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DFace (Deeplearning Face) •

Linux CPU	Linux GPU	Mac OS CPU	Windows CPU

MTCNN Structure　　

Installation

DFace has two major module, detection and recognition.In these two, We provide all tutorials about how to train a model and running. First setting a pytorch and cv2. We suggest Anaconda to make a virtual and independent python envirment.If you want to train on GPU,please install Nvidia cuda and cudnn.

Requirements

• pytorch==0.4.0
• torchvision
• cv2

git clone https://github.com/inisis/DFace.git

Add DFace to your local python path

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

Face Detetion and Recognition

If you are interested in how to train a mtcnn model, you can follow next step.

Train mtcnn Model

MTCNN have three networks called PNet, RNet and ONet.So we should train it on three stage, and each stage depend on previous network which will generate train data to feed current train net, also propel the minimum loss between two networks. Please download the train face datasets before your training. We use WIDER FACE and CelebA .WIDER FACE is used for training face classification and face bounding box, also CelebA is used for face landmarks. The original wider face annotation file is matlab format, you must transform it to text. I have put the transformed annotation text file into anno_store/wider_origin_anno.txt. This file is related to the following parameter called --anno_file.

• Generate PNet Train data and annotation file

python dface/prepare_data/gen_Pnet_train_data.py --prefix_path WIDER_train/images/ --dface_traindata_store data/ --anno_file anno_store/wider_origin_anno.txt

• Assemble annotation file and shuffle it

python dface/prepare_data/assemble_pnet_imglist.py

• Train PNet model

python dface/train_net/train_p_net.py

• Generate RNet Train data and annotation file

python dface/prepare_data/gen_Rnet_train_data.py --prefix_path WIDER_train/images/ --dface_traindata_store data/ --anno_file  anno_store/wider_origin_anno.txt --pmodel_file model_store/pnet_epoch_10.pt

• Assemble annotation file and shuffle it

python dface/prepare_data/assemble_rnet_imglist.py

• Train RNet model

python dface/train_net/train_r_net.py

• Generate ONet Train data and annotation file

python dface/prepare_data/gen_Onet_train_data.py --prefix_path WIDER_train/images/ --dface_traindata_store data/ --anno_file anno_store/wider_origin_anno.txt --pmodel_file model_store/pnet_epoch_10.pt --rmodel_file model_store/rnet_epoch_10.pt

• Generate ONet Train landmarks data
  To generate testImageList.txt, you need to cpoy merge_file.py to celeba/CelebA/Anno/ floder

python dface/prepare_data/gen_landmark_48.py --dface_traindata_store data/ --anno_file celeba/CelebA/Anno/testImageList.txt --prefix_path celeba/CelebA/Img/img_celeba.7z/img_celeba

• Assemble annotation file and shuffle it

python dface/prepare_data/assemble_onet_imglist.py

• Train ONet model

python dface/train_net/train_o_net.py

Test face detection

If you don't want to train,i have put onet_epoch10.pt,pnet_epoch10.pt,rnet_epoch10.pt in model_store folder.You just try test_image.py

python test_image.py

License

Apache License 2.0