blob: 361ed6d5bbe25fbb281c10c98fec0fabf41b0624 (
plain)
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
|
#!/usr/bin/env python
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework
from logging import debug
import numpy as np
from sklearn.cluster import KMeans
def create_kmean_clusters(feature_vectors, labels, gallery_idx, camId):
gallery = ([],[])
gallerylab = ([],[])
for i in range(gallery_idx.size):
cam = camId[gallery_idx[i]] - 1
gallery[cam].append(feature_vectors[gallery_idx[i]])
gallerylab[cam].append(labels[gallery_idx[i]])
train = np.array(gallery)
tlabel = np.array(gallerylab)
km_train_data = []
km_idx = []
km_labels = []
for i in range(2):
km_train_data.append(KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train[i]))
km_idx.append(km_train_data[i].labels_)
km_labels.append(list(range(np.max(labels))))
for j in range(np.max(labels)):
class_vote = np.zeros(np.max(labels))
for q in range(km_idx[i].size):
if km_idx[i][q]==j:
class_vote[int(tlabel[i][q])-1] += 1
km_labels[i][j] = np.argmax(class_vote) + 1
#MERGE CLUSTERS
cl = []
cllab = []
clustercam = []
for i in range(2):
clustercam.append(np.add(np.ones(len(km_labels[i])), i))
for j in range(len(km_labels[i])):
cl.append(km_train_data[i].cluster_centers_[j])
cllab.append(km_labels[i][j])
train_data = np.array(cl)
debug("Kmean data has shape", train_data.shape)
train_label = np.array(cllab)
train_cam = np.array([clustercam[i] for i in range(2)]).reshape(train_label.shape[0])
return train_data, train_label, train_cam
|
