深度学习卷积神经网络识别驾驶员是否闭眼的代码

以下是一个基于卷积神经网络的闭眼检测代码示例：

import cv2
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist

# 加载数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 调整数据格式
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')

# 将像素值标准化到0-1之间
X_train = X_train / 255
X_test = X_test / 255

# 将标签转换为分类问题
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

# 定义模型
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)

# 加载视频
cap = cv2.VideoCapture(0)

while True:
    # 读取帧
    ret, frame = cap.read()
    
    # 将帧调整为28x28像素的大小
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    roi = cv2.resize(gray, (28, 28), interpolation=cv2.INTER_AREA)
    
    # 将像素值标准化到0-1之间
    roi = roi / 255
    
    # 预测图像
    img = roi.reshape(1, 28, 28, 1)
    predictions = model.predict(img)
    
    # 获取预测结果的标签
    label = np.argmax(predictions, axis=1)[0]
    
    # 在视频帧上绘制预测结果
    cv2.putText(frame, str(label), (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    
    # 显示视频帧
    cv2.imshow('frame', frame)
    
    # 按下'q'键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放视频对象和窗口
cap.release()
cv2.destroyAllWindows()

这个示例代码使用Keras库来训练一个卷积神经网络，用于识别手写数字。然后，它使用OpenCV库来捕获视频流，并将每一帧调整为28x28像素的大小，并将其传递给模型进行预测，最后在视频帧上绘制预测结果。你可以将这个示例代码修改为你的具体应用场景。