[Image-SIG] PIL vs Numpy in raster calculations

[Christopher Barker]

cp wrote:
> Following your advice I wrote three different scripts and profiled them.
> 
> #Script 1 - indexing
> for i in range(10):
>     imarr[:,:,0].mean()
>     imarr[:,:,1].mean()
>     imarr[:,:,2].mean()
> 
> #Script 2 - slicing
> for i in range(10):
>     imarr[:,:,0:1].mean()
>     imarr[:,:,1:2].mean()
>     imarr[:,:,2:3].mean()
> 
> #Script 3 - reshape
> for i in range(10):
>     imarr.reshape(-1,3).mean(axis=0)
> 
> For an RGB image 2000x2000 of ~11mb the times were:
> script 1: 5.432sec
> script 2: 10.234sec
> script 3: 4.980sec
> 
> I tried the same without the mean(), but for 1000 loops, and the results were:
> script 1: 0.463sec (~6mb of RAM)
> script 2: 0.465sec (~3mb of RAM)
> script 3: 0.462sec (~2mb of RAM)
> 
> Script 3, you proposed, has the best performance, while script 2 is very slow.

I think this is because with slicing, the resulting array is 
discontiguous in memory -- this may slow down the mean() computation. 
The fact that all three run at the same speed when not calculating the 
mean supports that concept.

Shows you that you need to profile.

Script 3 is probably as fast as you can get with numpy -- it's probably 
slower than the PIL version because it does need to copy memory one way 
or the other.

-CHB

-- 
Christopher Barker, Ph.D.
Oceanographer

Emergency Response Division
NOAA/NOS/OR&R            (206) 526-6959   voice
7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chris.Barker at noaa.gov