Loading a directory of images into RippleImage objects

Ripplemapper is capable of loading a directory of images into a list of RippleImage objects.

from ripplemapper.data.example import example_dir
from ripplemapper.io import load_dir_to_obj

imgs = load_dir_to_obj(example_dir)
print(imgs)

[RippleImage: 1_00052_sample.tif, RippleImage: 1_00205_sample.tif, RippleImage: 1_00375_sample.tif, RippleImage: 1_00443_sample.tif]

We can also select a subset of the images in our path with start, end and skip kwargs. These integer valued parameters work like python slicing [start:end:skip].

imgs = load_dir_to_obj(example_dir, start=0, end=3, skip=2)
print(imgs)

[RippleImage: 1_00052_sample.tif, RippleImage: 1_00375_sample.tif]

This allows you to chunk up your input into independent processes or to quickly load a subset of your data for testing.

Since you can save and load RippleImage objects, you can also save the subset of images. If this is done without the image arrays then you can fit far more images in memory at once