Rotating images by 90 degrees for a multidimensional NumPy array
image, numpy, python
Solution
One solution without using `np.rot90` to rotate in clockwise direction would be to swap the last two axes and then flip the last one -
img.swapaxes(-2,-1)[...,::-1]
For counter-clockwise rotation, flip the second last axis -
img.swapaxes(-2,-1)[...,::-1,:]
With `np.rot90`, the counter-clockwise rotation would be -
np.rot90(img,axes=(-2,-1))
Sample run -
In [39]: img = np.random.randint(0,255,(7,4,3,5))
In [40]: out_CW = img.swapaxes(-2,-1)[...,::-1] # Clockwise
In [41]: out_CCW = img.swapaxes(-2,-1)[...,::-1,:] # Counter-Clockwise
In [42]: img[0,0,:,:]
Out[42]:
array([[142, 181, 141, 81, 42],
[ 1, 126, 145, 242, 118],
[112, 115, 128, 0, 151]])
In [43]: out_CW[0,0,:,:]
Out[43]:
array([[112, 1, 142],
[115, 126, 181],
[128, 145, 141],
[ 0, 242, 81],
[151, 118, 42]])
In [44]: out_CCW[0,0,:,:]
Out[44]:
array([[ 42, 118, 151],
[ 81, 242, 0],
[141, 145, 128],
[181, 126, 115],
[142, 1, 112]])
Runtime test
In [41]: img = np.random.randint(0,255,(800,600))
# @Manel Fornos's Scipy based rotate func
In [42]: %timeit rotate(img, 90)
10 loops, best of 3: 60.8 ms per loop
In [43]: %timeit np.rot90(img,axes=(-2,-1))
100000 loops, best of 3: 4.19 µs per loop
In [44]: %timeit img.swapaxes(-2,-1)[...,::-1,:]
1000000 loops, best of 3: 480 ns per loop
Thus, for rotating by `90` degrees or multiples of it, `numpy.dot` or `swapping axes` based ones seem pretty good in terms of performance and also more importantly do not perform any interpolation that would change the values otherwise as done by Scipy's rotate based function.
Problem
I have a numpy array of shape (7,4,100,100) which means that I have 7 images of 100x100 with depth 4. I want to rotate these images at 90 degrees. I have tried: ``` rotated= numpy.rot90(array, 1) ``` but it changes the shape of the array to (4,7,100,100) which is not desired. Any solution for that?