1 files changed, 62 insertions, 0 deletions
diff --git a/kmean.py b/kmean.py
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+++ b/kmean.py
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+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import sys
+import random
+import os
+import json
+import scipy.io
+from random import randint
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.neighbors import DistanceMetric
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+import argparse
+import numpy as np
+from numpy import genfromtxt
+from numpy import linalg as LA
+from timeit import default_timer as timer
+from scipy.spatial.distance import cdist
+from rerank import re_ranking
+
+def create_kmean_clusters(feature_vectors, labels, gallery_idx, camId):
+    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)
+
+    for i in range(2):
+        km_train_data[i] =  KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train[i])
+        km_labels[i] = np.zeros(int(np.max(labels))) # clusters size
+        km_idx[i] = km_train_data[i].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[0] = np.ones(km_labels_1.size)
+    clustercam[1] = np.add(np.ones(km_labels_2.size), 1)
+    for j in range(2):
+        for j in range(km_labels_1.size):
+            cl.append(km_train_data[i].cluster_centers_[j])
+            cllab.append(km_labels[i][j])
+
+    train_data = np.array(cl)
+    train_label = np.array(cllab)
+    train_cam = np.concatenate(clustercam, axis=1)
+    return train_data, train_label, train_cam
+