Enhancing Image Processing Speed using Python's multiprocessing

Question

I am seeking assistance in optimizing the performance of my image processing project in Python. My goal is to leverage multi-core processing capabilities using Python's multiprocessing module. While I'm aware of the module's potential, I'm uncertain about the most effective approach to implement it.

Objective: To parallelize the process_image function across multiple CPU cores and achieve efficient image processing, returning a list of processed images.

import numpy as np
import cv2
import multiprocessing

def process_image(image_array):
    # Placeholder for actual image processing code
    _, processed_image = cv2.threshold(image_array, 128, 255, cv2.THRESH_BINARY)
    return processed_image

def process_images_parallel(image_list, num_processes):
    pool = multiprocessing.Pool(processes=num_processes)
    processed_images = pool.map(process_image, image_list)
    pool.close()
    pool.join()
    return processed_images

if __name__ == "__main__":
    image_list = [np.random.randint(0, 256, size=(256, 256), dtype=np.uint8) for _ in range(10)]
    num_processes = multiprocessing.cpu_count()
    processed_images = process_images_parallel(image_list, num_processes)

I kindly request guidance on enhancing my current approach with the multiprocessing module. Specifically, I seek insights into how to maximize CPU utilization effectively and optimize image processing performance. Your expertise and explanations will greatly contribute to my understanding of implementing this solution.

Specific Questions:

How can I ensure thread safety while modifying the process_image function?
Are there any adjustments needed in the provided code to improve its efficiency further?
Could you please elaborate on how the multiprocessing pool distributes tasks and maximizes CPU usage?

I am committed to improving the quality of my inquiries and contributing positively to the Stack Overflow community. Your valuable assistance will aid me in achieving these goals.

This question is really broad and difficult to answer in a concise way. For your remark about mutliprocessing vs. concurrent.futures, you could have a look at [the answeres here](https://stackoverflow.com/questions/20776189/concurrent-futures-vs-multiprocessing-in-python-3). For multiprocessing, good place to start might be to have a read through some online tutorials (e.g. [this one](https://www.digitalocean.com/community/tutorials/python-multiprocessing-example)), and of course, when in doubt, have a look at the [docs](https://docs.python.org/3/library/multiprocessing.html). — Hoodlum, Jul 29 '23 at 07:30
@Jocefyneroot The code you've added to the question isn't in the least bit helpful. For example, we have no idea how you load the images or what you do with them post-processing — DarkKnight, Jul 29 '23 at 08:05

DarkKnight · Answer 1 · 2023-07-29T09:50:37.317

Let's assume that you want to process all JPEG files from a specific directory. You can start by globbing the directory for files of interest. You can then pass those filenames to a function that runs as a sub-process.

Something like this:

import cv2
from sys import stderr
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor

IMAGE_DIR = '/Volumes/G-Drive/Pictures'

def process_image(path: Path):
    try:
        image = cv2.imread(str(path))
        result = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY)
        # do something with result here
    except Exception as e:
        print(e, file=stderr)

def main():
    images = Path(IMAGE_DIR).glob('*.jpeg')
    # note that *images* is a generator
    with ProcessPoolExecutor() as executor:
        executor.map(process_image, images)

if __name__ == '__main__':
    main()

Enhancing Image Processing Speed using Python's multiprocessing

1 Answers1