How to resize an image faster in python

Question

I have an image i.e an array of pixel values, lets say 5000x5000 (this is the typical size). Now I want to expand it by 2 times to 10kx10k. The value of (0,0) pixel value goes to (0,0), (0,1), (1,0), (1,1) in the expanded image.

After that I am rotating the expanded image using scipy.interpolate.rotate (I believe there is no faster way than this given the size of my array)

Next I have to again resize this 10kx10k array to original size i.e. 5kx5k. To do this I have to take the average pixel values of (0,0), (0,1), (1,0), (1,1) in the expanded image and put them in (0,0) of the new image.

However it turns out that this whole thing is an expensive procedure an takes a lot of time given the size of my array. Is there a faster way to do it? I am using the following code to expand the original image

#Assume the original image is already given
largeImg=np.zeros((10000,10000), dtype=np.float32)
for j in range(5000):
    for k in range(5000):
          pixel_value=original_img[j][k]
          for x in range((2*k), (2*(k+1))):
                for y in range((2*j), (2*(j+1))):
                    largeImg[y][x] = pixel_value

A similar method is used to reduce the image to original size after rotation.

Paul Panzer · Accepted Answer · 2017-02-10 03:42:06Z

5

In numpy you can use repeat:

large_img = original_img.repeat(2, axis=1).repeat(2, axis=0)

and

final_img = 0.25 * rotated_img.reshape(5000,2,5000,2).sum(axis=(3,1))

or use scipy.ndimage.zoom. this can give you smoother results than the numpy methods.

edited Feb 10, 2017 at 3:42

answered Feb 10, 2017 at 3:31

Paul Panzer

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Comments

mistakeNot · Accepted Answer · 2017-02-10 02:27:58Z

0

there is a nice library that probably has all the functions you need for handling images, including rotate:

http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.rotate

answered Feb 10, 2017 at 2:27

mistakeNot

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