Hi folks,
I'm working on a live-video display for some microscope control tools
I'm building. For this, I need a fast histogram function to work on
large-ish images (1000x2000 or so) at video rate, with cycles left
over for more interesting calculations (like autofocus).
Now, numpy.histogram is a bit slower than I'd like, probably because
it's pretty general (and of course cf. the recent discussion about its
speed). I just need even bins within a set range. This is easy enough
to do with a C-extension, or perhaps even cython, but before I go
there, I was wondering if there's a numpy function that can help.
Here's what I have in mind:
def histogram(arr, bins, range):
min, max = range
indices = numpy.clip(((arr.astype(float) - min) * bins / (max -
min)).astype(int), 0, bins-1)
histogram = numpy.zeros(bins, numpy.uint32)
for i in indices:
histogram[i] += 1
Now, clearly, the last loop is what needs speeding up. Are there any
numpy functions that can do this kind of operation? Also perhaps
unnecessarily slow is the conversion of 'arr' to a float -- I do this
to avoid overflow issues with integer arrays.
Any advice? Should I go ahead and write this up in C (easy enough), or
can I do this in numpy? Probably the indices-computation line I'll
speed up with numexpr, if I use a pure-numpy approach.
Thanks,
Zach
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