请在你的代码中增加一个函数，自动抽取n个X_processed中的图片验证识别是否准确，返回准确率

以下是增加了验证准确率的函数的修改代码：

python
import random
from sklearn import svmfrom sklearn.metrics import accuracy_score
from skimage.feature hog

# 将X_processed列表按3:2的比例随机划分为"员"和"陌生人"两个集合
def split_dataset(X_processed):
random.shuffle(X_processed)
split_index = int(len(X_processed) * 3 / 5)
employee_set = X_processed[:split_index]
stranger_set = X_processed[split_index:]
return employee_set, stranger_set

# 使用HOG特征提取进行人脸识别训练
def train_face_recognition(employee_set):
X = []
y = []
for i, face_images in enumerate(employee_set):
for face_image in face_images:
feature = hog(face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False)
X.append(feature)
y.append(i) # i代表员工的标签
clf = svm.SVC()
clf.fit(X, y)
return clf

# 随机抽取一张图片进行识别
def recognize_random_face(clf, X_processed):
random_index = random.randint(0, len(X_processed)-1)
random_face_images = X_processed[random_index]
random_face_image = random.choice(random_face_images)
feature = hog(random_face_image, orientations=8, pixels_per_cell=(10, 10), cells_per_block=(1, 1), visualize=False)
prediction = clf.predict([feature])
return prediction[0] == random_index

# 验证识别准确率
def validate_recognition_accuracy(clf, X_processed, n):
correct_count = 0
for _ in range(n):
is_correct = recognize_random_face(clf, X_processed)
if is_correct:
correct_count += 1
accuracy = correct_count / n
return accuracy

# 示例用法
X_processed = [...] # X_processed列表的具体内容
employee_set, stranger_set = split_dataset(X_processed)
clf = train_face_recognition(employee_set)
accuracy = validate_recognition_accuracy(clf, X_processed, 100)
print("识别准确率：", accuracy)

上述代码中，新增了一个名为`validate_recognition_accuracy`的函数，用于验证识别准确率。该函数接受分类器、X_processed列表和抽取图片的数量n作为参数，并通过调用`recognize_random_face`函数进行识别，统计正确的结果数量并计算准确率。示例中验证了100次识别并输出准确率。您可以根据需要修改参数n来调整验证次数。