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pythonarraysnumpyimage-processing
Dragos Strugar
Dragos Strugar

Reputation: 1674

Preserve 3-dimensionality when converting PIL image to numpy array

I have a list of PIL images: p0, p1, ..., p85999 (a total of 86000 of them). They are all RGB, of size 30x30px.

I need to convert them to normalized numpy arrays, I did the following:

[np.asarray(r).astype('float32') / 255.0) for r in images]

where r is a PIL image.

This gives an array of numpy arrays.

However, these arrays are sometimes of shape (30,30,3) and sometimes of shape (30,30).

I want them always to be of shape (30,30,3).

I'm guessing numpy does this for performance reasons (when RGB is not needed, eg. white images?).

Anyway, how to get the desired result - get all numpy arrays to be of size (30,30,3)?

Also, ideally I would want my final numpy array to be of size (30, 30, 3, 86000). Is there a shortcut to create such an array straight from PIL images?

Upvotes: 0

Views: 819

Answers (1)

GPhilo
GPhilo

Reputation: 19153

I'm guessing numpy does this for performance reasons

Numpy has nothing to do with it, this is your PIL Image having one channel only. The simplest solution is to just convert everything to RGB:

ims = [np.asarray(r.convert('RGB')).astype('float32') / 255.0) for r in images]

If you then call np.asarray(ims), you'll obtain an array of shape [N,30,30,3] where N is the number of images, which you can then transpose to your desired ordering.

Upvotes: 1

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