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https://github.com/omimo/pymo
A library for machine learning research on motion capture data
https://github.com/omimo/pymo
library machine-learning mocap motion-capture python scikit-learn
Last synced: 1 day ago
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A library for machine learning research on motion capture data
- Host: GitHub
- URL: https://github.com/omimo/pymo
- Owner: omimo
- License: mit
- Created: 2017-11-07T19:13:29.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-09-01T10:37:02.000Z (over 2 years ago)
- Last Synced: 2024-12-16T21:11:37.130Z (8 days ago)
- Topics: library, machine-learning, mocap, motion-capture, python, scikit-learn
- Language: Python
- Homepage: https://omid.al/projects/pymo/
- Size: 5.87 MB
- Stars: 351
- Watchers: 23
- Forks: 70
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#read-bvh-files)
* Write BVH Files
* Pre-processing pipelines
* [Supporting `scikit-learn` API](#scikit-learn-pipeline-api)
* Convert data representations
* [Euler angles to positions](#convert-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
* [Skeleton hierarchy](#get-skeleton-info)
* [2D frame visualization](#visualize-a-single-2d-frame)
* [3D webgl-based animation](#animate-in-3d-inside-a-jupyter-notebook)
* Annotations
* Foot/ground contact detector
### Read BVH Files
```python
from pymo.parsers import BVHParser
parser = BVHParser()
parsed_data = parser.parse('demos/data/AV_8Walk_Meredith_HVHA_Rep1.bvh')
```
### Get Skeleton Info
```python
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
```python
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
```python
mp = MocapParameterizer('position')
positions = mp.fit_transform([parsed_data])
```
### Visualize a single 2D Frame
```python
draw_stickfigure(positions[0], frame=10)
```
### Animate in 3D (inside a Jupyter Notebook)
```python
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
```python
from pymo.features import *
plot_foot_up_down(positions[0], 'RightFoot_Yposition')
```
```python
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](https://github.com/omimo/PyMO/issues).
## Credits
Created by [Omid Alemi](https://omid.al/projects/)
## License
This code is available under the [MIT license](http://opensource.org/licenses/MIT).