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.