https://stackoverflow.com/questions/59669715/fastest-way-to-find-the-rgb-pixel-color-count-of-image
If you look at the answers on this link, there is a good answer.
Reshape the numpy array with (-1, 3) and count the number with unique.
I've changed it to counting pairs, not triple, so look at this is an example.
>>> import numpy as np
>>> arr = np.array([[[1,1],[2,2],[1,1]],
[[2,3],[1,1],[4,4]]])
>>> arr
array([[[1, 1],
[2, 2],
[1, 1]],
[[2, 3],
[1, 1],
[4, 4]]])
>>> arr.shape
(2, 3, 2)
>>> arr1 = arr.reshape((-1, 2))
>>> arr1
array([[1, 1],
[2, 2],
[1, 1],
[2, 3],
[1, 1],
[4, 4]])
>>> np.unique(arr1)
array([1, 2, 3, 4])
>>> np.unique(arr1, axis=0, return_counts=True)
(array([[1, 1],
[2, 2],
[2, 3],
[4, 4]]), array([3, 1, 1, 1], dtype=int64))
>>> values, counts = np.unique(arr1, axis=0, return_counts=1)
>>> values
array([[1, 1],
[2, 2],
[2, 3],
[4, 4]])
>>> counts
array([3, 1, 1, 1], dtype=int64)
>>> for value, count in zip(*np.unique(arr1, axis=0, return_counts=1)):
print(f"{value} {count})"
[11] These 3
[22] This one
[23] This one
[44] This one
>>>
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