Satellite-cloud-generator

Latest version: v0.4

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0.4.1

0.4

The package can now be imported via pip
bash
pip install git+https://github.com/strath-ai/SatelliteCloudGenerator

0.3

Major Changes 🔥
* :factory: **Introducing `CloudGenerator()`** which encapsulates a specific configuration, and generation probabilities (`cloud_p` and `shadow_p`). It is compatible as PyTorch module (you can plug it into augmentation pipelines, like `torchvision` or `albumentations`)

python
my_gen=CloudGenerator(WIDE_CONFIG,cloud_p=1.0,shadow_p=0.5)
my_gen(my_image) will act just like add_cloud_and_shadow() but will preserve the same configuration!


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* :rainbow: **Channel-Specific Cloud Magnitude** allows for channels to have slightly different cloud strengths (since this strength is generally dependent on carrier wavelength) by setting channel_magnitude_shift` to a non-zero value:

![ch](https://user-images.githubusercontent.com/13435425/207385813-1e4f8065-2cb8-4af4-b6ea-eb82aacdca78.png)

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* :mask: **Segmentation Mask Functionality** allows you to call the `segmentation_mask(cloud_mask,shadow_mask)` method, which will return a segmentation mask for your generated clouds and shadows!

![simple_seg](https://user-images.githubusercontent.com/13435425/207386086-2b2d8798-89e4-44f6-b622-f1eac02bf7b5.png)

...you can even set a range `thin_range` to something like `(0.05,0.5)` to also differentiate between thin and thick clouds

![thin_seg](https://user-images.githubusercontent.com/13435425/207386371-85dbca61-64b6-4001-b4c0-6bfb0fbe0879.png)

and this is an example content of each label:

![labels1](https://user-images.githubusercontent.com/13435425/207386548-3322bcda-662d-4c05-8dbf-d74fb3db0a5a.png)

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I hope these features prove useful! :rocket:

0.2

Major Changes 🔥
- [x] `min_lvl` and `max_lvl` redefined (to refer to cloud strength)
- [x] Introduction of shadow functionality with `add_cloud_and_shadow`
- [x] Introduction of `locality_index` argument that can control the sparsity of the cloud shape
- [x] Datasets of cloud and shadow masks can be easily generated using the `02-Dataset-Generation.ipynb` notebook

Minor Changes 🪛
- [x] Prevent NaN in `cloud_hue`
- [x] Fixed Perlin generation for small images and non-standard aspect ratios

0.1

Updated DOI, Colab links and citation details.

0.1a

Pre-release for setting up Zenodo DOI

Links

Releases

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