PFLD-pytorch

Implementation of PFLD A Practical Facial Landmark Detector by pytorch.

install requirements

pip3 install -r requirements.txt

Datasets

• WFLW Dataset Download

​ Wider Facial Landmarks in-the-wild (WFLW) is a new proposed face dataset. It contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks.

1. WFLW Training and Testing images [Google Drive] [Baidu Drive]
2. WFLW Face Annotations
3. Unzip above two packages and put them on

   ./data/WFLW/

4. move

   Mirror98.txt

   to

   WFLW/WFLW_annotations

$ cd data 
$ python3 SetPreparation.py

training & testing

training :

$ python3 train.py

use tensorboard, open a new terminal ~~~ $ tensorboard --logdir=./checkpoint/tensorboard/ ~~~ testing:

$ python3 test.py

results:

pytorch -> onnx -> ncnn

Pytorch -> onnx

python3 pytorch2onnx.py

onnx -> ncnn

how to build :https://github.com/Tencent/ncnn/wiki/how-to-build

cd ncnn/build/tools/onnx
./onnx2ncnn pfld-sim.onnx pfld-sim.param pfld-sim.bin

Now you can use pfld-sim.param and pfld-sim.bin in ncnn:

ncnn::Net pfld;
pfld.load_param("path/to/pfld-sim.param");
pfld.load_model("path/to/pfld-sim.bin");

cv::Mat img = cv::imread(imagepath, 1);
ncnn::Mat in = ncnn::Mat::from_pixels_resize(img.data, ncnn::Mat::PIXEL_BGR, img.cols, img.rows, 112, 112);
const float norm_vals[3] = {1/255.f, 1/255.f, 1/255.f};
in.substract_mean_normalize(0, norm_vals);
ncnn::Extractor ex = pfld.create_extractor();
ex.input("input_1", in);
ncnn::Mat out;
ex.extract("415", out);

reference:

PFLD: A Practical Facial Landmark Detector https://arxiv.org/pdf/1902.10859.pdf

Tensorflow Implementation: https://github.com/guoqiangqi/PFLD

TODO:

• [x] Train on CPU and GPU

• [x] ncnn inference

• [ ] retrain on datasets AFLW and 300W

• [ ] fix bugs