mocaplib 0.0.2

Library for Motion Capture data processing and analysis

MoCapLib

MoCapLib: Library for Motion Capture data processing and analysis

Description

The aim of this repository is to provide a simple Python-based library for motion capture data processing and analysis.

Installation

MoCapLib can be installed from PyPI using pip on Python>=3.7.

pip install mocaplib

Usage

Currently, there is only one sub-module availabe, mocaplib.gapfill.

Rows of ndarray values for marker coordinates should be filled with numpy.nan for occluded (blocked) frames.

import mocaplib.gapfill as gf

# numpy is required in order to provide necessary markers' coordinate values
import numpy as np

# 'fill_marker_gap_interp()' function will update the given ndarray by filling its gaps using bspline interpolation
# 'tgt_mkr_pos0': a 2D (n, 3) ndarray of a target marker position to fill the gaps
# 'n' is the total number of frames
tgt_mkr_pos0 = np.array((n, 3), dtype=np.float32)
# 'ret0': either True or False, True if there is any frame updated, False if there is no frame updated
# 'updated_frs_mask0': a boolean ndarray to indicate which frames are updated
ret0, updated_frs_mask0 = gf.fill_marker_gap_interp(tgt_mkr_pos0)

# 'fill_marker_gap_pattern()' function will update the given ndarray by filling its gaps using a donor marker
# 'tgt_mkr_pos1': a 2D (n, 3) ndarray of a target marker position to fill the gaps
# 'n' is the total number of frames
tgt_mkr_pos1 = np.array((n, 3), dtype=np.float32)
# 'dnr_mkr_pos': a 2D (n, 3) ndarray of a donor marker position
# 'n' is the total number of frames
dnr_mkr_pos = np.array((n, 3), dtype=np.float32)
# 'ret1': either True or False, True if there is any frame updated, False if there is no frame updated
# 'updated_frs_mask1': a boolean ndarray to indicate which frames are updated
ret1, updated_frs_mask1 = gf.fill_marker_gap_pattern(tgt_mkr_pos1, dnr_mkr_pos)

# 'fill_marker_gap_rbt()' function will update the given ndarray by filling its gaps using a cluster of 3 markers
# 'tgt_mkr_pos2': a 2D (n, 3) ndarray of a target marker position to fill the gaps
# 'n' is the total number of frames
tgt_mkr_pos2 = np.array((n, 3), dtype=np.float32)
# 'cl_mkr_pos': a 3D (m, n, 3) ndarray of the cluster markers
# 'm' (at least 3) is the number of markers, and 'n' is the total number of frames
cl_mkr_pos = np.array((m, n, 3), dtype=np.float32)
# 'ret2': either True or False, True if there is any frame updated, False if there is no frame updated
# 'updated_frs_mask2': a boolean ndarray to indicate which frames are updated
ret2, updated_frs_mask2 = gf.fill_marker_gap_rbt(tgt_mkr_pos2, cl_mkr_pos)

Dependencies

• NumPy
• SciPy

References

• Smolka, J. and Lukasik, E., 2016, July. "The rigid body gap filling algorithm". In 2016 9th International Conference on Human System Interactions (HSI) (pp. 337-343). IEEE.
• Wikipedia: "Kabsch algorithm"
• Kwon3D: "Computation of the Rotation Matrix"
• Vicon: "What Gap Filling Algorithms are used Nexus 2?"
• Qualisys: "Featuring the Trajectory Editor in QTM"

License

MIT