Welcome to fastgs
Introduction
This library is currently in alpha, neither the functionality nor the API is stable
This library provides geospatial MSData image support for fastai. FastAI already has extensive support for RGB images in the pipeline. I try to achieve feature parity for multi-spectral images with this library, specifically in the context of Sentinel 2 geospatial imaging.
Install
pip install -Uqq fastgs
conda install -c restlessronin fastgs
How to use
The low-level functionality is wrapped into a class that loads sets of
Sentinel 2 channels into a multi-spectral tensor (a
TensorImageMS
subclass of fastai TensorImage which itself is a subclass of the
pytorch Tensor).
from fastgs.geospatial.sentinel import *
The following code creates a class that can load 11 Sentinel 2 channels
into a
TensorImageMS.
from fastgs.vision.testio import * # defines read_multichan_files_as_tensor
sentinel2 = createSentinel2Descriptor()
snt_12 = MSData(
sentinel2,
["B02","B03","B04","B05","B06","B07","B08","B8A","B11","B12","AOT"],
[sentinel2.rgb_combo["natural_color"], ["B07","B06","B05"],["B12","B11","B8A"],["B08"]],
get_channel_filenames,
read_multichan_files
)
The second parameter is a list of 4 channel sets that are minimally required to visualize all the individual channels.
img_12 = snt_12.load_image(66)
img_12.show()
[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]
Acknowledgements
This library is inspired by the following notebooks (and related works by the authors)
1 maintainer
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