Metadata-Version: 2.1
Name: volumentations
Version: 0.1.5
Summary: Point augmentations library as hard-fork of albu-team/albumentations
Home-page: https://github.com/kumuji/volumentations
License: MIT
Author: kumuji
Author-email: alexey@nekrasov.dev
Requires-Python: >=3.6,<4.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: importlib-metadata (>=1.6.0,<2.0.0)
Requires-Dist: numpy (>=1.18.3,<2.0.0)
Requires-Dist: pyyaml (>=5.3.1,<6.0.0)
Project-URL: Documentation, https://volumentations.readthedocs.io/en/latest/
Project-URL: Repository, https://github.com/kumuji/volumentations
Description-Content-Type: text/markdown

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# Volumentations

Python library for 3d data augmentaiton. Hard fork from [alumentations](https://github.com/albumentations-team/albumentations).

For more information on available augmentations check [documentation](https://volumentations.readthedocs.io/en/latest/index.html).

Or, check simple example in colab:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CT9nIGME_M4kIDc3BfEF4pCb_8JdFLpH)

# Setup

`pip install volumentations`

# Usage example

```python
import volumentations as V
import numpy as np

volume_aug = V.Compose(
    [
        V.Scale3d(scale_limit=[0.1, 0.1, 0.1], bias=[1, 1, 1]),
        V.RotateAroundAxis3d(axis=[0, 0, 1], rotation_limit=np.pi / 6),
        V.RotateAroundAxis3d(axis=[0, 1, 0], rotation_limit=np.pi / 6),
        V.RotateAroundAxis3d(axis=[1, 0, 0], rotation_limit=np.pi / 6),
        V.RandomDropout3d(dropout_ratio=0.2),
    ]
)
original_point_cloud = np.empty((1000, 3))
augmented_point_cloud = volume_aug(points=original_point_cloud)["points"]

```

```
# So far the package in WIP stage
```

