It's not secret that installing openCV is by far the most annoying part about using it... but once it's running on your machine it's great! That said, to make a simple script super user-friendly I'd rather not require (or assume) that a user already has compiled openCV. I recently found myself only needing the function cv2.blur from openCV. To implement the same function without openCV, you can use the function scipy.ndimage.filters.convolve; e.g.:
| import numpy as np from scipy import ndimage A = np.ones((10,10)) pix_blur = (5,5) k = k = np.ones(pix_blur) / float(pix_blur[0]*pix_blur[1]) B = ndimage.convolve(A, k, mode='mirror') |
In the above example, the array B would be the same as from:
| import cv2 A = np.ones((10,10)) pix_blur = (5,5) B = cv2.blur(A, pix_blur) |
That said, cv2.blur is marginally faster (it's written C++). e.g., I found that with an 255 x 512 array, cv2.blur was on average 0.008 seconds faster than ndimage.convolve as used above (tested on n = 50 different arrays)---and of course that scales for your larger images. So you might consider writing your code with both options -- e.g., in your import statements have something like:
| openCV = False try: import cv2 openCV = True except: from scipy import ndimage |
This was developed with Chedy Raïssi. Hope it helps someone. Please post any comments below.