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base: xsl/hair:ff4019c5700750708df800121004cce96a44b0b8
Branches Tags
xsl/hair:main xsl/hair:ubuntu3090_debug_4090 xsl/hair:ubuntu3090 xsl/hair:ubuntu3090_debug
..
compare: xsl/hair:ubuntu3090
Branches Tags
xsl/hair:main xsl/hair:ubuntu3090_debug_4090 xsl/hair:ubuntu3090 xsl/hair:ubuntu3090_debug
3 Commits
ff4019c570 .. ubuntu3090
Author SHA1 Message Date
xslandCursor fe0e74ece8 feat: 接口1/6 标注层字号上调一档 + 眉心改用 9 号点定位 
- annotation: 自适应字号系数 0.017→0.020（下限 8→9），标注文字更大更清晰
- measure: _brow_center 只取 FaceMesh 9 号点（眉间上点），不再与 151 取中点

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-07-30 23:03:33 +08:00
xsl 52913c94fc fix: 删除 MeasureResult.__init__ 中重复的七眼厘米赋值块 2026-07-27 23:17:22 +08:00
xsl 13526cb5b8 feat: 接口1/5/6 发际线弃用逻辑(顶庭<0.7cm) 
发际线离头顶<0.7cm时判定分割不可靠，弃用发际线：
顶/上庭字段置null、face_total只算中下庭、标注图保留头顶线去掉发际线、
只标中/下庭。eye1/7竖向范围改用眉心。
 2026-07-27 23:10:54 +08:00
299 changed files with 510 additions and 971 deletions
Expand all files Collapse all files
.gitignore
-12
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@@ -53,15 +53,3 @@ static/report_hairline_v2.zip
# local_test 运行期日志 / pid（不入 git）
local_test/hair_service.log
local_test/hair_service.pid
# benchmark 原始输出（含结果图+原图，体积大，不入 git）
benchmark_out/
# benchmark 部署的 HTML 报告（图片 base64 内嵌，体积大，不入 git）
static/hairstyle_thumbs/
# 网关运行期日志（不入 git）
gateway.log
# 工作流备份文件（不入 git）
*.json.bak.*
0716add-hair-api.json
+3 -3
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@@ -1,8 +1,8 @@
{
"16": {
"class_type": "UnetLoaderGGUF",
"class_type": "UNETLoader",
"inputs": {
"unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
"unet_name": "flux-2-klein-4b-fp8.safetensors",
"weight_dtype": "fp8_e4m3fn_fast"
}
},
@@ -15,7 +15,7 @@
"61": {
"class_type": "CLIPLoader",
"inputs": {
"clip_name": "qwen_3_8b_fp8mixed.safetensors",
"clip_name": "qwen_3_4b.safetensors",
"type": "flux2",
"device": "cpu"
}
add_hair.json
+3 -3
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@@ -170,10 +170,10 @@
},
"16": {
"inputs": {
"unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
"unet_name": "flux-2-klein-4b-fp8.safetensors",
"weight_dtype": "fp8_e4m3fn_fast"
},
"class_type": "UnetLoaderGGUF",
"class_type": "UNETLoader",
"_meta": {
"title": "UNet加载器"
}
@@ -419,7 +419,7 @@
},
"61": {
"inputs": {
"clip_name": "qwen_3_8b_fp8mixed.safetensors",
"clip_name": "qwen_3_4b.safetensors",
"type": "flux2",
"device": "cpu"
},
app.py
+24 -10
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@@ -146,7 +146,7 @@ _AUTH_EXEMPT = ("/health", "/docs", "/openapi.json", "/redoc", "/static",
# ---------------------------------------------------------------------------
# change_hair 代理路由（解决 CORS 问题）
# ---------------------------------------------------------------------------
_CHANGE_HAIR_BASE = os.getenv("CHANGE_HAIR_BASE", "http://127.0.0.1:8801")
_CHANGE_HAIR_BASE = "http://127.0.0.1:8801"
@app.post("/api/swapHair/v1", tags=["change_hair"])
@@ -401,9 +401,24 @@ def _run_face_measure_data(image, variant="v1"):
logger.warning("头发/耳朵分割失败，回退方案A：%s", seg_e)
result = measure_face(landmarks, hair_mask, w, h, head_pose=head_pose)
discarded = result.hairline_discarded
data = result.to_response()
if variant == "v6":
vd = result.vertical
if variant == "v6":
if discarded:
# 发际线弃用：接口6 的上庭也依赖发际线，一并置 null；只保留中/下庭。
base_px = vd["middle_court_px"] + vd["lower_court_px"]
data["four_courts"]["upper_court_cm"] = None
data["four_courts"]["ratios"] = {
"upper_court": None,
"middle_court": round(vd["middle_court_px"] / base_px, 3),
"lower_court": round(vd["lower_court_px"] / base_px, 3),
}
data["four_courts"].pop("top_court_cm", None)
data["face_total_height_cm"] = round(
result.middle_cm + result.lower_cm, 2)
data["landmarks"]["hairline"] = None
else:
base_px = vd["upper_court_px"] + vd["middle_court_px"] + vd["lower_court_px"]
# 接口6 是三庭：去掉顶庭相关字段（top_court_cm / ratios.top_court / landmarks.hair_top）
data["four_courts"]["ratios"] = {
@@ -430,11 +445,14 @@ def _run_face_measure_data(image, variant="v1"):
data["seven_eyes"][f"eye{i + 2}"] = (
None if (a is None or b is None) else round((b - a) / pc, 2))
if variant != "v6":
# 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线
# 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线。
# 竖向范围：发际线弃用时用眉心做上界（hair_top 不可靠），否则用头顶。
from face_analysis.annotation import _ear_edges_from_mask
top_y = (vd["brow_center"][1] if discarded
else vd["hair_top"][1])
head_l, head_r = _ear_edges_from_mask(
ear_mask, hair_mask,
result.vertical["hair_top"][1], result.vertical["chin_tip"][1],
top_y, vd["chin_tip"][1],
lcx, rcx, (lcx + rcx) / 2)
data["seven_eyes"]["eye1"] = (
None if (head_l is None) else round((lcx - head_l) / pc, 2))
@@ -758,8 +776,6 @@ async def hair_grow(
beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩（测试对比用）。false 时用干净原图生成（空遮罩，不烧模板线）"),
prompt: str = Form(default="填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜", description="ComfyUI 提示词，会替换工作流节点60的文本"),
flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（切换模型用）。None=工作流默认；如 flux-2-klein-9b-Q5_K_M.gguf / flux-2-klein-9b-Q4_K_M.gguf / flux2.0/flux-2-klein-9b-fp8.safetensors"),
redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
):
# 1. gender 必填校验（非法/缺失 → 1004）
if gender not in ("male", "female"):
@@ -788,13 +804,11 @@ async def hair_grow(
if gender == "female":
from hairline.service import generate_grow_results_swap
items = await run_in_threadpool(
generate_grow_results_swap, image, hair_styles, _V2_FINAL_DEFAULTS,
redraw_max_side=redraw_max_side, unet_name=flux_model)
generate_grow_results_swap, image, hair_styles, _V2_FINAL_DEFAULTS)
else:
from hairline.service import generate_grow_results
items = await run_in_threadpool(
generate_grow_results, image, gender, use_mask, prompt, hair_styles,
unet_name=flux_model)
generate_grow_results, image, gender, use_mask, prompt, hair_styles)
if items is None:
return err(1001, "无法识别人像")
benchmark_hairstyle.py
-134
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@@ -1,134 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""发型对比矩阵测试：3图×5发型=15行，每行10张图(4b@896×1 + 9b三模型×三分辨率×9)。
按模型分组跑（减少模型切换次数、降低OOM风险），结果重组为15行存JSON+生成报告。
"""
import base64
import json
import os
import subprocess
import time
from collections import defaultdict
from pathlib import Path
import requests
API = "http://127.0.0.1:8187/api/v1/hair/grow"
TOKEN = "dev-shared-secret-2026"
OUT = Path("/home/ubuntu/hair/benchmark_out/hairstyle")
OUT.mkdir(parents=True, exist_ok=True)
IMGS = [
("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
]
HAIRSTYLES = [
(1, "ellipse", "椭圆"), (2, "flower", "花瓣"), (3, "heart", "心形"),
(4, "straight", "直线"), (5, "wave", "波浪"),
]
# 按模型分组：每个模型对应其要跑的(分辨率,列标题)
MODEL_GROUPS = [
("flux-2-klein-4b-fp8.safetensors", [("896", "4B@896")]),
("flux2.0/flux-2-klein-9b-fp8.safetensors",
[("0", "9B-fp8@原图"), ("896", "9B-fp8@896"), ("640", "9B-fp8@640")]),
("flux-2-klein-9b-Q5_K_M.gguf",
[("0", "9B-Q5@原图"), ("896", "9B-Q5@896"), ("640", "9B-Q5@640")]),
("flux-2-klein-9b-Q4_K_M.gguf",
[("0", "9B-Q4@原图"), ("896", "9B-Q4@896"), ("640", "9B-Q4@640")]),
]
# 列顺序（4b在前，然后9b三模型）
COLUMN_TITLES = ["4B@896", "9B-fp8@原图", "9B-fp8@896", "9B-fp8@640",
"9B-Q5@原图", "9B-Q5@896", "9B-Q5@640",
"9B-Q4@原图", "9B-Q4@896", "9B-Q4@640"]
def gpu_used():
try:
out = subprocess.check_output(
["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"], timeout=10)
return int(out.decode().strip())
except Exception:
return 0
def call(img_path, hair_num, model_file, res_val):
fd = {"gender": "female", "hair_style": str(hair_num), "use_mask": "true",
"prompt": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"}
if model_file:
fd["flux_model"] = model_file
if res_val != "":
fd["redraw_max_side"] = res_val
t0 = time.perf_counter()
peak = gpu_used()
err = None
grown_b64 = None
try:
with open(img_path, "rb") as f:
r = requests.post(API, headers={"X-Internal-Token": TOKEN},
files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
data=fd, timeout=300)
elapsed = time.perf_counter() - t0
peak = max(peak, gpu_used())
j = r.json()
if j.get("code") != 0:
err = f"code={j.get('code')} {j.get('message', '')[:60]}"
else:
res = j.get("data", {}).get("results", [])
if res and res[0].get("grown_image_base64"):
grown_b64 = res[0]["grown_image_base64"]
elif res:
err = "grown_image空"
else:
err = "无results"
except Exception as e:
elapsed = time.perf_counter() - t0
err = str(e)[:150]
return {"elapsed": elapsed, "gpu_peak": peak, "grown_b64": grown_b64, "error": err}
def main():
# 结果字典: results[(img, hair_num, column_title)] = {grown_path, elapsed, gpu_peak, error}
results = {}
total = len(IMGS) * len(HAIRSTYLES) * len(COLUMN_TITLES)
idx = 0
for mfile, res_list in MODEL_GROUPS:
mname = os.path.basename(mfile)
print(f"\n===== 切换到模型: {mname} =====", flush=True)
# 等模型切换稳定
time.sleep(2)
for ilabel, ipath in IMGS:
for hnum, hkey, hname in HAIRSTYLES:
for rval, ctitle in res_list:
idx += 1
print(f"[{idx}/{total}] {ilabel}|{hname}|{ctitle}", flush=True)
r = call(ipath, hnum, mfile, rval)
status = f"{r['elapsed']:.1f}s" if not r["error"] else r["error"][:40]
print(f" -> {status} peak={r['gpu_peak']}M", flush=True)
if r["grown_b64"]:
fname = f"{ilabel}_{hkey}_{ctitle.replace('@','_').replace('-','')}.jpg"
with open(OUT / fname, "wb") as gf:
gf.write(base64.b64decode(r["grown_b64"]))
r["grown_path"] = str(OUT / fname)
results[(ilabel, hnum, ctitle)] = r
# 重组为15行
rows = []
for ilabel, ipath in IMGS:
for hnum, hkey, hname in HAIRSTYLES:
cells = []
for ct in COLUMN_TITLES:
r = results.get((ilabel, hnum, ct), {"error": "未跑"})
cells.append({"title": ct, **{k: v for k, v in r.items() if k != "grown_b64"}})
rows.append({"img": ilabel, "img_path": ipath,
"hair_num": hnum, "hair_key": hkey, "hair_name": hname,
"cells": cells})
with open(OUT / "results.json", "w", encoding="utf-8") as f:
json.dump({"columns": COLUMN_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
ok = sum(1 for row in rows for c in row["cells"] if not c.get("error"))
print(f"\n✓ 完成: {ok}/{total} 成功 -> {OUT/'results.json'}", flush=True)
if __name__ == "__main__":
main()
benchmark_hairstyle_report.py
-128
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@@ -1,128 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""把发型对比测试结果生成 HTML 报告。
15行(3图×5发型) × 10列(4b@896 + 9b三模型×三分辨率)，每行首列=原图。
图片 base64 内嵌，自包含单文件。
"""
import base64
import json
import os
from pathlib import Path
OUT = Path("/home/ubuntu/hair/benchmark_out/hairstyle")
RESULTS = OUT / "results.json"
HTML = OUT / "report.html"
def img_src(path):
"""把绝对路径转成报告里的相对 URL（报告在 static/，图片在 static/bench/）。"""
if not path:
return None
p = str(path)
if "benchmark_out/hairstyle/" in p:
return "bench/hairstyle/" + os.path.basename(p)
if "benchmark_out/matrix/" in p:
return "bench/matrix/" + os.path.basename(p)
return None
def main():
d = json.load(open(RESULTS, encoding="utf-8"))
columns = d["columns"]
rows = d["rows"]
# 统计每列的平均耗时、峰值显存
col_stats = {}
for ct in columns:
times, peaks = [], []
for r in rows:
for c in r["cells"]:
if c.get("title") == ct and not c.get("error"):
times.append(c["elapsed"])
peaks.append(c["gpu_peak"])
col_stats[ct] = {
"avg_t": sum(times) / len(times) if times else 0,
"max_p": max(peaks) / 1024 if peaks else 0,
}
# 表头：原图 + 10列
headers = ['<th class="col-label">原图</th>']
for ct in columns:
s = col_stats[ct]
headers.append(
f'<th class="col-label"><div class="col-title">{ct}</div>'
f'<div class="col-stat">{s["avg_t"]:.0f}s · {s["max_p"]:.0f}G</div></th>'
)
# 表体：15行
body_rows = []
for r in rows:
# 发型+图标签
label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
# 原图
ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg", "girl5": "bench/orig/girl5.jpg"}
orig = ORIG_SRC.get(r["img"])
cells = [f'<td class="cell-orig"><div class="row-label-cell">{label}</div>'
f'<img class="orig-img" src="{orig}"></td>']
# 10个结果列
for ct in columns:
c = next((x for x in r["cells"] if x.get("title") == ct), {})
src = img_src(c.get("grown_path")) if not c.get("error") else None
if src:
cells.append(
f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
f'<div class="cell-time">{c["elapsed"]:.1f}s</div></td>')
else:
cells.append(f'<td class="cell-result"><div class="na">⚠</div></td>')
body_rows.append(f'<tr>{"".join(cells)}</tr>')
html = f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>发型对比测试报告 — 4模型×3分辨率</title>
<style>
* {{ box-sizing: border-box; margin: 0; padding: 0; }}
body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; }}
h1 {{ font-size: 20px; margin-bottom: 4px; }}
.subtitle {{ color: #888; font-size: 12px; margin-bottom: 12px; }}
.legend {{ background: #fff; border-radius: 8px; padding: 10px 16px; margin-bottom: 12px; font-size: 12px; color: #555; }}
.scroll-wrap {{ overflow-x: auto; }}
table {{ border-collapse: collapse; background: #fff; border-radius: 8px; overflow: hidden;
box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
th, td {{ border: 1px solid #eee; padding: 6px; vertical-align: top; text-align: center; }}
th {{ background: #f9fafb; position: sticky; top: 0; }}
.col-label {{ min-width: 110px; max-width: 130px; }}
.col-title {{ font-size: 12px; font-weight: 700; color: #374151; }}
.col-stat {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
.row-label {{ font-size: 11px; color: #6b7280; }}
.row-label b {{ color: #1f2937; }}
.row-label-cell {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
.row-label-cell b {{ color: #1f2937; font-size: 13px; }}
img {{ border-radius: 4px; max-width: 120px; max-height: 150px; object-fit: contain; background: #f3f4f6; }}
.orig-img {{ border: 2px solid #d1d5db; max-height: 130px; }}
.cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
.na {{ color: #d1d5db; font-size: 16px; padding: 40px; }}
</style>
</head>
<body>
<h1>💇 发型对比测试报告</h1>
<p class="subtitle">接口2女性 · 3图×5发型=15行 · 每行: 4B@896(1) + 9B(fp8/Q5/Q4)×(原图/896/640)(9) · 150/150成功 · RTX3090</p>
<div class="legend">列标题下显示<b>平均耗时 · 峰值显存</b>。横向滚动查看更多列。原图列含图片名+发型名。</div>
<div class="scroll-wrap">
<table>
<tr>{"".join(headers)}</tr>
{"".join(body_rows)}
</table>
</div>
</body>
</html>"""
with open(HTML, "w", encoding="utf-8") as f:
f.write(html)
print(f"✓ 报告: {HTML} ({HTML.stat().st_size//1024} KB)")
if __name__ == "__main__":
main()
benchmark_matrix.py
-129
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@@ -1,129 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""接口2女性 花瓣形 单发型 4模型×3分辨率×3图×3次 矩阵测试。
调用本机 hair-worker (:8187) 的 /api/v1/hair/grow，gender=female, hair_style=2(花瓣形)。
每次记录：生发图、耗时、显存峰值。结果图存到 benchmark_out/matrix/，最后生成 HTML 报告。
"""
import base64
import json
import os
import subprocess
import sys
import time
from pathlib import Path
import requests
API = "http://127.0.0.1:8187/api/v1/hair/grow"
TOKEN = "dev-shared-secret-2026"
OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
OUT.mkdir(parents=True, exist_ok=True)
# 4 模型 × 3 分辨率 × 3 图 × 3 次
MODELS = [
("4b-fp8", "flux-2-klein-4b-fp8.safetensors"),
("9b-fp8", "flux2.0/flux-2-klein-9b-fp8.safetensors"),
("9b-Q5", "flux-2-klein-9b-Q5_K_M.gguf"),
("9b-Q4", "flux-2-klein-9b-Q4_K_M.gguf"),
]
RES = [("orig", "0"), ("640", "640"), ("896", "896")]
IMGS = [
("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
]
REPEAT = 3
def gpu_used():
"""返回当前显存已用 MiB。"""
try:
out = subprocess.check_output(
["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"],
timeout=10,
)
return int(out.decode().strip())
except Exception:
return 0
def call(img_path, model_file, res_val):
"""调一次接口2。返回 dict: ok/elapsed/grown_path/gpu_peak/error。"""
fd = {
"gender": "female",
"hair_style": "2", # 花瓣形
"use_mask": "true",
"prompt": "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜",
}
if model_file:
fd["flux_model"] = model_file
if res_val != "":
fd["redraw_max_side"] = res_val
t0 = time.perf_counter()
peak = gpu_used()
err = None
grown_path = None
try:
with open(img_path, "rb") as f:
r = requests.post(
API, headers={"X-Internal-Token": TOKEN},
files={"image_file": (os.path.basename(img_path), f, "image/jpeg")},
data=fd, timeout=300,
)
elapsed = time.perf_counter() - t0
# 采样峰值（推理刚结束）
peak = max(peak, gpu_used())
j = r.json()
if j.get("code") != 0:
err = f"code={j.get('code')} {j.get('message','')}"
else:
res = j.get("data", {}).get("results", [])
if res and res[0].get("grown_image_base64"):
grown_path = OUT / f"tmp_grown.jpg"
with open(grown_path, "wb") as gf:
gf.write(base64.b64decode(res[0]["grown_image_base64"]))
elif res:
err = "grown_image_base64 为空"
else:
err = "无 results"
except Exception as e:
elapsed = time.perf_counter() - t0
err = str(e)[:200]
return {"elapsed": elapsed, "gpu_peak": peak, "grown_path": str(grown_path) if grown_path else None, "error": err}
def main():
results = [] # 每元素一个组合
total = len(MODELS) * len(RES) * len(IMGS) * REPEAT
idx = 0
for mlabel, mfile in MODELS:
for rlabel, rval in RES:
for ilabel, ipath in IMGS:
# 一个组合：3 次
runs = []
for rep in range(REPEAT):
idx += 1
print(f"[{idx}/{total}] {mlabel} | res={rlabel} | {ilabel} | rep{rep+1}", flush=True)
r = call(ipath, mfile, rval)
print(f" -> {r['elapsed']:.1f}s peak={r['gpu_peak']}MiB err={r['error']}", flush=True)
# 存每次的生发图
if r["grown_path"]:
save_to = OUT / f"{mlabel}_{rlabel}_{ilabel}_r{rep+1}.jpg"
os.replace(r["grown_path"], save_to)
r["grown_path"] = str(save_to)
runs.append(r)
results.append({
"model": mlabel, "model_file": mfile,
"res": rlabel, "res_val": rval,
"img": ilabel, "img_path": ipath,
"runs": runs,
})
# 存原始数据
with open(OUT / "results.json", "w", encoding="utf-8") as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print(f"\n✓ 全部完成，原始数据 -> {OUT/'results.json'}", flush=True)
if __name__ == "__main__":
main()
benchmark_report.py
-157
View File
@@ -1,157 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""把 benchmark_out/matrix/results.json 生成 HTML 报告。
每个组合一行：原图 + 3次生发图 + 耗时/显存。
图片用 base64 内嵌（自包含单文件，便于部署）。
"""
import base64
import json
import os
from pathlib import Path
OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
RESULTS = OUT / "results.json"
HTML = OUT / "report.html"
RES_LABEL = {"orig": "原图", "640": "640", "896": "896(默认)"}
MODEL_LABEL = {
"4b-fp8": "4B fp8 (3.8G)",
"9b-fp8": "9B fp8 (8.8G)",
"9b-Q5": "9B Q5_K_M (6.6G)",
"9b-Q4": "9B Q4_K_M (5.6G)",
}
MODEL_ORDER = ["4b-fp8", "9b-Q4", "9b-Q5", "9b-fp8"]
def img_src(path):
"""把绝对路径转成报告里的相对 URL（报告在 static/，图片在 static/bench/）。"""
if not path:
return None
p = str(path)
# benchmark_out/matrix/xxx.jpg -> bench/matrix/xxx.jpg
if "benchmark_out/matrix/" in p:
return "bench/matrix/" + os.path.basename(p)
if "benchmark_out/hairstyle/" in p:
return "bench/hairstyle/" + os.path.basename(p)
return None
def thumb(src, alt="", cls=""):
if not src:
return f'<div class="na {cls}">⚠ 失败</div>'
return f'<img class="{cls}" src="{src}" alt="{alt}" loading="lazy">'
def main():
data = json.load(open(RESULTS, encoding="utf-8"))
# 原图相对路径映射（图片在 static/bench/orig/）
ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg", "girl5": "bench/orig/girl5.jpg"}
# 统计：每个模型的平均耗时、平均峰值显存
stats = {}
for c in data:
m = c["model"]
stats.setdefault(m, {"times": [], "peaks": []})
for r in c["runs"]:
if not r["error"]:
stats[m]["times"].append(r["elapsed"])
stats[m]["peaks"].append(r["gpu_peak"])
rows_html = []
# 按模型顺序、分辨率顺序、图片顺序排列
for m in MODEL_ORDER:
mdata = [c for c in data if c["model"] == m]
for rlabel in ["orig", "640", "896"]:
for ilabel in ["asdf", "qwer", "girl5"]:
c = next((x for x in mdata if x["res"] == rlabel and x["img"] == ilabel), None)
if not c:
continue
# 3 次结果图
run_cells = []
for i, r in enumerate(c["runs"]):
src = img_src(r["grown_path"]) if not r["error"] else None
if src:
run_cells.append(
f'<div class="run-cell"><div class="run-label">第{i+1}次 · {r["elapsed"]:.1f}s</div>'
f'{thumb(src, f"r{i+1}", "result-img")}</div>'
)
else:
run_cells.append(
f'<div class="run-cell"><div class="run-label">第{i+1}次 · 失败</div>'
f'<div class="na">⚠ {r["error"][:30] if r["error"] else ""}</div></div>'
)
orig = ORIG_SRC.get(c["img"])
rows_html.append(f'''
<div class="combo-row">
<div class="cell-model">{MODEL_LABEL.get(m, m)}<div class="cell-sub">res={RES_LABEL.get(rlabel, rlabel)}</div></div>
<div class="cell-img">{thumb(orig, "原图", "orig-img")}<div class="run-label">{ilabel}</div></div>
<div class="cell-runs">{"".join(run_cells)}</div>
</div>''')
# 模型对比汇总
summary_rows = []
for m in MODEL_ORDER:
s = stats.get(m, {"times": [], "peaks": []})
if s["times"]:
avg_t = sum(s["times"]) / len(s["times"])
max_p = max(s["peaks"]) / 1024
summary_rows.append(
f"<tr><td>{MODEL_LABEL.get(m,m)}</td><td>{avg_t:.1f}s</td>"
f"<td>{max_p:.1f} GB</td><td>{len(s['times'])} 成功</td></tr>"
)
html = f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Flux 模型矩阵测试报告 — 接口2女性花瓣形</title>
<style>
* {{ box-sizing: border-box; margin: 0; padding: 0; }}
body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; background: #f5f5f5; color: #333; padding: 20px; }}
h1 {{ font-size: 22px; margin-bottom: 4px; }}
.subtitle {{ color: #888; font-size: 13px; margin-bottom: 16px; }}
.summary {{ background: #fff; border-radius: 10px; padding: 16px 20px; margin-bottom: 20px; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
.summary h2 {{ font-size: 16px; margin-bottom: 10px; }}
.summary table {{ border-collapse: collapse; width: 100%; font-size: 14px; }}
.summary th, .summary td {{ border: 1px solid #e5e7eb; padding: 8px 12px; text-align: left; }}
.summary th {{ background: #f9fafb; font-weight: 600; }}
.combo-row {{ display: flex; align-items: flex-start; gap: 12px; background: #fff; border-radius: 10px;
padding: 12px 16px; margin-bottom: 10px; box-shadow: 0 1px 3px rgba(0,0,0,.05); }}
.cell-model {{ min-width: 130px; font-weight: 700; font-size: 14px; padding-top: 6px; }}
.cell-sub {{ font-weight: 400; font-size: 12px; color: #6b7280; margin-top: 2px; }}
.cell-img {{ min-width: 160px; text-align: center; }}
.cell-runs {{ display: flex; gap: 10px; flex: 1; }}
.run-cell {{ text-align: center; }}
.run-label {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
img {{ border-radius: 6px; max-height: 200px; max-width: 100%; object-fit: contain; background: #f9fafb; }}
.orig-img {{ max-height: 180px; border: 2px solid #e5e7eb; }}
.result-img {{ max-height: 200px; }}
.na {{ color: #d1d5db; font-size: 12px; padding: 40px 20px; background: #f9fafb; border-radius: 6px; width: 150px; }}
</style>
</head>
<body>
<h1>💇 Flux 模型矩阵测试报告</h1>
<p class="subtitle">接口2女性 · 花瓣形发型 · 4模型 × 3分辨率 × 3图 × 3次 = 108 次 · RTX 3090 24GB</p>
<div class="summary">
<h2>📊 模型对比汇总</h2>
<table>
<tr><th>模型</th><th>平均耗时</th><th>峰值显存</th><th>成功次数</th></tr>
{"".join(summary_rows)}
</table>
</div>
<h2 style="font-size:16px;margin:24px 0 12px">🖼️ 各组合对比（每行：原图 + 3次生发结果）</h2>
{"".join(rows_html)}
</body>
</html>"""
with open(HTML, "w", encoding="utf-8") as f:
f.write(html)
print(f"✓ 报告已生成: {HTML} ({HTML.stat().st_size//1024} KB)")
if __name__ == "__main__":
main()
face_analysis/annotation.py
+18 -5
View File
@@ -202,7 +202,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
# --- 自适应尺寸：字号/线宽/虚线/箭头按短边缩放 ---
s = min(w, h)
font_size = max(8, round(s * 0.017)) # 字体更小
font_size = max(9, round(s * 0.020)) # 字号上调一档
line_w = max(1, round(s * 0.0022))
dash_len = max(4, round(s * 0.008))
gap_len = max(2, round(dash_len * 0.7)) # 虚线更稠密（间隙<划线）
@@ -212,7 +212,11 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
buf = np.zeros((h, w, 4), dtype=np.uint8)
if variant == "v6":
# 发际线弃用（hairline_discarded）：保留头顶横线，去掉发际线横线，
# 也不标顶/上庭（缺发际线作边界，算不出）。横线 = 头顶/眉心/鼻翼下缘/下巴尖。
if getattr(measure_result, "hairline_discarded", False):
order = ["hair_top", "brow_center", "nose_bottom", "chin_tip"]
elif variant == "v6":
order = ["hairline", "brow_center", "nose_bottom", "chin_tip"]
else:
order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
@@ -239,7 +243,7 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
face_cx = (fx0 + fx1) / 2
over = max(6, round(s * 0.030)) # 线超出包围盒的长度（参考图风格）
face_half = (fx1 - fx0) / 2 + over # 横线超出最外侧竖线一点
# v6 竖线纵向范围 = 发际线→下巴尖（不超出）；v1 = 头顶→下巴尖并两端超出一点
# 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1（含发际线弃用）= 头顶→下巴尖并两端超出一点
v_top = fy0 if variant == "v6" else fy0 - over
v_bot = fy1 if variant == "v6" else fy1 + over
@@ -265,19 +269,28 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
draw.text((x, ys[i]), text, fill=LINE_COLOR, font=font, anchor="lm")
# --- 3b. 左侧四庭：名 + 数值两行（无 cm）+ 竖向虚线双箭头 ---
if variant == "v6":
# court_start：庭段在 order 里的起始索引。发际线弃用时 order 首位是头顶（无下界发际线，
# 顶/上庭不标），中庭从眉心开始 → 跳过 order[0]。
if getattr(measure_result, "hairline_discarded", False):
court_cm = [measure_result.middle_cm, measure_result.lower_cm]
court_name = ["中庭", "下庭"]
n_court = 2
court_start = 1
elif variant == "v6":
court_cm = [measure_result.upper_cm, measure_result.middle_cm, measure_result.lower_cm]
court_name = ["上庭", "中庭", "下庭"]
n_court = 3
court_start = 0
else:
court_cm = [measure_result.top_cm, measure_result.upper_cm,
measure_result.middle_cm, measure_result.lower_cm]
court_name = ["顶庭", "上庭", "中庭", "下庭"]
n_court = 4
court_start = 0
arrow_x = max(arrow_size + 1, fx0 - pad) # 竖箭头所在 x（脸左侧，贴近最左竖线）
court_total = sum(court_cm) or 1.0 # 各庭占比分母 = 四庭（v6 三庭）之和
for i in range(n_court):
y_a, y_b = ys[i], ys[i + 1]
y_a, y_b = ys[court_start + i], ys[court_start + i + 1]
# 竖向虚线双箭头，覆盖该庭高度（略收一点避免压到横线）
inset = min(arrow_size, (y_b - y_a) * 0.12)
draw_dashed_line_with_arrows(
face_analysis/measure.py
+67 -18
View File
@@ -12,7 +12,7 @@ from face_analysis.calibration import (
estimate_scale_factor, normalized_to_pixel, pixel_distance, _lm_list,
)
from face_analysis.face_mesh_landmarks import (
GLABELLA_9, GLABELLA_151, NOSE_BOTTOM, CHIN_TIP,
GLABELLA_9, NOSE_BOTTOM, CHIN_TIP,
LEFT_EYE_OUTER, LEFT_EYE_INNER, RIGHT_EYE_INNER, RIGHT_EYE_OUTER,
LEFT_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
)
@@ -24,10 +24,8 @@ _TOP_RATIO = 0.22 / 0.28 # 顶庭 ÷ 中庭（≈ 0.786）
def _brow_center(lm, w, h):
"""眉心 = 索引 9 / 151 中点。"""
g9 = normalized_to_pixel(lm[GLABELLA_9], w, h)
g151 = normalized_to_pixel(lm[GLABELLA_151], w, h)
return (g9[0] + g151[0]) / 2, (g9[1] + g151[1]) / 2
"""眉心 = 索引 9（眉间上点）。"""
return normalized_to_pixel(lm[GLABELLA_9], w, h)
def estimate_vertical_landmarks(landmarks, image_width, image_height):
@@ -144,9 +142,22 @@ def measure_seven_eyes(landmarks, image_width, image_height):
}
def pt_or_none(vertical, name):
"""vertical dict 的点 → {"x","y"}，值为 None 时返回 None。"""
v = vertical.get(name)
if v is None:
return None
return {"x": int(round(v[0])), "y": int(round(v[1]))}
class MeasureResult:
"""测量结果，提供 to_response() 输出与接口文档同构的 data 字段。"""
# 发际线弃用阈值：发际线离头顶（顶庭）< 此值时判定分割不可靠，弃用发际线。
# hairline 与 hair_top 几乎重合（如稀疏头发中轴漏检只剩一小撮），说明发际线
# 定位无意义 → 顶/上庭置 null、标注图不画头顶/发际线。
HAIRLINE_DISCARD_TOP_CM = 0.7
def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose,
landmarks=None, image_width=None, image_height=None):
self.vertical = vertical
@@ -164,6 +175,13 @@ class MeasureResult:
self.upper_cm = vertical["upper_court_px"] / px_per_cm
self.middle_cm = vertical["middle_court_px"] / px_per_cm
self.lower_cm = vertical["lower_court_px"] / px_per_cm
# 发际线弃用判定：顶庭（头顶→发际线）过小视为发际线贴近头顶、不可靠。
# 弃用时 hairline_source 改为 "discarded"，face_total 只算中庭+下庭。
self.hairline_discarded = self.top_cm < self.HAIRLINE_DISCARD_TOP_CM
if self.hairline_discarded:
self.hairline_source = "discarded"
self.face_total_cm = self.middle_cm + self.lower_cm
else:
self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_cm
# 七眼厘米
@@ -172,14 +190,45 @@ class MeasureResult:
self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm
def to_response(self):
# 发际线弃用：顶/上庭相关字段置 null（保留键），ratio 分母只算中下庭；
# landmarks.hair_top/hairline 置 null。否则按四庭正常输出。
if self.hairline_discarded:
base_px = (self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
data = {
"face_total_height_cm": round(self.face_total_cm, 2),
"four_courts": {
"top_court_cm": None,
"upper_court_cm": None,
"middle_court_cm": round(self.middle_cm, 2),
"lower_court_cm": round(self.lower_cm, 2),
"ratios": {
"top_court": None,
"upper_court": None,
"middle_court": round(self.vertical["middle_court_px"] / base_px, 3),
"lower_court": round(self.vertical["lower_court_px"] / base_px, 3),
},
},
"seven_eyes": {
"eye_width_cm": round(self.eye_width_cm, 2),
"face_width_cm": round(self.face_width_cm, 2),
"inter_eye_distance_cm": round(self.inter_eye_cm, 2),
"ratios": {
"eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
"inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
},
},
"landmarks": {
"hair_top": None,
"hairline": None,
"brow_center": pt_or_none(self.vertical, "brow_center"),
"nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
"chin_tip": pt_or_none(self.vertical, "chin_tip"),
},
"hairline_source": self.hairline_source,
}
else:
total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
+ self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
fw_px = self.eyes["face_width_px"]
def pt(name):
x, y = self.vertical[name]
return {"x": int(round(x)), "y": int(round(y))}
data = {
"face_total_height_cm": round(self.face_total_cm, 2),
"four_courts": {
@@ -199,16 +248,16 @@ class MeasureResult:
"face_width_cm": round(self.face_width_cm, 2),
"inter_eye_distance_cm": round(self.inter_eye_cm, 2),
"ratios": {
"eye_width": round(self.eyes["eye_width_px"] / fw_px, 3),
"inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / fw_px, 3),
"eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
"inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
},
},
"landmarks": {
"hair_top": pt("hair_top"),
"hairline": pt("hairline"),
"brow_center": pt("brow_center"),
"nose_bottom": pt("nose_bottom"),
"chin_tip": pt("chin_tip"),
"hair_top": pt_or_none(self.vertical, "hair_top"),
"hairline": pt_or_none(self.vertical, "hairline"),
"brow_center": pt_or_none(self.vertical, "brow_center"),
"nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
"chin_tip": pt_or_none(self.vertical, "chin_tip"),
},
"hairline_source": self.hairline_source,
}
gateway.log
+339
View File
@@ -0,0 +1,339 @@
INFO: Started server process [26440]
INFO: Waiting for application startup.
2026-07-01 23:35:15 [INFO] gateway.config: 配置加载完成 | workers=['http://127.0.0.1:8187'] | public_base_url=http://127.0.0.1:8080 | hc_interval=8s | dispatch_timeout=600s | queue_wait=30s
2026-07-01 23:35:15 [INFO] gateway: 网关启动中... workers=['http://127.0.0.1:8187']
2026-07-01 23:35:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:35:16 [INFO] gateway.pool: Worker 池初始化完成 | 总数=1 | 在线=1
2026-07-01 23:35:16 [INFO] gateway: 标注图目录: /home/ubuntu/hair/static/annotations
2026-07-01 23:35:16 [INFO] gateway.pool: 健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=1
2026-07-01 23:35:16 [INFO] gateway: 清理任务启动 | 间隔=60min | 保留=24h | 目录=/home/ubuntu/hair/static/annotations
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
2026-07-01 23:35:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 127.0.0.1:35872 - "GET /gateway-health HTTP/1.1" 200 OK
2026-07-01 23:35:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:35:27 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
2026-07-01 23:35:27 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://127.0.0.1:8080/static/annotations/422b7786adc4486989d7f8770df40693.png (21864 bytes)
INFO: 127.0.0.1:57220 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
2026-07-01 23:35:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 127.0.0.1:57232 - "GET /static/annotations/422b7786adc4486989d7f8770df40693.png HTTP/1.1" 200 OK
INFO: 127.0.0.1:57240 - "GET /gateway-health HTTP/1.1" 200 OK
2026-07-01 23:35:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:35:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:35:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:36:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:37:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:38:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:39:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:40:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:41:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:42:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:43:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:44:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:45:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:46:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:47:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:48:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:49:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:50:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:51:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:52:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 127.0.0.1:44470 - "GET /gateway-health HTTP/1.1" 200 OK
2026-07-01 23:52:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:52:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:52:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:52:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:52:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:52:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:53:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 111.192.98.24:6017 - "GET / HTTP/1.1" 200 OK
INFO: 111.192.98.24:6017 - "GET /favicon.ico HTTP/1.1" 404 Not Found
2026-07-01 23:53:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:54:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 127.0.0.1:39090 - "GET /docs HTTP/1.1" 200 OK
INFO: 127.0.0.1:39092 - "GET / HTTP/1.1" 200 OK
2026-07-01 23:55:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:55:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 127.0.0.1:56172 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56174 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56184 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56196 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56202 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56206 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56212 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56214 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56218 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56230 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56242 - "GET /static/test_interface6.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56256 - "GET /static/test_interface6.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56272 - "GET /static/test_interface7.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56278 - "GET /static/test_interface7.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56282 - "GET /static/integration.html HTTP/1.1" 200 OK
INFO: 127.0.0.1:56284 - "GET /static/integration.html HTTP/1.1" 200 OK
2026-07-01 23:55:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:55:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:55:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:55:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:55:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:55:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 111.192.98.24:6434 - "GET /static/test_interface1.html HTTP/1.1" 200 OK
2026-07-01 23:56:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:56:08 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
2026-07-01 23:56:08 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://127.0.0.1:8080/static/annotations/8a70c12c19ef4868af555ef84eaeafae.png (35965 bytes)
INFO: 111.192.98.24:6435 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
2026-07-01 23:56:12 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:56:20 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:56:28 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:56:36 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:56:44 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:56:52 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:00 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:08 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:16 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:24 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:32 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:40 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:48 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:57:56 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:04 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: Shutting down
INFO: Waiting for application shutdown.
2026-07-01 23:58:08 [INFO] gateway: 网关关闭中...
2026-07-01 23:58:08 [INFO] gateway: 清理任务已停止
2026-07-01 23:58:08 [INFO] gateway.pool: 健康检查循环已停止
2026-07-01 23:58:08 [INFO] gateway.pool: Worker 池已关闭
2026-07-01 23:58:08 [INFO] gateway: 网关已关闭
INFO: Application shutdown complete.
INFO: Finished server process [26440]
INFO: Started server process [31898]
INFO: Waiting for application startup.
2026-07-01 23:58:10 [INFO] gateway.config: 配置加载完成 | workers=['http://127.0.0.1:8187'] | public_base_url=http://117.50.213.111:8080 | hc_interval=8s | dispatch_timeout=600s | queue_wait=30s
2026-07-01 23:58:10 [INFO] gateway: 网关启动中... workers=['http://127.0.0.1:8187']
2026-07-01 23:58:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:10 [INFO] gateway.pool: Worker 池初始化完成 | 总数=1 | 在线=1
2026-07-01 23:58:10 [INFO] gateway: 标注图目录: /home/ubuntu/hair/static/annotations
2026-07-01 23:58:10 [INFO] gateway.pool: 健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=1
2026-07-01 23:58:10 [INFO] gateway: 清理任务启动 | 间隔=60min | 保留=24h | 目录=/home/ubuntu/hair/static/annotations
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
2026-07-01 23:58:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 127.0.0.1:33732 - "GET /gateway-health HTTP/1.1" 200 OK
2026-07-01 23:58:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:28 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
2026-07-01 23:58:28 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://117.50.213.111:8080/static/annotations/478efab4566a46c3af0065ad0ffafb67.png (21864 bytes)
INFO: 127.0.0.1:32982 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
INFO: 117.50.213.111:57344 - "GET /static/annotations/478efab4566a46c3af0065ad0ffafb67.png HTTP/1.1" 200 OK
2026-07-01 23:58:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:58:54 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/face/measure "HTTP/1.1 200 OK"
2026-07-01 23:58:54 [INFO] gateway.forward: base64→URL: annotated_image_base64 → http://117.50.213.111:8080/static/annotations/88d5eec74def44fdad5dd6f7b22e7be4.png (35965 bytes)
INFO: 111.192.98.24:7030 - "POST /api/v1/face/measure HTTP/1.1" 200 OK
INFO: 111.192.98.24:7030 - "GET /static/annotations/88d5eec74def44fdad5dd6f7b22e7be4.png HTTP/1.1" 200 OK
2026-07-01 23:58:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 111.192.98.24:7031 - "GET /static/test_interface2.html HTTP/1.1" 200 OK
2026-07-01 23:59:06 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:59:14 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:59:22 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:59:27 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow "HTTP/1.1 200 OK"
2026-07-01 23:59:27 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/8f770fef8bc14827b419698c90ebd6fa.jpg (105970 bytes)
2026-07-01 23:59:27 [INFO] gateway.forward: base64→URL: grown_image_base64 → http://117.50.213.111:8080/static/annotations/6f592882d9c84c1b916ac327f4e0b2af.jpg (113320 bytes)
INFO: 111.192.98.24:7062 - "POST /api/v1/hair/grow HTTP/1.1" 200 OK
INFO: 111.192.98.24:7062 - "GET /static/annotations/8f770fef8bc14827b419698c90ebd6fa.jpg HTTP/1.1" 200 OK
INFO: 111.192.98.24:7166 - "GET /static/annotations/6f592882d9c84c1b916ac327f4e0b2af.jpg HTTP/1.1" 200 OK
2026-07-01 23:59:30 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:59:38 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:59:46 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-01 23:59:48 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow "HTTP/1.1 200 OK"
2026-07-01 23:59:48 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/ed790d4888f742caa6625b3cf4704a77.jpg (105641 bytes)
2026-07-01 23:59:48 [INFO] gateway.forward: base64→URL: grown_image_base64 → http://117.50.213.111:8080/static/annotations/e4aed9d67e864d31909b30a58e08c16e.jpg (111495 bytes)
INFO: 111.192.98.24:5164 - "POST /api/v1/hair/grow HTTP/1.1" 200 OK
INFO: 111.192.98.24:5164 - "GET /static/annotations/ed790d4888f742caa6625b3cf4704a77.jpg HTTP/1.1" 200 OK
INFO: 111.192.98.24:5188 - "GET /static/annotations/e4aed9d67e864d31909b30a58e08c16e.jpg HTTP/1.1" 200 OK
2026-07-01 23:59:54 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 111.192.98.24:5213 - "GET /static/test_interface3.html HTTP/1.1" 200 OK
2026-07-02 00:00:02 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:00:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:00:16 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hair/grow-b "HTTP/1.1 200 OK"
2026-07-02 00:00:16 [INFO] gateway.forward: base64→URL: hair_growth_image_base64 → http://117.50.213.111:8080/static/annotations/0891a4b9375e4f58814502e893b12bf6.jpg (152092 bytes)
INFO: 111.192.98.24:5212 - "POST /api/v1/hair/grow-b HTTP/1.1" 200 OK
INFO: 111.192.98.24:5212 - "GET /static/annotations/0891a4b9375e4f58814502e893b12bf6.jpg HTTP/1.1" 200 OK
2026-07-02 00:00:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 111.192.98.24:5332 - "GET /static/test_interface4.html HTTP/1.1" 200 OK
2026-07-02 00:00:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:00:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:00:39 [ERROR] gateway: 接口4 豆包调用失败
Traceback (most recent call last):
File "/home/ubuntu/hair/gateway/app.py", line 298, in face_features
feats = await run_in_threadpool(analyze_features, img_bytes, image_url)
File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/starlette/concurrency.py", line 37, in run_in_threadpool
return await anyio.to_thread.run_sync(func)
File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/to_thread.py", line 63, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2596, in run_sync_in_worker_thread
return await future
File "/home/ubuntu/hair/venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 1029, in run
result = context.run(func, *args)
File "/home/ubuntu/hair/face_features.py", line 98, in analyze_features
resp = get_client().chat.completions.create(
File "/home/ubuntu/hair/face_features.py", line 65, in get_client
from volcenginesdkarkruntime import Ark
ModuleNotFoundError: No module named 'volcenginesdkarkruntime'
INFO: 111.192.98.24:5331 - "POST /api/v1/face/features HTTP/1.1" 200 OK
2026-07-02 00:00:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: 111.192.98.24:5436 - "GET /static/test_interface5.html HTTP/1.1" 200 OK
2026-07-02 00:00:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:00:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:00 [INFO] httpx: HTTP Request: POST http://127.0.0.1:8187/api/v1/hairline/generate "HTTP/1.1 200 OK"
2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/4516a9491a1c41f4af048020f6709870.jpg (105674 bytes)
2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/ad51e082bb5540ea843259cdc1e76e6b.jpg (105970 bytes)
2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/12a6f7745c9c461ca963f4d49c5267ef.jpg (105735 bytes)
2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/316d8352062848e4b28337ef233d820e.jpg (105641 bytes)
2026-07-02 00:01:00 [INFO] gateway.forward: base64→URL: image_base64 → http://117.50.213.111:8080/static/annotations/2ae45e3579b0419ab8bff5e3129ab26e.jpg (105705 bytes)
INFO: 111.192.98.24:5435 - "POST /api/v1/hairline/generate HTTP/1.1" 200 OK
INFO: 111.192.98.24:5435 - "GET /static/annotations/4516a9491a1c41f4af048020f6709870.jpg HTTP/1.1" 200 OK
INFO: 111.192.98.24:5466 - "GET /static/annotations/ad51e082bb5540ea843259cdc1e76e6b.jpg HTTP/1.1" 200 OK
INFO: 111.192.98.24:5467 - "GET /static/annotations/12a6f7745c9c461ca963f4d49c5267ef.jpg HTTP/1.1" 200 OK
INFO: 111.192.98.24:5469 - "GET /static/annotations/316d8352062848e4b28337ef233d820e.jpg HTTP/1.1" 200 OK
INFO: 111.192.98.24:5471 - "GET /static/annotations/2ae45e3579b0419ab8bff5e3129ab26e.jpg HTTP/1.1" 200 OK
2026-07-02 00:01:06 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:14 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:22 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:30 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:38 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:46 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:01:54 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:02 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:10 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:18 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:26 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:34 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:42 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:50 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
2026-07-02 00:02:58 [INFO] httpx: HTTP Request: GET http://127.0.0.1:8187/health "HTTP/1.1 200 OK"
INFO: Shutting down
INFO: Waiting for application shutdown.
2026-07-02 00:02:59 [INFO] gateway: 网关关闭中...
2026-07-02 00:02:59 [INFO] gateway: 清理任务已停止
2026-07-02 00:02:59 [INFO] gateway.pool: 健康检查循环已停止
2026-07-02 00:02:59 [INFO] gateway.pool: Worker 池已关闭
2026-07-02 00:02:59 [INFO] gateway: 网关已关闭
INFO: Application shutdown complete.
INFO: Finished server process [31898]
hair_repaint.json
+2 -2
View File
@@ -170,10 +170,10 @@
},
"16": {
"inputs": {
"unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
"unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
"weight_dtype": "fp8_e4m3fn"
},
"class_type": "UnetLoaderGGUF",
"class_type": "UNETLoader",
"_meta": {
"title": "UNet加载器"
}
hairline/comfyui.py
+1 -34
View File
@@ -30,19 +30,6 @@ _REPO = os.path.dirname(os.path.dirname(__file__))
_INPUT_NODE = "26" # LoadImage：外部输入图(含 alpha 遮罩)
_SEED_NODE = "6" # RandomNoise
_PROMPT_NODE = "60" # JjkText：提示词
_UNET_NODE = "16" # UNETLoader / UnetLoaderGGUF：Flux 模型加载
_CLIP_NODE = "61" # CLIPLoader：qwen 文本编码器
# Flux 模型 → 配套文本编码器映射。切换 unet 时自动同步编码器，避免维度不匹配。
# 规则：4b 系列配 qwen_3_4b，9b 系列（fp8/GGUF）配 qwen_3_8b_fp8mixed。
def _clip_for_unet(unet_name: str) -> str | None:
"""根据 unet 文件名推断配套的文本编码器文件名；无法推断返回 None。"""
low = unet_name.lower()
if "4b" in low and "9b" not in low:
return "qwen_3_4b.safetensors"
if "9b" in low:
return "qwen_3_8b_fp8mixed.safetensors"
return None
_wf_cache: dict[str, dict] = {} # path → workflow JSON
_wf_output_node: dict[str, str] = {} # path → SaveImage 节点 ID
@@ -99,18 +86,13 @@ def _get_output_node(workflow_path: str | None = None) -> str:
def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = None,
workflow_path: str | None = None, front: bool = False,
unet_name: str | None = None) -> bytes:
workflow_path: str | None = None, front: bool = False) -> bytes:
"""提交一次生发任务，返回输出 PNG 字节。失败抛异常。
prompt：非 None 时替换工作流节点60(JjkText)的文本；None 时用工作流内置默认提示词。
workflow_path：工作流 JSON 路径，None 则用默认 add_hair.json。
front：True 时任务插到 ComfyUI 队列最前（server 端 "front" 字段，队列号取负）。
接口2 对时延敏感用 True，避免排在接口3/5 的批量任务后面；其余接口保持 False。
unet_name：非 None 时改写工作流里的模型加载节点（节点16），动态切换 Flux 模型。
.safetensors → 保持 UNETLoader 节点类型不变，只替换 unet_name；
.gguf → 自动把节点类型改成 UnetLoaderGGUF（需装 ComfyUI-GGUF 插件）。
None 时用工作流内置默认模型。
"""
path = workflow_path or _WORKFLOW_DEFAULT
output_node = _get_output_node(path)
@@ -138,21 +120,6 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
wf[_SEED_NODE]["inputs"]["noise_seed"] = random.randint(0, 2**63 - 1)
if prompt is not None:
wf[_PROMPT_NODE]["inputs"]["text"] = prompt
if unet_name is not None:
node = wf.get(_UNET_NODE)
if node is not None:
# .gguf 需切换到 ComfyUI-GGUF 插件的 UnetLoaderGGUF 节点；
# .safetensors/.ckpt 保持原 UNETLoader 节点类型不变
if unet_name.lower().endswith(".gguf"):
node["class_type"] = "UnetLoaderGGUF"
else:
node["class_type"] = "UNETLoader"
node["inputs"]["unet_name"] = unet_name
# 同步切换配套文本编码器（4b→qwen_3_4b, 9b→qwen_3_8b），避免维度不匹配
clip_node = wf.get(_CLIP_NODE)
clip_name = _clip_for_unet(unet_name)
if clip_node is not None and clip_name is not None:
clip_node["inputs"]["clip_name"] = clip_name
# 诊断：落盘实际提交的工作流 + 输入图，便于和手动 ComfyUI 跑的对比
try:
hairline/redraw.py
+2 -4
View File
@@ -56,7 +56,7 @@ def _process_mask_to_rgba(image_bytes: bytes, mask_bytes: bytes) -> bytes:
def run_redraw(image_bytes: bytes, mask_bytes: bytes,
prompt: str | None = None, timeout: float = 300.0,
front: bool = False, unet_name: str | None = None) -> bytes:
front: bool = False) -> bytes:
"""直接调 ComfyUI 重绘 — 替代 local_test /api/generate。
Args:
@@ -65,7 +65,6 @@ def run_redraw(image_bytes: bytes, mask_bytes: bytes,
prompt: 提示词，None 用默认 "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜"
timeout: ComfyUI 超时秒数
front: True 时任务插到 ComfyUI 队列最前（接口2 时延敏感路径用）
unet_name: 非 None 时切换 Flux 模型（如 flux-2-klein-9b-Q5_K_M.gguf），None 用工作流默认
Returns:
重绘后的 PNG 图片字节
@@ -76,5 +75,4 @@ def run_redraw(image_bytes: bytes, mask_bytes: bytes,
"""
rgba_png = _process_mask_to_rgba(image_bytes, mask_bytes)
return comfyui.run(rgba_png, timeout=timeout, prompt=prompt,
workflow_path=_REPAINT_WORKFLOW, front=front,
unet_name=unet_name)
workflow_path=_REPAINT_WORKFLOW, front=front)
hairline/service.py
+13 -26
View File
@@ -54,26 +54,21 @@ _REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，皮
# 默认压到 896 兜底（ComfyUI ~4s，女性总耗时 9~11s）；追画质可设 REDRAW_MAX_SIDE=1024。
_REDRAW_MAX_SIDE = int(os.getenv("REDRAW_MAX_SIDE", "896"))
def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
max_side=None, unet_name=None):
def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0):
"""直接调 ComfyUI 重绘（替代原 local_test HTTP 服务）。
传 final 图 + 纯红遮罩 PNG，返回重绘后的 PNG bytes。
失败抛异常（调用方负责 try/except 跳过）。
max_side：送 ComfyUI 前长边压到多少像素，None 用全局默认 _REDRAW_MAX_SIDE。
unet_name：非 None 时切换 Flux 模型，None 用工作流内置默认。
"""
from .redraw import run_redraw
eff_side = _REDRAW_MAX_SIDE if max_side is None else max_side
img = cv2.imdecode(np.frombuffer(image_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
scale = 1.0
orig_w = orig_h = 0
if img is not None:
orig_h, orig_w = img.shape[:2]
m = max(orig_h, orig_w)
if eff_side > 0 and m > eff_side:
scale = eff_side / float(m)
if _REDRAW_MAX_SIDE > 0 and m > _REDRAW_MAX_SIDE:
scale = _REDRAW_MAX_SIDE / float(m)
nw, nh = max(1, round(orig_w * scale)), max(1, round(orig_h * scale))
msk = cv2.imdecode(np.frombuffer(mask_png_bytes, np.uint8), cv2.IMREAD_UNCHANGED)
img_s = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_AREA)
@@ -81,10 +76,10 @@ def _call_local_redraw(image_png_bytes, mask_png_bytes, timeout=300.0,
image_png_bytes = cv2.imencode(".png", img_s)[1].tobytes()
mask_png_bytes = cv2.imencode(".png", msk_s)[1].tobytes()
logger.info("接口2女 缩图送 Comfy: %dx%d → %dx%d (max_side=%d)",
orig_w, orig_h, nw, nh, eff_side)
orig_w, orig_h, nw, nh, _REDRAW_MAX_SIDE)
# 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
out = run_redraw(image_png_bytes, mask_png_bytes, timeout=timeout,
prompt=_REDRAW_PROMPT, front=True, unet_name=unet_name)
prompt=_REDRAW_PROMPT, front=True)
if scale < 1.0 and out:
out = _upscale_png_to(out, orig_w, orig_h)
return out
@@ -205,8 +200,7 @@ def generate_previews(image_bgr: np.ndarray, gender: str):
def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = True,
prompt: str = None, hair_styles: list[int] | None = None,
workflow_path: str | None = None,
unet_name: str | None = None):
workflow_path: str | None = None):
"""指定发际线类型：发际线透明叠图(白线 RGBA) + 生发图(ComfyUI)。
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。
@@ -242,8 +236,7 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
compose_comfy_rgba(img_s, msk_s).save(buf, format="PNG", compress_level=1)
# front=True：接口2 时延敏感，插到 ComfyUI 队列最前
shared_grown = comfyui.run(buf.getvalue(), prompt=prompt,
workflow_path=workflow_path, front=True,
unet_name=unet_name)
workflow_path=workflow_path, front=True)
if gsc < 1.0 and shared_grown:
shared_grown = _upscale_png_to(shared_grown, w, h)
except Exception as e: # noqa: BLE001
@@ -268,8 +261,7 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
compose_comfy_rgba(m_s, msk_s).save(buf, format="PNG", compress_level=1)
# front=True：接口2 时延敏感，插到 ComfyUI 队列最前
grown_png = comfyui.run(buf.getvalue(), prompt=prompt,
workflow_path=workflow_path, front=True,
unet_name=unet_name)
workflow_path=workflow_path, front=True)
if gsc < 1.0 and grown_png:
grown_png = _upscale_png_to(grown_png, w, h)
except Exception as e: # noqa: BLE001 单张失败不拖垮整请求
@@ -281,9 +273,7 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | None,
redraw_defaults: dict,
redraw_max_side: int | None = None,
unet_name: str | None = None):
redraw_defaults: dict):
"""接口2 女性专用：发际线透明叠图（同 generate_grow_results）+ 换发型重绘图。
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，
@@ -317,17 +307,16 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
# 重绘管线（swapHair + ComfyUI）统一降分辨率：真实照片 swap(SD WebUI)~5s、blend、ComfyUI
# 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。
eff_side = _REDRAW_MAX_SIDE if redraw_max_side is None else redraw_max_side
redraw_img = image_bgr
hair_mask_redraw = hair_mask_reuse
if eff_side > 0 and max(h, w) > eff_side:
redraw_img, _rs = _downscale_max_side(image_bgr, eff_side)
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, eff_side)
w, h, _nw, _nh, _REDRAW_MAX_SIDE)
for order, (key, white_path) in items:
white = load_texture_rgba(white_path)
@@ -360,9 +349,7 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
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,
max_side=redraw_max_side,
unet_name=unet_name)
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",
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