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https://github.com/zfturbo/volumentations
Library for 3D augmentations
https://github.com/zfturbo/volumentations
3d 3d-augmentation augmentation transformations
Last synced: 6 days ago
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Library for 3D augmentations
- Host: GitHub
- URL: https://github.com/zfturbo/volumentations
- Owner: ZFTurbo
- License: mit
- Created: 2020-07-18T22:59:10.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-17T23:50:35.000Z (4 months ago)
- Last Synced: 2025-02-01T14:46:09.635Z (6 days ago)
- Topics: 3d, 3d-augmentation, augmentation, transformations
- Language: Python
- Homepage:
- Size: 2.51 MB
- Stars: 238
- Watchers: 3
- Forks: 35
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Volumentations 3D
3D Volume data augmentation package inspired by albumentations.
Volumentations is a working project, which originated from the following Git repositories:
- Original: https://github.com/albumentations-team/albumentations
- 3D Conversion: https://github.com/ashawkey/volumentations
- Continued Development: https://github.com/ZFTurbo/volumentations
Nevertheless, if you are using this subpackage, please give credit to all authors including ashawkey, ZFTurbo, qubvel and muellerdo.
Initially inspired by [albumentations](https://github.com/albumentations-team/albumentations) library for augmentation of 2D images.
# Installation
```sh
pip install volumentations-3D
```
# Simple Example
```python
from volumentations import *
def get_augmentation(patch_size):
return Compose([
Rotate((-15, 15), (0, 0), (0, 0), p=0.5),
RandomCropFromBorders(crop_value=0.1, p=0.5),
ElasticTransform((0, 0.25), interpolation=2, p=0.1),
Resize(patch_size, interpolation=1, resize_type=0, always_apply=True, p=1.0),
Flip(0, p=0.5),
Flip(1, p=0.5),
Flip(2, p=0.5),
RandomRotate90((1, 2), p=0.5),
GaussianNoise(var_limit=(0, 5), p=0.2),
RandomGamma(gamma_limit=(80, 120), p=0.2),
], p=1.0)
aug = get_augmentation((64, 128, 128))
img = np.random.randint(0, 255, size=(128, 256, 256), dtype=np.uint8)
lbl = np.random.randint(0, 1, size=(128, 256, 256), dtype=np.uint8)
# with mask
data = {'image': img, 'mask': lbl}
aug_data = aug(**data)
img, lbl = aug_data['image'], aug_data['mask']
# without mask
data = {'image': img}
aug_data = aug(**data)
img = aug_data['image']
```
* Check working usage example in [tst_volumentations_type_1.py](tst_volumentations_type_1.py)
* Added another usage example / testing in [tst_volumentations_type_2.py](tst_volumentations_type_2.py)
# Difference from initial version
* Diverse bug fixes.
* Implemented multiple augmentations.
* Approximation enhancements to be closer to Albumentations.
# Implemented 3D augmentations
Check the [EXAMPLES](EXAMPLES.md) page for visual demonstrations
```python
CenterCrop
ColorJitter
Contiguous
CropNonEmptyMaskIfExists
Downscale
ElasticTransform
ElasticTransformPseudo2D
Flip
Float
GaussianNoise
GlassBlur
GridDistortion
GridDropout
ImageCompression
Normalize
PadIfNeeded
RandomBrightnessContrast
RandomCrop
RandomCropFromBorders
RandomDropPlane
RandomGamma
RandomResizedCrop
RandomRotate90
RandomScale
RandomScale2
RemoveEmptyBorder
Resize
ResizedCropNonEmptyMaskIfExists
Rotate
RotatePseudo2D
Transpose
```
# Speed table
Speed in seconds per one sample.
| Aug name | Cube = 64px | Cube = 96px | Cube = 128px | Cube = 224px | Cube = 256px |
|----------|-------------|-------------|--------------|--------------|--------------|
| Rotate | 0.0402 | 0.1366 | 0.3246 | 1.7546 | 2.6349 |
| RandomCropFromBorders| 0.0037 | 0.0129 | 0.0315 | 0.1634 | 0.2426 |
| ElasticTransform | 0.1588 | 0.5439 | 2.8649 | 11.8937 | 42.3886 |
| Resize (type = 0) | 0.4029 | 0.4077 | 0.4245 | 0.5545 | 0.6278 |
| Resize (type = 1) | 0.3618 | 0.3696 | 0.3871 | 0.5174 | 0.5896 |
| Flip | 0.0042 | 0.0134 | 0.0314 | 0.1649 | 0.2453 |
| RandomRotate90 | 0.0040 | 0.0140 | 0.0306 | 0.1672 | 0.2439 |
| GaussianNoise | 0.0143 | 0.0406 | 0.0956 | 0.4992 | 0.7381 |
| RandomGamma | 0.0066 | 0.0211 | 0.0505 | 0.2654 | 0.3989 |
| RandomScale | 0.0158 | 0.0518 | 0.1198 | 0.6391 | 0.9457 |
### Related repositories
* [timm_3d](https://github.com/ZFTurbo/timm_3d) - classification models in 3D for PyTorch
* [classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) - 3D volumes classification models for Keras/Tensorflow
* [segmentation_models_pytorch_3d](https://github.com/ZFTurbo/segmentation_models_pytorch_3d) - 3D volumes segmentation models for PyTorch
* [segmentation_models_3D](https://github.com/ZFTurbo/segmentation_models_3D) - segmentation models in 3D for Keras/Tensorflow
# Citation
For more details, please refer to the publication: https://doi.org/10.1016/j.compbiomed.2021.105089
If you find this code useful, please cite it as:
```
@article{solovyev20223d,
title={3D convolutional neural networks for stalled brain capillary detection},
author={Solovyev, Roman and Kalinin, Alexandr A and Gabruseva, Tatiana},
journal={Computers in Biology and Medicine},
volume={141},
pages={105089},
year={2022},
publisher={Elsevier},
doi={10.1016/j.compbiomed.2021.105089}
}
```