Resize a batch of images in numpy

Question

I have close to 10000 greyscale images in a numpy array (10000 x 480 x 752) and would like to resize them with the imresize function from scipy.misc. It works with a for loop build around all the images, but it takes 15 minutes.

images_resized = np.zeros([0, newHeight, newWidth], dtype=np.uint8)
for image in range(images.shape[0]):
    temp = imresize(images[image], [newHeight, newWidth], 'bilinear')
    images_resized = np.append(images_resized, np.expand_dims(temp, axis=0), axis=0)

Is there any way to do this faster with an apply like function? I looked into apply_along_axis

def resize_image(image):
    return imresize(image, [newHeight, newWidth], 'bilinear')
np.apply_along_axis(lambda x: resize_image(x), 0, images)

but this gives an

'arr' does not have a suitable array shape for any mode

error.

Answer 1

the time is long probably because resize is long :

In [22]: %timeit for i in range(10000) : pass
1000 loops, best of 3: 1.06 ms per loop

so the time is spend by the resize function: Vectorisation will not improve performance here.

Estimate time for one image is 15*60/10000= 90ms. resize use splines. It is good for quality, but time consuming. if the goal is to reduce size, resampling can give acceptable results and is faster:

In [24]: a=np.rand(752,480)

In [25]: %timeit b=a[::2,::2].copy()
1000 loops, best of 3: 369 µs per loop #

In [27]: b.shape
Out[27]: (376, 240) 

It's about 300x faster. This way, you can achieve the job in a few seconds.