Use np.stack() to merge the grayscale images of different 1 channel into 3 channel RGB images

The np.stack document explains:

---

Join a sequence of arrays along a new axis.

The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

Generate a new axis for a given axis, and stack arrays along that axis

Here is the code test:

a = np.arange(8).reshape((2,2,2))
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])

b = np.arange(8).reshape((2,2,2))+1
array([[[1, 2],
        [3, 4]],

       [[5, 6],
        [7, 8]]])

The first 2 is 2 samples, and the next 2*2 is the pixel matrix. To turn a and b into 2*2*2*2, the fourth 2 represents the color channel:

c = np.stack((a,b),axis=3)
array([[[[0, 1],
         [1, 2]],

        [[2, 3],
         [3, 4]]],


       [[[4, 5],
         [5, 6]],

        [[6, 7],
         [7, 8]]]])

Verify, see c[:,:,:,0]:

 c[:,:,:,0]
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])

It can also become the second 2 is the color channel:

c = np.stack((a,b),axis=1)
array([[[[0, 1],
         [2, 3]],

        [[1, 2],
         [3, 4]]],


       [[[4, 5],
         [6, 7]],

        [[5, 6],
         [7, 8]]]])

c[:,1,:,:]
array([[[1, 2],
        [3, 4]],

       [[5, 6],
        [7, 8]]])


c[:,0,:,:]
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])