Code from pose-eval paper

[cleong110]

Code to:

1. Collect multiple datasets together to a common DataFrame/CSV format, with GLOSS, POSE_FILE_PATH, SPLIT and unique VIDEO_ID columns
2. Parser scripts for ASL Citizen, Sem-Lex, and PopSign ASL to the common csv format
3. Load in splits all three datasets, e.g. all the train/val or just the test sets
4. Construct metrics automatically by generating combinations of Distance Measure, keypoint selection, sequence alignment, etc. Resulting in dozens of metrics
5. Run "in-Gloss+4x Outgloss"
6. Save the results to specified folder as csvs.

Example usage:

clone and setup

# clone the repo, and checkout this branch
# cd into the repo
conda create -n pose_eval_src pip
conda activate pose_eval_src
# which pip should show the pip inside the env
which pip

# install editable
pip install -e -U .

Then generate csv files

python pose_evaluation/evaluation/dataset_parsing/popsign_to_df.py ~/data/PopSignASL/ --out ~/projects/pose-evaluation/dataset_dfs/popsign_asl.csv
python pose_evaluation/evaluation/dataset_parsing/sem_lex_to_dataframe.py ~/data/Sem-Lex/ --out dataset_dfs/semlex.csv
python pose_evaluation/evaluation/dataset_parsing/asl_citizen_to_dataframe.py ~/data/ASL_Citizen/ --pose-files-path ~/data/ASL_Citizen/poses/ --metadata-path ~/data/ASL_Citizen/splits/ --out dataset_dfs/asl-citizen.csv 

Note that the popsign ASL one can optionally "translate" some but not all of the glosses if given a path to the ASL Knowledge graph --asl-knowledge-graph-path, see #28

python pose_evaluation/evaluation/dataset_parsing/popsign_to_df.py ~/data/PopSignASL/ --out dataset_dfs/popsign_asl.csv --asl-knowledge-graph-path ~/data/ASLKG/edges_v2_noweights.tsv

Then load them and run metrics

python pose_evaluation/evaluation/load_splits_and_run_metrics.py dataset_dfs/*.csv

# usage instructions
python pose_evaluation/evaluation/load_splits_and_run_metrics.py --help

analysis (TODO)

Script that will load all the score csv files and run analysis

[cleong110]

Something else I'm realizing as I construct metrics is that it would be nice if some of them automatically populated their own Preprocessors based on the DistanceMeasure. Like, DTW metrics do not need a Sequence Alignment processor such as ZeroPadShorter

Similarly, dtai distance needs a strategy for dealing with nan (masked) values. Or we get nan trajectory distances, which become nan distances when aggregated. So when one instantiates a metric one NEEDs a masked value preprocessor, or a strategy for dealing with them, e.g. returning a default distance if the trajectory distance is nan

[AmitMY]

All plotting, latex generation files, etc, should go in another directory, maybe analysis or something.
then i'll merge.

README should be overhauled to show the metrics and some charts from the paper maybe

then code cleanup

[cleong110]

• Analysis folder
• README Overhaul with picture from the paper
• Code Cleanup Post-Study technical debt #32