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Install miniconda. Then, you can install all packages required by running:
conda env create -f environment.yml && conda activate thor-magni-tools
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
.
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
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.
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 |