Resize image faster in OpenCV Python

If these images are stored on a magnetic hard drive, you may very well find you are limited by read/write speed (lots of small reads and writes are very slow on spinning magnetic disks).

Otherwise you can always throw the problem at a processing pool to utilize multiple cores:

from multiprocessing.dummy import Pool
from multiprocessing.sharedctypes import Value
from ctypes import c_int
import time, cv2, os

wdir = r'C:\folder full of large images'
os.chdir(wdir)

def read_imagecv2(img_path, counter):
    # print(img_path)
    img = cv2.imread(img_path)
    img = cv2.resize(img, (128, 128))
    cv2.imwrite('resized_'+img_path, img) #write the image in the child process (I didn't want to overwrite my images)
    with counter.get_lock(): #processing pools give no way to check up on progress, so we make our own
        counter.value += 1

if __name__ == '__main__':
    # start 4 worker processes
    with Pool(processes=4) as pool: #this should be the same as your processor cores (or less)
        counter = Value(c_int, 0) #using sharedctypes with mp.dummy isn't needed anymore, but we already wrote the code once...
        chunksize = 4 #making this larger might improve speed (less important the longer a single function call takes)
        result = pool.starmap_async(read_imagecv2, #function to send to the worker pool
                                    ((file, counter) for file in os.listdir(os.getcwd()) if file.endswith('.jpg')),  #generator to fill in function args
                                    chunksize) #how many jobs to submit to each worker at once
        while not result.ready(): #print out progress to indicate program is still working.
            #with counter.get_lock(): #you could lock here but you're not modifying the value, so nothing bad will happen if a write occurs simultaneously
            #just don't `time.sleep()` while you're holding the lock
            print("\rcompleted {} images   ".format(counter.value), end='')
            time.sleep(.5)
        print('\nCompleted all images')

Due to a somewhat known problem with cv2 not playing nice with multiprocessing, we can use threads instead of processes by replacing multiprocessing.Pool with multiprocessing.dummy.Pool. Many openCV functions release the GIL anyway, so we still should see the computational benefit of using multiple cores at once. Additionally this reduces some amount of overhead, as threads aren't as heavy as processes. After some investigating I have not found an image library that does play nice with processes. They all seem to fail when trying to pickle a function to send to the child processes (how items of work are sent to child processes for computation).