Getting rid of for loops in Python with Numpy

Question

So, I have the following code for resizing an image using nearest neighbor interpolation. The solution seems straightforward to me using 2 for loops, but I can't think of any way to do this while taking advantage of numpy to avoid those pesky loops. Here is my code:

def scale(img, factor): # Calculate new image shape and create new image with it. height, width = img.shape[:2] new_height, new_width = (int(height * factor), int(width * factor))[:2] scaled_img = np.zeros([new_height, new_width]) # Iterate over all pixels and set their values based on the input image. for x in range(new_height): for y in range(new_width): scaled_img[x, y] = img[int(x / factor), int(y / factor)] return scaled_img

Any input on how to avoid the for loops?

V. Ayrat · Accepted Answer · 2020-05-26 14:24:56Z

You can calculate the mapping of indices of new image to old indices and then use numpy.ix_ to unite them

import numpy as np def scale(img, factor): """ >>> img = np.arange(9).reshape(3, 3) >>> print(img) [[0 1 2] [3 4 5] [6 7 8]] >>> print(scale(img, 1.67)) [[0 0 1 1 2] [0 0 1 1 2] [3 3 4 4 5] [3 3 4 4 5] [6 6 7 7 8]] """ x_indices = (np.arange(int(img.shape[1] * factor)) / factor).astype(int) # [0 0 1 1 2] y_indices = (np.arange(int(img.shape[0] * factor)) / factor).astype(int) return img[np.ix_(y_indices, x_indices)]

Nils L. · Accepted Answer · 2020-05-26 14:07:02Z

I would avoid writing this code entirely and use the Pillow image processing library to resize the image. Not sure about the performance, but I would think they would have optimized such basic tasks quite well.

Richard · Accepted Answer · 2020-05-26 16:31:11Z

While the first answer is really great from a "how do I do this in numpy" point of view, I'd second Nils L's answer: if what you're wanting to do is resize an image, then you're likely much better off using one of the image processing libraries.

Pillow works well, as does ndimage: http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html#scipy.ndimage.zoom

Both will likely be faster than numpy, and also give you a lot more options in the details of how you want the resizing to work, and let you do different interpolation methods if that's what you need.

Thanks for the heads-up, but this is for an assignment and I was mostly wondering how to avoid using for loops with numpy in general ^^

Collectives™ on Stack Overflow

Getting rid of for loops in Python with Numpy

3 Answers 3

1 Comment

Comments

1 Comment

Hot Network Questions

Collectives™ on Stack Overflow

3 Answers 3

1 Comment

Comments

1 Comment

Related