In image processing, we frequently apply the same algorithm on a large batch of images. In this paragraph, we propose to use joblib to parallelize loops. Here is an example of such repetitive tasks:
Sourcefrom skimage import data, color, util
from skimage.restoration import denoise_tv_chambolle
from skimage.feature import hog
def task(image):
"""
Apply some functions and return an image.
"""
image = denoise_tv_chambolle(image[0][0], weight=0.1, multichannel=True)
fd, hog_image = hog(color.rgb2gray(image), orientations=8,
pixels_per_cell=(16, 16), cells_per_block=(1, 1),
visualise=True)
return hog_image
# Prepare images
hubble = data.hubble_deep_field()
width = 10
pics = util.view_as_windows(hubble, (width, hubble.shape[1], hubble.shape[2]), step=width)
To call the function task on each element of the list pics, it is
usual to write a for loop. To measure the execution time of this loop, you can
use ipython and measure the execution time with %timeit.
def classic_loop():
for image in pics:
task(image)
%timeit classic_loop()
Another equivalent way to code this loop is to use a comprehension list which has the same efficiency.
def comprehension_loop():
[task(image) for image in pics]
%timeit comprehension_loop()
joblib is a library providing an easy way to parallelize for loops once we have a comprehension list.
The number of jobs can be specified.
from joblib import Parallel, delayed
def joblib_loop():
Parallel(n_jobs=4)(delayed(task)(i) for i in pics)
%timeit joblib_loop()