I have some numpy arrays which its elements are pixels of 28*28 images like this:
25 of these arrays are in one array in shape of (25,28,28) or (5,5,28,28). Is there any efficient way to stack them to have one image: 5*5 of 28*28 images.
I tried np.reshape to (140,140) array and plt.imgshow. But the output was a messed image.
"I tried np.reshape to (140,140)..." That will work if you first transpose the input appropriately.
Suppose the input x has shape (5, 5, 28, 28). To get the array y with shape (140, 140) that contains the images arranged the way you want, you can do:
xshp = x.shp
y = x.transpose((0, 2, 1, 3)).reshape((xshp[0]*xshp[2], xshp[1]*xshp[3]))
If x always has shape (5, 5, 28, 28), you can hardcode the constant 140:
y = x.transpose((0, 2, 1, 3)).reshape((140, 140))
For example, here I create x with shape (5, 5, 28, 28) where each 28x28 image is a constant. The constants are chosen randomly. The tranposed, reshaped array y is plotted, and you can see that all the constant blocks are arranged correctly.
In [148]: rng = np.random.default_rng()
In [149]: x = np.repeat(rng.integers(0, 256, size=(5, 5)), 28*28, axis=-1).reshape((5, 5, 28, 28))
In [150]: y = x.transpose((0, 2, 1, 3)).reshape((140, 140))
In [151]: imshow(y)
