRAW_MAX_SIDE 缩图逻辑，front=True 直接加在 _call_local_redraw / generate_grow_results 的调用点；另把 main 独有的 benchmark_*.py 里的 prompt 一并统一) Co-authored-by: Cursor <cursoragent@cursor.com>
433 lines
20 KiB
Python
433 lines
20 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 base64
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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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# 接口2 女性发型 key → change_hair hair_id（chang_*）映射：换发型+Flux-2 整帧重绘用。
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# 与接口12 final 的 5 型一一对应。
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_FEMALE_KEY_TO_CHANG = {
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"ellipse": "chang_tuoyuan", # 椭圆
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"flower": "chang_huaban", # 花瓣
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"heart": "chang_xinxing", # 心形
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"straight": "chang_zhixian", # 直线
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"wave": "chang_bolang", # 波浪
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}
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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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def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
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"""直接调 ComfyUI 重绘（替代原 local_test HTTP 服务）。
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传 final 图 + 纯红遮罩 PNG，返回重绘后的 PNG bytes。
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失败抛异常（调用方负责 try/except 跳过）。
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"""
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from .redraw import run_redraw
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# front=True：接口2 时延敏感，插到 ComfyUI 队列最前，避免排在接口3/5 的批量任务后面
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return run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout, front=True)
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# 发际线贴图档位：middle=默认(hairline_texture/)，high/low 各自独立文件夹。
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_TEXTURE_DIRS = {
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"middle": _TEXTURE_DIR,
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"high": os.path.join(_REPO, "hairline_texture_high"),
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"low": os.path.join(_REPO, "hairline_texture_low"),
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}
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# torch 2.7.1+cu128 已支持 RTX 5090 (sm_120)，SegFormer 走 GPU（~0.05s/张）
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_SEG_DEVICE = os.getenv("SEG_DEVICE", "cuda")
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_landmarker = None
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_parser = None
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_texture_maps: dict = {} # {level: {gender: [(key, path)]}}，按档位缓存
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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(level: str = "middle") -> dict:
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"""扫描指定档位贴图目录建 {gender: [(key, path)]}，按 key 排序、按档位缓存。
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level：middle(默认) / high / low，分别对应 hairline_texture[/_high|/_low]。
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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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if level not in _TEXTURE_DIRS:
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raise ValueError(f"hairline_level 必须是 middle/high/low，收到 {level!r}")
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cached = _texture_maps.get(level)
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if cached is not None:
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return cached
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mapping: dict[str, list] = {"female": [], "male": []}
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for path in sorted(glob.glob(os.path.join(_TEXTURE_DIRS[level], "*.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_maps[level] = mapping
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return mapping
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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, use_mask: bool = True,
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prompt: str = None, hair_styles: list[int] | None = None,
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workflow_path: str | None = None):
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"""指定发际线类型：发际线透明叠图(白线 RGBA) + 生发图(ComfyUI)。
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hair_styles（1-indexed 列表）：指定生成哪几张发际线（按贴图排序）。female: 1..5，male: 1..4。
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为 None 时生成全部（兼容旧调用）。
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use_mask（默认 True）：是否启用 inpaint 遮罩，用于测试对比（同接口3）。
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False 时用**干净原图 + 空遮罩**送 ComfyUI（不烧黑色模板线）。
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prompt（默认 None）：ComfyUI 提示词，非 None 时替换工作流节点60文本。
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workflow_path（默认 None）：ComfyUI 工作流 JSON 路径，None 用默认 add_hair.json。
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Returns: list[dict] {"hairline_type","order","overlay"((H,W,4) RGBA 透明层),
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"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，不抛异常。
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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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textures = get_texture_map()[gender] # [(key, path), ...] 已排序
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if hair_styles is not None:
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items = [(s, textures[s - 1]) for s in hair_styles]
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else:
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items = list(enumerate(textures, start=1))
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# 禁用遮罩：干净原图 + 空遮罩，与模板无关 → 只跑一次 ComfyUI，下面 N 项复用
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shared_grown = None
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if not use_mask:
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try:
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h, w = image_bgr.shape[:2]
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buf = io.BytesIO()
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compose_comfy_rgba(image_bgr, np.zeros((h, w), np.uint8)).save(buf, format="PNG")
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# front=True：接口2 时延敏感，插到 ComfyUI 队列最前
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shared_grown = comfyui.run(buf.getvalue(), prompt=prompt,
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workflow_path=workflow_path, front=True)
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except Exception as e: # noqa: BLE001
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logger.warning("接口2 生发图失败(无遮罩)：%s", e)
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results = []
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h, w = image_bgr.shape[:2]
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for order, (key, white_path) in items:
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white = load_texture_rgba(white_path)
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overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
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if not use_mask:
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grown_png = shared_grown
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else:
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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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# front=True：接口2 时延敏感，插到 ComfyUI 队列最前
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grown_png = comfyui.run(buf.getvalue(), prompt=prompt,
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workflow_path=workflow_path, front=True)
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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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"overlay": overlay, "grown_png": grown_png})
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return results
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def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
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redraw_defaults: dict):
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"""接口2 女性专用：发际线透明叠图（同 generate_grow_results）+ 换发型重绘图。
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grown 图来源（新流程）：对每个选中发型把 female key 映射到 change_hair 的 chang_* hair_id，
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调 face_analysis.hairline_grow.generate_hairline_redraw（= 接口12 final 管线，参数用
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redraw_defaults）拿到 ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG，再**后端直接调
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ComfyUI**（0716add-hair-api.json 工作流）完成发际线带重绘，重绘结果作为生发图。
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overlay 仍是发际线曲线透明层（与 generate_grow_results 完全一致）。
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Returns: list[dict] {"hairline_type","order","overlay","grown_png"(jpg bytes 或 None)}；
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无人脸返回 None。单个发型换发型/重绘失败时 grown_png=None，不抛异常。
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"""
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from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError
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from face_analysis.head_mask import SEGFORMER_HAIR
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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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# 复用 extract_context 已算好的 SegFormer parse_map，避免 generate_hairline_redraw 内部重复分割
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hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
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textures = get_texture_map()["female"] # [(key, path), ...] 已排序
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if hair_styles is not None:
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items = [(s, textures[s - 1]) for s in hair_styles]
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else:
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items = list(enumerate(textures, start=1))
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results = []
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h, w = image_bgr.shape[:2]
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for order, (key, white_path) in items:
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white = load_texture_rgba(white_path)
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overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
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grown_png = None
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chang_id = _FEMALE_KEY_TO_CHANG.get(key)
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if chang_id is None:
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logger.warning("接口2 换发型：female key=%s 无对应 chang_id，跳过生发图", key)
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else:
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try:
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data = generate_hairline_redraw(image_bgr, chang_id, hair_mask=hair_mask_reuse, **redraw_defaults)
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steps = data.get("steps") or {}
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# ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG
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final_b64 = steps.get("final_base64") or ""
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mask_b64 = steps.get("redraw_band_mask_base64") or ""
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if not final_b64 or not mask_b64:
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logger.warning("接口2 换发型：type=%s final/遮罩缺失（final=%d mask=%d）",
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key, len(final_b64), len(mask_b64))
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else:
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# 去掉 data URI 前缀
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if final_b64.startswith("data:"):
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final_b64 = final_b64.split(",", 1)[1]
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if mask_b64.startswith("data:"):
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mask_b64 = mask_b64.split(",", 1)[1]
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final_bytes = base64.b64decode(final_b64)
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mask_bytes = base64.b64decode(mask_b64)
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# 后端直接调 ComfyUI 重绘，返回重绘后的 PNG
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grown_png = _call_local_redraw(final_bytes, mask_bytes)
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if grown_png is None:
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logger.warning("接口2 换发型：type=%s 重绘结果为空", key)
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except NoFaceError:
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logger.warning("接口2 换发型：type=%s 未检出人脸", key)
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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 换发型图失败 type=%s：%s", key, e)
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results.append({"hairline_type": key, "order": order,
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"overlay": overlay, "grown_png": grown_png})
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return results
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def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
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use_mask: bool, prompt: str | None):
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"""对单个发际线做生发(ComfyUI)。黑模板固定取 hairline_texture_black/（middle），
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与 hairline_level 无关（high/low 贴图与 middle 同名，basename 映射即落回 middle 黑模板）。
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use_mask=False 时用干净原图+空遮罩（与贴图无关，white_path 可为 None）。
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失败返回 None，不抛异常。
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"""
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try:
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if use_mask:
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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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else:
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h, w = image_bgr.shape[:2]
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marked, mask = image_bgr, np.zeros((h, w), np.uint8)
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buf = io.BytesIO()
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compose_comfy_rgba(marked, mask).save(buf, format="PNG")
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return comfyui.run(buf.getvalue(), prompt=prompt)
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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5 生发图失败：%s", e)
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return None
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def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
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hair_styles: list[int], use_mask: bool = True,
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prompt: str | None = None):
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"""接口5：对选中发型返回 middle/high/low 三档发际线透明叠图 + 生发图(同接口2)。
|
||
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||
入参同接口2：先选 gender，再多选 hair_styles（必填，1-indexed 按贴图排序）。
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每个选中发型返回三档叠图（middle/high/low，RGBA 透明层只含发际线曲线）与一张生发图；
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三档贴图同名，生发黑模板固定取自 hairline_texture_black/（middle），故生发目标固定 middle 档。
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use_mask/prompt：同接口2 的生发参数。
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||
Returns: {"images":[{hairline_type,order,overlays:{middle,high,low}((H,W,4) RGBA 透明层),grown_png}],
|
||
"best_centers":{"middle":(x,y),"high":(x,y),"low":(x,y)}}；无人脸 None。
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||
best_centers 取首个选中发型三档各自的发际线中点。
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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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||
if not hair_styles:
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||
raise ValueError("hair_styles 必填且不能为空")
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||
ctx = extract_context(image_bgr)
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||
if ctx is None:
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||
return None
|
||
h, w = image_bgr.shape[:2]
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||
uv, ext_faces = load_ext_mesh()
|
||
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)
|
||
|
||
# 三档贴图表（同性别、同 key 顺序，因三个文件夹同名）
|
||
tex_by_level = {lv: get_texture_map(lv)[gender] for lv in _TEXTURE_DIRS}
|
||
|
||
# use_mask=False：干净原图+空遮罩与贴图无关，只跑一次 ComfyUI，选中项复用
|
||
shared_grown = None
|
||
if not use_mask:
|
||
shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
|
||
|
||
def _center_of(overlay):
|
||
"""从某档发际线透明叠图取面部中轴处的发际线中点 (x,y)，无像素返回 None。"""
|
||
ys, xs = np.where(overlay[:, :, 3] > 40)
|
||
if not xs.size:
|
||
return None
|
||
near = np.abs(xs - face_cx) <= max(2, int(w * 0.02))
|
||
col_ys = ys[near] if near.any() else ys[np.argsort(np.abs(xs - face_cx))[:20]]
|
||
return (int(round(face_cx)), int(round(float(col_ys.mean()))))
|
||
|
||
images, best_centers = [], None
|
||
for s in hair_styles: # s = 1-indexed 发型序号
|
||
key, mid_path = tex_by_level["middle"][s - 1]
|
||
overlays = {}
|
||
for lv in _TEXTURE_DIRS:
|
||
white = load_texture_rgba(tex_by_level[lv][s - 1][1])
|
||
overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
|
||
# 生发：固定 middle 黑模板
|
||
grown_png = shared_grown if not use_mask else \
|
||
_grow_from_texture(image_bgr, ctx, mid_path, use_mask=True, prompt=prompt)
|
||
images.append({"hairline_type": key, "order": s,
|
||
"overlays": overlays, "grown_png": grown_png})
|
||
# best_centers：首个选中发型三档（middle/high/low）发际线中点
|
||
if best_centers is None:
|
||
best_centers = {lv: _center_of(overlays[lv]) for lv in _TEXTURE_DIRS}
|
||
return {"images": images, "best_centers": best_centers}
|
||
|
||
|
||
def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str = None):
|
||
"""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）。
|
||
|
||
检测路径只用来**建遮罩**；ComfyUI 输入图用 **marked 原图**（含医生手绘线，
|
||
工作流提示词会清除黑线再生发）。
|
||
|
||
use_mask（默认 True）：是否启用自动检测的遮罩，用于测试对比。
|
||
- True：检测手绘线 → 建遮罩 → alpha=255−mask（透明区=重绘区，节点44 画黄色参考区）。
|
||
- False：跳过检测，直接送划线图，alpha 全 255（空遮罩，节点26 mask 为空），
|
||
模型仅凭医生黑线参考生发。无需改工作流，唯一变量是遮罩。
|
||
Returns: {"grown_png": bytes 或 None, "status": "ok"|"no_face"|"no_line"}。
|
||
"""
|
||
h, w = marked_bgr.shape[:2]
|
||
if use_mask:
|
||
rgb = cv2.cvtColor(marked_bgr, cv2.COLOR_BGR2RGB)
|
||
landmarks = get_landmarker().detect(rgb)
|
||
if landmarks is None:
|
||
return {"grown_png": None, "status": "no_face"}
|
||
parse_map = get_parser().parse(rgb)
|
||
path = detect_marker_hairline(marked_bgr, landmarks, parse_map)
|
||
if path is None:
|
||
return {"grown_png": None, "status": "no_line"}
|
||
line_w = max(2, int(w * 0.006))
|
||
curve_mask = path_to_curve_mask(path, h, w, thickness=max(3, line_w))
|
||
mask = mask_from_curve(curve_mask, landmarks, parse_map)
|
||
else:
|
||
mask = np.zeros((h, w), np.uint8) # 空遮罩：alpha 全 255，跳过检测
|
||
|
||
buf = io.BytesIO()
|
||
compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG") # marked 原图 + 遮罩
|
||
grown_png = comfyui.run(buf.getvalue(), prompt=prompt)
|
||
return {"grown_png": grown_png, "status": "ok"}
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import sys
|
||
g = sys.argv[2] if len(sys.argv) > 2 else "female"
|
||
img = cv2.imread(sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg")
|
||
os.makedirs("tests/output", exist_ok=True)
|
||
print("texture map:", {k: [kp[0] for kp in v] for k, v in get_texture_map().items()})
|
||
res = generate_previews(img, g)
|
||
if res is None:
|
||
print("无人脸")
|
||
sys.exit(1)
|
||
for r in res:
|
||
out = f"tests/output/preview_{g}_{r['hairline_type']}.png"
|
||
cv2.imwrite(out, r["image_bgr"])
|
||
print(f" order={r['order']} type={r['hairline_type']} -> {out}")
|