base on ⚡️HivisionIDPhotos: a lightweight and efficient AI ID photos tools. 一个轻量级的AI证件照制作算法。 <div align="center">
<img alt="hivision_logo" src="assets/hivision_logo.png" width=120 height=120>
<h1>HivisionIDPhoto</h1>
[English](README_EN.md) / 中文 / [日本語](README_JP.md) / [한국어](README_KO.md)
[![][release-shield]][release-link]
[![][dockerhub-shield]][dockerhub-link]
[![][github-stars-shield]][github-stars-link]
[![][github-issues-shield]][github-issues-link]
[![][github-contributors-shield]][github-contributors-link]
[![][github-forks-shield]][github-forks-link]
[![][license-shield]][license-link]
[![][wechat-shield]][wechat-link]
[![][spaces-shield]][spaces-link]
[![][swanhub-demo-shield]][swanhub-demo-link]
[![][modelscope-shield]][modelscope-link]
[![][modelers-shield]][modelers-link]
[![][trendshift-shield]][trendshift-link]
[![][hellogithub-shield]][hellogithub-link]
<img src="assets/demoImage.jpg" width=900>
</div>
> **相关项目**：
>
> - [SwanLab](https://github.com/SwanHubX/SwanLab)：训练人像抠图模型全程用它来分析和监控，以及和实验室同学协作交流，大幅提升了训练效率。
<br>
# 目录
- [最近更新](#-最近更新)
- [项目简介](#-项目简介)
- [社区](#-社区)
- [准备工作](#-准备工作)
- [Demo启动](#-运行-gradio-demo)
- [Python推理](#-python-推理)
- [API服务部署](#️-部署-api-服务)
- [Docker部署](#-docker-部署)
- [联系我们](#-联系我们)
- [FAQ](#faq)
- [感谢支持](#-感谢支持)
- [License](#-lincese)
- [引用](#-引用)
<br>
# 🤩 最近更新
- 在线体验： [](https://swanhub.co/ZeYiLin/HivisionIDPhotos/demo)、[](https://huggingface.co/spaces/TheEeeeLin/HivisionIDPhotos)、[![][modelscope-shield]][modelscope-link]
- 2024.11.20: Gradio Demo增加**打印排版**选项卡，支持六寸、五寸、A4、3R、4R五种排版尺寸
- 2024.11.16: API接口增加美颜参数
- 2024.09.25: 增加**五寸相纸**和**JPEG下载**选项｜默认照片下载支持300DPI
- 2024.09.24: API接口增加base64图像传入选项 | Gradio Demo增加**排版照裁剪线**功能
- 2024.09.22: Gradio Demo增加**野兽模式**，可设置内存加载策略 | API接口增加**dpi、face_alignment**参数
- 2024.09.18: Gradio Demo增加**分享模版照**功能、增加**美式证件照**背景选项
- 2024.09.17: Gradio Demo增加**自定义底色-HEX输入**功能 | **（社区贡献）C++版本** - [HivisionIDPhotos-cpp](https://github.com/zjkhahah/HivisionIDPhotos-cpp) 贡献 by [zjkhahah](https://github.com/zjkhahah)
- 2024.09.16: Gradio Demo增加**人脸旋转对齐**功能，自定义尺寸输入支持**毫米**单位
<br>
# 项目简介
> 🚀 谢谢你对我们的工作感兴趣。您可能还想查看我们在图像领域的其他成果，欢迎来信:
[email protected].
HivisionIDPhoto 旨在开发一种实用、系统性的证件照智能制作算法。
它利用一套完善的AI模型工作流程，实现对多种用户拍照场景的识别、抠图与证件照生成。
**HivisionIDPhoto 可以做到：**
1. 轻量级抠图（纯离线，仅需 **CPU** 即可快速推理）
2. 根据不同尺寸规格生成不同的标准证件照、六寸排版照
3. 支持 纯离线 或 端云 推理
4. 美颜
5. 智能换正装（waiting）
<div align="center">
<img src="assets/demo.png" width=900>
</div>
---
如果 HivisionIDPhoto 对你有帮助，请 star 这个 repo 或推荐给你的朋友，解决证件照应急制作问题！
<br>
# 🏠 社区
我们分享了一些由社区构建的HivisionIDPhotos的有趣应用和扩展：
| [HivisionIDPhotos-ComfyUI][community-hivision-comfyui] | [HivisionIDPhotos-wechat-weapp][community-hivision-wechat] |
| :----------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------: |
| <a href="https://github.com/AIFSH/HivisionIDPhotos-ComfyUI"> <img src="assets/comfyui.png" width="900" alt="ComfyUI workflow"> </a> | <a href="https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp"> <img src="assets/community-wechat-miniprogram.png" width="900" alt="ComfyUI workflow"> </a> |
|ComfyUI证件照处理工作流 | 证件照微信小程序（JAVA后端+原生前端） |
| [HivisionIDPhotos-Uniapp][community-hivision-uniapp] | [HivisionIDPhotos-web](https://github.com/jkm199/HivisionIDPhotos-web)|
| :------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------: |
| <a href="https://github.com/soulerror/HivisionIDPhotos-Uniapp"> <img src="assets/community-uniapp-wechat-miniprogram.png" width="900" alt="HivisionIDPhotos-uniapp"> </a> | <a href="https://github.com/jkm199/HivisionIDPhotos-web"> <img src="assets/community-web.png" width="900" alt="HivisionIDPhotos-uniapp"> </a> |
| 证件照微信小程序（uniapp）| 证件照应用网页版 |
- [HivisionIDPhotos-cpp](https://github.com/zjkhahah/HivisionIDPhotos-cpp): HivisionIDphotos C++版本，由 [zjkhahah](https://github.com/zjkhahah) 构建
- [ai-idphoto](https://github.com/wmlcjj/ai-idphoto): [HivisionIDPhotos-wechat-weapp](https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp) 的uniapp多端兼容版，由 [wmlcjj](https://github.com/wmlcjj) 贡献
- [HivisionIDPhotos-uniapp-WeChat-gpto1](https://github.com/jkm199/HivisionIDPhotos-uniapp-WeChat-gpto1/): 由gpt-o1辅助完成开发的证件照微信小程序，由 [jkm199](https://github.com/jkm199) 贡献
- [HivisionIDPhotos-windows-GUI](https://github.com/zhaoyun0071/HivisionIDPhotos-windows-GUI)：Windows客户端应用，由 [zhaoyun0071](https://github.com/zhaoyun0071) 构建
- [HivisionIDPhotos-NAS](https://github.com/ONG-Leo/HivisionIDPhotos-NAS): 群晖NAS部署中文教程，由 [ONG-Leo](https://github.com/ONG-Leo) 贡献
<br>
# 🔧 准备工作
环境安装与依赖：
- Python >= 3.7（项目主要测试在 python 3.10）
- OS: Linux, Windows, MacOS
## 1. 克隆项目
```bash
git clone https://github.com/Zeyi-Lin/HivisionIDPhotos.git
cd HivisionIDPhotos
```
## 2. 安装依赖环境
> 建议 conda 创建一个 python3.10 虚拟环境后，执行以下命令
```bash
pip install -r requirements.txt
pip install -r requirements-app.txt
```
## 3. 下载人像抠图模型权重文件
**方式一：脚本下载**
```bash
python scripts/download_model.py --models all
# 如需指定下载某个模型
# python scripts/download_model.py --models modnet_photographic_portrait_matting
```
**方式二：直接下载**
模型均存到项目的`hivision/creator/weights`目录下：
| 人像抠图模型 | 介绍 | 下载 |
| -- | -- | -- |
| MODNet | [MODNet](https://github.com/ZHKKKe/MODNet)官方权重 | [下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/download/pretrained-model/modnet_photographic_portrait_matting.onnx)(24.7MB)|
| hivision_modnet | 对纯色换底适配性更好的抠图模型 | [下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/download/pretrained-model/hivision_modnet.onnx)(24.7MB) |
| rmbg-1.4 | [BRIA AI](https://huggingface.co/briaai/RMBG-1.4) 开源的抠图模型 | [下载](https://huggingface.co/briaai/RMBG-1.4/resolve/main/onnx/model.onnx?download=true)(176.2MB)后重命名为`rmbg-1.4.onnx` |
| birefnet-v1-lite | [ZhengPeng7](https://github.com/ZhengPeng7/BiRefNet) 开源的抠图模型，拥有最好的分割精度 | [下载](https://github.com/ZhengPeng7/BiRefNet/releases/download/v1/BiRefNet-general-bb_swin_v1_tiny-epoch_232.onnx)(224MB)后重命名为`birefnet-v1-lite.onnx` |
> 如果下载网速不顺利：前往[SwanHub](https://swanhub.co/ZeYiLin/HivisionIDPhotos_models/tree/main)下载。
## 4. 人脸检测模型配置（可选）
| 拓展人脸检测模型 | 介绍 | 使用文档 |
| -- | -- | -- |
| MTCNN | **离线**人脸检测模型，高性能CPU推理（毫秒级），为默认模型，检测精度较低 | Clone此项目后直接使用 |
| RetinaFace | **离线**人脸检测模型，CPU推理速度中等（秒级），精度较高| [下载](https://github.com/Zeyi-Lin/HivisionIDPhotos/releases/download/pretrained-model/retinaface-resnet50.onnx)后放到`hivision/creator/retinaface/weights`目录下 |
| Face++ | 旷视推出的在线人脸检测API，检测精度较高，[官方文档](https://console.faceplusplus.com.cn/documents/4888373) | [使用文档](docs/face++_CN.md)|
## 5. 性能参考
> 测试环境为Mac M1 Max 64GB，非GPU加速，测试图片分辨率为 512x715(1) 与 764×1146(2)。
| 模型组合 | 内存占用 | 推理时长(1) | 推理时长(2) |
| -- | -- | -- | -- |
| MODNet + mtcnn | 410MB | 0.207s | 0.246s |
| MODNet + retinaface | 405MB | 0.571s | 0.971s |
| birefnet-v1-lite + retinaface | 6.20GB | 7.063s | 7.128s |
## 6. GPU推理加速（可选）
在当前版本，可被英伟达GPU加速的模型为`birefnet-v1-lite`，并请确保你有16GB左右的显存。
如需使用英伟达GPU加速推理，在确保你已经安装[CUDA](https://developer.nvidia.com/cuda-downloads)与[cuDNN](https://developer.nvidia.com/cudnn)后，根据[onnxruntime-gpu文档](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#cuda-12x)找到对应的`onnxruntime-gpu`版本安装，以及根据[pytorch官网](https://pytorch.org/get-started/locally/)找到对应的`torch`版本安装。
```bash
# 假如你的电脑安装的是CUDA 12.x, cuDNN 8
# 安装torch是可选的，如果你始终配置不好cuDNN，那么试试安装torch
pip install onnxruntime-gpu==1.18.0
pip install torch --index-url https://download.pytorch.org/whl/cu121
```
完成安装后，调用`birefnet-v1-lite`模型即可利用GPU加速推理。
> TIPS: CUDA 支持向下兼容。比如你的 CUDA 版本为 12.6，`torch` 官方目前支持的最高版本为 12.4（<12.6），`torch`仍可以正常使用CUDA。
<br>
# ⚡️ 运行 Gradio Demo
```bash
python app.py
```
运行程序将生成一个本地 Web 页面，在页面中可完成证件照的操作与交互。
<img src="assets/harry.png" width=900>
<br>
# 🚀 Python 推理
核心参数：
- `-i`: 输入图像路径
- `-o`: 保存图像路径
- `-t`: 推理类型，有idphoto、human_matting、add_background、generate_layout_photos可选
- `--matting_model`: 人像抠图模型权重选择
- `--face_detect_model`: 人脸检测模型选择
更多参数可通过`python inference.py --help`查看
## 1. 证件照制作
输入 1 张照片，获得 1 张标准证件照和 1 张高清证件照的 4 通道透明 png
```python
python inference.py -i demo/images/test0.jpg -o ./idphoto.png --height 413 --width 295
```
## 2. 人像抠图
输入 1 张照片，获得 1张 4 通道透明 png
```python
python inference.py -t human_matting -i demo/images/test0.jpg -o ./idphoto_matting.png --matting_model hivision_modnet
```
## 3. 透明图增加底色
输入 1 张 4 通道透明 png，获得 1 张增加了底色的 3通道图像
```python
python inference.py -t add_background -i ./idphoto.png -o ./idphoto_ab.jpg -c 4f83ce -k 30 -r 1
```
## 4. 得到六寸排版照
输入 1 张 3 通道照片，获得 1 张六寸排版照
```python
python inference.py -t generate_layout_photos -i ./idphoto_ab.jpg -o ./idphoto_layout.jpg --height 413 --width 295 -k 200
```
## 5. 证件照裁剪
输入 1 张 4 通道照片（抠图好的图像），获得 1 张标准证件照和 1 张高清证件照的 4 通道透明 png
```python
python inference.py -t idphoto_crop -i ./idphoto_matting.png -o ./idphoto_crop.png --height 413 --width 295
```
<br>
# ⚡️ 部署 API 服务
## 启动后端
```
python deploy_api.py
```
## 请求 API 服务
详细请求方式请参考 [API 文档](docs/api_CN.md)，包含以下请求示例：
- [cURL](docs/api_CN.md#curl-请求示例)
- [Python](docs/api_CN.md#python-请求示例)
<br>
# 🐳 Docker 部署
## 1. 拉取或构建镜像
> 以下方式三选一
**方式一：拉取最新镜像：**
```bash
docker pull linzeyi/hivision_idphotos
```
**方式二：Dockrfile 直接构建镜像：**
在确保将至少一个[抠图模型权重文件](#3-下载权重文件)放到`hivision/creator/weights`下后，在项目根目录执行：
```bash
docker build -t linzeyi/hivision_idphotos .
```
**方式三：Docker compose 构建：**
在确保将至少一个[抠图模型权重文件](#3-下载权重文件)放到`hivision/creator/weights`下后，在项目根目录下执行：
```bash
docker compose build
```
## 2. 运行服务
**启动 Gradio Demo 服务**
运行下面的命令，在你的本地访问 [http://127.0.0.1:7860](http://127.0.0.1:7860/) 即可使用。
```bash
docker run -d -p 7860:7860 linzeyi/hivision_idphotos
```
**启动 API 后端服务**
```bash
docker run -d -p 8080:8080 linzeyi/hivision_idphotos python3 deploy_api.py
```
**两个服务同时启动**
```bash
docker compose up -d
```
## 环境变量
本项目提供了一些额外的配置项，使用环境变量进行设置：
| 环境变量 | 类型 | 描述 | 示例 |
|--|--|--|--|
| FACE_PLUS_API_KEY | 可选 | 这是你在 Face++ 控制台申请的 API 密钥 | `7-fZStDJ····` |
| FACE_PLUS_API_SECRET | 可选 | Face++ API密钥对应的Secret | `VTee824E····` |
| RUN_MODE | 可选 | 运行模式，可选值为`beast`(野兽模式)。野兽模式下人脸检测和抠图模型将不释放内存，从而获得更快的二次推理速度。建议内存16GB以上尝试。 | `beast` |
| DEFAULT_LANG | 可选 | Gradio Demo启动时的默认语言| `en` |
docker使用环境变量示例：
```bash
docker run -d -p 7860:7860 \
-e FACE_PLUS_API_KEY=7-fZStDJ···· \
-e FACE_PLUS_API_SECRET=VTee824E···· \
-e RUN_MODE=beast \
-e DEFAULT_LANG=en \
linzeyi/hivision_idphotos
```
<br>
# FAQ
## 1. 如何修改预设尺寸和颜色？
- 尺寸：修改[size_list_CN.csv](demo/assets/size_list_CN.csv)后再次运行 `app.py` 即可，其中第一列为尺寸名，第二列为高度，第三列为宽度。
- 颜色：修改[color_list_CN.csv](demo/assets/color_list_CN.csv)后再次运行 `app.py` 即可，其中第一列为颜色名，第二列为Hex值。
## 2. 如何修改水印字体？
1. 将字体文件放到`hivision/plugin/font`文件夹下
2. 修改`hivision/plugin/watermark.py`的`font_file`参数值为字体文件名
## 3. 如何添加社交媒体模板照？
1. 将模板图片放到`hivision/plugin/template/assets`文件夹下。模板图片是一个4通道的透明png。
2. 在`hivision/plugin/template/assets/template_config.json`文件中添加最新的模板信息，其中`width`为模板图宽度(px)，`height`为模板图高度(px)，`anchor_points`为模板中透明区域的四个角的坐标(px)；`rotation`为透明区域相对于垂直方向的旋转角度，>0为逆时针，<0为顺时针。
3. 在`demo/processor.py`的`_generate_image_template`函数中的`TEMPLATE_NAME_LIST`变量添加最新的模板名
<img src="assets/social_template.png" width="500">
## 4. 如何修改Gradio Demo的顶部导航栏？
- 修改`demo/assets/title.md`
<br>
# 📧 联系我们
如果您有任何问题，请发邮件至
[email protected]
<br>
# 🙏 感谢支持
[](https://github.com/Zeyi-Lin/HivisionIDPhotos/stargazers)
[](https://github.com/Zeyi-Lin/HivisionIDPhotos/network/members)
[](https://star-history.com/#Zeyi-Lin/HivisionIDPhotos&Date)
贡献者们：
<a href="https://github.com/Zeyi-Lin/HivisionIDPhotos/graphs/contributors">
<img src="https://contrib.rocks/image?repo=Zeyi-Lin/HivisionIDPhotos" />
</a>
[Zeyi-Lin](https://github.com/Zeyi-Lin)、[SAKURA-CAT](https://github.com/SAKURA-CAT)、[Feudalman](https://github.com/Feudalman)、[swpfY](https://github.com/swpfY)、[Kaikaikaifang](https://github.com/Kaikaikaifang)、[ShaohonChen](https://github.com/ShaohonChen)、[KashiwaByte](https://github.com/KashiwaByte)
<br>
# 📜 Lincese
This repository is licensed under the [Apache-2.0 License](LICENSE).
<br>
# 📚 引用
如果您在研究或项目中使用了HivisionIDPhotos，请考虑引用我们的工作。您可以使用以下BibTeX条目：
```bibtex
@misc{hivisionidphotos,
title={{HivisionIDPhotos: A Lightweight and Efficient AI ID Photos Tool}},
author={Zeyi Lin and SwanLab Team},
year={2024},
publisher={GitHub},
url = {\url{https://github.com/Zeyi-Lin/HivisionIDPhotos}},
}
```
[github-stars-shield]: https://img.shields.io/github/stars/zeyi-lin/hivisionidphotos?color=ffcb47&labelColor=black&style=flat-square
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[swanhub-demo-shield]: https://swanhub.co/git/repo/SwanHub%2FAuto-README/file/preview?ref=main&path=swanhub.svg
[swanhub-demo-link]: https://swanhub.co/ZeYiLin/HivisionIDPhotos/demo
[spaces-shield]: https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue
[spaces-link]: https://huggingface.co/spaces/TheEeeeLin/HivisionIDPhotos
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[wechat-link]: https://docs.qq.com/doc/DUkpBdk90eWZFS2JW
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[dockerhub-link]: https://hub.docker.com/r/linzeyi/hivision_idphotos/tags
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[hellogithub-link]: https://hellogithub.com/repository/8ea1457289fb4062ba661e5299e733d6
[github-contributors-shield]: https://img.shields.io/github/contributors/zeyi-lin/hivisionidphotos?color=c4f042&labelColor=black&style=flat-square
[github-contributors-link]: https://github.com/zeyi-lin/hivisionidphotos/graphs/contributors
[github-forks-shield]: https://img.shields.io/github/forks/zeyi-lin/hivisionidphotos?color=8ae8ff&labelColor=black&style=flat-square
[github-forks-link]: https://github.com/zeyi-lin/hivisionidphotos/network/members
[modelscope-shield]: https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white
[modelscope-link]: https://modelscope.cn/studios/SwanLab/HivisionIDPhotos
[modelers-shield]: https://img.shields.io/badge/Demo_on_Modelers-c42a2a?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxMjQiIGhlaWdodD0iNjQiIHZpZXdCb3g9IjAgMCAxMjQgNjQiIGZpbGw9Im5vbmUiPgo8cGF0aCBkPSJNNDIuNzc4MyAwSDI2LjU5NzdWMTUuNzc4N0g0Mi43NzgzVjBaIiBmaWxsPSIjREUwNDI5Ii8+CjxwYXRoIGQ9Ik0xNi41MDg4IDQuMTc5MkgwLjMyODEyNVYxOS45NTc5SDE2LjUwODhWNC4xNzkyWiIgZmlsbD0iIzI0NDk5QyIvPgo8cGF0aCBkPSJNMTIzLjk1MiA0LjE3OTJIMTA3Ljc3MVYxOS45NTc5SDEyMy45NTJWNC4xNzkyWiIgZmlsbD0iIzI0NDk5QyIvPgo8cGF0aCBkPSJNMTYuNTA4OCA0NS40NjE5SDAuMzI4MTI1VjYxLjI0MDZIMTYuNTA4OFY0NS40NjE5WiIgZmlsbD0iIzI0NDk5QyIvPgo8cGF0aCBkPSJNMTIzLjk1MiA0NS40NjE5SDEwNy43NzFWNjEuMjQwNkgxMjMuOTUyVjQ1LjQ2MTlaIiBmaWxsPSIjMjQ0OTlDIi8+CjxwYXRoIGQ9Ik0zMi43MDggMTUuNzc4OEgxNi41MjczVjMxLjU1NzVIMzIuNzA4VjE1Ljc3ODhaIiBmaWxsPSIjREUwNDI5Ii8+CjxwYXRoIGQ9Ik01Mi44NDg2IDE1Ljc3ODhIMzYuNjY4VjMxLjU1NzVINTIuODQ4NlYxNS43Nzg4WiIgZmlsbD0iI0RFMDQyOSIvPgo8cGF0aCBkPSJNOTcuNzIzNyAwSDgxLjU0M1YxNS43Nzg3SDk3LjcyMzdWMFoiIGZpbGw9IiNERTA0MjkiLz4KPHBhdGggZD0iTTg3LjY1MzQgMTUuNzc4OEg3MS40NzI3VjMxLjU1NzVIODcuNjUzNFYxNS43Nzg4WiIgZmlsbD0iI0RFMDQyOSIvPgo8cGF0aCBkPSJNMTA3Ljc5NCAxNS43Nzg4SDkxLjYxMzNWMzEuNTU3NUgxMDcuNzk0VjE1Ljc3ODhaIiBmaWxsPSIjREUwNDI5Ii8+CjxwYXRoIGQ9Ik0yNC42NzQ4IDMxLjU1NzZIOC40OTQxNFY0Ny4zMzYzSDI0LjY3NDhWMzEuNTU3NloiIGZpbGw9IiNERTA0MjkiLz4KPHBhdGggZD0iTTYwLjg3OTkgMzEuNTU3Nkg0NC42OTkyVjQ3LjMzNjNINjAuODc5OVYzMS41NTc2WiIgZmlsbD0iI0RFMDQyOSIvPgo8cGF0aCBkPSJNNzkuNjIwMSAzMS41NTc2SDYzLjQzOTVWNDcuMzM2M0g3OS42MjAxVjMxLjU1NzZaIiBmaWxsPSIjREUwNDI5Ii8+CjxwYXRoIGQ9Ik0xMTUuODI1IDMxLjU1NzZIOTkuNjQ0NVY0Ny4zMzYzSDExNS44MjVWMzEuNTU3NloiIGZpbGw9IiNERTA0MjkiLz4KPHBhdGggZD0iTTcwLjI1NDkgNDcuMzM1OUg1NC4wNzQyVjYzLjExNDdINzAuMjU0OVY0Ny4zMzU5WiIgZmlsbD0iI0RFMDQyOSIvPgo8L3N2Zz4=&labelColor=white
[modelers-link]: https://modelers.cn/spaces/SwanLab/HivisionIDPhotos
<!-- 社区项目链接 -->
[community-hivision-comfyui]: https://github.com/AIFSH/HivisionIDPhotos-ComfyUI
[community-hivision-wechat]: https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp
[community-hivision-uniapp]: https://github.com/soulerror/HivisionIDPhotos-Uniapp
[community-hivision-cpp]: https://github.com/zjkhahah/HivisionIDPhotos-cpp
[community-hivision-windows-gui]: https://github.com/zhaoyun0071/HivisionIDPhotos-windows-GUI
[community-hivision-nas]: https://github.com/ONG-Leo/HivisionIDPhotos-NAS", Assign "at most 3 tags" to the expected json: {"id":"11622","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"