I am trying to replace an old code (biliteral filter) that rely on ndimage.generic_filter with the neighborhood iterator. In the old code, the generic_filter generates a contiguous copy of the neighborhood, thus the (cython) code could use C loop to iterate over the neighbourhood copy. In the new code version the PyArrayNeighborhoodIter_Next must be called to retrieve every neighbourhood item. The results of rough benchmarking to compare bilateral filtering on a 1000x1000 array: Old code (ndimage.generic_filter): 16.5 sec New code (neighborhood iteration): 60.5 sec New code with PyArrayNeighborhoodIter_Next omitted: 1.5 sec
* The last benchmark is not "real" since the omitted call is a must. It just demonstrates the iterator overhead. * I assune the main overhead in the old code is the python function callback process. There are instructions in the manual how to wrap a C code for a faster callback, but I rather use the neighbourhood iterator as I consider it as more generic. If the PyArrayNeighborhoodIter_Reset could (optionally) copy the relevant data (as the generic_filter does) it would provide a major speed up in many cases. Nadav _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
