[Numpy-discussion] Optimized half-sizing of images?

[Christopher Barker]

(second try on this message. the fist time I included a test PNG that 
made it too large)

Hi folks,

We have a need to to generate half-size version of RGB images as quickly
as possible. PIL does a pretty good job, but it dawned on me that in the
special case of a half-size, one might be able to do it faster with
numpy, simply averaging the four pixels in the larger image to create
one in the small. I'm doing tiling, and thus reducing 512x512 images to
256x256, so I imagine I'm making good use of cache (it does get pretty
pokey with really large images!)


What I have now is essentially this :

# a is a (h, w, 3) RGB array

      a2 =  a[0::2, 0::2, :].astype(np.uint16)
      a2 += a[0::2, 1::2, :]
      a2 += a[1::2, 0::2, :]
      a2 += a[1::2, 1::2, :]
      a2 /= 4
      return a2.astype(np.uint8)

time:  67.2 ms per loop

I can speed it up a bit if I accumulate in a uint8 and divide as I go to
prevent overflow:

      a2 =  a[0::2, 0::2, :].astype(np.uint8) / 4
      a2 += a[0::2, 1::2, :] / 4
      a2 += a[1::2, 0::2, :] / 4
      a2 += a[1::2, 1::2, :] / 4
      return a2

time:  46.6 ms per loop

That does lose a touch of accuracy, I suppose, but nothing I can see.


Method 1 is about twice as slow as PIL's bilinear scaling.

Can I do better? It seems it should be faster if I can avoid so many
separate loops through the array. I figure there may be some way with
filter or convolve or ndimage, but they all seem to return an array the
same size.

Any ideas?

(Cython is another option, of course)

-Chris

Test code  enclosed.









-- 
Christopher Barker, Ph.D.
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