My main aim is to read in around 16k images for a Data science project and I am barely able to perform that serially.
I have performed some parallelization in c++, but I am unfamiliar with using it in python. Essentially, all I need is to parallelize a for loop that calls a function that reads in the image using the matplotlib.image package and returns the image object. I then simply append that object to list. Here is the function,
def read_img(name):
try:
img = mpimg.imread(name)
return img
except:
return("Did not find image")
I ran my code for 100, 1000 and then 5000 images in one go to see if it can run at all, and it ran fine until I ran it for 5000 and my jupyter notebook just crashed. My system has 24gb ram and 12 cores so I def need to find a way to parallelize this.
I know there are 2 modules in python for parallelization, multiprocessing and joblib but I am not sure how to approach this problem which I know is very basic but any guidance would be much appreciated.
You can use the python ThreadPoolExecutor link
Here is the general program which is not perfect but if you fill this should work
# import or some variable from your code mpimg
def read_img(name):
try:
img = mpimg.imread(name)
return img
except:
return("Did not find image")
from concurrent.futures import ThreadPoolExecutor, as_completed
# suppose the files contains th 16k file names
files = ['f1.jpg', 'f2.jpg']
future_to_file = {}
images_read = []
with ThreadPoolExecutor(max_workers=4) as executor:
for file in files:
future = executor.submit(read_img, file)
future_to_file[future] = file
for future in as_completed(future_to_file):
file = future_to_file[future]
img_read = future.result()
if img_read != 'Did not find image':
images_read.append((file, img_read))
I have the following minimal code that gets the bytes from an image:
import Image
im = Image.open("kitten.png")
im_data = [pix for pixdata in im.getdata() for pix in pixdata]
This is rather slow (I have gigabytes of images to process) so how could this be sped up? I'm also unfamiliar with what exactly that code is trying to do. All my data is 1280 x 960 x 8-bit RGB, so I can ignore corner cases, etc.
(FYI, the full code is here - I've already replaced the ImageFile loop with the above Image.open().)
You can try
scipy.ndimage.imread()
If you mean speeding up by algorythamically i can suggest you accessing file with multiple threads simultaneously (only if you don't have a connection between processing sequence)
divide file logically by few sections and access each part simultaneously with threads (you have to put your operation inside a function and call it with threads)
here is a link to tutorial about threading in python
threding in python
I solved my problem, I think:
>>> [pix for pixdata in im.getdata() for pix in pixdata] ==
numpy.ndarray.tolist(numpy.ndarray.flatten(numpy.asarray(im)))
True
This cuts down the runtime by half, and with a bit of bash magic I can run the conversion on the 56 directories in parallel.
I'm trying to diagnose why my Python server app is leaking memory. The app takes a request for an image url resizes it using Vips and returns the image. After every request the memory usage grows roughly by the size of the original image.
from fapws import base
import fapws._evwsgi as evwsgi
from gi.repository import Vips
import urllib2
import hmac
import hashlib
import base64
import StringIO
from boto.s3.connection import S3Connection
from boto.s3.bucket import Bucket
def start():
evwsgi.start('0.0.0.0', '80')
evwsgi.set_base_module(base)
def lfrThumbnail(environ, start_response):
try:
parameters = environ['PATH_INFO'].split('/')
s3File = 'my s3 url' + parameters[0]
width = float(parameters[1])
height = float(parameters[2])
hmacSignatureUser = parameters[3]
hmacSignature = some hasing code...
if not (hmacSignatureUser == hmacSignature):
print hmacSignatureUser
print hmacSignature
print hmacSignatureUser == hmacSignature
raise Exception
bufferedImage = urllib2.urlopen(s3File).read()
image = Vips.Image.new_from_buffer(bufferedImage, '')
imageWidth = float(image.width)
imageHeight = float(image.height)
imageAspectRatio = imageWidth / imageHeight
if (width > imageWidth) or (height > imageHeight):
image = image
elif abs((imageAspectRatio / (width/height)) - 1) < 0.05:
image = image.resize(width / imageWidth)
else:
scaleRatioWidth = width / imageWidth
scaleRatioHeight = height / imageHeight
maxScale = max(scaleRatioWidth, scaleRatioHeight)
image = image.resize(maxScale)
cropStartX = (image.width - width) / 2
cropStartY = (image.height - height) / 2
image = image.crop(cropStartX, cropStartY, width, height)
except Exception, e:
start_response('500 INTERNAL SERVER ERROR', [('Content-Type','text')])
return ['Error generating thumbnail']
start_response('200 OK', [
('Content-Type','image/jpeg'),
('Cache-Control: max-stale', '31536000')
])
return [image.write_to_buffer('.jpg[Q=90]')]
evwsgi.wsgi_cb(('/lfr/', lfrThumbnail))
evwsgi.set_debug(0)
evwsgi.run()
if __name__ == '__main__':
start()
I've tried using muppy , the pympler tracker but each diff after the image open/close operations showed only a couple of bytes being used.
Could the external C libraries be the cause of the memory leak? if so, how does one debug that.
If it's anything related I'm running the python server inside a docker container
I'm the libvips maintainer. It sounds like the vips operation cache: vips keeps the last few operations in memory and reuses the results if it can. This can be a huge performance win in some cases.
For a web service, you're probably caching elsewhere so you won't want this, or you won't want a large cache at least. You can control the cache size with vips_cache_set_max() and friends:
http://www.vips.ecs.soton.ac.uk/supported/current/doc/html/libvips/VipsOperation.html#vips-cache-set-max
From Python it's:
Vips.cache_set_max(0)
To turn off the cache completely. You can set the cache to limit by memory use, file descriptor use, or number of operations.
There are a couple of other useful things you can set to watch resource usage. Vips.leak_set(True) makes vips report leaked objects on exit, and also report peak pixel buffer memory use. Vips.cache_set_trace(True) makes it trace all operations as they are called, and shows cache hits.
In your code, I would also enable sequential mode. Add access = Vips.Access.SEQUENTIAL to your new_from_buffer().
The default behaviour is to open images for full random access (since vips doesn't know what operations you'll end up running on the image). For things like JPG, this means that vips will decode the image to a large uncompressed array on open. If the image is under 100mb, it'll keep this array in memory.
However for a simple resize, you only need to access pixels top-to-bottom, so you can hint sequential access on open. In this mode, vips will only decompress a few scanlines at once from your input and won't ever keep the whole uncompressed image around. You should see a nice drop in memory use and latency.
There are a lot of other things you could handle, like exif autorotate, colour management, transparency, jpeg shrink-on-load, and many others, I'm sure you know. The sources to vipsthumbnail might be a useful reference:
https://github.com/jcupitt/libvips/blob/master/tools/vipsthumbnail.c
I have written a motion detection/ video program using opencv2 which saves video output for x seconds. if motion in detected during that time, the output is saved as an alternate named file, but if not motion is detected then the file is overwritten. To avoid needless wear on a flash based memory system, I want to write the file to the RAM, and if motion is detected then save it to the non-volatile memory.
I am trying to create this file in the RAM using pyfilesystem-fs.memoryfs
import numpy as np
import cv2, time, os, threading, thread
from Tkinter import *
from fs.memoryfs import MemoryFS
class stuff:
mem=MemoryFS()
output = mem.createfile('output.avi')
rectime=0
delay=0
kill=0
cap = cv2.VideoCapture(0)
#out = cv2.VideoWriter('C:\motion\\output.avi',cv2.cv.CV_FOURCC('F','M','P','4'), 30, (640,480),True)
out = cv2.VideoWriter(output, cv2.cv.CV_FOURCC('F','M','P','4'), 30, (640,480),True)
This is the motion detection part
if value > 100:
print "saving"
movement=time.time()
while time.time()<int(movement)+stuff.rectime:
stuff.out.write(frame)
ret, frame = stuff.cap.read()
if stuff.out.isOpened() is True:
stuff.out.release()
os.rename(stuff.output, 'c:\motion\\' + time.strftime('%m-%d-%y_%H-%M-%S') + '.avi')
the os.rename function returns TypeError must be string, not None
I'm clearly using memoryfs incorrectly, but cannot find any examples of its use.
EDIT
I use the following line to open the file object and write to it
stuff.out.open(stuff.output, cv2.cv.CV_FOURCC(*'FMP4'),24,(640,480),True)
however this returns False , I'm not sure but it appears it can't open the file object.
To move your file form MemoryFS to real file system, you should read orig file and write it to dest file, something like
with mem.open('output.avi', 'b') as orig:
with open('c:\\motion\\' + time.strftime('%m-%d-%y_%H-%M-%S') + '.avi')) as dest:
dest.write(orig.read())
I'm building a photo gallery in Python and want to be able to quickly generate thumbnails for the high resolution images.
What's the fastest way to generate high quality thumbnails for a variety of image sources?
Should I be using an external library like imagemagick, or is there an efficient internal way to do this?
The dimensions of the resized images will be (max size):
120x120
720x720
1600x1600
Quality is an issue, as I want to preserve as many of the original colors as possible and minimize compression artifacts.
Thanks.
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
You want PIL it does this with ease
from PIL import Image
sizes = [(120,120), (720,720), (1600,1600)]
files = ['a.jpg','b.jpg','c.jpg']
for image in files:
for size in sizes:
im = Image.open(image)
im.thumbnail(size)
im.save("thumbnail_%s_%s" % (image, "_".join(size)))
If you desperately need speed. Then thread it, multiprocess it or get another language.
A little late to the question (only a year!), but I'll piggy backing on the "multiprocess it" part of #JakobBowyer's answer.
This is a good example of an embarrassingly parallel problem, as the main bit of code doesn't mutate any state external to itself. It simply reads an input, performs its computation and saves the result.
Python is actually pretty good at these kinds of problems thanks to the map function provided by multiprocessing.Pool.
from PIL import Image
from multiprocessing import Pool
def thumbnail(image_details):
size, filename = image_details
try:
im = Image.open(filename)
im.thumbnail(size)
im.save("thumbnail_%s" % filename)
return 'OK'
except Exception as e:
return e
sizes = [(120,120), (720,720), (1600,1600)]
files = ['a.jpg','b.jpg','c.jpg']
pool = Pool(number_of_cores_to_use)
results = pool.map(thumbnail, zip(sizes, files))
The core of the code is exactly the same as #JakobBowyer, but instead of running it in a loop in a single thread, we wrapped it in a function spread it out across multiple cores via the multiprocessing map function.
Another option is to use the python bindings to OpenCV. This may be faster than PIL or Imagemagick.
import cv2
sizes = [(120, 120), (720, 720), (1600, 1600)]
image = cv2.imread("input.jpg")
for size in sizes:
resized_image = cv2.resize(image, size)
cv2.imwrite("thumbnail_%d.jpg" % size[0], resized_image)
There's a more complete walkthrough here.
If you want to run it in parallel, use concurrent.futures on Py3 or the futures package on Py2.7:
import concurrent.futures
import cv2
def resize(input_filename, size):
image = cv2.imread(input_filename)
resized_image = cv2.resize(image, size)
cv2.imwrite("thumbnail_%s%d.jpg" % (input_filename.split('.')[0], size[0]), resized_image)
executor = concurrent.futures.ThreadPoolExecutor(max_workers=3)
sizes = [(120, 120), (720, 720), (1600, 1600)]
for size in sizes:
executor.submit(resize, "input.jpg", size)
One more answer, since (I think?) no one has mentioned quality.
Here's a photo I took with an iPhone 6S at the Olympic park in East London:
The roof is made from a set of wooden slats and unless you downsize rather carefully you'll get very nasty Moire effects. I had to compress the image quite heavily to upload to stackoverflow --- if you're interested, the original is here.
Here's cv2 resize:
$ python3
Python 3.7.3 (default, Apr 3 2019, 05:39:12)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
>>> x = cv2.imread("IMG_1869.JPG")
>>> y = cv2.resize(x, (120, 90))
>>> cv2.imwrite("cv2.png", y)
True
Here's vipsthumbnail:
$ vipsthumbnail IMG_1869.JPG -s 120 -o vips.png
And here are the two downsized images side-by-side and zoomed by x2, with vipsthumbnail on the left:
(ImageMagick gives the same results as vipsthumbnail)
cv2 is defaults to BILINEAR, so it has a fixed 2x2 mask. For every point in the output image, it calculates the corresponding point in the input and takes the 2x2 average. This means it's really only sampling at most 240 points in each line, and simply ignoring the other 3750! This produces ugly aliasing.
vipsthumbnail is doing a a more complex three stage downsize.
It uses the libjpeg shrink-on-load feature to shrink the image by a factor of 8 in each axis with a box filter to turn the 4032 pixel across image to 504 x 378 pixels.
It does a further 2 x 2 box filter shrink to get 252 x 189 pixels.
It finishes with a 5 x 5 Lanczos3 kernel to get the output 120 x 90 pixel image.
This is supposed to give equivalent quality to a full Lanczos3 kernel, but be quicker because it can box filter most of the way.
If you are already familiar with imagemagick, why not stick with the python-bindings?
PythonMagick
Python 2.7, Windows, x64 users
In addition to #JakobBowyer & #Audionautics, PIL is quite old and you can find yourself troubleshooting and looking for the right version... instead, use Pillow from here (source)
the updated snippet will look like this:
im = Image.open(full_path)
im.thumbnail(thumbnail_size)
im.save(new_path, "JPEG")
full enumeration script for thumbnail creation:
import os
from PIL import Image
output_dir = '.\\output'
thumbnail_size = (200,200)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for dirpath, dnames, fnames in os.walk(".\\input"):
for f in fnames:
full_path = os.path.join(dirpath, f)
if f.endswith(".jpg"):
filename = 'thubmnail_{0}'.format(f)
new_path = os.path.join(output_dir, filename)
if os.path.exists(new_path):
os.remove(new_path)
im = Image.open(full_path)
im.thumbnail(thumbnail_size)
im.save(new_path, "JPEG")
I stumbled upon this when trying to figure out which library I should use:
It seems like OpenCV is clearly faster than PIL.
That said, I'm working with spreadsheets and it turns out that the module I was using openpyxl already requires me to import PIL to insert images.