[PYTHON] A story that I was addicted to at np.where


Since I couldn't output the index extraction of np.where during image processing at all. I understood it while referring to the sample code of here ... So it's a memorandum! If you understand how to read this, you can experience aha! ?? ??

Output of np.where! !! ??

Reference sample code and output. This time the 3rd floor tensor ($ Width \ times Height \ times Channels = 2 \ times 3 \ times 4 $) It is.

a_3d = np.arange(24).reshape(2, 3, 4)
# output
# [[[ 0  1  2  3]
#   [ 4  5  6  7]
#   [ 8  9 10 11]]
#  [[12 13 14 15]
#   [16 17 18 19]
#   [20 21 22 23]]]

print(np.where(a_3d < 5))
# output
# (array([0, 0, 0, 0, 0]), array([0, 0, 0, 0, 1]), array([0, 1, 2, 3, 0]))

Here is the output of `np.where (a_3d <5)` Widtharray([0, 0, 0, 0, 0]) Heightarray([0, 0, 0, 0, 1]) Channelsarray([0, 1, 2, 3, 0]) See it as a list of WidthW HeightH ChannelsC To access the elements of the 3rd floor tensor

a_3d[W[0]][H[0]][C[0]] = 0 
a_3d[W[1]][H[1]][C[1]] = 1 
a_3d[W[2]][H[2]][C[2]] = 2 
a_3d[W[3]][H[3]][C[3]] = 3 
a_3d[W[4]][H[4]][C[4]] = 4 

It becomes. Now you can finally read the output of ``` np.where (a_3d <5)` ``!

at the end

At first, the array was just returned, so what is this? I was wondering When you can see the shapes of rows, columns, and depths, they change to meaningful numbers. Aha experience is good ~

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