Challenge: Parallel Image Processing
Imagine you are building a tool to automate the processing of a large collection of images. Your goal is to apply a transformation—such as resizing each image to a standard dimension or applying a simple filter—across all files in a directory. Processing each image one by one would be slow, especially with hundreds or thousands of files. By leveraging multiprocessing, you can process many images at once, dramatically reducing the total time required. This approach is especially useful for tasks like preparing image datasets for machine learning or batch editing photographs.
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Implement a function to process a list of image file paths in parallel using a process pool.
- Use the
concurrent.futuresmodule'sProcessPoolExecutorto execute theprocess_funcon each image path in theimage_pathslist. - Collect the results into a list and return it.
- Use the
num_workersparameter to set the number of processes in the pool. - Call the
process_images_parallelfunction with a sample list of image paths, a simple processing function (such as returning the file name in uppercase), and a specified number of workers. Print the result using the following template:print(result)
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What are some common transformations I can apply to images using multiprocessing?
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How do I set up multiprocessing for image processing in Python?
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Imagine you are building a tool to automate the processing of a large collection of images. Your goal is to apply a transformation—such as resizing each image to a standard dimension or applying a simple filter—across all files in a directory. Processing each image one by one would be slow, especially with hundreds or thousands of files. By leveraging multiprocessing, you can process many images at once, dramatically reducing the total time required. This approach is especially useful for tasks like preparing image datasets for machine learning or batch editing photographs.
Swipe to start coding
Implement a function to process a list of image file paths in parallel using a process pool.
- Use the
concurrent.futuresmodule'sProcessPoolExecutorto execute theprocess_funcon each image path in theimage_pathslist. - Collect the results into a list and return it.
- Use the
num_workersparameter to set the number of processes in the pool. - Call the
process_images_parallelfunction with a sample list of image paths, a simple processing function (such as returning the file name in uppercase), and a specified number of workers. Print the result using the following template:print(result)
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