Custom image augmentations specifically designed around astronomical instruments and data. Please open an issue to highlight missing augmentations and / or datasets. This is an open source project, so feel free to fork, make changes and submit a pull request of your additions and modifications!
This package is in active development and although it should work, it may be a bit temporamental and require some love to get to know. Feel free to make suggestions using the issue tracker.
This package is based on Albumentations. This should allow scalability and applicability in a multitude of cases, including both TensorFlow and PyTorch.
- Augmentations designed for specific astronomical domains and data formats.
- Access to standardized default data sets.
- Most recent version covers:
- Radio image augmentations (designed with interferometers in mind)
Importing: import astroaugmentations as AA
.
Install:
pip install -U git+https://github.com/mb010/AstroAugmentations.git
pip install -U git+https://github.com/albumentations-team/albumentations
Developed using: torch>=1.10.2+cu113
and 'torchvision>=0.11.3+cu113
.
The default is to import the package as AA
: import astroaugmentations as AA
.
Augmentations for each data type are seperated into individual modules,
each of which will contain submodules with regime specific augmentations e.g.:
AA.image_domain
contains transformations designed for imaging / computer vision tasks.AA.image_domain.optical
provides augmentations specifically designed around optical imaging.AA.image_domain.radio
provides augmentations specifically designed around radio imaging.
AA.composed
contains 'ready to go'
example compositions
of multiple transforms explicitly designed for a data type and regime.
AA.CustomKernelConvolution()
requires a kernel to be available in a directory as
a saved numpy array (e.g. ./kernels/FIRST_kernel.npy
). We provide a kernel we generated
here
(designed for the FIRST Survey).
Please see the ipython notebooks provided for demonstrations of the various augmentations. These are implemented using Torch. The interaction with the Albumentations package should allow for AstroAugmentations to be applied to other frameworks. See examples of their implementations here.
Data sets are provided in astroaugmentations/datasets. See use examples in the demonstration ipython notebooks.
Following Albumentions notation, we adapt respective torch data loaders from a functional call to an Albumnetations call as shown in their PyTorch Example which allows respective transformations to be applied simultaneously to segmentation masks. We present an example of what this can look like.
Assuming there is a self.transform
attribute as a parameter in our data class. In which case, normally inside the __getitem__
method, a conditional application of the transform is made:
if self.transform is not None:
image = self.transform(image)
For Albumentations, and thus our package, we need to adapt this notation. In the case of image augmentations (no mask augmentations) we write:
if self.transform is not None:
image = self.transform(image=image)["image"]
This seems unnecessary, until we consider an example of what happens when we try to apply our transformations to masks as well as the input:
if self.transform is not None:
transformed = self.transform(image=image, mask=mask)
image = transformed["image"]
mask = transformed["mask"]
AstroAugmentations
├── LICENSE
├── astroaugmentations
│ ├── __init__.py
│ ├── image_domain
│ │ ├── general.py
│ │ ├── optical.py
│ │ └── radio.py
│ ├── utils
│ │ ├── __init__.py
│ │ ├── VLA_raw_antenna_position.py
│ │ └── kernel_creation.py
│ ├── datasets
│ │ ├── __init__.py
│ │ ├── galaxy_mnist.py
│ │ └── MiraBest_F.py
│ └── composed.py
├── README.md
└── setup.py
@software{Bowles_AstroAugmentations_2023,
author = {Bowles, Micah},
month = jun,
title = {{AstroAugmentations}},
url = {https://github.com/mb010/AstroAugmentations},
version = {0.1.0},
year = {2023}
}
For questions please contact: [email protected]
For bugs or any issues with implementing this package, please open an issue.