Simple tools for creating multiscale representations of large images.
pip install xarray-multiscale
Many image processing applications benefit from representing images at multiple scales (also known as image pyramids. This package provides tools for generating lazy multiscale representations of N-dimensional data using xarray to ensure that the downsampled images have the correct coordinates.
Why are coordinates important for this application? Because a downsampled image is typically scaled and translated relative to the source image. Without a coordinate-aware representation of the data, the scaling and translation information is easily lost.
Generate a multiscale representation of a numpy array:
from xarray_multiscale import multiscale, windowed_mean
import numpy as np
data = np.arange(4)
print(*multiscale(data, windowed_mean, 2), sep='\n')
"""
<xarray.DataArray 's0' (dim_0: 4)> Size: 32B
array([0, 1, 2, 3])
Coordinates:
* dim_0 (dim_0) float64 32B 0.0 1.0 2.0 3.0
<xarray.DataArray 's1' (dim_0: 2)> Size: 16B
array([0, 2])
Coordinates:
* dim_0 (dim_0) float64 16B 0.5 2.5
"""read more in the project documentation.