Assume I created an image in NumPy:
image = imread(...)
And the image is RGB:
assert len(image.shape) == 3 and image.shape == 3
I want to check (i.e. get a boolean mask) which pixels are (R=255, G=127, B=63) in a cleaner and efficient way:
mask = (img[:, :, 0] == 255) & (img[:, :, 1] == 127) & (img[:, :, 2] == 63)
This code worked for me. However - please correct me if I'm wrong - this code is creating three intermediate masks and, since I'm processing images, those masks will be pretty big.
I need help with:
- Is there a way to make boolean masks occupy 1 bit per element, instead of 1 byte per element?
- Is there a way to solve the same problem without creating the three intermediate boolean masks?
This code did not work:
mask = img == (255, 127, 63)
mask = img[:, :, :] == (255, 127, 63)
since the comparison was performed element-wise, and the resulting values were performed element-wise, yielding a 3-dimension (w, h, 3) boolean mask.