This is the repository for all the code that was ceated during the GSoC 2021 at Red Hen Lab for the project topic "Red Hen Open Dataset for gestures with performance baselines at Red Hen Lab".
For more info on the work and some information / How to use / documentations on Singularity and HPC, check my blog @ swadesh13.github.io/gestures-dataset-blog
The OpenPose container used in this project is derived from frankierr/openpose_containers:focal_nvcaffe
at Docker. (Check the singularity.df
file for reference). workflow.py
file works as the entry to all the code and handles all the tasks.
The src
directory contains all the code. There is a code
directory for python files such as workflow.py
. Also, the singularity def file and .openpose_env
are under the singularity
directory. I updated the .openpose_env
and the def files, so I have kept them here.
- Changed access permissions of
/.openpose_env
to allow execution. - Changed the
/.openpose_env
to add the workflow.py file path. - Openpose searches for
models
directory atOPENPOSE_SRC
i.e. base OpenPose folder. So, copied the directory/home/opt/openpose_models
to/home/opt/openpose/models
- Added this github repo to
/home/opt/openpose/
.
Go to src/code
. Workflow.py handles all input and outputs. Inside data, data related tasks such as arranging keypoints to numpy data is present in gestures_data.py
. The model directory contains the training and detection code. The pose
folder contains code to generate keypoints from OpenPose output in the keypoints.py
file. utils
contains some simple utility stuff. config.py
includes the basic default configuration. singularity
. singularity
folder contains the basic def file on which the container is built and also the .openpose_env
file, which contains an extra environment path to be added. models
contains the model with default parameters.