CODEWITHSUNDEEP
pythonpython-3.xtensorflowimage-resizingscikit-image
Austin
Austin

Reputation: 7319

Efficiently resize batch of np.array images

I have a 4D np.array size (10000,32,32,3) that represents a set of 10000 RGB images.

How can I use skimage.transform.resize or other function to resize all images efficiently so that the (32,32) is interpolated to (224,224)? I'd prefer to do this with skimage, but I'm open to any solutions that don't use tf.image.resize_images.

My current solution is using tf.image.resize_images, but it's causing GPU memory issues later down in my pipeline (won't free up memory after finishing in jupyter notebook) so I'd like to replace it.

Example:

import tensorflow as tf
X = tf.image.resize_images(X,[224, 224])
with tf.Session() as sess:
    X = X.eval()

Upvotes: 8

Views: 6630

Answers (2)

Austin
Austin

Reputation: 7319

I won't likely accept my own answer, but it seems that a simple for loop is actually fairly fast (says ~300% cpu utilization from top).

from skimage.transform import resize

imgs_in = np.random.rand(100, 32, 32, 3)
imgs_out = np.zeros((100,224,224,3))

for n,i in enumerate(imgs_in):
    imgs_out[n,:,:,:] = resize(imgs_in[n,:,:,:], imgs_out.shape[1:], anti_aliasing=True)

print(imgs_out.shape)

Seems to be 7-8x faster than ndi.zoom on my machine. Trying to parallelize this further with multiprocessing would be even better I think.

Upvotes: 6

Andras Deak -- Слава Україні
Andras Deak -- Слава Україні

Reputation: 35080

One possibility is scipy.ndimage.zoom which can work with your collection of images and use spline interpolation of a given order to upsample your images:

import numpy as np
import scipy.ndimage as ndi

imgs_in = np.random.rand(100, 32, 32, 3)
factor = 224/imgs_in.shape[1]
imgs_out = ndi.zoom(imgs_in, (1, factor, factor, 1), order=2)
print(imgs_out.shape)

The resulting shape is (100, 224, 224, 3) as expected.

You'll have to check whether the runtime and result is acceptable for your needs. Twiddling with the order of interpolation will probably affect this: there is a noticeable speed difference between second-order and (the default) third-order splines, at the cost of interpolation quality.

Upvotes: 3

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