Data preparation

Assume that we have a georeferenced RGB image and a map, and want to create a dataset for image segmentation. We will use split() function to save each channel of the image as a separate file, that is in compatible way. Then we use rasterize() to transform the map to the segmentation mask

from aeronet.dataset import BandCollection, FeatureCollection, rasterize
from aeronet.converters.split import split

# configuration
IMAGE_FILE = '/path/to/image.tif'
MASK_FILE = '/path/to/mask.geojson'
OUT_PATH = '/path/to/dataset/'

channels = ['RED', 'GRN', 'BLU']
label = '100'

# split the multi-channel image to the separate files for each band
# it saves the files to the filesystem and returns the BandCollection handle to them
bc = split(IMAGE_FILE, OUT_PATH, channels)

# Read the vector data
fc = FeatureCollection.read(MASK_FILE)

# The files can have different coordinate systems, so we will need to reproject vector data
# crs is coordinate system feature for a georeferenced object
fc = fc.reproject(dst_crs=bc.crs)

# Now we can rasterize the map data, the mask will be saved in the same folder
# the result is a BandSample of the size out_shape, with the given georeference and name

mask_sample = rasterize(feature_collection=fc,
          transform=bc.transform,
          out_shape=bc[0].shape,
          name=label)

# Now save it, the path is a folder to save, and the filename is derived from the BandSample name
mask_sample.save(OUT_PATH)