This repository shows a detail example of using shape fitting by deepregression (called shapenet) for landmark detection. The original method and source repository is: https://github.com/justusschock/shapenet and please consider that first.

This code is used for the following research. If you found it usefull, please cite the following document:

https://www.nature.com/articles/s41598-020-58103-6

@article{eslami2020automatic, title={Automatic vocal tract landmark localization from midsagittal MRI data}, author={Eslami, Mohammad and Neuschaefer-Rube, Christiane and Serrurier, Antoine}, journal={Scientific Reports}, volume={10}, number={1}, pages={1--13}, year={2020}, publisher={Nature Publishing Group} }

Following repositories are also used for the mentioned paper:

https://github.com/mohaEs/Train-Predict-Landmarks-by-DAN

https://github.com/mohaEs/Train-Predict-Landmarks-by-MCAM

https://github.com/mohaEs/Train-Predict-Landmarks-by-Autoencoder

https://github.com/mohaEs/Train-Predict-Landmarks-by-dlib

https://github.com/mohaEs/Train-Predict-Landmarks-by-flat-net

• make sure python version is 3.6 ,
  i.e. have a conda environment with python 3.6

• install pytorch suitable version

• install delira this version
  pip install delira[torch]==0.3.1

• install shapenet:
  https://github.com/justusschock/shapenet

• download your kaggle.json from kaggle site and put in in C:\Users\User.kaggle.
  Generally you should get this file first from our homepage www.kaggle.com -> Your Account -> Create New API token.
  This will download a ready-to-go JSON file to place in you [user-home]/.kaggle folder.
  If there is no .kaggle folder yet, please create it first.

1- first prepare data from a zip file:

python ./scripts/prepare_datasets.py --helen --hzip ./helen.zip --ddir ./prepared_helen

2-correct the config file and locate it
set pca path, data path, epochs, saving model path and etc.

3- train it by
passing the config file as argument

python ./scripts/train_single_shapenet.py --config ./helen.config --verbose

4- predict
pas the input path, output path, weight file and config file locations as arguments:

python ./scripts/predict_from_net.py --in_path ./prepared_helen/helen/testset --out_path ./output_predict/ --weight_file ./Trained_Model/checkpoint_best.pth --config_file ./helen.config --visualize

Output of our experiment: (it was just 5 epochs which was trained on a few images)

This an exemplary code to create a zip file called helen.zip from your own dataset and landmarks which suitable format for training/predicting by above mentioned procedure.

It is supposed our image files (.png) and landmark files (.csv) are in [path_train_data '\temp_train_png'] and [ path_train_data '\temp_train_lm'] folders with the same name correspondigly. My n_points was 23.

validation_ratio=0.05;

path_train_png=[path_train_data '\temp_train_png\'];
path_train_lm=[ path_train_data '\temp_train_lm\'];

Path_write_train=[path_train_data '\helen\trainset\'];
Path_write_test=[path_train_data  '\helen\testset\'];
mkdir(Path_write_train);
mkdir(Path_write_test);

DirPngs=dir(fullfile(path_train_png,'*.png'));

Num_samples=length(DirPngs);
Ind_val=randperm(Num_samples,round(Num_samples*validation_ratio));

for SampleCounter=1:Num_samples

  ImageFileName= DirPngs(SampleCounter).name;
  FileName=ImageFileName(1:end-3); 
  LMs=csvread([path_train_lm ImageFileName(1:end-4) '.csv']);
  if isempty(find(SampleCounter==Ind_val))
    fileID = fopen([Path_write_train '\' FileName 'pts'],'w');
      copyfile([path_train_png ImageFileName],[Path_write_train ImageFileName]);
  else
    fileID = fopen([Path_write_test '\' FileName 'pts'],'w');
      copyfile([path_train_png ImageFileName],[Path_write_test ImageFileName]);
  end

  fprintf(fileID, "version: 1 \n");
  fprintf(fileID, "n_points:  23 \n");
  fprintf(fileID, "{ \n");
  for lm_counter=1:length(LMs)
     fprintf(fileID, "%d %d \n",LMs(lm_counter,1),LMs(lm_counter,2));
  end

  fprintf(fileID, "} \n");      
  fclose(fileID);


end

zippedfiles = zip([path_train_data 'Fold_' num2str(FoldCounter) '\helen.zip'] ,[path_train_data 'Fold_' num2str(FoldCounter) '\helen\']);
status = rmdir([path_train_data 'Fold_' num2str(FoldCounter) '\helen\'], 's');