人工智能在智慧城市中的应用：智能交通与智慧安防

    import numpy as np
    import pandas as pd
    import tensorflow as tf
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import MinMaxScaler
    
    # 加载交通流量数据
    traffic_data = pd.read_csv('traffic_flow.csv')
    
    # 数据预处理
    scaler = MinMaxScaler()
    traffic_data_scaled = scaler.fit_transform(traffic_data)
    
    # 划分训练集和测试集
    X = traffic_data_scaled[:, :-1]
    y = traffic_data_scaled[:, -1]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # 构建LSTM神经网络模型
    model = tf.keras.Sequential([
    tf.keras.layers.LSTM(50, activation='relu', input_shape=(X_train.shape[1], 1)),
    tf.keras.layers.Dense(1)
    ])
    
    # 编译模型
    model.compile(optimizer='adam', loss='mean_squared_error')
    
    # 调整数据形状以适应LSTM输入
    X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
    X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
    
    # 训练模型
    model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test), verbose=1)
    
    # 预测交通流量
    predictions = model.predict(X_test)

    import rospy
    from geometry_msgs.msg import Twist
    
    def move_forward():
    # 初始化ROS节点
    rospy.init_node('move_forward_node', anonymous=True)
    # 创建发布者，发布到/cmd_vel主题
    pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
    rate = rospy.Rate(10)  # 10hz
    
    while not rospy.is_shutdown():
        # 创建Twist消息
        vel_msg = Twist()
        vel_msg.linear.x = 0.5  # 设置线速度为0.5m/s
        vel_msg.angular.z = 0.0  # 设置角速度为0
    
        # 发布消息
        pub.publish(vel_msg)
        rate.sleep()
    
    if __name__ == '__main__':
    try:
        move_forward()
    except rospy.ROSInterruptException:
        pass

    import cv2
    import dlib
    
    # 加载人脸检测器
    detector = dlib.get_frontal_face_detector()
    
    # 打开摄像头
    cap = cv2.VideoCapture(0)
    
    while True:
    # 读取帧
    ret, frame = cap.read()
    if not ret:
        break
    
    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # 检测人脸
    faces = detector(gray)
    
    # 绘制矩形框标记人脸
    for face in faces:
        x, y, w, h = face.left(), face.top(), face.width(), face.height()
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
    # 显示结果
    cv2.imshow('Face Detection', frame)
    
    # 按下'q'键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    
    # 释放摄像头并关闭窗口
    cap.release()
    cv2.destroyAllWindows()

    import tensorflow as tf
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
    from tensorflow.keras.preprocessing.image import ImageDataGenerator
    
    # 数据预处理
    train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)
    test_datagen = ImageDataGenerator(rescale=1./255)
    
    train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    subset='training'
    )
    
    validation_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    subset='validation'
    )
    
    test_generator = test_datagen.flow_from_directory(
    'data/test',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
    )
    
    # 构建卷积神经网络模型
    model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    MaxPooling2D(pool_size=(2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Flatten(),
    Dense(512, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
    ])
    
    # 编译模型
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
    # 训练模型
    model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // train_generator.batch_size,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // validation_generator.batch_size,
    epochs=10
    )
    
    # 评估模型
    test_loss, test_acc = model.evaluate(test_generator, steps=test_generator.samples // test_generator.batch_size)
    print(f'Test accuracy: {test_acc}')