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recovering of motion capture marker data\n\n## Installation\nGapFill can be installed from [PyPI](https://pypi.org/project/gapfill/) using ```pip``` on Python\u003e=3.7.\n\n```bash\npip install gapfill\n```\n\n## Usage\nRows of ndarray values for marker coordinates should be filled with numpy.nan for occluded (blocked) frames.\n```python\nimport gapfill as gf\n\n# numpy is required in order to provide necessary markers' coordinate values\nimport numpy as np\n\n# 'fill_marker_gap_interp()' function will update the given ndarray by filling its gaps using bspline interpolation\n# 'tgt_mkr_pos0': a 2D (n, 3) ndarray of a target marker position to fill the gaps\n# 'n' is the total number of frames\ntgt_mkr_pos0 = np.array((n, 3), dtype=np.float32)\n# 'ret0': either True or False, True if there is any frame updated, False if there is no frame updated\n# 'updated_frs_mask0': a boolean ndarray to indicate which frames are updated\nret0, updated_frs_mask0 = gf.fill_marker_gap_interp(tgt_mkr_pos0)\n\n# 'fill_marker_gap_pattern()' function will update the given ndarray by filling its gaps using a donor marker\n# 'tgt_mkr_pos1': a 2D (n, 3) ndarray of a target marker position to fill the gaps\n# 'n' is the total number of frames\ntgt_mkr_pos1 = np.array((n, 3), dtype=np.float32)\n# 'dnr_mkr_pos': a 2D (n, 3) ndarray of a donor marker position\n# 'n' is the total number of frames\ndnr_mkr_pos = np.array((n, 3), dtype=np.float32)\n# 'ret1': either True or False, True if there is any frame updated, False if there is no frame updated\n# 'updated_frs_mask1': a boolean ndarray to indicate which frames are updated\nret1, updated_frs_mask1 = gf.fill_marker_gap_pattern(tgt_mkr_pos1, dnr_mkr_pos)\n\n# 'fill_marker_gap_rbt()' function will update the given ndarray by filling its gaps using a cluster of 3 markers\n# 'tgt_mkr_pos2': a 2D (n, 3) ndarray of a target marker position to fill the gaps\n# 'n' is the total number of frames\ntgt_mkr_pos2 = np.array((n, 3), dtype=np.float32)\n# 'cl_mkr_pos': a 3D (m, n, 3) ndarray of the cluster markers\n# 'm' (at least 3) is the number of markers, and 'n' is the total number of frames\ncl_mkr_pos = np.array((m, n, 3), dtype=np.float32)\n# 'ret2': either True or False, True if there is any frame updated, False if there is no frame updated\n# 'updated_frs_mask2': a boolean ndarray to indicate which frames are updated\nret2, updated_frs_mask2 = gf.fill_marker_gap_rbt(tgt_mkr_pos2, cl_mkr_pos)\n```\n## Dependencies\n- [NumPy](https://numpy.org/)\n- [SciPy](https://www.scipy.org/)\n\n## References\n- [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.](https://doi.org/10.1109/HSI.2016.7529654)\n- [Arun, K.S., Huang, T.S. and Blostein, S.D., 1987. Least-squares fitting of two 3-D point sets. IEEE Transactions on pattern analysis and machine intelligence, (5), pp.698-700.](https://doi.org/10.1109/TPAMI.1987.4767965)\n- [Wikipedia: Kabsch algorithm](https://en.wikipedia.org/wiki/Kabsch_algorithm)\n- [Kwon3D: Computation of the Rotation Matrix](http://www.kwon3d.com/theory/jkinem/rotmat.html)\n- [Vicon: What Gap Filling Algorithms are used Nexus 2?](http://www.vicon.com/support/faqs/?q=what-gap-filling-algorithms-are-used-nexus-2)\n- [Qualisys: Featuring the Trajectory Editor in QTM](https://www.qualisys.com/webinars/viewing-gap-filling-and-smoothing-data-with-the-trajectory-editor/)\n\n## How to cite this work\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3903481.svg)](https://doi.org/10.5281/zenodo.3903481)\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkjung99%2Fgapfill","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmkjung99%2Fgapfill","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkjung99%2Fgapfill/lists"}