according to my experiment , OpenCV turned out to be the fastest to perform Image rotation. take a look at the link .
Edit: I should also include the answer here as well :
I am going to focus on three most used libraries for image editing in python namely , Pillow, OpenCV and Scipy.
In the following code you can learn how to import these libraries and how to rotate an image using them. I have defined a function for each library to use it for our experiments
import numpy as np
import PIL
import cv2
import matplotlib.pylab as plt
from PIL import Image
from scipy.ndimage import rotate
from scipy.ndimage import interpolation
def rotate_PIL (image, angel, interpolation):
'''
input :
image : image : PIL image Object
angel : rotation angel : int
interpolation : interpolation mode : PIL.Image.interpolation_mode
Interpolation modes :
PIL.Image.NEAREST (use nearest neighbour), PIL.Image.BILINEAR (linear interpolation in a 2×2 environment), or PIL.Image.BICUBIC
https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.rotate
returns :
rotated image
'''
return image.rotate(angel,interpolation)
def rotate_CV(image, angel , interpolation):
'''
input :
image : image : ndarray
angel : rotation angel : int
interpolation : interpolation mode : cv2 Interpolation object
Interpolation modes :
interpolation cv2.INTER_CUBIC (slow) & cv2.INTER_LINEAR
https://theailearner.com/2018/11/15/image-interpolation-using-opencv-python/
returns :
rotated image : ndarray
'''
#in OpenCV we need to form the tranformation matrix and apply affine calculations
#
h,w = image.shape[:2]
cX,cY = (w//2,h//2)
M = cv2.getRotationMatrix2D((cX,cY),angel,1)
rotated = cv2.warpAffine(image,M , (w,h),flags=interpolation)
return rotated
def rotate_scipy(image, angel , interpolation):
'''
input :
image : image : ndarray
angel : rotation angel : int
interpolation : interpolation mode : int
Interpolation modes :
https://stackoverflow.com/questions/57777370/set-interpolation-method-in-scipy-ndimage-map-coordinates-to-nearest-and-bilinea
order=0 for nearest interpolation
order=1 for linear interpolation
returns :
rotated image : ndarray
'''
return scipy.ndimage.interpolation.rotate(image,angel,reshape=False,order=interpolation)
To understand which library is more efficient in rotating and interpolating images, we design a simple experiment at first. We apply a 20 degree rotation using all three libraries on a 200 x 200 pixel 8bit image generated by our function rand_8bit().
def rand_8bit(n):
im =np.random.rand(n,n)*255
im = im.astype(np.uint8)
im[n//2:n//2+n//2,n//2:n//4+n//2]= 0 # a self scaling rectangle
im[n//3:50+n//3,n//3:200+n//3]= 0 # a constant rectangle
return im
#generate images of 200x200 pixels
im = rand_8bit(200)
#for PIL library we need to first convert the image array into a PIL image object
image_for_PIL=Image.fromarray(im)
%timeit rotate_PIL(image_for_PIL,20,PIL.Image.BILINEAR)
%timeit rotate_CV(im,20,cv2.INTER_LINEAR)
%timeit rotate_scipy(im,20,1)
the result is :
987 µs ± 76 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
414 µs ± 79.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
4.46 ms ± 1.07 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
This means that OpenCV is the most efficient and Scipy is the slowest of them when it comes to image rotation.
