请用python写出对指定目录内图片进行基于PCA与KNN人脸分类并计算正确率的代码

请使用以下库：numpy，sklearn，cv2

import numpy as np
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
import cv2

# 从指定目录读取图片
def read_images(path):
    images = []
    labels = []
    for i in range(1, 11):
        for j in range(1, 11):
            image = cv2.imread(path + str(i) + '_' + str(j) + '.jpg', 0)
            images.append(image.flatten())
            labels.append(i)
    return np.array(images), np.array(labels)

# 读取训练图片
train_images, train_labels = read_images('train/')

# 使用PCA进行降维
pca = PCA(n_components=100)
pca.fit(train_images)
train_images = pca.transform(train_images)

# 使用KNN进行分类
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(train_images, train_labels)

# 读取测试图片
test_images, test_labels = read_images('test/')
test_images = pca.transform(test_images)

# 计算分类准确率
accuracy = knn.score(test_images, test_labels)
print(accuracy)

上述代码会读取名称类似于"train/1_1.jpg"的图片，使用PCA降维，使用KNN分类，并输出分类准确率。