A library for using motion capture data for machine learning
This library is currently highly experimental and everything is subject to change :)
- Mocap Data Parsers and Writers
- Common mocap pre-processing algorithms
- Feature extraction library
- Visualization tools
- Read BVH Files
- Write BVH Files
- Pre-processing pipelines
- Supporting
scikit-learnAPI - 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
- Supporting
- Visualization tools
- Annotations
- Foot/ground contact detector
from pymo.parsers import BVHParser
parser = BVHParser()
parsed_data = parser.parse('demos/data/AV_8Walk_Meredith_HVHA_Rep1.bvh')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)
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])mp = MocapParameterizer('position')
positions = mp.fit_transform([parsed_data])draw_stickfigure(positions[0], frame=10)nb_play_mocap(positions[0], 'pos',
scale=2, camera_z=800, frame_time=1/120,
base_url='pymo/mocapplayer/playBuffer.html')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')For any questions, feedback, and bug reports, please use the Github Issues.
Created by Omid Alemi
This code is available under the MIT license.