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
Annotations
- Foot/ground contact detector

Read BVH Files

from pymo.parsers import BVHParser
import pymo.viz_tools

parser = BVHParser()

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

Get Skeleton Info

import pymo.viz_tools

viz_tools.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

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

piped_data = data_pipe.fit_transform(train_X)

Convert to Positions

mp = MocapParameterizer('positions')

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(pos_data[3], 'RightFoot_Yposition')
plt.figure(figsize=(12,5))
plt.plot(signal, 'r')
plt.plot(pos_data[3].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.