Numpy array not displaying color dimension of greyscale image after converting from PIL image

Question

I'm trying to convert an RGB image to a greyscale image, then to a numpy array using the following code snippet:

img = Image.open("image1.png")
img = img.convert('L')
img = np.array(img, dtype='f')
print(img.shape)

The result is a numpy array of shape (128, 128). Is there anyway that I could convert a greyscale image to a numpy array so that it would have the color channel as well, i.e. the shape would be (128, 128, 1)?

You can do img = img[..., np.newaxis] to get that shape but I don't see how that gives you colour. — Mark Setchell
– Mark Setchell, Commented Oct 28, 2020 at 7:36
To expand on Mark's comment, this is a somewhat confusing question because the values of the (128, 128) array are the "colors" here. Sometimes you need to have a singleton dimension, but it's usually because you are using numpy broadcasting or something similar. Adding a singleton dimension changes nothing about the underlying data. So you have a good answer now from @crazycoder, but I am not sure you have a solution to your actual problem. — senderle
– senderle, Commented Oct 28, 2020 at 9:29

Crazy Coder · Accepted Answer · 2020-10-28 09:07:29Z

2

Like @Mark mentioned in comments, add a dimension to the end if your array using newaxis:

img=img[...,None]

None will do similar as np.newaxis. It does not create a color, but adds a dimension similar to a single channel image.

answered Oct 28, 2020 at 9:07

Crazy Coder

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