PyMO

A library for using motion capture data for machine learning

This library is currently highly experimental and everything is subject to change :)

Roadmap

Mocap Data Parsers and Writers
Common mocap pre-processing algorithms
Feature extraction library
Visualization tools

Current Features

Read BVH Files
Write BVH Files
Pre-processing pipelines
- Supporting scikit-learn API
- Convert data representations
  - Euler angles to positions
  - Euler angles to exponential maps
  - Exponential maps to euler angles
- Body-oriented global translation and rotation calculation with inverse tranform
- Root-centric position normalizer with inverse tranform
- Standard scaler
- Joint selectors
Annotations
- Foot/ground contact detector

Read BVH Files

from pymo.parsers import BVHParser

parser = BVHParser()

parsed_data = parser.parse('demos/data/AV_8Walk_Meredith_HVHA_Rep1.bvh')

scikit-learn Pipeline API

from pymo.preprocessing import *
from sklearn.pipeline import Pipeline

data_pipe = Pipeline([
    ('param', MocapParameterizer('position')),
    ('rcpn', RootCentricPositionNormalizer()),
    ('delta', RootTransformer('abdolute_translation_deltas')),
    ('const', ConstantsRemover()),
    ('np', Numpyfier()),
    ('down', DownSampler(2)),
    ('stdscale', ListStandardScaler())
])

piped_data = data_pipe.fit_transform([parsed_data])

Convert to Positions

mp = MocapParameterizer('position')

positions = mp.fit_transform([parsed_data])

Foot/Ground Contact Detector

from pymo.features import *

plot_foot_up_down(positions[0], 'RightFoot_Yposition')

signal = create_foot_contact_signal(positions[0], 'RightFoot_Yposition')
plt.figure(figsize=(12,5))
plt.plot(signal, 'r')
plt.plot(positions[0].values['RightFoot_Yposition'].values, 'g')

Feedback, Bugs, and Questions

For any questions, feedback, and bug reports, please use the Github Issues.

Credits

Created by Omid Alemi

License

This code is available under the MIT license.

lched/PyMO