What is the fastest way to generate image thumbnails in Python?

I fancied some fun so I did some benchmarking on the various methods suggested above and a few ideas of my own.

I collected together 1000 high resolution 12MP iPhone 6S images, each 4032×3024 pixels and use an 8-core iMac.

Here are the techniques and results – each in its own section.

Method 1 – Sequential ImageMagick

This is simplistic, unoptimised code. Each image is read and a thumbnail is produced. Then it is read again and a different sized thumbnail is produced.

#!/bin/bash

start=$SECONDS
# Loop over all files
for f in image*.jpg; do
   # Loop over all sizes
   for s in 1600 720 120; do
      echo Reducing $f to ${s}x${s}
      convert "$f" -resize ${s}x${s} t-$f-$s.jpg
   done
done
echo Time: $((SECONDS-start))

Result: 170 seconds

Method 2 – Sequential ImageMagick with single load and successive resizing

This is still sequential but slightly smarter. Each image is only read one time and the loaded image is then resized down three times and saved at three resolutions. The improvement is that each image is read just once, not 3 times.

#!/bin/bash

start=$SECONDS
# Loop over all files
N=1
for f in image*.jpg; do
   echo Resizing $f
   # Load once and successively scale down
   convert "$f"                              \
      -resize 1600x1600 -write t-$N-1600.jpg \
      -resize 720x720   -write t-$N-720.jpg  \
      -resize 120x120          t-$N-120.jpg
   ((N=N+1))
done
echo Time: $((SECONDS-start))

Result: 76 seconds

Method 3 – GNU Parallel + ImageMagick

This builds on the previous method, by using GNU Parallel to process N images in parallel, where N is the number of CPU cores on your machine.

#!/bin/bash

start=$SECONDS

doit() {
   file=$1
   index=$2
   convert "$file"                               \
      -resize 1600x1600 -write t-$index-1600.jpg \
      -resize 720x720   -write t-$index-720.jpg  \
      -resize 120x120          t-$index-120.jpg
}

# Export doit() to subshells for GNU Parallel   
export -f doit

# Use GNU Parallel to do them all in parallel
parallel doit {} {#} ::: *.jpg

echo Time: $((SECONDS-start))

Result: 18 seconds

Method 4 – GNU Parallel + vips

This is the same as the previous method, but it uses vips at the command-line instead of ImageMagick.

#!/bin/bash

start=$SECONDS

doit() {
   file=$1
   index=$2
   r0=t-$index-1600.jpg
   r1=t-$index-720.jpg
   r2=t-$index-120.jpg
   vipsthumbnail "$file"  -s 1600 -o "$r0"
   vipsthumbnail "$r0"    -s 720  -o "$r1"
   vipsthumbnail "$r1"    -s 120  -o "$r2"
}

# Export doit() to subshells for GNU Parallel   
export -f doit

# Use GNU Parallel to do them all in parallel
parallel doit {} {#} ::: *.jpg

echo Time: $((SECONDS-start))

Result: 8 seconds

Method 5 – Sequential PIL

This is intended to correspond to Jakob’s answer.

#!/usr/local/bin/python3

import glob
from PIL import Image

sizes = [(120,120), (720,720), (1600,1600)]
files = glob.glob('image*.jpg')

N=0
for image in files:
    for size in sizes:
      im=Image.open(image)
      im.thumbnail(size)
      im.save("t-%d-%s.jpg" % (N,size[0]))
    N=N+1

Result: 38 seconds

Method 6 – Sequential PIL with single load & successive resize

This is intended as an improvement to Jakob’s answer, wherein the image is loaded just once and then resized down three times instead of re-loading each time to produce each new resolution.

#!/usr/local/bin/python3

import glob
from PIL import Image

sizes = [(120,120), (720,720), (1600,1600)]
files = glob.glob('image*.jpg')

N=0
for image in files:
   # Load just once, then successively scale down
   im=Image.open(image)
   im.thumbnail((1600,1600))
   im.save("t-%d-1600.jpg" % (N))
   im.thumbnail((720,720))
   im.save("t-%d-720.jpg"  % (N))
   im.thumbnail((120,120))
   im.save("t-%d-120.jpg"  % (N))
   N=N+1

Result: 27 seconds

Method 7 – Parallel PIL

This is intended to correspond to Audionautics’ answer, insofar as it uses Python’s multiprocessing. It also obviates the need to re-load the image for each thumbnail size.

#!/usr/local/bin/python3

import glob
from PIL import Image
from multiprocessing import Pool 

def thumbnail(params): 
    filename, N = params
    try:
        # Load just once, then successively scale down
        im=Image.open(filename)
        im.thumbnail((1600,1600))
        im.save("t-%d-1600.jpg" % (N))
        im.thumbnail((720,720))
        im.save("t-%d-720.jpg"  % (N))
        im.thumbnail((120,120))
        im.save("t-%d-120.jpg"  % (N))
        return 'OK'
    except Exception as e: 
        return e 


files = glob.glob('image*.jpg')
pool = Pool(8)
results = pool.map(thumbnail, zip(files,range((len(files)))))

Result: 6 seconds

Method 8 – Parallel OpenCV

This is intended to be an improvement on bcattle’s answer, insofar as it uses OpenCV but it also obviates the need to re-load the image to generate each new resolution output.

#!/usr/local/bin/python3

import cv2
import glob
from multiprocessing import Pool 

def thumbnail(params): 
    filename, N = params
    try:
        # Load just once, then successively scale down
        im = cv2.imread(filename)
        im = cv2.resize(im, (1600,1600))
        cv2.imwrite("t-%d-1600.jpg" % N, im) 
        im = cv2.resize(im, (720,720))
        cv2.imwrite("t-%d-720.jpg" % N, im) 
        im = cv2.resize(im, (120,120))
        cv2.imwrite("t-%d-120.jpg" % N, im) 
        return 'OK'
    except Exception as e: 
        return e 


files = glob.glob('image*.jpg')
pool = Pool(8)
results = pool.map(thumbnail, zip(files,range((len(files)))))

Result: 5 seconds

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