Awesome

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
• Visualization tools
  • Skeleton hierarchy
  • 2D frame visualization
  • 3D webgl-based animation
• 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')

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)

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')

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.