thor-magni-tools: Tools for Preprocessing and Filtering THÖR ̵ MAGNI Human Motion Dataset

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THÖR-MAGNI provides human gaze data aligned with 6D motion and human-robot interactions.

Install

Install miniconda. Then, you can install all packages required by running:

conda env create -f environment.yml && conda activate thor-magni-tools

Running

Download the dataset

First, the most important step is to download the dataset from zenodo. Run:

curl -O https://zenodo.org/records/10407223/files/THOR_MAGNI.zip\?download\=1 && unzip -r THOR_MAGNI.zip && rm -rf THOR_MAGNI.zip

The CSV files for each Scenario can be found in THOR_MAGNI\CSVs_Scenarios.

CSV headers checker

To check the alignment and consistency of headers in the csv files:

python -m thor_magni_tools.run_header_check --dir_path=PATH_TO_SCENARIO_FOLDER --sc_id=Scenario_1

Preprocessing

To preprocess the data with interpolation (and optional downsampling and moving average filter), first one should set the parameters in the cfg file and then run:

python -m thor_magni_tools.run_preprocessing 

If in_path is a folder, it will preprocess the files in the folder in parallel. After finishing, the files will be stored in the pre-specified output path with the format | time | frame_id | x | y | z | ag_id | data_label, where ag_id is the helmet number and data_label is the role of the participant.

Analysis

python -m thor_magni_tools.run_analysis --data_path=DATASET_FOLDER OR DATASET FILE --dataset DATASET_NAME 

Such that DATASET_NAME in {"thor_magni", "thor", "eth_ucy", "sdd", "atc"}.

Optional Arguments:

Parameter	Default	Description
--interpolation	None	used to preprocess the dataset. Max frames without tracking
--average_window	None	used to preprocess the dataset. Number of periods to average
--filtering_markers	3D-restoration	filtering markers type used in THÖR/THÖR-MAGNI tracks