omimo / Pymo
Licence: mit
A library for machine learning research on motion capture data
Stars: ✭ 150
Programming Languages
python
139335 projects - #7 most used programming language
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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
- Supporting
- Visualization tools
- 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.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].