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fcm.py
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import sys
import copy
import math
import numpy as np
from PIL import Image
from collections import namedtuple
from operator import attrgetter
import random
import matplotlib.pyplot as plt
def clustering(centroids,num_centroids):
num_data=height*width
cond=0
e=0.01
loop=0
m=2
while cond==0:
loop=loop+1
v = np.zeros(num_centroids)
tmp=np.zeros((num_centroids,num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i]=tmp[i]+((distki/distkj)**(2/(m-1)))
tmp[i]=1/tmp[i]
for i in range(num_centroids):
num=np.sum((tmp[i]**m)*pixels)
den=np.sum(tmp[i]**m)
v[i]=num/den
if np.average(abs(centroids-v))<e or loop==150:
print("Final centroids are : ",v)
cond=cond+1
else:
print(v)
centroids=np.copy(v)
db(v, num_centroids)
d(v, num_centroids)
print(loop)
draw_img(v, num_centroids)
return
def db(centroids,num_centroids):
num_data = height * width
m=2
max_c=np.zeros(num_centroids)
tmp = np.zeros((num_centroids,num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i] = tmp[i] + ((distki / distkj) ** (2 / (m - 1)))
tmp[i] = 1 / tmp[i]
for i in range(num_centroids):
for j in range(num_centroids):
if i!=j:
dist=abs(centroids[i]-centroids[j])
i_spr = np.sum((tmp[i]**m)*((pixels-centroids[i])**2))/np.sum(tmp[i]**m)
j_spr = np.sum((tmp[j]**m)*((pixels-centroids[j])**2))/np.sum(tmp[j]**m)
final=(i_spr+j_spr)/dist
if final>max_c[i]:
max_c[i]=final
db_val=np.sum(max_c)/num_centroids
print("DB index value is : ",db_val)
return
def d(centroids,num_centroids):
num_data=height*width
m=2
final=0
tmp = np.zeros((num_centroids, num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i] = tmp[i] + ((distki / distkj) ** (2 / (m - 1)))
tmp[i] = 1 / tmp[i]
for i in range(num_centroids):
i_spr = np.sum((tmp[i] ** m) * ((pixels - centroids[i]) ** 2)) / np.sum(tmp[i] ** m)
if i_spr>final:
final=i_spr
i_spr=final
d_val=sys.maxsize
for i in range(num_centroids):
for j in range(num_centroids):
if i!=j:
dist=abs(centroids[i]-centroids[j])
final=dist/i_spr
if final<d_val:
d_val=final
print("Dunn index value is : ",d_val)
return
def draw_img(centroids,num_centroids):
num_data = height * width
m = 2
tmp = np.zeros((num_centroids, num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i] = tmp[i] + ((distki / distkj) ** (2 / (m - 1)))
tmp[i] = 1 / tmp[i]
img=np.zeros((num_centroids,height,width))
for k in range(num_centroids):
z=0
for i in range(height):
for j in range(width):
img[k][i][j]=tmp[k][z]
z=z+1
fig = plt.figure()
for i in range(num_centroids):
ax = fig.add_subplot(2,2,i+1)
ax.imshow(img[i])
plt.show()
return
im=Image.open("C:/Users/User/Desktop/lena.jpg")
pixels = list(im.getdata())
width,height=im.size
#for i in range(len(pixels)):
# pixels[i]=int(round(sum(pixels[i]) / float(len(pixels[i]))))
pixels=np.array(pixels)
c=3
r=np.zeros(c)
for i in range(c):
r[i]=random.uniform(0,255)
clustering(r,c)