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I have loaded image as a numpy array using pillow and the dimensions of the resulting numpy array is (184, 184, 4), i.e it's a 184x184 pixel image with each pixel having RGBA dimensions.

For all practical purposes my application just needs to know if a pixel is black or not and hence I just need a 184x184 np array with 0, 1's.

I am new to numpy and particularly image manipulation, wanted to know if there is a faster method to do it.

I tried the naive implementation of checking each pixel in a loop, which appears to be too costly.

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If I understand correctly, you have an array with shape (184,184,4) and you want to get a boolean array with shape (184,184) depending on whether the final dimension is equal to [0,0,0,0]

image = np.random.randint(0, 255, size=(184, 184, 4)) #Generate random image
isBlack = np.int64(np.all(image[:, :, :3] == 0, axis=2))

Done!

But how does it work? It seems like magic! Numpy is kind of magical. That's why we like it. In this case:

  • the image==0 converts to a (184,184,4) boolean array depending on whether each number is equal to 0
  • in this case we use image[:,:,:3] to exclude the 4th number from the equation
  • the np.all asks whether all the elements are equal to 0.
  • the axis parameter changes the axis that it looks at, remembering that python starts counting at 0, so the third axis is number 2.
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6 Comments

what if I want 0 or 1 instead of True or false.
isBlack = np.int16((np.all(image==0,axis=2)))
Also I want the final dimension to be equal to [0,0,0,<I don't care about this place>]
Although I don't know why you would want it that way. The True / False is useful for masking other arrays
The reason being the last pixel value is transparency in the image and I don't care about it, at all. I'd be glad if you can provide a simple example of masking.
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