Slow because instead of C working on C arrays, even assuming the operation on the python side is perfectly fast, you still have overhead to the tune of amount_of_pixels * (convert from C to python, convert from python to C)

Putdata also lets you scale (multiply) and offset (add) arguments out.putdata( for x in im.getdata()]) treats the image as a flattened, one-dimensional iterable (of color tuples according to whatever mode/bands it has)

get/put arrays - which works for a few simpler operations,.

I would suggest this for all nontrivial calculation

PIL supports the array interface, meaning you can use numpy, scipy, and image-aware libraries and often get their C-like speed.

if you like the idea of Halide, there's a python interface.

Your options include ( roughly from fast to slow): You want fast array/pixel access, and you want loops to be fast. (Feel free to ignore, or tell me)Īn image behaves like a two-dimensional mapping (or a one-dimensional one if you like). Pixels and speed This article/section is a stub - probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. This was also slightly confusing, in that for a while you might get either, depending on which you installed. Pillow intentionally uses the import name PIL - to be a drop-in at the time. PIL has apparently not seen development since apparenty 2009 or so (which also meants it's not very p圓k, and is annoying to package), so the fork named Pillow is now the more complete replacement of the original PIL. We even tend to just call it PIL, except when pointing out this particular install difference. Pillow is an interface-compatible drop-in fork of PIL. Tl dr: the fork named Pillow is now the more complete replacement of the original PIL. Because that'll work with Pillow (this is apparently by design) ( verify)

Opencv interlacing image