Dfyjkt

This is a bit of a naive question, but I'm new to data science, thus the question.
I'm following a course which reads a 2D image and performs the following operation,

image = mpimg.imread('test.jpg')
duplicate = np.copy(image)
red_threshold = green_threshold = blue_threshold = 0
rgb_threshold = [red_threshold, green_threshold, blue_threshold]

Specifically this line

thresholds = (image[:,:,0] < rgb_threshold[0]) | (image[:,:,1] < rgb_threshold[1]) | (image[:,:,2] < rgb_threshold[2])
duplicate[thresholds] = [0,0,0]

The explanation for this line of code is,

The result, duplicate, is an image in which pixels that were above the
threshold have been retained, and pixels below the threshold have been
blacked out.

I just don't understand how?
Can someone break this up a little and help me understand what's going on here?

The above expression,

thresholds = (image[:,:,0] < rgb_threshold[0]) | (image[:,:,1] < rgb_threshold[1]) | (image[:,:,2] < rgb_threshold[2])

unfolded:

image[:,:,0] 

here the 3rd index, 0 is the channel of the image from RGB, thus image[:,:,0], image[:,:,1], image[:,:,2] is RGB channels' pixels respectively.

image[:,:,0] < rgb_threshold[0]) 

means, that we only need pixels with are below the value of the actual channel's threshold value, here 0.

In this case we intend get the sum of numpy arrays of the thresholded color channels's values with bitwise or operator | like e.g:

import numpy as np

a = np.array([26,0,46,])
b = np.array([0,55,1,])

print(a | b)

Out:

[26 55 47]