Added ability to map faces

modules/cluster_analysis.py

@@ -0,0 +1,32 @@
+import numpy as np
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+from typing import Any
+
+
+def find_cluster_centroids(embeddings, max_k=10) -> Any:
+    inertia = []
+    cluster_centroids = []
+    K = range(1, max_k+1)
+
+    for k in K:
+        kmeans = KMeans(n_clusters=k, random_state=0)
+        kmeans.fit(embeddings)
+        inertia.append(kmeans.inertia_)
+        cluster_centroids.append({"k": k, "centroids": kmeans.cluster_centers_})
+
+    diffs = [inertia[i] - inertia[i+1] for i in range(len(inertia)-1)]
+    optimal_centroids = cluster_centroids[diffs.index(max(diffs)) + 1]['centroids']
+
+    return optimal_centroids
+
+def find_closest_centroid(centroids: list, normed_face_embedding) -> list:
+    try:
+        centroids = np.array(centroids)
+        normed_face_embedding = np.array(normed_face_embedding)
+        similarities = np.dot(centroids, normed_face_embedding)
+        closest_centroid_index = np.argmax(similarities)
+        
+        return closest_centroid_index, centroids[closest_centroid_index]
+    except ValueError:
+        return None