def main():
profile, pipeline = driver.init() #初始化D435
pos = config.Region_config(pipeline) #设置监测范围
#inferprogram,inferexe = model.model_init("./model") #读取深度学习模型
#s = network.init() #初始化与服务器的连接
while True:
background_color, background_deep = perception.background_init(
pipeline, pos) #获取冰箱状态
processer.BackgroundSave(background_color) #存储冰箱状态照片
#network.sendBackground(s,"background.jpg") #上传冰箱状态照片
perception.waitForAction(pipeline, pos) #等待冰箱内出现人为操作
perception.waitForStop(pipeline, pos) #等待人为操作停止
background_color_now, background_deep_now = perception.background_init(
pipeline, pos) #获取新的冰箱状态
diff = processer.DifferentialImage(
background_color, background_color_now) #对比变化前后，获取变化位置
boundGather = processer.getArea(diff) #获取变化区域坐标集
ActionGather = perception.ActionAdjust(background_deep,
background_deep_now,
boundGather) #判断动作（放置，拿取）
perception.resultDisplay(boundGather, background_color_now,
ActionGather)
# result = perception.Objectinfer(inferprogram, inferexe, boundGather, background_color, background_color_now)#使用深度学习判断放置/拿取的事物名称
# for i in range(len(result)):
# if result[i][1] > 0.5: #置信度大于50%
# if ActionGather[i] == 1:
# network.add(s, True, result[i][0], str(i)+".jpg") #上传用户操作信息
# elif ActionGather[i] == -1:
# network.delete(s, True, result[i][0], str(i)+".jpg")
# else:
# if ActionGather[i] == 1:
# network.add(s, False, "NULL", str(i)+".jpg")
# elif ActionGather[i] == -1:
# network.delete(s, False, "NULL", str(i)+".jpg")
volume = driver.getVolume(pipeline, pos, profile) #获取当前冰箱内剩余空间
