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

在这里给出一种可能的代码实现:

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

# 定义人脸图片所在目录
face_dir = 'path/to/face/images'

# 读取人脸图片并处理
X, y = [], []
for i, file in enumerate(os.listdir(face_dir)):
    # 读取图片并转化为灰度图
    img = Image.open(os.path.join(face_dir, file)).convert('L')
    # 将图片转化为一维数组
    img_data = np.array(img).flatten()
    X.append(img_data)
    y.append(i)

# 将数据集分为训练集和测试集
X, y = np.array(X), np.array(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# LDA降维
lda = LDA(n_components=50)
X_train_lda = lda.fit_transform(X_train, y_train)
X_test_lda = lda.transform(X_test)

# KNN分类
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train_lda, y_train)
y_pred = knn.predict(X_test_lda)

# 计算正确率
acc = accuracy_score(y_test, y_pred)
print('Accuracy: ', acc)

请注意，这段代码仅供参考，可能需要根据具体情况进行修改。

需要注意的是，这段代码仅提供一种思路，具体实现可能需要根据具体情况进行修改。