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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent