xslandCursor
e7b62f2b2e
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>
563 lines
27 KiB
Python
563 lines
27 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 (
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smooth_hairline, sample_hairline_clamped,
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)
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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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# 三接口（接口2女重绘 / 接口2男 / 接口3）统一的 ComfyUI 重绘 prompt。
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# 关键：ComfyUI 单卡显存装不下 Flux(7.7G)+qwen CLIP(3.9G) 同驻，靠缓存 CLIP 文本条件避免重载。
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# prompt 不同会使缓存失效 → 重载 CLIP 并挤出 Flux（每次 +4s）。三接口用同一字符串即可全程命中。
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# 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
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_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜")
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# 接口2 女重绘整条管线（swapHair + ComfyUI）送模型前限边。真实照片常达 1257x1495：
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# 全分辨率 ComfyUI 重绘要 13~21s 且激活显存把模型挤出。女性路径含 swapHair(SD WebUI ~5.3s
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# 固定地板) + ComfyUI 两段串行。用 1024 优先画质（女性总耗时 ~11~12s，偶尔踩 12s）；
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# 若要更稳的 <12s 余量可设 REDRAW_MAX_SIDE=896（ComfyUI ~4s，女性 9~11s）。
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_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "1024"))
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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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img = cv2.imdecode(np.frombuffer(image_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
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scale = 1.0
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orig_w = orig_h = 0
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if img is not None:
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orig_h, orig_w = img.shape[:2]
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m = max(orig_h, orig_w)
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if _REDRAW_MAX_SIDE > 0 and m > _REDRAW_MAX_SIDE:
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scale = _REDRAW_MAX_SIDE / float(m)
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nw, nh = max(1, round(orig_w * scale)), max(1, round(orig_h * scale))
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msk = cv2.imdecode(np.frombuffer(mask_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
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img_s = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_AREA)
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msk_s = cv2.resize(msk, (nw, nh), interpolation=cv2.INTER_NEAREST)
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image_png_bytes = cv2.imencode(".png", img_s)[1].tobytes()
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mask_png_bytes = cv2.imencode(".png", msk_s)[1].tobytes()
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logger.info("接口2女 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
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orig_w, orig_h, nw, nh, _REDRAW_MAX_SIDE)
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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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out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
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prompt=_REDRAW_PROMPT, front=True)
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if scale < 1.0 and out:
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out = _upscale_png_to(out, orig_w, orig_h)
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return out
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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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发际线几何检测固定用 `sample_hairline_clamped`（射线检测 + 头部轮廓钳制）：
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短发/剃光头照片（如 man_test.jpg）中间锚点检测失效时，纯射线检测的固定 fallback
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偏移会把点顶到头部轮廓外面的背景，产生"发际线贴到头部外面"的视觉 bug；钳制兜底后
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fallback 点不会再跑出头部轮廓，正常长发照片结果与旧行为一致。
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"""
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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_clamped(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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img_s, msk_s, gsc = _prep_comfy_input(image_bgr, np.zeros((h, w), np.uint8))
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buf = io.BytesIO()
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compose_comfy_rgba(img_s, msk_s).save(buf, format="PNG", compress_level=1)
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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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if gsc < 1.0 and shared_grown:
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shared_grown = _upscale_png_to(shared_grown, w, h)
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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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m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
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buf = io.BytesIO()
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compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
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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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if gsc < 1.0 and grown_png:
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grown_png = _upscale_png_to(grown_png, w, h)
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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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# 重绘管线（swapHair + ComfyUI）统一降分辨率：真实照片 swap(SD WebUI)~5s、blend、ComfyUI
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# 均随分辨率线性下降。overlay 预览仍用全分辨率；grown_png 最后放大回原尺寸。
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redraw_img = image_bgr
|
||
hair_mask_redraw = hair_mask_reuse
|
||
if _REDRAW_MAX_SIDE > 0 and max(h, w) > _REDRAW_MAX_SIDE:
|
||
redraw_img, _rs = _downscale_max_side(image_bgr, _REDRAW_MAX_SIDE)
|
||
_nh, _nw = redraw_img.shape[:2]
|
||
if hair_mask_redraw is not None:
|
||
hair_mask_redraw = cv2.resize(hair_mask_reuse.astype(np.uint8), (_nw, _nh),
|
||
interpolation=cv2.INTER_NEAREST).astype(bool)
|
||
logger.info("接口2女 管线降分辨率: %dx%d → %dx%d (max_side=%d)",
|
||
w, h, _nw, _nh, _REDRAW_MAX_SIDE)
|
||
|
||
for order, (key, white_path) in items:
|
||
white = load_texture_rgba(white_path)
|
||
overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
|
||
|
||
grown_png = None
|
||
chang_id = _FEMALE_KEY_TO_CHANG.get(key)
|
||
if chang_id is None:
|
||
logger.warning("接口2 换发型：female key=%s 无对应 chang_id，跳过生发图", key)
|
||
else:
|
||
try:
|
||
import time as _t
|
||
_ts0 = _t.perf_counter()
|
||
data = generate_hairline_redraw(redraw_img, chang_id, hair_mask=hair_mask_redraw, **redraw_defaults)
|
||
_ts1 = _t.perf_counter()
|
||
steps = data.get("steps") or {}
|
||
# ④ final（接缝融合基底）+ ⑤-② 纯红遮罩 PNG
|
||
final_b64 = steps.get("final_base64") or ""
|
||
mask_b64 = steps.get("redraw_band_mask_base64") or ""
|
||
if not final_b64 or not mask_b64:
|
||
logger.warning("接口2 换发型：type=%s final/遮罩缺失（final=%d mask=%d）",
|
||
key, len(final_b64), len(mask_b64))
|
||
else:
|
||
# 去掉 data URI 前缀
|
||
if final_b64.startswith("data:"):
|
||
final_b64 = final_b64.split(",", 1)[1]
|
||
if mask_b64.startswith("data:"):
|
||
mask_b64 = mask_b64.split(",", 1)[1]
|
||
final_bytes = base64.b64decode(final_b64)
|
||
mask_bytes = base64.b64decode(mask_b64)
|
||
# 后端直接调 ComfyUI 重绘，返回重绘后的 PNG
|
||
_tr0 = _t.perf_counter()
|
||
grown_png = _call_local_redraw(final_bytes, mask_bytes)
|
||
_tr1 = _t.perf_counter()
|
||
_tm = data.get("timings_ms") or {}
|
||
logger.info("接口2女 分段计时 type=%s: swapHair管线=%.2fs (mask=%dms swap=%dms blend=%dms), ComfyUI重绘=%.2fs",
|
||
key, _ts1 - _ts0,
|
||
_tm.get("mask", 0), _tm.get("swap", 0), _tm.get("blend", 0),
|
||
_tr1 - _tr0)
|
||
if grown_png is None:
|
||
logger.warning("接口2 换发型：type=%s 重绘结果为空", key)
|
||
elif redraw_img is not image_bgr:
|
||
# 管线在降分辨率图上跑，结果放大回原尺寸
|
||
grown_png = _upscale_png_to(grown_png, w, h)
|
||
except NoFaceError:
|
||
logger.warning("接口2 换发型：type=%s 未检出人脸", key)
|
||
except Exception as e: # noqa: BLE001 单张失败不拖垮整请求
|
||
logger.warning("接口2 换发型图失败 type=%s：%s", key, e)
|
||
|
||
results.append({"hairline_type": key, "order": order,
|
||
"overlay": overlay, "grown_png": grown_png})
|
||
return results
|
||
|
||
|
||
def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
|
||
use_mask: bool, prompt: str | None):
|
||
"""对单个发际线做生发(ComfyUI)。黑模板固定取 hairline_texture_black/（middle），
|
||
与 hairline_level 无关（high/low 贴图与 middle 同名，basename 映射即落回 middle 黑模板）。
|
||
use_mask=False 时用干净原图+空遮罩（与贴图无关，white_path 可为 None）。
|
||
失败返回 None，不抛异常。
|
||
"""
|
||
try:
|
||
if use_mask:
|
||
black = load_texture_rgba(_black_texture_path(white_path))
|
||
marked, mask = build_inpaint_mask(
|
||
image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
|
||
else:
|
||
h, w = image_bgr.shape[:2]
|
||
marked, mask = image_bgr, np.zeros((h, w), np.uint8)
|
||
buf = io.BytesIO()
|
||
compose_comfy_rgba(marked, mask).save(buf, format="PNG", compress_level=1)
|
||
return comfyui.run(buf.getvalue(), prompt=prompt)
|
||
except Exception as e: # noqa: BLE001 单张失败不拖垮整请求
|
||
logger.warning("接口5 生发图失败：%s", e)
|
||
return None
|
||
|
||
|
||
def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
|
||
hair_styles: list[int], use_mask: bool = True,
|
||
prompt: str | None = None):
|
||
"""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)。
|
||
|
||
入参同接口2：先选 gender，再多选 hair_styles（必填，1-indexed 按贴图排序）。
|
||
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；
|
||
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。
|
||
use_mask/prompt：同接口2 的生发参数。
|
||
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。
|
||
best_centers 取首个选中发型三档各自的发际线中点。
|
||
"""
|
||
if gender not in ("male", "female"):
|
||
raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
|
||
if not hair_styles:
|
||
raise ValueError("hair_styles 必填且不能为空")
|
||
ctx = extract_context(image_bgr)
|
||
if ctx is None:
|
||
return None
|
||
h, w = image_bgr.shape[:2]
|
||
uv, ext_faces = load_ext_mesh()
|
||
lm = ctx["landmarks"]
|
||
# 面部中轴 x = 眉心(9/151 中点)
|
||
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}
|
||
|
||
|
||
# 接口3 送 ComfyUI 前限边，降低峰值显存，避免与接口2 切换时把 Flux 挤出。
|
||
# 统一 prompt 后 Flux 不再被 CLIP 挤出，接口3 可用较高分辨率。可用 GROW_B_MAX_SIDE 覆盖。
|
||
_GROW_B_MAX_SIDE = int(os.getenv("GROW_B_MAX_SIDE", "1024"))
|
||
|
||
def _downscale_max_side(img_bgr: np.ndarray, max_side: int) -> tuple[np.ndarray, float]:
|
||
"""长边超过 max_side 时等比例缩小；返回 (图, scale)，scale=新/旧。"""
|
||
h, w = img_bgr.shape[:2]
|
||
m = max(h, w)
|
||
if max_side <= 0 or m <= max_side:
|
||
return img_bgr, 1.0
|
||
scale = max_side / float(m)
|
||
nw = max(1, int(round(w * scale)))
|
||
nh = max(1, int(round(h * scale)))
|
||
out = cv2.resize(img_bgr, (nw, nh), interpolation=cv2.INTER_AREA)
|
||
return out, scale
|
||
|
||
|
||
def _upscale_png_to(png_bytes: bytes, out_w: int, out_h: int) -> bytes:
|
||
"""把 Comfy 输出 PNG 双线性拉回原图尺寸（仅展示对齐，不增加推理细节）。"""
|
||
arr = np.frombuffer(png_bytes, np.uint8)
|
||
img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
|
||
if img is None:
|
||
return png_bytes
|
||
if img.shape[1] == out_w and img.shape[0] == out_h:
|
||
return png_bytes
|
||
resized = cv2.resize(img, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
|
||
ok, buf = cv2.imencode(".png", resized)
|
||
return buf.tobytes() if ok else png_bytes
|
||
|
||
|
||
def _prep_comfy_input(img_bgr: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
|
||
"""单段 ComfyUI 生发（接口2男 / 接口3）送图前限边到 GROW_B_MAX_SIDE。
|
||
返回 (缩后图, 缩后遮罩, scale)；scale<1 时调用方需把结果放大回原尺寸。"""
|
||
h, w = img_bgr.shape[:2]
|
||
if _GROW_B_MAX_SIDE <= 0 or max(h, w) <= _GROW_B_MAX_SIDE:
|
||
return img_bgr, mask, 1.0
|
||
out, scale = _downscale_max_side(img_bgr, _GROW_B_MAX_SIDE)
|
||
nh, nw = out.shape[:2]
|
||
msk = cv2.resize(mask, (nw, nh), interpolation=cv2.INTER_NEAREST)
|
||
logger.info("接口2男/接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
|
||
w, h, nw, nh, _GROW_B_MAX_SIDE)
|
||
return out, msk, scale
|
||
|
||
|
||
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）。
|
||
|
||
进 Comfy 前若长边 > GROW_B_MAX_SIDE（默认 896）会先等比例缩小，降低峰值显存；
|
||
输出再拉回原图尺寸。
|
||
|
||
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"}。
|
||
"""
|
||
orig_h, orig_w = marked_bgr.shape[:2]
|
||
marked_bgr, _scale = _downscale_max_side(marked_bgr, _GROW_B_MAX_SIDE)
|
||
if _scale < 1.0:
|
||
logger.info(
|
||
"接口3 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
|
||
orig_w, orig_h, marked_bgr.shape[1], marked_bgr.shape[0], _GROW_B_MAX_SIDE,
|
||
)
|
||
|
||
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", compress_level=1) # marked + 遮罩
|
||
grown_png = comfyui.run(buf.getvalue(), prompt=prompt)
|
||
if _scale < 1.0 and grown_png:
|
||
grown_png = _upscale_png_to(grown_png, orig_w, orig_h)
|
||
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}")
|