0、任务需求
采集摄像头、烟雾传感器、温湿度传感器 （DHT11）、火焰传感器、光敏传感器
执行器：风扇、花洒、led灯
pc端订阅：video_stream、shu_data pc端发布：pc_data
树莓派：发布视频流到：video_stream， 发布数据：shu_data，接收(订阅)数据在:pc_data
温度：tem 温度阈值: th_tem
湿度：hum 湿度阈值:th_hum
空气质量:mq2 空气阈值: th_mq2
光照：light 光照阈值: th_light
火焰: fire
0、 树莓派的基本操作
1、如何查看树莓派的引脚资源
pinout
2、固定CPU的频率
树莓派4B全核心最大频率为1.5 GHz，单核最大频率为1.5 GHz
所以可以设置cpu的频率为1.5GHz
方法一
用cpufrequtils工具
sudo apt install cpufrequtils #安装该工具
alias get_cpu='sudo cpufreq-info -w -m' #在.bashrc中定义快捷键，用于查看cpu频率
alias set_cpu='sudo cpufreq-set -f 1.5GHz' #在.bashrc中定义快捷键，用于设置cpu频率（固定频率）
alias reset_cpu='sudo cpufreq-set -g ondemand' #在.bashrc中定义快捷键，用于恢复cpu频率调节的默认方式（ondemand)方法二（推荐）
修改/boot/config.txt文件
sudo nano /boot/config.txt
# 放开修改 arm_freq=1500
查看是否成功
get_cpu3、设置一个树莓派启动的时候自己设置环境的脚本
sudo nano /etc/rc.local
# 在rc.local的exit 0前面设置 /home/xq/startup.sh启动脚本的位置
/bin/bash /home/xq/startup.sh &1、qt编译mqtt
2、树莓派读取dht11
1、利用Adafruit_DHT
安装dht11驱动库
pip install Adafruit_DHTimport Adafruit_DHT
import time
sensor = Adafruit_DHT.DHT11
pin = 4 # 设置GPIO引脚号
while True:
humidity, temperature = Adafruit_DHT.read_retry(sensor, pin)
if humidity is not None and temperature is not None:
print('温度：{0:0.1f}°C 湿度：{1:0.1f}%'.format(temperature, humidity))
else:
print('读取失败，请检查传感器连接是否正确。')
time.sleep(2)
2、到时候补充，我自己试的时候一直读不出数据
3、使用usb摄像头在树莓派上做人脸识别
关于树莓派的摄像头如何使用我在 树莓派使用摄像头网络实时监控 | 哈哈 (hahaxiong0204.github.io)这个博客上已经说明好了。
并且在树莓派上必须安装好opencv，numpy
sudo apt-get install python3-opencv -y
sudo apt-get install numpy以下代码是通过mqtt向上位机传输数据的，也可以在树莓派上显示，打开对应的代码：cv2.imshow('image', img)
在文件路径下必须要有haarcascade_frontalface_alt2.xml文件
# face_my.py
import cv2
import os
import numpy as np
import pickle
import paho.mqtt.client as mqtt
import base64
import time
# MQTT连接参数
mqtt_broker = "192.168.137.1" # 请替换为PC的IP地址
mqtt_port = 1883
mqtt_topic = "video_stream"
# 创建MQTT客户端
client = mqtt.Client("RaspberryPi")
client.connect(mqtt_broker, mqtt_port, 60)
cap = cv2.VideoCapture(0)
cap.set(3, 640) # set video width
cap.set(4, 480) # set video height
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')
current_id = 0
label_ids = {}
x_train = []
y_labels = []
def face_recognition():
num_count = 0
label_id_name = {}
with open("label.pickle", 'rb') as f:
origin_labels = pickle.load(f).items()
# print("源文件数据:" + origin_labels)
label_id_name = {v: h for h, v in origin_labels}
print(label_id_name)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('my_trainer.xml')
while True:
ret, img = cap.read();
img = cv2.flip(img, 1) # flip video image vertically
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
num_count += 1
for (x, y, w, h) in faces:
if num_count >= 5:
id_, conf = recognizer.predict(gray[y:y + h, x:x + w])
print("id = ",label_id_name[id_],"conf = ",conf)
if 20 <= conf <= 54:
cv2.putText(img, str(label_id_name[id_]), (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2)
else:
cv2.putText(img, str("NULL"), (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
# cv2.imshow('image', img)
# ============================== 发送mqtt数据帧 ============================
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
result, encoded_frame = cv2.imencode('.jpg', img, encode_param)
if result:
# 将MJPEG格式的帧转换为base64编码
frame_base64 = base64.b64encode(encoded_frame.tobytes())
client.publish(mqtt_topic, frame_base64)
# time.sleep(1 / 5)
# ======================================================================
k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
def input_face(name):
count = 0
all_num = 60
folder = os.path.exists('./dataset/' + name)
if not folder: # 判断是否存在文件夹如果不存在则创建为文件夹
os.makedirs('./dataset/' + name) # makedirs 创建文件时如果路径不存在会创建这个路径
print("创建文件夹成功")
while True:
ret, img = cap.read()
img = cv2.flip(img, 1) # flip video image vertically
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# 创建文件夹
folder = os.path.exists('./dataset/' + name)
if not folder: # 判断是否存在文件夹如果不存在则创建为文件夹
os.makedirs('./dataset/' + name) # makedirs 创建文件时如果路径不存在会创建这个路径
print("创建文件夹成功")
for (x, y, w, h) in faces:
if count%20==0:
print("请输入任意键继续")
if count == 0:
print("请摆正你的脸，等待开启")
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
count += 1
path_jpg = "./dataset/" + name + '/' + str(count) + ".jpg"
cv2.imwrite(path_jpg, gray[y:y + h, x:x + w])
# cv2.imwrite(path_jpg, img)
print(path_jpg)
# cv2.imshow('image', img)
# ============================== 发送mqtt数据帧 ============================
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
result, encoded_frame = cv2.imencode('.jpg', img, encode_param)
if result:
# 将MJPEG格式的帧转换为base64编码
frame_base64 = base64.b64encode(encoded_frame.tobytes())
client.publish(mqtt_topic, frame_base64)
# time.sleep(1 / 5)
# ======================================================================
k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
elif count >= all_num: # Take 30 face sample and stop video
break
# 训练人脸
def train_face():
global current_id
recognizer = cv2.face.LBPHFaceRecognizer_create()
for root, dirs, files in os.walk('dataset'):
# print(root, dirs, files)
for file in files:
path = os.path.join(root, file)
image = cv2.imread(path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image_array = np.array(gray, "uint8")
label = os.path.basename(root)
if label not in label_ids:
label_ids = current_id
current_id += 1
id_ = label_ids
faces = face_cascade.detectMultiScale(image_array, scaleFactor=1.5, minNeighbors=5)
for (x, y, w, h) in faces:
roi = image_array[y:y + h, x:x + w]
x_train.append(roi)
y_labels.append(id_)
with open("label.pickle", "wb") as f:
pickle.dump(label_ids, f)
print(label_ids)
recognizer.train(x_train, np.array(y_labels))
recognizer.save("my_trainer.xml")
# while True:
name = input("please input name :")
if len(name) != 0:
input_face(name)
train_face()
face_recognition()
cap.release()
cv2.destroyAllWindows()
