# Voxelmentations
[](https://badge.fury.io/py/voxelmentations)
[](https://pepy.tech/project/voxelmentations?versions=0.0.*)
Voxelmentations is a Python library for 3d image (voxel) augmentation. Voxel augmentation is used in deep learning to increase the quality of trained models. The purpose of voxel augmentation is to create new training samples from the existing data.
Here is an example of how you can apply some augmentations from voxelmentations to create new voxel from the original one:
## Table of contents
- [Authors](#authors)
- [Installation](#installation)
- [A simple example](#a-simple-example)
- [List of augmentations](#list-of-augmentations)
- [Citing](#citing)
## Authors
[**Rostislav Epifanov** — Researcher in Novosibirsk]()
## Installation
Installation from PyPI:
```
pip install voxelmentations
```
Installation from GitHub:
```
pip install git+https://github.com/rostepifanov/voxelmentations
```
## A simple example
```python
import numpy as np
import voxelmentations as V
# Declare an augmentation pipeline
transform = V.Sequential([
V.Flip(p=0.5),
])
# Create example 3d image (height, width, depth, nchannels)
input = np.ones((32, 32, 32, 1))
# Augment exg
transformed = transform(voxel=input)
output = transformed['voxel']
```
## List of augmentations
The list of transforms:
- [Flip]()
- [AxialFlip]()
- [AxialPlaneFlip]()
- [AxialPlaneDropout]()
- [AxialPlaneRotate]()
- [AxialPlaneScale]()
- [AxialPlaneAffine]()
## Citing
If you find this library useful for your research, please consider citing:
```
@misc{epifanov2024exgment,
Author = {Rostislav Epifanov},
Title = {voxelmentations},
Year = {2024},
Publisher = {GitHub},
Journal = {GitHub repository},
Howpublished = {\url{https://github.com/rostepifanov/voxelmentations}}
}
```
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"description": "# Voxelmentations\n\n\n[](https://badge.fury.io/py/voxelmentations)\n[](https://pepy.tech/project/voxelmentations?versions=0.0.*)\n\nVoxelmentations is a Python library for 3d image (voxel) augmentation. Voxel augmentation is used in deep learning to increase the quality of trained models. The purpose of voxel augmentation is to create new training samples from the existing data.\n\nHere is an example of how you can apply some augmentations from voxelmentations to create new voxel from the original one:\n\n## Table of contents\n- [Authors](#authors)\n- [Installation](#installation)\n- [A simple example](#a-simple-example)\n- [List of augmentations](#list-of-augmentations)\n- [Citing](#citing)\n\n## Authors\n[**Rostislav Epifanov** \u2014 Researcher in Novosibirsk]()\n\n## Installation\nInstallation from PyPI:\n\n```\npip install voxelmentations\n```\n\nInstallation from GitHub:\n\n```\npip install git+https://github.com/rostepifanov/voxelmentations\n```\n\n## A simple example\n```python\nimport numpy as np\nimport voxelmentations as V\n\n# Declare an augmentation pipeline\ntransform = V.Sequential([\n V.Flip(p=0.5),\n])\n\n# Create example 3d image (height, width, depth, nchannels)\ninput = np.ones((32, 32, 32, 1))\n\n# Augment exg\ntransformed = transform(voxel=input)\noutput = transformed['voxel']\n```\n\n## List of augmentations\n\nThe list of transforms:\n\n- [Flip]()\n- [AxialFlip]()\n- [AxialPlaneFlip]()\n- [AxialPlaneDropout]()\n- [AxialPlaneRotate]()\n- [AxialPlaneScale]()\n- [AxialPlaneAffine]()\n\n## Citing\n\nIf you find this library useful for your research, please consider citing:\n\n```\n@misc{epifanov2024exgment,\n Author = {Rostislav Epifanov},\n Title = {voxelmentations},\n Year = {2024},\n Publisher = {GitHub},\n Journal = {GitHub repository},\n Howpublished = {\\url{https://github.com/rostepifanov/voxelmentations}}\n}\n```\n",
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