How to convert a PIL Image into a numpy array?
You're not saying how exactly putdata()
is not behaving. I'm assuming you're doing
>>> pic.putdata(a)
Traceback (most recent call last):
File "...blablabla.../PIL/Image.py", line 1185, in putdata
self.im.putdata(data, scale, offset)
SystemError: new style getargs format but argument is not a tuple
This is because putdata
expects a sequence of tuples and you're giving it a numpy array. This
>>> data = list(tuple(pixel) for pixel in pix)
>>> pic.putdata(data)
will work but it is very slow.
As of PIL 1.1.6, the "proper" way to convert between images and numpy arrays is simply
>>> pix = numpy.array(pic)
although the resulting array is in a different format than yours (3-d array or rows/columns/rgb in this case).
Then, after you make your changes to the array, you should be able to do either pic.putdata(pix)
or create a new image with Image.fromarray(pix)
.
Open I
as an array:
>>> I = numpy.asarray(PIL.Image.open('test.jpg'))
Do some stuff to I
, then, convert it back to an image:
>>> im = PIL.Image.fromarray(numpy.uint8(I))
Filter numpy images with FFT, Python
If you want to do it explicitly for some reason, there are pil2array() and array2pil() functions using getdata() on this page in correlation.zip.
I am using Pillow 4.1.1 (the successor of PIL) in Python 3.5. The conversion between Pillow and numpy is straightforward.
from PIL import Image
import numpy as np
im = Image.open('1.jpg')
im2arr = np.array(im) # im2arr.shape: height x width x channel
arr2im = Image.fromarray(im2arr)
One thing that needs noticing is that Pillow-style im
is column-major while numpy-style im2arr
is row-major. However, the function Image.fromarray
already takes this into consideration. That is, arr2im.size == im.size
and arr2im.mode == im.mode
in the above example.
We should take care of the HxWxC data format when processing the transformed numpy arrays, e.g. do the transform im2arr = np.rollaxis(im2arr, 2, 0)
or im2arr = np.transpose(im2arr, (2, 0, 1))
into CxHxW format.