Creating numpy masked array on 3 channel array never works

Question

I have a 3 channel numpy array, ie an image and I want to mask out some areas then calculate the mean on the unmasked areas. When I go to convert my numpy array to a masked numpy array I always get the following error:

raise MaskError(msg % (nd, nm))
numpy.ma.core.MaskError: Mask and data not compatible: data size is 325080, mask size is 108360.

My array (image) shape is: (301, 360, 3) for reference. I create my mask by creating a duplicate array of zeros then drawing a polygon shape of 1's (True) on the mask.

My code is:

mask = np.zeros((src.shape[0], src.shape[1], 1), dtype='uint8')
cv2.drawContours(mask, [np.array(poly)], -1, (1,), -1)
msrc = np.ma.array(src, mask=mask, dtype='uint8')  # error on this line
mean = np.ma.mean(msrc)

What am I doing wrong and how can I fix it to successfully create a masked array in numpy?

@hpaulj, isn't that right though? Because we are masking complete pixels/locations not per channel? Thats how you mask a 3 channel image in opencv (using a 1 channel mask) so I am assuming its the same in numpy? — sazr
– sazr, Commented Jul 4, 2019 at 7:16
Maybe try msrc = np.ma.array(src, mask=np.dstack((mask,mask,mask)), dtype='uint8') — Mark Setchell
– Mark Setchell, Commented Jul 4, 2019 at 10:20
What @hpaulj is trying to say is that numpy is an arbitrary array manipulation library. It has no concept of channels or pixels or any such thing. If you want to mask all of the "channels", you need to translate that from image-processing-speak to numpy-speak. "Masking all of the channels" == "Broadcasting a mask across the 2nd axis". Sometimes numpy can handle broadcasting for you. This is not one of those times. When numpy does not broadcast for you, you simply need to duplicate your mask — Him
– Him, Commented Jul 4, 2019 at 11:34

J.D. · Accepted Answer · 2019-07-04 11:40:28Z

0

As stated in the comments, numpy doesn't consider images, it's just math. OpenCV abstracts that math into easy image manipulations.
To mask an image using OpenCV, you can use
masked_img = cv2.bitwise_and(src,src,mask=mask).
(docs)

edited Jul 4, 2019 at 11:40

answered Jul 4, 2019 at 11:34

J.D.

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