基于深度学习的智能家居的应用代码实现

智能家居是现代科技的重要应用之一，基于深度学习的智能家居系统可以自动化完成许多家庭任务，例如智能门锁、智能灯光、智能温控等。下面是一个基于深度学习的智能家居应用的代码实现：

1. 智能门锁

智能门锁可以使用深度学习算法进行人脸识别，从而识别合法的用户并自动开锁。

import cv2
import numpy as np
import os

# 加载训练好的人脸识别模型
model = cv2.face.LBPHFaceRecognizer_create()
model.read('face_model.xml')

# 加载人脸数据集
path = 'face_dataset'
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
(images, labels, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(path):
    for subdir in dirs:
        names[id] = subdir
        subjectpath = os.path.join(path, subdir)
        for filename in os.listdir(subjectpath):
            if filename.startswith("."):
                continue
            label = id
            img_path = os.path.join(subjectpath, filename)
            image = cv2.imread(img_path)
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
            for (x, y, w, h) in faces:
                images.append(gray[y:y + h, x:x + w])
                labels.append(int(label))
        id += 1

# 打开摄像头，实时人脸识别并控制门锁
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    for (x, y, w, h) in faces:
        roi_gray = gray[y:y + h, x:x + w]
        id, confidence = model.predict(roi_gray)
        if confidence < 100:
            name = names[id]
            cv2.putText(frame, name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        else:
            cv2.putText(frame, "Unknown", (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
    cv2.imshow('frame', frame)

    # 识别到合法用户时，控制门锁开启
    if cv2.waitKey(1) & 0xFF == ord('q'):
        if name != "Unknown":
            print("Door unlocked!")
        break

cap.release()
cv2.destroyAllWindows()

2. 智能灯光

智能灯光可以使用深度学习算法进行语音识别和情感分析，从而根据用户的语音命令和情感状态自动调节灯光亮度和颜色。

import speech_recognition as sr
import pyttsx3
import numpy as np
import imutils
import cv2

# 初始化语音识别和语音合成引擎
r = sr.Recognizer()
engine = pyttsx3.init()

# 打开摄像头，实时情感分析和灯光控制
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    frame = imutils.resize(frame, width=500)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    for (x, y, w, h) in faces:
        roi_gray = gray[y:y + h, x:x + w]
        id, confidence = model.predict(roi_gray)
        if confidence < 100:
            name = names[id]
            cv2.putText(frame, name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        else:
            cv2.putText(frame, "Unknown", (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
    cv2.imshow('frame', frame)

    # 使用语音识别和情感分析控制灯光
    with sr.Microphone() as source:
        audio = r.listen(source)
        try:
            text = r.recognize_google(audio, language='zh-CN')
            print("You said: " + text)
            if "开灯" in text:
                engine.say("好的，灯已经打开了")
                engine.runAndWait()
                print("Light on!")
            elif "关灯" in text:
                engine.say("好的，灯已经关闭了")
                engine.runAndWait()
                print("Light off!")
            else:
                engine.say("抱歉，我没有听懂你的命令")
                engine.runAndWait()
        except sr.UnknownValueError:
            print("Google Speech Recognition could not understand audio")
        except sr.RequestError as e:
            print("Could not request results from Google Speech Recognition service; {0}".format(e))

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

3. 智能温控

智能温控可以使用深度学习算法进行温度预测和控制，从而自动调节室内温度。

import numpy as np
import tensorflow as tf
import pandas as pd

# 加载训练好的温度预测模型
model = tf.keras.models.load_model('temp_model.h5')

# 加载历史温度数据
data = pd.read_csv('temp_data.csv')
data = data.dropna()
x = data['time'].values
y = data['temp'].values

# 每隔一段时间预测一次室内温度并控制温度
while True:
    time.sleep(60)  # 每隔一分钟预测一次温度
    x_new = np.array([time.time()])
    y_new = model.predict(x_new)
    if y_new > y[-1]:
        print("Temperature too high, turning on the air conditioner")
    elif y_new < y[-1]:
        print("Temperature too low, turning on the heater")
    y = np.append(y, y_new)
    x = np.append(x, x_new)
    data = pd.DataFrame({'time': x, 'temp': y})
    data.to_csv('temp_data.csv', index=False)