numpy - Load just part of an image in python

Question

This might be a silly question, but...

I have several thousand images that I would like to load into Python and then convert into numpy arrays. Obviously this goes a little slowly. But, I am actually only interested in a small portion of each image. (The same portion, just 100x100 pixels in the center of the image.)

Is there any way to load just part of the image to make things go faster?

Here is some sample code where I generate some sample images, save them, and load them back in.

import numpy as np
import matplotlib.pyplot as plt
import Image, time

#Generate sample images
num_images = 5

for i in range(0,num_images):
    Z = np.random.rand(2000,2000)
    print 'saving %i'%i
    plt.imsave('%03i.png'%i,Z)

%load the images
for i in range(0,num_images):
    t = time.time()

    im = Image.open('%03i.png'%i)
    w,h = im.size
    imc = im.crop((w-50,h-50,w+50,h+50))

    print 'Time to open: %.4f seconds'%(time.time()-t)

    #convert them to numpy arrays
    data = np.array(imc)

Answer 1

While you can't get much faster than PIL crop in a single thread, you can use multiple cores to speed up everything! :)

I ran the below code on my 8 core i7 machine as well as my 7 year old, two core, barely 2ghz laptop. Both saw significant improvements in run time. Much as you would expect, the improvement was dependent on the number of cores available.

The core of your code is the same, I just separated the looping from the actual computation so that the function could be applies to a list of values in parallel.

So, this:

for i in range(0,num_images):
    t = time.time()

    im = Image.open('%03i.png'%i)
    w,h = im.size
    imc = im.crop((w-50,h-50,w+50,h+50))

    print 'Time to open: %.4f seconds'%(time.time()-t)

    #convert them to numpy arrays
    data = np.array(imc)

Became:

def convert(filename):  
    im = Image.open(filename)
    w,h = im.size
    imc = im.crop((w-50,h-50,w+50,h+50))
    return numpy.array(imc)

The key to the speedup is the Pool feature of the multiprocessing library. It makes it trivial to run things across multiple processors.

Full code:

import os 
import time
import numpy 
from PIL import Image
from multiprocessing import Pool 

# Path to where my test images are stored
img_folder = os.path.join(os.getcwd(), 'test_images')

# Collects all of the filenames for the images
# I want to process
images = [os.path.join(img_folder,f) 
        for f in os.listdir(img_folder)
        if '.jpeg' in f]

# Your code, but wrapped up in a function       
def convert(filename):  
    im = Image.open(filename)
    w,h = im.size
    imc = im.crop((w-50,h-50,w+50,h+50))
    return numpy.array(imc)

def main():
    # This is the hero of the code. It creates pool of 
    # worker processes across which you can "map" a function
    pool = Pool()

    t = time.time()
    # We run it normally (single core) first
    np_arrays = map(convert, images)
    print 'Time to open %i images in single thread: %.4f seconds'%(len(images), time.time()-t)

    t = time.time()
    # now we run the same thing, but this time leveraging the worker pool.
    np_arrays = pool.map(convert, images)
    print 'Time to open %i images with multiple threads: %.4f seconds'%(len(images), time.time()-t)

if __name__ == '__main__':
    main()

Pretty basic. Only a few extra lines of code, and a little refactoring to move the conversion bit into its own function. The results speak for themselves:

Results:

8-Core i7

Time to open 858 images in single thread: 6.0040 seconds
Time to open 858 images with multiple threads: 1.4800 seconds

2-Core Intel Duo

Time to open 858 images in single thread: 8.7640 seconds
Time to open 858 images with multiple threads: 4.6440 seconds

So there ya go! Even if you have a super old 2 core machine you can halve the time you spend opening and processing your images.

Caveats

Memory. If you're processing 1000s of images, you're probably going to pop Pythons Memory limit at some point. To get around this, you'll just have to process the data in chunks. You can still leverage all of the multiprocessing goodness, just in smaller bites. Something like:

for i in range(0, len(images), chunk_size): 
    results = pool.map(convert, images[i : i+chunk_size]) 
    # rest of code.