MachineLearning/bai5_Object_Segmentation.py at master · PhamKhoa96/MachineLearning · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 12 12:22:16 2020

@author: phamk
"""


import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans

img = mpimg.imread('girl3.jpg')
plt.imshow(img)
imgplot = plt.imshow(img)
plt.axis('off')
plt.show()

X = img.reshape((img.shape[0]*img.shape[1], img.shape[2]))


#cluster = 2,5,10,15,20
for K in [2, 5, 10, 15, 20]:
    kmeans = KMeans(n_clusters=K).fit(X)
    label = kmeans.predict(X)

    img4 = np.zeros_like(X)
    # replace each pixel by its center
    for k in range(K):
        img4[label == k] = kmeans.cluster_centers_[k]
    # reshape and display output image
    img5 = img4.reshape((img.shape[0], img.shape[1], img.shape[2]))
    plt.imshow(img5, interpolation='nearest')
    plt.axis('off')
    plt.show()

#cluster = 3
for K in [3]:
    kmeans = KMeans(n_clusters=K).fit(X)
    label = kmeans.predict(X)

    img4 = np.zeros_like(X)
    # replace each pixel by its center
    for k in range(K):
        img4[label == k] = kmeans.cluster_centers_[k]
    # reshape and display output image
    img5 = img4.reshape((img.shape[0], img.shape[1], img.shape[2]))
    plt.imshow(img5, interpolation='nearest')
    plt.axis('off')
    plt.show()