1 files changed, 48 insertions, 0 deletions
diff --git a/lib/kmean.py b/lib/kmean.py
new file mode 100644
index 0000000..2e34702
--- /dev/null
+++ b/lib/kmean.py
@@ -0,0 +1,48 @@
+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(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))
+        print(len(km_labels[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)
+    print(train_data.shape)
+    train_label = np.array(cllab)
+    print(train_label.shape)
+    train_cam = np.array([clustercam[i] for i in range(2)]).reshape(train_label.shape[0])
+    print(train_cam.shape)
+    return train_data, train_label, train_cam