Broadcast one channel in Numpy array into three channels

np.repeat(mask.reshape(2560L, 1920L, 1L), 3, axis=2)

If you absolutely want to have the mask being (2560, 1920, 3), you can simply expand it along an axis (there are several ways to do that, but this one is quite straightforward):

>>> mask = np.random.random_integers(0, 255, (15, 12))>>> mask_3d = mask[:, :, None] * np.ones(3, dtype=int)[None, None, :]>>> mask.shape(15L, 12L)>>> mask_3d.shape(15L, 12L, 3L)

However, in general, you can use these broadcasts directly. For instance, if you want to multiply your image by your mask:

>>> img = np.random.random_integers(0, 255, (15, 12, 3))>>> img.shape(15L, 12L, 3L)>>> converted = img * mask[:, :, None]>>> converted.shape(15L, 12L, 3L)

So you never have to create the (n, m, 3) mask: broadcasting is done on the fly by manipulating the strides of the mask array, rather than creating a bigger, redundant one. Most of the numpy operations support this kind of broadcasting: binary operations (as above), but also indexing:

>>> # Take the lower part of the image>>> mask = np.tri(15, 12, dtype=bool)>>> # Apply mask to first channel>>> one_channel = img[:, :, 0][mask]>>> one_channel.shape(114L,)>>> # Apply mask to all channels>>> pixels = img[mask]>>> pixels.shape(114L, 3L)>>> np.all(pixels[:, 0] == one_channel)True

The dimensions can be expanded along the last axis and then tiled as follows.

mask = np.random.randn(200, 150)mask3d = np.tile(mask[:, :, None], [1, 1, 3])

[1, 1, 3] tiles the mask 3 times in the last dimension.