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README
MulanPSL-2.0

爱美丽

帮你变美 [软著登字第10293875号]


ENGLISH

爱美丽是一款美颜智能应用,目标是帮助用户提高颜值,包括:

颜值评测,颜值报告,改进方案,颜值PK等

目前版本实现了颜值评测、颜值报告(仅适用亚洲女性)


人类的美分为两个层次:

1.基于生物工程的美;

2.基于基因诉求的美;

生物工程代表了生物进化的普世价值,而基因诉求则代表了个体追求的目标;

使用大数据样本建模,最后通过模型预测给出最优得分,是普世价值美的体现;

而个体追求目标则需要用户自身诉求的表达和参与,所以模型必须在端上运行,且具备与用户互动的能力。


每个人的审美标准不同,这就决定了颜值的标准并不是唯一的,

每个人的美也不是唯一的,这也说明了靠单一的标准不能发现一个人的全部,

但如今医美机构大行其道,试图通过封闭的测试导向对其有利的标准获客结果。

这样的评测不是对美丽的追求,而是对金钱的追求!

虽然公司这样的行为在不损害客户利益的情况下无可厚非,但这就导致很多人被误导!

每个人都有自己独特的美,需要的是对自我的发现和展现。

所以我坚信,坚持开放透明的评测标准,坚持与用户密切的互动,

才能真正做到公正的挖掘和展示出用户的美!

才能真正为帮助用户追求美丽,提出有意义的解决方案!

指引和帮助用户成就自我!

所以爱美丽将会一直开源,其评测模型的标准也是开放可查可复现的。


最新Android版下载(所有推断均在本地进行):

应用宝:

https://sj.qq.com/appdetail/com.ml.projects.beautydetection

华为应用市场:

https://appgallery.huawei.com/app/C105812963

google应用市场:

https://play.google.com/store/apps/details?id=com.ml.projects.beautydetection

| | | | |---|---|---|

Face Rank Project

颜值评测 检测原理

由于特征较多,使用 MLFeatureSelection 筛选特征

1.人脸轮廓检测

Dlib 人脸关键点检测

2.皮肤检测

byol + lda

3.整体特征

resnet

运行环境

  • Python 3.8

使用方法

1.clone整个项目;

git clone https://gitee.com/knifecms/beauty.git
or
git clone https://github.com/showkeyjar/beauty.git

2.安装依赖;

2.1 独立安装:
conda install cmake
conda install nodejs
conda install dlib
2.2 导入conda环境:
conda env create -f face.yaml
默认windows环境
linux环境请使用pip install

3.修改 predict.py 中的图片路径

# 修改为需要预测的美女图片
test = "data/2.jpg"

4.执行预测,即可得到颜值分[0-5],分数越高颜值越高

python predict.py

5.预测结果解释:

依次执行 landmarks/ 目录下的 1_gen_feature.py 2_prepare_data.py 即可生成 data/face/features.csv 文件

python predict_interpret.py

6.执行摄像头下的实时预测

python predict_cam.py

7.运行web预测服务

python predict_server.py
或者启动服务
./restart_server.sh

预览地址:

http://locahost:5000/pred

包含两种解释lime和shap,推荐使用shap的解释

face point

face_reoprt

Questions

1.使用关键点位置判断是否科学?

关键点位置 + 皮肤 + 配比

2.使用人脸变换(face morph)作为美颜目标是否恰当?

使用Face Pose Net重建3d人脸

3.检测美是否可以反其道行之,用模型检测丑?

todo 缺陷检测

Problems

1.颜值解释运行过于缓慢,需要优化

已优化(todo 改用集成评估策略)

2.颜值解释说明需要配截图

已优化

3.需要对人脸校正

已优化

Todo

1.尝试使用尺度熵+xgb替换CNN;

DEV:

训练数据集:

https://github.com/HCIILAB/SCUT-FBP5500-Database-Release

未来计划

1.颜值解释(已添加点位和身体部位对应名称); (使用传统切割手段 和 胶囊图网络Capsule GNN 对比使用 https://github.com/benedekrozemberczki/CapsGNN https://github.com/brjathu/deepcaps )

2.美肤评测(已添加 lbph 特征);

3.使用带语义结构的特征(识别特定皮肤纹理等);

4.使用深度网络对特征进行抽取 (使用autokeras探索SCUT-FBP5500数据集生成模型,仅包含亚洲人和白人);

ak net

5.端上应用:

由于cordova摄像头插件无法通过录像的方式捕捉人脸轮廓,暂时弃用
Android Native C++配置过于复杂,windows下与python兼容性不好

端上开发

使用 Android Studio 打开 App/beauty

代替 firebase -> 21yunbox.com

参考

《女性美容美体小百科》

https://wenku.baidu.com/view/b10e711ba58da0116c1749e6.html

https://wenku.baidu.com/view/29392bbb9fc3d5bbfd0a79563c1ec5da50e2d6eb.html

https://max.book118.com/html/2017/1115/140076049.shtm

其他研究进展

https://github.com/bknyaz/beauty_vision

https://github.com/ustcqidi/BeautyPredict

http://antitza.com/assessment_female_beauty.pdf

The Beauty of Capturing Faces: Rating the Quality of Digital Portraits https://arxiv.org/abs/1501.07304v1

SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction https://arxiv.org/abs/1801.06345v1

Understanding Beauty via Deep Facial Features: https://arxiv.org/pdf/1902.05380.pdf

Automatic Facial Spots and Acnes Detection System https://www.researchgate.net/publication/276040820_Automatic_Facial_Spots_and_Acnes_Detection_System

欢迎贡献

欢迎提出宝贵意见及贡献代码

QQ交流群:740807335

加微信进微信群:

wechat

开发目录说明:

App     	移动端项目
dl          深度神经网络训练过程
doc         文档
feature     特征处理
landmarks   人脸关键点提取过程
leaderboard 人脸排行榜
logs        日志目录
model       模型二进制文件
static      flask服务静态文件
template    flask服务模版文件
test        测试目录

###相关文章:

1.一次无监督模型的尝试 https://zhuanlan.zhihu.com/p/482841898

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简介

爱美丽是一款美颜智能应用,目标是提高用户颜值,包括颜值评测,缺陷报告,颜值PK等 展开 收起
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