numpy operation 1--task 1: single-channel image to three-channel understanding / task 2: press axis = 0 or axis = 1 or axis = 2 (axis) stack

Task one:

import numpy as np
a=np.asarray([[10,20],[101,201]])

# a=a[:,:,np.newaxis]
# print(a.shape)
# b= a.repeat([3],axis=2)
# print(b.shape,b)

image = np.expand_dims(a, axis=2)
image = np.concatenate((image, image, image), axis=-1)

print(image)

 

 

This understanding attached to the schematic diagram helps the visualization, there may be mistakes, I hope everyone will express their opinions!

Task two:

import numpy as np
a=np.arange(1,10).reshape((3,3))
b=np.arange(11,20).reshape((3,3))
c=np.arange(101,110).reshape((3,3))

Print ( ' Axis = 0 \ n- ' , np.stack ((A, B, C), Axis = 0))   # stacked By List 
Print ( ' Axis. 1 = \ n- ' , np.stack ((A, B, C), Axis =. 1))   # rows stacked 
Print ( ' Axis = 2 \ n- ' , np.stack ((A, B, C), Axis = 2))   # stacked in each element

 

This blog is just to help you understand. Please also think a lot. If there are mistakes or you have a better learning method, you can comment. Welcome to share!

 link:

https://blog.csdn.net/jacke121/article/details/80086866 (python single channel to 3 channel)

https://www.cnblogs.com/xzcfightingup/p/7598293.html (more numpy operations)

 

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Origin www.cnblogs.com/wywshtc/p/12699590.html