Note: This code is not maintained. Please refer to Peppa_Pig_Face_Landmark, The TRAIN subdir.There is better model.

face_landmark

A simple face aligment method, based on tensorflow2.0

introduction

This is the tensorflow2.0 branch, if u need to work on tf1 switch to branch tf1, it still work.

It is simple and flexible, trained with wingloss , multi task learning, also with data augmentation based on headpose and face attributes(eyes state and mouth state).

CN blog

And i suggest that you could try with another project,including face detect and keypoints, and some optimizations were made, u can check it there [pappa_pig_face_engine].

Contact me if u have problem about it. 2120140200@mail.nankai.edu.cn :)

demo pictures:

this gif is from github.com/610265158/Peppa_Pig_Face_Engine, but it is the same model : )

pretrained model:

shufflenetv2_1.0 tflite is not ok, retrain is needed, sad, i need more time.

• baidu disk (code rt7p)
• google drive

shufflenetv2_0.75 including tflite model, (time cost: mac i5-8279U@2.4GHz， tf2.0 5ms+， tflite 3.7ms+-)

• baidu disk (code fcdc)
• google drive

requirment

• tensorflow2.0

• tensorpack (for data provider)

• opencv

• python 3.6

useage

train

1. download all the 300W data set including the 300VW(parse as images, and make the label the same formate as 300W)

├── 300VW
│   ├── 001_annot
│   ├── 002_annot
│       ....
├── 300W
│   ├── 01_Indoor
│   └── 02_Outdoor
├── AFW
│   └── afw
├── HELEN
│   ├── testset
│   └── trainset
├── IBUG
│   └── ibug
├── LFPW
│   ├── testset
│   └── trainset

2. run python make_json.py produce train.json and val.json (if u like train u own data, please read the json produced , it is quite simple)

3. then, run: python train.py

4. by default it trained with shufflenetv2_1.0

finetune

1. download the pretrained model keypoints, put it into ./model and the model dir structure is :

 ./model/
  └── keypoints
      ├── saved_model.pb
      └── variables
          ├── variables.data-00000-of-00002
          ├── variables.data-00001-of-00002
          └── variables.index

2. set config.MODEL.pretrained_model='./model/keypoints/variables/variables', in train_config.py

3. adjust the lr policy

4. python train.py

convert to tflite

1. modify the model path in toos/convert_to_tflite.py

2. python toos/convert_to_tflite.py it will produce converted_model.tflite

3. CAUTION: the pretrained model shufflenentv2_1.0 is not ok with tflite, because the shuffle op, but it was fixed, if u need 1.0 please retrain, or wait for me.

visualization

python vis.py --model ./model/keypoints  
or python vis.py --model ./model/keypoints.tflite  (need conver to tflite first) 

TODO:

• A face detector is needed.

  dsfd_tensorflow

  faceboxes-tensorflow

  pappa_pig_face_engine

• tflite model