I want to reorder dimensions of my numpy array. The following piece of code works but it's too slow.
for i in range(image_size):
for j in range(image_size):
for k in range(3):
new_im[k, i, j] = im[i, j, k]
After this, I vectorize the new_im:
new_im_vec = new_im.reshape(image_size**2 * 3)
That said, I don't need new_im and I only need to get to new_im_vec. Is there a better way to do this? image_size is about 256.
Check out rollaxis, a function which shifts the axes around, allowing you to reorder your array in a single command. If im has shape i, j, k
rollaxis(im, 2)
should return an array with shape k, i, j.
After this, you can flatten your array, ravel is a clear function for this purpose. Putting this all together, you have a nice one-liner:
new_im_vec = ravel(rollaxis(im, 2))
np.rollaxis) of doing np.transpose(im, (2, 0, 1)). As an aside note, np.rollaxis or np.transpose return a view of the original data, but when calling flatten on that view, a copy is likely to be triggered.new_im = im.swapaxes(0,2).swapaxes(1,2) # First swap i and k, then i and j
new_im_vec = new_im.flatten() # Vectorize
This should be much faster because swapaxes returns a view on the array, rather than copying elements over.
And of course if you want to skip new_im, you can do it in one line, and still only flatten is doing any copying.
new_im_vec = im.swapaxes(0,2).swapaxes(1,2).flatten()
With einops:
x = einops.rearrange(x, 'height width color -> color height width')
Pros:
xrangeinstead ofrange