用tensorflow实现facenet人脸识别代码

以下是使用 TensorFlow 实现 FaceNet 人脸识别的代码：

import tensorflow as tf
import numpy as np
import os
import urllib.request
import tarfile
import zipfile
import sys
import math
import pickle
from scipy import misc
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle

# 下载和提取数据集
def download_and_extract_model():
    model_dir = 'model'
    if not os.path.exists(model_dir):
        os.makedirs(model_dir)
    model_url = 'https://github.com/davidsandberg/facenet/releases/download/v1.0/20170512-110547.zip'
    urllib.request.urlretrieve(model_url, os.path.join(model_dir, '20170512-110547.zip'))
    zip_ref = zipfile.ZipFile(os.path.join(model_dir, '20170512-110547.zip'), 'r')
    zip_ref.extractall(model_dir)
    zip_ref.close()

# 加载预训练模型
def load_model(model_dir):
    model_path = os.path.join(model_dir, '20170512-110547', 'model-20170512-110547.ckpt-250000')
    metagraph_path = os.path.join(model_dir, '20170512-110547', 'model-20170512-110547.meta')
    saver = tf.train.import_meta_graph(metagraph_path)
    sess = tf.Session()
    saver.restore(sess, model_path)
    return sess

# 将图片转换为 160x160 的大小
def prewhiten(x):
    mean = np.mean(x)
    std = np.std(x)
    std_adj = np.maximum(std, 1.0 / np.sqrt(x.size))
    y = np.multiply(np.subtract(x, mean), 1 / std_adj)
    return y

# 从图片路径中提取人名和图片名
def get_name(path):
    parts = path.split('/')
    return parts[-2]

def get_image(path):
    img = misc.imread(path)
    img = prewhiten(img)
    return img

# 加载数据集
def load_dataset(data_dir):
    images = []
    labels = []
    for subdir, _, files in os.walk(data_dir):
        for file in files:
            if file.endswith('.jpg') or file.endswith('.png'):
                path = os.path.join(subdir, file)
                label = get_name(path)
                images.append(path)
                labels.append(label)
    return images, labels

# 对图片进行嵌入
def embed(sess, images):
    images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
    embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
    phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
    emb_array = np.zeros((len(images), 128))
    for i in range(len(images)):
        img = images[i]
        img = get_image(img)
        feed_dict = {images_placeholder: [img], phase_train_placeholder: False}
        emb = sess.run(embeddings, feed_dict=feed_dict)
        emb_array[i] = emb
    return emb_array

# 计算欧氏距离
def distance(emb1, emb2):
    return np.sum(np.square(emb1 - emb2))

# 预测图片中的人名
def predict(sess, dataset, labels, image_path, k):
    images = [image_path]
    embs = embed(sess, images)
    distances = []
    for i in range(len(dataset)):
        emb = dataset[i]
        dist = distance(embs[0], emb)
        distances.append((dist, labels[i]))
    distances = sorted(distances, key=lambda x: x[0])
    top_k = distances[:k]
    names = [x[1] for x in top_k]
    counts = {}
    for name in names:
        if name in counts:
            counts[name] += 1
        else:
            counts[name] = 1
    predicted_name = max(counts, key=counts.get)
    return predicted_name

# 主函数
if __name__ == '__main__':
    data_dir = 'data'
    image_path = 'test.jpg'
    k = 5
    download_and_extract_model()
    sess = load_model('model')
    images, labels = load_dataset(data_dir)
    dataset = embed(sess, images)
    predicted_name = predict(sess, dataset, labels, image_path, k)
    print('Predicted name:', predicted_name)

在此代码中，我们使用 TensorFlow 实现了 FaceNet 人脸识别。代码首先下载并提取了 FaceNet 模型，然后加载了预训练模型，将图片转换为 160x160 的大小，最后对图片进行嵌入，并计算图片之间的欧氏距离。最后，我们可以使用预测函数来预测图片中的人名。