深度学习卷积神经网络识别驾驶员是否闭眼的代码
时间: 2023-10-14 09:04:48 浏览: 71
以下是一个基于卷积神经网络的闭眼检测代码示例:
```python
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。
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