请用python写出对指定目录内训练与测试图片进行基于LDA与KNN人脸分类并计算正确率的代码

这是一个可以实现基于LDA和KNN的人脸分类的Python代码示例：

import os
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 训练数据目录
train_dir = 'path/to/train/data'
# 测试数据目录
test_dir = 'path/to/test/data'

# 读取训练数据
train_data = []
train_labels = []
for label in os.listdir(train_dir):
    label_dir = os.path.join(train_dir, label)
    for image_file in os.listdir(label_dir):
        image = cv2.imread(os.path.join(label_dir, image_file))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        train_data.append(image.flatten())
        train_labels.append(label)

# 读取测试数据
test_data = []
test_labels = []
for label in os.listdir(test_dir):
    label_dir = os.path.join(test_dir, label)
    for image_file in os.listdir(label_dir):
        image = cv2.imread(os.path.join(label_dir, image_file))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        test_data.append(image.flatten())
        test_labels.append(label)

# 使用LDA进行降维
lda = LinearDiscriminantAnalysis(n_components=100)
train_data = lda.fit_transform(train_data, train_labels)
test_data = lda.transform(test_data)

# 使用KNN进行分类
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(train_data, train_labels)
pred_labels = knn.predict(test_data)

# 计算正确率
accuracy = accuracy_score(test_labels, pred_labels)
print('Accuracy:', accuracy)

你需要替换训练数据目录和测试数据目录为你的路径，且确保安装了sklearn,numpy,opencv-python-headless这些库。

注意：这个代码