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 been cropped from it using the array-indexing and -slicing options that NumPy provides to slice OpenCV image objects. If you are curious as to how these images were generated, check out the Chapter15/generate_input.py file.

Our goal in this section is to...