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

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

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)
then, run: python train.py
by default it trained with shufflenetv2_1.0

finetune

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

set config.MODEL.pretrained_model='./model/keypoints/variables/variables', in train_config.py
adjust the lr policy
python train.py

convert to tflite

modify the model path in toos/convert_to_tflite.py
python toos/convert_to_tflite.py it will produce converted_model.tflite
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

nsl2014fm/face_landmark