PIL vs Python-GD for crop and resize

I am creating custom开发者_运维技巧 images that I later convert to an image pyramid for Seadragon AJAX. The images and image pyramid are created using PIL. It currently take a few hours to generate the images and image pyramid for approximately 100 pictures that have a combined width and height of about 32,000,000 by 1000 (yes, the image is very long and narrow). The performance is roughly similar another algorithm I have tried (i.e. deepzoom.py). I plan to see if python-gd would perform better due to most of its functionality being coded in C (from the GD library). I would assume a significant performance increase however I am curious to hear the opinion of others. In particular the resizing and cropping is slow in PIL (w/ Image.ANTIALIAS). Will this improve considerable if I use Python-GD?

Thanks in advance for the comments and suggestions.

EDIT: The performance difference between PIL and python-GD seems minimal. I will refactor my code to reduce performance bottlenecks and include support for multiple processors. I've tested out the python 'multiprocessing' module. Results are encouraging.

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PIL is mostly in C.

Antialiasing is slow. When you turn off antialiasing, what happens to the speed?

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VIPS includes a fast deepzoom creator. I timed deepzoom.py and on my machine I see:

$ time ./wtc.py 
real    0m29.601s
user    0m29.158s
sys     0m0.408s
peak RES 450mb

where wtc.jpg is a 10,000 x 10,000 pixel RGB JPG image, and wtc.py is using these settings.

VIPS is around three times faster and needs a quarter of the memory:

$ time vips dzsave wtc.jpg wtc --overlap 2 --tile-size 128 --suffix .png[compression=0]
real    0m10.819s
user    0m37.084s
sys     0m15.314s
peak RES 100mb

I'm not sure why sys is so much higher.