services/embedding.py

import numpy as np
import tensorflow as tf
import cv2

class FaceEmbedding:
    def __init__(self, model_path="models/face_recognition.tflite"):
        self.interpreter = tf.lite.Interpreter(model_path=model_path)
        self.interpreter.allocate_tensors()
        
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        
    def get_embedding(self, face_image):
        """استخراج المتجه المميز للوجه"""
        # تحضير الوجه
        face_resized = cv2.resize(face_image, (112, 112))
        face_normalized = face_resized.astype(np.float32) / 127.5 - 1.0
        face_input = np.expand_dims(face_normalized, axis=0)
        
        # إنشاء المتجه
        self.interpreter.set_tensor(self.input_details[0]['index'], face_input)
        self.interpreter.invoke()
        
        embedding = self.interpreter.get_tensor(self.output_details[0]['index'])
        
        # تطبيع المتجه
        embedding = embedding / np.linalg.norm(embedding)
        
        return embedding[0]
    
    def compare_faces(self, embedding1, embedding2, threshold=0.6):
        """مقارنة وجهين"""
        similarity = np.dot(embedding1, embedding2)
        return similarity > threshold, similarity