Applying concurrency to image processing

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 of different regions in our original ship image, and they have been cropped from it using the array-indexing and -slicing options that NumPy provides for slicing OpenCV image objects. If you are curious as to how these images were generated, check out the generate_input.py file.

Our goal in this section is to implement a program that can concurrently process these images using thresholding. To do this, let's look at the example5.py file:

from multiprocessing import Pool
import cv2
    
import sys
from timeit import default_timer as timer
    
    
THRESH_METHOD = cv2.ADAPTIVE_THRESH_GAUSSIAN_C
INPUT_PATH = 'input/large_input/'
OUTPUT_PATH = 'output...