[Numpy-discussion] Speed performance on array constant set

[Mark H. <ma...@mi...>]

Im doing some work with the OpenCv* project.  Im using swig typemaps to 
convert the Cv data structures to numarray which works well.  Id like to 
restrict Cv use to what its strengths: complicated vision processing 
algorithms like optical flow.  For the case of simple Cv data 
manipulations, I'd rather use NumPy functions & methods but was 
surprised at the performance comparison. 

 - A simple scalar constant fill with cvSet.  'im' here is a wrapped Cv 
image data structure. 

> python -m timeit -s "import opencv.cv as cv; im = 
> cv.cvCreateImage(cv.cvSize(1000,1000), 8, 1)" "cv.cvSet( im, 
> cv.cvRealScalar( 7 ) )"
> 100 loops, best of 3: 2.58 msec per loop

- If I try the equivalent with NumPy

> python -m timeit -s "import numarray as na; a = na.zeros((1000,1000) 
> )" "a[:,:] = 7"

> 10 loops, best of 3: 45.1 msec per loop

A >10x hit.   Am I using the preferred / optimal NumPy method here?  I 
scanned the earlier Scalar  posts but thought that was boolean  type 
only issue.

Mark

*OpenCv is an computer vision library, open source, and is sponsored by 
Intel.  It includes many video capable functions for application to 
motion analysis, tracking and the like.