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9b76f08 | 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 | """In-memory FAISS gallery assigning persistent person IDs to face embeddings.
Duplicated from ../app/identity_store.py for this standalone web demo deployable
(see face_engine.py for why). Here, one instance lives per browser session
(held in gr.State), not per process - nothing persists between visitors.
"""
from typing import List
import faiss
import numpy as np
class IdentityStore:
"""Cosine-similarity gallery over L2-normalized ArcFace embeddings.
Uses FAISS IndexFlatIP (inner product), which equals cosine similarity
when all stored and query vectors are L2-normalized.
"""
def __init__(self, embedding_dim: int = 512, match_threshold: float = 0.38) -> None:
self.embedding_dim = embedding_dim
self.match_threshold = match_threshold
self.index = faiss.IndexFlatIP(embedding_dim)
self._next_person_id = 1
self.person_ids: List[int] = []
def match_or_register(self, normed_embedding: np.ndarray) -> int:
"""Finds the closest known identity; registers a new one if none is close enough."""
query = normed_embedding.astype(np.float32).reshape(1, -1)
if self.index.ntotal > 0:
similarities, indices = self.index.search(query, 1)
best_similarity = float(similarities[0, 0])
best_index = int(indices[0, 0])
if best_similarity >= self.match_threshold:
return self.person_ids[best_index]
person_id = self._next_person_id
self._next_person_id += 1
self.index.add(query)
self.person_ids.append(person_id)
return person_id
def __len__(self) -> int:
return self.index.ntotal
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