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:
from 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, 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, hubble.shape), 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
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()
© 2011 the scikit-image team
Licensed under the BSD 3-clause License.