facenet人脸识别代码

Facenet是一种基于深度学习的人脸识别算法，其代码实现可以使用TensorFlow框架。以下是一个简单的Facenet人脸识别代码示例：

import tensorflow as tf
import numpy as np
import cv2
import os

# 加载模型
model_path = 'models/20180402-114759.pb'
model = tf.Graph()
with model.as_default():
    graph_def = tf.GraphDef()
    with tf.gfile.GFile(model_path, 'rb') as f:
        serialized_graph = f.read()
        graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(graph_def, name='')

# 获取输入和输出张量
input_name = 'input:0'
embeddings_name = 'embeddings:0'
phase_train_name = 'phase_train:0'
with model.as_default():
    input_tensor = model.get_tensor_by_name(input_name)
    embeddings_tensor = model.get_tensor_by_name(embeddings_name)
    phase_train_tensor = model.get_tensor_by_name(phase_train_name)

# 加载人脸检测器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# 加载人脸图像
img_path = 'test.jpg'
img = cv2.imread(img_path)

# 检测人脸
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

# 提取人脸特征
with tf.Session(graph=model) as sess:
    sess.run(tf.global_variables_initializer())
    for (x, y, w, h) in faces:
        face_img = img[y:y+h, x:x+w]
        face_img = cv2.resize(face_img, (160, 160))
        face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
        face_img = np.expand_dims(face_img, axis=0)
        feed_dict = {input_tensor: face_img, phase_train_tensor: False}
        embeddings = sess.run(embeddings_tensor, feed_dict=feed_dict)
        print(embeddings)

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