rgb_nearest_nbr.py

import numpy as np
from scipy.spatial import KDTree


rgb_list = np.array([(0, 255, 126), (255, 34, 121)]) # imagine 10k items
rgb_list_normalized = rgb_list / 255.0

tree = KDTree(rgb_list_normalized)

 def custom_distance(rgb1, rgb2):
     return np.sum(np.abs(rgb1 - rgb2))

query_rgb = np.array([128, 64, 32]) / 255.0
distances, indices = tree.query(query_rgb, k=5, distance_upper_bound=custom_distance(query_rgb, rgb_list_normalized))
nearest_rgb = rgb_list[indices]