This is a pytorch version of Convolutional Pose Machines for facial landmarks detection,you can find more details in original paper for Convolutional Pose Machines
- Python3.5
- Pytorch 0.4.0
- torchvision
The raw dataset should be put into the datasets folder.
Each dataset is saved as one file, in which each row indicates one specific face in one image or one video frame. The format of one line :
image_path annotation_path x1 y1 x2 y2 (face_size)
- image_path: the image file path of that face.
- annotation_path: the annotation file path of that face (annotation is the coordinates of all landmarks)
- x1, y1, x2, y2: the coordinates of left-upper and right-lower points of the face bounding box.
- face_size: an optional item. If set this value, we use the
face_sizeto compute the NME; otherwise, we use the distance between two pre-defined points to compute the NME.
- 300-W consits of several different datasets
- Create directory to save images and annotations: mkdir ./datasets/300W
- To download i-bug: https://ibug.doc.ic.ac.uk/download/annotations/ibug.zip
- To download afw: https://ibug.doc.ic.ac.uk/download/annotations/afw.zip
- To download helen: https://ibug.doc.ic.ac.uk/download/annotations/helen.zip
- To download lfpw: https://ibug.doc.ic.ac.uk/download/annotations/lfpw.zip
- To download the bounding box annotations: https://ibug.doc.ic.ac.uk/media/uploads/competitions/bounding_boxes.zip
- In the folder of
./datasets/300W, there are four zip files ibug.zip, afw.zip, helen.zip, and lfpw.zip
unzip ibug.zip -d ibug
mv ibug/image_092\ _01.jpg ibug/image_092_01.jpg
mv ibug/image_092\ _01.pts ibug/image_092_01.pts
unzip afw.zip -d afw
unzip helen.zip -d helen
unzip lfpw.zip -d lfpw
unzip bounding_boxes.zip ; mv Bounding\ Boxes Bounding_Boxes
The 300W directory is in ./datasets/300W and the sturecture is:
.
├── afw
├── afw.zip
├── Bounding_Boxes
├── bounding_boxes.zip
├── helen
├── helen.zip
├── ibug
├── ibug.zip
├── lfpw
└── lfpw.zip
Then you use the script to generate the 300-W list files.
python generate_300W.py
All list files will be saved into ./datasets/300W_lists. The files *.DET use the face detecter results for face bounding box. *.GTB use the ground-truth results for face bounding box.
The download link is in the official 300-W website.
https://ibug.doc.ic.ac.uk/media/uploads/competitions/bounding_boxes.zip
The zip file should be unzipped, and all extracted mat files should be put into ./datasets//300W/Bounding_Boxes.
download AFLW datasets from the official website,you can alse download it from my baiduyun aflw-images-0.tar.gz --Baiduyun Password: oi38 aflw-images-2.tar.gz --Baiduyun Password: u39h aflw-images-3.tar.gz --Baiduyun Password: ka2b
Download the AFLW datasets in datasets/AFLW and extract it by tar xzvf aflw-images-*.tar.gz.
mv aflw/data/flickr/0 images/
mv aflw/da1ta/flickr/2 images/
mv aflw/data/flickr/3 images/
Download the AFLWinfo_release.mat from this website into ./cache_data. This is the revised annotation of the full AFLW dataset.
put AFLWinfo_release.mat file in datasets/AFLW folder, and he structure of AFLW is:
.
├── AFLWinfo_release.mat
└── images
├── 0
├── 2
└── 3
use follow script to generate the AFLW dataset list file into ./datasets/AFLW_lists.
python generate_AFLW.py
set the training parameter in configs/Detector.config ,configs/SGD.config and script/300W-DET.sh
bash script/300W-DET-TRAIN.sh #for trainning 300W
bash script/AFLW-DET-TRAIN.sh #for trainning AFLW
ash script/300W-DET-EVAL.sh #for evaluating 300W
bash script/AFLW-DET-EVAL.sh #for evaluating AFLW
Pre-trained model which trained on 300W datasets can be downloaded from here 300w_68pts_cpm_vgg16-epoch-049-050.pth --Baiduyun passwd:fe6e
Pre-trained model which trained on AFLW datasets can be downloaded from here AFLW_19pts_cpm_vgg16-epoch-049-050.pth --Baiduyun passwd:sia1
python ./demo.py --model cpm_vgg16-epoch-049-050.pth --image datasets/images/image_0019.png
the repository is based on this repo