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@@ -9,6 +9,7 @@ gateway/config.json
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# worker 配置（含鉴权密码，不入 git）
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worker_config.json
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ip_access.json
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# ComfyUI Basic Auth 密码（不入 git）
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password.txt
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@@ -29,7 +29,7 @@
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"60": {
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"class_type": "JjkText",
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"inputs": {
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"text": "填充遮罩区域的头发"
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"text": "填充遮罩区域的头发，人物增加一点磨皮"
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}
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},
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"22": {
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+1
-1
File diff suppressed because one or more lines are too long
@@ -4,80 +4,100 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
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## Overview
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"旷视五接口" — a FastAPI facial analysis API service providing 5 endpoints for face measurement, hair growth simulation (C-end and B-end), user feature extraction, and hairline PNG generation. Currently a **Mock v1**: all endpoints return hardcoded sample data; real ML logic is pending.
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旷视接口服务 — FastAPI 人像分析/生发服务，**一个仓库、两种部署角色**：
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- **Base URL (prod)**: `https://hair.xiangsilian.com`
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- **Swagger docs**: `https://hair.xiangsilian.com/docs`
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- **Internal**: uvicorn on `127.0.0.1:8000` behind nginx reverse proxy
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- **worker**（`app.py` + `face_analysis/` + `hairline/`）：跑真实算法（本地模型 + ComfyUI + change_hair），监听 `0.0.0.0:8187`。**本机（GPU 机）即 worker 角色**，不跑网关。
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- **网关**（`gateway/`）：外网机器上的薄反向代理，负责鉴权/健康检查/派发，并把 worker 返回的 `*_base64` 落盘改写为 `*_url`。接口4（豆包）和接口99（人脸特殊点，纯 CPU）在网关本机实现，不转发 worker。
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> 历史包袱：`app.py` 的 FastAPI `description` 和 `docs/接口文档.md` 部分示例仍写「Mock 第一版」字样，实际全部接口已是真实算法实现。**架构以 `docs/实现说明.md` 为准，契约以 `docs/接口文档.md` 为准。**
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## Commands
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```bash
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# Development server
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./venv/bin/uvicorn app:app --host 127.0.0.1 --port 8000 --reload
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# ---- worker（本机）----
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./run_worker.sh # 后台启动（0.0.0.0:8187，日志 worker.log）
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FOREGROUND=1 ./run_worker.sh # 前台调试
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./stop_worker.sh # 停止
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sudo systemctl restart hair-worker # systemd 方式（生产）
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journalctl -u hair-worker -f
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# Or via the start script
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./start.sh
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# ---- 依赖服务（worker 之前要先起）----
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sudo systemctl start comfyui # ComfyUI 127.0.0.1:8188（Flux-2 生图）
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cd /home/ubuntu/change_hair && bash start_all.sh start # 换发型三件套 8801/32678/57860
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# Production (systemd)
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sudo systemctl restart hair
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sudo systemctl status hair
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journalctl -u hair -f
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# Install dependencies (uses Tencent Cloud PyPI mirror)
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./venv/bin/pip install -r requirements.txt
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# ---- 测试 ----
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./venv/bin/pytest tests/ # 单元测试（conftest 会准备 fixtures）
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./venv/bin/python -m hairline.comfyui tests/output/comfy_input.png # 手动跑一次生发工作流
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```
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依赖装在 `./venv`（Python 3.10，torch 2.7.1+cu128，RTX 4090 CUDA 可用）。`requirements.txt` 里的 torch 2.2.2/5090 注释是旧机器的记录，本机 venv 实际是 cu128 版。
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## Architecture
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**Single-file app** — `app.py` (~555 lines) contains the entire server: FastAPI app creation, Pydantic models, all 5 endpoint handlers, and health check routes. There are no separate routers, services, or middleware modules.
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### 接口一览（worker 侧路由，`app.py`）
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### Endpoints
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| 接口 | 路径 | 实现位置 | 说明 |
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|------|------|----------|------|
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| 1 | `POST /api/v1/face/measure` | `face_analysis/` | 四庭七眼测量 + 标注 PNG |
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| 6 | `POST /api/v1/face/measure-v2` | 同上（`variant="v6"`） | 接口1 去顶庭变体 |
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| 2 | `POST /api/v1/hair/grow` | `hairline/service.py` | C端生发。**female 1..5** 走 change_hair swapHair + Flux-2 整帧重绘（接口12 final 管线）；**female 6/7 与 male 全部**走 ComfyUI `add_hair.json` inpaint |
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| 7 | `POST /api/v1/hair/grow-v2`（hidden/deprecated） | 同上 | 同接口2，用 `add_hair2.json` 工作流 |
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| 3 | `POST /api/v1/hair/grow-b` | `hairline/marker_detect.py` | B端马克笔划线生发（黑帽响应 + Dijkstra） |
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| 4 | `POST /api/v1/face/features` | worker 有实现；生产走**网关本机**（豆包） | 用户特征 |
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| 5 | `POST /api/v1/hairline/generate` | `hairline/service.py` | 发际线 PNG 三档（middle/high/low）+ 生发 |
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| 9 | `POST /api/v1/head/mask` | `face_analysis/head_mask.py` | 头发遮罩 + 分步可视化（BiSeNet/SegFormer 双路） |
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| 10 | `POST /api/v1/head/band` | `face_analysis/head_band.py` | 头部外缘膨胀带遮罩 |
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| 11 | `POST /api/v1/hairline/grow` | `face_analysis/hairline_grow.py` | 发际线生发（接口9 遮罩 + change_hair + 接缝融合），不含重绘 |
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| 12 | `POST /api/v1/hairline/grow_v2` | 同上 | 接口11 final + 发际线重绘带 → Flux-2 保色重绘 |
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| 12f | `POST /api/v1/hairline/grow_v2_final` | 同上（`_V2_FINAL_DEFAULTS`） | 精简版：仅图片 + hairline_id，取整帧重绘 |
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| 12f2 | `POST /api/v1/hairline/grow_v2_final_v2` | 同上 | 同上，取 B 局部加发+全脸美颜 |
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| 99 | `POST /api/v1/face/special-points` | `face_analysis/special_points.py`（网关直接 import 本机实现 + `_if99_lock` 单路排队；worker 保留同路径备份） | 固定编号关键点 + 发际线特殊点（射线求交头发 mask）像素坐标 |
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| 代理 | `POST /api/swapHair/v1`、`/hairColor/v2` | httpx 转发 127.0.0.1:8801 | 解决测试页 CORS |
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| 工具 | `POST /api/v1/redraw` | `hairline/redraw.py` | 直接 ComfyUI 重绘（图+遮罩+prompt） |
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| 调试 | `POST /api/v1/debug/grow-timing`、`GET /api/v1/debug/hairline_log` | — | 免鉴权，分步计时/下载日志 |
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| Method | Path | Purpose |
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||||
|--------|------|---------|
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||||
| POST | `/api/v1/face/measure` | 四庭七眼 measurement + annotated PNG |
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| POST | `/api/v1/hair/grow` | C-end hair growth (user-facing) |
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| POST | `/api/v1/hair/grow-b` | B-end hair growth (doctor/operator, with marked image) |
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| POST | `/api/v1/face/features` | User facial feature analysis |
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| POST | `/api/v1/hairline/generate` | Hairline PNG generation |
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| GET | `/health` | Health check (excluded from OpenAPI schema) |
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| GET | `/` | Service info (excluded from OpenAPI schema) |
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配套测试页在 `static/test_interface{1..12}*.html`，经 `/static` 挂载。
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### Image input convention (all endpoints)
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### 关键模式
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Every endpoint accepts images via **exactly one** of three mutually exclusive fields:
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- `image_file` — multipart file upload (≤ 1 MB)
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- `image_url` — HTTP/HTTPS URL string
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- `image_base64` — base64 string with `data:image/...;base64,` prefix
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- **统一信封**：`ok(data)` / `err(code, message)` 构造 `{code, message, request_id, data}`；业务错误 HTTP 一律 200。错误码：1001 无人像 / 1002 分辨率低 / 1003 非正面 / 1004 gender 非法 / 1005 多人 / 1006 >1MB / 1007 参数或处理失败 / 1008 格式不支持。
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- **图片三选一**：每个接口 `image_file`(multipart) / `image_url` / `image_base64`（带 `data:` 前缀）严格互斥，由 `resolve_image_bytes()` 统一处理（0 个或多个 → 1007）。接口3 是 `marked_image_*`。
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- **阻塞算法进线程池**：所有推理 handler 用 `run_in_threadpool(...)`，避免卡事件循环。
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- **内网鉴权**：HTTP 中间件校验 `/api/*` 的 `X-Internal-Token` 头，密码来自 `worker_config.json` 的 `accept_passwords`（或环境变量 `WORKER_ACCEPT_PASSWORDS`）。豁免前缀见 `_AUTH_EXEMPT`（health/docs/static/debug/redraw/swapHair/hairColor）。
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||||
- **发型序号映射**：`hairline/service.py` 的 `_HAIRSTYLE_ORDER` 显式定义 1..7 顺序（不靠文件名字母序）；female 1..5 → change_hair `chang_*` 映射在 `_FEMALE_KEY_TO_CHANG`。
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- **接口12 固化参数**：`_V2_FINAL_DEFAULTS`（app.py）是生产默认值（push=0.8cm / two_stage / color_match=关 等），接口2 female 重绘也复用它。
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||||
Endpoint 3 (B-end) has **two** sets of image params: `marked_image_*` (the marked-up image) and `original_image_*` (the original photo). Each set follows the same three-way exclusive rule independently.
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||||
### 外部依赖（worker 视角）
|
||||
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||||
### Unified response structure
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||||
| 依赖 | 地址 | 用途 | 配置 |
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||||
|------|------|------|------|
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||||
| ComfyUI | `http://127.0.0.1:8188`（`COMFYUI_URL`） | Flux-2 生图/重绘，走 HTTP API（`hairline/comfyui.py`） | Basic Auth `admin`，密码：`COMFYUI_PASSWORD` → `worker_config.json.comfyui_password` → `password.txt` |
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| change_hair | `http://127.0.0.1:8801`（`SWAP_HAIR_URL`/`HAIR_GROW_URL`/`CHANGE_HAIR_BASE`） | 换发型（SD WebUI + LoRA）、hairGrow | 服务目录 `/home/ubuntu/change_hair`，`start_all.sh` 启停 |
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|
||||
```json
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||||
{"code": 0, "message": "success", "request_id": "mock-request-id", "data": {...}}
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||||
```
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ComfyUI 工作流 JSON 在仓库根：`add_hair.json`（接口2/3/5 生发）、`add_hair2.json`（接口7）、`0716add-hair-api.json`（接口12/2女 重绘）、`hair_repaint.json`。节点约定：**26** 输入图 / **6** seed / **60** 提示词 / **16** unet（.gguf 自动切 UnetLoaderGGUF）/ **61** CLIP（4b↔9b 自动配套）。
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Error codes: 1001–1008 (see `app.py` docstring or `docs/接口文档.md`).
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### 性能/显存相关的坑
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### Key code patterns
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- 三个生发接口共用**同一提示词字符串**以命中 ComfyUI 的 CLIP 文本条件缓存——prompt 变了会触发 Flux(7.7G)+qwen CLIP(3.9G) 互相挤显存重载（每次 +4s）。
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- 接口2 女性重绘管线默认把输入长边压到 `REDRAW_MAX_SIDE`（env，默认 1024）再放大回原尺寸；`redraw_max_side=0` 表示不缩图。
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- `comfyui.run(front=True)` 可插队（接口2 对时延敏感）；超时 `COMFY_TIMEOUT`（默认 600s）。
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- `ok(data)` / `err(code, msg)` — helper functions at module level that build the standard response envelope
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- `ImageJsonBody` — Pydantic model for JSON-based image params (URL/base64); form-upload endpoints use FastAPI `Form()` / `File()` parameters directly
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- `SAMPLE_IMAGE_URL` — `https://hair.xiangsilian.com/static/sample.jpg`, returned as the image URL in all mock responses
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- Static files (`/static/*`) are mounted via `app.mount("/static", StaticFiles(directory="static"), name="static")`
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- All 5 POST endpoints use `multipart/form-data` with both `File` and `Form` fields — there are no JSON-body POST endpoints despite `ImageJsonBody` being defined (it exists for the OpenAPI schema but the actual handlers use `Form()` for all params)
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## Deployment（本机 = gpu_worker）
|
||||
|
||||
### Deployment
|
||||
systemd 单元（仓库根有副本，实装在 `/etc/systemd/system/`）：
|
||||
|
||||
- **Process manager**: systemd (`hair.service` → `ExecStart` runs uvicorn directly from venv)
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||||
- **Reverse proxy**: nginx (`nginx/hair.conf`) proxies port 80 → `127.0.0.1:8000` for `hair.xiangsilian.com`
|
||||
- **Python**: venv at `./venv`, dependencies in `requirements.txt`
|
||||
- **PyPI mirror**: Tencent Cloud (`mirrors.cloud.tencent.com`), configured in `pip.conf`
|
||||
| 服务 | 端口 | 说明 |
|
||||
|------|------|------|
|
||||
| `comfyui.service` | 127.0.0.1:8188 | `ComfyUI/venv` 的 `main.py --cache-classic --fast` |
|
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| `hair-worker.service` | 0.0.0.0:8187 | 本仓库 `app:app`（Wants comfyui + change_hair-hair） |
|
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| `change_hair-{webui,photo,hair}.service` | 57860 / 32678 / 8801 | conda envs `sdwebui`/`py310`/`my_hair`，依赖顺序 webui→photo→hair |
|
||||
|
||||
### Documentation
|
||||
网关机另行部署 `gateway/`（config：`workers` 列表 + `shared_password` + `ark` key），见 `gateway/config.example.json` 与 `docs/网关待改动.md`。
|
||||
|
||||
- `docs/接口文档.md` — complete API documentation (inputs, outputs, examples, error codes)
|
||||
- `docs/旷视具体需求.md` — original requirements spec (converted from `.docx`)
|
||||
- The FastAPI app's `description` docstring in `app.py` is an additional source of API docs visible in Swagger UI
|
||||
## Documentation
|
||||
|
||||
- `docs/接口文档.md` — 对外 API 契约（唯一权威）
|
||||
- `docs/实现说明.md` — 架构总览 + 各接口实现简述 + 部署要点
|
||||
- `docs/发际线增强算法.md`、`docs/发际线生发遮罩算法_pushed模式.md` — 接口11/12 算法推导
|
||||
- `docs/gpu-worker改动说明.md`、`docs/网关待改动.md` — worker↔网关 协同变更记录
|
||||
- `OFFLINE_ASSETS.md` — 离线模型权重/字体清单
|
||||
|
||||
+1
-1
@@ -410,7 +410,7 @@
|
||||
},
|
||||
"60": {
|
||||
"inputs": {
|
||||
"text": "填充遮罩区域的头发"
|
||||
"text": "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
},
|
||||
"class_type": "JjkText",
|
||||
"_meta": {
|
||||
|
||||
@@ -2,15 +2,20 @@
|
||||
|
||||
接口 1（四庭七眼测量）已为**真实算法实现**（见 face_analysis 包）；接口 2~5 仍为 Mock。
|
||||
拆分架构：worker 跑算法、返回 `annotated_image_base64`（不落盘、不拼 URL，由网关完成）。
|
||||
worker 对 `/api/*` 校验内网鉴权头 `X-Internal-Token`，`/health` 供网关探测不校验。
|
||||
worker 对 `/api/*` 校验内网鉴权头 `X-Internal-Token`，`/health` 供网关探测不校验
|
||||
（深检：磁盘水位 + 下游依赖 TCP 探测，异常返回 503 degraded，见 _load_health_cfg）。
|
||||
"""
|
||||
import asyncio
|
||||
import base64
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import shutil
|
||||
import time
|
||||
from contextlib import asynccontextmanager
|
||||
from io import BytesIO
|
||||
from typing import Any, List, Optional
|
||||
from urllib.parse import urlsplit
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -189,6 +194,10 @@ async def internal_token_auth(request: Request, call_next):
|
||||
本地起步），生产务必通过 worker_config.json / 环境变量配置密码。
|
||||
"""
|
||||
path = request.url.path
|
||||
from ip_access import rejection
|
||||
denied = rejection(request)
|
||||
if denied is not None:
|
||||
return denied
|
||||
if path == "/" or path.startswith(_AUTH_EXEMPT):
|
||||
return await call_next(request)
|
||||
if path.startswith("/api/") and ACCEPT_PASSWORDS:
|
||||
@@ -365,22 +374,22 @@ class ImageJsonBody(BaseModel):
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _run_face_measure_data(image, variant="v1"):
|
||||
def _run_face_measure_data(image, variant="v1", landmarks_override=None):
|
||||
"""接口1测量数值核心：detect → 姿态校验 → 头发/耳朵分割 → measure_face → eye1~eye7。
|
||||
|
||||
返回 (data_dict, result, hair_mask, ear_mask)，或检测/姿态失败时返回 None。
|
||||
不含标注图（annotated_image_*），供接口5 容错复用；接口1 调用后再自行生成标注图。
|
||||
任何分割/七眼计算异常均内部吞掉（七眼相关字段不出现），不影响主测量结果。
|
||||
|
||||
variant="v1"（接口1/接口5）：完整四庭七眼 + eye1~eye7（含头部端线段）。
|
||||
variant="v6"（接口6）：去顶庭、不画端线、无 eye1~eye7。
|
||||
variant="v1"（接口1/接口5）：完整四庭七眼 + eye1~eye7（含头部端线段）+ vertical_lines。
|
||||
variant="v6"（接口6）：去顶庭、不画端线、无 eye1/eye7，vertical_lines 仅 6 条内线。
|
||||
"""
|
||||
h, w = image.shape[:2]
|
||||
from face_analysis.detector import detector
|
||||
from face_analysis.pose import estimate_head_pose, check_frontal_face
|
||||
from face_analysis.measure import measure_face
|
||||
|
||||
landmarks = detector.detect(image)
|
||||
landmarks = landmarks_override if landmarks_override is not None else detector.detect(image)
|
||||
if landmarks is None:
|
||||
return None
|
||||
if not check_frontal_face(landmarks, w, h):
|
||||
@@ -419,30 +428,29 @@ def _run_face_measure_data(image, variant="v1"):
|
||||
# 占比、标注图仍按三庭处理，显示效果不变。
|
||||
|
||||
# 七眼段宽度（cm）。eye1=左耳外段 eye2=左脸颊 eye3=左眼 eye4=两眼间距 eye5=右眼 eye6=右脸颊 eye7=右耳外段。
|
||||
# eye2~eye6（5段）只用内部分点，接口1/6 共用；eye1/eye7 需耳朵分割端线，仅接口1 有。
|
||||
# eye2~eye6（5段）只用内部分点，接口1/6 共用；eye1/eye7 用竖线坐标里的人头端线，仅接口1 有。
|
||||
# vertical_lines 与标注图竖线同一套几何。
|
||||
try:
|
||||
from face_analysis.annotation import vertical_line_geometry
|
||||
epts = result.eyes["points"]
|
||||
lcx, rcx = epts["left_cheek"][0], epts["right_cheek"][0]
|
||||
pc = result.px_per_cm
|
||||
inner_xs = [lcx, epts["left_outer"][0], epts["left_inner"][0],
|
||||
epts["right_inner"][0], epts["right_outer"][0], rcx]
|
||||
geom = vertical_line_geometry(
|
||||
result, w, h, ear_mask=ear_mask, hair_mask=hair_mask, variant=variant)
|
||||
data["vertical_lines"] = geom
|
||||
inner_xs = [epts["left_cheek"][0], epts["left_outer"][0], epts["left_inner"][0],
|
||||
epts["right_inner"][0], epts["right_outer"][0], epts["right_cheek"][0]]
|
||||
for i in range(5):
|
||||
a, b = inner_xs[i], inner_xs[i + 1]
|
||||
data["seven_eyes"][f"eye{i + 2}"] = (
|
||||
None if (a is None or b is None) else round((b - a) / pc, 2))
|
||||
if variant != "v6":
|
||||
# 接口1 额外算 eye1/eye7（左/右耳外段），需耳朵分割端线。
|
||||
# 竖向范围上界用头顶（hair_top 始终可用）。
|
||||
from face_analysis.annotation import _ear_edges_from_mask
|
||||
top_y = vd["hair_top"][1]
|
||||
head_l, head_r = _ear_edges_from_mask(
|
||||
ear_mask, hair_mask,
|
||||
top_y, vd["chin_tip"][1],
|
||||
lcx, rcx, (lcx + rcx) / 2)
|
||||
by_name = {line["name"]: line["x"] for line in geom["lines"]}
|
||||
head_l = by_name.get("left_head_edge")
|
||||
head_r = by_name.get("right_head_edge")
|
||||
data["seven_eyes"]["eye1"] = (
|
||||
None if (head_l is None) else round((lcx - head_l) / pc, 2))
|
||||
None if head_l is None else round((by_name["left_cheek"] - head_l) / pc, 2))
|
||||
data["seven_eyes"]["eye7"] = (
|
||||
None if (head_r is None) else round((head_r - rcx) / pc, 2))
|
||||
None if head_r is None else round((head_r - by_name["right_cheek"]) / pc, 2))
|
||||
except Exception as seg_e: # noqa: BLE001
|
||||
logger.warning("七眼段宽度计算失败：%s", seg_e)
|
||||
|
||||
@@ -892,6 +900,7 @@ async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
|
||||
- 四庭（顶庭/上庭/中庭/下庭）各段**厘米数值及占比**
|
||||
- 七眼（眼宽/脸宽/两眼间距）**厘米数值及占比**
|
||||
- 五个关键分界点的**原图像素坐标**（头顶/发际线/眉心/鼻翼下缘/下巴尖）
|
||||
- 竖线坐标 `vertical_lines`（与标注图同一套：共用 y 范围 + 从左到右的 x）
|
||||
|
||||
{_image_fields_desc}
|
||||
|
||||
@@ -947,6 +956,19 @@ async def _face_measure_impl(image_file, image_url, image_base64, variant="v1"):
|
||||
"nose_bottom": {"x": 540, "y": 1050},
|
||||
"chin_tip": {"x": 540, "y": 1360},
|
||||
},
|
||||
"vertical_lines": {
|
||||
"y_top": 98, "y_bottom": 1410,
|
||||
"lines": [
|
||||
{"name": "left_head_edge", "x": 120},
|
||||
{"name": "left_cheek", "x": 210},
|
||||
{"name": "left_eye_outer", "x": 310},
|
||||
{"name": "left_eye_inner", "x": 420},
|
||||
{"name": "right_eye_inner", "x": 560},
|
||||
{"name": "right_eye_outer", "x": 670},
|
||||
{"name": "right_cheek", "x": 780},
|
||||
{"name": "right_head_edge", "x": 890},
|
||||
],
|
||||
},
|
||||
},
|
||||
}
|
||||
}
|
||||
@@ -1023,6 +1045,7 @@ async def face_measure_debug(
|
||||
- 三庭（上庭/中庭/下庭）各段**厘米数值及占比**（**不含顶庭**）
|
||||
- 五眼段宽（eye2~eye6：左脸颊/左眼/两眼间距/右眼/右脸颊）**厘米数值**，另含眼宽/脸宽/两眼间距
|
||||
- 四个关键分界点的**原图像素坐标**（发际线/眉心/鼻翼下缘/下巴尖）
|
||||
- 竖线坐标 `vertical_lines`（发际线→下巴尖，仅 6 条内线，无人头两端）
|
||||
|
||||
基于接口1 的变体，与接口1 的差异：
|
||||
- **去顶庭**：不画头顶横线、不返回顶庭数据；`face_total_height_cm` 为三庭之和
|
||||
@@ -1080,6 +1103,17 @@ async def face_measure_debug(
|
||||
"nose_bottom": {"x": 540, "y": 1050},
|
||||
"chin_tip": {"x": 540, "y": 1360},
|
||||
},
|
||||
"vertical_lines": {
|
||||
"y_top": 430, "y_bottom": 1360,
|
||||
"lines": [
|
||||
{"name": "left_cheek", "x": 210},
|
||||
{"name": "left_eye_outer", "x": 310},
|
||||
{"name": "left_eye_inner", "x": 420},
|
||||
{"name": "right_eye_inner", "x": 560},
|
||||
{"name": "right_eye_outer", "x": 670},
|
||||
{"name": "right_cheek", "x": 780},
|
||||
],
|
||||
},
|
||||
},
|
||||
}
|
||||
}
|
||||
@@ -1186,7 +1220,7 @@ async def hair_grow(
|
||||
hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填），如 1,2,3。female:1-7 male:1-7"),
|
||||
beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
|
||||
use_mask: bool = Form(default=True, description="是否启用 inpaint 遮罩（测试对比用）。false 时用干净原图生成（空遮罩，不烧模板线）"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发，人物增加一点磨皮", description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
flux_model: Optional[str] = Form(default=None, description="Flux 模型文件名（切换模型用）。None=工作流默认；如 flux-2-klein-9b-Q5_K_M.gguf / flux-2-klein-9b-Q4_K_M.gguf / flux2.0/flux-2-klein-9b-fp8.safetensors"),
|
||||
redraw_max_side: Optional[int] = Form(default=None, description="重绘压图长边像素。None=默认896；0=不缩图（原图直送）；其他如 768/640/1024"),
|
||||
):
|
||||
@@ -1258,7 +1292,7 @@ async def debug_grow_timing(
|
||||
hair_style: str = Form(default="2", description="发型序号(花瓣=2)，逗号分隔多选"),
|
||||
webui_steps: Optional[int] = Form(default=None, description="swapHair webui img2img 采样步数，None=服务端默认(15)，可填10/15/20/25对比"),
|
||||
redraw_max_side: Optional[int] = Form(default=None, description="ComfyUI重绘分辨率(长边像素)。None=默认896；0=原图不缩；其他如640/768/1024"),
|
||||
redraw_prompt: Optional[str] = Form(default=None, description="ComfyUI重绘提示词，None=默认'填充遮罩区域的头发'"),
|
||||
redraw_prompt: Optional[str] = Form(default=None, description="ComfyUI重绘提示词，None=默认'填充遮罩区域的头发，人物增加一点磨皮'"),
|
||||
):
|
||||
"""单图跑接口2女性生发，返回每个步骤的耗时 + 结果图，用于定位性能瓶颈。
|
||||
|
||||
@@ -1444,7 +1478,7 @@ async def hair_grow_b(
|
||||
marked_image_url: Optional[str] = Form(default=None, description="划线图片 URL"),
|
||||
marked_image_base64: Optional[str] = Form(default=None, description="划线图片 base64"),
|
||||
use_mask: bool = Form(default=True, description="是否画发际线（测试对比用）。false 时跳过划线检测、直接送划线图"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发，人物增加一点磨皮", description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
):
|
||||
# 划线图三选一取图（只需这一张）
|
||||
marked_raw, e = await resolve_image_bytes(marked_image_file, marked_image_url, marked_image_base64)
|
||||
@@ -1552,7 +1586,7 @@ async def face_features(
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 接口 5：发际线 PNG 生成
|
||||
# 接口 5：发际线 PNG 生成（恢复 a3fddbb 历史版本）
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@app.post(
|
||||
@@ -1655,7 +1689,7 @@ async def hairline_generate(
|
||||
gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
|
||||
hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-7 male:1-7"),
|
||||
use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，测试对比用）"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发，人物增加一点磨皮", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
|
||||
generate_grow_image: bool = Form(default=True, description="是否生成生发效果图（ComfyUI 生发，最耗时）。默认 true 出图；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"),
|
||||
):
|
||||
if gender not in ("male", "female"):
|
||||
@@ -1722,6 +1756,285 @@ async def hairline_generate(
|
||||
return err(1007, f"处理失败：{ex}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 接口 52：中下庭规则、扶正裁切与矩形叠加预览
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def _generate_hairline52_data(image, gender, hair_styles, use_mask=True,
|
||||
prompt=None, generate_grow_image=False,
|
||||
compare_rules=False, normalize_image=True):
|
||||
"""Share measurement coordinates between interface 52 and its comparison page."""
|
||||
from fastapi.concurrency import run_in_threadpool
|
||||
from hairline.service import generate_hairline_pngs_52, get_landmarker
|
||||
|
||||
original = image
|
||||
framing, normalized_landmarks, landmarks_override = None, None, None
|
||||
if normalize_image:
|
||||
from hairline.framing import normalize_portrait
|
||||
from mediapipe.framework.formats import landmark_pb2
|
||||
landmarks = await run_in_threadpool(
|
||||
get_landmarker().detect, cv2.cvtColor(image, cv2.COLOR_BGR2RGB), include_iris=True)
|
||||
if landmarks is None:
|
||||
return None, None
|
||||
image, normalized_landmarks, framing = await run_in_threadpool(normalize_portrait, image, landmarks)
|
||||
landmarks_override = landmark_pb2.NormalizedLandmarkList()
|
||||
for point in normalized_landmarks:
|
||||
landmarks_override.landmark.add(x=float(point[0]), y=float(point[1]), z=float(point[2]))
|
||||
fm, reference = None, None
|
||||
try:
|
||||
measured = await run_in_threadpool(_run_face_measure_data, image, landmarks_override=landmarks_override)
|
||||
if measured is not None:
|
||||
fm, result = measured[:2]
|
||||
reference = {key: result.vertical[key]
|
||||
for key in ("brow_center", "nose_bottom", "chin_tip")}
|
||||
reference["px_per_cm"] = result.px_per_cm
|
||||
except Exception as ex:
|
||||
logger.warning("接口52 当前底图测量失败，使用发际线关键点计算中下庭：%s", ex)
|
||||
res = await run_in_threadpool(
|
||||
generate_hairline_pngs_52, image, gender, hair_styles, use_mask, prompt,
|
||||
generate_grow_image=generate_grow_image, height_reference=reference,
|
||||
compare_rules=compare_rules,
|
||||
precomputed_landmarks=normalized_landmarks[:468] if normalized_landmarks is not None else None)
|
||||
if res is not None:
|
||||
res.update(processed_bgr=image, original_bgr=original, framing=framing)
|
||||
return res, fm
|
||||
|
||||
|
||||
@app.post(
|
||||
"/api/v1/hairline/generate-52",
|
||||
summary="接口52 发际线PNG生成",
|
||||
tags=["人脸分析"],
|
||||
description=f"""
|
||||
输入用户照片 + 性别 + 多选发型，各发型 middle 中点统一对齐到眉心上方（中庭+下庭）/2。
|
||||
返回设计曲线、生发图、目标高度与定位误差；high/low 旧字段保留为 null。
|
||||
|
||||
{_image_fields_desc}
|
||||
|
||||
图片同时支持 `multipart/form-data` 文件上传（字段名 `image_file`）。
|
||||
|
||||
---
|
||||
|
||||
**入参**（同接口2：先选性别，再多选发型）：
|
||||
- 必填 `gender`（`male`/`female`），决定发型集合（female 7 / male 7）。
|
||||
- 必填 `hair_style`（发型序号，逗号分隔如 `1,2,3`），决定返回哪些发际线类型。缺失/越界/非法返回 `1007`。
|
||||
`female`：1=ellipse,2=flower,3=heart,4=straight,5=wave,6=bigflower,7=clasicalflower；
|
||||
`male`：1=ellipse,2=inverse_arc,3=m,4=straight,5=heart,6=Softpetal,7=wave。
|
||||
- 可选 `use_mask` / `prompt`：同接口2 的生发控制参数。
|
||||
生发遮罩直接使用新位置的 middle 曲线，与预览保持一致。
|
||||
- 可选 `generate_grow_image`（默认 `true`）：是否生成生发效果图（ComfyUI 生发，全流程最耗时）。
|
||||
`false` 时跳过生发，各发型 `grown_image_*` 恒为 `null`，仅返回设计叠图与中心点。
|
||||
- 新增可选 `normalize_image`（默认 `false`）：显式传 `true` 时扶正并裁切，长图采用3:4。
|
||||
不传时保留旧调用的原图尺寸与坐标。测试页显式开启此参数，保留实际像素、不放大图片。
|
||||
|
||||
**返回说明**：
|
||||
|
||||
- `hairline_images`：**选中发型**的列表，数量 = 所选发型数，`order` = 发型序号。每项含：
|
||||
- `image_middle_base64`：新高度规则的透明发际线 PNG；`image_high_base64` / `image_low_base64` 固定 null。
|
||||
- `height_placement`：旧/新中心坐标、移动距离和目标误差（像素）。
|
||||
- `grown_image_base64`：该发型的**生发图**（生发失败时为 `null`）。
|
||||
- `hairline_type`：发际线类型 key。
|
||||
本接口仅部署在GPU worker，图片始终返回 `*_base64`；空图片保留字段为 null。
|
||||
- `best_hairline_center_point`：**首个选中发型**的 **middle 档**发际线曲线**面部中间点**坐标，
|
||||
以**当前底图像素**为基准（左上角为原点，x 向右，y 向下）。
|
||||
- `high_hairline_center_point` / `low_hairline_center_point`：旧字段，固定 null。
|
||||
- `height_rule`：中庭、下庭、目标上庭高度、目标中心与测量来源；`face_measure` 为当前底图测量。
|
||||
- `input_image_base64`：处理后底图，须用它叠加发际线；`original_image_base64`：处理前图片。
|
||||
- `coordinate_space`：默认 `original_image`，开启预处理后为 `processed_image`。
|
||||
- `framing`：是否标准裁切、旋转角、前后尺寸及双向坐标变换；关闭预处理时为 null。
|
||||
""",
|
||||
responses={
|
||||
200: {
|
||||
"description": "成功",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"example": {
|
||||
"code": 0,
|
||||
"message": "success",
|
||||
"request_id": "mock-request-id",
|
||||
"data": {
|
||||
"hairline_images": [
|
||||
{"hairline_type": "ellipse", "image_middle_base64": "iVBORw0KGgo...", "image_high_base64": None, "image_low_base64": None, "grown_image_base64": "iVBORw0KGgo...", "order": 1},
|
||||
{"hairline_type": "flower", "image_middle_base64": "iVBORw0KGgo...", "image_high_base64": None, "image_low_base64": None, "grown_image_base64": None, "order": 2},
|
||||
],
|
||||
"best_hairline_center_point": {"x": 540, "y": 430},
|
||||
"high_hairline_center_point": None,
|
||||
"low_hairline_center_point": None,
|
||||
"face_measure": {
|
||||
"face_total_height_cm": 13.76,
|
||||
"four_courts": {
|
||||
"top_court_cm": 3.44, "upper_court_cm": 3.44,
|
||||
"middle_court_cm": 3.44, "lower_court_cm": 3.44,
|
||||
"ratios": {"top_court": 0.25, "upper_court": 0.25,
|
||||
"middle_court": 0.25, "lower_court": 0.25},
|
||||
},
|
||||
"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},
|
||||
"eye1": 3.44, "eye2": 3.44, "eye3": 3.44, "eye4": 3.44,
|
||||
"eye5": 3.44, "eye6": 3.44, "eye7": 3.44,
|
||||
},
|
||||
"landmarks": {
|
||||
"hair_top": {"x": 540, "y": 120},
|
||||
"hairline": {"x": 540, "y": 430},
|
||||
"brow_center": {"x": 540, "y": 740},
|
||||
"nose_bottom": {"x": 540, "y": 1050},
|
||||
"chin_tip": {"x": 540, "y": 1360},
|
||||
},
|
||||
"hairline_source": "segmentation",
|
||||
"head_pose": {"yaw": -1.39, "pitch": 2.49, "roll": -0.06},
|
||||
},
|
||||
},
|
||||
}
|
||||
}
|
||||
},
|
||||
},
|
||||
400: {
|
||||
"description": "参数错误 / 图片识别失败",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"example": {"code": 1001, "message": "无法识别人像", "request_id": "x", "data": None}
|
||||
}
|
||||
},
|
||||
},
|
||||
},
|
||||
)
|
||||
async def hairline_generate_52(
|
||||
image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
|
||||
image_url: Optional[str] = Form(default=None, description="图片 URL"),
|
||||
image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
|
||||
gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
|
||||
hair_style: Optional[str] = Form(default=None, description="发型序号逗号分隔（必填，如 1,2,3）。female:1-7 male:1-7"),
|
||||
use_mask: bool = Form(default=True, description="生发是否启用 inpaint 遮罩（同接口2，测试对比用）"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发，人物增加一点磨皮", description="ComfyUI 提示词（同接口2），会替换工作流节点60的文本"),
|
||||
generate_grow_image: bool = Form(default=True, description="是否生成生发效果图。false 时仅返回新规则发际线叠图与中心点"),
|
||||
normalize_image: bool = Form(default=False, description="新增可选开关，默认false保留原图尺寸和坐标；true先扶正再裁切，长图采用3:4。测试页显式开启"),
|
||||
):
|
||||
if gender not in ("male", "female"):
|
||||
return err(1004, "gender 必填且只能为 male / female")
|
||||
|
||||
# hair_style 必填（同接口2）：解析逗号分隔，缺失/越界/非法 → 1007
|
||||
max_styles = {"female": 7, "male": 7}[gender]
|
||||
hair_styles = _parse_hair_styles(hair_style, max_styles)
|
||||
if hair_styles is None:
|
||||
return err(1007, f"hair_style 必填且为 1..{max_styles} 的整数（逗号分隔），收到 {hair_style!r}")
|
||||
|
||||
raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
|
||||
if e is not None:
|
||||
return e
|
||||
|
||||
from hairline.framing import decode_portrait
|
||||
image = decode_portrait(raw)
|
||||
if image is None:
|
||||
return err(1008, "图片格式不支持（仅 JPG / PNG）")
|
||||
|
||||
try:
|
||||
res, fm = await _generate_hairline52_data(
|
||||
image, gender, hair_styles, use_mask, prompt,
|
||||
generate_grow_image=generate_grow_image, normalize_image=normalize_image)
|
||||
if res is None:
|
||||
return err(1001, "无法识别人像")
|
||||
|
||||
hairline_images = []
|
||||
for it in res["images"]:
|
||||
ov = it["overlays"]
|
||||
hairline_images.append({
|
||||
"hairline_type": it["hairline_type"],
|
||||
"image_middle_base64": _rgba_png_b64(ov["middle"]),
|
||||
"image_high_base64": None,
|
||||
"image_low_base64": None,
|
||||
"grown_image_base64": (_png_to_jpg_b64(it["grown_png"]) # 生发图 JPG（失败为 null）
|
||||
if it.get("grown_png") else None),
|
||||
"order": it["order"],
|
||||
"height_placement": it["height_placement"],
|
||||
})
|
||||
c = res.get("best_centers") or {}
|
||||
def _pt(p):
|
||||
return ({"x": p[0], "y": p[1]} if p else None)
|
||||
data = {
|
||||
"hairline_images": hairline_images,
|
||||
"best_hairline_center_point": _pt(c.get("middle")),
|
||||
"high_hairline_center_point": _pt(c.get("high")),
|
||||
"low_hairline_center_point": _pt(c.get("low")),
|
||||
"height_rule": res["height_rule"],
|
||||
"guide_landmarks": res["guide_landmarks"],
|
||||
"face_measure": fm,
|
||||
"framing": res["framing"],
|
||||
"input_image_base64": _jpg_b64(res["processed_bgr"]),
|
||||
"original_image_base64": _jpg_b64(res["original_bgr"]),
|
||||
"coordinate_space": "processed_image" if normalize_image else "original_image",
|
||||
"width": res["processed_bgr"].shape[1], "height": res["processed_bgr"].shape[0],
|
||||
}
|
||||
return ok(data)
|
||||
except Exception as ex: # noqa: BLE001
|
||||
logger.exception("接口52 处理异常")
|
||||
return err(1007, f"处理失败：{ex}")
|
||||
|
||||
|
||||
@app.get("/api/v1/debug/hairline-height-cases", include_in_schema=False)
|
||||
async def hairline_height_cases():
|
||||
path = os.path.join(os.path.dirname(__file__), "tests", "fixtures", "hairline_height_cases.json")
|
||||
with open(path, encoding="utf-8") as f:
|
||||
return ok(json.load(f))
|
||||
|
||||
|
||||
@app.get("/api/v1/debug/hairline-framing-reference", include_in_schema=False)
|
||||
async def hairline_framing_reference():
|
||||
from hairline.framing import load_profile
|
||||
return ok(load_profile())
|
||||
|
||||
|
||||
@app.post("/api/v1/debug/hairline-height-compare", include_in_schema=False)
|
||||
async def hairline_height_compare(
|
||||
image_file: Optional[UploadFile] = File(default=None),
|
||||
image_url: Optional[str] = Form(default=None),
|
||||
image_base64: Optional[str] = Form(default=None),
|
||||
gender: str = Form(default="female"),
|
||||
hair_style: str = Form(default="1,2,3,4,5,6,7"),
|
||||
generate_grow_image: bool = Form(default=False),
|
||||
normalize_image: bool = Form(default=True),
|
||||
):
|
||||
if gender not in ("female", "male"):
|
||||
return err(1004, "gender 必须为 male / female")
|
||||
styles = _parse_hair_styles(hair_style, 7)
|
||||
if styles is None:
|
||||
return err(1007, "hair_style 必须为 1..7 的整数，逗号分隔")
|
||||
raw, error = await resolve_image_bytes(image_file, image_url, image_base64)
|
||||
if error is not None:
|
||||
return error
|
||||
from hairline.framing import decode_portrait
|
||||
photo = decode_portrait(raw)
|
||||
if photo is None:
|
||||
return err(1008, "图片格式不支持")
|
||||
try:
|
||||
res, fm = await _generate_hairline52_data(
|
||||
photo, gender, styles, prompt="填充遮罩区域的头发，人物增加一点磨皮",
|
||||
generate_grow_image=generate_grow_image, compare_rules=True,
|
||||
normalize_image=normalize_image)
|
||||
if res is None:
|
||||
return err(1001, "无法识别人像")
|
||||
h, w = res["processed_bgr"].shape[:2]
|
||||
return ok({
|
||||
"gender": gender, "width": w, "height": h,
|
||||
"input_base64": _jpg_b64(res["processed_bgr"]),
|
||||
"original_base64": _jpg_b64(res["original_bgr"]),
|
||||
"framing": res["framing"],
|
||||
"coordinate_space": "processed_image" if normalize_image else "original_image",
|
||||
"height_rule": res["height_rule"], "face_measure": fm,
|
||||
"guide_landmarks": res["guide_landmarks"],
|
||||
"results": [{
|
||||
"hairline_type": item["hairline_type"], "order": item["order"],
|
||||
"old_overlay_base64": _rgba_png_b64(item["legacy_overlay"]),
|
||||
"new_overlay_base64": _rgba_png_b64(item["overlays"]["middle"]),
|
||||
"height_placement": item["height_placement"],
|
||||
"grown_image_base64": _png_to_jpg_b64(item["grown_png"]) if item["grown_png"] else None,
|
||||
} for item in res["images"]],
|
||||
})
|
||||
except Exception as ex:
|
||||
logger.exception("接口52 高度规则对比失败")
|
||||
return err(1007, str(ex))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 接口 9：头发遮罩生成
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -1893,6 +2206,70 @@ async def head_band(
|
||||
return err(1007, f"处理失败：{ex}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 接口 99：人脸特殊点检测（固定关键点 + 发际线特殊点）
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@app.post(
|
||||
"/api/v1/face/special-points",
|
||||
summary="接口99 人脸特殊点检测",
|
||||
tags=["人脸分析"],
|
||||
description=f"""
|
||||
输入一张人脸照片，返回 MediaPipe Face Mesh 一组**固定编号特殊点**的像素坐标，
|
||||
以及 5 个**发际线特殊点**（射线与 BiSeNet 头发分割发际线的交点，见 hairline_grow 同款分割）。
|
||||
|
||||
**字段命名**：`<组英文名>_<点ID>`，如 `eyebrow_107`、`mouth_17`；发际线点用给定字段名
|
||||
（`hair_line_center` 等）。坐标为原图像素坐标（int），发际线点不可得（无头发/分割失败）时为 null。
|
||||
|
||||
| 组 | 字段前缀 | 点 ID |
|
||||
|----|----------|-------|
|
||||
| 眉毛 | eyebrow | 107 66 105 63 70 336 296 334 293 300 |
|
||||
| 眼睛 | eye | 189 222 224 113 226 413 442 444 342 446 |
|
||||
| 嘴 | mouth | 0 37 40 67 91 84 17 405 375 409 269 |
|
||||
| 中线鼻 | nose_midline | 9 168 197 5 4 194 278 48 437 217 |
|
||||
| 面颊 | cheek | 101 117 123 187 205 50 330 346 352 280 425 |
|
||||
| 边线 | face_edge | 162 234 58 136 149 152 378 365 288 401 366 389 251 |
|
||||
|
||||
**发际线点算法**：从关键点 a 沿 a→b 方向逐像素步进，首次进入头发 mask 处即交点；
|
||||
双射线的点取两交点中点（仅一条命中返回该点，均未命中为 null）：
|
||||
- `hair_line_center`：151→10
|
||||
- `hair_line_right`：mid(108→109, 69→67)
|
||||
- `hair_line_right_right`：mid(104→103, 68→54)
|
||||
- `hair_line_left`：mid(337→338, 299→297)
|
||||
- `hair_line_left_left`：mid(333→332, 298→284)
|
||||
|
||||
{_image_fields_desc}
|
||||
""",
|
||||
)
|
||||
async def face_special_points(
|
||||
image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
|
||||
image_url: Optional[str] = Form(default=None, description="图片 URL"),
|
||||
image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
|
||||
):
|
||||
"""接口99：固定关键点 + 发际线特殊点，像素坐标 JSON 返回。"""
|
||||
raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
|
||||
if e is not None:
|
||||
return e
|
||||
|
||||
image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR)
|
||||
if image is None:
|
||||
return err(1008, "图片格式不支持（仅 JPG / PNG）")
|
||||
|
||||
try:
|
||||
from fastapi.concurrency import run_in_threadpool
|
||||
from face_analysis.special_points import detect_special_points, NoFaceError, NonFrontalError
|
||||
try:
|
||||
data = await run_in_threadpool(detect_special_points, image)
|
||||
except NoFaceError:
|
||||
return err(1001, "无法识别人像")
|
||||
except NonFrontalError:
|
||||
return err(1003, "角度问题，请上传正面照")
|
||||
return ok(data)
|
||||
except Exception as ex: # noqa: BLE001
|
||||
logger.exception("接口99 处理异常")
|
||||
return err(1007, f"处理失败：{ex}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 接口 11：发际线生发（接口9 遮罩 + change_hair 换发型 + 按遮罩羽化贴回）
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -2049,7 +2426,7 @@ async def hairline_grow_v2(
|
||||
inpainting_fill: int = Form(default=1, description="change_hair服务端重绘填充：0=保留原图 | 1=噪声 | 2=纯色 | 3=潜变量。默认 1"),
|
||||
mask_blur: int = Form(default=11, description="change_hair服务端遮罩边缘模糊像素，默认 11"),
|
||||
mask_dilate_scale: float = Form(default=1.0, description="change_hair服务端遮罩膨胀缩放，默认 1.0"),
|
||||
comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，None 用默认「填充遮罩区域的头发」"),
|
||||
comfyui_prompt: Optional[str] = Form(default=None, description="Flux-2 重绘提示词，None 用默认「填充遮罩区域的头发，人物增加一点磨皮」"),
|
||||
beauty_alpha: float = Form(default=0.6, description="redraw_band 版 band 外的全脸美颜融入强度（0=band外无美颜纯用final，1≈整帧版），默认 0.6"),
|
||||
band_lo_mult: float = Form(default=0.5, description="重绘带外推倍率下限（相对 hairline_push_cm，内轮廓=0×、原外推线=1.0×），默认 0.5"),
|
||||
band_hi_mult: float = Form(default=1.5, description="重绘带外推倍率上限（相对 hairline_push_cm），默认 1.5"),
|
||||
@@ -2215,7 +2592,7 @@ async def hairline_grow_v2_final_v2(
|
||||
async def api_redraw(
|
||||
image_file: UploadFile = File(..., description="人物图片（JPG/PNG）"),
|
||||
mask_file: UploadFile = File(..., description="遮罩图片（PNG，支持红/白/alpha 格式）"),
|
||||
prompt: str = Form(default="填充遮罩区域的头发",
|
||||
prompt: str = Form(default="填充遮罩区域的头发，人物增加一点磨皮",
|
||||
description="ComfyUI 提示词"),
|
||||
):
|
||||
image_bytes = await image_file.read()
|
||||
@@ -2264,12 +2641,127 @@ async def download_hairline_log(rid: Optional[str] = None, tail: int = 500):
|
||||
# 健康检查
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _load_health_cfg() -> dict:
|
||||
"""深健康检查配置：环境变量优先，回退 worker_config.json 的 "health" 段。
|
||||
|
||||
min_free_gb=0 关闭磁盘检查；check_deps=false 关闭依赖探测（均退化为旧行为，
|
||||
即只看模型就绪标志）。
|
||||
"""
|
||||
cfg_path = os.path.join(os.path.dirname(__file__), "worker_config.json")
|
||||
file_cfg: dict = {}
|
||||
if os.path.isfile(cfg_path):
|
||||
try:
|
||||
with open(cfg_path, encoding="utf-8") as f:
|
||||
file_cfg = json.load(f).get("health") or {}
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("读取 worker_config.json health 段失败：%s", e)
|
||||
|
||||
def _get(env_key, file_key, default):
|
||||
raw = os.getenv(env_key)
|
||||
return raw if raw not in (None, "") else file_cfg.get(file_key, default)
|
||||
|
||||
def _as_float(value, default):
|
||||
try:
|
||||
return float(value)
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
|
||||
return {
|
||||
"min_free_gb": _as_float(_get("HEALTH_MIN_FREE_GB", "min_free_gb", 5.0), 5.0),
|
||||
"check_deps": str(_get("HEALTH_CHECK_DEPS", "check_deps", True)).strip().lower()
|
||||
not in ("0", "false", "no", "off"),
|
||||
"dep_comfyui": str(_get("HEALTH_DEP_COMFYUI", "dep_comfyui", "127.0.0.1:8188")),
|
||||
"deps_cache_seconds": max(0.0, _as_float(
|
||||
_get("HEALTH_DEPS_CACHE_SECONDS", "deps_cache_seconds", 5.0), 5.0)),
|
||||
}
|
||||
|
||||
|
||||
_HEALTH_CFG = _load_health_cfg()
|
||||
|
||||
|
||||
def _disk_free_gb() -> float:
|
||||
"""worker 所在文件系统的剩余空间（GB）。"""
|
||||
st = shutil.disk_usage(os.path.dirname(os.path.abspath(__file__)))
|
||||
return st.free / (1 << 30)
|
||||
|
||||
|
||||
def _parse_host_port(addr: str, default_port: int):
|
||||
"""解析 "host:port"（仅支持 IPv4/主机名形式）。"""
|
||||
host, _, port = str(addr).partition(":")
|
||||
try:
|
||||
return host or "127.0.0.1", int(port) if port else default_port
|
||||
except ValueError:
|
||||
return host or "127.0.0.1", default_port
|
||||
|
||||
|
||||
def _change_hair_addr():
|
||||
"""从 _CHANGE_HAIR_BASE 解析 (host, port)，失败返回 None。"""
|
||||
try:
|
||||
u = urlsplit(_CHANGE_HAIR_BASE)
|
||||
if u.hostname:
|
||||
return u.hostname, u.port or 80
|
||||
except ValueError:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
async def _tcp_probe(host: str, port: int, timeout: float = 0.8) -> bool:
|
||||
"""TCP 连通性探测：只判下游进程死活，不过 HTTP 认证、不受业务繁忙影响。"""
|
||||
try:
|
||||
_, writer = await asyncio.wait_for(asyncio.open_connection(host, port), timeout)
|
||||
writer.close()
|
||||
return True
|
||||
except Exception: # noqa: BLE001
|
||||
return False
|
||||
|
||||
|
||||
_DEP_CACHE = {"ts": -1.0, "detail": None}
|
||||
|
||||
|
||||
async def _check_deps():
|
||||
"""并发探测下游依赖（ComfyUI、change_hair），结果缓存 deps_cache_seconds 秒。"""
|
||||
now = time.monotonic()
|
||||
detail = _DEP_CACHE["detail"]
|
||||
if detail is None or now - _DEP_CACHE["ts"] >= _HEALTH_CFG["deps_cache_seconds"]:
|
||||
probes = {"comfyui": _tcp_probe(*_parse_host_port(_HEALTH_CFG["dep_comfyui"], 8188))}
|
||||
ch = _change_hair_addr()
|
||||
if ch:
|
||||
probes["change_hair"] = _tcp_probe(*ch)
|
||||
names = list(probes)
|
||||
results = await asyncio.gather(*probes.values())
|
||||
detail = dict(zip(names, results))
|
||||
_DEP_CACHE["ts"], _DEP_CACHE["detail"] = now, detail
|
||||
return all(detail.values()), detail
|
||||
|
||||
|
||||
@app.get("/health", include_in_schema=False)
|
||||
async def health():
|
||||
# 模型就绪后才返回 200，供网关探测；未就绪返回 503。
|
||||
"""网关探测入口。
|
||||
|
||||
模型未就绪 → 503 starting；磁盘剩余低于阈值 / 下游依赖 TCP 不通 → 503 degraded；
|
||||
正常 → 200（附 disk_free_gb 与 deps 明细，供人工排查）。深检自身异常时按就绪返回
|
||||
（健康检查不能自己成为故障点）。
|
||||
"""
|
||||
if not _STATE["ready"]:
|
||||
return JSONResponse(status_code=503, content={"status": "starting"})
|
||||
return {"status": "ok"}
|
||||
try:
|
||||
free_gb = _disk_free_gb()
|
||||
body = {"status": "ok", "disk_free_gb": round(free_gb, 1)}
|
||||
min_free = _HEALTH_CFG["min_free_gb"]
|
||||
if min_free > 0 and free_gb < min_free:
|
||||
body.update(status="degraded", reason=f"disk_free_below_{min_free}g")
|
||||
return JSONResponse(status_code=503, content=body)
|
||||
if _HEALTH_CFG["check_deps"]:
|
||||
deps_ok, detail = await _check_deps()
|
||||
body["deps"] = detail
|
||||
if not deps_ok:
|
||||
down = ",".join(k for k, v in detail.items() if not v)
|
||||
body.update(status="degraded", reason=f"dep_down:{down}")
|
||||
return JSONResponse(status_code=503, content=body)
|
||||
return body
|
||||
except Exception: # noqa: BLE001
|
||||
logger.exception("深健康检查异常，按就绪返回")
|
||||
return {"status": "ok"}
|
||||
|
||||
|
||||
@app.get("/", include_in_schema=False)
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
#!/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"。
|
||||
"""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，人物增加一点磨皮"。
|
||||
预热1次+正式1次。
|
||||
"""
|
||||
import base64, json, os, time
|
||||
@@ -9,7 +9,7 @@ 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"
|
||||
PROMPT = "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
OUT = Path("/home/ubuntu/hair/benchmark_out/bench6")
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
@@ -55,7 +55,7 @@ def gpu_used():
|
||||
|
||||
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"}
|
||||
"prompt": "填充遮罩区域的头发，人物增加一点磨皮"}
|
||||
if model_file:
|
||||
fd["flux_model"] = model_file
|
||||
if res_val != "":
|
||||
|
||||
+1
-1
@@ -54,7 +54,7 @@ def call(img_path, model_file, res_val):
|
||||
"gender": "female",
|
||||
"hair_style": "2", # 花瓣形
|
||||
"use_mask": "true",
|
||||
"prompt": "填充遮罩区域的头发",
|
||||
"prompt": "填充遮罩区域的头发，人物增加一点磨皮",
|
||||
}
|
||||
if model_file:
|
||||
fd["flux_model"] = model_file
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
#!/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"。
|
||||
提示词固定为 "填充遮罩区域的头发，人物增加一点磨皮"。
|
||||
热数据：预热1次+正式1次。
|
||||
"""
|
||||
import base64
|
||||
@@ -14,7 +14,7 @@ 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"
|
||||
PROMPT = "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
OUT = Path("/home/ubuntu/hair/benchmark_out/bench7")
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
@@ -16,7 +16,7 @@
|
||||
Handler 签名新增：
|
||||
|
||||
```python
|
||||
prompt: str = Form(default="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
prompt: str = Form(default=填充遮罩区域的头发，人物增加一点磨皮, description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
```
|
||||
|
||||
透传链路：
|
||||
@@ -31,7 +31,7 @@ handler → generate_grow_results(image, gender, use_mask, prompt)
|
||||
Handler 签名新增：
|
||||
|
||||
```python
|
||||
prompt: str = Form(default="补充遮罩区域的头发", description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
prompt: str = Form(default=填充遮罩区域的头发，人物增加一点磨皮, description="ComfyUI 提示词，会替换工作流节点60的文本"),
|
||||
```
|
||||
|
||||
透传链路：
|
||||
@@ -80,17 +80,17 @@ if prompt is not None:
|
||||
| 2 | `image_base64` | string | — | 同上 |
|
||||
| 2 | `gender` | string | `female` | `male` / `female` |
|
||||
| 2 | `use_mask` | bool | `true` | inpaint 遮罩开关 |
|
||||
| 2 | `prompt` | string | `补充遮罩区域的头发` | ComfyUI 提示词 |
|
||||
| 2 | `prompt` | string | 填充遮罩区域的头发，人物增加一点磨皮 | ComfyUI 提示词 |
|
||||
| 3 | `marked_image_file` | file | — | 划线图（三选一） |
|
||||
| 3 | `marked_image_url` | string | — | 同上 |
|
||||
| 3 | `marked_image_base64` | string | — | 同上 |
|
||||
| 3 | `use_mask` | bool | `true` | 画发际线开关 |
|
||||
| 3 | `prompt` | string | `补充遮罩区域的头发` | ComfyUI 提示词 |
|
||||
| 3 | `prompt` | string | 填充遮罩区域的头发，人物增加一点磨皮 | ComfyUI 提示词 |
|
||||
|
||||
## 3. 当前工作流提示词节点
|
||||
|
||||
`add_hair.json` 中节点 **60**（`JjkText`）为提示词节点，当前硬编码文本：
|
||||
|
||||
> 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
|
||||
> 填充遮罩区域的头发，人物增加一点磨皮
|
||||
|
||||
`comfyui.run()` 收到 `prompt` 参数后将替换此文本。
|
||||
|
||||
+74
-24
@@ -12,19 +12,23 @@
|
||||
|
||||
```
|
||||
客户端 ──HTTPS──> 外网网关(gateway/) ──HTTP(X-Internal-Token)──> worker(GPU 机, app.py)
|
||||
│ 薄代理 + 落盘改URL │ 跑算法(本地模型/ComfyUI)
|
||||
└ 接口4 本机直接调豆包(不转发) └ 接口1/2/3/5/6/7
|
||||
│ 薄代理 + 落盘改URL │ 跑算法(本地模型/ComfyUI/change_hair)
|
||||
└ 接口4(豆包)/99(特殊点) 本机实现(不转发) └ 接口1/2/3/5/52/6/7/9/10/11/12 + 工具路由
|
||||
```
|
||||
|
||||
- **worker**（`app.py` + `face_analysis/` + `hairline/`）：跑真正的算法，**纯本地、无外网依赖**。
|
||||
对 `/api/*` 校验 `X-Internal-Token`（密码在 `worker_config.json` 的 `accept_passwords`）；
|
||||
`/health` 模型就绪才返回 200。监听 **8187**（`./start.sh` 控制开关，`./run_worker.sh` 热重载）。
|
||||
- **worker**（`app.py` + `face_analysis/` + `hairline/`）：跑真正的算法，除 ComfyUI/change_hair 走本机
|
||||
回环外无外网依赖。对 `/api/*` 校验 `X-Internal-Token`（密码在 `worker_config.json` 的 `accept_passwords`）；
|
||||
`/health` 模型就绪才返回 200。监听 **8187**（systemd `hair-worker.service`，也可 `./run_worker.sh` 手动起）。
|
||||
另有 worker 本机的工具路由：`/api/swapHair/v1`、`/hairColor/v2`（CORS 代理转发 change_hair:8801）、
|
||||
`/api/v1/redraw`（直接 ComfyUI 重绘）、`/api/v1/debug/*`（免Token调试，仍受IP限制）。
|
||||
所有动态路径先校验直连来源IP：允许本机与网关`49.232.51.171`，其余返回HTTP403/code1010；`/static/`除外。配置见[`接口IP白名单.md`](接口IP白名单.md)，网关侧不增加IP限制。
|
||||
- **网关**（`gateway/`）：薄反向代理，健康检查/派发/鉴权/把 worker 的 `*_base64` 落盘改成 `*_url`。
|
||||
唯一例外是**接口4 在网关本机直接实现**（调外网豆包，不转发 worker）。
|
||||
网关本机实现的接口（不转发 worker）：**接口4**（调外网豆包）与**接口99**（纯 CPU 特殊点检测，
|
||||
直接 import 同仓库 `face_analysis/special_points`，`_if99_lock` 单路排队）。
|
||||
- **图片三选一**：所有接口图片入参 `image_file`/`image_url`/`image_base64` 严格三选一（接口3 是 `marked_image_*`）。
|
||||
- **响应信封**：`{code, message, request_id, data}`；业务错误用 `code`（HTTP 一律 200）。
|
||||
- **响应信封**：`{code, message, request_id, data}`；业务错误用 `code`（HTTP 200）；IP拒绝为HTTP403/code1010，Token错误为HTTP401/code1009，依赖健康异常为HTTP503。
|
||||
|
||||
### base64 → URL 映射（网关落盘改写，递归进数组、可空保留 null）
|
||||
### base64 → URL 映射（网关落盘改写，递归进数组）
|
||||
|
||||
| 接口 | worker 字段（内部） | 对外字段 |
|
||||
|------|--------------------|----------|
|
||||
@@ -33,24 +37,29 @@
|
||||
| 2 | `results[].image_base64` / `results[].grown_image_base64`(可空) | `results[].image_url` / `results[].grown_image_url` |
|
||||
| 3 | `hair_growth_image_base64`(可空) | `hair_growth_image_url` |
|
||||
| 5 | `hairline_images[].image_{middle,high,low}_base64` / `grown_image_base64`(可空) | `hairline_images[].image_{middle,high,low}_url` / `grown_image_url` |
|
||||
| 7 | `results[].image_base64` / `results[].grown_image_base64`(可空) | `results[].image_url` / `results[].grown_image_url` |
|
||||
| 52 | `hairline_images[].image_{middle,high,low}_base64` / `grown_image_base64`、`input_image_base64` / `original_image_base64` | 仅GPU worker，本次不新增网关路由；直接返回`*_base64` |
|
||||
| 9 | `steps_common.*_base64`、`bisenet|segformer.{hair,closed_region,final_overlay,mask}_mask_base64` 等 | 同名 `*_url` |
|
||||
| 10 | `steps_common.*_base64`、`bisenet|segformer.{base_mask,contour,band_overlay,mask}_base64` 等 | 同名 `*_url` |
|
||||
| 11 | `steps.{input,baseline,hair_mask,mask_overlay,mask,swap_raw,alpha,final}_base64` | `steps.*_url` |
|
||||
| 12 | `steps.{input,final,redraw_band_overlay,redraw_full,redraw_band,redraw_c}_base64` | `steps.*_url` |
|
||||
| 4 | （网关本机产出，无图片字段，`features` 为 JSON 字符串） | — |
|
||||
|
||||
> 实现建议：递归遍历 data，凡 key 以 `_base64` 结尾就落盘改 `_url`，自动覆盖嵌套/新增字段。
|
||||
> 兼容约定：网关保持原样，有图片时落盘为`*_url`，空图片保留`*_base64: null`。接口52仅在worker返回base64，与网关的图片改名无关。
|
||||
> **图片格式**：接口1 标注图含透明用 **PNG**；接口2/3/5 是不透明照片用 **JPG**(小很多，~9×)。
|
||||
> 网关落盘按内容嗅探扩展名（PNG 头→`.png`，否则 `.jpg`）。
|
||||
|
||||
### 错误码
|
||||
|
||||
`1001` 无法识别人像 ｜ `1002` 分辨率过低 ｜ `1003` 非正面 ｜ `1004` gender 必填/非法（接口2/5/7）｜
|
||||
`1006` >1MB ｜ `1007` 图片参数错误（0或多个）/未预期异常 ｜ `1008` 格式不支持。
|
||||
`1006` >1MB ｜ `1007` 图片参数错误（0或多个）/未预期异常 ｜ `1008` 格式不支持 ｜ `1009` Token鉴权失败 ｜ `1010` worker来源IP不允许或白名单配置无效。
|
||||
|
||||
---
|
||||
|
||||
## 2. 五个接口实现简述
|
||||
## 2. 各接口实现简述
|
||||
|
||||
### 接口1 四庭七眼测量 `/api/v1/face/measure`（worker）
|
||||
- **做什么**：正面照 → 四庭(顶/上/中/下庭) + 七眼(眼宽/脸宽/间距) 的 cm 与占比、5 个关键点坐标、一张透明底标注 PNG。
|
||||
- **做什么**：正面照 → 四庭(顶/上/中/下庭) + 七眼(眼宽/脸宽/间距) 的 cm 与占比、5 个关键点坐标、竖线坐标 `vertical_lines`、一张透明底标注 PNG。
|
||||
- **怎么实现**（`face_analysis/`）：MediaPipe Face Mesh 468+虹膜点 → solvePnP 姿态校验(非正面 1003) →
|
||||
虹膜直径法定标(px→cm) → **眉心以下实测**；**眉心以上**用 BiSeNet 头发分割取真实发际线/头顶(方案B)，
|
||||
失败回退比例推算(方案A，`hairline_source` 透出)。标注图 numpy 向量化渐变线 + 思源黑体。返回 `annotated_image_base64`。
|
||||
@@ -59,16 +68,17 @@
|
||||
### 接口6 四庭七眼测量 v2 `/api/v1/face/measure-v2`（worker）—— 接口1 的去顶庭变体
|
||||
- **做什么**：基于接口 1，**去顶庭**：不画头顶横线、不返回顶庭数据（`four_courts` 仅上/中/下庭，`landmarks` 无 `hair_top`，`face_total_height_cm` 为三庭之和）。
|
||||
- **与接口1 的标注差异**（`create_annotated_image(variant="v6")`）：①竖线范围改为发际线→下巴尖；②不画人头最左/最右端线（仅七眼 6 点 5 段，接口1 为 8 线 7 段）；③左侧只标上/中/下庭。箭头/虚线/字体等与接口1 一致。
|
||||
- **怎么实现**：与接口 1 共用 `_face_measure_impl(variant="v6")`；v6 时标注走变体分支、数据由 app.py 边界删顶庭字段并重算三庭比例。
|
||||
- **怎么实现**：与接口 1 共用 `_face_measure_impl(variant="v6")`；v6 时标注走变体分支、数据由 app.py 边界删顶庭字段并重算三庭比例。`vertical_lines` 与标注图共用 `vertical_line_geometry`：接口 6 的 y 范围是发际线到下巴尖，`lines` 只有 6 条内线。
|
||||
- **网关改动**：新增路由 `POST /api/v1/face/measure-v2`，转发到 worker 同路径；base64→URL 改写无需改动。
|
||||
|
||||
### 接口2 C端生发 `/api/v1/hair/grow`（worker）—— 预览 + 生发图
|
||||
- **做什么**：正面照 + `gender`(必填) + `hair_style`(必填，逗号分隔多选，如 `1,2,3`) → 指定发际线类型 **N 组**：**预览图**(发际线叠在照片上) + **生发后图**(植发3个月效果)。
|
||||
- **怎么实现**（`hairline/`）：移植 head3d——MediaPipe(Tasks) + SegFormer 分割 + 17 锚点射线检测 → 502 点 mesh，
|
||||
按 `face_ext.obj` 的 UV 把发际线贴图渲染到额头(预览)。生发：黑贴图渲染遮罩 → 调本机 **ComfyUI 8182** 的
|
||||
按 `face_ext.obj` 的 UV 把发际线贴图渲染到额头(预览)。原生生发：黑贴图渲染遮罩 → 调本机 **ComfyUI 8188** 的
|
||||
`add_hair.json`(Flux-2) 出图。**关键坑**：obj 是重排序，需 `INDEX_MAP_468` 把 MP 序→OBJ 序。
|
||||
返回 `results[].image_base64` + `grown_image_base64`。
|
||||
- `hair_style` 映射：female 1=ellipse 2=flower 3=heart 4=straight 5=wave 6=bigflower 7=clasicalflower；male 1=ellipse 2=inverse_arc 3=m 4=straight 5=heart 6=Softpetal 7=wave。female 1..5 走「换发型(change_hair)」+Flux-2 重绘管线；female 6/7 与 male 全部走原生发(ComfyUI add_hair)管线。
|
||||
- 模板对齐：左右刘海边界高低不同时，按 MediaPipe 左右对应额头点协调沿人脸向上方向的延伸距离，避免把 M 型等设计模板拉歪；保留人脸姿态，之后仍做头部轮廓钳制。预览白线与生发黑线/遮罩共用修正后的 502 点几何，不改变真实发际线测量算法。
|
||||
- `hair_style` 映射：female 1=ellipse 2=flower 3=heart 4=straight 5=wave 6=bigflower 7=clasicalflower；male 1=ellipse 2=inverse_arc 3=m 4=straight 5=heart 6=Softpetal 7=wave。female 1..5 走「换发型(change_hair)」+Flux-2 重绘管线（`generate_grow_results_swap`，复用接口12 final 的 `_V2_FINAL_DEFAULTS`，长边默认压到 `REDRAW_MAX_SIDE`=1024 再放大回原尺寸）；female 6/7 与 male 全部走原生发(ComfyUI add_hair)管线。可选参数 `flux_model`（切换 Flux unet，.gguf 自动配 UnetLoaderGGUF + 配套 qwen CLIP）、`redraw_max_side`（0=不缩图）。
|
||||
|
||||
### 接口7 C端生发 v2 `/api/v1/hair/grow-v2`（worker）—— 接口2同款，add_hair2 工作流
|
||||
- **做什么**：与接口 2 完全一致（正面照 + `gender` + `hair_style` 逗号分隔多选 → N 组预览+生发图）。
|
||||
@@ -92,20 +102,60 @@
|
||||
- **做什么**：入参同接口2（`gender` + 多选 `hair_style` 必填）。对每个选中发型 → `middle`/`high`/`low` 三档发际线叠图 + 生发图 + 首个选中发型的面部中间点坐标。
|
||||
- **怎么实现**：复用接口2 的 502 点渲染管线，三档分别用 `hairline_texture[/_high|/_low]` 同名贴图渲染叠图；生发同接口2（ComfyUI inpaint），**黑模板固定取 `hairline_texture_black/`（middle）**，每发型 1 张生发图。`best_hairline_center_point`=眉心 x × 首个选中发型 middle 档发际线 y。
|
||||
|
||||
### 接口52 发际线PNG生成 `/api/v1/hairline/generate-52`（worker）
|
||||
- **预处理**：`hairline/framing.py`先扶正9→152，再按高(3÷0.9)L、宽2.5L（3:4）做原像素裁切，人物相对原3L规则缩小约10%。L为9→152距离；眉心位置沿用已扶正参考图比例。长图的标准窗口不足时，求原图内最大的3:4窗口，通过可行区域约束保留全部人脸关键点，并尽量贴近目标眉心位置；无法保留人脸时回退仅扶正。其余图片空间不足时仍仅扶正、去除旋转空角。参考图与参数由`scripts/calibrate_hairline_framing.py`生成。公开接口为兼容旧调用默认关闭预处理，`normalize_image=true`开启；规则页显式开启，对比调试页默认开启。返回当前底图、原图及双向坐标矩阵。
|
||||
- **目标高度**：`上庭=(中庭+下庭)/2`，用当前底图测量的未取整像素坐标（眉心9、鼻翼下缘94、下巴152）算目标中心。测量失败时用发际线管线同编号点计算并标注来源。
|
||||
- **模板定位**：原有502点/贴图生成形状，`hairline/height_rule.py` 将每种 middle 曲线沿面部向上方向平移、校准中心 Y，保留曲线形状与倾斜。此规则仅接口52使用，接口2保持原逻辑。
|
||||
- **生发**：移动后的预览曲线直接生成黑线和重绘遮罩，再送ComfyUI，避免预览与黑模板位置不一致。
|
||||
- **裁切叠加预览**：测试页新增“裁切发际线叠加”。`static/hairline_reference_box.js`共享同一个162/389/9/152参考框几何，按原图像素裁切middle透明图层，再叠加到处理后人像；四栏顺序为原图、middle、裁切发际线叠加、生发图。
|
||||
- **兼容**：high/low不再生成，图片与中心点字段保留为null。接口52始终返回`*_base64`，空图片保留字段并返回null。本次不修改网关路由、落盘、字段改名或IP策略。`face_measure`为当前底图测量；新增`height_rule`与各项`height_placement`说明设计目标与实际误差。
|
||||
- **测试**：规则页`static/test_interface52.html`，对比页`static/test_interface52_compare.html`；运行`scripts/compare_hairline_height.py`默认保存全部30张×男女各7型，共420组对比。每人一行，顺序为未裁切原图、单张旧规则图、全部新规则样式，支持位置对照、额头特写、同图叠线和上传新图。新图可叠加162/389两侧、底边经过9、高度为9→152距离70%的参考框。原图与叠图按需加载。
|
||||
|
||||
### 接口9 头发遮罩 `/api/v1/head/mask`（worker）
|
||||
- **做什么**：人像照 → 头发遮罩 + 全流程分步可视化。
|
||||
- **怎么实现**（`face_analysis/head_mask.py`）：MediaPipe 底部分割线（21/68/104/69/108/151/337/299/333/298/251 连线，21/251 水平延伸）→ 线以上为上半区 → BiSeNet 与 SegFormer 双路头发分割、逐列向下填充到分割线得闭合区域（头发+额头）→ 外缘朝 151 内缩 `erode_cm`(默认1.2cm，虹膜标定) → 最终遮罩。返回 `steps_common` + `bisenet`/`segformer` 两组各四张图。
|
||||
|
||||
### 接口10 头部外缘膨胀带 `/api/v1/head/band`（worker）
|
||||
- **做什么**：在接口9 基准遮罩基础上，沿头部外缘生成一条膨胀带遮罩。
|
||||
- **怎么实现**（`face_analysis/head_band.py`）：取基准遮罩外轮廓、去掉贴底分割线段 → 膨胀成总宽 `dilate_cm`(默认2cm) 的带子 → 裁到分割线以上。返回 `base_mask`/`contour`/`band_overlay`/`mask` 分步图。
|
||||
|
||||
### 接口11 发际线生发 `/api/v1/hairline/grow`（worker）—— 不含重绘
|
||||
- **做什么**：发际线偏高/头发稀少正脸图 + `hairline_id`（= change_hair `hair_id`，如 `chang_tuoyuan`）→ 压低发际线图 + 全步骤可视化。
|
||||
- **怎么实现**（`face_analysis/hairline_grow.py`，算法见 `发际线增强算法.md`）：① 接口9 遮罩（`seg_model` 选 bisenet/segformer）→ ② 调 change_hair 生成该发际线类型图（`swap_mode=ext_mask` 把遮罩传给 webui 精确重绘 / `as_is` 用其内部遮罩；`gen_backend` 可换 hairgrow 区域生发 inpaint）→ ③ 严格按遮罩贴回 → ④ 接缝融合 `blend_method`（multiband 默认 / seamless / two_stage / feather / alpha_gradient，可选 Reinhard `color_match`）。返回 `steps.{input,baseline,hair_mask,mask_overlay,mask,swap_raw,alpha,final}`。
|
||||
|
||||
### 接口12 发际线带重绘 `/api/v1/hairline/grow_v2[_final[_v2]]`（worker）
|
||||
- **做什么**：接口11 的下游重绘阶段——先跑接口11 拿 ④final，再取发际线重绘带（外推 `band_lo_mult×push`~`band_hi_mult×push`，默认 0.5×~1.5×）作遮罩，用 **Flux-2（ComfyUI，`0716add-hair-api.json`，reference-latent 保色）** 重绘，产出两版：`redraw_full`（整帧美颜+整帧重绘）与 `redraw_band`（band 内加发、band 外 final 结构 + `beauty_alpha` 融入全脸美颜）。
|
||||
- **三个变体**：`grow_v2` 全参数暴露（接口11 参数 + `comfyui_prompt`/`beauty_alpha`/`band_lo_mult`/`band_hi_mult`）；`grow_v2_final` / `grow_v2_final_v2` 精简生产版——仅图片 + `hairline_id`，其余固化 `_V2_FINAL_DEFAULTS`（push=0.8cm / two_stage / color_match=关 / beauty_alpha=0.6 等），两者后端计算完全一致（同一次输出含 A/B 两版），差异只是测试页展示哪版。接口2 女性 1..5 的生发图即复用此管线。
|
||||
- ⚠️ 依赖 ComfyUI(:8188)；重绘失败在 `data.redraw.c_error` 报告。
|
||||
|
||||
### 接口99 人脸特殊点 `/api/v1/face/special-points`（**网关本机**，worker 保留同路径备份）
|
||||
- **做什么**：人脸照 → 60 个固定编号 MediaPipe 关键点（6 组：eyebrow/eye/mouth/nose_midline/cheek/face_edge）+ 5 个发际线特殊点，原图像素坐标 JSON（字段 `<组名>_<点ID>`，如 `eyebrow_107`）。
|
||||
- **怎么实现**（`face_analysis/special_points.py`）：MediaPipe 单例取关键点；发际线点=接口1 方案B 的 BiSeNet 头发 mask 上沿指定关键点方向（如 151→10）射线步进取首个头发像素，双射线的（right/right_right/left/left_left）取中点。**纯 CPU 可跑**（BiSeNet 单核 <1s），网关直接 import 本机实现（`_if99_lock` 单路排队）；worker 侧保留同路径接口供测试页直连。部署依赖清单见 `网关待改动.md` §9。
|
||||
- 测试页：`static/test_interface99.html`（canvas 可视化 + JSON，worker 本机直连）。
|
||||
|
||||
---
|
||||
|
||||
## 3. 部署 / 环境要点
|
||||
|
||||
**worker（GPU 机）**
|
||||
- Python **3.12**（系统 3.13 无 mediapipe/torch wheel）；venv 在 `./venv`，依赖 `requirements.txt`。
|
||||
- `numpy<2`(1.26.4)，`scikit-image==0.24.0`（**别升 0.25+，会顶 numpy≥2 顶崩 mediapipe**）。
|
||||
- ⚠️ 本机 **RTX 5090(sm_120)**，pinned `torch 2.2.2(cu121)` 只到 sm_90 → GPU 算子报 "no kernel image"，
|
||||
代码已自动**回退 CPU**（BiSeNet/SegFormer CPU 推理可用）。要用 5090 GPU 需换 torch cu128(≥2.7)。
|
||||
**worker（GPU 机，本机 = gpu_worker 角色）**
|
||||
- 启动使用`./run_worker.sh`或`hair-worker.service`，均显式传`--no-proxy-headers`以保留直连来源IP。部署时复制`ip_access.example.json`为`ip_access.json`并核对网关IP；运行配置不入Git，改动即时生效。旧启动方式也应补上此参数，详见[`接口IP白名单.md`](接口IP白名单.md)。
|
||||
- Python **3.10.12**（本机 venv 在 `./venv`）；`numpy<2`(1.26.4)，`scikit-image==0.24.0`
|
||||
（**别升 0.25+，会顶 numpy≥2 顶崩 mediapipe**）。
|
||||
- 本机 **RTX 4090**：venv 实装 `torch 2.7.1+cu128`，CUDA 可用（BiSeNet/SegFormer GPU 推理）。
|
||||
`requirements.txt` 里 pinned 的 `torch 2.2.2(cu121)` 及 5090 注释是旧机（sm_120 无 cu121 kernel、
|
||||
自动回退 CPU）的记录，本机以 venv 实际版本为准。
|
||||
- 模型权重/字体见 [`../OFFLINE_ASSETS.md`](../OFFLINE_ASSETS.md)；BiSeNet/SegFormer/face_landmarker.task 本地。
|
||||
- 生发接口依赖本机 **ComfyUI(8188)**（Flux-2，它自带支持 5090 的 torch）；worker 只调其 HTTP API，不跑 Flux。
|
||||
ComfyUI 开了 **HTTP Basic Auth**（user `admin` + 密码）；密码放 `password.txt`(不入 git) /
|
||||
`worker_config.json.comfyui_password` / 环境变量 `COMFYUI_PASSWORD`，URL 用 `COMFYUI_URL`。
|
||||
- `worker_config.json`(不入 git)：`accept_passwords`(鉴权) + 鉴权头 `X-Internal-Token`。
|
||||
- 生发接口依赖本机 **ComfyUI(8188)**（Flux-2，systemd `comfyui.service`，`--cache-classic --fast`）；
|
||||
worker 只调其 HTTP API，不跑 Flux。ComfyUI 开了 **HTTP Basic Auth**（user `admin` + 密码）；
|
||||
密码放 `password.txt`(不入 git) / `worker_config.json.comfyui_password` / 环境变量 `COMFYUI_PASSWORD`，
|
||||
URL 用 `COMFYUI_URL`。默认模型 flux-2-klein-9b Q4_K_M GGUF（接口2 `flux_model` 参数可切）。
|
||||
- 换发型依赖本机 **change_hair**（`/home/ubuntu/change_hair`，三进程 webui:57860(sdwebui) →
|
||||
photo_service:32678(py310/kohya) → hair_service_sd:8801(my_hair)，`start_all.sh start|stop|status`，
|
||||
也有 systemd `change_hair-{webui,photo,hair}.service`）。不跑时接口2女1..5、接口11/12、
|
||||
swapHair/hairColor 代理不可用。
|
||||
- `worker_config.json`(不入 git)：`accept_passwords`(鉴权) + `comfyui_password`；鉴权头 `X-Internal-Token`。
|
||||
- systemd 单元：`hair-worker.service`(8187) / `comfyui.service`(8188)，仓库根有副本；手动启停用
|
||||
`./run_worker.sh` / `./stop_worker.sh`。
|
||||
|
||||
**网关机**
|
||||
- 很轻：FastAPI/uvicorn/httpx + **接口4 的 `volcengine-python-sdk[ark]`**（或直接 httpx 调，OpenAI 兼容）。
|
||||
|
||||
@@ -0,0 +1,120 @@
|
||||
# 接口52上传图片裁切与扶正规划
|
||||
|
||||
状态：2026-11-04 在原3L规则上将人物显示缩小约10%，裁切高度改为(3÷0.9)L≈3.3333L、宽度2.5L，已接入接口52与对比页。参考图先扶正后重新标定，以下旧预检数值仅为历史记录，当前标准以 `hairline/reference/framing_profile.json` 为准。
|
||||
|
||||
## 接口拆分
|
||||
|
||||
- 接口5已按11月2日前最新提交`a3fddbb`恢复：`POST /api/v1/hairline/generate`，返回middle/high/low三档，测试页`/static/test_interface5.html`。
|
||||
- 当前新规则完整迁至接口52：`POST /api/v1/hairline/generate-52`，测试页`/static/test_interface52.html`，30案例对比`/static/test_interface52_compare.html`。接口52保留迁移前所有参数和返回字段，页面显式开启`normalize_image=true`。
|
||||
- 原`test_interface5_compare.html`书签跳转至52号对比页；5号与52号的worker处理路由和生成函数独立；52号仅部署在GPU worker，本次不新增网关转发。
|
||||
|
||||
## 已确定的要求
|
||||
|
||||
- 仅用于接口52与当前旧/新规则对比页。
|
||||
- 先扶正：变换后的9号点与152号点保持同一X坐标，9在上、152在下。
|
||||
- 原图空间够时按目标人脸比例和位置裁切；标准窗口不足时，偏长图片在保留人脸的前提下裁为3:4，其余仍仅扶正。3:4无法保留完整人脸时不强裁。
|
||||
- 输出保留实际像素尺寸，不为了凑参考图尺寸而放大小图。
|
||||
- 当前展示：全部30个案例，每个案例一行，未裁切原图放最前，旧规则图仅一张，新规则男女各7型横排，人物图片完整显示。选择新样式时旧图同步对应形状；失败案例仍保留原图并显示原因。
|
||||
|
||||
## 已扶正标准（当前生效）
|
||||
|
||||
- 标准图：`static/framing_reference.png`，786×1048，3:4。
|
||||
- 两点X均为385.18783691；9号Y为360.65437127，152号Y为725.82510480。
|
||||
- 相对位置：9号点 x=49.00608612%，y=34.41358504%。
|
||||
- 当前裁切高/L=3÷0.9≈3.3333、宽/L=2.5（3:4），L为9→152长度。参考图自身测得的宽/L=2.15241783589、高/L=2.86989044785作为原始标定记录保留；眉心在画面中的位置比例继续使用参考图数值。
|
||||
- 校准旋转约0.25962484°，变换后横向误差约5.7e-14像素。参数取自扶正并去空角后的图，不再使用未扶正原图的比例。
|
||||
- 重标定脚本：`./venv/bin/python scripts/calibrate_hairline_framing.py <参考图路径>`。
|
||||
- 使用同一套原始检测点作刚体变换与后续测量，避免二次检测抖动破坏轴线竖直。
|
||||
- 公开接口新增`normalize_image`默认false，旧请求保持原图尺寸和坐标；显式true开启预处理。规则页主动传true，对比调试页默认开启。响应明确返回`coordinate_space`、当前底图和坐标变换。
|
||||
|
||||
## 原始参考构图（旧测量）
|
||||
|
||||
[用户指定参考图](https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/misc/20261103_user.jpg)
|
||||
|
||||
测量采用当前发际线管线的MediaPipe FaceLandmarker：
|
||||
|
||||
| 指标 | 测量值 |
|
||||
|---|---|
|
||||
| 画面 | 792×1056，3:4 |
|
||||
| 9号点 | (388.93, 364.62) |
|
||||
| 152号点 | (387.27, 729.79) |
|
||||
| 9→152长度L | 365.17像素 |
|
||||
| 面部轴线距竖直方向 | 约0.26° |
|
||||
| 9号点画面相对位置 | x约49.1%，y约34.5% |
|
||||
| 152号点画面相对高度 | y约69.1% |
|
||||
| 画面宽度/L | 约2.16885 |
|
||||
| 画面高度/L | 约2.89180 |
|
||||
|
||||
参考图接近竖直。以9号点为定位锚点，用9→152长度作为缩放无关的构图标尺。这里的L仅为眉心到下巴的距离，不是头顶到下巴的全头高度。
|
||||
|
||||
## 实施流程
|
||||
|
||||
1. 读取上传原图、处理EXIF方向，再检测人脸关键点。
|
||||
2. 用9→152方向计算二维旋转，使转换后两点竖直。不改变脸部比例。
|
||||
3. 按本人的L算目标裁切尺寸：高(3÷0.9)L、宽2.5L；令9号点位于裁切画面约(49.0061%,34.4136%)处。高度取最接近的4的倍数，宽=高×3/4，不改变原图像素尺度。
|
||||
4. 将裁切窗口四角反算回原图，四角均在有效图片区域内才执行标准构图。采用一次仿射变换合并旋转与裁切，降低重复插值。
|
||||
5. 标准窗口空间不足时，偏长图片采用原图内最大的3:4有效窗口，约束所有人脸关键点在框内，并尽量靠近目标眉心位置。若无法保留完整人脸则仅扶正并给出原因。其余图片仍仅扶正，最小去除旋转空角。
|
||||
6. 后续测量、分割、旧/新发际线和70%参考框使用同一个处理后底图与坐标系。保留原图及原图/处理后坐标转换关系，防止叠图错位。
|
||||
7. 页面展示原图、处理后图、旋转角、是否标准裁切、跳过原因与前后尺寸。旧/新规则对比使用同一处理后底图，避免构图变化混入高度规则差异。
|
||||
|
||||
## 当前：长图与矩形发际线预览
|
||||
|
||||
- 长图即使放不下标准人脸占比窗口，也尝试在原图有效区域裁出最大的3:4窗口；通过可行区域约束保留468个人脸关键点，并尽量贴近参考眉心位置。无法完整保留人脸时回退仅扶正，响应说明原因。
|
||||
- 案例7从1080×1920裁为1062×1416，保持9→152竖直。长图7、20、21、22、50、51的男女各7型已更新，84组均成功；对比页30张、378组结果统一使用当前规则。
|
||||
- 规则页展示原图、middle、裁切发际线叠加、生发图。新增预览使用共享矩形几何裁切透明发际线，矩形外清空透明，底图仍是完整处理后人像。
|
||||
|
||||
## 人物缩小10%的验证（长图规则加入前）
|
||||
|
||||
- 裁切高=(3÷0.9)L、宽=2.5L，使同样展示尺寸下的人物线性尺寸约为原3L规则的90%。
|
||||
- 案例1由828×1104改为918×1224，人脸相对大小约90.20%，差异来自原像素窗口取整；底图、发际线和实际生发图均为918×1224。
|
||||
- 对比页保留全部30张。重新计算了1、3、42、43、44、45；其余48组结果验证新窗口仍超出有效范围，且只扶正后的矩阵和尺寸完全一致，复用原定位图并更新裁切参数。
|
||||
- 当前生成378组对比，43、44、45的男女组仍显示原图及定位失败原因。布局、眉心比例和9→152扶正方向保持一致。
|
||||
|
||||
## 历史：3L规则的五个案例验证
|
||||
|
||||
2026-11-04 按3L规则重跑男女各7型，共70组，失败0组。保留原始像素，不缩放人脸；实际尺寸如下。
|
||||
|
||||
| 案例 | 原尺寸 | 处理后尺寸 | 实际处理 |
|
||||
|---|---|---|---|
|
||||
| 1 | 1088×1406 | 828×1104 | 按3L规则裁切 |
|
||||
| 2 | 1088×1406 | 1042×1346 | 空间不足，仅扶正并去除旋转空角 |
|
||||
| 3 | 1088×1406 | 810×1080 | 按3L规则裁切 |
|
||||
| 4 | 1088×1406 | 1042×1346 | 空间不足，仅扶正并去除旋转空角 |
|
||||
| 5 | 1088×1367 | 1071×1346 | 空间不足，仅扶正并去除旋转空角 |
|
||||
|
||||
## 历史几何预检（未扶正参考图）
|
||||
|
||||
以下为修改前的预检记录，当前生效尺寸见上表。
|
||||
|
||||
| 案例 | 原尺寸 | 所需裁切尺寸（约） | 预计处理 |
|
||||
|---|---|---|---|
|
||||
| 1 | 1088×1406 | 797×1063 | 可按参考构图裁切并扶正 |
|
||||
| 2 | 1088×1406 | 1442×1923 | 空间不足，仅扶正 |
|
||||
| 3 | 1088×1406 | 782×1042 | 可按参考构图裁切并扶正 |
|
||||
| 4 | 1088×1406 | 1465×1953 | 空间不足，仅扶正 |
|
||||
| 5 | 1088×1367 | 1463×1950 | 空间不足，仅扶正 |
|
||||
|
||||
## 历史：3L规则的全部30张验证
|
||||
|
||||
- 按3L规则运行全部30张、男女各7型，共60次请求；55组请求成功，生成385组旧/新规则对比。27张男女均成功，案例43额外生成男性7型。
|
||||
- 案例43女性：模板在面部中轴处没有有效曲线；案例44、45男女：扶正后无法保留9/152参考点。三个案例均在对应行显示完整原图和未生成组合的原因，不隐藏失败案例。
|
||||
- 页面顺序为未裁切原图、旧规则、全部新规则；案例筛选包括失败图片，重跑按钮运行完整30张。
|
||||
- 页面筛选、旧图切换、末尾图片加载及移动端横向浏览均通过；3L调参后再次检查全部输出尺寸与混合成功/失败案例的筛选。
|
||||
|
||||
## 验收重点
|
||||
|
||||
- 处理后的9、152号点X差不超过1像素，保证9在152上方。
|
||||
- 标准裁切成功时，画面比例及两点位置接近参考构图，保留实际像素，不放大小图。
|
||||
- 空间不足的案例明确提示，仍完成扶正。
|
||||
- 旧/新规则叠图、参考框与底图坐标一致，原图坐标可通过逆变换还原。
|
||||
- 全部30案例均可查看，成功案例显示男女各7型，失败案例显示原图与原因；沿用每人一行、单张旧规则图的展示。
|
||||
|
||||
## 2026-11-04 提交前 Review 与验证
|
||||
|
||||
- 接口5的请求处理函数、三档PNG生成函数与`a3fddbb`逐项AST比较一致，测试页逐字节一致；新高度规则、扶正裁切和矩形叠加均在接口52。
|
||||
- 范围修正：接口52仅属于GPU worker，返回`*_base64`及null；网关路由、落盘、字段改名、mock worker均恢复`a3fddbb`，没有“仅52号改名”的特殊逻辑。测试确认旧网关行为保持原样且未暴露52号路由。
|
||||
- 范围修正后的回归检查：`./venv/bin/pytest tests/test_ip_access.py tests/test_gateway_hairline_routes.py tests/test_gateway_nullable_images.py tests/test_api.py -q`，54项通过。
|
||||
- GPU worker所有动态接口启用直连IP白名单，本机和网关`49.232.51.171`可访问；网关侧不启用IP限制。配置与部署见[接口IP白名单](接口IP白名单.md)。
|
||||
- 上一轮`./venv/bin/pytest -q`：113项通过、1项已有失败（撤回网关新增功能前的测试数）。`tests/test_pipeline.py::test_numeric_regression`期望全脸总高24.01cm，当前实测22.47cm；从未修改的`a3fddbb`导出代码、夹具后独立运行，同样失败。此次保留历史断言，未通过修改基线掩盖差异。
|
||||
- 浏览器实测：接口5返回middle/high/low且保持868×1166；接口52长图案例7返回1062×1416、`status=aspect_crop`，四栏图和裁切边框正常；对比页显示30个案例、378组结果，旧书签可跳转52，未出现页面脚本错误。定位验证均关闭ComfyUI生发，本轮未重复运行耗时的真实生发。
|
||||
- 批量结果与上传图位于被Git忽略的`static/annotations/`及`tests/output/`，不随提交分发；新环境执行`scripts/compare_hairline_height.py`重建。420是计划组合数，378为当前成功结果，失败组及原因保留在报告中。
|
||||
@@ -0,0 +1,29 @@
|
||||
# GPU worker 接口 IP白名单
|
||||
|
||||
仅GPU worker使用项目根目录的`ip_access.json`，不存在时仅允许本机。网关不启用IP限制。配置每次请求重新读取，修改后立即生效。现有`X-Internal-Token`鉴权继续有效。
|
||||
|
||||
```json
|
||||
{
|
||||
"allowed_ips": ["49.232.51.171"]
|
||||
}
|
||||
```
|
||||
|
||||
当前已将网关IP `49.232.51.171`加入白名单；本机回环地址和网卡自身地址自动允许，其余来源返回HTTP403、`code=1010`。支持IPv4、IPv6及CIDR，不自动允许整个内网。
|
||||
|
||||
worker的所有动态路径统一保护，包括5号、52号、其他业务接口、调试接口、代理接口、健康检查和文档接口。`/static/`下的测试页和图片可正常访问，但页面发出的API请求仍需通过IP限制。
|
||||
|
||||
启动GPU worker时使用`uvicorn ... --no-proxy-headers`，保留真实连接来源。`run_worker.sh`与`hair-worker.service`已配置该选项。来源被服务器提前改写、地址格式错误或白名单配置无效时拒绝访问。
|
||||
|
||||
判断依据是直接连接worker的IP，忽略`X-Forwarded-For`、`X-Real-IP`和`Forwarded`，这些请求头不能用于绕过限制。由网关转发的请求按网关IP判断，网关后面的最终用户无需加入worker白名单。网关保持原有鉴权和转发流程，无需部署此配置。
|
||||
|
||||
`ip_access.json`不入Git；配置格式参考`ip_access.example.json`。本机网卡地址在请求时自动获取。
|
||||
|
||||
## 部署与维护
|
||||
|
||||
首次部署复制`ip_access.example.json`为`ip_access.json`，示例已包含网关`49.232.51.171`；已有配置不覆盖。该操作仅在GPU worker执行，网关服务和启动参数保持原样。
|
||||
|
||||
修改`allowed_ips`后保存为有效JSON即可生效，无需重启；建议写入同目录临时文件再原子替换，避免编辑过程中出现半份JSON。文件不存在时仅允许本机，内容损坏时动态接口一律拒绝。更新程序时保留本机配置文件。
|
||||
|
||||
IP检查先于Token检查。白名单内请求仍需原来的`X-Internal-Token`；`/health`和`/api/v1/debug/*`等原本免Token的路径仍需通过IP检查。静态页可打开不代表该来源可以调用API。
|
||||
|
||||
验证命令：`./venv/bin/pytest tests/test_ip_access.py -q`，覆盖本机、精确白名单、CIDR、IPv6、转发头伪造、热更新、全接口拦截与网关不受限制。
|
||||
+402
-16
@@ -20,11 +20,14 @@
|
||||
| 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` |
|
||||
| 52 发际线新规则与裁切 | POST | `/api/v1/hairline/generate-52` |
|
||||
|
||||
---
|
||||
|
||||
## 通用约定
|
||||
|
||||
GPU worker的所有动态接口均启用IP限制：允许本机及`ip_access.json`白名单（当前为网关IP `49.232.51.171`），其他来源返回HTTP403、code=1010；静态页面与图片除外。按直接连接worker的IP判断，网关不启用此限制，已有鉴权继续有效。
|
||||
|
||||
以下约定适用于全部 5 个接口（最终以联调为准）。
|
||||
|
||||
### 请求
|
||||
@@ -35,7 +38,7 @@
|
||||
| 请求方式 | `POST` |
|
||||
| 图片传参方式 | 三选一，三种都支持：① `multipart/form-data` 上传文件、② 图片 URL、③ base64 字符串 |
|
||||
| 字符编码 | UTF-8 |
|
||||
| 坐标基准 | 所有返回的坐标均以**原图像素**为基准（原点为图片左上角，x 向右、y 向下） |
|
||||
| 坐标基准 | 默认以**原图像素**为基准；接口52开启预处理时使用响应中的处理后底图坐标（原点为左上角，x向右、y向下） |
|
||||
|
||||
图片传参字段（三者**严格互斥**，必须且只能传其一；传了 0 个或 ≥2 个均报错）：
|
||||
|
||||
@@ -81,12 +84,13 @@
|
||||
| 1001 | 无法识别人像 | 图片中未检测到人脸 |
|
||||
| 1002 | 人像分辨率过低 | 低于最低分辨率要求 |
|
||||
| 1003 | 角度问题，非正面照 | 非正面 / 角度过大 |
|
||||
| 1004 | gender 必填/非法 | 接口2/5 的 `gender` 缺失或非 `male`/`female` |
|
||||
| 1004 | gender 必填/非法 | 接口2/5/52/7 的 `gender` 缺失或非 `male`/`female` |
|
||||
| 1005 | 检测到多张人脸 | 默认仅支持单人，检测到 2 人或以上时返回 |
|
||||
| 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） |
|
||||
| 1010 | 来源IP不允许 | 仅GPU worker：直连IP不在本机/白名单内，或来源/白名单配置无效；HTTP403 |
|
||||
|
||||
---
|
||||
|
||||
@@ -109,6 +113,7 @@
|
||||
| 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 |
|
||||
| vertical_lines | object | 标注图竖线坐标，见下表。与标注 PNG 上的竖线同一套几何 |
|
||||
| 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 }` |
|
||||
|
||||
@@ -138,7 +143,28 @@
|
||||
| eye6 | number | 从左到右第 6 段宽度（cm）：右眼外角 → 右脸颊 |
|
||||
| eye7 | number \| null | 从左到右第 7 段宽度（cm）：右脸颊 → 人头最右（右耳外侧段）。该侧耳朵不可见时为 null |
|
||||
|
||||
> `eye1`~`eye7` 为从左到右共 7 段宽度，与标注图竖线一一对应。最左/最右端线取自耳朵分割外缘；某侧耳朵被头发或侧脸遮挡（不可见）时该侧端线省略，对应 `eye1` 或 `eye7` 为 `null`（键始终保留），实际有效段为 5 或 6 段。`eye3`/`eye5` 为左右眼宽、`eye4` 为两眼间距，与 `eye_width_cm` / `inter_eye_distance_cm` 语义一致。
|
||||
> `eye1`~`eye7` 为从左到右共 7 段宽度，与标注图竖线一一对应。最左/最右端线取自耳朵分割外缘；某侧耳朵被头发或侧脸遮挡（不可见）时该侧端线省略，对应 `eye1` 或 `eye7` 为 `null`（键始终保留），实际有效段为 5 或 6 段。`eye3`/`eye5` 为左右眼宽、`eye4` 为两眼间距，与 `eye_width_cm` / `inter_eye_distance_cm` 语义一致。`eye1`/`eye7` 的厘米数由 `vertical_lines` 里对应端点的 x 相减得到。
|
||||
|
||||
`vertical_lines`（竖线，原图像素，原点左上，x 向右，y 向下）：
|
||||
|
||||
| 字段 | 类型 | 说明 |
|
||||
|------|------|------|
|
||||
| y_top | int | 竖线共用上端。接口 1 在头顶之上，伸出 `max(6, round(短边 × 0.03))` 像素，可能略小于 0 |
|
||||
| y_bottom | int | 竖线共用下端。接口 1 在下巴尖之下，伸出同样像素，可能略大于图高 |
|
||||
| lines | array | 从左到右实际画出的竖线。每项 `{ "name": string, "x": int }` |
|
||||
|
||||
`lines[].name` 从左到右：
|
||||
|
||||
| name | 竖线 |
|
||||
|------|------|
|
||||
| left_head_edge | 人头最左（耳朵外缘）。该侧耳朵不可见时不出现，此时 `eye1` 为 `null` |
|
||||
| left_cheek | 左脸颊 |
|
||||
| left_eye_outer | 左眼外角 |
|
||||
| left_eye_inner | 左眼内角 |
|
||||
| right_eye_inner | 右眼内角 |
|
||||
| right_eye_outer | 右眼外角 |
|
||||
| right_cheek | 右脸颊 |
|
||||
| right_head_edge | 人头最右（耳朵外缘）。该侧耳朵不可见时不出现，此时 `eye7` 为 `null` |
|
||||
|
||||
### 标注图片（UI）规范
|
||||
|
||||
@@ -182,6 +208,19 @@
|
||||
"brow_center": { "x": 540, "y": 740 },
|
||||
"nose_bottom": { "x": 540, "y": 1050 },
|
||||
"chin_tip": { "x": 540, "y": 1360 }
|
||||
},
|
||||
"vertical_lines": {
|
||||
"y_top": 98, "y_bottom": 1410,
|
||||
"lines": [
|
||||
{ "name": "left_head_edge", "x": 120 },
|
||||
{ "name": "left_cheek", "x": 210 },
|
||||
{ "name": "left_eye_outer", "x": 310 },
|
||||
{ "name": "left_eye_inner", "x": 420 },
|
||||
{ "name": "right_eye_inner", "x": 560 },
|
||||
{ "name": "right_eye_outer", "x": 670 },
|
||||
{ "name": "right_cheek", "x": 780 },
|
||||
{ "name": "right_head_edge", "x": 890 }
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -213,6 +252,7 @@
|
||||
| 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 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） |
|
||||
| vertical_lines | object | 标注图竖线坐标。结构同接口 1，但 `y_top`/`y_bottom` 为发际线到下巴尖（不伸出），`lines` 只有中间 6 条，无 `left_head_edge` / `right_head_edge` |
|
||||
| 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 }` |
|
||||
|
||||
@@ -242,6 +282,17 @@
|
||||
"brow_center": { "x": 540, "y": 740 },
|
||||
"nose_bottom": { "x": 540, "y": 1050 },
|
||||
"chin_tip": { "x": 540, "y": 1360 }
|
||||
},
|
||||
"vertical_lines": {
|
||||
"y_top": 430, "y_bottom": 1360,
|
||||
"lines": [
|
||||
{ "name": "left_cheek", "x": 210 },
|
||||
{ "name": "left_eye_outer", "x": 310 },
|
||||
{ "name": "left_eye_inner", "x": 420 },
|
||||
{ "name": "right_eye_inner", "x": 560 },
|
||||
{ "name": "right_eye_outer", "x": 670 },
|
||||
{ "name": "right_cheek", "x": 780 }
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -267,8 +318,10 @@
|
||||
| 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`），最多不超过该性别的预设数。female：1=ellipse, 2=flower, 3=heart, 4=straight, 5=wave, 6=bigflower, 7=clasicalflower；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight, 5=heart, 6=Softpetal, 7=wave。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 |
|
||||
| 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。仅对原生发（male / female 6/7）管线生效 |
|
||||
| prompt | string | 否 | ComfyUI 提示词，默认「填充遮罩区域的头发，人物增加一点磨皮」，会替换工作流节点 60 的文本。所有分支生效（male / female 6/7 原生生发，female 1..5 换发型后的发际线带重绘；不进 change_hair）。注：非默认提示词会使 ComfyUI 的 CLIP 条件缓存失效，换词后首次请求明显变慢（实测 45s vs 命中缓存 8s），相同提示词的后续请求恢复正常 |
|
||||
| flux_model | string | 否 | Flux 模型文件名（如 `flux-2-klein-9b-Q5_K_M.gguf`）。缺省用工作流默认（9B-Q4_K_M）；`.gguf` 自动切 UnetLoaderGGUF 并同步配套 qwen CLIP |
|
||||
| redraw_max_side | int | 否 | female 1..5 重绘管线送模型前的长边上限像素。缺省=服务端默认（`REDRAW_MAX_SIDE`，当前 1024）；`0`=不缩图（原图直送，更慢） |
|
||||
|
||||
### 输出（data）
|
||||
|
||||
@@ -281,7 +334,8 @@
|
||||
| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`/`wave`（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**。
|
||||
> ⚠️ 生发图由本机 ComfyUI（Flux-2，端口 8188）生成，**一次请求生成指定发型的 1 张、同步返回**；
|
||||
> female 1..5 的生发图另依赖 change_hair 换发型服务（:8801）。
|
||||
> 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`。
|
||||
>
|
||||
@@ -446,6 +500,7 @@
|
||||
| four_courts | object | 四庭数据（顶/上/中/下庭 cm + 占比 ratios），结构同接口1 |
|
||||
| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + eye1~eye7 从左到右 7 段宽度），结构同接口1 |
|
||||
| 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 |
|
||||
| vertical_lines | object | 竖线坐标，结构同接口1 |
|
||||
| hairline_source | string | 发际线来源：`segmentation`（真实分割）/ `estimated`（比例估算）/ `corrected`（发际线贴近头顶、分割不可靠，已按 0.95×中庭反推修正） |
|
||||
| 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 }` |
|
||||
@@ -515,16 +570,347 @@
|
||||
|
||||
---
|
||||
|
||||
## 接口 52：发际线 PNG 生成接口
|
||||
|
||||
**说明**：入参同接口2（先选性别、再多选发型）。对每个选中发型返回一张 `middle` 设计叠图与生发图。**目标上庭 =（中庭 + 下庭）÷2**，从眉心向上定位；所有形状的中点对齐同一目标。`high` / `low` 已停用，旧字段保留为 `null`。
|
||||
|
||||
**请求**：`POST /api/v1/hairline/generate-52`（仅GPU worker，端口8187；本次不在网关新增路由）
|
||||
|
||||
**IP限制**：GPU worker的所有动态接口统一仅允许本机回环地址、本机网卡地址及白名单；目前白名单含网关IP `49.232.51.171`，其他直连来源需加入`ip_access.json`的`allowed_ips`。该文件支持单个IP与CIDR，修改后立即生效；拒绝时返回HTTP403、code=1010。网关不启用此限制，已有鉴权仍保留。详见[接口IP白名单](接口IP白名单.md)。
|
||||
|
||||
### 图片扶正与标准构图
|
||||
|
||||
显式传`normalize_image=true`时，先扶正9→152，再按**高(3÷0.9)×L≈3.3333×L、宽2.5×L（3:4）**裁切，L为9→152的直线像素距离。相对原3L规则，裁切框宽高均扩大约11.11%，同样显示尺寸下人物缩小约10%。高度取最接近的4的倍数，宽度取高度的3/4。眉心定位沿用已扶正参考图：9号点相对位置约(49.0061%,34.4136%)。参考图仍为786×1048，两点X均为385.18784。
|
||||
|
||||
目标窗口完全在原图有效区域内才进行标准裁切。标准窗口不足时，偏长图片（宽/高<3/4）采用原图有效范围内最大的3:4窗口，尽量贴近眉心目标位置并保留完整人脸，`status=aspect_crop`；人脸放不进3:4窗口时回退仅扶正并返回原因。其余图片空间不足时仍仅扶正、去除旋转空角。不放大小图。**所有叠图与坐标使用响应中的处理后底图 `input_image_*`**，调用方应使用该底图叠加；`normalize_image=false` 可关闭此预处理。
|
||||
|
||||
`framing`包含处理前后尺寸、旋转角、是否标准裁切、跳过原因、`source_to_processed`/`processed_to_source`仿射矩阵。`status`区分`reference_crop`（标准窗口）、`aspect_crop`（长图3:4裁切）和`rotation_only`（仅扶正）；`standard_crop=false`时仍可能有`aspect_crop=true`。`aspect_crop_skip_reason`说明长图无法保留人脸时的回退原因。`original_image_*`保留EXIF方向修正后的上传图，原图坐标可通过逆矩阵还原。
|
||||
|
||||
### 输入
|
||||
|
||||
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`。决定发型集合（female 7 / male 7）。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, 6=bigflower, 7=clasicalflower；male：1=ellipse, 2=inverse_arc, 3=m, 4=straight, 5=heart, 6=Softpetal, 7=wave。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「填充遮罩区域的头发，人物增加一点磨皮」，会替换工作流节点 60 的文本 |
|
||||
| normalize_image | bool | 否 | 新增可选开关，默认false：保留原图尺寸与坐标。传true开启扶正和裁切；规则测试页显式传true，对比调试页默认开启 |
|
||||
| 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`，仅返回设计叠图、中心点与高度规则数据，可大幅降低耗时 |
|
||||
|
||||
> 生发划线与遮罩直接取新位置的 middle 曲线；旧规则的头发边界与模板用于保留形状和倾斜，不决定新中心高度。计算使用未取整的像素高度，与四庭测量采用同一 Y 方向；测量主流程失败时用发际线管线的同编号关键点计算，`measurement_source` 标明来源。目标越出图片或无法对齐时返回 1007，不静默回退旧规则。
|
||||
|
||||
### 高度规则与对比
|
||||
|
||||
- **参数与字段**：原有8个请求参数、响应外层结构和所有旧返回字段均保留。`use_mask=true`、默认prompt、`generate_grow_image=true`保持原值。新功能只新增可选参数和返回字段；旧请求未传`normalize_image`时保留原图坐标，high/low按已约定保留null，worker始终返回`*_base64`，不负责保存图片、生成公网URL或改名；网关保持原样。
|
||||
- `height_rule`：`middle_height_px`、`lower_height_px`、`target_upper_height_px`、`target_upper_height_cm`（可空）、`target_center`、`brow_center`、`nose_bottom`、`chin_tip`、`measurement_source`。
|
||||
- 每项 `height_placement`：`old_center`、`new_center`、`shift_x_px`、`shift_y_px`（正=下移）、`target_error_px`。浮点坐标保留精度，旧中心点字段继续返回取整像素。
|
||||
- `face_measure` 为处理后底图的实际测量值，未被设计目标覆盖。
|
||||
- `guide_landmarks`：9、150、152、162、251、389 号 MediaPipe 点的图片像素坐标，供对比页绘制参考框。参考框左/右边分别经过162/389，底边经过9，高度为9→152距离的70%；两侧均固定平行9→152。
|
||||
- 规则页：`/static/test_interface52.html`；旧/新规则对比：`/static/test_interface52_compare.html`。
|
||||
- 规则页每个发型依次展示未裁切原图、middle、裁切发际线叠加、生发图。“裁切发际线叠加”由页面按`guide_landmarks`生成：在相同处理后底图上叠加矩形内的middle PNG，矩形外保持透明；左/右边过162/389，底边过9，高度0.7×9→152，左右边平行9→152。
|
||||
- 对比调试接口：`POST /api/v1/debug/hairline-height-compare`，图片三选一、gender、hair_style；默认只出旧/新曲线，可选 `generate_grow_image=true` 额外验证新规则生发。
|
||||
- 测试清单：`tests/fixtures/hairline_height_cases.json`（用户指定30张）；批量运行：`./venv/bin/python scripts/compare_hairline_height.py`，默认全部30张、男女各7型，共420组。每人一行，未裁切原图放在最前，后接一张可切换样式的旧规则图和全部新规则样式。可用 `--cases 1,2,3,4,5` 指定部分案例。
|
||||
|
||||
### 输出（data）
|
||||
|
||||
| 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 \| null | **首个选中发型**的 **middle 档**发际线曲线「面部中间点」坐标，当前底图像素：`{ "x": number, "y": number }` |
|
||||
| high_hairline_center_point | object \| null | 已停用，固定 `null` |
|
||||
| low_hairline_center_point | object \| null | 已停用，固定 `null` |
|
||||
| input_image_base64 | string | 当前底图的JPEG base64，发际线叠图、参考点和测量值均使用该图的坐标系 |
|
||||
| original_image_base64 | string | EXIF方向修正后的上传原图JPEG base64 |
|
||||
| width / height | int | 当前底图的实际像素宽 / 高 |
|
||||
| coordinate_space | string | 开启预处理时为 `processed_image`，关闭时为 `original_image` |
|
||||
| framing | object \| null | 裁切状态、旋转角、前后尺寸、轴线误差及双向仿射矩阵；关闭预处理时为 `null` |
|
||||
| height_rule | object | 中庭/下庭、目标上庭高度、目标中心及测量来源，详见“高度规则与对比” |
|
||||
| guide_landmarks | object | 9、150、152、162、251、389 号点的当前底图像素坐标；参考框实际使用9、152、162、389 |
|
||||
| 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：
|
||||
|
||||
| 字段 | 类型 | 说明 |
|
||||
|------|------|------|
|
||||
| hairline_type | string | 发际线类型 key：`ellipse`/`flower`/`heart`/`straight`/`wave`/`bigflower`/`clasicalflower`（female），`ellipse`/`m`/`straight`/`inverse_arc`/`heart`/`Softpetal`/`wave`（male） |
|
||||
| image_middle_base64 | string | middle 档发际线曲线**透明 PNG** base64（仅曲线，透明底，**不含人物**，需叠加 `input_image_base64` 显示） |
|
||||
| image_high_base64 | null | 已停用，保留字段为 `null` |
|
||||
| image_low_base64 | null | 已停用，保留字段为 `null` |
|
||||
| grown_image_base64 | string \| null | **生发后图片** JPEG base64（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） |
|
||||
| height_placement | object | 旧/新中心、XY移动距离、目标误差，均为未取整的像素值 |
|
||||
|
||||
> 上表为GPU worker的实际响应字段；所有图片使用base64，空图片保留原字段并返回null。网关的落盘与URL改写属于网关职责，本次不修改。
|
||||
>
|
||||
> 前端将base64转为data URI后显示。middle透明图叠加到`input_image_base64`，生发图直接显示。GPU worker本机测试页为`http://localhost:8187/static/test_interface52.html`。
|
||||
|
||||
`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）：
|
||||
|
||||
| 字段 | 类型 | 说明 |
|
||||
|------|------|------|
|
||||
| face_total_height_cm | number | 全脸总高度（cm）= 四庭之和 |
|
||||
| four_courts | object | 四庭数据（顶/上/中/下庭 cm + 占比 ratios），结构同接口1 |
|
||||
| seven_eyes | object | 七眼数据（眼宽/脸宽/两眼间距 cm + 占比 ratios + eye1~eye7 从左到右 7 段宽度），结构同接口1 |
|
||||
| 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 |
|
||||
| vertical_lines | object | 竖线坐标，结构同接口1 |
|
||||
| hairline_source | string | 发际线来源：`segmentation`（真实分割）/ `estimated`（比例估算）/ `corrected`（发际线贴近头顶、分割不可靠，已按 0.95×中庭反推修正） |
|
||||
| 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），当前底图像素：`{ "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），当前底图像素：`{ "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。
|
||||
|
||||
### GPU worker响应结构示例（normalize_image=false）
|
||||
|
||||
此例未开启裁切，坐标基于原图；`face_measure=null`时仍可按发际线关键点计算目标。图片内容以缩略base64字符串示意，空图片字段返回null，字段名不变。
|
||||
|
||||
```json
|
||||
{
|
||||
"code": 0,
|
||||
"message": "success",
|
||||
"request_id": "mock-request-id",
|
||||
"data": {
|
||||
"hairline_images": [
|
||||
{
|
||||
"hairline_type": "ellipse",
|
||||
"image_middle_base64": "iVBORw0KGgo...",
|
||||
"image_high_base64": null,
|
||||
"image_low_base64": null,
|
||||
"grown_image_base64": "/9j/4AAQ...",
|
||||
"order": 1,
|
||||
"height_placement": {
|
||||
"old_center": {"x": 540.0, "y": 450.0},
|
||||
"new_center": {"x": 540.0, "y": 430.0},
|
||||
"shift_x_px": 0.0, "shift_y_px": -20.0, "target_error_px": 0.0
|
||||
}
|
||||
}
|
||||
],
|
||||
"best_hairline_center_point": {"x": 540, "y": 430},
|
||||
"high_hairline_center_point": null,
|
||||
"low_hairline_center_point": null,
|
||||
"input_image_base64": "/9j/4AAQ...",
|
||||
"original_image_base64": "/9j/4AAQ...",
|
||||
"width": 1080, "height": 1440,
|
||||
"coordinate_space": "original_image",
|
||||
"framing": null,
|
||||
"height_rule": {
|
||||
"name": "court_average", "formula": "上庭 = (中庭 + 下庭) / 2",
|
||||
"measurement_source": "hairline_landmarks",
|
||||
"brow_center": {"x": 540.0, "y": 740.0},
|
||||
"nose_bottom": {"x": 540.0, "y": 1050.0},
|
||||
"chin_tip": {"x": 540.0, "y": 1360.0},
|
||||
"middle_height_px": 310.0, "lower_height_px": 310.0,
|
||||
"target_upper_height_px": 310.0, "target_upper_height_cm": null,
|
||||
"target_center": {"x": 540.0, "y": 430.0}
|
||||
},
|
||||
"guide_landmarks": {
|
||||
"9": {"x": 540.0, "y": 740.0}, "150": {"x": 470.0, "y": 1340.0},
|
||||
"152": {"x": 540.0, "y": 1360.0}, "162": {"x": 300.0, "y": 680.0},
|
||||
"251": {"x": 770.0, "y": 630.0}, "389": {"x": 780.0, "y": 680.0}
|
||||
},
|
||||
"face_measure": null
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
开启`normalize_image=true`后，`coordinate_space=processed_image`，`framing`包含前述裁切状态和坐标变换，所有发际线图片须叠加到`input_image_base64`。原始图片仅用于展示和坐标回溯。
|
||||
|
||||
---
|
||||
|
||||
## 接口 9：头发遮罩生成接口
|
||||
|
||||
**说明**：输入一张含人头的照片，生成头发遮罩，并返回**每一步的可视化图**（供对比调试）。
|
||||
|
||||
**请求**：`POST /api/v1/head/mask`
|
||||
|
||||
### 输入
|
||||
|
||||
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 |
|
||||
|------|------|------|------|
|
||||
| erode_cm | float | 否 | 外缘朝中心（鼻梁 151 点）内缩的距离（厘米），默认 1.2 |
|
||||
|
||||
### 处理流程
|
||||
|
||||
1. MediaPipe 关键点 → 底部分割线（关键点 21,68,104,69,108,151,337,299,333,298,251 的连线，21 水平延伸到最左、251 延伸到最右）。
|
||||
2. 分割线以上为「上半区」。
|
||||
3. 头发分割（**BiSeNet** 与 **SegFormer** 两套）；从每列最顶端头发向下填充到分割线，得到**含额头**的闭合区域（头发+额头，不割断）。
|
||||
4. 外缘朝中心点 151 内缩 `erode_cm`（虹膜标定换算像素）、底线不动 → 最终遮罩。
|
||||
|
||||
### 输出（data）
|
||||
|
||||
| 字段 | 类型 | 说明 |
|
||||
|------|------|------|
|
||||
| px_per_cm | float | 虹膜标定出的像素/厘米 |
|
||||
| erode_cm / erode_px | float / int | 内缩距离（厘米 / 像素） |
|
||||
| image_size | object | `{width, height}` 原图尺寸 |
|
||||
| steps_common | object | `landmarks_baseline_*`（关键点/分割线）、`upper_region_*`（上半区） |
|
||||
| bisenet / segformer | object | 两套分割模型各自的结果：`hair_mask_*`（原始头发分割）、`closed_region_*`（含额头闭合区域）、`final_overlay_*`（最终遮罩叠加可视化）、`mask_*`（最终遮罩 PNG）及 `hair_pixels`/`closed_pixels`/`mask_pixels` 像素计数 |
|
||||
|
||||
> worker 返回 `*_base64`（data URI），经网关改写为 `*_url`。
|
||||
|
||||
---
|
||||
|
||||
## 接口 10：头部外缘膨胀带遮罩接口
|
||||
|
||||
**说明**：先和接口9 一样得到内缩后的基准遮罩，再沿头部外缘生成一条膨胀带遮罩。
|
||||
|
||||
**请求**：`POST /api/v1/head/band`
|
||||
|
||||
### 输入
|
||||
|
||||
| 参数 | 类型 | 必填 | 说明 |
|
||||
|------|------|------|------|
|
||||
| erode_cm | float | 否 | 基准遮罩外缘朝 151 内缩距离（厘米，同接口9），默认 1.2 |
|
||||
| dilate_cm | float | 否 | 外轮廓线膨胀后带子总宽（厘米），默认 2.0 |
|
||||
|
||||
### 处理流程
|
||||
|
||||
1. 取基准遮罩的**外轮廓线**，去掉贴着底部分割线的那一段（只留头发/头部外缘弧线）。
|
||||
2. 把外轮廓线膨胀成带子（总宽 `dilate_cm`；半径=总宽/2，虹膜标定换算像素）。
|
||||
3. 裁到分割线以上（不越过底线）。
|
||||
|
||||
### 输出（data）
|
||||
|
||||
结构同接口9，但每组结果为 `base_mask_*`（基准遮罩）/ `contour_*`（外轮廓线）/ `band_overlay_*`（带子叠加可视化）/ `mask_*`（最终带状遮罩）及 `base_pixels`/`band_pixels` 计数，另有 `dilate_cm`/`dilate_radius_px`。
|
||||
|
||||
---
|
||||
|
||||
## 接口 11：发际线生发接口（不含重绘）
|
||||
|
||||
**说明**：输入一张**发际线较高 / 头发稀少**的正脸图 + 发际线类型 ID，输出同一个人、同一发型、按该类型压低发际线后的图片，并返回**每一步可视化**。重绘版见接口12。
|
||||
|
||||
**请求**：`POST /api/v1/hairline/grow`
|
||||
|
||||
### 输入
|
||||
|
||||
| 参数 | 类型 | 必填 | 说明 |
|
||||
|------|------|------|------|
|
||||
| hairline_id | string | **是** | 发际线类型 ID（= change_hair `hair_id`）：`chang_tuoyuan` / `chang_huaban` / `chang_xinxing` / `chang_zhixian` / `chang_bolang` |
|
||||
| gen_backend | string | 否 | 生成后端：`swaphair`（换发型 LoRA，默认）｜`hairgrow`（区域生发 inpaint） |
|
||||
| hairgrow_strength | float | 否 | 区域生发强度（仅 hairgrow 后端），默认 0.75 |
|
||||
| is_hr | bool | 否 | 高清模式（换发型输出 1152×1536，否则 576×768），默认 false |
|
||||
| seg_model | string | 否 | 头发分割模型：`bisenet` ｜ `segformer`（默认） |
|
||||
| erode_cm | float | 否 | baseline 参考内缩距离（厘米），默认 0.6 |
|
||||
| hairline_push_cm | float | 否 | 发际线外推距离（厘米，往头发方向推进），默认 1.0 |
|
||||
| hairline_edge | string | 否 | 发际线提取方式：`column`（默认）｜`contour` |
|
||||
| swap_mode | string | 否 | 换发型取图模式：`ext_mask`（默认，把接口9 遮罩传给 webui 精确重绘）｜`as_is`（用内部遮罩，贴回时再裁） |
|
||||
| edge_erode_px | int | 否 | 贴图前遮罩内缩像素（防边缘露皮/光晕），默认 3 |
|
||||
| denoising_strength | float | 否 | 换发型 webui 重绘强度（越大生发越激进），默认 0.6 |
|
||||
| mb_levels | int | 否 | 多频段金字塔层数（2~6），默认 5 |
|
||||
| blend_method | string | 否 | 接缝融合：`multiband`（默认）｜`seamless`（泊松）｜`two_stage`（泊松→多频段）｜`feather`（高斯羽化）｜`alpha_gradient`（距离渐变） |
|
||||
| color_match | bool | 否 | 融合前 Reinhard 颜色迁移消除整体色差，默认 true |
|
||||
| color_match_strength | float | 否 | 颜色迁移强度（0~1），默认 1.0 |
|
||||
| mb_feather_px | int | 否 | 多频段最细层掩码轻羽化像素，默认 1 |
|
||||
| transition_band_px | int | 否 | keep-region 过渡带边距（-1=自动按层数 2^n），默认 -1 |
|
||||
| inpainting_fill | int | 否 | change_hair 服务端重绘填充：0=保留原图 ｜ 1=噪声（默认）｜ 2=纯色 ｜ 3=潜变量噪声 |
|
||||
| mask_blur | int | 否 | change_hair 服务端遮罩边缘模糊像素，默认 11 |
|
||||
| mask_dilate_scale | float | 否 | change_hair 服务端遮罩膨胀缩放（<1 收缩防越界），默认 1.0 |
|
||||
|
||||
### 输出（data）
|
||||
|
||||
`steps`：`input`（原图）/ `baseline`（底部分割线）/ `hair_mask`（头发分割）/ `mask_overlay`（遮罩叠加）/ `mask`（遮罩）/ `swap_raw`（换发型原始输出）/ `alpha`（融合权重）/ `final`（最终结果）各步图（`*_base64`，经网关改写为 `*_url`），另有 `timings_ms` 分步耗时。
|
||||
|
||||
> ⚠️ `swaphair` 后端依赖 change_hair（:8801）在跑；`hairgrow` 后端依赖 ComfyUI（:8188）。
|
||||
|
||||
---
|
||||
|
||||
## 接口 12：发际线带重绘接口
|
||||
|
||||
**说明**：接口11 的**下游重绘阶段**——内部先跑接口11 核心管线拿 ④ 接缝融合最终图（final），再取发际线重绘带（外推 `band_lo_mult×push` ~ `band_hi_mult×push` 之间、经 baseline 截断只留上部的带状区域）作遮罩，用 **Flux-2（ComfyUI，reference-latent 保色重绘）** 重绘。
|
||||
|
||||
**同时产出两版结果**：
|
||||
|
||||
- `redraw_full`：ComfyUI 整帧输出（全脸美颜 + 全脸重绘）。
|
||||
- `redraw_band`：加发只在发际线带内、band 外保留 final 结构并按 `beauty_alpha` 融入全脸美颜。
|
||||
|
||||
**请求**：`POST /api/v1/hairline/grow_v2`
|
||||
|
||||
### 输入
|
||||
|
||||
接口11 的全部参数（见上表）再叠加：
|
||||
|
||||
| 参数 | 类型 | 必填 | 说明 |
|
||||
|------|------|------|------|
|
||||
| comfyui_prompt | string | 否 | Flux-2 重绘提示词，缺省「填充遮罩区域的头发，人物增加一点磨皮」 |
|
||||
| beauty_alpha | float | 否 | redraw_band 版 band 外的全脸美颜融入强度（0=band 外纯用 final，1≈整帧版），默认 0.6 |
|
||||
| band_lo_mult | float | 否 | 重绘带外推倍率下限（相对 hairline_push_cm），默认 0.5 |
|
||||
| band_hi_mult | float | 否 | 重绘带外推倍率上限（相对 hairline_push_cm），默认 1.5 |
|
||||
|
||||
### 输出（data）
|
||||
|
||||
`steps`：`input` / `final`（重绘输入基底）/ `redraw_band_overlay`（重绘带可视化）/ `redraw_full`（A 整帧）/ `redraw_band`（B 局部+美颜）/ `redraw_c`（兼容旧字段，=redraw_full）；重绘异常在 `redraw.c_error` 报告。
|
||||
|
||||
### 精简变体（生产推荐）
|
||||
|
||||
| 路径 | 说明 |
|
||||
|------|------|
|
||||
| `POST /api/v1/hairline/grow_v2_final` | 仅需图片 + `hairline_id`，其余参数固化为调优默认值（push=0.8cm / two_stage / color_match=关 等），**最终取整帧重绘 `redraw_full`** |
|
||||
| `POST /api/v1/hairline/grow_v2_final_v2` | 参数同上，**最终取 B 局部加发+全脸美颜 `redraw_band`** |
|
||||
|
||||
> ⚠️ 依赖 ComfyUI（:8188）与 change_hair（:8801）均在跑。接口2 女性 1..5 的生发图即复用 `grow_v2_final` 管线。
|
||||
|
||||
---
|
||||
|
||||
## 接口 99：人脸特殊点检测接口
|
||||
|
||||
**说明**：输入一张人脸照片，返回一组**固定编号 MediaPipe Face Mesh 关键点**的像素坐标，外加 5 个**发际线特殊点**（射线与头发分割发际线的交点）。**生产路径：网关本机实现（纯 CPU，不转发 worker，同接口4 模式）**；worker 侧保留同路径接口供测试页直连。
|
||||
|
||||
**请求**：`POST /api/v1/face/special-points`
|
||||
|
||||
### 输入
|
||||
|
||||
图片参数见「通用约定 → 图片传参字段」，无专属参数。要求单人正面照（非正面返回 `1003`）。
|
||||
|
||||
### 输出（data）
|
||||
|
||||
`points`：字段名 = `<组英文名>_<点ID>`，值为原图像素坐标 `{x, y}`（int）；发际线点不可得（秃顶/分割失败）时为 null。
|
||||
|
||||
| 组 | 字段前缀 | 点 ID |
|
||||
|----|----------|-------|
|
||||
| 眉毛 | `eyebrow` | 107 66 105 63 70 336 296 334 293 300 |
|
||||
| 眼睛 | `eye` | 189 222 224 113 226 413 442 444 342 446 |
|
||||
| 嘴 | `mouth` | 0 37 40 67 91 84 17 405 375 409 269 |
|
||||
| 中线鼻 | `nose_midline` | 9 168 197 5 4 194 278 48 437 217 |
|
||||
| 面颊 | `cheek` | 101 117 123 187 205 50 330 346 352 280 425 |
|
||||
| 边线 | `face_edge` | 162 234 58 136 149 152 378 365 288 401 366 389 251 |
|
||||
|
||||
**发际线特殊点**（沿关键点 a→b 方向射线步进，首次进入 BiSeNet 头发 mask 处为交点；双射线取中点，仅一条命中返回该点）：
|
||||
|
||||
| 字段 | 计算 |
|
||||
|------|------|
|
||||
| `hair_line_center` | 151→10 方向交点 |
|
||||
| `hair_line_right` | mid(108→109 交点A, 69→67 交点B) |
|
||||
| `hair_line_right_right` | mid(104→103 交点A, 68→54 交点B) |
|
||||
| `hair_line_left` | mid(337→338 交点A, 299→297 交点B) |
|
||||
| `hair_line_left_left` | mid(333→332 交点A, 298→284 交点B) |
|
||||
|
||||
另有 `image_size`（宽高）与 `hairline_source`（`segmentation`=发际线点已算出 / `unavailable`=分割不可用，发际线点为 null）。
|
||||
|
||||
---
|
||||
|
||||
## 汇总：输入输出一览
|
||||
|
||||
| 接口 | 输入 | 主要输出 |
|
||||
|------|------|----------|
|
||||
| 1 四庭七眼测量 | 用户照片 | 标注 PNG（无人物）+ 四庭/七眼厘米数值与坐标 |
|
||||
| 6 四庭七眼测量 v2 | 用户照片 | 同接口1，复刻实现 |
|
||||
| 2 C 端生发 | 用户照片 | 生发后图片 + 指定发际线预览（单/多张） |
|
||||
| 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 |
|
||||
| 接口 | 路径 | 输入 | 主要输出 |
|
||||
|------|------|------|----------|
|
||||
| 1 四庭七眼测量 | `/api/v1/face/measure` | 用户照片 | 标注 PNG（无人物）+ 四庭/七眼厘米数值与坐标 |
|
||||
| 6 四庭七眼测量 v2 | `/api/v1/face/measure-v2` | 用户照片 | 同接口1，去顶庭变体 |
|
||||
| 2 C 端生发 | `/api/v1/hair/grow` | 用户照片 + gender + hair_style（多选） | 每发型预览 PNG + 生发图（female 1..5=换发型+重绘，其余=原生生发） |
|
||||
| 7 C 端生发 v2 | `/api/v1/hair/grow-v2` | 同接口2 | 同接口2（add_hair2 工作流，已弃用隐藏） |
|
||||
| 3 B 端生发 | `/api/v1/hair/grow-b` | 划线图片 | 检测到的发际线 + 生发后图片 |
|
||||
| 4 用户特征 | `/api/v1/face/features` | 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），网关本机实现 |
|
||||
| 5 发际线 PNG | `/api/v1/hairline/generate` | 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 |
|
||||
| 52 发际线新规则 | `/api/v1/hairline/generate-52` | 用户照片 + gender + hair_style（多选） | 每个选中发型的新规则 middle 设计叠图 + 生发图 + 目标中心坐标；high/low 旧字段为 null |
|
||||
| 9 头发遮罩 | `/api/v1/head/mask` | 用户照片 | 头发遮罩 + 分步可视化（BiSeNet/SegFormer 双路） |
|
||||
| 10 外缘膨胀带 | `/api/v1/head/band` | 用户照片 | 头部外缘带状遮罩 + 分步可视化 |
|
||||
| 11 发际线生发 | `/api/v1/hairline/grow` | 用户照片 + hairline_id | 压低发际线图 + 全步骤可视化（不含重绘） |
|
||||
| 12 发际线带重绘 | `/api/v1/hairline/grow_v2` | 同接口11 + 重绘带参数 | final 基底 + Flux-2 保色重绘（整帧/局部两版）；精简版 `grow_v2_final[_v2]` 仅需图片+hairline_id |
|
||||
| 99 人脸特殊点 | `/api/v1/face/special-points` | 用户照片 | 60 个固定编号关键点 + 5 个发际线特殊点像素坐标 JSON（**网关本机实现**，同接口4） |
|
||||
|
||||
> 另有 worker 本机工具路由（不在对外契约内）：`/api/swapHair/v1`、`/hairColor/v2`（CORS 代理 change_hair）、`/api/v1/redraw`（直接 ComfyUI 重绘）、`/api/v1/debug/*`。
|
||||
|
||||
---
|
||||
|
||||
@@ -532,9 +918,9 @@
|
||||
|
||||
- 图片传参：**文件 / URL / base64 三种都支持，严格互斥**（必须且只能传一个，base64 需带 `data:image/...;base64,` 前缀）。
|
||||
- 默认单人；检测到 2~3 人报错提示客户端；单文件 **≤ 1 MB**；格式仅 **JPG / PNG**。
|
||||
- 所有坐标基准：**原图像素**。
|
||||
- 坐标基准：默认**原图像素**；接口52开启预处理时使用响应中的**处理后底图像素**。
|
||||
- 接口 2：提供「美颜」开关 `beauty_enabled`。
|
||||
- 接口 3：需同时上传原图，需返回发际线类型。
|
||||
- 接口 3：仅传**划线图**（`marked_image_*` 三选一），需返回发际线类型（`custom`）。
|
||||
- 接口 4：`data` 返回**一个 JSON 字符串**，字段不固定，可随时调整。
|
||||
- 接口 5：发际线 PNG 数量 **N 不固定**，需按合适度**排序**。
|
||||
- 英文字段命名由本文档约定（见各接口表格）。
|
||||
|
||||
+99
-1
@@ -70,7 +70,7 @@ async def hair_grow_v2(request: Request):
|
||||
|------|------|------|------|
|
||||
| image_file / image_url / image_base64 | — | **三选一** | 用户正面照 |
|
||||
| gender | string | **是** | `male` / `female` |
|
||||
| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female: 1–5，male: 1–4 |
|
||||
| hair_style | string | **是** | 发型序号，**逗号分隔多选**（如 `1,2,3`）。female / male 均为 1–7 |
|
||||
| beauty_enabled | bool | 否 | 美颜开关（本期不生效） |
|
||||
| use_mask | bool | 否 | 默认 `true`，`false` 跳过遮罩 |
|
||||
| prompt | string | 否 | ComfyUI 提示词 |
|
||||
@@ -135,6 +135,104 @@ async def face_measure_v2(request: Request):
|
||||
|
||||
---
|
||||
|
||||
## 7. ✅【已完成】接口9/10 头部遮罩（`/api/v1/head/mask`、`/api/v1/head/band`）
|
||||
|
||||
`gateway/app.py` 已新增两条盲转发路由（同接口6 的写法）：
|
||||
|
||||
```python
|
||||
@app.post("/api/v1/head/mask", tags=["人脸分析"])
|
||||
@app.post("/api/v1/head/band", tags=["人脸分析"])
|
||||
```
|
||||
|
||||
- 入参：图片三选一 + `erode_cm`（接口10 另有 `dilate_cm`）。
|
||||
- 出参：`steps_common.*_base64` 与 `bisenet`/`segformer` 组内的 `*_base64` 字段——现有递归
|
||||
`rewrite_base64_to_url` 已覆盖（嵌套 dict/数组），无需额外改动。
|
||||
- 两个接口响应较慢（双分割模型跑两遍），注意 `request_timeout_seconds`。
|
||||
|
||||
---
|
||||
|
||||
## 8. 🔲【新增】接口11/12 发际线生发/带重绘（`/api/v1/hairline/grow*`）
|
||||
|
||||
worker 侧已上线 4 条路由，**网关尚无对应转发路由**（当前仅测试页直连 worker）。如需对外提供，参考接口9 加法：
|
||||
|
||||
```python
|
||||
@app.post("/api/v1/hairline/grow", tags=["生发"]) # 接口11
|
||||
@app.post("/api/v1/hairline/grow_v2", tags=["生发"]) # 接口12 全参数
|
||||
@app.post("/api/v1/hairline/grow_v2_final", tags=["生发"]) # 接口12 final 精简版
|
||||
@app.post("/api/v1/hairline/grow_v2_final_v2", tags=["生发"])
|
||||
```
|
||||
|
||||
- 入参：图片三选一 + `hairline_id`（接口12 final 两变体仅需这两个）。接口11/12 全参数版还有
|
||||
20+ 个调优 Form 参数，网关盲转发即可，无需逐个声明。
|
||||
- 出参：`data.steps.*_base64`（input/final/redraw_full/redraw_band 等各步图）→ 现有递归改写覆盖。
|
||||
- ⚠️ 依赖 worker 机的 change_hair(:8801) 与 ComfyUI(:8188) 都在跑；单请求 10s+，超时建议 ≥120s。
|
||||
- 契约细节见 [`接口文档.md`](接口文档.md) 接口11/12 章节。
|
||||
|
||||
---
|
||||
|
||||
## 9. ✅【新增】接口99 人脸特殊点（`/api/v1/face/special-points`）— **网关本机实现，不转发 worker**
|
||||
|
||||
**背景**：接口99（固定编号 MediaPipe 关键点 6 组 60 点 + 5 个发际线特殊点，返回像素坐标 JSON）
|
||||
**纯 CPU 可跑**（MediaPipe 本就 CPU；BiSeNet 分割单核实测 <1s）且调用频率低。
|
||||
按约定：**不用 GPU、低频的接口默认网关本机实现**，不占 worker 队列
|
||||
（worker 池 `per_worker_concurrency=1`，转发会排在生发任务后面等）。
|
||||
|
||||
**网关代码已在本仓库 `gateway/app.py` 改好**——与接口4 同款本机实现（显式 Form 参数 +
|
||||
惰性导入 + `run_in_threadpool` + `gw-` request_id），直接 import 同仓库的
|
||||
`face_analysis/special_points.detect_special_points`。**单路排队**：`_if99_lock` 全局互斥，
|
||||
同一时刻只跑一路推理（限制 CPU 峰值 + 规避 MediaPipe 单例线程安全问题），请求 FIFO 排队。
|
||||
|
||||
### 网关机部署前检查
|
||||
|
||||
1. **Python 3.10~3.12**（mediapipe 0.10.14 无 3.13 wheel）。
|
||||
2. **系统库**（无 GUI 服务器跑 opencv/mediapipe 必需，已装可跳过）：
|
||||
|
||||
```bash
|
||||
sudo apt install -y libgl1 libglib2.0-0 libegl1
|
||||
```
|
||||
|
||||
3. **安装依赖**（网关 venv，国内镜像）：
|
||||
|
||||
```bash
|
||||
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple \
|
||||
mediapipe==0.10.14 opencv-python==4.10.0.84 numpy==1.26.4
|
||||
|
||||
# 可选：BiSeNet 头发分割（发际线 5 点需要）。CPU 版 torch 仅 ~200MB：
|
||||
pip install torch==2.2.2 torchvision==0.17.2 --index-url https://download.pytorch.org/whl/cpu
|
||||
# 不装 torch 也能跑：60 个关键点正常返回，发际线 5 点为 null（hairline_source="unavailable"）
|
||||
# ⚠️ 网关无 GPU——务必用上面的 CPU 专用源装 torch；装默认 PyPI 版会带 2.5G 的 CUDA 包，浪费但也能跑（自动回退 CPU）
|
||||
```
|
||||
|
||||
4. **权重已入 git**，`git clone/pull` 自动到位：`face_analysis/weights/79999_iter.pth`（51MB）。
|
||||
无需手动下载任何模型文件。
|
||||
5. **无 GPU 已实测**：`CUDA_VISIBLE_DEVICES=""` 模拟验证通过（70/70 点，与 GPU 结果一致）；
|
||||
CPU 推理 16 核 0.18s/次，网关机核少时约 0.5~2s/次，低频可接受。
|
||||
|
||||
### 部署与冒烟
|
||||
|
||||
```bash
|
||||
cd /path/to/hair && git pull
|
||||
sudo systemctl restart hair-gateway # 或网关实际服务名（uvicorn gateway.app:app）
|
||||
|
||||
# 冒烟（multipart 传图；经网关无需 X-Internal-Token）
|
||||
curl -s -X POST http://127.0.0.1:8080/api/v1/face/special-points \
|
||||
-F "image_file=@/tmp/face.jpg" | python3 -m json.tool | head -30
|
||||
# 期望 code=0；data.points 含 eyebrow_107 等 60 个关键点 + hair_line_* 5 点
|
||||
# 缺依赖时返回 code=1007 且 message 提示"网关依赖未安装"
|
||||
```
|
||||
|
||||
### 注意
|
||||
|
||||
- **超时无需调大**：单路 CPU 推理 <1s；即使排队，低频下积压可忽略。排队本身无超时，
|
||||
调用方按需自带客户端超时。
|
||||
- **错误码透传契约**：1001（无人脸）/ 1003（非正面）/ 1007（参数/处理失败/缺依赖）/
|
||||
1008（图片格式/下载失败），HTTP 一律 200。
|
||||
- **worker 侧同路径接口保留**（`app.py`），供测试页 `static/test_interface99.html`
|
||||
直连调试（需 token）；生产路径是网关本机实现。
|
||||
- 契约详见 [`接口文档.md`](接口文档.md) 接口99 章节。
|
||||
|
||||
---
|
||||
|
||||
## 已经做好、无需再动的
|
||||
|
||||
- **接口4 在网关本机实现**（调豆包，不转发 worker）——已完成；config 里配 `ark`。
|
||||
|
||||
@@ -0,0 +1,234 @@
|
||||
# 网关摘除隔离期实施方案（方案 B）
|
||||
|
||||
> 目标读者：网关机器的部署/维护者。本文档给出基于 **main 分支** `gateway/` 代码的
|
||||
> 完整改动说明，可直接照做。**不需要改 worker 侧任何代码。**
|
||||
|
||||
## 1. 背景与事故复盘
|
||||
|
||||
2026-09-19 晚，GPU worker 机器（ubuntu3090_debug_4090 所在主机）磁盘写满，业务接口
|
||||
全部失败，但网关**持续把请求打到该机器近 16 个小时**（21:30 故障发生 → 次日 13:36
|
||||
人工重启），没有切换到其他 worker。复盘结论是三层防线同时失效：
|
||||
|
||||
| 层 | 问题 | 后果 |
|
||||
|---|---|---|
|
||||
| 1. worker `/health` 太浅 | 只返回内存里的"模型就绪"标志，磁盘满/下游 ComfyUI 挂掉不影响它 | 探测一直 200 |
|
||||
| 2. 业务错误不触发摘除 | 下游出错经包装后以 HTTP 200/400 + 错误码返回，`forward.py` 只对 连接异常/超时/≥500/401 摘除 | 请求失败但 worker 被视为健康 |
|
||||
| 3. 摘除后 8 秒即复活（**本方案要修的**） | `mark_worker_unhealthy()` 下线后，下一轮探测（8s 后）`/health` 返回 200，`healthy_threshold=1` 使其**一次成功立即重新上线** | 即使被摘除也会反复横跳，流量持续打入 |
|
||||
|
||||
worker 侧的第 1 层已经修复（方案 A：`/health` 深检磁盘水位 + 下游依赖 TCP 探测，
|
||||
异常返回 503，代码在分支 `ubuntu3090_debug_4090`，提交 `14562d4`）。本方案修复
|
||||
第 3 层：**被动摘除后进入隔离期，隔离期内即使探测成功也不复活**。两层配合后：
|
||||
worker 磁盘满 → `/health` 503 → 网关 2 次探测失败摘除 → 隔离期内不再接流量。
|
||||
|
||||
> 第 2 层（错误码语义化，涉及调用方兼容）暂不处理，单独立项。
|
||||
|
||||
## 2. 现状代码行为（main 分支 gateway/pool.py）
|
||||
|
||||
- `mark_worker_unhealthy(w)`：被 `forward.py` 在转发失败（连接异常/超时/5xx/401）时
|
||||
调用，置 `w.online = False`。**没有任何冷却机制**。
|
||||
- `_health_check_loop(cfg)`：每 `interval_seconds`（默认 8s）探测所有 worker 的
|
||||
`/health`；返回 200 时 `consecutive_successes += 1`，达到 `healthy_threshold`
|
||||
（默认 **1**）且当前 offline → 立即 `online = True`。
|
||||
- 因此被动摘除最多 8 秒后就被一次探测成功拉回——如果 worker 是"浅健康"或故障
|
||||
间歇性表现，就会陷入 摘除→复活→失败→摘除 的横跳。
|
||||
- 另外 58-96 行有一段**从未被调用的死代码** `_check_worker_health()` /
|
||||
`_mark_failure()`（写了一半的旧实现），本次顺带删除，避免后人改错地方。
|
||||
|
||||
## 3. 代码改动（共 2 个文件 + 1 个配置）
|
||||
|
||||
### 3.1 `gateway/pool.py`
|
||||
|
||||
**(1) `WorkerState` 增加一个字段：**
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class WorkerState:
|
||||
"""单个 worker 的运行时状态。"""
|
||||
url: str
|
||||
online: bool = False # 初始 offline，等健康检查通过后上线
|
||||
busy: bool = False
|
||||
consecutive_failures: int = 0
|
||||
consecutive_successes: int = 0
|
||||
offline_until: float = 0.0 # 被动摘除后的隔离截止时刻（time.monotonic() 基准）
|
||||
```
|
||||
|
||||
**(2) 模块顶部（`logger = ...` 之后）加一个配置读取 helper：**
|
||||
|
||||
```python
|
||||
def _cooldown_seconds() -> float:
|
||||
"""被动摘除后的隔离期时长（秒），配置 health_check.cooldown_seconds，默认 60。"""
|
||||
try:
|
||||
from gateway.config import get_config
|
||||
return float(get_config()["health_check"].get("cooldown_seconds", 60))
|
||||
except Exception: # noqa: BLE001
|
||||
return 60.0
|
||||
```
|
||||
|
||||
（函数内 import 是为了不引入模块级循环依赖；`gateway/config.py` 不依赖 pool，顶层
|
||||
import 也可以。）
|
||||
|
||||
**(3) `mark_worker_unhealthy()` 摘除时同时设置隔离期：**
|
||||
|
||||
```python
|
||||
async def mark_worker_unhealthy(w: WorkerState) -> None:
|
||||
"""被动标记：转发失败时立即将该 worker 下线，并进入隔离期。
|
||||
|
||||
隔离期内即使 /health 探测成功也不复活（由 _health_check_loop 守卫），
|
||||
避免"摘除→8 秒复活→再失败"的横跳。
|
||||
"""
|
||||
async with _pool_condition:
|
||||
if w.online:
|
||||
w.online = False
|
||||
w.consecutive_failures += 1
|
||||
w.offline_until = time.monotonic() + _cooldown_seconds()
|
||||
logger.warning("⚠ Worker 被动下线: %s（转发失败，隔离 %.0fs）",
|
||||
w.url, _cooldown_seconds())
|
||||
```
|
||||
|
||||
**(4) `_health_check_loop()` 的探测成功分支加隔离期守卫。** 找到这段：
|
||||
|
||||
```python
|
||||
if resp.status_code == 200:
|
||||
w.consecutive_failures = 0
|
||||
w.consecutive_successes += 1
|
||||
if w.consecutive_successes >= healthy_threshold and not w.online:
|
||||
w.online = True
|
||||
logger.info("✅ Worker 上线: %s", w.url)
|
||||
```
|
||||
|
||||
改为：
|
||||
|
||||
```python
|
||||
if resp.status_code == 200:
|
||||
w.consecutive_failures = 0
|
||||
w.consecutive_successes += 1
|
||||
if w.consecutive_successes >= healthy_threshold and not w.online:
|
||||
if time.monotonic() < w.offline_until:
|
||||
logger.debug("Worker %s 探测成功但处于隔离期，暂不复活", w.url)
|
||||
else:
|
||||
w.online = True
|
||||
logger.info("✅ Worker 上线: %s", w.url)
|
||||
```
|
||||
|
||||
注意：`except`（连接异常）分支只累计失败、不涉及复活，无需改动；
|
||||
`init_pool()` 启动时的首轮探测同理无需改动（进程刚启动时 `offline_until` 为 0，
|
||||
不构成隔离）。
|
||||
|
||||
**(5) 删除死代码**：`_check_worker_health()` 与
|
||||
`_mark_failure()` 两个函数整体删除（约 58-96 行，以函数名为准，别删到
|
||||
`_health_check_loop`）。删除前可确认无调用方：
|
||||
`grep -rn "_check_worker_health\|_mark_failure" .` 应只剩定义处。
|
||||
|
||||
### 3.2 `gateway/config.py`
|
||||
|
||||
`DEFAULTS["health_check"]` 增加一个 key：
|
||||
|
||||
```python
|
||||
"health_check": {
|
||||
"path": "/health",
|
||||
"interval_seconds": 8,
|
||||
"timeout_seconds": 3,
|
||||
"unhealthy_threshold": 2,
|
||||
"healthy_threshold": 1,
|
||||
"cooldown_seconds": 60, # ← 新增：被动摘除后的隔离期（秒）
|
||||
},
|
||||
```
|
||||
|
||||
`config.example.json` 同步加这个 key。网关机器的实际 `config.json` **不加也不会报错**
|
||||
（代码里有 60 的默认兜底），但建议显式加上，便于运维感知。
|
||||
|
||||
### 3.3 建议单测：`tests/test_gateway_pool.py`（新文件）
|
||||
|
||||
gateway 子包目前没有任何测试。隔离期是纯逻辑，建议至少覆盖以下三个用例
|
||||
（不依赖真实网络）：
|
||||
|
||||
```python
|
||||
"""gateway/pool.py 隔离期逻辑单测。"""
|
||||
import asyncio
|
||||
import time
|
||||
from unittest.mock import patch
|
||||
|
||||
from gateway import pool
|
||||
from gateway.pool import WorkerState
|
||||
|
||||
|
||||
def _drain(coro):
|
||||
return asyncio.run(coro)
|
||||
|
||||
|
||||
def test_passive_ejection_sets_cooldown():
|
||||
w = WorkerState(url="http://127.0.0.1:9000", online=True)
|
||||
async def run():
|
||||
pool._pool_condition = asyncio.Condition()
|
||||
with patch.object(pool, "_cooldown_seconds", return_value=60.0):
|
||||
await pool.mark_worker_unhealthy(w)
|
||||
_drain(run())
|
||||
assert w.online is False
|
||||
assert w.offline_until > time.monotonic()
|
||||
|
||||
|
||||
def test_probe_success_revives_after_cooldown():
|
||||
w = WorkerState(url="http://127.0.0.1:9000") # offline
|
||||
w.offline_until = time.monotonic() - 1 # 隔离期已过
|
||||
# 直接验证状态迁移逻辑（若按 3.1(4) 内联实现，可复制该分支逻辑做断言，
|
||||
# 或将判断抽成 _try_revive(w, healthy_threshold) 小函数后单测）
|
||||
...
|
||||
|
||||
|
||||
def test_probe_success_blocked_during_cooldown():
|
||||
w = WorkerState(url="http://127.0.0.1:9000")
|
||||
w.offline_until = time.monotonic() + 60
|
||||
... # 断言保持 offline
|
||||
```
|
||||
|
||||
> 提示：如果按 3.1(4) 原样内联修改，后两个用例不好直接驱动，可以把"是否允许复活"
|
||||
> 抽成一个 6 行的纯函数 `_try_revive(w, healthy_threshold) -> bool` 再测，主循环
|
||||
> 调用它即可——实现方式自定，覆盖语义即可。
|
||||
|
||||
## 4. 部署步骤（网关机器）
|
||||
|
||||
1. `git pull` main 分支（含本方案代码改动）。
|
||||
2. （可选但建议）`config.json` 的 `health_check` 段加 `"cooldown_seconds": 60`。
|
||||
3. 重启网关服务（`hair-gateway.service` 或等价的 uvicorn 进程）。重启期间网关
|
||||
本身短暂不可用，选低峰执行。
|
||||
4. 观察启动日志：`健康检查循环启动 | 间隔=8s | 下线阈值=2 | 上线阈值=1 | workers=N`
|
||||
正常出现。
|
||||
|
||||
## 5. 验证
|
||||
|
||||
**功能验证（低峰演练）：**
|
||||
|
||||
1. 人工把主 worker 摘除一次最直接的方法：临时在 worker 机器
|
||||
`systemctl stop hair-worker`（或临时设 `HEALTH_MIN_FREE_GB=999` 重启）。
|
||||
2. 观察网关日志：
|
||||
- 连续 2 次探测失败 → `⚠ Worker 下线`（原有行为）；
|
||||
- **转发失败触发的** `⚠ Worker 被动下线: ...（转发失败，隔离 60s）`（新日志）；
|
||||
- 恢复 worker 后，若刚被被动摘除过，即使 `/health` 恢复 200，也应看到 60 秒内
|
||||
**不出现** `✅ Worker 上线`，隔离期满后下一次探测成功才上线。
|
||||
3. `GET /gateway-health` 观察 `workers_healthy` 的变化节奏。
|
||||
|
||||
**回归确认：** 正常场景 worker 上下线不受影响（隔离期只对"被动摘除"生效；
|
||||
探测失败导致的下线，恢复后仍按 `healthy_threshold` 立即上线）。
|
||||
|
||||
## 6. 回滚
|
||||
|
||||
- 代码回滚：`git revert` 对应提交即可，无数据/配置迁移。
|
||||
- 紧急关闭隔离期：网关机器 `config.json` 设 `"cooldown_seconds": 0`
|
||||
（等价于旧行为——摘除后下一次探测成功即复活），无需回代码。
|
||||
|
||||
## 7. 附：worker 侧方案 A 的行为（已上线，供联调参考）
|
||||
|
||||
worker（分支 `ubuntu3090_debug_4090`，提交 `14562d4`）的 `/health` 现在会在以下
|
||||
情况返回 **503**（网关 2 次探测失败即摘除）：
|
||||
|
||||
- 模型加载中：`503 {"status":"starting"}`（原有行为不变）
|
||||
- 磁盘剩余低于阈值：`503 {"status":"degraded","reason":"disk_free_below_5.0g","disk_free_gb":...}`
|
||||
- 下游依赖（ComfyUI 8188 / change_hair 8801）TCP 不通：
|
||||
`503 {"status":"degraded","reason":"dep_down:comfyui,...","deps":{...}}`
|
||||
|
||||
正常时返回 `200 {"status":"ok","disk_free_gb":24.6,"deps":{"comfyui":true,"change_hair":true}}`。
|
||||
|
||||
worker 侧配置（`worker_config.json` 的 `health` 段，环境变量可覆盖）：
|
||||
`min_free_gb`（默认 5，0=关闭）、`check_deps`（默认 true）、`dep_comfyui`（默认
|
||||
127.0.0.1:8188）、`deps_cache_seconds`（默认 5）。**其他 worker 机器如果也想启用
|
||||
深检，cherry-pick 该提交并补一份 worker_config.json 的 health 段即可。**
|
||||
+59
-16
@@ -180,6 +180,60 @@ def _ear_edges_from_mask(ear_mask, hair_mask, y0, y1, left_cheek_x, right_cheek_
|
||||
return head_l, head_r
|
||||
|
||||
|
||||
# 返回给接口的竖线名 → eyes["points"] 里的键。从左到右的解剖顺序。
|
||||
_VERTICAL_INNER_LINES = (
|
||||
("left_cheek", "left_cheek"),
|
||||
("left_eye_outer", "left_outer"),
|
||||
("left_eye_inner", "left_inner"),
|
||||
("right_eye_inner", "right_inner"),
|
||||
("right_eye_outer", "right_outer"),
|
||||
("right_cheek", "right_cheek"),
|
||||
)
|
||||
|
||||
|
||||
def vertical_line_geometry(measure_result, image_width, image_height,
|
||||
ear_mask=None, hair_mask=None, variant="v1"):
|
||||
"""标注图竖线的像素几何，供画线与接口返回共用。
|
||||
|
||||
返回 {"y_top", "y_bottom", "lines": [{"name", "x"}, ...]}。
|
||||
x / y 均为原图像素整数（四舍五入）。lines 从左到右，只含实际画出的线。
|
||||
variant="v1"：竖线从头顶上方延伸到下巴下方；有耳朵外缘时含
|
||||
left_head_edge / right_head_edge。
|
||||
variant="v6"：竖线从发际线到下巴尖，不含人头两端。
|
||||
"""
|
||||
v = measure_result.vertical
|
||||
s = min(image_width, image_height)
|
||||
if variant == "v6":
|
||||
order = ["hairline", "brow_center", "nose_bottom", "chin_tip"]
|
||||
else:
|
||||
order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
|
||||
ys = [v[name][1] for name in order]
|
||||
|
||||
pts = measure_result.eyes["points"]
|
||||
named = [(public, float(pts[internal][0])) for public, internal in _VERTICAL_INNER_LINES]
|
||||
if variant != "v6":
|
||||
lcx, rcx = pts["left_cheek"][0], pts["right_cheek"][0]
|
||||
head_l, head_r = _ear_edges_from_mask(
|
||||
ear_mask, hair_mask, ys[0], ys[-1], lcx, rcx, (lcx + rcx) / 2)
|
||||
if head_l is not None:
|
||||
named.append(("left_head_edge", float(head_l)))
|
||||
if head_r is not None:
|
||||
named.append(("right_head_edge", float(head_r)))
|
||||
named.sort(key=lambda item: item[1])
|
||||
|
||||
fy0, fy1 = ys[0], ys[-1]
|
||||
over = max(6, round(s * 0.030))
|
||||
if variant == "v6":
|
||||
y_top, y_bottom = fy0, fy1
|
||||
else:
|
||||
y_top, y_bottom = fy0 - over, fy1 + over
|
||||
return {
|
||||
"y_top": int(round(y_top)),
|
||||
"y_bottom": int(round(y_bottom)),
|
||||
"lines": [{"name": name, "x": int(round(x))} for name, x in named],
|
||||
}
|
||||
|
||||
|
||||
def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=None,
|
||||
variant="v1"):
|
||||
"""生成标注图层 PNG（透明底 RGBA，尺寸同原图）。返回 PIL.Image。
|
||||
@@ -221,19 +275,11 @@ def create_annotated_image(image_bgr, measure_result, ear_mask=None, hair_mask=N
|
||||
ys = [v[name][1] for name in order]
|
||||
|
||||
pts = measure_result.eyes["points"]
|
||||
seven_keys = ["left_cheek", "left_outer", "left_inner",
|
||||
"right_inner", "right_outer", "right_cheek"]
|
||||
base_xs = [pts[k][0] for k in seven_keys]
|
||||
if variant == "v6":
|
||||
xs = sorted(base_xs) # 接口6：仅七眼 6 点，不画人头最左/最右端线
|
||||
else:
|
||||
# 人头最左/最右：取自耳朵分割掩膜外缘（方案B，类7/8），脸纵向范围内统计。
|
||||
# 看不到耳朵（被头发/侧脸遮挡 → 掩膜空）或外缘未越过脸颊线则省略该侧端线。
|
||||
lcx, rcx = pts["left_cheek"][0], pts["right_cheek"][0]
|
||||
head_l, head_r = _ear_edges_from_mask(
|
||||
ear_mask, hair_mask, ys[0], ys[-1], lcx, rcx, (lcx + rcx) / 2)
|
||||
head_xs = [x for x in (head_l, head_r) if x is not None]
|
||||
xs = sorted(base_xs + head_xs) # 自左向右（6 或 7/8 点）
|
||||
# 竖线 x 与纵向范围与接口返回的 vertical_lines 同一份几何。
|
||||
geom = vertical_line_geometry(
|
||||
measure_result, w, h, ear_mask=ear_mask, hair_mask=hair_mask, variant=variant)
|
||||
xs = [line["x"] for line in geom["lines"]]
|
||||
v_top, v_bot = geom["y_top"], geom["y_bottom"]
|
||||
|
||||
# 人脸/人头包围盒
|
||||
fx0, fx1 = xs[0], xs[-1]
|
||||
@@ -241,9 +287,6 @@ 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
|
||||
# 竖线纵向范围：v6 = 发际线→下巴尖（不超出）；v1 = 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
|
||||
|
||||
# --- 1. 横向分界线（渐变，覆盖头宽并超出一点） ---
|
||||
for cy in ys:
|
||||
|
||||
@@ -1110,7 +1110,7 @@ def generate_hairline_redraw(image_bgr, hairline_id, is_hr=False, seg_model="seg
|
||||
"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",
|
||||
"comfyui_prompt": comfyui_prompt or "填充遮罩区域的头发，人物增加一点磨皮",
|
||||
"beauty_alpha": beauty_alpha,
|
||||
"px_per_cm": round(float(px_per_cm), 4),
|
||||
"mask_pixels": mask_viz["mask_pixels"],
|
||||
|
||||
@@ -0,0 +1,166 @@
|
||||
"""接口99：人脸特殊点检测。
|
||||
|
||||
返回 MediaPipe Face Mesh 关键点中一组固定编号的像素坐标（英文组名_点ID 为字段名），
|
||||
外加 5 个发际线特殊点：从指定关键点沿指定方向射线步进，与 BiSeNet 头发分割 mask
|
||||
首次相交处即发际线点（参考接口1 方案B 的发际线定位方式，见 hair_segmenter.py）。
|
||||
|
||||
组与编号（需求方给定）：
|
||||
- eyebrow 107 66 105 63 70 336 296 334 293 300
|
||||
- eye 189 222 224 113 226 413 442 444 342 446
|
||||
- mouth 0 37 40 67 91 84 17 405 375 409 269
|
||||
- nose_midline 9 168 197 5 4 194 278 48 437 217
|
||||
- cheek 101 117 123 187 205 50 330 346 352 280 425
|
||||
- face_edge 162 234 58 136 149 152 378 365 288 401 366 389 251
|
||||
|
||||
发际线点（字段名需求方给定；A/B 两射线时取中点，仅一条命中则返回该点，均未命中为 null）：
|
||||
- hair_line_center 151 → 10 方向与发际线交点
|
||||
- hair_line_right mid(108→109 交点A, 69→67 交点B)
|
||||
- hair_line_right_right mid(104→103 交点A, 68→54 交点B)
|
||||
- hair_line_left mid(337→338 交点A, 299→297 交点B)
|
||||
- hair_line_left_left mid(333→332 交点A, 298→284 交点B) # 与 right_right 镜像对称
|
||||
"""
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
|
||||
# 固定关键点组：组英文名 → MediaPipe 点 ID 列表（顺序即需求方给定顺序）
|
||||
GROUPS = {
|
||||
"eyebrow": [107, 66, 105, 63, 70, 336, 296, 334, 293, 300],
|
||||
"eye": [189, 222, 224, 113, 226, 413, 442, 444, 342, 446],
|
||||
"mouth": [0, 37, 40, 67, 91, 84, 17, 405, 375, 409, 269],
|
||||
"nose_midline": [9, 168, 197, 5, 4, 194, 278, 48, 437, 217],
|
||||
"cheek": [101, 117, 123, 187, 205, 50, 330, 346, 352, 280, 425],
|
||||
"face_edge": [162, 234, 58, 136, 149, 152, 378, 365, 288, 401, 366, 389, 251],
|
||||
}
|
||||
|
||||
# 发际线点定义：字段名 → 参与计算的 (起点ID, 方向点ID) 射线组；单射线直接取交点，
|
||||
# 双射线取两交点中点。
|
||||
HAIRLINE_RAYS = {
|
||||
"hair_line_center": [(151, 10)],
|
||||
"hair_line_right": [(108, 109), (69, 67)],
|
||||
"hair_line_right_right": [(104, 103), (68, 54)],
|
||||
"hair_line_left": [(337, 338), (299, 297)],
|
||||
"hair_line_left_left": [(333, 332), (298, 284)],
|
||||
}
|
||||
|
||||
|
||||
class NoFaceError(Exception):
|
||||
"""未检出人脸。"""
|
||||
|
||||
|
||||
class NonFrontalError(Exception):
|
||||
"""非正面照（复用接口1 姿态阈值）。"""
|
||||
|
||||
|
||||
def ray_hairline_intersection(p1, p2, hair_mask):
|
||||
"""从 p1 沿 p1→p2 方向逐像素步进，返回首次进入头发 mask 的整数像素坐标。
|
||||
|
||||
p1/p2: (x, y) 像素坐标（float）。起点本身已在头发内时直接返回起点（已到发际线）。
|
||||
步进直到出图或步数上限（3×图对角线，防极端死循环）；未命中返回 None。
|
||||
"""
|
||||
mask = np.asarray(hair_mask).astype(bool)
|
||||
if mask.size == 0:
|
||||
return None
|
||||
h, w = mask.shape
|
||||
x0, y0 = float(p1[0]), float(p1[1])
|
||||
dx, dy = float(p2[0]) - x0, float(p2[1]) - y0
|
||||
length = math.hypot(dx, dy)
|
||||
if length < 1e-6:
|
||||
return None
|
||||
ux, uy = dx / length, dy / length
|
||||
max_steps = int(3 * math.hypot(h, w))
|
||||
|
||||
def _hit(x, y):
|
||||
xi, yi = int(round(x)), int(round(y))
|
||||
if 0 <= xi < w and 0 <= yi < h:
|
||||
return mask[yi, xi]
|
||||
return False
|
||||
|
||||
x, y = x0, y0
|
||||
for _ in range(max_steps):
|
||||
if _hit(x, y):
|
||||
return int(round(x)), int(round(y))
|
||||
x += ux
|
||||
y += uy
|
||||
if not (0 <= int(round(x)) < w and 0 <= int(round(y)) < h):
|
||||
return None # 已步出图像，方向上无头发
|
||||
return None
|
||||
|
||||
|
||||
def detect_special_points(image_bgr):
|
||||
"""主入口：图片 → {字段名: {"x","y"} 或 None} + 元信息。
|
||||
|
||||
Returns: {"points": {...}, "image_size": {"width","height"},
|
||||
"hairline_source": "segmentation"|"unavailable"}
|
||||
无人脸抛 NoFaceError；非正面抛 NonFrontalError。
|
||||
"""
|
||||
from face_analysis.detector import detector
|
||||
from face_analysis.calibration import normalized_to_pixel
|
||||
from face_analysis.pose import check_frontal_face
|
||||
|
||||
h, w = image_bgr.shape[:2]
|
||||
landmarks = detector.detect(image_bgr)
|
||||
if landmarks is None:
|
||||
raise NoFaceError("未检出人脸")
|
||||
if not check_frontal_face(landmarks, w, h):
|
||||
raise NonFrontalError("非正面照")
|
||||
|
||||
lm = landmarks.landmark
|
||||
px = {i: normalized_to_pixel(lm[i], w, h) for i in range(len(lm))}
|
||||
|
||||
points = {}
|
||||
for group, ids in GROUPS.items():
|
||||
for idx in ids:
|
||||
x, y = px[idx]
|
||||
points[f"{group}_{idx}"] = {"x": int(round(x)), "y": int(round(y))}
|
||||
|
||||
# 头发分割（与接口1 相同：按人脸框裁剪后再分割，脸外背景不干扰）
|
||||
hair_mask = None
|
||||
try:
|
||||
from face_analysis.hair_segmenter import get_segmenter
|
||||
face_box = (min(p[0] for p in px.values()), min(p[1] for p in px.values()),
|
||||
max(p[0] for p in px.values()), max(p[1] for p in px.values()))
|
||||
hair_mask, _ = get_segmenter().segment_hair_and_ears(
|
||||
image_bgr, face_box=face_box)
|
||||
except Exception: # noqa: BLE001 分割不可用 → 发际线点置 null，不阻塞关键点
|
||||
hair_mask = None
|
||||
|
||||
for name, rays in HAIRLINE_RAYS.items():
|
||||
hits = []
|
||||
if hair_mask is not None:
|
||||
for a, b in rays:
|
||||
hits.append(ray_hairline_intersection(px[a], px[b], hair_mask))
|
||||
found = [p for p in hits if p is not None]
|
||||
if not found:
|
||||
points[name] = None
|
||||
elif len(found) == 1:
|
||||
points[name] = {"x": found[0][0], "y": found[0][1]}
|
||||
else:
|
||||
points[name] = {
|
||||
"x": int(round(sum(p[0] for p in found) / len(found))),
|
||||
"y": int(round(sum(p[1] for p in found) / len(found))),
|
||||
}
|
||||
|
||||
return {
|
||||
"points": points,
|
||||
"image_size": {"width": w, "height": h},
|
||||
"hairline_source": "segmentation" if hair_mask is not None else "unavailable",
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
import cv2
|
||||
import json
|
||||
|
||||
path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
|
||||
img = cv2.imread(path)
|
||||
if img is None:
|
||||
print(f"无法读取图片: {path}")
|
||||
sys.exit(1)
|
||||
try:
|
||||
out = detect_special_points(img)
|
||||
except (NoFaceError, NonFrontalError) as e:
|
||||
print(f"检测失败: {e}")
|
||||
sys.exit(1)
|
||||
print(json.dumps(out, ensure_ascii=False, indent=2))
|
||||
@@ -607,3 +607,79 @@ async def head_mask(request: Request):
|
||||
async def head_band(request: Request):
|
||||
"""接口10：头部外缘膨胀带遮罩 + 分步可视化"""
|
||||
return await _proxy(request, "/api/v1/head/band")
|
||||
|
||||
|
||||
# 接口99 单路排队：网关机推理全局互斥。调用频率低，无需并发——串行既限制 CPU 峰值
|
||||
# （MediaPipe + BiSeNet 全 CPU 推理），也规避检测器单例的线程安全风险。排队无超时，
|
||||
# 请求方按需自带客户端超时。
|
||||
_if99_lock = asyncio.Lock()
|
||||
|
||||
|
||||
@app.post("/api/v1/face/special-points", tags=["人脸分析"])
|
||||
async def face_special_points(
|
||||
image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
|
||||
image_url: Optional[str] = Form(default=None, description="图片 URL"),
|
||||
image_base64: Optional[str] = Form(default=None, description="图片 base64（需带前缀）"),
|
||||
):
|
||||
"""接口99：人脸特殊点检测 — 网关本机实现（纯 CPU：MediaPipe + BiSeNet CPU），不经过 worker。
|
||||
|
||||
与 worker 同仓库，直接 import face_analysis.special_points；权重已入 git，clone 即用。
|
||||
依赖惰性导入：网关缺依赖时仅本接口报 1007，不影响其他路由。
|
||||
"""
|
||||
import uuid as _uuid
|
||||
rid = f"gw-{_uuid.uuid4().hex[:8]}"
|
||||
|
||||
def _err(code, message):
|
||||
return JSONResponse(status_code=200, content={
|
||||
"code": code, "message": message, "request_id": rid, "data": None})
|
||||
|
||||
# 三选一校验
|
||||
provided = [x for x in (image_file, image_url, image_base64) if x]
|
||||
if len(provided) != 1:
|
||||
return _err(1007, "图片参数错误：必须且只能传 image_file / image_url / image_base64 其中一个")
|
||||
|
||||
img_bytes = None
|
||||
if image_file:
|
||||
img_bytes = await image_file.read()
|
||||
elif image_base64:
|
||||
b64 = image_base64.split(",", 1)[1] if "," in image_base64 else image_base64
|
||||
try:
|
||||
img_bytes = base64.b64decode(b64)
|
||||
except Exception:
|
||||
return _err(1008, "图片格式不支持（base64 解码失败）")
|
||||
else:
|
||||
try:
|
||||
import httpx
|
||||
async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as cli:
|
||||
r = await cli.get(image_url)
|
||||
r.raise_for_status()
|
||||
img_bytes = r.content
|
||||
except Exception:
|
||||
return _err(1008, "图片下载失败（URL 不可达）")
|
||||
|
||||
try:
|
||||
import cv2
|
||||
import numpy as np # noqa: F401 （detect_special_points 依赖）
|
||||
from face_analysis.special_points import (
|
||||
detect_special_points, NoFaceError, NonFrontalError)
|
||||
except ImportError as ex:
|
||||
return _err(1007, f"网关依赖未安装（安装清单见 docs/网关待改动.md §9）：{ex}")
|
||||
|
||||
image = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), cv2.IMREAD_COLOR)
|
||||
if image is None:
|
||||
return _err(1008, "图片格式不支持（仅 JPG / PNG）")
|
||||
|
||||
from fastapi.concurrency import run_in_threadpool
|
||||
try:
|
||||
async with _if99_lock: # 单路排队：同一时刻只跑一路推理
|
||||
data = await run_in_threadpool(detect_special_points, image)
|
||||
except NoFaceError:
|
||||
return _err(1001, "无法识别人像")
|
||||
except NonFrontalError:
|
||||
return _err(1003, "角度问题，请上传正面照")
|
||||
except Exception as ex: # noqa: BLE001
|
||||
logger.exception("接口99 处理异常")
|
||||
return _err(1007, f"处理失败：{ex}")
|
||||
|
||||
return JSONResponse(status_code=200, content={
|
||||
"code": 0, "message": "success", "request_id": rid, "data": data})
|
||||
|
||||
+1
-1
@@ -7,7 +7,7 @@ Wants=comfyui.service change_hair-hair.service
|
||||
Type=simple
|
||||
User=ubuntu
|
||||
WorkingDirectory=/home/ubuntu/hair
|
||||
ExecStart=/home/ubuntu/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187
|
||||
ExecStart=/home/ubuntu/hair/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8187 --no-proxy-headers
|
||||
Restart=on-failure
|
||||
RestartSec=5
|
||||
|
||||
|
||||
+1
-1
@@ -410,7 +410,7 @@
|
||||
},
|
||||
"60": {
|
||||
"inputs": {
|
||||
"text": "填充遮罩区域的头发"
|
||||
"text": "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
},
|
||||
"class_type": "JjkText",
|
||||
"_meta": {
|
||||
|
||||
@@ -20,6 +20,13 @@ MP_TOP_ANCHORS: list[int] = [
|
||||
]
|
||||
N_ANCHORS = len(MP_TOP_ANCHORS) # 17
|
||||
|
||||
# Anatomical left/right pairs, in MediaPipe indices. The anchor list is
|
||||
# NOT mirror-ordered at its ends: 127 pairs with 356, 234 with 454.
|
||||
MP_TOP_ANCHOR_PAIRS = [
|
||||
(127, 356), (234, 454), (162, 389), (21, 251),
|
||||
(54, 284), (103, 332), (67, 297), (109, 338),
|
||||
]
|
||||
|
||||
# Vertex ID layout in the extended array of length 468 + 2*N_ANCHORS.
|
||||
# [0 .. 468) : MediaPipe canonical landmarks
|
||||
# [468 .. 468+N) : middle row (between MP boundary and hairline)
|
||||
|
||||
@@ -44,18 +44,18 @@ class FaceLandmarker:
|
||||
self._detector = vision.FaceLandmarker.create_from_options(options)
|
||||
self._mp = mp
|
||||
|
||||
def detect(self, image_rgb: np.ndarray) -> np.ndarray | None:
|
||||
"""Returns (468, 3) float32 of normalized x, y and relative z, or None.
|
||||
def detect(self, image_rgb: np.ndarray, include_iris: bool = False) -> np.ndarray | None:
|
||||
"""Returns (468, 3), or (478, 3) with iris, normalized float32 points.
|
||||
|
||||
The task model produces 478 landmarks (468 face + 10 iris); we return
|
||||
only the first 468 to match the canonical FaceMesh topology used by
|
||||
the SDK's OBJ file.
|
||||
The task model produces 478 landmarks (468 face + 10 iris). By default
|
||||
only the first 468 match the SDK's canonical FaceMesh topology;
|
||||
include_iris=True retains all 478 for face-measurement calibration.
|
||||
"""
|
||||
mp_image = self._mp.Image(image_format=self._mp.ImageFormat.SRGB, data=image_rgb)
|
||||
result = self._detector.detect(mp_image)
|
||||
if not result.face_landmarks:
|
||||
return None
|
||||
lm = result.face_landmarks[0][:468]
|
||||
lm = result.face_landmarks[0] if include_iris else result.face_landmarks[0][:468]
|
||||
arr = np.array([[p.x, p.y, p.z] for p in lm], dtype=np.float32)
|
||||
return arr
|
||||
|
||||
|
||||
@@ -0,0 +1,218 @@
|
||||
"""Interface 52: upright face axis, then native-resolution reference framing."""
|
||||
from __future__ import annotations
|
||||
|
||||
from functools import lru_cache
|
||||
from io import BytesIO
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps
|
||||
|
||||
PROFILE_PATH = Path(__file__).parent / "reference/framing_profile.json"
|
||||
|
||||
|
||||
def decode_portrait(raw):
|
||||
"""Respect phone EXIF orientation before detecting or transforming points."""
|
||||
try:
|
||||
with Image.open(BytesIO(raw)) as image:
|
||||
return cv2.cvtColor(np.array(ImageOps.exif_transpose(image).convert("RGB")), cv2.COLOR_RGB2BGR)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def load_profile():
|
||||
return json.loads(PROFILE_PATH.read_text(encoding="utf-8"))
|
||||
|
||||
|
||||
def transform_points(points, matrix):
|
||||
return np.asarray(points, np.float64) @ matrix[:, :2].T + matrix[:, 2]
|
||||
|
||||
|
||||
def _rotation(landmarks, width, height):
|
||||
xy = landmarks[:, :2].astype(np.float64) * [width, height]
|
||||
delta = xy[152] - xy[9]
|
||||
length = float(np.linalg.norm(delta))
|
||||
if not np.isfinite(xy).all() or length < 2 or delta[1] <= 0:
|
||||
raise ValueError("9号点与152号点无效，无法扶正人脸")
|
||||
down = delta / length
|
||||
rotation = np.array([[down[1], -down[0]], [down[0], down[1]]], np.float64)
|
||||
return xy, rotation, length
|
||||
|
||||
|
||||
def _matrix(rotation, source_anchor, target_anchor):
|
||||
return np.column_stack((rotation, np.asarray(target_anchor) - rotation @ source_anchor))
|
||||
|
||||
|
||||
def crop_fits(matrix, source_size, output_size):
|
||||
w, h = output_size
|
||||
corners = transform_points([[0, 0], [w-1, 0], [w-1, h-1], [0, h-1]], cv2.invertAffineTransform(matrix))
|
||||
sw, sh = source_size
|
||||
return bool((corners[:, 0] >= -1e-7).all() and (corners[:, 0] <= sw-1+1e-7).all()
|
||||
and (corners[:, 1] >= -1e-7).all() and (corners[:, 1] <= sh-1+1e-7).all())
|
||||
|
||||
|
||||
def _inscribed_frame(rotation, source_size, aspect=None):
|
||||
"""Largest centred valid rectangle: no generated background or upscaling."""
|
||||
sw, sh = source_size
|
||||
ratio = sw / sh if aspect is None else aspect
|
||||
source_center = np.array([(sw-1)/2, (sh-1)/2])
|
||||
low, high = 2.0, min(float(sh), sw/ratio)
|
||||
for _ in range(48):
|
||||
h = (low+high)/2
|
||||
w = h*ratio
|
||||
matrix = _matrix(rotation, source_center, [(w-1)/2, (h-1)/2])
|
||||
if crop_fits(matrix, source_size, (w, h)):
|
||||
low = h
|
||||
else:
|
||||
high = h
|
||||
if aspect == .75:
|
||||
h = max(4, int(low+1e-7)//4*4)
|
||||
w = h//4*3
|
||||
else:
|
||||
h, w = max(2, int(low+1e-7)), max(2, int(low*ratio+1e-7))
|
||||
matrix = _matrix(rotation, source_center, [(w-1)/2, (h-1)/2])
|
||||
if not crop_fits(matrix, source_size, (w, h)):
|
||||
raise ValueError("图片边缘空间不足，无法得到有效扶正画面")
|
||||
return matrix, (w, h)
|
||||
|
||||
|
||||
def _tall_portrait_frame(xy, rotation, source_size, profile):
|
||||
"""Largest native 3:4 frame, positioned to retain the entire face."""
|
||||
_, size = _inscribed_frame(rotation, source_size, aspect=.75)
|
||||
half = (np.array(size, np.float64)-1)/2
|
||||
extent = np.abs(rotation.T) @ half
|
||||
low, high = extent, np.array(source_size)-1-extent
|
||||
polygon = [np.array(p) for p in ((low[0], low[1]), (high[0], low[1]),
|
||||
(high[0], high[1]), (low[0], high[1]))]
|
||||
face = xy[:468] @ rotation.T
|
||||
# The crop centre must satisfy both the source boundaries and the face
|
||||
# bounds. Clip the feasible polygon in double precision to avoid padding.
|
||||
face_low, face_high = face.max(axis=0)-half, face.min(axis=0)+half
|
||||
for axis in range(2):
|
||||
for normal, bound in ((rotation[axis], face_high[axis]),
|
||||
(-rotation[axis], -face_low[axis])):
|
||||
clipped = []
|
||||
for start, end in zip(polygon, polygon[1:]+polygon[:1]):
|
||||
a, b = float(normal @ start-bound-1e-8), float(normal @ end-bound-1e-8)
|
||||
if (a <= 0) != (b <= 0):
|
||||
clipped.append(start+(end-start)*(a/(a-b)))
|
||||
if b <= 0:
|
||||
clipped.append(end)
|
||||
polygon = clipped
|
||||
if not polygon:
|
||||
raise ValueError("3:4裁切窗口无法保留完整人脸")
|
||||
target = np.array([profile["brow_x_ratio"]*size[0], profile["brow_y_ratio"]*size[1]])
|
||||
preferred = xy[9]-rotation.T @ (target-half)
|
||||
# Euclidean projection keeps the brow as close as possible to its target.
|
||||
candidates = []
|
||||
if (preferred >= low-1e-8).all() and (preferred <= high+1e-8).all():
|
||||
upright = rotation @ preferred
|
||||
if (upright >= face_low-1e-8).all() and (upright <= face_high+1e-8).all():
|
||||
candidates.append(preferred)
|
||||
for start, end in zip(polygon, polygon[1:]+polygon[:1]):
|
||||
edge = end-start
|
||||
length2 = float(edge @ edge)
|
||||
t = np.clip(float((preferred-start) @ edge)/length2, 0, 1) if length2 > 1e-16 else 0
|
||||
candidates.append(start+t*edge)
|
||||
centre = min(candidates, key=lambda p: float(np.sum((p-preferred)**2)))
|
||||
matrix = _matrix(rotation, centre, half)
|
||||
if not crop_fits(matrix, source_size, size):
|
||||
raise ValueError("3:4裁切窗口超出原图有效范围")
|
||||
return matrix, size
|
||||
|
||||
|
||||
def _apply(photo, landmarks, matrix, size):
|
||||
h, w = photo.shape[:2]
|
||||
ow, oh = size
|
||||
moved = np.asarray(landmarks, np.float64).copy()
|
||||
moved[:, :2] = transform_points(landmarks[:, :2]*[w, h], matrix) / [ow, oh]
|
||||
# MediaPipe Z is measured in normalized image-width units.
|
||||
moved[:, 2] *= w/ow
|
||||
result = cv2.warpAffine(photo, matrix, size, flags=cv2.INTER_LINEAR,
|
||||
borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0))
|
||||
return result, moved
|
||||
|
||||
|
||||
def calibrate_reference(photo, landmarks):
|
||||
"""First straighten the actual reference; derive ratios from its final frame."""
|
||||
h, w = photo.shape[:2]
|
||||
_xy, rotation, length = _rotation(landmarks, w, h)
|
||||
matrix, size = _inscribed_frame(rotation, (w, h), aspect=.75)
|
||||
image, moved = _apply(photo, landmarks, matrix, size)
|
||||
ow, oh = size
|
||||
b, c = moved[[9, 152], :2]*[ow, oh]
|
||||
profile = {
|
||||
"version": "2026-11-04-upright-9-152-subject-90pct-tall-3x4",
|
||||
"source_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/misc/20261103_user.jpg",
|
||||
"reference_image_url": "/static/framing_reference.png",
|
||||
"reference_size": {"width": ow, "height": oh},
|
||||
"source_size": {"width": w, "height": h},
|
||||
"brow_9": {"x": float(b[0]), "y": float(b[1])},
|
||||
"chin_152": {"x": float(c[0]), "y": float(c[1])},
|
||||
"face_axis_length_px": length,
|
||||
"brow_x_ratio": float(b[0]/ow), "brow_y_ratio": float(b[1]/oh),
|
||||
"reference_width_per_face_length": ow/length,
|
||||
"reference_height_per_face_length": oh/length,
|
||||
"width_per_face_length": 2.5, "height_per_face_length": 3.0/.9,
|
||||
"rotation_degrees": float(np.degrees(np.arctan2(rotation[0, 1], rotation[0, 0]))),
|
||||
"axis_x_error_px": float(abs(b[0]-c[0])),
|
||||
"source_to_reference": matrix.tolist(),
|
||||
"landmark_source": "source_landmarks_transformed_without_redetection",
|
||||
}
|
||||
return image, moved, profile
|
||||
|
||||
|
||||
def normalize_portrait(photo, landmarks, profile=None):
|
||||
profile = load_profile() if profile is None else profile
|
||||
h, w = photo.shape[:2]
|
||||
xy, rotation, length = _rotation(landmarks, w, h)
|
||||
# Round to an exact 3:4 frame without changing pixel scale.
|
||||
target_h = max(4, round(profile["height_per_face_length"]*length/4)*4)
|
||||
target_w = target_h//4*3
|
||||
matrix = _matrix(rotation, xy[9], [profile["brow_x_ratio"]*target_w,
|
||||
profile["brow_y_ratio"]*target_h])
|
||||
cropped = crop_fits(matrix, (w, h), (target_w, target_h))
|
||||
aspect_cropped = False
|
||||
aspect_skip_reason = None
|
||||
if cropped:
|
||||
size = (target_w, target_h)
|
||||
elif h*3 > w*4:
|
||||
try:
|
||||
matrix, size = _tall_portrait_frame(xy, rotation, (w, h), profile)
|
||||
aspect_cropped = True
|
||||
except ValueError as ex:
|
||||
aspect_skip_reason = str(ex)
|
||||
matrix, size = _inscribed_frame(rotation, (w, h))
|
||||
else:
|
||||
matrix, size = _inscribed_frame(rotation, (w, h))
|
||||
image, moved = _apply(photo, landmarks, matrix, size)
|
||||
ow, oh = size
|
||||
axis = moved[[9, 152], :2]*[ow, oh]
|
||||
if not ((axis[:, 0] >= 0).all() and (axis[:, 0] < ow).all()
|
||||
and (axis[:, 1] >= 0).all() and (axis[:, 1] < oh).all()):
|
||||
raise ValueError("人脸过于贴近边缘，扶正后无法保留9号点与152号点")
|
||||
metadata = {
|
||||
"enabled": True, "standard_crop": cropped, "aspect_crop": aspect_cropped,
|
||||
"aspect_crop_skip_reason": aspect_skip_reason,
|
||||
"status": "reference_crop" if cropped else ("aspect_crop" if aspect_cropped else "rotation_only"),
|
||||
"skip_reason": None if cropped else "insufficient_space",
|
||||
"message": ("已扶正并按参考图裁切" if cropped else
|
||||
"原图偏长，已扶正并裁成3:4，保留完整人脸" if aspect_cropped else
|
||||
"3:4窗口无法完整保留人脸，已仅扶正" if aspect_skip_reason else
|
||||
"空间不足，仅扶正，未按参考比例裁切"),
|
||||
"rotation_degrees": float(np.degrees(np.arctan2(rotation[0, 1], rotation[0, 0]))),
|
||||
"source_size": {"width": w, "height": h}, "output_size": {"width": ow, "height": oh},
|
||||
"requested_crop_size": {"width": target_w, "height": target_h},
|
||||
"face_axis_length_px": length,
|
||||
"height_per_face_length": profile["height_per_face_length"],
|
||||
"width_per_face_length": profile["height_per_face_length"]*.75,
|
||||
"source_to_processed": matrix.tolist(),
|
||||
"processed_to_source": cv2.invertAffineTransform(matrix).tolist(),
|
||||
"axis_x_error_px": float(abs(axis[0, 0]-axis[1, 0])),
|
||||
"profile_version": profile["version"],
|
||||
"reference_image_url": profile["reference_image_url"],
|
||||
}
|
||||
return image, moved, metadata
|
||||
@@ -124,6 +124,46 @@ def build_hairline_mask(parse_map: np.ndarray) -> np.ndarray:
|
||||
return build_hair_mask(parse_map)
|
||||
|
||||
|
||||
def align_hairline_for_template(
|
||||
landmarks_norm: np.ndarray,
|
||||
hairline_norm: np.ndarray,
|
||||
valid: np.ndarray,
|
||||
image_shape: tuple[int, int],
|
||||
) -> np.ndarray:
|
||||
"""Keep a designed template aligned with the face rather than the fringe.
|
||||
|
||||
Uneven bangs can make the detected hair boundary much higher on one
|
||||
side, shearing an otherwise balanced template. Average the upward
|
||||
extension of each anatomical anchor pair, measured in image pixels.
|
||||
Keep the face anchors and the perpendicular component unchanged so
|
||||
facial roll/perspective remain visible; do not level the curve in the
|
||||
image. Missing ray hits retain their fallback and never influence a
|
||||
valid partner. Call silhouette clamping afterwards to bound the result.
|
||||
|
||||
This is for template placement only, not measurement of the real hairline.
|
||||
"""
|
||||
h, w = image_shape
|
||||
scale = np.array([w, h], dtype=np.float64)
|
||||
up = face_up_vector(landmarks_norm) * scale
|
||||
length = np.linalg.norm(up)
|
||||
if length < 1e-9:
|
||||
return hairline_norm.copy()
|
||||
up /= length
|
||||
|
||||
anchors = landmarks_norm[C.MP_TOP_ANCHORS, :2] * scale
|
||||
points = hairline_norm * scale
|
||||
distances = (points - anchors) @ up
|
||||
indices = {mp_idx: i for i, mp_idx in enumerate(C.MP_TOP_ANCHORS)}
|
||||
for left, right in C.MP_TOP_ANCHOR_PAIRS:
|
||||
i, j = indices[left], indices[right]
|
||||
if not (valid[i] and valid[j]):
|
||||
continue
|
||||
mean_distance = (distances[i] + distances[j]) / 2.0
|
||||
for k in (i, j):
|
||||
points[k] += (mean_distance - distances[k]) * up
|
||||
return (points / scale).astype(np.float32)
|
||||
|
||||
|
||||
def smooth_hairline(
|
||||
hairline_norm: np.ndarray,
|
||||
valid: np.ndarray,
|
||||
|
||||
@@ -0,0 +1,117 @@
|
||||
"""接口52设计高度：上庭 = (中庭 + 下庭) / 2；不改变接口2/5的几何。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from .mask import mask_from_curve
|
||||
|
||||
|
||||
def height_target(landmarks, width, height, reference=None):
|
||||
"""Use unrounded measurement coordinates; fall back to the same MP indices.
|
||||
|
||||
Distances use image Y, matching face_analysis.measure's court heights.
|
||||
No rounded centimetre values or existing-hairline estimates enter the rule.
|
||||
"""
|
||||
scale = np.array([width, height], dtype=np.float64)
|
||||
if reference is None:
|
||||
brow, nose, chin = landmarks[[9, 94, 152], :2] * scale
|
||||
source = "hairline_landmarks"
|
||||
px_per_cm = None
|
||||
else:
|
||||
brow = np.asarray(reference["brow_center"], dtype=np.float64)
|
||||
nose = np.asarray(reference["nose_bottom"], dtype=np.float64)
|
||||
chin = np.asarray(reference["chin_tip"], dtype=np.float64)
|
||||
source = "face_measure"
|
||||
px_per_cm = reference.get("px_per_cm")
|
||||
if not np.isfinite([brow, nose, chin]).all() or not brow[1] < nose[1] < chin[1]:
|
||||
raise ValueError("眉心、鼻翼下缘与下巴坐标异常，无法计算目标上庭高度")
|
||||
middle, lower = float(nose[1] - brow[1]), float(chin[1] - nose[1])
|
||||
upper = (middle + lower) / 2.0
|
||||
target_y = float(brow[1] - upper)
|
||||
if not (0 <= brow[0] < width and 1 <= target_y < height - 1):
|
||||
raise ValueError("按中下庭均值计算的目标发际线超出图片范围，请使用完整正面照")
|
||||
def pt(p):
|
||||
return {"x": float(p[0]), "y": float(p[1])}
|
||||
return {
|
||||
"name": "court_average",
|
||||
"formula": "上庭 = (中庭 + 下庭) / 2",
|
||||
"measurement_source": source,
|
||||
"brow_center": pt(brow), "nose_bottom": pt(nose), "chin_tip": pt(chin),
|
||||
"middle_height_px": middle, "lower_height_px": lower,
|
||||
"target_upper_height_px": upper,
|
||||
"target_upper_height_cm": upper / px_per_cm if px_per_cm else None,
|
||||
"target_center": {"x": float(brow[0]), "y": target_y},
|
||||
}
|
||||
|
||||
|
||||
def clean_curve(overlay):
|
||||
"""Keep the white design line; discard opaque black texture-border pixels."""
|
||||
out = np.zeros_like(overlay, dtype=np.float32)
|
||||
out[:, :, :3] = 255
|
||||
out[:, :, 3] = overlay[:, :, 3] * (overlay[:, :, :3].mean(axis=2) > 128)
|
||||
return out
|
||||
|
||||
|
||||
def curve_center(overlay, center_x):
|
||||
"""Alpha-weighted centre of the line at the facial midline, in image pixels."""
|
||||
h, w = overlay.shape[:2]
|
||||
column = int(round(center_x))
|
||||
if not 0 <= column < w:
|
||||
raise ValueError("面部中轴超出图片范围")
|
||||
alpha = overlay[:, column, 3].astype(np.float64)
|
||||
alpha[alpha <= 1] = 0
|
||||
if not alpha.any():
|
||||
# Rasterized endpoints may leave a 1px gap, but distant sideburns
|
||||
# must never be silently substituted for a missing central curve.
|
||||
columns = np.flatnonzero((overlay[:, :, 3] > 1).any(axis=0))
|
||||
if not columns.size or np.min(np.abs(columns - column)) > 2:
|
||||
raise ValueError("发际线模板在面部中轴处没有有效曲线")
|
||||
column = int(columns[np.argmin(np.abs(columns - column))])
|
||||
alpha = overlay[:, column, 3].astype(np.float64)
|
||||
alpha[alpha <= 1] = 0
|
||||
return {"x": float(center_x), "y": float(np.dot(np.arange(h), alpha) / alpha.sum())}
|
||||
|
||||
|
||||
def place_curve(overlay, target, landmarks):
|
||||
"""Rigidly shift a rendered template along face-up to meet the target Y.
|
||||
|
||||
Keeping the curve rigid preserves roll and the template's shape. Pixel Y
|
||||
remains the height convention, even for a rolled face. A short correction
|
||||
compensates for the curve's slope as the local-axis shift also changes X.
|
||||
"""
|
||||
curve = clean_curve(overlay)
|
||||
h, w = curve.shape[:2]
|
||||
center_x, target_y = target["x"], target["y"]
|
||||
before = curve_center(curve, center_x)
|
||||
up = (landmarks[10, :2] - landmarks[152, :2]) * [w, h]
|
||||
if abs(up[1]) < 1e-6:
|
||||
raise ValueError("无法确定人脸向上方向")
|
||||
x_per_y = float(np.clip(up[0] / up[1], -1.0, 1.0))
|
||||
dy = target_y - before["y"]
|
||||
moved = curve
|
||||
for _ in range(8):
|
||||
transform = np.array([[1, 0, dy * x_per_y], [0, 1, dy]], np.float32)
|
||||
moved = cv2.warpAffine(curve, transform, (w, h), flags=cv2.INTER_LINEAR,
|
||||
borderMode=cv2.BORDER_CONSTANT, borderValue=0)
|
||||
center = curve_center(moved, center_x)
|
||||
residual = target_y - center["y"]
|
||||
if abs(residual) <= 0.15:
|
||||
break
|
||||
dy += residual
|
||||
if abs(center["y"] - target_y) > 1.0:
|
||||
raise ValueError("发际线中心无法对齐目标高度，请检查照片姿态与裁剪范围")
|
||||
return moved, {
|
||||
"old_center": before, "new_center": center,
|
||||
"shift_x_px": float(dy * x_per_y), "shift_y_px": float(dy),
|
||||
"target_error_px": float(abs(center["y"] - target_y)),
|
||||
}
|
||||
|
||||
|
||||
def inpaint_from_curve(photo, landmarks, parse_map, overlay):
|
||||
"""The preview line is also the exact boundary used for the black line/mask."""
|
||||
alpha = np.clip(overlay[:, :, 3:4] / 255.0, 0, 1)
|
||||
marked = np.clip(photo * (1.0 - alpha), 0, 255).astype(np.uint8)
|
||||
curve = (overlay[:, :, 3] > 40).astype(np.uint8) * 255
|
||||
mask = mask_from_curve(curve, landmarks, parse_map)
|
||||
return marked, mask
|
||||
+2
-2
@@ -16,7 +16,7 @@ from . import comfyui
|
||||
|
||||
logger = logging.getLogger("hair.worker")
|
||||
|
||||
_DEFAULT_PROMPT = "填充遮罩区域的头发"
|
||||
_DEFAULT_PROMPT = "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
_REPO = os.path.dirname(os.path.dirname(__file__))
|
||||
_REPAINT_WORKFLOW = os.path.join(_REPO, "0716add-hair-api.json")
|
||||
|
||||
@@ -62,7 +62,7 @@ def run_redraw(image_bytes: bytes, mask_bytes: bytes,
|
||||
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"
|
||||
prompt: 提示词，None 用默认 "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
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
|
||||
|
||||
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"version": "2026-11-04-upright-9-152-subject-90pct-tall-3x4",
|
||||
"source_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/misc/20261103_user.jpg",
|
||||
"reference_image_url": "/static/framing_reference.png",
|
||||
"reference_size": {
|
||||
"width": 786,
|
||||
"height": 1048
|
||||
},
|
||||
"source_size": {
|
||||
"width": 792,
|
||||
"height": 1056
|
||||
},
|
||||
"brow_9": {
|
||||
"x": 385.1878369059212,
|
||||
"y": 360.65437126595765
|
||||
},
|
||||
"chin_152": {
|
||||
"x": 385.18783690592113,
|
||||
"y": 725.8251047982238
|
||||
},
|
||||
"face_axis_length_px": 365.1707335322661,
|
||||
"brow_x_ratio": 0.4900608612034621,
|
||||
"brow_y_ratio": 0.3441358504446161,
|
||||
"reference_width_per_face_length": 2.152417835890317,
|
||||
"reference_height_per_face_length": 2.8698904478537566,
|
||||
"width_per_face_length": 2.5,
|
||||
"height_per_face_length": 3.333333333333333,
|
||||
"rotation_degrees": 0.2596248408989324,
|
||||
"axis_x_error_px": 5.684341886080802e-14,
|
||||
"source_to_reference": [
|
||||
[
|
||||
0.9999897336401403,
|
||||
0.004531292786961665,
|
||||
-5.386196599797756
|
||||
],
|
||||
[
|
||||
-0.004531292786961665,
|
||||
0.9999897336401403,
|
||||
-2.202458197930582
|
||||
]
|
||||
],
|
||||
"landmark_source": "source_landmarks_transformed_without_redetection"
|
||||
}
|
||||
+93
-9
@@ -15,12 +15,14 @@ from . import comfyui
|
||||
from .face_landmarks import FaceLandmarker
|
||||
from .face_parsing import FaceParser
|
||||
from .hairline_2d import (
|
||||
smooth_hairline, sample_hairline_clamped,
|
||||
smooth_hairline, sample_hairline, align_hairline_for_template,
|
||||
clamp_hairline_to_silhouette,
|
||||
)
|
||||
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
|
||||
from .height_rule import height_target, clean_curve, place_curve, inpaint_from_curve
|
||||
|
||||
import base64
|
||||
import io
|
||||
@@ -58,7 +60,7 @@ _BLACK_TEXTURE_DIR = os.path.join(_REPO, "hairline_texture_black")
|
||||
# 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")
|
||||
_REDRAW_PROMPT = os.getenv("REDRAW_PROMPT", "填充遮罩区域的头发，人物增加一点磨皮")
|
||||
|
||||
# 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
|
||||
@@ -177,20 +179,25 @@ def extract_502(image_bgr: np.ndarray):
|
||||
return ctx["points"], ctx["valid"]
|
||||
|
||||
|
||||
def extract_context(image_bgr: np.ndarray):
|
||||
def extract_context(image_bgr: np.ndarray, landmarks: np.ndarray | None = 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）：
|
||||
发际线几何使用射线检测 + 人脸对应点对齐 + 头部轮廓钳制：
|
||||
左右刘海高低不同时，协调对应锚点的延伸距离，避免把设计模板拉歪；
|
||||
保留人脸本身的倾斜。预览白线与生发黑线/遮罩共用同一套 points。
|
||||
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。
|
||||
fallback 点不会再跑出头部轮廓。
|
||||
"""
|
||||
rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
|
||||
landmarks = get_landmarker().detect(rgb)
|
||||
if landmarks is None:
|
||||
landmarks = get_landmarker().detect(rgb)
|
||||
if landmarks is None:
|
||||
return None
|
||||
parse_map = get_parser().parse(rgb)
|
||||
hairline_2d, valid = sample_hairline_clamped(landmarks, parse_map)
|
||||
hairline_2d, valid = sample_hairline(landmarks, parse_map)
|
||||
hairline_2d = align_hairline_for_template(landmarks, hairline_2d, valid, parse_map.shape)
|
||||
hairline_2d = clamp_hairline_to_silhouette(hairline_2d, 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)
|
||||
@@ -315,7 +322,10 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
|
||||
一致的原生生发（ComfyUI add_hair），由 _grow_native_one 完成（黑模板 + inpaint 遮罩）。
|
||||
|
||||
overlay 仍是发际线曲线透明层（与 generate_grow_results 完全一致）。
|
||||
prompt 仅用于原生生发分支（换发型分支的提示词由 redraw 流程内部固定）。
|
||||
prompt 两条分支都生效：换发型分支传入 _call_local_redraw，原生生发分支传给
|
||||
_grow_native_one。None/默认「填充遮罩区域的头发，人物增加一点磨皮」时与全局 _REDRAW_PROMPT 一致，
|
||||
不破坏 CLIP 缓存命中的快路径；传不同提示词会使缓存失效并重载 CLIP/Flux
|
||||
（实测换词后首次请求重绘 45s vs 缓存命中 8s），后续相同提示词恢复快路径。
|
||||
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。
|
||||
"""
|
||||
@@ -389,7 +399,8 @@ def generate_grow_results_swap(image_bgr: np.ndarray, hair_styles: list[int] | N
|
||||
_tr0 = _t.perf_counter()
|
||||
grown_png = _call_local_redraw(final_bytes, mask_bytes,
|
||||
max_side=redraw_max_side,
|
||||
unet_name=unet_name)
|
||||
unet_name=unet_name,
|
||||
prompt=prompt)
|
||||
_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",
|
||||
@@ -529,6 +540,79 @@ def generate_hairline_pngs(image_bgr: np.ndarray, gender: str,
|
||||
return {"images": images, "best_centers": best_centers}
|
||||
|
||||
|
||||
def generate_hairline_pngs_52(image_bgr: np.ndarray, gender: str,
|
||||
hair_styles: list[int], use_mask: bool = True,
|
||||
prompt: str | None = None,
|
||||
generate_grow_image: bool = True,
|
||||
height_reference: dict | None = None,
|
||||
compare_rules: bool = False,
|
||||
precomputed_landmarks: np.ndarray | None = None):
|
||||
"""接口52：每种 middle 曲线中点对齐到眉心上方 (中庭+下庭)/2。
|
||||
|
||||
只读取 middle 贴图，high/low 保留空值。生发直接复用移动后的预览曲线，
|
||||
不再使用位置不同的黑模板。compare_rules 仅为调试页附带旧规则叠图。
|
||||
接口2仍使用原有管线。
|
||||
"""
|
||||
if gender not in ("male", "female"):
|
||||
raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
|
||||
if not hair_styles:
|
||||
raise ValueError("hair_styles 必填且不能为空")
|
||||
ctx = extract_context(image_bgr, landmarks=precomputed_landmarks)
|
||||
if ctx is None:
|
||||
return None
|
||||
h, w = image_bgr.shape[:2]
|
||||
uv, ext_faces = load_ext_mesh()
|
||||
rule = height_target(ctx["landmarks"], w, h, height_reference)
|
||||
textures = get_texture_map()[gender]
|
||||
|
||||
# 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 generate_grow_image and not use_mask:
|
||||
shared_grown = _grow_from_texture(image_bgr, ctx, None, use_mask=False, prompt=prompt)
|
||||
|
||||
images, best_centers = [], None
|
||||
for s in hair_styles: # s = 1-indexed 发型序号
|
||||
key, mid_path = textures[s - 1]
|
||||
old = build_overlay_layer(h, w, ctx["points"], ext_faces, uv, load_texture_rgba(mid_path))
|
||||
overlay, placement = place_curve(old, rule["target_center"], ctx["landmarks"])
|
||||
overlays = {"middle": overlay, "high": None, "low": None}
|
||||
if not generate_grow_image:
|
||||
grown_png = None
|
||||
elif not use_mask:
|
||||
grown_png = shared_grown
|
||||
else:
|
||||
grown_png = None
|
||||
try:
|
||||
marked, mask = inpaint_from_curve(
|
||||
image_bgr, ctx["landmarks"], ctx["parse_map"], overlay)
|
||||
buf = io.BytesIO()
|
||||
compose_comfy_rgba(marked, mask).save(buf, format="PNG", compress_level=1)
|
||||
grown_png = comfyui.run(buf.getvalue(), prompt=prompt)
|
||||
except Exception as ex: # 单张生发失败不丢弃设计曲线
|
||||
logger.warning("接口52 生发失败 type=%s：%s", key, ex)
|
||||
# Flux may round dimensions to a multiple of its latent stride.
|
||||
# Restore the input size so the preview and generated photo align.
|
||||
if grown_png:
|
||||
grown_png = _upscale_png_to(grown_png, w, h)
|
||||
item = {"hairline_type": key, "order": s, "overlays": overlays,
|
||||
"grown_png": grown_png, "height_placement": placement}
|
||||
if compare_rules:
|
||||
item["legacy_overlay"] = clean_curve(old)
|
||||
images.append(item)
|
||||
if best_centers is None:
|
||||
center = placement["new_center"]
|
||||
best_centers = {"middle": (round(center["x"]), round(center["y"])),
|
||||
"high": None, "low": None}
|
||||
guide_landmarks = {
|
||||
str(index): {"x": float(ctx["landmarks"][index, 0] * w),
|
||||
"y": float(ctx["landmarks"][index, 1] * h)}
|
||||
for index in (9, 150, 152, 162, 251, 389)
|
||||
}
|
||||
return {"images": images, "best_centers": best_centers, "height_rule": rule,
|
||||
"guide_landmarks": guide_landmarks}
|
||||
|
||||
|
||||
# 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"))
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"allowed_ips": ["49.232.51.171"]
|
||||
}
|
||||
@@ -0,0 +1,77 @@
|
||||
"""Direct-peer IP access policy for the GPU worker's dynamic endpoints."""
|
||||
import ipaddress
|
||||
import json
|
||||
from pathlib import Path
|
||||
import socket
|
||||
|
||||
CONFIG_PATH = Path(__file__).resolve().parent / "ip_access.json"
|
||||
|
||||
|
||||
def _address(value):
|
||||
address = ipaddress.ip_address(value)
|
||||
return address.ipv4_mapped if isinstance(address, ipaddress.IPv6Address) and address.ipv4_mapped else address
|
||||
|
||||
|
||||
def local_addresses():
|
||||
addresses = {_address("127.0.0.1"), _address("::1")}
|
||||
try:
|
||||
addresses.update(_address(item[4][0].split("%", 1)[0])
|
||||
for item in socket.getaddrinfo(socket.gethostname(), None))
|
||||
except (OSError, ValueError):
|
||||
pass
|
||||
# Include all assigned Linux interface addresses, not entire private ranges.
|
||||
try:
|
||||
import fcntl
|
||||
import struct
|
||||
with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as sock:
|
||||
for _, name in socket.if_nameindex():
|
||||
try:
|
||||
data = fcntl.ioctl(sock.fileno(), 0x8915, struct.pack("256s", name.encode()[:15]))
|
||||
addresses.add(_address(socket.inet_ntoa(data[20:24])))
|
||||
except OSError:
|
||||
continue
|
||||
path = Path("/proc/net/if_inet6")
|
||||
if path.exists():
|
||||
addresses.update(ipaddress.IPv6Address(line.split()[0]) for line in path.read_text().splitlines())
|
||||
except (ImportError, OSError, ValueError):
|
||||
pass
|
||||
return addresses
|
||||
|
||||
|
||||
def _networks(values):
|
||||
if not isinstance(values, list) or not all(isinstance(value, str) for value in values):
|
||||
raise ValueError("IP配置必须是字符串列表")
|
||||
return [ipaddress.ip_network(value, strict=False) for value in values]
|
||||
|
||||
|
||||
def _matches(address, networks):
|
||||
return any(address.version == network.version and address in network for network in networks)
|
||||
|
||||
|
||||
def check_access(request):
|
||||
"""Return (allowed, reason), re-reading the whitelist for each request."""
|
||||
try:
|
||||
config = json.loads(CONFIG_PATH.read_text()) if CONFIG_PATH.exists() else {}
|
||||
allowed = _networks(config.get("allowed_ips", []))
|
||||
if request.client is None or request.client.port == 0:
|
||||
# Uvicorn proxy middleware has discarded the socket peer at port 0.
|
||||
# Reject rather than treating an unverified forwarded IP as local.
|
||||
return False, "无法确认直连来源，请使用 --no-proxy-headers 启动服务"
|
||||
peer = _address(request.client.host)
|
||||
# Authorize the gateway's socket IP, never its end-user forwarding headers.
|
||||
if peer.is_unspecified:
|
||||
return False, "来源地址无效"
|
||||
return (True, "") if peer.is_loopback or peer in local_addresses() or _matches(peer, allowed) else (False, "来源IP不在接口白名单")
|
||||
except (OSError, ValueError, TypeError, AttributeError):
|
||||
return False, "IP白名单配置或来源地址无效"
|
||||
|
||||
|
||||
def rejection(request):
|
||||
from fastapi.responses import JSONResponse
|
||||
if request.url.path == "/static" or request.url.path.startswith("/static/"):
|
||||
return None
|
||||
allowed, reason = check_access(request)
|
||||
if allowed:
|
||||
return None
|
||||
return JSONResponse(status_code=403, content={"code": 1010, "message": reason,
|
||||
"request_id": "ip-access", "data": None})
|
||||
@@ -48,7 +48,7 @@ cd /home/ubuntu/hair/local_test
|
||||
|--------|------|------|------|
|
||||
| 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" |
|
||||
| prompt | String | 否 | 提示词，默认值："填充遮罩区域的头发，人物增加一点磨皮" |
|
||||
|
||||
#### 遮罩图片格式说明
|
||||
|
||||
@@ -70,7 +70,7 @@ cd /home/ubuntu/hair/local_test
|
||||
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" \
|
||||
-F "prompt=填充遮罩区域的头发，人物增加一点磨皮" \
|
||||
--output result.png
|
||||
```
|
||||
|
||||
@@ -85,7 +85,7 @@ files = {
|
||||
"mask": open("mask.png", "rb"),
|
||||
}
|
||||
data = {
|
||||
"prompt": "填充遮罩区域的头发"
|
||||
"prompt": "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
}
|
||||
|
||||
resp = requests.post(url, files=files, data=data, timeout=600)
|
||||
|
||||
+1
-1
@@ -206,7 +206,7 @@ def generate():
|
||||
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")
|
||||
prompt_text = request.form.get("prompt", "填充遮罩区域的头发，人物增加一点磨皮")
|
||||
log.info(
|
||||
"收到请求: image=%s mask=%s prompt=%r",
|
||||
image_file.filename, mask_file.filename, prompt_text,
|
||||
|
||||
+1
-1
@@ -27,7 +27,7 @@ def prep_and_upload():
|
||||
|
||||
|
||||
def run_once(fname, model, dtype, steps):
|
||||
wf = A.build_workflow(fname, "填充遮罩区域的头发")
|
||||
wf = A.build_workflow(fname, "填充遮罩区域的头发，人物增加一点磨皮")
|
||||
wf["16"]["inputs"]["unet_name"] = model
|
||||
wf["16"]["inputs"]["weight_dtype"] = dtype
|
||||
wf["1"]["inputs"]["steps"] = steps
|
||||
|
||||
@@ -33,7 +33,7 @@ def upload(scale=1.0):
|
||||
|
||||
|
||||
def run(fname, steps):
|
||||
wf = A.build_workflow(fname, "填充遮罩区域的头发")
|
||||
wf = A.build_workflow(fname, "填充遮罩区域的头发，人物增加一点磨皮")
|
||||
wf["16"]["inputs"]["unet_name"] = MODEL
|
||||
wf["16"]["inputs"]["weight_dtype"] = DTYPE
|
||||
wf["1"]["inputs"]["steps"] = steps
|
||||
|
||||
@@ -30,7 +30,7 @@ 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 = A.build_workflow(fname, "填充遮罩区域的头发，人物增加一点磨皮", seed=SEED)
|
||||
wf["16"]["inputs"]["unet_name"] = MODEL
|
||||
wf["16"]["inputs"]["weight_dtype"] = DTYPE
|
||||
wf["1"]["inputs"]["steps"] = steps
|
||||
|
||||
@@ -55,7 +55,7 @@ button { padding: 10px 24px; border: none; border-radius: 6px; cursor: pointer;
|
||||
</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">
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发，人物增加一点磨皮">
|
||||
</div>
|
||||
<div style="text-align:center; margin-top:16px">
|
||||
<button class="btn-generate" id="generateBtn" disabled>🚀 生成</button>
|
||||
|
||||
@@ -25,7 +25,7 @@ resp = requests.post(
|
||||
"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"},
|
||||
data={"prompt": "填充遮罩区域的头发，人物增加一点磨皮"},
|
||||
timeout=600,
|
||||
)
|
||||
|
||||
|
||||
+2
-2
@@ -31,11 +31,11 @@ fi
|
||||
|
||||
if [ "${FOREGROUND:-0}" = "1" ]; then
|
||||
echo ">> 前台运行 worker: ${HOST}:${PORT}"
|
||||
exec "$VENV_UVICORN" app:app --host "$HOST" --port "$PORT"
|
||||
exec "$VENV_UVICORN" app:app --host "$HOST" --port "$PORT" --no-proxy-headers
|
||||
fi
|
||||
|
||||
echo ">> 后台启动 worker: ${HOST}:${PORT}"
|
||||
nohup "$VENV_UVICORN" app:app --host "$HOST" --port "$PORT" >> "$LOG_FILE" 2>&1 &
|
||||
nohup "$VENV_UVICORN" app:app --host "$HOST" --port "$PORT" --no-proxy-headers >> "$LOG_FILE" 2>&1 &
|
||||
echo $! > "$PID_FILE"
|
||||
sleep 1
|
||||
if kill -0 "$(cat "$PID_FILE")" 2>/dev/null; then
|
||||
|
||||
@@ -0,0 +1,25 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Straighten the specified reference first, then recalibrate framing ratios."""
|
||||
import json
|
||||
from pathlib import Path
|
||||
import sys
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
sys.path.insert(0, str(ROOT))
|
||||
|
||||
import cv2
|
||||
from hairline.framing import calibrate_reference, decode_portrait, PROFILE_PATH
|
||||
from hairline.service import get_landmarker
|
||||
|
||||
if __name__ == "__main__":
|
||||
source = Path(sys.argv[1]) if len(sys.argv)>1 else ROOT/"tests/output/upload_framing/reference.jpg"
|
||||
photo = decode_portrait(source.read_bytes())
|
||||
if photo is None:
|
||||
raise ValueError("Reference image cannot be decoded")
|
||||
landmarks = get_landmarker().detect(cv2.cvtColor(photo, cv2.COLOR_BGR2RGB), include_iris=True)
|
||||
if landmarks is None:
|
||||
raise ValueError("Reference face not detected")
|
||||
image, _, profile = calibrate_reference(photo, landmarks)
|
||||
cv2.imwrite(str(ROOT/"static/framing_reference.png"), image)
|
||||
PROFILE_PATH.write_text(json.dumps(profile, ensure_ascii=False, indent=2)+"\n", encoding="utf-8")
|
||||
print(json.dumps(profile, ensure_ascii=False, indent=2))
|
||||
@@ -0,0 +1,122 @@
|
||||
#!/usr/bin/env python3
|
||||
"""保存接口52旧/新高度规则对比，供 static/test_interface52_compare.html 展示。
|
||||
|
||||
默认验证用户清单全部30张，男女各7型，共420组；只跑定位，不调用ComfyUI。
|
||||
用法：./venv/bin/python scripts/compare_hairline_height.py
|
||||
可用 --cases 1,2,3,4,5 只验证指定案例。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import base64
|
||||
from datetime import datetime
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
import time
|
||||
from zoneinfo import ZoneInfo
|
||||
|
||||
import cv2
|
||||
import httpx
|
||||
import numpy as np
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
def run(args):
|
||||
manifest = json.loads((ROOT / "tests/fixtures/hairline_height_cases.json").read_text())
|
||||
available = {c["image_number"]: c for c in manifest["cases"]}
|
||||
requested = list(available) if args.cases == "all" else [int(n) for n in args.cases.split(",")]
|
||||
cases = [available[n] for n in requested]
|
||||
inputs = ROOT / "tests/output/hairline_height_compare/inputs"
|
||||
inputs.mkdir(parents=True, exist_ok=True)
|
||||
output = ROOT / "static/annotations/hairline_height_compare"
|
||||
output.mkdir(parents=True, exist_ok=True)
|
||||
report = {
|
||||
"created_at": datetime.now(ZoneInfo("Asia/Shanghai")).isoformat(),
|
||||
"case_numbers": requested, "groups": [], "errors": [],
|
||||
}
|
||||
started = time.monotonic()
|
||||
with httpx.Client(timeout=180, follow_redirects=True) as client:
|
||||
for case in cases:
|
||||
number = case["image_number"]
|
||||
source = inputs / f"case_{number}.png"
|
||||
if not source.exists():
|
||||
response = client.get(case["image_url"])
|
||||
response.raise_for_status()
|
||||
source.write_bytes(response.content)
|
||||
photo = cv2.imdecode(np.frombuffer(source.read_bytes(), np.uint8), cv2.IMREAD_COLOR)
|
||||
if photo is None:
|
||||
raise ValueError(f"案例 {number} 无法解码")
|
||||
# Keep the source photo available even when positioning fails.
|
||||
original_path = output / f"case_{number}_original.jpg"
|
||||
cv2.imwrite(str(original_path), photo, [cv2.IMWRITE_JPEG_QUALITY, 95])
|
||||
original_url = "/static/annotations/hairline_height_compare/" + original_path.name
|
||||
# Interface 52 frames native-resolution input before any size changes.
|
||||
content = cv2.imencode(".png", photo)[1].tobytes()
|
||||
for gender in args.genders.split(","):
|
||||
step = time.monotonic()
|
||||
try:
|
||||
response = client.post(args.base_url.rstrip("/") + "/api/v1/debug/hairline-height-compare",
|
||||
files={"image_file": (f"case_{number}.png", content, "image/png")},
|
||||
data={"gender": gender, "hair_style": "1,2,3,4,5,6,7"})
|
||||
response.raise_for_status()
|
||||
body = response.json()
|
||||
if body["code"] != 0:
|
||||
raise ValueError(body["message"])
|
||||
group = body["data"]
|
||||
group.update(case_number=number, source_url=case["image_url"],
|
||||
elapsed_seconds=round(time.monotonic() - step, 2))
|
||||
def save_image(obj, field, suffix):
|
||||
value = obj.pop(field + "_base64", None)
|
||||
if not value:
|
||||
return
|
||||
# Both gender sets use the same source and processed
|
||||
# photos; share their URLs and browser image cache.
|
||||
prefix = f"case_{number}" if obj is group else f"case_{number}_{gender}"
|
||||
path = output / f"{prefix}_{suffix}"
|
||||
path.write_bytes(base64.b64decode(value.split(",", 1)[-1]))
|
||||
obj[field + "_url"] = "/static/annotations/hairline_height_compare/" + path.name
|
||||
save_image(group, "input", "input.jpg")
|
||||
save_image(group, "original", "original.jpg")
|
||||
for result in group["results"]:
|
||||
save_image(result, "old_overlay", f"{result['order']}_old.png")
|
||||
save_image(result, "new_overlay", f"{result['order']}_new.png")
|
||||
save_image(result, "grown_image", f"{result['order']}_grown.jpg")
|
||||
if result["height_placement"]["target_error_px"] > 1:
|
||||
raise ValueError(f"{result['hairline_type']} 中心误差超过1像素")
|
||||
if group["framing"]["axis_x_error_px"] > 1:
|
||||
raise ValueError("扶正后的9/152横向误差超过1像素")
|
||||
report["groups"].append(group)
|
||||
print(f"case {number} / {gender}: {len(group['results'])} shapes OK ({group['elapsed_seconds']}s)", flush=True)
|
||||
except Exception as ex:
|
||||
report["errors"].append({"case_number": number, "gender": gender, "message": str(ex),
|
||||
"source_url": case["image_url"], "original_url": original_url,
|
||||
"width": photo.shape[1], "height": photo.shape[0]})
|
||||
print(f"case {number} / {gender}: FAILED {ex}", flush=True)
|
||||
# Atomically expose partial results to the page while running.
|
||||
temporary = output / "latest.json.tmp"
|
||||
temporary.write_text(json.dumps(report, ensure_ascii=False, indent=2))
|
||||
os.replace(temporary, output / "latest.json")
|
||||
results = [r for g in report["groups"] for r in g["results"]]
|
||||
summary = {
|
||||
"cases": len(cases), "successful_groups": len(report["groups"]),
|
||||
"comparisons": len(results), "failures": len(report["errors"]),
|
||||
"max_center_error_px": max((r["height_placement"]["target_error_px"] for r in results), default=None),
|
||||
"elapsed_seconds": round(time.monotonic() - started, 2),
|
||||
}
|
||||
report["summary"] = summary
|
||||
temporary = output / "latest.json.tmp"
|
||||
temporary.write_text(json.dumps(report, ensure_ascii=False, indent=2))
|
||||
os.replace(temporary, output / "latest.json")
|
||||
print(json.dumps(summary, ensure_ascii=False), flush=True)
|
||||
if report["errors"]:
|
||||
raise SystemExit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--cases", default="all", help="all 或逗号分隔的图片编号")
|
||||
parser.add_argument("--genders", default="female,male")
|
||||
parser.add_argument("--base-url", default="http://127.0.0.1:8187")
|
||||
run(parser.parse_args())
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 520 KiB |
File diff suppressed because one or more lines are too long
@@ -0,0 +1,254 @@
|
||||
"use strict";
|
||||
const $ = id => document.getElementById(id);
|
||||
const styleNames = {
|
||||
female: ["椭圆形", "花瓣形", "心形", "直线形", "波浪形", "大花瓣形", "古典花瓣形"],
|
||||
male: ["椭圆形", "倒弧形", "M 型", "直线形", "心形", "柔和花瓣形", "波浪形"]
|
||||
};
|
||||
let dataset = {groups: [], errors: []}, mode = "side", drawVersion = 0, busy = false, cardObserver = null;
|
||||
const inputImages = new Map();
|
||||
const rowSelections = new Map();
|
||||
const esc = value => String(value).replace(/[&<>"']/g, c => ({"&":"&","<":"<",">":">",'"':""","'":"'"}[c]));
|
||||
const fixed = (value, n = 2) => Number(value).toFixed(n);
|
||||
const imageSource = (object, field, mime = "image/png") => object[field + "_url"] || window.resolveImgSrc(null, object[field + "_base64"], mime);
|
||||
function status(text, error = false) { $("status").textContent = text; $("status").classList.toggle("error", error); }
|
||||
function loadImage(src) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const im = new Image();
|
||||
im.onload = () => resolve(im); im.onerror = () => reject(new Error("无法读取对比图片，请重新运行案例")); im.src = src;
|
||||
});
|
||||
}
|
||||
function inputImage(group) {
|
||||
const src = imageSource(group, "input", "image/jpeg");
|
||||
if (!inputImages.has(src)) inputImages.set(src, loadImage(src));
|
||||
return inputImages.get(src);
|
||||
}
|
||||
function originalCard(group,caseName){
|
||||
const src=imageSource(group,"original","image/jpeg")||group.source_url||imageSource(group,"input","image/jpeg");
|
||||
const size=group.framing?.source_size||{width:group.width,height:group.height};
|
||||
const dimensions=size.width&&size.height?`width="${size.width}" height="${size.height}"`:"";
|
||||
const sizeLabel=size.width&&size.height?`原尺寸 ${size.width}×${size.height}`:"完整上传图片";
|
||||
const panel=document.createElement("article");panel.className="original-reference-card";
|
||||
panel.innerHTML=`<div class="case-header"><div><span class="case-number original-reference-label">未裁切原图</span><h3>${esc(caseName)} · 上传原图</h3><p>处理前完整图片</p></div></div><div class="metric-line">${sizeLabel}</div><div class="image-pair overlay"><figure><figcaption>未扶正、未裁切</figcaption><a href="${esc(src)}" target="_blank" rel="noopener"><img src="${esc(src)}" ${dimensions} alt="${esc(caseName)}未裁切原图" loading="lazy" decoding="async"></a></figure></div><div class="case-bottom"><span>保留完整画面</span><a href="${esc(src)}" target="_blank" rel="noopener">查看原图 ↗</a></div>`;
|
||||
return panel;
|
||||
}
|
||||
function failureCard(errors){
|
||||
const panel=document.createElement("article");panel.className="failed-reference-card";
|
||||
panel.innerHTML='<h3>定位失败，未生成对比图</h3><p>左侧保留完整上传原图。</p>';
|
||||
for(const error of errors){
|
||||
const message=document.createElement("p");
|
||||
message.textContent=`${error.gender==="female"?"女性":"男性"}7型：${error.message}`;
|
||||
panel.append(message);
|
||||
}
|
||||
return panel;
|
||||
}
|
||||
function refreshStyleOptions() {
|
||||
const old = $("styleFilter").value || "all", gender = $("genderFilter").value;
|
||||
$("styleFilter").replaceChildren();
|
||||
for (let i = 0; i < 7; i++) {
|
||||
const label = gender === "all" ? `${i+1}. 女：${styleNames.female[i]} / 男：${styleNames.male[i]}` : `${i+1}. ${styleNames[gender][i]}`;
|
||||
$("styleFilter").add(new Option(label, String(i+1)));
|
||||
}
|
||||
$("styleFilter").add(new Option("全部 7 种形状", "all"));
|
||||
$("styleFilter").value = old;
|
||||
}
|
||||
function refreshCases() {
|
||||
const selected = $("caseFilter").value;
|
||||
const cases = [...new Set([...dataset.groups,...dataset.errors].map(g => String(g.case_number)))].sort((a,b)=>a.localeCompare(b,"en",{numeric:true}));
|
||||
$("caseFilter").replaceChildren(new Option(`全部 ${cases.length} 个案例`, "all"));
|
||||
for (const key of cases) $("caseFilter").add(new Option(key.startsWith("custom") ? "自定义图片" : `案例 ${key}`, key));
|
||||
if (cases.includes(selected)) $("caseFilter").value = selected;
|
||||
}
|
||||
function refreshStats() {
|
||||
const results = dataset.groups.flatMap(g => g.results);
|
||||
const errors = results.map(r => r.height_placement.target_error_px);
|
||||
$("statCases").textContent = new Set([...dataset.groups,...dataset.errors].map(g => g.case_number)).size;
|
||||
$("statComparisons").textContent = results.length;
|
||||
$("statError").textContent = errors.length ? `${fixed(Math.max(...errors))} px` : "—";
|
||||
$("statStatus").textContent = busy ? "运行中" : (dataset.errors.length ? "有异常" : (results.length ? "已完成" : "待运行"));
|
||||
$("runTime").textContent = dataset.created_at ? `更新于 ${new Date(dataset.created_at).toLocaleString("zh-CN", {timeZone:"Asia/Shanghai",hour12:false})}` : "尚无保存结果";
|
||||
}
|
||||
function cropBox(group) {
|
||||
if (!$("zoomFace").checked) return [0, 0, group.width, group.height];
|
||||
if (group.crop_box) return group.crop_box;
|
||||
const rule = group.height_rule, upper = rule.target_upper_height_px, center = rule.target_center;
|
||||
const oldY = group.results.map(r => r.height_placement.old_center.y);
|
||||
const left = Math.max(0, center.x - 1.7 * upper), top = Math.max(0, Math.min(center.y, ...oldY) - .55 * upper);
|
||||
const right = Math.min(group.width, center.x + 1.7 * upper), bottom = Math.min(group.height, rule.brow_center.y + .75 * upper);
|
||||
return [left, top, right, bottom];
|
||||
}
|
||||
function paint(canvas, photo, oldLine, newLine, group, kind) {
|
||||
const [x0,y0,x1,y1] = cropBox(group), cw = x1-x0, ch = y1-y0;
|
||||
canvas.width = kind === "overlay" ? 1000 : 650;
|
||||
canvas.height = Math.round(canvas.width * ch / cw);
|
||||
const ctx = canvas.getContext("2d"), W = canvas.width, H = canvas.height;
|
||||
ctx.drawImage(photo, x0,y0,cw,ch,0,0,W,H);
|
||||
const tint = (line, color) => {
|
||||
const layer = document.createElement("canvas"); layer.width=W; layer.height=H;
|
||||
const ink=layer.getContext("2d"); ink.drawImage(line,x0,y0,cw,ch,0,0,W,H);
|
||||
ink.globalCompositeOperation="source-in";ink.fillStyle=color;ink.fillRect(0,0,W,H);ctx.drawImage(layer,0,0);
|
||||
};
|
||||
if (kind !== "new") tint(oldLine, "#f3a338");
|
||||
if (kind !== "old") tint(newLine, "#10c6b6");
|
||||
if ($("showTarget").checked) {
|
||||
const target = group.height_rule.target_center, tx=(target.x-x0)/cw*W, ty=(target.y-y0)/ch*H;
|
||||
ctx.save();ctx.strokeStyle="rgba(255,255,255,.85)";ctx.lineWidth=1.7;ctx.setLineDash([7,6]);
|
||||
ctx.beginPath();ctx.moveTo(0,ty);ctx.lineTo(W,ty);ctx.stroke();ctx.setLineDash([]);
|
||||
ctx.fillStyle="#fff";ctx.beginPath();ctx.arc(tx,ty,4,0,2*Math.PI);ctx.fill();ctx.restore();
|
||||
}
|
||||
if(kind!=="old"&&$("showBox").checked&&group.guide_landmarks){
|
||||
window.drawHairlineReferenceBox(ctx,group.guide_landmarks,[x0,y0,x1,y1],W,H);
|
||||
}
|
||||
}
|
||||
async function render() {
|
||||
if(cardObserver)cardObserver.disconnect();
|
||||
const version=++drawVersion,gender=$("genderFilter").value,number=$("caseFilter").value,style=$("styleFilter").value;
|
||||
const cases=new Map();
|
||||
for(const group of dataset.groups){
|
||||
if(gender!=="all"&&group.gender!==gender)continue;
|
||||
if(number!=="all"&&String(group.case_number)!==number)continue;
|
||||
for(const result of group.results){
|
||||
if(style!=="all"&&result.order!==Number(style))continue;
|
||||
if(!cases.has(group.case_number))cases.set(group.case_number,[]);
|
||||
cases.get(group.case_number).push({group,result});
|
||||
}
|
||||
}
|
||||
const visibleErrors=dataset.errors.filter(error=>(gender==="all"||error.gender===gender)&&(number==="all"||String(error.case_number)===number));
|
||||
for(const error of visibleErrors)if(!cases.has(error.case_number))cases.set(error.case_number,[]);
|
||||
$("results").replaceChildren();
|
||||
let drawErrors=0,total=0;
|
||||
const rowUpdates=[],drawJobs=new WeakMap();
|
||||
cardObserver=new IntersectionObserver(entries=>{
|
||||
for(const entry of entries)if(entry.isIntersecting){
|
||||
cardObserver.unobserve(entry.target);drawJobs.get(entry.target)?.();
|
||||
}
|
||||
},{rootMargin:"600px 0px"});
|
||||
for(const [caseNumber,cards] of [...cases].sort((a,b)=>String(a[0]).localeCompare(String(b[0]),"en",{numeric:true}))){
|
||||
total+=cards.length;
|
||||
const row=document.createElement("section");row.className="person-row";
|
||||
const caseName=String(caseNumber).startsWith("custom")?"自定义图片":`案例 ${caseNumber}`;
|
||||
const caseErrors=visibleErrors.filter(error=>error.case_number===caseNumber);
|
||||
if(!cards.length){
|
||||
row.classList.add("failed-case");
|
||||
row.innerHTML=`<div class="person-row-header"><h2 class="case-group-heading">${esc(caseName)} · 定位失败</h2></div><div class="person-body"><div class="person-styles" role="region" tabindex="0" aria-label="${esc(caseName)}的原图与定位结果"></div></div>`;
|
||||
row.querySelector(".person-styles").append(originalCard(caseErrors[0],caseName),failureCard(caseErrors));
|
||||
$("results").append(row);continue;
|
||||
}
|
||||
row.innerHTML=`<div class="person-row-header"><h2 class="case-group-heading">${esc(caseName)} · ${cards.length} 种发际线样式</h2><div class="row-controls"><span>点击样式，左侧旧图同步切换</span><button type="button" aria-label="查看前面的样式">←</button><button type="button" aria-label="查看后面的样式">→</button></div></div><div class="person-body"><article class="old-reference-card"><div class="case-header"><div><span class="case-number old-reference-label"><span class="dot old"></span>旧规则 · 头发边界定位</span><h3 class="old-style-name">正在加载…</h3><p>与右侧选中的形状对应</p></div></div><div class="metric-line old-style-metrics"></div><div class="image-pair overlay"><figure><figcaption>当前选中样式的原位置</figcaption><canvas aria-label="旧规则参考图"></canvas></figure></div><div class="case-bottom">本行仅显示一次 · 点击右侧样式切换</div></article><div class="person-styles" role="region" tabindex="0" aria-label="${esc(caseName)}的全部新规则样式"></div></div>`;
|
||||
$("results").append(row);
|
||||
const groupForRow=cards[0].group,framing=groupForRow.framing;
|
||||
if(framing){
|
||||
const info=document.createElement("div");info.className="framing-info";
|
||||
info.innerHTML=`${esc(framing.message)} · 旋转 ${fixed(framing.rotation_degrees,3)}° · ${framing.source_size.width}×${framing.source_size.height} → ${framing.output_size.width}×${framing.output_size.height} · 9/152 横向差 ${fixed(framing.axis_x_error_px,3)} px <a target="_blank" rel="noopener" href="${esc(imageSource(groupForRow,"original","image/jpeg")||groupForRow.source_url)}">上传原图 ↗</a> <a target="_blank" rel="noopener" href="${esc(imageSource(groupForRow,"input","image/jpeg"))}">处理后底图 ↗</a>`;
|
||||
row.insertBefore(info,row.querySelector(".person-body"));
|
||||
}
|
||||
const strip=row.querySelector(".person-styles"),oldPanel=row.querySelector(".old-reference-card"),oldCanvas=oldPanel.querySelector("canvas");
|
||||
// One horizontal row keeps the unprocessed photo first on narrow screens
|
||||
// too, without squeezing the new styles into a zero-width column.
|
||||
strip.append(originalCard(groupForRow,caseName),oldPanel);
|
||||
if(caseErrors.length)strip.append(failureCard(caseErrors));
|
||||
const [previous,next]=row.querySelectorAll(".row-controls button");
|
||||
previous.addEventListener("click",()=>strip.scrollBy({left:-strip.clientWidth*.85,behavior:"smooth"}));
|
||||
next.addEventListener("click",()=>strip.scrollBy({left:strip.clientWidth*.85,behavior:"smooth"}));
|
||||
const updateArrows=()=>{previous.disabled=strip.scrollLeft<=2;next.disabled=strip.scrollWidth-strip.clientWidth-strip.scrollLeft<=2;};
|
||||
strip.addEventListener("scroll",updateArrows,{passive:true});rowUpdates.push(updateArrows);
|
||||
let selectionVersion=0;
|
||||
const selectOld=async(group,result,drawNow=true)=>{
|
||||
const selected=++selectionVersion,key=`${group.gender}:${result.order}`;
|
||||
oldPanel.dataset.drawn="false";
|
||||
rowSelections.set(caseNumber,key);row.dataset.selectedStyle=key;
|
||||
for(const card of strip.querySelectorAll(".case-card")){
|
||||
const active=card.dataset.styleKey===key;card.classList.toggle("selected",active);
|
||||
card.setAttribute("aria-current",String(active));card.querySelector(".select-style").textContent=active?"已对照":"对照此样式";
|
||||
}
|
||||
const label=styleNames[group.gender][result.order-1];
|
||||
oldPanel.querySelector(".old-style-name").textContent=`${group.gender==="female"?"女性":"男性"} · ${label}`;
|
||||
oldPanel.querySelector(".old-style-metrics").textContent=`原中心 y ${fixed(result.height_placement.old_center.y)} · 目标 y ${fixed(group.height_rule.target_center.y)}`;
|
||||
const box=cropBox(group);oldCanvas.width=650;oldCanvas.height=Math.round(650*(box[3]-box[1])/(box[2]-box[0]));
|
||||
const draw=async()=>{
|
||||
try{
|
||||
const [photo,line]=await Promise.all([inputImage(group),loadImage(imageSource(result,"old_overlay"))]);
|
||||
if(version!==drawVersion||selected!==selectionVersion)return;
|
||||
paint(oldCanvas,photo,line,null,group,"old");oldPanel.dataset.drawn="true";
|
||||
}catch(error){if(version===drawVersion)status(error.message,true);}
|
||||
};
|
||||
if(drawNow)await draw();
|
||||
else{drawJobs.set(oldPanel,draw);cardObserver.observe(oldPanel);}
|
||||
};
|
||||
for(const {group,result} of cards){
|
||||
const rule=group.height_rule,p=result.height_placement,label=styleNames[group.gender][result.order-1];
|
||||
const card=document.createElement("article");card.className="case-card";card.tabIndex=0;
|
||||
card.dataset.styleKey=`${group.gender}:${result.order}`;
|
||||
card.innerHTML=`<div class="case-header"><div><span class="case-number">${group.gender==="female"?"女性":"男性"}发际线</span><h3>${esc(label)} <span style="font-size:11px;color:#7c8c88;font-weight:400">${esc(result.hairline_type)}</span></h3><p>目标上庭 (${fixed(rule.middle_height_px)} + ${fixed(rule.lower_height_px)}) ÷ 2 = ${fixed(rule.target_upper_height_px)} px</p></div></div><div class="metric-line"><span>新 y <b>${fixed(p.new_center.y)}</b></span><span><b>${p.shift_y_px<0?"上移":"下移"} ${fixed(Math.abs(p.shift_y_px))} px</b></span></div><div class="image-pair overlay"><figure><figcaption class="new"><span class="dot new"></span>${mode==="overlay"?"橙色旧线 / 青色新线":"新规则 · 中下庭均值"}</figcaption><canvas aria-label="新规则发际线"></canvas></figure></div><div class="case-bottom"><span>中心误差 ${fixed(p.target_error_px)} px</span><button class="select-style" type="button">对照此样式</button><a href="${esc(group.source_url||imageSource(group,"input","image/jpeg"))}" target="_blank" rel="noopener">原图 ↗</a></div>`;
|
||||
strip.append(card);
|
||||
card.addEventListener("click",event=>{if(!event.target.closest("a"))selectOld(group,result);});
|
||||
card.addEventListener("keydown",event=>{if(event.target===card&&(event.key==="Enter"||event.key===" ")){event.preventDefault();selectOld(group,result);}});
|
||||
const canvas=card.querySelector("canvas"),box=cropBox(group);canvas.width=650;canvas.height=Math.round(650*(box[3]-box[1])/(box[2]-box[0]));
|
||||
drawJobs.set(card,async()=>{
|
||||
try{
|
||||
const [photo,line,oldLine]=await Promise.all([inputImage(group),loadImage(imageSource(result,"new_overlay")),mode==="overlay"?loadImage(imageSource(result,"old_overlay")):Promise.resolve(null)]);
|
||||
if(version!==drawVersion)return;
|
||||
paint(canvas,photo,oldLine,line,group,mode==="overlay"?"overlay":"new");card.dataset.drawn="true";
|
||||
}catch(error){drawErrors++;const message=document.createElement("p");message.className="error-card";message.textContent=error.message;card.append(message);status(`有 ${drawErrors} 组图片加载失败，可重跑案例。`,true);}
|
||||
});
|
||||
cardObserver.observe(card);
|
||||
}
|
||||
const active=cards.find(c=>`${c.group.gender}:${c.result.order}`===rowSelections.get(caseNumber))||cards[0];
|
||||
selectOld(active.group,active.result,false);
|
||||
}
|
||||
if(!cases.size&&!dataset.errors.length)$("results").innerHTML='<div class="empty">暂无对比结果。可上传图片或点击“重跑全部30个案例”。</div>';
|
||||
requestAnimationFrame(()=>rowUpdates.forEach(update=>update()));
|
||||
if(version===drawVersion&&!busy){
|
||||
const failures=new Set(visibleErrors.map(error=>error.case_number)).size;
|
||||
status(`显示 ${cases.size} 个案例、${total} 种新规则样式${failures?`，${failures} 个案例存在定位失败`:""}。每人一行，最前面为未裁切原图，旧图仅一次；左右滑动查看全部样式。`,failures>0);
|
||||
}
|
||||
}
|
||||
function setBusy(value) {busy=value;$("rerunBatch").disabled=value;$("customRun").disabled=value;refreshStats();}
|
||||
async function compare(form) {
|
||||
const response=await fetch("/api/v1/debug/hairline-height-compare",{method:"POST",body:form});
|
||||
if(!response.ok)throw new Error(`请求失败：HTTP ${response.status}`);
|
||||
const json=await response.json();if(json.code!==0)throw new Error(json.message);return json.data;
|
||||
}
|
||||
async function rerun() {
|
||||
if(busy)return;setBusy(true);let failure=null;
|
||||
try {
|
||||
const response=await fetch("/api/v1/debug/hairline-height-cases");const json=await response.json();
|
||||
if(json.code!==0)throw new Error(json.message);
|
||||
dataset={groups:[],errors:[],created_at:new Date().toISOString()};inputImages.clear();
|
||||
const cases=json.data.cases,total=cases.length*2;let done=0;
|
||||
for(const test of cases)for(const gender of ["female","male"]){
|
||||
status(`正在运行案例 ${test.image_number} / ${gender==="female"?"女性":"男性"} 7 型（${done}/${total}）…`);
|
||||
const form=new FormData();form.append("image_url",test.image_url);form.append("gender",gender);form.append("hair_style","1,2,3,4,5,6,7");
|
||||
try{const data=await compare(form);dataset.groups.push({...data,case_number:test.image_number,source_url:test.image_url});}
|
||||
catch(error){dataset.errors.push({case_number:test.image_number,gender,message:error.message,source_url:test.image_url,original_url:test.image_url});}
|
||||
done++;refreshCases();refreshStats();await render();
|
||||
}
|
||||
}catch(error){failure=error.message;}finally{setBusy(false);await render();if(failure)status(failure,true);}
|
||||
}
|
||||
async function custom(event) {
|
||||
event.preventDefault();if(busy)return;const file=$("customFile").files[0];if(!file)return;setBusy(true);status("正在计算自定义图片的旧 / 新位置…");let failure=null;
|
||||
try {
|
||||
const photo=file,form=new FormData(),gender=$("customGender").value;
|
||||
form.append("image_file",photo);form.append("gender",gender);form.append("hair_style","1,2,3,4,5,6,7");
|
||||
const data=await compare(form),number=`custom-${Date.now()}`;
|
||||
dataset.groups.push({...data,case_number:number});dataset.created_at=new Date().toISOString();
|
||||
$("genderFilter").value=gender;refreshStyleOptions();refreshCases();$("caseFilter").value=number;
|
||||
}catch(error){failure=error.message;}finally{setBusy(false);await render();if(failure)status(failure,true);}
|
||||
}
|
||||
async function init() {
|
||||
fetch("/api/v1/debug/hairline-framing-reference").then(r=>r.json()).then(json=>{
|
||||
if(json.code!==0)throw new Error(json.message);
|
||||
const p=json.data;
|
||||
$("framingStandard").innerHTML=`<img src="${esc(p.reference_image_url)}" alt="9号与152号点已竖直对齐的参考图" loading="lazy"><div><strong>已扶正参考图：${p.reference_size.width}×${p.reference_size.height}（3:4）</strong><p>参考图9号点 (${fixed(p.brow_9.x)}, ${fixed(p.brow_9.y)})<br>参考图152号点 (${fixed(p.chin_152.x)}, ${fixed(p.chin_152.y)})<br>两点X相同；旋转 ${fixed(p.rotation_degrees,3)}°</p><p><strong>当前裁切：高约 ${Number(fixed(p.height_per_face_length,4))}×L，宽 ${Number(fixed(p.width_per_face_length,4))}×L</strong>L 为9号点到152号点的像素距离。<br>眉心位置：横向 ${fixed(p.brow_x_ratio*100,2)}%，纵向 ${fixed(p.brow_y_ratio*100,2)}%</p><p>用户图先扶正，有空间时按上述尺寸裁切；长图保留人脸裁为3:4，其余空间不足时仅扶正。保留实际像素，不放大图片。</p></div>`;
|
||||
}).catch(error=>{$("framingStandard").textContent=error.message;});
|
||||
refreshStyleOptions();
|
||||
$("genderFilter").addEventListener("change",()=>{refreshStyleOptions();render();});
|
||||
for(const id of ["styleFilter","caseFilter","zoomFace","showTarget","showBox"])$(id).addEventListener("change",render);
|
||||
for(const [id,value] of [["sideMode","side"],["overlayMode","overlay"]])$(id).addEventListener("click",()=>{mode=value;$("sideMode").classList.toggle("active",value==="side");$("overlayMode").classList.toggle("active",value==="overlay");render();});
|
||||
$("customForm").addEventListener("submit",custom);$("rerunBatch").addEventListener("click",rerun);
|
||||
try {
|
||||
const response=await fetch("/static/annotations/hairline_height_compare/latest.json",{cache:"no-store"});
|
||||
if(!response.ok)throw new Error("尚无保存的批量结果，可上传图片或重跑全部30个案例。");
|
||||
dataset=await response.json();dataset.errors ||= [];refreshCases();refreshStats();await render();
|
||||
}catch(error){refreshStats();status(error.message,true);}
|
||||
}
|
||||
document.addEventListener("DOMContentLoaded",init);
|
||||
@@ -0,0 +1,62 @@
|
||||
"use strict";
|
||||
// Reference geometry is in input-image pixels; no screen-vertical assumptions.
|
||||
window.hairlineReferenceBox = function(points) {
|
||||
const p = id => {
|
||||
const value = points[String(id)];
|
||||
if (!value || !Number.isFinite(value.x) || !Number.isFinite(value.y)) throw new Error(`缺少 ${id} 号关键点`);
|
||||
return [value.x, value.y];
|
||||
};
|
||||
const add = (a,b) => [a[0]+b[0],a[1]+b[1]];
|
||||
const sub = (a,b) => [a[0]-b[0],a[1]-b[1]];
|
||||
const mul = (a,s) => [a[0]*s,a[1]*s];
|
||||
const dot = (a,b) => a[0]*b[0]+a[1]*b[1];
|
||||
const brow=p(9),chin=p(152),left=p(162),right=p(389);
|
||||
const delta=sub(chin,brow),length=Math.hypot(...delta);
|
||||
if(length<1)throw new Error("9→152 方向无法确定");
|
||||
const up=mul(delta,-1/length),horizontal=[-up[1],up[0]],height=.7*length;
|
||||
const sidePoint=(anchor,direction,level)=>{
|
||||
const denominator=dot(direction,up);
|
||||
if(Math.abs(denominator)<1e-6)throw new Error("参考框侧边与底边平行");
|
||||
return add(anchor,mul(direction,(level-dot(sub(anchor,brow),up))/denominator));
|
||||
};
|
||||
const corners=[sidePoint(left,up,0),sidePoint(right,up,0),
|
||||
sidePoint(right,up,height),sidePoint(left,up,height)];
|
||||
if(dot(sub(corners[1],corners[0]),horizontal)<=0)throw new Error("参考框左右位置异常");
|
||||
return {corners,height,faceLength:length,
|
||||
landmarks:{9:brow,152:chin,162:left,389:right}};
|
||||
};
|
||||
|
||||
window.clipHairlineToReferenceBox = function(image, points) {
|
||||
const box=window.hairlineReferenceBox(points);
|
||||
const width=image.naturalWidth||image.width,height=image.naturalHeight||image.height;
|
||||
if(!width||!height)throw new Error("发际线图片尚未加载");
|
||||
const canvas=document.createElement("canvas");canvas.width=width;canvas.height=height;
|
||||
const ctx=canvas.getContext("2d");
|
||||
ctx.beginPath();box.corners.forEach((p,i)=>i?ctx.lineTo(...p):ctx.moveTo(...p));ctx.closePath();
|
||||
ctx.clip();ctx.drawImage(image,0,0,width,height);
|
||||
return canvas;
|
||||
};
|
||||
|
||||
window.drawHairlineReferenceBox = function(ctx, points, crop, width, height) {
|
||||
const box=window.hairlineReferenceBox(points);
|
||||
const [x0,y0,x1,y1]=crop;
|
||||
const screen=p=>[(p[0]-x0)/(x1-x0)*width,(p[1]-y0)/(y1-y0)*height];
|
||||
const color="#e33399";
|
||||
ctx.save();ctx.strokeStyle=color;ctx.lineWidth=Math.max(2.5,width/280);ctx.lineJoin="round";
|
||||
ctx.beginPath();box.corners.forEach((p,i)=>{const q=screen(p);i?ctx.lineTo(...q):ctx.moveTo(...q);});ctx.closePath();ctx.stroke();
|
||||
ctx.lineWidth=1.4;ctx.setLineDash([7,6]);ctx.beginPath();ctx.moveTo(...screen(box.landmarks[9]));ctx.lineTo(...screen(box.landmarks[152]));ctx.stroke();ctx.setLineDash([]);
|
||||
const fontSize=Math.max(13,width/43);ctx.font=`600 ${fontSize}px sans-serif`;
|
||||
for(const id of [9,152,162,389]){
|
||||
const [x,y]=screen(box.landmarks[id]);
|
||||
if(x<0||x>width||y<0||y>height)continue;
|
||||
ctx.fillStyle=color;ctx.beginPath();ctx.arc(x,y,Math.max(3,width/160),0,Math.PI*2);ctx.fill();
|
||||
const label=String(id),tw=ctx.measureText(label).width;
|
||||
const tx=Math.max(3,Math.min(width-tw-8,x+7)),ty=Math.max(fontSize+4,Math.min(height-5,y-7));
|
||||
ctx.fillStyle="rgba(255,255,255,.9)";ctx.fillRect(tx-3,ty-fontSize,tw+6,fontSize+4);ctx.fillStyle="#a51868";ctx.fillText(label,tx,ty);
|
||||
}
|
||||
const top=screen([(box.corners[2][0]+box.corners[3][0])/2,(box.corners[2][1]+box.corners[3][1])/2]);
|
||||
const text="70% · 9→152",tw=ctx.measureText(text).width;
|
||||
const tx=Math.max(4,Math.min(width-tw-6,top[0]-tw/2)),ty=Math.max(fontSize+4,top[1]-8);
|
||||
ctx.fillStyle="rgba(255,255,255,.9)";ctx.fillRect(tx-3,ty-fontSize,tw+6,fontSize+4);ctx.fillStyle="#a51868";ctx.fillText(text,tx,ty);
|
||||
ctx.restore();
|
||||
};
|
||||
@@ -56,6 +56,7 @@
|
||||
<a href="#if3">接口3</a>
|
||||
<a href="#if4">接口4</a>
|
||||
<a href="#if5">接口5</a>
|
||||
<a href="#if52">接口52</a>
|
||||
<a href="#if6">接口6</a>
|
||||
<a href="#errors">错误码</a>
|
||||
<a href="#test">在线测试</a>
|
||||
@@ -106,6 +107,7 @@
|
||||
<tr><td><code>four_courts</code></td><td>object</td><td>四庭：top/upper/middle/lower，各含 <code>_cm</code> 和 <code>ratios</code></td></tr>
|
||||
<tr><td><code>seven_eyes</code></td><td>object</td><td>七眼：<code>eye_width_cm</code>/<code>face_width_cm</code>/<code>inter_eye_distance_cm</code> + <code>ratios</code> + <code>eye1</code>~<code>eye7</code>（从左到右 7 段宽度 cm，<code>eye1</code>/<code>eye7</code> 耳朵不可见时为 null）</td></tr>
|
||||
<tr><td><code>landmarks</code></td><td>object</td><td>5 个关键点像素坐标：hair_top/hairline/brow_center/nose_bottom/chin_tip</td></tr>
|
||||
<tr><td><code>vertical_lines</code></td><td>object</td><td>标注图竖线坐标，与标注 PNG 同一套几何。原图像素：<code>y_top</code> / <code>y_bottom</code> 为竖线共用上下端（头顶之上到下巴之下，两端各伸出约短边的 3%，可能略超出图片）；<code>lines</code> 从左到右，每项 <code>{ name, x }</code>。name：<code>left_head_edge</code>（人头最左，耳朵不可见则不出现，此时 eye1 为 null）、<code>left_cheek</code>、<code>left_eye_outer</code>、<code>left_eye_inner</code>、<code>right_eye_inner</code>、<code>right_eye_outer</code>、<code>right_cheek</code>、<code>right_head_edge</code>（人头最右，不可见则不出现，此时 eye7 为 null）</td></tr>
|
||||
<tr><td><code>hairline_source</code></td><td>string</td><td>发际线来源：<code>"segmentation"</code>（真实分割，可信度高）/ <code>"estimated"</code>（比例估算，可信度低）</td></tr>
|
||||
<tr><td><code>head_pose</code></td><td>object</td><td>头部姿态角度：<code>{ yaw, pitch, roll }</code>（度），接近 0 表示正面照</td></tr>
|
||||
<tr><td><code>left_position</code></td><td>object</td><td>MediaPipe 21 号关键点坐标（左脸定位点），原图像素：<code>{ x: number, y: number }</code></td></tr>
|
||||
@@ -126,6 +128,7 @@ const { code, data } = await res.json();
|
||||
// data.annotated_image_url → 标注 PNG
|
||||
// data.face_total_height_cm → 全脸高度
|
||||
// data.four_courts.ratios → { top_court: 0.25, ... }
|
||||
// data.vertical_lines → { y_top, y_bottom, lines: [{ name, x }, ...] }
|
||||
// 前端叠加显示：原图 + annotated_image_url（absolute 叠加）</pre>
|
||||
</div>
|
||||
</div>
|
||||
@@ -251,6 +254,7 @@ console.log(features.gene_style); // "自然型"</pre>
|
||||
<tr><td><code>four_courts</code></td><td>object</td><td>四庭：top/upper/middle/lower，各含 <code>_cm</code> 和 <code>ratios</code></td></tr>
|
||||
<tr><td><code>seven_eyes</code></td><td>object</td><td>七眼：<code>eye_width_cm</code>/<code>face_width_cm</code>/<code>inter_eye_distance_cm</code> + <code>ratios</code> + <code>eye1</code>~<code>eye7</code>（从左到右 7 段宽度 cm，<code>eye1</code>/<code>eye7</code> 耳朵不可见时为 null）</td></tr>
|
||||
<tr><td><code>landmarks</code></td><td>object</td><td>5 个关键点像素坐标：hair_top/hairline/brow_center/nose_bottom/chin_tip</td></tr>
|
||||
<tr><td><code>vertical_lines</code></td><td>object</td><td>竖线坐标，结构同接口1</td></tr>
|
||||
<tr><td><code>hairline_source</code></td><td>string</td><td>发际线来源：<code>"segmentation"</code>（真实分割）/ <code>"estimated"</code>（比例估算）</td></tr>
|
||||
<tr><td><code>head_pose</code></td><td>object</td><td>头部姿态角度：<code>{ yaw, pitch, roll }</code>（度）</td></tr>
|
||||
<tr><td><code>left_position</code></td><td>object</td><td>MediaPipe 21 号关键点坐标（左脸定位点），原图像素：<code>{ x: number, y: number }</code></td></tr>
|
||||
@@ -261,6 +265,16 @@ console.log(features.gene_style); // "自然型"</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card" id="if52">
|
||||
<h2>52. 发际线新规则与裁切 <span class="badge post">POST</span> <code>/api/v1/hairline/generate-52</code></h2>
|
||||
<div class="card-body">
|
||||
<p class="desc">仅部署在GPU worker（8187），本次不新增网关路由。中下庭均值定位 middle，支持9→152扶正、人像裁切和矩形发际线预览。接口5保留原有三档实现；新功能使用此独立接口。</p>
|
||||
<p>基础图片参数、<code>gender</code>、<code>hair_style</code>、<code>use_mask</code>、<code>prompt</code>、<code>generate_grow_image</code>沿用接口5。新增可选<code>normalize_image</code>，默认false，传true开启扶正与裁切；测试页显式开启。</p>
|
||||
<p style="margin-top:12px">图片返回<code>*_base64</code>，空图片保留字段并返回null。另有<code>height_rule</code>、<code>framing</code>、<code>guide_landmarks</code>、原图及处理后底图。middle和坐标使用当前底图，high/low字段为null。标准裁切高约3.3333L、宽2.5L；长图在保留人脸的前提下裁为3:4。</p>
|
||||
<p style="margin-top:12px"><a href="http://localhost:8187/static/test_interface52.html" class="link">52号规则测试页</a> · <a href="http://localhost:8187/static/test_interface52_compare.html" class="link">52号30案例对比页</a></p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ======== 接口6 ======== -->
|
||||
<div class="card" id="if6">
|
||||
<h2>6. 四庭七眼测量 v2 <span class="badge post">POST</span> <code>/api/v1/face/measure-v2</code> <span class="badge warn">v2</span></h2>
|
||||
@@ -286,6 +300,7 @@ console.log(features.gene_style); // "自然型"</pre>
|
||||
<tr><td><code>four_courts</code></td><td>object</td><td>三庭：upper/middle/lower，各含 _cm 和 ratios（<strong>无 top_court</strong>）</td></tr>
|
||||
<tr><td><code>seven_eyes</code></td><td>object</td><td>七眼：<code>eye_width_cm</code>/<code>face_width_cm</code>/<code>inter_eye_distance_cm</code> + <code>ratios</code> + <strong><code>eye2</code>~<code>eye6</code></strong>（左脸颊/左眼/两眼间距/右眼/右脸颊，5 段宽度 cm；<strong>无 eye1/eye7</strong>）</td></tr>
|
||||
<tr><td><code>landmarks</code></td><td>object</td><td>4 个关键点：hairline/brow_center/nose_bottom/chin_tip（<strong>无 hair_top</strong>）</td></tr>
|
||||
<tr><td><code>vertical_lines</code></td><td>object</td><td>竖线坐标。结构同接口1，但 <code>y_top</code> / <code>y_bottom</code> 为发际线到下巴尖（不伸出），<code>lines</code> 只有中间 6 条，无 <code>left_head_edge</code> / <code>right_head_edge</code></td></tr>
|
||||
<tr><td><code>left_position</code></td><td>object</td><td>MediaPipe 21 号关键点坐标（左脸定位点），原图像素：<code>{ x: number, y: number }</code></td></tr>
|
||||
<tr><td><code>right_position</code></td><td>object</td><td>MediaPipe 251 号关键点坐标（右脸定位点，与 21 号镜像），原图像素：<code>{ x: number, y: number }</code></td></tr>
|
||||
</table>
|
||||
@@ -323,6 +338,8 @@ console.log(features.gene_style); // "自然型"</pre>
|
||||
<tr><td>3. B端生发</td><td><a href="/static/test_interface3.html" class="link">/static/test_interface3.html</a></td><td>划线图上传 → 生发效果图</td></tr>
|
||||
<tr><td>4. 用户特征</td><td><a href="/static/test_interface4.html" class="link">/static/test_interface4.html</a></td><td>上传照片 → 6项面部特征 + 原始JSON</td></tr>
|
||||
<tr><td>5. 发际线PNG</td><td><a href="/static/test_interface5.html" class="link">/static/test_interface5.html</a></td><td>上传+性别 → 发际线方案+中心点坐标</td></tr>
|
||||
<tr><td>52. 发际线新规则</td><td><a href="http://localhost:8187/static/test_interface52.html" class="link">/static/test_interface52.html</a></td><td>中下庭定位、扶正裁切、矩形发际线叠加及生发</td></tr>
|
||||
<tr><td>52. 规则对比</td><td><a href="http://localhost:8187/static/test_interface52_compare.html" class="link">/static/test_interface52_compare.html</a></td><td>30个案例，原图与旧/新规则样式对照</td></tr>
|
||||
<tr><td>6. 四庭七眼 v2</td><td><a href="/static/test_interface6.html" class="link">/static/test_interface6.html</a></td><td>同接口1，去顶庭 · 竖线发际线→下巴 · 无头部端线</td></tr>
|
||||
</table>
|
||||
<p style="font-size:12px;color:#94a3b8;margin-top:12px">
|
||||
|
||||
@@ -69,7 +69,7 @@
|
||||
</div>
|
||||
<div class="upload-row" style="margin-top:10px">
|
||||
<label style="font-size:13px;font-weight:600;white-space:nowrap">重绘提示词</label>
|
||||
<input type="text" id="localTestPrompt" value="填充遮罩区域的头发" style="flex:1;min-width:200px;padding:8px;border:1px solid #ddd;border-radius:8px">
|
||||
<input type="text" id="localTestPrompt" value="填充遮罩区域的头发，人物增加一点磨皮" style="flex:1;min-width:200px;padding:8px;border:1px solid #ddd;border-radius:8px">
|
||||
</div>
|
||||
<div class="upload-row" style="margin-top:10px">
|
||||
<label style="font-size:13px;font-weight:600;white-space:nowrap">X-Internal-Token</label>
|
||||
@@ -354,7 +354,7 @@ function dataUriToBlob(dataUri) {
|
||||
|
||||
async function runLocalRedraw() {
|
||||
if (!_finalDataUri || !_maskDataUri) { setStatus('缺少 final 或遮罩，请先生成', 'error'); return; }
|
||||
const prompt = $('localTestPrompt').value.trim() || '填充遮罩区域的头发';
|
||||
const prompt = $('localTestPrompt').value.trim() || '填充遮罩区域的头发，人物增加一点磨皮';
|
||||
const btn = $('redrawBtn');
|
||||
btn.disabled = true; btn.textContent = '⏳ 重绘中...';
|
||||
flog('===== 调后端重绘 =====', 'info');
|
||||
|
||||
@@ -56,7 +56,7 @@
|
||||
</div>
|
||||
<div class="upload-row" style="margin-top:10px">
|
||||
<label style="font-size:13px;font-weight:600;white-space:nowrap">重绘提示词</label>
|
||||
<input type="text" id="localTestPrompt" value="填充遮罩区域的头发" style="flex:1;min-width:200px;padding:8px;border:1px solid #ddd;border-radius:8px">
|
||||
<input type="text" id="localTestPrompt" value="填充遮罩区域的头发，人物增加一点磨皮" style="flex:1;min-width:200px;padding:8px;border:1px solid #ddd;border-radius:8px">
|
||||
</div>
|
||||
<div class="params">
|
||||
<div class="pf">
|
||||
@@ -169,7 +169,7 @@ async function submitTest() {
|
||||
|
||||
async function runLocalRedraw() {
|
||||
if (!_finalDataUri || !_maskDataUri) { setStatus('缺少 final 或遮罩，请先生成', 'error'); return; }
|
||||
const prompt = $('localTestPrompt').value.trim() || '填充遮罩区域的头发';
|
||||
const prompt = $('localTestPrompt').value.trim() || '填充遮罩区域的头发，人物增加一点磨皮';
|
||||
const btn = $('redrawBtn');
|
||||
btn.disabled = true; btn.textContent = '⏳ 重绘中...';
|
||||
|
||||
|
||||
@@ -107,7 +107,7 @@
|
||||
<div class="hint">JPG/PNG | 生发图生成较慢（数十秒~数分钟），请耐心等待</div>
|
||||
<div style="margin-top:10px;display:flex;align-items:center;gap:8px">
|
||||
<label style="font-size:13px;font-weight:600;color:#374151;white-space:nowrap">💬 提示词</label>
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发" style="flex:1;padding:8px 12px;border:1px solid #d1d5db;border-radius:6px;font-size:13px;max-width:500px">
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发，人物增加一点磨皮" style="flex:1;padding:8px 12px;border:1px solid #d1d5db;border-radius:6px;font-size:13px;max-width:500px">
|
||||
</div>
|
||||
<div id="statusBar" class="status hidden"></div>
|
||||
</div>
|
||||
|
||||
@@ -94,7 +94,7 @@
|
||||
</div>
|
||||
<div class="upload-group" style="margin-top:14px">
|
||||
<div class="label">💬 提示词（prompt）</div>
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发" style="width:100%;padding:8px 12px;border:1px solid #d1d5db;border-radius:8px;font-size:14px;max-width:500px">
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发，人物增加一点磨皮" style="width:100%;padding:8px 12px;border:1px solid #d1d5db;border-radius:8px;font-size:14px;max-width:500px">
|
||||
</div>
|
||||
<div style="margin-top:14px;display:flex;gap:12px;align-items:center">
|
||||
<button class="btn btn-primary" id="submitBtn" onclick="submitTest()">🚀 提交</button>
|
||||
|
||||
@@ -0,0 +1,418 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>接口52 — 发际线PNG生成 测试页</title>
|
||||
<style>
|
||||
* { box-sizing: border-box; margin: 0; padding: 0; }
|
||||
body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; background: #f5f5f5; color: #333; }
|
||||
.container { max-width: 1200px; margin: 0 auto; padding: 24px; }
|
||||
h1 { font-size: 22px; margin-bottom: 6px; }
|
||||
.subtitle { color: #888; font-size: 13px; margin-bottom: 24px; }
|
||||
|
||||
.card { background: #fff; border-radius: 12px; box-shadow: 0 1px 4px rgba(0,0,0,.06); margin-bottom: 20px; }
|
||||
.card-header { font-weight: 700; font-size: 14px; padding: 14px 18px; border-bottom: 1px solid #f0f0f0; background: #fafafa; display: flex; justify-content: space-between; align-items: center; }
|
||||
.card-body { padding: 18px; }
|
||||
|
||||
.upload-row { display: flex; gap: 12px; align-items: center; flex-wrap: wrap; }
|
||||
.file-input { flex: 1; min-width: 200px; }
|
||||
.file-input input[type=file] { width: 100%; padding: 8px; border: 2px dashed #ddd; border-radius: 8px; cursor: pointer; }
|
||||
.form-group { display: flex; align-items: center; gap: 6px; white-space: nowrap; }
|
||||
.form-group label { font-size: 13px; font-weight: 600; color: #374151; }
|
||||
.form-group select { padding: 8px 12px; border: 1px solid #d1d5db; border-radius: 6px; font-size: 14px; }
|
||||
.hair-checkboxes { display: flex; flex-wrap: wrap; gap: 4px 14px; align-items: center; }
|
||||
.hair-checkboxes label { font-size: 13px; font-weight: 500; color: #374151; cursor: pointer; display: inline-flex; align-items: center; gap: 3px; white-space: nowrap; }
|
||||
.hair-checkboxes label input[type=checkbox] { cursor: pointer; }
|
||||
.checkbox-actions { display: flex; gap: 6px; margin-left: 4px; }
|
||||
.checkbox-actions button { font-size: 11px; padding: 2px 8px; border-radius: 4px; border: 1px solid #d1d5db; background: #f9fafb; cursor: pointer; }
|
||||
.checkbox-actions button:hover { background: #e5e7eb; }
|
||||
.btn { padding: 10px 28px; border: none; border-radius: 8px; font-size: 15px; cursor: pointer; font-weight: 600; transition: .2s; }
|
||||
.btn-primary { background: #2563eb; color: #fff; }
|
||||
.btn-primary:hover { background: #1d4ed8; }
|
||||
.btn-primary:disabled { background: #93c5fd; cursor: not-allowed; }
|
||||
.btn-sm { padding: 6px 14px; font-size: 13px; }
|
||||
.btn-outline { background: #fff; border: 1px solid #d1d5db; color: #374151; }
|
||||
.btn-outline:hover { background: #f9fafb; }
|
||||
.hint { font-size: 12px; color: #9ca3af; margin-top: 8px; }
|
||||
|
||||
.status { padding: 10px 16px; border-radius: 8px; font-size: 14px; margin-bottom: 16px; display: none; }
|
||||
.status.info { background: #dbeafe; color: #1e40af; display: block; }
|
||||
.status.error { background: #fee2e2; color: #991b1b; display: block; }
|
||||
.status.success { background: #d1fae5; color: #065f46; display: block; }
|
||||
|
||||
.results-layout { display: flex; gap: 24px; }
|
||||
.col-main { flex: 1.5; min-width: 0; }
|
||||
.col-side { flex: 1; min-width: 0; }
|
||||
|
||||
/* 每个发型区块 */
|
||||
.hair-block { padding: 16px 0; border-bottom: 1px solid #f0f0f0; }
|
||||
.hair-block:first-child { padding-top: 4px; }
|
||||
.hair-block:last-child { border-bottom: none; padding-bottom: 4px; }
|
||||
.hair-block-title { font-size: 15px; font-weight: 700; color: #111827; margin-bottom: 10px; }
|
||||
|
||||
/* 未裁切原图 + middle + 矩形裁切发际线叠加 + 生发图 */
|
||||
.level-row { display: grid; grid-template-columns: repeat(4, minmax(0, 1fr)); gap: 10px; align-items: start; }
|
||||
.level-cell { background: #222; border-radius: 8px; overflow: hidden; display: flex; flex-direction: column; }
|
||||
.level-cell .level-label { font-size: 12px; font-weight: 600; color: #fff; background: rgba(0,0,0,.55); padding: 4px 8px; min-height: 38px; display: flex; align-items: center; }
|
||||
.level-cell img { width: 100%; max-height: 380px; object-fit: contain; display: block; }
|
||||
.level-cell .level-none { color: #9ca3af; font-size: 13px; padding: 28px 0; text-align: center; }
|
||||
/* 处理后底图 + 同尺寸透明设计曲线叠加 */
|
||||
.level-cell .img-stack { position: relative; line-height: 0; }
|
||||
.level-cell .img-stack .layer-base { width: 100%; max-height: 380px; object-fit: contain; display: block; }
|
||||
.level-cell .img-stack .layer-anno { position: absolute; top: 0; left: 0; width: 100%; height: 100%; object-fit: contain; }
|
||||
.hairline-crop-border { position: absolute; inset: 0; width: 100%; height: 100%; pointer-events: none; overflow: visible; }
|
||||
.hairline-crop-border polygon { fill: none; stroke: #e33399; stroke-width: 2; stroke-dasharray: 6 4; stroke-linejoin: round; vector-effect: non-scaling-stroke; }
|
||||
@media (max-width: 700px) { .level-row { grid-template-columns: repeat(2, 1fr); } }
|
||||
|
||||
/* 坐标高亮 */
|
||||
.coord-box { background: #f9fafb; border-radius: 8px; padding: 12px 16px; margin-top: 10px; font-size: 14px; }
|
||||
.coord-box .label { font-size: 11px; color: #9ca3af; margin-bottom: 4px; }
|
||||
.coord-box .value { font-weight: 700; font-size: 18px; color: #111827; }
|
||||
|
||||
/* 四庭七眼测量（face_measure） */
|
||||
.fm-section { margin-bottom: 18px; }
|
||||
.fm-section:last-child { margin-bottom: 0; }
|
||||
.fm-title { font-size: 13px; font-weight: 700; color: #374151; margin-bottom: 8px; }
|
||||
.fm-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(120px, 1fr)); gap: 8px; }
|
||||
.fm-cell { background: #f9fafb; border-radius: 6px; padding: 8px 10px; border: 1px solid #f0f0f0; }
|
||||
.fm-cell .k { font-size: 11px; color: #9ca3af; margin-bottom: 2px; }
|
||||
.fm-cell .v { font-size: 15px; font-weight: 700; color: #111827; }
|
||||
.fm-cell .v .sub { font-size: 11px; font-weight: 500; color: #6b7280; margin-left: 4px; }
|
||||
.fm-cell.muted .v { color: #9ca3af; font-weight: 600; }
|
||||
.fm-tag { display: inline-block; font-size: 11px; padding: 2px 8px; border-radius: 10px; margin-left: 6px; font-weight: 600; }
|
||||
.fm-tag.ok { background: #d1fae5; color: #065f46; }
|
||||
.fm-tag.warn { background: #fef3c7; color: #92400e; }
|
||||
|
||||
.json-panel { max-height: 550px; overflow: auto; }
|
||||
.json-content { padding: 14px 16px; font-family: "SF Mono", "Fira Code", monospace; font-size: 12px; line-height: 1.6; white-space: pre-wrap; word-break: break-all; }
|
||||
|
||||
.hidden { display: none !important; }
|
||||
.rule-banner { background:#ecfdf5; border:1px solid #a7f3d0; border-radius:12px; padding:18px; margin-bottom:20px; line-height:1.8; }
|
||||
.rule-banner strong { color:#065f46; font-size:19px; }
|
||||
.rule-banner a { color:#065f46; margin-left:18px; font-weight:600; }
|
||||
.placement { font-size:12px; color:#64748b; font-weight:400; margin:6px 0 10px; }
|
||||
@media (max-width: 800px) { .results-layout { flex-direction: column; } }
|
||||
</style>
|
||||
<script src="/static/img_downscale.js?v=2"></script>
|
||||
<script src="/static/hairline_reference_box.js?v=2"></script>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1>💈 接口52 — 发际线 PNG 生成 测试</h1>
|
||||
<p class="subtitle">上传正面照，选择性别与发型，查看按中下庭均值定位的设计曲线和生发效果。</p>
|
||||
<div class="rule-banner"><strong>目标上庭高度 =（中庭高度 + 下庭高度）÷ 2</strong><a href="/static/test_interface52_compare.html">查看旧 / 新规则对比 →</a><br>上传图先扶正9→152，再裁切为高约 3.3333×L、宽 2.5×L，人物比原3L规则缩小约10%；L 是9号点到152号点的像素距离，眉心位置沿用参考图比例。长图会在保留人脸的前提下裁为3:4，其余空间不足时仅扶正。从眉心向上定位，各发型中点对齐同一目标。high、low 已停用，旧字段返回 null。</div>
|
||||
|
||||
<div class="card">
|
||||
<div class="card-body">
|
||||
<div class="upload-row">
|
||||
<div class="file-input"><input type="file" id="imageFile" accept="image/jpeg,image/png,.jpg,.jpeg,.png"></div>
|
||||
<div class="form-group">
|
||||
<label>性别</label>
|
||||
<select id="gender" onchange="onGenderChange()"><option value="female" selected>👩 Female（7种）</option><option value="male">👨 Male（7种）</option></select>
|
||||
</div>
|
||||
<div class="form-group" style="align-items:flex-start">
|
||||
<label style="padding-top:3px">发型 *</label>
|
||||
<div>
|
||||
<div class="hair-checkboxes" id="hairStyleGroup"></div>
|
||||
<div class="checkbox-actions" style="margin-top:4px">
|
||||
<button type="button" onclick="selectAllHair(true)">全选</button>
|
||||
<button type="button" onclick="selectAllHair(false)">全不选</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="form-group">
|
||||
<label>生发效果图</label>
|
||||
<label class="checkbox-inline" style="font-weight:normal;display:flex;align-items:center;gap:6px">
|
||||
<input type="checkbox" id="genGrowImg" checked> 同时生成生发效果（关闭后只查看设计曲线，速度更快）
|
||||
</label>
|
||||
</div>
|
||||
<button class="btn btn-primary" id="submitBtn" onclick="submitTest()">🚀 提交</button>
|
||||
<button class="btn btn-outline btn-sm" onclick="clearResults()">清除</button>
|
||||
</div>
|
||||
<div class="hint">JPG/PNG 正面照 | Female: 椭圆/花瓣/心形/直线/波浪/大花瓣/古典花瓣 | Male: 椭圆/倒弧/M形/直线/桃心/柔和花瓣/波浪 | 每个发型返回一张设计曲线，生发遮罩同步到新位置</div>
|
||||
<div id="statusBar" class="status hidden"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="results-layout hidden" id="resultsArea">
|
||||
<div class="col-main">
|
||||
<!-- 四庭七眼测量（face_measure，复用接口1数值） -->
|
||||
<div class="card">
|
||||
<div class="card-header"><span>📐 处理后图像的四庭七眼测量</span><span style="font-weight:400;font-size:12px;color:#9ca3af">与当前显示底图使用同一坐标系</span></div>
|
||||
<div class="card-body"><div id="faceMeasureArea"><span style="color:#9ca3af">—</span></div></div>
|
||||
</div>
|
||||
<!-- 所有发型平铺 -->
|
||||
<div class="card">
|
||||
<div class="card-header"><span>🎯 未裁切原图 + middle + 裁切发际线叠加 + 生发图</span><span style="font-weight:400;font-size:12px;color:#9ca3af" id="resultCount">共 0 个发型</span></div>
|
||||
<div class="card-body">
|
||||
<div class="coord-box" style="margin-top:0;margin-bottom:16px">
|
||||
<div class="label">📍 设计中心点 — 图片像素坐标</div>
|
||||
<div class="value">
|
||||
<span id="centerPoint">—</span>
|
||||
</div>
|
||||
</div>
|
||||
<div id="ruleDetails" style="font-size:13px;color:#475569;margin-bottom:16px;line-height:1.8"></div>
|
||||
<div id="resultsGrid"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col-side">
|
||||
<div class="card">
|
||||
<div class="card-header"><span>📋 JSON</span><button class="btn btn-outline btn-sm" onclick="copyJson()">📋 复制</button></div>
|
||||
<div class="json-panel"><pre class="json-content" id="jsonContent"></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
const API_BASE = window.location.origin;
|
||||
let _images = [], _uploadUrl = '', _originalSrc = '', _processedSrc = '', _center = null, _guideLandmarks = null, _renderVersion = 0;
|
||||
|
||||
function $(id) { return document.getElementById(id); }
|
||||
function setStatus(t, type) { const b=$('statusBar'); b.textContent=t; b.className='status '+type; }
|
||||
|
||||
// 发型列表（同接口2）
|
||||
const HAIR_STYLES = {
|
||||
female: [
|
||||
{ value:'1', label:'1. ellipse 椭圆' },
|
||||
{ value:'2', label:'2. flower 花瓣' },
|
||||
{ value:'3', label:'3. heart 心形' },
|
||||
{ value:'4', label:'4. straight 直线' },
|
||||
{ value:'5', label:'5. wave 波浪' },
|
||||
{ value:'6', label:'6. bigflower 大花瓣型' },
|
||||
{ value:'7', label:'7. clasicalflower 古典花瓣型' },
|
||||
],
|
||||
male: [
|
||||
{ value:'1', label:'1. ellipse 椭圆' },
|
||||
{ value:'2', label:'2. inverse_arc 倒弧' },
|
||||
{ value:'3', label:'3. m M形' },
|
||||
{ value:'4', label:'4. straight 直线' },
|
||||
{ value:'5', label:'5. heart 桃心形' },
|
||||
{ value:'6', label:'6. Softpetal 柔和花瓣形' },
|
||||
{ value:'7', label:'7. wave 波浪形' },
|
||||
]
|
||||
};
|
||||
|
||||
function onGenderChange() {
|
||||
const g = $('gender').value;
|
||||
const div = $('hairStyleGroup');
|
||||
div.innerHTML = '';
|
||||
HAIR_STYLES[g].forEach(s => {
|
||||
const cb = document.createElement('input');
|
||||
cb.type = 'checkbox'; cb.value = s.value; cb.id = 'hs_' + s.value;
|
||||
const lb = document.createElement('label');
|
||||
lb.htmlFor = cb.id; lb.appendChild(cb);
|
||||
lb.appendChild(document.createTextNode(' ' + s.label));
|
||||
div.appendChild(lb);
|
||||
});
|
||||
}
|
||||
|
||||
function selectAllHair(select) {
|
||||
document.querySelectorAll('#hairStyleGroup input[type=checkbox]').forEach(cb => { cb.checked = select; });
|
||||
}
|
||||
|
||||
async function submitTest() {
|
||||
let f = $('imageFile').files[0];
|
||||
if (!f) { setStatus('请选择图片', 'error'); return; }
|
||||
const checked = [...document.querySelectorAll('#hairStyleGroup input:checked')].map(cb => cb.value);
|
||||
if (!checked.length) { setStatus('请至少选择一个发型', 'error'); return; }
|
||||
// Preserve native pixels: the worker frames and straightens the image first.
|
||||
|
||||
if (_uploadUrl) URL.revokeObjectURL(_uploadUrl);
|
||||
_uploadUrl = URL.createObjectURL(f);
|
||||
_originalSrc = _uploadUrl; _processedSrc = ''; _images = []; _guideLandmarks = null; _renderVersion++;
|
||||
$('resultsGrid').innerHTML = '';
|
||||
$('submitBtn').disabled = true; $('submitBtn').textContent = '⏳ ...';
|
||||
setStatus('请求中...', 'info'); $('resultsArea').classList.add('hidden');
|
||||
|
||||
const fd = new FormData(); fd.append('image_file', f); fd.append('gender', $('gender').value); fd.append('hair_style', checked.join(',')); fd.append('generate_grow_image', $('genGrowImg').checked ? 'true' : 'false'); fd.append('normalize_image', 'true');
|
||||
const _reqStart = performance.now();
|
||||
try {
|
||||
const r = await fetch(API_BASE + '/api/v1/hairline/generate-52', { method:'POST', headers: { 'X-Internal-Token': 'dev-shared-secret-2026' }, body:fd });
|
||||
const json = await r.json();
|
||||
const _elapsed = ((performance.now() - _reqStart) / 1000).toFixed(2);
|
||||
$('jsonContent').textContent = JSON.stringify(json, null, 2);
|
||||
$('resultsArea').classList.remove('hidden');
|
||||
if (json.code === 0) {
|
||||
_images = json.data.hairline_images || [];
|
||||
_originalSrc = resolveImgSrc(json.data.original_image_url, json.data.original_image_base64, 'image/jpeg') || _uploadUrl;
|
||||
_processedSrc = resolveImgSrc(json.data.input_image_url, json.data.input_image_base64, 'image/jpeg');
|
||||
_guideLandmarks = json.data.guide_landmarks;
|
||||
if (_originalSrc !== _uploadUrl) { URL.revokeObjectURL(_uploadUrl); _uploadUrl = ''; }
|
||||
_center = json.data.best_hairline_center_point;
|
||||
const _fmtPt = p => p ? '(' + p.x + ', ' + p.y + ')' : '—';
|
||||
$('centerPoint').textContent = _fmtPt(json.data.best_hairline_center_point);
|
||||
const rule = json.data.height_rule;
|
||||
$('ruleDetails').textContent = rule
|
||||
? '中庭 ' + rule.middle_height_px.toFixed(2) + ' px + 下庭 ' + rule.lower_height_px.toFixed(2) + ' px → 目标上庭 ' + rule.target_upper_height_px.toFixed(2) + ' px' + (rule.target_upper_height_cm == null ? '' : '（约 ' + rule.target_upper_height_cm.toFixed(2) + ' cm）') + '；目标 y = ' + rule.target_center.y.toFixed(2) + '。测量来源：' + (rule.measurement_source === 'face_measure' ? '当前底图测量' : '发际线关键点')
|
||||
: '—';
|
||||
if (json.data.framing) { const f = json.data.framing; $('ruleDetails').textContent += '；' + f.message + '，旋转 ' + f.rotation_degrees.toFixed(3) + '°，底图 ' + f.output_size.width + '×' + f.output_size.height + '。'; }
|
||||
$('resultCount').textContent = '共 ' + _images.length + ' 个发型';
|
||||
setStatus('✅ ' + _images.length + ' 个发型已按新规则定位 (' + _elapsed + 's)', 'success');
|
||||
await renderGrid();
|
||||
renderFaceMeasure(json.data.face_measure);
|
||||
} else {
|
||||
setStatus('❌ (' + _elapsed + 's) code=' + json.code + ' ' + json.message, 'error');
|
||||
}
|
||||
} catch(e) {
|
||||
const _elapsed = ((performance.now() - _reqStart) / 1000).toFixed(2);
|
||||
setStatus('❌ (' + _elapsed + 's) ' + e.message, 'error'); $('resultsArea').classList.remove('hidden'); }
|
||||
finally { $('submitBtn').disabled = false; $('submitBtn').textContent = '🚀 提交'; }
|
||||
}
|
||||
|
||||
async function buildClippedOverlay(src, points) {
|
||||
const image = await new Promise((resolve, reject) => {
|
||||
const img = new Image(); img.crossOrigin = 'anonymous';
|
||||
img.onload = () => resolve(img); img.onerror = () => reject(new Error('无法读取发际线图片'));
|
||||
img.src = src;
|
||||
});
|
||||
const canvas = window.clipHairlineToReferenceBox(image, points);
|
||||
const corners = window.hairlineReferenceBox(points).corners.map(p => p.join(',')).join(' ');
|
||||
const result = {
|
||||
src: canvas.toDataURL('image/png'),
|
||||
border: '<svg class="hairline-crop-border" viewBox="0 0 '+canvas.width+' '+canvas.height+'" role="img" aria-label="粉色虚线：发际线裁切边界"><polygon points="'+corners+'"></polygon></svg>'
|
||||
};
|
||||
canvas.width = 0; canvas.height = 0;
|
||||
return result;
|
||||
}
|
||||
|
||||
async function renderGrid() {
|
||||
const version = ++_renderVersion;
|
||||
if (!_images.length) { $('resultsGrid').innerHTML = '<span style="color:#9ca3af">无数据</span>'; return; }
|
||||
const images = _images, originalSrc = _originalSrc, processedSrc = _processedSrc, guideLandmarks = _guideLandmarks;
|
||||
const escapeText = text => String(text).replace(/[&<>"']/g, c => ({'&':'&','<':'<','>':'>','"':'"',"'":'''}[c]));
|
||||
// The overlay uses the server's processed image, with matching coordinates.
|
||||
const overlayCell = (label, src, className, emptyText = '暂无处理后图片', border = '') => (src && processedSrc)
|
||||
? '<div class="level-cell '+className+'"><div class="level-label">'+label+'</div><div class="img-stack">'+
|
||||
'<img class="layer-base" src="'+processedSrc+'" alt="处理后底图">'+
|
||||
'<img class="layer-anno" src="'+src+'" alt="'+label+'">'+border+'</div></div>'
|
||||
: '<div class="level-cell '+className+'"><div class="level-label">'+label+'</div><div class="level-none">'+escapeText(emptyText)+'</div></div>';
|
||||
const photoCell = (label, src, className) => src
|
||||
? '<div class="level-cell '+className+'"><div class="level-label">'+label+'</div><img src="'+src+'" alt="'+label+'"></div>'
|
||||
: '<div class="level-cell '+className+'"><div class="level-label">'+label+'</div><div class="level-none">无</div></div>';
|
||||
const blocks = await Promise.all(images.map(async function(it) {
|
||||
// 兼容网关 *_url 与 worker *_base64
|
||||
const mid = resolveImgSrc(it.image_middle_url, it.image_middle_base64, 'image/png');
|
||||
const grown = resolveImgSrc(it.grown_image_url, it.grown_image_base64, 'image/jpeg');
|
||||
let clipped = '', clipBorder = '', clipError = '缺少发际线图片或参考点';
|
||||
if (mid && processedSrc && guideLandmarks) {
|
||||
try { const preview = await buildClippedOverlay(mid, guideLandmarks); clipped = preview.src; clipBorder = preview.border; }
|
||||
catch (error) { clipError = error.message; }
|
||||
}
|
||||
return '<div class="hair-block">' +
|
||||
'<div class="hair-block-title">#' + (it.order||'—') + ' ' + (it.hairline_type||'') +
|
||||
(grown ? ' <span style="font-size:11px;color:#7c3aed">含生发图</span>' : '') + '</div>' +
|
||||
(it.height_placement ? '<div class="placement">中心 y：' + it.height_placement.new_center.y.toFixed(2) + ' · 相对旧规则 ' + (it.height_placement.shift_y_px >= 0 ? '下移 ' : '上移 ') + Math.abs(it.height_placement.shift_y_px).toFixed(2) + ' px · 目标误差 ' + it.height_placement.target_error_px.toFixed(2) + ' px</div>' : '') +
|
||||
'<div class="level-row">' +
|
||||
photoCell('未裁切原图', originalSrc, 'original-cell') +
|
||||
overlayCell('middle · 发际线叠加', mid, 'middle-cell') +
|
||||
overlayCell('裁切发际线叠加', clipped, 'clipped-cell', clipError, clipBorder) +
|
||||
photoCell('生发图', grown, 'grown-cell') +
|
||||
'</div>' +
|
||||
'</div>';
|
||||
}));
|
||||
if (version === _renderVersion) $('resultsGrid').innerHTML = blocks.join('');
|
||||
}
|
||||
|
||||
function clearResults() {
|
||||
if (_uploadUrl) URL.revokeObjectURL(_uploadUrl);
|
||||
_images=[]; _uploadUrl=''; _originalSrc=''; _processedSrc=''; _center=null; _guideLandmarks=null; _renderVersion++;
|
||||
$('ruleDetails').textContent = '';
|
||||
$('resultsArea').classList.add('hidden'); $('statusBar').className='status hidden';
|
||||
$('imageFile').value=''; $('jsonContent').textContent=''; $('resultsGrid').innerHTML='';
|
||||
$('centerPoint').textContent='—';
|
||||
$('resultCount').textContent = '共 0 个发型';
|
||||
$('faceMeasureArea').innerHTML = '<span style="color:#9ca3af">—</span>';
|
||||
}
|
||||
|
||||
// 四庭七眼测量渲染（face_measure，接口1数值）
|
||||
const _PCT = r => (r == null ? '' : '（' + (r * 100).toFixed(1) + '%）');
|
||||
const _FMT = v => (v == null ? '—' : Number(v).toFixed(2));
|
||||
function renderFaceMeasure(fm) {
|
||||
const box = $('faceMeasureArea');
|
||||
if (!fm) {
|
||||
box.innerHTML = '<div style="padding:24px;text-align:center;color:#9ca3af;font-size:14px">⚠️ face_measure = null（测量失败或未启用）</div>';
|
||||
return;
|
||||
}
|
||||
// 发际线来源 + 头部姿态
|
||||
const src = fm.hairline_source;
|
||||
const srcTag = src === 'segmentation'
|
||||
? '<span class="fm-tag ok">segmentation 真实分割</span>'
|
||||
: (src === 'estimated' ? '<span class="fm-tag warn">estimated 比例估算</span>' : '');
|
||||
let poseHtml = '—';
|
||||
if (fm.head_pose) {
|
||||
const p = fm.head_pose;
|
||||
poseHtml = 'yaw ' + _FMT(p.yaw) + '° pitch ' + _FMT(p.pitch) + '° roll ' + _FMT(p.roll) + '°';
|
||||
}
|
||||
let h = '<div class="coord-box" style="margin-top:0;margin-bottom:16px;display:flex;gap:32px;flex-wrap:wrap;align-items:center">'
|
||||
+ '<div><div class="label">脸总高</div><div class="value">' + _FMT(fm.face_total_height_cm) + ' cm</div></div>'
|
||||
+ '<div><div class="label">发际线来源</div><div class="value" style="font-size:15px">' + (src || '—') + srcTag + '</div></div>'
|
||||
+ '<div><div class="label">头部姿态</div><div class="value" style="font-size:14px">' + poseHtml + '</div></div>'
|
||||
+ '</div>';
|
||||
|
||||
// 四庭
|
||||
const fc = fm.four_courts || {};
|
||||
const fr = (fc.ratios || {});
|
||||
const courts = [
|
||||
['顶庭', fc.top_court_cm, fr.top_court],
|
||||
['上庭', fc.upper_court_cm, fr.upper_court],
|
||||
['中庭', fc.middle_court_cm, fr.middle_court],
|
||||
['下庭', fc.lower_court_cm, fr.lower_court],
|
||||
];
|
||||
h += '<div class="fm-section"><div class="fm-title">📏 四庭（纵向，自上而下）</div><div class="fm-grid">';
|
||||
courts.forEach(c => {
|
||||
h += '<div class="fm-cell"><div class="k">' + c[0] + '</div><div class="v">' + _FMT(c[1]) + ' cm<span class="sub">' + _PCT(c[2]).replace(/[（）]/g, '') + '</span></div></div>';
|
||||
});
|
||||
h += '</div></div>';
|
||||
|
||||
// 七眼
|
||||
const se = fm.seven_eyes || {};
|
||||
const ser = (se.ratios || {});
|
||||
h += '<div class="fm-section"><div class="fm-title">👁 七眼（横向汇总）</div><div class="fm-grid">';
|
||||
[['单眼宽', se.eye_width_cm, ser.eye_width], ['脸宽', se.face_width_cm, null], ['两眼间距', se.inter_eye_distance_cm, ser.inter_eye_distance]].forEach(e => {
|
||||
h += '<div class="fm-cell"><div class="k">' + e[0] + '</div><div class="v">' + _FMT(e[1]) + ' cm' + (e[2] != null ? '<span class="sub">' + (e[2] * 100).toFixed(1) + '%</span>' : '') + '</div></div>';
|
||||
});
|
||||
h += '</div></div>';
|
||||
|
||||
// 七眼 7 段（从左到右）
|
||||
const segLabels = ['eye1 左耳外段', 'eye2 左脸颊段', 'eye3 左眼', 'eye4 两眼间距', 'eye5 右眼', 'eye6 右脸颊段', 'eye7 右耳外段'];
|
||||
h += '<div class="fm-section"><div class="fm-title">↔️ 七眼 7 段宽度（从左到右，cm）</div><div class="fm-grid" style="grid-template-columns:repeat(7,1fr)">';
|
||||
segLabels.forEach((lab, i) => {
|
||||
const v = se['eye' + (i + 1)];
|
||||
const muted = v == null ? ' muted' : '';
|
||||
h += '<div class="fm-cell' + muted + '"><div class="k">' + lab + '</div><div class="v">' + (v == null ? '—' : _FMT(v)) + '</div></div>';
|
||||
});
|
||||
h += '</div></div>';
|
||||
|
||||
// 关键点坐标
|
||||
const lm = fm.landmarks || {};
|
||||
const lmLabels = { hair_top: '头顶', hairline: '发际线', brow_center: '眉心', nose_bottom: '鼻翼下缘', chin_tip: '下巴尖' };
|
||||
h += '<div class="fm-section"><div class="fm-title">📍 关键分界点（当前底图像素坐标）</div><div class="fm-grid">';
|
||||
Object.keys(lmLabels).forEach(k => {
|
||||
const p = lm[k];
|
||||
h += '<div class="fm-cell"><div class="k">' + lmLabels[k] + '</div><div class="v" style="font-size:14px">' + (p ? '(' + p.x + ', ' + p.y + ')' : '—') + '</div></div>';
|
||||
});
|
||||
h += '</div></div>';
|
||||
|
||||
box.innerHTML = h;
|
||||
}
|
||||
|
||||
function copyJson() {
|
||||
navigator.clipboard.writeText($('jsonContent').textContent).then(() => {
|
||||
const b = event.target, o = b.textContent; b.textContent = '✅ 已复制'; setTimeout(() => b.textContent = o, 1500);
|
||||
});
|
||||
}
|
||||
|
||||
document.addEventListener('DOMContentLoaded', () => {
|
||||
onGenderChange(); // 初始填充发型列表
|
||||
const dz = $('imageFile');
|
||||
dz.addEventListener('dragover', e => { e.preventDefault(); dz.style.borderColor='#2563eb'; });
|
||||
dz.addEventListener('dragleave', () => dz.style.borderColor='#ddd');
|
||||
dz.addEventListener('drop', e => { e.preventDefault(); dz.style.borderColor='#ddd'; if(e.dataTransfer.files.length){ dz.files=e.dataTransfer.files; setStatus('已选择: '+e.dataTransfer.files[0].name, 'info'); } });
|
||||
dz.addEventListener('change', () => { if(dz.files.length) setStatus('已选择: '+dz.files[0].name, 'info'); });
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,52 @@
|
||||
<!doctype html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width,initial-scale=1">
|
||||
<title>接口 52 · 发际线高度规则对比</title>
|
||||
<link rel="stylesheet" href="/static/hairline_height_compare.css">
|
||||
<script src="/static/img_downscale.js?v=2" defer></script>
|
||||
<script src="/static/hairline_reference_box.js" defer></script>
|
||||
<script src="/static/hairline_height_compare.js" defer></script>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<header class="page-header">
|
||||
<div><div class="eyebrow">接口 52 / 规则验证</div><h1>发际线高度规则对比</h1><p>用同一张照片、同一个形状，查看旧规则与中下庭均值规则的位置差异。</p></div>
|
||||
<a class="button secondary" href="/static/test_interface52.html">打开规则测试页 ↗</a>
|
||||
</header>
|
||||
<section class="rule-card" aria-label="规则说明">
|
||||
<div><span class="pill">新规则</span><h2>目标上庭 =（中庭 + 下庭）÷ 2</h2><p>从眉心向上定位，所有形状的中点对齐同一高度。各发型保留自身轮廓，叠加在扶正后的底图上。</p></div>
|
||||
<div class="rule-note"><span class="dot old"></span>旧规则：根据头发边界和模板定位<br><span class="dot new"></span>新规则：根据中庭、下庭定位<br><span class="dash"></span>虚线：新规则的目标高度</div>
|
||||
</section>
|
||||
<details class="framing-standard"><summary>查看已扶正的标准图与裁切参数</summary><div id="framingStandard">正在加载标准图参数…</div></details>
|
||||
<section class="stats" aria-label="验证概况">
|
||||
<div><span>已验证图片</span><strong id="statCases">—</strong><small>独立测试图片</small></div>
|
||||
<div><span>发际线对比</span><strong id="statComparisons">—</strong><small>男性 / 女性各 7 型</small></div>
|
||||
<div><span>最大中心误差</span><strong id="statError">—</strong><small>新中心与目标的像素差</small></div>
|
||||
<div><span>验证状态</span><strong id="statStatus">加载中</strong><small id="runTime">正在读取已保存结果</small></div>
|
||||
</section>
|
||||
<section class="toolbar" aria-label="显示设置">
|
||||
<label>发型集合<select id="genderFilter"><option value="all" selected>全部集合（男女各 7 型）</option><option value="female">女性发际线</option><option value="male">男性发际线</option></select></label>
|
||||
<label>形状<select id="styleFilter"></select></label>
|
||||
<label>图片<select id="caseFilter"><option value="all">全部 30 个案例</option></select></label>
|
||||
<div class="segmented" aria-label="对比方式"><button id="sideMode" class="active" type="button">左右对比</button><button id="overlayMode" type="button">同图叠线</button></div>
|
||||
<label class="check"><input type="checkbox" id="zoomFace">额头特写</label>
|
||||
<label class="check"><input type="checkbox" id="showTarget" checked>目标参考线</label>
|
||||
<label class="check"><input type="checkbox" id="showBox" checked>新图参考框</label>
|
||||
</section>
|
||||
<p id="status" class="status" role="status" aria-live="polite">正在加载30个案例的对比结果…</p>
|
||||
<section id="results" class="results" aria-label="案例对比"></section>
|
||||
<section class="tools-card">
|
||||
<div class="tools-heading"><div><h2>继续验证</h2><p>上传新图片，或重新运行清单全部30张。每人先显示未裁切原图，再显示旧、新规则；对比只生成定位曲线，无需等待生发。</p></div><button id="rerunBatch" class="button secondary" type="button">重跑全部30个案例</button></div>
|
||||
<form id="customForm" class="custom-form">
|
||||
<label>选择图片<input id="customFile" type="file" accept="image/png,image/jpeg" required></label>
|
||||
<label>发型集合<select id="customGender"><option value="female">女性 · 7 型</option><option value="male">男性 · 7 型</option></select></label>
|
||||
<button class="button primary" id="customRun" type="submit">运行自定义对比</button>
|
||||
</form>
|
||||
<p class="footnote">high、low 已停用；接口旧字段保留为 null。当前页的“旧规则”是本次高度修改前的定位逻辑。旧、新规则使用同一张处理后底图。</p>
|
||||
</section>
|
||||
<footer>旧线 <span class="dot old"></span> 橙色 · 新线 <span class="dot new"></span> 青色 · 粉色参考框：两侧均平行 9→152，左边经过 162、右边经过 389、底边经过 9；高度为 9→152 距离的 70%<br>向上移动为负，向下移动为正 · 所有距离基于接口实际输入图片</footer>
|
||||
</main>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,3 @@
|
||||
<!doctype html>
|
||||
<html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>接口52 · 规则对比</title><meta http-equiv="refresh" content="0;url=/static/test_interface52_compare.html"></head>
|
||||
<body><p>新规则对比已迁移至接口52：<a href="/static/test_interface52_compare.html">打开接口52对比页</a></p><script>location.replace('/static/test_interface52_compare.html'+location.search+location.hash);</script></body></html>
|
||||
@@ -104,7 +104,7 @@
|
||||
<div class="hint">JPG/PNG | 生发图生成较慢（数十秒~数分钟），请耐心等待 | 工作流: add_hair2.json</div>
|
||||
<div style="margin-top:10px;display:flex;align-items:center;gap:8px">
|
||||
<label style="font-size:13px;font-weight:600;color:#374151;white-space:nowrap">💬 提示词</label>
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发" style="flex:1;padding:8px 12px;border:1px solid #d1d5db;border-radius:6px;font-size:13px;max-width:500px">
|
||||
<input type="text" id="promptInput" value="填充遮罩区域的头发，人物增加一点磨皮" style="flex:1;padding:8px 12px;border:1px solid #d1d5db;border-radius:6px;font-size:13px;max-width:500px">
|
||||
</div>
|
||||
<div id="statusBar" class="status hidden"></div>
|
||||
</div>
|
||||
|
||||
@@ -0,0 +1,266 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>接口99 — 人脸特殊点检测 测试页</title>
|
||||
<style>
|
||||
* { box-sizing: border-box; margin: 0; padding: 0; }
|
||||
body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; background: #f5f5f5; color: #333; }
|
||||
.container { max-width: 1200px; margin: 0 auto; padding: 24px; }
|
||||
h1 { font-size: 22px; margin-bottom: 8px; }
|
||||
.subtitle { color: #888; font-size: 13px; margin-bottom: 20px; }
|
||||
h2 { font-size: 16px; margin: 24px 0 12px; }
|
||||
|
||||
.card { background: #fff; border-radius: 12px; padding: 20px; box-shadow: 0 1px 4px rgba(0,0,0,.06); margin-bottom: 20px; }
|
||||
.upload-row { display: flex; gap: 12px; align-items: center; flex-wrap: wrap; }
|
||||
input[type=file] { flex: 1; min-width: 200px; padding: 8px; border: 2px dashed #ddd; border-radius: 8px; cursor: pointer; }
|
||||
.btn { padding: 10px 24px; border: none; border-radius: 8px; font-size: 15px; cursor: pointer; font-weight: 600; }
|
||||
.btn-primary { background: #2563eb; color: #fff; }
|
||||
.btn-primary:disabled { background: #93c5fd; cursor: not-allowed; }
|
||||
.btn-outline { background: #fff; border: 1px solid #d1d5db; color: #374151; }
|
||||
.hint { font-size: 12px; color: #9ca3af; margin-top: 8px; }
|
||||
|
||||
.status { padding: 10px 16px; border-radius: 8px; font-size: 14px; margin-bottom: 16px; display: none; }
|
||||
.status.info { background: #dbeafe; color: #1e40af; display: block; }
|
||||
.status.error { background: #fee2e2; color: #991b1b; display: block; }
|
||||
.status.success { background: #d1fae5; color: #065f46; display: block; }
|
||||
|
||||
.hidden { display: none; }
|
||||
|
||||
.canvas-wrap { position: relative; display: inline-block; max-width: 100%; border-radius: 8px; overflow: hidden; }
|
||||
canvas { max-width: 100%; display: block; }
|
||||
.legend { display: flex; gap: 14px; flex-wrap: wrap; margin-top: 12px; font-size: 13px; }
|
||||
.legend .item { display: flex; align-items: center; gap: 6px; }
|
||||
.legend .dot { width: 12px; height: 12px; border-radius: 50%; display: inline-block; }
|
||||
|
||||
.json-panel { background: #1e293b; color: #e2e8f0; font-family: "SF Mono", Consolas, monospace; font-size: 12px; line-height: 1.6; padding: 16px; border-radius: 8px; max-height: 480px; overflow: auto; white-space: pre; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1>接口99 — 人脸特殊点检测</h1>
|
||||
<div class="subtitle">POST /api/v1/face/special-points · 固定编号 MediaPipe 关键点 + 5 个发际线特殊点（射线与头发分割 mask 交点），返回原图像素坐标</div>
|
||||
|
||||
<div class="card">
|
||||
<div class="upload-row">
|
||||
<input type="file" id="imageFile" accept="image/jpeg,image/png">
|
||||
<button class="btn btn-primary" id="submitBtn" onclick="submitTest()">🚀 提交测试</button>
|
||||
<button class="btn btn-outline" onclick="clearResults()">清空</button>
|
||||
</div>
|
||||
<div class="hint">支持 JPG / PNG；单人正面照效果最佳。发际线点依赖头发分割，秃顶/遮挡时对应字段为 null。</div>
|
||||
<div class="status" id="statusBar"></div>
|
||||
</div>
|
||||
|
||||
<div id="resultsArea" class="hidden">
|
||||
<div class="card">
|
||||
<h2 style="margin-top:0; display:flex; justify-content:space-between; align-items:center">
|
||||
<span>可视化结果（点击图片可放大）</span>
|
||||
<label style="font-size:13px; font-weight:400; display:flex; align-items:center; gap:6px; cursor:pointer">
|
||||
<input type="checkbox" id="showIds" checked onchange="redraw()"> 显示点ID
|
||||
</label>
|
||||
</h2>
|
||||
<div class="canvas-wrap"><canvas id="resultCanvas"></canvas></div>
|
||||
<div class="legend" id="legendBar"></div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h2 style="margin-top:0; display:flex; justify-content:space-between; align-items:center">
|
||||
<span>原始 JSON</span>
|
||||
<button class="btn btn-outline" style="padding:6px 14px; font-size:13px" onclick="copyJson()">复制</button>
|
||||
</h2>
|
||||
<div class="json-panel" id="jsonContent"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="lightbox" id="lightbox" onclick="this.style.display='none'"
|
||||
style="display:none; position:fixed; inset:0; background:rgba(0,0,0,.85); z-index:99; cursor:zoom-out; align-items:center; justify-content:center">
|
||||
<img id="lightboxImg" style="max-width:96vw; max-height:96vh" alt="">
|
||||
</div>
|
||||
|
||||
<script>
|
||||
const API_BASE = window.location.origin;
|
||||
const ENDPOINT = '/api/v1/face/special-points';
|
||||
|
||||
// 组 → 颜色（与下方 legend 一致）
|
||||
const GROUP_COLORS = {
|
||||
eyebrow: '#ef4444', // 红
|
||||
eye: '#3b82f6', // 蓝
|
||||
mouth: '#22c55e', // 绿
|
||||
nose_midline: '#a855f7', // 紫
|
||||
cheek: '#f97316', // 橙
|
||||
face_edge: '#06b6d4', // 青
|
||||
hairline: '#e11d48', // 玫红（发际线特殊点，大圆+标签）
|
||||
};
|
||||
const GROUP_LABELS = {
|
||||
eyebrow: '眉毛', eye: '眼睛', mouth: '嘴', nose_midline: '中线鼻',
|
||||
cheek: '面颊', face_edge: '边线', hairline: '发际线特殊点',
|
||||
};
|
||||
const HAIRLINE_KEYS = ['hair_line_center', 'hair_line_right', 'hair_line_right_right',
|
||||
'hair_line_left', 'hair_line_left_left'];
|
||||
|
||||
let lastImage = null; // 提交的原图（用于画布底图）
|
||||
let lastJson = null;
|
||||
let lastData = null; // 最近一次成功的 data（供「显示点ID」切换时重画）
|
||||
|
||||
function $(id) { return document.getElementById(id); }
|
||||
|
||||
function setStatus(text, type) {
|
||||
const bar = $('statusBar');
|
||||
bar.textContent = text;
|
||||
bar.className = 'status ' + type;
|
||||
}
|
||||
|
||||
async function submitTest() {
|
||||
let file = $('imageFile').files[0];
|
||||
if (!file) { setStatus('请先选择一张图片', 'error'); return; }
|
||||
|
||||
const t0 = performance.now();
|
||||
const btn = $('submitBtn');
|
||||
btn.disabled = true; btn.textContent = '⏳ 请求中...';
|
||||
setStatus('正在请求 ' + API_BASE + ENDPOINT + ' ...', 'info');
|
||||
$('resultsArea').classList.add('hidden');
|
||||
|
||||
// 本地预览原图（原图直送，不缩放，坐标即原图像素）
|
||||
lastImage = await loadImage(await file.arrayBuffer());
|
||||
|
||||
const form = new FormData();
|
||||
form.append('image_file', file);
|
||||
|
||||
try {
|
||||
const resp = await fetch(API_BASE + ENDPOINT, { method: 'POST', headers: { 'X-Internal-Token': 'dev-shared-secret-2026' }, body: form });
|
||||
const json = await resp.json();
|
||||
const dt = ((performance.now() - t0) / 1000).toFixed(2);
|
||||
lastJson = json;
|
||||
|
||||
$('jsonContent').textContent = JSON.stringify(json, null, 2);
|
||||
$('resultsArea').classList.remove('hidden');
|
||||
|
||||
if (json.code === 0) {
|
||||
const n = Object.values(json.data.points || {}).filter(Boolean).length;
|
||||
setStatus('✅ 请求成功 (' + dt + 's) — request_id: ' + json.request_id
|
||||
+ '，有效点 ' + n + '/' + Object.keys(json.data.points || {}).length
|
||||
+ '，hairline_source=' + json.data.hairline_source, 'success');
|
||||
lastData = json.data;
|
||||
renderCanvas(json.data);
|
||||
renderLegend();
|
||||
} else {
|
||||
setStatus('❌ 业务错误 (' + dt + 's) — code: ' + json.code + ' message: ' + json.message, 'error');
|
||||
}
|
||||
} catch (err) {
|
||||
setStatus('❌ 网络错误: ' + err.message, 'error');
|
||||
$('jsonContent').textContent = 'Error: ' + err.message;
|
||||
$('resultsArea').classList.remove('hidden');
|
||||
} finally {
|
||||
btn.disabled = false; btn.textContent = '🚀 提交测试';
|
||||
}
|
||||
}
|
||||
|
||||
function loadImage(buf) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const img = new Image();
|
||||
img.onload = () => resolve(img);
|
||||
img.onerror = reject;
|
||||
img.src = URL.createObjectURL(new Blob([buf]));
|
||||
});
|
||||
}
|
||||
|
||||
function renderCanvas(data) {
|
||||
const canvas = $('resultCanvas');
|
||||
const ctx = canvas.getContext('2d');
|
||||
const img = lastImage;
|
||||
const scale = Math.min(1, 900 / img.naturalWidth); // 画布显示尺寸（坐标按比例缩放）
|
||||
canvas.width = Math.round(img.naturalWidth * scale);
|
||||
canvas.height = Math.round(img.naturalHeight * scale);
|
||||
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
|
||||
|
||||
const r = Math.max(1.5, 1.6 * scale * 2); // 点半径随图缩小适度收缩
|
||||
const font = Math.max(9, Math.round(9 * scale * 2)); // 发际线标签字号
|
||||
const idFont = Math.max(8, Math.round(6.5 * scale * 2)); // 关键点ID字号
|
||||
const showIds = $('showIds').checked;
|
||||
|
||||
// 1) 固定关键点：小实心圆 + 白描边 + 点ID
|
||||
for (const [key, pt] of Object.entries(data.points || {})) {
|
||||
if (!pt || HAIRLINE_KEYS.includes(key)) continue;
|
||||
const group = key.split('_')[0];
|
||||
const color = GROUP_COLORS[group] || '#888';
|
||||
const x = pt.x * scale, y = pt.y * scale;
|
||||
drawDot(ctx, x, y, r, color);
|
||||
if (showIds) {
|
||||
const id = key.split('_').pop(); // 如 eyebrow_107 → 107
|
||||
ctx.font = idFont + 'px sans-serif';
|
||||
ctx.textBaseline = 'alphabetic';
|
||||
ctx.lineWidth = 2.5;
|
||||
ctx.strokeStyle = 'rgba(0,0,0,.75)'; // 黑描边保证任意底色可读
|
||||
ctx.strokeText(id, x + r + 1, y + 3);
|
||||
ctx.fillStyle = '#fff';
|
||||
ctx.fillText(id, x + r + 1, y + 3);
|
||||
}
|
||||
}
|
||||
|
||||
// 2) 发际线特殊点：大空心圆 + 字段名标签
|
||||
for (const key of HAIRLINE_KEYS) {
|
||||
const pt = (data.points || {})[key];
|
||||
if (!pt) continue;
|
||||
const x = pt.x * scale, y = pt.y * scale;
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r * 2, 0, Math.PI * 2);
|
||||
ctx.strokeStyle = GROUP_COLORS.hairline;
|
||||
ctx.lineWidth = Math.max(1.5, r * 0.55);
|
||||
ctx.stroke();
|
||||
drawDot(ctx, x, y, r, GROUP_COLORS.hairline);
|
||||
ctx.font = '600 ' + font + 'px sans-serif';
|
||||
ctx.textBaseline = 'bottom';
|
||||
const label = key.replace('hair_line_', 'HL ');
|
||||
const tw = ctx.measureText(label).width;
|
||||
ctx.fillStyle = 'rgba(0,0,0,.55)';
|
||||
ctx.fillRect(x + r * 2.4, y - font - 4, tw + 8, font + 6);
|
||||
ctx.fillStyle = '#fecdd3';
|
||||
ctx.fillText(label, x + r * 2.6 + 4, y - 2);
|
||||
}
|
||||
}
|
||||
|
||||
function drawDot(ctx, x, y, r, color) {
|
||||
ctx.beginPath();
|
||||
ctx.arc(x, y, r, 0, Math.PI * 2);
|
||||
ctx.fillStyle = color;
|
||||
ctx.fill();
|
||||
ctx.lineWidth = Math.max(1, r * 0.35);
|
||||
ctx.strokeStyle = 'rgba(255,255,255,.9)';
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
function renderLegend() {
|
||||
const bar = $('legendBar');
|
||||
bar.innerHTML = Object.entries(GROUP_LABELS)
|
||||
.map(([g, label]) => `<span class="item"><span class="dot" style="background:${GROUP_COLORS[g]}"></span>${label}</span>`)
|
||||
.join('');
|
||||
}
|
||||
|
||||
function clearResults() {
|
||||
$('resultsArea').classList.add('hidden');
|
||||
$('statusBar').className = 'status';
|
||||
$('imageFile').value = '';
|
||||
lastImage = null; lastJson = null; lastData = null;
|
||||
}
|
||||
|
||||
function redraw() {
|
||||
if (lastData && lastImage) renderCanvas(lastData);
|
||||
}
|
||||
|
||||
function copyJson() {
|
||||
if (!lastJson) return;
|
||||
navigator.clipboard.writeText(JSON.stringify(lastJson, null, 2))
|
||||
.then(() => setStatus('✅ JSON 已复制到剪贴板', 'success'));
|
||||
}
|
||||
|
||||
// 点击画布放大
|
||||
$('resultCanvas').addEventListener('click', () => {
|
||||
const lb = $('lightbox');
|
||||
$('lightboxImg').src = $('resultCanvas').toDataURL('image/png');
|
||||
lb.style.display = 'flex';
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
+38
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"description": "用户指定的接口 52 发际线高度规则回归验证图片清单",
|
||||
"recorded_on": "2026-11-03",
|
||||
"endpoint": "/api/v1/hairline/generate-52",
|
||||
"total": 30,
|
||||
"cases": [
|
||||
{"image_number": 1, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%201.png"},
|
||||
{"image_number": 2, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%202.png"},
|
||||
{"image_number": 3, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%203.png"},
|
||||
{"image_number": 4, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%204.png"},
|
||||
{"image_number": 5, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%205.png"},
|
||||
{"image_number": 6, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%206.png"},
|
||||
{"image_number": 7, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%207.png"},
|
||||
{"image_number": 20, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2020.png"},
|
||||
{"image_number": 21, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2021.png"},
|
||||
{"image_number": 22, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2022.png"},
|
||||
{"image_number": 23, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2023.png"},
|
||||
{"image_number": 27, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2027.png"},
|
||||
{"image_number": 32, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2032.png"},
|
||||
{"image_number": 33, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2033.png"},
|
||||
{"image_number": 34, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2034.png"},
|
||||
{"image_number": 35, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2035.png"},
|
||||
{"image_number": 36, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2036.png"},
|
||||
{"image_number": 37, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2037.png"},
|
||||
{"image_number": 40, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2040.png"},
|
||||
{"image_number": 41, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2041.png"},
|
||||
{"image_number": 42, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2042.png"},
|
||||
{"image_number": 43, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2043.png"},
|
||||
{"image_number": 44, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2044.png"},
|
||||
{"image_number": 45, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2045.png"},
|
||||
{"image_number": 46, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2046.png"},
|
||||
{"image_number": 47, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2047.png"},
|
||||
{"image_number": 50, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2050.png"},
|
||||
{"image_number": 51, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2051.png"},
|
||||
{"image_number": 52, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2052.png"},
|
||||
{"image_number": 53, "image_url": "https://raw.githubusercontent.com/feastfuyan/yonghe-badcase-0917/main/%E5%9B%BE%E7%89%87%2053.png"}
|
||||
]
|
||||
}
|
||||
+313
-3
@@ -18,7 +18,7 @@ H = {"X-Internal-Token": "testpass"}
|
||||
@pytest.fixture(scope="module")
|
||||
def client():
|
||||
# with 触发 lifespan：加载模型单例（detector + 尽力加载 segmenter）
|
||||
with TestClient(app_module.app) as c:
|
||||
with TestClient(app_module.app, client=("127.0.0.1", 50000)) as c:
|
||||
yield c
|
||||
|
||||
|
||||
@@ -36,12 +36,76 @@ def test_auth_missing_token_401(client):
|
||||
assert r.status_code == 401
|
||||
|
||||
|
||||
def test_health_no_token_200(client):
|
||||
def _patch_health_ok(monkeypatch):
|
||||
"""让 /health 深检确定性通过：不依赖本机磁盘水位与 8188/8801 服务存活。"""
|
||||
monkeypatch.setattr(app_module, "_HEALTH_CFG", {
|
||||
**app_module._HEALTH_CFG, "min_free_gb": 0.0, "check_deps": True})
|
||||
|
||||
async def _deps_ok():
|
||||
return True, {"comfyui": True, "change_hair": True}
|
||||
monkeypatch.setattr(app_module, "_check_deps", _deps_ok)
|
||||
|
||||
|
||||
def test_health_no_token_200(client, monkeypatch):
|
||||
_patch_health_ok(monkeypatch)
|
||||
r = client.get("/health")
|
||||
assert r.status_code == 200
|
||||
assert r.json()["status"] == "ok"
|
||||
|
||||
|
||||
def test_health_disk_low_503(client, monkeypatch):
|
||||
# 阈值调到不可能的 99999G → 必然触发磁盘水位 503
|
||||
monkeypatch.setattr(app_module, "_HEALTH_CFG", {
|
||||
**app_module._HEALTH_CFG, "min_free_gb": 99999.0, "check_deps": False})
|
||||
r = client.get("/health")
|
||||
assert r.status_code == 503
|
||||
body = r.json()
|
||||
assert body["status"] == "degraded"
|
||||
assert body["reason"].startswith("disk_free_below")
|
||||
|
||||
|
||||
def test_health_dep_down_503(client, monkeypatch):
|
||||
monkeypatch.setattr(app_module, "_HEALTH_CFG", {
|
||||
**app_module._HEALTH_CFG, "min_free_gb": 0.0, "check_deps": True})
|
||||
|
||||
async def _deps_down():
|
||||
return False, {"comfyui": False, "change_hair": True}
|
||||
monkeypatch.setattr(app_module, "_check_deps", _deps_down)
|
||||
r = client.get("/health")
|
||||
assert r.status_code == 503
|
||||
body = r.json()
|
||||
assert body["status"] == "degraded"
|
||||
assert "comfyui" in body["reason"]
|
||||
|
||||
|
||||
def test_health_ok_carries_detail(client, monkeypatch):
|
||||
_patch_health_ok(monkeypatch)
|
||||
r = client.get("/health")
|
||||
assert r.status_code == 200
|
||||
body = r.json()
|
||||
assert "disk_free_gb" in body
|
||||
assert body["deps"] == {"comfyui": True, "change_hair": True}
|
||||
|
||||
|
||||
def test_tcp_probe_real_socket():
|
||||
# 用进程内真实 socket 验证探测本身（曾因 open_connection 返回元组误判全部不可达）
|
||||
import asyncio
|
||||
|
||||
async def scenario():
|
||||
server = await asyncio.start_server(lambda r, w: None, host="127.0.0.1", port=0)
|
||||
port = server.sockets[0].getsockname()[1]
|
||||
up = await app_module._tcp_probe("127.0.0.1", port)
|
||||
server.close()
|
||||
await server.wait_closed()
|
||||
# 端口 1 无监听，本机 connect 立即被拒
|
||||
down = await app_module._tcp_probe("127.0.0.1", 1)
|
||||
return up, down
|
||||
|
||||
up, down = asyncio.run(scenario())
|
||||
assert up is True
|
||||
assert down is False
|
||||
|
||||
|
||||
def test_param_none_provided_1007(client):
|
||||
r = client.post(URL, headers=H)
|
||||
assert r.json()["code"] == 1007
|
||||
@@ -125,6 +189,40 @@ def test_growb_success(client, monkeypatch):
|
||||
HLGEN = "/api/v1/hairline/generate"
|
||||
|
||||
|
||||
def test_hairline5_restored_schema_has_only_historical_parameters(client):
|
||||
schema = client.get("/openapi.json", headers=H).json()
|
||||
ref = schema["paths"][HLGEN]["post"]["requestBody"]["content"]["multipart/form-data"]["schema"]["$ref"]
|
||||
fields = schema["components"]["schemas"][ref.rsplit("/", 1)[-1]]["properties"]
|
||||
assert set(fields) == {"image_file", "image_url", "image_base64", "gender", "hair_style",
|
||||
"use_mask", "prompt", "generate_grow_image"}
|
||||
assert fields["use_mask"]["default"] is True
|
||||
assert fields["generate_grow_image"]["default"] is True
|
||||
assert fields["prompt"]["default"] == "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("gender", ["female", "male"])
|
||||
def test_hairline5_restored_three_levels_and_original_coordinate_contract(client, gender):
|
||||
from pathlib import Path
|
||||
photo = Path(fixture("frontal.jpg")).read_bytes()
|
||||
body = client.post(HLGEN, headers=H, files={"image_file": ("frontal.jpg", photo, "image/jpeg")},
|
||||
data={"gender": gender, "hair_style": "1,3", "generate_grow_image": "false"}).json()
|
||||
assert body["code"] == 0, body
|
||||
data = body["data"]
|
||||
assert set(data) == {"hairline_images", "best_hairline_center_point", "high_hairline_center_point",
|
||||
"low_hairline_center_point", "face_measure"}
|
||||
expected = _cv2.imdecode(_np.frombuffer(photo, _np.uint8), _cv2.IMREAD_COLOR).shape[:2]
|
||||
for key in ("best_hairline_center_point", "high_hairline_center_point", "low_hairline_center_point"):
|
||||
assert set(data[key]) == {"x", "y"}
|
||||
assert isinstance(data[key]["x"], int) and isinstance(data[key]["y"], int)
|
||||
for item in data["hairline_images"]:
|
||||
assert set(item) == {"hairline_type", "image_middle_base64", "image_high_base64", "image_low_base64",
|
||||
"grown_image_base64", "order"}
|
||||
assert item["grown_image_base64"] is None
|
||||
for level in ("middle", "high", "low"):
|
||||
image = _cv2.imdecode(_np.frombuffer(base64.b64decode(item[f"image_{level}_base64"]), _np.uint8), _cv2.IMREAD_UNCHANGED)
|
||||
assert image.shape == (*expected, 4)
|
||||
|
||||
|
||||
def test_hairline_gen_missing_gender_1004(client):
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
assert client.post(HLGEN, headers=H, files=files).json()["code"] == 1004
|
||||
@@ -177,6 +275,181 @@ def test_hairline_gen_female(client, monkeypatch):
|
||||
assert set(["yaw", "pitch", "roll"]).issubset(fm["head_pose"].keys())
|
||||
|
||||
|
||||
HLGEN52 = "/api/v1/hairline/generate-52"
|
||||
|
||||
|
||||
def test_hairline52_legacy_form_parameters_and_defaults_are_preserved(client):
|
||||
schema = client.get("/openapi.json", headers=H).json()
|
||||
body = schema["paths"][HLGEN52]["post"]["requestBody"]["content"]["multipart/form-data"]["schema"]
|
||||
body = schema["components"]["schemas"][body["$ref"].rsplit("/", 1)[-1]]
|
||||
fields = body["properties"]
|
||||
legacy = {"image_file", "image_url", "image_base64", "gender", "hair_style",
|
||||
"use_mask", "prompt", "generate_grow_image"}
|
||||
assert legacy.issubset(fields)
|
||||
assert not set(body.get("required", [])) - legacy
|
||||
for key in ("image_file", "image_url", "image_base64", "gender", "hair_style"):
|
||||
assert any(choice.get("type") == "string" for choice in fields[key].get("anyOf", [fields[key]]))
|
||||
assert fields["use_mask"]["type"] == "boolean" and fields["use_mask"]["default"] is True
|
||||
assert fields["generate_grow_image"]["type"] == "boolean" and fields["generate_grow_image"]["default"] is True
|
||||
assert fields["prompt"]["default"] == "填充遮罩区域的头发，人物增加一点磨皮"
|
||||
assert fields["normalize_image"]["default"] is False
|
||||
|
||||
|
||||
@pytest.mark.parametrize("image_kind", ["file", "base64", "url"])
|
||||
def test_hairline52_legacy_request_keeps_fields_and_original_coordinates(client, monkeypatch, image_kind):
|
||||
import contextlib
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
import httpx
|
||||
import hairline.comfyui as comfy
|
||||
|
||||
raw = Path(fixture("frontal.jpg")).read_bytes()
|
||||
calls = []
|
||||
def generate(*args, **kwargs):
|
||||
calls.append(kwargs)
|
||||
return _PNG_1x1
|
||||
monkeypatch.setattr(comfy, "run", generate)
|
||||
form = {"gender": "male", "hair_style": "1", "use_mask": "false", "prompt": "legacy prompt"}
|
||||
kwargs = {"headers": H, "data": form}
|
||||
if image_kind == "file":
|
||||
kwargs["files"] = {"image_file": ("frontal.jpg", raw, "image/jpeg")}
|
||||
elif image_kind == "base64":
|
||||
form["image_base64"] = "data:image/jpeg;base64," + base64.b64encode(raw).decode()
|
||||
else:
|
||||
url = "https://legacy.example.test/frontal.jpg"
|
||||
form["image_url"] = url
|
||||
response = httpx.Response(200, content=raw, request=httpx.Request("GET", url))
|
||||
def get_image(actual_url):
|
||||
assert actual_url == url
|
||||
return response
|
||||
monkeypatch.setattr(httpx, "Client", lambda **kw: contextlib.nullcontext(SimpleNamespace(get=get_image)))
|
||||
# No new parameters: old clients keep their original-size overlays.
|
||||
body = client.post(HLGEN52, **kwargs).json()
|
||||
assert body["code"] == 0, body
|
||||
assert {"code", "message", "request_id", "data"}.issubset(body)
|
||||
data = body["data"]
|
||||
assert {"hairline_images", "best_hairline_center_point", "high_hairline_center_point",
|
||||
"low_hairline_center_point", "face_measure"}.issubset(data)
|
||||
assert data["coordinate_space"] == "original_image" and data["framing"] is None
|
||||
assert data["input_image_base64"] == data["original_image_base64"]
|
||||
original = _cv2.imdecode(_np.frombuffer(raw, _np.uint8), _cv2.IMREAD_COLOR)
|
||||
expected = original.shape[:2]
|
||||
assert (data["height"], data["width"]) == expected
|
||||
item = data["hairline_images"][0]
|
||||
assert {"hairline_type", "image_middle_base64", "image_high_base64", "image_low_base64",
|
||||
"grown_image_base64", "order"}.issubset(item)
|
||||
assert item["image_high_base64"] is None and item["image_low_base64"] is None
|
||||
assert data["high_hairline_center_point"] is None and data["low_hairline_center_point"] is None
|
||||
for key in ("image_middle_base64", "grown_image_base64"):
|
||||
image = _cv2.imdecode(_np.frombuffer(base64.b64decode(item[key]), _np.uint8), _cv2.IMREAD_UNCHANGED)
|
||||
assert image.shape[:2] == expected
|
||||
assert len(calls) == 1 and calls[0]["prompt"] == "legacy prompt"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("normalize_image", [True, False])
|
||||
def test_hairline52_height_compare_uses_same_new_rule(client, monkeypatch, normalize_image):
|
||||
import hairline.comfyui as comfy
|
||||
def unexpected_generation(*args, **kwargs):
|
||||
pytest.fail("Position comparison must not invoke ComfyUI by default")
|
||||
monkeypatch.setattr(comfy, "run", unexpected_generation)
|
||||
with open(fixture("frontal.jpg"), "rb") as photo:
|
||||
response = client.post("/api/v1/debug/hairline-height-compare",
|
||||
files={"image_file": ("frontal.jpg", photo, "image/jpeg")},
|
||||
data={"gender": "male", "hair_style": "1,3", "normalize_image": str(normalize_image).lower()})
|
||||
data = response.json()
|
||||
assert data["code"] == 0, data
|
||||
data = data["data"]
|
||||
assert [r["order"] for r in data["results"]] == [1, 3]
|
||||
assert set(data["guide_landmarks"]) == {"9", "150", "152", "162", "251", "389"}
|
||||
assert data["guide_landmarks"]["9"]["y"] < data["guide_landmarks"]["152"]["y"]
|
||||
expected_size = (data["height"], data["width"])
|
||||
base = _cv2.imdecode(_np.frombuffer(base64.b64decode(data["input_base64"]), _np.uint8), _cv2.IMREAD_COLOR)
|
||||
assert base.shape[:2] == expected_size
|
||||
assert data["coordinate_space"] == ("processed_image" if normalize_image else "original_image")
|
||||
if normalize_image:
|
||||
assert data["framing"]["enabled"] is True
|
||||
assert data["framing"]["axis_x_error_px"] < 1e-8
|
||||
assert data["guide_landmarks"]["9"]["x"] == pytest.approx(data["guide_landmarks"]["152"]["x"], abs=1e-8)
|
||||
else:
|
||||
assert data["framing"] is None
|
||||
original = _cv2.imread(fixture("frontal.jpg"))
|
||||
assert base.shape == original.shape
|
||||
for result in data["results"]:
|
||||
assert result["height_placement"]["target_error_px"] <= 1
|
||||
assert result["grown_image_base64"] is None
|
||||
for key in ("old_overlay_base64", "new_overlay_base64"):
|
||||
assert base64.b64decode(result[key])[:8] == b"\x89PNG\r\n\x1a\n"
|
||||
overlay = _cv2.imdecode(_np.frombuffer(base64.b64decode(result[key]), _np.uint8), _cv2.IMREAD_UNCHANGED)
|
||||
assert overlay.shape[:2] == expected_size
|
||||
|
||||
|
||||
def test_hairline52_gen_missing_gender_1004(client):
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
assert client.post(HLGEN52, headers=H, files=files).json()["code"] == 1004
|
||||
|
||||
|
||||
def test_hairline52_gen_missing_hairstyle_1007(client):
|
||||
# hair_style 已改为必填（同接口2）：只给 gender、不给 hair_style → 1007
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
assert client.post(HLGEN52, headers=H, files=files, data={"gender": "female"}).json()["code"] == 1007
|
||||
|
||||
|
||||
def test_hairline52_gen_female(client, monkeypatch):
|
||||
# mock ComfyUI：验证处理后底图、发际线和生发图使用相同尺寸与坐标。
|
||||
import hairline.comfyui as comfy
|
||||
monkeypatch.setattr(comfy, "run", lambda *a, **k: _PNG_1x1)
|
||||
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
body = client.post(HLGEN52, headers=H, files=files,
|
||||
data={"gender": "female", "hair_style": "1,3", "normalize_image": "true"}).json()
|
||||
assert body["code"] == 0, body
|
||||
d = body["data"]
|
||||
assert d["coordinate_space"] == "processed_image"
|
||||
assert d["framing"]["enabled"] is True
|
||||
assert d["framing"]["axis_x_error_px"] < 1e-8
|
||||
expected_size = (d["height"], d["width"])
|
||||
for value in (d["input_image_base64"], d["hairline_images"][0]["image_middle_base64"],
|
||||
d["hairline_images"][0]["grown_image_base64"]):
|
||||
image = _cv2.imdecode(_np.frombuffer(base64.b64decode(value), _np.uint8), _cv2.IMREAD_UNCHANGED)
|
||||
assert image.shape[:2] == expected_size
|
||||
imgs = d["hairline_images"]
|
||||
# 只返回选中发型，order = 发型序号
|
||||
assert [x["order"] for x in imgs] == [1, 3]
|
||||
assert [x["hairline_type"] for x in imgs] == ["ellipse", "heart"]
|
||||
# Only the designed middle curve is generated; deprecated fields stay null.
|
||||
assert base64.b64decode(imgs[0]["image_middle_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
|
||||
assert all(x["image_high_base64"] is None and x["image_low_base64"] is None for x in imgs)
|
||||
assert d["high_hairline_center_point"] is None and d["low_hairline_center_point"] is None
|
||||
rule = d["height_rule"]
|
||||
assert rule["target_upper_height_px"] == pytest.approx(
|
||||
(rule["middle_height_px"] + rule["lower_height_px"]) / 2)
|
||||
for x in imgs:
|
||||
assert x["height_placement"]["new_center"]["y"] == pytest.approx(rule["target_center"]["y"], abs=1)
|
||||
assert base64.b64decode(imgs[0]["grown_image_base64"])[:3] == b"\xff\xd8\xff"
|
||||
assert "image_middle_url" not in imgs[0]
|
||||
c = d["best_hairline_center_point"]
|
||||
assert 0 <= c["x"] < d["width"] and 0 <= c["y"] < d["height"]
|
||||
# face_measure：复用接口1测量数值（容错，失败为 null；本用例正常 → 必须有完整结构）
|
||||
fm = d["face_measure"]
|
||||
assert fm is not None, "face_measure 不应为 null（正常正面照）"
|
||||
assert set(["face_total_height_cm", "four_courts", "seven_eyes",
|
||||
"landmarks", "hairline_source", "head_pose"]).issubset(fm.keys())
|
||||
# 不应含标注图字段（接口52 只要数值，不要画线图）
|
||||
assert "annotated_image_base64" not in fm
|
||||
assert "annotated_image_url" not in fm
|
||||
# 子结构
|
||||
assert set(["top_court_cm", "upper_court_cm", "middle_court_cm",
|
||||
"lower_court_cm", "ratios"]).issubset(fm["four_courts"].keys())
|
||||
assert set(["eye_width_cm", "face_width_cm", "inter_eye_distance_cm",
|
||||
"ratios"]).issubset(fm["seven_eyes"].keys())
|
||||
# 七眼 eye1~eye7 键必须存在（eye1/eye7 在耳朵不可见时可为 null）
|
||||
assert set([f"eye{i}" for i in range(1, 8)]).issubset(fm["seven_eyes"].keys())
|
||||
assert set(["hair_top", "hairline", "brow_center",
|
||||
"nose_bottom", "chin_tip"]).issubset(fm["landmarks"].keys())
|
||||
assert fm["hairline_source"] in ("segmentation", "estimated", "corrected")
|
||||
assert set(["yaw", "pitch", "roll"]).issubset(fm["head_pose"].keys())
|
||||
|
||||
|
||||
# 接口4（用户特征）已迁到网关本机实现（直接调豆包），不再在 worker；
|
||||
# 其测试随实现一起在网关侧做，worker 这边不再覆盖。
|
||||
|
||||
@@ -188,7 +461,7 @@ def test_success_structure(client):
|
||||
data = body["data"]
|
||||
# 业务字段对齐文档
|
||||
assert set(["face_total_height_cm", "four_courts", "seven_eyes",
|
||||
"landmarks", "hairline_source", "head_pose",
|
||||
"landmarks", "hairline_source", "head_pose", "vertical_lines",
|
||||
"annotated_image_base64"]).issubset(data.keys())
|
||||
assert set(["top_court_cm", "upper_court_cm", "middle_court_cm",
|
||||
"lower_court_cm", "ratios"]).issubset(data["four_courts"].keys())
|
||||
@@ -205,3 +478,40 @@ def test_success_structure(client):
|
||||
assert "annotated_image_url" not in data
|
||||
png = base64.b64decode(data["annotated_image_base64"])
|
||||
assert png[:8] == b"\x89PNG\r\n\x1a\n"
|
||||
_assert_vertical_lines(data, "v1")
|
||||
|
||||
|
||||
_INNER_VLINES = [
|
||||
"left_cheek", "left_eye_outer", "left_eye_inner",
|
||||
"right_eye_inner", "right_eye_outer", "right_cheek",
|
||||
]
|
||||
|
||||
|
||||
def _assert_vertical_lines(data, variant):
|
||||
vl = data["vertical_lines"]
|
||||
assert isinstance(vl["y_top"], int) and isinstance(vl["y_bottom"], int)
|
||||
assert vl["y_top"] < vl["y_bottom"]
|
||||
names = [line["name"] for line in vl["lines"]]
|
||||
xs = [line["x"] for line in vl["lines"]]
|
||||
assert xs == sorted(xs)
|
||||
assert all(isinstance(x, int) for x in xs)
|
||||
if variant == "v6":
|
||||
assert names == _INNER_VLINES
|
||||
assert vl["y_top"] == data["landmarks"]["hairline"]["y"]
|
||||
assert vl["y_bottom"] == data["landmarks"]["chin_tip"]["y"]
|
||||
assert "eye1" not in data["seven_eyes"]
|
||||
assert "eye7" not in data["seven_eyes"]
|
||||
return
|
||||
assert set(_INNER_VLINES).issubset(names)
|
||||
assert names[0] in ("left_head_edge", "left_cheek")
|
||||
assert names[-1] in ("right_head_edge", "right_cheek")
|
||||
by_name = {line["name"]: line["x"] for line in vl["lines"]}
|
||||
assert ("left_head_edge" in by_name) == (data["seven_eyes"]["eye1"] is not None)
|
||||
assert ("right_head_edge" in by_name) == (data["seven_eyes"]["eye7"] is not None)
|
||||
|
||||
|
||||
def test_measure_v2_vertical_lines_have_no_head_edges(client):
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
body = client.post("/api/v1/face/measure-v2", headers=H, files=files).json()
|
||||
assert body["code"] == 0, body
|
||||
_assert_vertical_lines(body["data"], "v6")
|
||||
|
||||
@@ -0,0 +1,122 @@
|
||||
"""Framing must align real pixel coordinates without scaling or invented pixels."""
|
||||
import io
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import pytest
|
||||
|
||||
from hairline.framing import calibrate_reference, crop_fits, decode_portrait, normalize_portrait, transform_points
|
||||
|
||||
|
||||
PROFILE = {"version": "test", "reference_image_url": "/reference.png",
|
||||
"width_per_face_length": 2.1, "height_per_face_length": 2.8,
|
||||
"brow_x_ratio": .49, "brow_y_ratio": .34}
|
||||
|
||||
|
||||
def landmarks(w, h, brow, length=100, angle=0):
|
||||
lm = np.zeros((478, 3), np.float64)
|
||||
down = np.array([np.sin(np.deg2rad(angle)), np.cos(np.deg2rad(angle))])
|
||||
lm[:, :2] = np.array(brow)/[w, h]
|
||||
lm[152, :2] = (np.array(brow)+down*length)/[w, h]
|
||||
lm[:, 2] = .01
|
||||
return lm
|
||||
|
||||
|
||||
@pytest.mark.parametrize("angle", [-22, 0, 17])
|
||||
def test_native_scale_upright_and_inverse_mapping(angle):
|
||||
photo = np.full((700, 600, 3), 127, np.uint8)
|
||||
lm = landmarks(600, 700, (300, 280), angle=angle)
|
||||
result, moved, metadata = normalize_portrait(photo, lm, PROFILE)
|
||||
h, w = result.shape[:2]
|
||||
assert metadata["standard_crop"] is True
|
||||
assert w/h == .75
|
||||
xy = moved[[9, 152], :2]*[w, h]
|
||||
assert abs(xy[0, 0]-xy[1, 0]) < 1e-9
|
||||
assert xy[1, 1]-xy[0, 1] == pytest.approx(100)
|
||||
assert xy[0, 0]/w == pytest.approx(PROFILE["brow_x_ratio"])
|
||||
assert xy[0, 1]/h == pytest.approx(PROFILE["brow_y_ratio"])
|
||||
matrix = np.array(metadata["source_to_processed"])
|
||||
np.testing.assert_allclose(np.linalg.svd(matrix[:, :2])[1], [1, 1], atol=1e-10)
|
||||
restored = transform_points(moved[:, :2]*[w, h], np.array(metadata["processed_to_source"]))
|
||||
np.testing.assert_allclose(restored, lm[:, :2]*[600, 700], atol=1e-9)
|
||||
assert result.min() == 127 # no black triangles / padded background
|
||||
|
||||
|
||||
def test_insufficient_space_keeps_upright_original_size_when_no_rotation_needed():
|
||||
photo = np.full((300, 230, 3), 127, np.uint8)
|
||||
result, moved, metadata = normalize_portrait(photo, landmarks(230, 300, (115, 80), 150), PROFILE)
|
||||
assert metadata["standard_crop"] is False
|
||||
assert metadata["skip_reason"] == "insufficient_space"
|
||||
assert result.shape == photo.shape
|
||||
np.testing.assert_array_equal(result, photo)
|
||||
assert abs(moved[9, 0]-moved[152, 0]) < 1e-10
|
||||
|
||||
|
||||
def test_rotated_fallback_has_no_empty_corners():
|
||||
photo = np.full((400, 300, 3), 127, np.uint8)
|
||||
result, moved, metadata = normalize_portrait(photo, landmarks(300, 400, (150, 130), 160, 12), PROFILE)
|
||||
assert metadata["standard_crop"] is False
|
||||
assert result.min() == 127
|
||||
assert metadata["axis_x_error_px"] < 1e-10
|
||||
assert crop_fits(np.array(metadata["source_to_processed"]), (300, 400), result.shape[1::-1])
|
||||
|
||||
|
||||
@pytest.mark.parametrize("brow,angle", [((150, 330), 0), ((150, 330), 8), ((150, 70), -8)])
|
||||
def test_tall_fallback_is_three_by_four_and_retains_face(brow, angle):
|
||||
photo = np.full((700, 300, 3), 127, np.uint8)
|
||||
lm = landmarks(300, 700, brow, length=200, angle=angle)
|
||||
image, moved, metadata = normalize_portrait(photo, lm, PROFILE)
|
||||
h, w = image.shape[:2]
|
||||
assert metadata["standard_crop"] is False
|
||||
assert metadata["aspect_crop"] is True
|
||||
assert metadata["status"] == "aspect_crop"
|
||||
assert w*4 == h*3
|
||||
xy = moved[:, :2]*[w, h]
|
||||
assert (xy >= -1e-7).all() and (xy <= [w-1+1e-7, h-1+1e-7]).all()
|
||||
assert abs(xy[9, 0]-xy[152, 0]) < 1e-9
|
||||
assert np.linalg.norm(xy[152]-xy[9]) == pytest.approx(200)
|
||||
assert image.min() == 127
|
||||
assert crop_fits(np.array(metadata["source_to_processed"]), (300, 700), (w, h))
|
||||
|
||||
|
||||
def test_tall_fallback_does_not_cut_face_that_cannot_fit_three_by_four():
|
||||
photo = np.full((700, 300, 3), 127, np.uint8)
|
||||
image, moved, metadata = normalize_portrait(photo, landmarks(300, 700, (150, 100), length=450), PROFILE)
|
||||
assert metadata["aspect_crop"] is False
|
||||
assert metadata["status"] == "rotation_only"
|
||||
assert image.shape == photo.shape
|
||||
|
||||
|
||||
def test_reference_parameters_are_measured_after_straightening():
|
||||
photo = np.full((400, 300, 3), 127, np.uint8)
|
||||
image, moved, profile = calibrate_reference(photo, landmarks(300, 400, (145, 140), angle=8))
|
||||
h, w = image.shape[:2]
|
||||
assert w/h == .75 and w < 300 and h < 400
|
||||
assert profile["brow_9"]["x"] == pytest.approx(profile["chin_152"]["x"], abs=1e-9)
|
||||
assert profile["brow_x_ratio"] == pytest.approx(moved[9, 0])
|
||||
assert profile["brow_y_ratio"] == pytest.approx(moved[9, 1])
|
||||
assert profile["reference_width_per_face_length"] == pytest.approx(w/100)
|
||||
assert profile["reference_height_per_face_length"] == pytest.approx(h/100)
|
||||
assert profile["width_per_face_length"] == 2.5
|
||||
assert profile["height_per_face_length"] == pytest.approx(3/.9)
|
||||
assert image.min() == 127
|
||||
|
||||
|
||||
def test_default_crop_makes_face_ten_percent_smaller_than_three_l():
|
||||
photo = np.full((700, 600, 3), 127, np.uint8)
|
||||
result, moved, metadata = normalize_portrait(photo, landmarks(600, 700, (300, 280), length=120))
|
||||
assert metadata["standard_crop"] is True
|
||||
assert result.shape[:2] == (400, 300)
|
||||
assert metadata["face_axis_length_px"] == pytest.approx(120)
|
||||
assert metadata["height_per_face_length"] == pytest.approx(3/.9)
|
||||
assert metadata["width_per_face_length"] == 2.5
|
||||
old_height = 3*120
|
||||
assert old_height/result.shape[0] == pytest.approx(.9)
|
||||
|
||||
|
||||
def test_phone_orientation_is_applied_before_landmarks():
|
||||
image = Image.new("RGB", (120, 80), "white")
|
||||
exif = Image.Exif(); exif[274] = 6
|
||||
buf = io.BytesIO(); image.save(buf, format="JPEG", exif=exif)
|
||||
assert decode_portrait(buf.getvalue()).shape[:2] == (120, 80)
|
||||
@@ -0,0 +1,29 @@
|
||||
"""Interface 52 belongs to the GPU worker; the gateway keeps its legacy routes."""
|
||||
import asyncio
|
||||
import importlib
|
||||
|
||||
from starlette.requests import Request
|
||||
|
||||
|
||||
def test_legacy_hairline_forwarding(monkeypatch):
|
||||
name, path = "hairline_generate", "/api/v1/hairline/generate"
|
||||
gateway = importlib.import_module("gateway.app")
|
||||
request = Request({"type": "http", "method": "POST", "path": path, "headers": []})
|
||||
calls = []
|
||||
|
||||
async def proxy(actual_request, actual_path):
|
||||
assert actual_request is request
|
||||
calls.append(actual_path)
|
||||
return {"forwarded_to": actual_path}
|
||||
|
||||
monkeypatch.setattr(gateway, "_proxy", proxy)
|
||||
endpoint = getattr(gateway, name)
|
||||
assert next(route for route in gateway.app.routes if getattr(route, "path", None) == path).endpoint is endpoint
|
||||
assert asyncio.run(endpoint(request)) == {"forwarded_to": path}
|
||||
assert calls == [path]
|
||||
|
||||
|
||||
def test_interface52_is_not_exposed_by_gateway():
|
||||
gateway = importlib.import_module("gateway.app")
|
||||
assert not any(getattr(route, "path", None) == "/api/v1/hairline/generate-52"
|
||||
for route in gateway.app.routes)
|
||||
@@ -0,0 +1,62 @@
|
||||
"""Keep the gateway's historical image-field conversion unchanged."""
|
||||
import asyncio
|
||||
import json
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import AsyncMock
|
||||
|
||||
import httpx
|
||||
import pytest
|
||||
from starlette.requests import Request
|
||||
|
||||
from gateway.forward import rewrite_base64_to_url
|
||||
|
||||
|
||||
def test_nullable_image_fields_keep_original_names(tmp_path):
|
||||
value = {"hairline_images": [{"image_high_base64": None, "image_low_base64": None,
|
||||
"grown_image_base64": None}],
|
||||
"high_hairline_center_point": None, "face_measure": None}
|
||||
actual = rewrite_base64_to_url(value, "https://example.test", str(tmp_path))
|
||||
assert actual == value
|
||||
assert not list(tmp_path.iterdir())
|
||||
|
||||
|
||||
def test_nonempty_images_still_rewrite_alongside_null_fields(tmp_path):
|
||||
import base64
|
||||
import io
|
||||
from PIL import Image
|
||||
buf = io.BytesIO()
|
||||
Image.new("RGBA", (2, 2), (255, 255, 255, 0)).save(buf, format="PNG")
|
||||
value = {"image_middle_base64": base64.b64encode(buf.getvalue()).decode(),
|
||||
"image_high_base64": None}
|
||||
actual = rewrite_base64_to_url(value, "https://example.test", str(tmp_path))
|
||||
assert actual["image_middle_url"].startswith("https://example.test/static/annotations/")
|
||||
assert actual["image_high_base64"] is None
|
||||
assert "image_high_url" not in actual
|
||||
assert len(list(tmp_path.iterdir())) == 1
|
||||
|
||||
|
||||
@pytest.mark.parametrize("path,expected_key", [
|
||||
("/api/v1/hairline/generate", "grown_image_base64"),
|
||||
("/api/v1/hair/grow", "grown_image_base64"),
|
||||
])
|
||||
def test_proxy_keeps_original_null_image_fields(tmp_path, monkeypatch, path, expected_key):
|
||||
import gateway.config as config
|
||||
import gateway.forward as forward
|
||||
|
||||
monkeypatch.setattr(config, "get_config", lambda: {
|
||||
"dispatch": {"request_timeout_seconds": 10}, "shared_password": "test-token",
|
||||
"public_base_url": "https://example.test", "static_dir": str(tmp_path),
|
||||
})
|
||||
monkeypatch.setattr(forward, "acquire_worker", AsyncMock(return_value=SimpleNamespace(url="http://worker")))
|
||||
monkeypatch.setattr(forward, "release_worker", AsyncMock())
|
||||
worker_response = httpx.Response(200, json={"code": 0, "data": {"hairline_images": [
|
||||
{"grown_image_base64": None}]}})
|
||||
monkeypatch.setattr(forward, "get_client", lambda: SimpleNamespace(request=AsyncMock(return_value=worker_response)))
|
||||
|
||||
async def receive():
|
||||
return {"type": "http.request", "body": b"", "more_body": False}
|
||||
|
||||
request = Request({"type": "http", "method": "POST", "path": path, "headers": []}, receive)
|
||||
response = asyncio.run(forward.proxy_request(request, path))
|
||||
assert response.status_code == 200
|
||||
assert json.loads(response.body)["data"]["hairline_images"] == [{expected_key: None}]
|
||||
@@ -27,5 +27,9 @@ def test_mp_to_obj_anchor_mapping():
|
||||
|
||||
def test_texture_map():
|
||||
m = get_texture_map()
|
||||
assert [k for k, _ in m["female"]] == ["ellipse", "flower", "heart", "straight", "wave"]
|
||||
assert [k for k, _ in m["male"]] == ["ellipse", "inverse_arc", "m", "straight"]
|
||||
assert [k for k, _ in m["female"]] == [
|
||||
"ellipse", "flower", "heart", "straight", "wave", "bigflower", "clasicalflower",
|
||||
]
|
||||
assert [k for k, _ in m["male"]] == [
|
||||
"ellipse", "inverse_arc", "m", "straight", "heart", "Softpetal", "wave",
|
||||
]
|
||||
|
||||
@@ -0,0 +1,82 @@
|
||||
"""Template placement must follow face pose, not unequal left/right bangs."""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from hairline import constants as C
|
||||
from hairline.hairline_2d import (
|
||||
align_hairline_for_template,
|
||||
clamp_hairline_to_silhouette,
|
||||
)
|
||||
|
||||
|
||||
def _scene(angle=0.0, shape=(600, 900)):
|
||||
h, w = shape
|
||||
scale = np.array([w, h])
|
||||
lm = np.zeros((468, 3), np.float32)
|
||||
xy = np.full((468, 2), [450.0, 300.0])
|
||||
for i, (left, right) in enumerate(C.MP_TOP_ANCHOR_PAIRS):
|
||||
xy[left] = [300.0 + i * 12, 210.0 - i * 8]
|
||||
xy[right] = [600.0 - i * 12, 210.0 - i * 8]
|
||||
xy[10] = [450, 150]
|
||||
xy[152] = [450, 450]
|
||||
theta = np.deg2rad(angle)
|
||||
rotation = np.array([[np.cos(theta), -np.sin(theta)],
|
||||
[np.sin(theta), np.cos(theta)]])
|
||||
xy = (xy - [450, 300]) @ rotation.T + [450, 300]
|
||||
lm[:, :2] = xy / scale
|
||||
up = rotation @ [0.0, -1.0]
|
||||
anchors = lm[C.MP_TOP_ANCHORS, :2] * scale
|
||||
distances = np.full(C.N_ANCHORS, 50.0)
|
||||
indices = {k: i for i, k in enumerate(C.MP_TOP_ANCHORS)}
|
||||
distances[indices[67]] = 80.0
|
||||
distances[indices[297]] = 20.0
|
||||
hairline = ((anchors + distances[:, None] * up) / scale).astype(np.float32)
|
||||
return lm, hairline, np.ones(C.N_ANCHORS, bool), anchors, up
|
||||
|
||||
|
||||
@pytest.mark.parametrize("angle,shape", [(0, (600, 900)), (23, (600, 900)),
|
||||
(-17, (1200, 900))])
|
||||
def test_uneven_bangs_do_not_shear_template(angle, shape):
|
||||
lm, raw, valid, anchors, up = _scene(angle, shape)
|
||||
raw_copy, lm_copy = raw.copy(), lm.copy()
|
||||
aligned = align_hairline_for_template(lm, raw, valid, shape)
|
||||
displacement = aligned * shape[::-1] - anchors
|
||||
# Every anchor extends 50 px along the rotated face axis, even when
|
||||
# one side's hair boundary is 60 px higher than its counterpart.
|
||||
np.testing.assert_allclose(displacement, np.tile(50 * up, (C.N_ANCHORS, 1)), atol=1e-4)
|
||||
np.testing.assert_array_equal(raw, raw_copy)
|
||||
np.testing.assert_array_equal(lm, lm_copy)
|
||||
|
||||
|
||||
def test_anatomical_pairs_are_not_reversed_anchor_order():
|
||||
shape = (600, 900)
|
||||
lm, raw, valid, anchors, up = _scene(shape=shape)
|
||||
# Endpoints of MP_TOP_ANCHORS are not mirror-ordered.
|
||||
distances = {127: 10, 356: 30, 234: 60, 454: 100}
|
||||
for mp_idx, distance in distances.items():
|
||||
i = C.MP_TOP_ANCHORS.index(mp_idx)
|
||||
raw[i] = (anchors[i] + distance * up) / shape[::-1]
|
||||
aligned = align_hairline_for_template(lm, raw, valid, shape)
|
||||
actual = (aligned * shape[::-1] - anchors) @ up
|
||||
for mp_idx, expected in [(127, 20), (356, 20), (234, 80), (454, 80)]:
|
||||
assert actual[C.MP_TOP_ANCHORS.index(mp_idx)] == pytest.approx(expected, abs=1e-4)
|
||||
|
||||
|
||||
def test_missing_ray_hit_does_not_pull_valid_partner():
|
||||
shape = (600, 900)
|
||||
lm, raw, valid, _anchors, _up = _scene(shape=shape)
|
||||
left, right = C.MP_TOP_ANCHORS.index(67), C.MP_TOP_ANCHORS.index(297)
|
||||
valid[right] = False
|
||||
raw[right, 1] = -0.5 # An extrapolated fallback outside the head.
|
||||
aligned = align_hairline_for_template(lm, raw, valid, shape)
|
||||
np.testing.assert_allclose(aligned[[left, right]], raw[[left, right]], atol=1e-7)
|
||||
|
||||
|
||||
def test_template_alignment_still_allows_silhouette_clamping():
|
||||
shape = (600, 900)
|
||||
lm, raw, valid, _anchors, _up = _scene(shape=shape)
|
||||
aligned = align_hairline_for_template(lm, raw, valid, shape)
|
||||
parse_map = np.zeros(shape, np.int32)
|
||||
parse_map[140:450, 250:650] = C.PARSE_SKIN
|
||||
clamped = clamp_hairline_to_silhouette(aligned, parse_map)
|
||||
assert np.min(clamped[:, 1] * shape[0]) >= 142 - 1e-4
|
||||
@@ -0,0 +1,91 @@
|
||||
"""Court-based design placement, including preview/inpaint consistency."""
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from hairline.height_rule import (
|
||||
height_target, curve_center, place_curve, inpaint_from_curve,
|
||||
)
|
||||
|
||||
|
||||
def _landmarks(width=320, height=400):
|
||||
lm = np.zeros((468, 3), np.float32)
|
||||
for index, xy in {9: (160, 180), 94: (160, 260), 152: (160, 320),
|
||||
10: (160, 140), 21: (70, 170), 251: (250, 170)}.items():
|
||||
lm[index, :2] = np.array(xy) / [width, height]
|
||||
return lm
|
||||
|
||||
|
||||
def test_target_uses_mean_courts_without_old_estimation_ratio():
|
||||
rule = height_target(_landmarks(), 320, 400)
|
||||
assert rule["middle_height_px"] == pytest.approx(80, abs=1e-4)
|
||||
assert rule["lower_height_px"] == pytest.approx(60, abs=1e-4)
|
||||
assert rule["target_upper_height_px"] == pytest.approx(70, abs=1e-4)
|
||||
assert rule["target_center"]["y"] == pytest.approx(110, abs=1e-4)
|
||||
|
||||
|
||||
def test_reference_uses_unrounded_measurement_and_scale():
|
||||
ref = {"brow_center": (161.4, 181.3), "nose_bottom": (160.0, 261.9),
|
||||
"chin_tip": (160.0, 321.5), "px_per_cm": 20}
|
||||
rule = height_target(_landmarks(), 320, 400, ref)
|
||||
assert rule["target_upper_height_px"] == pytest.approx(70.1)
|
||||
assert rule["target_center"]["y"] == pytest.approx(111.2)
|
||||
assert rule["target_upper_height_cm"] == pytest.approx(3.505)
|
||||
assert rule["measurement_source"] == "face_measure"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("scale", [1, 2])
|
||||
@pytest.mark.parametrize("center_y", [90, 125, 150])
|
||||
def test_all_shapes_align_to_same_height_at_multiple_scales(scale, center_y):
|
||||
h, w = 400 * scale, 320 * scale
|
||||
lm = _landmarks()
|
||||
overlay = np.zeros((h, w, 4), np.float32)
|
||||
# A non-horizontal design, preserving a relative 20px arc after movement.
|
||||
pts = np.array([[70, center_y + 20], [160, center_y], [250, center_y + 20]]) * scale
|
||||
cv2.polylines(overlay, [pts.astype(np.int32)], False, (255, 255, 255, 255), 3 * scale)
|
||||
original = overlay.copy()
|
||||
target = height_target(lm, w, h)["target_center"]
|
||||
moved, placement = place_curve(overlay, target, lm)
|
||||
assert abs(curve_center(moved, target["x"])["y"] - target["y"]) < .2
|
||||
assert placement["target_error_px"] < .2
|
||||
# Shape's relative heights stay constant after translation.
|
||||
before_delta = curve_center(original, 100 * scale)["y"] - curve_center(original, 160 * scale)["y"]
|
||||
after_delta = curve_center(moved, 100 * scale)["y"] - curve_center(moved, 160 * scale)["y"]
|
||||
assert after_delta == pytest.approx(before_delta, abs=.1)
|
||||
np.testing.assert_array_equal(overlay, original)
|
||||
|
||||
|
||||
def test_rolled_face_preserves_curve_angle():
|
||||
lm = _landmarks()
|
||||
lm[10, 0] -= .08
|
||||
overlay = np.zeros((400, 320, 4), np.float32)
|
||||
cv2.line(overlay, (60, 95), (260, 135), (255, 255, 255, 255), 3)
|
||||
moved, _ = place_curve(overlay, {"x": 160, "y": 150}, lm)
|
||||
original_slope = curve_center(overlay, 200)["y"] - curve_center(overlay, 120)["y"]
|
||||
moved_slope = curve_center(moved, 200)["y"] - curve_center(moved, 120)["y"]
|
||||
assert moved_slope == pytest.approx(original_slope, abs=.2)
|
||||
|
||||
|
||||
def test_inpaint_mask_follows_new_line_not_old_black_template():
|
||||
lm = _landmarks()
|
||||
photo = np.full((400, 320, 3), 200, np.uint8)
|
||||
parsing = np.ones((400, 320), np.int32)
|
||||
overlay = np.zeros((400, 320, 4), np.float32)
|
||||
cv2.line(overlay, (20, 140), (300, 140), (255, 255, 255, 255), 3)
|
||||
moved, _ = place_curve(overlay, {"x": 160, "y": 100}, lm)
|
||||
marked, mask = inpaint_from_curve(photo, lm, parsing, moved)
|
||||
assert marked[100, 160, 0] == 0
|
||||
assert marked[140, 160, 0] == 200
|
||||
assert mask[90, 160] > 240
|
||||
assert mask[130, 160] < 10
|
||||
|
||||
|
||||
def test_out_of_frame_target_is_reported_instead_of_silent_clamping():
|
||||
ref = {"brow_center": (160, 50), "nose_bottom": (160, 200), "chin_tip": (160, 350)}
|
||||
with pytest.raises(ValueError, match="超出图片"):
|
||||
height_target(_landmarks(), 320, 400, ref)
|
||||
|
||||
|
||||
def test_missing_midline_curve_is_reported():
|
||||
with pytest.raises(ValueError, match="有效曲线"):
|
||||
place_curve(np.zeros((400, 320, 4), np.float32), {"x": 160, "y": 100}, _landmarks())
|
||||
@@ -0,0 +1,101 @@
|
||||
import json
|
||||
from ipaddress import ip_address
|
||||
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
from starlette.requests import Request
|
||||
|
||||
import ip_access as access
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def policy(tmp_path, monkeypatch):
|
||||
path = tmp_path / "access.json"
|
||||
path.write_text(json.dumps({"allowed_ips": []}))
|
||||
monkeypatch.setattr(access, "CONFIG_PATH", path)
|
||||
monkeypatch.setattr(access, "local_addresses", lambda: {ip_address("192.168.10.2"), ip_address("::1")})
|
||||
return path
|
||||
|
||||
|
||||
def request(host, headers=None, port=50000):
|
||||
return Request({"type": "http", "path": "/api/v1/hairline/generate-52", "scheme": "http",
|
||||
"client": (host, port), "headers": [(k.encode(), v.encode()) for k, v in (headers or {}).items()]})
|
||||
|
||||
|
||||
@pytest.mark.parametrize("host", ["127.0.0.1", "::1", "::ffff:127.0.0.1", "192.168.10.2"])
|
||||
def test_only_actual_local_addresses_allowed_by_default(policy, host):
|
||||
assert access.check_access(request(host))[0]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("host", ["203.0.113.9", "192.168.10.3", "0.0.0.0", "invalid"])
|
||||
def test_other_addresses_denied(policy, host):
|
||||
assert not access.check_access(request(host))[0]
|
||||
|
||||
|
||||
def test_whitelist_ipv4_ipv6_cidr_and_hot_reload(policy):
|
||||
policy.write_text(json.dumps({"allowed_ips": ["203.0.113.9", "198.51.100.0/24", "2001:db8::/64"]}))
|
||||
for host in ["203.0.113.9", "::ffff:203.0.113.9", "198.51.100.12", "2001:db8::12"]:
|
||||
assert access.check_access(request(host))[0]
|
||||
policy.write_text('{"allowed_ips": []}')
|
||||
assert not access.check_access(request("203.0.113.9"))[0]
|
||||
|
||||
|
||||
def test_configured_public_whitelist_is_exact(policy):
|
||||
policy.write_text('{"allowed_ips": ["49.232.51.171"]}')
|
||||
assert access.check_access(request("49.232.51.171"))[0]
|
||||
assert not access.check_access(request("49.232.51.172"))[0]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("header", ["x-forwarded-for", "x-real-ip", "forwarded"])
|
||||
def test_forwarding_headers_cannot_override_socket_peer(policy, header):
|
||||
assert not access.check_access(request("203.0.113.9", {header: "127.0.0.1"}))[0]
|
||||
assert access.check_access(request("127.0.0.1", {header: "203.0.113.9"}))[0]
|
||||
|
||||
|
||||
def test_gateway_is_allowed_independently_of_end_user_ip(policy):
|
||||
policy.write_text('{"allowed_ips": ["49.232.51.171"]}')
|
||||
assert access.check_access(request("49.232.51.171", {"x-forwarded-for": "203.0.113.9"}))[0]
|
||||
assert not access.check_access(request("203.0.113.9", {"x-forwarded-for": "49.232.51.171"}))[0]
|
||||
|
||||
|
||||
def test_rewritten_peer_or_invalid_policy_fail_closed(policy):
|
||||
assert not access.check_access(request("127.0.0.1", port=0))[0]
|
||||
policy.write_text('{"allowed_ips": ["not-an-ip"]}')
|
||||
assert not access.check_access(request("127.0.0.1"))[0]
|
||||
policy.write_text('invalid JSON')
|
||||
assert not access.check_access(request("127.0.0.1"))[0]
|
||||
|
||||
|
||||
def test_worker_guard_covers_all_interfaces_before_auth(policy, monkeypatch):
|
||||
import app as worker
|
||||
monkeypatch.setattr(worker, "ACCEPT_PASSWORDS", ["access-test"])
|
||||
headers = {"X-Internal-Token": "access-test"}
|
||||
with TestClient(worker.app, client=("203.0.113.9", 50000)) as client:
|
||||
assert client.post('/api/v1/hairline/generate-52').status_code == 403
|
||||
assert client.post('/api/v1/hairline/generate-52/', headers=headers).status_code == 403
|
||||
assert client.post('/api/v1/debug/hairline-height-compare').status_code == 403
|
||||
assert client.post('/api/v1/hairline/generate', headers=headers).status_code == 403
|
||||
assert client.post('/api/v1/face/measure', headers=headers).status_code == 403
|
||||
assert client.post('/api/v1/redraw', headers=headers).status_code == 403
|
||||
assert client.get('/health').status_code == 403
|
||||
assert client.get('/openapi.json').status_code == 403
|
||||
policy.write_text('{"allowed_ips": ["203.0.113.9"]}')
|
||||
assert client.post('/api/v1/hairline/generate-52', headers=headers).json()['code'] == 1004
|
||||
assert client.post('/api/v1/hairline/generate-52').status_code == 401
|
||||
|
||||
|
||||
def test_gateway_does_not_apply_worker_ip_policy(policy, monkeypatch):
|
||||
import importlib
|
||||
gateway = importlib.import_module('gateway.app')
|
||||
calls = []
|
||||
async def proxy(request, path):
|
||||
calls.append(path)
|
||||
return {'code': 0}
|
||||
monkeypatch.setattr(gateway, '_proxy', proxy)
|
||||
# Deliberately skip lifespan: no workers or configuration are needed.
|
||||
client = TestClient(gateway.app, client=('203.0.113.9', 50000))
|
||||
assert client.post('/api/v1/hairline/generate-52').status_code == 404
|
||||
assert client.post('/api/v1/hairline/generate').json()['code'] == 0
|
||||
assert client.post('/api/v1/hair/grow').json()['code'] == 0
|
||||
assert client.get('/static/test_interface52.html').status_code == 200
|
||||
assert calls == ['/api/v1/hairline/generate', '/api/v1/hair/grow']
|
||||
@@ -0,0 +1,83 @@
|
||||
"""竖线坐标：与标注图同一套几何，接口1/6 的返回字段由此而来。"""
|
||||
import numpy as np
|
||||
|
||||
from face_analysis.measure import MeasureResult
|
||||
from face_analysis.annotation import vertical_line_geometry
|
||||
|
||||
|
||||
def _result():
|
||||
"""已知像素坐标的测量结果。顶庭 2cm，不会触发发际线修正。"""
|
||||
vertical = {
|
||||
"hair_top": (500.0, 100.0),
|
||||
"hairline": (500.0, 200.2),
|
||||
"brow_center": (500.0, 350.0),
|
||||
"nose_bottom": (500.0, 500.0),
|
||||
"chin_tip": (500.0, 700.4),
|
||||
"top_court_px": 100.0,
|
||||
"upper_court_px": 150.0,
|
||||
"middle_court_px": 150.0,
|
||||
"lower_court_px": 200.0,
|
||||
}
|
||||
eyes = {
|
||||
"eye_width_px": 80.0,
|
||||
"face_width_px": 400.0,
|
||||
"inter_eye_distance_px": 90.0,
|
||||
"points": {
|
||||
"left_cheek": (300.4, 400.0),
|
||||
"left_outer": (360.6, 400.0),
|
||||
"left_inner": (440.2, 400.0),
|
||||
"right_inner": (530.8, 400.0),
|
||||
"right_outer": (610.4, 400.0),
|
||||
"right_cheek": (700.7, 400.0),
|
||||
},
|
||||
}
|
||||
return MeasureResult(vertical, eyes, 50.0, "estimated", None)
|
||||
|
||||
|
||||
_INNER = [
|
||||
"left_cheek", "left_eye_outer", "left_eye_inner",
|
||||
"right_eye_inner", "right_eye_outer", "right_cheek",
|
||||
]
|
||||
|
||||
|
||||
def test_v1_inner_lines_and_extended_span():
|
||||
geom = vertical_line_geometry(_result(), 1000, 800, variant="v1")
|
||||
# 短边 800，伸出 max(6, round(800*0.03)) = 24
|
||||
assert geom["y_top"] == 76
|
||||
assert geom["y_bottom"] == 724
|
||||
assert [line["name"] for line in geom["lines"]] == _INNER
|
||||
assert [line["x"] for line in geom["lines"]] == [300, 361, 440, 531, 610, 701]
|
||||
|
||||
|
||||
def test_v6_span_is_hairline_to_chin_without_head_edges():
|
||||
ear = np.zeros((800, 1000), dtype=np.uint8)
|
||||
ear[120:680, 250] = 255
|
||||
ear[120:680, 760] = 255
|
||||
geom = vertical_line_geometry(
|
||||
_result(), 1000, 800, ear_mask=ear, hair_mask=None, variant="v6")
|
||||
assert geom["y_top"] == 200
|
||||
assert geom["y_bottom"] == 700
|
||||
assert [line["name"] for line in geom["lines"]] == _INNER
|
||||
|
||||
|
||||
def test_v1_includes_ear_edges_left_to_right():
|
||||
ear = np.zeros((800, 1000), dtype=np.uint8)
|
||||
ear[120:680, 250] = 255
|
||||
ear[120:680, 760] = 255
|
||||
geom = vertical_line_geometry(
|
||||
_result(), 1000, 800, ear_mask=ear, hair_mask=None, variant="v1")
|
||||
names = [line["name"] for line in geom["lines"]]
|
||||
xs = [line["x"] for line in geom["lines"]]
|
||||
assert names == ["left_head_edge", *_INNER, "right_head_edge"]
|
||||
assert xs[0] == 250 and xs[-1] == 760
|
||||
assert xs == sorted(xs)
|
||||
|
||||
|
||||
def test_missing_ear_omits_that_edge():
|
||||
ear = np.zeros((800, 1000), dtype=np.uint8)
|
||||
ear[120:680, 250] = 255
|
||||
geom = vertical_line_geometry(
|
||||
_result(), 1000, 800, ear_mask=ear, hair_mask=None, variant="v1")
|
||||
names = [line["name"] for line in geom["lines"]]
|
||||
assert names[0] == "left_head_edge"
|
||||
assert "right_head_edge" not in names
|
||||
@@ -1,5 +1,11 @@
|
||||
{
|
||||
"_comment": "复制为 worker_config.json（不入 git）。accept_passwords：worker 内网鉴权密码列表，网关带 X-Internal-Token: <其中之一>（也可用环境变量 WORKER_ACCEPT_PASSWORDS 覆盖）。comfyui_password：本机 ComfyUI(8188) Basic Auth 密码（user 默认 admin；也可放 password.txt 或环境变量 COMFYUI_PASSWORD）。注：接口4 火山方舟 ark_api_key 已迁到网关配置。",
|
||||
"_comment": "复制为 worker_config.json（不入 git）。accept_passwords：worker 内网鉴权密码列表，网关带 X-Internal-Token: <其中之一>（也可用环境变量 WORKER_ACCEPT_PASSWORDS 覆盖）。comfyui_password：本机 ComfyUI(8188) Basic Auth 密码（user 默认 admin；也可放 password.txt 或环境变量 COMFYUI_PASSWORD）。health：深健康检查（/health 磁盘水位+下游依赖 TCP 探测，均可用环境变量 HEALTH_MIN_FREE_GB / HEALTH_CHECK_DEPS / HEALTH_DEP_COMFYUI / HEALTH_DEPS_CACHE_SECONDS 覆盖）。注：接口4 火山方舟 ark_api_key 已迁到网关配置。",
|
||||
"accept_passwords": ["change-me-to-a-strong-secret"],
|
||||
"comfyui_password": "your-comfyui-basic-auth-password"
|
||||
"comfyui_password": "your-comfyui-basic-auth-password",
|
||||
"health": {
|
||||
"min_free_gb": 5,
|
||||
"check_deps": true,
|
||||
"dep_comfyui": "127.0.0.1:8188",
|
||||
"deps_cache_seconds": 5
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user