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My goal is to find the nearest x,y point co-ordinate for every pixel. Based on that i have to colour the pixel points.

Here is what i have tried, The below code will draw the points.

import numpy as np
import matplotlib.pyplot as plt 

points = np.array([[0,40],[0,0],[5,30],[4,10],[10,25],[20,5],[30,35],[35,3],[50,0],[45,15],[40,22],[50,40]]) 
print (points)
x1, y1 = zip(*points)
plt.plot(x1,y1,'.')
plt.show()

Here is the output of the above code Now to find the nearest point for each pixel. I am found something like this where i have to give manually each pixel co-ordinates, to get the nearest point.

from scipy import spatial
import numpy as np
A = np.random.random((10,2))*100
print (A)
pt = np.array([[6, 30],[9,80]])
print (pt)
for each in pt:
    A[spatial.KDTree(A).query(each)[1]] # <-- the nearest point 
    distance,index = spatial.KDTree(A).query(each)
    print (distance) # <-- The distances to the nearest neighbors
    print (index) # <-- The locations of the neighbors
    print  (A[index])

The output will be like this,

[[1.76886192e+01 1.75054781e+01]
 [4.17533199e+01 9.94619127e+01]
 [5.30943347e+01 9.73358766e+01]
 [3.05607891e+00 8.14782701e+01]
 [5.88049334e+01 3.46475520e+01]
 [9.86076676e+01 8.98375851e+01]
 [9.54423012e+01 8.97209269e+01]
 [2.62715747e+01 3.81651805e-02]
 [6.59340306e+00 4.44893348e+01]
 [6.66997434e+01 3.62820929e+01]]
[[ 6 30]
 [ 9 80]]
14.50148095039858
8
[ 6.59340306 44.48933479]
6.124988197559344
3
[ 3.05607891 81.4782701 ]

Instead of giving each point manually i want to take each pixel from the image and i wanted to find the nearest blue point. This is my first question.

After that i want to classify those points into two categories, Based on pixel and point i want to colour it, basically i want to do a cluster on it. Something like this am looking for

This is not in proper form. But at the end i want it like this. Thanks in advance guys.

Community
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venkat
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2 Answers2

1
  • Use cKDTree instead of KDTree, which is faster (see this answer).
  • You can give the kdtree an array of points to query instead of looping over all of them.
  • Constructing a kdtree is a costly operation compared to querying it, so construct it once and query many times.

Compare the following two code snippets, on my tests the second one run x800 times faster. 

from timeit import default_timer as timer

np.random.seed(0)
A = np.random.random((1000,2))*100
pt = np.random.randint(0,100,(100,2))

start1 = timer()
for each in pt:
    A[spatial.KDTree(A).query(each)[1]] 
    distance,index = spatial.KDTree(A).query(each)
end1 = timer()
print end1-start1

start2 = timer()
kdt = spatial.cKDTree(A)  # cKDTree + outside construction 
distance,index = kdt.query(pt)  
A[index] 
end2 = timer()
print end2-start2
Iddo Hanniel
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0

you can use scikit-learn for this:

from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=1)
labels = list(range(len(points)))
neigh.fit(points, labels) 
pred = neigh.predict(np.random.random((10,2))*50)

if you want the points itself and not their class labels you can do

points[pred]
Nico
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