This network architecture is presented in which detects the locations of landmarks for vocal tract area.
The network is based on heatmap generation and location of the argmax.
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-SFD
code is based on the tensorflow 1.14 which the embedded keras is also used.
it is assumed that, the png files are stored in a folder and corresponding cvs files with same filenames are located in another folder and contains the x,y locations of the landmarks.
For example one image and corresponding landmark file is placed in data folder.
The landmark file contanis 20 landmarks.
The mentioned network were able to handle just 5 heatmaps for 256x256 images and batch size of 10.
Therefore for having a full machine predicting 20 landmarks we need to train 4 different netwroks.
The desired landmark for each network can be set by arguments.
the input arguments are as follow:
"--mode", choices=["train", "test"])
"--input_dir", the path of the directory contains png files
"--target_dir", the path of directory contains csv files of corressponding ladnmarks
"--checkpoint", the path of directorry for the trained model
"--output_dir", the path of output, in training mode the trained model would be saved here and in testing mode the results.
"--landmarks", the string contains the number of desired landmarks
for example for a machine:
target_dir='../Images_Data/CV_10_Folding/Fold_1/temp_train_lm/'
input_dir='../Images_Data/CV_10_Folding/Fold_1/temp_train_png/'
checkpoint='../Images_Data/CV_10_Folding/Fold_1/Models/Models_flat/Models_lm_2/'
output_dir='../Images_Data/CV_10_Folding/Fold_1/Models/Models_flat/Models_lm_2/'
landmarks='2,12,11,10,5'
python3 Pr_LandMarkDetection_FlatArc+HeatMap.py --mode 'train' --input_dir $input_dir --target_dir $target_dir --checkpoint $checkpoint --output_dir $output_dir --landmarks $landmarks
of the checkpoint is empty, the new training session would be done otherwise the training would be continued from previous session.
Do not forget to set the suitable number of epochs in the python scripts.
target_dir='../Images_Data/CV_10_Folding/Fold_1/temp_test_lm/'
input_dir='../Images_Data/CV_10_Folding/Fold_1/temp_test_png/'
checkpoint='../Images_Data/CV_10_Folding/Fold_1/Models/Models_flat/Models_lm_2/'
output_dir='../Images_Data/CV_10_Folding/Fold_1/Results/Models_flat/Models_lm_2/'
landmarks='2,12,11,10,5'
python3 Pr_LandMarkDetection_FlatArc+HeatMap.py --mode 'test' --input_dir $input_dir --target_dir $target_dir --checkpoint $checkpoint --output_dir $output_dir --landmarks $landmarks
The predicted landmarks plus the truth locations would be saved in csv files. Also a visualization image of the prediction will be saved:
Notice that, in the save csv of output folder, the x and y columns may changed from the original ones in targer_dir: x->y y->x
