👀 Views: 92 💬 Answers: 1 📅 Created: 2025-06-06

python-3.x multiprocessing image-processing Python

I'm converting an old project and I'm working through a tutorial and I'm trying to implement I'm working on a project and hit a roadblock. I'm working with a scenario while trying to use the `multiprocessing` module in Python 3.11 to process a large batch of images. My goal is to leverage multiple processes for image transformations to speed up the handling of numerous files. However, I encountered unexpected behavior when accessing shared memory using `multiprocessing.Array`. Here's a simplified version of my code: ```python import multiprocessing import numpy as np from PIL import Image # Function to process an image def process_image(image_path, shared_array, index): image = Image.open(image_path) image_array = np.array(image) # Perform some image transformation (e.g., grayscale) transformed_image = np.mean(image_array, axis=2).astype(np.uint8) # Store the transformed image in shared memory shared_array[index:index + transformed_image.size] = transformed_image.flatten() if __name__ == '__main__': image_paths = ['image1.jpg', 'image2.jpg'] # List of image paths size = 1024 * 768 # Example size for simplicity shared_array = multiprocessing.Array('B', size * len(image_paths)) # Shared memory array processes = [] for i, path in enumerate(image_paths): p = multiprocessing.Process(target=process_image, args=(path, shared_array, i * size)) processes.append(p) p.start() for p in processes: p.join() # Attempt to reconstruct the images from the shared array for i in range(len(image_paths)): image_data = np.frombuffer(shared_array.get_obj(), dtype=np.uint8)[i * size:(i + 1) * size] # Code to convert image_data back to image and save it ``` I expected each process to write the transformed images into the `shared_array`, but I noticed that the output images seem corrupted or incorrectly sized. When I print the dimensions of `transformed_image`, it returns the correct size, but the reconstructed images produce a runtime warning about shape mismatches. Additionally, I tried debugging with prints and found that the data written to `shared_array` during each process execution does not seem to align with the expected indices. I'm also unsure if there's a need for synchronization between processes while accessing `shared_array`. The warning I’m working with is: ``` VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or tuple-of lists-or tuples-or ndarrays) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. ``` Can someone guide to understand how to properly manage the shared memory access in this context? Are there best practices for using `multiprocessing` with shared data structures in Python 3.11? Is there a better approach? My development environment is Ubuntu 20.04. I'm open to any suggestions. The project is a application built with Python. Any examples would be super helpful. I'm open to any suggestions.