Lane detection using simple thresholding

basic_thresholding.py

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np

# Read in the image and print out some stats
# Note: in the previous example we were reading a .jpg 
# Here we read a .png and convert to 0,255 bytescale
image = (mpimg.imread('test.png')*255).astype('uint8')
print('This image is: ',type(image), 
         'with dimensions:', image.shape)

# Grab the x and y size and make a copy of the image
ysize = image.shape[0]
xsize = image.shape[1]
color_select = np.copy(image)

# Define color selection criteria
###### MODIFY THESE VARIABLES TO MAKE YOUR COLOR SELECTION
red_threshold = 200
green_threshold = 100
blue_threshold = 10
######

rgb_threshold = [red_threshold, green_threshold, blue_threshold]

# Do a bitwise or with the "|" character to identify
# pixels below the thresholds
thresholds = (image[:,:,0] < rgb_threshold[0]) \
            | (image[:,:,1] < rgb_threshold[1]) \
            | (image[:,:,2] < rgb_threshold[2])
color_select[thresholds] = [0,0,0]
plt.imshow(color_select)
# Display the image                 
plt.imshow(color_select)

# Uncomment the following code if you are running the code locally and wish to save the image
# mpimg.imsave("test-after.png", color_select)

I want to understand what is happening between line 17 to 31. Explain me in depth