Welcome to fastgs

Introduction

This library is currently in alpha, neither the functionality nor the API is stable

This library provides geospatial multispectral 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 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 *

If we have the following functions to map an image index to an array of channel files

def get_input(stem: str) -> str:
    "Get full input path for stem"
    return "./images/" + stem

def tile_img_name(chn_id: str, tile_num: int) -> str:
    "File name from channel id and tile number"
    return f"Sentinel20m-{chn_id}-20200215-{tile_num:03d}.png"

def get_channel_filenames(chn_ids, tile_idx):
    "Get list of all channel filenames for one tile idx"
    return [get_input(tile_img_name(x, tile_idx)) for x in chn_ids]

then the following code creates a class that can load 11 Sentinel 2 channels into a TensorImageMS.

from fastgs.vision.io import * # defines read_multichan_files_as_tensor

snt_12 = Sentinel2(
    ["B02","B03","B04","B05","B06","B07","B08","B8A","B11","B12","AOT"],
    [Sentinel2.natural_color, ["B07","B06","B05"],["B12","B11","B8A"],["B08"]],
    get_channel_filenames,
    read_multichan_files_as_tensor
)

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_tensor(66)
img_12.show()

[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]

Acknowledgements

This library is inspired by the following notebooks (and related works by the authors)

• https://dpird-dma.github.io/blog/Multispectral-image-classification-Transfer-Learning/
• https://github.com/cordmaur/Fastai2-Medium/blob/master/01_Create_Datablock.ipynb