Benchmarking script for Pillow

[hugovk]

PIL hasn't had any releases in five years. Pillow is a maintained fork.

The only difference to the PIL test is the imports. To run, make sure to uninstall PIL first, then pip install pillow. More details here: http://pillow.readthedocs.org/installation.html

It'd be interesting to see Pillow added to the results as well as PIL here: http://www.vips.ecs.soton.ac.uk/index.php?title=Speed_and_Memory_Use

[jcupitt]

Hi @hugovk, looks interesting, I'll update the benchmarks. Thanks!

[jcupitt]

Looks like there's no performance change between pil and pillow on this benchmark on this laptop:

program, time (s), peak memory (MB)
pillow, 2.49, 205.70703125
pil, 2.50, 205.62890625

I'll update the speed and memuse page anyway.

[hugovk]

@jcupitt Thanks for running it.

Please could you retest with the latest Pillow 2.7.0 (released 2015-01-01) rather than 2.3.0 (2014-01-01)? It contains a number of performance improvements.

[jcupitt]

I see a modest improvement: down from 2.5s to 2.3s.

Reading the docs on the performance improvements (which look very nice), I see that downsizing now always uses convolution. The other systems in this benchmark are using simple affine + bilinear, so this might be hurting Pillow's place.

Will Pillow use a convolution for BILINEAR with just a 10% shrink factor? Or does it fall back to simple affine plus interpolator for small shrinks?

I'll try with NEAREST and see how much difference it makes.

[jcupitt]

Ah, 1.72s with NEAREST. I've updated the page and added a some notes on this.

Is it possible to call affine directly with a bilinear interpolator?

[hugovk]

Let's ask @homm, he implemented much of the recent Pillow improvements.

[homm]

Indeed, Pillow always uses convolutions regardless of the scale factor.

Affine transformations still possible through Image.transform, if you sure the scale factor is always between 1 and 0.5.

[jcupitt]

I tried Image.transform, but it's a lot slower:

im = im.transform((int (im.size[0] * 0.9), int (im.size[1] * 0.9)), 
                  Image.AFFINE,
                  (0.9, 0, 0, 0, 0.9, 0),
                  resample = Image.BILINEAR)

Back to 2.5s and 260mb peak RES. I'll leave it as image.resize(NEAREST).