机器学习第六章应用案例中人脸识别的代码

以下是《机器学习实战》第六章应用案例中人脸识别的代码，包括数据预处理、特征提取和分类器训练等过程：

# 导入所需的库
import os
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
import cv2

# 定义数据预处理函数
def preprocess_data(data_path):
    images = []
    labels = []
    for sub_folder in os.listdir(data_path):
        sub_folder_path = os.path.join(data_path, sub_folder)
        for file_name in os.listdir(sub_folder_path):
            if file_name.startswith('.'):
                continue
            image_path = os.path.join(sub_folder_path, file_name)
            image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
            image = cv2.resize(image, (128, 128))
            images.append(np.asarray(image, dtype=np.uint8))
            labels.append(sub_folder)
    return images, labels

# 定义特征提取函数
def extract_features(images):
    features = []
    for image in images:
        hist = cv2.calcHist([image], [0], None, [256], [0, 256])
        features.append(hist)
    features = np.asarray(features)
    features = np.reshape(features, (features.shape[0], -1))
    return features

# 加载数据
data_path = 'path_to_data_folder'
images, labels = preprocess_data(data_path)

# 特征提取
features = extract_features(images)

# 标签编码
le = LabelEncoder()
labels = le.fit_transform(labels)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 训练分类器
clf = SVC(kernel='linear', C=1.0, random_state=42)
clf.fit(X_train, y_train)

# 预测并计算准确率
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: %.2f%%' % (accuracy * 100.0))

需要注意的是，以上代码只是一个示例，请根据实际情况进行修改和调整。同时，为了让模型具有更好的性能，建议使用更多的训练数据和更复杂的特征提取和分类器模型。