How to create an increasing, multi-dimensional array in numpy

Question

I am trying to perform inverse warping given a homography matrix, and to do that efficiently I need a numpy array that looks like this:

([[0, 0, 1], [0, 1, 1], [0, 2, 1], ... [1, 0, 1], [1, 1, 1], ... [n, p, 1]])

Where n is an image's width (im.shape[0]) and p is the image's height (im.shape[1]). Any idea on how to efficiently construct numpy arrays that look like that?

Edit:

There is some discussion on which is the fastest, if anyone has any info on that I think it'd be interesting to hear. I appreciate everyone's help!

Answer 1

Using indices_merged_arr_generic_using_cp by @unutbu -

def indices_one_grid(n,p):
    ar = np.ones((n,p),dtype=int)
    return indices_merged_arr_generic_using_cp(ar)

Sample run -

In [141]: indices_one_grid(n=3,p=4)
Out[141]: 
array([[0, 0, 1],
       [0, 1, 1],
       [0, 2, 1],
       [0, 3, 1],
       [1, 0, 1],
       [1, 1, 1],
       [1, 2, 1],
       [1, 3, 1],
       [2, 0, 1],
       [2, 1, 1],
       [2, 2, 1],
       [2, 3, 1]])

Benchmarking

Other approaches -

def MSeifert(n,p):
    x, y = np.mgrid[:n, :p]
    return np.stack([x.ravel(), y.ravel(), np.ones(x.size, dtype=int)], axis=1)

def DanielF(n,p):
    return np.vstack([np.indices((n,p)), np.ones((1, n,p))]).reshape(3,-1).T

def Aaron(n,p):
    arr = np.empty([n*p,3])
    arr[:,0] = np.repeat(np.arange(n),p)
    arr[:,1] = np.tile(np.arange(p),n)
    arr[:,2] = 1
    return arr

Timings -

In [152]: n=1000;p=1000

In [153]: %timeit MSeifert(n,p)
     ...: %timeit DanielF(n,p)
     ...: %timeit Aaron(n,p)
     ...: %timeit indices_one_grid(n,p)
     ...: 
100 loops, best of 3: 15.8 ms per loop
100 loops, best of 3: 8.46 ms per loop
100 loops, best of 3: 10.4 ms per loop
100 loops, best of 3: 4.78 ms per loop