GitHub - polarisZhao/PFLD-pytorch: PFLD pytorch Implementation (original) (raw)

PFLD-pytorch

Implementation of PFLD A Practical Facial Landmark Detector by pytorch.

1. install requirements

pip3 install -r requirements.txt

2. Datasets

​ 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

3. training & testing

training :

use tensorboard, open a new terminal

$ tensorboard  --logdir=./checkpoint/tensorboard/

testing:

4. results:

5. pytorch -> onnx -> ncnn

Pytorch -> onnx

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);

6. reference:

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

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