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 4032x3024 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
Best Answer
To achieve this I used MagickStripImage() to remove all the extraneous data, then ran
-convert [inputJPG] -profile [profile] [outputJPG]to add the sRGB profile again.This worked for my purposes (as I'd already done profile conversion beforehand, so all my profiles are sRGB). I did try Tom's suggestion of using ExifTool (which is fantastic) but couldn't get it to strip out EXIF+XMP+IPTC and leave the profile intact.