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README.md

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

from pymo.parsers import BVHParser

parser = BVHParser()

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

Get Skeleton Info

from pymo.viz_tools import *

print_skel(parsed_data)

Will print the skeleton hierarchy:

- Hips (None)
| | - RightUpLeg (Hips)
| | - RightLeg (RightUpLeg)
| | - RightFoot (RightLeg)
| | - RightToeBase (RightFoot)
| | - RightToeBase_Nub (RightToeBase)
| - LeftUpLeg (Hips)
| - LeftLeg (LeftUpLeg)
| - LeftFoot (LeftLeg)
| - LeftToeBase (LeftFoot)
| - LeftToeBase_Nub (LeftToeBase)
- Spine (Hips)
| | - RightShoulder (Spine)
| | - RightArm (RightShoulder)
| | - RightForeArm (RightArm)
| | - RightHand (RightForeArm)
| | | - RightHand_End (RightHand)
| | | - RightHand_End_Nub (RightHand_End)
| | - RightHandThumb1 (RightHand)
| | - RightHandThumb1_Nub (RightHandThumb1)
| - LeftShoulder (Spine)
| - LeftArm (LeftShoulder)
| - LeftForeArm (LeftArm)
| - LeftHand (LeftForeArm)
| | - LeftHand_End (LeftHand)
| | - LeftHand_End_Nub (LeftHand_End)
| - LeftHandThumb1 (LeftHand)
| - LeftHandThumb1_Nub (LeftHandThumb1)
- Head (Spine)
- Head_Nub (Head)

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])

Visualize a single 2D Frame

draw_stickfigure(positions[0], frame=10)

2D Skeleton Viz

Animate in 3D (inside a Jupyter Notebook)

nb_play_mocap(positions[0], 'pos', 
              scale=2, camera_z=800, frame_time=1/120, 
              base_url='pymo/mocapplayer/playBuffer.html')

Mocap Player

Foot/Ground Contact Detector

from pymo.features import *

plot_foot_up_down(positions[0], 'RightFoot_Yposition')

Foot Contact

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')

Foot Contact Signal

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.

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