Awesome
PFLD-pytorch
Implementation of PFLD A Practical Facial Landmark Detector by pytorch.
1. install requirements
pip3 install -r requirements.txt
2. 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.
- WFLW Training and Testing images [Google Drive] [Baidu Drive]
- WFLW Face Annotations
- Unzip above two packages and put them on
./data/WFLW/
- move
Mirror98.txt
toWFLW/WFLW_annotations
$ cd data
$ python3 SetPreparation.py
3. training & testing
training :
$ python3 train.py
use tensorboard, open a new terminal
$ tensorboard --logdir=./checkpoint/tensorboard/
testing:
$ python3 test.py
4. results:
5. 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);
6. reference:
PFLD: A Practical Facial Landmark Detector https://arxiv.org/pdf/1902.10859.pdf
Tensorflow Implementation: https://github.com/guoqiangqi/PFLD