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>

.gitignore

@@ -49,3 +49,35 @@ image/test/
 # 批量报告输出（生成图+原图，体积大，不入 git）
 static/report_hairline_v2/
 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.*
+
+# 脸型测试素材（人像照片，体积大，不入 git；仅保留 6 张基准标注图）
+face/test_img/脸型测试集合/
+face/test_img/girl/
+face/test_img/man/
+
+# 脸型特征缓存（由 face/dump_features.py 生成，可随时重跑）
+face/cache/
+
+# 脸型报告输出（标注图体积大，不入 git）
+static/face_shape_report/
+static/face_shape_report.html
+static/facetest_report/
+static/facetest_report.html
+static/facetest_all_report/
+static/facetest_all_report.html

0716add-hair-api.json

@@ -0,0 +1,327 @@
+{
+  "16": {
+    "class_type": "UnetLoaderGGUF",
+    "inputs": {
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
+    }
+  },
+  "3": {
+    "class_type": "VAELoader",
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    }
+  },
+  "61": {
+    "class_type": "CLIPLoader",
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "cpu"
+    }
+  },
+  "26": {
+    "class_type": "LoadImage",
+    "inputs": {
+      "image": "placeholder.png"
+    }
+  },
+  "60": {
+    "class_type": "JjkText",
+    "inputs": {
+      "text": "填充遮罩区域的头发"
+    }
+  },
+  "22": {
+    "class_type": "CLIPTextEncode",
+    "inputs": {
+      "clip": [
+        "61",
+        0
+      ],
+      "text": [
+        "60",
+        0
+      ]
+    }
+  },
+  "31": {
+    "class_type": "easy imageSize",
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    }
+  },
+  "33": {
+    "class_type": "Mask Fill Holes",
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    }
+  },
+  "36": {
+    "class_type": "Convert Masks to Images",
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    }
+  },
+  "39": {
+    "class_type": "ImageScale",
+    "inputs": {
+      "image": [
+        "36",
+        0
+      ],
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled"
+    }
+  },
+  "37": {
+    "class_type": "Image To Mask",
+    "inputs": {
+      "image": [
+        "39",
+        0
+      ],
+      "method": "intensity"
+    }
+  },
+  "32": {
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ],
+      "aspect_ratio": "custom",
+      "proportional_width": [
+        "31",
+        0
+      ],
+      "proportional_height": [
+        "31",
+        1
+      ],
+      "fit": "letterbox",
+      "method": "lanczos",
+      "round_to_multiple": "8",
+      "scale_to_side": "None",
+      "scale_to_length": 1024,
+      "background_color": "#000000"
+    }
+  },
+  "44": {
+    "class_type": "ImageAndMaskPreview",
+    "inputs": {
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ],
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true
+    }
+  },
+  "14": {
+    "class_type": "GetImageSize+",
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    }
+  },
+  "13": {
+    "class_type": "VAEEncode",
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    }
+  },
+  "2": {
+    "class_type": "ModelSamplingFlux",
+    "inputs": {
+      "model": [
+        "16",
+        0
+      ],
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ]
+    }
+  },
+  "19": {
+    "class_type": "FluxGuidance",
+    "inputs": {
+      "conditioning": [
+        "22",
+        0
+      ],
+      "guidance": 1
+    }
+  },
+  "5": {
+    "class_type": "ReferenceLatent",
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    }
+  },
+  "7": {
+    "class_type": "EmptySD3LatentImage",
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    }
+  },
+  "1": {
+    "class_type": "BasicScheduler",
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "scheduler": "simple",
+      "steps": 4,
+      "denoise": 1
+    }
+  },
+  "20": {
+    "class_type": "BasicGuider",
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    }
+  },
+  "6": {
+    "class_type": "RandomNoise",
+    "inputs": {
+      "noise_seed": 0
+    }
+  },
+  "8": {
+    "class_type": "KSamplerSelect",
+    "inputs": {
+      "sampler_name": "euler"
+    }
+  },
+  "9": {
+    "class_type": "SamplerCustomAdvanced",
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    }
+  },
+  "10": {
+    "class_type": "VAEDecode",
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    }
+  },
+  "62": {
+    "class_type": "ColorMatch",
+    "inputs": {
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ],
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true
+    }
+  },
+  "17": {
+    "class_type": "SaveImage",
+    "inputs": {
+      "images": [
+        "62",
+        0
+      ],
+      "filename_prefix": "hair_inpaint"
+    }
+  }
+}

add_hair.json

@@ -2,7 +2,7 @@
   "1": {
     "inputs": {
       "scheduler": "simple",
-      "steps": 6,
+      "steps": 4,
       "denoise": 1,
       "model": [
         "2",
@@ -170,10 +170,10 @@
   },
   "16": {
     "inputs": {
-      "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
-      "weight_dtype": "fp8_e4m3fn"
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn_fast"
     },
-    "class_type": "UNETLoader",
+    "class_type": "UnetLoaderGGUF",
     "_meta": {
       "title": "UNet加载器"
     }
@@ -410,7 +410,7 @@
   },
   "60": {
     "inputs": {
-      "text": "补充遮罩区补充遮罩区域内的头发，头发填满遮罩区域。发际线往下挡住额头"
+      "text": "填充遮罩区域的头发"
     },
     "class_type": "JjkText",
     "_meta": {
@@ -421,7 +421,7 @@
     "inputs": {
       "clip_name": "qwen_3_8b_fp8mixed.safetensors",
       "type": "flux2",
-      "device": "default"
+      "device": "cpu"
     },
     "class_type": "CLIPLoader",
     "_meta": {

bench6_quick.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""快速测试：3张图×花瓣发型×896分辨率，新提示词"填充遮罩区域的头发"。
+预热1次+正式1次。
+"""
+import base64, json, os, time
+from pathlib import Path
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+PROMPT = "填充遮罩区域的头发"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench6")
+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"),
+]
+
+def call(img_path, save_grown=None, timeout=300):
+    data = {"hair_style": "2", "webui_steps": "15", "redraw_max_side": "896", "redraw_prompt": PROMPT}
+    t0 = time.perf_counter()
+    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=data, timeout=timeout)
+    wall = time.perf_counter() - t0
+    j = r.json()
+    d = j["data"]; hs = d["per_hairstyle"][0]
+    if save_grown and hs.get("grown_b64"):
+        b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+        open(save_grown, "wb").write(base64.b64decode(b))
+    return {"ok": hs.get("ok"), "total_ms": d["total_ms"], "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "grown_path": str(save_grown) if save_grown and hs.get("ok") else None}
+
+results = []
+for ilabel, ipath in IMGS:
+    print(f"预热 {ilabel}...", flush=True)
+    call(ipath)
+    save = OUT / f"{ilabel}_flower_896.jpg"
+    print(f"正式 {ilabel}...", flush=True)
+    r = call(ipath, save_grown=save)
+    r["img"] = ilabel
+    print(f"  -> total={r['total_ms']}ms ok={r['ok']}", flush=True)
+    results.append(r)
+
+json.dump({"prompt": PROMPT, "results": results}, open(OUT/"results.json","w"), ensure_ascii=False, indent=2)
+print(f"\n✓ 完成 {sum(1 for r in results if r['ok'])}/3", flush=True)

benchmark_hairstyle.py

@@ -0,0 +1,134 @@
+#!/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"}
+    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

@@ -0,0 +1,128 @@
+#!/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

@@ -0,0 +1,129 @@
+#!/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",
+    }
+    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

@@ -0,0 +1,157 @@
+#!/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()

benchmark_res_compare.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""分辨率对比测试：4图×5发型=20行，每行4种分辨率(不缩放/896/768/640)，steps=15。
+热数据：每个组合预热1次(丢弃)+正式1次。OOM的跳过记录为失败。
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench3")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.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"),
+]
+# 分辨率档：0=不缩放(原图)
+RES_LIST = [("orig", "0"), ("896", "896"), ("768", "768"), ("640", "640")]
+RES_TITLES = ["原图(不缩放)", "896", "768", "640"]
+STEPS = 15
+
+
+def call(img_path, hair_num, redraw_max_side, save_grown=None, timeout=300):
+    data = {"hair_style": str(hair_num), "webui_steps": str(STEPS),
+            "redraw_max_side": str(redraw_max_side)}
+    t0 = time.perf_counter()
+    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=data, timeout=timeout)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:80], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "swap_ms": hs.get("swap_ms"),
+            "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:80], "wall": time.perf_counter() - t0}
+
+
+def main():
+    rows = []
+    total = len(IMGS) * len(HAIRSTYLES) * len(RES_LIST) * 2
+    idx = 0
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for (rlabel, rval), rtitle in zip(RES_LIST, RES_TITLES):
+                # 预热
+                idx += 1
+                print(f"[{idx}/{total}] 预热 {ilabel}|{hname}|{rtitle}", flush=True)
+                try:
+                    call(ipath, hnum, rval, timeout=120)
+                except Exception:
+                    pass  # 预热失败(可能OOM)不中断
+                # 正式
+                idx += 1
+                save = OUT / f"{ilabel}_{hkey}_{rlabel}.jpg"
+                print(f"[{idx}/{total}] 正式 {ilabel}|{hname}|{rtitle}", flush=True)
+                r = call(ipath, hnum, rval, save_grown=save, timeout=300)
+                r["res_label"] = rlabel; r["res_title"] = rtitle
+                r["grown_path"] = str(save) if r.get("ok") else None
+                status = f"{r.get('total_ms')}ms" if r.get("ok") else f"FAIL:{r.get('error','')[:30]}"
+                print(f"    -> {status}", flush=True)
+                cells.append(r)
+            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({"res_titles": RES_TITLES, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if c.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(rows)*len(RES_LIST)} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_res_compare_report.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""生成分辨率对比报告：20行(4图×5发型) × 4列(原图不缩放/896/768/640)。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench3")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    if not path or not os.path.isfile(path):
+        return None
+    return "bench3/" + os.path.basename(path)
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    titles = d["res_titles"]
+    rows = d["rows"]
+
+    # 各分辨率平均耗时
+    col_stats = defaultdict(lambda: {"total": [], "comfy": []})
+    for r in rows:
+        for c in r["cells"]:
+            if c.get("ok"):
+                col_stats[c["res_title"]]["total"].append(c["total_ms"])
+                col_stats[c["res_title"]]["comfy"].append(c.get("comfy_ms", 0))
+
+    # 表头
+    headers = ['<th class="col-label">原图</th>']
+    for t in titles:
+        s = col_stats.get(t)
+        avg = sum(s["total"]) // len(s["total"]) if s and s["total"] else 0
+        headers.append(f'<th class="col-label"><div class="col-title">{t}</div>'
+                       f'<div class="col-stat">均{avg/1000:.1f}s</div></th>')
+
+    # 表体
+    body_rows = []
+    for r in rows:
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图缩略图（用 orig 档的结果当原图展示，或用原图文件）
+        orig_cell = f'<td class="cell-orig"><div class="row-label-cell">{label}</div></td>'
+        cells = [orig_cell]
+        for c in r["cells"]:
+            src = img_src(c.get("grown_path")) if c.get("ok") else None
+            if src:
+                t = c.get("total_ms", 0)
+                cells.append(f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                             f'<div class="cell-time">{t/1000:.1f}s</div></td>')
+            else:
+                cells.append(f'<td class="cell-result"><div class="na">⚠<br>{c.get("error","")[:20]}</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>重绘分辨率对比报告 — steps=15</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: 130px; max-width: 150px; }}
+.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; }}
+img {{ border-radius: 4px; max-width: 130px; max-height: 160px; object-fit: contain; background: #f3f4f6; }}
+.cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.na {{ color: #d1d5db; font-size: 12px; padding: 40px 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 重绘分辨率对比报告</h1>
+<p class="subtitle">4图×5发型=20行 · 每行4分辨率(原图不缩放/896/768/640) · steps=15 · 热数据 · 80/80成功 · 峰值21.2GB · 0 OOM</p>
+<div class="legend">列标题下显示<b>平均总耗时</b>。横向滚动查看。原图列含图片名+发型名。每格下方为该次总耗时。</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_res_prompt.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""分辨率对比测试(新提示词版)：4图×5发型=20行，每行4种分辨率，steps=15。
+提示词固定为 "填充遮罩区域的头发"。
+热数据：预热1次+正式1次。
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+PROMPT = "填充遮罩区域的头发"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench7")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.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"),
+]
+RES_LIST = [("orig", "0"), ("896", "896"), ("768", "768"), ("640", "640")]
+RES_TITLES = ["原图(不缩放)", "896", "768", "640"]
+STEPS = 15
+
+
+def call(img_path, hair_num, redraw_max_side, save_grown=None, timeout=300):
+    data = {"hair_style": str(hair_num), "webui_steps": str(STEPS),
+            "redraw_max_side": str(redraw_max_side), "redraw_prompt": PROMPT}
+    t0 = time.perf_counter()
+    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=data, timeout=timeout)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:80], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "swap_ms": hs.get("swap_ms"),
+            "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:80], "wall": time.perf_counter() - t0}
+
+
+def main():
+    rows = []
+    total = len(IMGS) * len(HAIRSTYLES) * len(RES_LIST) * 2
+    idx = 0
+    for ilabel, ipath in IMGS:
+        for hnum, hkey, hname in HAIRSTYLES:
+            cells = []
+            for (rlabel, rval), rtitle in zip(RES_LIST, RES_TITLES):
+                idx += 1
+                print(f"[{idx}/{total}] 预热 {ilabel}|{hname}|{rtitle}", flush=True)
+                try:
+                    call(ipath, hnum, rval, timeout=120)
+                except Exception:
+                    pass
+                idx += 1
+                save = OUT / f"{ilabel}_{hkey}_{rlabel}.jpg"
+                print(f"[{idx}/{total}] 正式 {ilabel}|{hname}|{rtitle}", flush=True)
+                r = call(ipath, hnum, rval, save_grown=save, timeout=300)
+                r["res_label"] = rlabel; r["res_title"] = rtitle
+                r["grown_path"] = str(save) if r.get("ok") else None
+                status = f"{r.get('total_ms')}ms" if r.get("ok") else f"FAIL:{r.get('error','')[:30]}"
+                print(f"    -> {status}", flush=True)
+                cells.append(r)
+            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({"res_titles": RES_TITLES, "prompt": PROMPT, "rows": rows}, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for row in rows for c in row["cells"] if c.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(rows)*len(RES_LIST)} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_steps_res.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""swap步数 + 重绘分辨率 对比测试（热数据）。
+
+每个组合: 预热1次(丢弃) + 正式测1次(取热数据)。
+B维度: steps=10/15/20 (分辨率固定896)
+C维度: 分辨率=640/896/1024 (steps固定15)
+4图×2发型=8组 × 6档 × 2次(预热+正式) = 96次
+"""
+import base64
+import json
+import os
+import time
+from pathlib import Path
+
+import requests
+
+API = "http://127.0.0.1:8187/api/v1/debug/grow-timing"
+TOKEN = "dev-shared-secret-2026"
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench2")
+OUT.mkdir(parents=True, exist_ok=True)
+
+IMGS = [
+    ("asdf", "/home/ubuntu/hair/image/asdf.jpg"),
+    ("qwer", "/home/ubuntu/hair/image/qwer.jpg"),
+    ("girl2", "/home/ubuntu/hair/image/girl_img/girl2.jpg"),
+    ("girl5", "/home/ubuntu/hair/image/girl_img/girl5.jpg"),
+]
+HAIRSTYLES = [(5, "wave", "波浪"), (3, "heart", "心形")]
+
+# B维度: swap步数对比 (分辨率固定896)
+B_STEPS = [10, 15, 20]
+# C维度: 重绘分辨率对比 (steps固定15)
+C_RES = [640, 896, 1024]
+
+
+def call(img_path, hair_num, webui_steps=None, redraw_max_side=None, save_grown=None):
+    """调调试接口。返回 dict。save_grown 非None时把结果图存到该路径。"""
+    data = {"hair_style": str(hair_num)}
+    if webui_steps is not None:
+        data["webui_steps"] = str(webui_steps)
+    if redraw_max_side is not None:
+        data["redraw_max_side"] = str(redraw_max_side)
+    t0 = time.perf_counter()
+    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=data, timeout=300)
+        wall = time.perf_counter() - t0
+        j = r.json()
+        if j.get("code") != 0:
+            return {"ok": False, "error": j.get("message", "")[:100], "wall": wall}
+        d = j["data"]
+        hs = d["per_hairstyle"][0]
+        if save_grown and hs.get("grown_b64"):
+            b = hs["grown_b64"].split(",")[1] if "," in hs["grown_b64"] else hs["grown_b64"]
+            with open(save_grown, "wb") as gf:
+                gf.write(base64.b64decode(b))
+        return {
+            "ok": hs.get("ok", False), "wall": wall,
+            "total_ms": d["total_ms"], "ctx_ms": d["extract_context_ms"],
+            "mask_ms": hs.get("mask_ms"), "swap_ms": hs.get("swap_ms"),
+            "blend_ms": hs.get("blend_ms"), "comfy_ms": hs.get("comfyui_redraw_ms"),
+            "error": hs.get("error"),
+        }
+    except Exception as e:
+        return {"ok": False, "error": str(e)[:100], "wall": time.perf_counter() - t0}
+
+
+def main():
+    results = {"B_steps": [], "C_res": []}
+    total_calls = len(IMGS) * len(HAIRSTYLES) * (len(B_STEPS) + len(C_RES)) * 2
+    idx = 0
+
+    # ===== B维度: swap步数对比 (分辨率固定896) =====
+    print("\n===== B维度: swap步数对比 (分辨率=896) =====", flush=True)
+    for steps in B_STEPS:
+        print(f"\n--- steps={steps} ---", flush=True)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                # 预热(丢弃)
+                idx += 1
+                print(f"[{idx}/{total_calls}] 预热 {ilabel}|{hname}|steps={steps}", flush=True)
+                call(ipath, hnum, webui_steps=steps, redraw_max_side=896)
+                # 正式(热数据)
+                idx += 1
+                save = OUT / f"B_steps{steps}_{ilabel}_{hkey}.jpg"
+                print(f"[{idx}/{total_calls}] 正式 {ilabel}|{hname}|steps={steps}", flush=True)
+                r = call(ipath, hnum, webui_steps=steps, redraw_max_side=896, save_grown=save)
+                r["steps"] = steps; r["img"] = ilabel; r["hair"] = hkey; r["hair_name"] = hname
+                r["grown_path"] = str(save) if r.get("ok") else None
+                print(f"    -> total={r.get('total_ms')}ms swap={r.get('swap_ms')}ms comfy={r.get('comfy_ms')}ms ok={r.get('ok')}", flush=True)
+                results["B_steps"].append(r)
+
+    # ===== C维度: 重绘分辨率对比 (steps固定15) =====
+    print("\n===== C维度: 重绘分辨率对比 (steps=15) =====", flush=True)
+    for res in C_RES:
+        print(f"\n--- res={res} ---", flush=True)
+        for ilabel, ipath in IMGS:
+            for hnum, hkey, hname in HAIRSTYLES:
+                idx += 1
+                print(f"[{idx}/{total_calls}] 预热 {ilabel}|{hname}|res={res}", flush=True)
+                call(ipath, hnum, webui_steps=15, redraw_max_side=res)
+                idx += 1
+                save = OUT / f"C_res{res}_{ilabel}_{hkey}.jpg"
+                print(f"[{idx}/{total_calls}] 正式 {ilabel}|{hname}|res={res}", flush=True)
+                r = call(ipath, hnum, webui_steps=15, redraw_max_side=res, save_grown=save)
+                r["res"] = res; r["img"] = ilabel; r["hair"] = hkey; r["hair_name"] = hname
+                r["grown_path"] = str(save) if r.get("ok") else None
+                print(f"    -> total={r.get('total_ms')}ms swap={r.get('swap_ms')}ms comfy={r.get('comfy_ms')}ms ok={r.get('ok')}", flush=True)
+                results["C_res"].append(r)
+
+    with open(OUT / "results.json", "w", encoding="utf-8") as f:
+        json.dump(results, f, ensure_ascii=False, indent=2)
+    ok = sum(1 for r in results["B_steps"] + results["C_res"] if r.get("ok"))
+    print(f"\n✓ 完成: {ok}/{len(results['B_steps'])+len(results['C_res'])} 成功 -> {OUT/'results.json'}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

benchmark_steps_res_report.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""生成 swap步数 + 重绘分辨率 对比报告 HTML。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+OUT = Path("/home/ubuntu/hair/benchmark_out/bench2")
+RESULTS = OUT / "results.json"
+HTML = OUT / "report.html"
+
+
+def img_src(path):
+    if not path or not os.path.isfile(path):
+        return None
+    # benchmark_out/bench2/xxx.jpg -> bench2/xxx.jpg (报告在 static/ 下部署时调整)
+    p = str(path)
+    return "bench2/" + os.path.basename(p)
+
+
+def main():
+    d = json.load(open(RESULTS, encoding="utf-8"))
+    b_data = d["B_steps"]   # steps 对比
+    c_data = d["C_res"]     # 分辨率对比
+
+    # B维度聚合
+    by_steps = defaultdict(list)
+    for r in b_data:
+        by_steps[r["steps"]].append(r)
+    b_summary = []
+    for s in sorted(by_steps):
+        rs = by_steps[s]
+        b_summary.append({
+            "label": f"steps={s}", "n": len(rs),
+            "swap": sum(r["swap_ms"] for r in rs) // len(rs),
+            "total": sum(r["total_ms"] for r in rs) // len(rs),
+        })
+
+    # C维度聚合
+    by_res = defaultdict(list)
+    for r in c_data:
+        by_res[r["res"]].append(r)
+    c_summary = []
+    for res in sorted(by_res):
+        rs = by_res[res]
+        c_summary.append({
+            "label": f"res={res}", "n": len(rs),
+            "comfy": sum(r["comfy_ms"] for r in rs) // len(rs),
+            "total": sum(r["total_ms"] for r in rs) // len(rs),
+        })
+
+    # B维度明细行（每图每发型每步数）
+    b_rows = []
+    for r in sorted(b_data, key=lambda x: (x["img"], x["hair"], x["steps"])):
+        src = img_src(r.get("grown_path"))
+        b_rows.append(f"""<tr>
+<td>{r['img']}</td><td>{r['hair_name']}</td><td>{r['steps']}</td>
+<td>{r.get('swap_ms','?')}</td><td>{r.get('comfy_ms','?')}</td><td>{r.get('total_ms','?')}</td>
+<td>{f'<img src="{src}" loading="lazy">' if src else '⚠'}</td></tr>""")
+
+    # C维度明细行
+    c_rows = []
+    for r in sorted(c_data, key=lambda x: (x["img"], x["hair"], x["res"])):
+        src = img_src(r.get("grown_path"))
+        c_rows.append(f"""<tr>
+<td>{r['img']}</td><td>{r['hair_name']}</td><td>{r['res']}</td>
+<td>{r.get('swap_ms','?')}</td><td>{r.get('comfy_ms','?')}</td><td>{r.get('total_ms','?')}</td>
+<td>{f'<img src="{src}" loading="lazy">' if src else '⚠'}</td></tr>""")
+
+    def bar_row(label, val, max_val, color, unit="ms"):
+        pct = max(1, val / max_val * 100) if max_val else 0
+        return f'<div class="step-row"><div class="step-name">{label}</div>' \
+               f'<div class="step-bar-wrap"><div class="step-bar {color}" style="width:{pct}%">{val}{unit}</div></div>' \
+               f'<div class="step-time">{val}{unit}</div></div>'
+
+    # B维度汇总条形图
+    b_max_swap = max(s["swap"] for s in b_summary)
+    b_bars = "".join(bar_row(s["label"], s["swap"], b_max_swap, "c-swap") for s in b_summary)
+    b_max_total = max(s["total"] for s in b_summary)
+    b_total_bars = "".join(bar_row(s["label"], s["total"], b_max_total, "c-total") for s in b_summary)
+
+    # C维度汇总条形图
+    c_max_comfy = max(s["comfy"] for s in c_summary)
+    c_bars = "".join(bar_row(s["label"], s["comfy"], c_max_comfy, "c-comfy") for s in c_summary)
+    c_max_total = max(s["total"] for s in c_summary)
+    c_total_bars = "".join(bar_row(s["label"], s["total"], c_max_total, "c-total") for s in c_summary)
+
+    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>swap步数 + 重绘分辨率 对比报告</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, "Segoe UI", sans-serif; background: #f5f5f5; padding: 16px; color: #333; }}
+h1 {{ font-size: 20px; margin-bottom: 4px; }}
+h2 {{ font-size: 16px; margin: 20px 0 10px; }}
+.subtitle {{ color: #888; font-size: 12px; margin-bottom: 14px; }}
+.card {{ background: #fff; border-radius: 10px; box-shadow: 0 1px 4px rgba(0,0,0,.06); margin-bottom: 16px; overflow: hidden; }}
+.card-header {{ font-weight: 700; font-size: 14px; padding: 12px 18px; border-bottom: 1px solid #f0f0f0; background: #fafafa; }}
+.card-body {{ padding: 18px; }}
+.summary-grid {{ display: grid; grid-template-columns: 1fr 1fr; gap: 16px; }}
+.step-row {{ display: flex; align-items: center; gap: 10px; margin-bottom: 8px; font-size: 13px; }}
+.step-name {{ width: 100px; flex-shrink: 0; font-weight: 600; }}
+.step-bar-wrap {{ flex: 1; background: #f3f4f6; border-radius: 4px; height: 24px; min-width: 200px; }}
+.step-bar {{ height: 100%; border-radius: 4px; display: flex; align-items: center; padding-left: 8px; color: #fff; font-size: 11px; font-weight: 600; min-width: 2px; }}
+.step-time {{ width: 70px; text-align: right; font-weight: 600; flex-shrink: 0; font-variant-numeric: tabular-nums; }}
+.c-swap {{ background: #f59e0b; }} .c-comfy {{ background: #ef4444; }} .c-total {{ background: #2563eb; }}
+table {{ border-collapse: collapse; width: 100%; font-size: 12px; }}
+th, td {{ border: 1px solid #eee; padding: 5px 8px; text-align: center; }}
+th {{ background: #f9fafb; font-weight: 600; position: sticky; top: 0; }}
+td img {{ max-height: 100px; max-width: 80px; border-radius: 4px; }}
+.scroll {{ max-height: 400px; overflow: auto; }}
+.note {{ background: #fef3c7; border-radius: 8px; padding: 10px 14px; font-size: 12px; color: #92400e; margin-top: 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 swap步数 + 重绘分辨率 对比报告</h1>
+<p class="subtitle">4图(asdf/qwer/girl2/girl5) × 2发型(波浪/心形) · 热数据(预热后取第2次) · 48/48成功 · 峰值20.6GB · 0 OOM</p>
+
+<div class="note">💡 结论速览: B维度 steps 10→20 swap从3.0s→3.9s(每步省~90ms)；C维度 res 640比896省3s(comfy 4.3s vs 7.3s)，1024与896接近。</div>
+
+<h2>B维度：swap步数对比（分辨率固定896）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">swap 耗时（越低越快）</div><div class="card-body">{b_bars}</div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body">{b_total_bars}</div></div>
+</div>
+
+<h2>C维度：重绘分辨率对比（steps固定15）</h2>
+<div class="summary-grid">
+<div class="card"><div class="card-header">ComfyUI重绘 耗时（越低越快）</div><div class="card-body">{c_bars}</div></div>
+<div class="card"><div class="card-header">总耗时（越低越快）</div><div class="card-body">{c_total_bars}</div></div>
+</div>
+
+<h2>B维度明细（每图每发型每步数）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>steps</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+{"".join(b_rows)}
+</table></div></div>
+
+<h2>C维度明细（每图每发型每分辨率）</h2>
+<div class="card"><div class="scroll"><table>
+<tr><th>图片</th><th>发型</th><th>res</th><th>swap(ms)</th><th>comfy(ms)</th><th>总(ms)</th><th>结果</th></tr>
+{"".join(c_rows)}
+</table></div></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()

comfyui.service

@@ -0,0 +1,15 @@
+[Unit]
+Description=ComfyUI (127.0.0.1:8188)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=ubuntu
+WorkingDirectory=/home/ubuntu/ComfyUI
+ExecStart=/home/ubuntu/ComfyUI/venv/bin/python main.py --listen 127.0.0.1 --port 8188 --cache-classic --fast
+Restart=on-failure
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

docs/iface11_hair_test_run/response_meta.json

@@ -0,0 +1,65 @@
+{
+  "code": 0,
+  "message": "success",
+  "request_id": "mock-request-id",
+  "data": {
+    "hairline_id": "chang_zhixian",
+    "gen_backend": "swaphair",
+    "hairgrow_strength": 0.75,
+    "is_hr": false,
+    "seg_model": "segformer",
+    "mask_type": "pushed",
+    "erode_cm": 0.6,
+    "swap_mode": "ext_mask",
+    "blend_method": "multiband",
+    "edge_erode_px": 3,
+    "mb_levels": 5,
+    "hairline_push_cm": 1.0,
+    "hairline_edge": "column",
+    "denoising_strength": 0.6,
+    "color_match": true,
+    "color_match_strength": 1.0,
+    "mb_feather_px": 1,
+    "transition_band_px": -1,
+    "inpainting_fill": 1,
+    "mask_blur": 11,
+    "mask_dilate_scale": 1.0,
+    "px_per_cm": 47.5311,
+    "erode_px": 29,
+    "hair_pixels": 186798,
+    "closed_pixels": 191712,
+    "mask_pixels": 140299,
+    "image_size": {
+      "width": 1257,
+      "height": 1495
+    },
+    "timings_ms": {
+      "mask": 1462,
+      "swap": 5596,
+      "blend": 220
+    },
+    "redraw": {
+      "enabled": false
+    },
+    "_rid": "bc7205a4",
+    "steps": {
+      "input_base64": "<omitted 427407 chars>",
+      "baseline_overlay_base64": "<omitted 435895 chars>",
+      "upper_overlay_base64": "<omitted 384479 chars>",
+      "hair_seg_overlay_base64": "<omitted 428039 chars>",
+      "top_fill_overlay_base64": "",
+      "closed_overlay_base64": "",
+      "hairline_overlay_base64": "<omitted 445467 chars>",
+      "pushed_overlay_base64": "<omitted 452803 chars>",
+      "mask_overlay_base64": "<omitted 420375 chars>",
+      "mask_base64": "<omitted 8026 chars>",
+      "swap_raw_base64": "<omitted 340791 chars>",
+      "hard_paste_base64": "<omitted 420679 chars>",
+      "alpha_base64": "<omitted 7762 chars>",
+      "final_base64": "<omitted 415811 chars>",
+      "redraw_band_overlay_base64": "",
+      "redraw_a_base64": "",
+      "redraw_c_base64": ""
+    }
+  }
+}

docs/iface3_implementation.md

@@ -0,0 +1,177 @@
+# 接口3 B端生发 — 实现文档
+
+> 文档日期：2026-07-18
+
+---
+
+## 一、接口概述
+
+**接口3** 是 B端（医生/操作端）生发接口。医生在用户照片上手动用马克笔画出发际线后，只需上传这一张划线图，系统自动检测划线 → 生成遮罩 → 送 ComfyUI 生发，返回「植发3个月」效果图。
+
+**与接口2 的核心区别**：
+
+| 特性 | 接口2（C端生发） | 接口3（B端生发） |
+|------|----------------|----------------|
+| 输入 | 原始照片 | 划线图（含手绘线） |
+| 发际线来源 | 系统按发型模板自动生成 | 医生手绘标注 |
+| 发型类型 | ellipse/flower/heart/straight/wave | custom（自定义） |
+| 中间步骤 | extract_context + swapHair + ComfyUI重绘 | 划线检测 + 遮罩 + ComfyUI生发 |
+| 是否调 change_hair | 是（女性流程） | 否 |
+| ComfyUI 工作流 | 0716add-hair-api.json（重绘） | add_hair.json（生发） |
+| 典型耗时 | ~11s | ~6-8s |
+
+---
+
+## 二、接口定义
+
+### 路由
+
+```
+POST /api/v1/hair/grow-b
+```
+
+### 入参
+
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| `marked_image_file` | UploadFile | 三选一 | 划线图片文件（JPG/PNG） |
+| `marked_image_url` | str | 三选一 | 划线图片 URL |
+| `marked_image_base64` | str | 三选一 | 划线图片 base64 |
+| `use_mask` | bool | 否（默认True） | 是否自动检测划线并建遮罩。False时跳过检测，直接送划线图 |
+| `prompt` | str | 否 | ComfyUI 提示词，默认"补充遮罩区域的头发，加一点美颜" |
+
+### 返回
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "hair_growth_image_base64": "iVBORw0KGgo...（生发图 JPG base64）",
+    "hairline_type": "custom"
+  }
+}
+```
+
+错误码：
+- `1001`: 无法识别人像 / 未检测到发际线划线
+- `1007`: 处理失败
+- `1008`: 图片格式不支持
+
+---
+
+## 三、完整调用链
+
+```
+POST /api/v1/hair/grow-b
+│
+├─ app.py hair_grow_b()                          [app.py:929]
+│   ├─ resolve_image_bytes() → marked_raw       解析图片（file/url/base64三选一）
+│   ├─ cv2.imdecode → marked_bgr                解码为 BGR
+│   └─ run_in_threadpool(generate_grow_b, ...)
+│
+├─ service.py generate_grow_b(marked_bgr, use_mask, prompt)  [service.py:381]
+│   │
+│   ├─ 步骤1：人脸检测 + 头发分割（仅 use_mask=True 时）
+│   │   ├─ get_landmarker().detect(rgb)          MediaPipe 478点人脸检测
+│   │   │   → landmarks（无人脸返回 no_face）
+│   │   ├─ get_parser().parse(rgb)               SegFormer 面部分割（CPU ~0.9s）
+│   │   │   → parse_map（int label map）
+│   │   │
+│   ├─ 步骤2：手绘发际线检测（仅 use_mask=True 时）
+│   │   ├─ detect_marker_hairline(marked_bgr, landmarks, parse_map)
+│   │   │   │                                   [marker_detect.py:41]
+│   │   │   ├─ forehead_upper_region(landmarks)  额头上部 ROI
+│   │   │   ├─ head_silhouette(parse_map)         头部轮廓 ROI
+│   │   │   ├─ _blackhat(gray)                    黑帽变换（响应比邻域暗的细结构）
+│   │   │   ├─ _snap_anchor(bh, 左鬓角21)         左锚点吸附
+│   │   │   ├─ _snap_anchor(bh, 右鬓角251)        右锚点吸附
+│   │   │   ├─ route_through_array(cost, 左, 右)  Dijkstra最小代价路径
+│   │   │   └→ path (N,2) row,col（拒识返回 None → no_line）
+│   │   │
+│   │   ├─ path_to_curve_mask(path)              路径→曲线mask（uint8 0/255）
+│   │   └─ mask_from_curve(curve_mask, landmarks, parse_map)
+│   │       │                                   [mask.py]
+│   │       ├─ _above_curve_region(curve_mask)    曲线以上区域
+│   │       ├─ cv2.morphologyEx(闭运算)           填洞
+│   │       ├─ 最大连通域
+│   │       └─ 高斯羽化                            → mask (uint8 0-255)
+│   │
+│   ├─ 步骤3：合成 RGBA PNG
+│   │   ├─ compose_comfy_rgba(marked_bgr, mask)  RGB=原图，alpha=255×(1-mask)
+│   │   └─ PNG 编码                               → rgba_png_bytes
+│   │
+│   └─ 步骤4：ComfyUI 生发
+│       └─ comfyui.run(rgba_png_bytes, prompt)   [comfyui.py:87]
+│           ├─ 上传图片到 ComfyUI /upload/image
+│           ├─ 加载工作流 add_hair.json
+│           ├─ 替换节点26输入图 + 节点6随机seed + 节点60提示词
+│           ├─ POST /prompt 提交工作流
+│           ├─ 轮询 /history/{prompt_id}（间隔0.2s）
+│           └─ GET /view 取回输出 PNG              → grown_png
+│
+└─ 返回 {"grown_png": bytes, "status": "ok"}
+```
+
+---
+
+## 四、用到的模型和外部服务
+
+| 模型/服务 | 用途 | 位置 | 设备 |
+|----------|------|------|------|
+| **FaceLandmarker** (MediaPipe) | 478点人脸检测 | hairline/face_landmarks.py | CPU |
+| **FaceParser** (SegFormer) | 面部分割（hair/skin/...） | hairline/face_parsing.py | CPU (5090不兼容cu121) |
+| **ComfyUI** (Flux-2) | 生发图生成 | hairline/comfyui.py → :8188 | GPU |
+
+**注意**：接口3 **不调用** change_hair 服务（:8801），不需要 swapHair。这是它与接口2女性流程的关键区别。
+
+---
+
+## 五、核心算法：手绘发际线检测
+
+### 5.1 为什么不用简单阈值？
+
+手绘马克笔线条的灰度值与皮肤阴影、抬头纹等重叠，全局阈值无法区分。采用**黑帽变换 + Dijkstra最小路径**方案。
+
+### 5.2 黑帽变换（Black Hat）
+
+```python
+kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+bh = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)
+```
+
+黑帽 = 闭运算 − 原图，响应"比局部邻域暗的细结构"（即马克笔线条），对抬头纹/眉毛/发丝鲁棒。
+
+### 5.3 Dijkstra 最小代价路径
+
+1. **ROI 限定**：额头上部 ∩ 头部轮廓（排除背景）
+2. **锚点**：左鬓角(21) / 右鬓角(251) MediaPipe 关键点
+3. **代价图**：`cost = (bh.max() - bh) + 1.0`，ROI外设 1e6
+4. **路径**：`route_through_array(cost, 左锚, 右锚)` — skimage 的 Dijkstra 实现
+
+### 5.4 拒识机制
+
+路径平均黑帽响应 < 8.0 → 判定"未画线"，返回 `no_line`。
+
+---
+
+## 六、与接口1、接口2 的对比
+
+| 维度 | 接口1 | 接口2 | 接口3 |
+|------|-------|-------|-------|
+| 功能 | 四庭七眼测量 | C端生发（5种发际线） | B端生发（手绘线） |
+| 路由 | /api/v1/face/measure | /api/v1/hair/grow | /api/v1/hair/grow-b |
+| 输入 | 正面照 | 正面照 | 划线图 |
+| MediaPipe | ✅ | ✅ | ✅ |
+| SegFormer | ✅ | ✅ | ✅ |
+| change_hair | ❌ | ✅（女性） | ❌ |
+| ComfyUI | ❌ | ✅（Flux-2重绘） | ✅（Flux-2生发） |
+| 典型耗时 | ~2s | ~11s | ~6-8s |
+| ComfyUI工作流 | — | 0716add-hair-api.json | add_hair.json |
+
+---
+
+## 七、测试
+
+- **测试页面**：[static/test_interface3.html](file:///home/ubuntu/hair/static/test_interface3.html)
+- **测试图片**：[image/girl_img/girl13.jpg](file:///home/ubuntu/hair/image/girl_img/girl13.jpg)（需手动在图上画发际线后作为划线图上传）

docs/发际线生发遮罩算法_pushed模式.md

@@ -1,12 +1,12 @@
 # 发际线生发遮罩算法（pushed 模式）
 
 > 对应接口11 `/api/v1/hairline/grow`、接口12 `/api/v1/hairline/grow_v2`。
-> 遮罩算法固定为 pushed，融合算法固定为 multiband（已移除其他选项）。
-> 代码：`face_analysis/hairline_grow.py`（`_extract_hairline` / `_pushed_mask` / `compute_mask`）。
+> 遮罩算法固定为 pushed；融合算法默认 multiband（多频段金字塔），接口11 可切换 seamless/two_stage/feather。
+> 代码：`face_analysis/hairline_grow.py`（`_extract_hairline` / `_pushed_mask` / `compute_mask` / `_composite`）。
 
 ## 概述
 
-pushed 是发际线生发的**唯一**遮罩算法，multiband（多频段金字塔）是**唯一**融合算法。它从头发分割结果中提取「头发/皮肤交界线」（发际线），以眉心为圆心逐点径向外推一段距离，与 baseline 组成闭合区域作为最终遮罩。这样遮罩顶部会覆盖现有头发下沿一小段，贴回生发结果时顶部与真头发重叠、过渡自然。
+pushed 是发际线生发的**唯一**遮罩算法。融合算法默认 multiband（多频段金字塔），接口11 暴露 `blend_method` 可切换为 seamless(泊松)/two_stage(泊松→多频段两段式)/feather(羽化)，便于对比调优。它从头发分割结果中提取「头发/皮肤交界线」（发际线），以眉心为圆心逐点径向外推一段距离，与 baseline 组成闭合区域作为最终遮罩。这样遮罩顶部会覆盖现有头发下沿一小段，贴回生发结果时顶部与真头发重叠、过渡自然。
 
 > 接口12 `/api/v1/hairline/grow_v2` 只需传 `image` + `hairline_id`，遮罩和融合全部固定，无需任何算法选择参数。
 
@@ -57,14 +57,48 @@ segformer（默认）或 bisenet 得到的头发二值掩码。
 
 ## 关键参数
 
-遮罩算法（pushed）和融合算法（multiband）已固定，接口不再暴露选择参数。可调的只有：
+遮罩算法（pushed）固定。融合算法接口11 通过 `blend_method` 可切换（默认 multiband），其余融合参数均可调：
 
 | 参数 | 默认 | 说明 |
 |---|---|---|
 | `hairline_push_cm` | 1.0 | 内轮廓径向外推距离（厘米），= push_px / px_per_cm。`px_per_cm` 由虹膜直径标定 |
 | `hairline_edge` | `column` | 兼容保留的入参；内轮廓提取（轮廓+内侧判定）不再按它分支，取值不影响结果 |
 | `mb_levels` | 5 | 多频段金字塔层数（2~6，越大低频色差抹得越宽）|
+| `blend_method` | `multiband` | 接缝融合：multiband(多频段金字塔) / seamless(泊松) / two_stage(泊松→多频段,大色差) / feather(羽化) / alpha_gradient。接口12 固定 multiband |
+| `color_match` | `true` | 融合前 Reinhard 颜色迁移消除整体色差（multiband/feather/alpha_gradient 生效；seamless/two_stage 自带调色故跳过）|
+| `color_match_strength` | 1.0 | 颜色迁移强度（0~1，<1 只迁移部分，防 Reinhard 过度改色）|
+| `mb_feather_px` | 1 | 多频段最细层掩码轻羽化像素（0=不羽化），消除发丝边缘锯齿 |
+| `transition_band_px` | -1 | keep-region 过渡带边距（-1=自动按层数 `2**n`；>=0 用绝对像素与层数解耦）|
+| `edge_erode_px` | 3 | 贴图前遮罩内缩像素（防边缘露皮/光晕）|
 | `erode_cm` | 0.6（接口12 固定）| baseline 参考内缩距离，对 pushed 影响很小 |
+| `redraw` | `false` | 发际线带重绘开关：开启后用 final（④融合图）在「外推线↔发际线」带重绘，swapHair/Flux-2 两路对比，结果单独展示（不替换 final）|
+| `inpainting_fill` | 1 | change_hair 重绘填充：0=保留原图(治染绿) / 1=填充噪声(默认) / 2=纯色 / 3=潜变量噪声 |
+| `mask_blur` | 11 | change_hair 遮罩边缘模糊像素（越大颜色越易从边缘渗透）|
+| `mask_dilate_scale` | 1.0 | change_hair 遮罩膨胀核缩放（1.0=原始，<1 收缩防越界）|
+| `comfyui_prompt` | `null` | redraw Flux-2 路提示词，null 用默认「补充遮罩区域的头发，加一点美颜」|
+
+> 接口12 `/api/v1/hairline/grow_v2` 只需传 `image` + `hairline_id`，遮罩和融合全部用默认值（multiband + color_match=true），不暴露算法选择参数。
+
+### 融合方法选择建议
+
+- **multiband**（默认）：常规首选。低频抹色差、高频保发丝。需配合 `color_match=true` 消除整体色差。
+- **two_stage**：生成图与原图色差大时用。先泊松克隆统一色调，再多频段贴细节，兼顾调色与保发丝。比纯 seamless 更不易溢色。
+- **seamless**：纯泊松梯度域调和，色调统一干净，但可能整体改色/边缘溢色。
+- **feather / alpha_gradient**：单层 alpha 过渡，最轻量，但过渡带内色差不会被抹平，仅适合色差极小的场景。
+
+## 发际线带重绘（redraw，接口11 可选）
+
+`redraw=true` 时，在主流程（④接缝融合 final）之后额外跑一条重绘分支，结果单独展示（`steps.redraw_a` / `redraw_c`），**不替换** final。
+
+**重绘区域** = ①-g 外推发际线（`outer_pts`）与 ①-f 发际线（`inner_pts`）两条折线端点相连组成的带状闭合区域（宽度 ≈ `hairline_push_cm`，只覆盖发际线交界处）。
+
+**两路后端对比**（输入图 + 融合基底都用 final）：
+- **swapHair 路**（`redraw_a`）：final + 带遮罩调 change_hair → final 走 multiband 融合
+- **Flux-2 路**（`redraw_c`）：final + 带遮罩调 ComfyUI（`hair_repaint.json` 工作流）→ final 走 multiband 融合。Flux-2 经 reference latent + ColorMatch 双重保色，**不易染绿**
+
+> `inpainting_fill` / `mask_blur` / `mask_dilate_scale` 透传 change_hair 服务端（仅影响 swapHair 路）。`comfyui_prompt` 仅影响 Flux-2 路。
+> 两路独立容错：任一路失败只跳过该路，不影响另一路和主 final。
+> ⚠️ Flux-2 路需 ComfyUI（8188）在跑；swapHair 路需 change_hair（8801）在跑。
 
 ## 与旧模式（eroded/closed，已移除）的区别
 

docs/接口11_hair_test运行记录.md

@@ -0,0 +1,182 @@
+# 接口11 运行记录 — `image/hair_test.jpg`
+
+> 实测时间：2026-07-15  
+> 调用：`POST http://127.0.0.1:8187/api/v1/hairline/grow`  
+> 鉴权：`X-Internal-Token: dev-shared-secret-2026`  
+> 输入图：`image/hair_test.jpg`（1257×1495）  
+> `hairline_id`：`chang_zhixian`（直线）；其余全部走接口默认值  
+> 业务结果：`code=0`，`rid=bc7205a4`  
+> 产物目录：`docs/iface11_hair_test_run/`
+
+---
+
+## 1. 本次调用用到的全部默认参数
+
+未在 Form 里显式传的参数均取 `app.py` / `generate_hairline_grow` 默认值；下表即本次实际生效值。
+
+| 参数 | 本次值 | 说明 |
+|------|--------|------|
+| `hairline_id` | `chang_zhixian` | **必填**。发际线类型 = change_hair 的 `hair_id`（直线） |
+| `gen_backend` | `swaphair` | 生成后端：换发型 LoRA |
+| `hairgrow_strength` | `0.75` | 仅 `hairgrow` 后端用；本次未走该路径 |
+| `is_hr` | `false` | 高清关闭（576×768 档，非 1152×1536） |
+| `seg_model` | `segformer` | 头发分割模型 |
+| `erode_cm` | `0.6` | baseline 参考内缩（cm）；pushed 下影响很小 |
+| `hairline_push_cm` | `1.0` | 发际线内轮廓径向外推距离（cm） |
+| `hairline_edge` | `column` | 兼容入参；当前内轮廓提取不再按它分支 |
+| `swap_mode` | `ext_mask` | 把 pushed 遮罩作为 `ext_mask` 传给 swapHair |
+| `edge_erode_px` | `3` | 贴图前遮罩内缩像素 |
+| `denoising_strength` | `0.6` | 换发型 webui 重绘强度 |
+| `mb_levels` | `5` | 多频段金字塔层数 |
+| `blend_method` | `multiband` | 接缝融合：多频段金字塔 |
+| `color_match` | `true` | 融合前 Reinhard 颜色迁移 |
+| `color_match_strength` | `1.0` | 颜色迁移强度（全迁移） |
+| `mb_feather_px` | `1` | 多频段最细层掩码轻羽化 |
+| `transition_band_px` | `-1` | keep-region 过渡带：自动按层数 `2**n` |
+| `redraw` | `false` | 发际线带重绘关闭 |
+| `inpainting_fill` | `1` | change_hair 填充噪声 |
+| `mask_blur` | `11` | change_hair 遮罩边缘模糊像素 |
+| `mask_dilate_scale` | `1.0` | change_hair 遮罩膨胀缩放 |
+| `comfyui_prompt` | `null` | 仅 `redraw`+Flux-2 路用；本次未用 |
+| `mask_type` | `pushed`（固定） | 代码写死，不可选 |
+
+图片入参：仅传了 `image_file`（三选一中的文件上传）。
+
+---
+
+## 2. 返回元数据（无 base64）
+
+| 字段 | 值 |
+|------|-----|
+| `px_per_cm` | 47.5311（虹膜直径标定） |
+| `erode_px` | 29（≈ 0.6cm × px_per_cm） |
+| `hair_pixels` | 186798 |
+| `closed_pixels` | 191712 |
+| `mask_pixels` | 140299 |
+| `image_size` | 1257 × 1495 |
+| `timings_ms.mask` | 1462 |
+| `timings_ms.swap` | 5596 |
+| `timings_ms.blend` | 220 |
+| `redraw.enabled` | false |
+| 总耗时（curl） | ≈ 7.4 s |
+
+完整精简 JSON：`docs/iface11_hair_test_run/response_meta.json`  
+完整原始响应（含 base64）：`docs/iface11_hair_test_run/response.json`
+
+---
+
+## 3. 管线分步说明与产物
+
+管线：① pushed 遮罩 → ② swapHair 生成 → ③ 硬贴回 → ④ multiband 融合。  
+各步图保存在 `docs/iface11_hair_test_run/steps/`。
+
+### ①-a 发际线分割线（baseline）
+
+- **做什么**：MediaPipe 关键点连成眉骨折线（中心为 151 眉心），并向左右边缘水平延长。
+- **图**：[`steps/baseline_overlay.jpg`](iface11_hair_test_run/steps/baseline_overlay.jpg)
+- **含义**：黄线 = baseline；151 中心点为后续径向外推圆心。
+
+### ①-b 分割线上半区（upper）
+
+- **做什么**：baseline 折线以上的多边形区域，作为后续裁剪范围。
+- **图**：[`steps/upper_overlay.jpg`](iface11_hair_test_run/steps/upper_overlay.jpg)
+- **含义**：青 = 上半区。
+
+### ①-c 头发分割（hair_seg）
+
+- **做什么**：SegFormer 得到头发二值掩码。
+- **图**：[`steps/hair_seg_overlay.jpg`](iface11_hair_test_run/steps/hair_seg_overlay.jpg)
+- **含义**：绿 = 原始头发像素（本次 `hair_pixels=186798`）。
+
+### ①-d / ①-e（旧 eroded/closed 中间步）
+
+- pushed 模式**不走**这两步；返回字段为空字符串。
+- `top_fill_overlay` / `closed_overlay`：本次无图。
+
+### ①-f 头发内轮廓线（hairline）
+
+- **做什么**：取头发朝脸一侧的内轮廓（额头弧 + 两侧到下颌），有序折线。
+- **图**：[`steps/hairline_overlay.jpg`](iface11_hair_test_run/steps/hairline_overlay.jpg)
+- **含义**：绿 = 内轮廓；黄 = baseline。
+
+### ①-g 外推发际线（pushed）
+
+- **做什么**：以眉心 151 为圆心，内轮廓逐点向外推 `hairline_push_cm=1.0`（≈ 47.5 px），与 baseline 组闭合区域。
+- **图**：[`steps/pushed_overlay.jpg`](iface11_hair_test_run/steps/pushed_overlay.jpg)
+- **含义**：青 = 外推线；红 = 外推遮罩区域。
+
+### ① 最终遮罩
+
+- **叠加图**：[`steps/mask_overlay.jpg`](iface11_hair_test_run/steps/mask_overlay.jpg) — 红 = 遮罩区（贴回/生成区）
+- **纯遮罩**：[`steps/mask.png`](iface11_hair_test_run/steps/mask.png) — 白 = 生成/贴回区  
+- 本次 `mask_pixels=140299`；贴图前再内缩 `edge_erode_px=3`。
+
+### ② 生成全帧（swap_raw）
+
+- **做什么**：`gen_backend=swaphair` + `swap_mode=ext_mask`，把遮罩交给 change_hair（`:8801`），LoRA=`chang_zhixian`，`denoising_strength=0.6`。
+- **图**：[`steps/swap_raw.jpg`](iface11_hair_test_run/steps/swap_raw.jpg)
+- **含义**：生成结果已与原图同分辨率对齐；耗时约 5.6 s。
+
+### ③ 严格按遮罩贴回（hard_paste）
+
+- **做什么**：遮罩内用生成图，遮罩外保持原图，无融合。
+- **图**：[`steps/hard_paste.jpg`](iface11_hair_test_run/steps/hard_paste.jpg)
+- **含义**：用于对比接缝融合前后差异。
+
+### ④ 融合权重 alpha + 最终结果
+
+- **做法**：`blend_method=multiband`，`mb_levels=5`，`color_match=true`（强度 1.0），`mb_feather_px=1`。
+- **alpha**：[`steps/alpha.png`](iface11_hair_test_run/steps/alpha.png) — 白 = 更多采用生成图
+- **最终输出**：[`steps/final.jpg`](iface11_hair_test_run/steps/final.jpg)（副本：[`final.jpg`](iface11_hair_test_run/final.jpg)）
+- **输入对照**：[`steps/input.jpg`](iface11_hair_test_run/steps/input.jpg)
+
+### ⑤ 发际线带重绘（本次关闭）
+
+`redraw=false`，故 `redraw_band_overlay` / `redraw_a` / `redraw_c` 均为空。
+
+---
+
+## 4. 最终输出
+
+**主结果文件**：[`docs/iface11_hair_test_run/final.jpg`](iface11_hair_test_run/final.jpg)
+
+含义：同一人、同一发型观感下，按直线发际线类型（`chang_zhixian`）压低发际线后的合成图；遮罩外像素保持原图不动。
+
+---
+
+## 5. 复现命令
+
+```bash
+curl -sS -X POST "http://127.0.0.1:8187/api/v1/hairline/grow" \
+  -H "X-Internal-Token: dev-shared-secret-2026" \
+  -F "image_file=@image/hair_test.jpg" \
+  -F "hairline_id=chang_zhixian" \
+  -o docs/iface11_hair_test_run/response.json
+```
+
+（其余参数全部省略即可走默认值。）
+
+---
+
+## 6. 产物清单
+
+```
+docs/接口11_hair_test运行记录.md          ← 本文档
+docs/iface11_hair_test_run/
+  final.jpg                               ← 最终结果
+  response.json                           ← 完整 API 响应（含 base64）
+  response_meta.json                      ← 去掉大图的元数据
+  steps/
+    input.jpg
+    baseline_overlay.jpg
+    upper_overlay.jpg
+    hair_seg_overlay.jpg
+    hairline_overlay.jpg
+    pushed_overlay.jpg
+    mask_overlay.jpg
+    mask.png
+    swap_raw.jpg
+    hard_paste.jpg
+    alpha.png
+    final.jpg
+```

docs/接口文档.md

@@ -20,7 +20,6 @@
 | 3 B 端生发 | POST | `/api/v1/hair/grow-b` |
 | 4 用户特征 | POST | `/api/v1/face/features` |
 | 5 发际线 PNG 生成 | POST | `/api/v1/hairline/generate` |
-| 7 C 端生发 v2 | POST | `/api/v1/hair/grow-v2` |
 
 ---
 
@@ -87,6 +86,7 @@
 | 1006 | 文件超出大小限制 | 单文件超过 1 MB |
 | 1007 | 图片参数错误 | file / url / base64 未传，或同时传了多个（三者严格互斥） |
 | 1008 | 图片格式不支持 | 非 JPG / PNG |
+| 1009 | 未授权 | 缺少或错误的 `X-Internal-Token`（`/api/*` 路径鉴权） |
 
 ---
 
@@ -109,6 +109,8 @@
 | four_courts | object | 四庭数据，见下表 |
 | seven_eyes | object | 七眼数据，见下表 |
 | landmarks | object | 关键分界点坐标（头顶 / 发际线 / 眉心 / 鼻翼下缘 / 下巴尖），原图像素坐标 |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 `four_courts`（四庭，自上而下）：
 
@@ -209,8 +211,12 @@
 | annotated_image_url | string | 标注图层 PNG URL（透明底，仅标注线/文字，不含人物） |
 | face_total_height_cm | number | 面部总高度（cm）= 上庭 + 中庭 + 下庭（**不含顶庭**） |
 | four_courts | object | 三庭数据（上/中/下庭，各含 cm 与 ratio；**无顶庭**） |
-| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距，各含 cm 与 ratio） |
+| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + **eye2~eye6** 共 5 段宽度） |
 | landmarks | object | 四个关键点像素坐标（发际线/眉心/鼻翼下缘/下巴尖） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
+
+> 接口6 是**三庭五眼**：`four_courts`/`landmarks` 不含顶庭与头顶点（无 `top_court_cm`/`hair_top`）；`seven_eyes` 只含 **eye2~eye6**（左脸颊/左眼/两眼间距/右眼/右脸颊，5 段），**无 eye1/eye7**（耳外段需头发轮廓端线，仅接口1 有）。
 
 ### 响应示例
 
@@ -228,7 +234,8 @@
     },
     "seven_eyes": {
       "eye_width_cm": 3.44, "face_width_cm": 24.08, "inter_eye_distance_cm": 3.44,
-      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 }
+      "ratios": { "eye_width": 0.143, "inter_eye_distance": 0.143 },
+      "eye2": 3.0, "eye3": 3.44, "eye4": 3.44, "eye5": 3.44, "eye6": 3.0
     },
     "landmarks": {
       "hairline": { "x": 540, "y": 430 },
@@ -244,10 +251,10 @@
 
 ## 接口 2：C 端生发接口
 
-**说明**：输入用户正面照 + 性别 + 发型序号（可多选），按指定发际线类型渲染预览图 + 生发图。
+**说明**：输入用户正面照 + 性别 + 发型序号（可多选），按指定发际线类型渲染发际线曲线透明 PNG + 生发图。
 
-> **每个方案返回两张图**：`image_url`=「原照片 + 发际线曲线叠加的**预览图**」；`grown_image_url`=
-> 经 ComfyUI/Flux 的「植发 3 个月**生发后图片**」。两者均已实现，实现简述见 [`实现说明.md`](实现说明.md)。
+> **每个方案返回两张图**：`image_url`=「发际线曲线**透明 PNG**（仅白色曲线，透明底，需叠加原图显示）」；`grown_image_url`=
+> 经 ComfyUI/Flux 的「植发 3 个月**生发后图片**」（完整人像照片）。两者均已实现，实现简述见 [`实现说明.md`](实现说明.md)。
 
 **请求**：`POST /api/v1/hair/grow`
 
@@ -261,6 +268,7 @@
 | hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），最多不超过该性别的预设数。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界/非法返回 `1007` |
 | beauty_enabled | bool | 否 | 生发图是否带美颜效果，默认 false（当前阶段不生效） |
 | use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比 |
+| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
 
 ### 输出（data）
 
@@ -268,14 +276,16 @@
 
 | 字段 | 类型 | 说明 |
 |------|------|------|
-| image_url | string | 方案**预览图** URL（发际线曲线叠加图） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
+| image_url | string | 发际线曲线**透明 PNG** URL（仅白色发际线曲线，透明底，**不含人物**，需前端叠加原图显示） |
+| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图，完整人像照片） |
 | hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
 | order | int | 排序序号（当前阶段固定 `1..N`，按贴图顺序，暂不计算合适度） |
 
 > ⚠️ 生发图由本机 ComfyUI（Flux-2，端口 8182）生成，**一次请求生成指定发型的 1 张、同步返回**。
 > worker 侧返回 `image_base64` / `grown_image_base64`，
 > 网关落盘后改写为上表的 `image_url` / `grown_image_url`。
+>
+> 💡 `image_url` 为透明底 PNG，前端需用绝对定位叠加到原图上显示（参考[测试页](https://hair.xiangsilian.com/static/test_interface2.html)的 `.img-stack` 叠加结构）。
 
 ### 响应示例
 
@@ -313,6 +323,7 @@
 |------|------|------|------|
 | marked_image_* | file / string | 是 | 已用马克笔标注发际线的图片，三选一 |
 | use_mask | bool | 否 | 是否画发际线，默认 `true`。`false` 时跳过划线检测、直接送划线图，模型仅凭手绘黑线生发，供测试对比 |
+| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
 
 ### 输出（data）
 
@@ -397,7 +408,8 @@
 | gender | string | **是** | 性别：`male` / `female`。决定发型集合（female 5 / male 4）。缺失/非法返回 `1004` |
 | hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`），决定返回哪些发际线类型。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。缺失/越界/非法返回 `1007` |
 | use_mask | bool | 否 | 生发是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线），供测试对比 |
-| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发」，会替换工作流节点 60 的文本 |
+| prompt | string | 否 | ComfyUI 提示词，默认「补充遮罩区域的头发，加一点美颜」，会替换工作流节点 60 的文本 |
+| generate_grow_image | bool | 否 | 是否生成生发效果图（ComfyUI 生发，全流程最耗时），默认 `true`。传 `false` 时跳过生发，各发型 `grown_image_*` 恒为 `null`，仅返回三档发际线叠图与中心点，可大幅降低耗时 |
 
 > ⚠️ 三档叠图分别用 `hairline_texture` / `hairline_texture_high` / `hairline_texture_low` 三套同名贴图；**生发黑模板固定取自 `hairline_texture_black/`（middle 档）**，即生发目标固定压到 middle 档，每个发型仅 1 张生发图。
 
@@ -406,7 +418,9 @@
 | 字段 | 类型 | 说明 |
 |------|------|------|
 | hairline_images | object[] | **选中发型**列表，**数量 = 所选发型数**，元素见下表 |
-| best_hairline_center_point | object | **首个选中发型**的 middle 档发际线曲线「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| best_hairline_center_point | object \| null | **首个选中发型**的 **middle 档**发际线曲线「面部中间点」坐标，原图像素：`{ "x": number, "y": number }` |
+| high_hairline_center_point | object \| null | 同上，**high 档**发际线中点（发际线偏高） |
+| low_hairline_center_point | object \| null | 同上，**low 档**发际线中点（发际线偏低） |
 | face_measure | object \| null | **复用接口1**的四庭七眼测量**数值**（不含标注图）。独立流程，测量失败（无人脸/非正面/分割失败）时为 `null`，不影响发际线主结果。字段结构见下表 |
 
 `hairline_images` 元素：
@@ -414,13 +428,15 @@
 | 字段 | 类型 | 说明 |
 |------|------|------|
 | hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`（female），`ellipse`/`m`/`straight`/`inverse_arc`（male） |
-| image_middle_url | string | middle 档发际线叠加图 URL |
-| image_high_url | string | high 档发际线叠加图 URL |
-| image_low_url | string | low 档发际线叠加图 URL |
-| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，生发失败时为 `null`） |
+| image_middle_url | string | middle 档发际线曲线**透明 PNG** URL（仅曲线，透明底，**不含人物**，需叠加原图显示） |
+| image_high_url | string | high 档发际线曲线**透明 PNG** URL（同上，high 档曲线） |
+| image_low_url | string | low 档发际线曲线**透明 PNG** URL（同上，low 档曲线） |
+| grown_image_url | string \| null | **生发后图片** URL（ComfyUI「植发」效果图，完整人像照片，生发失败或 `generate_grow_image=false` 时为 `null`） |
 | order | int | 发型序号（= 传入的 hair_style 值） |
 
 > worker 侧返回 `image_middle_base64` / `image_high_base64` / `image_low_base64` / `grown_image_base64`，网关落盘后改写为上表对应的 `*_url`。
+>
+> 💡 三档 `image_*_url` 为透明底 PNG，前端需用绝对定位叠加到原图上显示（参考[测试页](https://hair.xiangsilian.com/static/test_interface5.html)的 `.img-stack` 叠加结构）。`grown_image_url` 是完整人像照片，直接显示即可。
 
 `face_measure` 元素（与[接口1](#接口-1四庭七眼测量标注接口)的 `data` 同构，**不含** `annotated_image_*` 标注图字段）：
 
@@ -432,6 +448,8 @@
 | landmarks | object | 5 个纵向关键点像素坐标（hair_top/hairline/brow_center/nose_bottom/chin_tip），结构同接口1 |
 | hairline_source | string | 发际线来源：`segmentation`（真实分割）/ `estimated`（比例估算） |
 | head_pose | object | 头部姿态角度（yaw/pitch/roll，单位：度） |
+| left_position | object | MediaPipe 21 号关键点坐标（左脸定位点），原图像素：`{ "x": int, "y": int }` |
+| right_position | object | MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：`{ "x": int, "y": int }` |
 
 > `eye1`~`eye7` 为从左到右共 7 段宽度，eye1=左耳外段、eye7=右耳外段，某侧耳朵不可见时对应段为 `null`。详见接口1说明。
 
@@ -462,6 +480,8 @@
       }
     ],
     "best_hairline_center_point": { "x": 540, "y": 430 },
+    "high_hairline_center_point": { "x": 540, "y": 380 },
+    "low_hairline_center_point": { "x": 540, "y": 480 },
     "face_measure": {
       "face_total_height_cm": 26.76,
       "four_courts": {
@@ -495,59 +515,6 @@
 
 ---
 
-## 接口 7：C 端生发 v2 接口
-
-**说明**：功能与[接口 2](#接口-2c-端生发接口)完全一致，仅 ComfyUI 工作流不同——使用 `add_hair2.json` 替代 `add_hair.json`。
-
-**请求**：`POST /api/v1/hair/grow-v2`
-
-### 输入
-
-与接口 2 完全相同。图片参数见「通用约定 → 图片传参字段」。专属参数：
-
-| 参数 | 类型 | 必填 | 说明 |
-|------|------|------|------|
-| gender | string | **是** | 性别：`male` / `female`。决定使用的发际线贴图集合 |
-| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight。越界/非法返回 `1007` |
-| beauty_enabled | bool | 否 | 生发图是否带美颜效果，默认 false（当前阶段不生效） |
-| use_mask | bool | 否 | 是否启用 inpaint 遮罩，默认 `true`。`false` 时用干净原图生成（空遮罩、不烧模板黑线） |
-
-### 输出（data）
-
-与接口 2 完全相同。`results`：发际线方案数组，**数量 = 所选发型数**。每个元素：
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| image_url | string | 方案**预览图** URL（发际线曲线叠加图） |
-| grown_image_url | string | **生发后图片** URL（ComfyUI/Flux「植发 3 个月」效果图） |
-| hairline_type | string | 发际线类型 key |
-| order | int | 排序序号 |
-
-> ⚠️ 与接口 2 的区别：本接口使用 `add_hair2.json` 工作流（Flux-2 Klein 9b），输入/遮罩节点同为 26，
-> SaveImage 输出节点为 75。
-
-### 响应示例
-
-```json
-{
-  "code": 0,
-  "message": "success",
-  "request_id": "mock-request-id",
-  "data": {
-    "results": [
-      {
-        "image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-        "grown_image_url": "https://hair.xiangsilian.com/static/sample.jpg",
-        "hairline_type": "ellipse",
-        "order": 1
-      }
-    ]
-  }
-}
-```
-
----
-
 ## 汇总：输入输出一览
 
 | 接口 | 输入 | 主要输出 |
@@ -558,7 +525,6 @@
 | 3 B 端生发 | 划线图片 | 最合适发际线图片 + 生发后图片 |
 | 4 用户特征 | 用户照片 | 6 个用户特征字段（脸形/眉形/年龄/动静/性别/基因风格） |
 | 5 发际线 PNG | 用户照片 + gender + hair_style（多选） | 每个选中发型 middle/high/low 三档发际线叠图 + 生发图 + 最合适发际线面部中间点坐标 |
-| 7 C 端生发 v2 | 用户照片 + gender + hair_style | 同接口2，使用 add_hair2.json 工作流 |
 
 ---
 

face/build_dataset_report.py

@@ -0,0 +1,506 @@
+"""
+build_dataset_report.py
+对任意图片目录（可含多层子目录）批量预测脸型并生成 HTML 报告。
+保留图片原始所属的子目录名作为「分组」，在报告中按分组展示与统计。
+
+用法:
+    ./venv/bin/python face/build_dataset_report.py --src <图片目录> [--sample 50] [--seed 42]
+
+示例:
+    ./venv/bin/python face/build_dataset_report.py \
+        --src face/test_img/脸型测试集合 --sample 50 --name 脸型测试集合
+
+输出:
+    static/<slug>_report.html
+    static/<slug>_report/images/*.jpg
+"""
+
+from __future__ import annotations
+
+import argparse
+import html
+import random
+import re
+import shutil
+import sys
+import unicodedata
+from collections import Counter, defaultdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List
+
+import cv2
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from face.face_shape_classifier import classify_from_image  # noqa: E402
+
+ROOT = Path(__file__).resolve().parents[1]
+IMAGE_SUFFIXES = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+MAX_IMAGE_SIDE = 900
+JPEG_QUALITY = 88
+
+SHAPE_ORDER = ["圆形脸", "心形脸", "菱形脸", "鹅蛋脸", "方形脸", "长形脸", "瓜子脸"]
+SHAPE_COLORS = {
+    "圆形脸": "#e67e22",
+    "心形脸": "#e74c3c",
+    "菱形脸": "#9b59b6",
+    "鹅蛋脸": "#27ae60",
+    "方形脸": "#2980b9",
+    "长形脸": "#16a085",
+    "瓜子脸": "#c0392b",
+    "检测失败": "#7f8c8d",
+}
+# 数据集分组名与分类器脸型口径的近似对应（仅用于交叉表高亮参考，非严格标签）
+TAXONOMY_EQUIV = {
+    "方形脸": "方形脸",
+    "长形脸": "长形脸",
+    "瓜子脸": "瓜子脸",
+    "标准脸": "鹅蛋脸",
+    "娃娃脸": "圆形脸",
+}
+
+FEATURE_KEYS = [
+    "face_width",
+    "face_height",
+    "jaw_angle",
+    "taper_ratio",
+    "forehead_ratio",
+    "cheekbone_ratio",
+    "jaw_ratio",
+    "chin_ratio",
+    "chin_sharpness",
+    "width_uniformity",
+    "face_curve_score",
+]
+
+
+def natural_key(text: str):
+    parts = re.split(r"(\d+)", text)
+    return [int(p) if p.isdigit() else p for p in parts]
+
+
+def collect_images(src: Path) -> List[Path]:
+    return sorted(
+        (p for p in src.rglob("*") if p.suffix.lower() in IMAGE_SUFFIXES),
+        key=lambda p: natural_key(str(p.relative_to(src))),
+    )
+
+
+def group_of(path: Path, src: Path) -> str:
+    """图片相对根目录的父目录名；直接位于根目录则记为「根目录」。"""
+    rel = path.relative_to(src).parent
+    return str(rel) if str(rel) != "." else "（根目录）"
+
+
+def ascii_slug(text: str, fallback: str) -> str:
+    """生成安全的 ASCII 文件名片段（中文目录名转拼音不可靠，直接编号兜底）。"""
+    norm = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode()
+    norm = re.sub(r"[^A-Za-z0-9_-]+", "_", norm).strip("_")
+    return norm or fallback
+
+
+def stratified_sample(
+    images: List[Path], src: Path, total: int, seed: int, min_per_group: int
+) -> List[Path]:
+    """
+    按分组分层抽样：先保证每组至少 min_per_group 张，剩余名额按组大小比例分配。
+    小分组（如只有 3 张的梨形脸）在纯随机抽样下几乎必然缺席，分层可保证覆盖。
+    """
+    rng = random.Random(seed)
+    buckets: Dict[str, List[Path]] = defaultdict(list)
+    for p in images:
+        buckets[group_of(p, src)].append(p)
+
+    groups = sorted(buckets, key=natural_key)
+    quota = {g: min(min_per_group, len(buckets[g])) for g in groups}
+
+    remaining = total - sum(quota.values())
+    if remaining > 0:
+        spare = {g: len(buckets[g]) - quota[g] for g in groups}
+        pool = sum(spare.values())
+        if pool > 0:
+            # 按剩余可选量比例分配，再把取整误差补给最大的分组
+            extra = {g: int(remaining * spare[g] / pool) for g in groups}
+            for g in sorted(groups, key=lambda g: -spare[g]):
+                if sum(extra.values()) >= remaining:
+                    break
+                if extra[g] < spare[g]:
+                    extra[g] += 1
+            for g in groups:
+                quota[g] += min(extra[g], spare[g])
+
+    chosen: List[Path] = []
+    for g in groups:
+        chosen.extend(rng.sample(buckets[g], min(quota[g], len(buckets[g]))))
+    return chosen
+
+
+def analyze(
+    src: Path, sample: int, seed: int, img_dir: Path, min_per_group: int
+) -> List[Dict]:
+    all_images = collect_images(src)
+    if not all_images:
+        raise SystemExit(f"目录中没有图片: {src}")
+
+    if sample and sample < len(all_images):
+        if min_per_group > 0:
+            chosen = stratified_sample(all_images, src, sample, seed, min_per_group)
+        else:
+            chosen = random.Random(seed).sample(all_images, sample)
+        chosen.sort(key=lambda p: natural_key(str(p.relative_to(src))))
+    else:
+        chosen = all_images
+
+    mode = f"分层抽样，每组至少 {min_per_group} 张" if min_per_group > 0 else "纯随机抽样"
+    print(f"共发现 {len(all_images)} 张图片，本次测试 {len(chosen)} 张（{mode}，seed={seed}）\n")
+
+    if img_dir.exists():
+        shutil.rmtree(img_dir)
+    img_dir.mkdir(parents=True)
+
+    group_slugs: Dict[str, str] = {}
+    rows: List[Dict] = []
+
+    for idx, path in enumerate(chosen, 1):
+        group = group_of(path, src)
+        if group not in group_slugs:
+            group_slugs[group] = ascii_slug(group, f"g{len(group_slugs) + 1}")
+        out_name = f"{group_slugs[group]}_{idx:03d}.jpg"
+
+        item = {
+            "index": idx,
+            "group": group,
+            "file": path.name,
+            "rel_path": str(path.relative_to(src)),
+            # 相对 static/ 的路径（报告 HTML 也放在 static/ 根下）
+            "img_src": f"{img_dir.relative_to(ROOT / 'static').as_posix()}/{out_name}",
+            "ok": False,
+            "predicted": None,
+            "display": None,
+            "confidence": None,
+            "score": None,
+            "top3": [],
+            "features": {},
+            "error": None,
+        }
+
+        try:
+            result = classify_from_image(path, return_details=True, return_annotated=True)
+            annotated = result["annotated"]
+            h, w = annotated.shape[:2]
+            if max(h, w) > MAX_IMAGE_SIDE:
+                scale = MAX_IMAGE_SIDE / max(h, w)
+                annotated = cv2.resize(
+                    annotated, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
+                )
+            cv2.imwrite(str(img_dir / out_name), annotated, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+
+            item.update(
+                {
+                    "ok": True,
+                    "predicted": result["face_shape"],
+                    "display": result["display"],
+                    "confidence": result["confidence"],
+                    "score": result["details"]["ranked"][0][1],
+                    "top3": result["details"]["ranked"][:3],
+                    "features": {k: result["features"][k] for k in FEATURE_KEYS},
+                }
+            )
+        except Exception as exc:  # noqa: BLE001 - 报告需要汇总所有失败样本
+            img = cv2.imread(str(path))
+            if img is not None:
+                h, w = img.shape[:2]
+                if max(h, w) > MAX_IMAGE_SIDE:
+                    scale = MAX_IMAGE_SIDE / max(h, w)
+                    img = cv2.resize(img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
+                cv2.imwrite(str(img_dir / out_name), img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+            item["error"] = str(exc)
+
+        rows.append(item)
+        print(f"[{idx:3d}/{len(chosen)}] [{group}] {path.name} -> {item['display'] or 'ERR: ' + str(item['error'])}")
+
+    return rows
+
+
+def bar_chart(counter: Counter) -> str:
+    if not counter:
+        return "<p class='muted'>无数据</p>"
+    total = sum(counter.values())
+    parts = []
+    order = [s for s in SHAPE_ORDER if counter.get(s)] + [
+        s for s in counter if s not in SHAPE_ORDER
+    ]
+    for shape in order:
+        n = counter[shape]
+        color = SHAPE_COLORS.get(shape, "#7f8c8d")
+        pct = n / total * 100
+        parts.append(
+            f"<div class='bar-row'><span class='bar-label'>{html.escape(shape)}</span>"
+            f"<div class='bar-track'><div class='bar-fill' style='width:{pct:.1f}%;background:{color}'></div></div>"
+            f"<span class='bar-num'>{n}（{pct:.0f}%）</span></div>"
+        )
+    return "".join(parts)
+
+
+def fmt_feat(key: str, value: float) -> str:
+    if key in {"face_width", "face_height"}:
+        return f"{value:.1f}px"
+    if key == "jaw_angle":
+        return f"{value:.1f}°"
+    return f"{value:.3f}"
+
+
+def card(item: Dict) -> str:
+    group_tag = f"<span class='group-tag'>{html.escape(item['group'])}</span>"
+    if not item["ok"]:
+        return f"""
+        <article class="card error">
+          <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank">
+            <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+          </a>
+          <div class="body">
+            <div class="meta"><h3>{html.escape(item['file'])}</h3>{group_tag}</div>
+            <p class="badge bad">检测失败</p>
+            <p class="muted">{html.escape(item['error'] or '')}</p>
+          </div>
+        </article>"""
+
+    color = SHAPE_COLORS.get(item["predicted"], "#34495e")
+    top3 = "".join(
+        f"<li><span>{html.escape(name)}</span><b>{score:.1f}</b></li>" for name, score in item["top3"]
+    )
+    feat_html = "".join(
+        f"<tr><td>{html.escape(k)}</td><td>{html.escape(fmt_feat(k, v))}</td></tr>"
+        for k, v in item["features"].items()
+    )
+    return f"""
+    <article class="card">
+      <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank" title="点击查看大图标注">
+        <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+      </a>
+      <div class="body">
+        <div class="meta"><h3>{html.escape(item['file'])}</h3>{group_tag}</div>
+        <p class="path muted">{html.escape(item['rel_path'])}</p>
+        <p class="badge" style="background:{color}">{html.escape(item['display'])}</p>
+        <p class="conf">匹配度 {item['score']:.1f} · 置信度 {item['confidence']:.3f}</p>
+        <ul class="scores">{top3}</ul>
+        <details>
+          <summary>标注特征数值</summary>
+          <table>{feat_html}</table>
+        </details>
+      </div>
+    </article>"""
+
+
+CSS = """
+:root { --bg:#f3efe6; --ink:#1c1915; --muted:#6b645a; --card:#fffdf8; --line:#e2d8c8; --accent:#0f6b5c; }
+* { box-sizing: border-box; }
+body {
+  margin:0; font-family:"PingFang SC","Noto Sans SC","Segoe UI",sans-serif; color:var(--ink);
+  background: radial-gradient(1200px 600px at 10% -10%, #ffe8c8 0%, transparent 55%),
+              radial-gradient(900px 500px at 100% 0%, #d9f2ea 0%, transparent 50%), var(--bg);
+}
+header { padding:40px 24px 20px; max-width:1320px; margin:0 auto; }
+header h1 { margin:0 0 8px; font-size:clamp(1.8rem,3vw,2.4rem); }
+header p { margin:4px 0; color:var(--muted); }
+.legend-box { max-width:1320px; margin:0 auto 20px; padding:0 24px; }
+.legend-box .inner { background:var(--card); border:1px solid var(--line); border-radius:14px;
+  padding:14px 16px; font-size:.9rem; line-height:1.55; }
+.legend-box code { background:#efe7da; padding:1px 6px; border-radius:4px; font-size:.84rem; }
+.swatch { display:inline-block; width:10px; height:10px; border-radius:2px; margin-right:4px; vertical-align:middle; }
+.stats { display:grid; grid-template-columns:repeat(auto-fit,minmax(280px,1fr)); gap:16px;
+  max-width:1320px; margin:0 auto 28px; padding:0 24px; }
+.stat { background:var(--card); border:1px solid var(--line); border-radius:16px; padding:16px 18px; }
+.stat h2 { margin:0 0 12px; font-size:1rem; }
+.bar-row { display:grid; grid-template-columns:72px 1fr 80px; gap:8px; align-items:center; margin:6px 0; font-size:.86rem; }
+.bar-track { height:8px; background:#efe7da; border-radius:999px; overflow:hidden; }
+.bar-fill { height:100%; border-radius:999px; }
+.bar-num { color:var(--muted); text-align:right; }
+section { max-width:1320px; margin:0 auto 36px; padding:0 24px; }
+section h2 { margin:0 0 14px; font-size:1.3rem; border-left:4px solid var(--accent); padding-left:10px; }
+section h2 small { color:var(--muted); font-weight:400; font-size:.8rem; margin-left:8px; }
+.grid { display:grid; grid-template-columns:repeat(auto-fill,minmax(270px,1fr)); gap:16px; }
+.card { background:var(--card); border:1px solid var(--line); border-radius:18px; overflow:hidden;
+  display:flex; flex-direction:column; box-shadow:0 8px 24px rgba(60,40,10,.05); }
+.card.error { opacity:.9; }
+.img-link { display:block; }
+.card img { width:100%; aspect-ratio:3/4; object-fit:cover; background:#ddd; display:block; }
+.card .body { padding:14px; }
+.meta { display:flex; justify-content:space-between; align-items:baseline; gap:8px; }
+.meta h3 { margin:0; font-size:.95rem; word-break:break-all; }
+.group-tag { font-size:.72rem; color:var(--accent); background:#e7f6f2; padding:2px 8px;
+  border-radius:999px; white-space:nowrap; }
+.path { font-size:.72rem; margin:4px 0 0; word-break:break-all; }
+.badge { display:inline-block; margin:10px 0 4px; color:#fff; padding:6px 10px; border-radius:999px;
+  font-weight:600; font-size:.92rem; }
+.badge.bad { background:#c0392b; }
+.conf { margin:0 0 8px; color:var(--muted); font-size:.85rem; }
+.scores { list-style:none; padding:0; margin:0 0 8px; }
+.scores li { display:flex; justify-content:space-between; padding:4px 0; border-bottom:1px dashed var(--line); font-size:.86rem; }
+details { margin-top:8px; }
+summary { cursor:pointer; color:var(--accent); font-size:.86rem; }
+table { width:100%; border-collapse:collapse; margin-top:8px; font-size:.8rem; }
+td { padding:3px 0; border-bottom:1px solid var(--line); }
+td:last-child { text-align:right; font-variant-numeric:tabular-nums; }
+.muted { color:var(--muted); }
+.cross-wrap { overflow-x:auto; background:var(--card); border:1px solid var(--line);
+  border-radius:16px; padding:14px 16px; }
+table.cross { border-collapse:collapse; width:100%; font-size:.88rem; }
+table.cross th, table.cross td { padding:7px 10px; text-align:center; border-bottom:1px solid var(--line);
+  white-space:nowrap; }
+table.cross thead th { background:#efe7da; font-weight:600; position:sticky; top:0; }
+table.cross th.rowh { text-align:left; font-weight:600; }
+table.cross th.rowh small { color:var(--muted); font-weight:400; }
+table.cross td.num { font-variant-numeric:tabular-nums; }
+table.cross td.hit { background:#d8f3e4; color:#0f6b5c; font-weight:700; font-variant-numeric:tabular-nums; }
+table.cross td.zero { color:#cfc6b6; }
+footer { max-width:1320px; margin:0 auto; padding:8px 24px 40px; color:var(--muted); font-size:.85rem; }
+"""
+
+
+def cross_table(by_group: Dict[str, List[Dict]]) -> str:
+    """原始分组 × 预测脸型 交叉表，对角线（口径对应的格子）高亮。"""
+    cols = SHAPE_ORDER + ["检测失败"]
+    head = "".join(f"<th>{html.escape(c)}</th>" for c in cols)
+    body = []
+    for group, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0])):
+        counts = Counter(i["predicted"] if i["ok"] else "检测失败" for i in items)
+        equiv = TAXONOMY_EQUIV.get(group)
+        cells = []
+        for c in cols:
+            n = counts.get(c, 0)
+            if n == 0:
+                cells.append("<td class='zero'>·</td>")
+                continue
+            cls = "hit" if c == equiv else "num"
+            cells.append(f"<td class='{cls}'>{n}</td>")
+        label = html.escape(group)
+        if equiv:
+            label += f" <small>≈{html.escape(equiv)}</small>"
+        body.append(f"<tr><th class='rowh'>{label}</th>{''.join(cells)}<th>{len(items)}</th></tr>")
+    return (
+        "<div class='cross-wrap'><table class='cross'>"
+        f"<thead><tr><th>原始分组 \\ 预测</th>{head}<th>合计</th></tr></thead>"
+        f"<tbody>{''.join(body)}</tbody></table></div>"
+    )
+
+
+def build_html(rows: List[Dict], name: str, src: Path, seed: int, img_dir_name: str) -> str:
+    overall = Counter(r["predicted"] if r["ok"] else "检测失败" for r in rows)
+    by_group: Dict[str, List[Dict]] = defaultdict(list)
+    for r in rows:
+        by_group[r["group"]].append(r)
+
+    group_stats = "".join(
+        f"<div class='stat'><h2>{html.escape(g)} <span class='muted'>（{len(items)} 张）</span></h2>"
+        f"{bar_chart(Counter(i['predicted'] if i['ok'] else '检测失败' for i in items))}</div>"
+        for g, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0]))
+    )
+
+    sections = "".join(
+        f"<section><h2>{html.escape(g)}<small>{len(items)} 张</small></h2>"
+        f"<div class='grid'>{''.join(card(i) for i in items)}</div></section>"
+        for g, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0]))
+    )
+
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    n_ok = sum(1 for r in rows if r["ok"])
+    n_kind = len([s for s in SHAPE_ORDER if overall.get(s)])
+
+    return f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>{html.escape(name)} — 脸型分类报告</title>
+<style>{CSS}</style>
+</head>
+<body>
+<header>
+  <h1>{html.escape(name)} — 脸型分类报告</h1>
+  <p>z 分数原型匹配分类 · 照片上标注 face_width / face_height 及各比例特征</p>
+  <p>生成时间：{html.escape(now)} · 抽样 {len(rows)} 张（随机种子 {seed}）· 成功 {n_ok} 张 ·
+     覆盖 {n_kind} 种脸型 · 共 {len(by_group)} 个原始分组 · 点击图片看大图</p>
+  <p class="muted">来源目录：{html.escape(str(src))}</p>
+</header>
+
+<div class="legend-box">
+  <div class="inner">
+    <b>图上标注说明</b><br/>
+    <span class="swatch" style="background:#00dc78"></span><code>face_width</code> 颧骨宽度&nbsp;
+    <span class="swatch" style="background:#28b4ff"></span><code>face_height</code> 额头顶→下巴&nbsp;
+    <span class="swatch" style="background:#ff5a00"></span><code>jaw_angle</code> 下巴到左右下颌角夹角&nbsp;
+    <span class="swatch" style="background:#ffc828"></span><code>taper_ratio</code> 额头→下巴收窄&nbsp;
+    <span class="swatch" style="background:#28a0ff"></span><code>forehead / jaw / chin ratio</code> 各级宽度比&nbsp;
+    <span class="swatch" style="background:#b4ff50"></span><code>face_curve_score</code> 下颌中点→下巴&nbsp;
+    右侧柱状条示意 <code>width_uniformity</code>；左上角是完整数值图例。
+  </div>
+</div>
+
+<section>
+  <h2>原始分组 × 预测脸型 对照<small>数据集分组本身是脸型标签，但命名口径与分类器不同</small></h2>
+  {cross_table(by_group)}
+  <p class="muted" style="margin-top:10px;font-size:.85rem">
+    绿色格子表示预测结果与该分组的对应口径一致（标准脸≈鹅蛋脸、娃娃脸≈圆形脸，方形/长形/瓜子同名直接对应）。
+    「梨形脸」「混合脸」在分类器的 7 分类里没有对应项，不作一致性判断。
+  </p>
+</section>
+
+<div class="stats">
+  <div class="stat"><h2>总体脸型分布（{len(rows)} 张）</h2>{bar_chart(overall)}</div>
+  {group_stats}
+</div>
+
+{sections}
+
+<footer>
+  分类实现：face/face_shape_classifier.py · 报告生成：face/build_dataset_report.py ·
+  图片目录：static/{html.escape(img_dir_name)}/
+</footer>
+</body>
+</html>
+"""
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="批量脸型预测并生成 HTML 报告")
+    ap.add_argument("--src", required=True, help="图片根目录（可含子目录）")
+    ap.add_argument("--sample", type=int, default=50, help="随机抽样张数，0 表示全部")
+    ap.add_argument("--seed", type=int, default=42, help="随机种子")
+    ap.add_argument("--name", default=None, help="报告标题，默认取目录名")
+    ap.add_argument("--slug", default="dataset", help="输出文件名前缀（ASCII）")
+    ap.add_argument(
+        "--min-per-group",
+        type=int,
+        default=2,
+        help="分层抽样时每个分组至少抽几张，0 表示纯随机抽样",
+    )
+    args = ap.parse_args()
+
+    src = Path(args.src).expanduser().resolve()
+    if not src.is_dir():
+        raise SystemExit(f"目录不存在: {src}")
+
+    name = args.name or src.name
+    img_dir_name = f"{args.slug}_report"
+    img_dir = ROOT / "static" / img_dir_name / "images"
+    out_html = ROOT / "static" / f"{args.slug}_report.html"
+
+    rows = analyze(src, args.sample, args.seed, img_dir, args.min_per_group)
+    out_html.write_text(
+        build_html(rows, name, src, args.seed, img_dir_name), encoding="utf-8"
+    )
+
+    overall = Counter(r["predicted"] if r["ok"] else "检测失败" for r in rows)
+    total = sum(overall.values())
+    print(f"\n写入 {out_html}")
+    print("=== 总体脸型分布 ===")
+    for shape in SHAPE_ORDER + ["检测失败"]:
+        n = overall.get(shape, 0)
+        if n:
+            print(f"  {shape}: {n:3d}  ({n / total * 100:4.1f}%)  {'#' * n}")
+
+
+if __name__ == "__main__":
+    main()

face/build_report.py

@@ -0,0 +1,359 @@
+"""
+build_report.py
+对 face/test_img/girl 与 face/test_img/man 下的照片批量预测脸型，
+在照片上标注 face_width / face_height 等特征，并生成 HTML 报告。
+
+用法:
+    ./venv/bin/python face/build_report.py
+输出:
+    static/face_shape_report.html
+    static/face_shape_report/images/*.jpg
+"""
+
+from __future__ import annotations
+
+import html
+import re
+import shutil
+import sys
+from collections import Counter
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List
+
+import cv2
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from face.face_shape_classifier import classify_from_image  # noqa: E402
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC_DIRS = {
+    "女": ROOT / "face/test_img/girl",
+    "男": ROOT / "face/test_img/man",
+}
+OUT_DIR = ROOT / "static/face_shape_report"
+IMG_DIR = OUT_DIR / "images"
+OUT_HTML = ROOT / "static/face_shape_report.html"
+
+MAX_IMAGE_SIDE = 900
+JPEG_QUALITY = 90
+
+SHAPE_ORDER = ["圆形脸", "心形脸", "菱形脸", "鹅蛋脸", "方形脸", "长形脸", "瓜子脸"]
+SHAPE_COLORS = {
+    "圆形脸": "#e67e22",
+    "心形脸": "#e74c3c",
+    "菱形脸": "#9b59b6",
+    "鹅蛋脸": "#27ae60",
+    "方形脸": "#2980b9",
+    "长形脸": "#16a085",
+    "瓜子脸": "#c0392b",
+}
+FEATURE_KEYS = [
+    "face_width",
+    "face_height",
+    "jaw_angle",
+    "taper_ratio",
+    "forehead_ratio",
+    "cheekbone_ratio",
+    "jaw_ratio",
+    "chin_ratio",
+    "chin_sharpness",
+    "width_uniformity",
+    "face_curve_score",
+]
+
+
+def natural_key(path: Path):
+    m = re.search(r"(\d+)", path.stem)
+    return (0, int(m.group(1))) if m else (1, path.stem)
+
+
+def analyze_all() -> tuple[List[Dict], Dict[str, Counter]]:
+    if IMG_DIR.exists():
+        shutil.rmtree(IMG_DIR)
+    IMG_DIR.mkdir(parents=True)
+
+    rows: List[Dict] = []
+    summary = {"女": Counter(), "男": Counter(), "all": Counter()}
+
+    for gender, src in SRC_DIRS.items():
+        prefix = "girl" if gender == "女" else "man"
+        paths = sorted(
+            [p for p in src.iterdir() if p.suffix.lower() in {".jpg", ".jpeg", ".png", ".webp"}],
+            key=natural_key,
+        )
+        for path in paths:
+            m = re.search(r"(\d+)", path.stem)
+            out_name = f"{prefix}_{int(m.group(1)) if m else 0:02d}.jpg"
+            dest = IMG_DIR / out_name
+
+            item = {
+                "gender": gender,
+                "file": path.name,
+                "img_src": f"face_shape_report/images/{out_name}",
+                "ok": False,
+                "predicted": None,
+                "display": None,
+                "confidence": None,
+                "score": None,
+                "top3": [],
+                "features": {},
+                "error": None,
+            }
+            try:
+                result = classify_from_image(path, return_details=True, return_annotated=True)
+                annotated = result["annotated"]
+                h, w = annotated.shape[:2]
+                if max(h, w) > MAX_IMAGE_SIDE:
+                    scale = MAX_IMAGE_SIDE / max(h, w)
+                    annotated = cv2.resize(
+                        annotated, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
+                    )
+                cv2.imwrite(str(dest), annotated, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+
+                item.update(
+                    {
+                        "ok": True,
+                        "predicted": result["face_shape"],
+                        "display": result["display"],
+                        "confidence": result["confidence"],
+                        "score": result["details"]["ranked"][0][1],
+                        "top3": result["details"]["ranked"][:3],
+                        "features": {k: result["features"][k] for k in FEATURE_KEYS},
+                    }
+                )
+                summary[gender][result["face_shape"]] += 1
+                summary["all"][result["face_shape"]] += 1
+            except Exception as exc:  # noqa: BLE001 - 报告需要汇总所有失败
+                img = cv2.imread(str(path))
+                if img is not None:
+                    cv2.imwrite(str(dest), img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+                item["error"] = str(exc)
+                summary[gender]["检测失败"] += 1
+                summary["all"]["检测失败"] += 1
+
+            rows.append(item)
+            print(f"[{gender}] {path.name} -> {item['display'] or 'ERR ' + str(item['error'])}")
+
+    return rows, summary
+
+
+def count_table(counter: Counter) -> str:
+    if not counter:
+        return "<p class='muted'>无数据</p>"
+    total = sum(counter.values())
+    parts = []
+    for shape, n in sorted(counter.items(), key=lambda x: (-x[1], x[0])):
+        color = SHAPE_COLORS.get(shape, "#7f8c8d")
+        pct = n / total * 100
+        parts.append(
+            f"<div class='bar-row'><span class='bar-label'>{html.escape(shape)}</span>"
+            f"<div class='bar-track'><div class='bar-fill' style='width:{pct:.1f}%;background:{color}'></div></div>"
+            f"<span class='bar-num'>{n}（{pct:.0f}%）</span></div>"
+        )
+    return "".join(parts)
+
+
+def fmt_feat(key: str, value: float) -> str:
+    if key in {"face_width", "face_height"}:
+        return f"{value:.1f}px"
+    if key == "jaw_angle":
+        return f"{value:.1f}°"
+    return f"{value:.3f}"
+
+
+def card(item: Dict) -> str:
+    if not item["ok"]:
+        return f"""
+        <article class="card error">
+          <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank">
+            <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+          </a>
+          <div class="body">
+            <h3>{html.escape(item['file'])}</h3>
+            <p class="badge bad">检测失败</p>
+            <p class="muted">{html.escape(item['error'] or '')}</p>
+          </div>
+        </article>"""
+
+    color = SHAPE_COLORS.get(item["predicted"], "#34495e")
+    top3 = "".join(
+        f"<li><span>{html.escape(name)}</span><b>{score:.1f}</b></li>" for name, score in item["top3"]
+    )
+    feat_html = "".join(
+        f"<tr><td>{html.escape(k)}</td><td>{html.escape(fmt_feat(k, v))}</td></tr>"
+        for k, v in item["features"].items()
+    )
+    return f"""
+    <article class="card">
+      <a class="img-link" href="{html.escape(item['img_src'])}" target="_blank" title="点击查看大图标注">
+        <img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
+      </a>
+      <div class="body">
+        <div class="meta">
+          <h3>{html.escape(item['file'])}</h3>
+          <span class="gender">{html.escape(item['gender'])}</span>
+        </div>
+        <p class="badge" style="background:{color}">{html.escape(item['display'])}</p>
+        <p class="conf">匹配度 {item['score']:.1f} · 置信度 {item['confidence']:.3f}</p>
+        <h4>Top-3 得分</h4>
+        <ul class="scores">{top3}</ul>
+        <details>
+          <summary>标注特征数值</summary>
+          <table>{feat_html}</table>
+        </details>
+      </div>
+    </article>"""
+
+
+CSS = """
+:root {
+  --bg: #f3efe6; --ink: #1c1915; --muted: #6b645a;
+  --card: #fffdf8; --line: #e2d8c8; --accent: #0f6b5c;
+}
+* { box-sizing: border-box; }
+body {
+  margin: 0;
+  font-family: "PingFang SC", "Noto Sans SC", "Segoe UI", sans-serif;
+  color: var(--ink);
+  background:
+    radial-gradient(1200px 600px at 10% -10%, #ffe8c8 0%, transparent 55%),
+    radial-gradient(900px 500px at 100% 0%, #d9f2ea 0%, transparent 50%),
+    var(--bg);
+}
+header { padding: 40px 24px 20px; max-width: 1280px; margin: 0 auto; }
+header h1 { margin: 0 0 8px; font-size: clamp(1.8rem, 3vw, 2.4rem); letter-spacing: .02em; }
+header p { margin: 4px 0; color: var(--muted); }
+.legend-box { max-width: 1280px; margin: 0 auto 20px; padding: 0 24px; }
+.legend-box .inner {
+  background: var(--card); border: 1px solid var(--line);
+  border-radius: 14px; padding: 14px 16px; font-size: .9rem; line-height: 1.55;
+}
+.legend-box code { background: #efe7da; padding: 1px 6px; border-radius: 4px; font-size: .84rem; }
+.swatch { display: inline-block; width: 10px; height: 10px; border-radius: 2px; margin-right: 4px; vertical-align: middle; }
+.stats {
+  display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr));
+  gap: 16px; max-width: 1280px; margin: 0 auto 28px; padding: 0 24px;
+}
+.stat { background: var(--card); border: 1px solid var(--line); border-radius: 16px; padding: 16px 18px; }
+.stat h2 { margin: 0 0 12px; font-size: 1rem; }
+.bar-row {
+  display: grid; grid-template-columns: 72px 1fr 76px; gap: 8px;
+  align-items: center; margin: 6px 0; font-size: .86rem;
+}
+.bar-track { height: 8px; background: #efe7da; border-radius: 999px; overflow: hidden; }
+.bar-fill { height: 100%; border-radius: 999px; }
+.bar-num { color: var(--muted); text-align: right; }
+section { max-width: 1280px; margin: 0 auto 36px; padding: 0 24px; }
+section h2 { margin: 0 0 14px; font-size: 1.35rem; border-left: 4px solid var(--accent); padding-left: 10px; }
+.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(280px, 1fr)); gap: 16px; }
+.card {
+  background: var(--card); border: 1px solid var(--line); border-radius: 18px;
+  overflow: hidden; display: flex; flex-direction: column;
+  box-shadow: 0 8px 24px rgba(60, 40, 10, .05);
+}
+.card.error { opacity: .9; }
+.img-link { display: block; }
+.card img { width: 100%; aspect-ratio: 3/4; object-fit: cover; background: #ddd; display: block; }
+.card .body { padding: 14px 14px 16px; }
+.meta { display: flex; justify-content: space-between; align-items: baseline; gap: 8px; }
+.meta h3 { margin: 0; font-size: 1rem; }
+.gender { font-size: .75rem; color: var(--accent); background: #e7f6f2; padding: 2px 8px; border-radius: 999px; }
+.badge {
+  display: inline-block; margin: 10px 0 4px; color: #fff;
+  padding: 6px 10px; border-radius: 999px; font-weight: 600; font-size: .92rem;
+}
+.badge.bad { background: #c0392b; }
+.conf { margin: 0 0 10px; color: var(--muted); font-size: .85rem; }
+.scores { list-style: none; padding: 0; margin: 0 0 8px; }
+.scores li {
+  display: flex; justify-content: space-between; padding: 4px 0;
+  border-bottom: 1px dashed var(--line); font-size: .88rem;
+}
+details { margin-top: 8px; }
+summary { cursor: pointer; color: var(--accent); font-size: .88rem; }
+table { width: 100%; border-collapse: collapse; margin-top: 8px; font-size: .8rem; }
+td { padding: 3px 0; border-bottom: 1px solid var(--line); }
+td:last-child { text-align: right; font-variant-numeric: tabular-nums; }
+.muted { color: var(--muted); }
+footer { max-width: 1280px; margin: 0 auto; padding: 8px 24px 40px; color: var(--muted); font-size: .85rem; }
+"""
+
+
+def build_html(rows: List[Dict], summary: Dict[str, Counter]) -> str:
+    girl_cards = "\n".join(card(r) for r in rows if r["gender"] == "女")
+    man_cards = "\n".join(card(r) for r in rows if r["gender"] == "男")
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    n_girl = sum(1 for r in rows if r["gender"] == "女")
+    n_man = sum(1 for r in rows if r["gender"] == "男")
+    n_ok = sum(1 for r in rows if r["ok"])
+    n_kind = len([s for s in SHAPE_ORDER if summary["all"].get(s)])
+
+    return f"""<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>脸型分类预测报告（特征标注）</title>
+<style>{CSS}</style>
+</head>
+<body>
+<header>
+  <h1>脸型分类预测报告</h1>
+  <p>z 分数原型匹配分类 · 照片上标注 face_width / face_height 及各比例特征</p>
+  <p>生成时间：{html.escape(now)} · 样本 {n_girl + n_man} 张（女 {n_girl} / 男 {n_man}）· 成功 {n_ok} · 覆盖 {n_kind} 种脸型 · 点击图片看大图</p>
+</header>
+
+<div class="legend-box">
+  <div class="inner">
+    <b>图上标注说明</b><br/>
+    <span class="swatch" style="background:#00dc78"></span><code>face_width</code> 颧骨宽度&nbsp;
+    <span class="swatch" style="background:#28b4ff"></span><code>face_height</code> 额头顶→下巴&nbsp;
+    <span class="swatch" style="background:#ff5a00"></span><code>jaw_angle</code> 下巴到左右下颌角夹角&nbsp;
+    <span class="swatch" style="background:#ffc828"></span><code>taper_ratio</code> 额头→下巴收窄&nbsp;
+    <span class="swatch" style="background:#28a0ff"></span><code>forehead / jaw / chin ratio</code> 各级宽度比&nbsp;
+    <span class="swatch" style="background:#b4ff50"></span><code>face_curve_score</code> 下颌中点→下巴&nbsp;
+    右侧柱状条示意 <code>width_uniformity</code>；左上角是完整数值图例。
+  </div>
+</div>
+
+<div class="stats">
+  <div class="stat"><h2>全部脸型分布</h2>{count_table(summary['all'])}</div>
+  <div class="stat"><h2>女性脸型分布</h2>{count_table(summary['女'])}</div>
+  <div class="stat"><h2>男性脸型分布</h2>{count_table(summary['男'])}</div>
+</div>
+
+<section>
+  <h2>女性样本（{n_girl}）</h2>
+  <div class="grid">{girl_cards}</div>
+</section>
+
+<section>
+  <h2>男性样本（{n_man}）</h2>
+  <div class="grid">{man_cards}</div>
+</section>
+
+<footer>
+  分类实现：face/face_shape_classifier.py · 报告生成：face/build_report.py
+</footer>
+</body>
+</html>
+"""
+
+
+def main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    rows, summary = analyze_all()
+    OUT_HTML.write_text(build_html(rows, summary), encoding="utf-8")
+
+    print(f"\n写入 {OUT_HTML}")
+    print("=== 脸型分布 ===")
+    total = sum(summary["all"].values())
+    for shape in SHAPE_ORDER:
+        n = summary["all"].get(shape, 0)
+        print(f"  {shape}: {n:2d}  ({n / total * 100:4.1f}%)  {'#' * n}")
+
+
+if __name__ == "__main__":
+    main()

face/dump_features.py

@@ -0,0 +1,83 @@
+"""
+把各数据集的人脸特征抽取一次并缓存为 JSON，供调参脚本反复使用。
+
+MediaPipe 关键点检测是调参循环里唯一的耗时环节，缓存后调参可以秒级迭代。
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+from typing import Dict, List
+
+import cv2
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from face_shape_classifier import (  # noqa: E402
+    _get_face_mesh,
+    extract_face_features,
+)
+
+ROOT = Path(__file__).resolve().parent
+IMG_EXT = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+
+
+def iter_images(root: Path) -> List[Path]:
+    return sorted(p for p in root.rglob("*") if p.suffix.lower() in IMG_EXT)
+
+
+def features_for(path: Path) -> Dict[str, float] | None:
+    bgr = cv2.imread(str(path))
+    if bgr is None:
+        return None
+    h, w = bgr.shape[:2]
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    res = _get_face_mesh().process(rgb)
+    if not res.multi_face_landmarks:
+        return None
+    return extract_face_features(res.multi_face_landmarks[0].landmark, image_size=(w, h))
+
+
+def main() -> None:
+    out_path = Path(sys.argv[1]) if len(sys.argv) > 1 else ROOT / "cache" / "features.json"
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    sources = {
+        # 6 张带标注的基准图，文件名即期望脸型
+        "benchmark": [p for p in ROOT.joinpath("test_img").glob("*.png")],
+        "girl": iter_images(ROOT / "test_img" / "girl"),
+        "man": iter_images(ROOT / "test_img" / "man"),
+        "dataset": iter_images(ROOT / "test_img" / "脸型测试集合"),
+    }
+
+    records = []
+    failed = 0
+    for source, paths in sources.items():
+        for i, path in enumerate(paths, 1):
+            feats = features_for(path)
+            if feats is None:
+                failed += 1
+                continue
+            rel = path.relative_to(ROOT)
+            records.append(
+                {
+                    "source": source,
+                    "path": rel.as_posix(),
+                    "file": path.name,
+                    # dataset 的上级目录名即原始分组（弱标签，非可信真值）
+                    "group": path.parent.name if source == "dataset" else source,
+                    "expected": path.stem if source == "benchmark" else None,
+                    "features": feats,
+                }
+            )
+            if i % 50 == 0 or i == len(paths):
+                print(f"[{source}] {i}/{len(paths)}", flush=True)
+
+    out_path.write_text(json.dumps(records, ensure_ascii=False), encoding="utf-8")
+    print(f"\n写入 {out_path}：{len(records)} 条，检测失败 {failed} 张")
+
+
+if __name__ == "__main__":
+    main()

face/face_shape_classification.md

@@ -0,0 +1,904 @@
+# 脸型判断规则优化报告
+
+> 基于 Mediapipe 468 点人脸关键点的脸型分类系统  
+> 优化日期：2026-07-28
+
+---
+
+## 一、优化总览
+
+### 原始规则主要问题
+
+| 问题 | 说明 |
+|------|------|
+| **规则冲突** | 7条 if 规则可能同时匹配，无优先级机制 |
+| **特征定义模糊** | `width_ratio`、`chin_narrowness` 等未给出精确计算方式 |
+| **关键点不足** | 额头宽度用 234/454（颧骨点）而非太阳穴点，导致测量不准 |
+| **无置信度** | 硬判断，混合脸型无处理 |
+| **阈值经验性** | 阈值未经统计校准，边界处容易误判 |
+| **缺少归一化** | 不同距离拍的照片结果不一致 |
+
+### 优化策略
+
+1. **精确特征提取**：增加关键点，所有测量归一化
+2. **评分制分类**：每个脸型计算匹配度分数（0-100），取最高分
+3. **置信度输出**：报告 Top-1 / Top-2 分差，判断是否为混合脸型
+4. **优先级仲裁**：分数接近时按"特异性优先"原则仲裁
+5. **鲁棒性增强**：clamp 防止除零、NaN，角度计算增加 3D 投影
+
+---
+
+## 二、优化后的完整 Python 代码
+
+```python
+"""
+face_shape_classifier.py
+基于 Mediapipe 468 点人脸关键点的脸型分类系统
+
+支持的脸型：圆形脸 / 心形脸 / 菱形脸 / 鹅蛋脸 / 方形脸 / 长形脸 / 瓜子脸
+分类策略：多维度特征提取 → 加权评分 → 置信度判断
+"""
+
+import math
+import numpy as np
+from typing import Dict, Tuple, List, Optional
+
+
+# ============================================================
+#  第一部分：关键点索引定义
+# ============================================================
+
+class FaceLandmarks:
+    """Mediapipe 468 点关键点索引（仅列出脸型分析所需）"""
+
+    # --- 中线关键点 ---
+    FOREHEAD_TOP    = 10    # 额头顶部（发际线附近）
+    NOSE_BRIDGE     = 1     # 鼻根（眉心位置）
+    NOSE_TIP        = 168   # 鼻尖
+    CHIN_BOTTOM      = 152   # 下巴最低点（menton）
+
+    # --- 太阳穴 / 额头两侧（额头宽度）---
+    LEFT_TEMPLE     = 127   # 左太阳穴
+    RIGHT_TEMPLE    = 356   # 右太阳穴
+
+    # --- 颧骨 / 脸颊最宽处 ---
+    LEFT_CHEEK      = 234   # 左颧弓最外侧
+    RIGHT_CHEEK     = 454   # 右颧弓最外侧
+
+    # --- 下颌角（gonion 区域）---
+    LEFT_JAW_ANGLE  = 172   # 左下颌角
+    RIGHT_JAW_ANGLE = 397   # 右下颌角
+
+    # --- 下巴两侧（下巴宽度）---
+    LEFT_CHIN       = 136   # 左下巴缘
+    RIGHT_CHIN      = 365   # 右下巴缘
+
+    # --- 嘴角（辅助参考）---
+    LEFT_MOUTH      = 61    # 左嘴角
+    RIGHT_MOUTH     = 291   # 右嘴角
+
+    # --- 眼角（辅助参考）---
+    LEFT_EYE_OUT    = 33    # 左眼外角
+    RIGHT_EYE_OUT   = 263   # 右眼外角
+
+    # --- 额头侧缘（辅助）---
+    LEFT_FOREHEAD   = 50    # 左额侧
+    RIGHT_FOREHEAD  = 280   # 右额侧
+
+
+# ============================================================
+#  第二部分：特征提取
+# ============================================================
+
+def extract_face_features(landmarks) -> Dict[str, float]:
+    """
+    从 Mediapipe 关键点中提取脸型特征向量。
+
+    参数:
+        landmarks: Mediapipe 的 NormalizedLandmark 列表（468 点）
+
+    返回:
+        features dict，包含以下归一化特征：
+        - aspect_ratio:      面部长宽比（face_width / face_height）
+        - jaw_angle:         下颌角度（度），越大越圆润
+        - taper_ratio:       额头→下巴收窄比例
+        - forehead_ratio:    额头宽度 / 面部宽度
+        - cheekbone_ratio:   颧骨宽度 / 面部宽度
+        - jaw_ratio:         下颌宽度 / 面部宽度
+        - chin_ratio:        下巴宽度 / 面部宽度
+        - chin_sharpness:    下巴尖锐度（下巴宽 / 下颌宽）
+        - width_uniformity:  宽度均匀度（越小越方正）
+        - face_curve_score:  面部曲线评分（越大越圆润）
+    """
+
+    def pt(idx):
+        """提取 3D 坐标"""
+        lm = landmarks[idx]
+        return np.array([lm.x, lm.y, lm.z])
+
+    def dist(p1, p2):
+        """欧氏距离"""
+        return float(np.linalg.norm(p1 - p2))
+
+    # --- 1. 提取关键点 ---
+    forehead_top  = pt(FaceLandmarks.FOREHEAD_TOP)
+    chin_bottom   = pt(FaceLandmarks.CHIN_BOTTOM)
+
+    left_temple   = pt(FaceLandmarks.LEFT_TEMPLE)
+    right_temple  = pt(FaceLandmarks.RIGHT_TEMPLE)
+
+    left_cheek    = pt(FaceLandmarks.LEFT_CHEEK)
+    right_cheek   = pt(FaceLandmarks.RIGHT_CHEEK)
+
+    left_jaw      = pt(FaceLandmarks.LEFT_JAW_ANGLE)
+    right_jaw     = pt(FaceLandmarks.RIGHT_JAW_ANGLE)
+
+    left_chin     = pt(FaceLandmarks.LEFT_CHIN)
+    right_chin    = pt(FaceLandmarks.RIGHT_CHIN)
+
+    # --- 2. 基础距离 ---
+    face_height = dist(forehead_top, chin_bottom)
+
+    forehead_width  = dist(left_temple, right_temple)
+    cheekbone_width = dist(left_cheek, right_cheek)
+    jaw_width       = dist(left_jaw, right_jaw)
+    chin_width      = dist(left_chin, right_chin)
+
+    # face_width 取颧骨宽度（通常是面部最宽处）
+    face_width = cheekbone_width
+
+    # 防止除零
+    eps = 1e-8
+
+    # --- 3. 计算下颌角度 ---
+    # 以下巴底为顶点，向左下颌角和右下颌角各做一向量
+    # 角度越大 → 下颌越圆润（圆形/鹅蛋）
+    # 角度越小 → 下颌越方正（方形）
+    v_left  = left_jaw  - chin_bottom
+    v_right = right_jaw - chin_bottom
+
+    cos_val = np.dot(v_left, v_right) / (np.linalg.norm(v_left) * np.linalg.norm(v_right) + eps)
+    cos_val = np.clip(cos_val, -1.0, 1.0)
+    jaw_angle = math.degrees(math.acos(cos_val))
+
+    # --- 4. 计算衍生特征 ---
+    aspect_ratio = face_width / (face_height + eps)
+
+    taper_ratio = (forehead_width - chin_width) / (forehead_width + eps)
+
+    # 归一化到面部宽度
+    forehead_ratio  = forehead_width  / (face_width + eps)
+    cheekbone_ratio = cheekbone_width / (face_width + eps)  # 始终 ≈ 1.0
+    jaw_ratio       = jaw_width       / (face_width + eps)
+    chin_ratio      = chin_width      / (face_width + eps)
+
+    # 下巴尖锐度：下巴宽 / 下颌宽
+    # 值越小 → 下巴越尖（瓜子/心形）
+    # 值越大 → 下巴越平（方形/圆形）
+    chin_sharpness = chin_width / (jaw_width + eps)
+
+    # 宽度均匀度：额头、颧骨、下颌三者的差异程度
+    # 值越小 → 三者越接近（方形/圆形）
+    # 值越大 → 差异越明显（菱形/心形/瓜子）
+    widths = [forehead_width, cheekbone_width, jaw_width]
+    width_uniformity = (max(widths) - min(widths)) / (max(widths) + eps)
+
+    # 面部曲线评分：下巴到下颌角的距离 / 面部高度
+    # 距离越短 → 线条越弯曲（圆润），越长 → 越直（方正）
+    jaw_midpoint = (left_jaw + right_jaw) / 2.0
+    jaw_to_chin = dist(jaw_midpoint, chin_bottom)
+    face_curve_score = jaw_to_chin / (face_height + eps)
+
+    # --- 5. 返回特征字典 ---
+    features = {
+        # 原始尺寸
+        'face_height':      face_height,
+        'face_width':       face_width,
+        'forehead_width':   forehead_width,
+        'cheekbone_width':  cheekbone_width,
+        'jaw_width':        jaw_width,
+        'chin_width':       chin_width,
+
+        # 比例特征
+        'aspect_ratio':     aspect_ratio,
+        'taper_ratio':      taper_ratio,
+        'forehead_ratio':   forehead_ratio,
+        'cheekbone_ratio':  cheekbone_ratio,
+        'jaw_ratio':        jaw_ratio,
+        'chin_ratio':       chin_ratio,
+
+        # 角度特征
+        'jaw_angle':        jaw_angle,
+
+        # 复合特征
+        'chin_sharpness':   chin_sharpness,
+        'width_uniformity': width_uniformity,
+        'face_curve_score': face_curve_score,
+    }
+
+    return features
+
+
+# ============================================================
+#  第三部分：评分制分类器
+# ============================================================
+
+def classify_face_shape(
+    features: Dict[str, float],
+    return_details: bool = False
+) -> Tuple[str, float, Optional[Dict]]:
+    """
+    基于评分的脸型分类器。
+
+    策略：
+        每种脸型计算 0-100 的匹配度分数。
+        取最高分作为结果，返回置信度和详细得分。
+
+    参数:
+        features: extract_face_features() 的输出
+        return_details: 是否返回详细评分
+
+    返回:
+        (face_shape: str, confidence: float, details: dict | None)
+    """
+
+    ar   = features['aspect_ratio']         # 长宽比（宽/高）
+    jaw  = features['jaw_angle']            # 下颌角度
+    tap  = features['taper_ratio']          # 额头→下巴收窄
+    fr   = features['forehead_ratio']       # 额头宽/面宽
+    jr   = features['jaw_ratio']            # 下颌宽/面宽
+    cr   = features['chin_ratio']           # 下巴宽/面宽
+    cs   = features['chin_sharpness']       # 下巴尖锐度
+    wu   = features['width_uniformity']     # 宽度均匀度
+    fcs  = features['face_curve_score']     # 面部曲线
+    fw   = features['forehead_width']
+    cw   = features['chin_width']
+    jw   = features['jaw_width']
+    sw   = features['cheekbone_width']
+    fh   = features['face_height']
+    eps  = 1e-8
+
+    scores: Dict[str, float] = {}
+
+    # ==========================================
+    #  1. 圆形脸 (Round)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比接近 1（脸几乎和宽一样长）
+    #    - 下颌角大（>135°，圆润线条）
+    #    - 额头≈颧骨≈下颌宽度（均匀）
+    #    - 下巴圆润不尖
+    #
+    #  理想值：aspect_ratio ≈ 0.88-1.0, jaw_angle ≈ 140-160°
+    # ==========================================
+    s = 0.0
+    s += _score_range(ar, 0.85, 1.0, peak=0.92, max_points=30)     # 长宽比
+    s += _score_range(jaw, 135, 165, peak=150, max_points=30)       # 下颌角度
+    s += _score_below(wu, 0.10, max_points=20)                      # 宽度均匀
+    s += _score_range(cs, 0.65, 0.90, peak=0.75, max_points=10)     # 下巴不尖
+    s += _score_range(tap, -0.05, 0.10, peak=0.02, max_points=10)   # 几乎不收窄
+    scores['圆形脸'] = s
+
+    # ==========================================
+    #  2. 心形脸 (Heart)
+    # ==========================================
+    #  核心特征：
+    #    - 额头明显宽于下巴（taper > 0.2）
+    #    - 下巴尖细（chin_sharpness < 0.55）
+    #    - 颧骨与额头接近（不是颧骨最宽）
+    #    - 前额发际线较宽
+    #
+    #  理想值：taper ≈ 0.25-0.40, chin_sharpness ≈ 0.35-0.55
+    # ==========================================
+    s = 0.0
+    s += _score_above(tap, 0.20, max_points=25)                     # 额头宽于下巴
+    s += _score_below(cs, 0.55, max_points=25)                      # 下巴尖
+    s += _score_above(fw, sw * 0.92, max_points=15)                 # 额头≥颧骨的92%
+    s += _score_range(jaw, 115, 150, peak=130, max_points=15)       # 下颌适中偏圆
+    s += _score_range(ar, 0.75, 0.92, peak=0.82, max_points=10)     # 长宽比适中
+    s += _score_above(cr, 0.0, max_points=10)                       # 下巴存在但窄
+    scores['心形脸'] = s
+
+    # ==========================================
+    #  3. 菱形脸 (Diamond)
+    # ==========================================
+    #  核心特征：
+    #    - 颧骨明显最宽（>额头和下颌的 105%+）
+    #    - 额头较窄（< 面宽的 90%）
+    #    - 下颌也较窄
+    #    - 整体呈菱形/钻石形
+    #
+    #  理想值：width_uniformity > 0.15
+    # ==========================================
+    s = 0.0
+    s += _score_above(sw, fw * 1.05, max_points=25)                 # 颧骨>额头5%+
+    s += _score_above(sw, jw * 1.10, max_points=25)                 # 颧骨>下颌10%+
+    s += _score_below(fr, 0.92, max_points=15)                      # 额头偏窄
+    s += _score_below(jr, 0.92, max_points=15)                      # 下颌偏窄
+    s += _score_above(wu, 0.12, max_points=10)                      # 宽度不均匀
+    s += _score_range(ar, 0.72, 0.90, peak=0.80, max_points=10)     # 长宽比适中
+    scores['菱形脸'] = s
+
+    # ==========================================
+    #  4. 鹅蛋脸 (Oval)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比适中（0.72-0.85，不过圆不过长）
+    #    - 轮廓柔和，下颌角度适中（125-155°）
+    #    - 额头略宽于下巴，但差距不大
+    #    - 宽度从上到下平滑递减
+    #    - 下巴圆润偏尖但不极端
+    #
+    #  理想值：aspect_ratio ≈ 0.75-0.82
+    # ==========================================
+    s = 0.0
+    s += _score_range(ar, 0.70, 0.85, peak=0.77, max_points=25)     # 长宽比
+    s += _score_range(jaw, 125, 155, peak=138, max_points=20)       # 下颌角度
+    s += _score_range(tap, 0.03, 0.20, peak=0.10, max_points=15)    # 适度收窄
+    s += _score_range(cs, 0.50, 0.75, peak=0.62, max_points=15)     # 下巴适中
+    s += _score_below(wu, 0.12, max_points=15)                      # 宽度比较均匀
+    s += _score_range(fcs, 0.15, 0.25, peak=0.19, max_points=10)    # 曲线适中
+    scores['鹅蛋脸'] = s
+
+    # ==========================================
+    #  5. 方形脸 (Square)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比较大（接近等宽，ar > 0.80）
+    #    - 下颌角小（<135°，线条硬朗）
+    #    - 额头≈颧骨≈下颌（宽度均匀）
+    #    - 下巴偏平不尖
+    #
+    #  理想值：aspect_ratio ≈ 0.85-0.95, jaw_angle ≈ 110-125°
+    # ==========================================
+    s = 0.0
+    s += _score_range(ar, 0.78, 0.95, peak=0.87, max_points=20)     # 长宽比偏大
+    s += _score_below(jaw, 135, max_points=30)                      # 下颌角小
+    s += _score_below(wu, 0.10, max_points=20)                      # 宽度均匀
+    s += _score_above(cs, 0.62, max_points=15)                      # 下巴偏宽
+    s += _score_range(jr, 0.90, 1.05, peak=0.96, max_points=15)     # 下颌宽接近面宽
+    scores['方形脸'] = s
+
+    # ==========================================
+    #  6. 长形脸 (Long/Oblong)
+    # ==========================================
+    #  核心特征：
+    #    - 长宽比低（< 0.72，脸明显比宽长很多）
+    #    - 面部高度 > 宽度的 1.4 倍
+    #    - 额头略宽于下巴
+    #    - 整体修长
+    #
+    #  理想值：aspect_ratio ≈ 0.58-0.70
+    # ==========================================
+    s = 0.0
+    s += _score_below(ar, 0.72, max_points=35)                      # 长宽比低
+    s += _score_above(fh, features['face_width'] * 1.35, max_points=20)  # 高>宽*1.35
+    s += _score_range(tap, 0.0, 0.20, peak=0.08, max_points=10)     # 适度收窄
+    s += _score_range(jaw, 120, 155, peak=135, max_points=15)       # 下颌适中
+    s += _score_range(cs, 0.45, 0.72, peak=0.58, max_points=10)     # 下巴适中
+    s += _score_range(wu, 0.02, 0.15, peak=0.08, max_points=10)     # 宽度比较均匀
+    scores['长形脸'] = s
+
+    # ==========================================
+    #  7. 瓜子脸 (Melon Seed / V-shape)
+    # ==========================================
+    #  核心特征：
+    #    - 额头宽，逐渐收窄到尖下巴
+    #    - 比心形脸更窄长（aspect_ratio < 0.82）
+    #    - 颧骨不超过额头
+    #    - 下巴尖锐（V 线条）
+    #    - 整体线条流畅
+    #
+    #  理想值：taper ≈ 0.20-0.35, chin_sharpness ≈ 0.30-0.55
+    # ==========================================
+    s = 0.0
+    s += _score_above(tap, 0.15, max_points=20)                     # 额头宽于下巴
+    s += _score_below(ar, 0.82, max_points=15)                      # 偏长
+    s += _score_below(cs, 0.58, max_points=25)                      # 下巴尖
+    s += _score_below(sw, fw * 1.02, max_points=15)                 # 颧骨≤额头
+    s += _score_below(jw, fw * 0.95, max_points=15)                 # 下颌<额头
+    s += _score_range(jaw, 120, 155, peak=135, max_points=10)       # 下颌适中
+    scores['瓜子脸'] = s
+
+    # --- 选择最高分 ---
+    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+    best_shape, best_score = ranked[0]
+    second_shape, second_score = ranked[1] if len(ranked) > 1 else (None, 0)
+
+    # 置信度：最高分 / 总分
+    total = sum(scores.values())
+    confidence = best_score / total if total > 0 else 0.0
+
+    # 判断是否混合脸型（Top-1 和 Top-2 分差太小）
+    score_gap = best_score - second_score
+    is_mixed = (score_gap < 8.0 and best_score > 30.0)
+
+    details = {
+        'scores':         scores,
+        'ranked':         ranked,
+        'confidence':     confidence,
+        'score_gap':      score_gap,
+        'is_mixed':       is_mixed,
+        'second_shape':   second_shape,
+        'second_score':   second_score,
+    } if return_details else None
+
+    return best_shape, confidence, details
+
+
+# ============================================================
+#  第四部分：评分辅助函数
+# ============================================================
+
+def _score_range(
+    value: float,
+    low: float,
+    high: float,
+    peak: float,
+    max_points: float = 10.0
+) -> float:
+    """
+    在 [low, high] 范围内评分，peak 处满分。
+    范围外线性衰减到 0。
+    使用三角窗函数（triangular window）。
+
+    例：_score_range(0.77, 0.70, 0.85, peak=0.77, max_points=25)
+        → value == peak → 返回 25.0
+        → value == low  → 返回 0.0（边界）
+        → value 在 peak 和 low 之间 → 线性插值
+    """
+    if value < low or value > high:
+        return 0.0
+
+    if value == peak:
+        return max_points
+
+    if value < peak:
+        # 在 [low, peak] 区间线性上升
+        ratio = (value - low) / (peak - low + 1e-8)
+    else:
+        # 在 [peak, high] 区间线性下降
+        ratio = (high - value) / (high - peak + 1e-8)
+
+    return max_points * ratio
+
+
+def _score_above(value: float, threshold: float, max_points: float = 10.0) -> float:
+    """
+    value >= threshold 时给满分，低于则线性衰减。
+    衰减区间：[threshold * 0.7, threshold]
+    """
+    if value >= threshold:
+        return max_points
+    floor = threshold * 0.7
+    if value <= floor:
+        return 0.0
+    ratio = (value - floor) / (threshold - floor + 1e-8)
+    return max_points * ratio
+
+
+def _score_below(value: float, threshold: float, max_points: float = 10.0) -> float:
+    """
+    value <= threshold 时给满分，高于则线性衰减。
+    衰减区间：[threshold, threshold * 1.3]
+    """
+    if value <= threshold:
+        return max_points
+    ceil = threshold * 1.3
+    if value >= ceil:
+        return 0.0
+    ratio = (ceil - value) / (ceil - threshold + 1e-8)
+    return max_points * ratio
+
+
+# ============================================================
+#  第五部分：完整调用示例
+# ============================================================
+
+def classify_from_mediapipe(multi_face_landmarks) -> List[Dict]:
+    """
+    完整调用示例：从 Mediapipe 结果到脸型分类。
+
+    参数:
+        multi_face_landmarks: mediapipe FaceMesh 的结果
+                              result.multi_face_landmarks
+
+    返回:
+        每张脸的分类结果列表
+    """
+    results = []
+    for face_lms in multi_face_landmarks:
+        features = extract_face_features(face_lms.landmark)
+        shape, conf, details = classify_face_shape(features, return_details=True)
+        results.append({
+            'face_shape':  shape,
+            'confidence':  conf,
+            'features':    features,
+            'details':     details,
+        })
+    return results
+
+
+# ============================================================
+#  第六部分：混合脸型输出（可选）
+# ============================================================
+
+def get_mixed_description(details: Dict) -> str:
+    """
+    当检测到混合脸型时，生成描述文本。
+
+    例："鹅蛋脸（偏瓜子脸）"
+    """
+    if not details or not details.get('is_mixed'):
+        return ""
+
+    shape1 = details['ranked'][0][0]
+    shape2 = details['ranked'][1][0]
+    return f"{shape1}（偏{shape2}）"
+```
+
+---
+
+## 三、各脸型详细特征说明
+
+### 1. 圆形脸 (Round)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.88-1.0 | 面部宽度和长度几乎相等 |
+| jaw_angle | 140-160° | 下颌线条圆润 |
+| width_uniformity | < 0.08 | 额头、颧骨、下颌宽度接近 |
+| chin_sharpness | 0.65-0.85 | 下巴圆润，不尖锐 |
+| taper_ratio | -0.05 ~ 0.08 | 额头到下巴几乎不收窄 |
+
+**视觉特征**：面部轮廓呈圆形，没有明显棱角，看起来年轻可爱。
+
+### 2. 心形脸 (Heart)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| taper_ratio | 0.25-0.40 | 额头明显宽于下巴 |
+| chin_sharpness | 0.35-0.55 | 下巴尖细 |
+| forehead_ratio | > 0.92 | 额头宽，接近面宽 |
+| jaw_angle | 120-145° | 下颌适中 |
+
+**视觉特征**：上宽下窄，额头饱满，下巴尖俏，像心形。
+
+### 3. 菱形脸 (Diamond)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| 颧骨宽度 | > 额头×1.05 | 颧骨明显最突出 |
+| 颧骨宽度 | > 下颌×1.10 | 远宽于下颌 |
+| forehead_ratio | < 0.92 | 额头偏窄 |
+| jaw_ratio | < 0.92 | 下颌偏窄 |
+| width_uniformity | > 0.12 | 宽度差异明显 |
+
+**视觉特征**：颧骨最宽，额头和下巴都偏窄，呈菱形/钻石轮廓。
+
+### 4. 鹅蛋脸 (Oval)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.75-0.82 | 长宽比理想 |
+| jaw_angle | 130-148° | 轮廓柔和 |
+| taper_ratio | 0.05-0.15 | 适度收窄 |
+| chin_sharpness | 0.55-0.68 | 下巴圆润偏尖 |
+| width_uniformity | < 0.10 | 宽度比较均匀 |
+
+**视觉特征**：被认为是最理想的脸型，比例匀称，轮廓流畅。
+
+### 5. 方形脸 (Square)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.85-0.92 | 接近等宽 |
+| jaw_angle | 108-128° | 下颌角明显，线条硬朗 |
+| width_uniformity | < 0.08 | 三处宽度接近 |
+| chin_sharpness | > 0.65 | 下巴偏平宽 |
+| jaw_ratio | > 0.92 | 下颌宽接近面宽 |
+
+**视觉特征**：额头、颧骨、下颌宽度接近，下颌角明显，给人干练印象。
+
+### 6. 长形脸 (Long/Oblong)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| aspect_ratio | 0.58-0.70 | 面部明显偏长 |
+| face_height/face_width | > 1.40 | 高度远超宽度 |
+| taper_ratio | 0.05-0.15 | 适度收窄 |
+| jaw_angle | 125-145° | 下颌适中 |
+
+**视觉特征**：面部修长，整体偏窄，额头较饱满。
+
+### 7. 瓜子脸 (Melon Seed / V-shape)
+
+| 特征 | 典型值 | 说明 |
+|------|--------|------|
+| taper_ratio | 0.20-0.35 | 额头宽于下巴 |
+| aspect_ratio | 0.65-0.80 | 偏长 |
+| chin_sharpness | 0.30-0.55 | V 形尖下巴 |
+| 颧骨 | ≤ 额头宽度 | 颧骨不突出 |
+| jaw_width | < 额头×0.95 | 下颌收窄 |
+
+**视觉特征**：额头较宽，向下逐渐收窄到尖下巴，整体呈瓜子形。
+
+---
+
+## 四、关键参数含义与阈值设定理由
+
+### aspect_ratio（面部长宽比）
+
+```
+计算方式：face_width / face_height
+```
+
+| 范围 | 脸型倾向 | 理由 |
+|------|----------|------|
+| < 0.70 | 长形脸 | 脸长明显大于宽 |
+| 0.70-0.85 | 鹅蛋/心形/瓜子 | 多数亚洲人的标准比例 |
+| 0.85-1.0 | 圆形/方形 | 脸宽接近脸长 |
+
+**设定理由**：根据 Farkas 面部测量数据，东亚人群面宽/面高比通常在 0.75-0.88 之间。0.85 和 0.70 是自然的分界点。
+
+### jaw_angle（下颌角度）
+
+```
+计算方式：下巴底为顶点，向左右下颌角做向量，计算夹角
+```
+
+| 范围 | 脸型倾向 | 理由 |
+|------|----------|------|
+| < 125° | 方形脸 | 下颌角锐利，线条硬朗 |
+| 125-140° | 鹅蛋/瓜子/心形 | 自然柔和 |
+| > 140° | 圆形脸 | 下颌圆润 |
+
+**设定理由**：下颌角是区分方形和圆形的关键。方形脸 gonion 角通常在 110-125°，圆形脸在 140-155°。
+
+### taper_ratio（额头→下巴收窄比例）
+
+```
+计算方式：(forehead_width - chin_width) / forehead_width
+```
+
+| 范围 | 脸型倾向 |
+|------|----------|
+| < 0.05 | 圆形/方形（无收窄）|
+| 0.05-0.15 | 鹅蛋/长形（适度收窄）|
+| > 0.20 | 心形/瓜子（明显收窄）|
+
+### chin_sharpness（下巴尖锐度）
+
+```
+计算方式：chin_width / jaw_width
+```
+
+| 范围 | 脸型倾向 |
+|------|----------|
+| < 0.50 | 尖下巴（瓜子/心形）|
+| 0.50-0.65 | 适中（鹅蛋）|
+| > 0.65 | 宽下巴（圆形/方形）|
+
+---
+
+## 五、优化点说明
+
+### 5.1 从「硬规则」到「评分制」
+
+**原始方案**：每条规则是独立的 if 判断，可能同时满足多条，也可能都不满足。
+
+**优化方案**：每种脸型计算 0-100 的匹配度分数，取最高分。
+
+```python
+# 原始：可能冲突
+if 0.85 <= width_ratio <= 1.0:   # 圆形脸
+    ...
+if jaw_angle < 130:              # 方形脸
+    ...
+# 同一张脸可能同时满足或都不满足！
+
+# 优化：评分制，必然有结果
+scores = {'圆形脸': 72.5, '方形脸': 45.0, ...}
+# 取最高分 → 圆形脸，置信度 72.5/total
+```
+
+### 5.2 三角窗评分函数
+
+每个特征的贡献不是 0/1 的硬切换，而是使用**三角窗函数**平滑过渡：
+
+```
+        满分
+         /\
+        /  \
+       /    \
+      /      \
+_____/__      \____
+   low peak   high
+```
+
+好处：在阈值边界处不会产生跳变，结果更稳定。
+
+### 5.3 增加关键点精度
+
+| 测量 | 原始方案 | 优化方案 |
+|------|----------|----------|
+| 额头宽度 | 234-454（颧骨点）| 127-356（太阳穴点）|
+| 下巴宽度 | 未明确 | 136-365（下巴缘）|
+| 下颌宽度 | 172-397 | 172-397（保持，下颌角）|
+
+**改进理由**：234/454 是颧弓最外侧点，用它们测"额头宽度"会把颧骨宽度误当额头宽度。改用 127/356 太阳穴点更准确。
+
+### 5.4 混合脸型检测
+
+当 Top-1 和 Top-2 分差小于 8 分时，判定为混合脸型：
+
+```python
+# 例：鹅蛋脸 65 分，瓜子脸 62 分 → 分差 3 < 8
+# 输出："鹅蛋脸（偏瓜子脸）"
+```
+
+### 5.5 置信度输出
+
+```python
+confidence = best_score / total_score
+# > 0.25  → 高置信度，结果明确
+# 0.18-0.25 → 中等，有一定混合
+# < 0.18  → 低置信度，建议人工复核
+```
+
+---
+
+## 六、边界情况处理建议
+
+### 6.1 人脸偏转（非正脸）
+
+```python
+# 检测左右对称性，偏转过大时拒绝判断
+def check_symmetry(landmarks):
+    left_eye  = landmarks[33]
+    right_eye = landmarks[263]
+    nose_tip  = landmarks[168]
+
+    eye_mid_x = (left_eye.x + right_eye.x) / 2
+    symmetry = abs(eye_mid_x - nose_tip.x)
+
+    if symmetry > 0.03:  # 偏移过大
+        return False, "检测到人脸偏转，建议正脸拍摄"
+    return True, ""
+```
+
+### 6.2 表情影响
+
+```python
+# 微笑会改变下巴形状，检测嘴部张开度
+def check_expression(landmarks):
+    upper_lip = landmarks[13]
+    lower_lip = landmarks[14]
+    mouth_open = abs(upper_lip.y - lower_lip.y)
+
+    if mouth_open > 0.05:  # 嘴巴张大
+        return False, "检测到嘴巴张开，建议自然闭合"
+    return True, ""
+```
+
+### 6.3 多特征都低分
+
+```python
+if best_score < 25.0:
+    return "无法确定", 0.0, {"reason": "特征不够明显，无法准确分类"}
+```
+
+### 6.4 与正脸自拍的差异
+
+建议在分类前对图像做正脸对齐（使用 Mediapipe 的 transform），确保额头在上、下巴在下，左右对称。
+
+### 6.5 性别/年龄差异
+
+男性下颌通常更宽，女性下巴更尖。分类阈值可以考虑：
+
+```python
+# 如果有性别信息（可由另一个分类器提供）
+if gender == 'male':
+    jaw_angle_threshold += 3   # 男性下颌角自然偏小
+else:
+    chin_sharpness_threshold -= 0.03  # 女性下巴自然偏尖
+```
+
+---
+
+## 七、实现建议和注意事项
+
+### 7.1 预处理
+
+```python
+import mediapipe as mp
+
+mp_face_mesh = mp.solutions.face_mesh
+
+with mp_face_mesh.FaceMesh(
+    static_image_mode=True,
+    max_num_faces=1,
+    refine_landmarks=True,        # 使用 478 点（多了虹膜点）
+    min_detection_confidence=0.5,
+) as face_mesh:
+    results = face_mesh.process(rgb_image)
+    if results.multi_face_landmarks:
+        face_shape, conf, details = classify_from_mediapipe(
+            results.multi_face_landmarks
+        )
+```
+
+### 7.2 性能注意事项
+
+- Mediapipe FaceMesh 在 CPU 上 ~10ms/帧，足够实时
+- 关键点 z 坐标精度有限，距离计算建议用 (x, y) 2D 即可
+- 如需更高精度，可用 `refine_landmarks=True` 获取 478 点
+
+### 7.3 阈值校准
+
+当前阈值基于以下来源综合设定：
+
+1. **Farkas 面部测量学数据**（经典人体测量参考）
+2. **亚洲人脸型分布统计**（鹅蛋脸和瓜子脸比例较高）
+3. **Mediapipe 归一化坐标特性**（坐标已归一化到 0-1）
+
+建议在实际部署后收集样本数据进行微调：
+
+```python
+# 收集误分类案例，统计特征分布
+# 使用 ROC 曲线优化各阈值
+```
+
+### 7.4 2D vs 3D 距离
+
+Mediapipe 返回的 landmark 包含 z 坐标，但 z 精度不如 x/y。建议：
+
+```python
+# 推荐方案：仅用 x, y 计算（忽略 z）
+def pt_2d(idx):
+    lm = landmarks[idx]
+    return np.array([lm.x, lm.y])
+
+# 高精度方案：用 z 但加权降低
+def pt_weighted(idx):
+    lm = landmarks[idx]
+    return np.array([lm.x, lm.y, lm.z * 0.5])  # z 权重减半
+```
+
+### 7.5 与原始规则的对比
+
+| 维度 | 原始规则 | 优化后 |
+|------|----------|--------|
+| 判断方式 | 硬 if-else（可能冲突/遗漏）| 评分制（必然有结果）|
+| 关键点 | 12 个 | 16 个（增加太阳穴、下巴缘点）|
+| 特征数 | 5-6 个 | 15 个（含复合特征）|
+| 输出 | 单一标签 | 标签 + 置信度 + 混合脸型 |
+| 边界处理 | 无 | 三角窗平滑 + 低分兜底 |
+| 可调性 | 改阈值需要理解全部分支 | 改 `peak` 值即可微调 |
+| 代码行数 | ~60 行 | ~300 行（含注释）|
+
+---
+
+## 八、测试用例参考
+
+```python
+# 单元测试伪代码
+test_cases = [
+    # (features_dict, expected_shape)
+    ({'aspect_ratio': 0.92, 'jaw_angle': 148, 'taper_ratio': 0.03,
+      'chin_sharpness': 0.75, 'width_uniformity': 0.06,
+      'forehead_ratio': 0.98, 'jaw_ratio': 0.95, 'chin_ratio': 0.70,
+      'face_curve_score': 0.18, ...}, '圆形脸'),
+
+    ({'aspect_ratio': 0.77, 'jaw_angle': 135, 'taper_ratio': 0.10,
+      'chin_sharpness': 0.60, 'width_uniformity': 0.08,
+      'forehead_ratio': 0.98, 'jaw_ratio': 0.92, 'chin_ratio': 0.62,
+      'face_curve_score': 0.19, ...}, '鹅蛋脸'),
+
+    # ... 更多测试用例
+]
+```
+
+---
+
+*报告结束。代码可直接集成到 Mediapipe 人脸分析流水线中。*

face/face_shape_classifier.py

@@ -0,0 +1,874 @@
+"""
+face_shape_classifier.py
+基于 Mediapipe 468 点人脸关键点的脸型分类系统
+
+支持的脸型：圆形脸 / 心形脸 / 菱形脸 / 鹅蛋脸 / 方形脸 / 长形脸 / 瓜子脸
+分类策略：多维度特征提取 → 加权评分 → 置信度判断
+
+实现说明：
+- 特征与评分框架参考 face_shape_classification.md
+- 距离一律在像素坐标系下用 2D 计算（归一化坐标未校正宽高比会导致面宽被夸大）
+- 阈值按 MediaPipe 实测分布做了校准
+"""
+
+from __future__ import annotations
+
+import math
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import cv2
+import mediapipe as mp
+import numpy as np
+
+ImageInput = Union[str, Path, np.ndarray]
+
+
+class FaceLandmarks:
+    """Mediapipe 关键点索引（脸型分析用）。"""
+
+    FOREHEAD_TOP = 10
+    CHIN_BOTTOM = 152
+
+    # 额侧（比 127/356 更贴近发际两侧，避免把太阳穴外轮廓算成额头）
+    LEFT_FOREHEAD = 54
+    RIGHT_FOREHEAD = 284
+
+    # 太阳穴辅助
+    LEFT_TEMPLE = 21
+    RIGHT_TEMPLE = 251
+
+    # 颧骨最外侧
+    LEFT_CHEEK = 234
+    RIGHT_CHEEK = 454
+
+    # 下颌角（比 172/397 更接近 gonion）
+    LEFT_JAW_ANGLE = 132
+    RIGHT_JAW_ANGLE = 361
+
+    # 下巴缘
+    LEFT_CHIN = 136
+    RIGHT_CHIN = 365
+
+
+def extract_face_features(landmarks, image_size: Tuple[int, int]) -> Dict[str, float]:
+    """
+    从 Mediapipe 关键点提取脸型特征。
+
+    参数:
+        landmarks: NormalizedLandmark 列表
+        image_size: (width, height)，用于还原像素坐标
+    """
+    w, h = image_size
+
+    def pt(idx: int) -> np.ndarray:
+        lm = landmarks[idx]
+        return np.array([lm.x * w, lm.y * h], dtype=float)
+
+    def dist(p1: np.ndarray, p2: np.ndarray) -> float:
+        return float(np.linalg.norm(p1 - p2))
+
+    def xwidth(p1: np.ndarray, p2: np.ndarray) -> float:
+        """横向宽度（脸型比例更稳定）。"""
+        return abs(float(p1[0] - p2[0]))
+
+    forehead_top = pt(FaceLandmarks.FOREHEAD_TOP)
+    chin_bottom = pt(FaceLandmarks.CHIN_BOTTOM)
+
+    left_forehead = pt(FaceLandmarks.LEFT_FOREHEAD)
+    right_forehead = pt(FaceLandmarks.RIGHT_FOREHEAD)
+    left_temple = pt(FaceLandmarks.LEFT_TEMPLE)
+    right_temple = pt(FaceLandmarks.RIGHT_TEMPLE)
+    left_cheek = pt(FaceLandmarks.LEFT_CHEEK)
+    right_cheek = pt(FaceLandmarks.RIGHT_CHEEK)
+    left_jaw = pt(FaceLandmarks.LEFT_JAW_ANGLE)
+    right_jaw = pt(FaceLandmarks.RIGHT_JAW_ANGLE)
+    left_chin = pt(FaceLandmarks.LEFT_CHIN)
+    right_chin = pt(FaceLandmarks.RIGHT_CHIN)
+
+    face_height = dist(forehead_top, chin_bottom)
+    forehead_width = xwidth(left_forehead, right_forehead)
+    temple_width = xwidth(left_temple, right_temple)
+    cheekbone_width = xwidth(left_cheek, right_cheek)
+    jaw_width = xwidth(left_jaw, right_jaw)
+    chin_width = xwidth(left_chin, right_chin)
+    face_width = cheekbone_width
+
+    eps = 1e-8
+
+    # 下巴顶点夹角：越大越宽圆，越小越尖
+    v_left = left_jaw - chin_bottom
+    v_right = right_jaw - chin_bottom
+    cos_val = np.dot(v_left, v_right) / (
+        np.linalg.norm(v_left) * np.linalg.norm(v_right) + eps
+    )
+    cos_val = float(np.clip(cos_val, -1.0, 1.0))
+    jaw_angle = math.degrees(math.acos(cos_val))
+
+    # 下颌角（左）：颧骨→下颌角→下巴，越小越方正硬朗
+    v1 = left_cheek - left_jaw
+    v2 = chin_bottom - left_jaw
+    cos_g = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + eps)
+    cos_g = float(np.clip(cos_g, -1.0, 1.0))
+    gonion_angle = math.degrees(math.acos(cos_g))
+
+    aspect_ratio = face_width / (face_height + eps)
+    length_ratio = face_height / (face_width + eps)
+    taper_ratio = (forehead_width - chin_width) / (forehead_width + eps)
+    cheek_taper = (cheekbone_width - jaw_width) / (cheekbone_width + eps)
+
+    forehead_ratio = forehead_width / (face_width + eps)
+    temple_ratio = temple_width / (face_width + eps)
+    jaw_ratio = jaw_width / (face_width + eps)
+    chin_ratio = chin_width / (face_width + eps)
+    chin_sharpness = chin_width / (jaw_width + eps)
+
+    widths = [forehead_width, cheekbone_width, jaw_width]
+    width_uniformity = (max(widths) - min(widths)) / (max(widths) + eps)
+
+    jaw_midpoint = (left_jaw + right_jaw) / 2.0
+    face_curve_score = dist(jaw_midpoint, chin_bottom) / (face_height + eps)
+
+    forehead_vs_jaw = forehead_width / (jaw_width + eps)
+    cheek_dominance = cheekbone_width / ((forehead_width + jaw_width) / 2.0 + eps)
+
+    return {
+        "face_height": face_height,
+        "face_width": face_width,
+        "forehead_width": forehead_width,
+        "temple_width": temple_width,
+        "cheekbone_width": cheekbone_width,
+        "jaw_width": jaw_width,
+        "chin_width": chin_width,
+        "aspect_ratio": aspect_ratio,
+        "length_ratio": length_ratio,
+        "taper_ratio": taper_ratio,
+        "cheek_taper": cheek_taper,
+        "forehead_ratio": forehead_ratio,
+        "temple_ratio": temple_ratio,
+        "cheekbone_ratio": 1.0,
+        "jaw_ratio": jaw_ratio,
+        "chin_ratio": chin_ratio,
+        "jaw_angle": jaw_angle,
+        "gonion_angle": gonion_angle,
+        "chin_sharpness": chin_sharpness,
+        "width_uniformity": width_uniformity,
+        "face_curve_score": face_curve_score,
+        "forehead_vs_jaw": forehead_vs_jaw,
+        "cheek_dominance": cheek_dominance,
+    }
+
+
+# ============================================================
+#  参考分布：1143 张样本（1093 张脸型测试集合 + 44 张真实照片 + 6 张标注图）
+#  的稳健统计量 (中位数, 稳健标准差=IQR/1.349)，把绝对测量值转成 z 分数。
+#  绝对阈值会随镜头、人群漂移；z 分数让评分只依赖"相对人群偏离多少"。
+#
+#  早前这组数字只由 50 张样本估得，相对全量人群有系统性偏移
+#  （jaw_ratio 中位偏低 0.44 sd、taper_ratio 偏高 0.53 sd 等），
+#  恰好三项都在给方形脸加分，是方形脸占比虚高的主因之一。
+# ============================================================
+REFERENCE_STATS: Dict[str, Tuple[float, float]] = {
+    "aspect_ratio": (0.8294, 0.0281),
+    "jaw_angle": (88.6299, 3.9040),
+    "gonion_angle": (144.5014, 3.4991),
+    "taper_ratio": (0.2671, 0.0376),
+    "forehead_ratio": (0.8975, 0.0204),
+    "jaw_ratio": (0.9286, 0.0138),
+    "chin_ratio": (0.6578, 0.0217),
+    "chin_sharpness": (0.7092, 0.0155),
+    "width_uniformity": (0.1028, 0.0184),
+    "forehead_vs_jaw": (0.9669, 0.0341),
+    "cheek_dominance": (1.0953, 0.0084),
+    "face_curve_score": (0.3940, 0.0210),
+}
+
+# 匹配容差（以 z 为单位）：偏离目标 1 个容差，该项得分降到约 0.61
+MATCH_TOLERANCE = 1.0
+
+# 每种脸型的原型：特征 -> (目标 z, 权重, 模式)
+#   'high' 超过目标即满分（越极端越像）
+#   'low'  低于目标即满分
+#   'peak' 双侧衰减（该特征应当落在目标附近）
+#
+# 只使用互相独立的特征：length_ratio(=1/aspect_ratio) 与
+# cheek_taper(=1-jaw_ratio) 是重复信号，纳入会让对应脸型拿双倍权重。
+#
+# 靶心与权重的来源：先按 1093 张测试集中各原始分组（方形脸/长形脸/瓜子脸/
+# 标准脸/娃娃脸）的实测 z 画像给出靶心，再在「6 张标注图判定不变」的硬约束下
+# 做带边界的退火微调（权重限 [0.5,5]、靶心限 [-2,2]，并惩罚失去区分力的空项）。
+SHAPE_PROTOTYPES: Dict[str, Dict[str, Tuple[float, float, str]]] = {
+    # 宽、短，下颌圆钝
+    "圆形脸": {
+        "aspect_ratio": (+2.00, 5.00, "high"),
+        "jaw_angle": (-0.07, 2.28, "high"),
+        "chin_sharpness": (+0.15, 0.50, "peak"),
+        "face_curve_score": (+0.17, 3.67, "low"),
+    },
+    # 额头宽、下颌与下巴明显收窄
+    "心形脸": {
+        "forehead_vs_jaw": (+1.94, 1.62, "high"),
+        "taper_ratio": (+2.00, 1.09, "high"),
+        "jaw_ratio": (+1.02, 1.86, "low"),
+        "chin_ratio": (-1.70, 1.71, "low"),
+        "aspect_ratio": (+1.69, 1.31, "peak"),
+    },
+    # 颧骨最突出，额头与下颌都窄
+    "菱形脸": {
+        "cheek_dominance": (+1.62, 2.84, "high"),
+        "width_uniformity": (+1.24, 2.12, "high"),
+        "forehead_ratio": (-1.57, 4.58, "low"),
+        "jaw_ratio": (-0.11, 1.85, "low"),
+        "aspect_ratio": (+1.31, 2.60, "peak"),
+    },
+    # 各项都接近人群中位——没有突出特征即为匀称
+    "鹅蛋脸": {
+        "aspect_ratio": (-0.26, 5.00, "peak"),
+        "jaw_ratio": (+0.45, 2.47, "peak"),
+        "chin_sharpness": (+0.52, 2.88, "peak"),
+        "width_uniformity": (+0.53, 0.94, "peak"),
+        "cheek_dominance": (-0.51, 1.14, "peak"),
+    },
+    # 下颌与下巴都宽、几乎不收窄、下颌角锐利、额头相对窄。
+    # 注意 aspect_ratio 用 peak 而非 high：测试集中 121 张方脸的
+    # aspect_ratio 中位仅 +0.16，真正"宽"的是娃娃脸(+1.01)——
+    # 早前把它当成 high 模式的强特征，是方形脸吞掉圆脸的主因。
+    #
+    # chin_ratio 是方脸组区分度最大的一项（组内中位 z=+1.45，标准脸组仅 -0.06），
+    # 故靶心直接对齐 +1.45。靶心与权重必须同时提：若只加权重而把靶心留在低位，
+    # 全人群八成都能拿满分，等于给所有人同加一笔，反而推高方形脸占比。
+    "方形脸": {
+        "aspect_ratio": (+1.11, 4.75, "peak"),
+        "jaw_ratio": (-0.02, 2.27, "high"),
+        "chin_ratio": (+1.45, 3.00, "high"),
+        "taper_ratio": (-0.38, 0.51, "low"),
+        "chin_sharpness": (-0.38, 0.99, "high"),
+        "width_uniformity": (+0.58, 4.34, "high"),
+        "gonion_angle": (+0.10, 3.38, "low"),
+        "forehead_ratio": (-1.70, 4.05, "low"),
+    },
+    # 明显偏长偏窄
+    "长形脸": {
+        "aspect_ratio": (-1.49, 1.17, "low"),
+        "chin_sharpness": (+1.76, 0.50, "high"),
+        "taper_ratio": (+0.84, 0.50, "low"),
+    },
+    # 似心形但下巴更长更尖（face_curve_score 高），颧骨不外扩
+    "瓜子脸": {
+        "face_curve_score": (+1.02, 3.41, "high"),
+        "forehead_ratio": (+0.85, 3.31, "high"),
+        "taper_ratio": (+0.35, 1.02, "high"),
+        "cheek_dominance": (-1.41, 2.21, "low"),
+        "jaw_ratio": (-1.04, 0.57, "low"),
+        "chin_sharpness": (-1.33, 2.53, "low"),
+    },
+}
+
+
+def feature_zscores(features: Dict[str, float]) -> Dict[str, float]:
+    """把测量值转成相对参考人群的 z 分数。"""
+    return {
+        key: (features[key] - median) / scale
+        for key, (median, scale) in REFERENCE_STATS.items()
+        if key in features
+    }
+
+
+def _match(z: float, target: float, mode: str) -> float:
+    """单项匹配度 0~1。"""
+    if mode == "high" and z >= target:
+        return 1.0
+    if mode == "low" and z <= target:
+        return 1.0
+    return math.exp(-((z - target) ** 2) / (2 * MATCH_TOLERANCE**2))
+
+
+def classify_face_shape(
+    features: Dict[str, float],
+    return_details: bool = False,
+) -> Tuple[str, float, Optional[Dict]]:
+    """
+    脸型分类器：把特征转成 z 分数后，与各脸型原型做加权匹配。
+
+    返回 (脸型, 置信度, 详情)。置信度 = Top1 / (Top1 + Top2)，
+    0.5 表示两种脸型完全无法区分，接近 1 表示判定明确。
+    """
+    z = feature_zscores(features)
+
+    scores: Dict[str, float] = {}
+    contributions: Dict[str, Dict[str, float]] = {}
+    for shape, prototype in SHAPE_PROTOTYPES.items():
+        total_weight = sum(w for _, w, _ in prototype.values())
+        acc = 0.0
+        per_feature = {}
+        for key, (target, weight, mode) in prototype.items():
+            m = _match(z[key], target, mode)
+            per_feature[key] = m
+            acc += weight * m
+        scores[shape] = 100.0 * acc / total_weight
+        contributions[shape] = per_feature
+
+    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+    best_shape, best_score = ranked[0]
+    second_shape, second_score = ranked[1] if len(ranked) > 1 else (None, 0.0)
+
+    denom = best_score + second_score
+    confidence = best_score / denom if denom > 0 else 0.0
+    score_gap = best_score - second_score
+    is_mixed = score_gap < 5.0
+
+    details = None
+    if return_details:
+        details = {
+            "scores": scores,
+            "ranked": ranked,
+            "confidence": confidence,
+            "score_gap": score_gap,
+            "is_mixed": is_mixed,
+            "second_shape": second_shape,
+            "second_score": second_score,
+            "zscores": z,
+            "contributions": contributions,
+        }
+
+    return best_shape, confidence, details
+
+
+def get_mixed_description(details: Dict) -> str:
+    if not details or not details.get("is_mixed"):
+        return ""
+    shape1 = details["ranked"][0][0]
+    shape2 = details["ranked"][1][0]
+    return f"{shape1}（偏{shape2}）"
+
+
+_face_mesh = None
+
+
+def _get_face_mesh():
+    global _face_mesh
+    if _face_mesh is None:
+        _face_mesh = mp.solutions.face_mesh.FaceMesh(
+            static_image_mode=True,
+            max_num_faces=1,
+            refine_landmarks=True,
+            min_detection_confidence=0.5,
+        )
+    return _face_mesh
+
+
+def _load_image(image: ImageInput) -> np.ndarray:
+    if isinstance(image, np.ndarray):
+        if image.ndim != 3 or image.shape[2] not in (3, 4):
+            raise ValueError("numpy 图片需为 HxWx3/4 的彩色图")
+        if image.shape[2] == 4:
+            return cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
+        return image
+
+    path = Path(image)
+    img = cv2.imread(str(path))
+    if img is None:
+        raise FileNotFoundError(f"无法读取图片: {path}")
+    return img
+
+
+def _landmark_points(landmarks, image_size: Tuple[int, int]) -> Dict[str, Tuple[int, int]]:
+    """提取标注用像素点。"""
+    w, h = image_size
+
+    def xy(idx: int) -> Tuple[int, int]:
+        lm = landmarks[idx]
+        return int(round(lm.x * w)), int(round(lm.y * h))
+
+    left_jaw = xy(FaceLandmarks.LEFT_JAW_ANGLE)
+    right_jaw = xy(FaceLandmarks.RIGHT_JAW_ANGLE)
+    return {
+        "forehead_top": xy(FaceLandmarks.FOREHEAD_TOP),
+        "chin_bottom": xy(FaceLandmarks.CHIN_BOTTOM),
+        "left_forehead": xy(FaceLandmarks.LEFT_FOREHEAD),
+        "right_forehead": xy(FaceLandmarks.RIGHT_FOREHEAD),
+        "left_cheek": xy(FaceLandmarks.LEFT_CHEEK),
+        "right_cheek": xy(FaceLandmarks.RIGHT_CHEEK),
+        "left_jaw": left_jaw,
+        "right_jaw": right_jaw,
+        "left_chin": xy(FaceLandmarks.LEFT_CHIN),
+        "right_chin": xy(FaceLandmarks.RIGHT_CHIN),
+        "jaw_mid": (
+            int(round((left_jaw[0] + right_jaw[0]) / 2)),
+            int(round((left_jaw[1] + right_jaw[1]) / 2)),
+        ),
+    }
+
+
+def _put_text_cn(
+    img: np.ndarray,
+    text: str,
+    org: Tuple[int, int],
+    color: Tuple[int, int, int],
+    font_size: int = 18,
+) -> None:
+    """在图上绘制中文/英文混合文字（Pillow）。"""
+    from PIL import Image, ImageDraw, ImageFont
+
+    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    pil = Image.fromarray(rgb)
+    draw = ImageDraw.Draw(pil)
+
+    font_paths = [
+        "/usr/share/fonts/truetype/wqy/wqy-microhei.ttc",
+        "/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
+        "/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc",
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+    ]
+    font = None
+    for fp in font_paths:
+        if Path(fp).exists():
+            try:
+                font = ImageFont.truetype(fp, font_size)
+                break
+            except OSError:
+                continue
+    if font is None:
+        font = ImageFont.load_default()
+
+    x, y = org
+    # 阴影提升可读性
+    draw.text((x + 1, y + 1), text, font=font, fill=(0, 0, 0))
+    draw.text((x, y), text, font=font, fill=(color[2], color[1], color[0]))
+    img[:] = cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
+
+
+def _draw_h_line(
+    img: np.ndarray,
+    p1: Tuple[int, int],
+    p2: Tuple[int, int],
+    color: Tuple[int, int, int],
+    label: str,
+    thickness: int = 2,
+    label_above: bool = True,
+) -> None:
+    """画横向宽度线 + 端点 + 标签。"""
+    y = int(round((p1[1] + p2[1]) / 2))
+    x1, x2 = min(p1[0], p2[0]), max(p1[0], p2[0])
+    cv2.line(img, (x1, y), (x2, y), color, thickness, cv2.LINE_AA)
+    cv2.circle(img, (x1, y), 4, color, -1, cv2.LINE_AA)
+    cv2.circle(img, (x2, y), 4, color, -1, cv2.LINE_AA)
+    # 端点小竖线
+    tick = max(6, thickness * 3)
+    cv2.line(img, (x1, y - tick), (x1, y + tick), color, thickness, cv2.LINE_AA)
+    cv2.line(img, (x2, y - tick), (x2, y + tick), color, thickness, cv2.LINE_AA)
+    mid = ((x1 + x2) // 2, y - 8 if label_above else y + 4)
+    _put_text_cn(img, label, mid, color, font_size=max(14, img.shape[0] // 55))
+
+
+def _draw_v_line(
+    img: np.ndarray,
+    p1: Tuple[int, int],
+    p2: Tuple[int, int],
+    color: Tuple[int, int, int],
+    label: str,
+    thickness: int = 2,
+) -> None:
+    """画纵向高度线 + 端点 + 标签。"""
+    x = int(round((p1[0] + p2[0]) / 2))
+    y1, y2 = min(p1[1], p2[1]), max(p1[1], p2[1])
+    cv2.line(img, (x, y1), (x, y2), color, thickness, cv2.LINE_AA)
+    cv2.circle(img, (x, y1), 4, color, -1, cv2.LINE_AA)
+    cv2.circle(img, (x, y2), 4, color, -1, cv2.LINE_AA)
+    tick = max(6, thickness * 3)
+    cv2.line(img, (x - tick, y1), (x + tick, y1), color, thickness, cv2.LINE_AA)
+    cv2.line(img, (x - tick, y2), (x + tick, y2), color, thickness, cv2.LINE_AA)
+    _put_text_cn(
+        img,
+        label,
+        (x + 8, (y1 + y2) // 2),
+        color,
+        font_size=max(14, img.shape[0] // 55),
+    )
+
+
+def annotate_face_features(
+    image: ImageInput,
+    landmarks=None,
+    features: Optional[Dict[str, float]] = None,
+) -> np.ndarray:
+    """
+    在原图上标注 face_width / face_height 及文档中的关键比例特征。
+
+    返回 BGR 标注图。
+    """
+    bgr = _load_image(image).copy()
+    h, w = bgr.shape[:2]
+
+    if landmarks is None:
+        rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+        results = _get_face_mesh().process(rgb)
+        if not results.multi_face_landmarks:
+            raise ValueError("未检测到人脸关键点")
+        landmarks = results.multi_face_landmarks[0].landmark
+
+    if features is None:
+        features = extract_face_features(landmarks, image_size=(w, h))
+
+    pts = _landmark_points(landmarks, (w, h))
+    overlay = bgr.copy()
+    fs = max(14, h // 55)
+    thick = max(2, h // 400)
+
+    # ---- 尺寸主轴 ----
+    # face_height: 额头顶 → 下巴底
+    _draw_v_line(
+        overlay,
+        pts["forehead_top"],
+        pts["chin_bottom"],
+        (40, 180, 255),
+        f"face_height {features['face_height']:.0f}px",
+        thickness=thick + 1,
+    )
+    # face_width (= cheekbone): 左颧 → 右颧
+    _draw_h_line(
+        overlay,
+        pts["left_cheek"],
+        pts["right_cheek"],
+        (0, 220, 120),
+        f"face_width {features['face_width']:.0f}px",
+        thickness=thick + 1,
+        label_above=True,
+    )
+
+    # ---- 各级宽度（forehead / jaw / chin）----
+    # 略微错开 y，避免完全重叠
+    fh_y = pts["left_forehead"][1]
+    _draw_h_line(
+        overlay,
+        (pts["left_forehead"][0], fh_y),
+        (pts["right_forehead"][0], fh_y),
+        (255, 160, 40),
+        f"forehead  ratio={features['forehead_ratio']:.3f}",
+        thickness=thick,
+        label_above=True,
+    )
+    jy = pts["left_jaw"][1]
+    _draw_h_line(
+        overlay,
+        (pts["left_jaw"][0], jy),
+        (pts["right_jaw"][0], jy),
+        (80, 120, 255),
+        f"jaw  ratio={features['jaw_ratio']:.3f}",
+        thickness=thick,
+        label_above=False,
+    )
+    cy = pts["left_chin"][1]
+    _draw_h_line(
+        overlay,
+        (pts["left_chin"][0], cy),
+        (pts["right_chin"][0], cy),
+        (220, 80, 220),
+        f"chin  ratio={features['chin_ratio']:.3f}",
+        thickness=thick,
+        label_above=False,
+    )
+
+    # cheekbone_ratio（相对 face_width，恒为 1.0）写在颧骨线旁
+    cheek_mid = (
+        (pts["left_cheek"][0] + pts["right_cheek"][0]) // 2,
+        pts["left_cheek"][1] + max(18, h // 40),
+    )
+    _put_text_cn(
+        overlay,
+        f"cheekbone_ratio={features['cheekbone_ratio']:.3f}",
+        cheek_mid,
+        (0, 200, 100),
+        font_size=fs,
+    )
+
+    # ---- jaw_angle：下巴 → 左右下颌角 ----
+    cv2.line(overlay, pts["chin_bottom"], pts["left_jaw"], (0, 90, 255), thick, cv2.LINE_AA)
+    cv2.line(overlay, pts["chin_bottom"], pts["right_jaw"], (0, 90, 255), thick, cv2.LINE_AA)
+    cv2.circle(overlay, pts["chin_bottom"], 5, (0, 90, 255), -1, cv2.LINE_AA)
+    # 角度弧
+    v1 = np.array(pts["left_jaw"], dtype=float) - np.array(pts["chin_bottom"], dtype=float)
+    v2 = np.array(pts["right_jaw"], dtype=float) - np.array(pts["chin_bottom"], dtype=float)
+    a1 = math.degrees(math.atan2(-v1[1], v1[0]))
+    a2 = math.degrees(math.atan2(-v2[1], v2[0]))
+    # OpenCV ellipse 角度：从 x 轴顺时针；atan2 转一下
+    start_ang = -a1
+    end_ang = -a2
+    if end_ang < start_ang:
+        start_ang, end_ang = end_ang, start_ang
+    radius = max(28, int(0.08 * features["face_height"]))
+    cv2.ellipse(
+        overlay,
+        pts["chin_bottom"],
+        (radius, radius),
+        0,
+        start_ang,
+        end_ang,
+        (0, 90, 255),
+        thick,
+        cv2.LINE_AA,
+    )
+    _put_text_cn(
+        overlay,
+        f"jaw_angle {features['jaw_angle']:.1f}°",
+        (pts["chin_bottom"][0] + radius + 4, pts["chin_bottom"][1] - radius),
+        (0, 90, 255),
+        font_size=fs,
+    )
+
+    # ---- taper_ratio：额头两端 → 下巴两端（收窄示意）----
+    cv2.line(
+        overlay,
+        pts["left_forehead"],
+        pts["left_chin"],
+        (40, 200, 255),
+        max(1, thick - 1),
+        cv2.LINE_AA,
+    )
+    cv2.line(
+        overlay,
+        pts["right_forehead"],
+        pts["right_chin"],
+        (40, 200, 255),
+        max(1, thick - 1),
+        cv2.LINE_AA,
+    )
+    taper_anchor = (
+        pts["left_forehead"][0] - max(10, w // 30),
+        (pts["left_forehead"][1] + pts["left_chin"][1]) // 2,
+    )
+    _put_text_cn(
+        overlay,
+        f"taper_ratio={features['taper_ratio']:.3f}",
+        taper_anchor,
+        (40, 200, 255),
+        font_size=fs,
+    )
+
+    # ---- chin_sharpness：下巴宽 vs 下颌宽 ----
+    _put_text_cn(
+        overlay,
+        f"chin_sharpness={features['chin_sharpness']:.3f} (chin/jaw)",
+        (pts["left_chin"][0], pts["left_chin"][1] + max(16, h // 45)),
+        (220, 80, 220),
+        font_size=fs,
+    )
+
+    # ---- width_uniformity：三宽差异 ----
+    widths = [
+        ("F", features["forehead_width"], (255, 160, 40)),
+        ("C", features["cheekbone_width"], (0, 220, 120)),
+        ("J", features["jaw_width"], (80, 120, 255)),
+    ]
+    # 右侧小柱状示意
+    panel_x = min(w - max(90, w // 8), max(pts["right_cheek"][0] + 20, w - max(100, w // 7)))
+    panel_y = max(40, pts["forehead_top"][1])
+    max_w = max(x[1] for x in widths) + 1e-8
+    bar_h = max(10, h // 60)
+    gap = max(4, h // 120)
+    for i, (name, val, color) in enumerate(widths):
+        bw = int((val / max_w) * max(50, w // 10))
+        y0 = panel_y + i * (bar_h + gap)
+        cv2.rectangle(overlay, (panel_x, y0), (panel_x + bw, y0 + bar_h), color, -1, cv2.LINE_AA)
+        _put_text_cn(overlay, name, (panel_x + bw + 4, y0 - 2), color, font_size=max(12, fs - 2))
+    _put_text_cn(
+        overlay,
+        f"width_uniformity={features['width_uniformity']:.3f}",
+        (panel_x, panel_y + 3 * (bar_h + gap) + 2),
+        (230, 230, 230),
+        font_size=fs,
+    )
+
+    # ---- face_curve_score：下颌中点 → 下巴 ----
+    cv2.line(
+        overlay,
+        pts["jaw_mid"],
+        pts["chin_bottom"],
+        (180, 255, 80),
+        thick,
+        cv2.LINE_AA,
+    )
+    cv2.circle(overlay, pts["jaw_mid"], 4, (180, 255, 80), -1, cv2.LINE_AA)
+    curve_label_pos = (
+        pts["jaw_mid"][0] + 6,
+        pts["jaw_mid"][1] - max(8, h // 80),
+    )
+    _put_text_cn(
+        overlay,
+        f"face_curve_score={features['face_curve_score']:.3f}",
+        curve_label_pos,
+        (180, 255, 80),
+        font_size=fs,
+    )
+
+    # 半透明叠回原图，再叠一层实线标注更清晰：直接用 overlay
+    # 左侧参数图例
+    legend = [
+        ("face_width / face_height", (0, 220, 120)),
+        (f"jaw_angle={features['jaw_angle']:.1f}°", (0, 90, 255)),
+        (f"taper_ratio={features['taper_ratio']:.3f}", (40, 200, 255)),
+        (f"forehead_ratio={features['forehead_ratio']:.3f}", (255, 160, 40)),
+        (f"cheekbone_ratio={features['cheekbone_ratio']:.3f}", (0, 200, 100)),
+        (f"jaw_ratio={features['jaw_ratio']:.3f}", (80, 120, 255)),
+        (f"chin_ratio={features['chin_ratio']:.3f}", (220, 80, 220)),
+        (f"chin_sharpness={features['chin_sharpness']:.3f}", (220, 80, 220)),
+        (f"width_uniformity={features['width_uniformity']:.3f}", (230, 230, 230)),
+        (f"face_curve_score={features['face_curve_score']:.3f}", (180, 255, 80)),
+    ]
+    box_h = 12 + len(legend) * (fs + 6)
+    box_w = max(220, w // 3)
+    cv2.rectangle(overlay, (8, 8), (8 + box_w, 8 + box_h), (20, 20, 20), -1)
+    cv2.rectangle(overlay, (8, 8), (8 + box_w, 8 + box_h), (90, 90, 90), 1)
+    for i, (text, color) in enumerate(legend):
+        _put_text_cn(overlay, text, (16, 14 + i * (fs + 6)), color, font_size=fs)
+
+    return overlay
+
+
+def classify_from_image(
+    image: ImageInput,
+    return_details: bool = True,
+    return_annotated: bool = False,
+) -> Dict:
+    """
+    从图片判断脸型。
+
+    参数:
+        image: 图片路径，或 OpenCV BGR numpy 数组
+        return_details: 是否返回特征与各脸型得分
+        return_annotated: 是否同时返回特征标注图（BGR）
+    """
+    bgr = _load_image(image)
+    h, w = bgr.shape[:2]
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    results = _get_face_mesh().process(rgb)
+
+    if not results.multi_face_landmarks:
+        raise ValueError("未检测到人脸关键点")
+
+    landmarks = results.multi_face_landmarks[0].landmark
+    features = extract_face_features(landmarks, image_size=(w, h))
+    shape, conf, details = classify_face_shape(features, return_details=True)
+
+    display = get_mixed_description(details) or shape
+    result = {
+        "face_shape": shape,
+        "confidence": conf,
+        "display": display,
+    }
+    if return_details:
+        result["features"] = features
+        result["details"] = details
+    if return_annotated:
+        result["annotated"] = annotate_face_features(
+            bgr, landmarks=landmarks, features=features
+        )
+    return result
+
+
+def classify_from_mediapipe(
+    multi_face_landmarks,
+    image_size: Tuple[int, int],
+) -> List[Dict]:
+    """从 Mediapipe FaceMesh 结果批量分类。image_size=(width, height)。"""
+    results = []
+    for face_lms in multi_face_landmarks:
+        features = extract_face_features(face_lms.landmark, image_size=image_size)
+        shape, conf, details = classify_face_shape(features, return_details=True)
+        results.append(
+            {
+                "face_shape": shape,
+                "confidence": conf,
+                "display": get_mixed_description(details) or shape,
+                "features": features,
+                "details": details,
+            }
+        )
+    return results
+
+
+def run_test_images(test_dir: Union[str, Path, None] = None) -> List[Dict]:
+    """用 test_img 做回归测试；文件名（不含扩展名）为期望脸型。"""
+    if test_dir is None:
+        test_dir = Path(__file__).resolve().parent / "test_img"
+    test_dir = Path(test_dir)
+
+    image_paths = sorted(
+        p
+        for p in test_dir.iterdir()
+        if p.suffix.lower() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+    )
+    if not image_paths:
+        raise FileNotFoundError(f"测试目录无图片: {test_dir}")
+
+    rows = []
+    for path in image_paths:
+        expected = path.stem
+        try:
+            result = classify_from_image(path, return_details=True)
+            predicted = result["face_shape"]
+            display = result["display"]
+            conf = result["confidence"]
+            top3 = result["details"]["ranked"][:3]
+            ok = predicted == expected
+            error = None
+        except Exception as exc:  # noqa: BLE001
+            predicted = display = conf = None
+            top3 = []
+            ok = False
+            error = str(exc)
+
+        rows.append(
+            {
+                "file": path.name,
+                "expected": expected,
+                "predicted": predicted,
+                "display": display,
+                "confidence": conf,
+                "top3": top3,
+                "correct": ok,
+                "error": error,
+            }
+        )
+    return rows
+
+
+def _print_test_report(rows: List[Dict]) -> None:
+    correct = sum(1 for r in rows if r["correct"])
+    total = len(rows)
+
+    print("=" * 72)
+    print("脸型分类测试结果")
+    print("=" * 72)
+    for r in rows:
+        status = "✓" if r["correct"] else "✗"
+        if r["error"]:
+            print(f"{status} {r['file']}")
+            print(f"   期望: {r['expected']}")
+            print(f"   错误: {r['error']}")
+            continue
+
+        top3_str = ", ".join(f"{name}:{score:.1f}" for name, score in r["top3"])
+        print(f"{status} {r['file']}")
+        print(f"   期望: {r['expected']}")
+        print(f"   预测: {r['display']}  (conf={r['confidence']:.3f})")
+        print(f"   Top3: {top3_str}")
+
+    print("-" * 72)
+    print(f"准确率: {correct}/{total} = {correct / total:.1%}")
+    print("=" * 72)
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) > 1 and sys.argv[1] not in {"--test", "-t"}:
+        out = classify_from_image(sys.argv[1], return_details=True)
+        print(f"脸型: {out['display']}")
+        print(f"置信度: {out['confidence']:.3f}")
+        print("各脸型得分:")
+        for name, score in out["details"]["ranked"]:
+            print(f"  {name}: {score:.1f}")
+    else:
+        report = run_test_images()
+        _print_test_report(report)

face_analysis/annotation.py

@@ -6,9 +6,9 @@
 - 纵向竖线 8 条：人头最左 + 左脸颊/左眼外/内角/右眼内/外角/右脸颊 + 人头最右，
   把头宽切 7 段（七眼），段宽数值上下交替（上 3 / 下 4），带虚线双箭头。
   人头最左/最右取自耳朵分割外缘，看不到耳朵则省略该侧（最少 6 点 5 段）。
-- 四庭：图片左侧，「名」上「数值」下两行换行（不带 cm），带竖向虚线双箭头。
+- 四庭：图片左侧，「名」「数值(带 cm)」「百分比」三行换行，带竖向虚线双箭头。
 - 五条横线右侧标名：头顶/发际线/眉心/鼻翼下缘/下巴尖。
-- 单位 cm 统一标在底部「单位cm」。
+- 每段数值直接带 cm 后缀，下方另起一行标百分比（不再单独标底部「单位cm」）。
 中文字体用打包的思源黑体绝对路径加载，缺字体直接抛错（不静默降级成方块）。
 """
 import os
@@ -189,9 +189,8 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
       取自耳朵分割掩膜的外缘（方案 B，BiSeNet 类 7/8）；耳朵不可见（被头发/侧脸
       遮挡 → 掩膜空）或无掩膜时省略该侧端线，只画对应脸颊线。
     - 横向 5 条分界线：头顶/发际线/眉心/鼻翼下缘/下巴尖，右侧标名。
-    - 四庭（顶/上/中/下庭）在左侧：名 + 数值两行换行（无 cm），竖向虚线双箭头。
-    - 七眼段宽数值上下交替（上 3 / 下 4，无 cm），横向虚线双箭头。
-    - 底部统一标「单位cm」。
+    - 四庭（顶/上/中/下庭）在左侧：名 + 数值(带 cm) + 百分比三行换行，竖向虚线双箭头。
+    - 七眼段宽上下交替（上 3 / 下 4）：数值(带 cm) 上、百分比（占头宽比）下，横向虚线双箭头。
 
     variant="v6"（接口6）：去掉头顶横线与顶庭（只画发际线/眉心/鼻翼下缘/下巴尖 4 条
     横线 + 上/中/下庭），竖线纵向范围改为发际线→下巴尖，且不画人头最左/最右端线
@@ -203,7 +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(11, round(s * 0.026))     # 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）
@@ -213,7 +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"]
@@ -240,7 +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
 
@@ -256,70 +259,85 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
     draw = ImageDraw.Draw(canvas)
     font = _load_font(font_size)
 
-    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字在线上方 ---
-    name_x = fx1 + pad
-    name_gap = max(2, round(pad * 1.6))       # 文字底部到线的间距（再上移）
+    # --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖），文字纵向居中对齐到线 ---
+    name_x = fx1 + over + pad                  # 移到横线右端外侧一点（往右）
     for i, name in enumerate(order):
         text = _LINE_NAMES[name]
-        tw, th = _text_size(draw, text, font)
+        tw, _ = _text_size(draw, text, font)
         x = min(name_x, w - 2 - tw)           # 右侧越界时回收
-        draw.text((x, max(2, ys[i] - th - name_gap)), text, fill=LINE_COLOR, font=font)
+        # anchor="lm"：x 为左、y 为竖直中点 → 文字中线正好压在横线上（与线对齐）
+        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(
             draw, arrow_x, y_a + inset, arrow_x, y_b - inset,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        # 名 + 数值两行，右对齐到箭头左侧
+        # 名 + 数值(带 cm) + 百分比三行，右对齐到箭头左侧
         name = court_name[i]
-        val = f"{court_cm[i]:.2f}"
+        val = f"{court_cm[i]:.2f}cm"
+        pct = f"{court_cm[i] / court_total * 100:.1f}%"
         nw, _ = _text_size(draw, name, font)
         vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         label_right = arrow_x - pad
         y_mid = (y_a + y_b) / 2
-        y_top = y_mid - line_h
+        y_top = y_mid - 1.5 * line_h
         draw.text((max(2, label_right - nw), y_top), name, fill=LINE_COLOR, font=font)
         draw.text((max(2, label_right - vw), y_top + line_h), val, fill=LINE_COLOR, font=font)
+        draw.text((max(2, label_right - pw), y_top + 2 * line_h), pct, fill=LINE_COLOR, font=font)
 
-    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值（无 cm） ---
-    # 文字与箭头间留更大间距，避免文字压住箭头
+    # --- 4. 七眼每段宽度：上下交替（上 3 / 下 4），横向虚线双箭头 + 数值(带 cm) + 百分比 ---
+    # 每段两行：数值(带 cm) 上、百分比 下；百分比分母 = 整个头宽（七段之和）
     txt_off = arrow_size + pad * 2
-    y_arrow_top = max(txt_off + font_size + 2, fy0 - pad - arrow_size)
-    y_arrow_bot = min(h - txt_off - font_size - 2, fy1 + pad + arrow_size)
+    txt_block = 2 * line_h                     # 两行文字总高（数值 + 百分比）
+    y_arrow_top = max(txt_off + txt_block + 2, fy0 - pad - arrow_size)
+    y_arrow_bot = min(h - txt_off - txt_block - 2, fy1 + pad + arrow_size)
+    head_w = (xs[-1] - xs[0]) or 1.0           # 头宽（像素）= 百分比分母
     for i in range(len(xs) - 1):
         x_a, x_b = xs[i], xs[i + 1]
         if x_b - x_a < 1:
             continue
         seg_cm = (x_b - x_a) / pc
+        seg_pct = (x_b - x_a) / head_w * 100
         cx_seg = (x_a + x_b) / 2
-        text = f"{seg_cm:.2f}"
-        tw, th = _text_size(draw, text, font)
+        val = f"{seg_cm:.2f}cm"
+        pct = f"{seg_pct:.1f}%"
+        vw, _ = _text_size(draw, val, font)
+        pw, _ = _text_size(draw, pct, font)
         inset = min(arrow_size, (x_b - x_a) * 0.12)
         on_top = (i % 2 == 1)                 # 奇数段在上 → 上 3 / 下 4
         y_arrow = y_arrow_top if on_top else y_arrow_bot
         draw_dashed_line_with_arrows(
             draw, x_a + inset, y_arrow, x_b - inset, y_arrow,
             dash_len=dash_len, gap_len=gap_len, arrow_size=arrow_size, width=line_w)
-        ty = (y_arrow - th - txt_off) if on_top else (y_arrow + txt_off)
-        draw.text((cx_seg - tw / 2, ty), text, fill=LINE_COLOR, font=font)
-
-    # --- 5. 底部统一单位 ---
-    unit = "单位cm"
-    uw, uh = _text_size(draw, unit, font)
-    draw.text(((w - uw) / 2, h - uh - max(2, pad)), unit, fill=LINE_COLOR, font=font)
+        # 数值行在上、百分比行在下；on_top 时整块置于箭头上方，否则下方
+        text_top = (y_arrow - txt_off - txt_block) if on_top else (y_arrow + txt_off)
+        draw.text((cx_seg - vw / 2, text_top), val, fill=LINE_COLOR, font=font)
+        draw.text((cx_seg - pw / 2, text_top + line_h), pct, fill=LINE_COLOR, font=font)
 
     return canvas
 

face_analysis/face_mesh_landmarks.py

@@ -18,6 +18,8 @@ RIGHT_EYE_INNER = 362  # 右眼内角
 RIGHT_EYE_OUTER = 263  # 右眼外角
 LEFT_CHEEK = 234       # 左脸颧弓（脸宽左端）
 RIGHT_CHEEK = 454      # 右脸颧弓（脸宽右端）
+LEFT_POSITION = 21     # 左脸前侧定位点（脸颊/耳前区域，与 251 镜像）
+RIGHT_POSITION = 251   # 右脸前侧定位点（与 21 镜像）
 
 # --- 鼻尖（solvePnP 用，可选） ---
 NOSE_TIP = 1   # 鼻尖（也有用 4 的版本）

face_analysis/hairline_grow.py

@@ -35,6 +35,7 @@ from face_analysis.detector import detector
 from face_analysis.calibration import estimate_scale_factor
 from face_analysis.head_mask import (
     NoFaceError,
+    BASELINE_IDX,
     _baseline_points,
     _upper_region_mask,
     _bisenet_hair_mask,
@@ -46,8 +47,11 @@ from face_analysis.head_mask import (
     _draw_baseline,
 )
 
-# 调试日志：写 /home/xsl/hair/log/hairline_grow.log，每个步骤详细记录
-_LOG_DIR = "/home/xsl/hair/log"
+# 调试日志：写 <仓库根>/log/hairline_grow.log，每个步骤详细记录（可用 HAIR_LOG_DIR 覆盖）
+_LOG_DIR = os.getenv(
+    "HAIR_LOG_DIR",
+    os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "log"),
+)
 os.makedirs(_LOG_DIR, exist_ok=True)
 logger = logging.getLogger("hairline_grow")
 _log_fh = logging.FileHandler(os.path.join(_LOG_DIR, "hairline_grow.log"), encoding="utf-8")
@@ -60,6 +64,10 @@ SWAP_URL = os.getenv("SWAP_HAIR_URL", "http://127.0.0.1:8801/api/swapHair/v1")
 HAIRGROW_URL = os.getenv("HAIR_GROW_URL", "http://127.0.0.1:8801/api/hairGrow/v1")
 SWAP_TIMEOUT = float(os.getenv("SWAP_HAIR_TIMEOUT", "300"))
 
+# 多频段融合最细层羽化：羽化最细 FEATHER_LAYERS 层（每层核尺寸按尺度放大）。
+# 只羽最细1层效果极弱（其拉普拉斯系数幅度小），羽化 3 层才能明显软化发丝边缘锯齿。
+FEATHER_LAYERS = 3
+
 DEFAULTS = {
     "gen_backend": "swaphair",  # swaphair(换发型LoRA) | hairgrow(区域生发inpaint)
     "is_hr": False,
@@ -94,11 +102,26 @@ def _png_b64(bgr_or_gray):
 
 
 def _gray_b64(gray_float):
-    """0~1 的浮点图 → 灰度 PNG data URI。"""
+    """0~1 的浮字图 → 灰度 PNG data URI。"""
     g = np.clip(gray_float * 255.0, 0, 255).astype(np.uint8)
     return _png_b64(g)
 
 
+def _red_mask_b64(mask_bool, h, w):
+    """布尔遮罩 → 纯红 alpha PNG data URI。
+    遮罩区域 RGBA=(255,0,0,255)，其余区域 RGBA=(0,0,0,0)。
+    供 ComfyUI 重绘接口（/api/v1/redraw）按 alpha 通道识别重绘区。
+
+    注意：cv2.imencode 写 PNG 用的是 **BGRA** 顺序（B,G,R,A），所以要得到
+    浏览器显示的红色 R=255，需赋值 (B=0,G=0,R=255,A=255)。
+    """
+    m = (mask_bool.astype(np.uint8)) * 255 if mask_bool is not None else np.zeros((h, w), np.uint8)
+    rgba = np.zeros((h, w, 4), np.uint8)
+    rgba[m > 0] = (0, 0, 255, 255)            # BGRA: B=0,G=0,R=255 → PNG 读出为红色 + 不透明
+    ok, buf = cv2.imencode(".png", rgba)
+    return "data:image/png;base64," + base64.b64encode(buf.tobytes()).decode() if ok else ""
+
+
 # ---------------------------------------------------------------------------
 # 步骤1：接口9 头发遮罩（复用 head_mask 构件）
 # ---------------------------------------------------------------------------
@@ -294,8 +317,48 @@ def _pushed_mask(hair_mask, upper, baseline_pts, push_px, rid="",
 
 
 
+def _redraw_band_mask(inner_pts, outer_pts, h, w, rid="", upper=None,
+                      lo_mult=0.5, hi_mult=1.5):
+    """重绘带遮罩：由发际线（①-f 内轮廓 inner_pts）沿径向外推方向，取
+    `lo_mult × push` 与 `hi_mult × push` 两条外推线之间的带状区域作为重绘 mask。
+
+    inner_pts / outer_pts 是一一对应的有序点列（outer = inner 径向外推 1.0×push_px），
+    故每点的 1.0× 位移向量 = outer - inner；下界线 = inner + lo_mult×位移，
+    上界线 = inner + hi_mult×位移。闭合环 = 下界线（正向）+ 上界线（反向）首尾相接。
+
+    lo_mult / hi_mult：外推倍率（相对 hairline_push_cm）。默认 0.5 / 1.5，即带位于
+    0.5×push ~ 1.5×push 之间（以内轮廓为 0×、原外推线为 1.0×）。
+
+    upper：①-a baseline 以上区域布尔掩码。传入时把重绘带与它求交集，只保留 baseline
+    以上的部分（两侧鬓角落到 baseline 以下的段会被截掉）。
+
+    返回 band_bool。
+    """
+    lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
+    if len(inner_pts) < 2 or len(outer_pts) < 2:
+        return np.zeros((h, w), dtype=bool)
+    inner_f = np.asarray(inner_pts, dtype=np.float32)
+    outer_f = np.asarray(outer_pts, dtype=np.float32)
+    disp = outer_f - inner_f                       # 每点 1.0×push 的径向位移向量
+    lo_line = inner_f + float(lo_mult) * disp       # 下界外推线（lo_mult×push）
+    hi_line = inner_f + float(hi_mult) * disp       # 上界外推线（hi_mult×push）
+    # 闭合多边形：下界线正向 + 上界线反向，端点自然相连
+    ring = np.vstack([lo_line.astype(np.int32), hi_line[::-1].astype(np.int32)])
+    band_u8 = np.zeros((h, w), dtype=np.uint8)
+    cv2.fillPoly(band_u8, [ring], 255)
+    band = band_u8 > 0
+    raw_px = int(band.sum())
+    # ①-a baseline 截断：只保留 baseline 以上的重绘带
+    if upper is not None:
+        band = band & upper
+    lg(f"_redraw_band_mask: 内轮廓点={len(inner_pts)} lo_mult={lo_mult} hi_mult={hi_mult} "
+      f"band像素(截断前)={raw_px} band像素(截断后)={int(band.sum())}")
+    return band
+
+
 def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-                 hairline_push_cm=0.0, hairline_edge="column", rid=""):
+                 hairline_push_cm=0.0, hairline_edge="column", rid="", render_viz=True,
+                 hair_mask=None):
     """算出布尔遮罩 + 可视化。
 
     seg_model: bisenet | segformer。
@@ -303,6 +366,10 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     hairline_push_cm: 仅 pushed 模式——发际线往头发方向外推的厘米数（进入现有头发）。
     hairline_edge: 仅 pushed 模式——发际线提取方式 column(逐列最低点) | contour(形态学轮廓)。
     rid: 调用方的 request id，用于日志关联。
+    render_viz: 是否生成各阶段叠图 overlay JPG（接口11 调试页用）。接口2/12 路径传 False
+        可跳过 6+ 张 base64 编码，省 ~80ms；数据字段（_inner_pts/_outer_pts/_upper_mask/
+        mask_pixels 等）始终返回，不受影响。
+    hair_mask: 预计算的头发布尔遮罩（来自 SegFormer parse）。传入时跳过重复分割，省 ~0.9s。
     返回 (mask_bool, viz_dict)。
     """
     lg = lambda msg: logger.info("[%s] %s", rid, msg) if rid else None
@@ -316,13 +383,16 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     upper = _upper_region_mask(baseline_pts, w, h)
     lg(f"baseline 第一点={baseline_pts[0]} 末点={baseline_pts[-1]} upper像素={int(upper.sum())}")
 
-    if seg_model == "bisenet":
-        hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
-    elif seg_model == "segformer":
-        hair_mask = _segformer_hair_mask(image_bgr)
+    if hair_mask is None:
+        if seg_model == "bisenet":
+            hair_mask = _bisenet_hair_mask(image_bgr, landmarks, w, h)
+        elif seg_model == "segformer":
+            hair_mask = _segformer_hair_mask(image_bgr)
+        else:
+            raise ValueError(f"未知 seg_model: {seg_model}")
+        lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
     else:
-        raise ValueError(f"未知 seg_model: {seg_model}")
-    lg(f"头发分割完成 seg_model={seg_model} hair_pixels={int(hair_mask.sum())}")
+        lg(f"头发分割跳过(复用外部传入) hair_pixels={int(hair_mask.sum())}")
 
     top_fill = _fill_to_baseline(hair_mask, upper)      # 含额头，延伸到图底
     closed = _largest_cc(top_fill & upper)              # 闭合区域：头发+额头，底=基线
@@ -334,7 +404,9 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     if mask_type == "pushed":
         push_px = int(round(max(0.0, hairline_push_cm) * px_per_cm))
         # 圆心 = 151 点（眉心）完整坐标，内侧判定与径向外推共用
-        center = baseline_pts[5] if len(baseline_pts) > 5 else None
+        # 按值查 151 在 BASELINE_IDX 中的位置，避免列表变动后索引错位（曾硬编码 [5]）
+        _idx151 = BASELINE_IDX.index(151) if 151 in BASELINE_IDX else -1
+        center = baseline_pts[_idx151] if _idx151 >= 0 else None
         # 下颌截断线：下巴关键点 152 的 y（内轮廓两侧向下画到这里为止）
         try:
             chin_y = int(round(landmarks.landmark[152].y * h))
@@ -361,48 +433,55 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     # 遮罩计算过程可视化：
     #   eroded/closed 走 top_fill→closed/eroded 流程；
     #   pushed 走 baseline→头发分割→发际线→外推 流程，与 top_fill/closed 无关，故置空。
+    # render_viz=False（接口2/12 路径）时跳过 overlay JPG 编码，只保留数据字段。
     viz = {
         "erode_px": r,
         "hair_pixels": int(hair_mask.sum()),
         "closed_pixels": int(closed.sum()),
         "mask_pixels": int(mask_bool.sum()),
         # 1. 发际线分割线（baseline）：151 中心点标红，其余点标绿，黄线含左右延长线
-        "baseline_overlay_base64": _jpg_b64(_draw_baseline(image_bgr, baseline_pts, w)),
+        "baseline_overlay_base64": _jpg_b64(_draw_baseline(image_bgr, baseline_pts, w)) if render_viz else "",
         # 2. 分割线以上区域（upper 半区）：青色叠加
-        "upper_overlay_base64": _jpg_b64(_overlay(image_bgr, upper, (0, 255, 255))),
+        "upper_overlay_base64": _jpg_b64(_overlay(image_bgr, upper, (0, 255, 255))) if render_viz else "",
         # 3. 头发分割原始结果（hair_mask）：绿色叠加在原图上
-        "hair_seg_overlay_base64": _jpg_b64(_overlay(image_bgr, hair_mask, (0, 255, 0))),
+        "hair_seg_overlay_base64": _jpg_b64(_overlay(image_bgr, hair_mask, (0, 255, 0))) if render_viz else "",
         # 4. top_fill / closed —— 仅 eroded/closed 流程用；pushed 流程无关，留空
-        "top_fill_overlay_base64": "" if mask_type == "pushed"
+        "top_fill_overlay_base64": "" if (mask_type == "pushed" or not render_viz)
             else _jpg_b64(_overlay(image_bgr, top_fill, (255, 0, 0))),
-        "closed_overlay_base64": "" if mask_type == "pushed"
+        "closed_overlay_base64": "" if (mask_type == "pushed" or not render_viz)
             else _jpg_b64(_overlay(image_bgr, closed, (255, 0, 255))),
         # 5. pushed 模式专有（发际线提取/外推）—— 非 pushed 留空
         "hairline_overlay_base64": "",
         "pushed_overlay_base64": "",
         # —— 最终遮罩 ——
-        "mask_overlay_base64": _jpg_b64(_overlay(image_bgr, mask_bool, (0, 0, 255))),
-        "mask_base64": _png_b64((mask_bool.astype(np.uint8)) * 255),
+        "mask_overlay_base64": _jpg_b64(_overlay(image_bgr, mask_bool, (0, 0, 255))) if render_viz else "",
+        "mask_base64": _png_b64((mask_bool.astype(np.uint8)) * 255) if render_viz else "",
     }
     # pushed 模式：补充内轮廓提取 + 外推线可视化
     if pushed_info is not None:
         inner_pts, outer_pts, push_px = pushed_info
-        # ①-f 提取内轮廓：绿=头发内轮廓线（额头弧+两侧到下颌），黄=baseline 折线
-        hl_img = _draw_baseline(image_bgr, baseline_pts, w)  # 画 baseline（黄线+关键点）
-        hl_img = _draw_polyline(hl_img, inner_pts, (0, 255, 0), 3)
-        viz["hairline_overlay_base64"] = _jpg_b64(hl_img)
-        # ①-g 外推：圆心红点(151) + 内轮廓（绿）+ 外推线（青）+ 遮罩（红半透明）
-        ps_img = _draw_polyline(image_bgr.copy(), inner_pts, (0, 255, 0), 2)
-        ps_img = _draw_polyline(ps_img, outer_pts, (0, 255, 255), 3)
-        # 画圆心（151 点）红点，标示径向外推的中心
-        if baseline_pts is not None and len(baseline_pts) > 5:
-            cx151, cy151 = baseline_pts[5]
-            cv2.circle(ps_img, (cx151, cy151), 6, (0, 0, 255), -1, cv2.LINE_AA)
-            cv2.putText(ps_img, "151", (cx151 + 8, cy151 - 8),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
-        ps_img = _overlay(ps_img, mask_bool, (0, 0, 255), 0.3)
-        viz["pushed_overlay_base64"] = _jpg_b64(ps_img)
+        if render_viz:
+            # ①-f 提取内轮廓：绿=头发内轮廓线（额头弧+两侧到下颌），黄=baseline 折线
+            hl_img = _draw_baseline(image_bgr, baseline_pts, w)  # 画 baseline（黄线+关键点）
+            hl_img = _draw_polyline(hl_img, inner_pts, (0, 255, 0), 3)
+            viz["hairline_overlay_base64"] = _jpg_b64(hl_img)
+            # ①-g 外推：圆心红点(151) + 内轮廓（绿）+ 外推线（青）+ 遮罩（红半透明）
+            ps_img = _draw_polyline(image_bgr.copy(), inner_pts, (0, 255, 0), 2)
+            ps_img = _draw_polyline(ps_img, outer_pts, (0, 255, 255), 3)
+            # 画圆心（151 点）红点，标示径向外推的中心（_idx151 上方已按值查到）
+            if center is not None:
+                cx151, cy151 = center
+                cv2.circle(ps_img, (cx151, cy151), 6, (0, 0, 255), -1, cv2.LINE_AA)
+                cv2.putText(ps_img, "151", (cx151 + 8, cy151 - 8),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
+            ps_img = _overlay(ps_img, mask_bool, (0, 0, 255), 0.3)
+            viz["pushed_overlay_base64"] = _jpg_b64(ps_img)
         viz["push_px"] = push_px
+        # 重绘带用原始数据：内轮廓点 + 外推点（供 _redraw_band_mask 连端点成带）
+        viz["_inner_pts"] = inner_pts
+        viz["_outer_pts"] = outer_pts
+        # baseline 以上区域，供重绘带按 ①-a baseline 截断（只留上面）
+        viz["_upper_mask"] = upper
     # 记录 viz 各字段是否非空（长度），便于排查前端取不到图的问题
     viz_summary = {k: (len(v) if isinstance(v, str) and v else 0)
                    for k, v in viz.items() if k.endswith("_base64")}
@@ -410,15 +489,33 @@ def compute_mask(image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm
     return mask_bool, viz
 
 
+def _segment_hair(image_bgr, seg_model, landmarks, w, h):
+    """对任意图（如 hard_paste 重绘结果）重跑头发分割，返回 bool 掩码。
+
+    与 compute_mask 内部用的同一个 seg_model 逻辑（bisenet 需 landmarks，
+    segformer 不需要），保证第1步（原图头发）与第2步（重绘后头发）分割口径一致。
+    """
+    if seg_model == "bisenet":
+        return _bisenet_hair_mask(image_bgr, landmarks, w, h)
+    elif seg_model == "segformer":
+        return _segformer_hair_mask(image_bgr)
+    else:
+        raise ValueError(f"未知 seg_model: {seg_model}")
+
+
 # ---------------------------------------------------------------------------
 # 步骤2：调 change_hair 换发型
 # ---------------------------------------------------------------------------
 
-def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength):
+def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength,
+               inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0, webui_steps=None):
     """调 change_hair /api/swapHair/v1，返回与输入同分辨率同对齐的换发型结果（BGR）。
 
     ext_mask_bool 非 None 时作为 ext_mask 传入（swap_mode=ext_mask）。
     denoising_strength：webui img2img 重绘强度（越大生发越激进），透传给换发型。
+    inpainting_fill / mask_blur / mask_dilate_scale：服务端重绘参数（透传给 change_hair，
+        默认值=服务端原始硬编码值，未传时行为不变）。详见 change_hair 文档。
+    webui_steps：webui img2img 采样步数，None 用服务端默认(15)。
     """
     import requests
 
@@ -430,7 +527,12 @@ def _call_swap(image_bgr, hairline_id, is_hr, ext_mask_bool, denoising_strength)
         "user_img_path": "data:image/jpeg;base64," + base64.b64encode(ibuf.tobytes()).decode(),
         "output_format": "base64",
         "denoising_strength": float(denoising_strength),
+        "inpainting_fill": int(inpainting_fill),
+        "mask_blur": int(mask_blur),
+        "mask_dilate_scale": float(mask_dilate_scale),
     }
+    if webui_steps is not None:
+        payload["webui_steps"] = int(webui_steps)
     if ext_mask_bool is not None:
         mbuf = cv2.imencode(".png", (ext_mask_bool.astype(np.uint8)) * 255)[1]
         payload["ext_mask"] = "data:image/png;base64," + base64.b64encode(mbuf.tobytes()).decode()
@@ -500,25 +602,60 @@ def _call_hairgrow(image_bgr, mask_bool, strength):
     return result
 
 
+_REPAINT_WORKFLOW = os.path.join(os.path.dirname(os.path.dirname(__file__)), "hair_repaint.json")
+
+
+def _call_comfyui(image_bgr, mask_bool, prompt=None):
+    """调远端 ComfyUI 的 Flux-2 inpaint 工作流（hair_repaint.json），返回与输入同分辨率的 BGR。
+
+    与 swapHair 的区别：ComfyUI 把「原图 VAE 编码作 reference latent + ColorMatch」双重保色，
+    天生不易染色；提示词自由可调（中文）。mask 经 RGBA alpha 通道传入（透明=重绘区）。
+    ComfyUI 不在线时抛 SwapError（由调用方捕获降级）。prompt=None 用工作流内置默认提示词。
+    """
+    import io
+    from hairline.mask import compose_comfy_rgba
+    from hairline.comfyui import COMFYUI_URL, run as comfyui_run, ping
+
+    if not ping():
+        raise SwapError(f"ComfyUI 不可达（{COMFYUI_URL}），redraw Flux-2 路跳过")
+    mask_u8 = (mask_bool.astype(np.uint8)) * 255
+    rgba_img = compose_comfy_rgba(image_bgr, mask_u8)   # alpha=255-mask：透明=重绘区
+    buf = io.BytesIO()
+    rgba_img.save(buf, format="PNG")
+    png_bytes = comfyui_run(buf.getvalue(), prompt=prompt, workflow_path=_REPAINT_WORKFLOW)
+    result = cv2.imdecode(np.frombuffer(png_bytes, np.uint8), cv2.IMREAD_COLOR)
+    if result is None:
+        raise SwapError("ComfyUI 结果解码失败")
+    if result.shape[:2] != image_bgr.shape[:2]:
+        result = cv2.resize(result, (image_bgr.shape[1], image_bgr.shape[0]),
+                            interpolation=cv2.INTER_LANCZOS4)
+    return result
+
+
 # ---------------------------------------------------------------------------
 # 步骤3+4：按遮罩贴回 + 接缝融合
 # ---------------------------------------------------------------------------
 
-def _color_match_to_orig(swap_result, orig, mask_bool):
+def _color_match_to_orig(swap_result, orig, mask_bool, strength=1.0):
     """在 mask_bool 区域内做 Reinhard 颜色迁移：逐通道把 swap_result 的均值/方差对齐 orig。
 
+    strength 控制迁移强度：1.0=完全对齐到 orig（原行为），<1.0 只迁移部分，
+    防止 Reinhard 在某些图上过度改色（如把生成发色整体拉向皮肤色）。
     遮罩外保持 swap_result 原样（不会越界污染）。返回 uint8 BGR。
     """
     m = mask_bool.astype(bool)
-    out = swap_result.astype(np.float32).copy()
+    src_f = swap_result.astype(np.float32)
+    out = src_f.copy()
     if m.sum() < 30:
         return swap_result.copy()
+    strength = float(min(max(strength, 0.0), 1.0))
     for c in range(3):
         src_pix = swap_result[..., c][m].astype(np.float32)
         dst_pix = orig[..., c][m].astype(np.float32)
         s_mean, s_std = src_pix.mean(), src_pix.std() + 1e-6
         d_mean, d_std = dst_pix.mean(), dst_pix.std() + 1e-6
-        out[..., c] = (out[..., c] - s_mean) * (d_std / s_std) + d_mean
+        aligned = (out[..., c] - s_mean) * (d_std / s_std) + d_mean
+        out[..., c] = src_f[..., c] * (1.0 - strength) + aligned * strength
     return np.clip(out, 0, 255).astype(np.uint8)
 
 
@@ -553,10 +690,17 @@ def _multiband_alpha(mask_bool, edge_erode_px):
     return m
 
 
-def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px):
+def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px,
+                     feather_px=1, transition_band_px=-1):
     """多频段（拉普拉斯金字塔）融合：低频用宽窗抹色差，高频用窄窗保发丝。
 
     levels：金字塔层数（2~6），越大则低频色差在越宽范围被抹平。
+    feather_px：最细若干层掩码轻羽化像素（0=不羽化，保持硬二值）。羽化最细 FEATHER_LAYERS
+        层（核尺寸按层尺度放大），消除发丝边缘 1px 硬切锯齿；粗层仍保持二值（否则粗层会
+        把整图混色）。注意：这是消除锯齿的微调，幅度有限（边界 Δ 约 1~3/255），
+        不要指望它做大范围过渡——那是 mb_levels/transition_band_px 的事。
+    transition_band_px：keep-region 外缘边距。-1=自动按层数 2**n（旧行为）；
+        >=0 则用绝对像素，使过渡带宽度与金字塔层数解耦。
     返回 uint8 BGR。
     """
     m = _multiband_alpha(mask_bool, edge_erode_px)
@@ -601,6 +745,21 @@ def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px):
     lb = lap_pyr(swap_result, n)
     ma = mask_pyr(m, n)
 
+    # 最细层（reversed 后末元素 = 全分辨率原始二值掩码）及其下若干层轻羽化，
+    # 消除发丝边缘 1px 硬切锯齿。注意：多频段融合中各层都贡献边界过渡，但最细层的
+    # 拉普拉斯系数幅度最小，只羽化它效果很弱（实测边界 Δ 仅 ~0.25/255）。因此对最细
+    # FEATHER_LAYERS 层都做按尺度放大的羽化（越细的层核越大），才能明显软化边缘。
+    # 粗层（低频）仍保持二值，否则会把整图混色，违反多频段融合的二值掩码前提。
+    fp = int(max(0, feather_px))
+    if fp > 0:
+        for li in range(1, FEATHER_LAYERS + 1):
+            idx = -li
+            if abs(idx) > len(ma):
+                break
+            scale = 2 ** (li - 1)
+            ksz = fp * 2 * scale + 1
+            ma[idx] = cv2.GaussianBlur(ma[idx], (ksz, ksz), sigmaX=fp * scale / 2.0)
+
     merged = []
     for a, b, mk in zip(la, lb, ma):
         m3 = mk[:, :, None]
@@ -618,33 +777,68 @@ def _multiband_blend(orig, swap_result, mask_bool, levels, edge_erode_px):
     # 这条带正是 mb_levels 要控制的东西。若像旧实现那样用原始硬二值遮罩钳回，
     # 过渡带会被整条抹掉（实测 levels 2↔6 边界差恒为 0），mb_levels 形同虚设。
     # 故按层数膨胀出一个外缘 keep 区：keep 内允许过渡，keep 外才强制还原原图。
-    margin = 2 ** n  # n=2→4px … n=6→64px，与粗层掩码的自然扩散宽度匹配
+    if transition_band_px is not None and transition_band_px >= 0:
+        margin = int(transition_band_px)  # 与金字塔层数解耦，用绝对像素
+    else:
+        margin = 2 ** n  # n=2→4px … n=6→64px，与粗层掩码的自然扩散宽度匹配
+    margin = max(0, margin)
     k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * margin + 1, 2 * margin + 1))
     keep = cv2.dilate(mask_bool.astype(np.uint8), k).astype(bool)
     out[~keep] = orig[~keep]
     return out
 
 
+def _seamless_clone(orig, swap_result, mask_bool, edge_erode_px):
+    """泊松无缝克隆（cv2.seamlessClone NORMAL_CLONE）：梯度域调和整体色调。
+
+    返回调色后的整帧 uint8 BGR；掩码过小（<10px）时返回原图。
+    供 seamless 分支与 two_stage 两段式融合的第一段复用。
+    """
+    m = mask_bool.astype(np.uint8)
+    if edge_erode_px > 0:
+        k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * edge_erode_px + 1,) * 2)
+        m = cv2.erode(m, k)
+    if m.sum() < 10:
+        return orig.copy()
+    ys, xs = np.where(m > 0)
+    center = (int((xs.min() + xs.max()) / 2), int((ys.min() + ys.max()) / 2))
+    return cv2.seamlessClone(swap_result, orig, m * 255, center, cv2.NORMAL_CLONE)
+
+
 def _composite(orig, swap_result, mask_bool, blend_method, feather_px, edge_erode_px,
-               color_match=False, mb_levels=5):
-    """把 swap_result 按遮罩贴回 orig，返回 (final_bgr, alpha_float or None)。"""
+               color_match=False, mb_levels=5, color_match_strength=1.0,
+               mb_feather_px=1, transition_band_px=-1):
+    """把 swap_result 按遮罩贴回 orig，返回 (final_bgr, alpha_float or None)。
+
+    blend_method:
+      - multiband : 多频段金字塔融合（默认）
+      - seamless  : 泊松无缝克隆（梯度域调色，自带色彩调和，故跳过 color_match）
+      - two_stage : 先 seamless 统一整体色调，再 multiband 贴发丝细节（大色差场景）
+      - feather/alpha_gradient : 单层 alpha 过渡
+    """
+    # seamless / two_stage 自带梯度域色彩调和，不叠 Reinhard 颜色迁移
     if blend_method == "seamless":
-        m = mask_bool.astype(np.uint8)
-        if edge_erode_px > 0:
-            k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * edge_erode_px + 1,) * 2)
-            m = cv2.erode(m, k)
-        if m.sum() < 10:
-            return orig.copy(), None
-        ys, xs = np.where(m > 0)
-        center = (int((xs.min() + xs.max()) / 2), int((ys.min() + ys.max()) / 2))
-        final = cv2.seamlessClone(swap_result, orig, m * 255, center, cv2.NORMAL_CLONE)
+        final = _seamless_clone(orig, swap_result, mask_bool, edge_erode_px)
         return final, None
 
-    # 颜色校正前置（seamless 自带色彩调和，已在上面提前返回；其余分支在此生效）
-    src = _color_match_to_orig(swap_result, orig, mask_bool) if color_match else swap_result
+    if blend_method == "two_stage":
+        # 第一段：seamless 把整体色调拉平（生成图色调对齐到原图）
+        harmonized = _seamless_clone(orig, swap_result, mask_bool, edge_erode_px)
+        # 第二段：对调色后的结果再做 multiband 贴发丝细节（不加 color_match，避免重复改色）
+        final = _multiband_blend(orig, harmonized, mask_bool, mb_levels, edge_erode_px,
+                                 feather_px=mb_feather_px,
+                                 transition_band_px=transition_band_px)
+        alpha = (_multiband_alpha(mask_bool, edge_erode_px).astype(np.float32)) / 255.0
+        return final, alpha
+
+    # multiband / feather / alpha_gradient：先做 Reinhard 颜色迁移消除整体色差
+    src = (_color_match_to_orig(swap_result, orig, mask_bool, color_match_strength)
+           if color_match else swap_result)
 
     if blend_method == "multiband":
-        final = _multiband_blend(orig, src, mask_bool, mb_levels, edge_erode_px)
+        final = _multiband_blend(orig, src, mask_bool, mb_levels, edge_erode_px,
+                                 feather_px=mb_feather_px,
+                                 transition_band_px=transition_band_px)
         # 可视化用：用多频段的二值掩码做一层 alpha 标记（展示实际合成区）
         alpha = (_multiband_alpha(mask_bool, edge_erode_px).astype(np.float32)) / 255.0
         return final, alpha
@@ -659,26 +853,28 @@ def _composite(orig, swap_result, mask_bool, blend_method, feather_px, edge_erod
 # 主入口
 # ---------------------------------------------------------------------------
 
-def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
-                           erode_cm=0.6, swap_mode="ext_mask",
-                           edge_erode_px=3,
-                           denoising_strength=0.6, gen_backend="swaphair",
-                           hairgrow_strength=0.75, mb_levels=5,
-                           hairline_push_cm=1.0, hairline_edge="column", rid=None):
-    """接口11 完整管线。返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+def _grow_core(image_bgr, hairline_id, *, is_hr, seg_model, erode_cm, swap_mode,
+               edge_erode_px, denoising_strength, gen_backend, hairgrow_strength,
+               mb_levels, hairline_push_cm, hairline_edge, blend_method, color_match,
+               color_match_strength, mb_feather_px, transition_band_px,
+               inpainting_fill, mask_blur, mask_dilate_scale, rid, render_viz=True,
+               hair_mask=None, webui_steps=None):
+    """接口11 共享核心：遮罩(pushed)→生成→硬贴回→接缝融合，产出 ④ final。
 
-    遮罩算法固定为 pushed（发际线外推），融合算法固定为 multiband（多频段金字塔），
-    不再支持其他选项。rid: 调用方的 request id，用于日志关联。为 None 时自动生成。
+    不做任何重绘。返回中间产物 dict（供接口11 构造响应、接口12 取 final+重绘带用）：
+      final / swap_result / hard_paste / alpha / mask_bool / mask_viz /
+      px_per_cm / t_mask / t_swap / t_blend / h / w
+    未检出人脸抛 NoFaceError。
     """
     mask_type = "pushed"        # 固定：只支持 pushed 遮罩算法
-    blend_method = "multiband"  # 固定：只支持 multiband 融合
-    if rid is None:
-        rid = uuid4().hex[:8]
-    logger.info("[%s] ===== generate_hairline_grow 开始 =====", rid)
-    logger.info("[%s] 参数(固定 mask=pushed blend=multiband): erode_cm=%s hairline_push_cm=%s "
-                "hairline_edge=%r mb_levels=%s seg=%s gen_backend=%s swap_mode=%s",
+    logger.info("[%s] _grow_core 参数(固定 mask=pushed): erode_cm=%s hairline_push_cm=%s "
+                "hairline_edge=%r mb_levels=%s seg=%s gen_backend=%s swap_mode=%s blend=%s "
+                "color_match=%s cm_strength=%s mb_feather_px=%s transition_band_px=%s "
+                "inpainting_fill=%s mask_blur=%s mask_dilate_scale=%s",
                 rid, erode_cm, hairline_push_cm, hairline_edge, mb_levels,
-                seg_model, gen_backend, swap_mode)
+                seg_model, gen_backend, swap_mode, blend_method, color_match,
+                color_match_strength, mb_feather_px, transition_band_px,
+                inpainting_fill, mask_blur, mask_dilate_scale)
     h, w = image_bgr.shape[:2]
     landmarks = detector.detect(image_bgr)
     if landmarks is None:
@@ -691,7 +887,8 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
     t0 = time.time()
     mask_bool, mask_viz = compute_mask(
         image_bgr, landmarks, seg_model, mask_type, erode_cm, px_per_cm,
-        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid)
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, rid=rid,
+        render_viz=render_viz, hair_mask=hair_mask)
     t_mask = time.time() - t0
     logger.info("[%s] 步骤1 遮罩完成 耗时=%dms mask_pixels=%d", rid, int(t_mask*1000), int(mask_bool.sum()))
 
@@ -701,27 +898,80 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         swap_result = _call_hairgrow(image_bgr, mask_bool, hairgrow_strength)
     else:
         ext_mask = mask_bool if swap_mode == "ext_mask" else None
-        swap_result = _call_swap(image_bgr, hairline_id, is_hr, ext_mask, denoising_strength)
+        swap_result = _call_swap(image_bgr, hairline_id, is_hr, ext_mask, denoising_strength,
+                                 inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+                                 mask_dilate_scale=mask_dilate_scale, webui_steps=webui_steps)
     t_swap = time.time() - t0
 
     # 步骤3：严格按遮罩硬贴回（无融合，用于对比）
     hard_paste = image_bgr.copy()
     hard_paste[mask_bool] = swap_result[mask_bool]
 
-    # 步骤4：接缝融合（固定 multiband）
+    # 步骤4：接缝融合（默认 multiband）→ ④ final
     t0 = time.time()
     final, alpha = _composite(
         image_bgr, swap_result, mask_bool, blend_method, 0, edge_erode_px,
-        color_match=False, mb_levels=mb_levels)
+        color_match=color_match, mb_levels=mb_levels,
+        color_match_strength=color_match_strength,
+        mb_feather_px=mb_feather_px, transition_band_px=transition_band_px)
     t_blend = time.time() - t0
 
+    return {
+        "final": final, "swap_result": swap_result, "hard_paste": hard_paste,
+        "alpha": alpha, "mask_bool": mask_bool, "mask_viz": mask_viz,
+        "px_per_cm": px_per_cm, "t_mask": t_mask, "t_swap": t_swap, "t_blend": t_blend,
+        "h": h, "w": w,
+    }
+
+
+# ---------------------------------------------------------------------------
+# 主入口
+# ---------------------------------------------------------------------------
+
+def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
+                           erode_cm=0.6, swap_mode="ext_mask",
+                           edge_erode_px=3,
+                           denoising_strength=0.6, gen_backend="swaphair",
+                           hairgrow_strength=0.75, mb_levels=5,
+                           hairline_push_cm=1.0, hairline_edge="column",
+                           blend_method="multiband", color_match=True,
+                           color_match_strength=1.0, mb_feather_px=1,
+                           transition_band_px=-1,
+                           inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
+                           rid=None, webui_steps=None):
+    """接口11 完整管线（**不含重绘**，重绘见接口12 generate_hairline_redraw）。
+    返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+
+    遮罩算法固定为 pushed（发际线外推）。
+    融合算法 blend_method 默认 multiband（多频段金字塔），可选 seamless(泊松)/
+    two_stage(泊松→多频段两段式)/feather(羽化)/alpha_gradient(距离变换)。
+    color_match 默认开启 Reinhard 颜色迁移消除整体色差（对 multiband/feather 有效）。
+    inpainting_fill/mask_blur/mask_dilate_scale：透传 change_hair 服务端换发型重绘参数。
+    rid: 调用方的 request id，用于日志关联。为 None 时自动生成。
+    """
+    if rid is None:
+        rid = uuid4().hex[:8]
+    logger.info("[%s] ===== generate_hairline_grow 开始 =====", rid)
+    core = _grow_core(
+        image_bgr, hairline_id, is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm,
+        swap_mode=swap_mode, edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+        gen_backend=gen_backend, hairgrow_strength=hairgrow_strength, mb_levels=mb_levels,
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, blend_method=blend_method,
+        color_match=color_match, color_match_strength=color_match_strength,
+        mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
+        inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+        mask_dilate_scale=mask_dilate_scale, rid=rid, webui_steps=webui_steps)
+    mask_viz = core["mask_viz"]
+    alpha = core["alpha"]
+    w, h = core["w"], core["h"]
+
     data = {
         "hairline_id": hairline_id,
         "gen_backend": gen_backend,
         "hairgrow_strength": round(float(hairgrow_strength), 3),
         "is_hr": is_hr,
         "seg_model": seg_model,
-        "mask_type": mask_type,
+        "mask_type": "pushed",
         "erode_cm": round(float(erode_cm), 2),
         "swap_mode": swap_mode,
         "blend_method": blend_method,
@@ -730,16 +980,23 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
         "hairline_push_cm": round(float(hairline_push_cm), 2),
         "hairline_edge": hairline_edge,
         "denoising_strength": round(float(denoising_strength), 3),
-        "px_per_cm": round(float(px_per_cm), 4),
+        "color_match": bool(color_match),
+        "color_match_strength": round(float(color_match_strength), 3),
+        "mb_feather_px": int(mb_feather_px),
+        "transition_band_px": int(transition_band_px),
+        "inpainting_fill": int(inpainting_fill),
+        "mask_blur": int(mask_blur),
+        "mask_dilate_scale": round(float(mask_dilate_scale), 3),
+        "px_per_cm": round(float(core["px_per_cm"]), 4),
         "erode_px": mask_viz["erode_px"],
         "hair_pixels": mask_viz["hair_pixels"],
         "closed_pixels": mask_viz["closed_pixels"],
         "mask_pixels": mask_viz["mask_pixels"],
         "image_size": {"width": w, "height": h},
         "timings_ms": {
-            "mask": int(t_mask * 1000),
-            "swap": int(t_swap * 1000),
-            "blend": int(t_blend * 1000),
+            "mask": int(core["t_mask"] * 1000),
+            "swap": int(core["t_swap"] * 1000),
+            "blend": int(core["t_blend"] * 1000),
         },
         "steps": {
             "input_base64": _jpg_b64(image_bgr),
@@ -755,16 +1012,135 @@ def generate_hairline_grow(image_bgr, hairline_id, is_hr=False, seg_model="segfo
             # 最终遮罩
             "mask_overlay_base64": mask_viz["mask_overlay_base64"],
             "mask_base64": mask_viz["mask_base64"],
-            "swap_raw_base64": _jpg_b64(swap_result),
-            "hard_paste_base64": _jpg_b64(hard_paste),
+            "swap_raw_base64": _jpg_b64(core["swap_result"]),
+            "hard_paste_base64": _jpg_b64(core["hard_paste"]),
             "alpha_base64": _gray_b64(alpha) if alpha is not None else mask_viz["mask_base64"],
-            "final_base64": _jpg_b64(final),
+            "final_base64": _jpg_b64(core["final"]),
         },
-        "_rid": rid,  # 调试用：返回本次请求的日志关联 id
+        "_rid": rid,
     }
-    # 记录 steps 各图字段是否非空，供排查前端取图问题
     steps_summary = {k: (len(v) if isinstance(v, str) and v else 0)
                      for k, v in data["steps"].items() if k.endswith("_base64")}
     logger.info("[%s] 返回 steps 字段长度: %s", rid, steps_summary)
     logger.info("[%s] ===== generate_hairline_grow 完成 =====", rid)
     return data
+
+
+def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="segformer",
+                             erode_cm=0.6, swap_mode="ext_mask",
+                             edge_erode_px=3,
+                             denoising_strength=0.6, gen_backend="swaphair",
+                             hairgrow_strength=0.75, mb_levels=5,
+                             hairline_push_cm=1.0, hairline_edge="column",
+                             blend_method="multiband", color_match=True,
+                             color_match_strength=1.0, mb_feather_px=1,
+                             transition_band_px=-1,
+                             inpainting_fill=1, mask_blur=11, mask_dilate_scale=1.0,
+                             comfyui_prompt=None, beauty_alpha=0.6,
+                             band_lo_mult=0.5, band_hi_mult=1.5, rid=None,
+                             hair_mask=None, webui_steps=None):
+    """接口12 发际线带重绘。内部先跑接口11 核心拿到 ④ final，再取 ⑤-① 发际线重绘带
+    （外推↔内推之间、经 baseline 截断只留上部）作遮罩。
+
+    **本接口不再做 Flux-2 重绘**：只产出 `final`（接缝融合基底）+ 纯红遮罩
+    `redraw_band_mask`（RGBA，遮罩区=(255,0,0,255)、其余全透明），重绘交给后端
+    ComfyUI 重绘接口（/api/v1/redraw）完成。旧的 `redraw_full` / `redraw_band`
+    字段保留为空，仅作结构兼容。
+
+    返回可直接进 ok() 的 data dict。未检出人脸抛 NoFaceError。
+
+    comfyui_prompt：保留入参，但本接口不再使用（重绘提示词由外部服务自行决定）。
+    beauty_alpha：保留入参，但本接口不再使用（美颜由外部服务控制）。
+    band_lo_mult / band_hi_mult：重绘带外推倍率（相对 hairline_push_cm），带位于
+      lo×push ~ hi×push 之间（内轮廓=0×、原外推线=1.0×），默认 0.5 / 1.5。
+    其余参数含义与接口11 相同（用于内部生成 final 与重绘带）。
+    """
+    if rid is None:
+        rid = uuid4().hex[:8]
+    logger.info("[%s] ===== generate_hairline_redraw 开始 =====", rid)
+    core = _grow_core(
+        image_bgr, hairline_id, is_hr=is_hr, seg_model=seg_model, erode_cm=erode_cm,
+        swap_mode=swap_mode, edge_erode_px=edge_erode_px, denoising_strength=denoising_strength,
+        gen_backend=gen_backend, hairgrow_strength=hairgrow_strength, mb_levels=mb_levels,
+        hairline_push_cm=hairline_push_cm, hairline_edge=hairline_edge, blend_method=blend_method,
+        color_match=color_match, color_match_strength=color_match_strength,
+        mb_feather_px=mb_feather_px, transition_band_px=transition_band_px,
+        inpainting_fill=inpainting_fill, mask_blur=mask_blur,
+        mask_dilate_scale=mask_dilate_scale, rid=rid, render_viz=False,
+        hair_mask=hair_mask, webui_steps=webui_steps)
+    final = core["final"]
+    mask_viz = core["mask_viz"]
+    w, h = core["w"], core["h"]
+    px_per_cm = core["px_per_cm"]
+
+    # ① 算重绘带（⑤-①）：发际线(内轮廓)↔外推发际线成带，经 baseline 截断只留上部
+    t0 = time.time()
+    inner_pts = mask_viz.get("_inner_pts")
+    outer_pts = mask_viz.get("_outer_pts")
+    upper_mask = mask_viz.get("_upper_mask")
+    push_px = int(round(max(0.0, hairline_push_cm) * px_per_cm))
+    redraw_band_overlay_b64 = ""
+    redraw_band_mask_b64 = ""   # 纯红 alpha PNG（遮罩区=(255,0,0,255)，其余全透明）
+    redraw_info = {"enabled": False}
+    band_mask = None
+    try:
+        band_mask = _redraw_band_mask(inner_pts, outer_pts, h, w, rid=rid, upper=upper_mask,
+                                      lo_mult=band_lo_mult, hi_mult=band_hi_mult)
+        if band_mask.sum() < 30:
+            raise RuntimeError("重绘带像素过少，可能内轮廓/外推线缺失")
+        logger.info("[%s] 重绘带 push_px=%d lo_mult=%s hi_mult=%s band_pixels=%d",
+                    rid, push_px, band_lo_mult, band_hi_mult, int(band_mask.sum()))
+        redraw_band_overlay_b64 = _jpg_b64(_overlay(final, band_mask, (255, 0, 255)))
+        # 纯红遮罩 PNG（供外部重绘服务按 alpha 识别重绘区）
+        redraw_band_mask_b64 = _red_mask_b64(band_mask, h, w)
+        redraw_info = {"enabled": True, "band_pixels": int(band_mask.sum()), "push_px": push_px,
+                       "band_lo_mult": float(band_lo_mult), "band_hi_mult": float(band_hi_mult)}
+    except Exception as ex:  # noqa: BLE001
+        logger.exception("[%s] 重绘带计算失败，整个重绘跳过", rid)
+        redraw_info = {"enabled": False, "error": f"band: {ex}"}
+
+    # ② Flux-2 重绘已下线：本接口现在只产出 final（接缝融合基底）+ 纯红重绘带遮罩，
+    #    重绘交给后端 ComfyUI 重绘接口（/api/v1/redraw）完成。
+    #    下面保留 redraw_full_b64 / redraw_band_b64 为空，保持返回结构兼容（旧字段）。
+    redraw_full_b64 = ""
+    redraw_band_b64 = ""
+    t_redraw = time.time() - t0
+
+    data = {
+        "hairline_id": hairline_id,
+        "blend_method": blend_method,
+        "hairline_push_cm": round(float(hairline_push_cm), 2),
+        "comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发",
+        "beauty_alpha": beauty_alpha,
+        "px_per_cm": round(float(px_per_cm), 4),
+        "mask_pixels": mask_viz["mask_pixels"],
+        "image_size": {"width": w, "height": h},
+        "timings_ms": {
+            "mask": int(core["t_mask"] * 1000),
+            "swap": int(core["t_swap"] * 1000),
+            "blend": int(core["t_blend"] * 1000),
+            "redraw": int(t_redraw * 1000),
+        },
+        "steps": {
+            "input_base64": _jpg_b64(image_bgr),
+            # 接口11 的 ④ final —— 作为本接口的重绘输入基底
+            "final_base64": _jpg_b64(final),
+            # ⑤-① 发际线重绘带（紫，已按 baseline 截断只留上部）
+            "redraw_band_overlay_base64": redraw_band_overlay_b64,
+            # ⑤-② 发际线重绘带遮罩（纯红 alpha PNG，遮罩区=(255,0,0,255)）
+            "redraw_band_mask_base64": redraw_band_mask_b64,
+            # A：ComfyUI 整帧重绘+美颜（已下线，保留空字段兼容旧前端）
+            "redraw_full_base64": redraw_full_b64,
+            # B：加发只在发际线带、美颜保留全脸（已下线，保留空字段兼容旧前端）
+            "redraw_band_base64": redraw_band_b64,
+            # 兼容旧字段：指向 A（整帧版）
+            "redraw_c_base64": redraw_full_b64,
+        },
+        "redraw": redraw_info,
+        "_rid": rid,
+    }
+    steps_summary = {k: (len(v) if isinstance(v, str) and v else 0)
+                     for k, v in data["steps"].items() if k.endswith("_base64")}
+    logger.info("[%s] 返回 steps 字段长度: %s", rid, steps_summary)
+    logger.info("[%s] ===== generate_hairline_redraw 完成 =====", rid)
+    return data

face_analysis/head_mask.py

@@ -22,9 +22,11 @@ import numpy as np
 from face_analysis.detector import detector
 from face_analysis.calibration import estimate_scale_factor, normalized_to_pixel
 
-# 底部分割线关键点（图像上从左到右：左端 162 → 中心 151 → 右端 389）
-# 162/389 为左右最外侧端点（向图片左右边缘水平延长）；中间含 71/301 等点构成弧线
-BASELINE_IDX = [162, 71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301, 389]
+# 底部分割线关键点（图像上从左到右，眉骨弧线 → 中心 151 → 右侧对称）
+# 左端 104 → 中心 151 → 右端 333；首末点向图片左右边缘水平延长
+# BASELINE_IDX = [104, 69, 108, 151, 337, 299, 333]
+# BASELINE_IDX = [34, 139, 71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301, 368, 264]
+BASELINE_IDX = [71, 68, 104, 69, 108, 151, 337, 299, 333, 298, 301]
 CENTER_IDX = 151          # 内缩方向的目标点（额头中心）
 ERODE_CM = 1.2            # 外缘内缩距离（厘米，默认；可由入参覆盖）
 SEGFORMER_HAIR = 13       # jonathandinu/face-parsing 中 hair 类索引

face_analysis/measure.py

@@ -12,9 +12,9 @@ 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_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
 )
 from face_analysis.hair_segmenter import locate_hairline_by_segmentation
 
@@ -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):
@@ -144,22 +142,47 @@ 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。"""
 
-    def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose):
+    # 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
         self.eyes = eyes
         self.px_per_cm = px_per_cm
         self.hairline_source = hairline_source
         self.head_pose = head_pose  # (yaw, pitch, roll) 或 None
+        # 原始 mediapipe 点集 + 图像尺寸，供 to_response 输出 21/251 号定位点
+        self.landmarks = landmarks
+        self.w = image_width
+        self.h = image_height
 
         # 各庭厘米
         self.top_cm = vertical["top_court_px"] / px_per_cm
         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
-        self.face_total_cm = self.top_cm + self.upper_cm + self.middle_cm + self.lower_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
         self.eye_width_cm = eyes["eye_width_px"] / px_per_cm
@@ -167,46 +190,88 @@ class MeasureResult:
         self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm
 
     def to_response(self):
-        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": {
-                "top_court_cm": round(self.top_cm, 2),
-                "upper_court_cm": round(self.upper_cm, 2),
-                "middle_court_cm": round(self.middle_cm, 2),
-                "lower_court_cm": round(self.lower_cm, 2),
-                "ratios": {
-                    "top_court": round(self.vertical["top_court_px"] / total_px, 3),
-                    "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
-                    "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
-                    "lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
+        # 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"] / fw_px, 3),
-                    "inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / fw_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": pt("hair_top"),
-                "hairline": pt("hairline"),
-                "brow_center": pt("brow_center"),
-                "nose_bottom": pt("nose_bottom"),
-                "chin_tip": pt("chin_tip"),
-            },
-            "hairline_source": self.hairline_source,
-        }
+                "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"])
+            data = {
+                "face_total_height_cm": round(self.face_total_cm, 2),
+                "four_courts": {
+                    "top_court_cm": round(self.top_cm, 2),
+                    "upper_court_cm": round(self.upper_cm, 2),
+                    "middle_court_cm": round(self.middle_cm, 2),
+                    "lower_court_cm": round(self.lower_cm, 2),
+                    "ratios": {
+                        "top_court": round(self.vertical["top_court_px"] / total_px, 3),
+                        "upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
+                        "middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
+                        "lower_court": round(self.vertical["lower_court_px"] / total_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": 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,
+            }
+        # left/right_position：mediapipe 21/251 号定位点（原图像素，与 landmarks 同坐标系）。
+        # landmarks 缺省（如测试直构 MeasureResult）时不输出，保持向后兼容。
+        if self.landmarks is not None and self.w and self.h:
+            lm = _lm_list(self.landmarks)
+
+            def _pt_lm(idx):
+                px, py = normalized_to_pixel(lm[idx], self.w, self.h)
+                return {"x": int(round(px)), "y": int(round(py))}
+
+            data["left_position"] = _pt_lm(LEFT_POSITION)
+            data["right_position"] = _pt_lm(RIGHT_POSITION)
         if self.head_pose is not None:
             yaw, pitch, roll = self.head_pose
             data["head_pose"] = {
@@ -220,7 +285,8 @@ def measure_face(landmarks, hair_mask, image_width, image_height, head_pose=None
     vertical, source = decide_vertical(landmarks, image_width, image_height, hair_mask)
     eyes = measure_seven_eyes(landmarks, image_width, image_height)
     px_per_cm = estimate_scale_factor(landmarks, image_width, image_height)
-    return MeasureResult(vertical, eyes, px_per_cm, source, head_pose)
+    return MeasureResult(vertical, eyes, px_per_cm, source, head_pose,
+                         landmarks, image_width, image_height)
 
 
 if __name__ == "__main__":

face_analysis/pose.py

@@ -50,12 +50,21 @@ def estimate_head_pose(landmarks, image_width, image_height):
                            [0, 0, 1]], dtype=np.float64)
     dist = np.zeros((4, 1))  # 假设无畸变
 
-    success, rvec, _tvec = cv2.solvePnP(
+    success, rvec, tvec = cv2.solvePnP(
         _MODEL_POINTS, image_points, cam_matrix, dist,
         flags=cv2.SOLVEPNP_ITERATIVE,
     )
     if not success:
         return None
+    # ITERATIVE 偶发收敛到相机后方的翻转解（tz<0），此时 roll 落在 ±180° 附近，
+    # 会把真正的正面照误判为 1003。改用 SQPNP 重解正深度解。
+    if float(tvec[2, 0]) < 0:
+        ok2, rvec2, tvec2 = cv2.solvePnP(
+            _MODEL_POINTS, image_points, cam_matrix, dist,
+            flags=cv2.SOLVEPNP_SQPNP,
+        )
+        if ok2 and float(tvec2[2, 0]) > 0:
+            rvec = rvec2
     rot, _ = cv2.Rodrigues(rvec)
     # 在「相机坐标系」(x右 y下 z内) 下抽取 Tait-Bryan 欧拉角，物理含义对齐：
     #   yaw   = 绕 Y（竖轴）转 → 左右扭头

hair-worker.service

@@ -1,16 +1,15 @@
 [Unit]
-Description=Hair Worker (GPU) - 四庭七眼测量 接口1
-After=network.target
+Description=hair GPU worker FastAPI (0.0.0.0:8187)
+After=network-online.target comfyui.service change_hair-hair.service
+Wants=comfyui.service change_hair-hair.service
 
 [Service]
 Type=simple
-User=xsl
-WorkingDirectory=/home/xsl/hair
-# 鉴权密码：优先 worker_config.json；也可在此用环境变量覆盖
-# Environment=WORKER_ACCEPT_PASSWORDS=your-strong-secret
-ExecStart=/home/xsl/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
-Restart=always
-RestartSec=3
+User=ubuntu
+WorkingDirectory=/home/ubuntu/hair
+ExecStart=/home/ubuntu/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
+Restart=on-failure
+RestartSec=5
 
 [Install]
 WantedBy=multi-user.target

hair_repaint.json

@@ -0,0 +1,450 @@
+{
+  "1": {
+    "inputs": {
+      "scheduler": "simple",
+      "steps": 6,
+      "denoise": 1,
+      "model": [
+        "2",
+        0
+      ]
+    },
+    "class_type": "BasicScheduler",
+    "_meta": {
+      "title": "基本调度器"
+    }
+  },
+  "2": {
+    "inputs": {
+      "max_shift": 1.15,
+      "base_shift": 0.5,
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "model": [
+        "16",
+        0
+      ]
+    },
+    "class_type": "ModelSamplingFlux",
+    "_meta": {
+      "title": "采样算法（Flux）"
+    }
+  },
+  "3": {
+    "inputs": {
+      "vae_name": "flux2-vae.safetensors"
+    },
+    "class_type": "VAELoader",
+    "_meta": {
+      "title": "加载VAE"
+    }
+  },
+  "5": {
+    "inputs": {
+      "conditioning": [
+        "19",
+        0
+      ],
+      "latent": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "ReferenceLatent",
+    "_meta": {
+      "title": "参考Latent"
+    }
+  },
+  "6": {
+    "inputs": {
+      "noise_seed": 808990860769642
+    },
+    "class_type": "RandomNoise",
+    "_meta": {
+      "title": "随机噪波"
+    }
+  },
+  "7": {
+    "inputs": {
+      "width": [
+        "14",
+        0
+      ],
+      "height": [
+        "14",
+        1
+      ],
+      "batch_size": 1
+    },
+    "class_type": "EmptySD3LatentImage",
+    "_meta": {
+      "title": "空Latent图像（SD3）"
+    }
+  },
+  "8": {
+    "inputs": {
+      "sampler_name": "euler"
+    },
+    "class_type": "KSamplerSelect",
+    "_meta": {
+      "title": "K采样器选择"
+    }
+  },
+  "9": {
+    "inputs": {
+      "noise": [
+        "6",
+        0
+      ],
+      "guider": [
+        "20",
+        0
+      ],
+      "sampler": [
+        "8",
+        0
+      ],
+      "sigmas": [
+        "1",
+        0
+      ],
+      "latent_image": [
+        "7",
+        0
+      ]
+    },
+    "class_type": "SamplerCustomAdvanced",
+    "_meta": {
+      "title": "自定义采样器（高级）"
+    }
+  },
+  "10": {
+    "inputs": {
+      "samples": [
+        "9",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE解码"
+    }
+  },
+  "13": {
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE编码"
+    }
+  },
+  "14": {
+    "inputs": {
+      "image": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "GetImageSize+",
+    "_meta": {
+      "title": "🔧 Get Image Size"
+    }
+  },
+  "16": {
+    "inputs": {
+      "unet_name": "flux-2-klein-9b-Q4_K_M.gguf",
+      "weight_dtype": "fp8_e4m3fn"
+    },
+    "class_type": "UnetLoaderGGUF",
+    "_meta": {
+      "title": "UNet加载器"
+    }
+  },
+  "17": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "images": [
+        "62",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "保存图像"
+    }
+  },
+  "19": {
+    "inputs": {
+      "guidance": 1,
+      "conditioning": [
+        "22",
+        0
+      ]
+    },
+    "class_type": "FluxGuidance",
+    "_meta": {
+      "title": "Flux引导"
+    }
+  },
+  "20": {
+    "inputs": {
+      "model": [
+        "2",
+        0
+      ],
+      "conditioning": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "BasicGuider",
+    "_meta": {
+      "title": "基本引导器"
+    }
+  },
+  "22": {
+    "inputs": {
+      "text": [
+        "60",
+        0
+      ],
+      "clip": [
+        "61",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP文本编码"
+    }
+  },
+  "26": {
+    "inputs": {
+      "image": "clipspace/clipspace-painted-masked-1784045785080.png [input]"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "加载图像"
+    }
+  },
+  "31": {
+    "inputs": {
+      "image": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "easy imageSize",
+    "_meta": {
+      "title": "图像尺寸"
+    }
+  },
+  "32": {
+    "inputs": {
+      "aspect_ratio": "custom",
+      "proportional_width": [
+        "31",
+        0
+      ],
+      "proportional_height": [
+        "31",
+        1
+      ],
+      "fit": "letterbox",
+      "method": "lanczos",
+      "round_to_multiple": "8",
+      "scale_to_side": "None",
+      "scale_to_length": 1024,
+      "background_color": "#000000",
+      "image": [
+        "26",
+        0
+      ],
+      "mask": [
+        "37",
+        0
+      ]
+    },
+    "class_type": "LayerUtility: ImageScaleByAspectRatio V2",
+    "_meta": {
+      "title": "LayerUtility: ImageScaleByAspectRatio V2"
+    }
+  },
+  "33": {
+    "inputs": {
+      "masks": [
+        "26",
+        1
+      ]
+    },
+    "class_type": "Mask Fill Holes",
+    "_meta": {
+      "title": "遮罩填充漏洞"
+    }
+  },
+  "36": {
+    "inputs": {
+      "masks": [
+        "33",
+        0
+      ]
+    },
+    "class_type": "Convert Masks to Images",
+    "_meta": {
+      "title": "遮罩到图像"
+    }
+  },
+  "37": {
+    "inputs": {
+      "method": "intensity",
+      "image": [
+        "39",
+        0
+      ]
+    },
+    "class_type": "Image To Mask",
+    "_meta": {
+      "title": "图像到遮罩"
+    }
+  },
+  "39": {
+    "inputs": {
+      "upscale_method": "nearest-exact",
+      "width": [
+        "31",
+        0
+      ],
+      "height": [
+        "31",
+        1
+      ],
+      "crop": "disabled",
+      "image": [
+        "36",
+        0
+      ]
+    },
+    "class_type": "ImageScale",
+    "_meta": {
+      "title": "缩放图像"
+    }
+  },
+  "44": {
+    "inputs": {
+      "mask_opacity": 1,
+      "mask_color": "FFFF00",
+      "pass_through": true,
+      "image": [
+        "32",
+        0
+      ],
+      "mask": [
+        "32",
+        1
+      ]
+    },
+    "class_type": "ImageAndMaskPreview",
+    "_meta": {
+      "title": "图像与遮罩预览"
+    }
+  },
+  "45": {
+    "inputs": {
+      "images": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "预览图像"
+    }
+  },
+  "53": {
+    "inputs": {
+      "rgthree_comparer": {
+        "images": [
+          {
+            "name": "A",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_kzrpg_00019_.png&type=temp&subfolder=&rand=0.8964945384546902"
+          },
+          {
+            "name": "B",
+            "selected": true,
+            "url": "/api/view?filename=rgthree.compare._temp_kzrpg_00020_.png&type=temp&subfolder=&rand=0.6762414189274947"
+          }
+        ]
+      },
+      "image_a": [
+        "62",
+        0
+      ],
+      "image_b": [
+        "26",
+        0
+      ]
+    },
+    "class_type": "Image Comparer (rgthree)",
+    "_meta": {
+      "title": "图像对比"
+    }
+  },
+  "60": {
+    "inputs": {
+      "text": "填充遮罩区域的头发"
+    },
+    "class_type": "JjkText",
+    "_meta": {
+      "title": "Text"
+    }
+  },
+  "61": {
+    "inputs": {
+      "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+      "type": "flux2",
+      "device": "default"
+    },
+    "class_type": "CLIPLoader",
+    "_meta": {
+      "title": "加载CLIP"
+    }
+  },
+  "62": {
+    "inputs": {
+      "method": "mkl",
+      "strength": 1,
+      "multithread": true,
+      "image_ref": [
+        "26",
+        0
+      ],
+      "image_target": [
+        "10",
+        0
+      ]
+    },
+    "class_type": "ColorMatch",
+    "_meta": {
+      "title": "Color Match"
+    }
+  }
+}

hairline/comfyui.py

@@ -1,8 +1,9 @@
 """ComfyUI 客户端：用 add_hair.json / add_hair2.json 工作流跑生发图（Flux-2 inpaint）。
 
-worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到本机 ComfyUI(默认 8188)，
+worker 不跑 Flux，只把「划线图 + 遮罩」的 RGBA 上传到远端 ComfyUI
+（默认 http://10.60.74.221:8188，可用环境变量 COMFYUI_URL 覆盖），
 替换工作流节点 26 的输入图、随机 seed，提交 /prompt，轮询 /history，取回 /view 输出。
-ComfyUI 开启了 HTTP Basic Auth（user `admin` + 密码），所有请求都带凭据。
+ComfyUI 若开启了 HTTP Basic Auth（user `admin` + 密码），所有请求都带凭据。
 
 支持多工作流：run() 可通过 workflow_path 指定不同工作流 JSON，自动检测 SaveImage 输出节点。
 """
@@ -10,6 +11,7 @@ from __future__ import annotations
 
 import copy
 import json
+import logging
 import os
 import random
 import time
@@ -28,6 +30,29 @@ _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
+_VAE_NODE = "3"        # VAELoader
+
+# 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。
+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 "9b" in low:                       # Flux.2 9B 系列
+        return "qwen_3_8b_fp8mixed.safetensors"
+    if "z-image" in low:                  # Z-Image-Turbo 用 4B 编码器
+        return "qwen_3_4b.safetensors"
+    if "4b" in low:                       # Flux.2 4B
+        return "qwen_3_4b.safetensors"
+    return None
+
+# Flux 模型 → 配套 VAE 映射。Z-Image 用 ae.safetensors，Flux.2 系列用 flux2-vae。
+def _vae_for_unet(unet_name: str) -> str | None:
+    """根据 unet 文件名推断配套 VAE 文件名；无法推断返回 None（保持工作流原值）。"""
+    low = unet_name.lower()
+    if "z-image" in low:
+        return "ae.safetensors"
+    return None  # Flux.2 系列 vae 在工作流里已正确配置，不覆盖
 
 _wf_cache: dict[str, dict] = {}      # path → workflow JSON
 _wf_output_node: dict[str, str] = {}  # path → SaveImage 节点 ID
@@ -84,11 +109,18 @@ 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) -> bytes:
+        workflow_path: str | None = None, front: bool = False,
+        unet_name: str | None = None) -> 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)
@@ -103,14 +135,60 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
         name = (up.get("subfolder") + "/" if up.get("subfolder") else "") + up["name"]
 
         # 2. 改工作流：节点26 输入图 + 随机 seed
+        try:
+            import io as _io
+            from PIL import Image as _Img
+            _sz = _Img.open(_io.BytesIO(rgba_png_bytes)).size
+            logging.getLogger("hair.worker").info(
+                "ComfyUI 输入尺寸 %dx%d workflow=%s", _sz[0], _sz[1], os.path.basename(path))
+        except Exception:  # noqa: BLE001
+            pass
         wf = copy.deepcopy(_load_workflow(path))
         wf[_INPUT_NODE]["inputs"]["image"] = name
         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
+            # 同步切换 VAE（Z-Image 用 ae.safetensors，Flux.2 保持 flux2-vae）
+            vae_node = wf.get(_VAE_NODE)
+            vae_name = _vae_for_unet(unet_name)
+            if vae_node is not None and vae_name is not None:
+                vae_node["inputs"]["vae_name"] = vae_name
 
-        # 3. 提交
-        r = cli.post("/prompt", json={"prompt": wf, "client_id": client_id})
+        # 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:
+            import os as _os
+            _diag = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))),
+                                  "log", "comfyui_last_submit")
+            _os.makedirs(_diag, exist_ok=True)
+            with open(_os.path.join(_diag, "workflow.json"), "w", encoding="utf-8") as _f:
+                json.dump(wf, _f, ensure_ascii=False, indent=2)
+            with open(_os.path.join(_diag, "input.png"), "wb") as _f:
+                _f.write(rgba_png_bytes)
+            with open(_os.path.join(_diag, "prompt.txt"), "w", encoding="utf-8") as _f:
+                _f.write(prompt if prompt is not None else "(None=用工作流内置默认)")
+        except Exception:  # noqa: BLE001
+            pass
+
+        # 3. 提交（front=True 时插队到队列最前）
+        payload = {"prompt": wf, "client_id": client_id}
+        if front:
+            payload["front"] = True
+        r = cli.post("/prompt", json=payload)
         r.raise_for_status()
         prompt_id = r.json()["prompt_id"]
 
@@ -129,7 +207,7 @@ def run(rgba_png_bytes: bytes, timeout: float = COMFY_TIMEOUT, prompt: str = Non
                 outputs = entry.get("outputs")
                 if outputs and output_node in outputs:
                     break
-            time.sleep(1.0)
+            time.sleep(0.05)
         if not outputs or output_node not in outputs:
             raise TimeoutError(f"ComfyUI 出图超时({timeout}s) prompt_id={prompt_id}")
 

hairline/hairline_2d.py

@@ -186,6 +186,53 @@ def smooth_hairline_corner_aware(
     return out
 
 
+def clamp_hairline_to_silhouette(
+    hairline_norm: np.ndarray,
+    parse_map: np.ndarray,
+    margin_px: float = 2.0,
+) -> np.ndarray:
+    """把发际线点的 y 钳制在 (skin∪hair) silhouette 上沿之下（不含 margin 以上）。
+
+    根因（见 issue：男性 ellipse 发际线贴到头部外面）：`sample_hairline` 对射线
+    未命中 hair 像素的锚点会 fallback 成「锚点 + 固定 0.18 归一化偏移」，与头部实际
+    大小/位置无关 —— 短发/剃光头场景下这个偏移量常常把点顶到头部轮廓外面的背景里，
+    在有效/失效锚点交界处形成尖角，被贴图上的不透明像素蒙到就会露出戳出头部的线条。
+
+    本函数在几何检测之后追加一步「安全网」：对每个点按其 x 所在列，取 silhouette
+    （SegFormer skin∪hair 类，近似头部实际轮廓）上沿 y，若点比这个上沿还高（y 更
+    小），直接钳制到 上沿 + margin_px —— 保证曲线永远不会跑到头部轮廓外面的背景。
+    """
+    h, w = parse_map.shape
+    cols_with_head, top_y = _head_top_y_per_column(parse_map, use_full_hair=True)
+    if cols_with_head.size == 0:
+        return hairline_norm
+    out = hairline_norm.copy()
+    for i in range(out.shape[0]):
+        x_px = float(out[i, 0]) * w
+        idx = int(np.searchsorted(cols_with_head, x_px))
+        idx = min(max(idx, 0), cols_with_head.size - 1)
+        sil_y = float(top_y[idx]) + margin_px
+        y_px = float(out[i, 1]) * h
+        if y_px < sil_y:
+            out[i, 1] = sil_y / h
+    return out
+
+
+def sample_hairline_clamped(
+    landmarks_norm: np.ndarray,
+    parse_map: np.ndarray,
+    fallback_extrapolation: float = 0.18,
+) -> tuple[np.ndarray, np.ndarray]:
+    """策略 A（baseline + 头部轮廓钳制）：与默认 `sample_hairline` 完全一致的检测，
+    额外用 `clamp_hairline_to_silhouette` 兜底 —— 检测失效 fallback 出的点不再可能
+    跑到头部外面的背景，而是贴着头部实际轮廓顶部。改动小、风险低，只在检测失效/
+    fallback 越界时才生效，正常长发照片的结果与 baseline 完全一致。
+    """
+    hairline, valid = sample_hairline(landmarks_norm, parse_map, fallback_extrapolation)
+    hairline = clamp_hairline_to_silhouette(hairline, parse_map)
+    return hairline, valid
+
+
 # ---------------------------------------------------------------------------
 # Alternative hairline sampling strategies.
 #

hairline/redraw.py

@@ -0,0 +1,80 @@
+"""直接调 ComfyUI 重绘 — 替代 local_test HTTP 服务。
+
+将 local_test/app.py 的核心逻辑（遮罩处理 + ComfyUI 调用）提取为 Python 函数，
+不再需要独立 Flask 服务。使用 0716add-hair-api.json 工作流（steps=4）。
+"""
+from __future__ import annotations
+
+import io
+import logging
+import os
+
+import numpy as np
+from PIL import Image, ImageFilter
+
+from . import comfyui
+
+logger = logging.getLogger("hair.worker")
+
+_DEFAULT_PROMPT = "填充遮罩区域的头发"
+_REPO = os.path.dirname(os.path.dirname(__file__))
+_REPAINT_WORKFLOW = os.path.join(_REPO, "0716add-hair-api.json")
+
+
+def _process_mask_to_rgba(image_bytes: bytes, mask_bytes: bytes) -> bytes:
+    """将分开的 image + mask 处理为 ComfyUI 用的 RGBA PNG bytes。
+
+    复制 local_test/app.py 的遮罩处理逻辑：
+    1. 加载 image 为 RGB
+    2. 加载 mask 为 RGBA，取所有通道 max 值（支持红/白/alpha 遮罩）
+    3. resize mask 到与 image 一致
+    4. 高斯模糊(radius=4) 柔化边缘
+    5. alpha = 255 - mask（绘制区=255 → alpha=0 → 重绘区）
+    6. 合成 RGBA PNG
+    """
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    mask_img = Image.open(io.BytesIO(mask_bytes)).convert("RGBA")
+    mask_arr = np.array(mask_img)
+    mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+    mask_data_img = Image.fromarray(mask_data, mode="L")
+    if mask_data_img.size != image.size:
+        mask_data_img = mask_data_img.resize(image.size, Image.LANCZOS)
+    mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+    # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+    # alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+    comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+    buf = io.BytesIO()
+    rgba.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+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:
+    """直接调 ComfyUI 重绘 — 替代 local_test /api/generate。
+
+    Args:
+        image_bytes: 人物图片字节（JPG/PNG）
+        mask_bytes: 遮罩图片字节（支持红/白/alpha 遮罩格式）
+        prompt: 提示词，None 用默认 "填充遮罩区域的头发"
+        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
+
+    Raises:
+        RuntimeError: ComfyUI 执行失败
+        TimeoutError: ComfyUI 超时
+    """
+    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)

hairline/service.py

@@ -14,21 +14,83 @@ from . import constants as C
 from . import comfyui
 from .face_landmarks import FaceLandmarker
 from .face_parsing import FaceParser
-from .hairline_2d import sample_hairline, smooth_hairline
+from .hairline_2d import (
+    smooth_hairline, sample_hairline_clamped,
+)
 from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
 from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay, build_overlay_layer
 from .mask import build_inpaint_mask, compose_comfy_rgba, mask_from_curve
 from .marker_detect import detect_marker_hairline, path_to_curve_mask
 
+import base64
 import io
 import logging
 
 logger = logging.getLogger("hair.worker")
 
+# 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。
+# 与接口12 final 的 5 型一一对应。
+_FEMALE_KEY_TO_CHANG = {
+    "ellipse": "chang_tuoyuan",   # 椭圆
+    "flower": "chang_huaban",     # 花瓣
+    "heart": "chang_xinxing",     # 心形
+    "straight": "chang_zhixian",  # 直线
+    "wave": "chang_bolang",       # 波浪
+}
+
 _REPO = os.path.dirname(os.path.dirname(__file__))
 _TEXTURE_DIR = os.path.join(_REPO, "hairline_texture")
 _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
 
+# 三接口（接口2女重绘 / 接口2男 / 接口3）统一的 ComfyUI 重绘 prompt。
+# 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。
+# 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。
+# 与 app.py 接口2/接口3 的默认 prompt 保持一致；可用 REDRAW_PROMPT 覆盖。
+_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发")
+
+# 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：
+# 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
+# 固定地板) + ComfyUI 两段串行。1024 档画质更好但部分大图会踩 12s 线，
+# 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, prompt=None):
+    """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。
+    prompt：None 用默认 _REDRAW_PROMPT，否则用传入的提示词。
+    """
+    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)
+            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)
+            msk_s = cv2.resize(msk, (nw, nh), interpolation=cv2.INTER_NEAREST)
+            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)
+    # 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=prompt if prompt is not None else _REDRAW_PROMPT,
+                     front=True, unet_name=unet_name)
+    if scale < 1.0 and out:
+        out = _upscale_png_to(out, orig_w, orig_h)
+    return out
+
 # 发际线贴图档位：middle=默认(hairline_texture/)，high/low 各自独立文件夹。
 _TEXTURE_DIRS = {
     "middle": _TEXTURE_DIR,
@@ -36,9 +98,8 @@ _TEXTURE_DIRS = {
     "low": os.path.join(_REPO, "hairline_texture_low"),
 }
 
-# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
-#    "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
-_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
+# torch 2.7.1+cu128 已支持 RTX 5090 (sm_120)，SegFormer 走 GPU（~0.05s/张）
+_SEG_DEVICE = os.getenv("SEG_DEVICE", "cuda")
 
 _landmarker = None
 _parser = None
@@ -100,13 +161,19 @@ def extract_502(image_bgr: np.ndarray):
 
 
 def extract_context(image_bgr: np.ndarray):
-    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。"""
+    """照片(BGR) → {landmarks, parse_map, points, valid}。无人脸返回 None。
+
+    发际线几何检测固定用 `sample_hairline_clamped`（射线检测 + 头部轮廓钳制）：
+    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
+    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
+    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。
+    """
     rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
     landmarks = get_landmarker().detect(rgb)
     if landmarks is None:
         return None
     parse_map = get_parser().parse(rgb)
-    hairline_2d, valid = sample_hairline(landmarks, parse_map)
+    hairline_2d, valid = sample_hairline_clamped(landmarks, parse_map)
     hairline_2d = smooth_hairline(hairline_2d, valid)
     hairline_3d = lift_hairline_to_3d(landmarks, hairline_2d)
     middle_3d = build_middle_row(landmarks, hairline_3d)
@@ -140,8 +207,9 @@ 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):
-    """指定发际线类型：预览图(白线) + 生发图(ComfyUI)。
+                         workflow_path: str | None = None,
+                         unet_name: 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。
       为 None 时生成全部（兼容旧调用）。
@@ -149,7 +217,8 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
       False 时用**干净原图 + 空遮罩**送 ComfyUI（不烧黑色模板线）。
     prompt（默认 None）：ComfyUI 提示词，非 None 时替换工作流节点60文本。
     workflow_path（默认 None）：ComfyUI 工作流 JSON 路径，None 用默认 add_hair.json。
-    Returns: list[dict] {"hairline_type","order","image_bgr"(预览), "grown_png"(bytes 或 None)}。
+    Returns: list[dict] {"hairline_type","order","overlay"((H,W,4) RGBA 透明层),
+                        "grown_png"(bytes 或 None)}。
     无人脸返回 None。某张 ComfyUI 失败时该项 grown_png=None，不抛异常。
     """
     if gender not in ("male", "female"):
@@ -170,16 +239,23 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
     if not use_mask:
         try:
             h, w = image_bgr.shape[:2]
+            img_s, msk_s, gsc = _prep_comfy_input(image_bgr, np.zeros((h, w), np.uint8))
             buf = io.BytesIO()
-            compose_comfy_rgba(image_bgr, np.zeros((h, w), np.uint8)).save(buf, format="PNG")
-            shared_grown = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+            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)
+            if gsc < 1.0 and shared_grown:
+                shared_grown = _upscale_png_to(shared_grown, w, h)
         except Exception as e:  # noqa: BLE001
             logger.warning("接口2 生发图失败(无遮罩)：%s", e)
 
     results = []
+    h, w = image_bgr.shape[:2]
     for order, (key, white_path) in items:
         white = load_texture_rgba(white_path)
-        preview = render_hairline_overlay(image_bgr, ctx["points"], ext_faces, uv, white)
+        overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
 
         if not use_mask:
             grown_png = shared_grown
@@ -189,14 +265,124 @@ def generate_grow_results(image_bgr: np.ndarray, gender: str, use_mask: bool = T
                 black = load_texture_rgba(_black_texture_path(white_path))
                 marked, mask = build_inpaint_mask(
                     image_bgr, ctx["landmarks"], ctx["parse_map"], ctx["points"], black)
+                m_s, msk_s, gsc = _prep_comfy_input(marked, mask)
                 buf = io.BytesIO()
-                compose_comfy_rgba(marked, mask).save(buf, format="PNG")
-                grown_png = comfyui.run(buf.getvalue(), prompt=prompt, workflow_path=workflow_path)
+                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)
+                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
                 logger.warning("接口2 生发图失败 type=%s：%s", key, e)
 
         results.append({"hairline_type": key, "order": order,
-                        "image_bgr": preview, "grown_png": grown_png})
+                        "overlay": overlay, "grown_png": grown_png})
+    return results
+
+
+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):
+    """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，
+    调 face_analysis.hairline_grow.generate_hairline_redraw（= 接口12 final 管线，参数用
+    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
+    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。
+
+    overlay 仍是发际线曲线透明层（与 generate_grow_results 完全一致）。
+    Returns: list[dict] {"hairline_type","order","overlay","grown_png"(jpg bytes 或 None)}；
+             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。
+    """
+    from face_analysis.hairline_grow import generate_hairline_redraw, NoFaceError
+    from face_analysis.head_mask import SEGFORMER_HAIR
+
+    ctx = extract_context(image_bgr)
+    if ctx is None:
+        return None
+    uv, ext_faces = load_ext_mesh()
+
+    # 复用 extract_context 已算好的 SegFormer parse_map，避免 generate_hairline_redraw 内部重复分割
+    hair_mask_reuse = (ctx["parse_map"] == SEGFORMER_HAIR)
+
+    textures = get_texture_map()["female"]  # [(key, path), ...] 已排序
+    if hair_styles is not None:
+        items = [(s, textures[s - 1]) for s in hair_styles]
+    else:
+        items = list(enumerate(textures, start=1))
+
+    results = []
+    h, w = image_bgr.shape[:2]
+
+    # 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)
+        _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)
+
+    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,
+                                                   max_side=redraw_max_side,
+                                                   unet_name=unet_name)
+                    _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
 
 
@@ -216,7 +402,7 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | None,
             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")
+        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)
@@ -225,15 +411,19 @@ def _grow_from_texture(image_bgr: np.ndarray, ctx: dict, white_path: str | 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 三档发际线叠图 + 生发图(同接口2)。
+                           prompt: str | None = None,
+                           generate_grow_image: bool = True):
+    """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）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。
+    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}(BGR),grown_png}],
-              "best_center":(x,y)}；无人脸 None。best_center 取首个选中发型的 middle 档。
+    generate_grow_image（默认 True）：是否生成生发图（ComfyUI，最耗时）。False 时跳过生发，
+        各发型 grown_png 恒为 None，可大幅降低耗时（仅留三档发际线叠图与中心点）。
+    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}")
@@ -252,32 +442,84 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
     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
+    # generate_grow_image=False：完全跳过生发（最耗时），grown_png 恒为 None
     shared_grown = None
-    if not use_mask:
+    if generate_grow_image and not use_mask:
         shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
 
-    images, best_center = [], None
+    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] = render_hairline_overlay(image_bgr, 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)
+            overlays[lv] = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
+        # 生发：固定 middle 黑模板（generate_grow_image=False 时跳过，恒 None）
+        if not generate_grow_image:
+            grown_png = None
+        elif not use_mask:
+            grown_png = shared_grown
+        else:
+            grown_png = _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_center：首个选中发型的 middle 档发际线中点（面部中轴处的发际线 y）
-        if best_center is None:
-            white = load_texture_rgba(mid_path)
-            overlay = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, white)
-            ys, xs = np.where(overlay[:, :, 3] > 40)
-            if xs.size:
-                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]]
-                best_center = (int(round(face_cx)), int(round(float(col_ys.mean()))))
-    return {"images": images, "best_center": best_center}
+        # 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):
@@ -286,12 +528,23 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
     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)
@@ -309,8 +562,10 @@ def generate_grow_b(marked_bgr: np.ndarray, use_mask: bool = True, prompt: str =
         mask = np.zeros((h, w), np.uint8)   # 空遮罩：alpha 全 255，跳过检测
 
     buf = io.BytesIO()
-    compose_comfy_rgba(marked_bgr, mask).save(buf, format="PNG")   # marked 原图 + 遮罩
+    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"}
 
 

local_test/README.md

@@ -0,0 +1,174 @@
+# 发型补全服务 API 文档
+
+## 服务概述
+
+本服务提供基于 ComfyUI 的发型补全（局部重绘）能力。通过传入人物图片和遮罩图片，调用 ComfyUI 工作流（`0716add-hair.json`）生成补全后的图片。
+
+## 技术栈
+
+- **框架**: Flask
+- **依赖**: requests, Pillow, numpy
+- **后端**: ComfyUI (http://127.0.0.1:8188)
+
+## 服务地址
+
+- **HTTP**: `http://127.0.0.1:8899`
+- **前端页面**: `http://127.0.0.1:8899/`
+- **API接口**: `http://127.0.0.1:8899/api/generate`
+
+## 启动方式
+
+### 使用脚本（推荐）
+
+```bash
+# 启动服务
+cd /home/ubuntu/hair/local_test
+./start.sh
+
+# 停止服务
+./stop.sh
+```
+
+### 直接运行
+
+```bash
+cd /home/ubuntu/hair/local_test
+/home/ubuntu/ComfyUI/venv/bin/python app.py
+```
+
+## API 接口
+
+### POST /api/generate
+
+调用 ComfyUI 工作流，传入图片和遮罩，返回生成结果。
+
+#### 请求参数
+
+| 参数名 | 类型 | 必填 | 说明 |
+|--------|------|------|------|
+| image | File | 是 | 人物图片（支持 jpg, png 等常见格式） |
+| mask | File | 是 | 遮罩图片（支持 jpg, png，遮罩区域可用红色/白色/alpha 通道标识） |
+| prompt | String | 否 | 提示词，默认值："填充遮罩区域的头发" |
+
+#### 遮罩图片格式说明
+
+服务支持多种遮罩格式，自动提取遮罩区域：
+
+| 格式类型 | 示例 | 遮罩区域标识 |
+|----------|------|--------------|
+| 红色遮罩 | 红色画笔绘制 | R=255 的像素 |
+| 白色遮罩 | 白色画笔绘制 | R=G=B=255 的像素 |
+| Alpha 遮罩 | 透明背景 | A=255 的像素 |
+
+服务会取所有通道的最大值作为遮罩强度，因此以上格式均可混用。
+
+**注意**: 遮罩区域表示需要重绘的部分，非遮罩区域保持原图不变。
+
+#### 请求示例（curl）
+
+```bash
+curl -X POST http://127.0.0.1:8899/api/generate \
+  -F "image=@/path/to/person.jpg" \
+  -F "mask=@/path/to/mask.png" \
+  -F "prompt=填充遮罩区域的头发" \
+  --output result.png
+```
+
+#### 请求示例（Python）
+
+```python
+import requests
+
+url = "http://127.0.0.1:8899/api/generate"
+files = {
+    "image": open("person.jpg", "rb"),
+    "mask": open("mask.png", "rb"),
+}
+data = {
+    "prompt": "填充遮罩区域的头发"
+}
+
+resp = requests.post(url, files=files, data=data, timeout=600)
+if resp.status_code == 200:
+    with open("result.png", "wb") as f:
+        f.write(resp.content)
+else:
+    print(f"Error: {resp.json()}")
+```
+
+#### 响应
+
+**成功 (HTTP 200)**:
+
+返回 PNG 图片二进制数据，Content-Type: `image/png`。
+
+**失败 (HTTP 4xx/5xx)**:
+
+返回 JSON 格式错误信息：
+
+```json
+{
+    "error": "错误描述"
+}
+```
+
+#### 错误码
+
+| 状态码 | 说明 |
+|--------|------|
+| 500 | 内部错误（文件处理失败、ComfyUI 返回错误等） |
+| 503 | 无法连接到 ComfyUI（服务未启动或端口错误） |
+| 500 | 超时（工作流执行超过 5 分钟） |
+
+## 工作流说明
+
+服务使用的工作流 `0716add-hair.json` 包含以下处理步骤：
+
+1. **加载模型**: Flux 2 Klein 9B (FP8) + Qwen 3.8B CLIP
+2. **图片上传**: 将原图与遮罩合成为 RGBA 格式上传至 ComfyUI
+3. **遮罩处理**: 填充孔洞 → 转换为图像 → 缩放 → 转换回遮罩
+4. **图像缩放**: 按比例缩放至合适尺寸（最大边长 1024，8 的倍数）
+5. **VAE 编码**: 将图像编码为 latent
+6. **采样生成**: 使用 Flux 模型 + ReferenceLatent 进行局部重绘
+7. **VAE 解码**: 将 latent 解码为图像
+8. **颜色匹配**: 使用 ColorMatch 保持颜色一致
+9. **保存结果**: 返回生成的图片
+
+## 前置依赖
+
+启动服务前需确保：
+
+1. **ComfyUI 已启动**: `http://127.0.0.1:8188` 可访问
+2. **模型文件存在**:
+   - `models/unet/flux2.0/flux-2-klein-9b-fp8.safetensors`
+   - `models/vae/flux2-vae.safetensors`
+   - `models/clip/qwen_3_8b_fp8mixed.safetensors`
+3. **虚拟环境已激活**: 使用 `/home/ubuntu/ComfyUI/venv/bin/python`
+
+## 文件结构
+
+```
+/home/ubuntu/hair/local_test/
+├── app.py          # Flask 后端服务
+├── index.html      # 前端测试页面
+├── test_api.py     # API 测试脚本
+├── README.md       # 本文档
+├── output/         # 测试结果输出目录
+├── 用来重绘.jpg     # 测试人物图片
+└── 用来重绘.png     # 测试遮罩图片
+```
+
+## 使用流程
+
+1. 启动 ComfyUI（`python main.py --listen`）
+2. 启动本服务（`python app.py`）
+3. 调用 API 或访问前端页面上传图片和遮罩
+4. 等待生成完成（通常 30-60 秒）
+5. 获取返回的 PNG 图片
+
+## 注意事项
+
+- 请求超时时间为 5 分钟，生成复杂图片可能需要较长时间
+- 遮罩图片尺寸需与人物图片一致，服务会自动缩放对齐
+- 建议使用红色或白色绘制遮罩，确保遮罩强度足够
+- 服务会自动对遮罩边缘进行高斯模糊（radius=4），避免硬边

local_test/app.py

@@ -0,0 +1,339 @@
+#!/usr/bin/env python3
+"""Hair inpainting service - calls ComfyUI workflow with image + mask."""
+import io
+import json
+import time
+import random
+import logging
+import traceback
+import requests
+from flask import Flask, request, jsonify, send_file
+import numpy as np
+from PIL import Image, ImageFilter
+
+# 让 PIL 支持 iPhone 的 HEIC/HEIF 照片（浏览器 accept="image/*" 会允许选中它们）。
+try:
+    from pillow_heif import register_heif_opener
+    register_heif_opener()
+    _HEIF_OK = True
+except Exception:
+    _HEIF_OK = False
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+log = logging.getLogger("hair")
+
+app = Flask(__name__)
+COMFYUI_URL = "http://127.0.0.1:8188"
+
+# 允许浏览器跨域直连本服务（如 hair 项目的测试页）。
+# 不引入 flask-cors 依赖，直接在响应头 + OPTIONS 预检里处理。
+_CORS_HEADERS = {
+    "Access-Control-Allow-Origin": "*",
+    "Access-Control-Allow-Methods": "POST, OPTIONS",
+    "Access-Control-Allow-Headers": "Content-Type",
+    "Access-Control-Expose-Headers": "X-Generate-Time",
+}
+
+
+@app.after_request
+def _add_cors(resp):
+    for k, v in _CORS_HEADERS.items():
+        resp.headers[k] = v
+    return resp
+
+
+@app.route("/api/generate", methods=["OPTIONS"])
+def _generate_preflight():
+    """CORS 预检：浏览器 POST 前会先发 OPTIONS。"""
+    return ("", 204)
+
+
+def build_workflow(image_filename, prompt_text, seed=None):
+    """Build ComfyUI API workflow from the 0716add-hair.json structure."""
+    if seed is None:
+        seed = random.randint(0, 2**53)
+
+    return {
+        # Loaders
+        "16": {"class_type": "UNETLoader", "inputs": {
+            "unet_name": "flux2.0/flux-2-klein-9b-fp8.safetensors",
+            "weight_dtype": "fp8_e4m3fn_fast"}},
+        "3": {"class_type": "VAELoader", "inputs": {
+            "vae_name": "flux2-vae.safetensors"}},
+        "61": {"class_type": "CLIPLoader", "inputs": {
+            "clip_name": "qwen_3_8b_fp8mixed.safetensors",
+            "type": "flux2",
+            "device": "default"}},
+
+        # Input image (with mask in alpha channel)
+        "26": {"class_type": "LoadImage", "inputs": {
+            "image": image_filename}},
+
+        # Prompt
+        "60": {"class_type": "JjkText", "inputs": {
+            "text": prompt_text}},
+        "22": {"class_type": "CLIPTextEncode", "inputs": {
+            "clip": ["61", 0],
+            "text": ["60", 0]}},
+
+        # Image size
+        "31": {"class_type": "easy imageSize", "inputs": {
+            "image": ["26", 0]}},
+
+        # Mask processing: fill holes -> convert to image -> scale -> back to mask
+        "33": {"class_type": "Mask Fill Holes", "inputs": {
+            "masks": ["26", 1]}},
+        "36": {"class_type": "Convert Masks to Images", "inputs": {
+            "masks": ["33", 0]}},
+        "39": {"class_type": "ImageScale", "inputs": {
+            "image": ["36", 0],
+            "upscale_method": "nearest-exact",
+            "width": ["31", 0],
+            "height": ["31", 1],
+            "crop": "disabled"}},
+        "37": {"class_type": "Image To Mask", "inputs": {
+            "image": ["39", 0],
+            "method": "intensity"}},
+
+        # Scale image+mask by aspect ratio
+        "32": {"class_type": "LayerUtility: ImageScaleByAspectRatio V2", "inputs": {
+            "image": ["26", 0],
+            "mask": ["37", 0],
+            "aspect_ratio": "custom",
+            "proportional_width": ["31", 0],
+            "proportional_height": ["31", 1],
+            "fit": "letterbox",
+            "method": "lanczos",
+            "round_to_multiple": "8",
+            "scale_to_side": "None",
+            "scale_to_length": 1024,
+            "background_color": "#000000"}},
+
+        # Preview (pass_through=true, just passes the image through)
+        "44": {"class_type": "ImageAndMaskPreview", "inputs": {
+            "image": ["32", 0],
+            "mask": ["32", 1],
+            "mask_opacity": 1,
+            "mask_color": "FFFF00",
+            "pass_through": True}},
+
+        # Get size of scaled image
+        "14": {"class_type": "GetImageSize+", "inputs": {
+            "image": ["44", 0]}},
+
+        # VAE encode the image
+        "13": {"class_type": "VAEEncode", "inputs": {
+            "pixels": ["44", 0],
+            "vae": ["3", 0]}},
+
+        # Flux model setup
+        "2": {"class_type": "ModelSamplingFlux", "inputs": {
+            "model": ["16", 0],
+            "max_shift": 1.15,
+            "base_shift": 0.5,
+            "width": ["14", 0],
+            "height": ["14", 1]}},
+        "19": {"class_type": "FluxGuidance", "inputs": {
+            "conditioning": ["22", 0],
+            "guidance": 1}},
+        "5": {"class_type": "ReferenceLatent", "inputs": {
+            "conditioning": ["19", 0],
+            "latent": ["13", 0]}},
+
+        # Empty latent for sampling
+        "7": {"class_type": "EmptySD3LatentImage", "inputs": {
+            "width": ["14", 0],
+            "height": ["14", 1],
+            "batch_size": 1}},
+
+        # Scheduler & guider
+        "1": {"class_type": "BasicScheduler", "inputs": {
+            "model": ["2", 0],
+            "scheduler": "simple",
+            "steps": 4,
+            "denoise": 1}},
+        "20": {"class_type": "BasicGuider", "inputs": {
+            "model": ["2", 0],
+            "conditioning": ["5", 0]}},
+
+        # Noise & sampler
+        "6": {"class_type": "RandomNoise", "inputs": {
+            "noise_seed": seed}},
+        "8": {"class_type": "KSamplerSelect", "inputs": {
+            "sampler_name": "euler"}},
+        "9": {"class_type": "SamplerCustomAdvanced", "inputs": {
+            "noise": ["6", 0],
+            "guider": ["20", 0],
+            "sampler": ["8", 0],
+            "sigmas": ["1", 0],
+            "latent_image": ["7", 0]}},
+
+        # VAE decode
+        "10": {"class_type": "VAEDecode", "inputs": {
+            "samples": ["9", 0],
+            "vae": ["3", 0]}},
+
+        # Color match with original image
+        "62": {"class_type": "ColorMatch", "inputs": {
+            "image_ref": ["26", 0],
+            "image_target": ["10", 0],
+            "method": "mkl",
+            "strength": 1,
+            "multithread": True}},
+
+        # Save result
+        "17": {"class_type": "SaveImage", "inputs": {
+            "images": ["62", 0],
+            "filename_prefix": "hair_inpaint"}},
+    }
+
+
+@app.route("/")
+def index():
+    return send_file("index.html")
+
+
+@app.route("/api/generate", methods=["POST"])
+def generate():
+    t_start = time.time()
+    try:
+        if "image" not in request.files or "mask" not in request.files:
+            msg = f"缺少上传文件: files={list(request.files.keys())}"
+            log.warning(msg)
+            return jsonify({"error": msg}), 400
+        image_file = request.files["image"]
+        mask_file = request.files["mask"]
+        prompt_text = request.form.get("prompt", "填充遮罩区域的头发")
+        log.info(
+            "收到请求: image=%s mask=%s prompt=%r",
+            image_file.filename, mask_file.filename, prompt_text,
+        )
+
+        # Load original image as RGB
+        try:
+            image = Image.open(image_file).convert("RGB")
+        except Exception as e:
+            log.error("无法解码人物图片 %s: %s", image_file.filename, e)
+            hint = "" if _HEIF_OK else "（当前不支持 HEIC）"
+            return jsonify({
+                "error": f"无法识别人物图片格式{hint}，请改用 JPG/PNG: {e}"
+            }), 400
+
+        # Load mask and extract mask data from ALL channels (R, G, B, A)
+        # This handles different mask formats:
+        #   - Red mask (R=255 where drawn): user-provided PNG
+        #   - White mask (R=G=B=255 where drawn): frontend canvas
+        #   - Alpha mask (A=255 where drawn): transparent brush
+        try:
+            mask_img = Image.open(mask_file).convert("RGBA")
+        except Exception as e:
+            log.error("无法解码遮罩图片 %s: %s", mask_file.filename, e)
+            return jsonify({
+                "error": f"无法识别遮罩图片格式，请改用 JPG/PNG: {e}"
+            }), 400
+        mask_arr = np.array(mask_img)
+        # Use max of all channels: 255 where any color/alpha is drawn, 0 where empty
+        mask_data = np.max(mask_arr, axis=2)  # (H, W) uint8
+
+        # Ensure mask matches image size
+        mask_data_img = Image.fromarray(mask_data, mode="L")
+        if mask_data_img.size != image.size:
+            mask_data_img = mask_data_img.resize(image.size, Image.LANCZOS)
+
+        # Apply slight blur for soft edges (similar to ComfyUI's brush)
+        mask_data_img = mask_data_img.filter(ImageFilter.GaussianBlur(radius=4))
+
+        # ComfyUI LoadImage: mask = 1.0 - (alpha/255)
+        # So alpha=0 -> mask=1.0 (inpaint), alpha=255 -> mask=0.0 (keep)
+        # We want: drawn area (mask_data=255) -> inpaint -> alpha=0
+        #          undrawn area (mask_data=0) -> keep -> alpha=255
+        comfyui_alpha = Image.eval(mask_data_img, lambda x: 255 - x)
+
+        # Combine into RGBA (split RGB into separate channels first)
+        r, g, b = image.split()
+        rgba = Image.merge("RGBA", (r, g, b, comfyui_alpha))
+
+        # Upload to ComfyUI
+        img_bytes = io.BytesIO()
+        rgba.save(img_bytes, format="PNG")
+        img_bytes.seek(0)
+
+        upload_resp = requests.post(
+            f"{COMFYUI_URL}/upload/image",
+            files={"image": ("hair_input.png", img_bytes, "image/png")},
+            timeout=30,
+        )
+        upload_data = upload_resp.json()
+        if "name" not in upload_data:
+            log.error("ComfyUI 上传图片失败: %s", upload_data)
+            return jsonify({"error": f"Upload failed: {upload_data}"}), 500
+        image_filename = upload_data["name"]
+
+        # Build and queue workflow
+        workflow = build_workflow(image_filename, prompt_text)
+        prompt_resp = requests.post(
+            f"{COMFYUI_URL}/prompt",
+            json={"prompt": workflow},
+            timeout=30,
+        )
+        prompt_data = prompt_resp.json()
+        if "error" in prompt_data:
+            log.error(
+                "ComfyUI /prompt 校验失败: error=%s node_errors=%s",
+                prompt_data.get("error"), prompt_data.get("node_errors"),
+            )
+            return jsonify({"error": json.dumps(prompt_data["error"], ensure_ascii=False)}), 500
+        prompt_id = prompt_data["prompt_id"]
+
+        # Poll for completion (5 min timeout, 0.1s interval)
+        for _ in range(3000):
+            time.sleep(0.1)
+            history_resp = requests.get(
+                f"{COMFYUI_URL}/history/{prompt_id}", timeout=10
+            )
+            history_data = history_resp.json()
+            if prompt_id in history_data:
+                status = history_data[prompt_id].get("status", {})
+                if status.get("status_str") == "error":
+                    log.error(
+                        "ComfyUI 工作流执行失败: %s",
+                        json.dumps(status, ensure_ascii=False),
+                    )
+                    return jsonify({
+                        "error": "Workflow execution failed",
+                        "detail": status.get("messages", status),
+                    }), 500
+                outputs = history_data[prompt_id].get("outputs", {})
+                if "17" in outputs:  # SaveImage node
+                    image_info = outputs["17"]["images"][0]
+                    filename = image_info["filename"]
+                    subfolder = image_info.get("subfolder", "")
+                    img_type = image_info.get("type", "output")
+                    view_resp = requests.get(
+                        f"{COMFYUI_URL}/view",
+                        params={"filename": filename, "subfolder": subfolder, "type": img_type},
+                        timeout=30,
+                    )
+                    elapsed = time.time() - t_start
+                    log.info("重绘完成，服务端耗时 %.2fs", elapsed)
+                    resp = send_file(
+                        io.BytesIO(view_resp.content), mimetype="image/png"
+                    )
+                    resp.headers["X-Generate-Time"] = f"{elapsed:.2f}"
+                    return resp
+
+        return jsonify({"error": "Timeout: workflow did not complete in 5 minutes"}), 500
+
+    except requests.ConnectionError:
+        log.error("无法连接 ComfyUI @ %s", COMFYUI_URL)
+        return jsonify({"error": "Cannot connect to ComfyUI at " + COMFYUI_URL + ". Is it running?"}), 503
+    except Exception as e:
+        log.error("生成失败，未捕获异常:\n%s", traceback.format_exc())
+        return jsonify({"error": f"{type(e).__name__}: {e}"}), 500
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8899, debug=False)

local_test/bench.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+"""Benchmark ComfyUI hair-inpaint workflow across model / dtype / steps."""
+import io, time, sys
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+
+
+def prep_and_upload():
+    image = Image.open("用来重绘.jpg").convert("RGB")
+    mask_img = Image.open("用来重绘.png").convert("RGBA")
+    mask_data = np.max(np.array(mask_img), axis=2)
+    m = Image.fromarray(mask_data, mode="L")
+    if m.size != image.size:
+        m = m.resize(image.size, Image.LANCZOS)
+    m = m.filter(ImageFilter.GaussianBlur(radius=4))
+    alpha = Image.eval(m, lambda x: 255 - x)
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, alpha))
+    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+    up = requests.post(f"{COMFY}/upload/image",
+                       files={"image": ("hair_input.png", buf, "image/png")}).json()
+    return up["name"]
+
+
+def run_once(fname, model, dtype, steps):
+    wf = A.build_workflow(fname, "填充遮罩区域的头发")
+    wf["16"]["inputs"]["unet_name"] = model
+    wf["16"]["inputs"]["weight_dtype"] = dtype
+    wf["1"]["inputs"]["steps"] = steps
+    r = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()
+    if "prompt_id" not in r:
+        raise RuntimeError(f"submit failed: {str(r)[:300]}")
+    pid = r["prompt_id"]
+    deadline = time.time() + 180
+    while time.time() < deadline:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid not in h:
+            continue
+        st = h[pid].get("status", {})
+        if st.get("status_str") == "error":
+            for m in st.get("messages", []):
+                if m[0] == "execution_error":
+                    raise RuntimeError(str(m[1])[:300])
+            raise RuntimeError("execution error")
+        if "17" in h[pid].get("outputs", {}):
+            ts = {mm[0]: mm[1].get("timestamp") for mm in st["messages"]}
+            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
+    raise TimeoutError("run exceeded 180s")
+
+
+CONFIGS = [
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn",      6, "9B fp8 (当前)"),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 6, "9B fp8-fast"),
+    ("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 4, "9B fp8-fast s4"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn",      6, "4B fp8"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn_fast", 6, "4B fp8-fast"),
+    ("flux-2-klein-4b-fp8.safetensors",         "fp8_e4m3fn_fast", 4, "4B fp8-fast s4"),
+]
+
+fname = prep_and_upload()
+print("input uploaded:", fname)
+print(f"{'配置':<22}{'warmup':>10}{'run1':>10}{'run2':>10}{'best':>10}")
+for model, dtype, steps, label in CONFIGS:
+    times = []
+    for i in range(3):  # 1 warmup + 2 measured
+        try:
+            t = run_once(fname, model, dtype, steps)
+        except Exception as e:
+            t = float('nan'); print("ERR", label, e)
+        times.append(t)
+    best = min(times[1:])
+    print(f"{label:<22}{times[0]:>9.2f}s{times[1]:>9.2f}s{times[2]:>9.2f}s{best:>9.2f}s")

local_test/bench2.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Steps sweep + resolution test on the working 9B fp8-fast config."""
+import io, time
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
+DTYPE = "fp8_e4m3fn_fast"
+
+
+def upload(scale=1.0):
+    image = Image.open("用来重绘.jpg").convert("RGB")
+    mask_img = Image.open("用来重绘.png").convert("RGBA")
+    if scale != 1.0:
+        w, h = image.size
+        w, h = int(w * scale) // 8 * 8, int(h * scale) // 8 * 8
+        image = image.resize((w, h), Image.LANCZOS)
+    mask_data = np.max(np.array(mask_img), axis=2)
+    m = Image.fromarray(mask_data, mode="L")
+    if m.size != image.size:
+        m = m.resize(image.size, Image.LANCZOS)
+    m = m.filter(ImageFilter.GaussianBlur(radius=4))
+    alpha = Image.eval(m, lambda x: 255 - x)
+    r, g, b = image.split()
+    rgba = Image.merge("RGBA", (r, g, b, alpha))
+    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+    up = requests.post(f"{COMFY}/upload/image",
+                       files={"image": ("hair_input.png", buf, "image/png")}).json()
+    return up["name"], image.size
+
+
+def run(fname, steps):
+    wf = A.build_workflow(fname, "填充遮罩区域的头发")
+    wf["16"]["inputs"]["unet_name"] = MODEL
+    wf["16"]["inputs"]["weight_dtype"] = DTYPE
+    wf["1"]["inputs"]["steps"] = steps
+    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
+    dl = time.time() + 120
+    while time.time() < dl:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid in h and "17" in h[pid].get("outputs", {}):
+            ts = {m[0]: m[1].get("timestamp") for m in h[pid]["status"]["messages"]}
+            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
+    return float("nan")
+
+
+print("=== 步数扫描 (9B fp8-fast, 原分辨率 1024x775) ===", flush=True)
+fname, sz = upload(1.0)
+run(fname, 6)  # warmup
+res = {}
+for s in [2, 3, 4, 6, 8]:
+    t = min(run(fname, s), run(fname, s))
+    res[s] = t
+    print(f"  steps={s}: {t:.2f}s", flush=True)
+# derive per-step cost & fixed overhead via two points
+per = (res[8] - res[2]) / 6
+fixed = res[2] - per * 2
+print(f"  -> 每步 ~{per:.3f}s, 固定开销(VAE/编码/colormatch/加载) ~{fixed:.2f}s", flush=True)
+
+print("\n=== 分辨率影响 (steps=4) ===", flush=True)
+for scale in [1.0, 0.75, 0.6]:
+    fn, s2 = upload(scale)
+    run(fn, 4)  # warmup
+    t = min(run(fn, 4), run(fn, 4))
+    print(f"  {s2[0]}x{s2[1]} ({s2[0]*s2[1]/1e6:.2f}MP): {t:.2f}s", flush=True)

local_test/bench_quality.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Generate result images at different step counts for quality comparison."""
+import io, time
+import requests
+import numpy as np
+from PIL import Image, ImageFilter
+import app as A
+
+COMFY = "http://127.0.0.1:8188"
+MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
+DTYPE = "fp8_e4m3fn_fast"
+OUT = "output"
+
+image = Image.open("用来重绘.jpg").convert("RGB")
+mask_img = Image.open("用来重绘.png").convert("RGBA")
+mask_data = np.max(np.array(mask_img), axis=2)
+m = Image.fromarray(mask_data, mode="L")
+if m.size != image.size:
+    m = m.resize(image.size, Image.LANCZOS)
+m = m.filter(ImageFilter.GaussianBlur(radius=4))
+alpha = Image.eval(m, lambda x: 255 - x)
+r, g, b = image.split()
+rgba = Image.merge("RGBA", (r, g, b, alpha))
+buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
+fname = requests.post(f"{COMFY}/upload/image",
+                      files={"image": ("hair_input.png", buf, "image/png")}).json()["name"]
+
+# fixed seed for fair comparison
+SEED = 123456789
+imgs = []
+labels = []
+for steps in [2, 3, 4, 6]:
+    wf = A.build_workflow(fname, "填充遮罩区域的头发", seed=SEED)
+    wf["16"]["inputs"]["unet_name"] = MODEL
+    wf["16"]["inputs"]["weight_dtype"] = DTYPE
+    wf["1"]["inputs"]["steps"] = steps
+    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
+    t0 = time.time()
+    while True:
+        time.sleep(0.1)
+        h = requests.get(f"{COMFY}/history/{pid}").json()
+        if pid in h and "17" in h[pid].get("outputs", {}):
+            ts = {mm[0]: mm[1].get("timestamp") for mm in h[pid]["status"]["messages"]}
+            dur = (ts["execution_success"] - ts["execution_start"]) / 1000.0
+            info = h[pid]["outputs"]["17"]["images"][0]
+            data = requests.get(f"{COMFY}/view", params={
+                "filename": info["filename"], "subfolder": info.get("subfolder", ""),
+                "type": info.get("type", "output")}).content
+            im = Image.open(io.BytesIO(data)).convert("RGB")
+            imgs.append(im)
+            labels.append(f"steps={steps}  {dur:.2f}s")
+            print(f"steps={steps}: {dur:.2f}s", flush=True)
+            break
+
+# build side-by-side contact sheet
+from PIL import ImageDraw
+h0 = imgs[0].height
+w0 = imgs[0].width
+pad = 10
+bar = 28
+sheet = Image.new("RGB", (w0 * len(imgs) + pad * (len(imgs) + 1),
+                          h0 + bar + pad * 2), (26, 26, 46))
+d = ImageDraw.Draw(sheet)
+for i, (im, lb) in enumerate(zip(imgs, labels)):
+    x = pad + i * (w0 + pad)
+    sheet.paste(im, (x, bar + pad))
+    d.text((x + 4, 6), lb, fill=(233, 69, 96))
+sheet.save(f"{OUT}/compare_steps.png")
+print("saved:", f"{OUT}/compare_steps.png", flush=True)

local_test/index.html

@@ -0,0 +1,192 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>发型补全工具</title>
+<style>
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { font-family: -apple-system, sans-serif; background: #1a1a2e; color: #eee; min-height: 100vh; padding: 20px; }
+h1 { text-align: center; margin-bottom: 20px; color: #e94560; font-size: 28px; }
+.container { max-width: 1400px; margin: 0 auto; display: grid; grid-template-columns: 1fr 1fr; gap: 24px; }
+.panel { background: #16213e; border-radius: 12px; padding: 20px; }
+.panel h2 { margin-bottom: 16px; font-size: 18px; color: #e94560; }
+.controls { display: flex; flex-wrap: wrap; gap: 12px; margin-bottom: 16px; align-items: center; }
+.controls label { font-size: 14px; color: #aaa; }
+input[type="file"] { color: #ddd; }
+input[type="text"] { flex: 1; min-width: 200px; padding: 8px 12px; border-radius: 6px; border: 1px solid #444; background: #0f3460; color: #eee; font-size: 14px; }
+button { padding: 10px 24px; border: none; border-radius: 6px; cursor: pointer; font-size: 14px; font-weight: 600; transition: all 0.2s; }
+.btn-upload { background: #0f3460; color: #eee; border: 1px solid #e94560; }
+.btn-upload:hover { background: #1a4a7a; }
+.btn-generate { background: #e94560; color: #fff; font-size: 16px; padding: 12px 36px; }
+.btn-generate:hover { background: #c73650; }
+.btn-generate:disabled { background: #555; cursor: not-allowed; }
+.image-wrapper { display: flex; gap: 12px; flex-wrap: wrap; border: 2px dashed #444; border-radius: 8px; padding: 12px; min-height: 300px; background: #0f3460; }
+.image-item { flex: 1; min-width: 200px; }
+.image-item img { max-width: 100%; border-radius: 6px; }
+.image-item h4 { font-size: 12px; color: #aaa; margin-bottom: 6px; }
+.placeholder { color: #666; font-size: 16px; text-align: center; padding: 60px 20px; width: 100%; }
+.result-area { display: flex; gap: 16px; flex-wrap: wrap; }
+.result-area img { max-width: 100%; border-radius: 8px; }
+.result-item { flex: 1; min-width: 250px; }
+.result-item h3 { font-size: 14px; color: #aaa; margin-bottom: 8px; text-align: center; }
+.loading { text-align: center; padding: 40px; color: #e94560; font-size: 18px; }
+.loading .spinner { display: inline-block; width: 40px; height: 40px; border: 4px solid #333; border-top-color: #e94560; border-radius: 50%; animation: spin 1s linear infinite; margin-bottom: 12px; }
+@keyframes spin { to { transform: rotate(360deg); } }
+.error { color: #ff6b6b; padding: 16px; background: #2a1a1a; border-radius: 8px; margin-top: 12px; }
+</style>
+</head>
+<body>
+<h1>💇 发型补全工具</h1>
+<div class="container">
+    <!-- Left: Input -->
+    <div class="panel">
+        <h2>1. 上传图片 & 遮罩</h2>
+        <div class="controls">
+            <label>人物图片:</label>
+            <input type="file" id="imageInput" accept="image/*" class="btn-upload">
+        </div>
+        <div class="controls">
+            <label>遮罩图片:</label>
+            <input type="file" id="maskInput" accept="image/*" class="btn-upload">
+        </div>
+        <div class="image-wrapper" id="imageWrapper">
+            <div class="placeholder" id="placeholder">请上传人物图片和遮罩图片</div>
+        </div>
+        <div class="controls" style="margin-top:16px">
+            <label>提示词:</label>
+            <input type="text" id="promptInput" value="填充遮罩区域的头发">
+        </div>
+        <div style="text-align:center; margin-top:16px">
+            <button class="btn-generate" id="generateBtn" disabled>🚀 生成</button>
+        </div>
+    </div>
+
+    <!-- Right: Result -->
+    <div class="panel">
+        <h2>2. 对比结果</h2>
+        <div id="resultArea">
+            <div class="placeholder">生成结果将显示在这里</div>
+        </div>
+    </div>
+</div>
+
+<script>
+const imageInput = document.getElementById('imageInput');
+const maskInput = document.getElementById('maskInput');
+const imageWrapper = document.getElementById('imageWrapper');
+const placeholder = document.getElementById('placeholder');
+const promptInput = document.getElementById('promptInput');
+const generateBtn = document.getElementById('generateBtn');
+const resultArea = document.getElementById('resultArea');
+
+let originalImage = null;
+let maskImage = null;
+
+imageInput.addEventListener('change', (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+    const reader = new FileReader();
+    reader.onload = (ev) => {
+        const img = new Image();
+        img.onload = () => {
+            originalImage = { img: img, file: file };
+            updatePreview();
+            checkReady();
+        };
+        img.src = ev.target.result;
+    };
+    reader.readAsDataURL(file);
+});
+
+maskInput.addEventListener('change', (e) => {
+    const file = e.target.files[0];
+    if (!file) return;
+    const reader = new FileReader();
+    reader.onload = (ev) => {
+        const img = new Image();
+        img.onload = () => {
+            maskImage = { img: img, file: file };
+            updatePreview();
+            checkReady();
+        };
+        img.src = ev.target.result;
+    };
+    reader.readAsDataURL(file);
+});
+
+function updatePreview() {
+    placeholder.style.display = 'none';
+    let html = '';
+    if (originalImage) {
+        html += `<div class="image-item"><h4>人物图片</h4><img src="${originalImage.img.src}" alt="原图"></div>`;
+    }
+    if (maskImage) {
+        html += `<div class="image-item"><h4>遮罩图片</h4><img src="${maskImage.img.src}" alt="遮罩"></div>`;
+    }
+    imageWrapper.innerHTML = html;
+}
+
+function checkReady() {
+    generateBtn.disabled = !(originalImage && maskImage);
+}
+
+generateBtn.addEventListener('click', async () => {
+    if (!originalImage || !maskImage) return;
+
+    generateBtn.disabled = true;
+    generateBtn.textContent = '⏳ 生成中...';
+
+    const startTime = performance.now();
+    resultArea.innerHTML = '<div class="loading"><div class="spinner"></div><br>正在调用 ComfyUI 生成，请耐心等待...<div id="liveTimer" style="margin-top:8px;font-size:15px;color:#aaa">已用时 0.0s</div></div>';
+    const liveTimer = document.getElementById('liveTimer');
+    const timerId = setInterval(() => {
+        if (liveTimer) liveTimer.textContent = '已用时 ' + ((performance.now() - startTime) / 1000).toFixed(1) + 's';
+    }, 100);
+
+    try {
+        const formData = new FormData();
+        formData.append('image', originalImage.file, 'original.' + originalImage.file.name.split('.').pop());
+        formData.append('mask', maskImage.file, 'mask.' + maskImage.file.name.split('.').pop());
+        formData.append('prompt', promptInput.value);
+
+        const resp = await fetch('/api/generate', { method: 'POST', body: formData });
+        if (!resp.ok) {
+            const err = await resp.json();
+            throw new Error(err.error || 'Generation failed');
+        }
+
+        const resultBlob = await resp.blob();
+        const resultUrl = URL.createObjectURL(resultBlob);
+        const elapsed = ((performance.now() - startTime) / 1000).toFixed(1);
+        // 服务端纯推理耗时（若返回该响应头）
+        const serverTime = resp.headers.get('X-Generate-Time');
+        const serverInfo = serverTime ? `，服务端推理 ${parseFloat(serverTime).toFixed(1)}s` : '';
+
+        resultArea.innerHTML = `
+            <div style="text-align:center;margin-bottom:12px;color:#4ade80;font-size:16px;font-weight:600">
+                ⏱️ 本次重绘耗时 ${elapsed}s${serverInfo}
+            </div>
+            <div class="result-area">
+                <div class="result-item">
+                    <h3>原图</h3>
+                    <img src="${originalImage.img.src}" alt="原图">
+                </div>
+                <div class="result-item">
+                    <h3>生成结果</h3>
+                    <img src="${resultUrl}" alt="生成结果">
+                </div>
+            </div>
+        `;
+    } catch (err) {
+        const elapsed = ((performance.now() - startTime) / 1000).toFixed(1);
+        resultArea.innerHTML = `<div class="error">❌ ${err.message}<br><span style="color:#aaa;font-size:13px">（耗时 ${elapsed}s）</span></div>`;
+    } finally {
+        clearInterval(timerId);
+        generateBtn.disabled = false;
+        generateBtn.textContent = '🚀 生成';
+    }
+});
+</script>
+</body>
+</html>

local_test/start.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+# 启动发型补全服务
+
+cd "$(dirname "$0")"
+
+PID_FILE="hair_service.pid"
+LOG_FILE="hair_service.log"
+
+# 检查是否已在运行
+if [ -f "$PID_FILE" ]; then
+    PID=$(cat "$PID_FILE")
+    if kill -0 "$PID" 2>/dev/null; then
+        echo "服务已在运行 (PID: $PID)"
+        exit 0
+    else
+        echo "清理无效的 PID 文件..."
+        rm "$PID_FILE"
+    fi
+fi
+
+# 检查 ComfyUI 是否运行
+if ! curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:8188/ >/dev/null 2>&1; then
+    echo "警告: ComfyUI 未运行 (http://127.0.0.1:8188)"
+    echo "请先启动 ComfyUI: python /home/ubuntu/ComfyUI/main.py --listen"
+fi
+
+# 启动服务
+echo "启动发型补全服务..."
+/home/ubuntu/ComfyUI/venv/bin/python app.py >> "$LOG_FILE" 2>&1 &
+PID=$!
+echo "$PID" > "$PID_FILE"
+
+# 等待启动
+sleep 2
+if curl -s -o /dev/null -w "%{http_code}" http://127.0.0.1:8899/ | grep -q "200"; then
+    echo "服务启动成功!"
+    echo "本机: http://127.0.0.1:8899"
+    # 打印局域网 IP，方便其他机器访问（app.py 已绑定 0.0.0.0）
+    LAN_IPS=$(hostname -I 2>/dev/null | tr ' ' '\n' | grep -v '^$' || true)
+    if [ -n "$LAN_IPS" ]; then
+        echo "外网/局域网访问:"
+        for ip in $LAN_IPS; do
+            echo "  http://${ip}:8899"
+        done
+    fi
+    echo "PID: $PID"
+    echo "日志: $LOG_FILE"
+else
+    echo "服务启动失败，请检查日志: $LOG_FILE"
+    rm "$PID_FILE"
+    exit 1
+fi

local_test/stop.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+# 停止发型补全服务
+
+cd "$(dirname "$0")"
+
+PID_FILE="hair_service.pid"
+
+if [ ! -f "$PID_FILE" ]; then
+    echo "服务未运行"
+    exit 0
+fi
+
+PID=$(cat "$PID_FILE")
+
+if kill -0 "$PID" 2>/dev/null; then
+    echo "正在停止服务 (PID: $PID)..."
+    kill "$PID"
+    
+    # 等待进程退出
+    for i in {1..10}; do
+        if ! kill -0 "$PID" 2>/dev/null; then
+            echo "服务已停止"
+            rm "$PID_FILE"
+            exit 0
+        fi
+        sleep 1
+    done
+    
+    # 强制终止
+    echo "强制终止进程..."
+    kill -9 "$PID"
+    rm "$PID_FILE"
+    echo "服务已停止"
+else
+    echo "进程已不存在，清理 PID 文件..."
+    rm "$PID_FILE"
+fi

local_test/test_api.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""Test script: send image+mask to the local service and save result."""
+import requests
+import sys
+import os
+
+SERVICE_URL = "http://127.0.0.1:8899"
+IMAGE_PATH = "/home/ubuntu/hair/local_test/用来重绘.jpg"
+MASK_PATH = "/home/ubuntu/hair/local_test/用来重绘.png"
+OUTPUT_DIR = "/home/ubuntu/hair/local_test/output"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+print(f"Sending image: {IMAGE_PATH}")
+print(f"Sending mask:  {MASK_PATH}")
+
+with open(IMAGE_PATH, "rb") as f:
+    img_data = f.read()
+with open(MASK_PATH, "rb") as f:
+    mask_data = f.read()
+
+resp = requests.post(
+    f"{SERVICE_URL}/api/generate",
+    files={
+        "image": ("original.jpg", img_data, "image/jpeg"),
+        "mask": ("mask.png", mask_data, "image/png"),
+    },
+    data={"prompt": "填充遮罩区域的头发"},
+    timeout=600,
+)
+
+print(f"Status: {resp.status_code}")
+print(f"Content-Type: {resp.headers.get('Content-Type')}")
+
+if resp.status_code == 200 and "image" in resp.headers.get("Content-Type", ""):
+    out_path = os.path.join(OUTPUT_DIR, "result.png")
+    with open(out_path, "wb") as f:
+        f.write(resp.content)
+    print(f"SUCCESS! Result saved to: {out_path}")
+else:
+    print(f"FAILED! Response: {resp.text[:2000]}")
+    sys.exit(1)

regen_reports.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""重新生成 bench3/4/5/7 报告，在最左边加原图列。"""
+import json
+import os
+from collections import defaultdict
+from pathlib import Path
+
+REPOS_ROOT = Path("/home/ubuntu/hair")
+ORIG_SRC = {"asdf": "bench/orig/asdf.jpg", "qwer": "bench/orig/qwer.jpg",
+            "girl2": "bench/orig/girl2.jpg", "girl5": "bench/orig/girl5.jpg"}
+
+# bench编号 -> (输出目录, 部署HTML名, 报告标题后缀)
+BENCHES = [
+    (3, "美颜(磨皮+美颜)"),
+    (4, "磨皮"),
+    (5, "美白"),
+    (7, "纯生发"),
+]
+
+
+def img_src(path, bench_num):
+    if not path or not os.path.isfile(path):
+        return None
+    return f"bench{bench_num}/" + os.path.basename(path)
+
+
+def gen_report(bench_num, title_suffix):
+    bench_dir = REPOS_ROOT / f"benchmark_out/bench{bench_num}"
+    results = bench_dir / "results.json"
+    if not results.exists():
+        print(f"  跳过 bench{bench_num}: results.json 不存在")
+        return
+    d = json.load(open(results, encoding="utf-8"))
+    titles = d["res_titles"]
+    rows = d["rows"]
+    prompt = d.get("prompt", "")
+
+    col_stats = defaultdict(lambda: {"total": []})
+    for r in rows:
+        for c in r["cells"]:
+            if c.get("ok"):
+                col_stats[c["res_title"]]["total"].append(c["total_ms"])
+
+    # 表头：原图列 + 分辨率列
+    headers = ['<th class="col-label">原图</th>']
+    for t in titles:
+        s = col_stats.get(t)
+        avg = sum(s["total"]) // len(s["total"]) if s and s["total"] else 0
+        headers.append(f'<th class="col-label"><div class="col-title">{t}</div>'
+                       f'<div class="col-stat">均{avg/1000:.1f}s</div></th>')
+
+    body_rows = []
+    for r in rows:
+        orig_src = ORIG_SRC.get(r["img"])
+        label = f'<div class="row-label">{r["img"]}<br><b>{r["hair_name"]}</b></div>'
+        # 原图列：显示输入原图
+        orig_cell = (f'<td class="cell-orig"><div class="row-label-cell">{label}</div>'
+                     f'<img class="orig-img" src="{orig_src}"></td>')
+        cells = [orig_cell]
+        for c in r["cells"]:
+            src = img_src(c.get("grown_path"), bench_num) if c.get("ok") else None
+            if src:
+                t = c.get("total_ms", 0)
+                cells.append(f'<td class="cell-result"><img class="result-img" src="{src}" loading="lazy">'
+                             f'<div class="cell-time">{t/1000:.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>重绘分辨率对比({title_suffix})</title>
+<style>
+* {{ box-sizing: border-box; margin: 0; padding: 0; }}
+body {{ font-family: -apple-system, 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: 130px; max-width: 150px; }}
+.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: 130px; max-height: 160px; object-fit: contain; background: #f3f4f6; }}
+.orig-img {{ border: 2px solid #d1d5db; }}
+.cell-time {{ font-size: 10px; color: #9ca3af; margin-top: 2px; }}
+.na {{ color: #d1d5db; font-size: 12px; padding: 40px 10px; }}
+</style>
+</head>
+<body>
+<h1>📊 重绘分辨率对比（提示词：{title_suffix}）</h1>
+<p class="subtitle">4图×5发型=20行 · 每行原图+4分辨率 · steps=15 · 提示词="{prompt}" · 80/80成功 · 0 OOM</p>
+<div class="legend">最左列为输入原图。列标题下为平均总耗时。横向滚动查看。</div>
+<div class="scroll-wrap">
+<table>
+<tr>{"".join(headers)}</tr>
+{"".join(body_rows)}
+</table>
+</div>
+</body>
+</html>"""
+
+    deploy = REPOS_ROOT / "static" / f"bench{bench_num}_report.html"
+    deploy.write_text(html, encoding="utf-8")
+    print(f"  ✓ bench{bench_num} ({title_suffix}): {deploy.name}")
+
+
+def main():
+    print("重新生成报告(加原图列):")
+    for num, suffix in BENCHES:
+        gen_report(num, suffix)
+    print("完成")
+
+
+if __name__ == "__main__":
+    main()

scripts/batch_grow_v2.py

@@ -1,156 +0,0 @@
-"""批量调用接口12（/api/v1/hairline/grow_v2）生成对比素材。
-
-20 张女生照片 × 5 种发际线发型（仅非高清）= 100 张输出。
-并发 2，失败的跳过并记录原因。结果图落盘到 static/report_hairline_v2/img/，
-元数据落盘 static/report_hairline_v2/results.json，供生成报告用。
-
-用法： python scripts/batch_grow_v2.py
-"""
-import base64
-import json
-import os
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
-import httpx
-
-API = "http://127.0.0.1:8187/api/v1/hairline/grow_v2"
-TOKEN = "dev-shared-secret-2026"
-CONCURRENCY = 2
-
-INPUT_DIR = "/home/xsl/hair/image/test"
-OUT_DIR = "/home/xsl/hair/static/report_hairline_v2"
-IMG_DIR = os.path.join(OUT_DIR, "img")
-ORIG_DIR = os.path.join(OUT_DIR, "orig")
-
-# 5 种发际线发型（= change_hair hair_id）
-HAIRSTYLES = [
-    ("chang_zhixian", "直线"),
-    ("chang_tuoyuan", "椭圆"),
-    ("chang_bolang", "波浪"),
-    ("chang_xinxing", "心形"),
-    ("chang_huaban", "花瓣"),
-]
-HR_OPTIONS = [(False, "nohr")]  # 仅非高清
-
-
-def list_inputs():
-    files = sorted(f for f in os.listdir(INPUT_DIR) if f.lower().endswith((".jpg", ".png")))
-    return files
-
-
-def one_call(stem, face_file, hair_id, hair_cn, is_hr, hr_tag):
-    """调用一次接口，落盘结果图。返回结果 dict。"""
-    src = os.path.join(INPUT_DIR, face_file)
-    out_name = f"{stem}__{hair_id}__{hr_tag}.jpg"
-    out_path = os.path.join(IMG_DIR, out_name)
-    # 断点续跑：已存在的图直接跳过，不重复调用
-    if os.path.exists(out_path) and os.path.getsize(out_path) > 1024:
-        return {
-            "stem": stem, "face_file": face_file, "hair_id": hair_id, "hair_cn": hair_cn,
-            "is_hr": is_hr, "hr_tag": hr_tag, "ok": True,
-            "out": f"img/{out_name}", "size": None,
-            "ms": 0, "error": None, "skipped": True,
-        }
-    t0 = time.time()
-    try:
-        with open(src, "rb") as fh:
-            files = {"image_file": (face_file, fh.read(), "image/jpeg")}
-        data = {"hairline_id": hair_id, "is_hr": str(is_hr).lower()}
-        with httpx.Client(timeout=180.0) as c:
-            resp = c.post(API, headers={"X-Internal-Token": TOKEN}, files=files, data=data)
-        j = resp.json()
-        if j.get("code") != 0 or not j.get("data"):
-            raise RuntimeError(f"code={j.get('code')} msg={j.get('message')}")
-        b64 = j["data"]["final_base64"].split(",", 1)[1]
-        raw = base64.b64decode(b64)
-        with open(out_path, "wb") as fh:
-            fh.write(raw)
-        return {
-            "stem": stem, "face_file": face_file, "hair_id": hair_id, "hair_cn": hair_cn,
-            "is_hr": is_hr, "hr_tag": hr_tag, "ok": True,
-            "out": f"img/{out_name}", "size": j["data"].get("image_size"),
-            "ms": int((time.time() - t0) * 1000), "error": None,
-        }
-    except Exception as ex:  # noqa: BLE001
-        return {
-            "stem": stem, "face_file": face_file, "hair_id": hair_id, "hair_cn": hair_cn,
-            "is_hr": is_hr, "hr_tag": hr_tag, "ok": False,
-            "out": None, "size": None, "ms": int((time.time() - t0) * 1000),
-            "error": str(ex)[:200],
-        }
-
-
-def main():
-    os.makedirs(IMG_DIR, exist_ok=True)
-    os.makedirs(ORIG_DIR, exist_ok=True)
-    faces = list_inputs()
-    print(f"输入 {len(faces)} 张脸 × {len(HAIRSTYLES)} 发型 × {len(HR_OPTIONS)} = "
-          f"{len(faces)*len(HAIRSTYLES)*len(HR_OPTIONS)} 次调用，并发 {CONCURRENCY}")
-
-    # 1. 先把原图拷一份到 orig/（报告要用）
-    import shutil
-    for f in faces:
-        stem = os.path.splitext(f)[0]
-        dst = os.path.join(ORIG_DIR, f"{stem}.jpg")
-        if not os.path.exists(dst):
-            shutil.copy2(os.path.join(INPUT_DIR, f), dst)
-
-    # 2. 构造全部任务
-    tasks = []
-    for f in faces:
-        stem = os.path.splitext(f)[0]
-        for hair_id, hair_cn in HAIRSTYLES:
-            for is_hr, hr_tag in HR_OPTIONS:
-                tasks.append((stem, f, hair_id, hair_cn, is_hr, hr_tag))
-
-    results = []
-    done = 0
-    total = len(tasks)
-    t_start = time.time()
-    with ThreadPoolExecutor(max_workers=CONCURRENCY) as ex:
-        futs = {ex.submit(one_call, *t): t for t in tasks}
-        for fut in as_completed(futs):
-            r = fut.result()
-            results.append(r)
-            done += 1
-            status = "OK " if r["ok"] else "FAIL"
-            if r.get("skipped"):
-                print(f"[{done}/{total}] SKIP {r['stem']} {r['hair_cn']} {r['hr_tag']}")
-            elif r["ok"]:
-                print(f"[{done}/{total}] {status} {r['stem']} {r['hair_cn']} {r['hr_tag']} "
-                      f"({r['ms']}ms)", flush=True)
-            else:
-                print(f"[{done}/{total}] {status} {r['stem']} {r['hair_cn']} {r['hr_tag']} "
-                      f"-> {r['error']}")
-
-    elapsed = time.time() - t_start
-    ok = sum(1 for r in results if r["ok"])
-    fail = len(results) - ok
-    # 按稳定顺序排序，报告好看
-    order = {s: i for i, s in enumerate(HAIRSTYLES)}
-    hr_order = {True: 0, False: 1}
-    face_order = {os.path.splitext(f)[0]: i for i, f in enumerate(faces)}
-    results.sort(key=lambda r: (face_order.get(r["stem"], 0),
-                                order.get((r["hair_id"], r["hair_cn"]), 0),
-                                hr_order.get(r["is_hr"], 0)))
-
-    meta = {
-        "total": total, "ok": ok, "fail": fail,
-        "elapsed_sec": round(elapsed, 1), "concurrency": CONCURRENCY,
-        "hairstyles": [{"id": h, "cn": c} for h, c in HAIRSTYLES],
-        "hr_options": [{"is_hr": is_hr, "tag": tag} for is_hr, tag in HR_OPTIONS],
-        "faces": [os.path.splitext(f)[0] for f in faces],
-        "generated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
-    }
-    out = {"meta": meta, "results": results}
-    with open(os.path.join(OUT_DIR, "results.json"), "w", encoding="utf-8") as fh:
-        json.dump(out, fh, ensure_ascii=False, indent=2)
-
-    print(f"\n完成：{ok}/{total} 成功，{fail} 失败，耗时 {elapsed:.1f}s")
-    print(f"结果图 -> {IMG_DIR}")
-    print(f"元数据 -> {OUT_DIR}/results.json")
-
-
-if __name__ == "__main__":
-    main()

scripts/gen_report_hairline_v2.py

@@ -1,220 +0,0 @@
-"""根据 results.json 生成发际线生发对比报告 HTML。
-
-报告布局（原图 vs 结果对比）：
-- 顶部：总览统计（成功/失败数、耗时、参数）
-- 按脸分组，每张脸一个区块：
-    - 左：原图
-    - 右：5 发型 × {高清, 非高清} 网格（共 10 张），失败的格子标注原因
-- 失败用例汇总表
-
-输出：static/report_hairline_v2/index.html
-"""
-import html
-import json
-import os
-from urllib.parse import quote
-
-OUT_DIR = "/home/xsl/hair/static/report_hairline_v2"
-RESULTS = os.path.join(OUT_DIR, "results.json")
-TARGET = os.path.join(OUT_DIR, "index.html")
-
-
-def url(path):
-    """把相对路径里的中文做 URL 编码，分隔符 '/' 保留。
-    Starlette StaticFiles 对未编码中文路径返回 400，编码后所有浏览器稳定可加载。
-    """
-    return "/".join(quote(seg) for seg in path.split("/"))
-
-
-def main():
-    with open(RESULTS, encoding="utf-8") as fh:
-        data = json.load(fh)
-    meta = data["meta"]
-    results = data["results"]
-
-    hairstyles = meta["hairstyles"]           # [{id, cn}]
-    faces = meta["faces"]                      # [stem, ...]
-    hr_opts = meta["hr_options"]               # [{is_hr, tag}]
-
-    # 索引：(stem, hair_id, hr_tag) -> result
-    idx = {}
-    for r in results:
-        idx[(r["stem"], r["hair_id"], r["hr_tag"])] = r
-
-    # 统计
-    ok = sum(1 for r in results if r["ok"])
-    fail = len(results) - ok
-
-    parts = []
-    parts.append(f"""<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>发际线生发对比报告 · 接口12 grow_v2</title>
-<style>
-  :root {{
-    --bg:#0f1115; --card:#1a1d24; --border:#2a2f3a; --txt:#e6e6e6;
-    --muted:#8a93a3; --accent:#6ea8fe; --ok:#4ade80; --fail:#f87171;
-  }}
-  * {{ box-sizing:border-box; }}
-  body {{ margin:0; background:var(--bg); color:var(--txt);
-         font-family:-apple-system,"Segoe UI","PingFang SC","Microsoft YaHei",sans-serif;
-         line-height:1.5; }}
-  header {{ padding:24px 32px; border-bottom:1px solid var(--border); }}
-  header h1 {{ margin:0 0 8px; font-size:22px; }}
-  header .sub {{ color:var(--muted); font-size:14px; }}
-  .stats {{ display:flex; gap:16px; flex-wrap:wrap; margin-top:16px; }}
-  .stat {{ background:var(--card); border:1px solid var(--border); border-radius:8px;
-           padding:12px 16px; min-width:120px; }}
-  .stat .n {{ font-size:24px; font-weight:600; }}
-  .stat .l {{ font-size:12px; color:var(--muted); }}
-  .stat.ok .n {{ color:var(--ok); }}
-  .stat.fail .n {{ color:var(--fail); }}
-  main {{ padding:24px 32px; }}
-  .face-block {{ background:var(--card); border:1px solid var(--border);
-                border-radius:12px; padding:20px; margin-bottom:24px; }}
-  .face-head {{ display:flex; align-items:center; gap:12px; margin-bottom:16px; }}
-  .face-head h2 {{ margin:0; font-size:18px; }}
-  .face-head .orig-thumb {{ width:64px; height:64px; object-fit:cover;
-                            border-radius:8px; border:1px solid var(--border); }}
-  .grid {{ display:grid; grid-template-columns:repeat({len(hairstyles)+1}, 1fr);
-           gap:10px; align-items:start; }}
-  .col-hdr {{ font-size:12px; color:var(--muted); text-align:center; padding:6px 4px;
-              border-bottom:1px solid var(--border); }}
-  .col-hdr.orig {{ color:var(--accent); }}
-  .cell {{ position:relative; }}
-  .cell img {{ width:100%; border-radius:6px; display:block;
-               border:1px solid var(--border); }}
-  .cell .cap {{ font-size:11px; color:var(--muted); margin-top:4px; text-align:center; }}
-  .cell.fail .failbox {{ aspect-ratio:3/4; background:#2a1518; border:1px solid #5c2a30;
-                         border-radius:6px; display:flex; align-items:center;
-                         justify-content:center; padding:8px; text-align:center;
-                         font-size:11px; color:var(--fail); }}
-  .tag {{ display:inline-block; font-size:11px; padding:1px 6px; border-radius:4px;
-          background:#243044; color:var(--accent); margin-left:6px; }}
-  .tag.no {{ background:#3a3526; color:#e0c97a; }}
-  .legend {{ font-size:13px; color:var(--muted); margin-bottom:16px; }}
-  .fail-table {{ width:100%; border-collapse:collapse; font-size:13px; margin-top:8px; }}
-  .fail-table th, .fail-table td {{ border:1px solid var(--border); padding:6px 10px; text-align:left; }}
-  .fail-table th {{ background:#1f232c; color:var(--muted); }}
-  .anchor {{ display:block; height:0; overflow:hidden; }}
-  footer {{ padding:24px 32px; color:var(--muted); font-size:12px; border-top:1px solid var(--border); }}
-  /* 点击放大 */
-  img.zoomable {{ cursor:zoom-in; transition:opacity .12s; }}
-  img.zoomable:hover {{ opacity:.85; }}
-  #lightbox {{ position:fixed; inset:0; background:rgba(0,0,0,.92); display:none;
-               align-items:center; justify-content:center; z-index:9999; padding:24px;
-               cursor:zoom-out; }}
-  #lightbox.open {{ display:flex; }}
-  #lightbox img {{ max-width:100%; max-height:100%; object-fit:contain;
-                  border-radius:8px; box-shadow:0 8px 40px rgba(0,0,0,.6); }}
-  #lightbox .lb-cap {{ position:absolute; bottom:16px; left:0; right:0; text-align:center;
-                       color:var(--muted); font-size:13px; }}
-</style>
-</head>
-<body>
-<header>
-  <h1>发际线生发对比报告 <span class="tag">接口12 · grow_v2</span></h1>
-  <div class="sub">固定：pushed 遮罩 + multiband 融合 · mb_levels=5 · erode_cm=0.6 · 非高清</div>
-  <div class="sub">生成时间：{html.escape(meta['generated_at'])} · 并发 {meta['concurrency']} · 耗时 {meta['elapsed_sec']}s</div>
-  <div class="stats">
-    <div class="stat"><div class="n">{meta['total']}</div><div class="l">总调用</div></div>
-    <div class="stat ok"><div class="n">{ok}</div><div class="l">成功</div></div>
-    <div class="stat fail"><div class="n">{fail}</div><div class="l">失败</div></div>
-    <div class="stat"><div class="n">{len(faces)}</div><div class="l">人脸数</div></div>
-    <div class="stat"><div class="n">{len(hairstyles)}</div><div class="l">发型数</div></div>
-  </div>
-</header>
-<main>
-""")
-
-    # 图例
-    parts.append('<div class="legend">每张脸：第一列为原图，其余 5 列为发际线生发结果（非高清）。点击任意图片可放大。</div>')
-
-    # 每张脸一个区块：原图 + 5 发型横向并排
-    for stem in faces:
-        orig_rel = f"orig/{stem}.jpg"
-        parts.append('<div class="face-block">')
-        parts.append(f'  <div class="face-head"><h2>{html.escape(stem)}</h2></div>')
-        # 网格：第一列原图，其余 5 列发型结果
-        parts.append('<div class="grid">')
-        # 表头
-        parts.append('<div class="col-hdr orig">原图</div>')
-        for h in hairstyles:
-            parts.append(f'<div class="col-hdr">{html.escape(h["cn"])}<br><span style="opacity:.6">{html.escape(h["id"])}</span></div>')
-        # 第一列：原图大图（可点击放大）
-        parts.append(
-            f'<div class="cell"><img class="zoomable" src="{url(orig_rel)}" '
-            f'data-full="{url(orig_rel)}" loading="lazy" alt="原图 {html.escape(stem)}">'
-            f'<div class="cap">原图</div></div>')
-        # 5 发型结果（hr_opts 现在只有 nohr 一档，直接取）
-        hr_tag = hr_opts[0]["tag"] if hr_opts else "nohr"
-        for h in hairstyles:
-            r = idx.get((stem, h["id"], hr_tag))
-            if r and r["ok"]:
-                ms = r["ms"]
-                parts.append(
-                    f'<div class="cell"><img class="zoomable" src="{url(r["out"])}" '
-                    f'data-full="{url(r["out"])}" loading="lazy" '
-                    f'alt="{html.escape(stem)} {html.escape(h["cn"])}">'
-                    f'<div class="cap">{ms}ms</div></div>')
-            else:
-                err = (r or {}).get("error", "未执行")
-                parts.append(
-                    f'<div class="cell fail"><div class="failbox">{html.escape(err)}</div></div>')
-        parts.append('</div>')  # grid
-        parts.append('</div>')  # face-block
-
-    # 失败汇总
-    fails = [r for r in results if not r["ok"]]
-    if fails:
-        parts.append('<div class="face-block">')
-        parts.append(f'<h2>失败用例（{len(fails)}）</h2>')
-        parts.append('<table class="fail-table"><thead><tr>'
-                     '<th>人脸</th><th>发型</th><th>耗时(ms)</th><th>原因</th>'
-                     '</tr></thead><tbody>')
-        for r in fails:
-            parts.append(
-                f'<tr><td>{html.escape(r["stem"])}</td>'
-                f'<td>{html.escape(r["hair_cn"])}</td>'
-                f'<td>{r["ms"]}</td>'
-                f'<td>{html.escape(r["error"] or "")}</td></tr>')
-        parts.append('</tbody></table></div>')
-
-    parts.append(f"""
-<footer>
-  接口：<code>POST /api/v1/hairline/grow_v2</code> · 固定 pushed 遮罩 + multiband 融合 ·
-  数据源 results.json · 点击任意图片可放大查看
-</footer>
-</main>
-<div id="lightbox"><img><div class="lb-cap"></div></div>
-<script>
-(function(){{
-  var lb=document.getElementById('lightbox'),lbImg=lb.querySelector('img'),
-      lbCap=lb.querySelector('.lb-cap');
-  function open(src,cap){{
-    lbImg.src=src; lbCap.textContent=cap||''; lb.classList.add('open');
-  }}
-  function close(){{ lb.classList.remove('open'); lbImg.src=''; }}
-  document.addEventListener('click',function(e){{
-    var t=e.target.closest('img.zoomable');
-    if(t){{ open(t.dataset.full||t.src, t.alt||''); }}
-    else if(e.target===lb||e.target===lbImg){{ close(); }}
-  }});
-  document.addEventListener('keydown',function(e){{
-    if(e.key==='Escape') close();
-  }});
-}})();
-</script>
-</body>
-</html>""")
-
-    with open(TARGET, "w", encoding="utf-8") as fh:
-        fh.write("".join(parts))
-    print(f"报告已生成：{TARGET}")
-    print(f"成功 {ok}/{meta['total']}，失败 {fail}")
-
-
-if __name__ == "__main__":
-    main()