I am trying to sort my colours (which are in colour space CieLAB) by euclidean distance. So I am using the following code however it rearranges my colours instead of sorting. Do I need to specify a different axis or use a different function. If I need a different function can you suggest which one will work?
a = np.array([(255,9,255), (0,0,0), (125,125,4)])
a.sort(axis=0)
print(a)
Result (notice how it has rearranged the colours?):
[[ 0 0 0]
[ 4 125 125]
[ 9 255 255]]
It should be:
[[ 0 0 0]
[ 125 125 4]
[ 255 9 255]]
I hope that I understood the question,
Maybe you should first calculate the pairwise distances, and then sort by these distances.
something like that:
import numpy as np
from scipy.spatial.distance import cdist
def sort_by_eucledian_distance(a):
dist = cdist(a, a)[:, 0] # calculate distances
dist = sorted(zip(dist, np.arange(len(dist)))) #add indexes and sort
idxs = [v[1] for v in dist] # get the new, sorted indexes
return a[idxs]
a = np.array([(255,9,255), (0,0,0), (125,125,4)])
b = sort_by_eucledian_distance(a)
print(b)
Will print
array([[ 0., 0., 0.],
[125., 9., 4.],
[255., 125., 255.]])
If I understood your question correctly you want to sort by Euclidean norm, right?
Something like
a[np.linalg.norm(a, axis=1).argsort()]
or using np.einsum
(sort by a dot a
row-wise)
a[np.einsum("ij,ij->i", a, a).argsort()]
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