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from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from sqlalchemy.orm import Session
from users.models import UserEmbeddings
from core.config import get_settings
settings = get_settings()
class FaceMatch:
def __init__(self, db: Session):
self.db = db
self.threshold = settings.FACE_RECOGNITION_THRESHOLD
def load_embeddings_from_db(self):
user_embeddings = self.db.query(UserEmbeddings).all()
return {ue.user_id: np.array(ue.embeddings) for ue in user_embeddings}
def match_faces(self, new_embeddings, saved_embeddings):
new_embeddings = np.array(new_embeddings)
max_similarity = 0
identity = None
for user_id, stored_embeddings in saved_embeddings.items():
similarity = cosine_similarity(new_embeddings.reshape(1, -1), stored_embeddings.reshape(1, -1))[0][0]
if similarity > max_similarity:
max_similarity = similarity
identity = user_id
return identity, max_similarity if max_similarity > self.threshold else (None, 0)
def new_face_matching(self, new_embeddings):
embeddings_dict = self.load_embeddings_from_db()
if not embeddings_dict:
return {'status': 'Error', 'message': 'No embeddings available in the database'}
identity, similarity = self.match_faces(new_embeddings, embeddings_dict)
if identity:
return {
'status': 'Success',
'message': 'Match Found',
'user_id': identity,
'similarity': float(similarity) # Convert numpy float to Python float
}
return {
'status': 'Error',
'message': 'No matching face found'
} |