How to use multiprocessing pool with a list?

I am trying to make the following code parallel: feret_diamater.py

When I call the get_min_max_feret_from_labelim() function with a labeled image (eg. 1000x1000 array, labels are numbers, between 0 and 1100), it calls the get_min_max_feret_from_mask() function for each label. As a result, get_min_max_feret_from_labelim() returns a list of 1101 elements. Works fine, but in case of a big image and many labels, it takes a lot a lot of time, so I want to call the get_min_max_feret_from_mask() using multiprocessing Pool.

The original code uses this:

for label in labels:
    results[label] = get_min_max_feret_from_mask(label_im == label)
return results

And I want to replace this part. I tried this after adding 'ncores' to the parameter list:

with Pool(ncores) as p:
    for label in labels:
        results[label] = p.map(get_min_max_feret_from_mask, label_im == label)
return results

But it is not working. How could I solve this problem? Thank you.

You can write:

with Pool(ncores) as p:
    return p.map(get_min_max_feret_from_mask, labels)

Pool.map takes the iterable (list or tuple, etc.) directly as the second parameter, no additional looping required:

https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map

Map expects a list of arguments and returns a list of results:

with Pool(ncores) as p:
    result_list = p.map(get_min_max_feret_from_mask, [label_im == label for label in labels])

The returned list will match the order of the arguments list, meaning the first result is the result for the first label and so on.

Another option is to use apply_async to schedule tasks without blocking, this immediately returns an AsyncResult object allowing you to retrieve the results later. This one allows you to keep the original structure:

with Pool(ncores) as p:
    for label in labels:
        async_results[label] = p.apply_async(get_min_max_feret_from_mask, (label_im == label,))

results = {label: result.get() for label, result in async_results.items()}