PFLD-pytorch

by polarisZhao

polarisZhao / PFLD-pytorch

PFLD pytorch Implementation

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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

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