How can I efficiently process a numpy array in blocks similar to Matlab’s blkproc (blockproc) function

I’m looking for a good approach for efficiently dividing an image into small regions, processing each region separately, and then re-assembling the results from each process into a single processed image. Matlab had a tool for this called blkproc (replaced by blockproc in newer versions of Matlab).

multiprocessing.Pool – PicklingError: Can’t pickle : attribute lookup thread.lock failed

multiprocessing.Pool is driving me crazy…
I want to upgrade many packages, and for every one of them I have to check whether there is a greater version or not. This is done by the check_one function.
The main code is in the Updater.update method: there I create the Pool object and call the map() method.

Length of generator output

Python provides a nice method for getting length of an eager iterable, len(x) that is. But I couldn’t find anything similar for lazy iterables represented by generator comprehensions and functions. Of course, it is not hard to write something like: def iterlen(x): n = 0 try: while True: next(x) n += 1 except StopIteration: pass … Read more