树莓派5驱动AI智能冰箱库存管理系统: 超声波传感器与定制YOLOv8模型实时监控技术全解析

import cv2  
import numpy as np  
# 加载定制产品检测模型（YOLOv8n导出为IMX500格式）  
model = load_model('path_to_imx500_model')  
def detect_products(frame):  
    # 预处理画面供模型使用  
    processed_frame = preprocess_frame(frame)  
    # 运行推理  
    detections = model(processed_frame)  
    # 解析检测结果并分类为'进'和'出'  
    in_products = []  
    out_products = []  
    for det in detections:  
        label = det['label']  
        x, y, w, h = det['bbox']  
        # 假设画面垂直分割：左半为"进"，右半为"出"  
        if x + w / 2 < frame.shape[1] // 2:  
            in_products.append(label)  
        else:  
            out_products.append(label)  
    return in_products, out_products  
# 辅助函数preprocess_frame()和load_model()在其他部分实现  

import time  
import socketio  
# 初始化SocketIO客户端  
sio = socketio.Client()  
sio.connect('http://your-ngrok-url')  
THRESHOLD = 50  # 距离阈值（单位：厘米）  
def read_ultrasonic_sensor():  
    # 模拟传感器读数；替换为实际逻辑  
    return get_distance()  
def sensor_loop():  
    detecting = False  
    in_list = []  
    out_list = []  
    while True:  
        distance = read_ultrasonic_sensor()  
        if distance < THRESHOLD and not detecting:  
            print("检测到冰箱门！启动库存检查...")  
            detecting = True  
            # 捕捉画面进行产品检测（模拟摄像头捕捉）  
            frame = capture_frame()  # 替换为实际摄像头逻辑  
            in_list, out_list = detect_products(frame)  
        elif distance >= THRESHOLD and detecting:  
            print("冰箱门关闭。向服务器发送库存数据。")  
            sio.emit('inventory_update', {'in': in_list, 'out': out_list})  
            detecting = False  
            in_list, out_list = [], []  
        time.sleep(0.5)  # 调整传感器轮询间隔  
# 在Raspberry Pi上运行sensor_loop()以持续监控门状态  

def pir_sensor_loop():  
    detecting = False  
    last_motion_time = time.time()  
    in_list = []  
    out_list = []  
    while True:  
        motion_detected = read_pir_sensor()  # 替换为实际传感器逻辑  
        if motion_detected:  
            last_motion_time = time.time()  
            if not detecting:  
                print("检测到动作！启动产品识别...")  
                detecting = True  
                frame = capture_frame()  # 使用AI摄像头捕捉画面  
                in_list, out_list = detect_products(frame)  
        elif detecting and (time.time() - last_motion_time) > 5:  
            print("5秒无动作。传输库存更新。")  
            sio.emit('inventory_update', {'in': in_list, 'out': out_list})  
            detecting = False  
            in_list, out_list = [], []  
        time.sleep(0.5)  

from flask import Flask  
from flask_socketio import SocketIO  
app = Flask(__name__)  
socketio = SocketIO(app, cors_allowed_origins="*")  
@socketio.on('inventory_update')  
def handle_inventory_update(data):  
    print("收到库存更新:", data)  
    # 向仪表盘客户端广播更新  
    socketio.emit('dashboard_update', data)  
if __name__ == '__main__':  
    socketio.run(app, host='0.0.0.0', port=5000)  

import React, { useEffect, useState } from "react";  
import io from "socket.io-client";  
const socket = io("http://your-ngrok-url");  
const FridgeInventory = () => {  
  const [inventory, setInventory] = useState({ in: [], out: [] });  
  useEffect(() => {  
    socket.on("dashboard_update", (data) => {  
      setInventory(data);  
    });  
    return () => {  
      socket.off("dashboard_update");  
    };  
  }, []);  
  return (  
    <div>  
      <h2>冰箱库存</h2>  
      <div>  
        <h3>在冰箱中</h3>  
        <ul>  
          {inventory.in.map((item, index) => (  
            <li key={index}>{item}</li>  
          ))}  
        </ul>  
      </div>  
      <div>  
        <h3>需补货物品</h3>  
        <ul>  
          {inventory.out.map((item, index) => (  
            <li key={index}>{item}</li>  
          ))}  
        </ul>  
      </div>  
    </div>  
  );  
};  
export default FridgeInventory;