Applying concurrency to image processing

We have talked a lot about the basics of image processing and some common image processing techniques. We also know why image processing is a heavy number-crunching task, and that concurrent and parallel programming can be applied to speed up independent processing tasks. In this section, we will be looking at a specific example on how to implement a concurrent image processing application that can handle a large number of input images.

First, head to the current folder for this chapter's code. Inside the input folder, there is a subfolder called large_input, which contains 400 images that we will be using for this example. These pictures are different regions in our original ship image, which have ...

Get Mastering Concurrency in Python now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.