请使用python写一个能对指定目录内图片进行基于KNN和LDA人脸识别并统计正确率的程序

使用python写一个基于KNN和LDA的人脸识别程序可以使用一些第三方库如OpenCV和scikit-learn来实现。以下是一个简单的示例代码：

import os
import cv2
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.neighbors import KNeighborsClassifier

def read_images(path):
    # 读取目录下的图片并返回
    images = []
    labels = []
    for dirpath, dirnames, filenames in os.walk(path):
        for filename in [f for f in filenames if f.endswith(".jpg")]:
            filepath = os.path.join(dirpath, filename)
            image = cv2.imread(filepath)
            images.append(image)
            labels.append(dirpath.split("/")[-1])
    return images, labels

def train_model(images, labels):
    # 训练KNN和LDA模型
    model_knn = KNeighborsClassifier(n_neighbors=1)
    model_lda = LDA()
    images = [cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) for image in images]
    images = [cv2.resize(image, (92, 112)) for image in images]
    images = [image.flatten() for image in images]
    model_knn.fit(images, labels)
    model_lda.fit(images, labels)
    return model_knn, model_lda

def test_model(model_knn, model_lda, images, labels):
    # 测试模型并统计正确率
    correct = 0
    total = 0
    for image, label in zip(images, labels):
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image = cv2.resize(image, (92, 112))
        image = image.flatten()
        predicted_knn = model_knn.predict([image])[0]
        predicted_lda = model_lda.predict([image])[0]
        if predicted_knn == label or predicted_lda == label:
            correct += 1
        total += 1
    return correct / total

# 使用示例
path = "path/to/your/images"
images, labels = read_images(path)
model_knn, model_lda = train_model(images, labels)
accuracy = test_model(model_knn, model_lda, images, labels)
print("Accuracy: {:.2f}%".format(accuracy * 100))

请注意