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

[Christopher Barker]

To finish off the thread for posterity:

Robert Bradshaw wrote:

<Robert's Cython code clipped.>

Robert's version operated on a 2-d array, so only one band at a time if
you have RGB. So I edited it a bit:

import cython
import numpy as np
cimport numpy as np

@cython.boundscheck(False)
def halfsize(np.ndarray[np.uint8_t, ndim=3, mode="c"] a):
      cdef unsigned int i, j, b, w, h, d
      w, h, d = a.shape[0], a.shape[1], a.shape[2]
      cdef np.ndarray[np.uint8_t, ndim=3, mode="c"] a2 =
np.ndarray((w/2, h/2, 3), np.uint8)
      for i in range(w/2):
          for j in range(h/2):
              for b in range(d): # color band
                  a2[i,j,b] = (<int>a[2*i,2*j,b] + a[2*i+1,2*j,b] +
a[2*i,2*j+1,b] + a[2*i+1,2*j+1,b])/4
      return a2


This now does the whole RGB image at once, and is pretty snappy. Here
are my timings for half-sizing a 512x512 RGB image:


     slicing, accumulating with a float32
       time: 89 ms per loop

     slicing, accumulating with a uint16
       time:  67 ms per loop

     slicing, all calculations in uint8
       time:  47 ms per loop

     using ndimage, one band at a time, 3rd order spline.
       time:  280 ms per loop

     using ndimage, one band at a time, 1st order spline.
       time:  40 ms per loop

     using cython, one band at a time
       time:  11.6 ms per loop

     using cython, all bands at once
       time:  2.66 ms per loop

     using PIL BILNEAR interpolation
       time:  2.66 ms per loop


So a ten times speed up over PIL, and a 17 times speed up over my 
fastest numpy version.

If anyone has any suggestions on how to improve on the Cython version, 
I'd like to hear it, though I doubt it would make a practical difference.

-Chris






-- 
Christopher Barker, Ph.D.
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