PIL to numpy ary = numpy.array( im ) # You can also convert while loading by specifying a dtype.Īnd you can get a no-copy (read-only, same-type) view like: You'ld often control your dimensionality and type/bit depth on the numpy side (though can also do the latter by converting it to a known type first), e.g. ndimage is, as a type, a little more tuned to images ( verify) (where array is completely generic).You can deal with arbitrary numyp arrays. "raw"), and any beyond are parameters to that decoderĪssuming you have a PIL after 1.1.6, you can use the fact that it supports the array interface (before that it was more involved). ombuffer("RGB",(640,480), rawpixels)įor more control: the next parameter is the decoder (e.g. Image.open( StringIO.StringIO( pngdata ) ) if you have fetched it from the network, memcache, etc. Read from various typical image files Image.open(filename)Į.g. Other-colorspace images and alpha channels can make things more involved, and sometimes be very hard. Note: This is biased to converting RGB images. PIL does supports varied formats, but sometimes makes you work for it. This can make sense if your data is varied, rather than necessarily 3-channel uint8 photos. Note that ImageIO reads a lot of formats, with means that if you focus on Halide or numpy/scipy, you can sometimes skip PIL. 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)
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