What is the best algorithm to rotate a non-square M×N array by 180° around its center, using the least memory and operations, for C langages and derivatives (Python, Cython, pure C) ?
Assuming out
is an initialized copy of array
, M
and N
their rows and columns numbers, and we are using a language indexing arrays from 0 to (M-1) and (N-1) :
In Python :
def rotate_180(array, M, N, out):
for i in range(M):
for j in range(N):
out[i, N-1-j] = array[M-1-i, j]
This takes 5.82 s on a 4000×3000 array.
In parallelized Cython + OpenMP using Memviews :
cdef void rotate_180(float[:, :] array, int M, int N, float[:, :] out) nogil:
cdef size_t i, j
with parallel(num_threads=8):
for i in prange(M):
for j in range(N):
out[i, N-1-j] = array[M-1-i, j]
This takes 5.45 s on a 4000×3000 array.
In comparison, numpy with np.rot90(array, 2)
takes 8.58 µs.
Edit : to avoid know-it-all comments out of the point, here is what it does :
a = array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
rotate_180(a, 3, 3, b)
b = array([[9, 8, 7],
[6, 5, 4],
[3, 2, 1]])
Using 2 successive 90° rotations with numpy built-in function, we get :
np.rot90(a, 2)
out = array([[9, 8, 7],
[6, 5, 4],
[3, 2, 1]])
So this rotate_180()
is indeed a 180° rotation. Now :
np.flip(a, 0)
out = array([[7, 8, 9],
[4, 5, 6],
[1, 2, 3]])
is not a rotation but a symmetry along the last line. If we compose 2 symmetries along each direction :
np.flip(np.flip(a, 1), 0)
out = array([[9, 8, 7],
[6, 5, 4],
[3, 2, 1]])
we also get a 180° rotation.
So, yes, thank you, my code does what it says.
It's relatively late to answer this question but still better than doing nothing.
For 2D numpy arrays being in shape of H x W
:
rotated_array = old_array[::-1,::-1] #rotate the array 180 degrees
For 2D numpy images being in shape of H x W x 3
:
rotated_image = image[::-1,::-1] #rotate the image 180 degrees
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