Parallel Image Processing Pipeline

You've been tasked with creating a more efficient image processing system. Many image operations can be performed independently on different parts of an image or on different images altogether. To leverage multi-core processors and significantly speed up these tasks, you need to implement a solution using Python's multiprocessing module. This challenge will test your ability to distribute computational work across multiple processes.

Problem Description

The goal is to create a parallel image processing pipeline. You will be given a list of image file paths. For each image, you need to apply a series of transformations (e.g., grayscale conversion, resizing, applying a filter). These transformations should be executed in parallel for different images.

What needs to be achieved:

1. Read and process images in parallel: For a given list of image file paths, apply a sequence of image processing functions to each image.
2. Distribute work across processes: Use the multiprocessing module to run these image processing tasks concurrently on multiple CPU cores.
3. Collect and save results: After processing, save the modified images to a specified output directory.

Key requirements:

• You must use the multiprocessing module (e.g., multiprocessing.Pool).
• The image processing functions themselves should be defined and then applied to individual images.
• The pipeline should handle a list of input image file paths.
• The processed images should be saved to a designated output directory.
• Error handling for file reading or processing should be considered.

Expected behavior:

Given a list of input image paths and an output directory, the script should spawn multiple worker processes. Each worker process will pick up an image, apply the defined transformations, and save the result to the output directory. The main process should wait for all tasks to complete.

Edge cases to consider:

• Empty list of input images.
• Input image files that do not exist or are corrupted.
• Output directory that does not exist (the script should ideally create it).
• Large images that might consume significant memory.

Examples

Example 1:

Let's assume we have two images: image1.jpg and image2.png. We want to convert them to grayscale and resize them to half their original dimensions.

Input:

• image_paths = ["path/to/image1.jpg", "path/to/image2.png"]
• output_dir = "processed_images/"
• Image processing functions: to_grayscale, resize_half

Output: Two new image files saved in processed_images/:

• processed_images/image1_processed.jpg (grayscale, half size)
• processed_images/image2_processed.png (grayscale, half size)

Explanation: The multiprocessing.Pool will distribute the processing of image1.jpg and image2.png to different worker processes. Each process will load its assigned image, apply the to_grayscale and resize_half functions sequentially, and then save the modified image to the processed_images/ directory.

Example 2:

Consider a scenario with many small images.

Input:

• image_paths = ["img_001.bmp", "img_002.bmp", ..., "img_100.bmp"]
• output_dir = "thumbnails/"
• Image processing functions: resize_thumbnail (e.g., to 128x128px)

Output: 100 thumbnail image files saved in thumbnails/:

• thumbnails/img_001_thumbnail.bmp
• ...
• thumbnails/img_100_thumbnail.bmp

Explanation: With many images, multiprocessing becomes crucial. The pool of worker processes will efficiently handle fetching, processing, and saving each thumbnail, significantly reducing the total processing time compared to a sequential approach.

Example 3: (Edge Case - No Images)

Input:

• image_paths = []
• output_dir = "empty_output/"

Output: The output_dir will be created if it doesn't exist, but no image files will be generated. The script should terminate gracefully without errors.

Explanation: The pipeline should detect an empty input list and exit without attempting to process any images.

Constraints

• The number of images to process can range from 0 to 1000.
• Image file formats can be JPEG, PNG, BMP, etc., supported by libraries like Pillow (PIL).
• Individual image processing operations (grayscale, resize, filter) should not take excessively long individually (e.g., less than 5 seconds per image on average for a single operation).
• The solution should aim to complete processing for 100 images (each taking ~1 second to process sequentially) within 15 seconds on a multi-core machine.
• Input paths will be strings. Output paths will be strings.
• The output directory may or may not exist.

Notes

• You will likely need an image processing library such as Pillow (pip install Pillow).
• Consider how to pass image processing functions to worker processes.
• Think about how to manage the output file naming to avoid overwriting.
• The multiprocessing.Pool class is a good starting point for managing worker processes. The map or starmap methods are particularly useful for applying a function to a list of arguments.
• Ensure proper cleanup of processes if necessary.
• Error handling should gracefully log or report issues with specific images without crashing the entire pipeline.