Voxelmentations
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
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
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:
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}}
}
1 maintainer
Release files for voxelmentations 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| voxelmentations-0.0.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Release files / voxelmentations-0.0.1-py2.py3-none-any.whl
| Download URL | voxelmentations-0.0.1-py2.py3-none-any.whl |
|---|---|
| Size | 38.0 kB |
| Tags | Python 2 Python 3 |
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