如何将一个函数并行应用于numpy数组中的多个图像？ [英] How to apply a function in parallel to multiple images in a numpy array?

问题描述

假设我有一个 images 数组，其中包含3个频道的100,000张图片。

Let's say I have an images array that holds 100,000 images with 3 channels.

images = np.random.randint(0,255,(100000,32,32,3))

我有一个函数 foo ，它接受一个图像并对其执行一些操作。

And I have a function foo which accepts an image and performs some operation on it.

def foo(img):
    #some operation on the image, say histogram equalization

我现在如何将 foo 函数并行应用于100000张图像？我认为numpy会有一些功能用于此目的，但我很失望，没有找到任何。我发现 numpy.apply_along_axis 但是我读到它是相当迭代的。我该怎么办？

How do I now apply the foo function to 100000 images in parallel? I thought numpy would have some function for this purpose, but I was disappointed to not find any. I found numpy.apply_along_axis but I read it is rather iterative. What should I do?

推荐答案

这是一个例子，使用 joblib 执行直方图均衡化， parallel ， n_jobs 等于 nprocs （这里有10个流程，但您可以根据需要进行更改）

Here is an example, using joblib which performs histogram equalization on the images, in parallel with n_jobs equal to nprocs (here 10 processes but you can change as per your need)

# imports
import numpy as np
from skimage import exposure
from joblib import Parallel, delayed

# number of processes
nprocs = 10

# batched image array
img_arr = np.random.randint(0, 255, (1000, 32, 32, 3))

# function to be applied on all images
def process_image(img):
     img_eq = exposure.equalize_hist(img)
     return img_eq

result = []

# run `process_image()` in parallel
result.extend(Parallel(n_jobs=nprocs)(delayed(process_image)(img_arr[idx]) for idx in range(img_arr.shape[0])))

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