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README
Apache-2.0

DFace (Deeplearning Face) • License

Linux CPU Linux GPU Mac OS CPU Windows CPU
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基于多任务卷积网络(MTCNN)和Center-Loss的多人实时人脸检测和人脸识别系统。

Github项目地址

Slack 聊天组

DFace 是个开源的深度学习人脸检测和人脸识别系统。所有功能都采用 pytorch 框架开发。pytorch是一个由facebook开发的深度学习框架,它包含了一些比较有趣的高级特性,例如自动求导,动态构图等。DFace天然的继承了这些优点,使得它的训练过程可以更加简单方便,并且实现的代码可以更加清晰易懂。 DFace可以利用CUDA来支持GPU加速模式。我们建议尝试linux GPU这种模式,它几乎可以实现实时的效果。 所有的灵感都来源于学术界最近的一些研究成果,例如 Joint Face Detection and Alignment using Multi-task Cascaded Convolutional NetworksFaceNet: A Unified Embedding for Face Recognition and Clustering

MTCNN 结构  

mtcnn

如果你对DFace感兴趣并且想参与到这个项目中, 以下TODO是一些需要实现的功能,我定期会更新,它会实时展示一些需要开发的清单。提交你的fork request,我会用issues来跟踪和反馈所有的问题。也可以加DFace的官方Q群 681403076 也可以加本人微信 jinkuaikuai005

TODO(需要开发的功能)

  • 基于center loss 或者triplet loss原理开发人脸对比功能,模型采用ResNet inception v2. 该功能能够比较两张人脸图片的相似性。具体可以参考 PaperFaceNet
  • 反欺诈功能,根据光线,质地等人脸特性来防止照片攻击,视频攻击,回放攻击等。具体可参考LBP算法和SVM训练模型。
  • 3D人脸反欺诈。
  • mobile移植,根据ONNX标准把pytorch训练好的模型迁移到caffe2,一些numpy算法改用c++实现。
  • Tensor RT移植,高并发。
  • Docker支持,gpu版

安装

DFace主要有两大模块,人脸检测和人脸识别。我会提供所有模型训练和运行的详细步骤。你首先需要构建一个pytorch和cv2的python环境,我推荐使用Anaconda来设置一个独立的虚拟环境。如果使用GPU训练模式,需要安装Nvidia的cuda和cudnn。 目前作者倾向于Linux Ubuntu安装环境。感谢热心网友提供windows DFace安装体验,windos安装教程具体 可参考他的博客

依赖

  • cuda 8.0
  • anaconda
  • pytorch
  • torchvision
  • cv2
  • matplotlib
git clone https://gitee.com/kuaikuaikim/dface.git

在这里我提供了一个anaconda的环境依赖文件environment.yml (windows请用environment-win64.yml,Mac OSX请用environment_osx.yaml),它能方便你构建自己的虚拟环境。

cd dface  

conda env create -f environment.yml

添加python搜索模块路径

export PYTHONPATH=$PYTHONPATH:{your local DFace root path}

人脸识别和检测

如果你对mtcnn模型感兴趣,以下过程可能会帮助到你。

训练mtcnn模型

MTCNN主要有三个网络,叫做PNet, RNetONet。因此我们的训练过程也需要分三步先后进行。为了更好的实现效果,当前被训练的网络都将依赖于上一个训练好的网络来生成数据。所有的人脸数据集都来自 WIDER FACECelebA。WIDER FACE仅提供了大量的人脸边框定位数据,而CelebA包含了人脸关键点定位数据。以下训练除了 生成ONet的人脸关键点训练数据和标注文件 该步骤使用CelebA数据集,其他一律使用WIDER FACE。如果使用wider face的 wider_face_train.mat 注解文件需要转换成txt格式的,我这里用h5py写了个 转换脚本. 这里我提供一个已经转换好的wider face注解文件 anno_store/wider_origin_anno.txt, 以下训练过程参数名--anno_file默认就是使用该转换好的注解文件。

  • 创建 dface 训练数据临时目录,对应于以下所有的参数名 --dface_traindata_store
mkdir {your dface traindata folder}
  • 生成PNet训练数据和标注文件
python dface/prepare_data/gen_Pnet_train_data.py --prefix_path {注解文件中图片的目录前缀,就是wider face图片所在目录} --dface_traindata_store {之前创建的dface训练数据临时目录} --anno_file {wider face 注解文件,可以不填,默认使用anno_store/wider_origin_anno.txt}
  • 乱序合并标注文件
python dface/prepare_data/assemble_pnet_imglist.py
  • 训练PNet模型
python dface/train_net/train_p_net.py
  • 生成RNet训练数据和标注文件
python dface/prepare_data/gen_Rnet_train_data.py --prefix_path {注解文件中图片的目录前缀,就是wider face图片所在目录} --dface_traindata_store {之前创建的dface训练数据临时目录} --anno_file {wider face 注解文件,可以不填,默认使用anno_store/wider_origin_anno.txt} --pmodel_file {之前训练的Pnet模型文件}
  • 乱序合并标注文件
python dface/prepare_data/assemble_rnet_imglist.py
  • 训练RNet模型
python dface/train_net/train_r_net.py
  • 生成ONet训练数据和标注文件
python dface/prepare_data/gen_Onet_train_data.py --prefix_path {注解文件中图片的目录前缀,就是wider face图片所在目录} --dface_traindata_store {之前创建的dface训练数据临时目录} --anno_file {wider face 注解文件,可以不填,默认使用anno_store/wider_origin_anno.txt} --pmodel_file {之前训练的Pnet模型文件} --rmodel_file {之前训练的Rnet模型文件}
  • 生成ONet的人脸五官关键点训练数据和标注文件
python dface/prepare_data/gen_landmark_48.py
  • 乱序合并标注文件(包括人脸五官关键点)
python dface/prepare_data/assemble_onet_imglist.py
  • 训练ONet模型
python dface/train_net/train_o_net.py

测试人脸检测

如果不想训练,我已经把onet_epoch.pt,pnet_epoch.pt,rnet_epoch.pt三个文件放到model_store目录,直接运行test_image.py即可

python test_image.py

人脸对比

TODO 根据center loss实现人脸识别

测试效果

mtcnn

QQ交流群(模型获取请加群)

681403076

本人微信

jinkuaikuai005

License

Apache License 2.0

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